From e4df6ec9f42ee7ad6596207610dc8ba657dc2fd0 Mon Sep 17 00:00:00 2001
From: keybangz <wyatt.wheeler@protonmail.com>
Date: Tue, 17 Mar 2026 02:39:16 +1300
Subject: [PATCH 01/13] Add AMD ROCm platform support

- Add ROCm execution provider detection in separator.py
- Add configure_rocm method for AMD GPU configuration
- Update log_onnxruntime_packages to include ROCm package
- Add rocm extra to pyproject.toml dependencies
- Create Dockerfile.rocm for ROCm-based containers
- Update README with ROCm installation instructions and troubleshooting
- ROCm detection prioritizes ROCMExecutionProvider when available
- Maintains backward compatibility with existing CUDA/NVIDIA support
---
 .opencode/.beacon/embeddings.db        | Bin 0 -> 14716928 bytes
 .opencode/.beacon/hnsw.entries.json    |   1 +
 .opencode/.beacon/hnsw.index           | Bin 0 -> 5331780 bytes
 Dockerfile.rocm                        |  26 +++++++++++++++
 README.md                              |  42 +++++++++++++++++++++++--
 audio_separator/separator/separator.py |  25 +++++++++++++--
 pyproject.toml                         |   1 +
 7 files changed, 90 insertions(+), 5 deletions(-)
 create mode 100644 .opencode/.beacon/embeddings.db
 create mode 100644 .opencode/.beacon/hnsw.entries.json
 create mode 100644 .opencode/.beacon/hnsw.index
 create mode 100644 Dockerfile.rocm

diff --git a/.opencode/.beacon/embeddings.db b/.opencode/.beacon/embeddings.db
new file mode 100644
index 0000000000000000000000000000000000000000..e0560ae0c2a105e6ab2dfcc757250445fbfb5569
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diff --git a/.opencode/.beacon/hnsw.entries.json b/.opencode/.beacon/hnsw.entries.json
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@@ -0,0 +1 @@
+{"entries":[[0,{"filePath":".cursor/commands/analyze.md","startLine":0,"endLine":11,"chunkText":"---\ndescription: Perform a non-destructive cross-artifact consistency and quality analysis across spec.md, plan.md, and tasks.md after task generation.\n---\n\nThe user input to you can be provided directly by the agent or as a command argument - you **MUST** consider it before proceeding with the prompt (if not empty).\n\nUser input:\n\n$ARGUMENTS\n\nGoal: Identify inconsistencies, duplications, ambiguities, and underspecified items across the three core artifacts (`spec.md`, `plan.md`, `tasks.md`) before implementation. This command MUST run only after `/tasks` has successfully produced a complete `tasks.md`.\n","chunkId":".cursor/commands/analyze.md:0"}],[1,{"filePath":".cursor/commands/analyze.md","startLine":9,"endLine":13,"chunkText":"\nGoal: Identify inconsistencies, duplications, ambiguities, and underspecified items across the three core artifacts (`spec.md`, `plan.md`, `tasks.md`) before implementation. This command MUST run only after `/tasks` has successfully produced a complete `tasks.md`.\n\nSTRICTLY READ-ONLY: Do **not** modify any files. Output a structured analysis report. Offer an optional remediation plan (user must explicitly approve before any follow-up editing commands would be invoked manually).\n","chunkId":".cursor/commands/analyze.md:1"}],[2,{"filePath":".cursor/commands/analyze.md","startLine":11,"endLine":12,"chunkText":"STRICTLY READ-ONLY: Do **not** modify any files. Output a structured analysis report. Offer an optional remediation plan (user must explicitly approve before any follow-up editing commands would be invoked manually).\n","chunkId":".cursor/commands/analyze.md:2"}],[3,{"filePath":".cursor/commands/analyze.md","startLine":12,"endLine":13,"chunkText":"STRICTLY READ-ONLY: Do **not** modify any files. Output a structured analysis report. Offer an optional remediation plan (user must explicitly approve before any follow-up editing commands would be invoked manually).\n","chunkId":".cursor/commands/analyze.md:3"}],[4,{"filePath":".cursor/commands/analyze.md","startLine":13,"endLine":17,"chunkText":"\nConstitution Authority: The project constitution (`.specify/memory/constitution.md`) is **non-negotiable** within this analysis scope. Constitution conflicts are automatically CRITICAL and require adjustment of the spec, plan, or tasks—not dilution, reinterpretation, or silent ignoring of the principle. If a principle itself needs to change, that must occur in a separate, explicit constitution update outside `/analyze`.\n\nExecution steps:\n","chunkId":".cursor/commands/analyze.md:4"}],[5,{"filePath":".cursor/commands/analyze.md","startLine":15,"endLine":25,"chunkText":"\nExecution steps:\n\n1. Run `.specify/scripts/bash/check-prerequisites.sh --json --require-tasks --include-tasks` once from repo root and parse JSON for FEATURE_DIR and AVAILABLE_DOCS. Derive absolute paths:\n   - SPEC = FEATURE_DIR/spec.md\n   - PLAN = FEATURE_DIR/plan.md\n   - TASKS = FEATURE_DIR/tasks.md\n   Abort with an error message if any required file is missing (instruct the user to run missing prerequisite command).\n\n2. Load artifacts:\n   - Parse spec.md sections: Overview/Context, Functional Requirements, Non-Functional Requirements, User Stories, Edge Cases (if present).","chunkId":".cursor/commands/analyze.md:5"}],[6,{"filePath":".cursor/commands/analyze.md","startLine":23,"endLine":30,"chunkText":"\n2. Load artifacts:\n   - Parse spec.md sections: Overview/Context, Functional Requirements, Non-Functional Requirements, User Stories, Edge Cases (if present).\n   - Parse plan.md: Architecture/stack choices, Data Model references, Phases, Technical constraints.\n   - Parse tasks.md: Task IDs, descriptions, phase grouping, parallel markers [P], referenced file paths.\n   - Load constitution `.specify/memory/constitution.md` for principle validation.\n\n3. Build internal semantic models:","chunkId":".cursor/commands/analyze.md:6"}],[7,{"filePath":".cursor/commands/analyze.md","startLine":28,"endLine":35,"chunkText":"   - Load constitution `.specify/memory/constitution.md` for principle validation.\n\n3. Build internal semantic models:\n   - Requirements inventory: Each functional + non-functional requirement with a stable key (derive slug based on imperative phrase; e.g., \"User can upload file\" -> `user-can-upload-file`).\n   - User story/action inventory.\n   - Task coverage mapping: Map each task to one or more requirements or stories (inference by keyword / explicit reference patterns like IDs or key phrases).\n   - Constitution rule set: Extract principle names and any MUST/SHOULD normative statements.\n","chunkId":".cursor/commands/analyze.md:7"}],[8,{"filePath":".cursor/commands/analyze.md","startLine":33,"endLine":41,"chunkText":"   - Task coverage mapping: Map each task to one or more requirements or stories (inference by keyword / explicit reference patterns like IDs or key phrases).\n   - Constitution rule set: Extract principle names and any MUST/SHOULD normative statements.\n\n4. Detection passes:\n   A. Duplication detection:\n      - Identify near-duplicate requirements. Mark lower-quality phrasing for consolidation.\n   B. Ambiguity detection:\n      - Flag vague adjectives (fast, scalable, secure, intuitive, robust) lacking measurable criteria.\n      - Flag unresolved placeholders (TODO, TKTK, ???, <placeholder>, etc.).","chunkId":".cursor/commands/analyze.md:8"}],[9,{"filePath":".cursor/commands/analyze.md","startLine":39,"endLine":47,"chunkText":"   B. Ambiguity detection:\n      - Flag vague adjectives (fast, scalable, secure, intuitive, robust) lacking measurable criteria.\n      - Flag unresolved placeholders (TODO, TKTK, ???, <placeholder>, etc.).\n   C. Underspecification:\n      - Requirements with verbs but missing object or measurable outcome.\n      - User stories missing acceptance criteria alignment.\n      - Tasks referencing files or components not defined in spec/plan.\n   D. Constitution alignment:\n      - Any requirement or plan element conflicting with a MUST principle.","chunkId":".cursor/commands/analyze.md:9"}],[10,{"filePath":".cursor/commands/analyze.md","startLine":45,"endLine":54,"chunkText":"      - Tasks referencing files or components not defined in spec/plan.\n   D. Constitution alignment:\n      - Any requirement or plan element conflicting with a MUST principle.\n      - Missing mandated sections or quality gates from constitution.\n   E. Coverage gaps:\n      - Requirements with zero associated tasks.\n      - Tasks with no mapped requirement/story.\n      - Non-functional requirements not reflected in tasks (e.g., performance, security).\n   F. Inconsistency:\n      - Terminology drift (same concept named differently across files).","chunkId":".cursor/commands/analyze.md:10"}],[11,{"filePath":".cursor/commands/analyze.md","startLine":52,"endLine":59,"chunkText":"      - Non-functional requirements not reflected in tasks (e.g., performance, security).\n   F. Inconsistency:\n      - Terminology drift (same concept named differently across files).\n      - Data entities referenced in plan but absent in spec (or vice versa).\n      - Task ordering contradictions (e.g., integration tasks before foundational setup tasks without dependency note).\n      - Conflicting requirements (e.g., one requires to use Next.js while other says to use Vue as the framework).\n\n5. Severity assignment heuristic:","chunkId":".cursor/commands/analyze.md:11"}],[12,{"filePath":".cursor/commands/analyze.md","startLine":57,"endLine":64,"chunkText":"      - Conflicting requirements (e.g., one requires to use Next.js while other says to use Vue as the framework).\n\n5. Severity assignment heuristic:\n   - CRITICAL: Violates constitution MUST, missing core spec artifact, or requirement with zero coverage that blocks baseline functionality.\n   - HIGH: Duplicate or conflicting requirement, ambiguous security/performance attribute, untestable acceptance criterion.\n   - MEDIUM: Terminology drift, missing non-functional task coverage, underspecified edge case.\n   - LOW: Style/wording improvements, minor redundancy not affecting execution order.\n","chunkId":".cursor/commands/analyze.md:12"}],[13,{"filePath":".cursor/commands/analyze.md","startLine":62,"endLine":70,"chunkText":"   - MEDIUM: Terminology drift, missing non-functional task coverage, underspecified edge case.\n   - LOW: Style/wording improvements, minor redundancy not affecting execution order.\n\n6. Produce a Markdown report (no file writes) with sections:\n\n   ### Specification Analysis Report\n   | ID | Category | Severity | Location(s) | Summary | Recommendation |\n   |----|----------|----------|-------------|---------|----------------|\n   | A1 | Duplication | HIGH | spec.md:L120-134 | Two similar requirements ... | Merge phrasing; keep clearer version |","chunkId":".cursor/commands/analyze.md:13"}],[14,{"filePath":".cursor/commands/analyze.md","startLine":68,"endLine":80,"chunkText":"   | ID | Category | Severity | Location(s) | Summary | Recommendation |\n   |----|----------|----------|-------------|---------|----------------|\n   | A1 | Duplication | HIGH | spec.md:L120-134 | Two similar requirements ... | Merge phrasing; keep clearer version |\n   (Add one row per finding; generate stable IDs prefixed by category initial.)\n\n   Additional subsections:\n   - Coverage Summary Table:\n     | Requirement Key | Has Task? | Task IDs | Notes |\n   - Constitution Alignment Issues (if any)\n   - Unmapped Tasks (if any)\n   - Metrics:\n     * Total Requirements\n     * Total Tasks","chunkId":".cursor/commands/analyze.md:14"}],[15,{"filePath":".cursor/commands/analyze.md","startLine":78,"endLine":90,"chunkText":"   - Metrics:\n     * Total Requirements\n     * Total Tasks\n     * Coverage % (requirements with >=1 task)\n     * Ambiguity Count\n     * Duplication Count\n     * Critical Issues Count\n\n7. At end of report, output a concise Next Actions block:\n   - If CRITICAL issues exist: Recommend resolving before `/implement`.\n   - If only LOW/MEDIUM: User may proceed, but provide improvement suggestions.\n   - Provide explicit command suggestions: e.g., \"Run /specify with refinement\", \"Run /plan to adjust architecture\", \"Manually edit tasks.md to add coverage for 'performance-metrics'\".\n","chunkId":".cursor/commands/analyze.md:15"}],[16,{"filePath":".cursor/commands/analyze.md","startLine":88,"endLine":96,"chunkText":"   - If only LOW/MEDIUM: User may proceed, but provide improvement suggestions.\n   - Provide explicit command suggestions: e.g., \"Run /specify with refinement\", \"Run /plan to adjust architecture\", \"Manually edit tasks.md to add coverage for 'performance-metrics'\".\n\n8. Ask the user: \"Would you like me to suggest concrete remediation edits for the top N issues?\" (Do NOT apply them automatically.)\n\nBehavior rules:\n- NEVER modify files.\n- NEVER hallucinate missing sections—if absent, report them.\n- KEEP findings deterministic: if rerun without changes, produce consistent IDs and counts.","chunkId":".cursor/commands/analyze.md:16"}],[17,{"filePath":".cursor/commands/analyze.md","startLine":94,"endLine":101,"chunkText":"- NEVER modify files.\n- NEVER hallucinate missing sections—if absent, report them.\n- KEEP findings deterministic: if rerun without changes, produce consistent IDs and counts.\n- LIMIT total findings in the main table to 50; aggregate remainder in a summarized overflow note.\n- If zero issues found, emit a success report with coverage statistics and proceed recommendation.\n\nContext: $ARGUMENTS\n","chunkId":".cursor/commands/analyze.md:17"}],[18,{"filePath":".cursor/commands/clarify.md","startLine":0,"endLine":11,"chunkText":"---\ndescription: Identify underspecified areas in the current feature spec by asking up to 5 highly targeted clarification questions and encoding answers back into the spec.\n---\n\nThe user input to you can be provided directly by the agent or as a command argument - you **MUST** consider it before proceeding with the prompt (if not empty).\n\nUser input:\n\n$ARGUMENTS\n\nGoal: Detect and reduce ambiguity or missing decision points in the active feature specification and record the clarifications directly in the spec file.\n","chunkId":".cursor/commands/clarify.md:0"}],[19,{"filePath":".cursor/commands/clarify.md","startLine":9,"endLine":15,"chunkText":"\nGoal: Detect and reduce ambiguity or missing decision points in the active feature specification and record the clarifications directly in the spec file.\n\nNote: This clarification workflow is expected to run (and be completed) BEFORE invoking `/plan`. If the user explicitly states they are skipping clarification (e.g., exploratory spike), you may proceed, but must warn that downstream rework risk increases.\n\nExecution steps:\n","chunkId":".cursor/commands/clarify.md:1"}],[20,{"filePath":".cursor/commands/clarify.md","startLine":13,"endLine":21,"chunkText":"\nExecution steps:\n\n1. Run `.specify/scripts/bash/check-prerequisites.sh --json --paths-only` from repo root **once** (combined `--json --paths-only` mode / `-Json -PathsOnly`). Parse minimal JSON payload fields:\n   - `FEATURE_DIR`\n   - `FEATURE_SPEC`\n   - (Optionally capture `IMPL_PLAN`, `TASKS` for future chained flows.)\n   - If JSON parsing fails, abort and instruct user to re-run `/specify` or verify feature branch environment.\n","chunkId":".cursor/commands/clarify.md:2"}],[21,{"filePath":".cursor/commands/clarify.md","startLine":19,"endLine":26,"chunkText":"   - (Optionally capture `IMPL_PLAN`, `TASKS` for future chained flows.)\n   - If JSON parsing fails, abort and instruct user to re-run `/specify` or verify feature branch environment.\n\n2. Load the current spec file. Perform a structured ambiguity & coverage scan using this taxonomy. For each category, mark status: Clear / Partial / Missing. Produce an internal coverage map used for prioritization (do not output raw map unless no questions will be asked).\n\n   Functional Scope & Behavior:\n   - Core user goals & success criteria\n   - Explicit out-of-scope declarations","chunkId":".cursor/commands/clarify.md:3"}],[22,{"filePath":".cursor/commands/clarify.md","startLine":26,"endLine":43,"chunkText":"   - Explicit out-of-scope declarations\n   - User roles / personas differentiation\n\n   Domain & Data Model:\n   - Entities, attributes, relationships\n   - Identity & uniqueness rules\n   - Lifecycle/state transitions\n   - Data volume / scale assumptions\n\n   Interaction & UX Flow:\n   - Critical user journeys / sequences\n   - Error/empty/loading states\n   - Accessibility or localization notes\n\n   Non-Functional Quality Attributes:\n   - Performance (latency, throughput targets)\n   - Scalability (horizontal/vertical, limits)\n   - Reliability & availability (uptime, recovery expectations)","chunkId":".cursor/commands/clarify.md:4"}],[23,{"filePath":".cursor/commands/clarify.md","startLine":41,"endLine":55,"chunkText":"   - Performance (latency, throughput targets)\n   - Scalability (horizontal/vertical, limits)\n   - Reliability & availability (uptime, recovery expectations)\n   - Observability (logging, metrics, tracing signals)\n   - Security & privacy (authN/Z, data protection, threat assumptions)\n   - Compliance / regulatory constraints (if any)\n\n   Integration & External Dependencies:\n   - External services/APIs and failure modes\n   - Data import/export formats\n   - Protocol/versioning assumptions\n\n   Edge Cases & Failure Handling:\n   - Negative scenarios\n   - Rate limiting / throttling","chunkId":".cursor/commands/clarify.md:5"}],[24,{"filePath":".cursor/commands/clarify.md","startLine":53,"endLine":71,"chunkText":"   Edge Cases & Failure Handling:\n   - Negative scenarios\n   - Rate limiting / throttling\n   - Conflict resolution (e.g., concurrent edits)\n\n   Constraints & Tradeoffs:\n   - Technical constraints (language, storage, hosting)\n   - Explicit tradeoffs or rejected alternatives\n\n   Terminology & Consistency:\n   - Canonical glossary terms\n   - Avoided synonyms / deprecated terms\n\n   Completion Signals:\n   - Acceptance criteria testability\n   - Measurable Definition of Done style indicators\n\n   Misc / Placeholders:\n   - TODO markers / unresolved decisions","chunkId":".cursor/commands/clarify.md:6"}],[25,{"filePath":".cursor/commands/clarify.md","startLine":69,"endLine":79,"chunkText":"\n   Misc / Placeholders:\n   - TODO markers / unresolved decisions\n   - Ambiguous adjectives (\"robust\", \"intuitive\") lacking quantification\n\n   For each category with Partial or Missing status, add a candidate question opportunity unless:\n   - Clarification would not materially change implementation or validation strategy\n   - Information is better deferred to planning phase (note internally)\n\n3. Generate (internally) a prioritized queue of candidate clarification questions (maximum 5). Do NOT output them all at once. Apply these constraints:\n    - Maximum of 5 total questions across the whole session.","chunkId":".cursor/commands/clarify.md:7"}],[26,{"filePath":".cursor/commands/clarify.md","startLine":77,"endLine":82,"chunkText":"\n3. Generate (internally) a prioritized queue of candidate clarification questions (maximum 5). Do NOT output them all at once. Apply these constraints:\n    - Maximum of 5 total questions across the whole session.\n    - Each question must be answerable with EITHER:\n       * A short multiple‑choice selection (2–5 distinct, mutually exclusive options), OR\n       * A one-word / short‑phrase answer (explicitly constrain: \"Answer in <=5 words\").","chunkId":".cursor/commands/clarify.md:8"}],[27,{"filePath":".cursor/commands/clarify.md","startLine":80,"endLine":83,"chunkText":"    - Each question must be answerable with EITHER:\n       * A short multiple‑choice selection (2–5 distinct, mutually exclusive options), OR\n       * A one-word / short‑phrase answer (explicitly constrain: \"Answer in <=5 words\").\n   - Only include questions whose answers materially impact architecture, data modeling, task decomposition, test design, UX behavior, operational readiness, or compliance validation.","chunkId":".cursor/commands/clarify.md:9"}],[28,{"filePath":".cursor/commands/clarify.md","startLine":81,"endLine":84,"chunkText":"       * A short multiple‑choice selection (2–5 distinct, mutually exclusive options), OR\n       * A one-word / short‑phrase answer (explicitly constrain: \"Answer in <=5 words\").\n   - Only include questions whose answers materially impact architecture, data modeling, task decomposition, test design, UX behavior, operational readiness, or compliance validation.\n   - Ensure category coverage balance: attempt to cover the highest impact unresolved categories first; avoid asking two low-impact questions when a single high-impact area (e.g., security posture) is unresolved.","chunkId":".cursor/commands/clarify.md:10"}],[29,{"filePath":".cursor/commands/clarify.md","startLine":82,"endLine":84,"chunkText":"       * A one-word / short‑phrase answer (explicitly constrain: \"Answer in <=5 words\").\n   - Only include questions whose answers materially impact architecture, data modeling, task decomposition, test design, UX behavior, operational readiness, or compliance validation.\n   - Ensure category coverage balance: attempt to cover the highest impact unresolved categories first; avoid asking two low-impact questions when a single high-impact area (e.g., security posture) is unresolved.","chunkId":".cursor/commands/clarify.md:11"}],[30,{"filePath":".cursor/commands/clarify.md","startLine":83,"endLine":85,"chunkText":"   - Only include questions whose answers materially impact architecture, data modeling, task decomposition, test design, UX behavior, operational readiness, or compliance validation.\n   - Ensure category coverage balance: attempt to cover the highest impact unresolved categories first; avoid asking two low-impact questions when a single high-impact area (e.g., security posture) is unresolved.\n   - Exclude questions already answered, trivial stylistic preferences, or plan-level execution details (unless blocking correctness).","chunkId":".cursor/commands/clarify.md:12"}],[31,{"filePath":".cursor/commands/clarify.md","startLine":84,"endLine":89,"chunkText":"   - Ensure category coverage balance: attempt to cover the highest impact unresolved categories first; avoid asking two low-impact questions when a single high-impact area (e.g., security posture) is unresolved.\n   - Exclude questions already answered, trivial stylistic preferences, or plan-level execution details (unless blocking correctness).\n   - Favor clarifications that reduce downstream rework risk or prevent misaligned acceptance tests.\n   - If more than 5 categories remain unresolved, select the top 5 by (Impact * Uncertainty) heuristic.\n\n4. Sequential questioning loop (interactive):","chunkId":".cursor/commands/clarify.md:13"}],[32,{"filePath":".cursor/commands/clarify.md","startLine":87,"endLine":99,"chunkText":"   - If more than 5 categories remain unresolved, select the top 5 by (Impact * Uncertainty) heuristic.\n\n4. Sequential questioning loop (interactive):\n    - Present EXACTLY ONE question at a time.\n    - For multiple‑choice questions render options as a Markdown table:\n\n       | Option | Description |\n       |--------|-------------|\n       | A | <Option A description> |\n       | B | <Option B description> |\n       | C | <Option C description> | (add D/E as needed up to 5)\n       | Short | Provide a different short answer (<=5 words) | (Include only if free-form alternative is appropriate)\n","chunkId":".cursor/commands/clarify.md:14"}],[33,{"filePath":".cursor/commands/clarify.md","startLine":97,"endLine":103,"chunkText":"       | C | <Option C description> | (add D/E as needed up to 5)\n       | Short | Provide a different short answer (<=5 words) | (Include only if free-form alternative is appropriate)\n\n    - For short‑answer style (no meaningful discrete options), output a single line after the question: `Format: Short answer (<=5 words)`.\n    - After the user answers:\n       * Validate the answer maps to one option or fits the <=5 word constraint.\n       * If ambiguous, ask for a quick disambiguation (count still belongs to same question; do not advance).","chunkId":".cursor/commands/clarify.md:15"}],[34,{"filePath":".cursor/commands/clarify.md","startLine":101,"endLine":108,"chunkText":"    - After the user answers:\n       * Validate the answer maps to one option or fits the <=5 word constraint.\n       * If ambiguous, ask for a quick disambiguation (count still belongs to same question; do not advance).\n       * Once satisfactory, record it in working memory (do not yet write to disk) and move to the next queued question.\n    - Stop asking further questions when:\n       * All critical ambiguities resolved early (remaining queued items become unnecessary), OR\n       * User signals completion (\"done\", \"good\", \"no more\"), OR\n       * You reach 5 asked questions.","chunkId":".cursor/commands/clarify.md:16"}],[35,{"filePath":".cursor/commands/clarify.md","startLine":106,"endLine":114,"chunkText":"       * All critical ambiguities resolved early (remaining queued items become unnecessary), OR\n       * User signals completion (\"done\", \"good\", \"no more\"), OR\n       * You reach 5 asked questions.\n    - Never reveal future queued questions in advance.\n    - If no valid questions exist at start, immediately report no critical ambiguities.\n\n5. Integration after EACH accepted answer (incremental update approach):\n    - Maintain in-memory representation of the spec (loaded once at start) plus the raw file contents.\n    - For the first integrated answer in this session:","chunkId":".cursor/commands/clarify.md:17"}],[36,{"filePath":".cursor/commands/clarify.md","startLine":112,"endLine":117,"chunkText":"5. Integration after EACH accepted answer (incremental update approach):\n    - Maintain in-memory representation of the spec (loaded once at start) plus the raw file contents.\n    - For the first integrated answer in this session:\n       * Ensure a `## Clarifications` section exists (create it just after the highest-level contextual/overview section per the spec template if missing).\n       * Under it, create (if not present) a `### Session YYYY-MM-DD` subheading for today.\n    - Append a bullet line immediately after acceptance: `- Q: <question> → A: <final answer>`.","chunkId":".cursor/commands/clarify.md:18"}],[37,{"filePath":".cursor/commands/clarify.md","startLine":115,"endLine":119,"chunkText":"       * Ensure a `## Clarifications` section exists (create it just after the highest-level contextual/overview section per the spec template if missing).\n       * Under it, create (if not present) a `### Session YYYY-MM-DD` subheading for today.\n    - Append a bullet line immediately after acceptance: `- Q: <question> → A: <final answer>`.\n    - Then immediately apply the clarification to the most appropriate section(s):\n       * Functional ambiguity → Update or add a bullet in Functional Requirements.","chunkId":".cursor/commands/clarify.md:19"}],[38,{"filePath":".cursor/commands/clarify.md","startLine":117,"endLine":121,"chunkText":"    - Append a bullet line immediately after acceptance: `- Q: <question> → A: <final answer>`.\n    - Then immediately apply the clarification to the most appropriate section(s):\n       * Functional ambiguity → Update or add a bullet in Functional Requirements.\n       * User interaction / actor distinction → Update User Stories or Actors subsection (if present) with clarified role, constraint, or scenario.\n       * Data shape / entities → Update Data Model (add fields, types, relationships) preserving ordering; note added constraints succinctly.","chunkId":".cursor/commands/clarify.md:20"}],[39,{"filePath":".cursor/commands/clarify.md","startLine":119,"endLine":122,"chunkText":"       * Functional ambiguity → Update or add a bullet in Functional Requirements.\n       * User interaction / actor distinction → Update User Stories or Actors subsection (if present) with clarified role, constraint, or scenario.\n       * Data shape / entities → Update Data Model (add fields, types, relationships) preserving ordering; note added constraints succinctly.\n       * Non-functional constraint → Add/modify measurable criteria in Non-Functional / Quality Attributes section (convert vague adjective to metric or explicit target).","chunkId":".cursor/commands/clarify.md:21"}],[40,{"filePath":".cursor/commands/clarify.md","startLine":120,"endLine":122,"chunkText":"       * User interaction / actor distinction → Update User Stories or Actors subsection (if present) with clarified role, constraint, or scenario.\n       * Data shape / entities → Update Data Model (add fields, types, relationships) preserving ordering; note added constraints succinctly.\n       * Non-functional constraint → Add/modify measurable criteria in Non-Functional / Quality Attributes section (convert vague adjective to metric or explicit target).","chunkId":".cursor/commands/clarify.md:22"}],[41,{"filePath":".cursor/commands/clarify.md","startLine":121,"endLine":124,"chunkText":"       * Data shape / entities → Update Data Model (add fields, types, relationships) preserving ordering; note added constraints succinctly.\n       * Non-functional constraint → Add/modify measurable criteria in Non-Functional / Quality Attributes section (convert vague adjective to metric or explicit target).\n       * Edge case / negative flow → Add a new bullet under Edge Cases / Error Handling (or create such subsection if template provides placeholder for it).\n       * Terminology conflict → Normalize term across spec; retain original only if necessary by adding `(formerly referred to as \"X\")` once.","chunkId":".cursor/commands/clarify.md:23"}],[42,{"filePath":".cursor/commands/clarify.md","startLine":122,"endLine":124,"chunkText":"       * Non-functional constraint → Add/modify measurable criteria in Non-Functional / Quality Attributes section (convert vague adjective to metric or explicit target).\n       * Edge case / negative flow → Add a new bullet under Edge Cases / Error Handling (or create such subsection if template provides placeholder for it).\n       * Terminology conflict → Normalize term across spec; retain original only if necessary by adding `(formerly referred to as \"X\")` once.","chunkId":".cursor/commands/clarify.md:24"}],[43,{"filePath":".cursor/commands/clarify.md","startLine":123,"endLine":126,"chunkText":"       * Edge case / negative flow → Add a new bullet under Edge Cases / Error Handling (or create such subsection if template provides placeholder for it).\n       * Terminology conflict → Normalize term across spec; retain original only if necessary by adding `(formerly referred to as \"X\")` once.\n    - If the clarification invalidates an earlier ambiguous statement, replace that statement instead of duplicating; leave no obsolete contradictory text.\n    - Save the spec file AFTER each integration to minimize risk of context loss (atomic overwrite).","chunkId":".cursor/commands/clarify.md:25"}],[44,{"filePath":".cursor/commands/clarify.md","startLine":124,"endLine":129,"chunkText":"       * Terminology conflict → Normalize term across spec; retain original only if necessary by adding `(formerly referred to as \"X\")` once.\n    - If the clarification invalidates an earlier ambiguous statement, replace that statement instead of duplicating; leave no obsolete contradictory text.\n    - Save the spec file AFTER each integration to minimize risk of context loss (atomic overwrite).\n    - Preserve formatting: do not reorder unrelated sections; keep heading hierarchy intact.\n    - Keep each inserted clarification minimal and testable (avoid narrative drift).\n","chunkId":".cursor/commands/clarify.md:26"}],[45,{"filePath":".cursor/commands/clarify.md","startLine":127,"endLine":134,"chunkText":"    - Preserve formatting: do not reorder unrelated sections; keep heading hierarchy intact.\n    - Keep each inserted clarification minimal and testable (avoid narrative drift).\n\n6. Validation (performed after EACH write plus final pass):\n   - Clarifications session contains exactly one bullet per accepted answer (no duplicates).\n   - Total asked (accepted) questions ≤ 5.\n   - Updated sections contain no lingering vague placeholders the new answer was meant to resolve.\n   - No contradictory earlier statement remains (scan for now-invalid alternative choices removed).","chunkId":".cursor/commands/clarify.md:27"}],[46,{"filePath":".cursor/commands/clarify.md","startLine":132,"endLine":141,"chunkText":"   - Total asked (accepted) questions ≤ 5.\n   - Updated sections contain no lingering vague placeholders the new answer was meant to resolve.\n   - No contradictory earlier statement remains (scan for now-invalid alternative choices removed).\n   - Markdown structure valid; only allowed new headings: `## Clarifications`, `### Session YYYY-MM-DD`.\n   - Terminology consistency: same canonical term used across all updated sections.\n\n7. Write the updated spec back to `FEATURE_SPEC`.\n\n8. Report completion (after questioning loop ends or early termination):\n   - Number of questions asked & answered.","chunkId":".cursor/commands/clarify.md:28"}],[47,{"filePath":".cursor/commands/clarify.md","startLine":139,"endLine":147,"chunkText":"\n8. Report completion (after questioning loop ends or early termination):\n   - Number of questions asked & answered.\n   - Path to updated spec.\n   - Sections touched (list names).\n   - Coverage summary table listing each taxonomy category with Status: Resolved (was Partial/Missing and addressed), Deferred (exceeds question quota or better suited for planning), Clear (already sufficient), Outstanding (still Partial/Missing but low impact).\n   - If any Outstanding or Deferred remain, recommend whether to proceed to `/plan` or run `/clarify` again later post-plan.\n   - Suggested next command.\n","chunkId":".cursor/commands/clarify.md:29"}],[48,{"filePath":".cursor/commands/clarify.md","startLine":145,"endLine":151,"chunkText":"   - If any Outstanding or Deferred remain, recommend whether to proceed to `/plan` or run `/clarify` again later post-plan.\n   - Suggested next command.\n\nBehavior rules:\n- If no meaningful ambiguities found (or all potential questions would be low-impact), respond: \"No critical ambiguities detected worth formal clarification.\" and suggest proceeding.\n- If spec file missing, instruct user to run `/specify` first (do not create a new spec here).\n- Never exceed 5 total asked questions (clarification retries for a single question do not count as new questions).","chunkId":".cursor/commands/clarify.md:30"}],[49,{"filePath":".cursor/commands/clarify.md","startLine":149,"endLine":153,"chunkText":"- If no meaningful ambiguities found (or all potential questions would be low-impact), respond: \"No critical ambiguities detected worth formal clarification.\" and suggest proceeding.\n- If spec file missing, instruct user to run `/specify` first (do not create a new spec here).\n- Never exceed 5 total asked questions (clarification retries for a single question do not count as new questions).\n- Avoid speculative tech stack questions unless the absence blocks functional clarity.\n- Respect user early termination signals (\"stop\", \"done\", \"proceed\").","chunkId":".cursor/commands/clarify.md:31"}],[50,{"filePath":".cursor/commands/clarify.md","startLine":151,"endLine":158,"chunkText":"- Never exceed 5 total asked questions (clarification retries for a single question do not count as new questions).\n- Avoid speculative tech stack questions unless the absence blocks functional clarity.\n- Respect user early termination signals (\"stop\", \"done\", \"proceed\").\n - If no questions asked due to full coverage, output a compact coverage summary (all categories Clear) then suggest advancing.\n - If quota reached with unresolved high-impact categories remaining, explicitly flag them under Deferred with rationale.\n\nContext for prioritization: $ARGUMENTS\n","chunkId":".cursor/commands/clarify.md:32"}],[51,{"filePath":".cursor/commands/constitution.md","startLine":0,"endLine":9,"chunkText":"---\ndescription: Create or update the project constitution from interactive or provided principle inputs, ensuring all dependent templates stay in sync.\n---\n\nThe user input to you can be provided directly by the agent or as a command argument - you **MUST** consider it before proceeding with the prompt (if not empty).\n\nUser input:\n\n$ARGUMENTS\n","chunkId":".cursor/commands/constitution.md:0"}],[52,{"filePath":".cursor/commands/constitution.md","startLine":7,"endLine":15,"chunkText":"\n$ARGUMENTS\n\nYou are updating the project constitution at `.specify/memory/constitution.md`. This file is a TEMPLATE containing placeholder tokens in square brackets (e.g. `[PROJECT_NAME]`, `[PRINCIPLE_1_NAME]`). Your job is to (a) collect/derive concrete values, (b) fill the template precisely, and (c) propagate any amendments across dependent artifacts.\n\nFollow this execution flow:\n\n1. Load the existing constitution template at `.specify/memory/constitution.md`.\n   - Identify every placeholder token of the form `[ALL_CAPS_IDENTIFIER]`.","chunkId":".cursor/commands/constitution.md:1"}],[53,{"filePath":".cursor/commands/constitution.md","startLine":13,"endLine":20,"chunkText":"\n1. Load the existing constitution template at `.specify/memory/constitution.md`.\n   - Identify every placeholder token of the form `[ALL_CAPS_IDENTIFIER]`.\n   **IMPORTANT**: The user might require less or more principles than the ones used in the template. If a number is specified, respect that - follow the general template. You will update the doc accordingly.\n\n2. Collect/derive values for placeholders:\n   - If user input (conversation) supplies a value, use it.\n   - Otherwise infer from existing repo context (README, docs, prior constitution versions if embedded).","chunkId":".cursor/commands/constitution.md:2"}],[54,{"filePath":".cursor/commands/constitution.md","startLine":18,"endLine":23,"chunkText":"2. Collect/derive values for placeholders:\n   - If user input (conversation) supplies a value, use it.\n   - Otherwise infer from existing repo context (README, docs, prior constitution versions if embedded).\n   - For governance dates: `RATIFICATION_DATE` is the original adoption date (if unknown ask or mark TODO), `LAST_AMENDED_DATE` is today if changes are made, otherwise keep previous.\n   - `CONSTITUTION_VERSION` must increment according to semantic versioning rules:\n     * MAJOR: Backward incompatible governance/principle removals or redefinitions.","chunkId":".cursor/commands/constitution.md:3"}],[55,{"filePath":".cursor/commands/constitution.md","startLine":21,"endLine":27,"chunkText":"   - For governance dates: `RATIFICATION_DATE` is the original adoption date (if unknown ask or mark TODO), `LAST_AMENDED_DATE` is today if changes are made, otherwise keep previous.\n   - `CONSTITUTION_VERSION` must increment according to semantic versioning rules:\n     * MAJOR: Backward incompatible governance/principle removals or redefinitions.\n     * MINOR: New principle/section added or materially expanded guidance.\n     * PATCH: Clarifications, wording, typo fixes, non-semantic refinements.\n   - If version bump type ambiguous, propose reasoning before finalizing.\n","chunkId":".cursor/commands/constitution.md:4"}],[56,{"filePath":".cursor/commands/constitution.md","startLine":25,"endLine":30,"chunkText":"     * PATCH: Clarifications, wording, typo fixes, non-semantic refinements.\n   - If version bump type ambiguous, propose reasoning before finalizing.\n\n3. Draft the updated constitution content:\n   - Replace every placeholder with concrete text (no bracketed tokens left except intentionally retained template slots that the project has chosen not to define yet—explicitly justify any left).\n   - Preserve heading hierarchy and comments can be removed once replaced unless they still add clarifying guidance.","chunkId":".cursor/commands/constitution.md:5"}],[57,{"filePath":".cursor/commands/constitution.md","startLine":28,"endLine":31,"chunkText":"3. Draft the updated constitution content:\n   - Replace every placeholder with concrete text (no bracketed tokens left except intentionally retained template slots that the project has chosen not to define yet—explicitly justify any left).\n   - Preserve heading hierarchy and comments can be removed once replaced unless they still add clarifying guidance.\n   - Ensure each Principle section: succinct name line, paragraph (or bullet list) capturing non‑negotiable rules, explicit rationale if not obvious.","chunkId":".cursor/commands/constitution.md:6"}],[58,{"filePath":".cursor/commands/constitution.md","startLine":29,"endLine":33,"chunkText":"   - Replace every placeholder with concrete text (no bracketed tokens left except intentionally retained template slots that the project has chosen not to define yet—explicitly justify any left).\n   - Preserve heading hierarchy and comments can be removed once replaced unless they still add clarifying guidance.\n   - Ensure each Principle section: succinct name line, paragraph (or bullet list) capturing non‑negotiable rules, explicit rationale if not obvious.\n   - Ensure Governance section lists amendment procedure, versioning policy, and compliance review expectations.\n","chunkId":".cursor/commands/constitution.md:7"}],[59,{"filePath":".cursor/commands/constitution.md","startLine":31,"endLine":35,"chunkText":"   - Ensure each Principle section: succinct name line, paragraph (or bullet list) capturing non‑negotiable rules, explicit rationale if not obvious.\n   - Ensure Governance section lists amendment procedure, versioning policy, and compliance review expectations.\n\n4. Consistency propagation checklist (convert prior checklist into active validations):\n   - Read `.specify/templates/plan-template.md` and ensure any \"Constitution Check\" or rules align with updated principles.","chunkId":".cursor/commands/constitution.md:8"}],[60,{"filePath":".cursor/commands/constitution.md","startLine":33,"endLine":37,"chunkText":"\n4. Consistency propagation checklist (convert prior checklist into active validations):\n   - Read `.specify/templates/plan-template.md` and ensure any \"Constitution Check\" or rules align with updated principles.\n   - Read `.specify/templates/spec-template.md` for scope/requirements alignment—update if constitution adds/removes mandatory sections or constraints.\n   - Read `.specify/templates/tasks-template.md` and ensure task categorization reflects new or removed principle-driven task types (e.g., observability, versioning, testing discipline).","chunkId":".cursor/commands/constitution.md:9"}],[61,{"filePath":".cursor/commands/constitution.md","startLine":35,"endLine":37,"chunkText":"   - Read `.specify/templates/plan-template.md` and ensure any \"Constitution Check\" or rules align with updated principles.\n   - Read `.specify/templates/spec-template.md` for scope/requirements alignment—update if constitution adds/removes mandatory sections or constraints.\n   - Read `.specify/templates/tasks-template.md` and ensure task categorization reflects new or removed principle-driven task types (e.g., observability, versioning, testing discipline).","chunkId":".cursor/commands/constitution.md:10"}],[62,{"filePath":".cursor/commands/constitution.md","startLine":36,"endLine":38,"chunkText":"   - Read `.specify/templates/spec-template.md` for scope/requirements alignment—update if constitution adds/removes mandatory sections or constraints.\n   - Read `.specify/templates/tasks-template.md` and ensure task categorization reflects new or removed principle-driven task types (e.g., observability, versioning, testing discipline).\n   - Read each command file in `.specify/templates/commands/*.md` (including this one) to verify no outdated references (agent-specific names like CLAUDE only) remain when generic guidance is required.","chunkId":".cursor/commands/constitution.md:11"}],[63,{"filePath":".cursor/commands/constitution.md","startLine":37,"endLine":40,"chunkText":"   - Read `.specify/templates/tasks-template.md` and ensure task categorization reflects new or removed principle-driven task types (e.g., observability, versioning, testing discipline).\n   - Read each command file in `.specify/templates/commands/*.md` (including this one) to verify no outdated references (agent-specific names like CLAUDE only) remain when generic guidance is required.\n   - Read any runtime guidance docs (e.g., `README.md`, `docs/quickstart.md`, or agent-specific guidance files if present). Update references to principles changed.\n","chunkId":".cursor/commands/constitution.md:12"}],[64,{"filePath":".cursor/commands/constitution.md","startLine":38,"endLine":44,"chunkText":"   - Read each command file in `.specify/templates/commands/*.md` (including this one) to verify no outdated references (agent-specific names like CLAUDE only) remain when generic guidance is required.\n   - Read any runtime guidance docs (e.g., `README.md`, `docs/quickstart.md`, or agent-specific guidance files if present). Update references to principles changed.\n\n5. Produce a Sync Impact Report (prepend as an HTML comment at top of the constitution file after update):\n   - Version change: old → new\n   - List of modified principles (old title → new title if renamed)\n   - Added sections","chunkId":".cursor/commands/constitution.md:13"}],[65,{"filePath":".cursor/commands/constitution.md","startLine":42,"endLine":54,"chunkText":"   - Version change: old → new\n   - List of modified principles (old title → new title if renamed)\n   - Added sections\n   - Removed sections\n   - Templates requiring updates (✅ updated / ⚠ pending) with file paths\n   - Follow-up TODOs if any placeholders intentionally deferred.\n\n6. Validation before final output:\n   - No remaining unexplained bracket tokens.\n   - Version line matches report.\n   - Dates ISO format YYYY-MM-DD.\n   - Principles are declarative, testable, and free of vague language (\"should\" → replace with MUST/SHOULD rationale where appropriate).\n","chunkId":".cursor/commands/constitution.md:14"}],[66,{"filePath":".cursor/commands/constitution.md","startLine":52,"endLine":62,"chunkText":"   - Dates ISO format YYYY-MM-DD.\n   - Principles are declarative, testable, and free of vague language (\"should\" → replace with MUST/SHOULD rationale where appropriate).\n\n7. Write the completed constitution back to `.specify/memory/constitution.md` (overwrite).\n\n8. Output a final summary to the user with:\n   - New version and bump rationale.\n   - Any files flagged for manual follow-up.\n   - Suggested commit message (e.g., `docs: amend constitution to vX.Y.Z (principle additions + governance update)`).\n\nFormatting & Style Requirements:","chunkId":".cursor/commands/constitution.md:15"}],[67,{"filePath":".cursor/commands/constitution.md","startLine":60,"endLine":69,"chunkText":"   - Suggested commit message (e.g., `docs: amend constitution to vX.Y.Z (principle additions + governance update)`).\n\nFormatting & Style Requirements:\n- Use Markdown headings exactly as in the template (do not demote/promote levels).\n- Wrap long rationale lines to keep readability (<100 chars ideally) but do not hard enforce with awkward breaks.\n- Keep a single blank line between sections.\n- Avoid trailing whitespace.\n\nIf the user supplies partial updates (e.g., only one principle revision), still perform validation and version decision steps.\n","chunkId":".cursor/commands/constitution.md:16"}],[68,{"filePath":".cursor/commands/constitution.md","startLine":67,"endLine":73,"chunkText":"\nIf the user supplies partial updates (e.g., only one principle revision), still perform validation and version decision steps.\n\nIf critical info missing (e.g., ratification date truly unknown), insert `TODO(<FIELD_NAME>): explanation` and include in the Sync Impact Report under deferred items.\n\nDo not create a new template; always operate on the existing `.specify/memory/constitution.md` file.\n","chunkId":".cursor/commands/constitution.md:17"}],[69,{"filePath":".cursor/commands/implement.md","startLine":0,"endLine":13,"chunkText":"---\ndescription: Execute the implementation plan by processing and executing all tasks defined in tasks.md\n---\n\nThe user input can be provided directly by the agent or as a command argument - you **MUST** consider it before proceeding with the prompt (if not empty).\n\nUser input:\n\n$ARGUMENTS\n\n1. Run `.specify/scripts/bash/check-prerequisites.sh --json --require-tasks --include-tasks` from repo root and parse FEATURE_DIR and AVAILABLE_DOCS list. All paths must be absolute.\n\n2. Load and analyze the implementation context:\n   - **REQUIRED**: Read tasks.md for the complete task list and execution plan","chunkId":".cursor/commands/implement.md:0"}],[70,{"filePath":".cursor/commands/implement.md","startLine":12,"endLine":21,"chunkText":"2. Load and analyze the implementation context:\n   - **REQUIRED**: Read tasks.md for the complete task list and execution plan\n   - **REQUIRED**: Read plan.md for tech stack, architecture, and file structure\n   - **IF EXISTS**: Read data-model.md for entities and relationships\n   - **IF EXISTS**: Read contracts/ for API specifications and test requirements\n   - **IF EXISTS**: Read research.md for technical decisions and constraints\n   - **IF EXISTS**: Read quickstart.md for integration scenarios\n\n3. Parse tasks.md structure and extract:\n   - **Task phases**: Setup, Tests, Core, Integration, Polish","chunkId":".cursor/commands/implement.md:1"}],[71,{"filePath":".cursor/commands/implement.md","startLine":20,"endLine":28,"chunkText":"3. Parse tasks.md structure and extract:\n   - **Task phases**: Setup, Tests, Core, Integration, Polish\n   - **Task dependencies**: Sequential vs parallel execution rules\n   - **Task details**: ID, description, file paths, parallel markers [P]\n   - **Execution flow**: Order and dependency requirements\n\n4. Execute implementation following the task plan:\n   - **Phase-by-phase execution**: Complete each phase before moving to the next\n   - **Respect dependencies**: Run sequential tasks in order, parallel tasks [P] can run together  ","chunkId":".cursor/commands/implement.md:2"}],[72,{"filePath":".cursor/commands/implement.md","startLine":27,"endLine":34,"chunkText":"   - **Phase-by-phase execution**: Complete each phase before moving to the next\n   - **Respect dependencies**: Run sequential tasks in order, parallel tasks [P] can run together  \n   - **Follow TDD approach**: Execute test tasks before their corresponding implementation tasks\n   - **File-based coordination**: Tasks affecting the same files must run sequentially\n   - **Validation checkpoints**: Verify each phase completion before proceeding\n\n5. Implementation execution rules:\n   - **Setup first**: Initialize project structure, dependencies, configuration","chunkId":".cursor/commands/implement.md:3"}],[73,{"filePath":".cursor/commands/implement.md","startLine":33,"endLine":42,"chunkText":"5. Implementation execution rules:\n   - **Setup first**: Initialize project structure, dependencies, configuration\n   - **Tests before code**: If you need to write tests for contracts, entities, and integration scenarios\n   - **Core development**: Implement models, services, CLI commands, endpoints\n   - **Integration work**: Database connections, middleware, logging, external services\n   - **Polish and validation**: Unit tests, performance optimization, documentation\n\n6. Progress tracking and error handling:\n   - Report progress after each completed task\n   - Halt execution if any non-parallel task fails","chunkId":".cursor/commands/implement.md:4"}],[74,{"filePath":".cursor/commands/implement.md","startLine":41,"endLine":51,"chunkText":"   - Report progress after each completed task\n   - Halt execution if any non-parallel task fails\n   - For parallel tasks [P], continue with successful tasks, report failed ones\n   - Provide clear error messages with context for debugging\n   - Suggest next steps if implementation cannot proceed\n   - **IMPORTANT** For completed tasks, make sure to mark the task off as [X] in the tasks file.\n\n7. Completion validation:\n   - Verify all required tasks are completed\n   - Check that implemented features match the original specification\n   - Validate that tests pass and coverage meets requirements","chunkId":".cursor/commands/implement.md:5"}],[75,{"filePath":".cursor/commands/implement.md","startLine":50,"endLine":56,"chunkText":"   - Check that implemented features match the original specification\n   - Validate that tests pass and coverage meets requirements\n   - Confirm the implementation follows the technical plan\n   - Report final status with summary of completed work\n\nNote: This command assumes a complete task breakdown exists in tasks.md. If tasks are incomplete or missing, suggest running `/tasks` first to regenerate the task list.\n","chunkId":".cursor/commands/implement.md:6"}],[76,{"filePath":".cursor/commands/plan.md","startLine":0,"endLine":12,"chunkText":"---\ndescription: Execute the implementation planning workflow using the plan template to generate design artifacts.\n---\n\nThe user input to you can be provided directly by the agent or as a command argument - you **MUST** consider it before proceeding with the prompt (if not empty).\n\nUser input:\n\n$ARGUMENTS\n\nGiven the implementation details provided as an argument, do this:\n\n1. Run `.specify/scripts/bash/setup-plan.sh --json` from the repo root and parse JSON for FEATURE_SPEC, IMPL_PLAN, SPECS_DIR, BRANCH. All future file paths must be absolute.","chunkId":".cursor/commands/plan.md:0"}],[77,{"filePath":".cursor/commands/plan.md","startLine":10,"endLine":12,"chunkText":"Given the implementation details provided as an argument, do this:\n\n1. Run `.specify/scripts/bash/setup-plan.sh --json` from the repo root and parse JSON for FEATURE_SPEC, IMPL_PLAN, SPECS_DIR, BRANCH. All future file paths must be absolute.","chunkId":".cursor/commands/plan.md:1"}],[78,{"filePath":".cursor/commands/plan.md","startLine":11,"endLine":11,"chunkText":"1. Run `.specify/scripts/bash/setup-plan.sh --json` from the repo root and parse JSON for FEATURE_SPEC, IMPL_PLAN, SPECS_DIR, BRANCH. All future file paths must be absolute.","chunkId":".cursor/commands/plan.md:2"}],[79,{"filePath":".cursor/commands/plan.md","startLine":12,"endLine":12,"chunkText":"1. Run `.specify/scripts/bash/setup-plan.sh --json` from the repo root and parse JSON for FEATURE_SPEC, IMPL_PLAN, SPECS_DIR, BRANCH. All future file paths must be absolute.","chunkId":".cursor/commands/plan.md:3"}],[80,{"filePath":".cursor/commands/plan.md","startLine":13,"endLine":15,"chunkText":"   - BEFORE proceeding, inspect FEATURE_SPEC for a `## Clarifications` section with at least one `Session` subheading. If missing or clearly ambiguous areas remain (vague adjectives, unresolved critical choices), PAUSE and instruct the user to run `/clarify` first to reduce rework. Only continue if: (a) Clarifications exist OR (b) an explicit user override is provided (e.g., \"proceed without clarification\"). Do not attempt to fabricate clarifications yourself.\n2. Read and analyze the feature specification to understand:\n   - The feature requirements and user stories","chunkId":".cursor/commands/plan.md:4"}],[81,{"filePath":".cursor/commands/plan.md","startLine":14,"endLine":25,"chunkText":"2. Read and analyze the feature specification to understand:\n   - The feature requirements and user stories\n   - Functional and non-functional requirements\n   - Success criteria and acceptance criteria\n   - Any technical constraints or dependencies mentioned\n\n3. Read the constitution at `.specify/memory/constitution.md` to understand constitutional requirements.\n\n4. Execute the implementation plan template:\n   - Load `.specify/templates/plan-template.md` (already copied to IMPL_PLAN path)\n   - Set Input path to FEATURE_SPEC\n   - Run the Execution Flow (main) function steps 1-9","chunkId":".cursor/commands/plan.md:5"}],[82,{"filePath":".cursor/commands/plan.md","startLine":23,"endLine":32,"chunkText":"   - Load `.specify/templates/plan-template.md` (already copied to IMPL_PLAN path)\n   - Set Input path to FEATURE_SPEC\n   - Run the Execution Flow (main) function steps 1-9\n   - The template is self-contained and executable\n   - Follow error handling and gate checks as specified\n   - Let the template guide artifact generation in $SPECS_DIR:\n     * Phase 0 generates research.md\n     * Phase 1 generates data-model.md, contracts/, quickstart.md\n     * Phase 2 generates tasks.md\n   - Incorporate user-provided details from arguments into Technical Context: $ARGUMENTS","chunkId":".cursor/commands/plan.md:6"}],[83,{"filePath":".cursor/commands/plan.md","startLine":30,"endLine":43,"chunkText":"     * Phase 1 generates data-model.md, contracts/, quickstart.md\n     * Phase 2 generates tasks.md\n   - Incorporate user-provided details from arguments into Technical Context: $ARGUMENTS\n   - Update Progress Tracking as you complete each phase\n\n5. Verify execution completed:\n   - Check Progress Tracking shows all phases complete\n   - Ensure all required artifacts were generated\n   - Confirm no ERROR states in execution\n\n6. Report results with branch name, file paths, and generated artifacts.\n\nUse absolute paths with the repository root for all file operations to avoid path issues.\n","chunkId":".cursor/commands/plan.md:7"}],[84,{"filePath":".cursor/commands/specify.md","startLine":0,"endLine":12,"chunkText":"---\ndescription: Create or update the feature specification from a natural language feature description.\n---\n\nThe user input to you can be provided directly by the agent or as a command argument - you **MUST** consider it before proceeding with the prompt (if not empty).\n\nUser input:\n\n$ARGUMENTS\n\nThe text the user typed after `/specify` in the triggering message **is** the feature description. Assume you always have it available in this conversation even if `$ARGUMENTS` appears literally below. Do not ask the user to repeat it unless they provided an empty command.\n\nGiven that feature description, do this","chunkId":".cursor/commands/specify.md:0"}],[85,{"filePath":".cursor/commands/specify.md","startLine":11,"endLine":16,"chunkText":"\nGiven that feature description, do this:\n\n1. Run the script `.specify/scripts/bash/create-new-feature.sh --json \"$ARGUMENTS\"` from repo root and parse its JSON output for BRANCH_NAME and SPEC_FILE. All file paths must be absolute.\n  **IMPORTANT** You must only ever run this script once. The JSON is provided in the terminal as output - always refer to it to get the actual content you're looking for.\n2. Load `.specify/templates/spec-template.md` to understand required sections.","chunkId":".cursor/commands/specify.md:1"}],[86,{"filePath":".cursor/commands/specify.md","startLine":14,"endLine":16,"chunkText":"1. Run the script `.specify/scripts/bash/create-new-feature.sh --json \"$ARGUMENTS\"` from repo root and parse its JSON output for BRANCH_NAME and SPEC_FILE. All file paths must be absolute.\n  **IMPORTANT** You must only ever run this script once. The JSON is provided in the terminal as output - always refer to it to get the actual content you're looking for.\n2. Load `.specify/templates/spec-template.md` to understand required sections.","chunkId":".cursor/commands/specify.md:2"}],[87,{"filePath":".cursor/commands/specify.md","startLine":15,"endLine":19,"chunkText":"  **IMPORTANT** You must only ever run this script once. The JSON is provided in the terminal as output - always refer to it to get the actual content you're looking for.\n2. Load `.specify/templates/spec-template.md` to understand required sections.\n3. Write the specification to SPEC_FILE using the template structure, replacing placeholders with concrete details derived from the feature description (arguments) while preserving section order and headings.\n4. Report completion with branch name, spec file path, and readiness for the next phase.\n","chunkId":".cursor/commands/specify.md:3"}],[88,{"filePath":".cursor/commands/specify.md","startLine":17,"endLine":21,"chunkText":"3. Write the specification to SPEC_FILE using the template structure, replacing placeholders with concrete details derived from the feature description (arguments) while preserving section order and headings.\n4. Report completion with branch name, spec file path, and readiness for the next phase.\n\nNote: The script creates and checks out the new branch and initializes the spec file before writing.\n","chunkId":".cursor/commands/specify.md:4"}],[89,{"filePath":".cursor/commands/tasks.md","startLine":0,"endLine":12,"chunkText":"---\ndescription: Generate an actionable, dependency-ordered tasks.md for the feature based on available design artifacts.\n---\n\nThe user input to you can be provided directly by the agent or as a command argument - you **MUST** consider it before proceeding with the prompt (if not empty).\n\nUser input:\n\n$ARGUMENTS\n\n1. Run `.specify/scripts/bash/check-prerequisites.sh --json` from repo root and parse FEATURE_DIR and AVAILABLE_DOCS list. All paths must be absolute.\n2. Load and analyze available design documents:\n   - Always read plan.md for tech stack and libraries","chunkId":".cursor/commands/tasks.md:0"}],[90,{"filePath":".cursor/commands/tasks.md","startLine":11,"endLine":24,"chunkText":"2. Load and analyze available design documents:\n   - Always read plan.md for tech stack and libraries\n   - IF EXISTS: Read data-model.md for entities\n   - IF EXISTS: Read contracts/ for API endpoints\n   - IF EXISTS: Read research.md for technical decisions\n   - IF EXISTS: Read quickstart.md for test scenarios\n\n   Note: Not all projects have all documents. For example:\n   - CLI tools might not have contracts/\n   - Simple libraries might not need data-model.md\n   - Generate tasks based on what's available\n\n3. Generate tasks following the template:\n   - Use `.specify/templates/tasks-template.md` as the base","chunkId":".cursor/commands/tasks.md:1"}],[91,{"filePath":".cursor/commands/tasks.md","startLine":22,"endLine":33,"chunkText":"\n3. Generate tasks following the template:\n   - Use `.specify/templates/tasks-template.md` as the base\n   - Replace example tasks with actual tasks based on:\n     * **Setup tasks**: Project init, dependencies, linting\n     * **Test tasks [P]**: One per contract, one per integration scenario\n     * **Core tasks**: One per entity, service, CLI command, endpoint\n     * **Integration tasks**: DB connections, middleware, logging\n     * **Polish tasks [P]**: Unit tests, performance, docs\n\n4. Task generation rules:\n   - Each contract file → contract test task marked [P]","chunkId":".cursor/commands/tasks.md:2"}],[92,{"filePath":".cursor/commands/tasks.md","startLine":31,"endLine":48,"chunkText":"\n4. Task generation rules:\n   - Each contract file → contract test task marked [P]\n   - Each entity in data-model → model creation task marked [P]\n   - Each endpoint → implementation task (not parallel if shared files)\n   - Each user story → integration test marked [P]\n   - Different files = can be parallel [P]\n   - Same file = sequential (no [P])\n\n5. Order tasks by dependencies:\n   - Setup before everything\n   - Tests before implementation (TDD)\n   - Models before services\n   - Services before endpoints\n   - Core before integration\n   - Everything before polish\n\n6. Include parallel execution examples:","chunkId":".cursor/commands/tasks.md:3"}],[93,{"filePath":".cursor/commands/tasks.md","startLine":46,"endLine":62,"chunkText":"   - Everything before polish\n\n6. Include parallel execution examples:\n   - Group [P] tasks that can run together\n   - Show actual Task agent commands\n\n7. Create FEATURE_DIR/tasks.md with:\n   - Correct feature name from implementation plan\n   - Numbered tasks (T001, T002, etc.)\n   - Clear file paths for each task\n   - Dependency notes\n   - Parallel execution guidance\n\nContext for task generation: $ARGUMENTS\n\nThe tasks.md should be immediately executable - each task must be specific enough that an LLM can complete it without additional context.\n","chunkId":".cursor/commands/tasks.md:4"}],[94,{"filePath":".github/prompts/analyze.prompt.md","startLine":0,"endLine":11,"chunkText":"---\ndescription: Perform a non-destructive cross-artifact consistency and quality analysis across spec.md, plan.md, and tasks.md after task generation.\n---\n\nThe user input to you can be provided directly by the agent or as a command argument - you **MUST** consider it before proceeding with the prompt (if not empty).\n\nUser input:\n\n$ARGUMENTS\n\nGoal: Identify inconsistencies, duplications, ambiguities, and underspecified items across the three core artifacts (`spec.md`, `plan.md`, `tasks.md`) before implementation. This command MUST run only after `/tasks` has successfully produced a complete `tasks.md`.\n","chunkId":".github/prompts/analyze.prompt.md:0"}],[95,{"filePath":".github/prompts/analyze.prompt.md","startLine":9,"endLine":13,"chunkText":"\nGoal: Identify inconsistencies, duplications, ambiguities, and underspecified items across the three core artifacts (`spec.md`, `plan.md`, `tasks.md`) before implementation. This command MUST run only after `/tasks` has successfully produced a complete `tasks.md`.\n\nSTRICTLY READ-ONLY: Do **not** modify any files. Output a structured analysis report. Offer an optional remediation plan (user must explicitly approve before any follow-up editing commands would be invoked manually).\n","chunkId":".github/prompts/analyze.prompt.md:1"}],[96,{"filePath":".github/prompts/analyze.prompt.md","startLine":11,"endLine":12,"chunkText":"STRICTLY READ-ONLY: Do **not** modify any files. Output a structured analysis report. Offer an optional remediation plan (user must explicitly approve before any follow-up editing commands would be invoked manually).\n","chunkId":".github/prompts/analyze.prompt.md:2"}],[97,{"filePath":".github/prompts/analyze.prompt.md","startLine":12,"endLine":13,"chunkText":"STRICTLY READ-ONLY: Do **not** modify any files. Output a structured analysis report. Offer an optional remediation plan (user must explicitly approve before any follow-up editing commands would be invoked manually).\n","chunkId":".github/prompts/analyze.prompt.md:3"}],[98,{"filePath":".github/prompts/analyze.prompt.md","startLine":13,"endLine":17,"chunkText":"\nConstitution Authority: The project constitution (`.specify/memory/constitution.md`) is **non-negotiable** within this analysis scope. Constitution conflicts are automatically CRITICAL and require adjustment of the spec, plan, or tasks—not dilution, reinterpretation, or silent ignoring of the principle. If a principle itself needs to change, that must occur in a separate, explicit constitution update outside `/analyze`.\n\nExecution steps:\n","chunkId":".github/prompts/analyze.prompt.md:4"}],[99,{"filePath":".github/prompts/analyze.prompt.md","startLine":15,"endLine":25,"chunkText":"\nExecution steps:\n\n1. Run `.specify/scripts/bash/check-prerequisites.sh --json --require-tasks --include-tasks` once from repo root and parse JSON for FEATURE_DIR and AVAILABLE_DOCS. Derive absolute paths:\n   - SPEC = FEATURE_DIR/spec.md\n   - PLAN = FEATURE_DIR/plan.md\n   - TASKS = FEATURE_DIR/tasks.md\n   Abort with an error message if any required file is missing (instruct the user to run missing prerequisite command).\n\n2. Load artifacts:\n   - Parse spec.md sections: Overview/Context, Functional Requirements, Non-Functional Requirements, User Stories, Edge Cases (if present).","chunkId":".github/prompts/analyze.prompt.md:5"}],[100,{"filePath":".github/prompts/analyze.prompt.md","startLine":23,"endLine":30,"chunkText":"\n2. Load artifacts:\n   - Parse spec.md sections: Overview/Context, Functional Requirements, Non-Functional Requirements, User Stories, Edge Cases (if present).\n   - Parse plan.md: Architecture/stack choices, Data Model references, Phases, Technical constraints.\n   - Parse tasks.md: Task IDs, descriptions, phase grouping, parallel markers [P], referenced file paths.\n   - Load constitution `.specify/memory/constitution.md` for principle validation.\n\n3. Build internal semantic models:","chunkId":".github/prompts/analyze.prompt.md:6"}],[101,{"filePath":".github/prompts/analyze.prompt.md","startLine":28,"endLine":35,"chunkText":"   - Load constitution `.specify/memory/constitution.md` for principle validation.\n\n3. Build internal semantic models:\n   - Requirements inventory: Each functional + non-functional requirement with a stable key (derive slug based on imperative phrase; e.g., \"User can upload file\" -> `user-can-upload-file`).\n   - User story/action inventory.\n   - Task coverage mapping: Map each task to one or more requirements or stories (inference by keyword / explicit reference patterns like IDs or key phrases).\n   - Constitution rule set: Extract principle names and any MUST/SHOULD normative statements.\n","chunkId":".github/prompts/analyze.prompt.md:7"}],[102,{"filePath":".github/prompts/analyze.prompt.md","startLine":33,"endLine":41,"chunkText":"   - Task coverage mapping: Map each task to one or more requirements or stories (inference by keyword / explicit reference patterns like IDs or key phrases).\n   - Constitution rule set: Extract principle names and any MUST/SHOULD normative statements.\n\n4. Detection passes:\n   A. Duplication detection:\n      - Identify near-duplicate requirements. Mark lower-quality phrasing for consolidation.\n   B. Ambiguity detection:\n      - Flag vague adjectives (fast, scalable, secure, intuitive, robust) lacking measurable criteria.\n      - Flag unresolved placeholders (TODO, TKTK, ???, <placeholder>, etc.).","chunkId":".github/prompts/analyze.prompt.md:8"}],[103,{"filePath":".github/prompts/analyze.prompt.md","startLine":39,"endLine":47,"chunkText":"   B. Ambiguity detection:\n      - Flag vague adjectives (fast, scalable, secure, intuitive, robust) lacking measurable criteria.\n      - Flag unresolved placeholders (TODO, TKTK, ???, <placeholder>, etc.).\n   C. Underspecification:\n      - Requirements with verbs but missing object or measurable outcome.\n      - User stories missing acceptance criteria alignment.\n      - Tasks referencing files or components not defined in spec/plan.\n   D. Constitution alignment:\n      - Any requirement or plan element conflicting with a MUST principle.","chunkId":".github/prompts/analyze.prompt.md:9"}],[104,{"filePath":".github/prompts/analyze.prompt.md","startLine":45,"endLine":54,"chunkText":"      - Tasks referencing files or components not defined in spec/plan.\n   D. Constitution alignment:\n      - Any requirement or plan element conflicting with a MUST principle.\n      - Missing mandated sections or quality gates from constitution.\n   E. Coverage gaps:\n      - Requirements with zero associated tasks.\n      - Tasks with no mapped requirement/story.\n      - Non-functional requirements not reflected in tasks (e.g., performance, security).\n   F. Inconsistency:\n      - Terminology drift (same concept named differently across files).","chunkId":".github/prompts/analyze.prompt.md:10"}],[105,{"filePath":".github/prompts/analyze.prompt.md","startLine":52,"endLine":59,"chunkText":"      - Non-functional requirements not reflected in tasks (e.g., performance, security).\n   F. Inconsistency:\n      - Terminology drift (same concept named differently across files).\n      - Data entities referenced in plan but absent in spec (or vice versa).\n      - Task ordering contradictions (e.g., integration tasks before foundational setup tasks without dependency note).\n      - Conflicting requirements (e.g., one requires to use Next.js while other says to use Vue as the framework).\n\n5. Severity assignment heuristic:","chunkId":".github/prompts/analyze.prompt.md:11"}],[106,{"filePath":".github/prompts/analyze.prompt.md","startLine":57,"endLine":64,"chunkText":"      - Conflicting requirements (e.g., one requires to use Next.js while other says to use Vue as the framework).\n\n5. Severity assignment heuristic:\n   - CRITICAL: Violates constitution MUST, missing core spec artifact, or requirement with zero coverage that blocks baseline functionality.\n   - HIGH: Duplicate or conflicting requirement, ambiguous security/performance attribute, untestable acceptance criterion.\n   - MEDIUM: Terminology drift, missing non-functional task coverage, underspecified edge case.\n   - LOW: Style/wording improvements, minor redundancy not affecting execution order.\n","chunkId":".github/prompts/analyze.prompt.md:12"}],[107,{"filePath":".github/prompts/analyze.prompt.md","startLine":62,"endLine":70,"chunkText":"   - MEDIUM: Terminology drift, missing non-functional task coverage, underspecified edge case.\n   - LOW: Style/wording improvements, minor redundancy not affecting execution order.\n\n6. Produce a Markdown report (no file writes) with sections:\n\n   ### Specification Analysis Report\n   | ID | Category | Severity | Location(s) | Summary | Recommendation |\n   |----|----------|----------|-------------|---------|----------------|\n   | A1 | Duplication | HIGH | spec.md:L120-134 | Two similar requirements ... | Merge phrasing; keep clearer version |","chunkId":".github/prompts/analyze.prompt.md:13"}],[108,{"filePath":".github/prompts/analyze.prompt.md","startLine":68,"endLine":80,"chunkText":"   | ID | Category | Severity | Location(s) | Summary | Recommendation |\n   |----|----------|----------|-------------|---------|----------------|\n   | A1 | Duplication | HIGH | spec.md:L120-134 | Two similar requirements ... | Merge phrasing; keep clearer version |\n   (Add one row per finding; generate stable IDs prefixed by category initial.)\n\n   Additional subsections:\n   - Coverage Summary Table:\n     | Requirement Key | Has Task? | Task IDs | Notes |\n   - Constitution Alignment Issues (if any)\n   - Unmapped Tasks (if any)\n   - Metrics:\n     * Total Requirements\n     * Total Tasks","chunkId":".github/prompts/analyze.prompt.md:14"}],[109,{"filePath":".github/prompts/analyze.prompt.md","startLine":78,"endLine":90,"chunkText":"   - Metrics:\n     * Total Requirements\n     * Total Tasks\n     * Coverage % (requirements with >=1 task)\n     * Ambiguity Count\n     * Duplication Count\n     * Critical Issues Count\n\n7. At end of report, output a concise Next Actions block:\n   - If CRITICAL issues exist: Recommend resolving before `/implement`.\n   - If only LOW/MEDIUM: User may proceed, but provide improvement suggestions.\n   - Provide explicit command suggestions: e.g., \"Run /specify with refinement\", \"Run /plan to adjust architecture\", \"Manually edit tasks.md to add coverage for 'performance-metrics'\".\n","chunkId":".github/prompts/analyze.prompt.md:15"}],[110,{"filePath":".github/prompts/analyze.prompt.md","startLine":88,"endLine":96,"chunkText":"   - If only LOW/MEDIUM: User may proceed, but provide improvement suggestions.\n   - Provide explicit command suggestions: e.g., \"Run /specify with refinement\", \"Run /plan to adjust architecture\", \"Manually edit tasks.md to add coverage for 'performance-metrics'\".\n\n8. Ask the user: \"Would you like me to suggest concrete remediation edits for the top N issues?\" (Do NOT apply them automatically.)\n\nBehavior rules:\n- NEVER modify files.\n- NEVER hallucinate missing sections—if absent, report them.\n- KEEP findings deterministic: if rerun without changes, produce consistent IDs and counts.","chunkId":".github/prompts/analyze.prompt.md:16"}],[111,{"filePath":".github/prompts/analyze.prompt.md","startLine":94,"endLine":101,"chunkText":"- NEVER modify files.\n- NEVER hallucinate missing sections—if absent, report them.\n- KEEP findings deterministic: if rerun without changes, produce consistent IDs and counts.\n- LIMIT total findings in the main table to 50; aggregate remainder in a summarized overflow note.\n- If zero issues found, emit a success report with coverage statistics and proceed recommendation.\n\nContext: $ARGUMENTS\n","chunkId":".github/prompts/analyze.prompt.md:17"}],[112,{"filePath":".github/prompts/clarify.prompt.md","startLine":0,"endLine":11,"chunkText":"---\ndescription: Identify underspecified areas in the current feature spec by asking up to 5 highly targeted clarification questions and encoding answers back into the spec.\n---\n\nThe user input to you can be provided directly by the agent or as a command argument - you **MUST** consider it before proceeding with the prompt (if not empty).\n\nUser input:\n\n$ARGUMENTS\n\nGoal: Detect and reduce ambiguity or missing decision points in the active feature specification and record the clarifications directly in the spec file.\n","chunkId":".github/prompts/clarify.prompt.md:0"}],[113,{"filePath":".github/prompts/clarify.prompt.md","startLine":9,"endLine":15,"chunkText":"\nGoal: Detect and reduce ambiguity or missing decision points in the active feature specification and record the clarifications directly in the spec file.\n\nNote: This clarification workflow is expected to run (and be completed) BEFORE invoking `/plan`. If the user explicitly states they are skipping clarification (e.g., exploratory spike), you may proceed, but must warn that downstream rework risk increases.\n\nExecution steps:\n","chunkId":".github/prompts/clarify.prompt.md:1"}],[114,{"filePath":".github/prompts/clarify.prompt.md","startLine":13,"endLine":21,"chunkText":"\nExecution steps:\n\n1. Run `.specify/scripts/bash/check-prerequisites.sh --json --paths-only` from repo root **once** (combined `--json --paths-only` mode / `-Json -PathsOnly`). Parse minimal JSON payload fields:\n   - `FEATURE_DIR`\n   - `FEATURE_SPEC`\n   - (Optionally capture `IMPL_PLAN`, `TASKS` for future chained flows.)\n   - If JSON parsing fails, abort and instruct user to re-run `/specify` or verify feature branch environment.\n","chunkId":".github/prompts/clarify.prompt.md:2"}],[115,{"filePath":".github/prompts/clarify.prompt.md","startLine":19,"endLine":26,"chunkText":"   - (Optionally capture `IMPL_PLAN`, `TASKS` for future chained flows.)\n   - If JSON parsing fails, abort and instruct user to re-run `/specify` or verify feature branch environment.\n\n2. Load the current spec file. Perform a structured ambiguity & coverage scan using this taxonomy. For each category, mark status: Clear / Partial / Missing. Produce an internal coverage map used for prioritization (do not output raw map unless no questions will be asked).\n\n   Functional Scope & Behavior:\n   - Core user goals & success criteria\n   - Explicit out-of-scope declarations","chunkId":".github/prompts/clarify.prompt.md:3"}],[116,{"filePath":".github/prompts/clarify.prompt.md","startLine":26,"endLine":43,"chunkText":"   - Explicit out-of-scope declarations\n   - User roles / personas differentiation\n\n   Domain & Data Model:\n   - Entities, attributes, relationships\n   - Identity & uniqueness rules\n   - Lifecycle/state transitions\n   - Data volume / scale assumptions\n\n   Interaction & UX Flow:\n   - Critical user journeys / sequences\n   - Error/empty/loading states\n   - Accessibility or localization notes\n\n   Non-Functional Quality Attributes:\n   - Performance (latency, throughput targets)\n   - Scalability (horizontal/vertical, limits)\n   - Reliability & availability (uptime, recovery expectations)","chunkId":".github/prompts/clarify.prompt.md:4"}],[117,{"filePath":".github/prompts/clarify.prompt.md","startLine":41,"endLine":55,"chunkText":"   - Performance (latency, throughput targets)\n   - Scalability (horizontal/vertical, limits)\n   - Reliability & availability (uptime, recovery expectations)\n   - Observability (logging, metrics, tracing signals)\n   - Security & privacy (authN/Z, data protection, threat assumptions)\n   - Compliance / regulatory constraints (if any)\n\n   Integration & External Dependencies:\n   - External services/APIs and failure modes\n   - Data import/export formats\n   - Protocol/versioning assumptions\n\n   Edge Cases & Failure Handling:\n   - Negative scenarios\n   - Rate limiting / throttling","chunkId":".github/prompts/clarify.prompt.md:5"}],[118,{"filePath":".github/prompts/clarify.prompt.md","startLine":53,"endLine":71,"chunkText":"   Edge Cases & Failure Handling:\n   - Negative scenarios\n   - Rate limiting / throttling\n   - Conflict resolution (e.g., concurrent edits)\n\n   Constraints & Tradeoffs:\n   - Technical constraints (language, storage, hosting)\n   - Explicit tradeoffs or rejected alternatives\n\n   Terminology & Consistency:\n   - Canonical glossary terms\n   - Avoided synonyms / deprecated terms\n\n   Completion Signals:\n   - Acceptance criteria testability\n   - Measurable Definition of Done style indicators\n\n   Misc / Placeholders:\n   - TODO markers / unresolved decisions","chunkId":".github/prompts/clarify.prompt.md:6"}],[119,{"filePath":".github/prompts/clarify.prompt.md","startLine":69,"endLine":79,"chunkText":"\n   Misc / Placeholders:\n   - TODO markers / unresolved decisions\n   - Ambiguous adjectives (\"robust\", \"intuitive\") lacking quantification\n\n   For each category with Partial or Missing status, add a candidate question opportunity unless:\n   - Clarification would not materially change implementation or validation strategy\n   - Information is better deferred to planning phase (note internally)\n\n3. Generate (internally) a prioritized queue of candidate clarification questions (maximum 5). Do NOT output them all at once. Apply these constraints:\n    - Maximum of 5 total questions across the whole session.","chunkId":".github/prompts/clarify.prompt.md:7"}],[120,{"filePath":".github/prompts/clarify.prompt.md","startLine":77,"endLine":82,"chunkText":"\n3. Generate (internally) a prioritized queue of candidate clarification questions (maximum 5). Do NOT output them all at once. Apply these constraints:\n    - Maximum of 5 total questions across the whole session.\n    - Each question must be answerable with EITHER:\n       * A short multiple‑choice selection (2–5 distinct, mutually exclusive options), OR\n       * A one-word / short‑phrase answer (explicitly constrain: \"Answer in <=5 words\").","chunkId":".github/prompts/clarify.prompt.md:8"}],[121,{"filePath":".github/prompts/clarify.prompt.md","startLine":80,"endLine":83,"chunkText":"    - Each question must be answerable with EITHER:\n       * A short multiple‑choice selection (2–5 distinct, mutually exclusive options), OR\n       * A one-word / short‑phrase answer (explicitly constrain: \"Answer in <=5 words\").\n   - Only include questions whose answers materially impact architecture, data modeling, task decomposition, test design, UX behavior, operational readiness, or compliance validation.","chunkId":".github/prompts/clarify.prompt.md:9"}],[122,{"filePath":".github/prompts/clarify.prompt.md","startLine":81,"endLine":84,"chunkText":"       * A short multiple‑choice selection (2–5 distinct, mutually exclusive options), OR\n       * A one-word / short‑phrase answer (explicitly constrain: \"Answer in <=5 words\").\n   - Only include questions whose answers materially impact architecture, data modeling, task decomposition, test design, UX behavior, operational readiness, or compliance validation.\n   - Ensure category coverage balance: attempt to cover the highest impact unresolved categories first; avoid asking two low-impact questions when a single high-impact area (e.g., security posture) is unresolved.","chunkId":".github/prompts/clarify.prompt.md:10"}],[123,{"filePath":".github/prompts/clarify.prompt.md","startLine":82,"endLine":84,"chunkText":"       * A one-word / short‑phrase answer (explicitly constrain: \"Answer in <=5 words\").\n   - Only include questions whose answers materially impact architecture, data modeling, task decomposition, test design, UX behavior, operational readiness, or compliance validation.\n   - Ensure category coverage balance: attempt to cover the highest impact unresolved categories first; avoid asking two low-impact questions when a single high-impact area (e.g., security posture) is unresolved.","chunkId":".github/prompts/clarify.prompt.md:11"}],[124,{"filePath":".github/prompts/clarify.prompt.md","startLine":83,"endLine":85,"chunkText":"   - Only include questions whose answers materially impact architecture, data modeling, task decomposition, test design, UX behavior, operational readiness, or compliance validation.\n   - Ensure category coverage balance: attempt to cover the highest impact unresolved categories first; avoid asking two low-impact questions when a single high-impact area (e.g., security posture) is unresolved.\n   - Exclude questions already answered, trivial stylistic preferences, or plan-level execution details (unless blocking correctness).","chunkId":".github/prompts/clarify.prompt.md:12"}],[125,{"filePath":".github/prompts/clarify.prompt.md","startLine":84,"endLine":89,"chunkText":"   - Ensure category coverage balance: attempt to cover the highest impact unresolved categories first; avoid asking two low-impact questions when a single high-impact area (e.g., security posture) is unresolved.\n   - Exclude questions already answered, trivial stylistic preferences, or plan-level execution details (unless blocking correctness).\n   - Favor clarifications that reduce downstream rework risk or prevent misaligned acceptance tests.\n   - If more than 5 categories remain unresolved, select the top 5 by (Impact * Uncertainty) heuristic.\n\n4. Sequential questioning loop (interactive):","chunkId":".github/prompts/clarify.prompt.md:13"}],[126,{"filePath":".github/prompts/clarify.prompt.md","startLine":87,"endLine":99,"chunkText":"   - If more than 5 categories remain unresolved, select the top 5 by (Impact * Uncertainty) heuristic.\n\n4. Sequential questioning loop (interactive):\n    - Present EXACTLY ONE question at a time.\n    - For multiple‑choice questions render options as a Markdown table:\n\n       | Option | Description |\n       |--------|-------------|\n       | A | <Option A description> |\n       | B | <Option B description> |\n       | C | <Option C description> | (add D/E as needed up to 5)\n       | Short | Provide a different short answer (<=5 words) | (Include only if free-form alternative is appropriate)\n","chunkId":".github/prompts/clarify.prompt.md:14"}],[127,{"filePath":".github/prompts/clarify.prompt.md","startLine":97,"endLine":103,"chunkText":"       | C | <Option C description> | (add D/E as needed up to 5)\n       | Short | Provide a different short answer (<=5 words) | (Include only if free-form alternative is appropriate)\n\n    - For short‑answer style (no meaningful discrete options), output a single line after the question: `Format: Short answer (<=5 words)`.\n    - After the user answers:\n       * Validate the answer maps to one option or fits the <=5 word constraint.\n       * If ambiguous, ask for a quick disambiguation (count still belongs to same question; do not advance).","chunkId":".github/prompts/clarify.prompt.md:15"}],[128,{"filePath":".github/prompts/clarify.prompt.md","startLine":101,"endLine":108,"chunkText":"    - After the user answers:\n       * Validate the answer maps to one option or fits the <=5 word constraint.\n       * If ambiguous, ask for a quick disambiguation (count still belongs to same question; do not advance).\n       * Once satisfactory, record it in working memory (do not yet write to disk) and move to the next queued question.\n    - Stop asking further questions when:\n       * All critical ambiguities resolved early (remaining queued items become unnecessary), OR\n       * User signals completion (\"done\", \"good\", \"no more\"), OR\n       * You reach 5 asked questions.","chunkId":".github/prompts/clarify.prompt.md:16"}],[129,{"filePath":".github/prompts/clarify.prompt.md","startLine":106,"endLine":114,"chunkText":"       * All critical ambiguities resolved early (remaining queued items become unnecessary), OR\n       * User signals completion (\"done\", \"good\", \"no more\"), OR\n       * You reach 5 asked questions.\n    - Never reveal future queued questions in advance.\n    - If no valid questions exist at start, immediately report no critical ambiguities.\n\n5. Integration after EACH accepted answer (incremental update approach):\n    - Maintain in-memory representation of the spec (loaded once at start) plus the raw file contents.\n    - For the first integrated answer in this session:","chunkId":".github/prompts/clarify.prompt.md:17"}],[130,{"filePath":".github/prompts/clarify.prompt.md","startLine":112,"endLine":117,"chunkText":"5. Integration after EACH accepted answer (incremental update approach):\n    - Maintain in-memory representation of the spec (loaded once at start) plus the raw file contents.\n    - For the first integrated answer in this session:\n       * Ensure a `## Clarifications` section exists (create it just after the highest-level contextual/overview section per the spec template if missing).\n       * Under it, create (if not present) a `### Session YYYY-MM-DD` subheading for today.\n    - Append a bullet line immediately after acceptance: `- Q: <question> → A: <final answer>`.","chunkId":".github/prompts/clarify.prompt.md:18"}],[131,{"filePath":".github/prompts/clarify.prompt.md","startLine":115,"endLine":119,"chunkText":"       * Ensure a `## Clarifications` section exists (create it just after the highest-level contextual/overview section per the spec template if missing).\n       * Under it, create (if not present) a `### Session YYYY-MM-DD` subheading for today.\n    - Append a bullet line immediately after acceptance: `- Q: <question> → A: <final answer>`.\n    - Then immediately apply the clarification to the most appropriate section(s):\n       * Functional ambiguity → Update or add a bullet in Functional Requirements.","chunkId":".github/prompts/clarify.prompt.md:19"}],[132,{"filePath":".github/prompts/clarify.prompt.md","startLine":117,"endLine":121,"chunkText":"    - Append a bullet line immediately after acceptance: `- Q: <question> → A: <final answer>`.\n    - Then immediately apply the clarification to the most appropriate section(s):\n       * Functional ambiguity → Update or add a bullet in Functional Requirements.\n       * User interaction / actor distinction → Update User Stories or Actors subsection (if present) with clarified role, constraint, or scenario.\n       * Data shape / entities → Update Data Model (add fields, types, relationships) preserving ordering; note added constraints succinctly.","chunkId":".github/prompts/clarify.prompt.md:20"}],[133,{"filePath":".github/prompts/clarify.prompt.md","startLine":119,"endLine":122,"chunkText":"       * Functional ambiguity → Update or add a bullet in Functional Requirements.\n       * User interaction / actor distinction → Update User Stories or Actors subsection (if present) with clarified role, constraint, or scenario.\n       * Data shape / entities → Update Data Model (add fields, types, relationships) preserving ordering; note added constraints succinctly.\n       * Non-functional constraint → Add/modify measurable criteria in Non-Functional / Quality Attributes section (convert vague adjective to metric or explicit target).","chunkId":".github/prompts/clarify.prompt.md:21"}],[134,{"filePath":".github/prompts/clarify.prompt.md","startLine":120,"endLine":122,"chunkText":"       * User interaction / actor distinction → Update User Stories or Actors subsection (if present) with clarified role, constraint, or scenario.\n       * Data shape / entities → Update Data Model (add fields, types, relationships) preserving ordering; note added constraints succinctly.\n       * Non-functional constraint → Add/modify measurable criteria in Non-Functional / Quality Attributes section (convert vague adjective to metric or explicit target).","chunkId":".github/prompts/clarify.prompt.md:22"}],[135,{"filePath":".github/prompts/clarify.prompt.md","startLine":121,"endLine":124,"chunkText":"       * Data shape / entities → Update Data Model (add fields, types, relationships) preserving ordering; note added constraints succinctly.\n       * Non-functional constraint → Add/modify measurable criteria in Non-Functional / Quality Attributes section (convert vague adjective to metric or explicit target).\n       * Edge case / negative flow → Add a new bullet under Edge Cases / Error Handling (or create such subsection if template provides placeholder for it).\n       * Terminology conflict → Normalize term across spec; retain original only if necessary by adding `(formerly referred to as \"X\")` once.","chunkId":".github/prompts/clarify.prompt.md:23"}],[136,{"filePath":".github/prompts/clarify.prompt.md","startLine":122,"endLine":124,"chunkText":"       * Non-functional constraint → Add/modify measurable criteria in Non-Functional / Quality Attributes section (convert vague adjective to metric or explicit target).\n       * Edge case / negative flow → Add a new bullet under Edge Cases / Error Handling (or create such subsection if template provides placeholder for it).\n       * Terminology conflict → Normalize term across spec; retain original only if necessary by adding `(formerly referred to as \"X\")` once.","chunkId":".github/prompts/clarify.prompt.md:24"}],[137,{"filePath":".github/prompts/clarify.prompt.md","startLine":123,"endLine":126,"chunkText":"       * Edge case / negative flow → Add a new bullet under Edge Cases / Error Handling (or create such subsection if template provides placeholder for it).\n       * Terminology conflict → Normalize term across spec; retain original only if necessary by adding `(formerly referred to as \"X\")` once.\n    - If the clarification invalidates an earlier ambiguous statement, replace that statement instead of duplicating; leave no obsolete contradictory text.\n    - Save the spec file AFTER each integration to minimize risk of context loss (atomic overwrite).","chunkId":".github/prompts/clarify.prompt.md:25"}],[138,{"filePath":".github/prompts/clarify.prompt.md","startLine":124,"endLine":129,"chunkText":"       * Terminology conflict → Normalize term across spec; retain original only if necessary by adding `(formerly referred to as \"X\")` once.\n    - If the clarification invalidates an earlier ambiguous statement, replace that statement instead of duplicating; leave no obsolete contradictory text.\n    - Save the spec file AFTER each integration to minimize risk of context loss (atomic overwrite).\n    - Preserve formatting: do not reorder unrelated sections; keep heading hierarchy intact.\n    - Keep each inserted clarification minimal and testable (avoid narrative drift).\n","chunkId":".github/prompts/clarify.prompt.md:26"}],[139,{"filePath":".github/prompts/clarify.prompt.md","startLine":127,"endLine":134,"chunkText":"    - Preserve formatting: do not reorder unrelated sections; keep heading hierarchy intact.\n    - Keep each inserted clarification minimal and testable (avoid narrative drift).\n\n6. Validation (performed after EACH write plus final pass):\n   - Clarifications session contains exactly one bullet per accepted answer (no duplicates).\n   - Total asked (accepted) questions ≤ 5.\n   - Updated sections contain no lingering vague placeholders the new answer was meant to resolve.\n   - No contradictory earlier statement remains (scan for now-invalid alternative choices removed).","chunkId":".github/prompts/clarify.prompt.md:27"}],[140,{"filePath":".github/prompts/clarify.prompt.md","startLine":132,"endLine":141,"chunkText":"   - Total asked (accepted) questions ≤ 5.\n   - Updated sections contain no lingering vague placeholders the new answer was meant to resolve.\n   - No contradictory earlier statement remains (scan for now-invalid alternative choices removed).\n   - Markdown structure valid; only allowed new headings: `## Clarifications`, `### Session YYYY-MM-DD`.\n   - Terminology consistency: same canonical term used across all updated sections.\n\n7. Write the updated spec back to `FEATURE_SPEC`.\n\n8. Report completion (after questioning loop ends or early termination):\n   - Number of questions asked & answered.","chunkId":".github/prompts/clarify.prompt.md:28"}],[141,{"filePath":".github/prompts/clarify.prompt.md","startLine":139,"endLine":147,"chunkText":"\n8. Report completion (after questioning loop ends or early termination):\n   - Number of questions asked & answered.\n   - Path to updated spec.\n   - Sections touched (list names).\n   - Coverage summary table listing each taxonomy category with Status: Resolved (was Partial/Missing and addressed), Deferred (exceeds question quota or better suited for planning), Clear (already sufficient), Outstanding (still Partial/Missing but low impact).\n   - If any Outstanding or Deferred remain, recommend whether to proceed to `/plan` or run `/clarify` again later post-plan.\n   - Suggested next command.\n","chunkId":".github/prompts/clarify.prompt.md:29"}],[142,{"filePath":".github/prompts/clarify.prompt.md","startLine":145,"endLine":151,"chunkText":"   - If any Outstanding or Deferred remain, recommend whether to proceed to `/plan` or run `/clarify` again later post-plan.\n   - Suggested next command.\n\nBehavior rules:\n- If no meaningful ambiguities found (or all potential questions would be low-impact), respond: \"No critical ambiguities detected worth formal clarification.\" and suggest proceeding.\n- If spec file missing, instruct user to run `/specify` first (do not create a new spec here).\n- Never exceed 5 total asked questions (clarification retries for a single question do not count as new questions).","chunkId":".github/prompts/clarify.prompt.md:30"}],[143,{"filePath":".github/prompts/clarify.prompt.md","startLine":149,"endLine":153,"chunkText":"- If no meaningful ambiguities found (or all potential questions would be low-impact), respond: \"No critical ambiguities detected worth formal clarification.\" and suggest proceeding.\n- If spec file missing, instruct user to run `/specify` first (do not create a new spec here).\n- Never exceed 5 total asked questions (clarification retries for a single question do not count as new questions).\n- Avoid speculative tech stack questions unless the absence blocks functional clarity.\n- Respect user early termination signals (\"stop\", \"done\", \"proceed\").","chunkId":".github/prompts/clarify.prompt.md:31"}],[144,{"filePath":".github/prompts/clarify.prompt.md","startLine":151,"endLine":158,"chunkText":"- Never exceed 5 total asked questions (clarification retries for a single question do not count as new questions).\n- Avoid speculative tech stack questions unless the absence blocks functional clarity.\n- Respect user early termination signals (\"stop\", \"done\", \"proceed\").\n - If no questions asked due to full coverage, output a compact coverage summary (all categories Clear) then suggest advancing.\n - If quota reached with unresolved high-impact categories remaining, explicitly flag them under Deferred with rationale.\n\nContext for prioritization: $ARGUMENTS\n","chunkId":".github/prompts/clarify.prompt.md:32"}],[145,{"filePath":".github/prompts/constitution.prompt.md","startLine":0,"endLine":9,"chunkText":"---\ndescription: Create or update the project constitution from interactive or provided principle inputs, ensuring all dependent templates stay in sync.\n---\n\nThe user input to you can be provided directly by the agent or as a command argument - you **MUST** consider it before proceeding with the prompt (if not empty).\n\nUser input:\n\n$ARGUMENTS\n","chunkId":".github/prompts/constitution.prompt.md:0"}],[146,{"filePath":".github/prompts/constitution.prompt.md","startLine":7,"endLine":15,"chunkText":"\n$ARGUMENTS\n\nYou are updating the project constitution at `.specify/memory/constitution.md`. This file is a TEMPLATE containing placeholder tokens in square brackets (e.g. `[PROJECT_NAME]`, `[PRINCIPLE_1_NAME]`). Your job is to (a) collect/derive concrete values, (b) fill the template precisely, and (c) propagate any amendments across dependent artifacts.\n\nFollow this execution flow:\n\n1. Load the existing constitution template at `.specify/memory/constitution.md`.\n   - Identify every placeholder token of the form `[ALL_CAPS_IDENTIFIER]`.","chunkId":".github/prompts/constitution.prompt.md:1"}],[147,{"filePath":".github/prompts/constitution.prompt.md","startLine":13,"endLine":20,"chunkText":"\n1. Load the existing constitution template at `.specify/memory/constitution.md`.\n   - Identify every placeholder token of the form `[ALL_CAPS_IDENTIFIER]`.\n   **IMPORTANT**: The user might require less or more principles than the ones used in the template. If a number is specified, respect that - follow the general template. You will update the doc accordingly.\n\n2. Collect/derive values for placeholders:\n   - If user input (conversation) supplies a value, use it.\n   - Otherwise infer from existing repo context (README, docs, prior constitution versions if embedded).","chunkId":".github/prompts/constitution.prompt.md:2"}],[148,{"filePath":".github/prompts/constitution.prompt.md","startLine":18,"endLine":23,"chunkText":"2. Collect/derive values for placeholders:\n   - If user input (conversation) supplies a value, use it.\n   - Otherwise infer from existing repo context (README, docs, prior constitution versions if embedded).\n   - For governance dates: `RATIFICATION_DATE` is the original adoption date (if unknown ask or mark TODO), `LAST_AMENDED_DATE` is today if changes are made, otherwise keep previous.\n   - `CONSTITUTION_VERSION` must increment according to semantic versioning rules:\n     * MAJOR: Backward incompatible governance/principle removals or redefinitions.","chunkId":".github/prompts/constitution.prompt.md:3"}],[149,{"filePath":".github/prompts/constitution.prompt.md","startLine":21,"endLine":27,"chunkText":"   - For governance dates: `RATIFICATION_DATE` is the original adoption date (if unknown ask or mark TODO), `LAST_AMENDED_DATE` is today if changes are made, otherwise keep previous.\n   - `CONSTITUTION_VERSION` must increment according to semantic versioning rules:\n     * MAJOR: Backward incompatible governance/principle removals or redefinitions.\n     * MINOR: New principle/section added or materially expanded guidance.\n     * PATCH: Clarifications, wording, typo fixes, non-semantic refinements.\n   - If version bump type ambiguous, propose reasoning before finalizing.\n","chunkId":".github/prompts/constitution.prompt.md:4"}],[150,{"filePath":".github/prompts/constitution.prompt.md","startLine":25,"endLine":30,"chunkText":"     * PATCH: Clarifications, wording, typo fixes, non-semantic refinements.\n   - If version bump type ambiguous, propose reasoning before finalizing.\n\n3. Draft the updated constitution content:\n   - Replace every placeholder with concrete text (no bracketed tokens left except intentionally retained template slots that the project has chosen not to define yet—explicitly justify any left).\n   - Preserve heading hierarchy and comments can be removed once replaced unless they still add clarifying guidance.","chunkId":".github/prompts/constitution.prompt.md:5"}],[151,{"filePath":".github/prompts/constitution.prompt.md","startLine":28,"endLine":31,"chunkText":"3. Draft the updated constitution content:\n   - Replace every placeholder with concrete text (no bracketed tokens left except intentionally retained template slots that the project has chosen not to define yet—explicitly justify any left).\n   - Preserve heading hierarchy and comments can be removed once replaced unless they still add clarifying guidance.\n   - Ensure each Principle section: succinct name line, paragraph (or bullet list) capturing non‑negotiable rules, explicit rationale if not obvious.","chunkId":".github/prompts/constitution.prompt.md:6"}],[152,{"filePath":".github/prompts/constitution.prompt.md","startLine":29,"endLine":33,"chunkText":"   - Replace every placeholder with concrete text (no bracketed tokens left except intentionally retained template slots that the project has chosen not to define yet—explicitly justify any left).\n   - Preserve heading hierarchy and comments can be removed once replaced unless they still add clarifying guidance.\n   - Ensure each Principle section: succinct name line, paragraph (or bullet list) capturing non‑negotiable rules, explicit rationale if not obvious.\n   - Ensure Governance section lists amendment procedure, versioning policy, and compliance review expectations.\n","chunkId":".github/prompts/constitution.prompt.md:7"}],[153,{"filePath":".github/prompts/constitution.prompt.md","startLine":31,"endLine":35,"chunkText":"   - Ensure each Principle section: succinct name line, paragraph (or bullet list) capturing non‑negotiable rules, explicit rationale if not obvious.\n   - Ensure Governance section lists amendment procedure, versioning policy, and compliance review expectations.\n\n4. Consistency propagation checklist (convert prior checklist into active validations):\n   - Read `.specify/templates/plan-template.md` and ensure any \"Constitution Check\" or rules align with updated principles.","chunkId":".github/prompts/constitution.prompt.md:8"}],[154,{"filePath":".github/prompts/constitution.prompt.md","startLine":33,"endLine":37,"chunkText":"\n4. Consistency propagation checklist (convert prior checklist into active validations):\n   - Read `.specify/templates/plan-template.md` and ensure any \"Constitution Check\" or rules align with updated principles.\n   - Read `.specify/templates/spec-template.md` for scope/requirements alignment—update if constitution adds/removes mandatory sections or constraints.\n   - Read `.specify/templates/tasks-template.md` and ensure task categorization reflects new or removed principle-driven task types (e.g., observability, versioning, testing discipline).","chunkId":".github/prompts/constitution.prompt.md:9"}],[155,{"filePath":".github/prompts/constitution.prompt.md","startLine":35,"endLine":37,"chunkText":"   - Read `.specify/templates/plan-template.md` and ensure any \"Constitution Check\" or rules align with updated principles.\n   - Read `.specify/templates/spec-template.md` for scope/requirements alignment—update if constitution adds/removes mandatory sections or constraints.\n   - Read `.specify/templates/tasks-template.md` and ensure task categorization reflects new or removed principle-driven task types (e.g., observability, versioning, testing discipline).","chunkId":".github/prompts/constitution.prompt.md:10"}],[156,{"filePath":".github/prompts/constitution.prompt.md","startLine":36,"endLine":38,"chunkText":"   - Read `.specify/templates/spec-template.md` for scope/requirements alignment—update if constitution adds/removes mandatory sections or constraints.\n   - Read `.specify/templates/tasks-template.md` and ensure task categorization reflects new or removed principle-driven task types (e.g., observability, versioning, testing discipline).\n   - Read each command file in `.specify/templates/commands/*.md` (including this one) to verify no outdated references (agent-specific names like CLAUDE only) remain when generic guidance is required.","chunkId":".github/prompts/constitution.prompt.md:11"}],[157,{"filePath":".github/prompts/constitution.prompt.md","startLine":37,"endLine":40,"chunkText":"   - Read `.specify/templates/tasks-template.md` and ensure task categorization reflects new or removed principle-driven task types (e.g., observability, versioning, testing discipline).\n   - Read each command file in `.specify/templates/commands/*.md` (including this one) to verify no outdated references (agent-specific names like CLAUDE only) remain when generic guidance is required.\n   - Read any runtime guidance docs (e.g., `README.md`, `docs/quickstart.md`, or agent-specific guidance files if present). Update references to principles changed.\n","chunkId":".github/prompts/constitution.prompt.md:12"}],[158,{"filePath":".github/prompts/constitution.prompt.md","startLine":38,"endLine":44,"chunkText":"   - Read each command file in `.specify/templates/commands/*.md` (including this one) to verify no outdated references (agent-specific names like CLAUDE only) remain when generic guidance is required.\n   - Read any runtime guidance docs (e.g., `README.md`, `docs/quickstart.md`, or agent-specific guidance files if present). Update references to principles changed.\n\n5. Produce a Sync Impact Report (prepend as an HTML comment at top of the constitution file after update):\n   - Version change: old → new\n   - List of modified principles (old title → new title if renamed)\n   - Added sections","chunkId":".github/prompts/constitution.prompt.md:13"}],[159,{"filePath":".github/prompts/constitution.prompt.md","startLine":42,"endLine":54,"chunkText":"   - Version change: old → new\n   - List of modified principles (old title → new title if renamed)\n   - Added sections\n   - Removed sections\n   - Templates requiring updates (✅ updated / ⚠ pending) with file paths\n   - Follow-up TODOs if any placeholders intentionally deferred.\n\n6. Validation before final output:\n   - No remaining unexplained bracket tokens.\n   - Version line matches report.\n   - Dates ISO format YYYY-MM-DD.\n   - Principles are declarative, testable, and free of vague language (\"should\" → replace with MUST/SHOULD rationale where appropriate).\n","chunkId":".github/prompts/constitution.prompt.md:14"}],[160,{"filePath":".github/prompts/constitution.prompt.md","startLine":52,"endLine":62,"chunkText":"   - Dates ISO format YYYY-MM-DD.\n   - Principles are declarative, testable, and free of vague language (\"should\" → replace with MUST/SHOULD rationale where appropriate).\n\n7. Write the completed constitution back to `.specify/memory/constitution.md` (overwrite).\n\n8. Output a final summary to the user with:\n   - New version and bump rationale.\n   - Any files flagged for manual follow-up.\n   - Suggested commit message (e.g., `docs: amend constitution to vX.Y.Z (principle additions + governance update)`).\n\nFormatting & Style Requirements:","chunkId":".github/prompts/constitution.prompt.md:15"}],[161,{"filePath":".github/prompts/constitution.prompt.md","startLine":60,"endLine":69,"chunkText":"   - Suggested commit message (e.g., `docs: amend constitution to vX.Y.Z (principle additions + governance update)`).\n\nFormatting & Style Requirements:\n- Use Markdown headings exactly as in the template (do not demote/promote levels).\n- Wrap long rationale lines to keep readability (<100 chars ideally) but do not hard enforce with awkward breaks.\n- Keep a single blank line between sections.\n- Avoid trailing whitespace.\n\nIf the user supplies partial updates (e.g., only one principle revision), still perform validation and version decision steps.\n","chunkId":".github/prompts/constitution.prompt.md:16"}],[162,{"filePath":".github/prompts/constitution.prompt.md","startLine":67,"endLine":73,"chunkText":"\nIf the user supplies partial updates (e.g., only one principle revision), still perform validation and version decision steps.\n\nIf critical info missing (e.g., ratification date truly unknown), insert `TODO(<FIELD_NAME>): explanation` and include in the Sync Impact Report under deferred items.\n\nDo not create a new template; always operate on the existing `.specify/memory/constitution.md` file.\n","chunkId":".github/prompts/constitution.prompt.md:17"}],[163,{"filePath":".github/prompts/implement.prompt.md","startLine":0,"endLine":13,"chunkText":"---\ndescription: Execute the implementation plan by processing and executing all tasks defined in tasks.md\n---\n\nThe user input can be provided directly by the agent or as a command argument - you **MUST** consider it before proceeding with the prompt (if not empty).\n\nUser input:\n\n$ARGUMENTS\n\n1. Run `.specify/scripts/bash/check-prerequisites.sh --json --require-tasks --include-tasks` from repo root and parse FEATURE_DIR and AVAILABLE_DOCS list. All paths must be absolute.\n\n2. Load and analyze the implementation context:\n   - **REQUIRED**: Read tasks.md for the complete task list and execution plan","chunkId":".github/prompts/implement.prompt.md:0"}],[164,{"filePath":".github/prompts/implement.prompt.md","startLine":12,"endLine":21,"chunkText":"2. Load and analyze the implementation context:\n   - **REQUIRED**: Read tasks.md for the complete task list and execution plan\n   - **REQUIRED**: Read plan.md for tech stack, architecture, and file structure\n   - **IF EXISTS**: Read data-model.md for entities and relationships\n   - **IF EXISTS**: Read contracts/ for API specifications and test requirements\n   - **IF EXISTS**: Read research.md for technical decisions and constraints\n   - **IF EXISTS**: Read quickstart.md for integration scenarios\n\n3. Parse tasks.md structure and extract:\n   - **Task phases**: Setup, Tests, Core, Integration, Polish","chunkId":".github/prompts/implement.prompt.md:1"}],[165,{"filePath":".github/prompts/implement.prompt.md","startLine":20,"endLine":28,"chunkText":"3. Parse tasks.md structure and extract:\n   - **Task phases**: Setup, Tests, Core, Integration, Polish\n   - **Task dependencies**: Sequential vs parallel execution rules\n   - **Task details**: ID, description, file paths, parallel markers [P]\n   - **Execution flow**: Order and dependency requirements\n\n4. Execute implementation following the task plan:\n   - **Phase-by-phase execution**: Complete each phase before moving to the next\n   - **Respect dependencies**: Run sequential tasks in order, parallel tasks [P] can run together  ","chunkId":".github/prompts/implement.prompt.md:2"}],[166,{"filePath":".github/prompts/implement.prompt.md","startLine":27,"endLine":34,"chunkText":"   - **Phase-by-phase execution**: Complete each phase before moving to the next\n   - **Respect dependencies**: Run sequential tasks in order, parallel tasks [P] can run together  \n   - **Follow TDD approach**: Execute test tasks before their corresponding implementation tasks\n   - **File-based coordination**: Tasks affecting the same files must run sequentially\n   - **Validation checkpoints**: Verify each phase completion before proceeding\n\n5. Implementation execution rules:\n   - **Setup first**: Initialize project structure, dependencies, configuration","chunkId":".github/prompts/implement.prompt.md:3"}],[167,{"filePath":".github/prompts/implement.prompt.md","startLine":33,"endLine":42,"chunkText":"5. Implementation execution rules:\n   - **Setup first**: Initialize project structure, dependencies, configuration\n   - **Tests before code**: If you need to write tests for contracts, entities, and integration scenarios\n   - **Core development**: Implement models, services, CLI commands, endpoints\n   - **Integration work**: Database connections, middleware, logging, external services\n   - **Polish and validation**: Unit tests, performance optimization, documentation\n\n6. Progress tracking and error handling:\n   - Report progress after each completed task\n   - Halt execution if any non-parallel task fails","chunkId":".github/prompts/implement.prompt.md:4"}],[168,{"filePath":".github/prompts/implement.prompt.md","startLine":41,"endLine":51,"chunkText":"   - Report progress after each completed task\n   - Halt execution if any non-parallel task fails\n   - For parallel tasks [P], continue with successful tasks, report failed ones\n   - Provide clear error messages with context for debugging\n   - Suggest next steps if implementation cannot proceed\n   - **IMPORTANT** For completed tasks, make sure to mark the task off as [X] in the tasks file.\n\n7. Completion validation:\n   - Verify all required tasks are completed\n   - Check that implemented features match the original specification\n   - Validate that tests pass and coverage meets requirements","chunkId":".github/prompts/implement.prompt.md:5"}],[169,{"filePath":".github/prompts/implement.prompt.md","startLine":50,"endLine":56,"chunkText":"   - Check that implemented features match the original specification\n   - Validate that tests pass and coverage meets requirements\n   - Confirm the implementation follows the technical plan\n   - Report final status with summary of completed work\n\nNote: This command assumes a complete task breakdown exists in tasks.md. If tasks are incomplete or missing, suggest running `/tasks` first to regenerate the task list.\n","chunkId":".github/prompts/implement.prompt.md:6"}],[170,{"filePath":".github/prompts/plan.prompt.md","startLine":0,"endLine":12,"chunkText":"---\ndescription: Execute the implementation planning workflow using the plan template to generate design artifacts.\n---\n\nThe user input to you can be provided directly by the agent or as a command argument - you **MUST** consider it before proceeding with the prompt (if not empty).\n\nUser input:\n\n$ARGUMENTS\n\nGiven the implementation details provided as an argument, do this:\n\n1. Run `.specify/scripts/bash/setup-plan.sh --json` from the repo root and parse JSON for FEATURE_SPEC, IMPL_PLAN, SPECS_DIR, BRANCH. All future file paths must be absolute.","chunkId":".github/prompts/plan.prompt.md:0"}],[171,{"filePath":".github/prompts/plan.prompt.md","startLine":10,"endLine":12,"chunkText":"Given the implementation details provided as an argument, do this:\n\n1. Run `.specify/scripts/bash/setup-plan.sh --json` from the repo root and parse JSON for FEATURE_SPEC, IMPL_PLAN, SPECS_DIR, BRANCH. All future file paths must be absolute.","chunkId":".github/prompts/plan.prompt.md:1"}],[172,{"filePath":".github/prompts/plan.prompt.md","startLine":11,"endLine":11,"chunkText":"1. Run `.specify/scripts/bash/setup-plan.sh --json` from the repo root and parse JSON for FEATURE_SPEC, IMPL_PLAN, SPECS_DIR, BRANCH. All future file paths must be absolute.","chunkId":".github/prompts/plan.prompt.md:2"}],[173,{"filePath":".github/prompts/plan.prompt.md","startLine":12,"endLine":12,"chunkText":"1. Run `.specify/scripts/bash/setup-plan.sh --json` from the repo root and parse JSON for FEATURE_SPEC, IMPL_PLAN, SPECS_DIR, BRANCH. All future file paths must be absolute.","chunkId":".github/prompts/plan.prompt.md:3"}],[174,{"filePath":".github/prompts/plan.prompt.md","startLine":13,"endLine":15,"chunkText":"   - BEFORE proceeding, inspect FEATURE_SPEC for a `## Clarifications` section with at least one `Session` subheading. If missing or clearly ambiguous areas remain (vague adjectives, unresolved critical choices), PAUSE and instruct the user to run `/clarify` first to reduce rework. Only continue if: (a) Clarifications exist OR (b) an explicit user override is provided (e.g., \"proceed without clarification\"). Do not attempt to fabricate clarifications yourself.\n2. Read and analyze the feature specification to understand:\n   - The feature requirements and user stories","chunkId":".github/prompts/plan.prompt.md:4"}],[175,{"filePath":".github/prompts/plan.prompt.md","startLine":14,"endLine":25,"chunkText":"2. Read and analyze the feature specification to understand:\n   - The feature requirements and user stories\n   - Functional and non-functional requirements\n   - Success criteria and acceptance criteria\n   - Any technical constraints or dependencies mentioned\n\n3. Read the constitution at `.specify/memory/constitution.md` to understand constitutional requirements.\n\n4. Execute the implementation plan template:\n   - Load `.specify/templates/plan-template.md` (already copied to IMPL_PLAN path)\n   - Set Input path to FEATURE_SPEC\n   - Run the Execution Flow (main) function steps 1-9","chunkId":".github/prompts/plan.prompt.md:5"}],[176,{"filePath":".github/prompts/plan.prompt.md","startLine":23,"endLine":32,"chunkText":"   - Load `.specify/templates/plan-template.md` (already copied to IMPL_PLAN path)\n   - Set Input path to FEATURE_SPEC\n   - Run the Execution Flow (main) function steps 1-9\n   - The template is self-contained and executable\n   - Follow error handling and gate checks as specified\n   - Let the template guide artifact generation in $SPECS_DIR:\n     * Phase 0 generates research.md\n     * Phase 1 generates data-model.md, contracts/, quickstart.md\n     * Phase 2 generates tasks.md\n   - Incorporate user-provided details from arguments into Technical Context: $ARGUMENTS","chunkId":".github/prompts/plan.prompt.md:6"}],[177,{"filePath":".github/prompts/plan.prompt.md","startLine":30,"endLine":43,"chunkText":"     * Phase 1 generates data-model.md, contracts/, quickstart.md\n     * Phase 2 generates tasks.md\n   - Incorporate user-provided details from arguments into Technical Context: $ARGUMENTS\n   - Update Progress Tracking as you complete each phase\n\n5. Verify execution completed:\n   - Check Progress Tracking shows all phases complete\n   - Ensure all required artifacts were generated\n   - Confirm no ERROR states in execution\n\n6. Report results with branch name, file paths, and generated artifacts.\n\nUse absolute paths with the repository root for all file operations to avoid path issues.\n","chunkId":".github/prompts/plan.prompt.md:7"}],[178,{"filePath":".github/prompts/specify.prompt.md","startLine":0,"endLine":12,"chunkText":"---\ndescription: Create or update the feature specification from a natural language feature description.\n---\n\nThe user input to you can be provided directly by the agent or as a command argument - you **MUST** consider it before proceeding with the prompt (if not empty).\n\nUser input:\n\n$ARGUMENTS\n\nThe text the user typed after `/specify` in the triggering message **is** the feature description. Assume you always have it available in this conversation even if `$ARGUMENTS` appears literally below. Do not ask the user to repeat it unless they provided an empty command.\n\nGiven that feature description, do this","chunkId":".github/prompts/specify.prompt.md:0"}],[179,{"filePath":".github/prompts/specify.prompt.md","startLine":11,"endLine":16,"chunkText":"\nGiven that feature description, do this:\n\n1. Run the script `.specify/scripts/bash/create-new-feature.sh --json \"$ARGUMENTS\"` from repo root and parse its JSON output for BRANCH_NAME and SPEC_FILE. All file paths must be absolute.\n  **IMPORTANT** You must only ever run this script once. The JSON is provided in the terminal as output - always refer to it to get the actual content you're looking for.\n2. Load `.specify/templates/spec-template.md` to understand required sections.","chunkId":".github/prompts/specify.prompt.md:1"}],[180,{"filePath":".github/prompts/specify.prompt.md","startLine":14,"endLine":16,"chunkText":"1. Run the script `.specify/scripts/bash/create-new-feature.sh --json \"$ARGUMENTS\"` from repo root and parse its JSON output for BRANCH_NAME and SPEC_FILE. All file paths must be absolute.\n  **IMPORTANT** You must only ever run this script once. The JSON is provided in the terminal as output - always refer to it to get the actual content you're looking for.\n2. Load `.specify/templates/spec-template.md` to understand required sections.","chunkId":".github/prompts/specify.prompt.md:2"}],[181,{"filePath":".github/prompts/specify.prompt.md","startLine":15,"endLine":19,"chunkText":"  **IMPORTANT** You must only ever run this script once. The JSON is provided in the terminal as output - always refer to it to get the actual content you're looking for.\n2. Load `.specify/templates/spec-template.md` to understand required sections.\n3. Write the specification to SPEC_FILE using the template structure, replacing placeholders with concrete details derived from the feature description (arguments) while preserving section order and headings.\n4. Report completion with branch name, spec file path, and readiness for the next phase.\n","chunkId":".github/prompts/specify.prompt.md:3"}],[182,{"filePath":".github/prompts/specify.prompt.md","startLine":17,"endLine":21,"chunkText":"3. Write the specification to SPEC_FILE using the template structure, replacing placeholders with concrete details derived from the feature description (arguments) while preserving section order and headings.\n4. Report completion with branch name, spec file path, and readiness for the next phase.\n\nNote: The script creates and checks out the new branch and initializes the spec file before writing.\n","chunkId":".github/prompts/specify.prompt.md:4"}],[183,{"filePath":".github/prompts/tasks.prompt.md","startLine":0,"endLine":12,"chunkText":"---\ndescription: Generate an actionable, dependency-ordered tasks.md for the feature based on available design artifacts.\n---\n\nThe user input to you can be provided directly by the agent or as a command argument - you **MUST** consider it before proceeding with the prompt (if not empty).\n\nUser input:\n\n$ARGUMENTS\n\n1. Run `.specify/scripts/bash/check-prerequisites.sh --json` from repo root and parse FEATURE_DIR and AVAILABLE_DOCS list. All paths must be absolute.\n2. Load and analyze available design documents:\n   - Always read plan.md for tech stack and libraries","chunkId":".github/prompts/tasks.prompt.md:0"}],[184,{"filePath":".github/prompts/tasks.prompt.md","startLine":11,"endLine":24,"chunkText":"2. Load and analyze available design documents:\n   - Always read plan.md for tech stack and libraries\n   - IF EXISTS: Read data-model.md for entities\n   - IF EXISTS: Read contracts/ for API endpoints\n   - IF EXISTS: Read research.md for technical decisions\n   - IF EXISTS: Read quickstart.md for test scenarios\n\n   Note: Not all projects have all documents. For example:\n   - CLI tools might not have contracts/\n   - Simple libraries might not need data-model.md\n   - Generate tasks based on what's available\n\n3. Generate tasks following the template:\n   - Use `.specify/templates/tasks-template.md` as the base","chunkId":".github/prompts/tasks.prompt.md:1"}],[185,{"filePath":".github/prompts/tasks.prompt.md","startLine":22,"endLine":33,"chunkText":"\n3. Generate tasks following the template:\n   - Use `.specify/templates/tasks-template.md` as the base\n   - Replace example tasks with actual tasks based on:\n     * **Setup tasks**: Project init, dependencies, linting\n     * **Test tasks [P]**: One per contract, one per integration scenario\n     * **Core tasks**: One per entity, service, CLI command, endpoint\n     * **Integration tasks**: DB connections, middleware, logging\n     * **Polish tasks [P]**: Unit tests, performance, docs\n\n4. Task generation rules:\n   - Each contract file → contract test task marked [P]","chunkId":".github/prompts/tasks.prompt.md:2"}],[186,{"filePath":".github/prompts/tasks.prompt.md","startLine":31,"endLine":48,"chunkText":"\n4. Task generation rules:\n   - Each contract file → contract test task marked [P]\n   - Each entity in data-model → model creation task marked [P]\n   - Each endpoint → implementation task (not parallel if shared files)\n   - Each user story → integration test marked [P]\n   - Different files = can be parallel [P]\n   - Same file = sequential (no [P])\n\n5. Order tasks by dependencies:\n   - Setup before everything\n   - Tests before implementation (TDD)\n   - Models before services\n   - Services before endpoints\n   - Core before integration\n   - Everything before polish\n\n6. Include parallel execution examples:","chunkId":".github/prompts/tasks.prompt.md:3"}],[187,{"filePath":".github/prompts/tasks.prompt.md","startLine":46,"endLine":62,"chunkText":"   - Everything before polish\n\n6. Include parallel execution examples:\n   - Group [P] tasks that can run together\n   - Show actual Task agent commands\n\n7. Create FEATURE_DIR/tasks.md with:\n   - Correct feature name from implementation plan\n   - Numbered tasks (T001, T002, etc.)\n   - Clear file paths for each task\n   - Dependency notes\n   - Parallel execution guidance\n\nContext for task generation: $ARGUMENTS\n\nThe tasks.md should be immediately executable - each task must be specific enough that an LLM can complete it without additional context.\n","chunkId":".github/prompts/tasks.prompt.md:4"}],[188,{"filePath":".specify/memory/constitution.md","startLine":0,"endLine":18,"chunkText":"<!--\nSync Impact Report:\nVersion change: Initial → 1.0.0\nModified principles: All (new constitution)\nAdded sections: Core Principles, Performance Standards, Quality Assurance\nRemoved sections: None (template placeholders)\nTemplates requiring updates:\n- ✅ plan-template.md (Constitution Check section updated)\n- ✅ spec-template.md (aligned with principles)\n- ✅ tasks-template.md (TDD enforcement aligned)\n- ✅ agent-file-template.md (no changes needed)\nFollow-up TODOs: None\n-->\n\n# Audio Separator Constitution\n\n## Core Principles\n\n### I. Library-First Architecture","chunkId":".specify/memory/constitution.md:0"}],[189,{"filePath":".specify/memory/constitution.md","startLine":16,"endLine":23,"chunkText":"## Core Principles\n\n### I. Library-First Architecture\nThe `Separator` class MUST be the primary interface for all audio separation functionality. CLI and remote API are thin wrappers around the library. Libraries MUST be self-contained, independently testable, and documented with clear separation of concerns between architectures (MDX, VR, Demucs, MDXC).\n\n**Rationale**: This ensures the core functionality can be integrated into other projects while maintaining a consistent API across all interfaces.\n\n### II. Multi-Interface Consistency","chunkId":".specify/memory/constitution.md:1"}],[190,{"filePath":".specify/memory/constitution.md","startLine":21,"endLine":27,"chunkText":"**Rationale**: This ensures the core functionality can be integrated into other projects while maintaining a consistent API across all interfaces.\n\n### II. Multi-Interface Consistency\nEvery core feature MUST be accessible via three interfaces: Python API, CLI, and Remote API. Parameter names and behavior MUST be identical across all interfaces. All interfaces MUST support the same model architectures and processing options.\n\n**Rationale**: Users should have consistent experience regardless of how they access the functionality, enabling seamless transition between local and remote processing.\n","chunkId":".specify/memory/constitution.md:2"}],[191,{"filePath":".specify/memory/constitution.md","startLine":25,"endLine":30,"chunkText":"\n**Rationale**: Users should have consistent experience regardless of how they access the functionality, enabling seamless transition between local and remote processing.\n\n### III. Test-First Development (NON-NEGOTIABLE)\nTDD is mandatory: Tests written → Tests fail → Implementation → Tests pass. All new features MUST include unit tests, integration tests with audio validation (SSIM comparison), and CLI tests. No code merges without passing tests.\n","chunkId":".specify/memory/constitution.md:3"}],[192,{"filePath":".specify/memory/constitution.md","startLine":28,"endLine":33,"chunkText":"### III. Test-First Development (NON-NEGOTIABLE)\nTDD is mandatory: Tests written → Tests fail → Implementation → Tests pass. All new features MUST include unit tests, integration tests with audio validation (SSIM comparison), and CLI tests. No code merges without passing tests.\n\n**Rationale**: Audio processing requires precision and consistency. Automated testing with perceptual validation ensures output quality remains stable across changes.\n\n### IV. Performance & Resource Efficiency","chunkId":".specify/memory/constitution.md:4"}],[193,{"filePath":".specify/memory/constitution.md","startLine":31,"endLine":37,"chunkText":"**Rationale**: Audio processing requires precision and consistency. Automated testing with perceptual validation ensures output quality remains stable across changes.\n\n### IV. Performance & Resource Efficiency\nHardware acceleration MUST be supported (CUDA, CoreML, DirectML). Memory usage MUST be optimized for large audio files through streaming and batch processing. Processing parameters MUST be tunable for different hardware capabilities.\n\n**Rationale**: Audio separation is computationally intensive. Efficient resource usage enables processing of longer files and broader hardware compatibility.\n","chunkId":".specify/memory/constitution.md:5"}],[194,{"filePath":".specify/memory/constitution.md","startLine":35,"endLine":44,"chunkText":"\n**Rationale**: Audio separation is computationally intensive. Efficient resource usage enables processing of longer files and broader hardware compatibility.\n\n### V. Model Architecture Separation\nEach model architecture (MDX, VR, Demucs, MDXC) MUST be implemented in separate modules inheriting from `CommonSeparator`. Loading one architecture MUST NOT load code from others. Architecture-specific parameters MUST be isolated and documented.\n\n**Rationale**: This prevents conflicts between different model types and keeps memory usage minimal by loading only required components.\n\n## Performance Standards\n","chunkId":".specify/memory/constitution.md:6"}],[195,{"filePath":".specify/memory/constitution.md","startLine":42,"endLine":55,"chunkText":"\n## Performance Standards\n\nAll audio processing operations MUST meet these requirements:\n- **Memory efficiency**: Support files larger than available RAM through streaming\n- **GPU utilization**: Automatically detect and utilize available hardware acceleration\n- **Batch processing**: Support processing multiple files without model reloading\n- **Output consistency**: Identical inputs MUST produce identical outputs (deterministic)\n\n## Quality Assurance\n\n### Testing Requirements\n- **Unit tests**: All core classes and functions\n- **Integration tests**: End-to-end audio processing with SSIM validation","chunkId":".specify/memory/constitution.md:7"}],[196,{"filePath":".specify/memory/constitution.md","startLine":53,"endLine":66,"chunkText":"### Testing Requirements\n- **Unit tests**: All core classes and functions\n- **Integration tests**: End-to-end audio processing with SSIM validation\n- **Performance tests**: Memory usage and processing speed benchmarks\n- **Cross-platform tests**: Windows, macOS, Linux compatibility\n\n### Audio Validation\nOutput quality MUST be validated using:\n- Waveform and spectrogram image comparison (SSIM ≥ 0.95)\n- Reference audio files for each supported model architecture\n- Automated regression testing on model output changes\n\n## Governance\n","chunkId":".specify/memory/constitution.md:8"}],[197,{"filePath":".specify/memory/constitution.md","startLine":64,"endLine":73,"chunkText":"\n## Governance\n\nThis constitution supersedes all other development practices. All pull requests MUST verify compliance with these principles. Any deviation MUST be explicitly justified and documented.\n\n**Amendment Process**: Changes require documentation of impact, approval from maintainers, and migration plan for affected code.\n\n**Compliance Review**: All features undergo constitutional compliance check during planning phase and post-implementation validation.\n\n**Version**: 1.0.0 | **Ratified**: 2025-09-25 | **Last Amended**: 2025-09-25","chunkId":".specify/memory/constitution.md:9"}],[198,{"filePath":".specify/templates/agent-file-template.md","startLine":0,"endLine":22,"chunkText":"# [PROJECT NAME] Development Guidelines\n\nAuto-generated from all feature plans. Last updated: [DATE]\n\n## Active Technologies\n[EXTRACTED FROM ALL PLAN.MD FILES]\n\n## Project Structure\n```\n[ACTUAL STRUCTURE FROM PLANS]\n```\n\n## Commands\n[ONLY COMMANDS FOR ACTIVE TECHNOLOGIES]\n\n## Code Style\n[LANGUAGE-SPECIFIC, ONLY FOR LANGUAGES IN USE]\n\n## Recent Changes\n[LAST 3 FEATURES AND WHAT THEY ADDED]\n\n<!-- MANUAL ADDITIONS START -->\n<!-- MANUAL ADDITIONS END -->","chunkId":".specify/templates/agent-file-template.md:0"}],[199,{"filePath":".specify/templates/plan-template.md","startLine":0,"endLine":13,"chunkText":"\n# Implementation Plan: [FEATURE]\n\n**Branch**: `[###-feature-name]` | **Date**: [DATE] | **Spec**: [link]\n**Input**: Feature specification from `/specs/[###-feature-name]/spec.md`\n\n## Execution Flow (/plan command scope)\n```\n1. Load feature spec from Input path\n   → If not found: ERROR \"No feature spec at {path}\"\n2. Fill Technical Context (scan for NEEDS CLARIFICATION)\n   → Detect Project Type from context (web=frontend+backend, mobile=app+api)\n   → Set Structure Decision based on project type\n3. Fill the Constitution Check section based on the content of the constitution document.","chunkId":".specify/templates/plan-template.md:0"}],[200,{"filePath":".specify/templates/plan-template.md","startLine":11,"endLine":19,"chunkText":"   → Detect Project Type from context (web=frontend+backend, mobile=app+api)\n   → Set Structure Decision based on project type\n3. Fill the Constitution Check section based on the content of the constitution document.\n4. Evaluate Constitution Check section below\n   → If violations exist: Document in Complexity Tracking\n   → If no justification possible: ERROR \"Simplify approach first\"\n   → Update Progress Tracking: Initial Constitution Check\n5. Execute Phase 0 → research.md\n   → If NEEDS CLARIFICATION remain: ERROR \"Resolve unknowns\"","chunkId":".specify/templates/plan-template.md:1"}],[201,{"filePath":".specify/templates/plan-template.md","startLine":17,"endLine":23,"chunkText":"   → Update Progress Tracking: Initial Constitution Check\n5. Execute Phase 0 → research.md\n   → If NEEDS CLARIFICATION remain: ERROR \"Resolve unknowns\"\n6. Execute Phase 1 → contracts, data-model.md, quickstart.md, agent-specific template file (e.g., `CLAUDE.md` for Claude Code, `.github/copilot-instructions.md` for GitHub Copilot, `GEMINI.md` for Gemini CLI, `QWEN.md` for Qwen Code or `AGENTS.md` for opencode).\n7. Re-evaluate Constitution Check section\n   → If new violations: Refactor design, return to Phase 1\n   → Update Progress Tracking: Post-Design Constitution Check","chunkId":".specify/templates/plan-template.md:2"}],[202,{"filePath":".specify/templates/plan-template.md","startLine":21,"endLine":35,"chunkText":"7. Re-evaluate Constitution Check section\n   → If new violations: Refactor design, return to Phase 1\n   → Update Progress Tracking: Post-Design Constitution Check\n8. Plan Phase 2 → Describe task generation approach (DO NOT create tasks.md)\n9. STOP - Ready for /tasks command\n```\n\n**IMPORTANT**: The /plan command STOPS at step 7. Phases 2-4 are executed by other commands:\n- Phase 2: /tasks command creates tasks.md\n- Phase 3-4: Implementation execution (manual or via tools)\n\n## Summary\n[Extract from feature spec: primary requirement + technical approach from research]\n\n## Technical Context","chunkId":".specify/templates/plan-template.md:3"}],[203,{"filePath":".specify/templates/plan-template.md","startLine":33,"endLine":41,"chunkText":"[Extract from feature spec: primary requirement + technical approach from research]\n\n## Technical Context\n**Language/Version**: [e.g., Python 3.11, Swift 5.9, Rust 1.75 or NEEDS CLARIFICATION]  \n**Primary Dependencies**: [e.g., FastAPI, UIKit, LLVM or NEEDS CLARIFICATION]  \n**Storage**: [if applicable, e.g., PostgreSQL, CoreData, files or N/A]  \n**Testing**: [e.g., pytest, XCTest, cargo test or NEEDS CLARIFICATION]  \n**Target Platform**: [e.g., Linux server, iOS 15+, WASM or NEEDS CLARIFICATION]\n**Project Type**: [single/web/mobile - determines source structure]  ","chunkId":".specify/templates/plan-template.md:4"}],[204,{"filePath":".specify/templates/plan-template.md","startLine":39,"endLine":46,"chunkText":"**Testing**: [e.g., pytest, XCTest, cargo test or NEEDS CLARIFICATION]  \n**Target Platform**: [e.g., Linux server, iOS 15+, WASM or NEEDS CLARIFICATION]\n**Project Type**: [single/web/mobile - determines source structure]  \n**Performance Goals**: [domain-specific, e.g., 1000 req/s, 10k lines/sec, 60 fps or NEEDS CLARIFICATION]  \n**Constraints**: [domain-specific, e.g., <200ms p95, <100MB memory, offline-capable or NEEDS CLARIFICATION]  \n**Scale/Scope**: [domain-specific, e.g., 10k users, 1M LOC, 50 screens or NEEDS CLARIFICATION]\n\n## Constitution Check","chunkId":".specify/templates/plan-template.md:5"}],[205,{"filePath":".specify/templates/plan-template.md","startLine":44,"endLine":55,"chunkText":"**Scale/Scope**: [domain-specific, e.g., 10k users, 1M LOC, 50 screens or NEEDS CLARIFICATION]\n\n## Constitution Check\n*GATE: Must pass before Phase 0 research. Re-check after Phase 1 design.*\n\n### I. Library-First Architecture\n- [ ] Core functionality implemented in `Separator` class or similar library pattern\n- [ ] CLI/Remote API are thin wrappers, not containing business logic\n- [ ] Clear separation between model architectures (MDX, VR, Demucs, MDXC)\n\n### II. Multi-Interface Consistency  \n- [ ] Feature accessible via Python API, CLI, and Remote API (if applicable)","chunkId":".specify/templates/plan-template.md:6"}],[206,{"filePath":".specify/templates/plan-template.md","startLine":53,"endLine":66,"chunkText":"\n### II. Multi-Interface Consistency  \n- [ ] Feature accessible via Python API, CLI, and Remote API (if applicable)\n- [ ] Parameter names identical across all interfaces\n- [ ] Same model architectures supported across interfaces\n\n### III. Test-First Development (NON-NEGOTIABLE)\n- [ ] Tests written before implementation\n- [ ] Unit tests for all core functionality\n- [ ] Integration tests with audio validation (SSIM comparison)\n- [ ] CLI tests for all exposed functionality\n\n### IV. Performance & Resource Efficiency\n- [ ] Hardware acceleration support considered (CUDA, CoreML, DirectML)","chunkId":".specify/templates/plan-template.md:7"}],[207,{"filePath":".specify/templates/plan-template.md","startLine":64,"endLine":80,"chunkText":"### IV. Performance & Resource Efficiency\n- [ ] Hardware acceleration support considered (CUDA, CoreML, DirectML)\n- [ ] Memory optimization for large files (streaming/batch processing)\n- [ ] Tunable parameters for different hardware capabilities\n\n### V. Model Architecture Separation\n- [ ] Each architecture in separate modules\n- [ ] Inherits from `CommonSeparator` pattern\n- [ ] Architecture-specific parameters isolated\n- [ ] Loading one architecture doesn't load others\n\n## Project Structure\n\n### Documentation (this feature)\n```\nspecs/[###-feature]/\n├── plan.md              # This file (/plan command output","chunkId":".specify/templates/plan-template.md:8"}],[208,{"filePath":".specify/templates/plan-template.md","startLine":79,"endLine":102,"chunkText":"```\nspecs/[###-feature]/\n├── plan.md              # This file (/plan command output)\n├── research.md          # Phase 0 output (/plan command)\n├── data-model.md        # Phase 1 output (/plan command)\n├── quickstart.md        # Phase 1 output (/plan command)\n├── contracts/           # Phase 1 output (/plan command)\n└── tasks.md             # Phase 2 output (/tasks command - NOT created by /plan)\n```\n\n### Source Code (repository root)\n```\n# Option 1: Single project (DEFAULT)\nsrc/\n├── models/\n├── services/\n├── cli/\n└── lib/\n\ntests/\n├── contract/\n├── integration/\n└── unit/\n","chunkId":".specify/templates/plan-template.md:9"}],[209,{"filePath":".specify/templates/plan-template.md","startLine":100,"endLine":129,"chunkText":"├── integration/\n└── unit/\n\n# Option 2: Web application (when \"frontend\" + \"backend\" detected)\nbackend/\n├── src/\n│   ├── models/\n│   ├── services/\n│   └── api/\n└── tests/\n\nfrontend/\n├── src/\n│   ├── components/\n│   ├── pages/\n│   └── services/\n└── tests/\n\n# Option 3: Mobile + API (when \"iOS/Android\" detected)\napi/\n└── [same as backend above]\n\nios/ or android/\n└── [platform-specific structure]\n```\n\n**Structure Decision**: [DEFAULT to Option 1 unless Technical Context indicates web/mobile app]\n\n## Phase 0: Outline & Research\n1. **Extract unknowns from Technical Context** above:","chunkId":".specify/templates/plan-template.md:10"}],[210,{"filePath":".specify/templates/plan-template.md","startLine":127,"endLine":144,"chunkText":"\n## Phase 0: Outline & Research\n1. **Extract unknowns from Technical Context** above:\n   - For each NEEDS CLARIFICATION → research task\n   - For each dependency → best practices task\n   - For each integration → patterns task\n\n2. **Generate and dispatch research agents**:\n   ```\n   For each unknown in Technical Context:\n     Task: \"Research {unknown} for {feature context}\"\n   For each technology choice:\n     Task: \"Find best practices for {tech} in {domain}\"\n   ```\n\n3. **Consolidate findings** in `research.md` using format:\n   - Decision: [what was chosen]\n   - Rationale: [why chosen]","chunkId":".specify/templates/plan-template.md:11"}],[211,{"filePath":".specify/templates/plan-template.md","startLine":142,"endLine":158,"chunkText":"3. **Consolidate findings** in `research.md` using format:\n   - Decision: [what was chosen]\n   - Rationale: [why chosen]\n   - Alternatives considered: [what else evaluated]\n\n**Output**: research.md with all NEEDS CLARIFICATION resolved\n\n## Phase 1: Design & Contracts\n*Prerequisites: research.md complete*\n\n1. **Extract entities from feature spec** → `data-model.md`:\n   - Entity name, fields, relationships\n   - Validation rules from requirements\n   - State transitions if applicable\n\n2. **Generate API contracts** from functional requirements:\n   - For each user action → endpoint","chunkId":".specify/templates/plan-template.md:12"}],[212,{"filePath":".specify/templates/plan-template.md","startLine":157,"endLine":172,"chunkText":"2. **Generate API contracts** from functional requirements:\n   - For each user action → endpoint\n   - Use standard REST/GraphQL patterns\n   - Output OpenAPI/GraphQL schema to `/contracts/`\n\n3. **Generate contract tests** from contracts:\n   - One test file per endpoint\n   - Assert request/response schemas\n   - Tests must fail (no implementation yet)\n\n4. **Extract test scenarios** from user stories:\n   - Each story → integration test scenario\n   - Quickstart test = story validation steps\n\n5. **Update agent file incrementally** (O(1) operation):\n   - Run `.specify/scripts/bash/update-agent-context.sh cursor`","chunkId":".specify/templates/plan-template.md:13"}],[213,{"filePath":".specify/templates/plan-template.md","startLine":170,"endLine":182,"chunkText":"\n5. **Update agent file incrementally** (O(1) operation):\n   - Run `.specify/scripts/bash/update-agent-context.sh cursor`\n     **IMPORTANT**: Execute it exactly as specified above. Do not add or remove any arguments.\n   - If exists: Add only NEW tech from current plan\n   - Preserve manual additions between markers\n   - Update recent changes (keep last 3)\n   - Keep under 150 lines for token efficiency\n   - Output to repository root\n\n**Output**: data-model.md, /contracts/*, failing tests, quickstart.md, agent-specific file\n\n## Phase 2: Task Planning Approach","chunkId":".specify/templates/plan-template.md:14"}],[214,{"filePath":".specify/templates/plan-template.md","startLine":180,"endLine":194,"chunkText":"**Output**: data-model.md, /contracts/*, failing tests, quickstart.md, agent-specific file\n\n## Phase 2: Task Planning Approach\n*This section describes what the /tasks command will do - DO NOT execute during /plan*\n\n**Task Generation Strategy**:\n- Load `.specify/templates/tasks-template.md` as base\n- Generate tasks from Phase 1 design docs (contracts, data model, quickstart)\n- Each contract → contract test task [P]\n- Each entity → model creation task [P] \n- Each user story → integration test task\n- Implementation tasks to make tests pass\n\n**Ordering Strategy**:\n- TDD order: Tests before implementation ","chunkId":".specify/templates/plan-template.md:15"}],[215,{"filePath":".specify/templates/plan-template.md","startLine":192,"endLine":206,"chunkText":"\n**Ordering Strategy**:\n- TDD order: Tests before implementation \n- Dependency order: Models before services before UI\n- Mark [P] for parallel execution (independent files)\n\n**Estimated Output**: 25-30 numbered, ordered tasks in tasks.md\n\n**IMPORTANT**: This phase is executed by the /tasks command, NOT by /plan\n\n## Phase 3+: Future Implementation\n*These phases are beyond the scope of the /plan command*\n\n**Phase 3**: Task execution (/tasks command creates tasks.md)  \n**Phase 4**: Implementation (execute tasks.md following constitutional principles)  ","chunkId":".specify/templates/plan-template.md:16"}],[216,{"filePath":".specify/templates/plan-template.md","startLine":204,"endLine":214,"chunkText":"\n**Phase 3**: Task execution (/tasks command creates tasks.md)  \n**Phase 4**: Implementation (execute tasks.md following constitutional principles)  \n**Phase 5**: Validation (run tests, execute quickstart.md, performance validation)\n\n## Complexity Tracking\n*Fill ONLY if Constitution Check has violations that must be justified*\n\n| Violation | Why Needed | Simpler Alternative Rejected Because |\n|-----------|------------|-------------------------------------|\n| [e.g., 4th project] | [current need] | [why 3 projects insufficient] |","chunkId":".specify/templates/plan-template.md:17"}],[217,{"filePath":".specify/templates/plan-template.md","startLine":212,"endLine":225,"chunkText":"| Violation | Why Needed | Simpler Alternative Rejected Because |\n|-----------|------------|-------------------------------------|\n| [e.g., 4th project] | [current need] | [why 3 projects insufficient] |\n| [e.g., Repository pattern] | [specific problem] | [why direct DB access insufficient] |\n\n\n## Progress Tracking\n*This checklist is updated during execution flow*\n\n**Phase Status**:\n- [ ] Phase 0: Research complete (/plan command)\n- [ ] Phase 1: Design complete (/plan command)\n- [ ] Phase 2: Task planning complete (/plan command - describe approach only)\n- [ ] Phase 3: Tasks generated (/tasks command)","chunkId":".specify/templates/plan-template.md:18"}],[218,{"filePath":".specify/templates/plan-template.md","startLine":223,"endLine":237,"chunkText":"- [ ] Phase 1: Design complete (/plan command)\n- [ ] Phase 2: Task planning complete (/plan command - describe approach only)\n- [ ] Phase 3: Tasks generated (/tasks command)\n- [ ] Phase 4: Implementation complete\n- [ ] Phase 5: Validation passed\n\n**Gate Status**:\n- [ ] Initial Constitution Check: PASS\n- [ ] Post-Design Constitution Check: PASS\n- [ ] All NEEDS CLARIFICATION resolved\n- [ ] Complexity deviations documented\n\n---\n*Based on Constitution v1.0.0 - See `.specify/memory/constitution.md`*\n","chunkId":".specify/templates/plan-template.md:19"}],[219,{"filePath":".specify/templates/spec-template.md","startLine":0,"endLine":17,"chunkText":"# Feature Specification: [FEATURE NAME]\n\n**Feature Branch**: `[###-feature-name]`  \n**Created**: [DATE]  \n**Status**: Draft  \n**Input**: User description: \"$ARGUMENTS\"\n\n## Execution Flow (main)\n```\n1. Parse user description from Input\n   → If empty: ERROR \"No feature description provided\"\n2. Extract key concepts from description\n   → Identify: actors, actions, data, constraints\n3. For each unclear aspect:\n   → Mark with [NEEDS CLARIFICATION: specific question]\n4. Fill User Scenarios & Testing section\n   → If no clear user flow: ERROR \"Cannot determine user scenarios\"\n5. Generate Functional Requirements","chunkId":".specify/templates/spec-template.md:0"}],[220,{"filePath":".specify/templates/spec-template.md","startLine":15,"endLine":31,"chunkText":"4. Fill User Scenarios & Testing section\n   → If no clear user flow: ERROR \"Cannot determine user scenarios\"\n5. Generate Functional Requirements\n   → Each requirement must be testable\n   → Mark ambiguous requirements\n6. Identify Key Entities (if data involved)\n7. Run Review Checklist\n   → If any [NEEDS CLARIFICATION]: WARN \"Spec has uncertainties\"\n   → If implementation details found: ERROR \"Remove tech details\"\n8. Return: SUCCESS (spec ready for planning)\n```\n\n---\n\n## ⚡ Quick Guidelines\n- ✅ Focus on WHAT users need and WHY\n- ❌ Avoid HOW to implement (no tech stack, APIs, code structure)","chunkId":".specify/templates/spec-template.md:1"}],[221,{"filePath":".specify/templates/spec-template.md","startLine":29,"endLine":41,"chunkText":"## ⚡ Quick Guidelines\n- ✅ Focus on WHAT users need and WHY\n- ❌ Avoid HOW to implement (no tech stack, APIs, code structure)\n- 👥 Written for business stakeholders, not developers\n\n### Section Requirements\n- **Mandatory sections**: Must be completed for every feature\n- **Optional sections**: Include only when relevant to the feature\n- When a section doesn't apply, remove it entirely (don't leave as \"N/A\")\n\n### For AI Generation\nWhen creating this spec from a user prompt:\n1. **Mark all ambiguities**: Use [NEEDS CLARIFICATION: specific question] for any assumption you'd need to make","chunkId":".specify/templates/spec-template.md:2"}],[222,{"filePath":".specify/templates/spec-template.md","startLine":39,"endLine":49,"chunkText":"### For AI Generation\nWhen creating this spec from a user prompt:\n1. **Mark all ambiguities**: Use [NEEDS CLARIFICATION: specific question] for any assumption you'd need to make\n2. **Don't guess**: If the prompt doesn't specify something (e.g., \"login system\" without auth method), mark it\n3. **Think like a tester**: Every vague requirement should fail the \"testable and unambiguous\" checklist item\n4. **Common underspecified areas**:\n   - User types and permissions\n   - Data retention/deletion policies  \n   - Performance targets and scale\n   - Error handling behaviors\n   - Integration requirements","chunkId":".specify/templates/spec-template.md:3"}],[223,{"filePath":".specify/templates/spec-template.md","startLine":47,"endLine":69,"chunkText":"   - Performance targets and scale\n   - Error handling behaviors\n   - Integration requirements\n   - Security/compliance needs\n\n---\n\n## User Scenarios & Testing *(mandatory)*\n\n### Primary User Story\n[Describe the main user journey in plain language]\n\n### Acceptance Scenarios\n1. **Given** [initial state], **When** [action], **Then** [expected outcome]\n2. **Given** [initial state], **When** [action], **Then** [expected outcome]\n\n### Edge Cases\n- What happens when [boundary condition]?\n- How does system handle [error scenario]?\n\n## Requirements *(mandatory)*\n\n### Functional Requirements","chunkId":".specify/templates/spec-template.md:4"}],[224,{"filePath":".specify/templates/spec-template.md","startLine":67,"endLine":76,"chunkText":"## Requirements *(mandatory)*\n\n### Functional Requirements\n- **FR-001**: System MUST [specific capability, e.g., \"allow users to create accounts\"]\n- **FR-002**: System MUST [specific capability, e.g., \"validate email addresses\"]  \n- **FR-003**: Users MUST be able to [key interaction, e.g., \"reset their password\"]\n- **FR-004**: System MUST [data requirement, e.g., \"persist user preferences\"]\n- **FR-005**: System MUST [behavior, e.g., \"log all security events\"]\n\n*Example of marking unclear requirements:*","chunkId":".specify/templates/spec-template.md:5"}],[225,{"filePath":".specify/templates/spec-template.md","startLine":74,"endLine":86,"chunkText":"- **FR-005**: System MUST [behavior, e.g., \"log all security events\"]\n\n*Example of marking unclear requirements:*\n- **FR-006**: System MUST authenticate users via [NEEDS CLARIFICATION: auth method not specified - email/password, SSO, OAuth?]\n- **FR-007**: System MUST retain user data for [NEEDS CLARIFICATION: retention period not specified]\n\n### Key Entities *(include if feature involves data)*\n- **[Entity 1]**: [What it represents, key attributes without implementation]\n- **[Entity 2]**: [What it represents, relationships to other entities]\n\n---\n\n## Review & Acceptance Checklist","chunkId":".specify/templates/spec-template.md:6"}],[226,{"filePath":".specify/templates/spec-template.md","startLine":84,"endLine":106,"chunkText":"---\n\n## Review & Acceptance Checklist\n*GATE: Automated checks run during main() execution*\n\n### Content Quality\n- [ ] No implementation details (languages, frameworks, APIs)\n- [ ] Focused on user value and business needs\n- [ ] Written for non-technical stakeholders\n- [ ] All mandatory sections completed\n\n### Requirement Completeness\n- [ ] No [NEEDS CLARIFICATION] markers remain\n- [ ] Requirements are testable and unambiguous  \n- [ ] Success criteria are measurable\n- [ ] Scope is clearly bounded\n- [ ] Dependencies and assumptions identified\n\n---\n\n## Execution Status\n*Updated by main() during processing*\n","chunkId":".specify/templates/spec-template.md:7"}],[227,{"filePath":".specify/templates/spec-template.md","startLine":104,"endLine":116,"chunkText":"## Execution Status\n*Updated by main() during processing*\n\n- [ ] User description parsed\n- [ ] Key concepts extracted\n- [ ] Ambiguities marked\n- [ ] User scenarios defined\n- [ ] Requirements generated\n- [ ] Entities identified\n- [ ] Review checklist passed\n\n---\n","chunkId":".specify/templates/spec-template.md:8"}],[228,{"filePath":".specify/templates/tasks-template.md","startLine":0,"endLine":15,"chunkText":"# Tasks: [FEATURE NAME]\n\n**Input**: Design documents from `/specs/[###-feature-name]/`\n**Prerequisites**: plan.md (required), research.md, data-model.md, contracts/\n\n## Execution Flow (main)\n```\n1. Load plan.md from feature directory\n   → If not found: ERROR \"No implementation plan found\"\n   → Extract: tech stack, libraries, structure\n2. Load optional design documents:\n   → data-model.md: Extract entities → model tasks\n   → contracts/: Each file → contract test task\n   → research.md: Extract decisions → setup tasks\n3. Generate tasks by category:\n   → Setup: project init, dependencies, linting","chunkId":".specify/templates/tasks-template.md:0"}],[229,{"filePath":".specify/templates/tasks-template.md","startLine":13,"endLine":27,"chunkText":"   → research.md: Extract decisions → setup tasks\n3. Generate tasks by category:\n   → Setup: project init, dependencies, linting\n   → Tests: contract tests, integration tests\n   → Core: models, services, CLI commands\n   → Integration: DB, middleware, logging\n   → Polish: unit tests, performance, docs\n4. Apply task rules:\n   → Different files = mark [P] for parallel\n   → Same file = sequential (no [P])\n   → Tests before implementation (TDD)\n5. Number tasks sequentially (T001, T002...)\n6. Generate dependency graph\n7. Create parallel execution examples\n8. Validate task completeness:","chunkId":".specify/templates/tasks-template.md:1"}],[230,{"filePath":".specify/templates/tasks-template.md","startLine":25,"endLine":41,"chunkText":"6. Generate dependency graph\n7. Create parallel execution examples\n8. Validate task completeness:\n   → All contracts have tests?\n   → All entities have models?\n   → All endpoints implemented?\n9. Return: SUCCESS (tasks ready for execution)\n```\n\n## Format: `[ID] [P?] Description`\n- **[P]**: Can run in parallel (different files, no dependencies)\n- Include exact file paths in descriptions\n\n## Path Conventions\n- **Single project**: `src/`, `tests/` at repository root\n- **Web app**: `backend/src/`, `frontend/src/`\n- **Mobile**: `api/src/`, `ios/src/` or `android/src/`","chunkId":".specify/templates/tasks-template.md:2"}],[231,{"filePath":".specify/templates/tasks-template.md","startLine":39,"endLine":50,"chunkText":"- **Single project**: `src/`, `tests/` at repository root\n- **Web app**: `backend/src/`, `frontend/src/`\n- **Mobile**: `api/src/`, `ios/src/` or `android/src/`\n- Paths shown below assume single project - adjust based on plan.md structure\n\n## Phase 3.1: Setup\n- [ ] T001 Create project structure per implementation plan\n- [ ] T002 Initialize [language] project with [framework] dependencies\n- [ ] T003 [P] Configure linting and formatting tools\n\n## Phase 3.2: Tests First (TDD) ⚠️ MUST COMPLETE BEFORE 3.3\n**CRITICAL: These tests MUST be written and MUST FAIL before ANY implementation**","chunkId":".specify/templates/tasks-template.md:3"}],[232,{"filePath":".specify/templates/tasks-template.md","startLine":48,"endLine":57,"chunkText":"\n## Phase 3.2: Tests First (TDD) ⚠️ MUST COMPLETE BEFORE 3.3\n**CRITICAL: These tests MUST be written and MUST FAIL before ANY implementation**\n- [ ] T004 [P] Contract test POST /api/users in tests/contract/test_users_post.py\n- [ ] T005 [P] Contract test GET /api/users/{id} in tests/contract/test_users_get.py\n- [ ] T006 [P] Integration test user registration in tests/integration/test_registration.py\n- [ ] T007 [P] Integration test auth flow in tests/integration/test_auth.py\n\n## Phase 3.3: Core Implementation (ONLY after tests are failing)\n- [ ] T008 [P] User model in src/models/user.py","chunkId":".specify/templates/tasks-template.md:4"}],[233,{"filePath":".specify/templates/tasks-template.md","startLine":55,"endLine":71,"chunkText":"\n## Phase 3.3: Core Implementation (ONLY after tests are failing)\n- [ ] T008 [P] User model in src/models/user.py\n- [ ] T009 [P] UserService CRUD in src/services/user_service.py\n- [ ] T010 [P] CLI --create-user in src/cli/user_commands.py\n- [ ] T011 POST /api/users endpoint\n- [ ] T012 GET /api/users/{id} endpoint\n- [ ] T013 Input validation\n- [ ] T014 Error handling and logging\n\n## Phase 3.4: Integration\n- [ ] T015 Connect UserService to DB\n- [ ] T016 Auth middleware\n- [ ] T017 Request/response logging\n- [ ] T018 CORS and security headers\n\n## Phase 3.5: Polish","chunkId":".specify/templates/tasks-template.md:5"}],[234,{"filePath":".specify/templates/tasks-template.md","startLine":69,"endLine":87,"chunkText":"- [ ] T018 CORS and security headers\n\n## Phase 3.5: Polish\n- [ ] T019 [P] Unit tests for validation in tests/unit/test_validation.py\n- [ ] T020 Performance tests (<200ms)\n- [ ] T021 [P] Update docs/api.md\n- [ ] T022 Remove duplication\n- [ ] T023 Run manual-testing.md\n\n## Dependencies\n- Tests (T004-T007) before implementation (T008-T014)\n- T008 blocks T009, T015\n- T016 blocks T018\n- Implementation before polish (T019-T023)\n\n## Parallel Example\n```\n# Launch T004-T007 together:\nTask: \"Contract test POST /api/users in tests/contract/test_users_post.py\"","chunkId":".specify/templates/tasks-template.md:6"}],[235,{"filePath":".specify/templates/tasks-template.md","startLine":85,"endLine":102,"chunkText":"```\n# Launch T004-T007 together:\nTask: \"Contract test POST /api/users in tests/contract/test_users_post.py\"\nTask: \"Contract test GET /api/users/{id} in tests/contract/test_users_get.py\"\nTask: \"Integration test registration in tests/integration/test_registration.py\"\nTask: \"Integration test auth in tests/integration/test_auth.py\"\n```\n\n## Notes\n- [P] tasks = different files, no dependencies\n- Verify tests fail before implementing\n- Commit after each task\n- Avoid: vague tasks, same file conflicts\n\n## Task Generation Rules\n*Applied during main() execution*\n\n1. **From Contracts**:","chunkId":".specify/templates/tasks-template.md:7"}],[236,{"filePath":".specify/templates/tasks-template.md","startLine":100,"endLine":121,"chunkText":"*Applied during main() execution*\n\n1. **From Contracts**:\n   - Each contract file → contract test task [P]\n   - Each endpoint → implementation task\n   \n2. **From Data Model**:\n   - Each entity → model creation task [P]\n   - Relationships → service layer tasks\n   \n3. **From User Stories**:\n   - Each story → integration test [P]\n   - Quickstart scenarios → validation tasks\n\n4. **Ordering**:\n   - Setup → Tests → Models → Services → Endpoints → Polish\n   - Dependencies block parallel execution\n\n## Validation Checklist\n*GATE: Checked by main() before returning*\n\n- [ ] All contracts have corresponding tests","chunkId":".specify/templates/tasks-template.md:8"}],[237,{"filePath":".specify/templates/tasks-template.md","startLine":119,"endLine":126,"chunkText":"*GATE: Checked by main() before returning*\n\n- [ ] All contracts have corresponding tests\n- [ ] All entities have model tasks\n- [ ] All tests come before implementation\n- [ ] Parallel tasks truly independent\n- [ ] Each task specifies exact file path\n- [ ] No task modifies same file as another [P] task","chunkId":".specify/templates/tasks-template.md:9"}],[238,{"filePath":"README.md","startLine":0,"endLine":7,"chunkText":"<div align=\"center\">\n \n# 🎶 Audio Separator 🎶\n\n[![PyPI version](https://badge.fury.io/py/audio-separator.svg)](https://badge.fury.io/py/audio-separator)\n[![Conda Version](https://img.shields.io/conda/vn/conda-forge/audio-separator.svg)](https://anaconda.org/conda-forge/audio-separator)\n[![Docker pulls](https://img.shields.io/docker/pulls/beveradb/audio-separator.svg)](https://hub.docker.com/r/beveradb/audio-separator/tags)\n[![codecov](https://codecov.io/gh/karaokenerds/python-audio-separator/graph/badge.svg?token=N7YK4ET5JP)](https://codecov.io/gh/karaokenerds/python-audio-separator)","chunkId":"README.md:0"}],[239,{"filePath":"README.md","startLine":5,"endLine":8,"chunkText":"[![Conda Version](https://img.shields.io/conda/vn/conda-forge/audio-separator.svg)](https://anaconda.org/conda-forge/audio-separator)\n[![Docker pulls](https://img.shields.io/docker/pulls/beveradb/audio-separator.svg)](https://hub.docker.com/r/beveradb/audio-separator/tags)\n[![codecov](https://codecov.io/gh/karaokenerds/python-audio-separator/graph/badge.svg?token=N7YK4ET5JP)](https://codecov.io/gh/karaokenerds/python-audio-separator)\n[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1gSlmSmna7f7fH6OjsiMEDLl-aJ9kGPkY?usp=sharing)","chunkId":"README.md:1"}],[240,{"filePath":"README.md","startLine":6,"endLine":8,"chunkText":"[![Docker pulls](https://img.shields.io/docker/pulls/beveradb/audio-separator.svg)](https://hub.docker.com/r/beveradb/audio-separator/tags)\n[![codecov](https://codecov.io/gh/karaokenerds/python-audio-separator/graph/badge.svg?token=N7YK4ET5JP)](https://codecov.io/gh/karaokenerds/python-audio-separator)\n[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1gSlmSmna7f7fH6OjsiMEDLl-aJ9kGPkY?usp=sharing)","chunkId":"README.md:2"}],[241,{"filePath":"README.md","startLine":7,"endLine":12,"chunkText":"[![codecov](https://codecov.io/gh/karaokenerds/python-audio-separator/graph/badge.svg?token=N7YK4ET5JP)](https://codecov.io/gh/karaokenerds/python-audio-separator)\n[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1gSlmSmna7f7fH6OjsiMEDLl-aJ9kGPkY?usp=sharing)\n[![Open In Huggingface](https://huggingface.co/datasets/huggingface/badges/resolve/main/open-in-hf-spaces-sm.svg)](https://huggingface.co/spaces/nomadkaraoke/audio-separator)\n\n</div>\n","chunkId":"README.md:3"}],[242,{"filePath":"README.md","startLine":10,"endLine":16,"chunkText":"\n</div>\n\n**Summary:** Easy to use audio stem separation from the command line or as a dependency in your own Python project, using the amazing MDX-Net, VR Arch, Demucs and MDXC models available in UVR by @Anjok07 & @aufr33.\n\nAudio Separator is a Python package that allows you to separate an audio file into various stems, using models trained by @Anjok07 for use with [Ultimate Vocal Remover](https://github.com/Anjok07/ultimatevocalremovergui).\n","chunkId":"README.md:4"}],[243,{"filePath":"README.md","startLine":14,"endLine":15,"chunkText":"Audio Separator is a Python package that allows you to separate an audio file into various stems, using models trained by @Anjok07 for use with [Ultimate Vocal Remover](https://github.com/Anjok07/ultimatevocalremovergui).\n","chunkId":"README.md:5"}],[244,{"filePath":"README.md","startLine":15,"endLine":16,"chunkText":"Audio Separator is a Python package that allows you to separate an audio file into various stems, using models trained by @Anjok07 for use with [Ultimate Vocal Remover](https://github.com/Anjok07/ultimatevocalremovergui).\n","chunkId":"README.md:6"}],[245,{"filePath":"README.md","startLine":16,"endLine":24,"chunkText":"\nThe simplest (and probably most used) use case for this package is to separate an audio file into two stems, Instrumental and Vocals, which can be very useful for producing karaoke videos! However, the models available in UVR can separate audio into many more stems, such as Drums, Bass, Piano, and Guitar, and perform other audio processing tasks, such as denoising or removing echo/reverb.\n\n<details>\n<summary align=\"center\"><b>Table of Contents</div></b></summary>\n\n- [🎶 Audio Separator 🎶](#-audio-separator-)\n  - [Features](#features)\n  - [Installation 🛠️](#installation-%EF%B8%8F)","chunkId":"README.md:7"}],[246,{"filePath":"README.md","startLine":22,"endLine":29,"chunkText":"- [🎶 Audio Separator 🎶](#-audio-separator-)\n  - [Features](#features)\n  - [Installation 🛠️](#installation-%EF%B8%8F)\n    - [🐳 Docker](#-docker)\n    - [🎮 Nvidia GPU with CUDA or 🧪 Google Colab](#-nvidia-gpu-with-cuda-or--google-colab)\n    - [ Apple Silicon, macOS Sonoma+ with M1 or newer CPU (CoreML acceleration)](#-apple-silicon-macos-sonoma-with-m1-or-newer-cpu-coreml-acceleration)\n    - [🐢 No hardware acceleration, CPU only](#-no-hardware-acceleration-cpu-only)\n    - [🎥 FFmpeg dependency](#-ffmpeg-dependency)","chunkId":"README.md:8"}],[247,{"filePath":"README.md","startLine":27,"endLine":33,"chunkText":"    - [ Apple Silicon, macOS Sonoma+ with M1 or newer CPU (CoreML acceleration)](#-apple-silicon-macos-sonoma-with-m1-or-newer-cpu-coreml-acceleration)\n    - [🐢 No hardware acceleration, CPU only](#-no-hardware-acceleration-cpu-only)\n    - [🎥 FFmpeg dependency](#-ffmpeg-dependency)\n  - [GPU / CUDA specific installation steps with Pip](#gpu--cuda-specific-installation-steps-with-pip)\n    - [Multiple CUDA library versions may be needed](#multiple-cuda-library-versions-may-be-needed)\n  - [Usage 🚀](#usage-)\n    - [Command Line Interface (CLI)](#command-line-interface-cli)","chunkId":"README.md:9"}],[248,{"filePath":"README.md","startLine":31,"endLine":39,"chunkText":"    - [Multiple CUDA library versions may be needed](#multiple-cuda-library-versions-may-be-needed)\n  - [Usage 🚀](#usage-)\n    - [Command Line Interface (CLI)](#command-line-interface-cli)\n    - [Listing and Filtering Available Models](#listing-and-filtering-available-models)\n      - [Filtering Models](#filtering-models)\n      - [Limiting Results](#limiting-results)\n      - [JSON Output](#json-output)\n    - [Full command-line interface options](#full-command-line-interface-options)\n    - [As a Dependency in a Python Project](#as-a-dependency-in-a-python-project)","chunkId":"README.md:10"}],[249,{"filePath":"README.md","startLine":37,"endLine":46,"chunkText":"      - [JSON Output](#json-output)\n    - [Full command-line interface options](#full-command-line-interface-options)\n    - [As a Dependency in a Python Project](#as-a-dependency-in-a-python-project)\n      - [Batch processing and processing with multiple models](#batch-processing-and-processing-with-multiple-models)\n      - [Renaming Stems](#renaming-stems)\n  - [Parameters for the Separator class](#parameters-for-the-separator-class)\n  - [Remote API Usage 🌐](#remote-api-usage-)\n  - [Requirements 📋](#requirements-)\n  - [Developing Locally](#developing-locally)\n    - [Prerequisites](#prerequisites)","chunkId":"README.md:11"}],[250,{"filePath":"README.md","startLine":44,"endLine":54,"chunkText":"  - [Requirements 📋](#requirements-)\n  - [Developing Locally](#developing-locally)\n    - [Prerequisites](#prerequisites)\n    - [Clone the Repository](#clone-the-repository)\n    - [Create and activate the Conda Environment](#create-and-activate-the-conda-environment)\n    - [Install Dependencies](#install-dependencies)\n    - [Running the Command-Line Interface Locally](#running-the-command-line-interface-locally)\n    - [Deactivate the Virtual Environment](#deactivate-the-virtual-environment)\n    - [Building the Package](#building-the-package)\n  - [Contributing 🤝](#contributing-)\n  - [License 📄](#license-","chunkId":"README.md:12"}],[251,{"filePath":"README.md","startLine":52,"endLine":67,"chunkText":"    - [Building the Package](#building-the-package)\n  - [Contributing 🤝](#contributing-)\n  - [License 📄](#license-)\n  - [Credits 🙏](#credits-)\n  - [Contact 💌](#contact-)\n  - [Thanks to all contributors for their efforts](#thanks-to-all-contributors-for-their-efforts)\n</details>\n\n---\n\n## Features\n\n- Separate audio into multiple stems, e.g. instrumental and vocals.\n- Supports all common audio formats (WAV, MP3, FLAC, M4A, etc.)\n- Ability to inference using a pre-trained model in PTH or ONNX format.\n- CLI support for easy use in scripts and batch processing.","chunkId":"README.md:13"}],[252,{"filePath":"README.md","startLine":67,"endLine":77,"chunkText":"- CLI support for easy use in scripts and batch processing.\n- Python API for integration into other projects.\n\n## Installation 🛠️\n\n### 🐳 Docker\n\nIf you're able to use docker, you don't actually need to _install_ anything - there are [images published on Docker Hub](https://hub.docker.com/r/beveradb/audio-separator/tags) for GPU (CUDA) and CPU inferencing, for both `amd64` and `arm64` platforms.\n\nYou probably want to volume-mount a folder containing whatever file you want to separate, which can then also be used as the output folder.\n","chunkId":"README.md:14"}],[253,{"filePath":"README.md","startLine":75,"endLine":86,"chunkText":"You probably want to volume-mount a folder containing whatever file you want to separate, which can then also be used as the output folder.\n\nFor instance, if your current directory has the file `input.wav`, you could execute `audio-separator` as shown below (see [usage](#usage-) section for more details):\n\n```sh\ndocker run -it -v `pwd`:/workdir beveradb/audio-separator input.wav\n```\n\nIf you're using a machine with a GPU, you'll want to use the GPU specific image and pass in the GPU device to the container, like this:\n\n```sh\ndocker run -it --gpus all -v `pwd`:/workdir beveradb/audio-separator:gpu input.wav","chunkId":"README.md:15"}],[254,{"filePath":"README.md","startLine":87,"endLine":100,"chunkText":"docker run -it --gpus all -v `pwd`:/workdir beveradb/audio-separator:gpu input.wav\n```\n\nIf the GPU isn't being detected, make sure your docker runtime environment is passing through the GPU correctly - there are [various guides](https://www.celantur.com/blog/run-cuda-in-docker-on-linux/) online to help with that.\n\n### 🎮 Nvidia GPU with CUDA or 🧪 Google Colab\n\n**Supported CUDA Versions:** 11.8 and 12.2\n\n💬 If successfully configured, you should see this log message when running `audio-separator --env_info`:\n `ONNXruntime has CUDAExecutionProvider available, enabling acceleration`\n\nConda:\n```sh","chunkId":"README.md:16"}],[255,{"filePath":"README.md","startLine":98,"endLine":127,"chunkText":"\nConda:\n```sh\nconda install pytorch=*=*cuda* onnxruntime=*=*cuda* audio-separator -c pytorch -c conda-forge\n```\n\nPip:\n```sh\npip install \"audio-separator[gpu]\"\n```\n\nDocker:\n```sh\nbeveradb/audio-separator:gpu\n```\n\n###  Apple Silicon, macOS Sonoma+ with M1 or newer CPU (CoreML acceleration)\n\n💬 If successfully configured, you should see this log message when running `audio-separator --env_info`:\n `ONNXruntime has CoreMLExecutionProvider available, enabling acceleration`\n\nPip:\n```sh\npip install \"audio-separator[cpu]\"\n```\n\n### 🐢 No hardware acceleration, CPU only\n\nConda:\n```sh","chunkId":"README.md:17"}],[256,{"filePath":"README.md","startLine":125,"endLine":150,"chunkText":"\nConda:\n```sh\nconda install audio-separator -c pytorch -c conda-forge\n```\n\nPip:\n```sh\npip install \"audio-separator[cpu]\"\n```\n\nDocker:\n```sh\nbeveradb/audio-separator\n```\n\n### 🎥 FFmpeg dependency\n\n💬 To test if `audio-separator` has been successfully configured to use FFmpeg, run `audio-separator --env_info`. The log will show `FFmpeg installed`.\n\nIf you installed `audio-separator` using `conda` or `docker`, FFmpeg should already be available in your environment.\n\nYou may need to separately install FFmpeg. It should be easy to install on most platforms, e.g.:\n\n🐧 Debian/Ubuntu:\n```sh","chunkId":"README.md:18"}],[257,{"filePath":"README.md","startLine":148,"endLine":167,"chunkText":"\n🐧 Debian/Ubuntu:\n```sh\napt-get update; apt-get install -y ffmpeg\n```\n\n macOS:\n```sh\nbrew update; brew install ffmpeg\n```\n\n## GPU / CUDA specific installation steps with Pip\n\nIn theory, all you should need to do to get `audio-separator` working with a GPU is install it with the `[gpu]` extra as above.\n\nHowever, sometimes getting both PyTorch and ONNX Runtime working with CUDA support can be a bit tricky so it may not work that easily.\n\nYou may need to reinstall both packages directly, allowing pip to calculate the right versions for your platform, for example:\n\n- `pip uninstall torch onnxruntime`","chunkId":"README.md:19"}],[258,{"filePath":"README.md","startLine":165,"endLine":176,"chunkText":"You may need to reinstall both packages directly, allowing pip to calculate the right versions for your platform, for example:\n\n- `pip uninstall torch onnxruntime`\n- `pip cache purge`\n- `pip install --force-reinstall torch torchvision torchaudio`\n- `pip install --force-reinstall onnxruntime-gpu`\n\nI generally recommend installing the latest version of PyTorch for your environment using the command recommended by the wizard here:\n<https://pytorch.org/get-started/locally/>\n\n### Multiple CUDA library versions may be needed\n","chunkId":"README.md:20"}],[259,{"filePath":"README.md","startLine":174,"endLine":187,"chunkText":"\n### Multiple CUDA library versions may be needed\n\nDepending on your CUDA version and environment, you may need to install specific version(s) of CUDA libraries for ONNX Runtime to use your GPU.\n\n🧪 Google Colab, for example, now uses CUDA 12 by default, but ONNX Runtime still needs CUDA 11 libraries to work.\n\nIf you see the error `Failed to load library` or `cannot open shared object file` when you run `audio-separator`, this is likely the issue.\n\nYou can install the CUDA 11 libraries _alongside_ CUDA 12 like so:\n```sh\napt update; apt install nvidia-cuda-toolkit\n```\n","chunkId":"README.md:21"}],[260,{"filePath":"README.md","startLine":185,"endLine":191,"chunkText":"apt update; apt install nvidia-cuda-toolkit\n```\n\nIf you encounter the following messages when running on Google Colab or in another environment:\n```\n[E:onnxruntime:Default, provider_bridge_ort.cc:1862 TryGetProviderInfo_CUDA] /onnxruntime_src/onnxruntime/core/session/provider_bridge_ort.cc:1539 onnxruntime::Provider& onnxruntime::ProviderLibrary::Get() [ONNXRuntimeError] : 1 : FAIL : Failed to load library libonnxruntime_providers_cuda.so with error: libcudnn_adv.so.9: cannot open shared object file: No such file or directory\n","chunkId":"README.md:22"}],[261,{"filePath":"README.md","startLine":189,"endLine":191,"chunkText":"```\n[E:onnxruntime:Default, provider_bridge_ort.cc:1862 TryGetProviderInfo_CUDA] /onnxruntime_src/onnxruntime/core/session/provider_bridge_ort.cc:1539 onnxruntime::Provider& onnxruntime::ProviderLibrary::Get() [ONNXRuntimeError] : 1 : FAIL : Failed to load library libonnxruntime_providers_cuda.so with error: libcudnn_adv.so.9: cannot open shared object file: No such file or directory\n","chunkId":"README.md:23"}],[262,{"filePath":"README.md","startLine":190,"endLine":191,"chunkText":"[E:onnxruntime:Default, provider_bridge_ort.cc:1862 TryGetProviderInfo_CUDA] /onnxruntime_src/onnxruntime/core/session/provider_bridge_ort.cc:1539 onnxruntime::Provider& onnxruntime::ProviderLibrary::Get() [ONNXRuntimeError] : 1 : FAIL : Failed to load library libonnxruntime_providers_cuda.so with error: libcudnn_adv.so.9: cannot open shared object file: No such file or directory\n","chunkId":"README.md:24"}],[263,{"filePath":"README.md","startLine":191,"endLine":198,"chunkText":"\n[W:onnxruntime:Default, onnxruntime_pybind_state.cc:993 CreateExecutionProviderInstance] Failed to create CUDAExecutionProvider. Require cuDNN 9.* and CUDA 12.*. Please install all dependencies as mentioned in the GPU requirements page (https://onnxruntime.ai/docs/execution-providers/CUDA-ExecutionProvider.html#requirements), make sure they're in the PATH, and that your GPU is supported.\n```\nYou can resolve this by running the following command:\n```sh\npython -m pip install ort-nightly-gpu --index-url=https://aiinfra.pkgs.visualstudio.com/PublicPackages/_packaging/ort-cuda-12-nightly/pypi/simple/\n```\n","chunkId":"README.md:25"}],[264,{"filePath":"README.md","startLine":196,"endLine":210,"chunkText":"python -m pip install ort-nightly-gpu --index-url=https://aiinfra.pkgs.visualstudio.com/PublicPackages/_packaging/ort-cuda-12-nightly/pypi/simple/\n```\n\n> Note: if anyone knows how to make this cleaner so we can support both different platform-specific dependencies for hardware acceleration without a separate installation process for each, please let me know or raise a PR!\n\n## Usage 🚀\n\n### Command Line Interface (CLI)\n\nYou can use Audio Separator via the command line, for example:\n\n```sh\naudio-separator /path/to/your/input/audio.wav --model_filename model_bs_roformer_ep_317_sdr_12.9755.ckpt\n```\n","chunkId":"README.md:26"}],[265,{"filePath":"README.md","startLine":208,"endLine":216,"chunkText":"audio-separator /path/to/your/input/audio.wav --model_filename model_bs_roformer_ep_317_sdr_12.9755.ckpt\n```\n\nThis command will download the specified model file, process the `audio.wav` input audio and generate two new files in the current directory, one containing vocals and one containing instrumental.\n\n**Note:** You do not need to download any files yourself - audio-separator does that automatically for you!\n\nTo see a list of supported models, run `audio-separator --list_models`\n","chunkId":"README.md:27"}],[266,{"filePath":"README.md","startLine":214,"endLine":227,"chunkText":"\nTo see a list of supported models, run `audio-separator --list_models`\n\nAny file listed in the list models output can be specified (with file extension) with the model_filename parameter (e.g. `--model_filename UVR_MDXNET_KARA_2.onnx`) and it will be automatically downloaded to the `--model_file_dir` (default: `/tmp/audio-separator-models/`) folder on first usage.\n\n### Listing and Filtering Available Models\n\nYou can view all available models using the `--list_models` (or `-l`) flag:\n\n```sh\naudio-separator --list_models\n```\n\nThe output shows a table with the following columns:","chunkId":"README.md:28"}],[267,{"filePath":"README.md","startLine":225,"endLine":242,"chunkText":"```\n\nThe output shows a table with the following columns:\n- Model Filename: The filename to use with `--model_filename`\n- Arch: The model architecture (MDX, MDXC, Demucs, etc.)\n- Output Stems (SDR): The stems this model can separate, with Signal-to-Distortion Ratio scores where available\n- Friendly Name: A human-readable name describing the model\n\n#### Filtering Models\n\nYou can filter and sort the model list by stem type using `--list_filter`. For example, to find models that can separate drums:\n\n```sh\naudio-separator -l --list_filter=drums\n```\n\nExample output:\n```","chunkId":"README.md:29"}],[268,{"filePath":"README.md","startLine":240,"endLine":246,"chunkText":"\nExample output:\n```\n-----------------------------------------------------------------------------------------------------------------------------------\nModel Filename        Arch    Output Stems (SDR)                                            Friendly Name\n-----------------------------------------------------------------------------------------------------------------------------------\nhtdemucs_ft.yaml      Demucs  vocals (10.8), drums (10.1), bass (11.9), other               Demucs v4: htdemucs_ft","chunkId":"README.md:30"}],[269,{"filePath":"README.md","startLine":244,"endLine":248,"chunkText":"Model Filename        Arch    Output Stems (SDR)                                            Friendly Name\n-----------------------------------------------------------------------------------------------------------------------------------\nhtdemucs_ft.yaml      Demucs  vocals (10.8), drums (10.1), bass (11.9), other               Demucs v4: htdemucs_ft\nhdemucs_mmi.yaml      Demucs  vocals (10.3), drums (9.7), bass (12.0), other                Demucs v4: hdemucs_mmi\nhtdemucs.yaml         Demucs  vocals (10.0), drums (9.4), bass (11.3), other                Demucs v4: htdemucs","chunkId":"README.md:31"}],[270,{"filePath":"README.md","startLine":246,"endLine":253,"chunkText":"htdemucs_ft.yaml      Demucs  vocals (10.8), drums (10.1), bass (11.9), other               Demucs v4: htdemucs_ft\nhdemucs_mmi.yaml      Demucs  vocals (10.3), drums (9.7), bass (12.0), other                Demucs v4: hdemucs_mmi\nhtdemucs.yaml         Demucs  vocals (10.0), drums (9.4), bass (11.3), other                Demucs v4: htdemucs\nhtdemucs_6s.yaml      Demucs  vocals (9.7), drums (8.5), bass (10.0), guitar, piano, other  Demucs v4: htdemucs_6s\n```\n\n#### Limiting Results\n","chunkId":"README.md:32"}],[271,{"filePath":"README.md","startLine":251,"endLine":263,"chunkText":"\n#### Limiting Results\n\nYou can limit the number of results shown using `--list_limit`. This is useful for finding the best performing models for a particular stem. For example, to see the top 5 vocal separation models:\n\n```sh\naudio-separator -l --list_filter=vocals --list_limit=5\n```\n\nExample output:\n```\n--------------------------------------------------------------------------------------------------------------------------------------------------------------\nModel Filename                             Arch  Output Stems (SDR)                   Friendly Name","chunkId":"README.md:33"}],[272,{"filePath":"README.md","startLine":261,"endLine":265,"chunkText":"```\n--------------------------------------------------------------------------------------------------------------------------------------------------------------\nModel Filename                             Arch  Output Stems (SDR)                   Friendly Name\n--------------------------------------------------------------------------------------------------------------------------------------------------------------\nmodel_bs_roformer_ep_317_sdr_12.9755.ckpt  MDXC  vocals* (12.9), instrumental (17.0)  Roformer Model: BS-Roformer-Viperx-1297","chunkId":"README.md:34"}],[273,{"filePath":"README.md","startLine":263,"endLine":266,"chunkText":"Model Filename                             Arch  Output Stems (SDR)                   Friendly Name\n--------------------------------------------------------------------------------------------------------------------------------------------------------------\nmodel_bs_roformer_ep_317_sdr_12.9755.ckpt  MDXC  vocals* (12.9), instrumental (17.0)  Roformer Model: BS-Roformer-Viperx-1297\nmodel_bs_roformer_ep_368_sdr_12.9628.ckpt  MDXC  vocals* (12.9), instrumental (17.0)  Roformer Model: BS-Roformer-Viperx-1296","chunkId":"README.md:35"}],[274,{"filePath":"README.md","startLine":264,"endLine":267,"chunkText":"--------------------------------------------------------------------------------------------------------------------------------------------------------------\nmodel_bs_roformer_ep_317_sdr_12.9755.ckpt  MDXC  vocals* (12.9), instrumental (17.0)  Roformer Model: BS-Roformer-Viperx-1297\nmodel_bs_roformer_ep_368_sdr_12.9628.ckpt  MDXC  vocals* (12.9), instrumental (17.0)  Roformer Model: BS-Roformer-Viperx-1296\nvocals_mel_band_roformer.ckpt              MDXC  vocals* (12.6), other                Roformer Model: MelBand Roformer | Vocals by Kimberley Jensen","chunkId":"README.md:36"}],[275,{"filePath":"README.md","startLine":265,"endLine":268,"chunkText":"model_bs_roformer_ep_317_sdr_12.9755.ckpt  MDXC  vocals* (12.9), instrumental (17.0)  Roformer Model: BS-Roformer-Viperx-1297\nmodel_bs_roformer_ep_368_sdr_12.9628.ckpt  MDXC  vocals* (12.9), instrumental (17.0)  Roformer Model: BS-Roformer-Viperx-1296\nvocals_mel_band_roformer.ckpt              MDXC  vocals* (12.6), other                Roformer Model: MelBand Roformer | Vocals by Kimberley Jensen\nmelband_roformer_big_beta4.ckpt            MDXC  vocals* (12.5), other                Roformer Model: MelBand Roformer Kim | Big Beta 4 FT by unwa","chunkId":"README.md:37"}],[276,{"filePath":"README.md","startLine":266,"endLine":273,"chunkText":"model_bs_roformer_ep_368_sdr_12.9628.ckpt  MDXC  vocals* (12.9), instrumental (17.0)  Roformer Model: BS-Roformer-Viperx-1296\nvocals_mel_band_roformer.ckpt              MDXC  vocals* (12.6), other                Roformer Model: MelBand Roformer | Vocals by Kimberley Jensen\nmelband_roformer_big_beta4.ckpt            MDXC  vocals* (12.5), other                Roformer Model: MelBand Roformer Kim | Big Beta 4 FT by unwa\nmel_band_roformer_kim_ft_unwa.ckpt         MDXC  vocals* (12.4), other                Roformer Model: MelBand Roformer Kim | FT by unwa\n```\n\n#### JSON Output\n","chunkId":"README.md:38"}],[277,{"filePath":"README.md","startLine":271,"endLine":290,"chunkText":"#### JSON Output\n\nFor programmatic use, you can output the model list in JSON format:\n\n```sh\naudio-separator -l --list_format=json\n```\n\n### Processing Large Files\n\nFor very long audio files (>1 hour), you may encounter out-of-memory errors. The `--chunk_duration` option automatically splits large files into smaller chunks, processes them separately, and merges the results:\n\n```sh\n# Process an 8-hour podcast in 10-minute chunks\naudio-separator long_podcast.wav --chunk_duration 600\n\n# Adjust chunk size based on available memory\naudio-separator very_long_audio.wav --chunk_duration 300  # 5-minute chunks\n```\n","chunkId":"README.md:39"}],[278,{"filePath":"README.md","startLine":289,"endLine":304,"chunkText":"audio-separator very_long_audio.wav --chunk_duration 300  # 5-minute chunks\n```\n\n#### How It Works\n\n1. **Split**: The input file is split into fixed-duration chunks (e.g., 10 minutes)\n2. **Process**: Each chunk is processed separately, reducing peak memory usage\n3. **Merge**: The results are merged back together with simple concatenation\n\nThe chunking feature supports all model types:\n- **2-stem models** (e.g., MDX): Vocals + Instrumental\n- **4-stem models** (e.g., Demucs): Drums, Bass, Other, Vocals\n- **6-stem models** (e.g., Demucs 6s): Bass, Drums, Other, Vocals, Guitar, Piano\n\n#### Benefits\n","chunkId":"README.md:40"}],[279,{"filePath":"README.md","startLine":302,"endLine":317,"chunkText":"\n#### Benefits\n\n- **Prevents OOM errors**: Process files of any length without running out of memory\n- **Predictable memory usage**: Memory usage stays bounded regardless of file length\n- **No quality loss**: Each chunk is fully processed with the selected model\n- **Multi-stem support**: Works seamlessly with 2, 4, and 6-stem models\n\n#### Recommendations\n\n- **Files > 1 hour**: Use `--chunk_duration 600` (10 minutes)\n- **Limited memory systems**: Use smaller chunks (300-600 seconds)\n- **Ample memory**: You may not need chunking at all\n\n#### Note on Audio Quality\n","chunkId":"README.md:41"}],[280,{"filePath":"README.md","startLine":315,"endLine":323,"chunkText":"\n#### Note on Audio Quality\n\nChunks are concatenated without crossfading, which may result in minor artifacts at chunk boundaries in rare cases. For most use cases, these are not noticeable. The simple concatenation approach keeps processing time minimal while solving out-of-memory issues.\n\n### Full command-line interface options\n\n```sh\nusage: audio-separator [-h] [-v] [-d] [-e] [-l] [--log_level LOG_LEVEL] [--list_filter LIST_FILTER] [--list_limit LIST_LIMIT] [--list_format {pretty,json}] [-m MODEL_FILENAME] [--output_format OUTPUT_FORMAT]","chunkId":"README.md:42"}],[281,{"filePath":"README.md","startLine":321,"endLine":325,"chunkText":"\n```sh\nusage: audio-separator [-h] [-v] [-d] [-e] [-l] [--log_level LOG_LEVEL] [--list_filter LIST_FILTER] [--list_limit LIST_LIMIT] [--list_format {pretty,json}] [-m MODEL_FILENAME] [--output_format OUTPUT_FORMAT]\n                       [--output_bitrate OUTPUT_BITRATE] [--output_dir OUTPUT_DIR] [--model_file_dir MODEL_FILE_DIR] [--download_model_only] [--invert_spect] [--normalization NORMALIZATION]\n                       [--amplification AMPLIFICATION] [--single_stem SINGLE_STEM] [--sample_rate SAMPLE_RATE] [--use_soundfile] [--use_autocast] [--custom_output_names CUSTOM_OUTPUT_NAMES]","chunkId":"README.md:43"}],[282,{"filePath":"README.md","startLine":323,"endLine":325,"chunkText":"usage: audio-separator [-h] [-v] [-d] [-e] [-l] [--log_level LOG_LEVEL] [--list_filter LIST_FILTER] [--list_limit LIST_LIMIT] [--list_format {pretty,json}] [-m MODEL_FILENAME] [--output_format OUTPUT_FORMAT]\n                       [--output_bitrate OUTPUT_BITRATE] [--output_dir OUTPUT_DIR] [--model_file_dir MODEL_FILE_DIR] [--download_model_only] [--invert_spect] [--normalization NORMALIZATION]\n                       [--amplification AMPLIFICATION] [--single_stem SINGLE_STEM] [--sample_rate SAMPLE_RATE] [--use_soundfile] [--use_autocast] [--custom_output_names CUSTOM_OUTPUT_NAMES]","chunkId":"README.md:44"}],[283,{"filePath":"README.md","startLine":324,"endLine":326,"chunkText":"                       [--output_bitrate OUTPUT_BITRATE] [--output_dir OUTPUT_DIR] [--model_file_dir MODEL_FILE_DIR] [--download_model_only] [--invert_spect] [--normalization NORMALIZATION]\n                       [--amplification AMPLIFICATION] [--single_stem SINGLE_STEM] [--sample_rate SAMPLE_RATE] [--use_soundfile] [--use_autocast] [--custom_output_names CUSTOM_OUTPUT_NAMES]\n                       [--mdx_segment_size MDX_SEGMENT_SIZE] [--mdx_overlap MDX_OVERLAP] [--mdx_batch_size MDX_BATCH_SIZE] [--mdx_hop_length MDX_HOP_LENGTH] [--mdx_enable_denoise] [--vr_batch_size VR_BATCH_SIZE]","chunkId":"README.md:45"}],[284,{"filePath":"README.md","startLine":325,"endLine":327,"chunkText":"                       [--amplification AMPLIFICATION] [--single_stem SINGLE_STEM] [--sample_rate SAMPLE_RATE] [--use_soundfile] [--use_autocast] [--custom_output_names CUSTOM_OUTPUT_NAMES]\n                       [--mdx_segment_size MDX_SEGMENT_SIZE] [--mdx_overlap MDX_OVERLAP] [--mdx_batch_size MDX_BATCH_SIZE] [--mdx_hop_length MDX_HOP_LENGTH] [--mdx_enable_denoise] [--vr_batch_size VR_BATCH_SIZE]\n                       [--vr_window_size VR_WINDOW_SIZE] [--vr_aggression VR_AGGRESSION] [--vr_enable_tta] [--vr_high_end_process] [--vr_enable_post_process]","chunkId":"README.md:46"}],[285,{"filePath":"README.md","startLine":326,"endLine":328,"chunkText":"                       [--mdx_segment_size MDX_SEGMENT_SIZE] [--mdx_overlap MDX_OVERLAP] [--mdx_batch_size MDX_BATCH_SIZE] [--mdx_hop_length MDX_HOP_LENGTH] [--mdx_enable_denoise] [--vr_batch_size VR_BATCH_SIZE]\n                       [--vr_window_size VR_WINDOW_SIZE] [--vr_aggression VR_AGGRESSION] [--vr_enable_tta] [--vr_high_end_process] [--vr_enable_post_process]\n                       [--vr_post_process_threshold VR_POST_PROCESS_THRESHOLD] [--demucs_segment_size DEMUCS_SEGMENT_SIZE] [--demucs_shifts DEMUCS_SHIFTS] [--demucs_overlap DEMUCS_OVERLAP]","chunkId":"README.md:47"}],[286,{"filePath":"README.md","startLine":327,"endLine":329,"chunkText":"                       [--vr_window_size VR_WINDOW_SIZE] [--vr_aggression VR_AGGRESSION] [--vr_enable_tta] [--vr_high_end_process] [--vr_enable_post_process]\n                       [--vr_post_process_threshold VR_POST_PROCESS_THRESHOLD] [--demucs_segment_size DEMUCS_SEGMENT_SIZE] [--demucs_shifts DEMUCS_SHIFTS] [--demucs_overlap DEMUCS_OVERLAP]\n                       [--demucs_segments_enabled DEMUCS_SEGMENTS_ENABLED] [--mdxc_segment_size MDXC_SEGMENT_SIZE] [--mdxc_override_model_segment_size] [--mdxc_overlap MDXC_OVERLAP]","chunkId":"README.md:48"}],[287,{"filePath":"README.md","startLine":328,"endLine":335,"chunkText":"                       [--vr_post_process_threshold VR_POST_PROCESS_THRESHOLD] [--demucs_segment_size DEMUCS_SEGMENT_SIZE] [--demucs_shifts DEMUCS_SHIFTS] [--demucs_overlap DEMUCS_OVERLAP]\n                       [--demucs_segments_enabled DEMUCS_SEGMENTS_ENABLED] [--mdxc_segment_size MDXC_SEGMENT_SIZE] [--mdxc_override_model_segment_size] [--mdxc_overlap MDXC_OVERLAP]\n                       [--mdxc_batch_size MDXC_BATCH_SIZE] [--mdxc_pitch_shift MDXC_PITCH_SHIFT]\n                       [audio_files ...]\n\nSeparate audio file into different stems.\n\npositional arguments:","chunkId":"README.md:49"}],[288,{"filePath":"README.md","startLine":333,"endLine":343,"chunkText":"Separate audio file into different stems.\n\npositional arguments:\n  audio_files                                            The audio file paths or directory to separate, in any common format.\n\noptions:\n  -h, --help                                             show this help message and exit\n\nInfo and Debugging:\n  -v, --version                                          Show the program's version number and exit.\n  -d, --debug                                            Enable debug logging, equivalent to --log_level=debug.","chunkId":"README.md:50"}],[289,{"filePath":"README.md","startLine":341,"endLine":345,"chunkText":"Info and Debugging:\n  -v, --version                                          Show the program's version number and exit.\n  -d, --debug                                            Enable debug logging, equivalent to --log_level=debug.\n  -e, --env_info                                         Print environment information and exit.\n  -l, --list_models                                      List all supported models and exit. Use --list_filter to filter/sort the list and --list_limit to show only top N results.","chunkId":"README.md:51"}],[290,{"filePath":"README.md","startLine":343,"endLine":346,"chunkText":"  -d, --debug                                            Enable debug logging, equivalent to --log_level=debug.\n  -e, --env_info                                         Print environment information and exit.\n  -l, --list_models                                      List all supported models and exit. Use --list_filter to filter/sort the list and --list_limit to show only top N results.\n  --log_level LOG_LEVEL                                  Log level, e.g. info, debug, warning (default: info).","chunkId":"README.md:52"}],[291,{"filePath":"README.md","startLine":344,"endLine":347,"chunkText":"  -e, --env_info                                         Print environment information and exit.\n  -l, --list_models                                      List all supported models and exit. Use --list_filter to filter/sort the list and --list_limit to show only top N results.\n  --log_level LOG_LEVEL                                  Log level, e.g. info, debug, warning (default: info).\n  --list_filter LIST_FILTER                              Filter and sort the model list by 'name', 'filename', or any stem e.g. vocals, instrumental, drums","chunkId":"README.md:53"}],[292,{"filePath":"README.md","startLine":345,"endLine":348,"chunkText":"  -l, --list_models                                      List all supported models and exit. Use --list_filter to filter/sort the list and --list_limit to show only top N results.\n  --log_level LOG_LEVEL                                  Log level, e.g. info, debug, warning (default: info).\n  --list_filter LIST_FILTER                              Filter and sort the model list by 'name', 'filename', or any stem e.g. vocals, instrumental, drums\n  --list_limit LIST_LIMIT                                Limit the number of models shown","chunkId":"README.md:54"}],[293,{"filePath":"README.md","startLine":346,"endLine":351,"chunkText":"  --log_level LOG_LEVEL                                  Log level, e.g. info, debug, warning (default: info).\n  --list_filter LIST_FILTER                              Filter and sort the model list by 'name', 'filename', or any stem e.g. vocals, instrumental, drums\n  --list_limit LIST_LIMIT                                Limit the number of models shown\n  --list_format {pretty,json}                            Format for listing models: 'pretty' for formatted output, 'json' for raw JSON dump\n\nSeparation I/O Params:","chunkId":"README.md:55"}],[294,{"filePath":"README.md","startLine":349,"endLine":353,"chunkText":"  --list_format {pretty,json}                            Format for listing models: 'pretty' for formatted output, 'json' for raw JSON dump\n\nSeparation I/O Params:\n  -m MODEL_FILENAME, --model_filename MODEL_FILENAME     Model to use for separation (default: model_bs_roformer_ep_317_sdr_12.9755.yaml). Example: -m 2_HP-UVR.pth\n  --output_format OUTPUT_FORMAT                          Output format for separated files, any common format (default: FLAC). Example: --output_format=MP3","chunkId":"README.md:56"}],[295,{"filePath":"README.md","startLine":351,"endLine":354,"chunkText":"Separation I/O Params:\n  -m MODEL_FILENAME, --model_filename MODEL_FILENAME     Model to use for separation (default: model_bs_roformer_ep_317_sdr_12.9755.yaml). Example: -m 2_HP-UVR.pth\n  --output_format OUTPUT_FORMAT                          Output format for separated files, any common format (default: FLAC). Example: --output_format=MP3\n  --output_bitrate OUTPUT_BITRATE                        Output bitrate for separated files, any ffmpeg-compatible bitrate (default: None). Example: --output_bitrate=320k","chunkId":"README.md:57"}],[296,{"filePath":"README.md","startLine":352,"endLine":354,"chunkText":"  -m MODEL_FILENAME, --model_filename MODEL_FILENAME     Model to use for separation (default: model_bs_roformer_ep_317_sdr_12.9755.yaml). Example: -m 2_HP-UVR.pth\n  --output_format OUTPUT_FORMAT                          Output format for separated files, any common format (default: FLAC). Example: --output_format=MP3\n  --output_bitrate OUTPUT_BITRATE                        Output bitrate for separated files, any ffmpeg-compatible bitrate (default: None). Example: --output_bitrate=320k","chunkId":"README.md:58"}],[297,{"filePath":"README.md","startLine":353,"endLine":355,"chunkText":"  --output_format OUTPUT_FORMAT                          Output format for separated files, any common format (default: FLAC). Example: --output_format=MP3\n  --output_bitrate OUTPUT_BITRATE                        Output bitrate for separated files, any ffmpeg-compatible bitrate (default: None). Example: --output_bitrate=320k\n  --output_dir OUTPUT_DIR                                Directory to write output files (default: <current dir>). Example: --output_dir=/app/separated","chunkId":"README.md:59"}],[298,{"filePath":"README.md","startLine":354,"endLine":358,"chunkText":"  --output_bitrate OUTPUT_BITRATE                        Output bitrate for separated files, any ffmpeg-compatible bitrate (default: None). Example: --output_bitrate=320k\n  --output_dir OUTPUT_DIR                                Directory to write output files (default: <current dir>). Example: --output_dir=/app/separated\n  --model_file_dir MODEL_FILE_DIR                        Model files directory (default: /tmp/audio-separator-models/). Example: --model_file_dir=/app/models\n  --download_model_only                                  Download a single model file only, without performing separation.\n","chunkId":"README.md:60"}],[299,{"filePath":"README.md","startLine":356,"endLine":361,"chunkText":"  --model_file_dir MODEL_FILE_DIR                        Model files directory (default: /tmp/audio-separator-models/). Example: --model_file_dir=/app/models\n  --download_model_only                                  Download a single model file only, without performing separation.\n\nCommon Separation Parameters:\n  --invert_spect                                         Invert secondary stem using spectrogram (default: False). Example: --invert_spect\n  --normalization NORMALIZATION                          Max peak amplitude to normalize input and output audio to (default: 0.9). Example: --normalization=0.7","chunkId":"README.md:61"}],[300,{"filePath":"README.md","startLine":359,"endLine":362,"chunkText":"Common Separation Parameters:\n  --invert_spect                                         Invert secondary stem using spectrogram (default: False). Example: --invert_spect\n  --normalization NORMALIZATION                          Max peak amplitude to normalize input and output audio to (default: 0.9). Example: --normalization=0.7\n  --amplification AMPLIFICATION                          Min peak amplitude to amplify input and output audio to (default: 0.0). Example: --amplification=0.4","chunkId":"README.md:62"}],[301,{"filePath":"README.md","startLine":360,"endLine":362,"chunkText":"  --invert_spect                                         Invert secondary stem using spectrogram (default: False). Example: --invert_spect\n  --normalization NORMALIZATION                          Max peak amplitude to normalize input and output audio to (default: 0.9). Example: --normalization=0.7\n  --amplification AMPLIFICATION                          Min peak amplitude to amplify input and output audio to (default: 0.0). Example: --amplification=0.4","chunkId":"README.md:63"}],[302,{"filePath":"README.md","startLine":361,"endLine":363,"chunkText":"  --normalization NORMALIZATION                          Max peak amplitude to normalize input and output audio to (default: 0.9). Example: --normalization=0.7\n  --amplification AMPLIFICATION                          Min peak amplitude to amplify input and output audio to (default: 0.0). Example: --amplification=0.4\n  --single_stem SINGLE_STEM                              Output only single stem, e.g. Instrumental, Vocals, Drums, Bass, Guitar, Piano, Other. Example: --single_stem=Instrumental","chunkId":"README.md:64"}],[303,{"filePath":"README.md","startLine":362,"endLine":364,"chunkText":"  --amplification AMPLIFICATION                          Min peak amplitude to amplify input and output audio to (default: 0.0). Example: --amplification=0.4\n  --single_stem SINGLE_STEM                              Output only single stem, e.g. Instrumental, Vocals, Drums, Bass, Guitar, Piano, Other. Example: --single_stem=Instrumental\n  --sample_rate SAMPLE_RATE                              Modify the sample rate of the output audio (default: 44100). Example: --sample_rate=44100","chunkId":"README.md:65"}],[304,{"filePath":"README.md","startLine":363,"endLine":365,"chunkText":"  --single_stem SINGLE_STEM                              Output only single stem, e.g. Instrumental, Vocals, Drums, Bass, Guitar, Piano, Other. Example: --single_stem=Instrumental\n  --sample_rate SAMPLE_RATE                              Modify the sample rate of the output audio (default: 44100). Example: --sample_rate=44100\n  --use_soundfile                                        Use soundfile to write audio output (default: False). Example: --use_soundfile","chunkId":"README.md:66"}],[305,{"filePath":"README.md","startLine":364,"endLine":366,"chunkText":"  --sample_rate SAMPLE_RATE                              Modify the sample rate of the output audio (default: 44100). Example: --sample_rate=44100\n  --use_soundfile                                        Use soundfile to write audio output (default: False). Example: --use_soundfile\n  --use_autocast                                         Use PyTorch autocast for faster inference (default: False). Do not use for CPU inference. Example: --use_autocast","chunkId":"README.md:67"}],[306,{"filePath":"README.md","startLine":365,"endLine":369,"chunkText":"  --use_soundfile                                        Use soundfile to write audio output (default: False). Example: --use_soundfile\n  --use_autocast                                         Use PyTorch autocast for faster inference (default: False). Do not use for CPU inference. Example: --use_autocast\n  --custom_output_names CUSTOM_OUTPUT_NAMES              Custom names for all output files in JSON format (default: None). Example: --custom_output_names='{\"Vocals\": \"vocals_output\", \"Drums\": \"drums_output\"}'\n\nMDX Architecture Parameters:","chunkId":"README.md:68"}],[307,{"filePath":"README.md","startLine":367,"endLine":371,"chunkText":"  --custom_output_names CUSTOM_OUTPUT_NAMES              Custom names for all output files in JSON format (default: None). Example: --custom_output_names='{\"Vocals\": \"vocals_output\", \"Drums\": \"drums_output\"}'\n\nMDX Architecture Parameters:\n  --mdx_segment_size MDX_SEGMENT_SIZE                    Larger consumes more resources, but may give better results (default: 256). Example: --mdx_segment_size=256\n  --mdx_overlap MDX_OVERLAP                              Amount of overlap between prediction windows, 0.001-0.999. Higher is better but slower (default: 0.25). Example: --mdx_overlap=0.25","chunkId":"README.md:69"}],[308,{"filePath":"README.md","startLine":369,"endLine":372,"chunkText":"MDX Architecture Parameters:\n  --mdx_segment_size MDX_SEGMENT_SIZE                    Larger consumes more resources, but may give better results (default: 256). Example: --mdx_segment_size=256\n  --mdx_overlap MDX_OVERLAP                              Amount of overlap between prediction windows, 0.001-0.999. Higher is better but slower (default: 0.25). Example: --mdx_overlap=0.25\n  --mdx_batch_size MDX_BATCH_SIZE                        Larger consumes more RAM but may process slightly faster (default: 1). Example: --mdx_batch_size=4","chunkId":"README.md:70"}],[309,{"filePath":"README.md","startLine":370,"endLine":372,"chunkText":"  --mdx_segment_size MDX_SEGMENT_SIZE                    Larger consumes more resources, but may give better results (default: 256). Example: --mdx_segment_size=256\n  --mdx_overlap MDX_OVERLAP                              Amount of overlap between prediction windows, 0.001-0.999. Higher is better but slower (default: 0.25). Example: --mdx_overlap=0.25\n  --mdx_batch_size MDX_BATCH_SIZE                        Larger consumes more RAM but may process slightly faster (default: 1). Example: --mdx_batch_size=4","chunkId":"README.md:71"}],[310,{"filePath":"README.md","startLine":371,"endLine":373,"chunkText":"  --mdx_overlap MDX_OVERLAP                              Amount of overlap between prediction windows, 0.001-0.999. Higher is better but slower (default: 0.25). Example: --mdx_overlap=0.25\n  --mdx_batch_size MDX_BATCH_SIZE                        Larger consumes more RAM but may process slightly faster (default: 1). Example: --mdx_batch_size=4\n  --mdx_hop_length MDX_HOP_LENGTH                        Usually called stride in neural networks, only change if you know what you're doing (default: 1024). Example: --mdx_hop_length=1024","chunkId":"README.md:72"}],[311,{"filePath":"README.md","startLine":372,"endLine":376,"chunkText":"  --mdx_batch_size MDX_BATCH_SIZE                        Larger consumes more RAM but may process slightly faster (default: 1). Example: --mdx_batch_size=4\n  --mdx_hop_length MDX_HOP_LENGTH                        Usually called stride in neural networks, only change if you know what you're doing (default: 1024). Example: --mdx_hop_length=1024\n  --mdx_enable_denoise                                   Enable denoising during separation (default: False). Example: --mdx_enable_denoise\n\nVR Architecture Parameters:","chunkId":"README.md:73"}],[312,{"filePath":"README.md","startLine":374,"endLine":378,"chunkText":"  --mdx_enable_denoise                                   Enable denoising during separation (default: False). Example: --mdx_enable_denoise\n\nVR Architecture Parameters:\n  --vr_batch_size VR_BATCH_SIZE                          Number of batches to process at a time. Higher = more RAM, slightly faster processing (default: 1). Example: --vr_batch_size=16\n  --vr_window_size VR_WINDOW_SIZE                        Balance quality and speed. 1024 = fast but lower, 320 = slower but better quality. (default: 512). Example: --vr_window_size=320","chunkId":"README.md:74"}],[313,{"filePath":"README.md","startLine":376,"endLine":379,"chunkText":"VR Architecture Parameters:\n  --vr_batch_size VR_BATCH_SIZE                          Number of batches to process at a time. Higher = more RAM, slightly faster processing (default: 1). Example: --vr_batch_size=16\n  --vr_window_size VR_WINDOW_SIZE                        Balance quality and speed. 1024 = fast but lower, 320 = slower but better quality. (default: 512). Example: --vr_window_size=320\n  --vr_aggression VR_AGGRESSION                          Intensity of primary stem extraction, -100 - 100. Typically, 5 for vocals & instrumentals (default: 5). Example: --vr_aggression=2","chunkId":"README.md:75"}],[314,{"filePath":"README.md","startLine":377,"endLine":379,"chunkText":"  --vr_batch_size VR_BATCH_SIZE                          Number of batches to process at a time. Higher = more RAM, slightly faster processing (default: 1). Example: --vr_batch_size=16\n  --vr_window_size VR_WINDOW_SIZE                        Balance quality and speed. 1024 = fast but lower, 320 = slower but better quality. (default: 512). Example: --vr_window_size=320\n  --vr_aggression VR_AGGRESSION                          Intensity of primary stem extraction, -100 - 100. Typically, 5 for vocals & instrumentals (default: 5). Example: --vr_aggression=2","chunkId":"README.md:76"}],[315,{"filePath":"README.md","startLine":378,"endLine":380,"chunkText":"  --vr_window_size VR_WINDOW_SIZE                        Balance quality and speed. 1024 = fast but lower, 320 = slower but better quality. (default: 512). Example: --vr_window_size=320\n  --vr_aggression VR_AGGRESSION                          Intensity of primary stem extraction, -100 - 100. Typically, 5 for vocals & instrumentals (default: 5). Example: --vr_aggression=2\n  --vr_enable_tta                                        Enable Test-Time-Augmentation; slow but improves quality (default: False). Example: --vr_enable_tta","chunkId":"README.md:77"}],[316,{"filePath":"README.md","startLine":379,"endLine":381,"chunkText":"  --vr_aggression VR_AGGRESSION                          Intensity of primary stem extraction, -100 - 100. Typically, 5 for vocals & instrumentals (default: 5). Example: --vr_aggression=2\n  --vr_enable_tta                                        Enable Test-Time-Augmentation; slow but improves quality (default: False). Example: --vr_enable_tta\n  --vr_high_end_process                                  Mirror the missing frequency range of the output (default: False). Example: --vr_high_end_process","chunkId":"README.md:78"}],[317,{"filePath":"README.md","startLine":380,"endLine":382,"chunkText":"  --vr_enable_tta                                        Enable Test-Time-Augmentation; slow but improves quality (default: False). Example: --vr_enable_tta\n  --vr_high_end_process                                  Mirror the missing frequency range of the output (default: False). Example: --vr_high_end_process\n  --vr_enable_post_process                               Identify leftover artifacts within vocal output; may improve separation for some songs (default: False). Example: --vr_enable_post_process","chunkId":"README.md:79"}],[318,{"filePath":"README.md","startLine":381,"endLine":385,"chunkText":"  --vr_high_end_process                                  Mirror the missing frequency range of the output (default: False). Example: --vr_high_end_process\n  --vr_enable_post_process                               Identify leftover artifacts within vocal output; may improve separation for some songs (default: False). Example: --vr_enable_post_process\n  --vr_post_process_threshold VR_POST_PROCESS_THRESHOLD  Threshold for post_process feature: 0.1-0.3 (default: 0.2). Example: --vr_post_process_threshold=0.1\n\nDemucs Architecture Parameters:","chunkId":"README.md:80"}],[319,{"filePath":"README.md","startLine":383,"endLine":387,"chunkText":"  --vr_post_process_threshold VR_POST_PROCESS_THRESHOLD  Threshold for post_process feature: 0.1-0.3 (default: 0.2). Example: --vr_post_process_threshold=0.1\n\nDemucs Architecture Parameters:\n  --demucs_segment_size DEMUCS_SEGMENT_SIZE              Size of segments into which the audio is split, 1-100. Higher = slower but better quality (default: Default). Example: --demucs_segment_size=256\n  --demucs_shifts DEMUCS_SHIFTS                          Number of predictions with random shifts, higher = slower but better quality (default: 2). Example: --demucs_shifts=4","chunkId":"README.md:81"}],[320,{"filePath":"README.md","startLine":385,"endLine":388,"chunkText":"Demucs Architecture Parameters:\n  --demucs_segment_size DEMUCS_SEGMENT_SIZE              Size of segments into which the audio is split, 1-100. Higher = slower but better quality (default: Default). Example: --demucs_segment_size=256\n  --demucs_shifts DEMUCS_SHIFTS                          Number of predictions with random shifts, higher = slower but better quality (default: 2). Example: --demucs_shifts=4\n  --demucs_overlap DEMUCS_OVERLAP                        Overlap between prediction windows, 0.001-0.999. Higher = slower but better quality (default: 0.25). Example: --demucs_overlap=0.25","chunkId":"README.md:82"}],[321,{"filePath":"README.md","startLine":386,"endLine":388,"chunkText":"  --demucs_segment_size DEMUCS_SEGMENT_SIZE              Size of segments into which the audio is split, 1-100. Higher = slower but better quality (default: Default). Example: --demucs_segment_size=256\n  --demucs_shifts DEMUCS_SHIFTS                          Number of predictions with random shifts, higher = slower but better quality (default: 2). Example: --demucs_shifts=4\n  --demucs_overlap DEMUCS_OVERLAP                        Overlap between prediction windows, 0.001-0.999. Higher = slower but better quality (default: 0.25). Example: --demucs_overlap=0.25","chunkId":"README.md:83"}],[322,{"filePath":"README.md","startLine":387,"endLine":391,"chunkText":"  --demucs_shifts DEMUCS_SHIFTS                          Number of predictions with random shifts, higher = slower but better quality (default: 2). Example: --demucs_shifts=4\n  --demucs_overlap DEMUCS_OVERLAP                        Overlap between prediction windows, 0.001-0.999. Higher = slower but better quality (default: 0.25). Example: --demucs_overlap=0.25\n  --demucs_segments_enabled DEMUCS_SEGMENTS_ENABLED      Enable segment-wise processing (default: True). Example: --demucs_segments_enabled=False\n\nMDXC Architecture Parameters:","chunkId":"README.md:84"}],[323,{"filePath":"README.md","startLine":389,"endLine":393,"chunkText":"  --demucs_segments_enabled DEMUCS_SEGMENTS_ENABLED      Enable segment-wise processing (default: True). Example: --demucs_segments_enabled=False\n\nMDXC Architecture Parameters:\n  --mdxc_segment_size MDXC_SEGMENT_SIZE                  Larger consumes more resources, but may give better results (default: 256). Example: --mdxc_segment_size=256\n  --mdxc_override_model_segment_size                     Override model default segment size instead of using the model default value. Example: --mdxc_override_model_segment_size","chunkId":"README.md:85"}],[324,{"filePath":"README.md","startLine":391,"endLine":394,"chunkText":"MDXC Architecture Parameters:\n  --mdxc_segment_size MDXC_SEGMENT_SIZE                  Larger consumes more resources, but may give better results (default: 256). Example: --mdxc_segment_size=256\n  --mdxc_override_model_segment_size                     Override model default segment size instead of using the model default value. Example: --mdxc_override_model_segment_size\n  --mdxc_overlap MDXC_OVERLAP                            Amount of overlap between prediction windows, 2-50. Higher is better but slower (default: 8). Example: --mdxc_overlap=8","chunkId":"README.md:86"}],[325,{"filePath":"README.md","startLine":392,"endLine":394,"chunkText":"  --mdxc_segment_size MDXC_SEGMENT_SIZE                  Larger consumes more resources, but may give better results (default: 256). Example: --mdxc_segment_size=256\n  --mdxc_override_model_segment_size                     Override model default segment size instead of using the model default value. Example: --mdxc_override_model_segment_size\n  --mdxc_overlap MDXC_OVERLAP                            Amount of overlap between prediction windows, 2-50. Higher is better but slower (default: 8). Example: --mdxc_overlap=8","chunkId":"README.md:87"}],[326,{"filePath":"README.md","startLine":393,"endLine":395,"chunkText":"  --mdxc_override_model_segment_size                     Override model default segment size instead of using the model default value. Example: --mdxc_override_model_segment_size\n  --mdxc_overlap MDXC_OVERLAP                            Amount of overlap between prediction windows, 2-50. Higher is better but slower (default: 8). Example: --mdxc_overlap=8\n  --mdxc_batch_size MDXC_BATCH_SIZE                      Larger consumes more RAM but may process slightly faster (default: 1). Example: --mdxc_batch_size=4","chunkId":"README.md:88"}],[327,{"filePath":"README.md","startLine":394,"endLine":400,"chunkText":"  --mdxc_overlap MDXC_OVERLAP                            Amount of overlap between prediction windows, 2-50. Higher is better but slower (default: 8). Example: --mdxc_overlap=8\n  --mdxc_batch_size MDXC_BATCH_SIZE                      Larger consumes more RAM but may process slightly faster (default: 1). Example: --mdxc_batch_size=4\n  --mdxc_pitch_shift MDXC_PITCH_SHIFT                    Shift audio pitch by a number of semitones while processing. May improve output for deep/high vocals. (default: 0). Example: --mdxc_pitch_shift=2\n```\n\n### As a Dependency in a Python Project\n","chunkId":"README.md:89"}],[328,{"filePath":"README.md","startLine":398,"endLine":414,"chunkText":"\n### As a Dependency in a Python Project\n\nYou can use Audio Separator in your own Python project. Here's a minimal example using the default two stem (Instrumental and Vocals) model:\n\n```python\nfrom audio_separator.separator import Separator\n\n# Initialize the Separator class (with optional configuration properties, below)\nseparator = Separator()\n\n# Load a machine learning model (if unspecified, defaults to 'model_mel_band_roformer_ep_3005_sdr_11.4360.ckpt')\nseparator.load_model()\n\n# Perform the separation on specific audio files without reloading the model\noutput_files = separator.separate('audio1.wav')\n","chunkId":"README.md:90"}],[329,{"filePath":"README.md","startLine":412,"endLine":430,"chunkText":"# Perform the separation on specific audio files without reloading the model\noutput_files = separator.separate('audio1.wav')\n\nprint(f\"Separation complete! Output file(s): {' '.join(output_files)}\")\n```\n\n#### Batch processing and processing with multiple models\n\nYou can process multiple files without reloading the model to save time and memory.\n\nYou only need to load a model when choosing or changing models. See example below:\n\n```python\nfrom audio_separator.separator import Separator\n\n# Initialize the Separator class (with optional configuration properties, below)\nseparator = Separator()\n\n# Load a model","chunkId":"README.md:91"}],[330,{"filePath":"README.md","startLine":428,"endLine":444,"chunkText":"separator = Separator()\n\n# Load a model\nseparator.load_model(model_filename='model_bs_roformer_ep_317_sdr_12.9755.ckpt')\n\n# Separate multiple audio files without reloading the model\noutput_files = separator.separate(['audio1.wav', 'audio2.wav', 'audio3.wav'])\n\n# Load a different model\nseparator.load_model(model_filename='UVR_MDXNET_KARA_2.onnx')\n\n# Separate the same files with the new model\noutput_files = separator.separate(['audio1.wav', 'audio2.wav', 'audio3.wav'])\n```\n\nYou can also specify the path to a folder containing audio files instead of listing the full paths to each of them:\n```python","chunkId":"README.md:92"}],[331,{"filePath":"README.md","startLine":442,"endLine":459,"chunkText":"You can also specify the path to a folder containing audio files instead of listing the full paths to each of them:\n```python\nfrom audio_separator.separator import Separator\n\n# Initialize the Separator class (with optional configuration properties, below)\nseparator = Separator()\n\n# Load a model\nseparator.load_model(model_filename='model_bs_roformer_ep_317_sdr_12.9755.ckpt')\n\n# Separate all audio files located in a folder\noutput_files = separator.separate('path/to/audio_directory')\n```\n\n#### Renaming Stems\n\nYou can rename the output files by specifying the desired names. For example:\n```python","chunkId":"README.md:93"}],[332,{"filePath":"README.md","startLine":459,"endLine":477,"chunkText":"You can rename the output files by specifying the desired names. For example:\n```python\noutput_names = {\n    \"Vocals\": \"vocals_output\",\n    \"Instrumental\": \"instrumental_output\",\n}\noutput_files = separator.separate('audio1.wav', output_names)\n```\nIn this case, the output file names will be: `vocals_output.wav` and `instrumental_output.wav`.\n\nYou can also rename specific stems:\n\n- To rename the Vocals stem:\n  ```python\n  output_names = {\n      \"Vocals\": \"vocals_output\",\n  }\n  output_files = separator.separate('audio1.wav', output_names)\n  ```","chunkId":"README.md:94"}],[333,{"filePath":"README.md","startLine":475,"endLine":490,"chunkText":"  }\n  output_files = separator.separate('audio1.wav', output_names)\n  ```\n  > The output files will be named: `vocals_output.wav` and `audio1_(Instrumental)_model_mel_band_roformer_ep_3005_sdr_11.wav`\n- To rename the Instrumental stem:\n  ```python\n  output_names = {\n      \"Instrumental\": \"instrumental_output\",\n  }\n  output_files = separator.separate('audio1.wav', output_names)\n  ```\n  > The output files will be named: `audio1_(Vocals)_model_mel_band_roformer_ep_3005_sdr_11.wav` and `instrumental_output.wav`\n- List of stems for Demucs models:\n  - htdemucs_6s.yaml\n    ```python\n    output_names = {","chunkId":"README.md:95"}],[334,{"filePath":"README.md","startLine":488,"endLine":510,"chunkText":"  - htdemucs_6s.yaml\n    ```python\n    output_names = {\n        \"Vocals\": \"vocals_output\",\n        \"Drums\": \"drums_output\",\n        \"Bass\": \"bass_output\",\n        \"Other\": \"other_output\",\n        \"Guitar\": \"guitar_output\",\n        \"Piano\": \"piano_output\",\n    }\n    ```\n  - Other Demucs models\n    ```python\n    output_names = {\n        \"Vocals\": \"vocals_output\",\n        \"Drums\": \"drums_output\",\n        \"Bass\": \"bass_output\",\n        \"Other\": \"other_output\",\n    }\n    ```\n\n## Parameters for the Separator class\n","chunkId":"README.md:96"}],[335,{"filePath":"README.md","startLine":508,"endLine":514,"chunkText":"\n## Parameters for the Separator class\n\n- **`log_level`:** (Optional) Logging level, e.g., INFO, DEBUG, WARNING. `Default: logging.INFO`\n- **`log_formatter`:** (Optional) The log format. Default: None, which falls back to '%(asctime)s - %(levelname)s - %(module)s - %(message)s'\n- **`model_file_dir`:** (Optional) Directory to cache model files in. `Default: /tmp/audio-separator-models/`\n- **`output_dir`:** (Optional) Directory where the separated files will be saved. If not specified, uses the current directory.","chunkId":"README.md:97"}],[336,{"filePath":"README.md","startLine":512,"endLine":515,"chunkText":"- **`log_formatter`:** (Optional) The log format. Default: None, which falls back to '%(asctime)s - %(levelname)s - %(module)s - %(message)s'\n- **`model_file_dir`:** (Optional) Directory to cache model files in. `Default: /tmp/audio-separator-models/`\n- **`output_dir`:** (Optional) Directory where the separated files will be saved. If not specified, uses the current directory.\n- **`output_format`:** (Optional) Format to encode output files, any common format (WAV, MP3, FLAC, M4A, etc.). `Default: WAV`","chunkId":"README.md:98"}],[337,{"filePath":"README.md","startLine":513,"endLine":516,"chunkText":"- **`model_file_dir`:** (Optional) Directory to cache model files in. `Default: /tmp/audio-separator-models/`\n- **`output_dir`:** (Optional) Directory where the separated files will be saved. If not specified, uses the current directory.\n- **`output_format`:** (Optional) Format to encode output files, any common format (WAV, MP3, FLAC, M4A, etc.). `Default: WAV`\n- **`normalization_threshold`:** (Optional) The amount by which the amplitude of the output audio will be multiplied. `Default: 0.9`","chunkId":"README.md:99"}],[338,{"filePath":"README.md","startLine":514,"endLine":516,"chunkText":"- **`output_dir`:** (Optional) Directory where the separated files will be saved. If not specified, uses the current directory.\n- **`output_format`:** (Optional) Format to encode output files, any common format (WAV, MP3, FLAC, M4A, etc.). `Default: WAV`\n- **`normalization_threshold`:** (Optional) The amount by which the amplitude of the output audio will be multiplied. `Default: 0.9`","chunkId":"README.md:100"}],[339,{"filePath":"README.md","startLine":515,"endLine":518,"chunkText":"- **`output_format`:** (Optional) Format to encode output files, any common format (WAV, MP3, FLAC, M4A, etc.). `Default: WAV`\n- **`normalization_threshold`:** (Optional) The amount by which the amplitude of the output audio will be multiplied. `Default: 0.9`\n- **`amplification_threshold`:** (Optional) The minimum amplitude level at which the waveform will be amplified. If the peak amplitude of the audio is below this threshold, the waveform will be scaled up to meet it. `Default: 0.0`\n- **`output_single_stem`:** (Optional) Output only a single stem, such as 'Instrumental' and 'Vocals'. `Default: None`","chunkId":"README.md:101"}],[340,{"filePath":"README.md","startLine":516,"endLine":519,"chunkText":"- **`normalization_threshold`:** (Optional) The amount by which the amplitude of the output audio will be multiplied. `Default: 0.9`\n- **`amplification_threshold`:** (Optional) The minimum amplitude level at which the waveform will be amplified. If the peak amplitude of the audio is below this threshold, the waveform will be scaled up to meet it. `Default: 0.0`\n- **`output_single_stem`:** (Optional) Output only a single stem, such as 'Instrumental' and 'Vocals'. `Default: None`\n- **`invert_using_spec`:** (Optional) Flag to invert using spectrogram. `Default: False`","chunkId":"README.md:102"}],[341,{"filePath":"README.md","startLine":517,"endLine":520,"chunkText":"- **`amplification_threshold`:** (Optional) The minimum amplitude level at which the waveform will be amplified. If the peak amplitude of the audio is below this threshold, the waveform will be scaled up to meet it. `Default: 0.0`\n- **`output_single_stem`:** (Optional) Output only a single stem, such as 'Instrumental' and 'Vocals'. `Default: None`\n- **`invert_using_spec`:** (Optional) Flag to invert using spectrogram. `Default: False`\n- **`sample_rate`:** (Optional) Set the sample rate of the output audio. `Default: 44100`","chunkId":"README.md:103"}],[342,{"filePath":"README.md","startLine":518,"endLine":522,"chunkText":"- **`output_single_stem`:** (Optional) Output only a single stem, such as 'Instrumental' and 'Vocals'. `Default: None`\n- **`invert_using_spec`:** (Optional) Flag to invert using spectrogram. `Default: False`\n- **`sample_rate`:** (Optional) Set the sample rate of the output audio. `Default: 44100`\n- **`use_soundfile`:** (Optional) Use soundfile for output writing, can solve OOM issues, especially on longer audio.\n- **`use_autocast`:** (Optional) Flag to use PyTorch autocast for faster inference. Do not use for CPU inference. `Default: False`","chunkId":"README.md:104"}],[343,{"filePath":"README.md","startLine":520,"endLine":523,"chunkText":"- **`sample_rate`:** (Optional) Set the sample rate of the output audio. `Default: 44100`\n- **`use_soundfile`:** (Optional) Use soundfile for output writing, can solve OOM issues, especially on longer audio.\n- **`use_autocast`:** (Optional) Flag to use PyTorch autocast for faster inference. Do not use for CPU inference. `Default: False`\n- **`mdx_params`:** (Optional) MDX Architecture Specific Attributes & Defaults. `Default: {\"hop_length\": 1024, \"segment_size\": 256, \"overlap\": 0.25, \"batch_size\": 1, \"enable_denoise\": False}`","chunkId":"README.md:105"}],[344,{"filePath":"README.md","startLine":521,"endLine":523,"chunkText":"- **`use_soundfile`:** (Optional) Use soundfile for output writing, can solve OOM issues, especially on longer audio.\n- **`use_autocast`:** (Optional) Flag to use PyTorch autocast for faster inference. Do not use for CPU inference. `Default: False`\n- **`mdx_params`:** (Optional) MDX Architecture Specific Attributes & Defaults. `Default: {\"hop_length\": 1024, \"segment_size\": 256, \"overlap\": 0.25, \"batch_size\": 1, \"enable_denoise\": False}`","chunkId":"README.md:106"}],[345,{"filePath":"README.md","startLine":522,"endLine":524,"chunkText":"- **`use_autocast`:** (Optional) Flag to use PyTorch autocast for faster inference. Do not use for CPU inference. `Default: False`\n- **`mdx_params`:** (Optional) MDX Architecture Specific Attributes & Defaults. `Default: {\"hop_length\": 1024, \"segment_size\": 256, \"overlap\": 0.25, \"batch_size\": 1, \"enable_denoise\": False}`\n- **`vr_params`:** (Optional) VR Architecture Specific Attributes & Defaults. `Default: {\"batch_size\": 1, \"window_size\": 512, \"aggression\": 5, \"enable_tta\": False, \"enable_post_process\": False, \"post_process_threshold\": 0.2, \"high_end_process\": False}`","chunkId":"README.md:107"}],[346,{"filePath":"README.md","startLine":523,"endLine":524,"chunkText":"- **`mdx_params`:** (Optional) MDX Architecture Specific Attributes & Defaults. `Default: {\"hop_length\": 1024, \"segment_size\": 256, \"overlap\": 0.25, \"batch_size\": 1, \"enable_denoise\": False}`\n- **`vr_params`:** (Optional) VR Architecture Specific Attributes & Defaults. `Default: {\"batch_size\": 1, \"window_size\": 512, \"aggression\": 5, \"enable_tta\": False, \"enable_post_process\": False, \"post_process_threshold\": 0.2, \"high_end_process\": False}`","chunkId":"README.md:108"}],[347,{"filePath":"README.md","startLine":524,"endLine":525,"chunkText":"- **`vr_params`:** (Optional) VR Architecture Specific Attributes & Defaults. `Default: {\"batch_size\": 1, \"window_size\": 512, \"aggression\": 5, \"enable_tta\": False, \"enable_post_process\": False, \"post_process_threshold\": 0.2, \"high_end_process\": False}`\n- **`demucs_params`:** (Optional) Demucs Architecture Specific Attributes & Defaults. `Default: {\"segment_size\": \"Default\", \"shifts\": 2, \"overlap\": 0.25, \"segments_enabled\": True}`","chunkId":"README.md:109"}],[348,{"filePath":"README.md","startLine":525,"endLine":529,"chunkText":"- **`demucs_params`:** (Optional) Demucs Architecture Specific Attributes & Defaults. `Default: {\"segment_size\": \"Default\", \"shifts\": 2, \"overlap\": 0.25, \"segments_enabled\": True}`\n- **`mdxc_params`:** (Optional) MDXC Architecture Specific Attributes & Defaults. `Default: {\"segment_size\": 256, \"override_model_segment_size\": False, \"batch_size\": 1, \"overlap\": 8, \"pitch_shift\": 0}`\n\n## Remote API Usage 🌐\n","chunkId":"README.md:110"}],[349,{"filePath":"README.md","startLine":527,"endLine":537,"chunkText":"\n## Remote API Usage 🌐\n\nAudio Separator includes a remote API client that allows you to connect to a deployed Audio Separator API service, enabling you to perform audio separation without running the models locally. The API uses asynchronous processing with job polling for efficient handling of separation tasks.\n\nTo deploy Audio Separator as an API on modal.com and use this for remote processing, please see the detailed documentation here: [audio_separator/remote/README.md](audio_separator/remote/README.md).\n\n## Requirements 📋\n\nPython >= 3.10\n","chunkId":"README.md:111"}],[350,{"filePath":"README.md","startLine":535,"endLine":553,"chunkText":"\nPython >= 3.10\n\nLibraries: torch, onnx, onnxruntime, numpy, librosa, requests, six, tqdm, pydub\n\n## Developing Locally\n\nThis project uses Poetry for dependency management and packaging. Follow these steps to setup a local development environment:\n\n### Prerequisites\n\n- Make sure you have Python 3.10 or newer installed on your machine.\n- Install Conda (I recommend Miniforge: [Miniforge GitHub](https://github.com/conda-forge/miniforge)) to manage your Python virtual environments\n\n### Clone the Repository\n\nClone the repository to your local machine:\n\n```sh","chunkId":"README.md:112"}],[351,{"filePath":"README.md","startLine":551,"endLine":574,"chunkText":"Clone the repository to your local machine:\n\n```sh\ngit clone https://github.com/YOUR_USERNAME/audio-separator.git\ncd audio-separator\n```\n\nReplace `YOUR_USERNAME` with your GitHub username if you've forked the repository, or use the main repository URL if you have the permissions.\n\n### Create and activate the Conda Environment\n\nTo create and activate the conda environment, use the following commands:\n\n```sh\nconda env create\nconda activate audio-separator-dev\n```\n\n### Install Dependencies\n\nOnce you're inside the conda env, run the following command to install the project dependencies:\n\n```sh\npoetry install","chunkId":"README.md:113"}],[352,{"filePath":"README.md","startLine":574,"endLine":607,"chunkText":"poetry install\n```\n\nInstall extra dependencies depending if you're running with GPU or CPU.\n```sh\npoetry install --extras \"cpu\"\n```\nor\n```sh\npoetry install --extras \"gpu\"\n```\nor\n```sh\npoetry install --extras \"dml\"\n```\n\n### Running the Command-Line Interface Locally\n\nYou can run the CLI command directly within the virtual environment. For example:\n\n```sh\naudio-separator path/to/your/audio-file.wav\n```\n\n### Deactivate the Virtual Environment\n\nOnce you are done with your development work, you can exit the virtual environment by simply typing:\n\n```sh\nconda deactivate\n```\n\n### Building the Package\n","chunkId":"README.md:114"}],[353,{"filePath":"README.md","startLine":605,"endLine":621,"chunkText":"\n### Building the Package\n\nTo build the package for distribution, use the following command:\n\n```sh\npoetry build\n```\n\nThis will generate the distribution packages in the dist directory - but for now only @beveradb will be able to publish to PyPI.\n\n\n## Contributing 🤝\n\nContributions are very much welcome! Please fork the repository and submit a pull request with your changes, and I'll try to review, merge and publish promptly!\n\n- This project is 100% open-source and free for anyone to use and modify as they wish.","chunkId":"README.md:115"}],[354,{"filePath":"README.md","startLine":619,"endLine":622,"chunkText":"Contributions are very much welcome! Please fork the repository and submit a pull request with your changes, and I'll try to review, merge and publish promptly!\n\n- This project is 100% open-source and free for anyone to use and modify as they wish.\n- If the maintenance workload for this repo somehow becomes too much for me I'll ask for volunteers to share maintainership of the repo, though I don't think that is very likely","chunkId":"README.md:116"}],[355,{"filePath":"README.md","startLine":620,"endLine":621,"chunkText":"- This project is 100% open-source and free for anyone to use and modify as they wish.\n- If the maintenance workload for this repo somehow becomes too much for me I'll ask for volunteers to share maintainership of the repo, though I don't think that is very likely","chunkId":"README.md:117"}],[356,{"filePath":"README.md","startLine":621,"endLine":622,"chunkText":"- This project is 100% open-source and free for anyone to use and modify as they wish.\n- If the maintenance workload for this repo somehow becomes too much for me I'll ask for volunteers to share maintainership of the repo, though I don't think that is very likely","chunkId":"README.md:118"}],[357,{"filePath":"README.md","startLine":622,"endLine":626,"chunkText":"- If the maintenance workload for this repo somehow becomes too much for me I'll ask for volunteers to share maintainership of the repo, though I don't think that is very likely\n- Development and support for the MDX-Net separation models is part of the main [UVR project](https://github.com/Anjok07/ultimatevocalremovergui), this repo is just a CLI/Python package wrapper to simplify running those models programmatically. So, if you want to try and improve the actual models, please get involved in the UVR project and look for guidance there!\n\n## License 📄\n","chunkId":"README.md:119"}],[358,{"filePath":"README.md","startLine":624,"endLine":632,"chunkText":"\n## License 📄\n\nThis project is licensed under the MIT [License](LICENSE).\n\n- **Please Note:** If you choose to integrate this project into some other project using the default model or any other model trained as part of the [UVR](https://github.com/Anjok07/ultimatevocalremovergui) project, please honor the MIT license by providing credit to UVR and its developers!\n\n## Credits 🙏\n","chunkId":"README.md:120"}],[359,{"filePath":"README.md","startLine":630,"endLine":635,"chunkText":"\n## Credits 🙏\n\n- [Anjok07](https://github.com/Anjok07) - Author of [Ultimate Vocal Remover GUI](https://github.com/Anjok07/ultimatevocalremovergui), which almost all of the code in this repo was copied from! Definitely deserving of credit for anything good from this project. Thank you!\n- [DilanBoskan](https://github.com/DilanBoskan) - Your contributions at the start of this project were essential to the success of UVR. Thank you!\n- [Kuielab & Woosung Choi](https://github.com/kuielab) - Developed the original MDX-Net AI code.","chunkId":"README.md:121"}],[360,{"filePath":"README.md","startLine":633,"endLine":635,"chunkText":"- [Anjok07](https://github.com/Anjok07) - Author of [Ultimate Vocal Remover GUI](https://github.com/Anjok07/ultimatevocalremovergui), which almost all of the code in this repo was copied from! Definitely deserving of credit for anything good from this project. Thank you!\n- [DilanBoskan](https://github.com/DilanBoskan) - Your contributions at the start of this project were essential to the success of UVR. Thank you!\n- [Kuielab & Woosung Choi](https://github.com/kuielab) - Developed the original MDX-Net AI code.","chunkId":"README.md:122"}],[361,{"filePath":"README.md","startLine":634,"endLine":637,"chunkText":"- [DilanBoskan](https://github.com/DilanBoskan) - Your contributions at the start of this project were essential to the success of UVR. Thank you!\n- [Kuielab & Woosung Choi](https://github.com/kuielab) - Developed the original MDX-Net AI code.\n- [KimberleyJSN](https://github.com/KimberleyJensen) - Advised and aided the implementation of the training scripts for MDX-Net and Demucs. Thank you!\n- [Hv](https://github.com/NaJeongMo/Colab-for-MDX_B) - Helped implement chunks into the MDX-Net AI code. Thank you!","chunkId":"README.md:123"}],[362,{"filePath":"README.md","startLine":635,"endLine":641,"chunkText":"- [Kuielab & Woosung Choi](https://github.com/kuielab) - Developed the original MDX-Net AI code.\n- [KimberleyJSN](https://github.com/KimberleyJensen) - Advised and aided the implementation of the training scripts for MDX-Net and Demucs. Thank you!\n- [Hv](https://github.com/NaJeongMo/Colab-for-MDX_B) - Helped implement chunks into the MDX-Net AI code. Thank you!\n- [zhzhongshi](https://github.com/zhzhongshi) - Helped add support for the MDXC models in `audio-separator`. Thank you!\n\n## Contact 💌\n","chunkId":"README.md:124"}],[363,{"filePath":"README.md","startLine":639,"endLine":656,"chunkText":"\n## Contact 💌\n\nFor questions or feedback, please raise an issue or reach out to @beveradb ([Andrew Beveridge](mailto:andrew@beveridge.uk)) directly.\n\n---\n<div align=\"center\">\n\n<!-- sponsors --><!-- sponsors -->\n\n## Thanks to all contributors for their efforts\n\n<a href=\"https://github.com/nomadkaraoke/python-audio-separator/graphs/contributors\">\n  <img src=\"https://contrib.rocks/image?repo=nomadkaraoke/python-audio-separator\" />\n</a>\n\n</div>\n","chunkId":"README.md:125"}],[364,{"filePath":"TODO.md","startLine":0,"endLine":9,"chunkText":"# Audio-Separator TO-DO list\n\nIf you see something here, Andrew is aware it needs to be done, and he will hopefully get to it soon but can't make any promises!\nThis isn't his full time job, and he's doing his best to keep up with everything.\n\nIf you'd like something to be done sooner, please consider trying to work on it yourself and submitting a pull request!\nIf you don't know how to code, please consider learning - it's free, and anyone can do it! https://www.freecodecamp.org/learn/scientific-computing-with-python/\n\n## TODO:\n","chunkId":"TODO.md:0"}],[365,{"filePath":"TODO.md","startLine":9,"endLine":18,"chunkText":"\n- Add unit tests to all uvr lib functions to ensure no obvious errors are missed\n- Add end-to-end tests which download all models and test separation with a very short input file for speed\n- Add ability for user to download all models ahead of time\n- Add tests for Windows, Linux and macOS separately\n- Add tests for Python 3.10, 3.11\n- Add support for Ensemble mode\n- Add support for Chaining multiple models\n- Add support for Splitting multiple stems from a single input file by running different models\n","chunkId":"TODO.md:1"}],[366,{"filePath":"audio_separator/remote/README.md","startLine":0,"endLine":4,"chunkText":"## Remote API Usage 🌐\n\nAudio Separator includes a remote API client that allows you to connect to a deployed Audio Separator API service, enabling you to perform audio separation without running the models locally. The API uses asynchronous processing with job polling for efficient handling of separation tasks.\n\nTo make this easy to set up and use cheaply, we've created a deployment script for [modal.com](https://modal.com) who currently (July 2025) offer $30/mo free in GPU credits.","chunkId":"audio_separator/remote/README.md:0"}],[367,{"filePath":"audio_separator/remote/README.md","startLine":2,"endLine":4,"chunkText":"Audio Separator includes a remote API client that allows you to connect to a deployed Audio Separator API service, enabling you to perform audio separation without running the models locally. The API uses asynchronous processing with job polling for efficient handling of separation tasks.\n\nTo make this easy to set up and use cheaply, we've created a deployment script for [modal.com](https://modal.com) who currently (July 2025) offer $30/mo free in GPU credits.","chunkId":"audio_separator/remote/README.md:1"}],[368,{"filePath":"audio_separator/remote/README.md","startLine":3,"endLine":10,"chunkText":"\nTo make this easy to set up and use cheaply, we've created a deployment script for [modal.com](https://modal.com) who currently (July 2025) offer $30/mo free in GPU credits.\nConsidering [their pricing](https://modal.com/pricing) for execution with an Nvidia T4 is $0.000164 / sec and most audio-separation jobs take less than 2 minutes, that's around $0.019 per separation job.\nWith $30/month in free credits, that's over **1,500 GPU-accelerated audio separation jobs per month, for free!**\n\n**✨ Key Features:**\n\n- **Multiple Model Support**: Upload once, separate with multiple models in a single job","chunkId":"audio_separator/remote/README.md:2"}],[369,{"filePath":"audio_separator/remote/README.md","startLine":8,"endLine":19,"chunkText":"**✨ Key Features:**\n\n- **Multiple Model Support**: Upload once, separate with multiple models in a single job\n- **Full Parameter Compatibility**: All local CLI parameters and architecture settings supported\n- **Efficient Processing**: Avoid repeated uploads when comparing different models\n- **Real-time Progress**: Track progress across multiple models with detailed status updates\n\n#### Multiple Model Workflow\n\n```mermaid\ngraph TD\n    A[\"🎵 Upload Audio File<br/>audio.wav\"] --> B[\"☁️ Remote API Server\"]","chunkId":"audio_separator/remote/README.md:3"}],[370,{"filePath":"audio_separator/remote/README.md","startLine":17,"endLine":25,"chunkText":"```mermaid\ngraph TD\n    A[\"🎵 Upload Audio File<br/>audio.wav\"] --> B[\"☁️ Remote API Server\"]\n    B --> C[\"📋 Queue Multiple Models<br/>• model_bs_roformer.ckpt<br/>• UVR-MDX-NET.onnx<br/>• htdemucs_6s.yaml\"]\n    C --> D[\"🚀 Process Model 1<br/>BS-Roformer\"]\n    C --> E[\"🚀 Process Model 2<br/>MDX-NET\"]\n    C --> F[\"🚀 Process Model 3<br/>Demucs 6-stem\"]\n    D --> G[\"📁 Generate Output<br/>audio_(Vocals)_bs_roformer.flac<br/>audio_(Instrumental)_bs_roformer.flac\"]\n    E --> H[\"📁 Generate Output<br/>audio_(Vocals)_mdx.flac<br/>audio_(Instrumental)_mdx.flac\"]","chunkId":"audio_separator/remote/README.md:4"}],[371,{"filePath":"audio_separator/remote/README.md","startLine":23,"endLine":33,"chunkText":"    C --> F[\"🚀 Process Model 3<br/>Demucs 6-stem\"]\n    D --> G[\"📁 Generate Output<br/>audio_(Vocals)_bs_roformer.flac<br/>audio_(Instrumental)_bs_roformer.flac\"]\n    E --> H[\"📁 Generate Output<br/>audio_(Vocals)_mdx.flac<br/>audio_(Instrumental)_mdx.flac\"]\n    F --> I[\"📁 Generate Output<br/>audio_(Vocals)_htdemucs.flac<br/>audio_(Drums)_htdemucs.flac<br/>audio_(Bass)_htdemucs.flac<br/>etc...\"]\n    G --> J[\"📥 Download All Results<br/>Compare quality across models\"]\n    H --> J\n    I --> J\n\n    style A fill:#e1f5fe,color:#000\n    style B fill:#f3e5f5,color:#000\n    style C fill:#fff3e0,color:#000","chunkId":"audio_separator/remote/README.md:5"}],[372,{"filePath":"audio_separator/remote/README.md","startLine":31,"endLine":45,"chunkText":"    style A fill:#e1f5fe,color:#000\n    style B fill:#f3e5f5,color:#000\n    style C fill:#fff3e0,color:#000\n    style D fill:#e8f5e8,color:#000\n    style E fill:#e8f5e8,color:#000\n    style F fill:#e8f5e8,color:#000\n    style J fill:#e1f5fe,color:#000\n```\n\n### Deploying the API Server\n\nTo use the remote API functionality, you'll need to deploy the Audio Separator API server. The easiest way is using Modal.com:\n\n1. **Sign up for Modal.com** at [modal.com](https://modal.com)\n2. **Install the Modal CLI** and authenticate:","chunkId":"audio_separator/remote/README.md:6"}],[373,{"filePath":"audio_separator/remote/README.md","startLine":43,"endLine":57,"chunkText":"\n1. **Sign up for Modal.com** at [modal.com](https://modal.com)\n2. **Install the Modal CLI** and authenticate:\n   (Note, modal will need access to the installed project dependencies in whatever virtual environment you're using for audio-separator)\n   ```bash\n   poetry install --extras cpu # need either cpu or gpu to ensure onnxruntime is installed\n   pip install modal\n   modal setup\n   ```\n3. **Deploy the Audio Separator API**:\n   ```bash\n   modal deploy audio_separator/remote/deploy_modal.py\n   ```\n4. **Get your API URL** from the deployment output. It will look like:\n   ```","chunkId":"audio_separator/remote/README.md:7"}],[374,{"filePath":"audio_separator/remote/README.md","startLine":55,"endLine":79,"chunkText":"   ```\n4. **Get your API URL** from the deployment output. It will look like:\n   ```\n   https://USERNAME--audio-separator-api.modal.run\n   ```\n\nSet this API URL as an environment variable:\n\n```bash\nexport AUDIO_SEPARATOR_API_URL=\"https://USERNAME--audio-separator-api.modal.run\"\n```\n\nOr pass it directly with the `--api_url` parameter.\n\n### Remote API Client (Python)\n\nYou can use the `AudioSeparatorAPIClient` class to interact with a remote Audio Separator API:\n\n```python\nimport logging\nfrom audio_separator.remote import AudioSeparatorAPIClient\n\n# Set up logging\nlogger = logging.getLogger(__name__)\n","chunkId":"audio_separator/remote/README.md:8"}],[375,{"filePath":"audio_separator/remote/README.md","startLine":77,"endLine":92,"chunkText":"# Set up logging\nlogger = logging.getLogger(__name__)\n\n# Initialize the API client\napi_client = AudioSeparatorAPIClient(\"https://USERNAME--audio-separator-api.modal.run\", logger)\n\n# Simple example: separate audio and get results\nresult = api_client.separate_audio_and_wait(\"audio.mp3\")\nif result[\"status\"] == \"completed\":\n    print(f\"✅ Separation completed! Downloaded files:\")\n    for file_path in result[\"downloaded_files\"]:\n        print(f\"  - {file_path}\")\nelse:\n    print(f\"❌ Separation failed: {result.get('error', 'Unknown error')}\")\n\n# Multiple models example: separate with multiple models in one upload","chunkId":"audio_separator/remote/README.md:9"}],[376,{"filePath":"audio_separator/remote/README.md","startLine":90,"endLine":105,"chunkText":"    print(f\"❌ Separation failed: {result.get('error', 'Unknown error')}\")\n\n# Multiple models example: separate with multiple models in one upload\nresult = api_client.separate_audio_and_wait(\n    \"path/to/audio.wav\",\n    models=[\n        \"model_bs_roformer_ep_317_sdr_12.9755.ckpt\",\n        \"UVR-MDX-NET-Inst_HQ_4.onnx\",\n        \"htdemucs_6s.yaml\"\n    ],\n    timeout=600,           # Wait up to 10 minutes for multiple models\n    poll_interval=10,      # Check status every 10 seconds\n    download=True,         # Automatically download files\n    output_dir=\"./output\"  # Save files to specific directory\n)\n","chunkId":"audio_separator/remote/README.md:10"}],[377,{"filePath":"audio_separator/remote/README.md","startLine":103,"endLine":116,"chunkText":"    output_dir=\"./output\"  # Save files to specific directory\n)\n\n# Complex example with custom options and separator parameters\nresult = api_client.separate_audio_and_wait(\n    \"path/to/audio.wav\",\n    model=\"model_bs_roformer_ep_317_sdr_12.9755.ckpt\",\n    timeout=300,           # Wait up to 5 minutes\n    poll_interval=10,      # Check status every 10 seconds\n    download=True,         # Automatically download files\n    output_dir=\"./output\", # Save files to specific directory\n    # Separator configuration options (same as local CLI)\n    output_format=\"wav\",\n    normalization_threshold=0.8,","chunkId":"audio_separator/remote/README.md:11"}],[378,{"filePath":"audio_separator/remote/README.md","startLine":114,"endLine":130,"chunkText":"    # Separator configuration options (same as local CLI)\n    output_format=\"wav\",\n    normalization_threshold=0.8,\n    custom_output_names={\"Vocals\": \"lead_vocals\", \"Instrumental\": \"backing_track\"},\n    # MDX parameters\n    mdx_segment_size=512,\n    mdx_batch_size=2,\n    # VR parameters\n    vr_aggression=10,\n    vr_window_size=320,\n    # And any other separator parameters...\n)\n\n# Advanced approach: manual job management (for custom polling logic)\nresult = api_client.separate_audio(\n    \"path/to/audio.wav\",\n    models=[\"model1.ckpt\", \"model2.onnx\"],","chunkId":"audio_separator/remote/README.md:12"}],[379,{"filePath":"audio_separator/remote/README.md","startLine":128,"endLine":144,"chunkText":"result = api_client.separate_audio(\n    \"path/to/audio.wav\",\n    models=[\"model1.ckpt\", \"model2.onnx\"],\n    custom_output_names={\"Vocals\": \"vocals_output\", \"Instrumental\": \"instrumental_output\"}\n)\ntask_id = result[\"task_id\"]\nprint(f\"Job submitted! Task ID: {task_id}\")\n\n# Custom polling logic\nimport time\nwhile True:\n    status = api_client.get_job_status(task_id)\n    print(f\"Job status: {status['status']}\")\n\n    # Show progress with model information\n    if \"progress\" in status:\n        progress_info = f\"Progress: {status['progress']}%\"","chunkId":"audio_separator/remote/README.md:13"}],[380,{"filePath":"audio_separator/remote/README.md","startLine":142,"endLine":153,"chunkText":"    # Show progress with model information\n    if \"progress\" in status:\n        progress_info = f\"Progress: {status['progress']}%\"\n        if \"current_model_index\" in status and \"total_models\" in status:\n            model_info = f\" (Model {status['current_model_index'] + 1}/{status['total_models']})\"\n            progress_info += model_info\n        print(progress_info)\n\n    if status[\"status\"] == \"completed\":\n        # Download files manually\n        for filehash, filename in status[\"files\"].items():\n            output_path = api_client.download_file_by_hash(task_id, filehash, filename)","chunkId":"audio_separator/remote/README.md:14"}],[381,{"filePath":"audio_separator/remote/README.md","startLine":151,"endLine":170,"chunkText":"        # Download files manually\n        for filehash, filename in status[\"files\"].items():\n            output_path = api_client.download_file_by_hash(task_id, filehash, filename)\n            print(f\"Downloaded: {output_path}\")\n        break\n    elif status[\"status\"] == \"error\":\n        print(f\"Job failed: {status.get('error', 'Unknown error')}\")\n        break\n    else:\n        time.sleep(10)  # Wait 10 seconds\n\n# List available models\nmodels = api_client.list_models()\nprint(models[\"text\"])\n\n# Get server version\nversion = api_client.get_server_version()\nprint(f\"Server version: {version}\")\n```\n","chunkId":"audio_separator/remote/README.md:15"}],[382,{"filePath":"audio_separator/remote/README.md","startLine":168,"endLine":186,"chunkText":"print(f\"Server version: {version}\")\n```\n\n### Remote API CLI\n\nAudio Separator also provides a command-line interface for interacting with remote APIs:\n\n#### Commands\n\n**Separate audio files:**\n\n```bash\n# Separate audio file (asynchronous processing)\naudio-separator-remote separate audio.wav --model model_bs_roformer_ep_317_sdr_12.9755.ckpt\n\n# Multiple models - upload once, separate with multiple models\naudio-separator-remote separate audio.wav --models model_bs_roformer_ep_317_sdr_12.9755.ckpt UVR-MDX-NET-Inst_HQ_4.onnx htdemucs_6s.yaml\n\n# Multiple files","chunkId":"audio_separator/remote/README.md:16"}],[383,{"filePath":"audio_separator/remote/README.md","startLine":184,"endLine":196,"chunkText":"audio-separator-remote separate audio.wav --models model_bs_roformer_ep_317_sdr_12.9755.ckpt UVR-MDX-NET-Inst_HQ_4.onnx htdemucs_6s.yaml\n\n# Multiple files\naudio-separator-remote separate audio1.wav audio2.wav audio3.wav\n\n# Use default model (if not specified)\naudio-separator-remote separate audio.wav\n\n# Advanced separation with custom parameters (all local CLI parameters supported)\naudio-separator-remote separate audio.wav \\\n  --model model_bs_roformer_ep_317_sdr_12.9755.ckpt \\\n  --output_format wav \\\n  --normalization 0.8 \\","chunkId":"audio_separator/remote/README.md:17"}],[384,{"filePath":"audio_separator/remote/README.md","startLine":194,"endLine":223,"chunkText":"  --model model_bs_roformer_ep_317_sdr_12.9755.ckpt \\\n  --output_format wav \\\n  --normalization 0.8 \\\n  --custom_output_names '{\"Vocals\": \"lead_vocals\", \"Instrumental\": \"backing_track\"}' \\\n  --mdx_segment_size 512 \\\n  --vr_aggression 10\n```\n\n**Check job status:**\n\n```bash\naudio-separator-remote status <task_id>\n```\n\n**List available models:**\n\n```bash\n# Pretty formatted list\naudio-separator-remote models\n\n# JSON output\naudio-separator-remote models --format json\n\n# Filter by stem type\naudio-separator-remote models --filter vocals\n```\n\n**Download specific files:**\n\n```bash","chunkId":"audio_separator/remote/README.md:18"}],[385,{"filePath":"audio_separator/remote/README.md","startLine":221,"endLine":245,"chunkText":"**Download specific files:**\n\n```bash\naudio-separator-remote download <task_id> filename1.wav filename2.wav\n```\n\n**Get version information:**\n\n```bash\naudio-separator-remote --version\n```\n\n#### CLI Options\n\n**Global Options:**\n\n- `--api_url`: Override the API URL\n- `--timeout`: Set timeout for polling (default: 600 seconds)\n- `--poll_interval`: Set polling interval (default: 10 seconds)\n- `--debug`: Enable debug logging\n- `--log_level`: Set log level (info, debug, warning, etc.)\n\n**Model Selection:**\n\n- `--model`: Single model to use for separation","chunkId":"audio_separator/remote/README.md:19"}],[386,{"filePath":"audio_separator/remote/README.md","startLine":243,"endLine":256,"chunkText":"**Model Selection:**\n\n- `--model`: Single model to use for separation\n- `--models`: Multiple models to use for separation (space-separated)\n\n**Output Parameters:**\n\n- `--output_format`: Output format (default: flac)\n- `--output_bitrate`: Output bitrate\n- `--normalization`: Max peak amplitude to normalize to (default: 0.9)\n- `--amplification`: Min peak amplitude to amplify to (default: 0.0)\n- `--single_stem`: Output only single stem (e.g. Vocals, Instrumental)\n- `--invert_spect`: Invert secondary stem using spectrogram\n- `--sample_rate`: Sample rate of output audio (default: 44100)","chunkId":"audio_separator/remote/README.md:20"}],[387,{"filePath":"audio_separator/remote/README.md","startLine":254,"endLine":264,"chunkText":"- `--single_stem`: Output only single stem (e.g. Vocals, Instrumental)\n- `--invert_spect`: Invert secondary stem using spectrogram\n- `--sample_rate`: Sample rate of output audio (default: 44100)\n- `--use_soundfile`: Use soundfile for output writing\n- `--use_autocast`: Use PyTorch autocast for faster inference\n- `--custom_output_names`: Custom output names in JSON format\n\n**Architecture-Specific Parameters:**\nAll MDX, VR, Demucs, and MDXC parameters from the local CLI are supported:\n\n- `--mdx_segment_size`, `--mdx_overlap`, `--mdx_batch_size`, etc.","chunkId":"audio_separator/remote/README.md:21"}],[388,{"filePath":"audio_separator/remote/README.md","startLine":262,"endLine":276,"chunkText":"All MDX, VR, Demucs, and MDXC parameters from the local CLI are supported:\n\n- `--mdx_segment_size`, `--mdx_overlap`, `--mdx_batch_size`, etc.\n- `--vr_batch_size`, `--vr_window_size`, `--vr_aggression`, etc.\n- `--demucs_segment_size`, `--demucs_shifts`, `--demucs_overlap`, etc.\n- `--mdxc_segment_size`, `--mdxc_overlap`, `--mdxc_batch_size`, etc.\n\n#### Examples\n\n```bash\n# Separate with multiple models and custom settings\naudio-separator-remote separate song.mp3 \\\n  --models model_bs_roformer_ep_317_sdr_12.9755.ckpt UVR-MDX-NET-Inst_HQ_4.onnx \\\n  --output_format wav \\\n  --normalization 0.8 \\","chunkId":"audio_separator/remote/README.md:22"}],[389,{"filePath":"audio_separator/remote/README.md","startLine":274,"endLine":290,"chunkText":"  --models model_bs_roformer_ep_317_sdr_12.9755.ckpt UVR-MDX-NET-Inst_HQ_4.onnx \\\n  --output_format wav \\\n  --normalization 0.8 \\\n  --api_url https://my-api.com \\\n  --timeout 600\n\n# Separate with custom output names\naudio-separator-remote separate song.mp3 \\\n  --model htdemucs_6s.yaml \\\n  --custom_output_names '{\"Vocals\": \"vocals\", \"Drums\": \"drums\", \"Bass\": \"bass\", \"Other\": \"other\"}'\n\n# Check status with debug logging\naudio-separator-remote status abc123 --debug\n\n# List vocal separation models in JSON format\naudio-separator-remote models --filter vocals --format json\n","chunkId":"audio_separator/remote/README.md:23"}],[390,{"filePath":"audio_separator/remote/README.md","startLine":288,"endLine":304,"chunkText":"# List vocal separation models in JSON format\naudio-separator-remote models --filter vocals --format json\n\n# Use VR parameters for better vocal isolation\naudio-separator-remote separate vocals.wav \\\n  --model 2_HP-UVR.pth \\\n  --vr_aggression 15 \\\n  --vr_window_size 320 \\\n  --vr_enable_tta\n```\n\n#### Key Features\n\nThe remote API client automatically handles:\n\n- **File uploading and downloading**: Seamless transfer of audio files and results\n- **Multiple model processing**: Upload once, separate with multiple models efficiently","chunkId":"audio_separator/remote/README.md:24"}],[391,{"filePath":"audio_separator/remote/README.md","startLine":302,"endLine":311,"chunkText":"\n- **File uploading and downloading**: Seamless transfer of audio files and results\n- **Multiple model processing**: Upload once, separate with multiple models efficiently\n- **Full separator compatibility**: All local CLI parameters and architectures supported\n- **Job polling and status updates**: Real-time progress tracking with model-specific information\n- **Error handling and retries**: Robust error handling for reliable processing\n- **Progress reporting**: Detailed progress updates including current model being processed\n\n#### Benefits of Multiple Model Support\n","chunkId":"audio_separator/remote/README.md:25"}],[392,{"filePath":"audio_separator/remote/README.md","startLine":309,"endLine":320,"chunkText":"\n#### Benefits of Multiple Model Support\n\nWhen using multiple models, the remote API provides significant advantages:\n\n- **Efficiency**: Upload your audio file once, process with multiple models without re-uploading\n- **Comparison**: Easily compare results from different models (e.g., vocals vs. instrumental quality)\n- **Workflow optimization**: Process with complementary models in a single job\n- **Time savings**: Avoid repeated upload times for large audio files\n\nExample use cases:\n","chunkId":"audio_separator/remote/README.md:26"}],[393,{"filePath":"audio_separator/remote/README.md","startLine":318,"endLine":324,"chunkText":"\nExample use cases:\n\n- Compare quality between `model_bs_roformer_ep_317_sdr_12.9755.ckpt` (high-quality vocals) and `UVR-MDX-NET-Inst_HQ_4.onnx` (high-quality instrumental)\n- Process with both 2-stem models (vocals/instrumental) and multi-stem models (vocals/drums/bass/other) in one job\n- Use different models optimized for different parts of the frequency spectrum\n","chunkId":"audio_separator/remote/README.md:27"}],[394,{"filePath":"audio_separator/remote/__init__.py","startLine":0,"endLine":3,"chunkText":"from .api_client import AudioSeparatorAPIClient\n\n__all__ = [\"AudioSeparatorAPIClient\"]\n","chunkId":"audio_separator/remote/__init__.py:0"}],[395,{"filePath":"audio_separator/remote/api_client.py","startLine":0,"endLine":21,"chunkText":"#!/usr/bin/env python\nimport os\nimport logging\nimport json\nfrom typing import Optional, List, Dict\nfrom urllib.parse import quote\n\nimport requests\n\n# Get package version for debugging\ntry:\n    from importlib.metadata import version\n    AUDIO_SEPARATOR_VERSION = version(\"audio-separator\")\nexcept ImportError:\n    try:\n        import pkg_resources\n        AUDIO_SEPARATOR_VERSION = pkg_resources.get_distribution(\"audio-separator\").version\n    except Exception:\n        AUDIO_SEPARATOR_VERSION = \"unknown\"\n\n\nclass AudioSeparatorAPIClient:","chunkId":"audio_separator/remote/api_client.py:0"}],[396,{"filePath":"audio_separator/remote/api_client.py","startLine":21,"endLine":37,"chunkText":"class AudioSeparatorAPIClient:\n    \"\"\"Client for interacting with a remotely deployed Audio Separator API.\"\"\"\n\n    def __init__(self, api_url: str, logger: logging.Logger):\n        self.api_url = api_url\n        self.logger = logger\n        self.session = requests.Session()\n\n    def separate_audio(\n        self,\n        file_path: str,\n        model: Optional[str] = None,\n        models: Optional[List[str]] = None,\n        # Output parameters\n        output_format: str = \"flac\",\n        output_bitrate: Optional[str] = None,\n        normalization_threshold: float = 0.9,","chunkId":"audio_separator/remote/api_client.py:1"}],[397,{"filePath":"audio_separator/remote/api_client.py","startLine":35,"endLine":49,"chunkText":"        output_format: str = \"flac\",\n        output_bitrate: Optional[str] = None,\n        normalization_threshold: float = 0.9,\n        amplification_threshold: float = 0.0,\n        output_single_stem: Optional[str] = None,\n        invert_using_spec: bool = False,\n        sample_rate: int = 44100,\n        use_soundfile: bool = False,\n        use_autocast: bool = False,\n        custom_output_names: Optional[Dict[str, str]] = None,\n        # MDX parameters\n        mdx_segment_size: int = 256,\n        mdx_overlap: float = 0.25,\n        mdx_batch_size: int = 1,\n        mdx_hop_length: int = 1024,","chunkId":"audio_separator/remote/api_client.py:2"}],[398,{"filePath":"audio_separator/remote/api_client.py","startLine":47,"endLine":62,"chunkText":"        mdx_overlap: float = 0.25,\n        mdx_batch_size: int = 1,\n        mdx_hop_length: int = 1024,\n        mdx_enable_denoise: bool = False,\n        # VR parameters\n        vr_batch_size: int = 1,\n        vr_window_size: int = 512,\n        vr_aggression: int = 5,\n        vr_enable_tta: bool = False,\n        vr_high_end_process: bool = False,\n        vr_enable_post_process: bool = False,\n        vr_post_process_threshold: float = 0.2,\n        # Demucs parameters\n        demucs_segment_size: str = \"Default\",\n        demucs_shifts: int = 2,\n        demucs_overlap: float = 0.25,","chunkId":"audio_separator/remote/api_client.py:3"}],[399,{"filePath":"audio_separator/remote/api_client.py","startLine":60,"endLine":74,"chunkText":"        demucs_segment_size: str = \"Default\",\n        demucs_shifts: int = 2,\n        demucs_overlap: float = 0.25,\n        demucs_segments_enabled: bool = True,\n        # MDXC parameters\n        mdxc_segment_size: int = 256,\n        mdxc_override_model_segment_size: bool = False,\n        mdxc_overlap: int = 8,\n        mdxc_batch_size: int = 1,\n        mdxc_pitch_shift: int = 0,\n    ) -> dict:\n        \"\"\"Submit audio separation job (asynchronous processing).\"\"\"\n        if not os.path.exists(file_path):\n            raise FileNotFoundError(f\"Audio file not found: {file_path}\")\n","chunkId":"audio_separator/remote/api_client.py:4"}],[400,{"filePath":"audio_separator/remote/api_client.py","startLine":72,"endLine":88,"chunkText":"        if not os.path.exists(file_path):\n            raise FileNotFoundError(f\"Audio file not found: {file_path}\")\n\n        files = {\"file\": (os.path.basename(file_path), open(file_path, \"rb\"))}\n        data = {}\n\n        # Handle model parameters (backwards compatibility)\n        if models:\n            data[\"models\"] = json.dumps(models)\n        elif model:\n            data[\"model\"] = model\n\n        # Add all separator parameters\n        data.update(\n            {\n                \"output_format\": output_format,\n                \"normalization_threshold\": normalization_threshold,","chunkId":"audio_separator/remote/api_client.py:5"}],[401,{"filePath":"audio_separator/remote/api_client.py","startLine":86,"endLine":97,"chunkText":"            {\n                \"output_format\": output_format,\n                \"normalization_threshold\": normalization_threshold,\n                \"amplification_threshold\": amplification_threshold,\n                \"invert_using_spec\": invert_using_spec,\n                \"sample_rate\": sample_rate,\n                \"use_soundfile\": use_soundfile,\n                \"use_autocast\": use_autocast,\n                # MDX parameters\n                \"mdx_segment_size\": mdx_segment_size,\n                \"mdx_overlap\": mdx_overlap,\n                \"mdx_batch_size\": mdx_batch_size,","chunkId":"audio_separator/remote/api_client.py:6"}],[402,{"filePath":"audio_separator/remote/api_client.py","startLine":95,"endLine":106,"chunkText":"                \"mdx_segment_size\": mdx_segment_size,\n                \"mdx_overlap\": mdx_overlap,\n                \"mdx_batch_size\": mdx_batch_size,\n                \"mdx_hop_length\": mdx_hop_length,\n                \"mdx_enable_denoise\": mdx_enable_denoise,\n                # VR parameters\n                \"vr_batch_size\": vr_batch_size,\n                \"vr_window_size\": vr_window_size,\n                \"vr_aggression\": vr_aggression,\n                \"vr_enable_tta\": vr_enable_tta,\n                \"vr_high_end_process\": vr_high_end_process,\n                \"vr_enable_post_process\": vr_enable_post_process,","chunkId":"audio_separator/remote/api_client.py:7"}],[403,{"filePath":"audio_separator/remote/api_client.py","startLine":104,"endLine":114,"chunkText":"                \"vr_enable_tta\": vr_enable_tta,\n                \"vr_high_end_process\": vr_high_end_process,\n                \"vr_enable_post_process\": vr_enable_post_process,\n                \"vr_post_process_threshold\": vr_post_process_threshold,\n                # Demucs parameters\n                \"demucs_segment_size\": demucs_segment_size,\n                \"demucs_shifts\": demucs_shifts,\n                \"demucs_overlap\": demucs_overlap,\n                \"demucs_segments_enabled\": demucs_segments_enabled,\n                # MDXC parameters\n                \"mdxc_segment_size\": mdxc_segment_size,","chunkId":"audio_separator/remote/api_client.py:8"}],[404,{"filePath":"audio_separator/remote/api_client.py","startLine":112,"endLine":125,"chunkText":"                \"demucs_segments_enabled\": demucs_segments_enabled,\n                # MDXC parameters\n                \"mdxc_segment_size\": mdxc_segment_size,\n                \"mdxc_override_model_segment_size\": mdxc_override_model_segment_size,\n                \"mdxc_overlap\": mdxc_overlap,\n                \"mdxc_batch_size\": mdxc_batch_size,\n                \"mdxc_pitch_shift\": mdxc_pitch_shift,\n            }\n        )\n\n        # Add optional parameters only if they have non-default values\n        if output_bitrate:\n            data[\"output_bitrate\"] = output_bitrate\n        if output_single_stem:","chunkId":"audio_separator/remote/api_client.py:9"}],[405,{"filePath":"audio_separator/remote/api_client.py","startLine":123,"endLine":135,"chunkText":"        if output_bitrate:\n            data[\"output_bitrate\"] = output_bitrate\n        if output_single_stem:\n            data[\"output_single_stem\"] = output_single_stem\n        if custom_output_names:\n            data[\"custom_output_names\"] = json.dumps(custom_output_names)\n\n        try:\n            # Increase timeout for large files (5 minutes)\n            response = self.session.post(f\"{self.api_url}/separate\", files=files, data=data, timeout=300)\n            response.raise_for_status()\n            return response.json()\n        except requests.RequestException as e:","chunkId":"audio_separator/remote/api_client.py:10"}],[406,{"filePath":"audio_separator/remote/api_client.py","startLine":133,"endLine":150,"chunkText":"            response.raise_for_status()\n            return response.json()\n        except requests.RequestException as e:\n            self.logger.error(f\"Separation request failed: {e}\")\n            raise\n        finally:\n            files[\"file\"][1].close()\n\n    def separate_audio_and_wait(\n        self,\n        file_path: str,\n        model: Optional[str] = None,\n        models: Optional[List[str]] = None,\n        timeout: int = 600,\n        poll_interval: int = 10,\n        download: bool = True,\n        output_dir: Optional[str] = None,\n        # All separator parameters (same as separate_audio)","chunkId":"audio_separator/remote/api_client.py:11"}],[407,{"filePath":"audio_separator/remote/api_client.py","startLine":148,"endLine":161,"chunkText":"        download: bool = True,\n        output_dir: Optional[str] = None,\n        # All separator parameters (same as separate_audio)\n        output_format: str = \"flac\",\n        output_bitrate: Optional[str] = None,\n        normalization_threshold: float = 0.9,\n        amplification_threshold: float = 0.0,\n        output_single_stem: Optional[str] = None,\n        invert_using_spec: bool = False,\n        sample_rate: int = 44100,\n        use_soundfile: bool = False,\n        use_autocast: bool = False,\n        custom_output_names: Optional[Dict[str, str]] = None,\n        mdx_segment_size: int = 256,","chunkId":"audio_separator/remote/api_client.py:12"}],[408,{"filePath":"audio_separator/remote/api_client.py","startLine":159,"endLine":173,"chunkText":"        use_autocast: bool = False,\n        custom_output_names: Optional[Dict[str, str]] = None,\n        mdx_segment_size: int = 256,\n        mdx_overlap: float = 0.25,\n        mdx_batch_size: int = 1,\n        mdx_hop_length: int = 1024,\n        mdx_enable_denoise: bool = False,\n        vr_batch_size: int = 1,\n        vr_window_size: int = 512,\n        vr_aggression: int = 5,\n        vr_enable_tta: bool = False,\n        vr_high_end_process: bool = False,\n        vr_enable_post_process: bool = False,\n        vr_post_process_threshold: float = 0.2,\n        demucs_segment_size: str = \"Default\",","chunkId":"audio_separator/remote/api_client.py:13"}],[409,{"filePath":"audio_separator/remote/api_client.py","startLine":171,"endLine":185,"chunkText":"        vr_enable_post_process: bool = False,\n        vr_post_process_threshold: float = 0.2,\n        demucs_segment_size: str = \"Default\",\n        demucs_shifts: int = 2,\n        demucs_overlap: float = 0.25,\n        demucs_segments_enabled: bool = True,\n        mdxc_segment_size: int = 256,\n        mdxc_override_model_segment_size: bool = False,\n        mdxc_overlap: int = 8,\n        mdxc_batch_size: int = 1,\n        mdxc_pitch_shift: int = 0,\n    ) -> dict:\n        \"\"\"\n        Submit audio separation job and wait for completion (convenience method).\n","chunkId":"audio_separator/remote/api_client.py:14"}],[410,{"filePath":"audio_separator/remote/api_client.py","startLine":183,"endLine":194,"chunkText":"        \"\"\"\n        Submit audio separation job and wait for completion (convenience method).\n\n        This method handles the full workflow: submit job, poll for completion,\n        and optionally download the result files.\n\n        Args:\n            file_path: Path to the audio file to separate\n            model: Single model to use for separation (for backwards compatibility)\n            models: List of models to use for separation\n            timeout: Maximum time to wait for completion in seconds (default: 600)\n            poll_interval: How often to check status in seconds (default: 10)","chunkId":"audio_separator/remote/api_client.py:15"}],[411,{"filePath":"audio_separator/remote/api_client.py","startLine":192,"endLine":202,"chunkText":"            models: List of models to use for separation\n            timeout: Maximum time to wait for completion in seconds (default: 600)\n            poll_interval: How often to check status in seconds (default: 10)\n            download: Whether to automatically download result files (default: True)\n            output_dir: Directory to save downloaded files (default: current directory)\n            **kwargs: All other separator parameters (same as separate_audio method)\n\n        Returns:\n            dict with keys:\n                - task_id: The job task ID\n                - status: \"completed\" or \"error","chunkId":"audio_separator/remote/api_client.py:16"}],[412,{"filePath":"audio_separator/remote/api_client.py","startLine":200,"endLine":210,"chunkText":"            dict with keys:\n                - task_id: The job task ID\n                - status: \"completed\" or \"error\"\n                - files: List of output filenames\n                - downloaded_files: List of local file paths (if download=True)\n                - error: Error message (if status=\"error\")\n        \"\"\"\n        import time\n\n        # Submit the separation job with all parameters\n        models_desc = models or ([model] if model else [\"default\"])","chunkId":"audio_separator/remote/api_client.py:17"}],[413,{"filePath":"audio_separator/remote/api_client.py","startLine":208,"endLine":223,"chunkText":"        # Submit the separation job with all parameters\n        models_desc = models or ([model] if model else [\"default\"])\n        self.logger.info(f\"Submitting separation job for '{file_path}' with models: {models_desc} (audio-separator v{AUDIO_SEPARATOR_VERSION})\")\n\n        result = self.separate_audio(\n            file_path,\n            model,\n            models,\n            output_format,\n            output_bitrate,\n            normalization_threshold,\n            amplification_threshold,\n            output_single_stem,\n            invert_using_spec,\n            sample_rate,\n            use_soundfile","chunkId":"audio_separator/remote/api_client.py:18"}],[414,{"filePath":"audio_separator/remote/api_client.py","startLine":222,"endLine":241,"chunkText":"            invert_using_spec,\n            sample_rate,\n            use_soundfile,\n            use_autocast,\n            custom_output_names,\n            mdx_segment_size,\n            mdx_overlap,\n            mdx_batch_size,\n            mdx_hop_length,\n            mdx_enable_denoise,\n            vr_batch_size,\n            vr_window_size,\n            vr_aggression,\n            vr_enable_tta,\n            vr_high_end_process,\n            vr_enable_post_process,\n            vr_post_process_threshold,\n            demucs_segment_size,\n            demucs_shifts,\n            demucs_overlap,","chunkId":"audio_separator/remote/api_client.py:19"}],[415,{"filePath":"audio_separator/remote/api_client.py","startLine":239,"endLine":258,"chunkText":"            demucs_segment_size,\n            demucs_shifts,\n            demucs_overlap,\n            demucs_segments_enabled,\n            mdxc_segment_size,\n            mdxc_override_model_segment_size,\n            mdxc_overlap,\n            mdxc_batch_size,\n            mdxc_pitch_shift,\n        )\n\n        task_id = result[\"task_id\"]\n        self.logger.info(f\"Job submitted! Task ID: {task_id}\")\n\n        # Poll for completion\n        self.logger.info(\"Waiting for separation to complete...\")\n        start_time = time.time()\n        last_progress = -1\n\n        while time.time() - start_time < timeout:","chunkId":"audio_separator/remote/api_client.py:20"}],[416,{"filePath":"audio_separator/remote/api_client.py","startLine":256,"endLine":267,"chunkText":"        last_progress = -1\n\n        while time.time() - start_time < timeout:\n            try:\n                status = self.get_job_status(task_id)\n                current_status = status.get(\"status\", \"unknown\")\n\n                # Show progress if it changed\n                if \"progress\" in status and status[\"progress\"] != last_progress:\n                    progress_info = f\"Progress: {status['progress']}%\"\n                    if \"current_model_index\" in status and \"total_models\" in status:\n                        model_info = f\" (Model {status['current_model_index'] + 1}/{status['total_models']})\"","chunkId":"audio_separator/remote/api_client.py:21"}],[417,{"filePath":"audio_separator/remote/api_client.py","startLine":265,"endLine":275,"chunkText":"                    progress_info = f\"Progress: {status['progress']}%\"\n                    if \"current_model_index\" in status and \"total_models\" in status:\n                        model_info = f\" (Model {status['current_model_index'] + 1}/{status['total_models']})\"\n                        progress_info += model_info\n                    self.logger.info(progress_info)\n                    last_progress = status[\"progress\"]\n\n                # Check if completed\n                if current_status == \"completed\":\n                    self.logger.info(\"✅ Separation completed!\")\n                    ","chunkId":"audio_separator/remote/api_client.py:22"}],[418,{"filePath":"audio_separator/remote/api_client.py","startLine":273,"endLine":282,"chunkText":"                if current_status == \"completed\":\n                    self.logger.info(\"✅ Separation completed!\")\n                    \n                    files_data = status.get(\"files\", {})\n                    \n                    # Handle both old (list) and new (dict) format for backward compatibility\n                    if isinstance(files_data, list):\n                        # Legacy format: list of filenames\n                        self.logger.info(f\"🔍 Job status returned {len(files_data)} files (legacy format)\")\n                        for i, filename in enumerate(files_data):","chunkId":"audio_separator/remote/api_client.py:23"}],[419,{"filePath":"audio_separator/remote/api_client.py","startLine":280,"endLine":286,"chunkText":"                        # Legacy format: list of filenames\n                        self.logger.info(f\"🔍 Job status returned {len(files_data)} files (legacy format)\")\n                        for i, filename in enumerate(files_data):\n                            self.logger.info(f\"  [{i}] '{filename}' (len={len(filename)})\")\n                        result = {\"task_id\": task_id, \"status\": \"completed\", \"files\": files_data}\n                    else:\n                        # New format: dictionary of hash -> filename","chunkId":"audio_separator/remote/api_client.py:24"}],[420,{"filePath":"audio_separator/remote/api_client.py","startLine":284,"endLine":291,"chunkText":"                        result = {\"task_id\": task_id, \"status\": \"completed\", \"files\": files_data}\n                    else:\n                        # New format: dictionary of hash -> filename\n                        self.logger.info(f\"🔍 Job status returned {len(files_data)} files (hash format)\")\n                        for i, (file_hash, filename) in enumerate(files_data.items()):\n                            self.logger.info(f\"  [{i}] hash={file_hash} -> '{filename}' (len={len(filename)})\")\n                        result = {\"task_id\": task_id, \"status\": \"completed\", \"files\": files_data}\n","chunkId":"audio_separator/remote/api_client.py:25"}],[421,{"filePath":"audio_separator/remote/api_client.py","startLine":289,"endLine":298,"chunkText":"                            self.logger.info(f\"  [{i}] hash={file_hash} -> '{filename}' (len={len(filename)})\")\n                        result = {\"task_id\": task_id, \"status\": \"completed\", \"files\": files_data}\n\n                    # Download files if requested\n                    if download:\n                        downloaded_files = []\n                        files_data = status.get(\"files\", {})\n                        \n                        # Handle both old (list) and new (dict) format\n                        if isinstance(files_data, list):","chunkId":"audio_separator/remote/api_client.py:26"}],[422,{"filePath":"audio_separator/remote/api_client.py","startLine":296,"endLine":304,"chunkText":"                        \n                        # Handle both old (list) and new (dict) format\n                        if isinstance(files_data, list):\n                            # Legacy format: list of filenames\n                            self.logger.info(f\"📥 Downloading {len(files_data)} output files (legacy format)...\")\n                            self.logger.info(f\"🔍 Files to download: {files_data}\")\n\n                            for i, filename in enumerate(files_data):\n                                try:","chunkId":"audio_separator/remote/api_client.py:27"}],[423,{"filePath":"audio_separator/remote/api_client.py","startLine":302,"endLine":311,"chunkText":"\n                            for i, filename in enumerate(files_data):\n                                try:\n                                    self.logger.info(f\"🔍 [{i+1}/{len(files_data)}] Attempting to download: '{filename}' (len={len(filename)})\")\n                                    \n                                    if output_dir:\n                                        output_path = f\"{output_dir.rstrip('/')}/{filename}\"\n                                    else:\n                                        output_path = filename\n","chunkId":"audio_separator/remote/api_client.py:28"}],[424,{"filePath":"audio_separator/remote/api_client.py","startLine":309,"endLine":317,"chunkText":"                                    else:\n                                        output_path = filename\n\n                                    downloaded_path = self.download_file(task_id, filename, output_path)\n                                    downloaded_files.append(downloaded_path)\n                                    self.logger.info(f\"  ✅ Downloaded: {downloaded_path}\")\n                                except Exception as e:\n                                    self.logger.error(f\"  ❌ Failed to download {filename}: {e}\")\n                                    self._log_server_version_on_error()","chunkId":"audio_separator/remote/api_client.py:29"}],[425,{"filePath":"audio_separator/remote/api_client.py","startLine":315,"endLine":323,"chunkText":"                                except Exception as e:\n                                    self.logger.error(f\"  ❌ Failed to download {filename}: {e}\")\n                                    self._log_server_version_on_error()\n                        else:\n                            # New format: dictionary of hash -> filename\n                            self.logger.info(f\"📥 Downloading {len(files_data)} output files (hash format)...\")\n                            filenames_list = list(files_data.values())\n                            self.logger.info(f\"🔍 Files to download: {filenames_list}\")\n","chunkId":"audio_separator/remote/api_client.py:30"}],[426,{"filePath":"audio_separator/remote/api_client.py","startLine":321,"endLine":328,"chunkText":"                            filenames_list = list(files_data.values())\n                            self.logger.info(f\"🔍 Files to download: {filenames_list}\")\n\n                            for i, (file_hash, filename) in enumerate(files_data.items()):\n                                try:\n                                    self.logger.info(f\"🔍 [{i+1}/{len(files_data)}] Attempting to download: '{filename}' (hash={file_hash}, len={len(filename)})\")\n                                    \n                                    if output_dir:","chunkId":"audio_separator/remote/api_client.py:31"}],[427,{"filePath":"audio_separator/remote/api_client.py","startLine":326,"endLine":333,"chunkText":"                                    self.logger.info(f\"🔍 [{i+1}/{len(files_data)}] Attempting to download: '{filename}' (hash={file_hash}, len={len(filename)})\")\n                                    \n                                    if output_dir:\n                                        output_path = f\"{output_dir.rstrip('/')}/{filename}\"\n                                    else:\n                                        output_path = filename\n\n                                    downloaded_path = self.download_file_by_hash(task_id, file_hash, filename, output_path)","chunkId":"audio_separator/remote/api_client.py:32"}],[428,{"filePath":"audio_separator/remote/api_client.py","startLine":331,"endLine":339,"chunkText":"                                        output_path = filename\n\n                                    downloaded_path = self.download_file_by_hash(task_id, file_hash, filename, output_path)\n                                    downloaded_files.append(downloaded_path)\n                                    self.logger.info(f\"  ✅ Downloaded: {downloaded_path}\")\n                                except Exception as e:\n                                    self.logger.error(f\"  ❌ Failed to download {filename}: {e}\")\n                                    self._log_server_version_on_error()\n","chunkId":"audio_separator/remote/api_client.py:33"}],[429,{"filePath":"audio_separator/remote/api_client.py","startLine":337,"endLine":347,"chunkText":"                                    self.logger.error(f\"  ❌ Failed to download {filename}: {e}\")\n                                    self._log_server_version_on_error()\n\n                        result[\"downloaded_files\"] = downloaded_files\n                        self.logger.info(f\"🎉 Successfully downloaded {len(downloaded_files)} files!\")\n\n                    return result\n\n                elif current_status == \"error\":\n                    error_msg = status.get(\"error\", \"Unknown error\")\n                    self.logger.error(f\"❌ Job failed: {error_msg}\")","chunkId":"audio_separator/remote/api_client.py:34"}],[430,{"filePath":"audio_separator/remote/api_client.py","startLine":345,"endLine":357,"chunkText":"                elif current_status == \"error\":\n                    error_msg = status.get(\"error\", \"Unknown error\")\n                    self.logger.error(f\"❌ Job failed: {error_msg}\")\n                    return {\"task_id\": task_id, \"status\": \"error\", \"error\": error_msg, \"files\": []}\n\n                # Wait before next poll\n                time.sleep(poll_interval)\n\n            except Exception as e:\n                self.logger.warning(f\"Error polling status: {e}\")\n                time.sleep(poll_interval)\n\n        # Timeout reached","chunkId":"audio_separator/remote/api_client.py:35"}],[431,{"filePath":"audio_separator/remote/api_client.py","startLine":355,"endLine":367,"chunkText":"                time.sleep(poll_interval)\n\n        # Timeout reached\n        self.logger.error(f\"❌ Job polling timed out after {timeout} seconds\")\n        return {\"task_id\": task_id, \"status\": \"timeout\", \"error\": f\"Job polling timed out after {timeout} seconds\", \"files\": []}\n\n    def get_job_status(self, task_id: str) -> dict:\n        \"\"\"Get job status.\"\"\"\n        try:\n            response = self.session.get(f\"{self.api_url}/status/{task_id}\", timeout=10)\n            response.raise_for_status()\n            return response.json()\n        except requests.RequestException as e:","chunkId":"audio_separator/remote/api_client.py:36"}],[432,{"filePath":"audio_separator/remote/api_client.py","startLine":365,"endLine":378,"chunkText":"            response.raise_for_status()\n            return response.json()\n        except requests.RequestException as e:\n            self.logger.error(f\"Status request failed: {e}\")\n            raise\n\n    def download_file(self, task_id: str, filename: str, output_path: Optional[str] = None) -> str:\n        \"\"\"Download a file from a completed job (legacy method for backward compatibility).\"\"\"\n        if output_path is None:\n            output_path = filename\n\n        try:\n            # URL encode the filename to handle spaces and special characters\n            encoded_filename = quote(filename, safe='')","chunkId":"audio_separator/remote/api_client.py:37"}],[433,{"filePath":"audio_separator/remote/api_client.py","startLine":376,"endLine":385,"chunkText":"        try:\n            # URL encode the filename to handle spaces and special characters\n            encoded_filename = quote(filename, safe='')\n            download_url = f\"{self.api_url}/download/{task_id}/{encoded_filename}\"\n            \n            # Debug logging to understand what's happening\n            self.logger.info(f\"🔍 Download details (legacy filename method):\")\n            self.logger.info(f\"  Original filename: '{filename}'\")\n            self.logger.info(f\"  Encoded filename: '{encoded_filename}'\")\n            self.logger.info(f\"  Download URL: {download_url}\")","chunkId":"audio_separator/remote/api_client.py:38"}],[434,{"filePath":"audio_separator/remote/api_client.py","startLine":383,"endLine":393,"chunkText":"            self.logger.info(f\"  Original filename: '{filename}'\")\n            self.logger.info(f\"  Encoded filename: '{encoded_filename}'\")\n            self.logger.info(f\"  Download URL: {download_url}\")\n            self.logger.info(f\"  Task ID: {task_id}\")\n            \n            response = self.session.get(download_url, timeout=60)\n            \n            # Log response details for debugging\n            self.logger.info(f\"🔍 Response status: {response.status_code}\")\n            if response.status_code != 200:\n                try:","chunkId":"audio_separator/remote/api_client.py:39"}],[435,{"filePath":"audio_separator/remote/api_client.py","startLine":391,"endLine":401,"chunkText":"            self.logger.info(f\"🔍 Response status: {response.status_code}\")\n            if response.status_code != 200:\n                try:\n                    self.logger.error(f\"🔍 Response headers: {dict(response.headers)}\")\n                except Exception:\n                    self.logger.error(f\"🔍 Response headers: {response.headers}\")\n                try:\n                    self.logger.error(f\"🔍 Response text (first 500 chars): {response.text[:500]}\")\n                except Exception:\n                    self.logger.error(f\"🔍 Response text: <unavailable>\")\n            ","chunkId":"audio_separator/remote/api_client.py:40"}],[436,{"filePath":"audio_separator/remote/api_client.py","startLine":399,"endLine":413,"chunkText":"                except Exception:\n                    self.logger.error(f\"🔍 Response text: <unavailable>\")\n            \n            response.raise_for_status()\n\n            with open(output_path, \"wb\") as f:\n                f.write(response.content)\n\n            return output_path\n        except requests.RequestException as e:\n            self.logger.error(f\"Download failed: {e}\")\n            raise\n\n    def download_file_by_hash(self, task_id: str, file_hash: str, filename: str, output_path: Optional[str] = None) -> str:\n        \"\"\"Download a file from a completed job using its hash identifier.\"\"\"","chunkId":"audio_separator/remote/api_client.py:41"}],[437,{"filePath":"audio_separator/remote/api_client.py","startLine":411,"endLine":422,"chunkText":"\n    def download_file_by_hash(self, task_id: str, file_hash: str, filename: str, output_path: Optional[str] = None) -> str:\n        \"\"\"Download a file from a completed job using its hash identifier.\"\"\"\n        if output_path is None:\n            output_path = filename\n\n        try:\n            # Use the file hash in the URL instead of the filename\n            download_url = f\"{self.api_url}/download/{task_id}/{file_hash}\"\n            \n            # Debug logging to understand what's happening\n            self.logger.info(f\"🔍 Download details (hash method):\")","chunkId":"audio_separator/remote/api_client.py:42"}],[438,{"filePath":"audio_separator/remote/api_client.py","startLine":420,"endLine":431,"chunkText":"            \n            # Debug logging to understand what's happening\n            self.logger.info(f\"🔍 Download details (hash method):\")\n            self.logger.info(f\"  Original filename: '{filename}'\")\n            self.logger.info(f\"  File hash: '{file_hash}'\")\n            self.logger.info(f\"  Download URL: {download_url}\")\n            self.logger.info(f\"  Task ID: {task_id}\")\n            \n            response = self.session.get(download_url, timeout=60)\n            \n            # Log response details for debugging\n            self.logger.info(f\"🔍 Response status: {response.status_code}\")","chunkId":"audio_separator/remote/api_client.py:43"}],[439,{"filePath":"audio_separator/remote/api_client.py","startLine":429,"endLine":439,"chunkText":"            \n            # Log response details for debugging\n            self.logger.info(f\"🔍 Response status: {response.status_code}\")\n            if response.status_code != 200:\n                try:\n                    self.logger.error(f\"🔍 Response headers: {dict(response.headers)}\")\n                except Exception:\n                    self.logger.error(f\"🔍 Response headers: {response.headers}\")\n                try:\n                    self.logger.error(f\"🔍 Response text (first 500 chars): {response.text[:500]}\")\n                except Exception:","chunkId":"audio_separator/remote/api_client.py:44"}],[440,{"filePath":"audio_separator/remote/api_client.py","startLine":437,"endLine":452,"chunkText":"                try:\n                    self.logger.error(f\"🔍 Response text (first 500 chars): {response.text[:500]}\")\n                except Exception:\n                    self.logger.error(f\"🔍 Response text: <unavailable>\")\n            \n            response.raise_for_status()\n\n            with open(output_path, \"wb\") as f:\n                f.write(response.content)\n\n            return output_path\n        except requests.RequestException as e:\n            self.logger.error(f\"Download failed: {e}\")\n            raise\n\n    def _log_server_version_on_error(self):","chunkId":"audio_separator/remote/api_client.py:45"}],[441,{"filePath":"audio_separator/remote/api_client.py","startLine":450,"endLine":463,"chunkText":"            raise\n\n    def _log_server_version_on_error(self):\n        \"\"\"Helper method to log server version when download fails.\"\"\"\n        try:\n            server_version = self.get_server_version()\n            self.logger.error(f\"🔍 Server version when download failed: {server_version}\")\n        except Exception as version_error:\n            self.logger.error(f\"🔍 Could not get server version: {version_error}\")\n\n    def list_models(self, format_type: str = \"pretty\", filter_by: Optional[str] = None) -> dict:\n        \"\"\"List available models.\"\"\"\n        try:\n            if format_type == \"json\":","chunkId":"audio_separator/remote/api_client.py:46"}],[442,{"filePath":"audio_separator/remote/api_client.py","startLine":461,"endLine":476,"chunkText":"        \"\"\"List available models.\"\"\"\n        try:\n            if format_type == \"json\":\n                response = self.session.get(f\"{self.api_url}/models-json\", timeout=10)\n            else:\n                url = f\"{self.api_url}/models\"\n                if filter_by:\n                    url += f\"?filter_sort_by={filter_by}\"\n                response = self.session.get(url, timeout=10)\n\n            response.raise_for_status()\n\n            if format_type == \"json\":\n                return response.json()\n            else:\n                return {\"text\": response.text}","chunkId":"audio_separator/remote/api_client.py:47"}],[443,{"filePath":"audio_separator/remote/api_client.py","startLine":474,"endLine":488,"chunkText":"                return response.json()\n            else:\n                return {\"text\": response.text}\n        except requests.RequestException as e:\n            self.logger.error(f\"Models request failed: {e}\")\n            raise\n\n    def get_server_version(self) -> str:\n        \"\"\"Get the server version.\"\"\"\n        try:\n            response = self.session.get(f\"{self.api_url}/health\", timeout=10)\n            response.raise_for_status()\n            health_data = response.json()\n            return health_data.get(\"version\", \"unknown\")\n        except requests.RequestException as e:","chunkId":"audio_separator/remote/api_client.py:48"}],[444,{"filePath":"audio_separator/remote/api_client.py","startLine":486,"endLine":491,"chunkText":"            health_data = response.json()\n            return health_data.get(\"version\", \"unknown\")\n        except requests.RequestException as e:\n            self.logger.error(f\"Health check request failed: {e}\")\n            raise\n","chunkId":"audio_separator/remote/api_client.py:49"}],[445,{"filePath":"audio_separator/remote/cli.py","startLine":0,"endLine":19,"chunkText":"#!/usr/bin/env python\nimport argparse\nimport json\nimport logging\nimport os\nimport sys\nimport time\nfrom importlib import metadata\n\nfrom audio_separator.remote import AudioSeparatorAPIClient\n\n\ndef main():\n    \"\"\"Main entry point for the remote CLI.\"\"\"\n    logger = logging.getLogger(__name__)\n    log_handler = logging.StreamHandler()\n    log_formatter = logging.Formatter(fmt=\"%(asctime)s.%(msecs)03d - %(levelname)s - %(module)s - %(message)s\", datefmt=\"%Y-%m-%d %H:%M:%S\")\n    log_handler.setFormatter(log_formatter)\n    logger.addHandler(log_handler)\n","chunkId":"audio_separator/remote/cli.py:0"}],[446,{"filePath":"audio_separator/remote/cli.py","startLine":17,"endLine":29,"chunkText":"    log_handler.setFormatter(log_formatter)\n    logger.addHandler(log_handler)\n\n    parser = argparse.ArgumentParser(description=\"Separate audio files using a remote audio-separator API.\", formatter_class=lambda prog: argparse.RawTextHelpFormatter(prog, max_help_position=60))\n\n    # Get package version\n    package_version = metadata.distribution(\"audio-separator\").version\n\n    # Main command subparsers\n    subparsers = parser.add_subparsers(dest=\"command\", help=\"Available commands\")\n\n    # Separate command\n    separate_parser = subparsers.add_parser(\"separate\", help=\"Separate audio files\")","chunkId":"audio_separator/remote/cli.py:1"}],[447,{"filePath":"audio_separator/remote/cli.py","startLine":27,"endLine":37,"chunkText":"\n    # Separate command\n    separate_parser = subparsers.add_parser(\"separate\", help=\"Separate audio files\")\n    separate_parser.add_argument(\"audio_files\", nargs=\"+\", help=\"Audio file paths to separate\")\n\n    # Model selection\n    model_group = separate_parser.add_mutually_exclusive_group()\n    model_group.add_argument(\"-m\", \"--model\", help=\"Single model to use for separation\")\n    model_group.add_argument(\"--models\", nargs=\"+\", help=\"Multiple models to use for separation\")\n\n    separate_parser.add_argument(\"--timeout\", type=int, default=600, help=\"Timeout in seconds for polling (default: 600)\")","chunkId":"audio_separator/remote/cli.py:2"}],[448,{"filePath":"audio_separator/remote/cli.py","startLine":35,"endLine":42,"chunkText":"    model_group.add_argument(\"--models\", nargs=\"+\", help=\"Multiple models to use for separation\")\n\n    separate_parser.add_argument(\"--timeout\", type=int, default=600, help=\"Timeout in seconds for polling (default: 600)\")\n    separate_parser.add_argument(\"--poll_interval\", type=int, default=10, help=\"Polling interval in seconds (default: 10)\")\n\n    # Output parameters\n    output_group = separate_parser.add_argument_group(\"Output Parameters\")\n    output_group.add_argument(\"--output_format\", default=\"flac\", help=\"Output format for separated files (default: %(default)s)\")","chunkId":"audio_separator/remote/cli.py:3"}],[449,{"filePath":"audio_separator/remote/cli.py","startLine":40,"endLine":45,"chunkText":"    # Output parameters\n    output_group = separate_parser.add_argument_group(\"Output Parameters\")\n    output_group.add_argument(\"--output_format\", default=\"flac\", help=\"Output format for separated files (default: %(default)s)\")\n    output_group.add_argument(\"--output_bitrate\", help=\"Output bitrate for separated files\")\n    output_group.add_argument(\"--normalization\", type=float, default=0.9, help=\"Max peak amplitude to normalize audio to (default: %(default)s)\")\n    output_group.add_argument(\"--amplification\", type=float, default=0.0, help=\"Min peak amplitude to amplify audio to (default: %(default)s)\")","chunkId":"audio_separator/remote/cli.py:4"}],[450,{"filePath":"audio_separator/remote/cli.py","startLine":43,"endLine":47,"chunkText":"    output_group.add_argument(\"--output_bitrate\", help=\"Output bitrate for separated files\")\n    output_group.add_argument(\"--normalization\", type=float, default=0.9, help=\"Max peak amplitude to normalize audio to (default: %(default)s)\")\n    output_group.add_argument(\"--amplification\", type=float, default=0.0, help=\"Min peak amplitude to amplify audio to (default: %(default)s)\")\n    output_group.add_argument(\"--single_stem\", help=\"Output only single stem (e.g. Vocals, Instrumental)\")\n    output_group.add_argument(\"--invert_spect\", action=\"store_true\", help=\"Invert secondary stem using spectrogram\")","chunkId":"audio_separator/remote/cli.py:5"}],[451,{"filePath":"audio_separator/remote/cli.py","startLine":45,"endLine":49,"chunkText":"    output_group.add_argument(\"--amplification\", type=float, default=0.0, help=\"Min peak amplitude to amplify audio to (default: %(default)s)\")\n    output_group.add_argument(\"--single_stem\", help=\"Output only single stem (e.g. Vocals, Instrumental)\")\n    output_group.add_argument(\"--invert_spect\", action=\"store_true\", help=\"Invert secondary stem using spectrogram\")\n    output_group.add_argument(\"--sample_rate\", type=int, default=44100, help=\"Sample rate of output audio (default: %(default)s)\")\n    output_group.add_argument(\"--use_soundfile\", action=\"store_true\", help=\"Use soundfile for output writing\")","chunkId":"audio_separator/remote/cli.py:6"}],[452,{"filePath":"audio_separator/remote/cli.py","startLine":47,"endLine":52,"chunkText":"    output_group.add_argument(\"--invert_spect\", action=\"store_true\", help=\"Invert secondary stem using spectrogram\")\n    output_group.add_argument(\"--sample_rate\", type=int, default=44100, help=\"Sample rate of output audio (default: %(default)s)\")\n    output_group.add_argument(\"--use_soundfile\", action=\"store_true\", help=\"Use soundfile for output writing\")\n    output_group.add_argument(\"--use_autocast\", action=\"store_true\", help=\"Use PyTorch autocast for faster inference\")\n    output_group.add_argument(\"--custom_output_names\", type=json.loads, help=\"Custom output names in JSON format\")\n","chunkId":"audio_separator/remote/cli.py:7"}],[453,{"filePath":"audio_separator/remote/cli.py","startLine":50,"endLine":56,"chunkText":"    output_group.add_argument(\"--use_autocast\", action=\"store_true\", help=\"Use PyTorch autocast for faster inference\")\n    output_group.add_argument(\"--custom_output_names\", type=json.loads, help=\"Custom output names in JSON format\")\n\n    # MDX parameters\n    mdx_group = separate_parser.add_argument_group(\"MDX Architecture Parameters\")\n    mdx_group.add_argument(\"--mdx_segment_size\", type=int, default=256, help=\"MDX segment size (default: %(default)s)\")\n    mdx_group.add_argument(\"--mdx_overlap\", type=float, default=0.25, help=\"MDX overlap (default: %(default)s)\")","chunkId":"audio_separator/remote/cli.py:8"}],[454,{"filePath":"audio_separator/remote/cli.py","startLine":54,"endLine":58,"chunkText":"    mdx_group = separate_parser.add_argument_group(\"MDX Architecture Parameters\")\n    mdx_group.add_argument(\"--mdx_segment_size\", type=int, default=256, help=\"MDX segment size (default: %(default)s)\")\n    mdx_group.add_argument(\"--mdx_overlap\", type=float, default=0.25, help=\"MDX overlap (default: %(default)s)\")\n    mdx_group.add_argument(\"--mdx_batch_size\", type=int, default=1, help=\"MDX batch size (default: %(default)s)\")\n    mdx_group.add_argument(\"--mdx_hop_length\", type=int, default=1024, help=\"MDX hop length (default: %(default)s)\")","chunkId":"audio_separator/remote/cli.py:9"}],[455,{"filePath":"audio_separator/remote/cli.py","startLine":56,"endLine":62,"chunkText":"    mdx_group.add_argument(\"--mdx_overlap\", type=float, default=0.25, help=\"MDX overlap (default: %(default)s)\")\n    mdx_group.add_argument(\"--mdx_batch_size\", type=int, default=1, help=\"MDX batch size (default: %(default)s)\")\n    mdx_group.add_argument(\"--mdx_hop_length\", type=int, default=1024, help=\"MDX hop length (default: %(default)s)\")\n    mdx_group.add_argument(\"--mdx_enable_denoise\", action=\"store_true\", help=\"Enable MDX denoising\")\n\n    # VR parameters\n    vr_group = separate_parser.add_argument_group(\"VR Architecture Parameters\")","chunkId":"audio_separator/remote/cli.py:10"}],[456,{"filePath":"audio_separator/remote/cli.py","startLine":60,"endLine":66,"chunkText":"\n    # VR parameters\n    vr_group = separate_parser.add_argument_group(\"VR Architecture Parameters\")\n    vr_group.add_argument(\"--vr_batch_size\", type=int, default=1, help=\"VR batch size (default: %(default)s)\")\n    vr_group.add_argument(\"--vr_window_size\", type=int, default=512, help=\"VR window size (default: %(default)s)\")\n    vr_group.add_argument(\"--vr_aggression\", type=int, default=5, help=\"VR aggression (default: %(default)s)\")\n    vr_group.add_argument(\"--vr_enable_tta\", action=\"store_true\", help=\"Enable VR Test-Time-Augmentation\")","chunkId":"audio_separator/remote/cli.py:11"}],[457,{"filePath":"audio_separator/remote/cli.py","startLine":64,"endLine":68,"chunkText":"    vr_group.add_argument(\"--vr_window_size\", type=int, default=512, help=\"VR window size (default: %(default)s)\")\n    vr_group.add_argument(\"--vr_aggression\", type=int, default=5, help=\"VR aggression (default: %(default)s)\")\n    vr_group.add_argument(\"--vr_enable_tta\", action=\"store_true\", help=\"Enable VR Test-Time-Augmentation\")\n    vr_group.add_argument(\"--vr_high_end_process\", action=\"store_true\", help=\"Enable VR high end processing\")\n    vr_group.add_argument(\"--vr_enable_post_process\", action=\"store_true\", help=\"Enable VR post processing\")","chunkId":"audio_separator/remote/cli.py:12"}],[458,{"filePath":"audio_separator/remote/cli.py","startLine":66,"endLine":72,"chunkText":"    vr_group.add_argument(\"--vr_enable_tta\", action=\"store_true\", help=\"Enable VR Test-Time-Augmentation\")\n    vr_group.add_argument(\"--vr_high_end_process\", action=\"store_true\", help=\"Enable VR high end processing\")\n    vr_group.add_argument(\"--vr_enable_post_process\", action=\"store_true\", help=\"Enable VR post processing\")\n    vr_group.add_argument(\"--vr_post_process_threshold\", type=float, default=0.2, help=\"VR post process threshold (default: %(default)s)\")\n\n    # Demucs parameters\n    demucs_group = separate_parser.add_argument_group(\"Demucs Architecture Parameters\")","chunkId":"audio_separator/remote/cli.py:13"}],[459,{"filePath":"audio_separator/remote/cli.py","startLine":70,"endLine":75,"chunkText":"\n    # Demucs parameters\n    demucs_group = separate_parser.add_argument_group(\"Demucs Architecture Parameters\")\n    demucs_group.add_argument(\"--demucs_segment_size\", default=\"Default\", help=\"Demucs segment size (default: %(default)s)\")\n    demucs_group.add_argument(\"--demucs_shifts\", type=int, default=2, help=\"Demucs shifts (default: %(default)s)\")\n    demucs_group.add_argument(\"--demucs_overlap\", type=float, default=0.25, help=\"Demucs overlap (default: %(default)s)\")","chunkId":"audio_separator/remote/cli.py:14"}],[460,{"filePath":"audio_separator/remote/cli.py","startLine":73,"endLine":79,"chunkText":"    demucs_group.add_argument(\"--demucs_segment_size\", default=\"Default\", help=\"Demucs segment size (default: %(default)s)\")\n    demucs_group.add_argument(\"--demucs_shifts\", type=int, default=2, help=\"Demucs shifts (default: %(default)s)\")\n    demucs_group.add_argument(\"--demucs_overlap\", type=float, default=0.25, help=\"Demucs overlap (default: %(default)s)\")\n    demucs_group.add_argument(\"--demucs_segments_enabled\", type=bool, default=True, help=\"Enable Demucs segments (default: %(default)s)\")\n\n    # MDXC parameters\n    mdxc_group = separate_parser.add_argument_group(\"MDXC Architecture Parameters\")","chunkId":"audio_separator/remote/cli.py:15"}],[461,{"filePath":"audio_separator/remote/cli.py","startLine":77,"endLine":83,"chunkText":"\n    # MDXC parameters\n    mdxc_group = separate_parser.add_argument_group(\"MDXC Architecture Parameters\")\n    mdxc_group.add_argument(\"--mdxc_segment_size\", type=int, default=256, help=\"MDXC segment size (default: %(default)s)\")\n    mdxc_group.add_argument(\"--mdxc_override_model_segment_size\", action=\"store_true\", help=\"Override MDXC model segment size\")\n    mdxc_group.add_argument(\"--mdxc_overlap\", type=int, default=8, help=\"MDXC overlap (default: %(default)s)\")\n    mdxc_group.add_argument(\"--mdxc_batch_size\", type=int, default=1, help=\"MDXC batch size (default: %(default)s)\")","chunkId":"audio_separator/remote/cli.py:16"}],[462,{"filePath":"audio_separator/remote/cli.py","startLine":81,"endLine":87,"chunkText":"    mdxc_group.add_argument(\"--mdxc_override_model_segment_size\", action=\"store_true\", help=\"Override MDXC model segment size\")\n    mdxc_group.add_argument(\"--mdxc_overlap\", type=int, default=8, help=\"MDXC overlap (default: %(default)s)\")\n    mdxc_group.add_argument(\"--mdxc_batch_size\", type=int, default=1, help=\"MDXC batch size (default: %(default)s)\")\n    mdxc_group.add_argument(\"--mdxc_pitch_shift\", type=int, default=0, help=\"MDXC pitch shift (default: %(default)s)\")\n\n    # Status command\n    status_parser = subparsers.add_parser(\"status\", help=\"Check job status\")","chunkId":"audio_separator/remote/cli.py:17"}],[463,{"filePath":"audio_separator/remote/cli.py","startLine":85,"endLine":96,"chunkText":"\n    # Status command\n    status_parser = subparsers.add_parser(\"status\", help=\"Check job status\")\n    status_parser.add_argument(\"task_id\", help=\"Task ID to check status for\")\n\n    # Models command\n    models_parser = subparsers.add_parser(\"models\", help=\"List available models\")\n    models_parser.add_argument(\"--format\", choices=[\"pretty\", \"json\"], default=\"pretty\", help=\"Output format\")\n    models_parser.add_argument(\"--filter\", help=\"Filter models by name, type, or stem\")\n\n    # Download command\n    download_parser = subparsers.add_parser(\"download\", help=\"Download specific files from a job\")","chunkId":"audio_separator/remote/cli.py:18"}],[464,{"filePath":"audio_separator/remote/cli.py","startLine":94,"endLine":103,"chunkText":"\n    # Download command\n    download_parser = subparsers.add_parser(\"download\", help=\"Download specific files from a job\")\n    download_parser.add_argument(\"task_id\", help=\"Task ID\")\n    download_parser.add_argument(\"filenames\", nargs=\"+\", help=\"Filenames to download\")\n\n    # Global options\n    parser.add_argument(\"-v\", \"--version\", action=\"store_true\", help=\"Show version information\")\n    parser.add_argument(\"-d\", \"--debug\", action=\"store_true\", help=\"Enable debug logging\")\n    parser.add_argument(\"--log_level\", default=\"info\", help=\"Log level (default: info)\")","chunkId":"audio_separator/remote/cli.py:19"}],[465,{"filePath":"audio_separator/remote/cli.py","startLine":101,"endLine":115,"chunkText":"    parser.add_argument(\"-v\", \"--version\", action=\"store_true\", help=\"Show version information\")\n    parser.add_argument(\"-d\", \"--debug\", action=\"store_true\", help=\"Enable debug logging\")\n    parser.add_argument(\"--log_level\", default=\"info\", help=\"Log level (default: info)\")\n    parser.add_argument(\"--api_url\", help=\"API URL (overrides AUDIO_SEPARATOR_API_URL env var)\")\n\n    args = parser.parse_args()\n\n    # Set up logging\n    if args.debug:\n        log_level = logging.DEBUG\n    else:\n        log_level = getattr(logging, args.log_level.upper())\n    logger.setLevel(log_level)\n\n    # Handle version command","chunkId":"audio_separator/remote/cli.py:20"}],[466,{"filePath":"audio_separator/remote/cli.py","startLine":113,"endLine":127,"chunkText":"    logger.setLevel(log_level)\n\n    # Handle version command\n    if args.version:\n        print(f\"Client version: {package_version}\")\n\n        # Try to get server version if API URL is available\n        api_url = args.api_url or os.environ.get(\"AUDIO_SEPARATOR_API_URL\")\n        if api_url:\n            api_url = api_url.rstrip(\"/\")\n            api_client = AudioSeparatorAPIClient(api_url, logger)\n            try:\n                server_version = api_client.get_server_version()\n                print(f\"Server version: {server_version}\")\n            except Exception as e:","chunkId":"audio_separator/remote/cli.py:21"}],[467,{"filePath":"audio_separator/remote/cli.py","startLine":125,"endLine":136,"chunkText":"                server_version = api_client.get_server_version()\n                print(f\"Server version: {server_version}\")\n            except Exception as e:\n                logger.warning(f\"Could not retrieve server version: {e}\")\n        else:\n            logger.warning(\"API URL not provided. Set AUDIO_SEPARATOR_API_URL environment variable or use --api_url to get server version\")\n\n        sys.exit(0)\n\n    # Get API URL\n    api_url = args.api_url or os.environ.get(\"AUDIO_SEPARATOR_API_URL\")\n    if not api_url:","chunkId":"audio_separator/remote/cli.py:22"}],[468,{"filePath":"audio_separator/remote/cli.py","startLine":134,"endLine":150,"chunkText":"    # Get API URL\n    api_url = args.api_url or os.environ.get(\"AUDIO_SEPARATOR_API_URL\")\n    if not api_url:\n        logger.error(\"API URL not provided. Set AUDIO_SEPARATOR_API_URL environment variable or use --api_url\")\n        sys.exit(1)\n\n    # Remove trailing slash\n    api_url = api_url.rstrip(\"/\")\n\n    # Create API client\n    api_client = AudioSeparatorAPIClient(api_url, logger)\n\n    # Handle commands\n    if args.command == \"separate\":\n        handle_separate_command(args, api_client, logger)\n    elif args.command == \"status\":\n        handle_status_command(args, api_client, logger)","chunkId":"audio_separator/remote/cli.py:23"}],[469,{"filePath":"audio_separator/remote/cli.py","startLine":148,"endLine":162,"chunkText":"        handle_separate_command(args, api_client, logger)\n    elif args.command == \"status\":\n        handle_status_command(args, api_client, logger)\n    elif args.command == \"models\":\n        handle_models_command(args, api_client, logger)\n    elif args.command == \"download\":\n        handle_download_command(args, api_client, logger)\n    else:\n        parser.print_help()\n        sys.exit(1)\n\n\ndef handle_separate_command(args, api_client: AudioSeparatorAPIClient, logger: logging.Logger):\n    \"\"\"Handle the separate command.\"\"\"\n    for audio_file in args.audio_files:","chunkId":"audio_separator/remote/cli.py:24"}],[470,{"filePath":"audio_separator/remote/cli.py","startLine":160,"endLine":172,"chunkText":"def handle_separate_command(args, api_client: AudioSeparatorAPIClient, logger: logging.Logger):\n    \"\"\"Handle the separate command.\"\"\"\n    for audio_file in args.audio_files:\n        logger.info(f\"Uploading '{audio_file}' to audio separator...\")\n\n        try:\n            # Prepare parameters for separation\n            kwargs = {\n                \"model\": args.model,\n                \"models\": args.models,\n                \"timeout\": args.timeout,\n                \"poll_interval\": args.poll_interval,\n                \"download\": True,  # Always download in CLI","chunkId":"audio_separator/remote/cli.py:25"}],[471,{"filePath":"audio_separator/remote/cli.py","startLine":170,"endLine":180,"chunkText":"                \"timeout\": args.timeout,\n                \"poll_interval\": args.poll_interval,\n                \"download\": True,  # Always download in CLI\n                \"output_dir\": None,  # Use current directory\n                # Output parameters\n                \"output_format\": args.output_format,\n                \"output_bitrate\": args.output_bitrate,\n                \"normalization_threshold\": args.normalization,\n                \"amplification_threshold\": args.amplification,\n                \"output_single_stem\": args.single_stem,\n                \"invert_using_spec\": args.invert_spect,","chunkId":"audio_separator/remote/cli.py:26"}],[472,{"filePath":"audio_separator/remote/cli.py","startLine":178,"endLine":188,"chunkText":"                \"amplification_threshold\": args.amplification,\n                \"output_single_stem\": args.single_stem,\n                \"invert_using_spec\": args.invert_spect,\n                \"sample_rate\": args.sample_rate,\n                \"use_soundfile\": args.use_soundfile,\n                \"use_autocast\": args.use_autocast,\n                \"custom_output_names\": args.custom_output_names,\n                # MDX parameters\n                \"mdx_segment_size\": args.mdx_segment_size,\n                \"mdx_overlap\": args.mdx_overlap,\n                \"mdx_batch_size\": args.mdx_batch_size,","chunkId":"audio_separator/remote/cli.py:27"}],[473,{"filePath":"audio_separator/remote/cli.py","startLine":186,"endLine":196,"chunkText":"                \"mdx_segment_size\": args.mdx_segment_size,\n                \"mdx_overlap\": args.mdx_overlap,\n                \"mdx_batch_size\": args.mdx_batch_size,\n                \"mdx_hop_length\": args.mdx_hop_length,\n                \"mdx_enable_denoise\": args.mdx_enable_denoise,\n                # VR parameters\n                \"vr_batch_size\": args.vr_batch_size,\n                \"vr_window_size\": args.vr_window_size,\n                \"vr_aggression\": args.vr_aggression,\n                \"vr_enable_tta\": args.vr_enable_tta,\n                \"vr_high_end_process\": args.vr_high_end_process,","chunkId":"audio_separator/remote/cli.py:28"}],[474,{"filePath":"audio_separator/remote/cli.py","startLine":194,"endLine":203,"chunkText":"                \"vr_aggression\": args.vr_aggression,\n                \"vr_enable_tta\": args.vr_enable_tta,\n                \"vr_high_end_process\": args.vr_high_end_process,\n                \"vr_enable_post_process\": args.vr_enable_post_process,\n                \"vr_post_process_threshold\": args.vr_post_process_threshold,\n                # Demucs parameters\n                \"demucs_segment_size\": args.demucs_segment_size,\n                \"demucs_shifts\": args.demucs_shifts,\n                \"demucs_overlap\": args.demucs_overlap,\n                \"demucs_segments_enabled\": args.demucs_segments_enabled,","chunkId":"audio_separator/remote/cli.py:29"}],[475,{"filePath":"audio_separator/remote/cli.py","startLine":201,"endLine":212,"chunkText":"                \"demucs_shifts\": args.demucs_shifts,\n                \"demucs_overlap\": args.demucs_overlap,\n                \"demucs_segments_enabled\": args.demucs_segments_enabled,\n                # MDXC parameters\n                \"mdxc_segment_size\": args.mdxc_segment_size,\n                \"mdxc_override_model_segment_size\": args.mdxc_override_model_segment_size,\n                \"mdxc_overlap\": args.mdxc_overlap,\n                \"mdxc_batch_size\": args.mdxc_batch_size,\n                \"mdxc_pitch_shift\": args.mdxc_pitch_shift,\n            }\n\n            # Use the convenience method that handles everythin","chunkId":"audio_separator/remote/cli.py:30"}],[476,{"filePath":"audio_separator/remote/cli.py","startLine":210,"endLine":220,"chunkText":"            }\n\n            # Use the convenience method that handles everything\n            result = api_client.separate_audio_and_wait(audio_file, **kwargs)\n\n            if result[\"status\"] == \"completed\":\n                if \"downloaded_files\" in result:\n                    logger.info(f\"✅ Separation completed! Downloaded {len(result['downloaded_files'])} files:\")\n                    for file_path in result[\"downloaded_files\"]:\n                        logger.info(f\"  - {file_path}\")\n                else:","chunkId":"audio_separator/remote/cli.py:31"}],[477,{"filePath":"audio_separator/remote/cli.py","startLine":218,"endLine":231,"chunkText":"                    for file_path in result[\"downloaded_files\"]:\n                        logger.info(f\"  - {file_path}\")\n                else:\n                    logger.info(f\"✅ Separation completed! Files available for download: {result['files']}\")\n            else:\n                logger.error(f\"❌ Separation failed: {result.get('error', 'Unknown error')}\")\n\n        except Exception as e:\n            logger.error(f\"❌ Error processing '{audio_file}': {e}\")\n\n\ndef handle_status_command(args, api_client: AudioSeparatorAPIClient, logger: logging.Logger):\n    \"\"\"Handle the status command.\"\"\"\n    try:","chunkId":"audio_separator/remote/cli.py:32"}],[478,{"filePath":"audio_separator/remote/cli.py","startLine":229,"endLine":240,"chunkText":"def handle_status_command(args, api_client: AudioSeparatorAPIClient, logger: logging.Logger):\n    \"\"\"Handle the status command.\"\"\"\n    try:\n        status = api_client.get_job_status(args.task_id)\n\n        logger.info(f\"Job Status: {status['status']}\")\n        if \"progress\" in status:\n            progress_info = f\"Progress: {status['progress']}%\"\n            if \"current_model_index\" in status and \"total_models\" in status:\n                model_info = f\" (Model {status['current_model_index'] + 1}/{status['total_models']})\"\n                progress_info += model_info\n            logger.info(progress_info)","chunkId":"audio_separator/remote/cli.py:33"}],[479,{"filePath":"audio_separator/remote/cli.py","startLine":238,"endLine":247,"chunkText":"                model_info = f\" (Model {status['current_model_index'] + 1}/{status['total_models']})\"\n                progress_info += model_info\n            logger.info(progress_info)\n        if \"original_filename\" in status:\n            logger.info(f\"Original File: {status['original_filename']}\")\n        if \"models_used\" in status:\n            logger.info(f\"Models Used: {', '.join(status['models_used'])}\")\n        if status[\"status\"] == \"error\" and \"error\" in status:\n            logger.error(f\"Error: {status['error']}\")\n        elif status[\"status\"] == \"completed\" and \"files\" in status:","chunkId":"audio_separator/remote/cli.py:34"}],[480,{"filePath":"audio_separator/remote/cli.py","startLine":245,"endLine":260,"chunkText":"        if status[\"status\"] == \"error\" and \"error\" in status:\n            logger.error(f\"Error: {status['error']}\")\n        elif status[\"status\"] == \"completed\" and \"files\" in status:\n            logger.info(\"Output Files:\")\n            for filename in status[\"files\"]:\n                logger.info(f\"  - {filename}\")\n\n    except Exception as e:\n        logger.error(f\"❌ Error getting status: {e}\")\n\n\ndef handle_models_command(args, api_client: AudioSeparatorAPIClient, logger: logging.Logger):\n    \"\"\"Handle the models command.\"\"\"\n    try:\n        models = api_client.list_models(args.format, args.filter)\n","chunkId":"audio_separator/remote/cli.py:35"}],[481,{"filePath":"audio_separator/remote/cli.py","startLine":258,"endLine":275,"chunkText":"    try:\n        models = api_client.list_models(args.format, args.filter)\n\n        if args.format == \"json\":\n            print(json.dumps(models, indent=2))\n        else:\n            print(models[\"text\"])\n\n    except Exception as e:\n        logger.error(f\"❌ Error listing models: {e}\")\n\n\ndef handle_download_command(args, api_client: AudioSeparatorAPIClient, logger: logging.Logger):\n    \"\"\"Handle the download command.\"\"\"\n    try:\n        for filename in args.filenames:\n            logger.info(f\"📂 Downloading: {filename}\")\n            output_path = api_client.download_file(args.task_id, filename)","chunkId":"audio_separator/remote/cli.py:36"}],[482,{"filePath":"audio_separator/remote/cli.py","startLine":273,"endLine":288,"chunkText":"        for filename in args.filenames:\n            logger.info(f\"📂 Downloading: {filename}\")\n            output_path = api_client.download_file(args.task_id, filename)\n            logger.info(f\"✅ Downloaded: {output_path}\")\n\n    except Exception as e:\n        logger.error(f\"❌ Error downloading files: {e}\")\n\n\ndef poll_for_completion(task_id: str, api_client: AudioSeparatorAPIClient, logger: logging.Logger, timeout: int = 600, poll_interval: int = 10) -> bool:\n    \"\"\"Poll for job completion.\"\"\"\n    start_time = time.time()\n    last_progress = -1\n\n    while time.time() - start_time < timeout:\n        try:","chunkId":"audio_separator/remote/cli.py:37"}],[483,{"filePath":"audio_separator/remote/cli.py","startLine":286,"endLine":297,"chunkText":"\n    while time.time() - start_time < timeout:\n        try:\n            status = api_client.get_job_status(task_id)\n            current_status = status.get(\"status\", \"unknown\")\n\n            # Show progress if it changed\n            if \"progress\" in status and status[\"progress\"] != last_progress:\n                progress_info = f\"📊 Progress: {status['progress']}%\"\n                if \"current_model_index\" in status and \"total_models\" in status:\n                    model_info = f\" (Model {status['current_model_index'] + 1}/{status['total_models']})\"\n                    progress_info += model_info","chunkId":"audio_separator/remote/cli.py:38"}],[484,{"filePath":"audio_separator/remote/cli.py","startLine":295,"endLine":305,"chunkText":"                if \"current_model_index\" in status and \"total_models\" in status:\n                    model_info = f\" (Model {status['current_model_index'] + 1}/{status['total_models']})\"\n                    progress_info += model_info\n                logger.info(progress_info)\n                last_progress = status[\"progress\"]\n\n            # Check if completed\n            if current_status == \"completed\":\n                logger.info(\"✅ Job completed!\")\n                return True\n            elif current_status == \"error\":","chunkId":"audio_separator/remote/cli.py:39"}],[485,{"filePath":"audio_separator/remote/cli.py","startLine":304,"endLine":320,"chunkText":"                return True\n            elif current_status == \"error\":\n                logger.error(f\"❌ Job failed: {status.get('error', 'Unknown error')}\")\n                return False\n\n            # Wait before next poll\n            time.sleep(poll_interval)\n\n        except Exception as e:\n            logger.warning(f\"Error polling status: {e}\")\n            time.sleep(poll_interval)\n\n    logger.error(f\"❌ Job polling timed out after {timeout} seconds\")\n    return False\n\n\ndef download_files(task_id: str, files: list, api_client: AudioSeparatorAPIClient, logger: logging.Logger):","chunkId":"audio_separator/remote/cli.py:40"}],[486,{"filePath":"audio_separator/remote/cli.py","startLine":318,"endLine":334,"chunkText":"\n\ndef download_files(task_id: str, files: list, api_client: AudioSeparatorAPIClient, logger: logging.Logger):\n    \"\"\"Download all files from a completed job.\"\"\"\n    if not files:\n        logger.warning(\"No files to download\")\n        return\n\n    logger.info(f\"📥 Downloading {len(files)} output files...\")\n\n    downloaded_count = 0\n    for filename in files:\n        try:\n            logger.info(f\"  📂 Downloading: {filename}\")\n            output_path = api_client.download_file(task_id, filename)\n            logger.info(f\"  ✅ Downloaded: {output_path}\")\n            downloaded_count += 1","chunkId":"audio_separator/remote/cli.py:41"}],[487,{"filePath":"audio_separator/remote/cli.py","startLine":332,"endLine":346,"chunkText":"            output_path = api_client.download_file(task_id, filename)\n            logger.info(f\"  ✅ Downloaded: {output_path}\")\n            downloaded_count += 1\n        except Exception as e:\n            logger.error(f\"  ❌ Failed to download {filename}: {e}\")\n\n    if downloaded_count > 0:\n        logger.info(f\"🎉 Successfully downloaded {downloaded_count} files to current directory!\")\n    else:\n        logger.error(\"❌ No files were successfully downloaded\")\n\n\nif __name__ == \"__main__\":\n    main()\n","chunkId":"audio_separator/remote/cli.py:42"}],[488,{"filePath":"audio_separator/remote/deploy_modal.py","startLine":0,"endLine":15,"chunkText":"\"\"\"\nAudio Separator API - Simple Modal Deployment\nA FastAPI service for separating vocals from instrumental tracks using audio-separator\n\nFeatures:\n- Asynchronous job processing\n- Progress tracking and status polling\n- Persistent storage for models and outputs\n- GPU acceleration support\n- Multiple audio format support\n\nUsage with Remote CLI:\n1. Install audio-separator package: pip install audio-separator\n2. Set environment variable: export AUDIO_SEPARATOR_API_URL=\"https://your-deployment-url.modal.run\"\n3. Use the remote CLI:\n   - audio-separator-remote separate song.mp3","chunkId":"audio_separator/remote/deploy_modal.py:0"}],[489,{"filePath":"audio_separator/remote/deploy_modal.py","startLine":13,"endLine":32,"chunkText":"2. Set environment variable: export AUDIO_SEPARATOR_API_URL=\"https://your-deployment-url.modal.run\"\n3. Use the remote CLI:\n   - audio-separator-remote separate song.mp3\n   - audio-separator-remote separate song.mp3 --model UVR-MDX-NET-Inst_HQ_4\n   - audio-separator-remote status <task_id>\n   - audio-separator-remote models\n   - audio-separator-remote download <task_id> <filename>\n\"\"\"\n\n# Standard library imports\nimport logging\nimport os\nimport shutil\nimport traceback\nimport uuid\nimport json\nimport hashlib\nfrom importlib.metadata import version\nimport typing\nfrom typing import Optional","chunkId":"audio_separator/remote/deploy_modal.py:1"}],[490,{"filePath":"audio_separator/remote/deploy_modal.py","startLine":30,"endLine":47,"chunkText":"from importlib.metadata import version\nimport typing\nfrom typing import Optional\nfrom urllib.parse import quote\n\n# Third-party imports\nfrom fastapi import FastAPI, File, Form, HTTPException, Response, UploadFile\nfrom fastapi.middleware.cors import CORSMiddleware\nfrom starlette.responses import Response as StarletteResponse, PlainTextResponse\nimport filetype\nimport modal\n\n# Note: Separator is imported inside functions to allow Modal to parse this file\n# without requiring audio_separator to be installed on the deployment machine\n\n# Constants\nDEFAULT_MODEL_NAME = \"default\"  # Used when no model is specified\n","chunkId":"audio_separator/remote/deploy_modal.py:2"}],[491,{"filePath":"audio_separator/remote/deploy_modal.py","startLine":45,"endLine":57,"chunkText":"# Constants\nDEFAULT_MODEL_NAME = \"default\"  # Used when no model is specified\n\ndef generate_file_hash(filename: str) -> str:\n    \"\"\"Generate a short, stable hash for a filename to use in download URLs.\"\"\"\n    # Use SHA-256 hash of the filename, take first 16 characters for brevity\n    # This gives us a stable, URL-safe identifier that's much shorter than the filename\n    return hashlib.sha256(filename.encode('utf-8')).hexdigest()[:16]\n\n# Get the version of the installed audio-separator package\ntry:\n    AUDIO_SEPARATOR_VERSION = version(\"audio-separator\")\nexcept Exception:","chunkId":"audio_separator/remote/deploy_modal.py:3"}],[492,{"filePath":"audio_separator/remote/deploy_modal.py","startLine":55,"endLine":72,"chunkText":"try:\n    AUDIO_SEPARATOR_VERSION = version(\"audio-separator\")\nexcept Exception:\n    # Fallback version if package version cannot be determined\n    AUDIO_SEPARATOR_VERSION = \"unknown\"\n\n# Create Modal app\napp = modal.App(\"audio-separator\")\n\n# Define the container image; we're using CUDA for hardware acceleration and Python 3.13 for optimal performance\nimage = (\n    modal.Image.from_registry(\"nvidia/cuda:12.9.1-devel-ubuntu22.04\", add_python=\"3.13\")\n    .apt_install(\n        [\n            # Core system packages\n            \"curl\",\n            \"wget\",\n            # Audio libraries and dependencies","chunkId":"audio_separator/remote/deploy_modal.py:4"}],[493,{"filePath":"audio_separator/remote/deploy_modal.py","startLine":70,"endLine":89,"chunkText":"            \"curl\",\n            \"wget\",\n            # Audio libraries and dependencies\n            \"libsndfile1\",\n            \"libsndfile1-dev\",\n            \"libsox-dev\",\n            \"sox\",\n            \"libportaudio2\",\n            \"portaudio19-dev\",\n            \"libasound2-dev\",\n            \"libpulse-dev\",\n            \"libjack-dev\",\n            # Sample rate conversion library\n            \"libsamplerate0\",\n            \"libsamplerate0-dev\",\n            # Build tools for compiling Python packages with C extensions\n            \"build-essential\",\n            \"clang\",\n            \"gcc\",\n            \"g++\",","chunkId":"audio_separator/remote/deploy_modal.py:5"}],[494,{"filePath":"audio_separator/remote/deploy_modal.py","startLine":87,"endLine":102,"chunkText":"            \"clang\",\n            \"gcc\",\n            \"g++\",\n            \"make\",\n            \"cmake\",\n            \"pkg-config\",\n        ]\n    )\n    .run_commands(\n        [\n            # Set up CUDA library paths for NVENC support\n            \"echo '/usr/local/cuda/lib64' >> /etc/ld.so.conf.d/cuda.conf\",\n            \"ldconfig\",\n            # Install latest FFmpeg\n            \"wget https://github.com/BtbN/FFmpeg-Builds/releases/download/latest/ffmpeg-master-latest-linux64-gpl.tar.xz\",\n            \"tar -xf ffmpeg-master-latest-linux64-gpl.tar.xz\",","chunkId":"audio_separator/remote/deploy_modal.py:6"}],[495,{"filePath":"audio_separator/remote/deploy_modal.py","startLine":100,"endLine":111,"chunkText":"            # Install latest FFmpeg\n            \"wget https://github.com/BtbN/FFmpeg-Builds/releases/download/latest/ffmpeg-master-latest-linux64-gpl.tar.xz\",\n            \"tar -xf ffmpeg-master-latest-linux64-gpl.tar.xz\",\n            \"cp ffmpeg-master-latest-linux64-gpl/bin/* /usr/local/bin/\",\n            \"chmod +x /usr/local/bin/ffmpeg /usr/local/bin/ffprobe\",\n            # Verify installations and NVENC support\n            \"ffmpeg -version\",\n        ]\n    )\n    .pip_install(\n        [\n            # Core audio-separator with GPU support","chunkId":"audio_separator/remote/deploy_modal.py:7"}],[496,{"filePath":"audio_separator/remote/deploy_modal.py","startLine":109,"endLine":125,"chunkText":"    .pip_install(\n        [\n            # Core audio-separator with GPU support\n            # Always pulls the latest version from PyPI when the image is rebuilt\n            \"audio-separator[gpu]\",\n            # FastAPI and web server dependencies for Modal API deployment\n            \"fastapi>=0.104.0\",\n            \"uvicorn[standard]>=0.24.0\",\n            \"python-multipart>=0.0.6\",\n            # File type detection for response content type\n            \"filetype>=1.2.0\",\n        ]\n    )\n    .env(\n        {\n            \"AUDIO_SEPARATOR_MODEL_DIR\": \"/models\",\n            # CUDA environment for NVENC support","chunkId":"audio_separator/remote/deploy_modal.py:8"}],[497,{"filePath":"audio_separator/remote/deploy_modal.py","startLine":123,"endLine":138,"chunkText":"        {\n            \"AUDIO_SEPARATOR_MODEL_DIR\": \"/models\",\n            # CUDA environment for NVENC support\n            \"LD_LIBRARY_PATH\": \"/usr/local/cuda/lib64:$LD_LIBRARY_PATH\",\n            \"PATH\": \"/usr/local/cuda/bin:$PATH\",\n        }\n    )\n)\n\n# Create persistent volume for storing separated files\nvolume = modal.Volume.from_name(\"audio-separator-storage\", create_if_missing=True)\n\n# Create persistent volume for caching downloaded models\nmodels_volume = modal.Volume.from_name(\"audio-separator-models\", create_if_missing=True)\n\n# Modal Dict for job status tracking is accessed by name in functions","chunkId":"audio_separator/remote/deploy_modal.py:9"}],[498,{"filePath":"audio_separator/remote/deploy_modal.py","startLine":136,"endLine":150,"chunkText":"models_volume = modal.Volume.from_name(\"audio-separator-models\", create_if_missing=True)\n\n# Modal Dict for job status tracking is accessed by name in functions\n# job_status_dict = modal.Dict.from_name(\"audio-separator-job-status\", create_if_missing=True)\n\n\nclass PrettyJSONResponse(StarletteResponse):\n    \"\"\"Custom JSON response class for pretty-printing JSON\"\"\"\n\n    media_type = \"application/json\"\n\n    def render(self, content: typing.Any) -> bytes:\n        return json.dumps(content, ensure_ascii=False, allow_nan=False, indent=4, separators=(\", \", \": \")).encode(\"utf-8\")\n\n","chunkId":"audio_separator/remote/deploy_modal.py:10"}],[499,{"filePath":"audio_separator/remote/deploy_modal.py","startLine":148,"endLine":161,"chunkText":"        return json.dumps(content, ensure_ascii=False, allow_nan=False, indent=4, separators=(\", \", \": \")).encode(\"utf-8\")\n\n\n@app.function(image=image, gpu=\"ANY\", timeout=1200, volumes={\"/storage\": volume, \"/models\": models_volume}, scaledown_window=300)\ndef separate_audio_function(\n    audio_data: bytes,\n    filename: str,\n    models: Optional[list] = None,\n    task_id: Optional[str] = None,\n    # Separator parameters\n    output_format: str = \"flac\",\n    output_bitrate: Optional[str] = None,\n    normalization_threshold: float = 0.9,\n    amplification_threshold: float = 0.0,","chunkId":"audio_separator/remote/deploy_modal.py:11"}],[500,{"filePath":"audio_separator/remote/deploy_modal.py","startLine":159,"endLine":175,"chunkText":"    output_bitrate: Optional[str] = None,\n    normalization_threshold: float = 0.9,\n    amplification_threshold: float = 0.0,\n    output_single_stem: Optional[str] = None,\n    invert_using_spec: bool = False,\n    sample_rate: int = 44100,\n    use_soundfile: bool = False,\n    use_autocast: bool = False,\n    custom_output_names: Optional[dict] = None,\n    # MDX parameters\n    mdx_segment_size: int = 256,\n    mdx_overlap: float = 0.25,\n    mdx_batch_size: int = 1,\n    mdx_hop_length: int = 1024,\n    mdx_enable_denoise: bool = False,\n    # VR parameters\n    vr_batch_size: int = 1,","chunkId":"audio_separator/remote/deploy_modal.py:12"}],[501,{"filePath":"audio_separator/remote/deploy_modal.py","startLine":173,"endLine":190,"chunkText":"    mdx_enable_denoise: bool = False,\n    # VR parameters\n    vr_batch_size: int = 1,\n    vr_window_size: int = 512,\n    vr_aggression: int = 5,\n    vr_enable_tta: bool = False,\n    vr_high_end_process: bool = False,\n    vr_enable_post_process: bool = False,\n    vr_post_process_threshold: float = 0.2,\n    # Demucs parameters\n    demucs_segment_size: str = \"Default\",\n    demucs_shifts: int = 2,\n    demucs_overlap: float = 0.25,\n    demucs_segments_enabled: bool = True,\n    # MDXC parameters\n    mdxc_segment_size: int = 256,\n    mdxc_override_model_segment_size: bool = False,\n    mdxc_overlap: int = 8,","chunkId":"audio_separator/remote/deploy_modal.py:13"}],[502,{"filePath":"audio_separator/remote/deploy_modal.py","startLine":188,"endLine":206,"chunkText":"    mdxc_segment_size: int = 256,\n    mdxc_override_model_segment_size: bool = False,\n    mdxc_overlap: int = 8,\n    mdxc_batch_size: int = 1,\n    mdxc_pitch_shift: int = 0,\n) -> dict:\n    \"\"\"\n    Separate audio into stems using one or more models\n    \"\"\"\n    from audio_separator.separator import Separator\n\n    if task_id is None:\n        task_id = str(uuid.uuid4())\n\n    # Default to single default model if no models specified\n    if models is None or len(models) == 0:\n        models = [None]  # None will use separator's default\n\n    all_output_files = {}  # Dictionary mapping file hashes to filenames","chunkId":"audio_separator/remote/deploy_modal.py:14"}],[503,{"filePath":"audio_separator/remote/deploy_modal.py","startLine":204,"endLine":219,"chunkText":"        models = [None]  # None will use separator's default\n\n    all_output_files = {}  # Dictionary mapping file hashes to filenames\n    models_used = []\n    current_model_index = 0\n    total_models = len(models)\n\n    def update_job_status(status: str, progress: int = 0, error: str = None, files: list = None):\n        \"\"\"Update job status in Modal Dict\"\"\"\n        status_data = {\n            \"task_id\": task_id,\n            \"status\": status,\n            \"progress\": progress,\n            \"original_filename\": filename,\n            \"models_used\": models_used,\n            \"total_models\": total_models,","chunkId":"audio_separator/remote/deploy_modal.py:15"}],[504,{"filePath":"audio_separator/remote/deploy_modal.py","startLine":217,"endLine":232,"chunkText":"            \"original_filename\": filename,\n            \"models_used\": models_used,\n            \"total_models\": total_models,\n            \"current_model_index\": current_model_index,\n            \"files\": files or [],\n        }\n        if error:\n            status_data[\"error\"] = error\n\n        # Access Modal Dict by name within function scope\n        job_status = modal.Dict.from_name(\"audio-separator-job-status\", create_if_missing=True)\n        job_status[task_id] = status_data\n\n    try:\n        # Ensure storage directories exist\n        os.makedirs(\"/storage/uploads\", exist_ok=True)","chunkId":"audio_separator/remote/deploy_modal.py:16"}],[505,{"filePath":"audio_separator/remote/deploy_modal.py","startLine":230,"endLine":245,"chunkText":"    try:\n        # Ensure storage directories exist\n        os.makedirs(\"/storage/uploads\", exist_ok=True)\n        os.makedirs(\"/storage/outputs\", exist_ok=True)\n        os.makedirs(\"/models\", exist_ok=True)\n\n        # Update status: starting\n        update_job_status(\"processing\", 5)\n\n        # Create output directory\n        output_dir = f\"/storage/outputs/{task_id}\"\n        os.makedirs(output_dir, exist_ok=True)\n\n        # Write uploaded file directly to output directory with original filename\n        input_file_path = os.path.join(output_dir, filename)\n        with open(input_file_path, \"wb\") as f:","chunkId":"audio_separator/remote/deploy_modal.py:17"}],[506,{"filePath":"audio_separator/remote/deploy_modal.py","startLine":243,"endLine":255,"chunkText":"        # Write uploaded file directly to output directory with original filename\n        input_file_path = os.path.join(output_dir, filename)\n        with open(input_file_path, \"wb\") as f:\n            f.write(audio_data)\n\n        update_job_status(\"processing\", 10)\n\n        # Process each model\n        for model_index, model_name in enumerate(models):\n            current_model_index = model_index\n            base_progress = 10 + (model_index * 80 // total_models)\n            model_progress_range = 80 // total_models\n","chunkId":"audio_separator/remote/deploy_modal.py:18"}],[507,{"filePath":"audio_separator/remote/deploy_modal.py","startLine":253,"endLine":264,"chunkText":"            base_progress = 10 + (model_index * 80 // total_models)\n            model_progress_range = 80 // total_models\n\n            print(f\"Processing model {model_index + 1}/{total_models}: {model_name or 'default'}\")\n            update_job_status(\"processing\", base_progress + (model_progress_range // 4))\n\n            # Initialize separator with all the provided parameters\n            separator = Separator(\n                log_level=logging.INFO,\n                model_file_dir=\"/models\",\n                output_dir=output_dir,\n                output_format=output_format,","chunkId":"audio_separator/remote/deploy_modal.py:19"}],[508,{"filePath":"audio_separator/remote/deploy_modal.py","startLine":262,"endLine":272,"chunkText":"                model_file_dir=\"/models\",\n                output_dir=output_dir,\n                output_format=output_format,\n                output_bitrate=output_bitrate,\n                normalization_threshold=normalization_threshold,\n                amplification_threshold=amplification_threshold,\n                output_single_stem=output_single_stem,\n                invert_using_spec=invert_using_spec,\n                sample_rate=sample_rate,\n                use_soundfile=use_soundfile,\n                use_autocast=use_autocast,","chunkId":"audio_separator/remote/deploy_modal.py:20"}],[509,{"filePath":"audio_separator/remote/deploy_modal.py","startLine":270,"endLine":279,"chunkText":"                sample_rate=sample_rate,\n                use_soundfile=use_soundfile,\n                use_autocast=use_autocast,\n                mdx_params={\"hop_length\": mdx_hop_length, \"segment_size\": mdx_segment_size, \"overlap\": mdx_overlap, \"batch_size\": mdx_batch_size, \"enable_denoise\": mdx_enable_denoise},\n                vr_params={\n                    \"batch_size\": vr_batch_size,\n                    \"window_size\": vr_window_size,\n                    \"aggression\": vr_aggression,\n                    \"enable_tta\": vr_enable_tta,\n                    \"enable_post_process\": vr_enable_post_process,","chunkId":"audio_separator/remote/deploy_modal.py:21"}],[510,{"filePath":"audio_separator/remote/deploy_modal.py","startLine":277,"endLine":285,"chunkText":"                    \"aggression\": vr_aggression,\n                    \"enable_tta\": vr_enable_tta,\n                    \"enable_post_process\": vr_enable_post_process,\n                    \"post_process_threshold\": vr_post_process_threshold,\n                    \"high_end_process\": vr_high_end_process,\n                },\n                demucs_params={\"segment_size\": demucs_segment_size, \"shifts\": demucs_shifts, \"overlap\": demucs_overlap, \"segments_enabled\": demucs_segments_enabled},\n                mdxc_params={\n                    \"segment_size\": mdxc_segment_size,","chunkId":"audio_separator/remote/deploy_modal.py:22"}],[511,{"filePath":"audio_separator/remote/deploy_modal.py","startLine":283,"endLine":293,"chunkText":"                demucs_params={\"segment_size\": demucs_segment_size, \"shifts\": demucs_shifts, \"overlap\": demucs_overlap, \"segments_enabled\": demucs_segments_enabled},\n                mdxc_params={\n                    \"segment_size\": mdxc_segment_size,\n                    \"batch_size\": mdxc_batch_size,\n                    \"overlap\": mdxc_overlap,\n                    \"override_model_segment_size\": mdxc_override_model_segment_size,\n                    \"pitch_shift\": mdxc_pitch_shift,\n                },\n            )\n\n            # Load the model","chunkId":"audio_separator/remote/deploy_modal.py:23"}],[512,{"filePath":"audio_separator/remote/deploy_modal.py","startLine":291,"endLine":304,"chunkText":"            )\n\n            # Load the model\n            update_job_status(\"processing\", base_progress + (model_progress_range // 2))\n            if model_name:\n                print(f\"Loading specified model: {model_name}\")\n                separator.load_model(model_name)\n                models_used.append(model_name)\n            else:\n                print(f\"No model specified, using default model\")\n                separator.load_model()\n                models_used.append(\"default\")\n\n            # Perform separation","chunkId":"audio_separator/remote/deploy_modal.py:24"}],[513,{"filePath":"audio_separator/remote/deploy_modal.py","startLine":302,"endLine":312,"chunkText":"                models_used.append(\"default\")\n\n            # Perform separation\n            update_job_status(\"processing\", base_progress + (3 * model_progress_range // 4))\n            print(f\"Separating audio file: {filename} with model: {models_used[-1]}\")\n\n            # Create custom output names for this model if multiple models\n            model_custom_output_names = None\n            if total_models > 1 and custom_output_names:\n                # Add model name suffix to custom output names\n                model_suffix = f\"_{models_used[-1].replace('.', '_').replace('/', '_')}\"","chunkId":"audio_separator/remote/deploy_modal.py:25"}],[514,{"filePath":"audio_separator/remote/deploy_modal.py","startLine":310,"endLine":319,"chunkText":"            if total_models > 1 and custom_output_names:\n                # Add model name suffix to custom output names\n                model_suffix = f\"_{models_used[-1].replace('.', '_').replace('/', '_')}\"\n                model_custom_output_names = {stem: f\"{name}{model_suffix}\" for stem, name in custom_output_names.items()}\n            elif custom_output_names:\n                model_custom_output_names = custom_output_names\n\n            output_files = separator.separate(input_file_path, custom_output_names=model_custom_output_names)\n\n            if not output_files:","chunkId":"audio_separator/remote/deploy_modal.py:26"}],[515,{"filePath":"audio_separator/remote/deploy_modal.py","startLine":317,"endLine":325,"chunkText":"            output_files = separator.separate(input_file_path, custom_output_names=model_custom_output_names)\n\n            if not output_files:\n                error_msg = f\"Separation with model {models_used[-1]} completed but produced no output files\"\n                print(f\"❌ {error_msg}\")\n                update_job_status(\"error\", 0, error=error_msg)\n                return {\"task_id\": task_id, \"status\": \"error\", \"error\": error_msg, \"models_used\": models_used, \"original_filename\": filename}\n\n            # Convert full paths to filenames and add to collection with hashes","chunkId":"audio_separator/remote/deploy_modal.py:27"}],[516,{"filePath":"audio_separator/remote/deploy_modal.py","startLine":323,"endLine":332,"chunkText":"                return {\"task_id\": task_id, \"status\": \"error\", \"error\": error_msg, \"models_used\": models_used, \"original_filename\": filename}\n\n            # Convert full paths to filenames and add to collection with hashes\n            model_result_files = [os.path.basename(f) for f in output_files]\n            for filename in model_result_files:\n                file_hash = generate_file_hash(filename)\n                all_output_files[file_hash] = filename\n            print(f\"Model {models_used[-1]} produced {len(model_result_files)} files: {model_result_files}\")\n\n        # Update final status","chunkId":"audio_separator/remote/deploy_modal.py:28"}],[517,{"filePath":"audio_separator/remote/deploy_modal.py","startLine":330,"endLine":344,"chunkText":"            print(f\"Model {models_used[-1]} produced {len(model_result_files)} files: {model_result_files}\")\n\n        # Update final status\n        update_job_status(\"processing\", 95)\n        print(f\"All separations completed. Total output files: {len(all_output_files)}\")\n\n        # Commit volume changes\n        volume.commit()\n        models_volume.commit()\n\n        # Update status: completed\n        update_job_status(\"completed\", 100, files=all_output_files)\n\n        return {\"task_id\": task_id, \"status\": \"completed\", \"files\": all_output_files, \"models_used\": models_used, \"original_filename\": filename}\n","chunkId":"audio_separator/remote/deploy_modal.py:29"}],[518,{"filePath":"audio_separator/remote/deploy_modal.py","startLine":342,"endLine":351,"chunkText":"\n        return {\"task_id\": task_id, \"status\": \"completed\", \"files\": all_output_files, \"models_used\": models_used, \"original_filename\": filename}\n\n    except FileNotFoundError as e:\n        print(f\"Input file not found: {str(e)}\")\n        traceback.print_exc()\n        try:\n            update_job_status(\"error\", 0, error=f\"Input file not found: {str(e)}\")\n        except Exception as status_error:\n            print(f\"WARNING: Failed to update job status: {status_error}\")","chunkId":"audio_separator/remote/deploy_modal.py:30"}],[519,{"filePath":"audio_separator/remote/deploy_modal.py","startLine":349,"endLine":359,"chunkText":"            update_job_status(\"error\", 0, error=f\"Input file not found: {str(e)}\")\n        except Exception as status_error:\n            print(f\"WARNING: Failed to update job status: {status_error}\")\n        return {\"task_id\": task_id, \"status\": \"error\", \"error\": f\"Input file not found: {str(e)}\", \"models_used\": models_used, \"original_filename\": filename}\n\n    except ValueError as e:\n        print(f\"Invalid input or configuration: {str(e)}\")\n        traceback.print_exc()\n        try:\n            update_job_status(\"error\", 0, error=f\"Invalid input: {str(e)}\")\n        except Exception as status_error:","chunkId":"audio_separator/remote/deploy_modal.py:31"}],[520,{"filePath":"audio_separator/remote/deploy_modal.py","startLine":357,"endLine":368,"chunkText":"        try:\n            update_job_status(\"error\", 0, error=f\"Invalid input: {str(e)}\")\n        except Exception as status_error:\n            print(f\"WARNING: Failed to update job status: {status_error}\")\n        return {\"task_id\": task_id, \"status\": \"error\", \"error\": f\"Invalid input: {str(e)}\", \"models_used\": models_used, \"original_filename\": filename}\n\n    except Exception as e:\n        print(f\"Unexpected error during separation: {str(e)}\")\n        traceback.print_exc()\n        try:\n            update_job_status(\"error\", 0, error=str(e))\n        except Exception as status_error:","chunkId":"audio_separator/remote/deploy_modal.py:32"}],[521,{"filePath":"audio_separator/remote/deploy_modal.py","startLine":366,"endLine":380,"chunkText":"        try:\n            update_job_status(\"error\", 0, error=str(e))\n        except Exception as status_error:\n            print(f\"WARNING: Failed to update job status: {status_error}\")\n\n        # Clean up on error\n        if os.path.exists(input_file_path):\n            os.unlink(input_file_path)\n        if os.path.exists(output_dir):\n            shutil.rmtree(output_dir, ignore_errors=True)\n\n        return {\"task_id\": task_id, \"status\": \"error\", \"error\": str(e), \"models_used\": models_used, \"original_filename\": filename}\n\n\n@app.function(image=image, timeout=300, volumes={\"/storage\": volume})","chunkId":"audio_separator/remote/deploy_modal.py:33"}],[522,{"filePath":"audio_separator/remote/deploy_modal.py","startLine":378,"endLine":392,"chunkText":"\n\n@app.function(image=image, timeout=300, volumes={\"/storage\": volume})\ndef get_job_status_function(task_id: str) -> dict:\n    \"\"\"\n    Get the status of a separation job\n    \"\"\"\n    try:\n        # Access Modal Dict by name within function scope\n        job_status = modal.Dict.from_name(\"audio-separator-job-status\", create_if_missing=True)\n\n        if task_id in job_status:\n            return job_status[task_id]\n        else:\n            # Job not found - might be initializing or doesn't exist","chunkId":"audio_separator/remote/deploy_modal.py:34"}],[523,{"filePath":"audio_separator/remote/deploy_modal.py","startLine":390,"endLine":400,"chunkText":"            return job_status[task_id]\n        else:\n            # Job not found - might be initializing or doesn't exist\n            return {\"task_id\": task_id, \"status\": \"not_found\", \"progress\": 0, \"error\": \"Job not found - may have been cleaned up or never existed\"}\n    except Exception as e:\n        print(f\"ERROR: Failed to access job status for {task_id}: {str(e)}\")\n        return {\"task_id\": task_id, \"status\": \"error\", \"error\": f\"Failed to read status: {str(e)}\"}\n\n\n@app.function(image=image, timeout=300, volumes={\"/storage\": volume})\ndef get_file_function(task_id: str, filename: str) -> bytes:","chunkId":"audio_separator/remote/deploy_modal.py:35"}],[524,{"filePath":"audio_separator/remote/deploy_modal.py","startLine":399,"endLine":415,"chunkText":"@app.function(image=image, timeout=300, volumes={\"/storage\": volume})\ndef get_file_function(task_id: str, filename: str) -> bytes:\n    \"\"\"\n    Retrieve a separated audio file\n    \"\"\"\n    file_path = f\"/storage/outputs/{task_id}/{filename}\"\n\n    if not os.path.exists(file_path):\n        raise FileNotFoundError(f\"File not found: {filename}\")\n\n    with open(file_path, \"rb\") as f:\n        return f.read()\n\n\n@app.function(image=image, timeout=300, volumes={\"/storage\": volume})\ndef get_file_by_hash_function(task_id: str, file_hash: str) -> tuple[bytes, str]:\n    \"\"\"","chunkId":"audio_separator/remote/deploy_modal.py:36"}],[525,{"filePath":"audio_separator/remote/deploy_modal.py","startLine":413,"endLine":424,"chunkText":"@app.function(image=image, timeout=300, volumes={\"/storage\": volume})\ndef get_file_by_hash_function(task_id: str, file_hash: str) -> tuple[bytes, str]:\n    \"\"\"\n    Retrieve a separated audio file by its hash identifier.\n    Returns tuple of (file_data, actual_filename)\n    \"\"\"\n    print(f\"🔍 get_file_by_hash_function called - Task ID: {task_id}, File hash: {file_hash}\")\n    \n    # Reload the volume to ensure we see the latest files written by other function executions\n    print(f\"🔍 Reloading volume to see latest files...\")\n    volume.reload()\n    ","chunkId":"audio_separator/remote/deploy_modal.py:37"}],[526,{"filePath":"audio_separator/remote/deploy_modal.py","startLine":422,"endLine":436,"chunkText":"    print(f\"🔍 Reloading volume to see latest files...\")\n    volume.reload()\n    \n    # Access Modal Dict to get the job status with file hash mappings\n    job_status = modal.Dict.from_name(\"audio-separator-job-status\", create_if_missing=True)\n    \n    if task_id not in job_status:\n        print(f\"❌ Task not found in job_status: {task_id}\")\n        raise FileNotFoundError(f\"Task not found: {task_id}\")\n    \n    status_data = job_status[task_id]\n    files_dict = status_data.get(\"files\", {})\n    print(f\"🔍 Retrieved files_dict: {files_dict}\")\n    print(f\"🔍 files_dict type: {type(files_dict)}\")\n    ","chunkId":"audio_separator/remote/deploy_modal.py:38"}],[527,{"filePath":"audio_separator/remote/deploy_modal.py","startLine":434,"endLine":444,"chunkText":"    print(f\"🔍 Retrieved files_dict: {files_dict}\")\n    print(f\"🔍 files_dict type: {type(files_dict)}\")\n    \n    # Check if files is still a list (backward compatibility)\n    if isinstance(files_dict, list):\n        print(f\"🔍 Using legacy list format with {len(files_dict)} files\")\n        # For backward compatibility, try to find file by regenerating hash\n        for filename in files_dict:\n            generated_hash = generate_file_hash(filename)\n            print(f\"🔍 Checking filename '{filename}' -> hash '{generated_hash}' vs requested '{file_hash}'\")\n            if generated_hash == file_hash:","chunkId":"audio_separator/remote/deploy_modal.py:39"}],[528,{"filePath":"audio_separator/remote/deploy_modal.py","startLine":442,"endLine":451,"chunkText":"            generated_hash = generate_file_hash(filename)\n            print(f\"🔍 Checking filename '{filename}' -> hash '{generated_hash}' vs requested '{file_hash}'\")\n            if generated_hash == file_hash:\n                file_path = f\"/storage/outputs/{task_id}/{filename}\"\n                print(f\"🔍 Hash match! Checking file path: {file_path}\")\n                if os.path.exists(file_path):\n                    print(f\"✅ File exists, returning content\")\n                    with open(file_path, \"rb\") as f:\n                        return f.read(), filename\n                else:","chunkId":"audio_separator/remote/deploy_modal.py:40"}],[529,{"filePath":"audio_separator/remote/deploy_modal.py","startLine":449,"endLine":460,"chunkText":"                    with open(file_path, \"rb\") as f:\n                        return f.read(), filename\n                else:\n                    print(f\"❌ File does not exist at path: {file_path}\")\n        raise FileNotFoundError(f\"File with hash {file_hash} not found in legacy format\")\n    \n    # Normal case: files is a dictionary mapping hashes to filenames\n    print(f\"🔍 Using new hash format with {len(files_dict)} files\")\n    print(f\"🔍 Available hashes: {list(files_dict.keys())}\")\n    \n    if file_hash not in files_dict:\n        print(f\"❌ Hash {file_hash} not found in files_dict\")","chunkId":"audio_separator/remote/deploy_modal.py:41"}],[530,{"filePath":"audio_separator/remote/deploy_modal.py","startLine":458,"endLine":471,"chunkText":"    \n    if file_hash not in files_dict:\n        print(f\"❌ Hash {file_hash} not found in files_dict\")\n        raise FileNotFoundError(f\"File with hash {file_hash} not found\")\n    \n    actual_filename = files_dict[file_hash]\n    file_path = f\"/storage/outputs/{task_id}/{actual_filename}\"\n    print(f\"🔍 Hash found! Filename: '{actual_filename}'\")\n    print(f\"🔍 Checking file path: {file_path}\")\n\n    if not os.path.exists(file_path):\n        print(f\"❌ File does not exist at path: {file_path}\")\n        # List what files actually exist in the directory\n        task_dir = f\"/storage/outputs/{task_id}\"","chunkId":"audio_separator/remote/deploy_modal.py:42"}],[531,{"filePath":"audio_separator/remote/deploy_modal.py","startLine":469,"endLine":479,"chunkText":"        print(f\"❌ File does not exist at path: {file_path}\")\n        # List what files actually exist in the directory\n        task_dir = f\"/storage/outputs/{task_id}\"\n        if os.path.exists(task_dir):\n            actual_files = os.listdir(task_dir)\n            print(f\"🔍 Files actually in directory ({len(actual_files)}):\")\n            for i, actual_file in enumerate(actual_files):\n                print(f\"  [{i}] '{actual_file}'\")\n                if actual_file == actual_filename:\n                    print(f\"    ✅ EXACT MATCH found!\")\n        else:","chunkId":"audio_separator/remote/deploy_modal.py:43"}],[532,{"filePath":"audio_separator/remote/deploy_modal.py","startLine":477,"endLine":492,"chunkText":"                if actual_file == actual_filename:\n                    print(f\"    ✅ EXACT MATCH found!\")\n        else:\n            print(f\"❌ Task directory does not exist: {task_dir}\")\n        raise FileNotFoundError(f\"File not found: {actual_filename}\")\n\n    print(f\"✅ File exists, returning content\")\n    with open(file_path, \"rb\") as f:\n        return f.read(), actual_filename\n\n\n@app.function(image=image, timeout=60, volumes={\"/models\": models_volume})\ndef list_available_models() -> dict:\n    \"\"\"\n    List available separation models using the same approach as CLI\n    \"\"\"","chunkId":"audio_separator/remote/deploy_modal.py:44"}],[533,{"filePath":"audio_separator/remote/deploy_modal.py","startLine":490,"endLine":510,"chunkText":"    \"\"\"\n    List available separation models using the same approach as CLI\n    \"\"\"\n    from audio_separator.separator import Separator\n\n    # Use the persistent model directory\n    model_dir = \"/models\"\n\n    # Ensure the model directory exists\n    os.makedirs(model_dir, exist_ok=True)\n\n    # Use the same approach as the CLI: create separator with info_only=True\n    separator = Separator(info_only=True, model_file_dir=model_dir)\n\n    # Get the list of supported models\n    model_list = separator.list_supported_model_files()\n\n    # Return the full model dictionary\n    return model_list\n\n","chunkId":"audio_separator/remote/deploy_modal.py:45"}],[534,{"filePath":"audio_separator/remote/deploy_modal.py","startLine":508,"endLine":526,"chunkText":"    return model_list\n\n\n@app.function(image=image, timeout=60, volumes={\"/models\": models_volume})\ndef get_simplified_models(filter_sort_by: str = None) -> dict:\n    \"\"\"\n    Get simplified model list using the same approach as CLI --list_models\n    \"\"\"\n    from audio_separator.separator import Separator\n\n    # Use the persistent model directory\n    model_dir = \"/models\"\n\n    # Ensure the model directory exists\n    os.makedirs(model_dir, exist_ok=True)\n\n    # Use the same approach as the CLI: create separator with info_only=True\n    separator = Separator(info_only=True, model_file_dir=model_dir)\n","chunkId":"audio_separator/remote/deploy_modal.py:46"}],[535,{"filePath":"audio_separator/remote/deploy_modal.py","startLine":524,"endLine":538,"chunkText":"    # Use the same approach as the CLI: create separator with info_only=True\n    separator = Separator(info_only=True, model_file_dir=model_dir)\n\n    # Get the simplified model list\n    simplified_models = separator.get_simplified_model_list(filter_sort_by=filter_sort_by)\n\n    return simplified_models\n\n\nweb_app = FastAPI(title=\"Audio Separator API\", description=\"Separate vocals from instrumental tracks using AI\", version=AUDIO_SEPARATOR_VERSION)\n\nweb_app.add_middleware(CORSMiddleware, allow_origins=[\"*\"], allow_credentials=True, allow_methods=[\"*\"], allow_headers=[\"*\"])\n\n\n@web_app.post(\"/separate\")","chunkId":"audio_separator/remote/deploy_modal.py:47"}],[536,{"filePath":"audio_separator/remote/deploy_modal.py","startLine":536,"endLine":545,"chunkText":"\n\n@web_app.post(\"/separate\")\nasync def separate_audio(\n    file: UploadFile = File(..., description=\"Audio file to separate\"),\n    # Model selection - support both single model and multiple models\n    model: Optional[str] = Form(None, description=\"Single model to use for separation (for backwards compatibility)\"),\n    models: Optional[str] = Form(None, description='JSON list of models to use for separation, e.g. [\"model1.ckpt\", \"model2.onnx\"]'),\n    # Output parameters\n    output_format: str = Form(\"flac\", description=\"Output format for separated files\"),","chunkId":"audio_separator/remote/deploy_modal.py:48"}],[537,{"filePath":"audio_separator/remote/deploy_modal.py","startLine":543,"endLine":548,"chunkText":"    models: Optional[str] = Form(None, description='JSON list of models to use for separation, e.g. [\"model1.ckpt\", \"model2.onnx\"]'),\n    # Output parameters\n    output_format: str = Form(\"flac\", description=\"Output format for separated files\"),\n    output_bitrate: Optional[str] = Form(None, description=\"Output bitrate for separated files\"),\n    normalization_threshold: float = Form(0.9, description=\"Max peak amplitude to normalize audio to\"),\n    amplification_threshold: float = Form(0.0, description=\"Min peak amplitude to amplify audio to\"),","chunkId":"audio_separator/remote/deploy_modal.py:49"}],[538,{"filePath":"audio_separator/remote/deploy_modal.py","startLine":546,"endLine":551,"chunkText":"    output_bitrate: Optional[str] = Form(None, description=\"Output bitrate for separated files\"),\n    normalization_threshold: float = Form(0.9, description=\"Max peak amplitude to normalize audio to\"),\n    amplification_threshold: float = Form(0.0, description=\"Min peak amplitude to amplify audio to\"),\n    output_single_stem: Optional[str] = Form(None, description=\"Output only single stem (e.g. Vocals, Instrumental)\"),\n    invert_using_spec: bool = Form(False, description=\"Invert secondary stem using spectrogram\"),\n    sample_rate: int = Form(44100, description=\"Sample rate of output audio\"),","chunkId":"audio_separator/remote/deploy_modal.py:50"}],[539,{"filePath":"audio_separator/remote/deploy_modal.py","startLine":549,"endLine":555,"chunkText":"    output_single_stem: Optional[str] = Form(None, description=\"Output only single stem (e.g. Vocals, Instrumental)\"),\n    invert_using_spec: bool = Form(False, description=\"Invert secondary stem using spectrogram\"),\n    sample_rate: int = Form(44100, description=\"Sample rate of output audio\"),\n    use_soundfile: bool = Form(False, description=\"Use soundfile for output writing\"),\n    use_autocast: bool = Form(False, description=\"Use PyTorch autocast for faster inference\"),\n    custom_output_names: Optional[str] = Form(None, description=\"JSON dict of custom output names\"),\n    # MDX parameters","chunkId":"audio_separator/remote/deploy_modal.py:51"}],[540,{"filePath":"audio_separator/remote/deploy_modal.py","startLine":553,"endLine":561,"chunkText":"    use_autocast: bool = Form(False, description=\"Use PyTorch autocast for faster inference\"),\n    custom_output_names: Optional[str] = Form(None, description=\"JSON dict of custom output names\"),\n    # MDX parameters\n    mdx_segment_size: int = Form(256, description=\"MDX segment size\"),\n    mdx_overlap: float = Form(0.25, description=\"MDX overlap\"),\n    mdx_batch_size: int = Form(1, description=\"MDX batch size\"),\n    mdx_hop_length: int = Form(1024, description=\"MDX hop length\"),\n    mdx_enable_denoise: bool = Form(False, description=\"Enable MDX denoising\"),\n    # VR parameters","chunkId":"audio_separator/remote/deploy_modal.py:52"}],[541,{"filePath":"audio_separator/remote/deploy_modal.py","startLine":559,"endLine":566,"chunkText":"    mdx_hop_length: int = Form(1024, description=\"MDX hop length\"),\n    mdx_enable_denoise: bool = Form(False, description=\"Enable MDX denoising\"),\n    # VR parameters\n    vr_batch_size: int = Form(1, description=\"VR batch size\"),\n    vr_window_size: int = Form(512, description=\"VR window size\"),\n    vr_aggression: int = Form(5, description=\"VR aggression\"),\n    vr_enable_tta: bool = Form(False, description=\"Enable VR Test-Time-Augmentation\"),\n    vr_high_end_process: bool = Form(False, description=\"Enable VR high end processing\"),","chunkId":"audio_separator/remote/deploy_modal.py:53"}],[542,{"filePath":"audio_separator/remote/deploy_modal.py","startLine":564,"endLine":571,"chunkText":"    vr_aggression: int = Form(5, description=\"VR aggression\"),\n    vr_enable_tta: bool = Form(False, description=\"Enable VR Test-Time-Augmentation\"),\n    vr_high_end_process: bool = Form(False, description=\"Enable VR high end processing\"),\n    vr_enable_post_process: bool = Form(False, description=\"Enable VR post processing\"),\n    vr_post_process_threshold: float = Form(0.2, description=\"VR post process threshold\"),\n    # Demucs parameters\n    demucs_segment_size: str = Form(\"Default\", description=\"Demucs segment size\"),\n    demucs_shifts: int = Form(2, description=\"Demucs shifts\"),","chunkId":"audio_separator/remote/deploy_modal.py:54"}],[543,{"filePath":"audio_separator/remote/deploy_modal.py","startLine":569,"endLine":577,"chunkText":"    # Demucs parameters\n    demucs_segment_size: str = Form(\"Default\", description=\"Demucs segment size\"),\n    demucs_shifts: int = Form(2, description=\"Demucs shifts\"),\n    demucs_overlap: float = Form(0.25, description=\"Demucs overlap\"),\n    demucs_segments_enabled: bool = Form(True, description=\"Enable Demucs segments\"),\n    # MDXC parameters\n    mdxc_segment_size: int = Form(256, description=\"MDXC segment size\"),\n    mdxc_override_model_segment_size: bool = Form(False, description=\"Override MDXC model segment size\"),\n    mdxc_overlap: int = Form(8, description=\"MDXC overlap\"),","chunkId":"audio_separator/remote/deploy_modal.py:55"}],[544,{"filePath":"audio_separator/remote/deploy_modal.py","startLine":575,"endLine":586,"chunkText":"    mdxc_segment_size: int = Form(256, description=\"MDXC segment size\"),\n    mdxc_override_model_segment_size: bool = Form(False, description=\"Override MDXC model segment size\"),\n    mdxc_overlap: int = Form(8, description=\"MDXC overlap\"),\n    mdxc_batch_size: int = Form(1, description=\"MDXC batch size\"),\n    mdxc_pitch_shift: int = Form(0, description=\"MDXC pitch shift\"),\n) -> dict:\n    \"\"\"\n    Upload an audio file and separate it into stems using one or more models (asynchronous processing)\n    \"\"\"\n    if not file.filename:\n        raise HTTPException(status_code=400, detail=\"No file provided\")\n","chunkId":"audio_separator/remote/deploy_modal.py:56"}],[545,{"filePath":"audio_separator/remote/deploy_modal.py","startLine":584,"endLine":598,"chunkText":"    if not file.filename:\n        raise HTTPException(status_code=400, detail=\"No file provided\")\n\n    try:\n        # Parse models parameter\n        models_list = None\n        if models:\n            try:\n                models_list = json.loads(models)\n                if not isinstance(models_list, list):\n                    raise ValueError(\"Models must be a JSON list\")\n            except json.JSONDecodeError as e:\n                raise HTTPException(status_code=400, detail=f\"Invalid JSON in models parameter: {e}\")\n        elif model:\n            # Backwards compatibility: single model parameter","chunkId":"audio_separator/remote/deploy_modal.py:57"}],[546,{"filePath":"audio_separator/remote/deploy_modal.py","startLine":596,"endLine":607,"chunkText":"                raise HTTPException(status_code=400, detail=f\"Invalid JSON in models parameter: {e}\")\n        elif model:\n            # Backwards compatibility: single model parameter\n            models_list = [model]\n        # If neither provided, models_list stays None (will use default)\n\n        # Parse custom_output_names if provided\n        custom_output_names_dict = None\n        if custom_output_names:\n            try:\n                custom_output_names_dict = json.loads(custom_output_names)\n                if not isinstance(custom_output_names_dict, dict):","chunkId":"audio_separator/remote/deploy_modal.py:58"}],[547,{"filePath":"audio_separator/remote/deploy_modal.py","startLine":605,"endLine":619,"chunkText":"            try:\n                custom_output_names_dict = json.loads(custom_output_names)\n                if not isinstance(custom_output_names_dict, dict):\n                    raise ValueError(\"Custom output names must be a JSON object\")\n            except json.JSONDecodeError as e:\n                raise HTTPException(status_code=400, detail=f\"Invalid JSON in custom_output_names parameter: {e}\")\n\n        # Read file data\n        audio_data = await file.read()\n\n        # Generate task ID\n        task_id = str(uuid.uuid4())\n\n        # Create initial status in Modal Dict\n        initial_status = {","chunkId":"audio_separator/remote/deploy_modal.py:59"}],[548,{"filePath":"audio_separator/remote/deploy_modal.py","startLine":617,"endLine":631,"chunkText":"\n        # Create initial status in Modal Dict\n        initial_status = {\n            \"task_id\": task_id,\n            \"status\": \"submitted\",\n            \"progress\": 0,\n            \"original_filename\": file.filename,\n            \"models_used\": models_list or [\"default\"],\n            \"total_models\": len(models_list) if models_list else 1,\n            \"current_model_index\": 0,\n            \"files\": [],\n        }\n\n        # Access Modal Dict by name to ensure proper scope\n        job_status = modal.Dict.from_name(\"audio-separator-job-status\", create_if_missing=True)","chunkId":"audio_separator/remote/deploy_modal.py:60"}],[549,{"filePath":"audio_separator/remote/deploy_modal.py","startLine":629,"endLine":645,"chunkText":"\n        # Access Modal Dict by name to ensure proper scope\n        job_status = modal.Dict.from_name(\"audio-separator-job-status\", create_if_missing=True)\n        job_status[task_id] = initial_status\n\n        # Submit job asynchronously with all parameters\n        separate_audio_function.spawn(\n            audio_data,\n            file.filename,\n            models_list,\n            task_id,\n            # Separator parameters\n            output_format,\n            output_bitrate,\n            normalization_threshold,\n            amplification_threshold,\n            output_single_stem,","chunkId":"audio_separator/remote/deploy_modal.py:61"}],[550,{"filePath":"audio_separator/remote/deploy_modal.py","startLine":643,"endLine":662,"chunkText":"            normalization_threshold,\n            amplification_threshold,\n            output_single_stem,\n            invert_using_spec,\n            sample_rate,\n            use_soundfile,\n            use_autocast,\n            custom_output_names_dict,\n            # MDX parameters\n            mdx_segment_size,\n            mdx_overlap,\n            mdx_batch_size,\n            mdx_hop_length,\n            mdx_enable_denoise,\n            # VR parameters\n            vr_batch_size,\n            vr_window_size,\n            vr_aggression,\n            vr_enable_tta,\n            vr_high_end_process,","chunkId":"audio_separator/remote/deploy_modal.py:62"}],[551,{"filePath":"audio_separator/remote/deploy_modal.py","startLine":660,"endLine":680,"chunkText":"            vr_aggression,\n            vr_enable_tta,\n            vr_high_end_process,\n            vr_enable_post_process,\n            vr_post_process_threshold,\n            # Demucs parameters\n            demucs_segment_size,\n            demucs_shifts,\n            demucs_overlap,\n            demucs_segments_enabled,\n            # MDXC parameters\n            mdxc_segment_size,\n            mdxc_override_model_segment_size,\n            mdxc_overlap,\n            mdxc_batch_size,\n            mdxc_pitch_shift,\n        )\n\n        return {\n            \"task_id\": task_id,\n            \"status\": \"submitted\",","chunkId":"audio_separator/remote/deploy_modal.py:63"}],[552,{"filePath":"audio_separator/remote/deploy_modal.py","startLine":678,"endLine":694,"chunkText":"        return {\n            \"task_id\": task_id,\n            \"status\": \"submitted\",\n            \"message\": \"Job submitted for processing. Use /status/{task_id} to check progress.\",\n            \"models_used\": models_list or [\"default\"],\n            \"total_models\": len(models_list) if models_list else 1,\n            \"original_filename\": file.filename,\n        }\n    except HTTPException:\n        raise\n    except Exception as e:\n        raise HTTPException(status_code=500, detail=f\"Separation failed: {str(e)}\") from e\n\n\n@web_app.get(\"/status/{task_id}\")\nasync def get_job_status(task_id: str) -> dict:\n    \"\"\"","chunkId":"audio_separator/remote/deploy_modal.py:64"}],[553,{"filePath":"audio_separator/remote/deploy_modal.py","startLine":692,"endLine":709,"chunkText":"@web_app.get(\"/status/{task_id}\")\nasync def get_job_status(task_id: str) -> dict:\n    \"\"\"\n    Get the status of a separation job\n    \"\"\"\n    try:\n        status_data = get_job_status_function.remote(task_id)\n        return status_data\n    except Exception as e:\n        raise HTTPException(status_code=500, detail=f\"Failed to get job status: {str(e)}\") from e\n\n\n@web_app.get(\"/download/{task_id}/{file_hash}\")\nasync def download_file(task_id: str, file_hash: str) -> Response:\n    \"\"\"\n    Download a separated audio file using its hash identifier\n    \"\"\"\n    try:","chunkId":"audio_separator/remote/deploy_modal.py:65"}],[554,{"filePath":"audio_separator/remote/deploy_modal.py","startLine":707,"endLine":721,"chunkText":"    Download a separated audio file using its hash identifier\n    \"\"\"\n    try:\n        file_data, actual_filename = get_file_by_hash_function.remote(task_id, file_hash)\n\n        # Detect file type from content\n        detected_type = filetype.guess(file_data)\n\n        if detected_type and detected_type.mime:\n            content_type = detected_type.mime\n        else:\n            # Log when we can't detect the file type\n            print(f\"WARNING: Could not detect MIME type for {actual_filename}, using generic type\")\n            content_type = \"application/octet-stream\"\n","chunkId":"audio_separator/remote/deploy_modal.py:66"}],[555,{"filePath":"audio_separator/remote/deploy_modal.py","startLine":719,"endLine":727,"chunkText":"            print(f\"WARNING: Could not detect MIME type for {actual_filename}, using generic type\")\n            content_type = \"application/octet-stream\"\n\n        # RFC 5987 encoding for Unicode filenames in Content-Disposition header\n        # Provide ASCII fallback for older clients, and UTF-8 encoded filename for modern clients\n        ascii_filename = \"\".join(c if ord(c) < 128 else \"_\" for c in actual_filename)\n        encoded_filename = quote(actual_filename, safe=\"\")\n        content_disposition = f\"attachment; filename=\\\"{ascii_filename}\\\"; filename*=UTF-8''{encoded_filename}\"\n","chunkId":"audio_separator/remote/deploy_modal.py:67"}],[556,{"filePath":"audio_separator/remote/deploy_modal.py","startLine":725,"endLine":737,"chunkText":"        encoded_filename = quote(actual_filename, safe=\"\")\n        content_disposition = f\"attachment; filename=\\\"{ascii_filename}\\\"; filename*=UTF-8''{encoded_filename}\"\n\n        return Response(content=file_data, media_type=content_type, headers={\"Content-Disposition\": content_disposition})\n\n    except FileNotFoundError as exc:\n        raise HTTPException(status_code=404, detail=\"File not found\") from exc\n    except Exception as e:\n        raise HTTPException(status_code=500, detail=f\"Download failed: {str(e)}\") from e\n\n\n@web_app.get(\"/models-json\")\nasync def get_available_models() -> PrettyJSONResponse","chunkId":"audio_separator/remote/deploy_modal.py:68"}],[557,{"filePath":"audio_separator/remote/deploy_modal.py","startLine":735,"endLine":754,"chunkText":"\n@web_app.get(\"/models-json\")\nasync def get_available_models() -> PrettyJSONResponse:\n    \"\"\"\n    Get list of available separation models\n    \"\"\"\n    models = list_available_models.remote()\n\n    # Return pretty-printed JSON for better readability\n    return PrettyJSONResponse(content=models)\n\n\n@web_app.get(\"/models\")\nasync def get_simplified_models_list(filter_sort_by: str = None) -> PlainTextResponse:\n    \"\"\"\n    Get simplified model list in plain text format (like CLI --list_models)\n    \"\"\"\n    models = get_simplified_models.remote(filter_sort_by=filter_sort_by)\n\n    if not models:","chunkId":"audio_separator/remote/deploy_modal.py:69"}],[558,{"filePath":"audio_separator/remote/deploy_modal.py","startLine":752,"endLine":762,"chunkText":"    models = get_simplified_models.remote(filter_sort_by=filter_sort_by)\n\n    if not models:\n        return PlainTextResponse(\"No models found\")\n\n    # Calculate maximum widths for each column\n    filename_width = max(len(\"Model Filename\"), max(len(filename) for filename in models.keys()))\n    arch_width = max(len(\"Arch\"), max(len(info[\"Type\"]) for info in models.values()))\n    stems_width = max(len(\"Output Stems (SDR)\"), max(len(\", \".join(info[\"Stems\"])) for info in models.values()))\n    name_width = max(len(\"Friendly Name\"), max(len(info[\"Name\"]) for info in models.values()))\n","chunkId":"audio_separator/remote/deploy_modal.py:70"}],[559,{"filePath":"audio_separator/remote/deploy_modal.py","startLine":760,"endLine":768,"chunkText":"    stems_width = max(len(\"Output Stems (SDR)\"), max(len(\", \".join(info[\"Stems\"])) for info in models.values()))\n    name_width = max(len(\"Friendly Name\"), max(len(info[\"Name\"]) for info in models.values()))\n\n    # Calculate total width for separator line\n    total_width = filename_width + arch_width + stems_width + name_width + 15  # 15 accounts for spacing between columns\n\n    # Format the output with dynamic widths and extra spacing\n    output_lines = []\n    output_lines.append(\"-\" * total_width)","chunkId":"audio_separator/remote/deploy_modal.py:71"}],[560,{"filePath":"audio_separator/remote/deploy_modal.py","startLine":766,"endLine":779,"chunkText":"    # Format the output with dynamic widths and extra spacing\n    output_lines = []\n    output_lines.append(\"-\" * total_width)\n    output_lines.append(f\"{'Model Filename':<{filename_width}}  {'Arch':<{arch_width}}  {'Output Stems (SDR)':<{stems_width}}  {'Friendly Name'}\")\n    output_lines.append(\"-\" * total_width)\n\n    for filename, info in models.items():\n        stems = \", \".join(info[\"Stems\"])\n        output_lines.append(f\"{filename:<{filename_width}}  {info['Type']:<{arch_width}}  {stems:<{stems_width}}  {info['Name']}\")\n\n    return PlainTextResponse(\"\\n\".join(output_lines))\n\n\n@web_app.get(\"/health\")","chunkId":"audio_separator/remote/deploy_modal.py:72"}],[561,{"filePath":"audio_separator/remote/deploy_modal.py","startLine":777,"endLine":796,"chunkText":"\n\n@web_app.get(\"/health\")\nasync def health_check() -> dict:\n    \"\"\"\n    Health check endpoint\n    \"\"\"\n    return {\"status\": \"healthy\", \"service\": \"audio-separator-api\", \"version\": AUDIO_SEPARATOR_VERSION}\n\n\n@web_app.get(\"/\")\nasync def root() -> dict:\n    \"\"\"\n    Root endpoint with API information\n    \"\"\"\n    return {\n        \"message\": \"Audio Separator API\",\n        \"version\": AUDIO_SEPARATOR_VERSION,\n        \"description\": (\"Separate vocals from instrumental tracks using AI - \" \"supports all formats and parameters that audio-separator CLI supports\"),\n        \"features\": [","chunkId":"audio_separator/remote/deploy_modal.py:73"}],[562,{"filePath":"audio_separator/remote/deploy_modal.py","startLine":794,"endLine":803,"chunkText":"        \"version\": AUDIO_SEPARATOR_VERSION,\n        \"description\": (\"Separate vocals from instrumental tracks using AI - \" \"supports all formats and parameters that audio-separator CLI supports\"),\n        \"features\": [\n            \"Multiple model processing in single job\",\n            \"Full separator parameter compatibility\",\n            \"Asynchronous processing with progress tracking\",\n            \"All MDX, VR, Demucs, and MDXC architectures supported\",\n            \"Custom output naming and format options\",\n        ],\n        \"endpoints\": {","chunkId":"audio_separator/remote/deploy_modal.py:74"}],[563,{"filePath":"audio_separator/remote/deploy_modal.py","startLine":801,"endLine":807,"chunkText":"            \"Custom output naming and format options\",\n        ],\n        \"endpoints\": {\n            \"POST /separate\": \"Upload and separate audio file (supports multiple models and all separator parameters)\",\n            \"GET /status/{task_id}\": \"Get job status and progress (includes model-specific progress and file hashes)\",\n            \"GET /download/{task_id}/{file_hash}\": \"Download separated file using hash identifier (avoids URL length limits)\",\n            \"GET /models-json\": \"List available models (JSON format)\",","chunkId":"audio_separator/remote/deploy_modal.py:75"}],[564,{"filePath":"audio_separator/remote/deploy_modal.py","startLine":805,"endLine":812,"chunkText":"            \"GET /status/{task_id}\": \"Get job status and progress (includes model-specific progress and file hashes)\",\n            \"GET /download/{task_id}/{file_hash}\": \"Download separated file using hash identifier (avoids URL length limits)\",\n            \"GET /models-json\": \"List available models (JSON format)\",\n            \"GET /models\": \"List available models (plain text format like CLI --list_models)\",\n            \"GET /health\": \"Health check\",\n        },\n        \"note\": (\"Full-featured wrapper around audio-separator with complete parameter compatibility\"),\n        \"remote_cli\": {","chunkId":"audio_separator/remote/deploy_modal.py:76"}],[565,{"filePath":"audio_separator/remote/deploy_modal.py","startLine":810,"endLine":818,"chunkText":"        },\n        \"note\": (\"Full-featured wrapper around audio-separator with complete parameter compatibility\"),\n        \"remote_cli\": {\n            \"install\": \"pip install audio-separator\",\n            \"setup\": 'export AUDIO_SEPARATOR_API_URL=\"https://your-deployment-url.modal.run\"',\n            \"usage\": [\n                \"audio-separator-remote separate song.mp3\",\n                \"audio-separator-remote separate song.mp3 --model UVR-MDX-NET-Inst_HQ_4\",\n                \"audio-separator-remote separate song.mp3 --models model1.ckpt model2.onnx\",","chunkId":"audio_separator/remote/deploy_modal.py:77"}],[566,{"filePath":"audio_separator/remote/deploy_modal.py","startLine":816,"endLine":827,"chunkText":"                \"audio-separator-remote separate song.mp3\",\n                \"audio-separator-remote separate song.mp3 --model UVR-MDX-NET-Inst_HQ_4\",\n                \"audio-separator-remote separate song.mp3 --models model1.ckpt model2.onnx\",\n                'audio-separator-remote separate song.mp3 --custom_output_names \\'{\"Vocals\": \"lead_vocals\"}\\'',\n                \"audio-separator-remote status <task_id>\",\n                \"audio-separator-remote models --filter vocals\",\n            ],\n        },\n    }\n\n\n@app.function(image=image, timeout=600, scaledown_window=300, volumes={\"/storage\": volume})","chunkId":"audio_separator/remote/deploy_modal.py:78"}],[567,{"filePath":"audio_separator/remote/deploy_modal.py","startLine":825,"endLine":834,"chunkText":"\n\n@app.function(image=image, timeout=600, scaledown_window=300, volumes={\"/storage\": volume})\n@modal.asgi_app()\ndef api() -> FastAPI:\n    \"\"\"\n    Deploy the FastAPI app as a Modal ASGI application\n    \"\"\"\n    return web_app\n","chunkId":"audio_separator/remote/deploy_modal.py:79"}],[568,{"filePath":"audio_separator/separator/__init__.py","startLine":0,"endLine":1,"chunkText":"from .separator import Separator\n","chunkId":"audio_separator/separator/__init__.py:0"}],[569,{"filePath":"audio_separator/separator/architectures/demucs_separator.py","startLine":0,"endLine":13,"chunkText":"import os\nimport sys\nfrom pathlib import Path\nimport torch\nimport numpy as np\nfrom audio_separator.separator.common_separator import CommonSeparator\nfrom audio_separator.separator.uvr_lib_v5.demucs.apply import apply_model, demucs_segments\nfrom audio_separator.separator.uvr_lib_v5.demucs.hdemucs import HDemucs\nfrom audio_separator.separator.uvr_lib_v5.demucs.pretrained import get_model as get_demucs_model\nfrom audio_separator.separator.uvr_lib_v5 import spec_utils\n\nDEMUCS_4_SOURCE = [\"drums\", \"bass\", \"other\", \"vocals\"]\n\nDEMUCS_2_SOURCE_MAPPER = {CommonSeparator.INST_STEM: 0, CommonSeparator.VOCAL_STEM: 1}","chunkId":"audio_separator/separator/architectures/demucs_separator.py:0"}],[570,{"filePath":"audio_separator/separator/architectures/demucs_separator.py","startLine":11,"endLine":26,"chunkText":"DEMUCS_4_SOURCE = [\"drums\", \"bass\", \"other\", \"vocals\"]\n\nDEMUCS_2_SOURCE_MAPPER = {CommonSeparator.INST_STEM: 0, CommonSeparator.VOCAL_STEM: 1}\nDEMUCS_4_SOURCE_MAPPER = {CommonSeparator.BASS_STEM: 0, CommonSeparator.DRUM_STEM: 1, CommonSeparator.OTHER_STEM: 2, CommonSeparator.VOCAL_STEM: 3}\nDEMUCS_6_SOURCE_MAPPER = {\n    CommonSeparator.BASS_STEM: 0,\n    CommonSeparator.DRUM_STEM: 1,\n    CommonSeparator.OTHER_STEM: 2,\n    CommonSeparator.VOCAL_STEM: 3,\n    CommonSeparator.GUITAR_STEM: 4,\n    CommonSeparator.PIANO_STEM: 5,\n}\n\n\nclass DemucsSeparator(CommonSeparator):\n    \"\"\"","chunkId":"audio_separator/separator/architectures/demucs_separator.py:1"}],[571,{"filePath":"audio_separator/separator/architectures/demucs_separator.py","startLine":24,"endLine":35,"chunkText":"\nclass DemucsSeparator(CommonSeparator):\n    \"\"\"\n    DemucsSeparator is responsible for separating audio sources using Demucs models.\n    It initializes with configuration parameters and prepares the model for separation tasks.\n    \"\"\"\n\n    def __init__(self, common_config, arch_config):\n        # Any configuration values which can be shared between architectures should be set already in CommonSeparator,\n        # e.g. user-specified functionality choices (self.output_single_stem) or common model parameters (self.primary_stem_name)\n        super().__init__(config=common_config)\n","chunkId":"audio_separator/separator/architectures/demucs_separator.py:2"}],[572,{"filePath":"audio_separator/separator/architectures/demucs_separator.py","startLine":33,"endLine":42,"chunkText":"        # e.g. user-specified functionality choices (self.output_single_stem) or common model parameters (self.primary_stem_name)\n        super().__init__(config=common_config)\n\n        # Initializing user-configurable parameters, passed through with an mdx_from the CLI or Separator instance\n\n        # Adjust segments to manage RAM or V-RAM usage:\n        # - Smaller sizes consume less resources.\n        # - Bigger sizes consume more resources, but may provide better results.\n        # - \"Default\" picks the optimal size.\n        # DEMUCS_SEGMENTS = (DEF_OPT, '1', '5', '10', '15', '20',","chunkId":"audio_separator/separator/architectures/demucs_separator.py:3"}],[573,{"filePath":"audio_separator/separator/architectures/demucs_separator.py","startLine":40,"endLine":49,"chunkText":"        # - Bigger sizes consume more resources, but may provide better results.\n        # - \"Default\" picks the optimal size.\n        # DEMUCS_SEGMENTS = (DEF_OPT, '1', '5', '10', '15', '20',\n        #           '25', '30', '35', '40', '45', '50',\n        #           '55', '60', '65', '70', '75', '80',\n        #           '85', '90', '95', '100')\n        self.segment_size = arch_config.get(\"segment_size\", \"Default\")\n\n        # Performs multiple predictions with random shifts of the input and averages them.\n        # The higher number of shifts, the longer the prediction will take.","chunkId":"audio_separator/separator/architectures/demucs_separator.py:4"}],[574,{"filePath":"audio_separator/separator/architectures/demucs_separator.py","startLine":47,"endLine":57,"chunkText":"\n        # Performs multiple predictions with random shifts of the input and averages them.\n        # The higher number of shifts, the longer the prediction will take.\n        # Not recommended unless you have a GPU.\n        # DEMUCS_SHIFTS = (0, 1, 2, 3, 4, 5,\n        #                 6, 7, 8, 9, 10, 11,\n        #                 12, 13, 14, 15, 16, 17,\n        #                 18, 19, 20)\n        self.shifts = arch_config.get(\"shifts\", 2)\n\n        # This option controls the amount of overlap between prediction windows.","chunkId":"audio_separator/separator/architectures/demucs_separator.py:5"}],[575,{"filePath":"audio_separator/separator/architectures/demucs_separator.py","startLine":55,"endLine":65,"chunkText":"        self.shifts = arch_config.get(\"shifts\", 2)\n\n        # This option controls the amount of overlap between prediction windows.\n        #  - Higher values can provide better results, but will lead to longer processing times.\n        #  - You can choose between 0.001-0.999\n        # DEMUCS_OVERLAP = (0.25, 0.50, 0.75, 0.99)\n        self.overlap = arch_config.get(\"overlap\", 0.25)\n\n        # Enables \"Segments\". Deselecting this option is only recommended for those with powerful PCs.\n        self.segments_enabled = arch_config.get(\"segments_enabled\", True)\n","chunkId":"audio_separator/separator/architectures/demucs_separator.py:6"}],[576,{"filePath":"audio_separator/separator/architectures/demucs_separator.py","startLine":63,"endLine":74,"chunkText":"        # Enables \"Segments\". Deselecting this option is only recommended for those with powerful PCs.\n        self.segments_enabled = arch_config.get(\"segments_enabled\", True)\n\n        self.logger.debug(f\"Demucs arch params: segment_size={self.segment_size}, segments_enabled={self.segments_enabled}\")\n        self.logger.debug(f\"Demucs arch params: shifts={self.shifts}, overlap={self.overlap}\")\n\n        self.demucs_source_map = DEMUCS_4_SOURCE_MAPPER\n\n        self.audio_file_path = None\n        self.audio_file_base = None\n        self.demucs_model_instance = None\n","chunkId":"audio_separator/separator/architectures/demucs_separator.py:7"}],[577,{"filePath":"audio_separator/separator/architectures/demucs_separator.py","startLine":72,"endLine":86,"chunkText":"        self.audio_file_base = None\n        self.demucs_model_instance = None\n\n        # Add uvr_lib_v5 folder to system path so pytorch serialization can find the demucs module\n        current_dir = os.path.dirname(__file__)\n        uvr_lib_v5_path = os.path.join(current_dir, \"..\", \"uvr_lib_v5\")\n        sys.path.insert(0, uvr_lib_v5_path)\n\n        self.logger.info(\"Demucs Separator initialisation complete\")\n\n    def separate(self, audio_file_path, custom_output_names=None):\n        \"\"\"\n        Separates the audio file into its component stems using the Demucs model.\n\n        Args:","chunkId":"audio_separator/separator/architectures/demucs_separator.py:8"}],[578,{"filePath":"audio_separator/separator/architectures/demucs_separator.py","startLine":84,"endLine":98,"chunkText":"        Separates the audio file into its component stems using the Demucs model.\n\n        Args:\n            audio_file_path (str): The path to the audio file to be processed.\n            custom_output_names (dict, optional): Custom names for the output files. Defaults to None.\n\n        Returns:\n            list: A list of paths to the output files generated by the separation process.\n        \"\"\"\n        self.logger.debug(\"Starting separation process...\")\n        source = None\n        stem_source = None\n        inst_source = {}\n\n        self.audio_file_path = audio_file_path","chunkId":"audio_separator/separator/architectures/demucs_separator.py:9"}],[579,{"filePath":"audio_separator/separator/architectures/demucs_separator.py","startLine":96,"endLine":109,"chunkText":"        inst_source = {}\n\n        self.audio_file_path = audio_file_path\n        self.audio_file_base = os.path.splitext(os.path.basename(audio_file_path))[0]\n\n        # Prepare the mix for processing\n        self.logger.debug(\"Preparing mix...\")\n        mix = self.prepare_mix(self.audio_file_path)\n\n        self.logger.debug(f\"Mix prepared for demixing. Shape: {mix.shape}\")\n\n        self.logger.debug(\"Loading model for demixing...\")\n\n        self.demucs_model_instance = HDemucs(sources=DEMUCS_4_SOURCE)","chunkId":"audio_separator/separator/architectures/demucs_separator.py:10"}],[580,{"filePath":"audio_separator/separator/architectures/demucs_separator.py","startLine":107,"endLine":118,"chunkText":"        self.logger.debug(\"Loading model for demixing...\")\n\n        self.demucs_model_instance = HDemucs(sources=DEMUCS_4_SOURCE)\n        self.demucs_model_instance = get_demucs_model(name=os.path.splitext(os.path.basename(self.model_path))[0], repo=Path(os.path.dirname(self.model_path)))\n        self.demucs_model_instance = demucs_segments(self.segment_size, self.demucs_model_instance)\n        self.demucs_model_instance.to(self.torch_device)\n        self.demucs_model_instance.eval()\n\n        self.logger.debug(\"Model loaded and set to evaluation mode.\")\n\n        source = self.demix_demucs(mix)\n","chunkId":"audio_separator/separator/architectures/demucs_separator.py:11"}],[581,{"filePath":"audio_separator/separator/architectures/demucs_separator.py","startLine":116,"endLine":128,"chunkText":"\n        source = self.demix_demucs(mix)\n\n        del self.demucs_model_instance\n        self.clear_gpu_cache()\n        self.logger.debug(\"Model and GPU cache cleared after demixing.\")\n\n        output_files = []\n        self.logger.debug(\"Processing output files...\")\n\n        if isinstance(inst_source, np.ndarray):\n            self.logger.debug(\"Processing instance source...\")\n            source_reshape = spec_utils.reshape_sources(inst_source[self.demucs_source_map[CommonSeparator.VOCAL_STEM]], source[self.demucs_source_map[CommonSeparator.VOCAL_STEM]])","chunkId":"audio_separator/separator/architectures/demucs_separator.py:12"}],[582,{"filePath":"audio_separator/separator/architectures/demucs_separator.py","startLine":126,"endLine":134,"chunkText":"        if isinstance(inst_source, np.ndarray):\n            self.logger.debug(\"Processing instance source...\")\n            source_reshape = spec_utils.reshape_sources(inst_source[self.demucs_source_map[CommonSeparator.VOCAL_STEM]], source[self.demucs_source_map[CommonSeparator.VOCAL_STEM]])\n            inst_source[self.demucs_source_map[CommonSeparator.VOCAL_STEM]] = source_reshape\n            source = inst_source\n\n        if isinstance(source, np.ndarray):\n            source_length = len(source)\n            self.logger.debug(f\"Processing source array, source length is {source_length}\")","chunkId":"audio_separator/separator/architectures/demucs_separator.py:13"}],[583,{"filePath":"audio_separator/separator/architectures/demucs_separator.py","startLine":132,"endLine":142,"chunkText":"        if isinstance(source, np.ndarray):\n            source_length = len(source)\n            self.logger.debug(f\"Processing source array, source length is {source_length}\")\n            match source_length:\n                case 2:\n                    self.logger.debug(\"Setting source map to 2-stem...\")\n                    self.demucs_source_map = DEMUCS_2_SOURCE_MAPPER\n                case 6:\n                    self.logger.debug(\"Setting source map to 6-stem...\")\n                    self.demucs_source_map = DEMUCS_6_SOURCE_MAPPER\n                case _:","chunkId":"audio_separator/separator/architectures/demucs_separator.py:14"}],[584,{"filePath":"audio_separator/separator/architectures/demucs_separator.py","startLine":140,"endLine":149,"chunkText":"                    self.logger.debug(\"Setting source map to 6-stem...\")\n                    self.demucs_source_map = DEMUCS_6_SOURCE_MAPPER\n                case _:\n                    self.logger.debug(\"Setting source map to 4-stem...\")\n                    self.demucs_source_map = DEMUCS_4_SOURCE_MAPPER\n\n        self.logger.debug(\"Processing for all stems...\")\n        for stem_name, stem_value in self.demucs_source_map.items():\n            if self.output_single_stem is not None:\n                if stem_name.lower() != self.output_single_stem.lower():","chunkId":"audio_separator/separator/architectures/demucs_separator.py:15"}],[585,{"filePath":"audio_separator/separator/architectures/demucs_separator.py","startLine":147,"endLine":158,"chunkText":"        for stem_name, stem_value in self.demucs_source_map.items():\n            if self.output_single_stem is not None:\n                if stem_name.lower() != self.output_single_stem.lower():\n                    self.logger.debug(f\"Skipping writing stem {stem_name} as output_single_stem is set to {self.output_single_stem}...\")\n                    continue\n\n            stem_path = self.get_stem_output_path(stem_name, custom_output_names)\n            stem_source = source[stem_value].T\n\n            self.final_process(stem_path, stem_source, stem_name)\n            output_files.append(stem_path)\n","chunkId":"audio_separator/separator/architectures/demucs_separator.py:16"}],[586,{"filePath":"audio_separator/separator/architectures/demucs_separator.py","startLine":156,"endLine":174,"chunkText":"            self.final_process(stem_path, stem_source, stem_name)\n            output_files.append(stem_path)\n\n        return output_files\n\n    def demix_demucs(self, mix):\n        \"\"\"\n        Demixes the input mix using the demucs model.\n        \"\"\"\n        self.logger.debug(\"Starting demixing process in demix_demucs...\")\n\n        processed = {}\n        mix = torch.tensor(mix, dtype=torch.float32)\n        ref = mix.mean(0)\n        mix = (mix - ref.mean()) / ref.std()\n        mix_infer = mix\n\n        with torch.no_grad():\n            self.logger.debug(\"Running model inference...\")","chunkId":"audio_separator/separator/architectures/demucs_separator.py:17"}],[587,{"filePath":"audio_separator/separator/architectures/demucs_separator.py","startLine":172,"endLine":187,"chunkText":"\n        with torch.no_grad():\n            self.logger.debug(\"Running model inference...\")\n            sources = apply_model(\n                model=self.demucs_model_instance,\n                mix=mix_infer[None],\n                shifts=self.shifts,\n                split=self.segments_enabled,\n                overlap=self.overlap,\n                static_shifts=1 if self.shifts == 0 else self.shifts,\n                set_progress_bar=None,\n                device=self.torch_device,\n                progress=True,\n            )[0]\n\n        sources = (sources * ref.std() + ref.mean()).cpu().numpy()","chunkId":"audio_separator/separator/architectures/demucs_separator.py:18"}],[588,{"filePath":"audio_separator/separator/architectures/demucs_separator.py","startLine":185,"endLine":195,"chunkText":"            )[0]\n\n        sources = (sources * ref.std() + ref.mean()).cpu().numpy()\n        sources[[0, 1]] = sources[[1, 0]]\n        processed[mix] = sources[:, :, 0:None].copy()\n        sources = list(processed.values())\n        sources = [s[:, :, 0:None] for s in sources]\n        sources = np.concatenate(sources, axis=-1)\n\n        return sources\n","chunkId":"audio_separator/separator/architectures/demucs_separator.py:19"}],[589,{"filePath":"audio_separator/separator/architectures/mdx_separator.py","startLine":0,"endLine":17,"chunkText":"\"\"\"Module for separating audio sources using MDX architecture models.\"\"\"\n\nimport os\nimport platform\nimport torch\nimport onnx\nimport onnxruntime as ort\nimport numpy as np\nimport onnx2torch\nfrom tqdm import tqdm\nfrom audio_separator.separator.uvr_lib_v5 import spec_utils\nfrom audio_separator.separator.uvr_lib_v5.stft import STFT\nfrom audio_separator.separator.common_separator import CommonSeparator\n\n\nclass MDXSeparator(CommonSeparator):\n    \"\"\"\n    MDXSeparator is responsible for separating audio sources using MDX models.","chunkId":"audio_separator/separator/architectures/mdx_separator.py:0"}],[590,{"filePath":"audio_separator/separator/architectures/mdx_separator.py","startLine":16,"endLine":25,"chunkText":"    \"\"\"\n    MDXSeparator is responsible for separating audio sources using MDX models.\n    It initializes with configuration parameters and prepares the model for separation tasks.\n    \"\"\"\n\n    def __init__(self, common_config, arch_config):\n        # Any configuration values which can be shared between architectures should be set already in CommonSeparator,\n        # e.g. user-specified functionality choices (self.output_single_stem) or common model parameters (self.primary_stem_name)\n        super().__init__(config=common_config)\n","chunkId":"audio_separator/separator/architectures/mdx_separator.py:1"}],[591,{"filePath":"audio_separator/separator/architectures/mdx_separator.py","startLine":23,"endLine":31,"chunkText":"        # e.g. user-specified functionality choices (self.output_single_stem) or common model parameters (self.primary_stem_name)\n        super().__init__(config=common_config)\n\n        # Initializing user-configurable parameters, passed through with an mdx_from the CLI or Separator instance\n\n        # Pick a segment size to balance speed, resource use, and quality:\n        # - Smaller sizes consume less resources.\n        # - Bigger sizes consume more resources, but may provide better results.\n        # - Default size is 256. Quality can change based on your pick.","chunkId":"audio_separator/separator/architectures/mdx_separator.py:2"}],[592,{"filePath":"audio_separator/separator/architectures/mdx_separator.py","startLine":29,"endLine":38,"chunkText":"        # - Smaller sizes consume less resources.\n        # - Bigger sizes consume more resources, but may provide better results.\n        # - Default size is 256. Quality can change based on your pick.\n        self.segment_size = arch_config.get(\"segment_size\")\n\n        # This option controls the amount of overlap between prediction windows.\n        #  - Higher values can provide better results, but will lead to longer processing times.\n        #  - For Non-MDX23C models: You can choose between 0.001-0.999\n        self.overlap = arch_config.get(\"overlap\")\n","chunkId":"audio_separator/separator/architectures/mdx_separator.py:3"}],[593,{"filePath":"audio_separator/separator/architectures/mdx_separator.py","startLine":36,"endLine":45,"chunkText":"        #  - For Non-MDX23C models: You can choose between 0.001-0.999\n        self.overlap = arch_config.get(\"overlap\")\n\n        # Number of batches to be processed at a time.\n        # - Higher values mean more RAM usage but slightly faster processing times.\n        # - Lower values mean less RAM usage but slightly longer processing times.\n        # - Batch size value has no effect on output quality.\n        # BATCH_SIZE = ('1', ''2', '3', '4', '5', '6', '7', '8', '9', '10')\n        self.batch_size = arch_config.get(\"batch_size\", 1)\n","chunkId":"audio_separator/separator/architectures/mdx_separator.py:4"}],[594,{"filePath":"audio_separator/separator/architectures/mdx_separator.py","startLine":43,"endLine":49,"chunkText":"        # BATCH_SIZE = ('1', ''2', '3', '4', '5', '6', '7', '8', '9', '10')\n        self.batch_size = arch_config.get(\"batch_size\", 1)\n\n        # hop_length is equivalent to the more commonly used term \"stride\" in convolutional neural networks\n        # In machine learning, particularly in the context of convolutional neural networks (CNNs),\n        # the term \"stride\" refers to the number of pixels by which we move the filter across the input image.\n        # Strides are a crucial component in the convolution operation, a fundamental building block of CNNs used primarily in the field of computer vision.","chunkId":"audio_separator/separator/architectures/mdx_separator.py:5"}],[595,{"filePath":"audio_separator/separator/architectures/mdx_separator.py","startLine":47,"endLine":50,"chunkText":"        # In machine learning, particularly in the context of convolutional neural networks (CNNs),\n        # the term \"stride\" refers to the number of pixels by which we move the filter across the input image.\n        # Strides are a crucial component in the convolution operation, a fundamental building block of CNNs used primarily in the field of computer vision.\n        # Stride is a parameter that dictates the movement of the kernel, or filter, across the input data, such as an image.","chunkId":"audio_separator/separator/architectures/mdx_separator.py:6"}],[596,{"filePath":"audio_separator/separator/architectures/mdx_separator.py","startLine":48,"endLine":52,"chunkText":"        # the term \"stride\" refers to the number of pixels by which we move the filter across the input image.\n        # Strides are a crucial component in the convolution operation, a fundamental building block of CNNs used primarily in the field of computer vision.\n        # Stride is a parameter that dictates the movement of the kernel, or filter, across the input data, such as an image.\n        # When performing a convolution operation, the stride determines how many units the filter shifts at each step.\n        # The choice of stride affects the model in several ways:","chunkId":"audio_separator/separator/architectures/mdx_separator.py:7"}],[597,{"filePath":"audio_separator/separator/architectures/mdx_separator.py","startLine":50,"endLine":54,"chunkText":"        # Stride is a parameter that dictates the movement of the kernel, or filter, across the input data, such as an image.\n        # When performing a convolution operation, the stride determines how many units the filter shifts at each step.\n        # The choice of stride affects the model in several ways:\n        # Output Size: A larger stride will result in a smaller output spatial dimension.\n        # Computational Efficiency: Increasing the stride can decrease the computational load.","chunkId":"audio_separator/separator/architectures/mdx_separator.py:8"}],[598,{"filePath":"audio_separator/separator/architectures/mdx_separator.py","startLine":52,"endLine":58,"chunkText":"        # The choice of stride affects the model in several ways:\n        # Output Size: A larger stride will result in a smaller output spatial dimension.\n        # Computational Efficiency: Increasing the stride can decrease the computational load.\n        # Field of View: A higher stride means that each step of the filter takes into account a wider area of the input image.\n        #   This can be beneficial when the model needs to capture more global features rather than focusing on finer details.\n        self.hop_length = arch_config.get(\"hop_length\")\n","chunkId":"audio_separator/separator/architectures/mdx_separator.py:9"}],[599,{"filePath":"audio_separator/separator/architectures/mdx_separator.py","startLine":56,"endLine":64,"chunkText":"        #   This can be beneficial when the model needs to capture more global features rather than focusing on finer details.\n        self.hop_length = arch_config.get(\"hop_length\")\n\n        # If enabled, model will be run twice to reduce noise in output audio.\n        self.enable_denoise = arch_config.get(\"enable_denoise\")\n\n        self.logger.debug(f\"MDX arch params: batch_size={self.batch_size}, segment_size={self.segment_size}\")\n        self.logger.debug(f\"MDX arch params: overlap={self.overlap}, hop_length={self.hop_length}, enable_denoise={self.enable_denoise}\")\n","chunkId":"audio_separator/separator/architectures/mdx_separator.py:10"}],[600,{"filePath":"audio_separator/separator/architectures/mdx_separator.py","startLine":62,"endLine":69,"chunkText":"        self.logger.debug(f\"MDX arch params: batch_size={self.batch_size}, segment_size={self.segment_size}\")\n        self.logger.debug(f\"MDX arch params: overlap={self.overlap}, hop_length={self.hop_length}, enable_denoise={self.enable_denoise}\")\n\n        # Initializing model-specific parameters from model_data JSON\n        self.compensate = self.model_data[\"compensate\"]\n        self.dim_f = self.model_data[\"mdx_dim_f_set\"]\n        self.dim_t = 2 ** self.model_data[\"mdx_dim_t_set\"]\n        self.n_fft = self.model_data[\"mdx_n_fft_scale_set\"]","chunkId":"audio_separator/separator/architectures/mdx_separator.py:11"}],[601,{"filePath":"audio_separator/separator/architectures/mdx_separator.py","startLine":67,"endLine":75,"chunkText":"        self.dim_f = self.model_data[\"mdx_dim_f_set\"]\n        self.dim_t = 2 ** self.model_data[\"mdx_dim_t_set\"]\n        self.n_fft = self.model_data[\"mdx_n_fft_scale_set\"]\n        self.config_yaml = self.model_data.get(\"config_yaml\", None)\n\n        self.logger.debug(f\"MDX arch params: compensate={self.compensate}, dim_f={self.dim_f}, dim_t={self.dim_t}, n_fft={self.n_fft}\")\n        self.logger.debug(f\"MDX arch params: config_yaml={self.config_yaml}\")\n\n        # In UVR, these variables are set but either aren't useful or are better handled in audio-separator.","chunkId":"audio_separator/separator/architectures/mdx_separator.py:12"}],[602,{"filePath":"audio_separator/separator/architectures/mdx_separator.py","startLine":73,"endLine":83,"chunkText":"        self.logger.debug(f\"MDX arch params: config_yaml={self.config_yaml}\")\n\n        # In UVR, these variables are set but either aren't useful or are better handled in audio-separator.\n        # Leaving these comments explaining to help myself or future developers understand why these aren't in audio-separator.\n\n        # \"chunks\" is not actually used for anything in UVR...\n        # self.chunks = 0\n\n        # \"adjust\" is hard-coded to 1 in UVR, and only used as a multiplier in run_model, so it does nothing.\n        # self.adjust = 1\n","chunkId":"audio_separator/separator/architectures/mdx_separator.py:13"}],[603,{"filePath":"audio_separator/separator/architectures/mdx_separator.py","startLine":81,"endLine":90,"chunkText":"        # \"adjust\" is hard-coded to 1 in UVR, and only used as a multiplier in run_model, so it does nothing.\n        # self.adjust = 1\n\n        # \"hop\" is hard-coded to 1024 in UVR. We have a \"hop_length\" parameter instead\n        # self.hop = 1024\n\n        # \"margin\" maps to sample rate and is set from the GUI in UVR (default: 44100). We have a \"sample_rate\" parameter instead.\n        # self.margin = 44100\n\n        # \"dim_c\" is hard-coded to 4 in UVR, seems to be a parameter for the number of channels, and is only used for checkpoint models.","chunkId":"audio_separator/separator/architectures/mdx_separator.py:14"}],[604,{"filePath":"audio_separator/separator/architectures/mdx_separator.py","startLine":88,"endLine":107,"chunkText":"        # self.margin = 44100\n\n        # \"dim_c\" is hard-coded to 4 in UVR, seems to be a parameter for the number of channels, and is only used for checkpoint models.\n        # We haven't implemented support for the checkpoint models here, so we're not using it.\n        # self.dim_c = 4\n\n        self.load_model()\n\n        self.n_bins = 0\n        self.trim = 0\n        self.chunk_size = 0\n        self.gen_size = 0\n        self.stft = None\n\n        self.primary_source = None\n        self.secondary_source = None\n        self.audio_file_path = None\n        self.audio_file_base = None\n\n    def load_model(self)","chunkId":"audio_separator/separator/architectures/mdx_separator.py:15"}],[605,{"filePath":"audio_separator/separator/architectures/mdx_separator.py","startLine":105,"endLine":120,"chunkText":"        self.audio_file_base = None\n\n    def load_model(self):\n        \"\"\"\n        Load the model into memory from file on disk, initialize it with config from the model data,\n        and prepare for inferencing using hardware accelerated Torch device.\n        \"\"\"\n        self.logger.debug(\"Loading ONNX model for inference...\")\n\n        if self.segment_size == self.dim_t:\n            ort_session_options = ort.SessionOptions()\n            if self.log_level > 10:\n                ort_session_options.log_severity_level = 3\n            else:\n                ort_session_options.log_severity_level = 0\n","chunkId":"audio_separator/separator/architectures/mdx_separator.py:16"}],[606,{"filePath":"audio_separator/separator/architectures/mdx_separator.py","startLine":118,"endLine":126,"chunkText":"            else:\n                ort_session_options.log_severity_level = 0\n\n            ort_inference_session = ort.InferenceSession(self.model_path, providers=self.onnx_execution_provider, sess_options=ort_session_options)\n            self.model_run = lambda spek: ort_inference_session.run(None, {\"input\": spek.cpu().numpy()})[0]\n            self.logger.debug(\"Model loaded successfully using ONNXruntime inferencing session.\")\n        else:\n            if platform.system() == 'Windows':\n                onnx_model = onnx.load(self.model_path)","chunkId":"audio_separator/separator/architectures/mdx_separator.py:17"}],[607,{"filePath":"audio_separator/separator/architectures/mdx_separator.py","startLine":124,"endLine":135,"chunkText":"        else:\n            if platform.system() == 'Windows':\n                onnx_model = onnx.load(self.model_path)\n                self.model_run = onnx2torch.convert(onnx_model)\n            else:\n                self.model_run = onnx2torch.convert(self.model_path)\n   \n            self.model_run.to(self.torch_device).eval()\n            self.logger.warning(\"Model converted from onnx to pytorch due to segment size not matching dim_t, processing may be slower.\")\n\n    def separate(self, audio_file_path, custom_output_names=None):\n        \"\"\"","chunkId":"audio_separator/separator/architectures/mdx_separator.py:18"}],[608,{"filePath":"audio_separator/separator/architectures/mdx_separator.py","startLine":133,"endLine":145,"chunkText":"\n    def separate(self, audio_file_path, custom_output_names=None):\n        \"\"\"\n        Separates the audio file into primary and secondary sources based on the model's configuration.\n        It processes the mix, demixes it into sources, normalizes the sources, and saves the output files.\n\n        Args:\n            audio_file_path (str): The path to the audio file to be processed.\n            custom_output_names (dict, optional): Custom names for the output files. Defaults to None.\n\n        Returns:\n            list: A list of paths to the output files generated by the separation process.\n        \"\"\"","chunkId":"audio_separator/separator/architectures/mdx_separator.py:19"}],[609,{"filePath":"audio_separator/separator/architectures/mdx_separator.py","startLine":143,"endLine":154,"chunkText":"        Returns:\n            list: A list of paths to the output files generated by the separation process.\n        \"\"\"\n        self.audio_file_path = audio_file_path\n        self.audio_file_base = os.path.splitext(os.path.basename(audio_file_path))[0]\n\n        # Prepare the mix for processing\n        self.logger.debug(f\"Preparing mix for input audio file {self.audio_file_path}...\")\n        mix = self.prepare_mix(self.audio_file_path)\n\n        self.logger.debug(\"Normalizing mix before demixing...\")\n        peak = np.abs(mix).max()","chunkId":"audio_separator/separator/architectures/mdx_separator.py:20"}],[610,{"filePath":"audio_separator/separator/architectures/mdx_separator.py","startLine":152,"endLine":167,"chunkText":"\n        self.logger.debug(\"Normalizing mix before demixing...\")\n        peak = np.abs(mix).max()\n        mix = spec_utils.normalize(wave=mix, max_peak=self.normalization_threshold, min_peak=self.amplification_threshold)\n\n        # Start the demixing process\n        source = self.demix(mix) * peak\n        self.logger.debug(\"Demixing completed.\")\n\n\n        if not isinstance(self.primary_source, np.ndarray):\n            self.primary_source = source.T\n\n        # In UVR, the source is cached here if it's a vocal split model, but we're not supporting that yet\n\n        # Initialize the list for output files","chunkId":"audio_separator/separator/architectures/mdx_separator.py:21"}],[611,{"filePath":"audio_separator/separator/architectures/mdx_separator.py","startLine":165,"endLine":176,"chunkText":"        # In UVR, the source is cached here if it's a vocal split model, but we're not supporting that yet\n\n        # Initialize the list for output files\n        output_files = []\n        self.logger.debug(\"Processing output files...\")\n\n        # Process the secondary source if not already an array\n        if not isinstance(self.secondary_source, np.ndarray):\n            self.logger.debug(\"Producing secondary source: demixing in match_mix mode\")\n            raw_mix = self.demix(mix, is_match_mix=True)\n\n            if self.invert_using_spec:","chunkId":"audio_separator/separator/architectures/mdx_separator.py:22"}],[612,{"filePath":"audio_separator/separator/architectures/mdx_separator.py","startLine":174,"endLine":182,"chunkText":"            raw_mix = self.demix(mix, is_match_mix=True)\n\n            if self.invert_using_spec:\n                self.logger.debug(\"Inverting secondary stem using spectogram as invert_using_spec is set to True\")\n                self.secondary_source = spec_utils.invert_stem(raw_mix, self.primary_source * self.compensate)\n            else:\n                self.logger.debug(\"Inverting secondary stem by subtracting of transposed demixed stem from transposed original mix\")\n                self.secondary_source = (-self.primary_source * self.compensate) + mix.T\n","chunkId":"audio_separator/separator/architectures/mdx_separator.py:23"}],[613,{"filePath":"audio_separator/separator/architectures/mdx_separator.py","startLine":180,"endLine":186,"chunkText":"                self.logger.debug(\"Inverting secondary stem by subtracting of transposed demixed stem from transposed original mix\")\n                self.secondary_source = (-self.primary_source * self.compensate) + mix.T\n\n        # Save and process the secondary stem if needed\n        if not self.output_single_stem or self.output_single_stem.lower() == self.secondary_stem_name.lower():\n            self.secondary_stem_output_path = self.get_stem_output_path(self.secondary_stem_name, custom_output_names)\n","chunkId":"audio_separator/separator/architectures/mdx_separator.py:24"}],[614,{"filePath":"audio_separator/separator/architectures/mdx_separator.py","startLine":184,"endLine":191,"chunkText":"        if not self.output_single_stem or self.output_single_stem.lower() == self.secondary_stem_name.lower():\n            self.secondary_stem_output_path = self.get_stem_output_path(self.secondary_stem_name, custom_output_names)\n\n            self.logger.info(f\"Saving {self.secondary_stem_name} stem to {self.secondary_stem_output_path}...\")\n            self.final_process(self.secondary_stem_output_path, self.secondary_source, self.secondary_stem_name)\n            output_files.append(self.secondary_stem_output_path)\n\n        # Save and process the primary stem if needed","chunkId":"audio_separator/separator/architectures/mdx_separator.py:25"}],[615,{"filePath":"audio_separator/separator/architectures/mdx_separator.py","startLine":189,"endLine":195,"chunkText":"            output_files.append(self.secondary_stem_output_path)\n\n        # Save and process the primary stem if needed\n        if not self.output_single_stem or self.output_single_stem.lower() == self.primary_stem_name.lower():\n            self.primary_stem_output_path = self.get_stem_output_path(self.primary_stem_name, custom_output_names)\n            self.logger.info(f\"Saving {self.primary_stem_name} stem to {self.primary_stem_output_path}...\")\n            self.final_process(self.primary_stem_output_path, self.primary_source, self.primary_stem_name)","chunkId":"audio_separator/separator/architectures/mdx_separator.py:26"}],[616,{"filePath":"audio_separator/separator/architectures/mdx_separator.py","startLine":193,"endLine":203,"chunkText":"            self.primary_stem_output_path = self.get_stem_output_path(self.primary_stem_name, custom_output_names)\n            self.logger.info(f\"Saving {self.primary_stem_name} stem to {self.primary_stem_output_path}...\")\n            self.final_process(self.primary_stem_output_path, self.primary_source, self.primary_stem_name)\n            output_files.append(self.primary_stem_output_path)\n\n        # Not yet implemented from UVR features:\n        # self.process_vocal_split_chain(secondary_sources)\n        # self.logger.debug(\"Vocal split chain processed.\")\n\n        return output_files\n","chunkId":"audio_separator/separator/architectures/mdx_separator.py:27"}],[617,{"filePath":"audio_separator/separator/architectures/mdx_separator.py","startLine":201,"endLine":212,"chunkText":"\n        return output_files\n\n    def initialize_model_settings(self):\n        \"\"\"\n        This function sets up the necessary parameters for the model, like the number of frequency bins (n_bins), the trimming size (trim),\n        the size of each audio chunk (chunk_size), and the window function for spectral transformations (window).\n        It ensures that the model is configured with the correct settings for processing the audio data.\n        \"\"\"\n        self.logger.debug(\"Initializing model settings...\")\n\n        # n_bins is half the FFT size plus one (self.n_fft // 2 + 1).","chunkId":"audio_separator/separator/architectures/mdx_separator.py:28"}],[618,{"filePath":"audio_separator/separator/architectures/mdx_separator.py","startLine":210,"endLine":223,"chunkText":"        self.logger.debug(\"Initializing model settings...\")\n\n        # n_bins is half the FFT size plus one (self.n_fft // 2 + 1).\n        self.n_bins = self.n_fft // 2 + 1\n\n        # trim is half the FFT size (self.n_fft // 2).\n        self.trim = self.n_fft // 2\n\n        # chunk_size is the hop_length size times the segment size minus one\n        self.chunk_size = self.hop_length * (self.segment_size - 1)\n\n        # gen_size is the chunk size minus twice the trim size\n        self.gen_size = self.chunk_size - 2 * self.trim\n","chunkId":"audio_separator/separator/architectures/mdx_separator.py:29"}],[619,{"filePath":"audio_separator/separator/architectures/mdx_separator.py","startLine":221,"endLine":230,"chunkText":"        # gen_size is the chunk size minus twice the trim size\n        self.gen_size = self.chunk_size - 2 * self.trim\n\n        self.stft = STFT(self.logger, self.n_fft, self.hop_length, self.dim_f, self.torch_device)\n\n        self.logger.debug(f\"Model input params: n_fft={self.n_fft} hop_length={self.hop_length} dim_f={self.dim_f}\")\n        self.logger.debug(f\"Model settings: n_bins={self.n_bins}, trim={self.trim}, chunk_size={self.chunk_size}, gen_size={self.gen_size}\")\n\n    def initialize_mix(self, mix, is_ckpt=False):\n        \"\"\"","chunkId":"audio_separator/separator/architectures/mdx_separator.py:30"}],[620,{"filePath":"audio_separator/separator/architectures/mdx_separator.py","startLine":228,"endLine":236,"chunkText":"\n    def initialize_mix(self, mix, is_ckpt=False):\n        \"\"\"\n        After prepare_mix segments the audio, initialize_mix further processes each segment.\n        It ensures each audio segment is in the correct format for the model, applies necessary padding,\n        and converts the segments into tensors for processing with the model.\n        This step is essential for preparing the audio data in a format that the neural network can process.\n        \"\"\"\n        # Log the initialization of the mix and whether checkpoint mode is used","chunkId":"audio_separator/separator/architectures/mdx_separator.py:31"}],[621,{"filePath":"audio_separator/separator/architectures/mdx_separator.py","startLine":234,"endLine":244,"chunkText":"        This step is essential for preparing the audio data in a format that the neural network can process.\n        \"\"\"\n        # Log the initialization of the mix and whether checkpoint mode is used\n        self.logger.debug(f\"Initializing mix with is_ckpt={is_ckpt}. Initial mix shape: {mix.shape}\")\n\n        # Ensure the mix is a 2-channel (stereo) audio signal\n        if mix.shape[0] != 2:\n            error_message = f\"Expected a 2-channel audio signal, but got {mix.shape[0]} channels\"\n            self.logger.error(error_message)\n            raise ValueError(error_message)\n","chunkId":"audio_separator/separator/architectures/mdx_separator.py:32"}],[622,{"filePath":"audio_separator/separator/architectures/mdx_separator.py","startLine":242,"endLine":253,"chunkText":"            self.logger.error(error_message)\n            raise ValueError(error_message)\n\n        # If in checkpoint mode, process the mix differently\n        if is_ckpt:\n            self.logger.debug(\"Processing in checkpoint mode...\")\n            # Calculate padding based on the generation size and trim\n            pad = self.gen_size + self.trim - (mix.shape[-1] % self.gen_size)\n            self.logger.debug(f\"Padding calculated: {pad}\")\n            # Add padding at the beginning and the end of the mix\n            mixture = np.concatenate(\n                (","chunkId":"audio_separator/separator/architectures/mdx_separator.py:33"}],[623,{"filePath":"audio_separator/separator/architectures/mdx_separator.py","startLine":251,"endLine":262,"chunkText":"            # Add padding at the beginning and the end of the mix\n            mixture = np.concatenate(\n                (\n                    np.zeros((2, self.trim), dtype=\"float32\"),  # Pad at the start\n                    mix,\n                    np.zeros((2, pad), dtype=\"float32\"),        # Pad in the middle (to match chunk size)\n                    np.zeros((2, self.trim), dtype=\"float32\"),  # Pad at the end\n                ),\n                1\n            )\n            # Determine the number of chunks based on the mixture's length\n            num_chunks = mixture.shape[-1] // self.gen_size","chunkId":"audio_separator/separator/architectures/mdx_separator.py:34"}],[624,{"filePath":"audio_separator/separator/architectures/mdx_separator.py","startLine":260,"endLine":269,"chunkText":"            )\n            # Determine the number of chunks based on the mixture's length\n            num_chunks = mixture.shape[-1] // self.gen_size\n            self.logger.debug(f\"Mixture shape after padding: {mixture.shape}, Number of chunks: {num_chunks}\")\n            # Split the mixture into chunks\n            mix_waves = [mixture[:, i * self.gen_size : i * self.gen_size + self.chunk_size] for i in range(num_chunks)]\n        else:\n            # If not in checkpoint mode, process normally\n            self.logger.debug(\"Processing in non-checkpoint mode...\")\n            mix_waves = []","chunkId":"audio_separator/separator/architectures/mdx_separator.py:35"}],[625,{"filePath":"audio_separator/separator/architectures/mdx_separator.py","startLine":267,"endLine":275,"chunkText":"            # If not in checkpoint mode, process normally\n            self.logger.debug(\"Processing in non-checkpoint mode...\")\n            mix_waves = []\n            n_sample = mix.shape[1]\n            # Calculate necessary padding to make the total length divisible by the generation size\n            pad = self.gen_size - n_sample % self.gen_size\n            self.logger.debug(f\"Number of samples: {n_sample}, Padding calculated: {pad}\")\n            # Apply padding to the mix\n            mix_p = np.concatenate((np.zeros((2, self.trim)), mix, np.zeros((2, pad)), np.zeros((2, self.trim))), 1)","chunkId":"audio_separator/separator/architectures/mdx_separator.py:36"}],[626,{"filePath":"audio_separator/separator/architectures/mdx_separator.py","startLine":273,"endLine":282,"chunkText":"            self.logger.debug(f\"Number of samples: {n_sample}, Padding calculated: {pad}\")\n            # Apply padding to the mix\n            mix_p = np.concatenate((np.zeros((2, self.trim)), mix, np.zeros((2, pad)), np.zeros((2, self.trim))), 1)\n            self.logger.debug(f\"Shape of mix after padding: {mix_p.shape}\")\n\n            # Process the mix in chunks\n            i = 0\n            while i < n_sample + pad:\n                waves = np.array(mix_p[:, i : i + self.chunk_size])\n                mix_waves.append(waves)","chunkId":"audio_separator/separator/architectures/mdx_separator.py:37"}],[627,{"filePath":"audio_separator/separator/architectures/mdx_separator.py","startLine":280,"endLine":289,"chunkText":"            while i < n_sample + pad:\n                waves = np.array(mix_p[:, i : i + self.chunk_size])\n                mix_waves.append(waves)\n                self.logger.debug(f\"Processed chunk {len(mix_waves)}: Start {i}, End {i + self.chunk_size}\")\n                i += self.gen_size\n\n        # Convert the list of wave chunks into a tensor for processing on the specified device\n        mix_waves_tensor = torch.tensor(mix_waves, dtype=torch.float32).to(self.torch_device)\n        self.logger.debug(f\"Converted mix_waves to tensor. Tensor shape: {mix_waves_tensor.shape}\")\n","chunkId":"audio_separator/separator/architectures/mdx_separator.py:38"}],[628,{"filePath":"audio_separator/separator/architectures/mdx_separator.py","startLine":287,"endLine":295,"chunkText":"        mix_waves_tensor = torch.tensor(mix_waves, dtype=torch.float32).to(self.torch_device)\n        self.logger.debug(f\"Converted mix_waves to tensor. Tensor shape: {mix_waves_tensor.shape}\")\n\n        return mix_waves_tensor, pad\n\n    def demix(self, mix, is_match_mix=False):\n        \"\"\"\n        Demixes the input mix into its constituent sources. If is_match_mix is True, the function adjusts the processing\n        to better match the mix, affecting chunk sizes and overlaps. The demixing process involves padding the mix,","chunkId":"audio_separator/separator/architectures/mdx_separator.py:39"}],[629,{"filePath":"audio_separator/separator/architectures/mdx_separator.py","startLine":293,"endLine":301,"chunkText":"        \"\"\"\n        Demixes the input mix into its constituent sources. If is_match_mix is True, the function adjusts the processing\n        to better match the mix, affecting chunk sizes and overlaps. The demixing process involves padding the mix,\n        processing it in chunks, applying windowing for overlaps, and accumulating the results to separate the sources.\n        \"\"\"\n        self.logger.debug(f\"Starting demixing process with is_match_mix: {is_match_mix}...\")\n        self.initialize_model_settings()\n\n        # Preserves the original mix for later use.","chunkId":"audio_separator/separator/architectures/mdx_separator.py:40"}],[630,{"filePath":"audio_separator/separator/architectures/mdx_separator.py","startLine":299,"endLine":311,"chunkText":"        self.initialize_model_settings()\n\n        # Preserves the original mix for later use.\n        # In UVR, this is used for the pitch fix and VR denoise processes, which aren't yet implemented here.\n        org_mix = mix\n        self.logger.debug(f\"Original mix stored. Shape: {org_mix.shape}\")\n\n        # Initializes a list to store the separated waveforms.\n        tar_waves_ = []\n\n        # Handling different chunk sizes and overlaps based on the matching requirement.\n        if is_match_mix:\n            # Sets a smaller chunk size specifically for matching the mix.","chunkId":"audio_separator/separator/architectures/mdx_separator.py:41"}],[631,{"filePath":"audio_separator/separator/architectures/mdx_separator.py","startLine":309,"endLine":318,"chunkText":"        # Handling different chunk sizes and overlaps based on the matching requirement.\n        if is_match_mix:\n            # Sets a smaller chunk size specifically for matching the mix.\n            chunk_size = self.hop_length * (self.segment_size - 1)\n            # Sets a small overlap for the chunks.\n            overlap = 0.02\n            self.logger.debug(f\"Chunk size for matching mix: {chunk_size}, Overlap: {overlap}\")\n        else:\n            # Uses the regular chunk size defined in model settings.\n            chunk_size = self.chunk_size","chunkId":"audio_separator/separator/architectures/mdx_separator.py:42"}],[632,{"filePath":"audio_separator/separator/architectures/mdx_separator.py","startLine":316,"endLine":327,"chunkText":"        else:\n            # Uses the regular chunk size defined in model settings.\n            chunk_size = self.chunk_size\n            # Uses the overlap specified in the model settings.\n            overlap = self.overlap\n            self.logger.debug(f\"Standard chunk size: {chunk_size}, Overlap: {overlap}\")\n\n        # Calculates the generated size after subtracting the trim from both ends of the chunk.\n        gen_size = chunk_size - 2 * self.trim\n        self.logger.debug(f\"Generated size calculated: {gen_size}\")\n\n        # Calculates padding to make the mix length a multiple of the generated size.","chunkId":"audio_separator/separator/architectures/mdx_separator.py:43"}],[633,{"filePath":"audio_separator/separator/architectures/mdx_separator.py","startLine":325,"endLine":333,"chunkText":"        self.logger.debug(f\"Generated size calculated: {gen_size}\")\n\n        # Calculates padding to make the mix length a multiple of the generated size.\n        pad = gen_size + self.trim - ((mix.shape[-1]) % gen_size)\n        # Prepares the mixture with padding at the beginning and the end.\n        mixture = np.concatenate((np.zeros((2, self.trim), dtype=\"float32\"), mix, np.zeros((2, pad), dtype=\"float32\")), 1)\n        self.logger.debug(f\"Mixture prepared with padding. Mixture shape: {mixture.shape}\")\n\n        # Calculates the step size for processing chunks based on the overlap.","chunkId":"audio_separator/separator/architectures/mdx_separator.py:44"}],[634,{"filePath":"audio_separator/separator/architectures/mdx_separator.py","startLine":331,"endLine":341,"chunkText":"        self.logger.debug(f\"Mixture prepared with padding. Mixture shape: {mixture.shape}\")\n\n        # Calculates the step size for processing chunks based on the overlap.\n        step = int((1 - overlap) * chunk_size)\n        self.logger.debug(f\"Step size for processing chunks: {step} as overlap is set to {overlap}.\")\n\n        # Initializes arrays to store the results and to account for overlap.\n        result = np.zeros((1, 2, mixture.shape[-1]), dtype=np.float32)\n        divider = np.zeros((1, 2, mixture.shape[-1]), dtype=np.float32)\n\n        # Initializes counters for processing chunks.","chunkId":"audio_separator/separator/architectures/mdx_separator.py:45"}],[635,{"filePath":"audio_separator/separator/architectures/mdx_separator.py","startLine":339,"endLine":352,"chunkText":"        divider = np.zeros((1, 2, mixture.shape[-1]), dtype=np.float32)\n\n        # Initializes counters for processing chunks.\n        total = 0\n        total_chunks = (mixture.shape[-1] + step - 1) // step\n        self.logger.debug(f\"Total chunks to process: {total_chunks}\")\n\n        # Processes each chunk of the mixture.\n        for i in tqdm(range(0, mixture.shape[-1], step)):\n            total += 1\n            start = i\n            end = min(i + chunk_size, mixture.shape[-1])\n            self.logger.debug(f\"Processing chunk {total}/{total_chunks}: Start {start}, End {end}\")\n","chunkId":"audio_separator/separator/architectures/mdx_separator.py:46"}],[636,{"filePath":"audio_separator/separator/architectures/mdx_separator.py","startLine":350,"endLine":362,"chunkText":"            end = min(i + chunk_size, mixture.shape[-1])\n            self.logger.debug(f\"Processing chunk {total}/{total_chunks}: Start {start}, End {end}\")\n\n            # Handles windowing for overlapping chunks.\n            chunk_size_actual = end - start\n            window = None\n            if overlap != 0:\n                window = np.hanning(chunk_size_actual)\n                window = np.tile(window[None, None, :], (1, 2, 1))\n                self.logger.debug(\"Window applied to the chunk.\")\n\n            # Zero-pad the chunk to prepare it for processing.\n            mix_part_ = mixture[:, start:end]","chunkId":"audio_separator/separator/architectures/mdx_separator.py:47"}],[637,{"filePath":"audio_separator/separator/architectures/mdx_separator.py","startLine":360,"endLine":370,"chunkText":"\n            # Zero-pad the chunk to prepare it for processing.\n            mix_part_ = mixture[:, start:end]\n            if end != i + chunk_size:\n                pad_size = (i + chunk_size) - end\n                mix_part_ = np.concatenate((mix_part_, np.zeros((2, pad_size), dtype=\"float32\")), axis=-1)\n\n            # Converts the chunk to a tensor for processing.\n            mix_part = torch.tensor([mix_part_], dtype=torch.float32).to(self.torch_device)\n            # Splits the chunk into smaller batches if necessary.\n            mix_waves = mix_part.split(self.batch_size)","chunkId":"audio_separator/separator/architectures/mdx_separator.py:48"}],[638,{"filePath":"audio_separator/separator/architectures/mdx_separator.py","startLine":368,"endLine":378,"chunkText":"            mix_part = torch.tensor([mix_part_], dtype=torch.float32).to(self.torch_device)\n            # Splits the chunk into smaller batches if necessary.\n            mix_waves = mix_part.split(self.batch_size)\n            total_batches = len(mix_waves)\n            self.logger.debug(f\"Mix part split into batches. Number of batches: {total_batches}\")\n\n            with torch.no_grad():\n                # Processes each batch in the chunk.\n                batches_processed = 0\n                for mix_wave in mix_waves:\n                    batches_processed += 1","chunkId":"audio_separator/separator/architectures/mdx_separator.py:49"}],[639,{"filePath":"audio_separator/separator/architectures/mdx_separator.py","startLine":376,"endLine":386,"chunkText":"                batches_processed = 0\n                for mix_wave in mix_waves:\n                    batches_processed += 1\n                    self.logger.debug(f\"Processing mix_wave batch {batches_processed}/{total_batches}\")\n\n                    # Runs the model to separate the sources.\n                    tar_waves = self.run_model(mix_wave, is_match_mix=is_match_mix)\n\n                    # Applies windowing if needed and accumulates the results.\n                    if window is not None:\n                        tar_waves[..., :chunk_size_actual] *= window","chunkId":"audio_separator/separator/architectures/mdx_separator.py:50"}],[640,{"filePath":"audio_separator/separator/architectures/mdx_separator.py","startLine":384,"endLine":395,"chunkText":"                    # Applies windowing if needed and accumulates the results.\n                    if window is not None:\n                        tar_waves[..., :chunk_size_actual] *= window\n                        divider[..., start:end] += window\n                    else:\n                        divider[..., start:end] += 1\n\n                    result[..., start:end] += tar_waves[..., : end - start]\n\n        # Normalizes the results by the divider to account for overlap.\n        self.logger.debug(\"Normalizing result by dividing result by divider.\")\n        tar_waves = result / divider","chunkId":"audio_separator/separator/architectures/mdx_separator.py:51"}],[641,{"filePath":"audio_separator/separator/architectures/mdx_separator.py","startLine":393,"endLine":405,"chunkText":"        # Normalizes the results by the divider to account for overlap.\n        self.logger.debug(\"Normalizing result by dividing result by divider.\")\n        tar_waves = result / divider\n        tar_waves_.append(tar_waves)\n\n        # Reshapes the results to match the original dimensions.\n        tar_waves_ = np.vstack(tar_waves_)[:, :, self.trim : -self.trim]\n        tar_waves = np.concatenate(tar_waves_, axis=-1)[:, : mix.shape[-1]]\n\n        # Extracts the source from the results.\n        source = tar_waves[:, 0:None]\n        self.logger.debug(f\"Concatenated tar_waves. Shape: {tar_waves.shape}\")\n","chunkId":"audio_separator/separator/architectures/mdx_separator.py:52"}],[642,{"filePath":"audio_separator/separator/architectures/mdx_separator.py","startLine":403,"endLine":415,"chunkText":"        source = tar_waves[:, 0:None]\n        self.logger.debug(f\"Concatenated tar_waves. Shape: {tar_waves.shape}\")\n\n        # TODO: In UVR, pitch changing happens here. Consider implementing this as a feature.\n\n        # TODO: In UVR, VR denoise model gets applied here. Consider implementing this as a feature.\n\n        self.logger.debug(\"Demixing process completed.\")\n        return source\n\n    def run_model(self, mix, is_match_mix=False):\n        \"\"\"\n        Processes the input mix through the model to separate the sources.","chunkId":"audio_separator/separator/architectures/mdx_separator.py:53"}],[643,{"filePath":"audio_separator/separator/architectures/mdx_separator.py","startLine":413,"endLine":422,"chunkText":"    def run_model(self, mix, is_match_mix=False):\n        \"\"\"\n        Processes the input mix through the model to separate the sources.\n        Applies STFT, handles spectrum modifications, and runs the model for source separation.\n        \"\"\"\n        # Applying the STFT to the mix. The mix is moved to the specified device (e.g., GPU) before processing.\n        # self.logger.debug(f\"Running STFT on the mix. Mix shape before STFT: {mix.shape}\")\n        spek = self.stft(mix.to(self.torch_device))\n        self.logger.debug(f\"STFT applied on mix. Spectrum shape: {spek.shape}\")\n","chunkId":"audio_separator/separator/architectures/mdx_separator.py:54"}],[644,{"filePath":"audio_separator/separator/architectures/mdx_separator.py","startLine":420,"endLine":430,"chunkText":"        spek = self.stft(mix.to(self.torch_device))\n        self.logger.debug(f\"STFT applied on mix. Spectrum shape: {spek.shape}\")\n\n        # Zeroing out the first 3 bins of the spectrum. This is often done to reduce low-frequency noise.\n        spek[:, :, :3, :] *= 0\n        # self.logger.debug(\"First 3 bins of the spectrum zeroed out.\")\n\n        # Handling the case where the mix needs to be matched (is_match_mix = True)\n        if is_match_mix:\n            # self.logger.debug(\"Match mix mode is enabled. Converting spectrum to NumPy array.\")\n            spec_pred = spek.cpu().numpy()","chunkId":"audio_separator/separator/architectures/mdx_separator.py:55"}],[645,{"filePath":"audio_separator/separator/architectures/mdx_separator.py","startLine":428,"endLine":435,"chunkText":"        if is_match_mix:\n            # self.logger.debug(\"Match mix mode is enabled. Converting spectrum to NumPy array.\")\n            spec_pred = spek.cpu().numpy()\n            self.logger.debug(\"is_match_mix: spectrum prediction obtained directly from STFT output.\")\n        else:\n            # If denoising is enabled, the model is run on both the negative and positive spectrums.\n            if self.enable_denoise:\n                # Assuming spek is a tensor and self.model_run can process it directly","chunkId":"audio_separator/separator/architectures/mdx_separator.py:56"}],[646,{"filePath":"audio_separator/separator/architectures/mdx_separator.py","startLine":433,"endLine":439,"chunkText":"            # If denoising is enabled, the model is run on both the negative and positive spectrums.\n            if self.enable_denoise:\n                # Assuming spek is a tensor and self.model_run can process it directly\n                spec_pred_neg = self.model_run(-spek)  # Ensure this line correctly negates spek and runs the model\n                spec_pred_pos = self.model_run(spek)\n                # Ensure both spec_pred_neg and spec_pred_pos are tensors before applying operations\n                spec_pred = (spec_pred_neg * -0.5) + (spec_pred_pos * 0.5)  # [invalid-unary-operand-type]","chunkId":"audio_separator/separator/architectures/mdx_separator.py:57"}],[647,{"filePath":"audio_separator/separator/architectures/mdx_separator.py","startLine":437,"endLine":445,"chunkText":"                spec_pred_pos = self.model_run(spek)\n                # Ensure both spec_pred_neg and spec_pred_pos are tensors before applying operations\n                spec_pred = (spec_pred_neg * -0.5) + (spec_pred_pos * 0.5)  # [invalid-unary-operand-type]\n                self.logger.debug(\"Model run on both negative and positive spectrums for denoising.\")\n            else:\n                spec_pred = self.model_run(spek)\n                self.logger.debug(\"Model run on the spectrum without denoising.\")\n\n        # Applying the inverse STFT to convert the spectrum back to the time domain.","chunkId":"audio_separator/separator/architectures/mdx_separator.py:58"}],[648,{"filePath":"audio_separator/separator/architectures/mdx_separator.py","startLine":443,"endLine":450,"chunkText":"                self.logger.debug(\"Model run on the spectrum without denoising.\")\n\n        # Applying the inverse STFT to convert the spectrum back to the time domain.\n        result = self.stft.inverse(torch.tensor(spec_pred).to(self.torch_device)).cpu().detach().numpy()\n        self.logger.debug(f\"Inverse STFT applied. Returning result with shape: {result.shape}\")\n\n        return result\n","chunkId":"audio_separator/separator/architectures/mdx_separator.py:59"}],[649,{"filePath":"audio_separator/separator/architectures/mdxc_separator.py","startLine":0,"endLine":17,"chunkText":"import os\nimport sys\n\nimport torch\nimport numpy as np\nfrom tqdm import tqdm\nfrom ml_collections import ConfigDict\nfrom scipy import signal\n\nfrom audio_separator.separator.common_separator import CommonSeparator\nfrom audio_separator.separator.uvr_lib_v5 import spec_utils\nfrom audio_separator.separator.uvr_lib_v5.tfc_tdf_v3 import TFC_TDF_net\n# Roformer direct constructors removed; loading handled via RoformerLoader in CommonSeparator.\n\n\nclass MDXCSeparator(CommonSeparator):\n    \"\"\"\n    MDXCSeparator is responsible for separating audio sources using MDXC models.","chunkId":"audio_separator/separator/architectures/mdxc_separator.py:0"}],[650,{"filePath":"audio_separator/separator/architectures/mdxc_separator.py","startLine":15,"endLine":25,"chunkText":"class MDXCSeparator(CommonSeparator):\n    \"\"\"\n    MDXCSeparator is responsible for separating audio sources using MDXC models.\n    It initializes with configuration parameters and prepares the model for separation tasks.\n    \"\"\"\n\n    def __init__(self, common_config, arch_config):\n        # Any configuration values which can be shared between architectures should be set already in CommonSeparator,\n        # e.g. user-specified functionality choices (self.output_single_stem) or common model parameters (self.primary_stem_name)\n        super().__init__(config=common_config)\n","chunkId":"audio_separator/separator/architectures/mdxc_separator.py:1"}],[651,{"filePath":"audio_separator/separator/architectures/mdxc_separator.py","startLine":23,"endLine":30,"chunkText":"        # e.g. user-specified functionality choices (self.output_single_stem) or common model parameters (self.primary_stem_name)\n        super().__init__(config=common_config)\n\n        # Model data is basic overview metadata about the model, e.g. which stem is primary and whether it's a karaoke model\n        # It's loaded in from model_data_new.json in Separator.load_model and there are JSON examples in that method\n        # The instance variable self.model_data is passed through from Separator and set in CommonSeparator\n        self.logger.debug(f\"Model data: {self.model_data}\")\n","chunkId":"audio_separator/separator/architectures/mdxc_separator.py:2"}],[652,{"filePath":"audio_separator/separator/architectures/mdxc_separator.py","startLine":28,"endLine":34,"chunkText":"        # The instance variable self.model_data is passed through from Separator and set in CommonSeparator\n        self.logger.debug(f\"Model data: {self.model_data}\")\n\n        # Arch Config is the MDXC architecture specific user configuration options, which should all be configurable by the user\n        # either by their Separator class instantiation or by passing in a CLI parameter.\n        # While there are similarities between architectures for some of these (e.g. batch_size), they are deliberately configured\n        # this way as they have architecture-specific default values.","chunkId":"audio_separator/separator/architectures/mdxc_separator.py:3"}],[653,{"filePath":"audio_separator/separator/architectures/mdxc_separator.py","startLine":32,"endLine":38,"chunkText":"        # either by their Separator class instantiation or by passing in a CLI parameter.\n        # While there are similarities between architectures for some of these (e.g. batch_size), they are deliberately configured\n        # this way as they have architecture-specific default values.\n        self.segment_size = arch_config.get(\"segment_size\", 256)\n\n        # Whether or not to use the segment size from model config, or the default\n        # The segment size is set based on the value provided in a chosen model's associated config file (yaml).","chunkId":"audio_separator/separator/architectures/mdxc_separator.py:4"}],[654,{"filePath":"audio_separator/separator/architectures/mdxc_separator.py","startLine":36,"endLine":45,"chunkText":"\n        # Whether or not to use the segment size from model config, or the default\n        # The segment size is set based on the value provided in a chosen model's associated config file (yaml).\n        self.override_model_segment_size = arch_config.get(\"override_model_segment_size\", False)\n\n        self.overlap = arch_config.get(\"overlap\", 8)\n        self.batch_size = arch_config.get(\"batch_size\", 1)\n\n        # Amount of pitch shift to apply during processing (this does NOT affect the pitch of the output audio):\n        # • Whole numbers indicate semitones.","chunkId":"audio_separator/separator/architectures/mdxc_separator.py:5"}],[655,{"filePath":"audio_separator/separator/architectures/mdxc_separator.py","startLine":43,"endLine":52,"chunkText":"\n        # Amount of pitch shift to apply during processing (this does NOT affect the pitch of the output audio):\n        # • Whole numbers indicate semitones.\n        # • Using higher pitches may cut the upper bandwidth, even in high-quality models.\n        # • Upping the pitch can be better for tracks with deeper vocals.\n        # • Dropping the pitch may take more processing time but works well for tracks with high-pitched vocals.\n        self.pitch_shift = arch_config.get(\"pitch_shift\", 0)\n\n        self.process_all_stems = arch_config.get(\"process_all_stems\", True)\n","chunkId":"audio_separator/separator/architectures/mdxc_separator.py:6"}],[656,{"filePath":"audio_separator/separator/architectures/mdxc_separator.py","startLine":50,"endLine":57,"chunkText":"\n        self.process_all_stems = arch_config.get(\"process_all_stems\", True)\n\n        self.logger.debug(f\"MDXC arch params: batch_size={self.batch_size}, segment_size={self.segment_size}, overlap={self.overlap}\")\n        self.logger.debug(f\"MDXC arch params: override_model_segment_size={self.override_model_segment_size}, pitch_shift={self.pitch_shift}\")\n        self.logger.debug(f\"MDXC multi-stem params: process_all_stems={self.process_all_stems}\")\n\n        # Align Roformer detection flag with CommonSeparator to ensure consistent stats/logging","chunkId":"audio_separator/separator/architectures/mdxc_separator.py:7"}],[657,{"filePath":"audio_separator/separator/architectures/mdxc_separator.py","startLine":55,"endLine":68,"chunkText":"        self.logger.debug(f\"MDXC multi-stem params: process_all_stems={self.process_all_stems}\")\n\n        # Align Roformer detection flag with CommonSeparator to ensure consistent stats/logging\n        self.is_roformer = getattr(self, \"is_roformer_model\", False)\n\n        self.load_model()\n\n        self.primary_source = None\n        self.secondary_source = None\n        self.audio_file_path = None\n        self.audio_file_base = None\n\n        # Only mark primary stem as main target for single-target models.\n        # Multi-stem models should not trigger residual subtraction logic.","chunkId":"audio_separator/separator/architectures/mdxc_separator.py:8"}],[658,{"filePath":"audio_separator/separator/architectures/mdxc_separator.py","startLine":66,"endLine":77,"chunkText":"\n        # Only mark primary stem as main target for single-target models.\n        # Multi-stem models should not trigger residual subtraction logic.\n        self.is_primary_stem_main_target = bool(self.model_data_cfgdict.training.target_instrument)\n\n        self.logger.debug(f\"is_primary_stem_main_target: {self.is_primary_stem_main_target}\")\n\n        self.logger.info(\"MDXC Separator initialisation complete\")\n\n    def load_model(self):\n        \"\"\"\n        Load the model into memory from file on disk, initialize it with config from the model data,","chunkId":"audio_separator/separator/architectures/mdxc_separator.py:9"}],[659,{"filePath":"audio_separator/separator/architectures/mdxc_separator.py","startLine":75,"endLine":87,"chunkText":"    def load_model(self):\n        \"\"\"\n        Load the model into memory from file on disk, initialize it with config from the model data,\n        and prepare for inferencing using hardware accelerated Torch device.\n        \"\"\"\n        self.logger.debug(\"Loading checkpoint model for inference...\")\n\n        self.model_data_cfgdict = ConfigDict(self.model_data)\n\n        try:\n            if self.is_roformer:\n                # Use the RoformerLoader exclusively; no legacy fallback\n                self.logger.debug(\"Loading Roformer model via RoformerLoader...\")","chunkId":"audio_separator/separator/architectures/mdxc_separator.py:10"}],[660,{"filePath":"audio_separator/separator/architectures/mdxc_separator.py","startLine":85,"endLine":95,"chunkText":"            if self.is_roformer:\n                # Use the RoformerLoader exclusively; no legacy fallback\n                self.logger.debug(\"Loading Roformer model via RoformerLoader...\")\n                result = self.roformer_loader.load_model(\n                    model_path=self.model_path,\n                    config=self.model_data,\n                    device=str(self.torch_device),\n                )\n\n                if getattr(result, \"success\", False) and getattr(result, \"model\", None) is not None:\n                    self.model_run = result.model","chunkId":"audio_separator/separator/architectures/mdxc_separator.py:11"}],[661,{"filePath":"audio_separator/separator/architectures/mdxc_separator.py","startLine":93,"endLine":103,"chunkText":"\n                if getattr(result, \"success\", False) and getattr(result, \"model\", None) is not None:\n                    self.model_run = result.model\n                    self.model_run.to(self.torch_device).eval()\n                else:\n                    error_msg = getattr(result, \"error_message\", \"RoformerLoader unsuccessful\")\n                    self.logger.error(f\"Failed to load Roformer model: {error_msg}\")\n                    raise RuntimeError(error_msg)\n\n            else:\n                self.logger.debug(\"Loading TFC_TDF_net model...\")","chunkId":"audio_separator/separator/architectures/mdxc_separator.py:12"}],[662,{"filePath":"audio_separator/separator/architectures/mdxc_separator.py","startLine":101,"endLine":111,"chunkText":"\n            else:\n                self.logger.debug(\"Loading TFC_TDF_net model...\")\n                self.model_run = TFC_TDF_net(self.model_data_cfgdict, device=self.torch_device)\n                self.logger.debug(\"Loading model onto cpu\")\n                # For some reason loading the state onto a hardware accelerated devices causes issues, \n                # so we load it onto CPU first then move it to the device\n                self.model_run.load_state_dict(torch.load(self.model_path, map_location=\"cpu\"))\n                self.model_run.to(self.torch_device).eval()\n\n        except RuntimeError as e:","chunkId":"audio_separator/separator/architectures/mdxc_separator.py:13"}],[663,{"filePath":"audio_separator/separator/architectures/mdxc_separator.py","startLine":109,"endLine":118,"chunkText":"                self.model_run.to(self.torch_device).eval()\n\n        except RuntimeError as e:\n            self.logger.error(f\"Error: {e}\")\n            self.logger.error(\"An error occurred while loading the model file. This often occurs when the model file is corrupt or incomplete.\")\n            self.logger.error(f\"Please try deleting the model file from {self.model_path} and run audio-separator again to re-download it.\")\n            sys.exit(1)\n\n    def separate(self, audio_file_path, custom_output_names=None):\n        \"\"\"","chunkId":"audio_separator/separator/architectures/mdxc_separator.py:14"}],[664,{"filePath":"audio_separator/separator/architectures/mdxc_separator.py","startLine":116,"endLine":128,"chunkText":"\n    def separate(self, audio_file_path, custom_output_names=None):\n        \"\"\"\n        Separates the audio file into primary and secondary sources based on the model's configuration.\n        It processes the mix, demixes it into sources, normalizes the sources, and saves the output files.\n\n        Args:\n            audio_file_path (str): The path to the audio file to be processed.\n            custom_output_names (dict, optional): Custom names for the output files. Defaults to None.\n\n        Returns:\n            list: A list of paths to the output files generated by the separation process.\n        \"\"\"","chunkId":"audio_separator/separator/architectures/mdxc_separator.py:15"}],[665,{"filePath":"audio_separator/separator/architectures/mdxc_separator.py","startLine":126,"endLine":139,"chunkText":"        Returns:\n            list: A list of paths to the output files generated by the separation process.\n        \"\"\"\n        self.primary_source = None\n        self.secondary_source = None\n\n        self.audio_file_path = audio_file_path\n        self.audio_file_base = os.path.splitext(os.path.basename(audio_file_path))[0]\n\n        self.logger.debug(f\"Preparing mix for input audio file {self.audio_file_path}...\")\n        mix = self.prepare_mix(self.audio_file_path)\n\n        # Check if audio is shorter than threshold\n        audio_duration_seconds = mix.shape[1] / self.sample_rate","chunkId":"audio_separator/separator/architectures/mdxc_separator.py:16"}],[666,{"filePath":"audio_separator/separator/architectures/mdxc_separator.py","startLine":137,"endLine":146,"chunkText":"\n        # Check if audio is shorter than threshold\n        audio_duration_seconds = mix.shape[1] / self.sample_rate\n        if audio_duration_seconds < 10.0:\n            # Only change and warn if it wasn't already set by the user\n            if not self.override_model_segment_size:\n                self.override_model_segment_size = True\n                self.logger.warning(f\"Audio duration ({audio_duration_seconds:.2f}s) is less than 10 seconds.\")\n                self.logger.warning(\"Automatically enabling override_model_segment_size for better processing of short audio.\")\n","chunkId":"audio_separator/separator/architectures/mdxc_separator.py:17"}],[667,{"filePath":"audio_separator/separator/architectures/mdxc_separator.py","startLine":144,"endLine":155,"chunkText":"                self.logger.warning(f\"Audio duration ({audio_duration_seconds:.2f}s) is less than 10 seconds.\")\n                self.logger.warning(\"Automatically enabling override_model_segment_size for better processing of short audio.\")\n\n        self.logger.debug(\"Normalizing mix before demixing...\")\n        mix = spec_utils.normalize(wave=mix, max_peak=self.normalization_threshold, min_peak=self.amplification_threshold)\n\n        source = self.demix(mix=mix)\n        self.logger.debug(\"Demixing completed.\")\n\n        output_files = []\n        self.logger.debug(\"Processing output files...\")\n","chunkId":"audio_separator/separator/architectures/mdxc_separator.py:18"}],[668,{"filePath":"audio_separator/separator/architectures/mdxc_separator.py","startLine":153,"endLine":167,"chunkText":"        output_files = []\n        self.logger.debug(\"Processing output files...\")\n\n        if isinstance(source, dict):\n            self.logger.debug(\"Source is a dict, processing each stem...\")\n            \n            stem_list = []\n            if self.model_data_cfgdict.training.target_instrument:\n                stem_list = [self.model_data_cfgdict.training.target_instrument]\n            else:\n                stem_list = self.model_data_cfgdict.training.instruments\n            \n            self.logger.debug(f\"Available stems: {stem_list}\")\n\n            is_multi_stem_model = len(stem_list) > 2","chunkId":"audio_separator/separator/architectures/mdxc_separator.py:19"}],[669,{"filePath":"audio_separator/separator/architectures/mdxc_separator.py","startLine":165,"endLine":175,"chunkText":"            self.logger.debug(f\"Available stems: {stem_list}\")\n\n            is_multi_stem_model = len(stem_list) > 2\n            should_process_all_stems = self.process_all_stems and is_multi_stem_model\n            \n            if should_process_all_stems:\n                self.logger.debug(\"Processing all stems from multi-stem model...\")\n                for stem_name in stem_list:\n                    stem_output_path = self.get_stem_output_path(stem_name, custom_output_names)\n                    stem_source = spec_utils.normalize(\n                        wave=source[stem_name], ","chunkId":"audio_separator/separator/architectures/mdxc_separator.py:20"}],[670,{"filePath":"audio_separator/separator/architectures/mdxc_separator.py","startLine":173,"endLine":182,"chunkText":"                    stem_output_path = self.get_stem_output_path(stem_name, custom_output_names)\n                    stem_source = spec_utils.normalize(\n                        wave=source[stem_name], \n                        max_peak=self.normalization_threshold, \n                        min_peak=self.amplification_threshold\n                    ).T\n                    \n                    self.logger.info(f\"Saving {stem_name} stem to {stem_output_path}...\")\n                    self.final_process(stem_output_path, stem_source, stem_name)\n                    output_files.append(stem_output_path)","chunkId":"audio_separator/separator/architectures/mdxc_separator.py:21"}],[671,{"filePath":"audio_separator/separator/architectures/mdxc_separator.py","startLine":180,"endLine":187,"chunkText":"                    self.logger.info(f\"Saving {stem_name} stem to {stem_output_path}...\")\n                    self.final_process(stem_output_path, stem_source, stem_name)\n                    output_files.append(stem_output_path)\n            else:\n                # Standard processing for primary and secondary stems\n                if not isinstance(self.primary_source, np.ndarray):\n                    self.logger.debug(f\"Normalizing primary source for primary stem {self.primary_stem_name}...\")\n                    self.primary_source = spec_utils.normalize(","chunkId":"audio_separator/separator/architectures/mdxc_separator.py:22"}],[672,{"filePath":"audio_separator/separator/architectures/mdxc_separator.py","startLine":185,"endLine":193,"chunkText":"                if not isinstance(self.primary_source, np.ndarray):\n                    self.logger.debug(f\"Normalizing primary source for primary stem {self.primary_stem_name}...\")\n                    self.primary_source = spec_utils.normalize(\n                        wave=source[self.primary_stem_name], \n                        max_peak=self.normalization_threshold, \n                        min_peak=self.amplification_threshold\n                    ).T\n\n                if not isinstance(self.secondary_source, np.ndarray):","chunkId":"audio_separator/separator/architectures/mdxc_separator.py:23"}],[673,{"filePath":"audio_separator/separator/architectures/mdxc_separator.py","startLine":191,"endLine":200,"chunkText":"                    ).T\n\n                if not isinstance(self.secondary_source, np.ndarray):\n                    self.logger.debug(f\"Normalizing secondary source for secondary stem {self.secondary_stem_name}...\")\n                    self.secondary_source = spec_utils.normalize(\n                        wave=source[self.secondary_stem_name], \n                        max_peak=self.normalization_threshold, \n                        min_peak=self.amplification_threshold\n                    ).T\n","chunkId":"audio_separator/separator/architectures/mdxc_separator.py:24"}],[674,{"filePath":"audio_separator/separator/architectures/mdxc_separator.py","startLine":198,"endLine":205,"chunkText":"                        min_peak=self.amplification_threshold\n                    ).T\n\n                if not self.output_single_stem or self.output_single_stem.lower() == self.secondary_stem_name.lower():\n                    self.secondary_stem_output_path = self.get_stem_output_path(self.secondary_stem_name, custom_output_names)\n\n                    self.logger.info(f\"Saving {self.secondary_stem_name} stem to {self.secondary_stem_output_path}...\")\n                    self.final_process(self.secondary_stem_output_path, self.secondary_source, self.secondary_stem_name)","chunkId":"audio_separator/separator/architectures/mdxc_separator.py:25"}],[675,{"filePath":"audio_separator/separator/architectures/mdxc_separator.py","startLine":203,"endLine":210,"chunkText":"\n                    self.logger.info(f\"Saving {self.secondary_stem_name} stem to {self.secondary_stem_output_path}...\")\n                    self.final_process(self.secondary_stem_output_path, self.secondary_source, self.secondary_stem_name)\n                    output_files.append(self.secondary_stem_output_path)\n                \n                if not self.output_single_stem or self.output_single_stem.lower() == self.primary_stem_name.lower():\n                    self.primary_stem_output_path = self.get_stem_output_path(self.primary_stem_name, custom_output_names)\n                    ","chunkId":"audio_separator/separator/architectures/mdxc_separator.py:26"}],[676,{"filePath":"audio_separator/separator/architectures/mdxc_separator.py","startLine":208,"endLine":215,"chunkText":"                if not self.output_single_stem or self.output_single_stem.lower() == self.primary_stem_name.lower():\n                    self.primary_stem_output_path = self.get_stem_output_path(self.primary_stem_name, custom_output_names)\n                    \n                    self.logger.info(f\"Saving {self.primary_stem_name} stem to {self.primary_stem_output_path}...\")\n                    self.final_process(self.primary_stem_output_path, self.primary_source, self.primary_stem_name)\n                    output_files.append(self.primary_stem_output_path)\n\n        else:","chunkId":"audio_separator/separator/architectures/mdxc_separator.py:27"}],[677,{"filePath":"audio_separator/separator/architectures/mdxc_separator.py","startLine":213,"endLine":222,"chunkText":"                    output_files.append(self.primary_stem_output_path)\n\n        else:\n            # Handle case when source is not a dictionary (single source model)\n            if not self.output_single_stem or self.output_single_stem.lower() == self.primary_stem_name.lower():\n                self.primary_stem_output_path = self.get_stem_output_path(self.primary_stem_name, custom_output_names)\n\n                if not isinstance(self.primary_source, np.ndarray):\n                    self.primary_source = source.T\n","chunkId":"audio_separator/separator/architectures/mdxc_separator.py:28"}],[678,{"filePath":"audio_separator/separator/architectures/mdxc_separator.py","startLine":220,"endLine":233,"chunkText":"                if not isinstance(self.primary_source, np.ndarray):\n                    self.primary_source = source.T\n\n                self.logger.info(f\"Saving {self.primary_stem_name} stem to {self.primary_stem_output_path}...\")\n                self.final_process(self.primary_stem_output_path, self.primary_source, self.primary_stem_name)\n                output_files.append(self.primary_stem_output_path)\n\n        return output_files\n\n    def pitch_fix(self, source, sr_pitched, orig_mix):\n        \"\"\"\n        Change the pitch of the source audio by a number of semitones.\n\n        Args:","chunkId":"audio_separator/separator/architectures/mdxc_separator.py:29"}],[679,{"filePath":"audio_separator/separator/architectures/mdxc_separator.py","startLine":231,"endLine":242,"chunkText":"        Change the pitch of the source audio by a number of semitones.\n\n        Args:\n            source (np.ndarray): The source audio to be pitch-shifted.\n            sr_pitched (int): The sample rate of the pitch-shifted audio.\n            orig_mix (np.ndarray): The original mix, used to match the shape of the pitch-shifted audio.\n\n        Returns:\n            np.ndarray: The pitch-shifted source audio.\n        \"\"\"\n        source = spec_utils.change_pitch_semitones(source, sr_pitched, semitone_shift=self.pitch_shift)[0]\n        source = spec_utils.match_array_shapes(source, orig_mix)","chunkId":"audio_separator/separator/architectures/mdxc_separator.py:30"}],[680,{"filePath":"audio_separator/separator/architectures/mdxc_separator.py","startLine":240,"endLine":252,"chunkText":"        \"\"\"\n        source = spec_utils.change_pitch_semitones(source, sr_pitched, semitone_shift=self.pitch_shift)[0]\n        source = spec_utils.match_array_shapes(source, orig_mix)\n        return source\n\n    def overlap_add(self, result, x, weights, start, length):\n        \"\"\"\n        Adds the overlapping part of the result to the result tensor.\n        \"\"\"\n        # Guard against minor shape mismatches from model output length\n        # Use the minimum of provided lengths to avoid broadcasting errors\n        safe_len = min(length, x.shape[-1], weights.shape[0])\n        if safe_len > 0:","chunkId":"audio_separator/separator/architectures/mdxc_separator.py:31"}],[681,{"filePath":"audio_separator/separator/architectures/mdxc_separator.py","startLine":250,"endLine":264,"chunkText":"        # Use the minimum of provided lengths to avoid broadcasting errors\n        safe_len = min(length, x.shape[-1], weights.shape[0])\n        if safe_len > 0:\n            result[..., start : start + safe_len] += x[..., :safe_len] * weights[:safe_len]\n        return result\n\n    def demix(self, mix: np.ndarray) -> dict:\n        \"\"\"\n        Demixes the input mix into primary and secondary sources using the model and model data.\n\n        Args:\n            mix (np.ndarray): The mix to be demixed.\n        Returns:\n            dict: A dictionary containing the demixed sources.\n        \"\"\"","chunkId":"audio_separator/separator/architectures/mdxc_separator.py:32"}],[682,{"filePath":"audio_separator/separator/architectures/mdxc_separator.py","startLine":262,"endLine":275,"chunkText":"        Returns:\n            dict: A dictionary containing the demixed sources.\n        \"\"\"\n        orig_mix = mix\n\n        if self.pitch_shift != 0:\n            self.logger.debug(f\"Shifting pitch by -{self.pitch_shift} semitones...\")\n            mix, sample_rate = spec_utils.change_pitch_semitones(mix, self.sample_rate, semitone_shift=-self.pitch_shift)\n\n        if self.is_roformer:\n            # Note: Currently, for Roformer models, `batch_size` is not utilized due to negligible performance improvements.\n\n            mix = torch.tensor(mix, dtype=torch.float32)\n","chunkId":"audio_separator/separator/architectures/mdxc_separator.py:33"}],[683,{"filePath":"audio_separator/separator/architectures/mdxc_separator.py","startLine":273,"endLine":284,"chunkText":"\n            mix = torch.tensor(mix, dtype=torch.float32)\n\n            if self.override_model_segment_size:\n                mdx_segment_size = self.segment_size\n                self.logger.debug(f\"Using configured segment size: {mdx_segment_size}\")\n            else:\n                mdx_segment_size = self.model_data_cfgdict.inference.dim_t\n                self.logger.debug(f\"Using model default segment size: {mdx_segment_size}\")\n\n            # num_stems aka \"S\" in UVR\n            num_stems = 1 if self.model_data_cfgdict.training.target_instrument else len(self.model_data_cfgdict.training.instruments)","chunkId":"audio_separator/separator/architectures/mdxc_separator.py:34"}],[684,{"filePath":"audio_separator/separator/architectures/mdxc_separator.py","startLine":282,"endLine":291,"chunkText":"\n            # num_stems aka \"S\" in UVR\n            num_stems = 1 if self.model_data_cfgdict.training.target_instrument else len(self.model_data_cfgdict.training.instruments)\n            self.logger.debug(f\"Number of stems: {num_stems}\")\n\n            # chunk_size aka \"C\" in UVR\n            # IMPORTANT: For Roformer models, use the model's STFT hop length to derive the temporal chunk size\n            stft_hop_len = getattr(self.model_data_cfgdict.model, \"stft_hop_length\", None)\n            if stft_hop_len is None:\n                # Fallback to audio.hop_length if not present, but log for visibility","chunkId":"audio_separator/separator/architectures/mdxc_separator.py:35"}],[685,{"filePath":"audio_separator/separator/architectures/mdxc_separator.py","startLine":289,"endLine":298,"chunkText":"            stft_hop_len = getattr(self.model_data_cfgdict.model, \"stft_hop_length\", None)\n            if stft_hop_len is None:\n                # Fallback to audio.hop_length if not present, but log for visibility\n                stft_hop_len = self.model_data_cfgdict.audio.hop_length\n                self.logger.debug(\n                    f\"Model.stft_hop_length missing; falling back to audio.hop_length={stft_hop_len}\"\n                )\n\n            chunk_size = int(stft_hop_len) * (int(mdx_segment_size) - 1)\n            self.logger.debug(","chunkId":"audio_separator/separator/architectures/mdxc_separator.py:36"}],[686,{"filePath":"audio_separator/separator/architectures/mdxc_separator.py","startLine":296,"endLine":305,"chunkText":"\n            chunk_size = int(stft_hop_len) * (int(mdx_segment_size) - 1)\n            self.logger.debug(\n                f\"Chunk size: {chunk_size} (using stft_hop_length={stft_hop_len} and dim_t={mdx_segment_size})\"\n            )\n\n            # Align step to chunk_size by default for Roformer to avoid stride mismatches\n            # If a user-specified overlap (in seconds) results in a step larger than chunk_size, clamp it\n            desired_step = int(self.overlap * self.model_data_cfgdict.audio.sample_rate)\n            step = chunk_size if desired_step <= 0 else min(desired_step, chunk_size)","chunkId":"audio_separator/separator/architectures/mdxc_separator.py:37"}],[687,{"filePath":"audio_separator/separator/architectures/mdxc_separator.py","startLine":303,"endLine":313,"chunkText":"            # If a user-specified overlap (in seconds) results in a step larger than chunk_size, clamp it\n            desired_step = int(self.overlap * self.model_data_cfgdict.audio.sample_rate)\n            step = chunk_size if desired_step <= 0 else min(desired_step, chunk_size)\n            self.logger.debug(f\"Step: {step} (desired={desired_step})\")\n\n            # Create a weighting table and convert it to a PyTorch tensor\n            window = torch.tensor(signal.windows.hamming(chunk_size), dtype=torch.float32)\n\n            device = next(self.model_run.parameters()).device\n\n","chunkId":"audio_separator/separator/architectures/mdxc_separator.py:38"}],[688,{"filePath":"audio_separator/separator/architectures/mdxc_separator.py","startLine":311,"endLine":323,"chunkText":"            device = next(self.model_run.parameters()).device\n\n\n            with torch.no_grad():\n                req_shape = (len(self.model_data_cfgdict.training.instruments),) + tuple(mix.shape)\n                result = torch.zeros(req_shape, dtype=torch.float32)\n                counter = torch.zeros(req_shape, dtype=torch.float32)\n\n                for i in tqdm(range(0, mix.shape[1], step)):\n                    part = mix[:, i : i + chunk_size]\n                    length = part.shape[-1]\n                    if i + chunk_size > mix.shape[1]:\n                        part = mix[:, -chunk_size:]","chunkId":"audio_separator/separator/architectures/mdxc_separator.py:39"}],[689,{"filePath":"audio_separator/separator/architectures/mdxc_separator.py","startLine":321,"endLine":331,"chunkText":"                    length = part.shape[-1]\n                    if i + chunk_size > mix.shape[1]:\n                        part = mix[:, -chunk_size:]\n                        length = chunk_size\n                    part = part.to(device)\n                    x = self.model_run(part.unsqueeze(0))[0]\n                    x = x.cpu()\n                    # Perform overlap_add on CPU\n                    if i + chunk_size > mix.shape[1]:\n                        # Fixed to correctly add to the end of the tensor\n                        start_idx = result.shape[-1] - chunk_size","chunkId":"audio_separator/separator/architectures/mdxc_separator.py:40"}],[690,{"filePath":"audio_separator/separator/architectures/mdxc_separator.py","startLine":329,"endLine":337,"chunkText":"                    if i + chunk_size > mix.shape[1]:\n                        # Fixed to correctly add to the end of the tensor\n                        start_idx = result.shape[-1] - chunk_size\n                        result = self.overlap_add(result, x, window, start_idx, length)\n                        safe_len = min(length, x.shape[-1], window.shape[0])\n                        if safe_len > 0:\n                            counter[..., start_idx : start_idx + safe_len] += window[:safe_len]\n                    else:\n                        result = self.overlap_add(result, x, window, i, length)","chunkId":"audio_separator/separator/architectures/mdxc_separator.py:41"}],[691,{"filePath":"audio_separator/separator/architectures/mdxc_separator.py","startLine":335,"endLine":347,"chunkText":"                            counter[..., start_idx : start_idx + safe_len] += window[:safe_len]\n                    else:\n                        result = self.overlap_add(result, x, window, i, length)\n                        safe_len = min(length, x.shape[-1], window.shape[0])\n                        if safe_len > 0:\n                            counter[..., i : i + safe_len] += window[:safe_len]\n\n            inferenced_outputs = result / counter.clamp(min=1e-10)\n\n        else:\n            mix = torch.tensor(mix, dtype=torch.float32)\n\n            try:","chunkId":"audio_separator/separator/architectures/mdxc_separator.py:42"}],[692,{"filePath":"audio_separator/separator/architectures/mdxc_separator.py","startLine":345,"endLine":357,"chunkText":"            mix = torch.tensor(mix, dtype=torch.float32)\n\n            try:\n                num_stems = self.model_run.num_target_instruments\n            except AttributeError:\n                num_stems = self.model_run.module.num_target_instruments\n            self.logger.debug(f\"Number of stems: {num_stems}\")\n\n            if self.override_model_segment_size:\n                mdx_segment_size = self.segment_size\n                self.logger.debug(f\"Using configured segment size: {mdx_segment_size}\")\n            else:\n                mdx_segment_size = self.model_data_cfgdict.inference.dim_t","chunkId":"audio_separator/separator/architectures/mdxc_separator.py:43"}],[693,{"filePath":"audio_separator/separator/architectures/mdxc_separator.py","startLine":355,"endLine":366,"chunkText":"                self.logger.debug(f\"Using configured segment size: {mdx_segment_size}\")\n            else:\n                mdx_segment_size = self.model_data_cfgdict.inference.dim_t\n                self.logger.debug(f\"Using model default segment size: {mdx_segment_size}\")\n\n            chunk_size = self.model_data_cfgdict.audio.hop_length * (mdx_segment_size - 1)\n            self.logger.debug(f\"Chunk size: {chunk_size}\")\n\n            hop_size = chunk_size // self.overlap\n            self.logger.debug(f\"Hop size: {hop_size}\")\n\n            mix_shape = mix.shape[1]","chunkId":"audio_separator/separator/architectures/mdxc_separator.py:44"}],[694,{"filePath":"audio_separator/separator/architectures/mdxc_separator.py","startLine":364,"endLine":375,"chunkText":"            self.logger.debug(f\"Hop size: {hop_size}\")\n\n            mix_shape = mix.shape[1]\n            pad_size = hop_size - (mix_shape - chunk_size) % hop_size\n            self.logger.debug(f\"Pad size: {pad_size}\")\n\n            mix = torch.cat([torch.zeros(2, chunk_size - hop_size), mix, torch.zeros(2, pad_size + chunk_size - hop_size)], 1)\n            self.logger.debug(f\"Mix shape: {mix.shape}\")\n\n            chunks = mix.unfold(1, chunk_size, hop_size).transpose(0, 1)\n            self.logger.debug(f\"Chunks length: {len(chunks)} and shape: {chunks.shape}\")\n","chunkId":"audio_separator/separator/architectures/mdxc_separator.py:45"}],[695,{"filePath":"audio_separator/separator/architectures/mdxc_separator.py","startLine":373,"endLine":380,"chunkText":"            chunks = mix.unfold(1, chunk_size, hop_size).transpose(0, 1)\n            self.logger.debug(f\"Chunks length: {len(chunks)} and shape: {chunks.shape}\")\n\n            batches = [chunks[i : i + self.batch_size] for i in range(0, len(chunks), self.batch_size)]\n            self.logger.debug(f\"Batch size: {self.batch_size}, number of batches: {len(batches)}\")\n\n            # accumulated_outputs is used to accumulate the output from processing each batch of chunks through the model.\n            # It starts as a tensor of zeros and is updated in-place as the model processes each batch.","chunkId":"audio_separator/separator/architectures/mdxc_separator.py:46"}],[696,{"filePath":"audio_separator/separator/architectures/mdxc_separator.py","startLine":378,"endLine":386,"chunkText":"\n            # accumulated_outputs is used to accumulate the output from processing each batch of chunks through the model.\n            # It starts as a tensor of zeros and is updated in-place as the model processes each batch.\n            # The variable holds the combined result of all processed batches, which, after post-processing, represents the separated audio sources.\n            accumulated_outputs = torch.zeros(num_stems, *mix.shape) if num_stems > 1 else torch.zeros_like(mix)\n\n            with torch.no_grad():\n                count = 0\n                for batch in tqdm(batches):","chunkId":"audio_separator/separator/architectures/mdxc_separator.py:47"}],[697,{"filePath":"audio_separator/separator/architectures/mdxc_separator.py","startLine":384,"endLine":391,"chunkText":"            with torch.no_grad():\n                count = 0\n                for batch in tqdm(batches):\n                    # Since the model processes the audio data in batches, single_batch_result temporarily holds the model's output\n                    # for each batch before it is accumulated into accumulated_outputs.\n                    single_batch_result = self.model_run(batch.to(self.torch_device))\n\n                    # Each individual output tensor from the current batch's processing result.","chunkId":"audio_separator/separator/architectures/mdxc_separator.py:48"}],[698,{"filePath":"audio_separator/separator/architectures/mdxc_separator.py","startLine":389,"endLine":395,"chunkText":"                    single_batch_result = self.model_run(batch.to(self.torch_device))\n\n                    # Each individual output tensor from the current batch's processing result.\n                    # Since single_batch_result can contain multiple output tensors (one for each piece of audio in the batch),\n                    # individual_output is used to iterate through these tensors and accumulate them into accumulated_outputs.\n                    for individual_output in single_batch_result:\n                        individual_output_cpu = individual_output.cpu()","chunkId":"audio_separator/separator/architectures/mdxc_separator.py:49"}],[699,{"filePath":"audio_separator/separator/architectures/mdxc_separator.py","startLine":393,"endLine":400,"chunkText":"                    # individual_output is used to iterate through these tensors and accumulate them into accumulated_outputs.\n                    for individual_output in single_batch_result:\n                        individual_output_cpu = individual_output.cpu()\n                        # Accumulate outputs on CPU\n                        accumulated_outputs[..., count * hop_size : count * hop_size + chunk_size] += individual_output_cpu\n                        count += 1\n\n            self.logger.debug(\"Calculating inferenced outputs based on accumulated outputs and overlap\")","chunkId":"audio_separator/separator/architectures/mdxc_separator.py:50"}],[700,{"filePath":"audio_separator/separator/architectures/mdxc_separator.py","startLine":398,"endLine":409,"chunkText":"                        count += 1\n\n            self.logger.debug(\"Calculating inferenced outputs based on accumulated outputs and overlap\")\n            inferenced_outputs = accumulated_outputs[..., chunk_size - hop_size : -(pad_size + chunk_size - hop_size)] / self.overlap\n            self.logger.debug(\"Deleting accumulated outputs to free up memory\")\n            del accumulated_outputs\n\n        if num_stems > 1:\n            self.logger.debug(\"Number of stems is greater than 1, detaching individual sources and correcting pitch if necessary...\")\n\n            sources = {}\n","chunkId":"audio_separator/separator/architectures/mdxc_separator.py:51"}],[701,{"filePath":"audio_separator/separator/architectures/mdxc_separator.py","startLine":407,"endLine":413,"chunkText":"\n            sources = {}\n\n            # Iterates over each instrument specified in the model's configuration and its corresponding separated audio source.\n            # self.model_data_cfgdict.training.instruments provides the list of stems.\n            # estimated_sources.cpu().detach().numpy() converts the separated sources tensor to a NumPy array for processing.\n            # Each iteration provides an instrument name ('key') and its separated audio ('value') for further processing.","chunkId":"audio_separator/separator/architectures/mdxc_separator.py:52"}],[702,{"filePath":"audio_separator/separator/architectures/mdxc_separator.py","startLine":411,"endLine":416,"chunkText":"            # self.model_data_cfgdict.training.instruments provides the list of stems.\n            # estimated_sources.cpu().detach().numpy() converts the separated sources tensor to a NumPy array for processing.\n            # Each iteration provides an instrument name ('key') and its separated audio ('value') for further processing.\n            for key, value in zip(self.model_data_cfgdict.training.instruments, inferenced_outputs.cpu().detach().numpy()):\n                self.logger.debug(f\"Processing instrument: {key}\")\n                if self.pitch_shift != 0:","chunkId":"audio_separator/separator/architectures/mdxc_separator.py:53"}],[703,{"filePath":"audio_separator/separator/architectures/mdxc_separator.py","startLine":414,"endLine":422,"chunkText":"            for key, value in zip(self.model_data_cfgdict.training.instruments, inferenced_outputs.cpu().detach().numpy()):\n                self.logger.debug(f\"Processing instrument: {key}\")\n                if self.pitch_shift != 0:\n                    self.logger.debug(f\"Applying pitch correction for {key}\")\n                    sources[key] = self.pitch_fix(value, sample_rate, orig_mix)\n                else:\n                    sources[key] = value\n\n            # Residual subtraction is only applicable for single-target models (not multi-stem)","chunkId":"audio_separator/separator/architectures/mdxc_separator.py:54"}],[704,{"filePath":"audio_separator/separator/architectures/mdxc_separator.py","startLine":420,"endLine":426,"chunkText":"                    sources[key] = value\n\n            # Residual subtraction is only applicable for single-target models (not multi-stem)\n            if self.is_primary_stem_main_target and num_stems == 1:\n                self.logger.debug(f\"Primary stem: {self.primary_stem_name} is main target, detaching and matching array shapes if necessary...\")\n                if sources[self.primary_stem_name].shape[1] != orig_mix.shape[1]:\n                    sources[self.primary_stem_name] = spec_utils.match_array_shapes(sources[self.primary_stem_name], orig_mix)","chunkId":"audio_separator/separator/architectures/mdxc_separator.py:55"}],[705,{"filePath":"audio_separator/separator/architectures/mdxc_separator.py","startLine":424,"endLine":431,"chunkText":"                self.logger.debug(f\"Primary stem: {self.primary_stem_name} is main target, detaching and matching array shapes if necessary...\")\n                if sources[self.primary_stem_name].shape[1] != orig_mix.shape[1]:\n                    sources[self.primary_stem_name] = spec_utils.match_array_shapes(sources[self.primary_stem_name], orig_mix)\n                sources[self.secondary_stem_name] = orig_mix - sources[self.primary_stem_name]\n\n            self.logger.debug(\"Deleting inferenced outputs to free up memory\")\n            del inferenced_outputs\n","chunkId":"audio_separator/separator/architectures/mdxc_separator.py:56"}],[706,{"filePath":"audio_separator/separator/architectures/mdxc_separator.py","startLine":429,"endLine":440,"chunkText":"            self.logger.debug(\"Deleting inferenced outputs to free up memory\")\n            del inferenced_outputs\n\n            self.logger.debug(\"Returning separated sources\")\n            return sources\n        else:\n            self.logger.debug(\"Processing single source...\")\n\n            if self.is_roformer:\n                sources = {k: v.cpu().detach().numpy() for k, v in zip([self.model_data_cfgdict.training.target_instrument], inferenced_outputs)}\n                inferenced_output = sources[self.model_data_cfgdict.training.target_instrument]\n            else:","chunkId":"audio_separator/separator/architectures/mdxc_separator.py:57"}],[707,{"filePath":"audio_separator/separator/architectures/mdxc_separator.py","startLine":438,"endLine":447,"chunkText":"                sources = {k: v.cpu().detach().numpy() for k, v in zip([self.model_data_cfgdict.training.target_instrument], inferenced_outputs)}\n                inferenced_output = sources[self.model_data_cfgdict.training.target_instrument]\n            else:\n                inferenced_output = inferenced_outputs.cpu().detach().numpy()\n\n            self.logger.debug(\"Demix process completed for single source.\")\n\n            self.logger.debug(\"Deleting inferenced outputs to free up memory\")\n            del inferenced_outputs\n","chunkId":"audio_separator/separator/architectures/mdxc_separator.py:58"}],[708,{"filePath":"audio_separator/separator/architectures/mdxc_separator.py","startLine":445,"endLine":455,"chunkText":"            self.logger.debug(\"Deleting inferenced outputs to free up memory\")\n            del inferenced_outputs\n\n            # For single-target models (e.g., karaoke), also return the residual as secondary\n            if self.pitch_shift != 0:\n                self.logger.debug(\"Applying pitch correction for single instrument\")\n                primary = self.pitch_fix(inferenced_output, sample_rate, orig_mix)\n            else:\n                primary = inferenced_output\n\n            if self.is_primary_stem_main_target:","chunkId":"audio_separator/separator/architectures/mdxc_separator.py:59"}],[709,{"filePath":"audio_separator/separator/architectures/mdxc_separator.py","startLine":453,"endLine":463,"chunkText":"                primary = inferenced_output\n\n            if self.is_primary_stem_main_target:\n                self.logger.debug(\"Single-target model detected; computing residual secondary stem from original mix\")\n                # Ensure shapes match before residual subtraction\n                if primary.shape[1] != orig_mix.shape[1]:\n                    primary = spec_utils.match_array_shapes(primary, orig_mix)\n                secondary = orig_mix - primary\n                return {\n                    self.primary_stem_name: primary,\n                    self.secondary_stem_name: secondary,","chunkId":"audio_separator/separator/architectures/mdxc_separator.py:60"}],[710,{"filePath":"audio_separator/separator/architectures/mdxc_separator.py","startLine":461,"endLine":468,"chunkText":"                return {\n                    self.primary_stem_name: primary,\n                    self.secondary_stem_name: secondary,\n                }\n\n            self.logger.debug(\"Returning inferenced output for single instrument\")\n            return primary\n","chunkId":"audio_separator/separator/architectures/mdxc_separator.py:61"}],[711,{"filePath":"audio_separator/separator/architectures/vr_separator.py","startLine":0,"endLine":19,"chunkText":"\"\"\"Module for separating audio sources using VR architecture models.\"\"\"\n\nimport os\nimport math\n\nimport torch\nimport librosa\nimport numpy as np\nfrom tqdm import tqdm\n\n# Check if we really need the rerun_mp3 function, remove if not\nimport audioread\n\nfrom audio_separator.separator.common_separator import CommonSeparator\nfrom audio_separator.separator.uvr_lib_v5 import spec_utils\nfrom audio_separator.separator.uvr_lib_v5.vr_network import nets\nfrom audio_separator.separator.uvr_lib_v5.vr_network import nets_new\nfrom audio_separator.separator.uvr_lib_v5.vr_network.model_param_init import ModelParameters\n\n","chunkId":"audio_separator/separator/architectures/vr_separator.py:0"}],[712,{"filePath":"audio_separator/separator/architectures/vr_separator.py","startLine":17,"endLine":27,"chunkText":"from audio_separator.separator.uvr_lib_v5.vr_network.model_param_init import ModelParameters\n\n\nclass VRSeparator(CommonSeparator):\n    \"\"\"\n    VRSeparator is responsible for separating audio sources using VR models.\n    It initializes with configuration parameters and prepares the model for separation tasks.\n    \"\"\"\n\n    def __init__(self, common_config, arch_config: dict):\n        # Any configuration values which can be shared between architectures should be set already in CommonSeparator,","chunkId":"audio_separator/separator/architectures/vr_separator.py:1"}],[713,{"filePath":"audio_separator/separator/architectures/vr_separator.py","startLine":25,"endLine":32,"chunkText":"\n    def __init__(self, common_config, arch_config: dict):\n        # Any configuration values which can be shared between architectures should be set already in CommonSeparator,\n        # e.g. user-specified functionality choices (self.output_single_stem) or common model parameters (self.primary_stem_name)\n        super().__init__(config=common_config)\n\n        # Model data is basic overview metadata about the model, e.g. which stem is primary and whether it's a karaoke model\n        # It's loaded in from model_data_new.json in Separator.load_model and there are JSON examples in that method","chunkId":"audio_separator/separator/architectures/vr_separator.py:2"}],[714,{"filePath":"audio_separator/separator/architectures/vr_separator.py","startLine":30,"endLine":37,"chunkText":"\n        # Model data is basic overview metadata about the model, e.g. which stem is primary and whether it's a karaoke model\n        # It's loaded in from model_data_new.json in Separator.load_model and there are JSON examples in that method\n        # The instance variable self.model_data is passed through from Separator and set in CommonSeparator\n        self.logger.debug(f\"Model data: {self.model_data}\")\n\n        # Most of the VR models use the same number of output channels, but the VR 51 models have specific values set in model_data JSON\n        self.model_capacity = 32, 128","chunkId":"audio_separator/separator/architectures/vr_separator.py:3"}],[715,{"filePath":"audio_separator/separator/architectures/vr_separator.py","startLine":35,"endLine":44,"chunkText":"\n        # Most of the VR models use the same number of output channels, but the VR 51 models have specific values set in model_data JSON\n        self.model_capacity = 32, 128\n        self.is_vr_51_model = False\n\n        if \"nout\" in self.model_data.keys() and \"nout_lstm\" in self.model_data.keys():\n            self.model_capacity = self.model_data[\"nout\"], self.model_data[\"nout_lstm\"]\n            self.is_vr_51_model = True\n\n        # Model params are additional technical parameter values from JSON files in separator/uvr_lib_v5/vr_network/modelparams/*.json,","chunkId":"audio_separator/separator/architectures/vr_separator.py:4"}],[716,{"filePath":"audio_separator/separator/architectures/vr_separator.py","startLine":42,"endLine":48,"chunkText":"            self.is_vr_51_model = True\n\n        # Model params are additional technical parameter values from JSON files in separator/uvr_lib_v5/vr_network/modelparams/*.json,\n        # with filenames referenced by the model_data[\"vr_model_param\"] value\n        package_root_filepath = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))\n        vr_params_json_dir = os.path.join(package_root_filepath, \"uvr_lib_v5\", \"vr_network\", \"modelparams\")\n        vr_params_json_filename = f\"{self.model_data['vr_model_param']}.json\"","chunkId":"audio_separator/separator/architectures/vr_separator.py:5"}],[717,{"filePath":"audio_separator/separator/architectures/vr_separator.py","startLine":46,"endLine":53,"chunkText":"        package_root_filepath = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))\n        vr_params_json_dir = os.path.join(package_root_filepath, \"uvr_lib_v5\", \"vr_network\", \"modelparams\")\n        vr_params_json_filename = f\"{self.model_data['vr_model_param']}.json\"\n        vr_params_json_filepath = os.path.join(vr_params_json_dir, vr_params_json_filename)\n        self.model_params = ModelParameters(vr_params_json_filepath)\n\n        self.logger.debug(f\"Model params: {self.model_params.param}\")\n","chunkId":"audio_separator/separator/architectures/vr_separator.py:6"}],[718,{"filePath":"audio_separator/separator/architectures/vr_separator.py","startLine":51,"endLine":59,"chunkText":"\n        self.logger.debug(f\"Model params: {self.model_params.param}\")\n\n        # Arch Config is the VR architecture specific user configuration options, which should all be configurable by the user\n        # either by their Separator class instantiation or by passing in a CLI parameter.\n        # While there are similarities between architectures for some of these (e.g. batch_size), they are deliberately configured\n        # this way as they have architecture-specific default values.\n\n        # This option performs Test-Time-Augmentation to improve the separation quality.","chunkId":"audio_separator/separator/architectures/vr_separator.py:7"}],[719,{"filePath":"audio_separator/separator/architectures/vr_separator.py","startLine":57,"endLine":63,"chunkText":"        # this way as they have architecture-specific default values.\n\n        # This option performs Test-Time-Augmentation to improve the separation quality.\n        # Note: Having this selected will increase the time it takes to complete a conversion\n        self.enable_tta = arch_config.get(\"enable_tta\", False)\n\n        # This option can potentially identify leftover instrumental artifacts within the vocal outputs; may improve the separation of some songs.","chunkId":"audio_separator/separator/architectures/vr_separator.py:8"}],[720,{"filePath":"audio_separator/separator/architectures/vr_separator.py","startLine":61,"endLine":69,"chunkText":"        self.enable_tta = arch_config.get(\"enable_tta\", False)\n\n        # This option can potentially identify leftover instrumental artifacts within the vocal outputs; may improve the separation of some songs.\n        # Note: Selecting this option can adversely affect the conversion process, depending on the track. Because of this, it is only recommended as a last resort.\n        self.enable_post_process = arch_config.get(\"enable_post_process\", False)\n\n        # post_process_threshold values = ('0.1', '0.2', '0.3')\n        self.post_process_threshold = arch_config.get(\"post_process_threshold\", 0.2)\n","chunkId":"audio_separator/separator/architectures/vr_separator.py:9"}],[721,{"filePath":"audio_separator/separator/architectures/vr_separator.py","startLine":67,"endLine":75,"chunkText":"        # post_process_threshold values = ('0.1', '0.2', '0.3')\n        self.post_process_threshold = arch_config.get(\"post_process_threshold\", 0.2)\n\n        # Number of batches to be processed at a time.\n        # - Higher values mean more RAM usage but slightly faster processing times.\n        # - Lower values mean less RAM usage but slightly longer processing times.\n        # - Batch size value has no effect on output quality.\n\n        # Andrew note: for some reason, lower batch sizes seem to cause broken output for VR arch; need to investigate why","chunkId":"audio_separator/separator/architectures/vr_separator.py:10"}],[722,{"filePath":"audio_separator/separator/architectures/vr_separator.py","startLine":73,"endLine":84,"chunkText":"        # - Batch size value has no effect on output quality.\n\n        # Andrew note: for some reason, lower batch sizes seem to cause broken output for VR arch; need to investigate why\n        self.batch_size = arch_config.get(\"batch_size\", 1)\n\n        # Select window size to balance quality and speed:\n        # - 1024 - Quick but lesser quality.\n        # - 512 - Medium speed and quality.\n        # - 320 - Takes longer but may offer better quality.\n        self.window_size = arch_config.get(\"window_size\", 512)\n\n        # The application will mirror the missing frequency range of the output.","chunkId":"audio_separator/separator/architectures/vr_separator.py:11"}],[723,{"filePath":"audio_separator/separator/architectures/vr_separator.py","startLine":82,"endLine":93,"chunkText":"        self.window_size = arch_config.get(\"window_size\", 512)\n\n        # The application will mirror the missing frequency range of the output.\n        self.high_end_process = arch_config.get(\"high_end_process\", False)\n        self.input_high_end_h = None\n        self.input_high_end = None\n\n        # Adjust the intensity of primary stem extraction:\n        # - Ranges from -100 - 100.\n        # - Bigger values mean deeper extractions.\n        # - Typically, it's set to 5 for vocals & instrumentals.\n        # - Values beyond 5 might muddy the sound for non-vocal models.","chunkId":"audio_separator/separator/architectures/vr_separator.py:12"}],[724,{"filePath":"audio_separator/separator/architectures/vr_separator.py","startLine":91,"endLine":99,"chunkText":"        # - Bigger values mean deeper extractions.\n        # - Typically, it's set to 5 for vocals & instrumentals.\n        # - Values beyond 5 might muddy the sound for non-vocal models.\n        self.aggression = float(int(arch_config.get(\"aggression\", 5)) / 100)\n\n        self.aggressiveness = {\"value\": self.aggression, \"split_bin\": self.model_params.param[\"band\"][1][\"crop_stop\"], \"aggr_correction\": self.model_params.param.get(\"aggr_correction\")}\n\n        self.model_samplerate = self.model_params.param[\"sr\"]\n","chunkId":"audio_separator/separator/architectures/vr_separator.py:13"}],[725,{"filePath":"audio_separator/separator/architectures/vr_separator.py","startLine":97,"endLine":102,"chunkText":"\n        self.model_samplerate = self.model_params.param[\"sr\"]\n\n        self.logger.debug(f\"VR arch params: enable_tta={self.enable_tta}, enable_post_process={self.enable_post_process}, post_process_threshold={self.post_process_threshold}\")\n        self.logger.debug(f\"VR arch params: batch_size={self.batch_size}, window_size={self.window_size}\")\n        self.logger.debug(f\"VR arch params: high_end_process={self.high_end_process}, aggression={self.aggression}\")","chunkId":"audio_separator/separator/architectures/vr_separator.py:14"}],[726,{"filePath":"audio_separator/separator/architectures/vr_separator.py","startLine":100,"endLine":104,"chunkText":"        self.logger.debug(f\"VR arch params: enable_tta={self.enable_tta}, enable_post_process={self.enable_post_process}, post_process_threshold={self.post_process_threshold}\")\n        self.logger.debug(f\"VR arch params: batch_size={self.batch_size}, window_size={self.window_size}\")\n        self.logger.debug(f\"VR arch params: high_end_process={self.high_end_process}, aggression={self.aggression}\")\n        self.logger.debug(f\"VR arch params: is_vr_51_model={self.is_vr_51_model}, model_samplerate={self.model_samplerate}, model_capacity={self.model_capacity}\")\n","chunkId":"audio_separator/separator/architectures/vr_separator.py:15"}],[727,{"filePath":"audio_separator/separator/architectures/vr_separator.py","startLine":102,"endLine":111,"chunkText":"        self.logger.debug(f\"VR arch params: high_end_process={self.high_end_process}, aggression={self.aggression}\")\n        self.logger.debug(f\"VR arch params: is_vr_51_model={self.is_vr_51_model}, model_samplerate={self.model_samplerate}, model_capacity={self.model_capacity}\")\n\n        self.model_run = lambda *args, **kwargs: self.logger.error(\"Model run method is not initialised yet.\")\n\n        # wav_subtype will be set based on input audio bit depth in prepare_mix()\n        # Removed hardcoded \"PCM_16\" to allow bit depth preservation\n\n        self.logger.info(\"VR Separator initialisation complete\")\n","chunkId":"audio_separator/separator/architectures/vr_separator.py:16"}],[728,{"filePath":"audio_separator/separator/architectures/vr_separator.py","startLine":109,"endLine":121,"chunkText":"\n        self.logger.info(\"VR Separator initialisation complete\")\n\n    def separate(self, audio_file_path, custom_output_names=None):\n        \"\"\"\n        Separates the audio file into primary and secondary sources based on the model's configuration.\n        It processes the mix, demixes it into sources, normalizes the sources, and saves the output files.\n\n        Args:\n            audio_file_path (str): The path to the audio file to be processed.\n            custom_output_names (dict, optional): Custom names for the output files. Defaults to None.\n\n        Returns:","chunkId":"audio_separator/separator/architectures/vr_separator.py:17"}],[729,{"filePath":"audio_separator/separator/architectures/vr_separator.py","startLine":119,"endLine":133,"chunkText":"            custom_output_names (dict, optional): Custom names for the output files. Defaults to None.\n\n        Returns:\n            list: A list of paths to the output files generated by the separation process.\n        \"\"\"\n        self.primary_source = None\n        self.secondary_source = None\n\n        self.audio_file_path = audio_file_path\n        self.audio_file_base = os.path.splitext(os.path.basename(audio_file_path))[ 0]\n\n        # Detect input audio bit depth for output preservation\n        try:\n            import soundfile as sf\n            info = sf.info(audio_file_path)","chunkId":"audio_separator/separator/architectures/vr_separator.py:18"}],[730,{"filePath":"audio_separator/separator/architectures/vr_separator.py","startLine":131,"endLine":142,"chunkText":"        try:\n            import soundfile as sf\n            info = sf.info(audio_file_path)\n            self.input_audio_subtype = info.subtype\n            self.logger.info(f\"Input audio subtype: {self.input_audio_subtype}\")\n            \n            # Map subtype to wav_subtype for soundfile and set input_bit_depth for pydub\n            if \"24\" in self.input_audio_subtype:\n                self.wav_subtype = \"PCM_24\"\n                self.input_bit_depth = 24\n                self.logger.info(\"Detected 24-bit input audio\")\n            elif \"32\" in self.input_audio_subtype:","chunkId":"audio_separator/separator/architectures/vr_separator.py:19"}],[731,{"filePath":"audio_separator/separator/architectures/vr_separator.py","startLine":140,"endLine":151,"chunkText":"                self.input_bit_depth = 24\n                self.logger.info(\"Detected 24-bit input audio\")\n            elif \"32\" in self.input_audio_subtype:\n                self.wav_subtype = \"PCM_32\"\n                self.input_bit_depth = 32\n                self.logger.info(\"Detected 32-bit input audio\")\n            else:\n                self.wav_subtype = \"PCM_16\"\n                self.input_bit_depth = 16\n                self.logger.info(\"Detected 16-bit input audio\")\n        except Exception as e:\n            self.logger.warning(f\"Could not detect input audio bit depth: {e}. Defaulting to PCM_16\")","chunkId":"audio_separator/separator/architectures/vr_separator.py:20"}],[732,{"filePath":"audio_separator/separator/architectures/vr_separator.py","startLine":149,"endLine":159,"chunkText":"                self.logger.info(\"Detected 16-bit input audio\")\n        except Exception as e:\n            self.logger.warning(f\"Could not detect input audio bit depth: {e}. Defaulting to PCM_16\")\n            self.wav_subtype = \"PCM_16\"\n            self.input_audio_subtype = None\n            self.input_bit_depth = 16\n\n        self.logger.debug(f\"Starting separation for input audio file {self.audio_file_path}...\")\n\n        nn_arch_sizes = [31191, 33966, 56817, 123821, 123812, 129605, 218409, 537238, 537227]  # default\n        vr_5_1_models = [56817, 218409]","chunkId":"audio_separator/separator/architectures/vr_separator.py:21"}],[733,{"filePath":"audio_separator/separator/architectures/vr_separator.py","startLine":157,"endLine":165,"chunkText":"\n        nn_arch_sizes = [31191, 33966, 56817, 123821, 123812, 129605, 218409, 537238, 537227]  # default\n        vr_5_1_models = [56817, 218409]\n        model_size = math.ceil(os.stat(self.model_path).st_size / 1024)\n        nn_arch_size = min(nn_arch_sizes, key=lambda x: abs(x - model_size))\n        self.logger.debug(f\"Model size determined: {model_size}, NN architecture size: {nn_arch_size}\")\n\n        if nn_arch_size in vr_5_1_models or self.is_vr_51_model:\n            self.logger.debug(\"Using CascadedNet for VR 5.1 model...\")","chunkId":"audio_separator/separator/architectures/vr_separator.py:22"}],[734,{"filePath":"audio_separator/separator/architectures/vr_separator.py","startLine":163,"endLine":171,"chunkText":"\n        if nn_arch_size in vr_5_1_models or self.is_vr_51_model:\n            self.logger.debug(\"Using CascadedNet for VR 5.1 model...\")\n            self.model_run = nets_new.CascadedNet(self.model_params.param[\"bins\"] * 2, nn_arch_size, nout=self.model_capacity[0], nout_lstm=self.model_capacity[1])\n            self.is_vr_51_model = True\n        else:\n            self.logger.debug(\"Determining model capacity...\")\n            self.model_run = nets.determine_model_capacity(self.model_params.param[\"bins\"] * 2, nn_arch_size)\n","chunkId":"audio_separator/separator/architectures/vr_separator.py:23"}],[735,{"filePath":"audio_separator/separator/architectures/vr_separator.py","startLine":169,"endLine":179,"chunkText":"            self.logger.debug(\"Determining model capacity...\")\n            self.model_run = nets.determine_model_capacity(self.model_params.param[\"bins\"] * 2, nn_arch_size)\n\n        self.model_run.load_state_dict(torch.load(self.model_path, map_location=\"cpu\"))\n        self.model_run.to(self.torch_device)\n        self.logger.debug(\"Model loaded and moved to device.\")\n\n        y_spec, v_spec = self.inference_vr(self.loading_mix(), self.torch_device, self.aggressiveness)\n        self.logger.debug(\"Inference completed.\")\n\n        # Sanitize y_spec and v_spec to replace NaN and infinite values","chunkId":"audio_separator/separator/architectures/vr_separator.py:24"}],[736,{"filePath":"audio_separator/separator/architectures/vr_separator.py","startLine":177,"endLine":186,"chunkText":"        self.logger.debug(\"Inference completed.\")\n\n        # Sanitize y_spec and v_spec to replace NaN and infinite values\n        y_spec = np.nan_to_num(y_spec, nan=0.0, posinf=0.0, neginf=0.0)\n        v_spec = np.nan_to_num(v_spec, nan=0.0, posinf=0.0, neginf=0.0)\n\n        self.logger.debug(\"Sanitization completed. Replaced NaN and infinite values in y_spec and v_spec.\")\n\n        # After inference_vr call\n        self.logger.debug(f\"Inference VR completed. y_spec shape: {y_spec.shape}, v_spec shape: {v_spec.shape}\")","chunkId":"audio_separator/separator/architectures/vr_separator.py:25"}],[737,{"filePath":"audio_separator/separator/architectures/vr_separator.py","startLine":184,"endLine":192,"chunkText":"        # After inference_vr call\n        self.logger.debug(f\"Inference VR completed. y_spec shape: {y_spec.shape}, v_spec shape: {v_spec.shape}\")\n        self.logger.debug(f\"y_spec stats - min: {np.min(y_spec)}, max: {np.max(y_spec)}, isnan: {np.isnan(y_spec).any()}, isinf: {np.isinf(y_spec).any()}\")\n        self.logger.debug(f\"v_spec stats - min: {np.min(v_spec)}, max: {np.max(v_spec)}, isnan: {np.isnan(v_spec).any()}, isinf: {np.isinf(v_spec).any()}\")\n\n        # Not yet implemented from UVR features:\n        #\n        # if not self.is_vocal_split_model:\n        #     self.cache_source((y_spec, v_spec))","chunkId":"audio_separator/separator/architectures/vr_separator.py:26"}],[738,{"filePath":"audio_separator/separator/architectures/vr_separator.py","startLine":192,"endLine":202,"chunkText":"        # if not self.is_vocal_split_model:\n        #     self.cache_source((y_spec, v_spec))\n\n        # if self.is_secondary_model_activated and self.secondary_model:\n        #     self.logger.debug(\"Processing secondary model...\")\n        #     self.secondary_source_primary, self.secondary_source_secondary = process_secondary_model(\n        #         self.secondary_model, self.process_data, main_process_method=self.process_method, main_model_primary=self.primary_stem\n        #     )\n\n        # Initialize the list for output files\n        output_files = []","chunkId":"audio_separator/separator/architectures/vr_separator.py:27"}],[739,{"filePath":"audio_separator/separator/architectures/vr_separator.py","startLine":200,"endLine":208,"chunkText":"\n        # Initialize the list for output files\n        output_files = []\n        self.logger.debug(\"Processing output files...\")\n\n        # Note: logic similar to the following should probably be added to the other architectures\n        # Check if output_single_stem is set to a value that would result in no output files\n        if self.output_single_stem and (self.output_single_stem.lower() != self.primary_stem_name.lower() and self.output_single_stem.lower() != self.secondary_stem_name.lower()):\n            # If so, reset output_single_stem to None to save both stems","chunkId":"audio_separator/separator/architectures/vr_separator.py:28"}],[740,{"filePath":"audio_separator/separator/architectures/vr_separator.py","startLine":206,"endLine":209,"chunkText":"        # Check if output_single_stem is set to a value that would result in no output files\n        if self.output_single_stem and (self.output_single_stem.lower() != self.primary_stem_name.lower() and self.output_single_stem.lower() != self.secondary_stem_name.lower()):\n            # If so, reset output_single_stem to None to save both stems\n            self.output_single_stem = None","chunkId":"audio_separator/separator/architectures/vr_separator.py:29"}],[741,{"filePath":"audio_separator/separator/architectures/vr_separator.py","startLine":207,"endLine":211,"chunkText":"        if self.output_single_stem and (self.output_single_stem.lower() != self.primary_stem_name.lower() and self.output_single_stem.lower() != self.secondary_stem_name.lower()):\n            # If so, reset output_single_stem to None to save both stems\n            self.output_single_stem = None\n            self.logger.warning(f\"The output_single_stem setting '{self.output_single_stem}' does not match any of the output files: '{self.primary_stem_name}' and '{self.secondary_stem_name}'. For this model '{self.model_name}', the output_single_stem setting will be ignored and all output files will be saved.\")\n","chunkId":"audio_separator/separator/architectures/vr_separator.py:30"}],[742,{"filePath":"audio_separator/separator/architectures/vr_separator.py","startLine":209,"endLine":214,"chunkText":"            self.output_single_stem = None\n            self.logger.warning(f\"The output_single_stem setting '{self.output_single_stem}' does not match any of the output files: '{self.primary_stem_name}' and '{self.secondary_stem_name}'. For this model '{self.model_name}', the output_single_stem setting will be ignored and all output files will be saved.\")\n\n        # Save and process the primary stem if needed\n        if not self.output_single_stem or self.output_single_stem.lower() == self.primary_stem_name.lower():\n            self.logger.debug(f\"Processing primary stem: {self.primary_stem_name}\")","chunkId":"audio_separator/separator/architectures/vr_separator.py:31"}],[743,{"filePath":"audio_separator/separator/architectures/vr_separator.py","startLine":212,"endLine":219,"chunkText":"        # Save and process the primary stem if needed\n        if not self.output_single_stem or self.output_single_stem.lower() == self.primary_stem_name.lower():\n            self.logger.debug(f\"Processing primary stem: {self.primary_stem_name}\")\n            if not isinstance(self.primary_source, np.ndarray):\n                self.logger.debug(f\"Preparing to convert spectrogram to waveform. Spec shape: {y_spec.shape}\")\n\n                self.primary_source = self.spec_to_wav(y_spec).T\n                self.logger.debug(\"Converting primary source spectrogram to waveform.\")","chunkId":"audio_separator/separator/architectures/vr_separator.py:32"}],[744,{"filePath":"audio_separator/separator/architectures/vr_separator.py","startLine":217,"endLine":225,"chunkText":"\n                self.primary_source = self.spec_to_wav(y_spec).T\n                self.logger.debug(\"Converting primary source spectrogram to waveform.\")\n                if not self.model_samplerate == 44100:\n                    self.primary_source = librosa.resample(self.primary_source.T, orig_sr=self.model_samplerate, target_sr=44100).T\n                    self.logger.debug(\"Resampling primary source to 44100Hz.\")\n\n            self.primary_stem_output_path = self.get_stem_output_path(self.primary_stem_name, custom_output_names)\n","chunkId":"audio_separator/separator/architectures/vr_separator.py:33"}],[745,{"filePath":"audio_separator/separator/architectures/vr_separator.py","startLine":223,"endLine":231,"chunkText":"\n            self.primary_stem_output_path = self.get_stem_output_path(self.primary_stem_name, custom_output_names)\n\n            self.logger.info(f\"Saving {self.primary_stem_name} stem to {self.primary_stem_output_path}...\")\n            self.final_process(self.primary_stem_output_path, self.primary_source, self.primary_stem_name)\n            output_files.append(self.primary_stem_output_path)\n\n        # Save and process the secondary stem if needed\n        if not self.output_single_stem or self.output_single_stem.lower() == self.secondary_stem_name.lower():","chunkId":"audio_separator/separator/architectures/vr_separator.py:34"}],[746,{"filePath":"audio_separator/separator/architectures/vr_separator.py","startLine":229,"endLine":237,"chunkText":"\n        # Save and process the secondary stem if needed\n        if not self.output_single_stem or self.output_single_stem.lower() == self.secondary_stem_name.lower():\n            self.logger.debug(f\"Processing secondary stem: {self.secondary_stem_name}\")\n            if not isinstance(self.secondary_source, np.ndarray):\n                self.logger.debug(f\"Preparing to convert spectrogram to waveform. Spec shape: {v_spec.shape}\")\n\n                self.secondary_source = self.spec_to_wav(v_spec).T\n                self.logger.debug(\"Converting secondary source spectrogram to waveform.\")","chunkId":"audio_separator/separator/architectures/vr_separator.py:35"}],[747,{"filePath":"audio_separator/separator/architectures/vr_separator.py","startLine":235,"endLine":243,"chunkText":"\n                self.secondary_source = self.spec_to_wav(v_spec).T\n                self.logger.debug(\"Converting secondary source spectrogram to waveform.\")\n                if not self.model_samplerate == 44100:\n                    self.secondary_source = librosa.resample(self.secondary_source.T, orig_sr=self.model_samplerate, target_sr=44100).T\n                    self.logger.debug(\"Resampling secondary source to 44100Hz.\")\n\n            self.secondary_stem_output_path = self.get_stem_output_path(self.secondary_stem_name, custom_output_names)\n","chunkId":"audio_separator/separator/architectures/vr_separator.py:36"}],[748,{"filePath":"audio_separator/separator/architectures/vr_separator.py","startLine":241,"endLine":253,"chunkText":"\n            self.secondary_stem_output_path = self.get_stem_output_path(self.secondary_stem_name, custom_output_names)\n\n            self.logger.info(f\"Saving {self.secondary_stem_name} stem to {self.secondary_stem_output_path}...\")\n            self.final_process(self.secondary_stem_output_path, self.secondary_source, self.secondary_stem_name)\n            output_files.append(self.secondary_stem_output_path)\n\n        # Not yet implemented from UVR features:\n        # self.process_vocal_split_chain(secondary_sources)\n        # self.logger.debug(\"Vocal split chain processed.\")\n\n        return output_files\n","chunkId":"audio_separator/separator/architectures/vr_separator.py:37"}],[749,{"filePath":"audio_separator/separator/architectures/vr_separator.py","startLine":251,"endLine":268,"chunkText":"\n        return output_files\n\n    def loading_mix(self):\n        X_wave, X_spec_s = {}, {}\n\n        bands_n = len(self.model_params.param[\"band\"])\n\n        audio_file = spec_utils.write_array_to_mem(self.audio_file_path, subtype=self.wav_subtype)\n        is_mp3 = audio_file.endswith(\".mp3\") if isinstance(audio_file, str) else False\n\n        self.logger.debug(f\"loading_mix iteraring through {bands_n} bands\")\n        for d in tqdm(range(bands_n, 0, -1)):\n            bp = self.model_params.param[\"band\"][d]\n\n            wav_resolution = bp[\"res_type\"]\n\n            if self.torch_device_mps is not None:","chunkId":"audio_separator/separator/architectures/vr_separator.py:38"}],[750,{"filePath":"audio_separator/separator/architectures/vr_separator.py","startLine":266,"endLine":277,"chunkText":"            wav_resolution = bp[\"res_type\"]\n\n            if self.torch_device_mps is not None:\n                wav_resolution = \"polyphase\"\n\n            if d == bands_n:  # high-end band\n                X_wave[d], _ = librosa.load(audio_file, sr=bp[\"sr\"], mono=False, dtype=np.float32, res_type=wav_resolution)\n                X_spec_s[d] = spec_utils.wave_to_spectrogram(X_wave[d], bp[\"hl\"], bp[\"n_fft\"], self.model_params, band=d, is_v51_model=self.is_vr_51_model)\n\n                if not np.any(X_wave[d]) and is_mp3:\n                    X_wave[d] = rerun_mp3(audio_file, bp[\"sr\"])\n","chunkId":"audio_separator/separator/architectures/vr_separator.py:39"}],[751,{"filePath":"audio_separator/separator/architectures/vr_separator.py","startLine":275,"endLine":283,"chunkText":"                if not np.any(X_wave[d]) and is_mp3:\n                    X_wave[d] = rerun_mp3(audio_file, bp[\"sr\"])\n\n                if X_wave[d].ndim == 1:\n                    X_wave[d] = np.asarray([X_wave[d], X_wave[d]])\n            else:  # lower bands\n                X_wave[d] = librosa.resample(X_wave[d + 1], orig_sr=self.model_params.param[\"band\"][d + 1][\"sr\"], target_sr=bp[\"sr\"], res_type=wav_resolution)\n                X_spec_s[d] = spec_utils.wave_to_spectrogram(X_wave[d], bp[\"hl\"], bp[\"n_fft\"], self.model_params, band=d, is_v51_model=self.is_vr_51_model)\n","chunkId":"audio_separator/separator/architectures/vr_separator.py:40"}],[752,{"filePath":"audio_separator/separator/architectures/vr_separator.py","startLine":281,"endLine":285,"chunkText":"                X_wave[d] = librosa.resample(X_wave[d + 1], orig_sr=self.model_params.param[\"band\"][d + 1][\"sr\"], target_sr=bp[\"sr\"], res_type=wav_resolution)\n                X_spec_s[d] = spec_utils.wave_to_spectrogram(X_wave[d], bp[\"hl\"], bp[\"n_fft\"], self.model_params, band=d, is_v51_model=self.is_vr_51_model)\n\n            if d == bands_n and self.high_end_process:\n                self.input_high_end_h = (bp[\"n_fft\"] // 2 - bp[\"crop_stop\"]) + (self.model_params.param[\"pre_filter_stop\"] - self.model_params.param[\"pre_filter_start\"])","chunkId":"audio_separator/separator/architectures/vr_separator.py:41"}],[753,{"filePath":"audio_separator/separator/architectures/vr_separator.py","startLine":283,"endLine":294,"chunkText":"\n            if d == bands_n and self.high_end_process:\n                self.input_high_end_h = (bp[\"n_fft\"] // 2 - bp[\"crop_stop\"]) + (self.model_params.param[\"pre_filter_stop\"] - self.model_params.param[\"pre_filter_start\"])\n                self.input_high_end = X_spec_s[d][:, bp[\"n_fft\"] // 2 - self.input_high_end_h : bp[\"n_fft\"] // 2, :]\n\n        X_spec = spec_utils.combine_spectrograms(X_spec_s, self.model_params, is_v51_model=self.is_vr_51_model)\n\n        del X_wave, X_spec_s, audio_file\n\n        return X_spec\n\n    def inference_vr(self, X_spec, device, aggressiveness):","chunkId":"audio_separator/separator/architectures/vr_separator.py:42"}],[754,{"filePath":"audio_separator/separator/architectures/vr_separator.py","startLine":292,"endLine":304,"chunkText":"        return X_spec\n\n    def inference_vr(self, X_spec, device, aggressiveness):\n        def _execute(X_mag_pad, roi_size):\n            X_dataset = []\n            patches = (X_mag_pad.shape[2] - 2 * self.model_run.offset) // roi_size\n\n            self.logger.debug(f\"inference_vr appending to X_dataset for each of {patches} patches\")\n            for i in tqdm(range(patches)):\n                start = i * roi_size\n                X_mag_window = X_mag_pad[:, :, start : start + self.window_size]\n                X_dataset.append(X_mag_window)\n","chunkId":"audio_separator/separator/architectures/vr_separator.py:43"}],[755,{"filePath":"audio_separator/separator/architectures/vr_separator.py","startLine":302,"endLine":314,"chunkText":"                X_mag_window = X_mag_pad[:, :, start : start + self.window_size]\n                X_dataset.append(X_mag_window)\n\n            total_iterations = patches // self.batch_size if not self.enable_tta else (patches // self.batch_size) * 2\n            self.logger.debug(f\"inference_vr iterating through {total_iterations} batches, batch_size = {self.batch_size}\")\n\n            X_dataset = np.asarray(X_dataset)\n            self.model_run.eval()\n            with torch.no_grad():\n                mask = []\n\n                for i in tqdm(range(0, patches, self.batch_size)):\n","chunkId":"audio_separator/separator/architectures/vr_separator.py:44"}],[756,{"filePath":"audio_separator/separator/architectures/vr_separator.py","startLine":312,"endLine":323,"chunkText":"\n                for i in tqdm(range(0, patches, self.batch_size)):\n\n                    X_batch = X_dataset[i : i + self.batch_size]\n                    X_batch = torch.from_numpy(X_batch).to(device)\n                    pred = self.model_run.predict_mask(X_batch)\n                    if not pred.size()[3] > 0:\n                        raise ValueError(f\"Window size error: h1_shape[3] must be greater than h2_shape[3]\")\n                    pred = pred.detach().cpu().numpy()\n                    pred = np.concatenate(pred, axis=2)\n                    mask.append(pred)\n                if len(mask) == 0:","chunkId":"audio_separator/separator/architectures/vr_separator.py:45"}],[757,{"filePath":"audio_separator/separator/architectures/vr_separator.py","startLine":321,"endLine":334,"chunkText":"                    pred = np.concatenate(pred, axis=2)\n                    mask.append(pred)\n                if len(mask) == 0:\n                    raise ValueError(f\"Window size error: h1_shape[3] must be greater than h2_shape[3]\")\n\n                mask = np.concatenate(mask, axis=2)\n            return mask\n\n        def postprocess(mask, X_mag, X_phase):\n            is_non_accom_stem = False\n            for stem in CommonSeparator.NON_ACCOM_STEMS:\n                if stem == self.primary_stem_name:\n                    is_non_accom_stem = True\n","chunkId":"audio_separator/separator/architectures/vr_separator.py:46"}],[758,{"filePath":"audio_separator/separator/architectures/vr_separator.py","startLine":332,"endLine":346,"chunkText":"                if stem == self.primary_stem_name:\n                    is_non_accom_stem = True\n\n            mask = spec_utils.adjust_aggr(mask, is_non_accom_stem, aggressiveness)\n\n            if self.enable_post_process:\n                mask = spec_utils.merge_artifacts(mask, thres=self.post_process_threshold)\n\n            y_spec = mask * X_mag * np.exp(1.0j * X_phase)\n            v_spec = (1 - mask) * X_mag * np.exp(1.0j * X_phase)\n\n            return y_spec, v_spec\n\n        X_mag, X_phase = spec_utils.preprocess(X_spec)\n        n_frame = X_mag.shape[2]","chunkId":"audio_separator/separator/architectures/vr_separator.py:47"}],[759,{"filePath":"audio_separator/separator/architectures/vr_separator.py","startLine":344,"endLine":356,"chunkText":"\n        X_mag, X_phase = spec_utils.preprocess(X_spec)\n        n_frame = X_mag.shape[2]\n        pad_l, pad_r, roi_size = spec_utils.make_padding(n_frame, self.window_size, self.model_run.offset)\n        X_mag_pad = np.pad(X_mag, ((0, 0), (0, 0), (pad_l, pad_r)), mode=\"constant\")\n        X_mag_pad /= X_mag_pad.max()\n        mask = _execute(X_mag_pad, roi_size)\n\n        if self.enable_tta:\n            pad_l += roi_size // 2\n            pad_r += roi_size // 2\n            X_mag_pad = np.pad(X_mag, ((0, 0), (0, 0), (pad_l, pad_r)), mode=\"constant\")\n            X_mag_pad /= X_mag_pad.max()","chunkId":"audio_separator/separator/architectures/vr_separator.py:48"}],[760,{"filePath":"audio_separator/separator/architectures/vr_separator.py","startLine":354,"endLine":367,"chunkText":"            pad_r += roi_size // 2\n            X_mag_pad = np.pad(X_mag, ((0, 0), (0, 0), (pad_l, pad_r)), mode=\"constant\")\n            X_mag_pad /= X_mag_pad.max()\n            mask_tta = _execute(X_mag_pad, roi_size)\n            mask_tta = mask_tta[:, :, roi_size // 2 :]\n            mask = (mask[:, :, :n_frame] + mask_tta[:, :, :n_frame]) * 0.5\n        else:\n            mask = mask[:, :, :n_frame]\n\n        y_spec, v_spec = postprocess(mask, X_mag, X_phase)\n\n        return y_spec, v_spec\n\n    def spec_to_wav(self, spec):","chunkId":"audio_separator/separator/architectures/vr_separator.py:49"}],[761,{"filePath":"audio_separator/separator/architectures/vr_separator.py","startLine":365,"endLine":376,"chunkText":"        return y_spec, v_spec\n\n    def spec_to_wav(self, spec):\n        if self.high_end_process and isinstance(self.input_high_end, np.ndarray) and self.input_high_end_h:\n            input_high_end_ = spec_utils.mirroring(\"mirroring\", spec, self.input_high_end, self.model_params)\n            wav = spec_utils.cmb_spectrogram_to_wave(spec, self.model_params, self.input_high_end_h, input_high_end_, is_v51_model=self.is_vr_51_model)\n        else:\n            wav = spec_utils.cmb_spectrogram_to_wave(spec, self.model_params, is_v51_model=self.is_vr_51_model)\n\n        return wav\n\n","chunkId":"audio_separator/separator/architectures/vr_separator.py:50"}],[762,{"filePath":"audio_separator/separator/architectures/vr_separator.py","startLine":374,"endLine":383,"chunkText":"        return wav\n\n\n# Check if we really need the rerun_mp3 function, refactor or remove if not\ndef rerun_mp3(audio_file, sample_rate=44100):\n    with audioread.audio_open(audio_file) as f:\n        track_length = int(f.duration)\n\n    return librosa.load(audio_file, duration=track_length, mono=False, sr=sample_rate)[0]\n","chunkId":"audio_separator/separator/architectures/vr_separator.py:51"}],[763,{"filePath":"audio_separator/separator/audio_chunking.py","startLine":0,"endLine":18,"chunkText":"\"\"\"Audio chunking utilities for processing large audio files to prevent OOM errors.\"\"\"\n\nimport os\nimport logging\nfrom typing import List\nfrom pydub import AudioSegment\n\n\nclass AudioChunker:\n    \"\"\"\n    Handles splitting and merging of large audio files.\n\n    This class provides utilities to:\n    - Split large audio files into fixed-duration chunks\n    - Merge processed chunks back together with simple concatenation\n    - Determine if a file should be chunked based on its duration\n\n    Example:\n        >>> chunker = AudioChunker(chunk_duration_seconds=600)  # 10-minute chunks","chunkId":"audio_separator/separator/audio_chunking.py:0"}],[764,{"filePath":"audio_separator/separator/audio_chunking.py","startLine":16,"endLine":31,"chunkText":"\n    Example:\n        >>> chunker = AudioChunker(chunk_duration_seconds=600)  # 10-minute chunks\n        >>> chunk_paths = chunker.split_audio(\"long_audio.wav\", \"/tmp/chunks\")\n        >>> # Process each chunk...\n        >>> output_path = chunker.merge_chunks(processed_chunks, \"output.wav\")\n    \"\"\"\n\n    def __init__(self, chunk_duration_seconds: float, logger: logging.Logger = None):\n        \"\"\"\n        Initialize the AudioChunker.\n\n        Args:\n            chunk_duration_seconds: Duration of each chunk in seconds\n            logger: Optional logger instance for logging operations\n        \"\"\"","chunkId":"audio_separator/separator/audio_chunking.py:1"}],[765,{"filePath":"audio_separator/separator/audio_chunking.py","startLine":29,"endLine":43,"chunkText":"            chunk_duration_seconds: Duration of each chunk in seconds\n            logger: Optional logger instance for logging operations\n        \"\"\"\n        self.chunk_duration_ms = int(chunk_duration_seconds * 1000)\n        self.logger = logger or logging.getLogger(__name__)\n\n    def split_audio(self, input_path: str, output_dir: str) -> List[str]:\n        \"\"\"\n        Split audio file into fixed-size chunks.\n\n        Args:\n            input_path: Path to the input audio file\n            output_dir: Directory where chunk files will be saved\n\n        Returns:","chunkId":"audio_separator/separator/audio_chunking.py:2"}],[766,{"filePath":"audio_separator/separator/audio_chunking.py","startLine":41,"endLine":58,"chunkText":"            output_dir: Directory where chunk files will be saved\n\n        Returns:\n            List of paths to the created chunk files\n\n        Raises:\n            FileNotFoundError: If input file doesn't exist\n            IOError: If there's an error reading or writing audio files\n        \"\"\"\n        if not os.path.exists(input_path):\n            raise FileNotFoundError(f\"Input file not found: {input_path}\")\n\n        if not os.path.exists(output_dir):\n            os.makedirs(output_dir)\n\n        self.logger.debug(f\"Loading audio file: {input_path}\")\n        audio = AudioSegment.from_file(input_path)\n","chunkId":"audio_separator/separator/audio_chunking.py:3"}],[767,{"filePath":"audio_separator/separator/audio_chunking.py","startLine":56,"endLine":68,"chunkText":"        self.logger.debug(f\"Loading audio file: {input_path}\")\n        audio = AudioSegment.from_file(input_path)\n\n        total_duration_ms = len(audio)\n        chunk_paths = []\n\n        # Calculate number of chunks\n        num_chunks = (total_duration_ms + self.chunk_duration_ms - 1) // self.chunk_duration_ms\n        self.logger.info(f\"Splitting {total_duration_ms / 1000:.1f}s audio into {num_chunks} chunks of {self.chunk_duration_ms / 1000:.1f}s each\")\n\n        # Get file extension from input\n        _, ext = os.path.splitext(input_path)\n        if not ext:","chunkId":"audio_separator/separator/audio_chunking.py:4"}],[768,{"filePath":"audio_separator/separator/audio_chunking.py","startLine":66,"endLine":79,"chunkText":"        # Get file extension from input\n        _, ext = os.path.splitext(input_path)\n        if not ext:\n            ext = \".wav\"  # Default to WAV if no extension\n\n        # Split into chunks\n        for i in range(num_chunks):\n            start_ms = i * self.chunk_duration_ms\n            end_ms = min(start_ms + self.chunk_duration_ms, total_duration_ms)\n\n            chunk = audio[start_ms:end_ms]\n            chunk_filename = f\"chunk_{i:04d}{ext}\"\n            chunk_path = os.path.join(output_dir, chunk_filename)\n","chunkId":"audio_separator/separator/audio_chunking.py:5"}],[769,{"filePath":"audio_separator/separator/audio_chunking.py","startLine":77,"endLine":91,"chunkText":"            chunk_filename = f\"chunk_{i:04d}{ext}\"\n            chunk_path = os.path.join(output_dir, chunk_filename)\n\n            self.logger.debug(f\"Exporting chunk {i + 1}/{num_chunks}: {start_ms / 1000:.1f}s - {end_ms / 1000:.1f}s to {chunk_path}\")\n            chunk.export(chunk_path, format=ext.lstrip('.'))\n            chunk_paths.append(chunk_path)\n\n        return chunk_paths\n\n    def merge_chunks(self, chunk_paths: List[str], output_path: str) -> str:\n        \"\"\"\n        Merge processed chunks with simple concatenation.\n\n        Args:\n            chunk_paths: List of paths to chunk files to merge","chunkId":"audio_separator/separator/audio_chunking.py:6"}],[770,{"filePath":"audio_separator/separator/audio_chunking.py","startLine":89,"endLine":106,"chunkText":"\n        Args:\n            chunk_paths: List of paths to chunk files to merge\n            output_path: Path where the merged output will be saved\n\n        Returns:\n            Path to the merged output file\n\n        Raises:\n            ValueError: If chunk_paths is empty\n            FileNotFoundError: If any chunk file doesn't exist\n            IOError: If there's an error reading or writing audio files\n        \"\"\"\n        if not chunk_paths:\n            raise ValueError(\"Cannot merge empty list of chunks\")\n\n        # Verify all chunks exist\n        for chunk_path in chunk_paths:","chunkId":"audio_separator/separator/audio_chunking.py:7"}],[771,{"filePath":"audio_separator/separator/audio_chunking.py","startLine":104,"endLine":118,"chunkText":"\n        # Verify all chunks exist\n        for chunk_path in chunk_paths:\n            if not os.path.exists(chunk_path):\n                raise FileNotFoundError(f\"Chunk file not found: {chunk_path}\")\n\n        self.logger.info(f\"Merging {len(chunk_paths)} chunks into {output_path}\")\n\n        # Start with empty audio segment\n        combined = AudioSegment.empty()\n\n        # Concatenate all chunks\n        for i, chunk_path in enumerate(chunk_paths):\n            self.logger.debug(f\"Loading chunk {i + 1}/{len(chunk_paths)}: {chunk_path}\")\n            chunk = AudioSegment.from_file(chunk_path)","chunkId":"audio_separator/separator/audio_chunking.py:8"}],[772,{"filePath":"audio_separator/separator/audio_chunking.py","startLine":116,"endLine":129,"chunkText":"        for i, chunk_path in enumerate(chunk_paths):\n            self.logger.debug(f\"Loading chunk {i + 1}/{len(chunk_paths)}: {chunk_path}\")\n            chunk = AudioSegment.from_file(chunk_path)\n            combined += chunk  # Simple concatenation\n\n        # Get output format from file extension\n        _, ext = os.path.splitext(output_path)\n        output_format = ext.lstrip('.') if ext else 'wav'\n\n        self.logger.info(f\"Exporting merged audio ({len(combined) / 1000:.1f}s) to {output_path}\")\n        combined.export(output_path, format=output_format)\n\n        return output_path\n","chunkId":"audio_separator/separator/audio_chunking.py:9"}],[773,{"filePath":"audio_separator/separator/audio_chunking.py","startLine":127,"endLine":141,"chunkText":"\n        return output_path\n\n    def should_chunk(self, audio_duration_seconds: float) -> bool:\n        \"\"\"\n        Determine if file is large enough to benefit from chunking.\n\n        Args:\n            audio_duration_seconds: Duration of the audio file in seconds\n\n        Returns:\n            True if the file should be chunked, False otherwise\n        \"\"\"\n        return audio_duration_seconds > (self.chunk_duration_ms / 1000)\n","chunkId":"audio_separator/separator/audio_chunking.py:10"}],[774,{"filePath":"audio_separator/separator/common_separator.py","startLine":0,"endLine":22,"chunkText":"\"\"\" This file contains the CommonSeparator class, common to all architecture-specific Separator classes. \"\"\"\n\nfrom logging import Logger\nimport os\nimport re\nimport gc\nimport numpy as np\nimport librosa\nimport torch\nfrom pydub import AudioSegment\nimport soundfile as sf\nfrom audio_separator.separator.uvr_lib_v5 import spec_utils\n\n\nclass CommonSeparator:\n    \"\"\"\n    This class contains the common methods and attributes common to all architecture-specific Separator classes.\n    \"\"\"\n\n    ALL_STEMS = \"All Stems\"\n    VOCAL_STEM = \"Vocals\"\n    INST_STEM = \"Instrumental\"\n    OTHER_STEM = \"Other\"","chunkId":"audio_separator/separator/common_separator.py:0"}],[775,{"filePath":"audio_separator/separator/common_separator.py","startLine":21,"endLine":40,"chunkText":"    INST_STEM = \"Instrumental\"\n    OTHER_STEM = \"Other\"\n    BASS_STEM = \"Bass\"\n    DRUM_STEM = \"Drums\"\n    GUITAR_STEM = \"Guitar\"\n    PIANO_STEM = \"Piano\"\n    SYNTH_STEM = \"Synthesizer\"\n    STRINGS_STEM = \"Strings\"\n    WOODWINDS_STEM = \"Woodwinds\"\n    BRASS_STEM = \"Brass\"\n    WIND_INST_STEM = \"Wind Inst\"\n    NO_OTHER_STEM = \"No Other\"\n    NO_BASS_STEM = \"No Bass\"\n    NO_DRUM_STEM = \"No Drums\"\n    NO_GUITAR_STEM = \"No Guitar\"\n    NO_PIANO_STEM = \"No Piano\"\n    NO_SYNTH_STEM = \"No Synthesizer\"\n    NO_STRINGS_STEM = \"No Strings\"\n    NO_WOODWINDS_STEM = \"No Woodwinds\"\n    NO_WIND_INST_STEM = \"No Wind Inst\"","chunkId":"audio_separator/separator/common_separator.py:1"}],[776,{"filePath":"audio_separator/separator/common_separator.py","startLine":38,"endLine":51,"chunkText":"    NO_STRINGS_STEM = \"No Strings\"\n    NO_WOODWINDS_STEM = \"No Woodwinds\"\n    NO_WIND_INST_STEM = \"No Wind Inst\"\n    NO_BRASS_STEM = \"No Brass\"\n    PRIMARY_STEM = \"Primary Stem\"\n    SECONDARY_STEM = \"Secondary Stem\"\n    LEAD_VOCAL_STEM = \"lead_only\"\n    BV_VOCAL_STEM = \"backing_only\"\n    LEAD_VOCAL_STEM_I = \"with_lead_vocals\"\n    BV_VOCAL_STEM_I = \"with_backing_vocals\"\n    LEAD_VOCAL_STEM_LABEL = \"Lead Vocals\"\n    BV_VOCAL_STEM_LABEL = \"Backing Vocals\"\n    NO_STEM = \"No \"\n","chunkId":"audio_separator/separator/common_separator.py:2"}],[777,{"filePath":"audio_separator/separator/common_separator.py","startLine":49,"endLine":61,"chunkText":"    BV_VOCAL_STEM_LABEL = \"Backing Vocals\"\n    NO_STEM = \"No \"\n\n    STEM_PAIR_MAPPER = {VOCAL_STEM: INST_STEM, INST_STEM: VOCAL_STEM, LEAD_VOCAL_STEM: BV_VOCAL_STEM, BV_VOCAL_STEM: LEAD_VOCAL_STEM, PRIMARY_STEM: SECONDARY_STEM}\n\n    NON_ACCOM_STEMS = (VOCAL_STEM, OTHER_STEM, BASS_STEM, DRUM_STEM, GUITAR_STEM, PIANO_STEM, SYNTH_STEM, STRINGS_STEM, WOODWINDS_STEM, BRASS_STEM, WIND_INST_STEM)\n\n    def __init__(self, config):\n\n        self.logger: Logger = config.get(\"logger\")\n        self.log_level: int = config.get(\"log_level\")\n\n        # Inferencing device / acceleration config","chunkId":"audio_separator/separator/common_separator.py:3"}],[778,{"filePath":"audio_separator/separator/common_separator.py","startLine":59,"endLine":72,"chunkText":"        self.log_level: int = config.get(\"log_level\")\n\n        # Inferencing device / acceleration config\n        self.torch_device = config.get(\"torch_device\")\n        self.torch_device_cpu = config.get(\"torch_device_cpu\")\n        self.torch_device_mps = config.get(\"torch_device_mps\")\n        self.onnx_execution_provider = config.get(\"onnx_execution_provider\")\n\n        # Model data\n        self.model_name = config.get(\"model_name\")\n        self.model_path = config.get(\"model_path\")\n        self.model_data = config.get(\"model_data\")\n\n        # Output directory and format","chunkId":"audio_separator/separator/common_separator.py:4"}],[779,{"filePath":"audio_separator/separator/common_separator.py","startLine":70,"endLine":80,"chunkText":"        self.model_data = config.get(\"model_data\")\n\n        # Output directory and format\n        self.output_dir = config.get(\"output_dir\")\n        self.output_format = config.get(\"output_format\")\n        self.output_bitrate = config.get(\"output_bitrate\")\n\n        # Functional options which are applicable to all architectures and the user may tweak to affect the output\n        self.normalization_threshold = config.get(\"normalization_threshold\")\n        self.amplification_threshold = config.get(\"amplification_threshold\")\n        self.enable_denoise = config.get(\"enable_denoise\")","chunkId":"audio_separator/separator/common_separator.py:5"}],[780,{"filePath":"audio_separator/separator/common_separator.py","startLine":78,"endLine":88,"chunkText":"        self.normalization_threshold = config.get(\"normalization_threshold\")\n        self.amplification_threshold = config.get(\"amplification_threshold\")\n        self.enable_denoise = config.get(\"enable_denoise\")\n        self.output_single_stem = config.get(\"output_single_stem\")\n        self.invert_using_spec = config.get(\"invert_using_spec\")\n        self.sample_rate = config.get(\"sample_rate\")\n        self.use_soundfile = config.get(\"use_soundfile\")\n        \n        # Roformer-specific loading support\n        self.roformer_loader = None\n        self.is_roformer_model = self._detect_roformer_model()","chunkId":"audio_separator/separator/common_separator.py:6"}],[781,{"filePath":"audio_separator/separator/common_separator.py","startLine":86,"endLine":101,"chunkText":"        # Roformer-specific loading support\n        self.roformer_loader = None\n        self.is_roformer_model = self._detect_roformer_model()\n        if self.is_roformer_model:\n            self._initialize_roformer_loader()\n\n        # Model specific properties\n\n        # Check if model_data has a \"training\" key with \"instruments\" list\n        self.primary_stem_name = None\n        self.secondary_stem_name = None\n        \n        # Audio bit depth tracking for preserving input quality\n        self.input_bit_depth = None\n        self.input_subtype = None\n","chunkId":"audio_separator/separator/common_separator.py:7"}],[782,{"filePath":"audio_separator/separator/common_separator.py","startLine":99,"endLine":109,"chunkText":"        self.input_bit_depth = None\n        self.input_subtype = None\n\n        if \"training\" in self.model_data and \"instruments\" in self.model_data[\"training\"]:\n            instruments = self.model_data[\"training\"][\"instruments\"]\n            if instruments:\n                self.primary_stem_name = instruments[0]\n                self.secondary_stem_name = instruments[1] if len(instruments) > 1 else self.secondary_stem(self.primary_stem_name)\n\n        if self.primary_stem_name is None:\n            self.primary_stem_name = self.model_data.get(\"primary_stem\", \"Vocals\")","chunkId":"audio_separator/separator/common_separator.py:8"}],[783,{"filePath":"audio_separator/separator/common_separator.py","startLine":107,"endLine":116,"chunkText":"\n        if self.primary_stem_name is None:\n            self.primary_stem_name = self.model_data.get(\"primary_stem\", \"Vocals\")\n            self.secondary_stem_name = self.secondary_stem(self.primary_stem_name)\n\n        self.is_karaoke = self.model_data.get(\"is_karaoke\", False)\n        self.is_bv_model = self.model_data.get(\"is_bv_model\", False)\n        self.bv_model_rebalance = self.model_data.get(\"is_bv_model_rebalanced\", 0)\n\n        self.logger.debug(f\"Common params: model_name={self.model_name}, model_path={self.model_path}\")","chunkId":"audio_separator/separator/common_separator.py:9"}],[784,{"filePath":"audio_separator/separator/common_separator.py","startLine":114,"endLine":119,"chunkText":"        self.bv_model_rebalance = self.model_data.get(\"is_bv_model_rebalanced\", 0)\n\n        self.logger.debug(f\"Common params: model_name={self.model_name}, model_path={self.model_path}\")\n        self.logger.debug(f\"Common params: output_dir={self.output_dir}, output_format={self.output_format}\")\n        self.logger.debug(f\"Common params: normalization_threshold={self.normalization_threshold}, amplification_threshold={self.amplification_threshold}\")\n        self.logger.debug(f\"Common params: enable_denoise={self.enable_denoise}, output_single_stem={self.output_single_stem}\")","chunkId":"audio_separator/separator/common_separator.py:10"}],[785,{"filePath":"audio_separator/separator/common_separator.py","startLine":117,"endLine":121,"chunkText":"        self.logger.debug(f\"Common params: output_dir={self.output_dir}, output_format={self.output_format}\")\n        self.logger.debug(f\"Common params: normalization_threshold={self.normalization_threshold}, amplification_threshold={self.amplification_threshold}\")\n        self.logger.debug(f\"Common params: enable_denoise={self.enable_denoise}, output_single_stem={self.output_single_stem}\")\n        self.logger.debug(f\"Common params: invert_using_spec={self.invert_using_spec}, sample_rate={self.sample_rate}\")\n","chunkId":"audio_separator/separator/common_separator.py:11"}],[786,{"filePath":"audio_separator/separator/common_separator.py","startLine":119,"endLine":124,"chunkText":"        self.logger.debug(f\"Common params: enable_denoise={self.enable_denoise}, output_single_stem={self.output_single_stem}\")\n        self.logger.debug(f\"Common params: invert_using_spec={self.invert_using_spec}, sample_rate={self.sample_rate}\")\n\n        self.logger.debug(f\"Common params: primary_stem_name={self.primary_stem_name}, secondary_stem_name={self.secondary_stem_name}\")\n        self.logger.debug(f\"Common params: is_karaoke={self.is_karaoke}, is_bv_model={self.is_bv_model}, bv_model_rebalance={self.bv_model_rebalance}\")\n","chunkId":"audio_separator/separator/common_separator.py:12"}],[787,{"filePath":"audio_separator/separator/common_separator.py","startLine":122,"endLine":132,"chunkText":"        self.logger.debug(f\"Common params: primary_stem_name={self.primary_stem_name}, secondary_stem_name={self.secondary_stem_name}\")\n        self.logger.debug(f\"Common params: is_karaoke={self.is_karaoke}, is_bv_model={self.is_bv_model}, bv_model_rebalance={self.bv_model_rebalance}\")\n\n        # File-specific variables which need to be cleared between processing different audio inputs\n        self.audio_file_path = None\n        self.audio_file_base = None\n\n        self.primary_source = None\n        self.secondary_source = None\n\n        self.primary_stem_output_path = None","chunkId":"audio_separator/separator/common_separator.py:13"}],[788,{"filePath":"audio_separator/separator/common_separator.py","startLine":130,"endLine":143,"chunkText":"        self.secondary_source = None\n\n        self.primary_stem_output_path = None\n        self.secondary_stem_output_path = None\n\n        self.cached_sources_map = {}\n\n    def secondary_stem(self, primary_stem: str):\n        \"\"\"Determines secondary stem name based on the primary stem name.\"\"\"\n        primary_stem = primary_stem if primary_stem else self.NO_STEM\n\n        if primary_stem in self.STEM_PAIR_MAPPER:\n            secondary_stem = self.STEM_PAIR_MAPPER[primary_stem]\n        else:","chunkId":"audio_separator/separator/common_separator.py:14"}],[789,{"filePath":"audio_separator/separator/common_separator.py","startLine":141,"endLine":155,"chunkText":"        if primary_stem in self.STEM_PAIR_MAPPER:\n            secondary_stem = self.STEM_PAIR_MAPPER[primary_stem]\n        else:\n            secondary_stem = primary_stem.replace(self.NO_STEM, \"\") if self.NO_STEM in primary_stem else f\"{self.NO_STEM}{primary_stem}\"\n\n        return secondary_stem\n\n    def separate(self, audio_file_path):\n        \"\"\"\n        Placeholder method for separating audio sources. Should be overridden by subclasses.\n        \"\"\"\n        raise NotImplementedError(\"This method should be overridden by subclasses.\")\n\n    def final_process(self, stem_path, source, stem_name):\n        \"\"\"","chunkId":"audio_separator/separator/common_separator.py:15"}],[790,{"filePath":"audio_separator/separator/common_separator.py","startLine":153,"endLine":167,"chunkText":"\n    def final_process(self, stem_path, source, stem_name):\n        \"\"\"\n        Finalizes the processing of a stem by writing the audio to a file and returning the processed source.\n        \"\"\"\n        self.logger.debug(f\"Finalizing {stem_name} stem processing and writing audio...\")\n        self.write_audio(stem_path, source)\n\n        return {stem_name: source}\n\n    def cached_sources_clear(self):\n        \"\"\"\n        Clears the cache dictionaries for VR, MDX, and Demucs models.\n\n        This function is essential for ensuring that the cache does not hold outdated or irrelevant data","chunkId":"audio_separator/separator/common_separator.py:16"}],[791,{"filePath":"audio_separator/separator/common_separator.py","startLine":165,"endLine":176,"chunkText":"        Clears the cache dictionaries for VR, MDX, and Demucs models.\n\n        This function is essential for ensuring that the cache does not hold outdated or irrelevant data\n        between different processing sessions or when a new batch of audio files is processed.\n        It helps in managing memory efficiently and prevents potential errors due to stale data.\n        \"\"\"\n        self.cached_sources_map = {}\n\n    def cached_source_callback(self, model_architecture, model_name=None):\n        \"\"\"\n        Retrieves the model and sources from the cache based on the processing method and model name.\n","chunkId":"audio_separator/separator/common_separator.py:17"}],[792,{"filePath":"audio_separator/separator/common_separator.py","startLine":174,"endLine":184,"chunkText":"        \"\"\"\n        Retrieves the model and sources from the cache based on the processing method and model name.\n\n        Args:\n            model_architecture: The architecture type (VR, MDX, or Demucs) being used for processing.\n            model_name: The specific model name within the architecture type, if applicable.\n\n        Returns:\n            A tuple containing the model and its sources if found in the cache; otherwise, None.\n\n        This function is crucial for optimizing performance by avoiding redundant processing.","chunkId":"audio_separator/separator/common_separator.py:18"}],[793,{"filePath":"audio_separator/separator/common_separator.py","startLine":182,"endLine":196,"chunkText":"            A tuple containing the model and its sources if found in the cache; otherwise, None.\n\n        This function is crucial for optimizing performance by avoiding redundant processing.\n        If the requested model and its sources are already in the cache, they can be reused directly,\n        saving time and computational resources.\n        \"\"\"\n        model, sources = None, None\n\n        mapper = self.cached_sources_map[model_architecture]\n\n        for key, value in mapper.items():\n            if model_name in key:\n                model = key\n                sources = value\n","chunkId":"audio_separator/separator/common_separator.py:19"}],[794,{"filePath":"audio_separator/separator/common_separator.py","startLine":194,"endLine":207,"chunkText":"                model = key\n                sources = value\n\n        return model, sources\n\n    def cached_model_source_holder(self, model_architecture, sources, model_name=None):\n        \"\"\"\n        Update the dictionary for the given model_architecture with the new model name and its sources.\n        Use the model_architecture as a key to access the corresponding cache source mapper dictionary.\n        \"\"\"\n        self.cached_sources_map[model_architecture] = {**self.cached_sources_map.get(model_architecture, {}), **{model_name: sources}}\n\n    def prepare_mix(self, mix):\n        \"\"\"","chunkId":"audio_separator/separator/common_separator.py:20"}],[795,{"filePath":"audio_separator/separator/common_separator.py","startLine":205,"endLine":218,"chunkText":"\n    def prepare_mix(self, mix):\n        \"\"\"\n        Prepares the mix for processing. This includes loading the audio from a file if necessary,\n        ensuring the mix is in the correct format, and converting mono to stereo if needed.\n        \"\"\"\n        # Store the original path or the mix itself for later checks\n        audio_path = mix\n\n        # Check if the input is a file path (string) and needs to be loaded\n        if not isinstance(mix, np.ndarray):\n            self.logger.debug(f\"Loading audio from file: {mix}\")\n            \n            # Get audio file info to capture bit depth before loading","chunkId":"audio_separator/separator/common_separator.py:21"}],[796,{"filePath":"audio_separator/separator/common_separator.py","startLine":216,"endLine":227,"chunkText":"            self.logger.debug(f\"Loading audio from file: {mix}\")\n            \n            # Get audio file info to capture bit depth before loading\n            try:\n                audio_info = sf.info(mix)\n                self.input_subtype = audio_info.subtype\n                self.logger.info(f\"Input audio subtype: {self.input_subtype}\")\n                \n                # Map subtype to bit depth\n                if 'PCM_16' in self.input_subtype or self.input_subtype == 'PCM_S8':\n                    self.input_bit_depth = 16\n                elif 'PCM_24' in self.input_subtype:","chunkId":"audio_separator/separator/common_separator.py:22"}],[797,{"filePath":"audio_separator/separator/common_separator.py","startLine":225,"endLine":233,"chunkText":"                if 'PCM_16' in self.input_subtype or self.input_subtype == 'PCM_S8':\n                    self.input_bit_depth = 16\n                elif 'PCM_24' in self.input_subtype:\n                    self.input_bit_depth = 24\n                elif 'PCM_32' in self.input_subtype or 'FLOAT' in self.input_subtype or 'DOUBLE' in self.input_subtype:\n                    self.input_bit_depth = 32\n                else:\n                    # Default to 16-bit for unknown formats\n                    self.input_bit_depth = 16","chunkId":"audio_separator/separator/common_separator.py:23"}],[798,{"filePath":"audio_separator/separator/common_separator.py","startLine":231,"endLine":241,"chunkText":"                else:\n                    # Default to 16-bit for unknown formats\n                    self.input_bit_depth = 16\n                    self.logger.warning(f\"Unknown audio subtype {self.input_subtype}, defaulting to 16-bit output\")\n                \n                self.logger.info(f\"Detected input bit depth: {self.input_bit_depth}-bit\")\n            except Exception as e:\n                self.logger.warning(f\"Could not read audio file info, defaulting to 16-bit output: {e}\")\n                self.input_bit_depth = 16\n                self.input_subtype = 'PCM_16'\n            ","chunkId":"audio_separator/separator/common_separator.py:24"}],[799,{"filePath":"audio_separator/separator/common_separator.py","startLine":239,"endLine":249,"chunkText":"                self.input_bit_depth = 16\n                self.input_subtype = 'PCM_16'\n            \n            mix, sr = librosa.load(mix, mono=False, sr=self.sample_rate)\n            self.logger.debug(f\"Audio loaded. Sample rate: {sr}, Audio shape: {mix.shape}\")\n        else:\n            # Transpose the mix if it's already an ndarray (expected shape: [channels, samples])\n            self.logger.debug(\"Transposing the provided mix array.\")\n            # Default to 16-bit if numpy array provided directly\n            if self.input_bit_depth is None:\n                self.input_bit_depth = 16","chunkId":"audio_separator/separator/common_separator.py:25"}],[800,{"filePath":"audio_separator/separator/common_separator.py","startLine":247,"endLine":259,"chunkText":"            # Default to 16-bit if numpy array provided directly\n            if self.input_bit_depth is None:\n                self.input_bit_depth = 16\n                self.input_subtype = 'PCM_16'\n            mix = mix.T\n            self.logger.debug(f\"Transposed mix shape: {mix.shape}\")\n\n        # If the original input was a filepath, check if the loaded mix is empty\n        if isinstance(audio_path, str):\n            if not np.any(mix):\n                error_msg = f\"Audio file {audio_path} is empty or not valid\"\n                self.logger.error(error_msg)\n                raise ValueError(error_msg)","chunkId":"audio_separator/separator/common_separator.py:26"}],[801,{"filePath":"audio_separator/separator/common_separator.py","startLine":257,"endLine":269,"chunkText":"                error_msg = f\"Audio file {audio_path} is empty or not valid\"\n                self.logger.error(error_msg)\n                raise ValueError(error_msg)\n            else:\n                self.logger.debug(\"Audio file is valid and contains data.\")\n\n        # Ensure the mix is in stereo format\n        if mix.ndim == 1:\n            self.logger.debug(\"Mix is mono. Converting to stereo.\")\n            mix = np.asfortranarray([mix, mix])\n            self.logger.debug(\"Converted to stereo mix.\")\n\n        # Final log indicating successful preparation of the mix","chunkId":"audio_separator/separator/common_separator.py:27"}],[802,{"filePath":"audio_separator/separator/common_separator.py","startLine":267,"endLine":277,"chunkText":"            self.logger.debug(\"Converted to stereo mix.\")\n\n        # Final log indicating successful preparation of the mix\n        self.logger.debug(\"Mix preparation completed.\")\n        return mix\n\n    def write_audio(self, stem_path: str, stem_source):\n        \"\"\"\n        Writes the separated audio source to a file using pydub or soundfile\n        Pydub supports a much wider range of audio formats and produces better encoded lossy files for some formats.\n        Soundfile is used for very large files (longer than 1 hour), as pydub has memory issues with large files:","chunkId":"audio_separator/separator/common_separator.py:28"}],[803,{"filePath":"audio_separator/separator/common_separator.py","startLine":275,"endLine":282,"chunkText":"        Writes the separated audio source to a file using pydub or soundfile\n        Pydub supports a much wider range of audio formats and produces better encoded lossy files for some formats.\n        Soundfile is used for very large files (longer than 1 hour), as pydub has memory issues with large files:\n        https://github.com/jiaaro/pydub/issues/135\n        \"\"\"\n        # Get the duration of the input audio file\n        duration_seconds = librosa.get_duration(filename=self.audio_file_path)\n        duration_hours = duration_seconds / 3600","chunkId":"audio_separator/separator/common_separator.py:29"}],[804,{"filePath":"audio_separator/separator/common_separator.py","startLine":280,"endLine":291,"chunkText":"        # Get the duration of the input audio file\n        duration_seconds = librosa.get_duration(filename=self.audio_file_path)\n        duration_hours = duration_seconds / 3600\n        self.logger.info(f\"Audio duration is {duration_hours:.2f} hours ({duration_seconds:.2f} seconds).\")\n\n        if self.use_soundfile:\n            self.logger.warning(f\"Using soundfile for writing.\")\n            self.write_audio_soundfile(stem_path, stem_source)\n        else:\n            self.logger.info(f\"Using pydub for writing.\")\n            self.write_audio_pydub(stem_path, stem_source)\n","chunkId":"audio_separator/separator/common_separator.py:30"}],[805,{"filePath":"audio_separator/separator/common_separator.py","startLine":289,"endLine":300,"chunkText":"            self.logger.info(f\"Using pydub for writing.\")\n            self.write_audio_pydub(stem_path, stem_source)\n\n    def write_audio_pydub(self, stem_path: str, stem_source):\n        \"\"\"\n        Writes the separated audio source to a file using pydub (ffmpeg)\n        \"\"\"\n        self.logger.debug(f\"Entering write_audio_pydub with stem_path: {stem_path}\")\n\n        stem_source = spec_utils.normalize(wave=stem_source, max_peak=self.normalization_threshold, min_peak=self.amplification_threshold)\n\n        # Check if the numpy array is empty or contains very low values","chunkId":"audio_separator/separator/common_separator.py:31"}],[806,{"filePath":"audio_separator/separator/common_separator.py","startLine":298,"endLine":309,"chunkText":"        stem_source = spec_utils.normalize(wave=stem_source, max_peak=self.normalization_threshold, min_peak=self.amplification_threshold)\n\n        # Check if the numpy array is empty or contains very low values\n        if np.max(np.abs(stem_source)) < 1e-6:\n            self.logger.warning(\"Warning: stem_source array is near-silent or empty.\")\n            return\n\n        # If output_dir is specified, create it and join it with stem_path\n        if self.output_dir:\n            os.makedirs(self.output_dir, exist_ok=True)\n            stem_path = os.path.join(self.output_dir, stem_path)\n","chunkId":"audio_separator/separator/common_separator.py:32"}],[807,{"filePath":"audio_separator/separator/common_separator.py","startLine":307,"endLine":316,"chunkText":"            os.makedirs(self.output_dir, exist_ok=True)\n            stem_path = os.path.join(self.output_dir, stem_path)\n\n        self.logger.debug(f\"Audio data shape before processing: {stem_source.shape}\")\n        self.logger.debug(f\"Data type before conversion: {stem_source.dtype}\")\n\n        # Determine bit depth for output (use input bit depth if available, otherwise default to 16)\n        output_bit_depth = self.input_bit_depth if self.input_bit_depth is not None else 16\n        self.logger.info(f\"Writing output with {output_bit_depth}-bit depth\")\n","chunkId":"audio_separator/separator/common_separator.py:33"}],[808,{"filePath":"audio_separator/separator/common_separator.py","startLine":314,"endLine":323,"chunkText":"        output_bit_depth = self.input_bit_depth if self.input_bit_depth is not None else 16\n        self.logger.info(f\"Writing output with {output_bit_depth}-bit depth\")\n\n        # For pydub, we always convert to int16 for the AudioSegment creation\n        # Then let ffmpeg handle the conversion to the target bit depth during export\n        if stem_source.dtype != np.int16:\n            stem_source = (stem_source * 32767).astype(np.int16)\n            self.logger.debug(\"Converted stem_source to int16 for pydub processing.\")\n\n        # Correctly interleave stereo channels","chunkId":"audio_separator/separator/common_separator.py:34"}],[809,{"filePath":"audio_separator/separator/common_separator.py","startLine":321,"endLine":331,"chunkText":"            self.logger.debug(\"Converted stem_source to int16 for pydub processing.\")\n\n        # Correctly interleave stereo channels\n        stem_source_interleaved = np.empty((2 * stem_source.shape[0],), dtype=np.int16)\n        stem_source_interleaved[0::2] = stem_source[:, 0]  # Left channel\n        stem_source_interleaved[1::2] = stem_source[:, 1]  # Right channel\n\n        self.logger.debug(f\"Interleaved audio data shape: {stem_source_interleaved.shape}\")\n\n        # Create a pydub AudioSegment (always from 16-bit data)\n        try:","chunkId":"audio_separator/separator/common_separator.py:35"}],[810,{"filePath":"audio_separator/separator/common_separator.py","startLine":329,"endLine":340,"chunkText":"\n        # Create a pydub AudioSegment (always from 16-bit data)\n        try:\n            audio_segment = AudioSegment(stem_source_interleaved.tobytes(), frame_rate=self.sample_rate, sample_width=2, channels=2)\n            self.logger.debug(\"Created AudioSegment successfully.\")\n        except (IOError, ValueError) as e:\n            self.logger.error(f\"Specific error creating AudioSegment: {e}\")\n            return\n\n        # Determine file format based on the file extension\n        file_format = stem_path.lower().split(\".\")[-1]\n","chunkId":"audio_separator/separator/common_separator.py:36"}],[811,{"filePath":"audio_separator/separator/common_separator.py","startLine":338,"endLine":351,"chunkText":"        # Determine file format based on the file extension\n        file_format = stem_path.lower().split(\".\")[-1]\n\n        # For m4a files, specify mp4 as the container format as the extension doesn't match the format name\n        if file_format == \"m4a\":\n            file_format = \"mp4\"\n        elif file_format == \"mka\":\n            file_format = \"matroska\"\n\n        # Set the bitrate to 320k for mp3 files if output_bitrate is not specified\n        bitrate = \"320k\" if file_format == \"mp3\" and self.output_bitrate is None else self.output_bitrate\n\n        # Export using the determined format\n        try:","chunkId":"audio_separator/separator/common_separator.py:37"}],[812,{"filePath":"audio_separator/separator/common_separator.py","startLine":349,"endLine":362,"chunkText":"\n        # Export using the determined format\n        try:\n            # Pass codec parameters to ffmpeg to enforce bit depth for lossless formats\n            export_params = {\"format\": file_format}\n            \n            if bitrate:\n                export_params[\"bitrate\"] = bitrate\n            \n            # For lossless formats (WAV/FLAC), specify the codec parameters to enforce bit depth\n            if file_format in [\"wav\", \"flac\"]:\n                if output_bit_depth == 16:\n                    export_params[\"parameters\"] = [\"-sample_fmt\", \"s16\"]\n                elif output_bit_depth == 24:","chunkId":"audio_separator/separator/common_separator.py:38"}],[813,{"filePath":"audio_separator/separator/common_separator.py","startLine":360,"endLine":369,"chunkText":"                if output_bit_depth == 16:\n                    export_params[\"parameters\"] = [\"-sample_fmt\", \"s16\"]\n                elif output_bit_depth == 24:\n                    export_params[\"parameters\"] = [\"-sample_fmt\", \"s32\"]\n                    # For 24-bit, we also need to specify the bit depth explicitly\n                    if file_format == \"wav\":\n                        export_params[\"codec\"] = \"pcm_s24le\"\n                    elif file_format == \"flac\":\n                        # FLAC supports 24-bit natively, no special handling needed\n                        pass","chunkId":"audio_separator/separator/common_separator.py:39"}],[814,{"filePath":"audio_separator/separator/common_separator.py","startLine":367,"endLine":376,"chunkText":"                    elif file_format == \"flac\":\n                        # FLAC supports 24-bit natively, no special handling needed\n                        pass\n                elif output_bit_depth == 32:\n                    export_params[\"parameters\"] = [\"-sample_fmt\", \"s32\"]\n                    if file_format == \"wav\":\n                        export_params[\"codec\"] = \"pcm_s32le\"\n            \n            audio_segment.export(stem_path, **export_params)\n            self.logger.debug(f\"Exported audio file successfully to {stem_path} with {output_bit_depth}-bit depth\")","chunkId":"audio_separator/separator/common_separator.py:40"}],[815,{"filePath":"audio_separator/separator/common_separator.py","startLine":374,"endLine":385,"chunkText":"            \n            audio_segment.export(stem_path, **export_params)\n            self.logger.debug(f\"Exported audio file successfully to {stem_path} with {output_bit_depth}-bit depth\")\n        except (IOError, ValueError) as e:\n            self.logger.error(f\"Error exporting audio file: {e}\")\n\n    def write_audio_soundfile(self, stem_path: str, stem_source):\n        \"\"\"\n        Writes the separated audio source to a file using soundfile library.\n        \"\"\"\n        self.logger.debug(f\"Entering write_audio_soundfile with stem_path: {stem_path}\")\n","chunkId":"audio_separator/separator/common_separator.py:41"}],[816,{"filePath":"audio_separator/separator/common_separator.py","startLine":383,"endLine":394,"chunkText":"        \"\"\"\n        self.logger.debug(f\"Entering write_audio_soundfile with stem_path: {stem_path}\")\n\n        stem_source = spec_utils.normalize(wave=stem_source, max_peak=self.normalization_threshold, min_peak=self.amplification_threshold)\n\n        # Check if the numpy array is empty or contains very low values\n        if np.max(np.abs(stem_source)) < 1e-6:\n            self.logger.warning(\"Warning: stem_source array is near-silent or empty.\")\n            return\n\n        # If output_dir is specified, create it and join it with stem_path\n        if self.output_dir:","chunkId":"audio_separator/separator/common_separator.py:42"}],[817,{"filePath":"audio_separator/separator/common_separator.py","startLine":392,"endLine":405,"chunkText":"\n        # If output_dir is specified, create it and join it with stem_path\n        if self.output_dir:\n            os.makedirs(self.output_dir, exist_ok=True)\n            stem_path = os.path.join(self.output_dir, stem_path)\n\n        # Determine the subtype based on the input audio's bit depth\n        output_subtype = None\n        if self.input_subtype:\n            output_subtype = self.input_subtype\n            self.logger.info(f\"Using input subtype for output: {output_subtype}\")\n        elif self.input_bit_depth:\n            # Map bit depth to subtype\n            if self.input_bit_depth == 16:","chunkId":"audio_separator/separator/common_separator.py:43"}],[818,{"filePath":"audio_separator/separator/common_separator.py","startLine":403,"endLine":415,"chunkText":"        elif self.input_bit_depth:\n            # Map bit depth to subtype\n            if self.input_bit_depth == 16:\n                output_subtype = 'PCM_16'\n            elif self.input_bit_depth == 24:\n                output_subtype = 'PCM_24'\n            elif self.input_bit_depth == 32:\n                output_subtype = 'PCM_32'\n            else:\n                output_subtype = 'PCM_16'  # Default fallback\n            self.logger.info(f\"Using output subtype based on bit depth: {output_subtype}\")\n        else:\n            # Default to PCM_16 if no bit depth info available","chunkId":"audio_separator/separator/common_separator.py:44"}],[819,{"filePath":"audio_separator/separator/common_separator.py","startLine":413,"endLine":423,"chunkText":"            self.logger.info(f\"Using output subtype based on bit depth: {output_subtype}\")\n        else:\n            # Default to PCM_16 if no bit depth info available\n            output_subtype = 'PCM_16'\n            self.logger.warning(\"No bit depth info available, defaulting to PCM_16\")\n\n        # Correctly interleave stereo channels if needed\n        if stem_source.shape[1] == 2:\n            # If the audio is already interleaved, ensure it's in the correct order\n            # Check if the array is Fortran contiguous (column-major)\n            if stem_source.flags[\"F_CONTIGUOUS\"]:","chunkId":"audio_separator/separator/common_separator.py:45"}],[820,{"filePath":"audio_separator/separator/common_separator.py","startLine":421,"endLine":431,"chunkText":"            # If the audio is already interleaved, ensure it's in the correct order\n            # Check if the array is Fortran contiguous (column-major)\n            if stem_source.flags[\"F_CONTIGUOUS\"]:\n                # Convert to C contiguous (row-major)\n                stem_source = np.ascontiguousarray(stem_source)\n            # No need to manually interleave for soundfile - it handles multi-channel properly\n            # Just ensure we don't have the wrong shape\n\n        self.logger.debug(f\"Audio data shape for soundfile: {stem_source.shape}\")\n\n        \"\"\"","chunkId":"audio_separator/separator/common_separator.py:46"}],[821,{"filePath":"audio_separator/separator/common_separator.py","startLine":429,"endLine":441,"chunkText":"        self.logger.debug(f\"Audio data shape for soundfile: {stem_source.shape}\")\n\n        \"\"\"\n        Write audio using soundfile (for formats other than M4A).\n        \"\"\"\n        # Save audio using soundfile with the specified subtype\n        try:\n            # Specify the subtype to match input bit depth\n            sf.write(stem_path, stem_source, self.sample_rate, subtype=output_subtype)\n            self.logger.debug(f\"Exported audio file successfully to {stem_path} with subtype {output_subtype}\")\n        except Exception as e:\n            self.logger.error(f\"Error exporting audio file: {e}\")\n","chunkId":"audio_separator/separator/common_separator.py:47"}],[822,{"filePath":"audio_separator/separator/common_separator.py","startLine":439,"endLine":454,"chunkText":"        except Exception as e:\n            self.logger.error(f\"Error exporting audio file: {e}\")\n\n    def clear_gpu_cache(self):\n        \"\"\"\n        This method clears the GPU cache to free up memory.\n        \"\"\"\n        self.logger.debug(\"Running garbage collection...\")\n        gc.collect()\n        if self.torch_device == torch.device(\"mps\"):\n            self.logger.debug(\"Clearing MPS cache...\")\n            torch.mps.empty_cache()\n        if self.torch_device == torch.device(\"cuda\"):\n            self.logger.debug(\"Clearing CUDA cache...\")\n            torch.cuda.empty_cache()\n","chunkId":"audio_separator/separator/common_separator.py:48"}],[823,{"filePath":"audio_separator/separator/common_separator.py","startLine":452,"endLine":469,"chunkText":"            self.logger.debug(\"Clearing CUDA cache...\")\n            torch.cuda.empty_cache()\n\n    def clear_file_specific_paths(self):\n        \"\"\"\n        Clears the file-specific variables which need to be cleared between processing different audio inputs.\n        \"\"\"\n        self.logger.info(\"Clearing input audio file paths, sources and stems...\")\n\n        self.audio_file_path = None\n        self.audio_file_base = None\n\n        self.primary_source = None\n        self.secondary_source = None\n\n        self.primary_stem_output_path = None\n        self.secondary_stem_output_path = None\n","chunkId":"audio_separator/separator/common_separator.py:49"}],[824,{"filePath":"audio_separator/separator/common_separator.py","startLine":467,"endLine":482,"chunkText":"        self.primary_stem_output_path = None\n        self.secondary_stem_output_path = None\n\n    def sanitize_filename(self, filename):\n        \"\"\"\n        Cleans the filename by replacing invalid characters with underscores.\n        \"\"\"\n        sanitized = re.sub(r'[<>:\"/\\\\|?*]', '_', filename)\n        sanitized = re.sub(r'_+', '_', sanitized)\n        sanitized = sanitized.strip('_. ')\n        return sanitized\n\n    def get_stem_output_path(self, stem_name, custom_output_names):\n        \"\"\"\n        Gets the output path for a stem based on the stem name and custom output names.\n        \"\"\"","chunkId":"audio_separator/separator/common_separator.py:50"}],[825,{"filePath":"audio_separator/separator/common_separator.py","startLine":480,"endLine":488,"chunkText":"        \"\"\"\n        Gets the output path for a stem based on the stem name and custom output names.\n        \"\"\"\n        # Convert custom_output_names keys to lowercase for case-insensitive comparison\n        if custom_output_names:\n            custom_output_names_lower = {k.lower(): v for k, v in custom_output_names.items()}\n            stem_name_lower = stem_name.lower()\n            if stem_name_lower in custom_output_names_lower:\n                sanitized_custom_name = self.sanitize_filename(custom_output_names_lower[stem_name_lower])","chunkId":"audio_separator/separator/common_separator.py:51"}],[826,{"filePath":"audio_separator/separator/common_separator.py","startLine":486,"endLine":494,"chunkText":"            stem_name_lower = stem_name.lower()\n            if stem_name_lower in custom_output_names_lower:\n                sanitized_custom_name = self.sanitize_filename(custom_output_names_lower[stem_name_lower])\n                return os.path.join(f\"{sanitized_custom_name}.{self.output_format.lower()}\")\n\n        sanitized_audio_base = self.sanitize_filename(self.audio_file_base)\n        sanitized_stem_name = self.sanitize_filename(stem_name)\n        sanitized_model_name = self.sanitize_filename(self.model_name) \n","chunkId":"audio_separator/separator/common_separator.py:52"}],[827,{"filePath":"audio_separator/separator/common_separator.py","startLine":492,"endLine":507,"chunkText":"        sanitized_stem_name = self.sanitize_filename(stem_name)\n        sanitized_model_name = self.sanitize_filename(self.model_name) \n\n        filename = f\"{sanitized_audio_base}_({sanitized_stem_name})_{sanitized_model_name}.{self.output_format.lower()}\"\n        return os.path.join(filename)\n    \n    def _detect_roformer_model(self):\n        \"\"\"\n        Detect if the current model is a Roformer model.\n        \n        Returns:\n            bool: True if this is a Roformer model, False otherwise\n        \"\"\"\n        if not self.model_data:\n            return False\n            ","chunkId":"audio_separator/separator/common_separator.py:53"}],[828,{"filePath":"audio_separator/separator/common_separator.py","startLine":505,"endLine":523,"chunkText":"        if not self.model_data:\n            return False\n            \n        # Check for explicit Roformer flag\n        if self.model_data.get(\"is_roformer\", False):\n            return True\n            \n        # Check model path for Roformer indicators\n        if self.model_path and \"roformer\" in self.model_path.lower():\n            return True\n            \n        # Check model name for Roformer indicators\n        if self.model_name and \"roformer\" in self.model_name.lower():\n            return True\n            \n        return False\n    \n    def _initialize_roformer_loader(self):\n        \"\"\"","chunkId":"audio_separator/separator/common_separator.py:54"}],[829,{"filePath":"audio_separator/separator/common_separator.py","startLine":521,"endLine":537,"chunkText":"    \n    def _initialize_roformer_loader(self):\n        \"\"\"\n        Initialize the Roformer loader for this model.\n        \"\"\"\n        try:\n            from .roformer.roformer_loader import RoformerLoader\n            self.roformer_loader = RoformerLoader()\n            self.logger.debug(\"Initialized Roformer loader for CommonSeparator\")\n        except ImportError as e:\n            self.logger.warning(f\"Could not import RoformerLoader: {e}\")\n            self.roformer_loader = None\n    \n    def get_roformer_loading_stats(self):\n        \"\"\"\n        Get Roformer loading statistics if available.\n        ","chunkId":"audio_separator/separator/common_separator.py:55"}],[830,{"filePath":"audio_separator/separator/common_separator.py","startLine":535,"endLine":553,"chunkText":"        \"\"\"\n        Get Roformer loading statistics if available.\n        \n        Returns:\n            dict: Loading statistics or empty dict if not available\n        \"\"\"\n        if self.roformer_loader:\n            return self.roformer_loader.get_loading_stats()\n        return {}\n    \n    def validate_roformer_config(self, config, model_type):\n        \"\"\"\n        Validate Roformer configuration if loader is available.\n        \n        Args:\n            config: Configuration dictionary to validate\n            model_type: Type of model to validate for\n            \n        Returns:","chunkId":"audio_separator/separator/common_separator.py:56"}],[831,{"filePath":"audio_separator/separator/common_separator.py","startLine":551,"endLine":559,"chunkText":"            model_type: Type of model to validate for\n            \n        Returns:\n            bool: True if valid or validation not available, False if invalid\n        \"\"\"\n        if self.roformer_loader:\n            return self.roformer_loader.validate_configuration(config, model_type)\n        return True  # Assume valid if no loader available\n","chunkId":"audio_separator/separator/common_separator.py:57"}],[832,{"filePath":"audio_separator/separator/roformer/README.md","startLine":0,"endLine":10,"chunkText":"# Roformer Implementation - Updated Parameter Support\n\nThis directory contains the updated Roformer implementation with support for new parameters and backward compatibility for legacy models (handled by the single, modern loader).\n\n## Overview\n\nThe updated Roformer implementation provides:\n\n- **New Parameter Support**: `mlp_expansion_factor`, `sage_attention`, `zero_dc`, `use_torch_checkpoint`, `skip_connection`\n- **Single Loader Path**: Unified loader supports both newer and older checkpoints\n- **Robust Validation**: Comprehensive parameter validation with detailed error messages","chunkId":"audio_separator/separator/roformer/README.md:0"}],[833,{"filePath":"audio_separator/separator/roformer/README.md","startLine":9,"endLine":20,"chunkText":"- **Single Loader Path**: Unified loader supports both newer and older checkpoints\n- **Robust Validation**: Comprehensive parameter validation with detailed error messages\n- **Configuration Normalization**: Handles different config formats and applies sensible defaults\n\n## Architecture\n\n```\nroformer/\n├── model_configuration.py      # Data model for standardized config\n├── parameter_validator.py      # Base parameter validation\n├── bs_roformer_validator.py    # BSRoformer-specific validation\n├── mel_band_roformer_validator.py  # MelBandRoformer-specific validation","chunkId":"audio_separator/separator/roformer/README.md:1"}],[834,{"filePath":"audio_separator/separator/roformer/README.md","startLine":19,"endLine":33,"chunkText":"├── bs_roformer_validator.py    # BSRoformer-specific validation\n├── mel_band_roformer_validator.py  # MelBandRoformer-specific validation\n├── configuration_normalizer.py # Config normalization and defaults\n├── parameter_validation_error.py  # Custom exception handling\n├── roformer_loader.py          # Main loader (new implementation only)\n├── model_loading_result.py     # Result dataclass\n└── README.md                   # This documentation\n```\n\n## New Parameters\n\n### Core New Parameters\n\n| Parameter | Type | Default | Description |\n|-----------|------|---------|-------------|","chunkId":"audio_separator/separator/roformer/README.md:2"}],[835,{"filePath":"audio_separator/separator/roformer/README.md","startLine":32,"endLine":41,"chunkText":"| Parameter | Type | Default | Description |\n|-----------|------|---------|-------------|\n| `mlp_expansion_factor` | int | 4 | MLP expansion factor in transformer layers |\n| `sage_attention` | bool | False | Use Sage attention mechanism instead of standard attention |\n| `zero_dc` | bool | True | Apply zero DC component filtering |\n| `use_torch_checkpoint` | bool | False | Enable gradient checkpointing for memory efficiency |\n| `skip_connection` | bool | False | Add skip connections in the architecture |\n\n### Compatibility Notes\n","chunkId":"audio_separator/separator/roformer/README.md:3"}],[836,{"filePath":"audio_separator/separator/roformer/README.md","startLine":40,"endLine":58,"chunkText":"### Compatibility Notes\n\n- **`sage_attention`**: Cannot be used simultaneously with `flash_attn=True` (warning will be issued)\n- **`mlp_expansion_factor`**: Higher values increase model capacity but also memory usage\n- **`use_torch_checkpoint`**: Trades computation for memory - useful for large models\n- **`zero_dc`**: Recommended for most audio separation tasks\n\n## Usage Examples\n\n### Basic Usage with New Parameters\n\n```python\nfrom audio_separator.separator.roformer.roformer_loader import RoformerLoader\n\nloader = RoformerLoader()\n\nconfig = {\n    'dim': 512,\n    'depth': 12,","chunkId":"audio_separator/separator/roformer/README.md:4"}],[837,{"filePath":"audio_separator/separator/roformer/README.md","startLine":57,"endLine":80,"chunkText":"    'dim': 512,\n    'depth': 12,\n    'freqs_per_bands': (2, 4, 8, 16, 32, 64),\n    'mlp_expansion_factor': 8,\n    'sage_attention': True,\n    'zero_dc': True,\n    'use_torch_checkpoint': True,\n    'skip_connection': False\n}\n\nresult = loader.load_model('/path/to/model.ckpt', config, device='cuda')\nif result.success:\n    print(\"Model loaded\")\nelse:\n    print(f\"Failed: {result.error_message}\")\n```\n\n### Configuration Validation\n\n```python\nfrom audio_separator.separator.roformer.parameter_validator import ParameterValidator\n\nvalidator = ParameterValidator()\nissues = validator.validate_all(config, \"bs_roformer\"","chunkId":"audio_separator/separator/roformer/README.md:5"}],[838,{"filePath":"audio_separator/separator/roformer/README.md","startLine":79,"endLine":99,"chunkText":"validator = ParameterValidator()\nissues = validator.validate_all(config, \"bs_roformer\")\nif issues:\n    for issue in issues:\n        print(f\"{issue.severity.value}: {issue.message}\")\nelse:\n    print(\"Configuration is valid!\")\n```\n\n### Configuration Normalization\n\n```python\nfrom audio_separator.separator.roformer.configuration_normalizer import ConfigurationNormalizer\n\nnormalizer = ConfigurationNormalizer()\nraw_config = {\n    'model': {'dim': '512', 'depth': 12.0, 'n_heads': 8},\n    'training': {'sample_rate': 44100},\n    'stereo': 'true',\n    'freq_bands': '[2, 4, 8, 16]'\n}","chunkId":"audio_separator/separator/roformer/README.md:6"}],[839,{"filePath":"audio_separator/separator/roformer/README.md","startLine":98,"endLine":110,"chunkText":"    'freq_bands': '[2, 4, 8, 16]'\n}\nnormalized = normalizer.normalize_config(raw_config, model_type=\"bs_roformer\", apply_defaults=True, validate=True)\nprint(f\"Normalized config has {len(normalized)} parameters\")\n```\n\n## Model Type Detection\n\nDetected via config contents (e.g., `freqs_per_bands` vs `num_bands`) and filename hints. Defaults to BSRoformer when ambiguous.\n\n## Integration with Audio Separator\n\n- Routing remains through the MDXC architecture path; Roformer models are detected and handled by the MDXC separator using the unified `RoformerLoader`.","chunkId":"audio_separator/separator/roformer/README.md:7"}],[840,{"filePath":"audio_separator/separator/roformer/README.md","startLine":109,"endLine":126,"chunkText":"\n- Routing remains through the MDXC architecture path; Roformer models are detected and handled by the MDXC separator using the unified `RoformerLoader`.\n- Loader statistics are surfaced via `CommonSeparator.get_roformer_loading_stats()` and logged by the top-level `Separator`.\n\n## Testing\n\n```bash\n# Unit tests (examples)\npython -m pytest tests/unit/test_parameter_validator.py -v\npython -m pytest tests/unit/test_configuration_normalizer.py -v\n\n# Integration\npython -m pytest tests/integration/test_roformer_*.py -v\n```\n\n## Contributing\n\n- Follow TDD","chunkId":"audio_separator/separator/roformer/README.md:8"}],[841,{"filePath":"audio_separator/separator/roformer/README.md","startLine":125,"endLine":133,"chunkText":"\n- Follow TDD\n- Maintain compatibility for existing checkpoints through the single loader path\n- Update documentation when adding parameters or behavior\n\n## License\n\nFollows the main Audio Separator project license.\n","chunkId":"audio_separator/separator/roformer/README.md:9"}],[842,{"filePath":"audio_separator/separator/roformer/__init__.py","startLine":0,"endLine":18,"chunkText":"\"\"\"\nRoformer implementation module.\nUpdated implementation supporting both old and new Roformer model parameters.\n\"\"\"\n\nfrom .model_configuration import ModelConfiguration\nfrom .bs_roformer_config import BSRoformerConfig\nfrom .mel_band_roformer_config import MelBandRoformerConfig\nfrom .model_loading_result import ModelLoadingResult\nfrom .parameter_validation_error import ParameterValidationError\n\n__all__ = [\n    'ModelConfiguration',\n    'BSRoformerConfig', \n    'MelBandRoformerConfig',\n    'ModelLoadingResult',\n    'ParameterValidationError'\n]\n","chunkId":"audio_separator/separator/roformer/__init__.py:0"}],[843,{"filePath":"audio_separator/separator/roformer/bs_roformer_config.py","startLine":0,"endLine":18,"chunkText":"\"\"\"\nBSRoformer-specific configuration class.\nExtends ModelConfiguration with BSRoformer-specific parameters.\n\"\"\"\n\nfrom dataclasses import dataclass\nfrom typing import Tuple, Optional, Dict, Any\nfrom .model_configuration import ModelConfiguration, RoformerType\n\n\n@dataclass(frozen=True, unsafe_hash=True)\nclass BSRoformerConfig(ModelConfiguration):\n    \"\"\"\n    Configuration class specifically for BSRoformer (Band-Split Roformer) models.\n    \n    BSRoformer processes audio by splitting it into frequency bands and applying\n    Roformer architecture to each band separately.\n    \"\"\"\n    ","chunkId":"audio_separator/separator/roformer/bs_roformer_config.py:0"}],[844,{"filePath":"audio_separator/separator/roformer/bs_roformer_config.py","startLine":16,"endLine":30,"chunkText":"    Roformer architecture to each band separately.\n    \"\"\"\n    \n    # BSRoformer-specific required parameters\n    freqs_per_bands: Tuple[int, ...] = (2, 4, 8, 16, 32, 64)  # Default frequency band configuration\n    \n    # BSRoformer-specific optional parameters\n    mask_estimator_depth: int = 2  # Depth of mask estimation network\n    \n    def __post_init__(self):\n        \"\"\"Validate BSRoformer-specific configuration after initialization.\"\"\"\n        super().__post_init__()\n        self._validate_bs_roformer_parameters()\n    \n    def _validate_bs_roformer_parameters(self):","chunkId":"audio_separator/separator/roformer/bs_roformer_config.py:1"}],[845,{"filePath":"audio_separator/separator/roformer/bs_roformer_config.py","startLine":28,"endLine":38,"chunkText":"        self._validate_bs_roformer_parameters()\n    \n    def _validate_bs_roformer_parameters(self):\n        \"\"\"Validate BSRoformer-specific parameters.\"\"\"\n        if not self.freqs_per_bands:\n            raise ValueError(\"freqs_per_bands must be provided for BSRoformer\")\n        \n        if not isinstance(self.freqs_per_bands, (tuple, list)):\n            raise ValueError(f\"freqs_per_bands must be a tuple or list, got {type(self.freqs_per_bands)}\")\n        \n        if not all(isinstance(freq, int) and freq > 0 for freq in self.freqs_per_bands):","chunkId":"audio_separator/separator/roformer/bs_roformer_config.py:2"}],[846,{"filePath":"audio_separator/separator/roformer/bs_roformer_config.py","startLine":36,"endLine":46,"chunkText":"            raise ValueError(f\"freqs_per_bands must be a tuple or list, got {type(self.freqs_per_bands)}\")\n        \n        if not all(isinstance(freq, int) and freq > 0 for freq in self.freqs_per_bands):\n            raise ValueError(\"All frequencies in freqs_per_bands must be positive integers\")\n        \n        if len(self.freqs_per_bands) < 2:\n            raise ValueError(\"freqs_per_bands must contain at least 2 frequency bands\")\n        \n        if self.mask_estimator_depth <= 0:\n            raise ValueError(f\"mask_estimator_depth must be positive, got {self.mask_estimator_depth}\")\n    ","chunkId":"audio_separator/separator/roformer/bs_roformer_config.py:3"}],[847,{"filePath":"audio_separator/separator/roformer/bs_roformer_config.py","startLine":44,"endLine":60,"chunkText":"        if self.mask_estimator_depth <= 0:\n            raise ValueError(f\"mask_estimator_depth must be positive, got {self.mask_estimator_depth}\")\n    \n    def get_total_frequency_bins(self) -> int:\n        \"\"\"Calculate total number of frequency bins.\"\"\"\n        return sum(self.freqs_per_bands)\n    \n    def validate_against_stft_config(self, n_fft: int) -> bool:\n        \"\"\"\n        Validate that frequency bands match STFT configuration.\n        \n        Args:\n            n_fft: STFT n_fft parameter\n            \n        Returns:\n            True if configuration is valid, False otherwise\n        \"\"\"","chunkId":"audio_separator/separator/roformer/bs_roformer_config.py:4"}],[848,{"filePath":"audio_separator/separator/roformer/bs_roformer_config.py","startLine":58,"endLine":76,"chunkText":"        Returns:\n            True if configuration is valid, False otherwise\n        \"\"\"\n        expected_freq_bins = n_fft // 2 + 1\n        total_freq_bins = self.get_total_frequency_bins()\n        \n        return total_freq_bins == expected_freq_bins\n    \n    def get_stft_compatibility_info(self, n_fft: int) -> Dict[str, Any]:\n        \"\"\"\n        Get information about STFT compatibility.\n        \n        Args:\n            n_fft: STFT n_fft parameter\n            \n        Returns:\n            Dictionary with compatibility information\n        \"\"\"\n        expected_freq_bins = n_fft // 2 + 1","chunkId":"audio_separator/separator/roformer/bs_roformer_config.py:5"}],[849,{"filePath":"audio_separator/separator/roformer/bs_roformer_config.py","startLine":74,"endLine":88,"chunkText":"            Dictionary with compatibility information\n        \"\"\"\n        expected_freq_bins = n_fft // 2 + 1\n        total_freq_bins = self.get_total_frequency_bins()\n        \n        return {\n            'expected_freq_bins': expected_freq_bins,\n            'actual_freq_bins': total_freq_bins,\n            'is_compatible': total_freq_bins == expected_freq_bins,\n            'difference': total_freq_bins - expected_freq_bins,\n            'freqs_per_bands': self.freqs_per_bands\n        }\n    \n    def get_model_type(self) -> RoformerType:\n        \"\"\"Get the model type.\"\"\"","chunkId":"audio_separator/separator/roformer/bs_roformer_config.py:6"}],[850,{"filePath":"audio_separator/separator/roformer/bs_roformer_config.py","startLine":87,"endLine":99,"chunkText":"    def get_model_type(self) -> RoformerType:\n        \"\"\"Get the model type.\"\"\"\n        return RoformerType.BS_ROFORMER\n    \n    def get_bs_roformer_kwargs(self) -> Dict[str, Any]:\n        \"\"\"Get BSRoformer-specific parameters for model initialization.\"\"\"\n        kwargs = self.get_transformer_kwargs()\n        kwargs.update({\n            'freqs_per_bands': self.freqs_per_bands,\n            'mask_estimator_depth': self.mask_estimator_depth,\n            'stereo': self.stereo,\n            'num_stems': self.num_stems,\n            'mlp_expansion_factor': self.mlp_expansion_factor,","chunkId":"audio_separator/separator/roformer/bs_roformer_config.py:7"}],[851,{"filePath":"audio_separator/separator/roformer/bs_roformer_config.py","startLine":97,"endLine":113,"chunkText":"            'stereo': self.stereo,\n            'num_stems': self.num_stems,\n            'mlp_expansion_factor': self.mlp_expansion_factor,\n            'use_torch_checkpoint': self.use_torch_checkpoint,\n            'skip_connection': self.skip_connection,\n        })\n        return kwargs\n    \n    @classmethod\n    def from_model_config(cls, config_dict: Dict[str, Any]) -> 'BSRoformerConfig':\n        \"\"\"\n        Create BSRoformerConfig from a model configuration dictionary.\n        \n        Args:\n            config_dict: Dictionary containing model configuration\n            \n        Returns:","chunkId":"audio_separator/separator/roformer/bs_roformer_config.py:8"}],[852,{"filePath":"audio_separator/separator/roformer/bs_roformer_config.py","startLine":111,"endLine":128,"chunkText":"            config_dict: Dictionary containing model configuration\n            \n        Returns:\n            BSRoformerConfig instance\n        \"\"\"\n        # Ensure freqs_per_bands is present\n        if 'freqs_per_bands' not in config_dict:\n            # Use default if not specified\n            config_dict = config_dict.copy()\n            config_dict['freqs_per_bands'] = (2, 4, 8, 16, 32, 64)\n        \n        return cls.from_dict(config_dict)\n    \n    def suggest_stft_n_fft(self) -> int:\n        \"\"\"\n        Suggest appropriate n_fft value for STFT based on frequency bands.\n        \n        Returns:","chunkId":"audio_separator/separator/roformer/bs_roformer_config.py:9"}],[853,{"filePath":"audio_separator/separator/roformer/bs_roformer_config.py","startLine":126,"endLine":138,"chunkText":"        Suggest appropriate n_fft value for STFT based on frequency bands.\n        \n        Returns:\n            Suggested n_fft value\n        \"\"\"\n        total_freq_bins = self.get_total_frequency_bins()\n        # n_fft = 2 * (freq_bins - 1)\n        return 2 * (total_freq_bins - 1)\n    \n    def get_parameter_summary(self) -> str:\n        \"\"\"Get a summary string of key BSRoformer parameters.\"\"\"\n        base_summary = super().get_parameter_summary()\n        bs_info = f\", freqs_per_bands={self.freqs_per_bands}, total_bins={self.get_total_frequency_bins()}\"","chunkId":"audio_separator/separator/roformer/bs_roformer_config.py:10"}],[854,{"filePath":"audio_separator/separator/roformer/bs_roformer_config.py","startLine":136,"endLine":144,"chunkText":"        \"\"\"Get a summary string of key BSRoformer parameters.\"\"\"\n        base_summary = super().get_parameter_summary()\n        bs_info = f\", freqs_per_bands={self.freqs_per_bands}, total_bins={self.get_total_frequency_bins()}\"\n        return base_summary.replace(\")\", bs_info + \")\")\n    \n    def __repr__(self) -> str:\n        \"\"\"String representation of the BSRoformer configuration.\"\"\"\n        return f\"BSRoformerConfig({self.get_parameter_summary()})\"\n","chunkId":"audio_separator/separator/roformer/bs_roformer_config.py:11"}],[855,{"filePath":"audio_separator/separator/roformer/bs_roformer_validator.py","startLine":0,"endLine":17,"chunkText":"\"\"\"\nBSRoformer-specific parameter validator.\nExtends the base ParameterValidator with BSRoformer-specific validation logic.\n\"\"\"\n\nfrom typing import Dict, Any, List, Tuple\nfrom .parameter_validator import ParameterValidator, ValidationIssue, ValidationSeverity\n\n\nclass BSRoformerValidator(ParameterValidator):\n    \"\"\"\n    Specialized validator for BSRoformer model parameters.\n    \n    Provides BSRoformer-specific validation beyond the base parameter validation,\n    including frequency band configuration and STFT parameter validation.\n    \"\"\"\n    \n    # BSRoformer-specific parameter constraints","chunkId":"audio_separator/separator/roformer/bs_roformer_validator.py:0"}],[856,{"filePath":"audio_separator/separator/roformer/bs_roformer_validator.py","startLine":15,"endLine":34,"chunkText":"    \"\"\"\n    \n    # BSRoformer-specific parameter constraints\n    DEFAULT_FREQS_PER_BANDS = (2, 4, 8, 16, 32, 64, 128, 256, 512, 1024)\n    MIN_BANDS = 2\n    MAX_BANDS = 32\n    \n    def validate_freqs_per_bands(self, config: Dict[str, Any]) -> List[ValidationIssue]:\n        \"\"\"\n        Validate freqs_per_bands parameter for BSRoformer.\n        \n        Args:\n            config: Model configuration dictionary\n            \n        Returns:\n            List of validation issues for freqs_per_bands\n        \"\"\"\n        issues = []\n        \n        if 'freqs_per_bands' not in config:","chunkId":"audio_separator/separator/roformer/bs_roformer_validator.py:1"}],[857,{"filePath":"audio_separator/separator/roformer/bs_roformer_validator.py","startLine":32,"endLine":45,"chunkText":"        issues = []\n        \n        if 'freqs_per_bands' not in config:\n            return issues  # Required parameter check handled by base class\n        \n        freqs_per_bands = config['freqs_per_bands']\n        \n        # Type check\n        if not isinstance(freqs_per_bands, (list, tuple)):\n            issue = ValidationIssue(\n                severity=ValidationSeverity.ERROR,\n                parameter_name=\"freqs_per_bands\",\n                message=\"freqs_per_bands must be a list or tuple\",\n                suggested_fix=\"Convert freqs_per_bands to a list or tuple of integers\",","chunkId":"audio_separator/separator/roformer/bs_roformer_validator.py:2"}],[858,{"filePath":"audio_separator/separator/roformer/bs_roformer_validator.py","startLine":43,"endLine":56,"chunkText":"                parameter_name=\"freqs_per_bands\",\n                message=\"freqs_per_bands must be a list or tuple\",\n                suggested_fix=\"Convert freqs_per_bands to a list or tuple of integers\",\n                current_value=freqs_per_bands,\n                expected_value=\"list or tuple of integers\"\n            )\n            issues.append(issue)\n            return issues\n        \n        # Length check\n        if len(freqs_per_bands) < self.MIN_BANDS:\n            issue = ValidationIssue(\n                severity=ValidationSeverity.ERROR,\n                parameter_name=\"freqs_per_bands\",","chunkId":"audio_separator/separator/roformer/bs_roformer_validator.py:3"}],[859,{"filePath":"audio_separator/separator/roformer/bs_roformer_validator.py","startLine":54,"endLine":65,"chunkText":"            issue = ValidationIssue(\n                severity=ValidationSeverity.ERROR,\n                parameter_name=\"freqs_per_bands\",\n                message=f\"freqs_per_bands must have at least {self.MIN_BANDS} bands\",\n                suggested_fix=f\"Add more frequency bands to reach minimum of {self.MIN_BANDS}\",\n                current_value=len(freqs_per_bands),\n                expected_value=f\">= {self.MIN_BANDS}\"\n            )\n            issues.append(issue)\n        \n        if len(freqs_per_bands) > self.MAX_BANDS:\n            issue = ValidationIssue(","chunkId":"audio_separator/separator/roformer/bs_roformer_validator.py:4"}],[860,{"filePath":"audio_separator/separator/roformer/bs_roformer_validator.py","startLine":63,"endLine":74,"chunkText":"        \n        if len(freqs_per_bands) > self.MAX_BANDS:\n            issue = ValidationIssue(\n                severity=ValidationSeverity.WARNING,\n                parameter_name=\"freqs_per_bands\",\n                message=f\"freqs_per_bands has {len(freqs_per_bands)} bands, which may impact performance\",\n                suggested_fix=f\"Consider reducing to {self.MAX_BANDS} or fewer bands for better performance\",\n                current_value=len(freqs_per_bands),\n                expected_value=f\"<= {self.MAX_BANDS} (recommended)\"\n            )\n            issues.append(issue)\n        ","chunkId":"audio_separator/separator/roformer/bs_roformer_validator.py:5"}],[861,{"filePath":"audio_separator/separator/roformer/bs_roformer_validator.py","startLine":72,"endLine":83,"chunkText":"            )\n            issues.append(issue)\n        \n        # Value validation\n        for i, freq_count in enumerate(freqs_per_bands):\n            if not isinstance(freq_count, int) or freq_count <= 0:\n                issue = ValidationIssue(\n                    severity=ValidationSeverity.ERROR,\n                    parameter_name=f\"freqs_per_bands[{i}]\",\n                    message=f\"Each frequency count must be a positive integer, got {freq_count}\",\n                    suggested_fix=\"Use positive integers for all frequency counts\",\n                    current_value=freq_count,","chunkId":"audio_separator/separator/roformer/bs_roformer_validator.py:6"}],[862,{"filePath":"audio_separator/separator/roformer/bs_roformer_validator.py","startLine":81,"endLine":90,"chunkText":"                    message=f\"Each frequency count must be a positive integer, got {freq_count}\",\n                    suggested_fix=\"Use positive integers for all frequency counts\",\n                    current_value=freq_count,\n                    expected_value=\"positive integer\"\n                )\n                issues.append(issue)\n        \n        # Check for reasonable progression (powers of 2 are common)\n        if all(isinstance(f, int) and f > 0 for f in freqs_per_bands):\n            # Check if values follow a reasonable pattern (not strictly enforced)","chunkId":"audio_separator/separator/roformer/bs_roformer_validator.py:7"}],[863,{"filePath":"audio_separator/separator/roformer/bs_roformer_validator.py","startLine":88,"endLine":96,"chunkText":"        # Check for reasonable progression (powers of 2 are common)\n        if all(isinstance(f, int) and f > 0 for f in freqs_per_bands):\n            # Check if values follow a reasonable pattern (not strictly enforced)\n            total_freqs = sum(freqs_per_bands)\n            if total_freqs > 8192:  # Unusually high for typical STFT\n                issue = ValidationIssue(\n                    severity=ValidationSeverity.WARNING,\n                    parameter_name=\"freqs_per_bands\",\n                    message=f\"Total frequency count ({total_freqs}) is very high\",","chunkId":"audio_separator/separator/roformer/bs_roformer_validator.py:8"}],[864,{"filePath":"audio_separator/separator/roformer/bs_roformer_validator.py","startLine":94,"endLine":106,"chunkText":"                    severity=ValidationSeverity.WARNING,\n                    parameter_name=\"freqs_per_bands\",\n                    message=f\"Total frequency count ({total_freqs}) is very high\",\n                    suggested_fix=\"Consider using fewer total frequencies for typical audio processing\",\n                    current_value=total_freqs,\n                    expected_value=\"<= 8192 (typical)\"\n                )\n                issues.append(issue)\n        \n        return issues\n    \n    def validate_stft_compatibility(self, config: Dict[str, Any]) -> List[ValidationIssue]:\n        \"\"\"","chunkId":"audio_separator/separator/roformer/bs_roformer_validator.py:9"}],[865,{"filePath":"audio_separator/separator/roformer/bs_roformer_validator.py","startLine":104,"endLine":122,"chunkText":"    \n    def validate_stft_compatibility(self, config: Dict[str, Any]) -> List[ValidationIssue]:\n        \"\"\"\n        Validate STFT-related parameters for compatibility with freqs_per_bands.\n        \n        Args:\n            config: Model configuration dictionary\n            \n        Returns:\n            List of validation issues for STFT compatibility\n        \"\"\"\n        issues = []\n        \n        if 'freqs_per_bands' not in config:\n            return issues\n        \n        freqs_per_bands = config.get('freqs_per_bands')\n        stft_n_fft = config.get('stft_n_fft', 2048)  # Common default\n        ","chunkId":"audio_separator/separator/roformer/bs_roformer_validator.py:10"}],[866,{"filePath":"audio_separator/separator/roformer/bs_roformer_validator.py","startLine":120,"endLine":130,"chunkText":"        freqs_per_bands = config.get('freqs_per_bands')\n        stft_n_fft = config.get('stft_n_fft', 2048)  # Common default\n        \n        if isinstance(freqs_per_bands, (list, tuple)) and all(isinstance(f, int) for f in freqs_per_bands):\n            total_freqs = sum(freqs_per_bands)\n            expected_freqs = stft_n_fft // 2 + 1\n            \n            if total_freqs != expected_freqs:\n                issue = ValidationIssue(\n                    severity=ValidationSeverity.WARNING,\n                    parameter_name=\"freqs_per_bands, stft_n_fft\",","chunkId":"audio_separator/separator/roformer/bs_roformer_validator.py:11"}],[867,{"filePath":"audio_separator/separator/roformer/bs_roformer_validator.py","startLine":128,"endLine":136,"chunkText":"                issue = ValidationIssue(\n                    severity=ValidationSeverity.WARNING,\n                    parameter_name=\"freqs_per_bands, stft_n_fft\",\n                    message=f\"freqs_per_bands sum ({total_freqs}) doesn't match STFT frequency bins ({expected_freqs})\",\n                    suggested_fix=f\"Adjust freqs_per_bands to sum to {expected_freqs} or modify stft_n_fft\",\n                    current_value=f\"sum={total_freqs}, expected={expected_freqs}\",\n                    expected_value=f\"freqs_per_bands sum == stft_n_fft//2 + 1\"\n                )\n                issues.append(issue)","chunkId":"audio_separator/separator/roformer/bs_roformer_validator.py:12"}],[868,{"filePath":"audio_separator/separator/roformer/bs_roformer_validator.py","startLine":134,"endLine":152,"chunkText":"                    expected_value=f\"freqs_per_bands sum == stft_n_fft//2 + 1\"\n                )\n                issues.append(issue)\n        \n        return issues\n    \n    def validate_mask_estimator_config(self, config: Dict[str, Any]) -> List[ValidationIssue]:\n        \"\"\"\n        Validate mask estimator configuration for BSRoformer.\n        \n        Args:\n            config: Model configuration dictionary\n            \n        Returns:\n            List of validation issues for mask estimator\n        \"\"\"\n        issues = []\n        \n        mask_depth = config.get('mask_estimator_depth', 2)","chunkId":"audio_separator/separator/roformer/bs_roformer_validator.py:13"}],[869,{"filePath":"audio_separator/separator/roformer/bs_roformer_validator.py","startLine":150,"endLine":163,"chunkText":"        issues = []\n        \n        mask_depth = config.get('mask_estimator_depth', 2)\n        \n        if not isinstance(mask_depth, int) or mask_depth < 1:\n            issue = ValidationIssue(\n                severity=ValidationSeverity.ERROR,\n                parameter_name=\"mask_estimator_depth\",\n                message=\"mask_estimator_depth must be a positive integer\",\n                suggested_fix=\"Set mask_estimator_depth to a positive integer (typically 2-8)\",\n                current_value=mask_depth,\n                expected_value=\"positive integer\"\n            )\n            issues.append(issue)","chunkId":"audio_separator/separator/roformer/bs_roformer_validator.py:14"}],[870,{"filePath":"audio_separator/separator/roformer/bs_roformer_validator.py","startLine":161,"endLine":174,"chunkText":"                expected_value=\"positive integer\"\n            )\n            issues.append(issue)\n        elif mask_depth > 8:\n            issue = ValidationIssue(\n                severity=ValidationSeverity.WARNING,\n                parameter_name=\"mask_estimator_depth\",\n                message=f\"mask_estimator_depth of {mask_depth} may be unnecessarily deep\",\n                suggested_fix=\"Consider using a smaller depth (2-8) for better efficiency\",\n                current_value=mask_depth,\n                expected_value=\"2-8 (recommended)\"\n            )\n            issues.append(issue)\n        ","chunkId":"audio_separator/separator/roformer/bs_roformer_validator.py:15"}],[871,{"filePath":"audio_separator/separator/roformer/bs_roformer_validator.py","startLine":172,"endLine":190,"chunkText":"            )\n            issues.append(issue)\n        \n        return issues\n    \n    def validate_all(self, config: Dict[str, Any], model_type: str = \"bs_roformer\") -> List[ValidationIssue]:\n        \"\"\"\n        Run all BSRoformer-specific validation checks.\n        \n        Args:\n            config: Model configuration dictionary\n            model_type: Type of model (should be \"bs_roformer\")\n            \n        Returns:\n            List of all validation issues found\n        \"\"\"\n        # Start with base validation\n        all_issues = super().validate_all(config, model_type)\n        ","chunkId":"audio_separator/separator/roformer/bs_roformer_validator.py:16"}],[872,{"filePath":"audio_separator/separator/roformer/bs_roformer_validator.py","startLine":188,"endLine":202,"chunkText":"        # Start with base validation\n        all_issues = super().validate_all(config, model_type)\n        \n        # Add BSRoformer-specific validation\n        all_issues.extend(self.validate_freqs_per_bands(config))\n        all_issues.extend(self.validate_stft_compatibility(config))\n        all_issues.extend(self.validate_mask_estimator_config(config))\n        \n        return all_issues\n    \n    def get_parameter_defaults(self, model_type: str = \"bs_roformer\") -> Dict[str, Any]:\n        \"\"\"\n        Get BSRoformer-specific parameter defaults.\n        \n        Args:","chunkId":"audio_separator/separator/roformer/bs_roformer_validator.py:17"}],[873,{"filePath":"audio_separator/separator/roformer/bs_roformer_validator.py","startLine":200,"endLine":216,"chunkText":"        Get BSRoformer-specific parameter defaults.\n        \n        Args:\n            model_type: Type of model\n            \n        Returns:\n            Dictionary of parameter names to default values\n        \"\"\"\n        defaults = super().get_parameter_defaults(model_type)\n        \n        # Override with BSRoformer-specific defaults\n        defaults.update({\n            'freqs_per_bands': self.DEFAULT_FREQS_PER_BANDS[:6],  # Use first 6 bands as default\n            'mask_estimator_depth': 2,\n            'stft_n_fft': 2048,\n            'stft_hop_length': 512,\n            'stft_win_length': 2048,","chunkId":"audio_separator/separator/roformer/bs_roformer_validator.py:18"}],[874,{"filePath":"audio_separator/separator/roformer/bs_roformer_validator.py","startLine":214,"endLine":221,"chunkText":"            'stft_n_fft': 2048,\n            'stft_hop_length': 512,\n            'stft_win_length': 2048,\n            'multi_stft_resolution_loss_weight': 1.0,\n        })\n        \n        return defaults\n","chunkId":"audio_separator/separator/roformer/bs_roformer_validator.py:19"}],[875,{"filePath":"audio_separator/separator/roformer/configuration_normalizer.py","startLine":0,"endLine":20,"chunkText":"\"\"\"\nConfiguration normalizer for Roformer models.\nNormalizes and standardizes configuration dictionaries from various sources.\n\"\"\"\n\nfrom typing import Dict, Any, Optional, Union, List\nimport copy\nimport logging\n\nfrom .parameter_validator import ParameterValidator\nfrom .bs_roformer_validator import BSRoformerValidator\nfrom .mel_band_roformer_validator import MelBandRoformerValidator\nfrom .parameter_validation_error import ParameterValidationError\n\nlogger = logging.getLogger(__name__)\n\n\nclass ConfigurationNormalizer:\n    \"\"\"\n    Normalizes configuration dictionaries for Roformer models.\n    ","chunkId":"audio_separator/separator/roformer/configuration_normalizer.py:0"}],[876,{"filePath":"audio_separator/separator/roformer/configuration_normalizer.py","startLine":18,"endLine":33,"chunkText":"    \"\"\"\n    Normalizes configuration dictionaries for Roformer models.\n    \n    Handles different configuration formats, applies defaults, and ensures\n    consistency across different model types and versions.\n    \"\"\"\n    \n    def __init__(self):\n        \"\"\"Initialize the configuration normalizer with validators.\"\"\"\n        self.base_validator = ParameterValidator()\n        self.bs_validator = BSRoformerValidator()\n        self.mel_validator = MelBandRoformerValidator()\n    \n    def normalize_config(self, \n                        config: Dict[str, Any], \n                        model_type: str, ","chunkId":"audio_separator/separator/roformer/configuration_normalizer.py:1"}],[877,{"filePath":"audio_separator/separator/roformer/configuration_normalizer.py","startLine":31,"endLine":44,"chunkText":"    def normalize_config(self, \n                        config: Dict[str, Any], \n                        model_type: str, \n                        apply_defaults: bool = True,\n                        validate: bool = True) -> Dict[str, Any]:\n        \"\"\"\n        Normalize a configuration dictionary.\n        \n        Args:\n            config: Raw configuration dictionary\n            model_type: Type of model (\"bs_roformer\" or \"mel_band_roformer\")\n            apply_defaults: Whether to apply default values for missing parameters\n            validate: Whether to validate the configuration\n            ","chunkId":"audio_separator/separator/roformer/configuration_normalizer.py:2"}],[878,{"filePath":"audio_separator/separator/roformer/configuration_normalizer.py","startLine":42,"endLine":56,"chunkText":"            apply_defaults: Whether to apply default values for missing parameters\n            validate: Whether to validate the configuration\n            \n        Returns:\n            Normalized configuration dictionary\n            \n        Raises:\n            ParameterValidationError: If validation fails and validate=True\n        \"\"\"\n        # Deep copy to avoid modifying original\n        normalized = copy.deepcopy(config)\n        \n        # Step 1: Normalize structure (flatten nested configs if needed)\n        normalized = self._normalize_structure(normalized, model_type)\n        ","chunkId":"audio_separator/separator/roformer/configuration_normalizer.py:3"}],[879,{"filePath":"audio_separator/separator/roformer/configuration_normalizer.py","startLine":54,"endLine":66,"chunkText":"        # Step 1: Normalize structure (flatten nested configs if needed)\n        normalized = self._normalize_structure(normalized, model_type)\n        \n        # Step 2: Normalize parameter names and values\n        normalized = self._normalize_parameter_names(normalized)\n        normalized = self._normalize_parameter_values(normalized, model_type)\n        \n        # Step 3: Apply defaults if requested\n        if apply_defaults:\n            normalized = self._apply_defaults(normalized, model_type)\n        \n        # Step 4: Validate if requested\n        if validate:","chunkId":"audio_separator/separator/roformer/configuration_normalizer.py:4"}],[880,{"filePath":"audio_separator/separator/roformer/configuration_normalizer.py","startLine":64,"endLine":79,"chunkText":"        \n        # Step 4: Validate if requested\n        if validate:\n            self._validate_config(normalized, model_type)\n        \n        logger.debug(f\"Normalized {model_type} configuration: {len(normalized)} parameters\")\n        return normalized\n    \n    def _normalize_structure(self, config: Dict[str, Any], model_type: str) -> Dict[str, Any]:\n        \"\"\"\n        Normalize the structure of the configuration dictionary.\n        \n        Some configurations may be nested or have different structures.\n        This flattens and standardizes the structure.\n        \"\"\"\n        normalized = {}","chunkId":"audio_separator/separator/roformer/configuration_normalizer.py:5"}],[881,{"filePath":"audio_separator/separator/roformer/configuration_normalizer.py","startLine":77,"endLine":88,"chunkText":"        This flattens and standardizes the structure.\n        \"\"\"\n        normalized = {}\n        \n        # Handle nested configurations (e.g., from YAML files)\n        for key, value in config.items():\n            if isinstance(value, dict) and key in ['model', 'architecture', 'params']:\n                # Flatten nested model parameters\n                normalized.update(value)\n            elif key in ['training', 'inference'] and isinstance(value, dict):\n                # Some configs have training/inference sections\n                # Extract relevant parameters with prefixes","chunkId":"audio_separator/separator/roformer/configuration_normalizer.py:6"}],[882,{"filePath":"audio_separator/separator/roformer/configuration_normalizer.py","startLine":86,"endLine":98,"chunkText":"            elif key in ['training', 'inference'] and isinstance(value, dict):\n                # Some configs have training/inference sections\n                # Extract relevant parameters with prefixes\n                for nested_key, nested_value in value.items():\n                    if nested_key in ['dim_t', 'hop_length', 'n_fft', 'sample_rate']:\n                        normalized[nested_key] = nested_value\n            else:\n                normalized[key] = value\n        \n        return normalized\n    \n    def _normalize_parameter_names(self, config: Dict[str, Any]) -> Dict[str, Any]:\n        \"\"\"","chunkId":"audio_separator/separator/roformer/configuration_normalizer.py:7"}],[883,{"filePath":"audio_separator/separator/roformer/configuration_normalizer.py","startLine":96,"endLine":113,"chunkText":"    \n    def _normalize_parameter_names(self, config: Dict[str, Any]) -> Dict[str, Any]:\n        \"\"\"\n        Normalize parameter names to standard format.\n        \n        Handles different naming conventions and aliases.\n        \"\"\"\n        normalized = {}\n        \n        # Parameter name mappings (old_name -> new_name)\n        name_mappings = {\n            # Common aliases\n            'n_fft': 'stft_n_fft',\n            'hop_length': 'stft_hop_length', \n            'win_length': 'stft_win_length',\n            'window_fn': 'stft_window_fn',\n            'normalized': 'stft_normalized',\n            ","chunkId":"audio_separator/separator/roformer/configuration_normalizer.py:8"}],[884,{"filePath":"audio_separator/separator/roformer/configuration_normalizer.py","startLine":111,"endLine":125,"chunkText":"            'window_fn': 'stft_window_fn',\n            'normalized': 'stft_normalized',\n            \n            # Transformer aliases\n            'n_heads': 'heads',\n            'num_heads': 'heads',\n            'head_dim': 'dim_head',\n            'dropout': 'attn_dropout',\n            'attention_dropout': 'attn_dropout',\n            'feedforward_dropout': 'ff_dropout',\n            \n            # Model-specific aliases\n            'expansion_factor': 'mlp_expansion_factor',\n            'mlp_ratio': 'mlp_expansion_factor',\n            'use_checkpoint': 'use_torch_checkpoint',","chunkId":"audio_separator/separator/roformer/configuration_normalizer.py:9"}],[885,{"filePath":"audio_separator/separator/roformer/configuration_normalizer.py","startLine":123,"endLine":137,"chunkText":"            'expansion_factor': 'mlp_expansion_factor',\n            'mlp_ratio': 'mlp_expansion_factor',\n            'use_checkpoint': 'use_torch_checkpoint',\n            'checkpoint': 'use_torch_checkpoint',\n            \n            # Frequency band aliases\n            'freq_bands': 'freqs_per_bands',\n            'frequency_bands': 'freqs_per_bands',\n            'mel_bands': 'num_bands',\n            'n_mels': 'num_bands',\n        }\n        \n        for key, value in config.items():\n            # Apply name mapping if exists\n            normalized_key = name_mappings.get(key, key)","chunkId":"audio_separator/separator/roformer/configuration_normalizer.py:10"}],[886,{"filePath":"audio_separator/separator/roformer/configuration_normalizer.py","startLine":135,"endLine":149,"chunkText":"        for key, value in config.items():\n            # Apply name mapping if exists\n            normalized_key = name_mappings.get(key, key)\n            normalized[normalized_key] = value\n        \n        return normalized\n    \n    def _normalize_parameter_values(self, config: Dict[str, Any], model_type: str) -> Dict[str, Any]:\n        \"\"\"\n        Normalize parameter values to expected types and formats.\n        \"\"\"\n        normalized = {}\n        \n        for key, value in config.items():\n            normalized_value = self._normalize_single_value(key, value, model_type)","chunkId":"audio_separator/separator/roformer/configuration_normalizer.py:11"}],[887,{"filePath":"audio_separator/separator/roformer/configuration_normalizer.py","startLine":147,"endLine":160,"chunkText":"        \n        for key, value in config.items():\n            normalized_value = self._normalize_single_value(key, value, model_type)\n            normalized[key] = normalized_value\n        \n        return normalized\n    \n    def _normalize_single_value(self, key: str, value: Any, model_type: str) -> Any:\n        \"\"\"Normalize a single parameter value.\"\"\"\n        \n        # Boolean normalization\n        if key in ['stereo', 'flash_attn', 'sage_attention', 'zero_dc', \n                  'use_torch_checkpoint', 'skip_connection', 'stft_normalized']:\n            if isinstance(value, str):","chunkId":"audio_separator/separator/roformer/configuration_normalizer.py:12"}],[888,{"filePath":"audio_separator/separator/roformer/configuration_normalizer.py","startLine":158,"endLine":167,"chunkText":"        if key in ['stereo', 'flash_attn', 'sage_attention', 'zero_dc', \n                  'use_torch_checkpoint', 'skip_connection', 'stft_normalized']:\n            if isinstance(value, str):\n                return value.lower() in ['true', '1', 'yes', 'on']\n            return bool(value)\n        \n        # Integer normalization\n        elif key in ['dim', 'depth', 'num_stems', 'time_transformer_depth', \n                    'freq_transformer_depth', 'dim_head', 'heads', \n                    'mlp_expansion_factor', 'num_bands', 'sample_rate',","chunkId":"audio_separator/separator/roformer/configuration_normalizer.py:13"}],[889,{"filePath":"audio_separator/separator/roformer/configuration_normalizer.py","startLine":165,"endLine":174,"chunkText":"        elif key in ['dim', 'depth', 'num_stems', 'time_transformer_depth', \n                    'freq_transformer_depth', 'dim_head', 'heads', \n                    'mlp_expansion_factor', 'num_bands', 'sample_rate',\n                    'stft_n_fft', 'stft_hop_length', 'stft_win_length',\n                    'mask_estimator_depth']:\n            if isinstance(value, str):\n                try:\n                    return int(float(value))  # Handle \"2.0\" -> 2\n                except (ValueError, TypeError):\n                    return value  # Let validator catch the error","chunkId":"audio_separator/separator/roformer/configuration_normalizer.py:14"}],[890,{"filePath":"audio_separator/separator/roformer/configuration_normalizer.py","startLine":172,"endLine":183,"chunkText":"                    return int(float(value))  # Handle \"2.0\" -> 2\n                except (ValueError, TypeError):\n                    return value  # Let validator catch the error\n            return int(value) if isinstance(value, (int, float)) else value\n        \n        # Float normalization  \n        elif key in ['attn_dropout', 'ff_dropout', 'multi_stft_resolution_loss_weight',\n                    'fmin', 'fmax']:\n            if isinstance(value, str):\n                try:\n                    return float(value)\n                except (ValueError, TypeError):","chunkId":"audio_separator/separator/roformer/configuration_normalizer.py:15"}],[891,{"filePath":"audio_separator/separator/roformer/configuration_normalizer.py","startLine":181,"endLine":192,"chunkText":"                try:\n                    return float(value)\n                except (ValueError, TypeError):\n                    return value  # Let validator catch the error\n            return float(value) if isinstance(value, (int, float)) else value\n        \n        # Tuple/list normalization\n        elif key.startswith('freqs_per_bands') or key in ['freqs_per_bands']:\n            if isinstance(value, str):\n                # Handle string representations like \"(2, 4, 8, 16)\"\n                try:\n                    # Remove parentheses and split","chunkId":"audio_separator/separator/roformer/configuration_normalizer.py:16"}],[892,{"filePath":"audio_separator/separator/roformer/configuration_normalizer.py","startLine":190,"endLine":201,"chunkText":"                # Handle string representations like \"(2, 4, 8, 16)\"\n                try:\n                    # Remove parentheses and split\n                    clean_str = value.strip('()[]').replace(' ', '')\n                    if clean_str:\n                        return tuple(int(x) for x in clean_str.split(','))\n                except (ValueError, TypeError):\n                    return value  # Let validator catch the error\n            elif isinstance(value, list):\n                return tuple(value)  # Convert lists to tuples for consistency\n            return value\n        ","chunkId":"audio_separator/separator/roformer/configuration_normalizer.py:17"}],[893,{"filePath":"audio_separator/separator/roformer/configuration_normalizer.py","startLine":199,"endLine":215,"chunkText":"                return tuple(value)  # Convert lists to tuples for consistency\n            return value\n        \n        # String normalization\n        elif key in ['norm', 'act', 'mel_scale']:\n            if value is not None:\n                return str(value).lower()\n            return value\n        \n        # No normalization needed\n        else:\n            return value\n    \n    def _apply_defaults(self, config: Dict[str, Any], model_type: str) -> Dict[str, Any]:\n        \"\"\"Apply default values for missing parameters.\"\"\"\n        \n        if model_type == \"bs_roformer\":","chunkId":"audio_separator/separator/roformer/configuration_normalizer.py:18"}],[894,{"filePath":"audio_separator/separator/roformer/configuration_normalizer.py","startLine":213,"endLine":227,"chunkText":"        \"\"\"Apply default values for missing parameters.\"\"\"\n        \n        if model_type == \"bs_roformer\":\n            validator = self.bs_validator\n        elif model_type == \"mel_band_roformer\":\n            validator = self.mel_validator\n        else:\n            validator = self.base_validator\n        \n        return validator.apply_parameter_defaults(config, model_type)\n    \n    def _validate_config(self, config: Dict[str, Any], model_type: str) -> None:\n        \"\"\"Validate the configuration and raise errors if invalid.\"\"\"\n        \n        if model_type == \"bs_roformer\":","chunkId":"audio_separator/separator/roformer/configuration_normalizer.py:19"}],[895,{"filePath":"audio_separator/separator/roformer/configuration_normalizer.py","startLine":225,"endLine":241,"chunkText":"        \"\"\"Validate the configuration and raise errors if invalid.\"\"\"\n        \n        if model_type == \"bs_roformer\":\n            validator = self.bs_validator\n        elif model_type == \"mel_band_roformer\":\n            validator = self.mel_validator\n        else:\n            validator = self.base_validator\n        \n        validator.validate_and_raise(config, model_type)\n    \n    def detect_model_type(self, config: Dict[str, Any]) -> Optional[str]:\n        \"\"\"\n        Attempt to detect the model type from configuration parameters.\n        \n        Args:\n            config: Configuration dictionary","chunkId":"audio_separator/separator/roformer/configuration_normalizer.py:20"}],[896,{"filePath":"audio_separator/separator/roformer/configuration_normalizer.py","startLine":239,"endLine":254,"chunkText":"        \n        Args:\n            config: Configuration dictionary\n            \n        Returns:\n            Detected model type or None if cannot be determined\n        \"\"\"\n        # Check for BSRoformer-specific parameters\n        if 'freqs_per_bands' in config:\n            return \"bs_roformer\"\n        \n        # Check for MelBandRoformer-specific parameters\n        if 'num_bands' in config or 'n_mels' in config or 'mel_bands' in config:\n            return \"mel_band_roformer\"\n        \n        # Check for model type hints in the config","chunkId":"audio_separator/separator/roformer/configuration_normalizer.py:21"}],[897,{"filePath":"audio_separator/separator/roformer/configuration_normalizer.py","startLine":252,"endLine":262,"chunkText":"            return \"mel_band_roformer\"\n        \n        # Check for model type hints in the config\n        model_type = config.get('model_type', config.get('type', config.get('architecture')))\n        if isinstance(model_type, str):\n            model_type_lower = model_type.lower()\n            if 'bs' in model_type_lower and 'roformer' in model_type_lower:\n                return \"bs_roformer\"\n            elif 'mel' in model_type_lower and 'roformer' in model_type_lower:\n                return \"mel_band_roformer\"\n            elif 'roformer' in model_type_lower:","chunkId":"audio_separator/separator/roformer/configuration_normalizer.py:22"}],[898,{"filePath":"audio_separator/separator/roformer/configuration_normalizer.py","startLine":260,"endLine":273,"chunkText":"            elif 'mel' in model_type_lower and 'roformer' in model_type_lower:\n                return \"mel_band_roformer\"\n            elif 'roformer' in model_type_lower:\n                # Default to BSRoformer if just \"roformer\"\n                return \"bs_roformer\"\n        \n        return None\n    \n    def normalize_from_file_path(self, \n                                config: Dict[str, Any], \n                                file_path: str,\n                                apply_defaults: bool = True,\n                                validate: bool = True) -> Dict[str, Any]:\n        \"\"\"","chunkId":"audio_separator/separator/roformer/configuration_normalizer.py:23"}],[899,{"filePath":"audio_separator/separator/roformer/configuration_normalizer.py","startLine":271,"endLine":285,"chunkText":"                                apply_defaults: bool = True,\n                                validate: bool = True) -> Dict[str, Any]:\n        \"\"\"\n        Normalize configuration with model type detection from file path.\n        \n        Args:\n            config: Configuration dictionary\n            file_path: Path to the model file (used for type detection)\n            apply_defaults: Whether to apply defaults\n            validate: Whether to validate\n            \n        Returns:\n            Normalized configuration\n        \"\"\"\n        # Try to detect model type from file path","chunkId":"audio_separator/separator/roformer/configuration_normalizer.py:24"}],[900,{"filePath":"audio_separator/separator/roformer/configuration_normalizer.py","startLine":283,"endLine":296,"chunkText":"            Normalized configuration\n        \"\"\"\n        # Try to detect model type from file path\n        file_path_lower = file_path.lower()\n        if 'bs' in file_path_lower and 'roformer' in file_path_lower:\n            model_type = \"bs_roformer\"\n        elif 'mel' in file_path_lower and 'roformer' in file_path_lower:\n            model_type = \"mel_band_roformer\"\n        else:\n            # Try to detect from config\n            model_type = self.detect_model_type(config)\n            if model_type is None:\n                # Default to BSRoformer\n                model_type = \"bs_roformer\"","chunkId":"audio_separator/separator/roformer/configuration_normalizer.py:25"}],[901,{"filePath":"audio_separator/separator/roformer/configuration_normalizer.py","startLine":294,"endLine":300,"chunkText":"            if model_type is None:\n                # Default to BSRoformer\n                model_type = \"bs_roformer\"\n                logger.warning(f\"Could not detect model type from config or path {file_path}, defaulting to bs_roformer\")\n        \n        return self.normalize_config(config, model_type, apply_defaults, validate)\n","chunkId":"audio_separator/separator/roformer/configuration_normalizer.py:26"}],[902,{"filePath":"audio_separator/separator/roformer/mel_band_roformer_config.py","startLine":0,"endLine":18,"chunkText":"\"\"\"\nMelBandRoformer-specific configuration class.\nExtends ModelConfiguration with MelBandRoformer-specific parameters.\n\"\"\"\n\nfrom dataclasses import dataclass\nfrom typing import Optional, Dict, Any\nfrom .model_configuration import ModelConfiguration, RoformerType\n\n\n@dataclass(frozen=True, unsafe_hash=True)\nclass MelBandRoformerConfig(ModelConfiguration):\n    \"\"\"\n    Configuration class specifically for MelBandRoformer models.\n    \n    MelBandRoformer processes audio using mel-scale frequency bands,\n    which are more aligned with human auditory perception.\n    \"\"\"\n    ","chunkId":"audio_separator/separator/roformer/mel_band_roformer_config.py:0"}],[903,{"filePath":"audio_separator/separator/roformer/mel_band_roformer_config.py","startLine":16,"endLine":29,"chunkText":"    which are more aligned with human auditory perception.\n    \"\"\"\n    \n    # MelBandRoformer-specific required parameters\n    num_bands: int = 64  # Number of mel-scale bands\n    \n    # MelBandRoformer-specific optional parameters\n    mel_scale: str = \"htk\"  # Mel scale type: \"htk\" or \"slaney\"\n    fmin: float = 0.0  # Minimum frequency for mel scale\n    fmax: Optional[float] = None  # Maximum frequency for mel scale (None = sample_rate/2)\n    \n    def __post_init__(self):\n        \"\"\"Validate MelBandRoformer-specific configuration after initialization.\"\"\"\n        super().__post_init__()","chunkId":"audio_separator/separator/roformer/mel_band_roformer_config.py:1"}],[904,{"filePath":"audio_separator/separator/roformer/mel_band_roformer_config.py","startLine":27,"endLine":37,"chunkText":"    def __post_init__(self):\n        \"\"\"Validate MelBandRoformer-specific configuration after initialization.\"\"\"\n        super().__post_init__()\n        self._validate_mel_band_roformer_parameters()\n    \n    def _validate_mel_band_roformer_parameters(self):\n        \"\"\"Validate MelBandRoformer-specific parameters.\"\"\"\n        if not isinstance(self.num_bands, int) or self.num_bands <= 0:\n            raise ValueError(f\"num_bands must be a positive integer, got {self.num_bands}\")\n        \n        if self.num_bands < 8:","chunkId":"audio_separator/separator/roformer/mel_band_roformer_config.py:2"}],[905,{"filePath":"audio_separator/separator/roformer/mel_band_roformer_config.py","startLine":35,"endLine":46,"chunkText":"            raise ValueError(f\"num_bands must be a positive integer, got {self.num_bands}\")\n        \n        if self.num_bands < 8:\n            raise ValueError(f\"num_bands should be at least 8 for meaningful separation, got {self.num_bands}\")\n        \n        if self.num_bands > 512:\n            raise ValueError(f\"num_bands should not exceed 512 for practical purposes, got {self.num_bands}\")\n        \n        if self.mel_scale not in [\"htk\", \"slaney\"]:\n            raise ValueError(f\"mel_scale must be 'htk' or 'slaney', got '{self.mel_scale}'\")\n        \n        if self.fmin < 0:","chunkId":"audio_separator/separator/roformer/mel_band_roformer_config.py:3"}],[906,{"filePath":"audio_separator/separator/roformer/mel_band_roformer_config.py","startLine":44,"endLine":56,"chunkText":"            raise ValueError(f\"mel_scale must be 'htk' or 'slaney', got '{self.mel_scale}'\")\n        \n        if self.fmin < 0:\n            raise ValueError(f\"fmin must be non-negative, got {self.fmin}\")\n        \n        if self.fmax is not None:\n            if self.fmax <= self.fmin:\n                raise ValueError(f\"fmax ({self.fmax}) must be greater than fmin ({self.fmin})\")\n            \n            if self.fmax > self.sample_rate / 2:\n                raise ValueError(f\"fmax ({self.fmax}) cannot exceed sample_rate/2 ({self.sample_rate/2})\")\n    \n    def get_effective_fmax(self) -> float:","chunkId":"audio_separator/separator/roformer/mel_band_roformer_config.py:4"}],[907,{"filePath":"audio_separator/separator/roformer/mel_band_roformer_config.py","startLine":54,"endLine":65,"chunkText":"                raise ValueError(f\"fmax ({self.fmax}) cannot exceed sample_rate/2 ({self.sample_rate/2})\")\n    \n    def get_effective_fmax(self) -> float:\n        \"\"\"Get the effective maximum frequency (fmax or sample_rate/2).\"\"\"\n        return self.fmax if self.fmax is not None else self.sample_rate / 2.0\n    \n    def get_frequency_range(self) -> tuple[float, float]:\n        \"\"\"Get the frequency range (fmin, fmax) for mel scale.\"\"\"\n        return (self.fmin, self.get_effective_fmax())\n    \n    def validate_sample_rate(self, sample_rate: int) -> bool:\n        \"\"\"","chunkId":"audio_separator/separator/roformer/mel_band_roformer_config.py:5"}],[908,{"filePath":"audio_separator/separator/roformer/mel_band_roformer_config.py","startLine":63,"endLine":80,"chunkText":"    \n    def validate_sample_rate(self, sample_rate: int) -> bool:\n        \"\"\"\n        Validate that the configuration is compatible with a given sample rate.\n        \n        Args:\n            sample_rate: Audio sample rate to validate against\n            \n        Returns:\n            True if compatible, False otherwise\n        \"\"\"\n        if sample_rate != self.sample_rate:\n            # Check if fmax is compatible with new sample rate\n            if self.fmax is not None and self.fmax > sample_rate / 2:\n                return False\n        \n        return True\n    ","chunkId":"audio_separator/separator/roformer/mel_band_roformer_config.py:6"}],[909,{"filePath":"audio_separator/separator/roformer/mel_band_roformer_config.py","startLine":78,"endLine":97,"chunkText":"        \n        return True\n    \n    def get_mel_scale_info(self) -> Dict[str, Any]:\n        \"\"\"\n        Get information about the mel scale configuration.\n        \n        Returns:\n            Dictionary with mel scale information\n        \"\"\"\n        return {\n            'num_bands': self.num_bands,\n            'mel_scale': self.mel_scale,\n            'fmin': self.fmin,\n            'fmax': self.get_effective_fmax(),\n            'sample_rate': self.sample_rate,\n            'frequency_range': self.get_frequency_range(),\n            'bands_per_octave': self._estimate_bands_per_octave()\n        }\n    ","chunkId":"audio_separator/separator/roformer/mel_band_roformer_config.py:7"}],[910,{"filePath":"audio_separator/separator/roformer/mel_band_roformer_config.py","startLine":95,"endLine":110,"chunkText":"            'bands_per_octave': self._estimate_bands_per_octave()\n        }\n    \n    def _estimate_bands_per_octave(self) -> float:\n        \"\"\"Estimate the number of mel bands per octave.\"\"\"\n        fmin, fmax = self.get_frequency_range()\n        if fmin <= 0:\n            fmin = 1.0  # Avoid log(0)\n        \n        # Rough estimation using logarithmic scale\n        import math\n        octave_range = math.log2(fmax / fmin)\n        return self.num_bands / octave_range if octave_range > 0 else self.num_bands\n    \n    def get_model_type(self) -> RoformerType:\n        \"\"\"Get the model type.\"\"\"","chunkId":"audio_separator/separator/roformer/mel_band_roformer_config.py:8"}],[911,{"filePath":"audio_separator/separator/roformer/mel_band_roformer_config.py","startLine":108,"endLine":122,"chunkText":"    \n    def get_model_type(self) -> RoformerType:\n        \"\"\"Get the model type.\"\"\"\n        return RoformerType.MEL_BAND_ROFORMER\n    \n    def get_mel_band_roformer_kwargs(self) -> Dict[str, Any]:\n        \"\"\"Get MelBandRoformer-specific parameters for model initialization.\"\"\"\n        kwargs = self.get_transformer_kwargs()\n        kwargs.update({\n            'num_bands': self.num_bands,\n            'sample_rate': self.sample_rate,\n            'mel_scale': self.mel_scale,\n            'fmin': self.fmin,\n            'fmax': self.fmax,\n            'stereo': self.stereo,","chunkId":"audio_separator/separator/roformer/mel_band_roformer_config.py:9"}],[912,{"filePath":"audio_separator/separator/roformer/mel_band_roformer_config.py","startLine":120,"endLine":135,"chunkText":"            'fmin': self.fmin,\n            'fmax': self.fmax,\n            'stereo': self.stereo,\n            'num_stems': self.num_stems,\n            'mlp_expansion_factor': self.mlp_expansion_factor,\n            'use_torch_checkpoint': self.use_torch_checkpoint,\n            'skip_connection': self.skip_connection,\n        })\n        return kwargs\n    \n    @classmethod\n    def from_model_config(cls, config_dict: Dict[str, Any]) -> 'MelBandRoformerConfig':\n        \"\"\"\n        Create MelBandRoformerConfig from a model configuration dictionary.\n        \n        Args:","chunkId":"audio_separator/separator/roformer/mel_band_roformer_config.py:10"}],[913,{"filePath":"audio_separator/separator/roformer/mel_band_roformer_config.py","startLine":133,"endLine":149,"chunkText":"        Create MelBandRoformerConfig from a model configuration dictionary.\n        \n        Args:\n            config_dict: Dictionary containing model configuration\n            \n        Returns:\n            MelBandRoformerConfig instance\n        \"\"\"\n        # Ensure num_bands is present\n        if 'num_bands' not in config_dict:\n            # Use default if not specified\n            config_dict = config_dict.copy()\n            config_dict['num_bands'] = 64\n        \n        return cls.from_dict(config_dict)\n    \n    def suggest_optimal_bands(self, target_frequency_resolution: float = 50.0) -> int:","chunkId":"audio_separator/separator/roformer/mel_band_roformer_config.py:11"}],[914,{"filePath":"audio_separator/separator/roformer/mel_band_roformer_config.py","startLine":147,"endLine":163,"chunkText":"        return cls.from_dict(config_dict)\n    \n    def suggest_optimal_bands(self, target_frequency_resolution: float = 50.0) -> int:\n        \"\"\"\n        Suggest optimal number of bands based on desired frequency resolution.\n        \n        Args:\n            target_frequency_resolution: Desired frequency resolution in Hz\n            \n        Returns:\n            Suggested number of bands\n        \"\"\"\n        fmin, fmax = self.get_frequency_range()\n        frequency_span = fmax - fmin\n        suggested_bands = int(frequency_span / target_frequency_resolution)\n        \n        # Clamp to reasonable range","chunkId":"audio_separator/separator/roformer/mel_band_roformer_config.py:12"}],[915,{"filePath":"audio_separator/separator/roformer/mel_band_roformer_config.py","startLine":161,"endLine":172,"chunkText":"        suggested_bands = int(frequency_span / target_frequency_resolution)\n        \n        # Clamp to reasonable range\n        return max(8, min(512, suggested_bands))\n    \n    def get_parameter_summary(self) -> str:\n        \"\"\"Get a summary string of key MelBandRoformer parameters.\"\"\"\n        base_summary = super().get_parameter_summary()\n        mel_info = f\", num_bands={self.num_bands}, sr={self.sample_rate}, fmax={self.get_effective_fmax():.0f}\"\n        return base_summary.replace(\")\", mel_info + \")\")\n    \n    def __repr__(self) -> str:","chunkId":"audio_separator/separator/roformer/mel_band_roformer_config.py:13"}],[916,{"filePath":"audio_separator/separator/roformer/mel_band_roformer_config.py","startLine":170,"endLine":175,"chunkText":"        return base_summary.replace(\")\", mel_info + \")\")\n    \n    def __repr__(self) -> str:\n        \"\"\"String representation of the MelBandRoformer configuration.\"\"\"\n        return f\"MelBandRoformerConfig({self.get_parameter_summary()})\"\n","chunkId":"audio_separator/separator/roformer/mel_band_roformer_config.py:14"}],[917,{"filePath":"audio_separator/separator/roformer/mel_band_roformer_validator.py","startLine":0,"endLine":16,"chunkText":"\"\"\"\nMelBandRoformer-specific parameter validator.\nExtends the base ParameterValidator with MelBandRoformer-specific validation logic.\n\"\"\"\n\nfrom typing import Dict, Any, List\nfrom .parameter_validator import ParameterValidator, ValidationIssue, ValidationSeverity\n\n\nclass MelBandRoformerValidator(ParameterValidator):\n    \"\"\"\n    Specialized validator for MelBandRoformer model parameters.\n    \n    Provides MelBandRoformer-specific validation beyond the base parameter validation,\n    including mel-scale frequency band configuration and sample rate validation.\n    \"\"\"\n    ","chunkId":"audio_separator/separator/roformer/mel_band_roformer_validator.py:0"}],[918,{"filePath":"audio_separator/separator/roformer/mel_band_roformer_validator.py","startLine":14,"endLine":33,"chunkText":"    including mel-scale frequency band configuration and sample rate validation.\n    \"\"\"\n    \n    # MelBandRoformer-specific parameter constraints\n    MIN_NUM_BANDS = 8\n    MAX_NUM_BANDS = 512\n    RECOMMENDED_NUM_BANDS = 64\n    \n    # Common sample rates and their typical mel band counts\n    SAMPLE_RATE_BAND_RECOMMENDATIONS = {\n        8000: (8, 32),\n        16000: (16, 64),\n        22050: (24, 80),\n        44100: (32, 128),\n        48000: (32, 128),\n        96000: (64, 256),\n    }\n    \n    def validate_num_bands(self, config: Dict[str, Any]) -> List[ValidationIssue]:\n        \"\"\"","chunkId":"audio_separator/separator/roformer/mel_band_roformer_validator.py:1"}],[919,{"filePath":"audio_separator/separator/roformer/mel_band_roformer_validator.py","startLine":31,"endLine":50,"chunkText":"    \n    def validate_num_bands(self, config: Dict[str, Any]) -> List[ValidationIssue]:\n        \"\"\"\n        Validate num_bands parameter for MelBandRoformer.\n        \n        Args:\n            config: Model configuration dictionary\n            \n        Returns:\n            List of validation issues for num_bands\n        \"\"\"\n        issues = []\n        \n        if 'num_bands' not in config:\n            return issues  # Required parameter check handled by base class\n        \n        num_bands = config['num_bands']\n        \n        # Type check\n        if not isinstance(num_bands, int):","chunkId":"audio_separator/separator/roformer/mel_band_roformer_validator.py:2"}],[920,{"filePath":"audio_separator/separator/roformer/mel_band_roformer_validator.py","startLine":48,"endLine":64,"chunkText":"        \n        # Type check\n        if not isinstance(num_bands, int):\n            issue = ValidationIssue(\n                severity=ValidationSeverity.ERROR,\n                parameter_name=\"num_bands\",\n                message=\"num_bands must be an integer\",\n                suggested_fix=\"Set num_bands to an integer value\",\n                current_value=num_bands,\n                expected_value=\"integer\"\n            )\n            issues.append(issue)\n            return issues\n        \n        # Range check\n        if num_bands < self.MIN_NUM_BANDS:\n            issue = ValidationIssue(","chunkId":"audio_separator/separator/roformer/mel_band_roformer_validator.py:3"}],[921,{"filePath":"audio_separator/separator/roformer/mel_band_roformer_validator.py","startLine":62,"endLine":75,"chunkText":"        # Range check\n        if num_bands < self.MIN_NUM_BANDS:\n            issue = ValidationIssue(\n                severity=ValidationSeverity.ERROR,\n                parameter_name=\"num_bands\",\n                message=f\"num_bands must be at least {self.MIN_NUM_BANDS}\",\n                suggested_fix=f\"Set num_bands to {self.MIN_NUM_BANDS} or higher\",\n                current_value=num_bands,\n                expected_value=f\">= {self.MIN_NUM_BANDS}\"\n            )\n            issues.append(issue)\n        \n        if num_bands > self.MAX_NUM_BANDS:\n            issue = ValidationIssue(","chunkId":"audio_separator/separator/roformer/mel_band_roformer_validator.py:4"}],[922,{"filePath":"audio_separator/separator/roformer/mel_band_roformer_validator.py","startLine":73,"endLine":84,"chunkText":"        \n        if num_bands > self.MAX_NUM_BANDS:\n            issue = ValidationIssue(\n                severity=ValidationSeverity.WARNING,\n                parameter_name=\"num_bands\",\n                message=f\"num_bands of {num_bands} is very high and may impact performance\",\n                suggested_fix=f\"Consider using {self.MAX_NUM_BANDS} or fewer bands for better performance\",\n                current_value=num_bands,\n                expected_value=f\"<= {self.MAX_NUM_BANDS} (recommended)\"\n            )\n            issues.append(issue)\n        ","chunkId":"audio_separator/separator/roformer/mel_band_roformer_validator.py:5"}],[923,{"filePath":"audio_separator/separator/roformer/mel_band_roformer_validator.py","startLine":82,"endLine":94,"chunkText":"            )\n            issues.append(issue)\n        \n        # Check if it's a power of 2 (often preferred for efficiency)\n        if num_bands > 0 and (num_bands & (num_bands - 1)) != 0:\n            # Find nearest powers of 2\n            lower_power = 1 << (num_bands.bit_length() - 1)\n            upper_power = 1 << num_bands.bit_length()\n            \n            issue = ValidationIssue(\n                severity=ValidationSeverity.INFO,\n                parameter_name=\"num_bands\",\n                message=f\"num_bands ({num_bands}) is not a power of 2, which may be less efficient\",","chunkId":"audio_separator/separator/roformer/mel_band_roformer_validator.py:6"}],[924,{"filePath":"audio_separator/separator/roformer/mel_band_roformer_validator.py","startLine":92,"endLine":103,"chunkText":"                severity=ValidationSeverity.INFO,\n                parameter_name=\"num_bands\",\n                message=f\"num_bands ({num_bands}) is not a power of 2, which may be less efficient\",\n                suggested_fix=f\"Consider using {lower_power} or {upper_power} for potentially better performance\",\n                current_value=num_bands,\n                expected_value=\"power of 2 (optional optimization)\"\n            )\n            issues.append(issue)\n        \n        return issues\n    \n    def validate_sample_rate_compatibility(self, config: Dict[str, Any]) -> List[ValidationIssue]:","chunkId":"audio_separator/separator/roformer/mel_band_roformer_validator.py:7"}],[925,{"filePath":"audio_separator/separator/roformer/mel_band_roformer_validator.py","startLine":101,"endLine":118,"chunkText":"        return issues\n    \n    def validate_sample_rate_compatibility(self, config: Dict[str, Any]) -> List[ValidationIssue]:\n        \"\"\"\n        Validate sample rate compatibility with num_bands.\n        \n        Args:\n            config: Model configuration dictionary\n            \n        Returns:\n            List of validation issues for sample rate compatibility\n        \"\"\"\n        issues = []\n        \n        num_bands = config.get('num_bands')\n        sample_rate = config.get('sample_rate')\n        \n        if not isinstance(num_bands, int) or not isinstance(sample_rate, int):","chunkId":"audio_separator/separator/roformer/mel_band_roformer_validator.py:8"}],[926,{"filePath":"audio_separator/separator/roformer/mel_band_roformer_validator.py","startLine":116,"endLine":127,"chunkText":"        sample_rate = config.get('sample_rate')\n        \n        if not isinstance(num_bands, int) or not isinstance(sample_rate, int):\n            return issues  # Type validation handled elsewhere\n        \n        # Check against known good combinations\n        if sample_rate in self.SAMPLE_RATE_BAND_RECOMMENDATIONS:\n            min_bands, max_bands = self.SAMPLE_RATE_BAND_RECOMMENDATIONS[sample_rate]\n            \n            if num_bands < min_bands:\n                issue = ValidationIssue(\n                    severity=ValidationSeverity.WARNING,","chunkId":"audio_separator/separator/roformer/mel_band_roformer_validator.py:9"}],[927,{"filePath":"audio_separator/separator/roformer/mel_band_roformer_validator.py","startLine":125,"endLine":134,"chunkText":"            if num_bands < min_bands:\n                issue = ValidationIssue(\n                    severity=ValidationSeverity.WARNING,\n                    parameter_name=\"num_bands, sample_rate\",\n                    message=f\"num_bands ({num_bands}) may be too low for sample_rate ({sample_rate})\",\n                    suggested_fix=f\"Consider using {min_bands}-{max_bands} bands for {sample_rate}Hz\",\n                    current_value=f\"bands={num_bands}, sr={sample_rate}\",\n                    expected_value=f\"{min_bands}-{max_bands} bands\"\n                )\n                issues.append(issue)","chunkId":"audio_separator/separator/roformer/mel_band_roformer_validator.py:10"}],[928,{"filePath":"audio_separator/separator/roformer/mel_band_roformer_validator.py","startLine":132,"endLine":141,"chunkText":"                    expected_value=f\"{min_bands}-{max_bands} bands\"\n                )\n                issues.append(issue)\n            elif num_bands > max_bands:\n                issue = ValidationIssue(\n                    severity=ValidationSeverity.WARNING,\n                    parameter_name=\"num_bands, sample_rate\",\n                    message=f\"num_bands ({num_bands}) may be too high for sample_rate ({sample_rate})\",\n                    suggested_fix=f\"Consider using {min_bands}-{max_bands} bands for {sample_rate}Hz\",\n                    current_value=f\"bands={num_bands}, sr={sample_rate}\",","chunkId":"audio_separator/separator/roformer/mel_band_roformer_validator.py:11"}],[929,{"filePath":"audio_separator/separator/roformer/mel_band_roformer_validator.py","startLine":139,"endLine":148,"chunkText":"                    message=f\"num_bands ({num_bands}) may be too high for sample_rate ({sample_rate})\",\n                    suggested_fix=f\"Consider using {min_bands}-{max_bands} bands for {sample_rate}Hz\",\n                    current_value=f\"bands={num_bands}, sr={sample_rate}\",\n                    expected_value=f\"{min_bands}-{max_bands} bands\"\n                )\n                issues.append(issue)\n        \n        # General heuristic: num_bands should be much smaller than nyquist frequency\n        nyquist = sample_rate // 2\n        if num_bands > nyquist // 100:  # Very rough heuristic","chunkId":"audio_separator/separator/roformer/mel_band_roformer_validator.py:12"}],[930,{"filePath":"audio_separator/separator/roformer/mel_band_roformer_validator.py","startLine":146,"endLine":154,"chunkText":"        # General heuristic: num_bands should be much smaller than nyquist frequency\n        nyquist = sample_rate // 2\n        if num_bands > nyquist // 100:  # Very rough heuristic\n            issue = ValidationIssue(\n                severity=ValidationSeverity.WARNING,\n                parameter_name=\"num_bands, sample_rate\",\n                message=f\"num_bands ({num_bands}) seems high relative to sample_rate ({sample_rate})\",\n                suggested_fix=\"Verify that this band count is appropriate for your mel-scale analysis\",\n                current_value=f\"bands={num_bands}, nyquist={nyquist}\",","chunkId":"audio_separator/separator/roformer/mel_band_roformer_validator.py:13"}],[931,{"filePath":"audio_separator/separator/roformer/mel_band_roformer_validator.py","startLine":152,"endLine":164,"chunkText":"                message=f\"num_bands ({num_bands}) seems high relative to sample_rate ({sample_rate})\",\n                suggested_fix=\"Verify that this band count is appropriate for your mel-scale analysis\",\n                current_value=f\"bands={num_bands}, nyquist={nyquist}\",\n                expected_value=\"num_bands << nyquist_frequency\"\n            )\n            issues.append(issue)\n        \n        return issues\n    \n    def validate_mel_scale_config(self, config: Dict[str, Any]) -> List[ValidationIssue]:\n        \"\"\"\n        Validate mel-scale specific configuration parameters.\n        ","chunkId":"audio_separator/separator/roformer/mel_band_roformer_validator.py:14"}],[932,{"filePath":"audio_separator/separator/roformer/mel_band_roformer_validator.py","startLine":162,"endLine":180,"chunkText":"        \"\"\"\n        Validate mel-scale specific configuration parameters.\n        \n        Args:\n            config: Model configuration dictionary\n            \n        Returns:\n            List of validation issues for mel-scale configuration\n        \"\"\"\n        issues = []\n        \n        # Check for mel-scale related parameters if present\n        mel_params = ['fmin', 'fmax', 'mel_scale']\n        \n        fmin = config.get('fmin', 0)\n        fmax = config.get('fmax')\n        sample_rate = config.get('sample_rate', 44100)\n        \n        if fmin is not None and not isinstance(fmin, (int, float)):","chunkId":"audio_separator/separator/roformer/mel_band_roformer_validator.py:15"}],[933,{"filePath":"audio_separator/separator/roformer/mel_band_roformer_validator.py","startLine":178,"endLine":191,"chunkText":"        sample_rate = config.get('sample_rate', 44100)\n        \n        if fmin is not None and not isinstance(fmin, (int, float)):\n            issue = ValidationIssue(\n                severity=ValidationSeverity.ERROR,\n                parameter_name=\"fmin\",\n                message=\"fmin must be a number\",\n                suggested_fix=\"Set fmin to a numeric value (typically 0-100 Hz)\",\n                current_value=fmin,\n                expected_value=\"number\"\n            )\n            issues.append(issue)\n        elif isinstance(fmin, (int, float)) and fmin < 0:\n            issue = ValidationIssue(","chunkId":"audio_separator/separator/roformer/mel_band_roformer_validator.py:16"}],[934,{"filePath":"audio_separator/separator/roformer/mel_band_roformer_validator.py","startLine":189,"endLine":203,"chunkText":"            issues.append(issue)\n        elif isinstance(fmin, (int, float)) and fmin < 0:\n            issue = ValidationIssue(\n                severity=ValidationSeverity.ERROR,\n                parameter_name=\"fmin\",\n                message=\"fmin must be non-negative\",\n                suggested_fix=\"Set fmin to 0 or a positive frequency value\",\n                current_value=fmin,\n                expected_value=\">= 0\"\n            )\n            issues.append(issue)\n        \n        if fmax is not None:\n            if not isinstance(fmax, (int, float)):\n                issue = ValidationIssue(","chunkId":"audio_separator/separator/roformer/mel_band_roformer_validator.py:17"}],[935,{"filePath":"audio_separator/separator/roformer/mel_band_roformer_validator.py","startLine":201,"endLine":213,"chunkText":"        if fmax is not None:\n            if not isinstance(fmax, (int, float)):\n                issue = ValidationIssue(\n                    severity=ValidationSeverity.ERROR,\n                    parameter_name=\"fmax\",\n                    message=\"fmax must be a number\",\n                    suggested_fix=\"Set fmax to a numeric value or None for automatic setting\",\n                    current_value=fmax,\n                    expected_value=\"number or None\"\n                )\n                issues.append(issue)\n            elif isinstance(fmax, (int, float)):\n                if fmax <= 0:","chunkId":"audio_separator/separator/roformer/mel_band_roformer_validator.py:18"}],[936,{"filePath":"audio_separator/separator/roformer/mel_band_roformer_validator.py","startLine":211,"endLine":223,"chunkText":"                issues.append(issue)\n            elif isinstance(fmax, (int, float)):\n                if fmax <= 0:\n                    issue = ValidationIssue(\n                        severity=ValidationSeverity.ERROR,\n                        parameter_name=\"fmax\",\n                        message=\"fmax must be positive\",\n                        suggested_fix=\"Set fmax to a positive frequency value\",\n                        current_value=fmax,\n                        expected_value=\"> 0\"\n                    )\n                    issues.append(issue)\n                elif fmax > sample_rate // 2:","chunkId":"audio_separator/separator/roformer/mel_band_roformer_validator.py:19"}],[937,{"filePath":"audio_separator/separator/roformer/mel_band_roformer_validator.py","startLine":221,"endLine":231,"chunkText":"                    )\n                    issues.append(issue)\n                elif fmax > sample_rate // 2:\n                    issue = ValidationIssue(\n                        severity=ValidationSeverity.WARNING,\n                        parameter_name=\"fmax, sample_rate\",\n                        message=f\"fmax ({fmax}) exceeds Nyquist frequency ({sample_rate//2})\",\n                        suggested_fix=f\"Set fmax to {sample_rate//2} or lower\",\n                        current_value=fmax,\n                        expected_value=f\"<= {sample_rate//2}\"\n                    )","chunkId":"audio_separator/separator/roformer/mel_band_roformer_validator.py:20"}],[938,{"filePath":"audio_separator/separator/roformer/mel_band_roformer_validator.py","startLine":229,"endLine":238,"chunkText":"                        current_value=fmax,\n                        expected_value=f\"<= {sample_rate//2}\"\n                    )\n                    issues.append(issue)\n                elif isinstance(fmin, (int, float)) and fmax <= fmin:\n                    issue = ValidationIssue(\n                        severity=ValidationSeverity.ERROR,\n                        parameter_name=\"fmin, fmax\",\n                        message=f\"fmax ({fmax}) must be greater than fmin ({fmin})\",\n                        suggested_fix=\"Set fmax to a value higher than fmin\",","chunkId":"audio_separator/separator/roformer/mel_band_roformer_validator.py:21"}],[939,{"filePath":"audio_separator/separator/roformer/mel_band_roformer_validator.py","startLine":236,"endLine":247,"chunkText":"                        parameter_name=\"fmin, fmax\",\n                        message=f\"fmax ({fmax}) must be greater than fmin ({fmin})\",\n                        suggested_fix=\"Set fmax to a value higher than fmin\",\n                        current_value=f\"fmin={fmin}, fmax={fmax}\",\n                        expected_value=\"fmax > fmin\"\n                    )\n                    issues.append(issue)\n        \n        return issues\n    \n    def validate_all(self, config: Dict[str, Any], model_type: str = \"mel_band_roformer\") -> List[ValidationIssue]:\n        \"\"\"","chunkId":"audio_separator/separator/roformer/mel_band_roformer_validator.py:22"}],[940,{"filePath":"audio_separator/separator/roformer/mel_band_roformer_validator.py","startLine":245,"endLine":260,"chunkText":"    \n    def validate_all(self, config: Dict[str, Any], model_type: str = \"mel_band_roformer\") -> List[ValidationIssue]:\n        \"\"\"\n        Run all MelBandRoformer-specific validation checks.\n        \n        Args:\n            config: Model configuration dictionary\n            model_type: Type of model (should be \"mel_band_roformer\")\n            \n        Returns:\n            List of all validation issues found\n        \"\"\"\n        # Start with base validation\n        all_issues = super().validate_all(config, model_type)\n        \n        # Add MelBandRoformer-specific validation","chunkId":"audio_separator/separator/roformer/mel_band_roformer_validator.py:23"}],[941,{"filePath":"audio_separator/separator/roformer/mel_band_roformer_validator.py","startLine":258,"endLine":273,"chunkText":"        all_issues = super().validate_all(config, model_type)\n        \n        # Add MelBandRoformer-specific validation\n        all_issues.extend(self.validate_num_bands(config))\n        all_issues.extend(self.validate_sample_rate_compatibility(config))\n        all_issues.extend(self.validate_mel_scale_config(config))\n        \n        return all_issues\n    \n    def get_parameter_defaults(self, model_type: str = \"mel_band_roformer\") -> Dict[str, Any]:\n        \"\"\"\n        Get MelBandRoformer-specific parameter defaults.\n        \n        Args:\n            model_type: Type of model\n            ","chunkId":"audio_separator/separator/roformer/mel_band_roformer_validator.py:24"}],[942,{"filePath":"audio_separator/separator/roformer/mel_band_roformer_validator.py","startLine":271,"endLine":288,"chunkText":"        Args:\n            model_type: Type of model\n            \n        Returns:\n            Dictionary of parameter names to default values\n        \"\"\"\n        defaults = super().get_parameter_defaults(model_type)\n        \n        # Override with MelBandRoformer-specific defaults\n        defaults.update({\n            'num_bands': self.RECOMMENDED_NUM_BANDS,\n            'fmin': 0,\n            'fmax': None,  # Will be set to sample_rate // 2 automatically\n            'mel_scale': 'htk',  # or 'slaney'\n        })\n        \n        return defaults\n","chunkId":"audio_separator/separator/roformer/mel_band_roformer_validator.py:25"}],[943,{"filePath":"audio_separator/separator/roformer/model_configuration.py","startLine":0,"endLine":16,"chunkText":"\"\"\"\nModel configuration dataclass for Roformer models.\nSupports both old and new parameter sets with backward compatibility.\n\"\"\"\n\nfrom dataclasses import dataclass, field\nfrom typing import Optional, Tuple, Any, Dict\nfrom enum import Enum\n\n\nclass RoformerType(Enum):\n    \"\"\"Supported Roformer model types.\"\"\"\n    BS_ROFORMER = \"bs_roformer\"\n    MEL_BAND_ROFORMER = \"mel_band_roformer\"\n\n\n@dataclass(frozen=True, unsafe_hash=True)","chunkId":"audio_separator/separator/roformer/model_configuration.py:0"}],[944,{"filePath":"audio_separator/separator/roformer/model_configuration.py","startLine":17,"endLine":37,"chunkText":"class ModelConfiguration:\n    \"\"\"\n    Model configuration parameters for Roformer models.\n    \n    This class supports both old and new parameter sets to maintain\n    backward compatibility while enabling new features.\n    \"\"\"\n    \n    # Required parameters (must be provided)\n    dim: int\n    depth: int\n    \n    # Common optional parameters (with sensible defaults)\n    stereo: bool = False\n    num_stems: int = 1\n    time_transformer_depth: int = 2\n    freq_transformer_depth: int = 2\n    dim_head: int = 64\n    heads: int = 8\n    attn_dropout: float = 0.0\n    ff_dropout: float = 0.0","chunkId":"audio_separator/separator/roformer/model_configuration.py:1"}],[945,{"filePath":"audio_separator/separator/roformer/model_configuration.py","startLine":35,"endLine":52,"chunkText":"    heads: int = 8\n    attn_dropout: float = 0.0\n    ff_dropout: float = 0.0\n    flash_attn: bool = True\n    \n    # New parameters (with defaults for backward compatibility)\n    mlp_expansion_factor: int = 4\n    sage_attention: bool = False\n    zero_dc: bool = True\n    use_torch_checkpoint: bool = False\n    skip_connection: bool = False\n    \n    # Normalization (may be None in some configs)\n    norm: Optional[str] = None\n    \n    # Model-specific parameters (set by subclasses)\n    freqs_per_bands: Optional[Tuple[int, ...]] = None  # BSRoformer\n    num_bands: Optional[int] = None  # MelBandRoformer","chunkId":"audio_separator/separator/roformer/model_configuration.py:2"}],[946,{"filePath":"audio_separator/separator/roformer/model_configuration.py","startLine":50,"endLine":63,"chunkText":"    # Model-specific parameters (set by subclasses)\n    freqs_per_bands: Optional[Tuple[int, ...]] = None  # BSRoformer\n    num_bands: Optional[int] = None  # MelBandRoformer\n    sample_rate: int = 44100  # Default sample rate\n    \n    # Additional configuration data (stored as tuple for hashability)\n    extra_config: Tuple[Tuple[str, Any], ...] = field(default_factory=tuple)\n    \n    def __post_init__(self):\n        \"\"\"Validate configuration after initialization.\"\"\"\n        self._validate_basic_parameters()\n        self._validate_parameter_ranges()\n    \n    def _validate_basic_parameters(self):","chunkId":"audio_separator/separator/roformer/model_configuration.py:3"}],[947,{"filePath":"audio_separator/separator/roformer/model_configuration.py","startLine":61,"endLine":71,"chunkText":"        self._validate_parameter_ranges()\n    \n    def _validate_basic_parameters(self):\n        \"\"\"Validate basic parameter types and requirements.\"\"\"\n        if not isinstance(self.dim, int) or self.dim <= 0:\n            raise ValueError(f\"dim must be a positive integer, got {self.dim}\")\n        \n        if not isinstance(self.depth, int) or self.depth <= 0:\n            raise ValueError(f\"depth must be a positive integer, got {self.depth}\")\n        \n        if not isinstance(self.num_stems, int) or self.num_stems <= 0:","chunkId":"audio_separator/separator/roformer/model_configuration.py:4"}],[948,{"filePath":"audio_separator/separator/roformer/model_configuration.py","startLine":69,"endLine":78,"chunkText":"            raise ValueError(f\"depth must be a positive integer, got {self.depth}\")\n        \n        if not isinstance(self.num_stems, int) or self.num_stems <= 0:\n            raise ValueError(f\"num_stems must be a positive integer, got {self.num_stems}\")\n        \n        if not isinstance(self.mlp_expansion_factor, int) or self.mlp_expansion_factor <= 0:\n            raise ValueError(f\"mlp_expansion_factor must be a positive integer, got {self.mlp_expansion_factor}\")\n    \n    def _validate_parameter_ranges(self):\n        \"\"\"Validate parameter value ranges.\"\"\"","chunkId":"audio_separator/separator/roformer/model_configuration.py:5"}],[949,{"filePath":"audio_separator/separator/roformer/model_configuration.py","startLine":76,"endLine":88,"chunkText":"    \n    def _validate_parameter_ranges(self):\n        \"\"\"Validate parameter value ranges.\"\"\"\n        if not (0.0 <= self.attn_dropout <= 1.0):\n            raise ValueError(f\"attn_dropout must be between 0.0 and 1.0, got {self.attn_dropout}\")\n        \n        if not (0.0 <= self.ff_dropout <= 1.0):\n            raise ValueError(f\"ff_dropout must be between 0.0 and 1.0, got {self.ff_dropout}\")\n        \n        if self.dim_head <= 0:\n            raise ValueError(f\"dim_head must be positive, got {self.dim_head}\")\n        \n        if self.heads <= 0:","chunkId":"audio_separator/separator/roformer/model_configuration.py:6"}],[950,{"filePath":"audio_separator/separator/roformer/model_configuration.py","startLine":86,"endLine":99,"chunkText":"            raise ValueError(f\"dim_head must be positive, got {self.dim_head}\")\n        \n        if self.heads <= 0:\n            raise ValueError(f\"heads must be positive, got {self.heads}\")\n        \n        if self.sample_rate <= 0:\n            raise ValueError(f\"sample_rate must be positive, got {self.sample_rate}\")\n    \n    def to_dict(self) -> Dict[str, Any]:\n        \"\"\"Convert configuration to dictionary.\"\"\"\n        result = {}\n        for field_info in self.__dataclass_fields__.values():\n            value = getattr(self, field_info.name)\n            if value is not None:","chunkId":"audio_separator/separator/roformer/model_configuration.py:7"}],[951,{"filePath":"audio_separator/separator/roformer/model_configuration.py","startLine":97,"endLine":112,"chunkText":"        for field_info in self.__dataclass_fields__.values():\n            value = getattr(self, field_info.name)\n            if value is not None:\n                result[field_info.name] = value\n        return result\n    \n    @classmethod\n    def from_dict(cls, config_dict: Dict[str, Any]) -> 'ModelConfiguration':\n        \"\"\"Create configuration from dictionary.\"\"\"\n        # Extract known parameters\n        known_params = {}\n        extra_params = {}\n        \n        for key, value in config_dict.items():\n            if key in cls.__dataclass_fields__:\n                known_params[key] = value","chunkId":"audio_separator/separator/roformer/model_configuration.py:8"}],[952,{"filePath":"audio_separator/separator/roformer/model_configuration.py","startLine":110,"endLine":125,"chunkText":"        for key, value in config_dict.items():\n            if key in cls.__dataclass_fields__:\n                known_params[key] = value\n            else:\n                extra_params[key] = value\n        \n        # Set extra_config as tuple if there are unknown parameters\n        if extra_params:\n            known_params['extra_config'] = tuple(extra_params.items())\n        \n        return cls(**known_params)\n    \n    def get_transformer_kwargs(self) -> Dict[str, Any]:\n        \"\"\"Get parameters to pass to transformer initialization.\"\"\"\n        return {\n            'dim': self.dim,","chunkId":"audio_separator/separator/roformer/model_configuration.py:9"}],[953,{"filePath":"audio_separator/separator/roformer/model_configuration.py","startLine":123,"endLine":138,"chunkText":"        \"\"\"Get parameters to pass to transformer initialization.\"\"\"\n        return {\n            'dim': self.dim,\n            'depth': self.depth,\n            'heads': self.heads,\n            'dim_head': self.dim_head,\n            'attn_dropout': self.attn_dropout,\n            'ff_dropout': self.ff_dropout,\n            'flash_attn': self.flash_attn,\n            'sage_attention': self.sage_attention,  # New parameter\n            'zero_dc': self.zero_dc,  # New parameter\n        }\n    \n    def has_new_parameters(self) -> bool:\n        \"\"\"Check if configuration uses any new parameters.\"\"\"\n        return (","chunkId":"audio_separator/separator/roformer/model_configuration.py:10"}],[954,{"filePath":"audio_separator/separator/roformer/model_configuration.py","startLine":136,"endLine":149,"chunkText":"    def has_new_parameters(self) -> bool:\n        \"\"\"Check if configuration uses any new parameters.\"\"\"\n        return (\n            self.mlp_expansion_factor != 4 or\n            self.sage_attention is True or\n            self.zero_dc is not True or\n            self.use_torch_checkpoint is True or\n            self.skip_connection is True\n        )\n    \n    def get_parameter_summary(self) -> str:\n        \"\"\"Get a summary string of key parameters.\"\"\"\n        return (\n            f\"ModelConfiguration(dim={self.dim}, depth={self.depth}, \"","chunkId":"audio_separator/separator/roformer/model_configuration.py:11"}],[955,{"filePath":"audio_separator/separator/roformer/model_configuration.py","startLine":147,"endLine":157,"chunkText":"        \"\"\"Get a summary string of key parameters.\"\"\"\n        return (\n            f\"ModelConfiguration(dim={self.dim}, depth={self.depth}, \"\n            f\"stems={self.num_stems}, mlp_factor={self.mlp_expansion_factor}, \"\n            f\"sage_attn={self.sage_attention}, new_params={self.has_new_parameters()})\"\n        )\n    \n    def __repr__(self) -> str:\n        \"\"\"String representation of the configuration.\"\"\"\n        return self.get_parameter_summary()\n","chunkId":"audio_separator/separator/roformer/model_configuration.py:12"}],[956,{"filePath":"audio_separator/separator/roformer/model_loading_result.py","startLine":0,"endLine":17,"chunkText":"\"\"\"\nModel loading result dataclass.\nContains the result of model loading operations with success/failure information.\n\"\"\"\n\nfrom dataclasses import dataclass, field\nfrom typing import Optional, List, Any, Dict\nfrom enum import Enum\n\n\nclass ImplementationVersion(Enum):\n    \"\"\"Available implementation versions.\"\"\"\n    OLD = \"old\"\n    NEW = \"new\"\n    FALLBACK = \"fallback\"\n\n\n@dataclass","chunkId":"audio_separator/separator/roformer/model_loading_result.py:0"}],[957,{"filePath":"audio_separator/separator/roformer/model_loading_result.py","startLine":18,"endLine":33,"chunkText":"class ModelLoadingResult:\n    \"\"\"\n    Result of a model loading operation.\n    \n    Contains information about whether the loading was successful,\n    the loaded model (if successful), error details (if failed),\n    and metadata about which implementation was used.\n    \"\"\"\n    \n    success: bool\n    model: Optional[Any] = None  # Actual model instance (torch.nn.Module)\n    error_message: Optional[str] = None\n    implementation_used: ImplementationVersion = ImplementationVersion.NEW\n    warnings: List[str] = field(default_factory=list)\n    \n    # Additional metadata","chunkId":"audio_separator/separator/roformer/model_loading_result.py:1"}],[958,{"filePath":"audio_separator/separator/roformer/model_loading_result.py","startLine":31,"endLine":43,"chunkText":"    warnings: List[str] = field(default_factory=list)\n    \n    # Additional metadata\n    loading_time_seconds: Optional[float] = None\n    model_info: Dict[str, Any] = field(default_factory=dict)\n    config_used: Optional[Any] = None  # The configuration that was actually used\n    \n    def __post_init__(self):\n        \"\"\"Validate result after initialization.\"\"\"\n        if self.success and self.model is None:\n            raise ValueError(\"success=True but model is None\")\n        \n        if not self.success and self.error_message is None:","chunkId":"audio_separator/separator/roformer/model_loading_result.py:2"}],[959,{"filePath":"audio_separator/separator/roformer/model_loading_result.py","startLine":42,"endLine":58,"chunkText":"        \n        if not self.success and self.error_message is None:\n            raise ValueError(\"success=False but error_message is None\")\n        \n        if self.warnings is None:\n            self.warnings = []\n    \n    def add_warning(self, warning: str):\n        \"\"\"Add a warning message.\"\"\"\n        if warning not in self.warnings:\n            self.warnings.append(warning)\n    \n    def add_model_info(self, key: str, value: Any):\n        \"\"\"Add model metadata information.\"\"\"\n        self.model_info[key] = value\n    \n    def get_summary(self) -> str:","chunkId":"audio_separator/separator/roformer/model_loading_result.py:3"}],[960,{"filePath":"audio_separator/separator/roformer/model_loading_result.py","startLine":56,"endLine":71,"chunkText":"        self.model_info[key] = value\n    \n    def get_summary(self) -> str:\n        \"\"\"Get a summary string of the loading result.\"\"\"\n        if self.success:\n            summary = f\"SUCCESS: Model loaded using {self.implementation_used.value} implementation\"\n            if self.loading_time_seconds:\n                summary += f\" in {self.loading_time_seconds:.2f}s\"\n            if self.warnings:\n                summary += f\" with {len(self.warnings)} warnings\"\n        else:\n            summary = f\"FAILED: {self.error_message}\"\n        \n        return summary\n    \n    def is_fallback_used(self) -> bool:","chunkId":"audio_separator/separator/roformer/model_loading_result.py:4"}],[961,{"filePath":"audio_separator/separator/roformer/model_loading_result.py","startLine":69,"endLine":83,"chunkText":"        return summary\n    \n    def is_fallback_used(self) -> bool:\n        \"\"\"Check if fallback implementation was used.\"\"\"\n        return self.implementation_used == ImplementationVersion.FALLBACK\n    \n    def is_new_implementation_used(self) -> bool:\n        \"\"\"Check if new implementation was used.\"\"\"\n        return self.implementation_used == ImplementationVersion.NEW\n    \n    def has_warnings(self) -> bool:\n        \"\"\"Check if there are any warnings.\"\"\"\n        return len(self.warnings) > 0\n    \n    def get_model_parameters_count(self) -> Optional[int]:","chunkId":"audio_separator/separator/roformer/model_loading_result.py:5"}],[962,{"filePath":"audio_separator/separator/roformer/model_loading_result.py","startLine":81,"endLine":97,"chunkText":"        return len(self.warnings) > 0\n    \n    def get_model_parameters_count(self) -> Optional[int]:\n        \"\"\"Get the number of model parameters if available.\"\"\"\n        if self.model is None:\n            return None\n        \n        try:\n            # Try to count parameters for PyTorch models\n            if hasattr(self.model, 'parameters'):\n                return sum(p.numel() for p in self.model.parameters())\n        except Exception:\n            pass\n        \n        return self.model_info.get('parameter_count')\n    \n    def get_model_size_mb(self) -> Optional[float]:","chunkId":"audio_separator/separator/roformer/model_loading_result.py:6"}],[963,{"filePath":"audio_separator/separator/roformer/model_loading_result.py","startLine":95,"endLine":109,"chunkText":"        return self.model_info.get('parameter_count')\n    \n    def get_model_size_mb(self) -> Optional[float]:\n        \"\"\"Get the model size in MB if available.\"\"\"\n        param_count = self.get_model_parameters_count()\n        if param_count is not None:\n            # Assume float32 parameters (4 bytes each)\n            return (param_count * 4) / (1024 * 1024)\n        \n        return self.model_info.get('size_mb')\n    \n    def to_dict(self) -> Dict[str, Any]:\n        \"\"\"Convert result to dictionary for serialization.\"\"\"\n        return {\n            'success': self.success,","chunkId":"audio_separator/separator/roformer/model_loading_result.py:7"}],[964,{"filePath":"audio_separator/separator/roformer/model_loading_result.py","startLine":107,"endLine":121,"chunkText":"        \"\"\"Convert result to dictionary for serialization.\"\"\"\n        return {\n            'success': self.success,\n            'error_message': self.error_message,\n            'implementation_used': self.implementation_used.value,\n            'warnings': self.warnings,\n            'loading_time_seconds': self.loading_time_seconds,\n            'model_info': self.model_info,\n            'has_model': self.model is not None,\n            'parameter_count': self.get_model_parameters_count(),\n            'model_size_mb': self.get_model_size_mb(),\n        }\n    \n    @classmethod\n    def success_result(","chunkId":"audio_separator/separator/roformer/model_loading_result.py:8"}],[965,{"filePath":"audio_separator/separator/roformer/model_loading_result.py","startLine":119,"endLine":137,"chunkText":"    \n    @classmethod\n    def success_result(\n        cls, \n        model: Any, \n        implementation: ImplementationVersion = ImplementationVersion.NEW,\n        config: Optional[Any] = None,\n        loading_time: Optional[float] = None\n    ) -> 'ModelLoadingResult':\n        \"\"\"\n        Create a successful loading result.\n        \n        Args:\n            model: The loaded model instance\n            implementation: Which implementation was used\n            config: The configuration that was used\n            loading_time: Time taken to load the model\n            \n        Returns:","chunkId":"audio_separator/separator/roformer/model_loading_result.py:9"}],[966,{"filePath":"audio_separator/separator/roformer/model_loading_result.py","startLine":135,"endLine":155,"chunkText":"            loading_time: Time taken to load the model\n            \n        Returns:\n            ModelLoadingResult indicating success\n        \"\"\"\n        return cls(\n            success=True,\n            model=model,\n            implementation_used=implementation,\n            config_used=config,\n            loading_time_seconds=loading_time\n        )\n    \n    @classmethod\n    def failure_result(\n        cls, \n        error_message: str,\n        implementation: ImplementationVersion = ImplementationVersion.NEW,\n        warnings: Optional[List[str]] = None\n    ) -> 'ModelLoadingResult':\n        \"\"\"","chunkId":"audio_separator/separator/roformer/model_loading_result.py:10"}],[967,{"filePath":"audio_separator/separator/roformer/model_loading_result.py","startLine":153,"endLine":171,"chunkText":"        warnings: Optional[List[str]] = None\n    ) -> 'ModelLoadingResult':\n        \"\"\"\n        Create a failed loading result.\n        \n        Args:\n            error_message: Description of what went wrong\n            implementation: Which implementation was attempted\n            warnings: Any warnings that occurred before failure\n            \n        Returns:\n            ModelLoadingResult indicating failure\n        \"\"\"\n        return cls(\n            success=False,\n            error_message=error_message,\n            implementation_used=implementation,\n            warnings=warnings or []\n        )","chunkId":"audio_separator/separator/roformer/model_loading_result.py:11"}],[968,{"filePath":"audio_separator/separator/roformer/model_loading_result.py","startLine":169,"endLine":187,"chunkText":"            implementation_used=implementation,\n            warnings=warnings or []\n        )\n    \n    @classmethod\n    def fallback_success_result(\n        cls,\n        model: Any,\n        original_error: str,\n        config: Optional[Any] = None,\n        loading_time: Optional[float] = None\n    ) -> 'ModelLoadingResult':\n        \"\"\"\n        Create a successful fallback loading result.\n        \n        Args:\n            model: The loaded model instance\n            original_error: The error that caused fallback\n            config: The configuration that was used","chunkId":"audio_separator/separator/roformer/model_loading_result.py:12"}],[969,{"filePath":"audio_separator/separator/roformer/model_loading_result.py","startLine":185,"endLine":199,"chunkText":"            model: The loaded model instance\n            original_error: The error that caused fallback\n            config: The configuration that was used\n            loading_time: Time taken to load the model\n            \n        Returns:\n            ModelLoadingResult indicating fallback success\n        \"\"\"\n        result = cls(\n            success=True,\n            model=model,\n            implementation_used=ImplementationVersion.FALLBACK,\n            config_used=config,\n            loading_time_seconds=loading_time\n        )","chunkId":"audio_separator/separator/roformer/model_loading_result.py:13"}],[970,{"filePath":"audio_separator/separator/roformer/model_loading_result.py","startLine":197,"endLine":210,"chunkText":"            config_used=config,\n            loading_time_seconds=loading_time\n        )\n        result.add_warning(f\"Fell back to old implementation due to: {original_error}\")\n        return result\n    \n    def __str__(self) -> str:\n        \"\"\"String representation of the loading result.\"\"\"\n        return self.get_summary()\n    \n    def __repr__(self) -> str:\n        \"\"\"Detailed string representation of the loading result.\"\"\"\n        return f\"ModelLoadingResult(success={self.success}, impl={self.implementation_used.value}, warnings={len(self.warnings)})\"\n","chunkId":"audio_separator/separator/roformer/model_loading_result.py:14"}],[971,{"filePath":"audio_separator/separator/roformer/parameter_validation_error.py","startLine":0,"endLine":24,"chunkText":"\"\"\"\nParameter validation error exception.\nRaised when model parameters are invalid or incompatible.\n\"\"\"\n\nfrom typing import Any, Optional\n\n\nclass ParameterValidationError(Exception):\n    \"\"\"\n    Exception raised when model parameters are invalid.\n    \n    This exception provides detailed information about what parameter\n    was invalid, what was expected, and suggestions for fixing the issue.\n    \"\"\"\n    \n    def __init__(\n        self, \n        parameter_name: str, \n        expected_type: str, \n        actual_value: Any, \n        suggested_fix: str,\n        context: Optional[str] = None\n    ):\n        \"\"","chunkId":"audio_separator/separator/roformer/parameter_validation_error.py:0"}],[972,{"filePath":"audio_separator/separator/roformer/parameter_validation_error.py","startLine":22,"endLine":36,"chunkText":"        context: Optional[str] = None\n    ):\n        \"\"\"\n        Initialize parameter validation error.\n        \n        Args:\n            parameter_name: Name of the invalid parameter\n            expected_type: Expected type or description of valid values\n            actual_value: The actual value that was provided\n            suggested_fix: Suggestion for how to fix the issue\n            context: Additional context about where the error occurred\n        \"\"\"\n        self.parameter_name = parameter_name\n        self.expected_type = expected_type\n        self.actual_value = actual_value","chunkId":"audio_separator/separator/roformer/parameter_validation_error.py:1"}],[973,{"filePath":"audio_separator/separator/roformer/parameter_validation_error.py","startLine":34,"endLine":49,"chunkText":"        self.parameter_name = parameter_name\n        self.expected_type = expected_type\n        self.actual_value = actual_value\n        self.suggested_fix = suggested_fix\n        self.context = context\n        \n        # Create detailed error message\n        message = self._create_error_message()\n        super().__init__(message)\n    \n    def _create_error_message(self) -> str:\n        \"\"\"Create a detailed error message.\"\"\"\n        actual_type = type(self.actual_value).__name__\n        \n        message_parts = [\n            f\"Invalid parameter '{self.parameter_name}': \",","chunkId":"audio_separator/separator/roformer/parameter_validation_error.py:2"}],[974,{"filePath":"audio_separator/separator/roformer/parameter_validation_error.py","startLine":47,"endLine":63,"chunkText":"        \n        message_parts = [\n            f\"Invalid parameter '{self.parameter_name}': \",\n            f\"expected {self.expected_type}, got {actual_type} ({self.actual_value})\"\n        ]\n        \n        if self.context:\n            message_parts.append(f\" in {self.context}\")\n        \n        message_parts.append(f\". Suggestion: {self.suggested_fix}\")\n        \n        return \"\".join(message_parts)\n    \n    def get_error_details(self) -> dict:\n        \"\"\"Get error details as a dictionary.\"\"\"\n        return {\n            'parameter_name': self.parameter_name,","chunkId":"audio_separator/separator/roformer/parameter_validation_error.py:3"}],[975,{"filePath":"audio_separator/separator/roformer/parameter_validation_error.py","startLine":61,"endLine":74,"chunkText":"        \"\"\"Get error details as a dictionary.\"\"\"\n        return {\n            'parameter_name': self.parameter_name,\n            'expected_type': self.expected_type,\n            'actual_value': self.actual_value,\n            'actual_type': type(self.actual_value).__name__,\n            'suggested_fix': self.suggested_fix,\n            'context': self.context,\n            'error_message': str(self)\n        }\n    \n    @classmethod\n    def missing_parameter(cls, parameter_name: str, parameter_type: str, context: Optional[str] = None) -> 'ParameterValidationError':\n        \"\"\"","chunkId":"audio_separator/separator/roformer/parameter_validation_error.py:4"}],[976,{"filePath":"audio_separator/separator/roformer/parameter_validation_error.py","startLine":72,"endLine":86,"chunkText":"    @classmethod\n    def missing_parameter(cls, parameter_name: str, parameter_type: str, context: Optional[str] = None) -> 'ParameterValidationError':\n        \"\"\"\n        Create error for missing required parameter.\n        \n        Args:\n            parameter_name: Name of missing parameter\n            parameter_type: Expected type of the parameter\n            context: Context where parameter is missing\n            \n        Returns:\n            ParameterValidationError for missing parameter\n        \"\"\"\n        return cls(\n            parameter_name=parameter_name,","chunkId":"audio_separator/separator/roformer/parameter_validation_error.py:5"}],[977,{"filePath":"audio_separator/separator/roformer/parameter_validation_error.py","startLine":84,"endLine":99,"chunkText":"        \"\"\"\n        return cls(\n            parameter_name=parameter_name,\n            expected_type=parameter_type,\n            actual_value=None,\n            suggested_fix=f\"Add '{parameter_name}' parameter with {parameter_type} value\",\n            context=context\n        )\n    \n    @classmethod\n    def wrong_type(cls, parameter_name: str, expected_type: str, actual_value: Any, context: Optional[str] = None) -> 'ParameterValidationError':\n        \"\"\"\n        Create error for wrong parameter type.\n        \n        Args:\n            parameter_name: Name of parameter with wrong type","chunkId":"audio_separator/separator/roformer/parameter_validation_error.py:6"}],[978,{"filePath":"audio_separator/separator/roformer/parameter_validation_error.py","startLine":97,"endLine":115,"chunkText":"        \n        Args:\n            parameter_name: Name of parameter with wrong type\n            expected_type: Expected type\n            actual_value: Actual value provided\n            context: Context where error occurred\n            \n        Returns:\n            ParameterValidationError for type mismatch\n        \"\"\"\n        return cls(\n            parameter_name=parameter_name,\n            expected_type=expected_type,\n            actual_value=actual_value,\n            suggested_fix=f\"Change '{parameter_name}' to {expected_type}\",\n            context=context\n        )\n    \n    @classmethod","chunkId":"audio_separator/separator/roformer/parameter_validation_error.py:7"}],[979,{"filePath":"audio_separator/separator/roformer/parameter_validation_error.py","startLine":113,"endLine":129,"chunkText":"        )\n    \n    @classmethod\n    def out_of_range(cls, parameter_name: str, valid_range: str, actual_value: Any, context: Optional[str] = None) -> 'ParameterValidationError':\n        \"\"\"\n        Create error for parameter value out of valid range.\n        \n        Args:\n            parameter_name: Name of parameter out of range\n            valid_range: Description of valid range\n            actual_value: Actual value provided\n            context: Context where error occurred\n            \n        Returns:\n            ParameterValidationError for out of range value\n        \"\"\"\n        return cls(","chunkId":"audio_separator/separator/roformer/parameter_validation_error.py:8"}],[980,{"filePath":"audio_separator/separator/roformer/parameter_validation_error.py","startLine":127,"endLine":139,"chunkText":"            ParameterValidationError for out of range value\n        \"\"\"\n        return cls(\n            parameter_name=parameter_name,\n            expected_type=f\"value in range {valid_range}\",\n            actual_value=actual_value,\n            suggested_fix=f\"Set '{parameter_name}' to a value within {valid_range}\",\n            context=context\n        )\n    \n    @classmethod\n    def incompatible_parameters(cls, parameter_names: list, issue_description: str, suggested_fix: str, context: Optional[str] = None) -> 'ParameterValidationError':\n        \"\"\"","chunkId":"audio_separator/separator/roformer/parameter_validation_error.py:9"}],[981,{"filePath":"audio_separator/separator/roformer/parameter_validation_error.py","startLine":137,"endLine":148,"chunkText":"    @classmethod\n    def incompatible_parameters(cls, parameter_names: list, issue_description: str, suggested_fix: str, context: Optional[str] = None) -> 'ParameterValidationError':\n        \"\"\"\n        Create error for incompatible parameter combination.\n        \n        Args:\n            parameter_names: List of parameter names that are incompatible\n            issue_description: Description of the incompatibility\n            suggested_fix: How to fix the incompatibility\n            context: Context where error occurred\n            \n        Returns:","chunkId":"audio_separator/separator/roformer/parameter_validation_error.py:10"}],[982,{"filePath":"audio_separator/separator/roformer/parameter_validation_error.py","startLine":146,"endLine":160,"chunkText":"            context: Context where error occurred\n            \n        Returns:\n            ParameterValidationError for incompatible parameters\n        \"\"\"\n        parameter_list = \", \".join(parameter_names)\n        return cls(\n            parameter_name=parameter_list,\n            expected_type=\"compatible parameter combination\",\n            actual_value=issue_description,\n            suggested_fix=suggested_fix,\n            context=context\n        )\n    \n    @classmethod","chunkId":"audio_separator/separator/roformer/parameter_validation_error.py:11"}],[983,{"filePath":"audio_separator/separator/roformer/parameter_validation_error.py","startLine":158,"endLine":172,"chunkText":"        )\n    \n    @classmethod\n    def invalid_normalization(cls, norm_value: Any, supported_norms: list, context: Optional[str] = None) -> 'ParameterValidationError':\n        \"\"\"\n        Create error for invalid normalization configuration.\n        \n        Args:\n            norm_value: The invalid normalization value\n            supported_norms: List of supported normalization types\n            context: Context where error occurred\n            \n        Returns:\n            ParameterValidationError for invalid normalization\n        \"\"\"","chunkId":"audio_separator/separator/roformer/parameter_validation_error.py:12"}],[984,{"filePath":"audio_separator/separator/roformer/parameter_validation_error.py","startLine":170,"endLine":183,"chunkText":"        Returns:\n            ParameterValidationError for invalid normalization\n        \"\"\"\n        supported_list = \", \".join(f\"'{norm}'\" for norm in supported_norms)\n        return cls(\n            parameter_name=\"norm\",\n            expected_type=f\"one of: {supported_list}\",\n            actual_value=norm_value,\n            suggested_fix=f\"Use one of the supported normalization types: {supported_list}\",\n            context=context\n        )\n    \n    def __repr__(self) -> str:\n        \"\"\"Detailed string representation.\"\"\"","chunkId":"audio_separator/separator/roformer/parameter_validation_error.py:13"}],[985,{"filePath":"audio_separator/separator/roformer/parameter_validation_error.py","startLine":181,"endLine":185,"chunkText":"    \n    def __repr__(self) -> str:\n        \"\"\"Detailed string representation.\"\"\"\n        return f\"ParameterValidationError(parameter='{self.parameter_name}', expected='{self.expected_type}', actual={self.actual_value})\"\n","chunkId":"audio_separator/separator/roformer/parameter_validation_error.py:14"}],[986,{"filePath":"audio_separator/separator/roformer/parameter_validator.py","startLine":0,"endLine":16,"chunkText":"\"\"\"\nParameter validator implementation.\nValidates Roformer model parameters according to interface contracts.\n\"\"\"\n\nfrom typing import Dict, Any, List, Optional, Union, Tuple\nimport sys\nimport os\n\n# Add contracts to path for interface imports (optional)\ntry:\n    # Find project root dynamically\n    current_dir = os.path.dirname(os.path.abspath(__file__))\n    project_root = current_dir\n    # Go up until we find the project root (contains specs/ directory)\n    while project_root and not os.path.exists(os.path.join(project_root, 'specs')):\n        parent = os.path.dirname(project_root)","chunkId":"audio_separator/separator/roformer/parameter_validator.py:0"}],[987,{"filePath":"audio_separator/separator/roformer/parameter_validator.py","startLine":14,"endLine":26,"chunkText":"    # Go up until we find the project root (contains specs/ directory)\n    while project_root and not os.path.exists(os.path.join(project_root, 'specs')):\n        parent = os.path.dirname(project_root)\n        if parent == project_root:  # Reached filesystem root\n            break\n        project_root = parent\n    \n    contracts_path = os.path.join(project_root, 'specs', '001-update-roformer-implementation', 'contracts')\n    if os.path.exists(contracts_path):\n        sys.path.append(contracts_path)\n    from parameter_validator_interface import (\n        ParameterValidatorInterface,\n        ValidationIssue","chunkId":"audio_separator/separator/roformer/parameter_validator.py:1"}],[988,{"filePath":"audio_separator/separator/roformer/parameter_validator.py","startLine":24,"endLine":40,"chunkText":"    from parameter_validator_interface import (\n        ParameterValidatorInterface,\n        ValidationIssue,\n        ValidationSeverity\n    )\n    _has_interface = True\nexcept ImportError:\n    # Create dummy interfaces for when contracts are not available\n    from enum import Enum\n    from dataclasses import dataclass\n    \n    class ValidationSeverity(Enum):\n        ERROR = \"error\"\n        WARNING = \"warning\"\n        INFO = \"info\"\n    \n    @dataclass","chunkId":"audio_separator/separator/roformer/parameter_validator.py:2"}],[989,{"filePath":"audio_separator/separator/roformer/parameter_validator.py","startLine":41,"endLine":48,"chunkText":"    class ValidationIssue:\n        severity: ValidationSeverity\n        parameter_name: str\n        message: str\n        suggested_fix: str\n        current_value: any = None\n        expected_value: any = None\n    ","chunkId":"audio_separator/separator/roformer/parameter_validator.py:3"}],[990,{"filePath":"audio_separator/separator/roformer/parameter_validator.py","startLine":49,"endLine":55,"chunkText":"    class ParameterValidatorInterface:\n        pass\n    \n    _has_interface = False\nfrom .parameter_validation_error import ParameterValidationError\n\n","chunkId":"audio_separator/separator/roformer/parameter_validator.py:4"}],[991,{"filePath":"audio_separator/separator/roformer/parameter_validator.py","startLine":56,"endLine":74,"chunkText":"class ParameterValidator(ParameterValidatorInterface):\n    \"\"\"\n    Implementation of parameter validation for Roformer models.\n    \n    Validates model parameters according to the interface contract,\n    providing detailed error messages and suggestions for fixes.\n    \"\"\"\n    \n    # Define valid parameter types and ranges\n    PARAMETER_TYPES = {\n        'dim': int,\n        'depth': int,\n        'stereo': bool,\n        'num_stems': int,\n        'time_transformer_depth': int,\n        'freq_transformer_depth': int,\n        'dim_head': int,\n        'heads': int,\n        'attn_dropout': float,","chunkId":"audio_separator/separator/roformer/parameter_validator.py:5"}],[992,{"filePath":"audio_separator/separator/roformer/parameter_validator.py","startLine":72,"endLine":92,"chunkText":"        'dim_head': int,\n        'heads': int,\n        'attn_dropout': float,\n        'ff_dropout': float,\n        'flash_attn': bool,\n        'mlp_expansion_factor': int,\n        'sage_attention': bool,\n        'zero_dc': bool,\n        'use_torch_checkpoint': bool,\n        'skip_connection': bool,\n        'sample_rate': int,\n        'freqs_per_bands': (tuple, list),\n        'num_bands': int,\n        'mask_estimator_depth': int,\n    }\n    \n    PARAMETER_RANGES = {\n        'dim': (1, 8192),\n        'depth': (1, 64),\n        'num_stems': (1, 16),\n        'time_transformer_depth': (1, 32),","chunkId":"audio_separator/separator/roformer/parameter_validator.py:6"}],[993,{"filePath":"audio_separator/separator/roformer/parameter_validator.py","startLine":90,"endLine":108,"chunkText":"        'depth': (1, 64),\n        'num_stems': (1, 16),\n        'time_transformer_depth': (1, 32),\n        'freq_transformer_depth': (1, 32),\n        'dim_head': (1, 1024),\n        'heads': (1, 64),\n        'attn_dropout': (0.0, 1.0),\n        'ff_dropout': (0.0, 1.0),\n        'mlp_expansion_factor': (1, 16),\n        'sample_rate': (8000, 192000),\n        'num_bands': (8, 512),\n        'mask_estimator_depth': (1, 8),\n    }\n    \n    REQUIRED_PARAMETERS = {\n        'bs_roformer': ['dim', 'depth', 'freqs_per_bands'],\n        'mel_band_roformer': ['dim', 'depth', 'num_bands'],\n    }\n    ","chunkId":"audio_separator/separator/roformer/parameter_validator.py:7"}],[994,{"filePath":"audio_separator/separator/roformer/parameter_validator.py","startLine":106,"endLine":122,"chunkText":"        'mel_band_roformer': ['dim', 'depth', 'num_bands'],\n    }\n    \n    SUPPORTED_NORMALIZATION_TYPES = [\n        'layer_norm', 'batch_norm', 'rms_norm', 'group_norm', \n        'instance_norm', None, 'none'\n    ]\n    \n    def validate_required_parameters(self, config: Dict[str, Any], model_type: str) -> List[ValidationIssue]:\n        \"\"\"\n        Validate that all required parameters are present.\n        \n        Args:\n            config: Model configuration dictionary\n            model_type: Type of model (\"bs_roformer\" or \"mel_band_roformer\")\n            \n        Returns:","chunkId":"audio_separator/separator/roformer/parameter_validator.py:8"}],[995,{"filePath":"audio_separator/separator/roformer/parameter_validator.py","startLine":120,"endLine":133,"chunkText":"            model_type: Type of model (\"bs_roformer\" or \"mel_band_roformer\")\n            \n        Returns:\n            List of validation issues for missing required parameters\n        \"\"\"\n        issues = []\n        \n        required_params = self.REQUIRED_PARAMETERS.get(model_type, [])\n        \n        for param_name in required_params:\n            if param_name not in config:\n                issue = ValidationIssue(\n                    severity=ValidationSeverity.ERROR,\n                    parameter_name=param_name,","chunkId":"audio_separator/separator/roformer/parameter_validator.py:9"}],[996,{"filePath":"audio_separator/separator/roformer/parameter_validator.py","startLine":131,"endLine":142,"chunkText":"                issue = ValidationIssue(\n                    severity=ValidationSeverity.ERROR,\n                    parameter_name=param_name,\n                    message=f\"Required parameter '{param_name}' is missing for {model_type}\",\n                    suggested_fix=f\"Add '{param_name}' parameter with appropriate {self._get_expected_type_description(param_name)} value\",\n                    current_value=None,\n                    expected_value=self._get_expected_type_description(param_name)\n                )\n                issues.append(issue)\n        \n        return issues\n    ","chunkId":"audio_separator/separator/roformer/parameter_validator.py:10"}],[997,{"filePath":"audio_separator/separator/roformer/parameter_validator.py","startLine":140,"endLine":158,"chunkText":"        \n        return issues\n    \n    def validate_parameter_types(self, config: Dict[str, Any]) -> List[ValidationIssue]:\n        \"\"\"\n        Validate parameter types match expected types.\n        \n        Args:\n            config: Model configuration dictionary\n            \n        Returns:\n            List of validation issues for type mismatches\n        \"\"\"\n        issues = []\n        \n        for param_name, value in config.items():\n            if param_name in self.PARAMETER_TYPES:\n                expected_type = self.PARAMETER_TYPES[param_name]\n                ","chunkId":"audio_separator/separator/roformer/parameter_validator.py:11"}],[998,{"filePath":"audio_separator/separator/roformer/parameter_validator.py","startLine":156,"endLine":165,"chunkText":"            if param_name in self.PARAMETER_TYPES:\n                expected_type = self.PARAMETER_TYPES[param_name]\n                \n                if not self._is_correct_type(value, expected_type):\n                    issue = ValidationIssue(\n                        severity=ValidationSeverity.ERROR,\n                        parameter_name=param_name,\n                        message=f\"Parameter '{param_name}' has incorrect type\",\n                        suggested_fix=f\"Change '{param_name}' to {self._get_type_name(expected_type)}\",\n                        current_value=value,","chunkId":"audio_separator/separator/roformer/parameter_validator.py:12"}],[999,{"filePath":"audio_separator/separator/roformer/parameter_validator.py","startLine":163,"endLine":176,"chunkText":"                        message=f\"Parameter '{param_name}' has incorrect type\",\n                        suggested_fix=f\"Change '{param_name}' to {self._get_type_name(expected_type)}\",\n                        current_value=value,\n                        expected_value=self._get_type_name(expected_type)\n                    )\n                    issues.append(issue)\n        \n        return issues\n    \n    def validate_parameter_ranges(self, config: Dict[str, Any]) -> List[ValidationIssue]:\n        \"\"\"\n        Validate parameter values are within acceptable ranges.\n        \n        Args:","chunkId":"audio_separator/separator/roformer/parameter_validator.py:13"}],[1000,{"filePath":"audio_separator/separator/roformer/parameter_validator.py","startLine":174,"endLine":189,"chunkText":"        Validate parameter values are within acceptable ranges.\n        \n        Args:\n            config: Model configuration dictionary\n            \n        Returns:\n            List of validation issues for out-of-range values\n        \"\"\"\n        issues = []\n        \n        for param_name, value in config.items():\n            if param_name in self.PARAMETER_RANGES:\n                min_val, max_val = self.PARAMETER_RANGES[param_name]\n                \n                if isinstance(value, (int, float)) and not (min_val <= value <= max_val):\n                    issue = ValidationIssue(","chunkId":"audio_separator/separator/roformer/parameter_validator.py:14"}],[1001,{"filePath":"audio_separator/separator/roformer/parameter_validator.py","startLine":187,"endLine":195,"chunkText":"                \n                if isinstance(value, (int, float)) and not (min_val <= value <= max_val):\n                    issue = ValidationIssue(\n                        severity=ValidationSeverity.ERROR,\n                        parameter_name=param_name,\n                        message=f\"Parameter '{param_name}' value {value} is outside valid range [{min_val}, {max_val}]\",\n                        suggested_fix=f\"Set '{param_name}' to a value between {min_val} and {max_val}\",\n                        current_value=value,\n                        expected_value=f\"{min_val} <= value <= {max_val}\"","chunkId":"audio_separator/separator/roformer/parameter_validator.py:15"}],[1002,{"filePath":"audio_separator/separator/roformer/parameter_validator.py","startLine":193,"endLine":208,"chunkText":"                        suggested_fix=f\"Set '{param_name}' to a value between {min_val} and {max_val}\",\n                        current_value=value,\n                        expected_value=f\"{min_val} <= value <= {max_val}\"\n                    )\n                    issues.append(issue)\n        \n        return issues\n    \n    def validate_parameter_compatibility(self, config: Dict[str, Any]) -> List[ValidationIssue]:\n        \"\"\"\n        Validate that parameter combinations are compatible.\n        \n        Args:\n            config: Model configuration dictionary\n            \n        Returns:","chunkId":"audio_separator/separator/roformer/parameter_validator.py:16"}],[1003,{"filePath":"audio_separator/separator/roformer/parameter_validator.py","startLine":206,"endLine":218,"chunkText":"            config: Model configuration dictionary\n            \n        Returns:\n            List of validation issues for incompatible parameter combinations\n        \"\"\"\n        issues = []\n        \n        # Check sage_attention and flash_attn compatibility\n        if config.get('sage_attention', False) and config.get('flash_attn', False):\n            issue = ValidationIssue(\n                severity=ValidationSeverity.WARNING,\n                parameter_name=\"sage_attention, flash_attn\",\n                message=\"Using both sage_attention=True and flash_attn=True may cause conflicts\",","chunkId":"audio_separator/separator/roformer/parameter_validator.py:17"}],[1004,{"filePath":"audio_separator/separator/roformer/parameter_validator.py","startLine":216,"endLine":227,"chunkText":"                severity=ValidationSeverity.WARNING,\n                parameter_name=\"sage_attention, flash_attn\",\n                message=\"Using both sage_attention=True and flash_attn=True may cause conflicts\",\n                suggested_fix=\"Consider using only one attention mechanism\",\n                current_value=\"both True\",\n                expected_value=\"only one True\"\n            )\n            issues.append(issue)\n        \n        # Check freqs_per_bands consistency for BSRoformer\n        if 'freqs_per_bands' in config:\n            freqs = config['freqs_per_bands']","chunkId":"audio_separator/separator/roformer/parameter_validator.py:18"}],[1005,{"filePath":"audio_separator/separator/roformer/parameter_validator.py","startLine":225,"endLine":234,"chunkText":"        # Check freqs_per_bands consistency for BSRoformer\n        if 'freqs_per_bands' in config:\n            freqs = config['freqs_per_bands']\n            if isinstance(freqs, (list, tuple)) and len(freqs) > 0:\n                total_freqs = sum(freqs)\n                # Check if it looks like a reasonable STFT frequency count\n                if total_freqs < 64 or total_freqs > 4096:\n                    issue = ValidationIssue(\n                        severity=ValidationSeverity.WARNING,\n                        parameter_name=\"freqs_per_bands\",","chunkId":"audio_separator/separator/roformer/parameter_validator.py:19"}],[1006,{"filePath":"audio_separator/separator/roformer/parameter_validator.py","startLine":232,"endLine":241,"chunkText":"                    issue = ValidationIssue(\n                        severity=ValidationSeverity.WARNING,\n                        parameter_name=\"freqs_per_bands\",\n                        message=f\"Sum of freqs_per_bands ({total_freqs}) may be incompatible with typical STFT configurations\",\n                        suggested_fix=\"Verify that freqs_per_bands sum matches your STFT n_fft//2 + 1\",\n                        current_value=total_freqs,\n                        expected_value=\"64 to 4096 (typical range)\"\n                    )\n                    issues.append(issue)\n        ","chunkId":"audio_separator/separator/roformer/parameter_validator.py:20"}],[1007,{"filePath":"audio_separator/separator/roformer/parameter_validator.py","startLine":239,"endLine":249,"chunkText":"                    )\n                    issues.append(issue)\n        \n        # Check num_bands vs sample_rate for MelBandRoformer\n        if 'num_bands' in config and 'sample_rate' in config:\n            num_bands = config['num_bands']\n            sample_rate = config['sample_rate']\n            if num_bands > sample_rate // 100:  # Very rough heuristic\n                issue = ValidationIssue(\n                    severity=ValidationSeverity.WARNING,\n                    parameter_name=\"num_bands, sample_rate\",","chunkId":"audio_separator/separator/roformer/parameter_validator.py:21"}],[1008,{"filePath":"audio_separator/separator/roformer/parameter_validator.py","startLine":247,"endLine":258,"chunkText":"                issue = ValidationIssue(\n                    severity=ValidationSeverity.WARNING,\n                    parameter_name=\"num_bands, sample_rate\",\n                    message=f\"num_bands ({num_bands}) may be too high for sample_rate ({sample_rate})\",\n                    suggested_fix=\"Consider reducing num_bands or verify it's appropriate for your use case\",\n                    current_value=f\"bands={num_bands}, sr={sample_rate}\",\n                    expected_value=\"num_bands << sample_rate\"\n                )\n                issues.append(issue)\n        \n        return issues\n    ","chunkId":"audio_separator/separator/roformer/parameter_validator.py:22"}],[1009,{"filePath":"audio_separator/separator/roformer/parameter_validator.py","startLine":256,"endLine":273,"chunkText":"        \n        return issues\n    \n    def validate_normalization_config(self, norm_config: Any) -> List[ValidationIssue]:\n        \"\"\"\n        Validate normalization configuration.\n        \n        Args:\n            norm_config: Normalization configuration (may be string, dict, or None)\n            \n        Returns:\n            List of validation issues for normalization configuration\n        \"\"\"\n        issues = []\n        \n        if norm_config is not None:\n            if isinstance(norm_config, str):\n                if norm_config not in self.SUPPORTED_NORMALIZATION_TYPES:","chunkId":"audio_separator/separator/roformer/parameter_validator.py:23"}],[1010,{"filePath":"audio_separator/separator/roformer/parameter_validator.py","startLine":271,"endLine":279,"chunkText":"        if norm_config is not None:\n            if isinstance(norm_config, str):\n                if norm_config not in self.SUPPORTED_NORMALIZATION_TYPES:\n                    issue = ValidationIssue(\n                        severity=ValidationSeverity.ERROR,\n                        parameter_name=\"norm\",\n                        message=f\"Unsupported normalization type '{norm_config}'\",\n                        suggested_fix=f\"Use one of: {', '.join(str(t) for t in self.SUPPORTED_NORMALIZATION_TYPES if t is not None)}\",\n                        current_value=norm_config,","chunkId":"audio_separator/separator/roformer/parameter_validator.py:24"}],[1011,{"filePath":"audio_separator/separator/roformer/parameter_validator.py","startLine":277,"endLine":286,"chunkText":"                        message=f\"Unsupported normalization type '{norm_config}'\",\n                        suggested_fix=f\"Use one of: {', '.join(str(t) for t in self.SUPPORTED_NORMALIZATION_TYPES if t is not None)}\",\n                        current_value=norm_config,\n                        expected_value=\"supported normalization type\"\n                    )\n                    issues.append(issue)\n            elif not isinstance(norm_config, dict):\n                issue = ValidationIssue(\n                    severity=ValidationSeverity.ERROR,\n                    parameter_name=\"norm\",","chunkId":"audio_separator/separator/roformer/parameter_validator.py:25"}],[1012,{"filePath":"audio_separator/separator/roformer/parameter_validator.py","startLine":284,"endLine":296,"chunkText":"                issue = ValidationIssue(\n                    severity=ValidationSeverity.ERROR,\n                    parameter_name=\"norm\",\n                    message=f\"Normalization config must be string, dict, or None, got {type(norm_config).__name__}\",\n                    suggested_fix=\"Use a string normalization type or None\",\n                    current_value=norm_config,\n                    expected_value=\"string, dict, or None\"\n                )\n                issues.append(issue)\n        \n        return issues\n    \n    def get_parameter_defaults(self, model_type: str) -> Dict[str, Any]:","chunkId":"audio_separator/separator/roformer/parameter_validator.py:26"}],[1013,{"filePath":"audio_separator/separator/roformer/parameter_validator.py","startLine":294,"endLine":313,"chunkText":"        return issues\n    \n    def get_parameter_defaults(self, model_type: str) -> Dict[str, Any]:\n        \"\"\"\n        Get default values for optional parameters.\n        \n        Args:\n            model_type: Type of model (\"bs_roformer\" or \"mel_band_roformer\")\n            \n        Returns:\n            Dictionary of parameter names to default values\n        \"\"\"\n        defaults = {\n            'stereo': False,\n            'num_stems': 2,\n            'time_transformer_depth': 2,\n            'freq_transformer_depth': 2,\n            'dim_head': 64,\n            'heads': 8,\n            'attn_dropout': 0.0,","chunkId":"audio_separator/separator/roformer/parameter_validator.py:27"}],[1014,{"filePath":"audio_separator/separator/roformer/parameter_validator.py","startLine":311,"endLine":328,"chunkText":"            'dim_head': 64,\n            'heads': 8,\n            'attn_dropout': 0.0,\n            'ff_dropout': 0.0,\n            'flash_attn': True,\n            'mlp_expansion_factor': 4,\n            'sage_attention': False,\n            'zero_dc': True,\n            'use_torch_checkpoint': False,\n            'skip_connection': False,\n            'sample_rate': 44100,\n            'norm': None,\n        }\n        \n        # Add model-specific defaults\n        if model_type == 'bs_roformer':\n            defaults.update({\n                'freqs_per_bands': (2, 4, 8, 16, 32, 64),","chunkId":"audio_separator/separator/roformer/parameter_validator.py:28"}],[1015,{"filePath":"audio_separator/separator/roformer/parameter_validator.py","startLine":326,"endLine":343,"chunkText":"        if model_type == 'bs_roformer':\n            defaults.update({\n                'freqs_per_bands': (2, 4, 8, 16, 32, 64),\n                'mask_estimator_depth': 2,\n            })\n        elif model_type == 'mel_band_roformer':\n            defaults.update({\n                'num_bands': 64,\n            })\n        \n        return defaults\n    \n    def apply_parameter_defaults(self, config: Dict[str, Any], model_type: str) -> Dict[str, Any]:\n        \"\"\"\n        Apply default values to missing optional parameters.\n        \n        Args:\n            config: Model configuration dictionary","chunkId":"audio_separator/separator/roformer/parameter_validator.py:29"}],[1016,{"filePath":"audio_separator/separator/roformer/parameter_validator.py","startLine":341,"endLine":356,"chunkText":"        \n        Args:\n            config: Model configuration dictionary\n            model_type: Type of model\n            \n        Returns:\n            Configuration with defaults applied\n        \"\"\"\n        defaults = self.get_parameter_defaults(model_type)\n        result_config = defaults.copy()\n        result_config.update(config)  # Override defaults with provided values\n        \n        return result_config\n    \n    def _is_correct_type(self, value: Any, expected_type: Union[type, Tuple[type, ...]]) -> bool:\n        \"\"\"Check if value matches expected type(s).\"\"\"","chunkId":"audio_separator/separator/roformer/parameter_validator.py:30"}],[1017,{"filePath":"audio_separator/separator/roformer/parameter_validator.py","startLine":354,"endLine":366,"chunkText":"    \n    def _is_correct_type(self, value: Any, expected_type: Union[type, Tuple[type, ...]]) -> bool:\n        \"\"\"Check if value matches expected type(s).\"\"\"\n        if isinstance(expected_type, tuple):\n            return isinstance(value, expected_type)\n        return isinstance(value, expected_type)\n    \n    def _get_type_name(self, expected_type: Union[type, Tuple[type, ...]]) -> str:\n        \"\"\"Get human-readable type name.\"\"\"\n        if isinstance(expected_type, tuple):\n            return \" or \".join(t.__name__ for t in expected_type)\n        return expected_type.__name__\n    ","chunkId":"audio_separator/separator/roformer/parameter_validator.py:31"}],[1018,{"filePath":"audio_separator/separator/roformer/parameter_validator.py","startLine":364,"endLine":377,"chunkText":"            return \" or \".join(t.__name__ for t in expected_type)\n        return expected_type.__name__\n    \n    def _get_expected_type_description(self, param_name: str) -> str:\n        \"\"\"Get description of expected type for a parameter.\"\"\"\n        if param_name in self.PARAMETER_TYPES:\n            return self._get_type_name(self.PARAMETER_TYPES[param_name])\n        return \"appropriate type\"\n    \n    def validate_all(self, config: Dict[str, Any], model_type: str) -> List[ValidationIssue]:\n        \"\"\"\n        Run all validation checks on a configuration.\n        \n        Args:","chunkId":"audio_separator/separator/roformer/parameter_validator.py:32"}],[1019,{"filePath":"audio_separator/separator/roformer/parameter_validator.py","startLine":375,"endLine":390,"chunkText":"        Run all validation checks on a configuration.\n        \n        Args:\n            config: Model configuration dictionary\n            model_type: Type of model\n            \n        Returns:\n            List of all validation issues found\n        \"\"\"\n        all_issues = []\n        \n        all_issues.extend(self.validate_required_parameters(config, model_type))\n        all_issues.extend(self.validate_parameter_types(config))\n        all_issues.extend(self.validate_parameter_ranges(config))\n        all_issues.extend(self.validate_parameter_compatibility(config))\n        ","chunkId":"audio_separator/separator/roformer/parameter_validator.py:33"}],[1020,{"filePath":"audio_separator/separator/roformer/parameter_validator.py","startLine":388,"endLine":402,"chunkText":"        all_issues.extend(self.validate_parameter_ranges(config))\n        all_issues.extend(self.validate_parameter_compatibility(config))\n        \n        # Validate normalization if present\n        if 'norm' in config:\n            all_issues.extend(self.validate_normalization_config(config['norm']))\n        \n        return all_issues\n    \n    def validate_and_raise(self, config: Dict[str, Any], model_type: str) -> None:\n        \"\"\"\n        Validate configuration and raise ParameterValidationError if issues found.\n        \n        Args:\n            config: Model configuration dictionary","chunkId":"audio_separator/separator/roformer/parameter_validator.py:34"}],[1021,{"filePath":"audio_separator/separator/roformer/parameter_validator.py","startLine":400,"endLine":416,"chunkText":"        \n        Args:\n            config: Model configuration dictionary\n            model_type: Type of model\n            \n        Raises:\n            ParameterValidationError: If validation issues are found\n        \"\"\"\n        issues = self.validate_all(config, model_type)\n        \n        # Find first error (not warning)\n        error_issues = [issue for issue in issues if issue.severity == ValidationSeverity.ERROR]\n        \n        if error_issues:\n            first_error = error_issues[0]\n            raise ParameterValidationError(\n                parameter_name=first_error.parameter_name,","chunkId":"audio_separator/separator/roformer/parameter_validator.py:35"}],[1022,{"filePath":"audio_separator/separator/roformer/parameter_validator.py","startLine":414,"endLine":422,"chunkText":"            first_error = error_issues[0]\n            raise ParameterValidationError(\n                parameter_name=first_error.parameter_name,\n                expected_type=first_error.expected_value or \"valid value\",\n                actual_value=first_error.current_value,\n                suggested_fix=first_error.suggested_fix,\n                context=f\"{model_type} model validation\"\n            )\n","chunkId":"audio_separator/separator/roformer/parameter_validator.py:36"}],[1023,{"filePath":"audio_separator/separator/roformer/roformer_loader.py","startLine":0,"endLine":17,"chunkText":"\"\"\"Roformer model loader with simplified new-implementation only path.\"\"\"\nfrom typing import Dict, Any\nimport logging\nimport os\n\nfrom .model_loading_result import ModelLoadingResult, ImplementationVersion\nfrom .configuration_normalizer import ConfigurationNormalizer\nfrom .parameter_validation_error import ParameterValidationError\n\nlogger = logging.getLogger(__name__)\n\n\nclass RoformerLoader:\n    \"\"\"Main Roformer model loader (new implementation only).\"\"\"\n\n    def __init__(self):\n        self.config_normalizer = ConfigurationNormalizer()\n        self._loading_stats = {","chunkId":"audio_separator/separator/roformer/roformer_loader.py:0"}],[1024,{"filePath":"audio_separator/separator/roformer/roformer_loader.py","startLine":15,"endLine":30,"chunkText":"    def __init__(self):\n        self.config_normalizer = ConfigurationNormalizer()\n        self._loading_stats = {\n            'new_implementation_success': 0,\n            'total_failures': 0\n        }\n\n    def load_model(self,\n                   model_path: str,\n                   config: Dict[str, Any],\n                   device: str = 'cpu') -> ModelLoadingResult:\n        logger.info(f\"Loading Roformer model from {model_path}\")\n        try:\n            normalized_config = self.config_normalizer.normalize_from_file_path(\n                config, model_path, apply_defaults=True, validate=True\n            ","chunkId":"audio_separator/separator/roformer/roformer_loader.py:1"}],[1025,{"filePath":"audio_separator/separator/roformer/roformer_loader.py","startLine":28,"endLine":39,"chunkText":"            normalized_config = self.config_normalizer.normalize_from_file_path(\n                config, model_path, apply_defaults=True, validate=True\n            )\n            model_type = self.config_normalizer.detect_model_type(normalized_config)\n            logger.debug(f\"Detected model type: {model_type}\")\n        except ParameterValidationError as e:\n            logger.error(f\"Configuration validation failed: {e}\")\n            return ModelLoadingResult.failure_result(\n                error_message=f\"Config validation: {e}\",\n                implementation=ImplementationVersion.NEW,\n            )\n","chunkId":"audio_separator/separator/roformer/roformer_loader.py:2"}],[1026,{"filePath":"audio_separator/separator/roformer/roformer_loader.py","startLine":37,"endLine":49,"chunkText":"                implementation=ImplementationVersion.NEW,\n            )\n\n        try:\n            result = self._load_with_new_implementation(\n                model_path, normalized_config, model_type, device\n            )\n            self._loading_stats['new_implementation_success'] += 1\n            logger.info(f\"Successfully loaded {model_type} model with new implementation\")\n            return result\n        except (RuntimeError, ValueError, TypeError) as e:\n            logger.error(f\"New implementation failed: {e}\")\n            # Attempt legacy fallback using the original (pre-normalized) configuratio","chunkId":"audio_separator/separator/roformer/roformer_loader.py:3"}],[1027,{"filePath":"audio_separator/separator/roformer/roformer_loader.py","startLine":47,"endLine":58,"chunkText":"        except (RuntimeError, ValueError, TypeError) as e:\n            logger.error(f\"New implementation failed: {e}\")\n            # Attempt legacy fallback using the original (pre-normalized) configuration\n            try:\n                fallback_result = self._load_with_legacy_implementation(\n                    model_path=model_path,\n                    original_config=config,\n                    device=device,\n                    original_error=str(e)\n                )\n                logger.warning(\"Fell back to legacy Roformer implementation successfully\")\n                return fallback_result","chunkId":"audio_separator/separator/roformer/roformer_loader.py:4"}],[1028,{"filePath":"audio_separator/separator/roformer/roformer_loader.py","startLine":56,"endLine":64,"chunkText":"                )\n                logger.warning(\"Fell back to legacy Roformer implementation successfully\")\n                return fallback_result\n            except (RuntimeError, ValueError, TypeError) as fallback_error:\n                logger.error(f\"Legacy implementation also failed: {fallback_error}\")\n                self._loading_stats['total_failures'] += 1\n                return ModelLoadingResult.failure_result(\n                    error_message=f\"New implementation failed: {e}; Legacy fallback failed: {fallback_error}\",\n                    implementation=ImplementationVersion.NEW,","chunkId":"audio_separator/separator/roformer/roformer_loader.py:5"}],[1029,{"filePath":"audio_separator/separator/roformer/roformer_loader.py","startLine":62,"endLine":73,"chunkText":"                return ModelLoadingResult.failure_result(\n                    error_message=f\"New implementation failed: {e}; Legacy fallback failed: {fallback_error}\",\n                    implementation=ImplementationVersion.NEW,\n                )\n\n    def validate_configuration(self, config: Dict[str, Any], model_type: str) -> bool:\n        try:\n            _ = self.config_normalizer.normalize_config(\n                config, model_type, apply_defaults=False, validate=True\n            )\n            logger.debug(f\"Configuration validation passed for {model_type}\")\n            return True","chunkId":"audio_separator/separator/roformer/roformer_loader.py:6"}],[1030,{"filePath":"audio_separator/separator/roformer/roformer_loader.py","startLine":71,"endLine":83,"chunkText":"            )\n            logger.debug(f\"Configuration validation passed for {model_type}\")\n            return True\n        except ParameterValidationError as e:\n            logger.warning(f\"Configuration validation failed for {model_type}: {e}\")\n            return False\n        except (RuntimeError, ValueError) as e:\n            logger.error(f\"Unexpected error during validation: {e}\")\n            return False\n\n    def _load_with_new_implementation(self,\n                                      model_path: str,\n                                      config: Dict[str, Any],","chunkId":"audio_separator/separator/roformer/roformer_loader.py:7"}],[1031,{"filePath":"audio_separator/separator/roformer/roformer_loader.py","startLine":81,"endLine":93,"chunkText":"    def _load_with_new_implementation(self,\n                                      model_path: str,\n                                      config: Dict[str, Any],\n                                      model_type: str,\n                                      device: str) -> ModelLoadingResult:\n        import torch\n\n        try:\n            if model_type == \"bs_roformer\":\n                model = self._create_bs_roformer(config)\n            elif model_type == \"mel_band_roformer\":\n                model = self._create_mel_band_roformer(config)\n            else:","chunkId":"audio_separator/separator/roformer/roformer_loader.py:8"}],[1032,{"filePath":"audio_separator/separator/roformer/roformer_loader.py","startLine":91,"endLine":101,"chunkText":"            elif model_type == \"mel_band_roformer\":\n                model = self._create_mel_band_roformer(config)\n            else:\n                raise ValueError(f\"Unknown model type: {model_type}\")\n\n            if os.path.exists(model_path):\n                state_dict = torch.load(model_path, map_location=device)\n                if isinstance(state_dict, dict) and 'state_dict' in state_dict:\n                    model.load_state_dict(state_dict['state_dict'])\n                elif isinstance(state_dict, dict) and 'model' in state_dict:\n                    model.load_state_dict(state_dict['model'])","chunkId":"audio_separator/separator/roformer/roformer_loader.py:9"}],[1033,{"filePath":"audio_separator/separator/roformer/roformer_loader.py","startLine":99,"endLine":113,"chunkText":"                    model.load_state_dict(state_dict['state_dict'])\n                elif isinstance(state_dict, dict) and 'model' in state_dict:\n                    model.load_state_dict(state_dict['model'])\n                else:\n                    model.load_state_dict(state_dict)\n                logger.debug(f\"Loaded state dict from {model_path}\")\n\n            model.to(device)\n            model.eval()\n\n            result = ModelLoadingResult.success_result(\n                model=model,\n                implementation=ImplementationVersion.NEW,\n                config=config,\n            )","chunkId":"audio_separator/separator/roformer/roformer_loader.py:10"}],[1034,{"filePath":"audio_separator/separator/roformer/roformer_loader.py","startLine":111,"endLine":124,"chunkText":"                implementation=ImplementationVersion.NEW,\n                config=config,\n            )\n            result.add_model_info('model_type', model_type)\n            result.add_model_info('loading_method', 'direct')\n            result.add_model_info('device', device)\n            return result\n        except (RuntimeError, ValueError) as e:\n            logger.error(f\"Failed to create {model_type} model: {e}\")\n            raise\n\n    def _create_bs_roformer(self, config: Dict[str, Any]):\n        from ..uvr_lib_v5.roformer.bs_roformer import BSRoformer\n        model_args = {","chunkId":"audio_separator/separator/roformer/roformer_loader.py:11"}],[1035,{"filePath":"audio_separator/separator/roformer/roformer_loader.py","startLine":122,"endLine":132,"chunkText":"    def _create_bs_roformer(self, config: Dict[str, Any]):\n        from ..uvr_lib_v5.roformer.bs_roformer import BSRoformer\n        model_args = {\n            'dim': config['dim'],\n            'depth': config['depth'],\n            'stereo': config.get('stereo', False),\n            'num_stems': config.get('num_stems', 2),\n            'time_transformer_depth': config.get('time_transformer_depth', 2),\n            'freq_transformer_depth': config.get('freq_transformer_depth', 2),\n            'freqs_per_bands': config['freqs_per_bands'],\n            'dim_head': config.get('dim_head', 64),","chunkId":"audio_separator/separator/roformer/roformer_loader.py:12"}],[1036,{"filePath":"audio_separator/separator/roformer/roformer_loader.py","startLine":130,"endLine":139,"chunkText":"            'freq_transformer_depth': config.get('freq_transformer_depth', 2),\n            'freqs_per_bands': config['freqs_per_bands'],\n            'dim_head': config.get('dim_head', 64),\n            'heads': config.get('heads', 8),\n            'attn_dropout': config.get('attn_dropout', 0.0),\n            'ff_dropout': config.get('ff_dropout', 0.0),\n            'flash_attn': config.get('flash_attn', True),\n            'mlp_expansion_factor': config.get('mlp_expansion_factor', 4),\n            'sage_attention': config.get('sage_attention', False),\n            'zero_dc': config.get('zero_dc', True),","chunkId":"audio_separator/separator/roformer/roformer_loader.py:13"}],[1037,{"filePath":"audio_separator/separator/roformer/roformer_loader.py","startLine":137,"endLine":147,"chunkText":"            'mlp_expansion_factor': config.get('mlp_expansion_factor', 4),\n            'sage_attention': config.get('sage_attention', False),\n            'zero_dc': config.get('zero_dc', True),\n            'use_torch_checkpoint': config.get('use_torch_checkpoint', False),\n            'skip_connection': config.get('skip_connection', False),\n        }\n        if 'stft_n_fft' in config:\n            model_args['stft_n_fft'] = config['stft_n_fft']\n        if 'stft_hop_length' in config:\n            model_args['stft_hop_length'] = config['stft_hop_length']\n        if 'stft_win_length' in config:","chunkId":"audio_separator/separator/roformer/roformer_loader.py:14"}],[1038,{"filePath":"audio_separator/separator/roformer/roformer_loader.py","startLine":145,"endLine":156,"chunkText":"        if 'stft_hop_length' in config:\n            model_args['stft_hop_length'] = config['stft_hop_length']\n        if 'stft_win_length' in config:\n            model_args['stft_win_length'] = config['stft_win_length']\n        logger.debug(f\"Creating BSRoformer with args: {list(model_args.keys())}\")\n        return BSRoformer(**model_args)\n\n    def _create_mel_band_roformer(self, config: Dict[str, Any]):\n        from ..uvr_lib_v5.roformer.mel_band_roformer import MelBandRoformer\n        model_args = {\n            'dim': config['dim'],\n            'depth': config['depth'],","chunkId":"audio_separator/separator/roformer/roformer_loader.py:15"}],[1039,{"filePath":"audio_separator/separator/roformer/roformer_loader.py","startLine":154,"endLine":164,"chunkText":"        model_args = {\n            'dim': config['dim'],\n            'depth': config['depth'],\n            'stereo': config.get('stereo', False),\n            'num_stems': config.get('num_stems', 2),\n            'time_transformer_depth': config.get('time_transformer_depth', 2),\n            'freq_transformer_depth': config.get('freq_transformer_depth', 2),\n            'num_bands': config['num_bands'],\n            'dim_head': config.get('dim_head', 64),\n            'heads': config.get('heads', 8),\n            'attn_dropout': config.get('attn_dropout', 0.0),","chunkId":"audio_separator/separator/roformer/roformer_loader.py:16"}],[1040,{"filePath":"audio_separator/separator/roformer/roformer_loader.py","startLine":162,"endLine":170,"chunkText":"            'dim_head': config.get('dim_head', 64),\n            'heads': config.get('heads', 8),\n            'attn_dropout': config.get('attn_dropout', 0.0),\n            'ff_dropout': config.get('ff_dropout', 0.0),\n            'flash_attn': config.get('flash_attn', True),\n            'mlp_expansion_factor': config.get('mlp_expansion_factor', 4),\n            'sage_attention': config.get('sage_attention', False),\n            'zero_dc': config.get('zero_dc', True),\n            'use_torch_checkpoint': config.get('use_torch_checkpoint', False),","chunkId":"audio_separator/separator/roformer/roformer_loader.py:17"}],[1041,{"filePath":"audio_separator/separator/roformer/roformer_loader.py","startLine":169,"endLine":182,"chunkText":"            'zero_dc': config.get('zero_dc', True),\n            'use_torch_checkpoint': config.get('use_torch_checkpoint', False),\n            'skip_connection': config.get('skip_connection', False),\n        }\n        if 'sample_rate' in config:\n            model_args['sample_rate'] = config['sample_rate']\n        # Optional parameters commonly present in legacy configs\n        for optional_key in [\n            'mask_estimator_depth',\n            'stft_n_fft',\n            'stft_hop_length',\n            'stft_win_length',\n            'stft_normalized',\n            'stft_window_fn',","chunkId":"audio_separator/separator/roformer/roformer_loader.py:18"}],[1042,{"filePath":"audio_separator/separator/roformer/roformer_loader.py","startLine":180,"endLine":192,"chunkText":"            'stft_win_length',\n            'stft_normalized',\n            'stft_window_fn',\n            'multi_stft_resolution_loss_weight',\n            'multi_stft_resolutions_window_sizes',\n            'multi_stft_hop_size',\n            'multi_stft_normalized',\n            'multi_stft_window_fn',\n            'match_input_audio_length',\n        ]:\n            if optional_key in config:\n                model_args[optional_key] = config[optional_key]\n        # Note: fmin and fmax are defined in config classes but not accepted by current constructor","chunkId":"audio_separator/separator/roformer/roformer_loader.py:19"}],[1043,{"filePath":"audio_separator/separator/roformer/roformer_loader.py","startLine":190,"endLine":199,"chunkText":"            if optional_key in config:\n                model_args[optional_key] = config[optional_key]\n        # Note: fmin and fmax are defined in config classes but not accepted by current constructor\n        logger.debug(f\"Creating MelBandRoformer with args: {list(model_args.keys())}\")\n        return MelBandRoformer(**model_args)\n\n    def _load_with_legacy_implementation(self,\n                                          model_path: str,\n                                          original_config: Dict[str, Any],\n                                          device: str,","chunkId":"audio_separator/separator/roformer/roformer_loader.py:20"}],[1044,{"filePath":"audio_separator/separator/roformer/roformer_loader.py","startLine":197,"endLine":209,"chunkText":"                                          model_path: str,\n                                          original_config: Dict[str, Any],\n                                          device: str,\n                                          original_error: str) -> ModelLoadingResult:\n        \"\"\"\n        Attempt to load the model using the legacy direct-constructor path\n        for maximum backward compatibility with existing checkpoints.\n        \"\"\"\n        import torch\n\n        # Use nested 'model' section if present; otherwise assume flat\n        model_cfg = original_config.get('model', original_config)\n","chunkId":"audio_separator/separator/roformer/roformer_loader.py:21"}],[1045,{"filePath":"audio_separator/separator/roformer/roformer_loader.py","startLine":207,"endLine":219,"chunkText":"        # Use nested 'model' section if present; otherwise assume flat\n        model_cfg = original_config.get('model', original_config)\n\n        # Determine model type from config\n        if 'num_bands' in model_cfg:\n            from ..uvr_lib_v5.roformer.mel_band_roformer import MelBandRoformer\n            model = MelBandRoformer(**model_cfg)\n        elif 'freqs_per_bands' in model_cfg:\n            from ..uvr_lib_v5.roformer.bs_roformer import BSRoformer\n            model = BSRoformer(**model_cfg)\n        else:\n            raise ValueError(\"Unknown Roformer model type in legacy configuration\")\n","chunkId":"audio_separator/separator/roformer/roformer_loader.py:22"}],[1046,{"filePath":"audio_separator/separator/roformer/roformer_loader.py","startLine":217,"endLine":231,"chunkText":"        else:\n            raise ValueError(\"Unknown Roformer model type in legacy configuration\")\n\n        # Load checkpoint as raw state dict (legacy behavior)\n        try:\n            checkpoint = torch.load(model_path, map_location='cpu', weights_only=True)\n        except TypeError:\n            # For older torch versions without weights_only\n            checkpoint = torch.load(model_path, map_location='cpu')\n\n        model.load_state_dict(checkpoint)\n        model.to(device).eval()\n\n        return ModelLoadingResult.fallback_success_result(\n            model=model,","chunkId":"audio_separator/separator/roformer/roformer_loader.py:23"}],[1047,{"filePath":"audio_separator/separator/roformer/roformer_loader.py","startLine":229,"endLine":246,"chunkText":"\n        return ModelLoadingResult.fallback_success_result(\n            model=model,\n            original_error=original_error,\n            config=original_config,\n        )\n\n    def get_loading_stats(self) -> Dict[str, int]:\n        return self._loading_stats.copy()\n\n    def reset_loading_stats(self) -> None:\n        self._loading_stats = {\n            'new_implementation_success': 0,\n            'total_failures': 0\n        }\n\n    def detect_model_type(self, model_path: str) -> str:\n        model_path_lower = model_path.lower()","chunkId":"audio_separator/separator/roformer/roformer_loader.py:24"}],[1048,{"filePath":"audio_separator/separator/roformer/roformer_loader.py","startLine":244,"endLine":253,"chunkText":"\n    def detect_model_type(self, model_path: str) -> str:\n        model_path_lower = model_path.lower()\n        if any(indicator in model_path_lower for indicator in ['bs_roformer', 'bs-roformer', 'bsroformer']):\n            return \"bs_roformer\"\n        if any(indicator in model_path_lower for indicator in ['mel_band_roformer', 'mel-band-roformer', 'melband']):\n            return \"mel_band_roformer\"\n        if 'roformer' in model_path_lower:\n            logger.warning(f\"Generic 'roformer' detected in {model_path}, defaulting to bs_roformer\")\n            return \"bs_roformer\"","chunkId":"audio_separator/separator/roformer/roformer_loader.py:25"}],[1049,{"filePath":"audio_separator/separator/roformer/roformer_loader.py","startLine":251,"endLine":263,"chunkText":"        if 'roformer' in model_path_lower:\n            logger.warning(f\"Generic 'roformer' detected in {model_path}, defaulting to bs_roformer\")\n            return \"bs_roformer\"\n        raise ValueError(f\"Cannot determine Roformer model type from path: {model_path}\")\n\n    def get_default_configuration(self, model_type: str) -> Dict[str, Any]:\n        if model_type == \"bs_roformer\":\n            return {\n                'dim': 512,\n                'depth': 12,\n                'stereo': False,\n                'num_stems': 2,\n                'time_transformer_depth': 2,","chunkId":"audio_separator/separator/roformer/roformer_loader.py:26"}],[1050,{"filePath":"audio_separator/separator/roformer/roformer_loader.py","startLine":261,"endLine":275,"chunkText":"                'stereo': False,\n                'num_stems': 2,\n                'time_transformer_depth': 2,\n                'freq_transformer_depth': 2,\n                'freqs_per_bands': (2, 4, 8, 16, 32, 64),\n                'dim_head': 64,\n                'heads': 8,\n                'attn_dropout': 0.0,\n                'ff_dropout': 0.0,\n                'flash_attn': True,\n                'mlp_expansion_factor': 4,\n                'sage_attention': False,\n                'zero_dc': True,\n                'use_torch_checkpoint': False,\n                'skip_connection': False,","chunkId":"audio_separator/separator/roformer/roformer_loader.py:27"}],[1051,{"filePath":"audio_separator/separator/roformer/roformer_loader.py","startLine":273,"endLine":289,"chunkText":"                'zero_dc': True,\n                'use_torch_checkpoint': False,\n                'skip_connection': False,\n                'mask_estimator_depth': 2,\n                'stft_n_fft': 2048,\n                'stft_hop_length': 512,\n                'stft_win_length': 2048,\n            }\n        elif model_type == \"mel_band_roformer\":\n            return {\n                'dim': 512,\n                'depth': 12,\n                'stereo': False,\n                'num_stems': 2,\n                'time_transformer_depth': 2,\n                'freq_transformer_depth': 2,\n                'num_bands': 64,","chunkId":"audio_separator/separator/roformer/roformer_loader.py:28"}],[1052,{"filePath":"audio_separator/separator/roformer/roformer_loader.py","startLine":287,"endLine":300,"chunkText":"                'time_transformer_depth': 2,\n                'freq_transformer_depth': 2,\n                'num_bands': 64,\n                'dim_head': 64,\n                'heads': 8,\n                'attn_dropout': 0.0,\n                'ff_dropout': 0.0,\n                'flash_attn': True,\n                'mlp_expansion_factor': 4,\n                'sage_attention': False,\n                'zero_dc': True,\n                'use_torch_checkpoint': False,\n                'skip_connection': False,\n                'sample_rate': 44100,","chunkId":"audio_separator/separator/roformer/roformer_loader.py:29"}],[1053,{"filePath":"audio_separator/separator/roformer/roformer_loader.py","startLine":298,"endLine":305,"chunkText":"                'use_torch_checkpoint': False,\n                'skip_connection': False,\n                'sample_rate': 44100,\n                # Note: fmin and fmax are not implemented in MelBandRoformer constructor\n            }\n        else:\n            raise ValueError(f\"Unknown model type: {model_type}\")\n","chunkId":"audio_separator/separator/roformer/roformer_loader.py:30"}],[1054,{"filePath":"audio_separator/separator/separator.py","startLine":0,"endLine":27,"chunkText":"\"\"\" This file contains the Separator class, to facilitate the separation of stems from audio. \"\"\"\n\nfrom importlib import metadata, resources\nimport os\nimport sys\nimport platform\nimport subprocess\nimport time\nimport logging\nimport warnings\nimport importlib\nimport io\nimport re\nfrom typing import Optional\n\nimport hashlib\nimport json\nimport yaml\nimport requests\nimport torch\nimport torch.amp.autocast_mode as autocast_mode\nimport onnxruntime as ort\nfrom tqdm import tqdm\n\n\nclass Separator:\n    \"\"\"\n    The Separator class is designed to facilitate the separation of audio sources from a given audio file.","chunkId":"audio_separator/separator/separator.py:0"}],[1055,{"filePath":"audio_separator/separator/separator.py","startLine":25,"endLine":32,"chunkText":"class Separator:\n    \"\"\"\n    The Separator class is designed to facilitate the separation of audio sources from a given audio file.\n    It supports various separation architectures and models, including MDX, VR, and Demucs. The class provides\n    functionalities to configure separation parameters, load models, and perform audio source separation.\n    It also handles logging, normalization, and output formatting of the separated audio stems.\n\n    The actual separation task is handled by one of the architecture-specific classes in the `architectures` module;","chunkId":"audio_separator/separator/separator.py:1"}],[1056,{"filePath":"audio_separator/separator/separator.py","startLine":30,"endLine":39,"chunkText":"    It also handles logging, normalization, and output formatting of the separated audio stems.\n\n    The actual separation task is handled by one of the architecture-specific classes in the `architectures` module;\n    this class is responsible for initialising logging, configuring hardware acceleration, loading the model,\n    initiating the separation process and passing outputs back to the caller.\n\n    Common Attributes:\n        log_level (int): The logging level.\n        log_formatter (logging.Formatter): The logging formatter.\n        model_file_dir (str): The directory where model files are stored.","chunkId":"audio_separator/separator/separator.py:2"}],[1057,{"filePath":"audio_separator/separator/separator.py","startLine":37,"endLine":44,"chunkText":"        log_level (int): The logging level.\n        log_formatter (logging.Formatter): The logging formatter.\n        model_file_dir (str): The directory where model files are stored.\n        output_dir (str): The directory where output files will be saved.\n        output_format (str): The format of the output audio file.\n        output_bitrate (str): The bitrate of the output audio file.\n        amplification_threshold (float): The threshold for audio amplification.\n        normalization_threshold (float): The threshold for audio normalization.","chunkId":"audio_separator/separator/separator.py:3"}],[1058,{"filePath":"audio_separator/separator/separator.py","startLine":42,"endLine":50,"chunkText":"        output_bitrate (str): The bitrate of the output audio file.\n        amplification_threshold (float): The threshold for audio amplification.\n        normalization_threshold (float): The threshold for audio normalization.\n        output_single_stem (str): Option to output a single stem.\n        invert_using_spec (bool): Flag to invert using spectrogram.\n        sample_rate (int): The sample rate of the audio.\n        use_soundfile (bool): Use soundfile for audio writing, can solve OOM issues.\n        use_autocast (bool): Flag to use PyTorch autocast for faster inference.\n","chunkId":"audio_separator/separator/separator.py:4"}],[1059,{"filePath":"audio_separator/separator/separator.py","startLine":48,"endLine":60,"chunkText":"        use_soundfile (bool): Use soundfile for audio writing, can solve OOM issues.\n        use_autocast (bool): Flag to use PyTorch autocast for faster inference.\n\n    MDX Architecture Specific Attributes:\n        hop_length (int): The hop length for STFT.\n        segment_size (int): The segment size for processing.\n        overlap (float): The overlap between segments.\n        batch_size (int): The batch size for processing.\n        enable_denoise (bool): Flag to enable or disable denoising.\n\n    VR Architecture Specific Attributes & Defaults:\n        batch_size: 16\n        window_size: 512","chunkId":"audio_separator/separator/separator.py:5"}],[1060,{"filePath":"audio_separator/separator/separator.py","startLine":58,"endLine":80,"chunkText":"    VR Architecture Specific Attributes & Defaults:\n        batch_size: 16\n        window_size: 512\n        aggression: 5\n        enable_tta: False\n        enable_post_process: False\n        post_process_threshold: 0.2\n        high_end_process: False\n\n    Demucs Architecture Specific Attributes & Defaults:\n        segment_size: \"Default\"\n        shifts: 2\n        overlap: 0.25\n        segments_enabled: True\n\n    MDXC Architecture Specific Attributes & Defaults:\n        segment_size: 256\n        override_model_segment_size: False\n        batch_size: 1\n        overlap: 8\n        pitch_shift: 0\n    \"\"\"\n","chunkId":"audio_separator/separator/separator.py:6"}],[1061,{"filePath":"audio_separator/separator/separator.py","startLine":78,"endLine":97,"chunkText":"        pitch_shift: 0\n    \"\"\"\n\n    def __init__(\n        self,\n        log_level=logging.INFO,\n        log_formatter=None,\n        model_file_dir=\"/tmp/audio-separator-models/\",\n        output_dir=None,\n        output_format=\"WAV\",\n        output_bitrate=None,\n        normalization_threshold=0.9,\n        amplification_threshold=0.0,\n        output_single_stem=None,\n        invert_using_spec=False,\n        sample_rate=44100,\n        use_soundfile=False,\n        use_autocast=False,\n        use_directml=False,\n        chunk_duration=None,","chunkId":"audio_separator/separator/separator.py:7"}],[1062,{"filePath":"audio_separator/separator/separator.py","startLine":95,"endLine":100,"chunkText":"        use_autocast=False,\n        use_directml=False,\n        chunk_duration=None,\n        mdx_params={\"hop_length\": 1024, \"segment_size\": 256, \"overlap\": 0.25, \"batch_size\": 1, \"enable_denoise\": False},\n        vr_params={\"batch_size\": 1, \"window_size\": 512, \"aggression\": 5, \"enable_tta\": False, \"enable_post_process\": False, \"post_process_threshold\": 0.2, \"high_end_process\": False},\n        demucs_params={\"segment_size\": \"Default\", \"shifts\": 2, \"overlap\": 0.25, \"segments_enabled\": True},","chunkId":"audio_separator/separator/separator.py:8"}],[1063,{"filePath":"audio_separator/separator/separator.py","startLine":98,"endLine":104,"chunkText":"        mdx_params={\"hop_length\": 1024, \"segment_size\": 256, \"overlap\": 0.25, \"batch_size\": 1, \"enable_denoise\": False},\n        vr_params={\"batch_size\": 1, \"window_size\": 512, \"aggression\": 5, \"enable_tta\": False, \"enable_post_process\": False, \"post_process_threshold\": 0.2, \"high_end_process\": False},\n        demucs_params={\"segment_size\": \"Default\", \"shifts\": 2, \"overlap\": 0.25, \"segments_enabled\": True},\n        mdxc_params={\"segment_size\": 256, \"override_model_segment_size\": False, \"batch_size\": 1, \"overlap\": 8, \"pitch_shift\": 0},\n        info_only=False,\n    ):\n        \"\"\"Initialize the separator.\"\"\"","chunkId":"audio_separator/separator/separator.py:9"}],[1064,{"filePath":"audio_separator/separator/separator.py","startLine":102,"endLine":119,"chunkText":"        info_only=False,\n    ):\n        \"\"\"Initialize the separator.\"\"\"\n        self.logger = logging.getLogger(__name__)\n        self.logger.setLevel(log_level)\n        self.log_level = log_level\n        self.log_formatter = log_formatter\n\n        self.log_handler = logging.StreamHandler()\n\n        if self.log_formatter is None:\n            self.log_formatter = logging.Formatter(\"%(asctime)s - %(levelname)s - %(module)s - %(message)s\")\n\n        self.log_handler.setFormatter(self.log_formatter)\n\n        if not self.logger.hasHandlers():\n            self.logger.addHandler(self.log_handler)\n","chunkId":"audio_separator/separator/separator.py:10"}],[1065,{"filePath":"audio_separator/separator/separator.py","startLine":117,"endLine":129,"chunkText":"        if not self.logger.hasHandlers():\n            self.logger.addHandler(self.log_handler)\n\n        # Filter out noisy warnings from PyTorch for users who don't care about them\n        if log_level > logging.DEBUG:\n            warnings.filterwarnings(\"ignore\")\n\n        # Skip initialization logs if info_only is True\n        if not info_only:\n            package_version = self.get_package_distribution(\"audio-separator\").version\n            self.logger.info(f\"Separator version {package_version} instantiating with output_dir: {output_dir}, output_format: {output_format}\")\n\n        if output_dir is None:","chunkId":"audio_separator/separator/separator.py:11"}],[1066,{"filePath":"audio_separator/separator/separator.py","startLine":127,"endLine":139,"chunkText":"            self.logger.info(f\"Separator version {package_version} instantiating with output_dir: {output_dir}, output_format: {output_format}\")\n\n        if output_dir is None:\n            output_dir = os.getcwd()\n            if not info_only:\n                self.logger.info(\"Output directory not specified. Using current working directory.\")\n\n        self.output_dir = output_dir\n\n        # Check for environment variable to override model_file_dir\n        env_model_dir = os.environ.get(\"AUDIO_SEPARATOR_MODEL_DIR\")\n        if env_model_dir:\n            self.model_file_dir = env_model_dir","chunkId":"audio_separator/separator/separator.py:12"}],[1067,{"filePath":"audio_separator/separator/separator.py","startLine":137,"endLine":146,"chunkText":"        env_model_dir = os.environ.get(\"AUDIO_SEPARATOR_MODEL_DIR\")\n        if env_model_dir:\n            self.model_file_dir = env_model_dir\n            self.logger.info(f\"Using model directory from AUDIO_SEPARATOR_MODEL_DIR env var: {self.model_file_dir}\")\n            if not os.path.exists(self.model_file_dir):\n                raise FileNotFoundError(f\"The specified model directory does not exist: {self.model_file_dir}\")\n        else:\n            self.logger.info(f\"Using model directory from model_file_dir parameter: {model_file_dir}\")\n            self.model_file_dir = model_file_dir\n","chunkId":"audio_separator/separator/separator.py:13"}],[1068,{"filePath":"audio_separator/separator/separator.py","startLine":144,"endLine":157,"chunkText":"            self.logger.info(f\"Using model directory from model_file_dir parameter: {model_file_dir}\")\n            self.model_file_dir = model_file_dir\n\n        # Create the model directory if it does not exist\n        os.makedirs(self.model_file_dir, exist_ok=True)\n        os.makedirs(self.output_dir, exist_ok=True)\n\n        self.output_format = output_format\n        self.output_bitrate = output_bitrate\n\n        if self.output_format is None:\n            self.output_format = \"WAV\"\n\n        self.normalization_threshold = normalization_threshold","chunkId":"audio_separator/separator/separator.py:14"}],[1069,{"filePath":"audio_separator/separator/separator.py","startLine":155,"endLine":165,"chunkText":"            self.output_format = \"WAV\"\n\n        self.normalization_threshold = normalization_threshold\n        if normalization_threshold <= 0 or normalization_threshold > 1:\n            raise ValueError(\"The normalization_threshold must be greater than 0 and less than or equal to 1.\")\n\n        self.amplification_threshold = amplification_threshold\n        if amplification_threshold < 0 or amplification_threshold > 1:\n            raise ValueError(\"The amplification_threshold must be greater than or equal to 0 and less than or equal to 1.\")\n\n        self.output_single_stem = output_single_stem","chunkId":"audio_separator/separator/separator.py:15"}],[1070,{"filePath":"audio_separator/separator/separator.py","startLine":163,"endLine":173,"chunkText":"            raise ValueError(\"The amplification_threshold must be greater than or equal to 0 and less than or equal to 1.\")\n\n        self.output_single_stem = output_single_stem\n        if output_single_stem is not None:\n            self.logger.debug(f\"Single stem output requested, so only one output file ({output_single_stem}) will be written\")\n\n        self.invert_using_spec = invert_using_spec\n        if self.invert_using_spec:\n            self.logger.debug(f\"Secondary step will be inverted using spectogram rather than waveform. This may improve quality but is slightly slower.\")\n\n        try:","chunkId":"audio_separator/separator/separator.py:16"}],[1071,{"filePath":"audio_separator/separator/separator.py","startLine":171,"endLine":179,"chunkText":"            self.logger.debug(f\"Secondary step will be inverted using spectogram rather than waveform. This may improve quality but is slightly slower.\")\n\n        try:\n            self.sample_rate = int(sample_rate)\n            if self.sample_rate <= 0:\n                raise ValueError(f\"The sample rate setting is {self.sample_rate} but it must be a non-zero whole number.\")\n            if self.sample_rate > 12800000:\n                raise ValueError(f\"The sample rate setting is {self.sample_rate}. Enter something less ambitious.\")\n        except ValueError:","chunkId":"audio_separator/separator/separator.py:17"}],[1072,{"filePath":"audio_separator/separator/separator.py","startLine":177,"endLine":188,"chunkText":"            if self.sample_rate > 12800000:\n                raise ValueError(f\"The sample rate setting is {self.sample_rate}. Enter something less ambitious.\")\n        except ValueError:\n            raise ValueError(\"The sample rate must be a non-zero whole number. Please provide a valid integer.\")\n\n        self.use_soundfile = use_soundfile\n        self.use_autocast = use_autocast\n        self.use_directml = use_directml\n\n        self.chunk_duration = chunk_duration\n        if chunk_duration is not None:\n            if chunk_duration <= 0:","chunkId":"audio_separator/separator/separator.py:18"}],[1073,{"filePath":"audio_separator/separator/separator.py","startLine":186,"endLine":198,"chunkText":"        self.chunk_duration = chunk_duration\n        if chunk_duration is not None:\n            if chunk_duration <= 0:\n                raise ValueError(\"chunk_duration must be greater than 0\")\n\n        # These are parameters which users may want to configure so we expose them to the top-level Separator class,\n        # even though they are specific to a single model architecture\n        self.arch_specific_params = {\"MDX\": mdx_params, \"VR\": vr_params, \"Demucs\": demucs_params, \"MDXC\": mdxc_params}\n\n        self.torch_device = None\n        self.torch_device_cpu = None\n        self.torch_device_mps = None\n","chunkId":"audio_separator/separator/separator.py:19"}],[1074,{"filePath":"audio_separator/separator/separator.py","startLine":196,"endLine":213,"chunkText":"        self.torch_device_cpu = None\n        self.torch_device_mps = None\n\n        self.onnx_execution_provider = None\n        self.model_instance = None\n\n        self.model_is_uvr_vip = False\n        self.model_friendly_name = None\n\n        if not info_only:\n            self.setup_accelerated_inferencing_device()\n\n    def setup_accelerated_inferencing_device(self):\n        \"\"\"\n        This method sets up the PyTorch and/or ONNX Runtime inferencing device, using GPU hardware acceleration if available.\n        \"\"\"\n        system_info = self.get_system_info()\n        self.check_ffmpeg_installed()","chunkId":"audio_separator/separator/separator.py:20"}],[1075,{"filePath":"audio_separator/separator/separator.py","startLine":211,"endLine":225,"chunkText":"        \"\"\"\n        system_info = self.get_system_info()\n        self.check_ffmpeg_installed()\n        self.log_onnxruntime_packages()\n        self.setup_torch_device(system_info)\n\n    def get_system_info(self):\n        \"\"\"\n        This method logs the system information, including the operating system, CPU archutecture and Python version\n        \"\"\"\n        os_name = platform.system()\n        os_version = platform.version()\n        self.logger.info(f\"Operating System: {os_name} {os_version}\")\n\n        system_info = platform.uname()","chunkId":"audio_separator/separator/separator.py:21"}],[1076,{"filePath":"audio_separator/separator/separator.py","startLine":223,"endLine":236,"chunkText":"        self.logger.info(f\"Operating System: {os_name} {os_version}\")\n\n        system_info = platform.uname()\n        self.logger.info(f\"System: {system_info.system} Node: {system_info.node} Release: {system_info.release} Machine: {system_info.machine} Proc: {system_info.processor}\")\n\n        python_version = platform.python_version()\n        self.logger.info(f\"Python Version: {python_version}\")\n\n        pytorch_version = torch.__version__\n        self.logger.info(f\"PyTorch Version: {pytorch_version}\")\n        return system_info\n\n    def check_ffmpeg_installed(self):\n        \"\"\"","chunkId":"audio_separator/separator/separator.py:22"}],[1077,{"filePath":"audio_separator/separator/separator.py","startLine":234,"endLine":244,"chunkText":"\n    def check_ffmpeg_installed(self):\n        \"\"\"\n        This method checks if ffmpeg is installed and logs its version.\n        \"\"\"\n        try:\n            ffmpeg_version_output = subprocess.check_output([\"ffmpeg\", \"-version\"], text=True)\n            first_line = ffmpeg_version_output.splitlines()[0]\n            self.logger.info(f\"FFmpeg installed: {first_line}\")\n        except FileNotFoundError:\n            self.logger.error(\"FFmpeg is not installed. Please install FFmpeg to use this package.\")","chunkId":"audio_separator/separator/separator.py:23"}],[1078,{"filePath":"audio_separator/separator/separator.py","startLine":242,"endLine":251,"chunkText":"            self.logger.info(f\"FFmpeg installed: {first_line}\")\n        except FileNotFoundError:\n            self.logger.error(\"FFmpeg is not installed. Please install FFmpeg to use this package.\")\n            # Raise an exception if this is being run by a user, as ffmpeg is required for pydub to write audio\n            # but if we're just running unit tests in CI, no reason to throw\n            if \"PYTEST_CURRENT_TEST\" not in os.environ:\n                raise\n\n    def log_onnxruntime_packages(self):\n        \"\"\"","chunkId":"audio_separator/separator/separator.py:24"}],[1079,{"filePath":"audio_separator/separator/separator.py","startLine":249,"endLine":259,"chunkText":"\n    def log_onnxruntime_packages(self):\n        \"\"\"\n        This method logs the ONNX Runtime package versions, including the GPU and Silicon packages if available.\n        \"\"\"\n        onnxruntime_gpu_package = self.get_package_distribution(\"onnxruntime-gpu\")\n        onnxruntime_silicon_package = self.get_package_distribution(\"onnxruntime-silicon\")\n        onnxruntime_cpu_package = self.get_package_distribution(\"onnxruntime\")\n        onnxruntime_dml_package = self.get_package_distribution(\"onnxruntime-directml\")\n\n        if onnxruntime_gpu_package is not None:","chunkId":"audio_separator/separator/separator.py:25"}],[1080,{"filePath":"audio_separator/separator/separator.py","startLine":257,"endLine":264,"chunkText":"        onnxruntime_dml_package = self.get_package_distribution(\"onnxruntime-directml\")\n\n        if onnxruntime_gpu_package is not None:\n            self.logger.info(f\"ONNX Runtime GPU package installed with version: {onnxruntime_gpu_package.version}\")\n        if onnxruntime_silicon_package is not None:\n            self.logger.info(f\"ONNX Runtime Silicon package installed with version: {onnxruntime_silicon_package.version}\")\n        if onnxruntime_cpu_package is not None:\n            self.logger.info(f\"ONNX Runtime CPU package installed with version: {onnxruntime_cpu_package.version}\")","chunkId":"audio_separator/separator/separator.py:26"}],[1081,{"filePath":"audio_separator/separator/separator.py","startLine":262,"endLine":269,"chunkText":"            self.logger.info(f\"ONNX Runtime Silicon package installed with version: {onnxruntime_silicon_package.version}\")\n        if onnxruntime_cpu_package is not None:\n            self.logger.info(f\"ONNX Runtime CPU package installed with version: {onnxruntime_cpu_package.version}\")\n        if onnxruntime_dml_package is not None:\n            self.logger.info(f\"ONNX Runtime DirectML package installed with version: {onnxruntime_dml_package.version}\")\n\n    def setup_torch_device(self, system_info):\n        \"\"\"","chunkId":"audio_separator/separator/separator.py:27"}],[1082,{"filePath":"audio_separator/separator/separator.py","startLine":267,"endLine":280,"chunkText":"\n    def setup_torch_device(self, system_info):\n        \"\"\"\n        This method sets up the PyTorch and/or ONNX Runtime inferencing device, using GPU hardware acceleration if available.\n        \"\"\"\n        hardware_acceleration_enabled = False\n        ort_providers = ort.get_available_providers()\n        has_torch_dml_installed = self.get_package_distribution(\"torch_directml\")\n\n        self.torch_device_cpu = torch.device(\"cpu\")\n\n        if torch.cuda.is_available():\n            self.configure_cuda(ort_providers)\n            hardware_acceleration_enabled = True","chunkId":"audio_separator/separator/separator.py:28"}],[1083,{"filePath":"audio_separator/separator/separator.py","startLine":278,"endLine":289,"chunkText":"        if torch.cuda.is_available():\n            self.configure_cuda(ort_providers)\n            hardware_acceleration_enabled = True\n        elif hasattr(torch.backends, \"mps\") and torch.backends.mps.is_available() and system_info.processor == \"arm\":\n            self.configure_mps(ort_providers)\n            hardware_acceleration_enabled = True\n        elif self.use_directml and has_torch_dml_installed:\n            import torch_directml\n            if torch_directml.is_available():\n                self.configure_dml(ort_providers)\n                hardware_acceleration_enabled = True\n","chunkId":"audio_separator/separator/separator.py:29"}],[1084,{"filePath":"audio_separator/separator/separator.py","startLine":287,"endLine":298,"chunkText":"                self.configure_dml(ort_providers)\n                hardware_acceleration_enabled = True\n\n        if not hardware_acceleration_enabled:\n            self.logger.info(\"No hardware acceleration could be configured, running in CPU mode\")\n            self.torch_device = self.torch_device_cpu\n            self.onnx_execution_provider = [\"CPUExecutionProvider\"]\n\n    def configure_cuda(self, ort_providers):\n        \"\"\"\n        This method configures the CUDA device for PyTorch and ONNX Runtime, if available.\n        \"\"\"","chunkId":"audio_separator/separator/separator.py:30"}],[1085,{"filePath":"audio_separator/separator/separator.py","startLine":296,"endLine":306,"chunkText":"        \"\"\"\n        This method configures the CUDA device for PyTorch and ONNX Runtime, if available.\n        \"\"\"\n        self.logger.info(\"CUDA is available in Torch, setting Torch device to CUDA\")\n        self.torch_device = torch.device(\"cuda\")\n        if \"CUDAExecutionProvider\" in ort_providers:\n            self.logger.info(\"ONNXruntime has CUDAExecutionProvider available, enabling acceleration\")\n            self.onnx_execution_provider = [\"CUDAExecutionProvider\"]\n        else:\n            self.logger.warning(\"CUDAExecutionProvider not available in ONNXruntime, so acceleration will NOT be enabled\")\n","chunkId":"audio_separator/separator/separator.py:31"}],[1086,{"filePath":"audio_separator/separator/separator.py","startLine":304,"endLine":316,"chunkText":"        else:\n            self.logger.warning(\"CUDAExecutionProvider not available in ONNXruntime, so acceleration will NOT be enabled\")\n\n    def configure_mps(self, ort_providers):\n        \"\"\"\n        This method configures the Apple Silicon MPS/CoreML device for PyTorch and ONNX Runtime, if available.\n        \"\"\"\n        self.logger.info(\"Apple Silicon MPS/CoreML is available in Torch and processor is ARM, setting Torch device to MPS\")\n        self.torch_device_mps = torch.device(\"mps\")\n\n        self.torch_device = self.torch_device_mps\n\n        if \"CoreMLExecutionProvider\" in ort_providers:","chunkId":"audio_separator/separator/separator.py:32"}],[1087,{"filePath":"audio_separator/separator/separator.py","startLine":314,"endLine":325,"chunkText":"        self.torch_device = self.torch_device_mps\n\n        if \"CoreMLExecutionProvider\" in ort_providers:\n            self.logger.info(\"ONNXruntime has CoreMLExecutionProvider available, enabling acceleration\")\n            self.onnx_execution_provider = [\"CoreMLExecutionProvider\"]\n        else:\n            self.logger.warning(\"CoreMLExecutionProvider not available in ONNXruntime, so acceleration will NOT be enabled\")\n\n    def configure_dml(self, ort_providers):\n        \"\"\"\n        This method configures the DirectML device for PyTorch and ONNX Runtime, if available.\n        \"\"\"","chunkId":"audio_separator/separator/separator.py:33"}],[1088,{"filePath":"audio_separator/separator/separator.py","startLine":323,"endLine":334,"chunkText":"        \"\"\"\n        This method configures the DirectML device for PyTorch and ONNX Runtime, if available.\n        \"\"\"\n        import torch_directml\n        self.logger.info(\"DirectML is available in Torch, setting Torch device to DirectML\")\n        self.torch_device_dml = torch_directml.device() \n        self.torch_device = self.torch_device_dml\n\n        if \"DmlExecutionProvider\" in ort_providers:\n            self.logger.info(\"ONNXruntime has DmlExecutionProvider available, enabling acceleration\")\n            self.onnx_execution_provider = [\"DmlExecutionProvider\"]\n        else:","chunkId":"audio_separator/separator/separator.py:34"}],[1089,{"filePath":"audio_separator/separator/separator.py","startLine":332,"endLine":343,"chunkText":"            self.logger.info(\"ONNXruntime has DmlExecutionProvider available, enabling acceleration\")\n            self.onnx_execution_provider = [\"DmlExecutionProvider\"]\n        else:\n            self.logger.warning(\"DmlExecutionProvider not available in ONNXruntime, so acceleration will NOT be enabled\")\n\n    def get_package_distribution(self, package_name):\n        \"\"\"\n        This method returns the package distribution for a given package name if installed, or None otherwise.\n        \"\"\"\n        try:\n            return metadata.distribution(package_name)\n        except metadata.PackageNotFoundError:","chunkId":"audio_separator/separator/separator.py:35"}],[1090,{"filePath":"audio_separator/separator/separator.py","startLine":341,"endLine":357,"chunkText":"        try:\n            return metadata.distribution(package_name)\n        except metadata.PackageNotFoundError:\n            self.logger.debug(f\"Python package: {package_name} not installed\")\n            return None\n\n    def get_model_hash(self, model_path):\n        \"\"\"\n        This method returns the MD5 hash of a given model file.\n        \"\"\"\n        self.logger.debug(f\"Calculating hash of model file {model_path}\")\n        # Use the specific byte count from the original logic\n        BYTES_TO_HASH = 10000 * 1024  # 10,240,000 bytes\n\n        try:\n            file_size = os.path.getsize(model_path)\n","chunkId":"audio_separator/separator/separator.py:36"}],[1091,{"filePath":"audio_separator/separator/separator.py","startLine":355,"endLine":365,"chunkText":"        try:\n            file_size = os.path.getsize(model_path)\n\n            with open(model_path, \"rb\") as f:\n                if file_size < BYTES_TO_HASH:\n                    # Hash the entire file if smaller than the target byte count\n                    self.logger.debug(f\"File size {file_size} < {BYTES_TO_HASH}, hashing entire file.\")\n                    hash_value = hashlib.md5(f.read()).hexdigest()\n                else:\n                    # Seek to the specific position before the end (from the beginning) and hash\n                    seek_pos = file_size - BYTES_TO_HASH","chunkId":"audio_separator/separator/separator.py:37"}],[1092,{"filePath":"audio_separator/separator/separator.py","startLine":363,"endLine":374,"chunkText":"                else:\n                    # Seek to the specific position before the end (from the beginning) and hash\n                    seek_pos = file_size - BYTES_TO_HASH\n                    self.logger.debug(f\"File size {file_size} >= {BYTES_TO_HASH}, seeking to {seek_pos} and hashing remaining bytes.\")\n                    f.seek(seek_pos, io.SEEK_SET)\n                    hash_value = hashlib.md5(f.read()).hexdigest()\n\n            # Log the calculated hash\n            self.logger.info(f\"Hash of model file {model_path} is {hash_value}\")\n            return hash_value\n\n        except FileNotFoundError:","chunkId":"audio_separator/separator/separator.py:38"}],[1093,{"filePath":"audio_separator/separator/separator.py","startLine":372,"endLine":384,"chunkText":"            return hash_value\n\n        except FileNotFoundError:\n            self.logger.error(f\"Model file not found at {model_path}\")\n            raise # Re-raise the specific error\n        except Exception as e:\n            # Catch other potential errors (e.g., permissions, other IOErrors)\n            self.logger.error(f\"Error calculating hash for {model_path}: {e}\")\n            raise # Re-raise other errors\n\n    def download_file_if_not_exists(self, url, output_path):\n        \"\"\"\n        This method downloads a file from a given URL to a given output path, if the file does not already exist.","chunkId":"audio_separator/separator/separator.py:39"}],[1094,{"filePath":"audio_separator/separator/separator.py","startLine":384,"endLine":395,"chunkText":"        This method downloads a file from a given URL to a given output path, if the file does not already exist.\n        \"\"\"\n\n        if os.path.isfile(output_path):\n            self.logger.debug(f\"File already exists at {output_path}, skipping download\")\n            return\n\n        self.logger.debug(f\"Downloading file from {url} to {output_path} with timeout 300s\")\n        response = requests.get(url, stream=True, timeout=300)\n\n        if response.status_code == 200:\n            total_size_in_bytes = int(response.headers.get(\"content-length\", 0))","chunkId":"audio_separator/separator/separator.py:40"}],[1095,{"filePath":"audio_separator/separator/separator.py","startLine":393,"endLine":406,"chunkText":"\n        if response.status_code == 200:\n            total_size_in_bytes = int(response.headers.get(\"content-length\", 0))\n            progress_bar = tqdm(total=total_size_in_bytes, unit=\"iB\", unit_scale=True)\n\n            with open(output_path, \"wb\") as f:\n                for chunk in response.iter_content(chunk_size=8192):\n                    progress_bar.update(len(chunk))\n                    f.write(chunk)\n            progress_bar.close()\n        else:\n            raise RuntimeError(f\"Failed to download file from {url}, response code: {response.status_code}\")\n\n    def list_supported_model_files(self):","chunkId":"audio_separator/separator/separator.py:41"}],[1096,{"filePath":"audio_separator/separator/separator.py","startLine":404,"endLine":418,"chunkText":"            raise RuntimeError(f\"Failed to download file from {url}, response code: {response.status_code}\")\n\n    def list_supported_model_files(self):\n        \"\"\"\n        This method lists the supported model files for audio-separator, by fetching the same file UVR uses to list these.\n        Also includes model performance scores where available.\n\n        Example response object:\n\n        {\n            \"MDX\": {\n                \"MDX-Net Model VIP: UVR-MDX-NET-Inst_full_292\": {\n                \"filename\": \"UVR-MDX-NET-Inst_full_292.onnx\",\n                \"scores\": {\n                    \"vocals\": {","chunkId":"audio_separator/separator/separator.py:42"}],[1097,{"filePath":"audio_separator/separator/separator.py","startLine":416,"endLine":432,"chunkText":"                \"filename\": \"UVR-MDX-NET-Inst_full_292.onnx\",\n                \"scores\": {\n                    \"vocals\": {\n                    \"SDR\": 10.6497,\n                    \"SIR\": 20.3786,\n                    \"SAR\": 10.692,\n                    \"ISR\": 14.848\n                    },\n                    \"instrumental\": {\n                    \"SDR\": 15.2149,\n                    \"SIR\": 25.6075,\n                    \"SAR\": 17.1363,\n                    \"ISR\": 17.7893\n                    }\n                },\n                \"download_files\": [\n                    \"UVR-MDX-NET-Inst_full_292.onnx\"","chunkId":"audio_separator/separator/separator.py:43"}],[1098,{"filePath":"audio_separator/separator/separator.py","startLine":430,"endLine":448,"chunkText":"                },\n                \"download_files\": [\n                    \"UVR-MDX-NET-Inst_full_292.onnx\"\n                ]\n                }\n            },\n            \"Demucs\": {\n                \"Demucs v4: htdemucs_ft\": {\n                \"filename\": \"htdemucs_ft.yaml\",\n                \"scores\": {\n                    \"vocals\": {\n                    \"SDR\": 11.2685,\n                    \"SIR\": 21.257,\n                    \"SAR\": 11.0359,\n                    \"ISR\": 19.3753\n                    },\n                    \"drums\": {\n                    \"SDR\": 13.235,\n                    \"SIR\": 23.3053,","chunkId":"audio_separator/separator/separator.py:44"}],[1099,{"filePath":"audio_separator/separator/separator.py","startLine":446,"endLine":460,"chunkText":"                    \"drums\": {\n                    \"SDR\": 13.235,\n                    \"SIR\": 23.3053,\n                    \"SAR\": 13.0313,\n                    \"ISR\": 17.2889\n                    },\n                    \"bass\": {\n                    \"SDR\": 9.72743,\n                    \"SIR\": 19.5435,\n                    \"SAR\": 9.20801,\n                    \"ISR\": 13.5037\n                    }\n                },\n                \"download_files\": [\n                    \"https://dl.fbaipublicfiles.com/demucs/hybrid_transformer/f7e0c4bc-ba3fe64a.th\",","chunkId":"audio_separator/separator/separator.py:45"}],[1100,{"filePath":"audio_separator/separator/separator.py","startLine":458,"endLine":465,"chunkText":"                },\n                \"download_files\": [\n                    \"https://dl.fbaipublicfiles.com/demucs/hybrid_transformer/f7e0c4bc-ba3fe64a.th\",\n                    \"https://dl.fbaipublicfiles.com/demucs/hybrid_transformer/d12395a8-e57c48e6.th\",\n                    \"https://dl.fbaipublicfiles.com/demucs/hybrid_transformer/92cfc3b6-ef3bcb9c.th\",\n                    \"https://dl.fbaipublicfiles.com/demucs/hybrid_transformer/04573f0d-f3cf25b2.th\",\n                    \"https://github.com/TRvlvr/model_repo/releases/download/all_public_uvr_models/htdemucs_ft.yaml\"\n                ]","chunkId":"audio_separator/separator/separator.py:46"}],[1101,{"filePath":"audio_separator/separator/separator.py","startLine":463,"endLine":476,"chunkText":"                    \"https://dl.fbaipublicfiles.com/demucs/hybrid_transformer/04573f0d-f3cf25b2.th\",\n                    \"https://github.com/TRvlvr/model_repo/releases/download/all_public_uvr_models/htdemucs_ft.yaml\"\n                ]\n                }\n            },\n            \"MDXC\": {\n                \"MDX23C Model: MDX23C-InstVoc HQ\": {\n                \"filename\": \"MDX23C-8KFFT-InstVoc_HQ.ckpt\",\n                \"scores\": {\n                    \"vocals\": {\n                    \"SDR\": 11.9504,\n                    \"SIR\": 23.1166,\n                    \"SAR\": 12.093,\n                    \"ISR\": 15.4782","chunkId":"audio_separator/separator/separator.py:47"}],[1102,{"filePath":"audio_separator/separator/separator.py","startLine":474,"endLine":492,"chunkText":"                    \"SIR\": 23.1166,\n                    \"SAR\": 12.093,\n                    \"ISR\": 15.4782\n                    },\n                    \"instrumental\": {\n                    \"SDR\": 16.3035,\n                    \"SIR\": 26.6161,\n                    \"SAR\": 18.5167,\n                    \"ISR\": 18.3939\n                    }\n                },\n                \"download_files\": [\n                    \"MDX23C-8KFFT-InstVoc_HQ.ckpt\",\n                    \"model_2_stem_full_band_8k.yaml\"\n                ]\n                }\n            }\n        }\n        \"\"\"","chunkId":"audio_separator/separator/separator.py:48"}],[1103,{"filePath":"audio_separator/separator/separator.py","startLine":490,"endLine":503,"chunkText":"            }\n        }\n        \"\"\"\n        download_checks_path = os.path.join(self.model_file_dir, \"download_checks.json\")\n\n        self.download_file_if_not_exists(\"https://raw.githubusercontent.com/TRvlvr/application_data/main/filelists/download_checks.json\", download_checks_path)\n\n        model_downloads_list = json.load(open(download_checks_path, encoding=\"utf-8\"))\n        self.logger.debug(f\"UVR model download list loaded\")\n\n        # Load the model scores with error handling\n        model_scores = {}\n        try:\n            with resources.open_text(\"audio_separator\", \"models-scores.json\") as f:","chunkId":"audio_separator/separator/separator.py:49"}],[1104,{"filePath":"audio_separator/separator/separator.py","startLine":501,"endLine":510,"chunkText":"        model_scores = {}\n        try:\n            with resources.open_text(\"audio_separator\", \"models-scores.json\") as f:\n                model_scores = json.load(f)\n            self.logger.debug(f\"Model scores loaded\")\n        except json.JSONDecodeError as e:\n            self.logger.warning(f\"Failed to load model scores: {str(e)}\")\n            self.logger.warning(\"Continuing without model scores\")\n\n        # Only show Demucs v4 models as we've only implemented support for v4","chunkId":"audio_separator/separator/separator.py:50"}],[1105,{"filePath":"audio_separator/separator/separator.py","startLine":508,"endLine":516,"chunkText":"            self.logger.warning(\"Continuing without model scores\")\n\n        # Only show Demucs v4 models as we've only implemented support for v4\n        filtered_demucs_v4 = {key: value for key, value in model_downloads_list[\"demucs_download_list\"].items() if key.startswith(\"Demucs v4\")}\n\n        # Modified Demucs handling to use YAML files as identifiers and include download files\n        demucs_models = {}\n        for name, files in filtered_demucs_v4.items():\n            # Find the YAML file in the model files","chunkId":"audio_separator/separator/separator.py:51"}],[1106,{"filePath":"audio_separator/separator/separator.py","startLine":514,"endLine":523,"chunkText":"        demucs_models = {}\n        for name, files in filtered_demucs_v4.items():\n            # Find the YAML file in the model files\n            yaml_file = next((filename for filename in files.keys() if filename.endswith(\".yaml\")), None)\n            if yaml_file:\n                model_score_data = model_scores.get(yaml_file, {})\n                demucs_models[name] = {\n                    \"filename\": yaml_file,\n                    \"scores\": model_score_data.get(\"median_scores\", {}),\n                    \"stems\": model_score_data.get(\"stems\", []),","chunkId":"audio_separator/separator/separator.py:52"}],[1107,{"filePath":"audio_separator/separator/separator.py","startLine":521,"endLine":530,"chunkText":"                    \"filename\": yaml_file,\n                    \"scores\": model_score_data.get(\"median_scores\", {}),\n                    \"stems\": model_score_data.get(\"stems\", []),\n                    \"target_stem\": model_score_data.get(\"target_stem\"),\n                    \"download_files\": list(files.values()),  # List of all download URLs/filenames\n                }\n\n        # Load the JSON file using importlib.resources\n        with resources.open_text(\"audio_separator\", \"models.json\") as f:\n            audio_separator_models_list = json.load(f)","chunkId":"audio_separator/separator/separator.py:53"}],[1108,{"filePath":"audio_separator/separator/separator.py","startLine":530,"endLine":541,"chunkText":"            audio_separator_models_list = json.load(f)\n        self.logger.debug(f\"Audio-Separator model list loaded\")\n\n        # Return object with list of model names\n        model_files_grouped_by_type = {\n            \"VR\": {\n                name: {\n                    \"filename\": filename,\n                    \"scores\": model_scores.get(filename, {}).get(\"median_scores\", {}),\n                    \"stems\": model_scores.get(filename, {}).get(\"stems\", []),\n                    \"target_stem\": model_scores.get(filename, {}).get(\"target_stem\"),\n                    \"download_files\": [filename],","chunkId":"audio_separator/separator/separator.py:54"}],[1109,{"filePath":"audio_separator/separator/separator.py","startLine":539,"endLine":548,"chunkText":"                    \"stems\": model_scores.get(filename, {}).get(\"stems\", []),\n                    \"target_stem\": model_scores.get(filename, {}).get(\"target_stem\"),\n                    \"download_files\": [filename],\n                }  # Just the filename for VR models\n                for name, filename in {**model_downloads_list[\"vr_download_list\"], **audio_separator_models_list[\"vr_download_list\"]}.items()\n            },\n            \"MDX\": {\n                name: {\n                    \"filename\": filename,\n                    \"scores\": model_scores.get(filename, {}).get(\"median_scores\", {}),","chunkId":"audio_separator/separator/separator.py:55"}],[1110,{"filePath":"audio_separator/separator/separator.py","startLine":546,"endLine":552,"chunkText":"                name: {\n                    \"filename\": filename,\n                    \"scores\": model_scores.get(filename, {}).get(\"median_scores\", {}),\n                    \"stems\": model_scores.get(filename, {}).get(\"stems\", []),\n                    \"target_stem\": model_scores.get(filename, {}).get(\"target_stem\"),\n                    \"download_files\": [filename],\n                }  # Just the filename for MDX models","chunkId":"audio_separator/separator/separator.py:56"}],[1111,{"filePath":"audio_separator/separator/separator.py","startLine":550,"endLine":558,"chunkText":"                    \"target_stem\": model_scores.get(filename, {}).get(\"target_stem\"),\n                    \"download_files\": [filename],\n                }  # Just the filename for MDX models\n                for name, filename in {**model_downloads_list[\"mdx_download_list\"], **model_downloads_list[\"mdx_download_vip_list\"], **audio_separator_models_list[\"mdx_download_list\"]}.items()\n            },\n            \"Demucs\": demucs_models,\n            \"MDXC\": {\n                name: {\n                    \"filename\": next(iter(files.keys())),","chunkId":"audio_separator/separator/separator.py:57"}],[1112,{"filePath":"audio_separator/separator/separator.py","startLine":556,"endLine":564,"chunkText":"            \"MDXC\": {\n                name: {\n                    \"filename\": next(iter(files.keys())),\n                    \"scores\": model_scores.get(next(iter(files.keys())), {}).get(\"median_scores\", {}),\n                    \"stems\": model_scores.get(next(iter(files.keys())), {}).get(\"stems\", []),\n                    \"target_stem\": model_scores.get(next(iter(files.keys())), {}).get(\"target_stem\"),\n                    \"download_files\": list(files.keys()) + list(files.values()),  # List of both model filenames and config filenames\n                }\n                for name, files in {","chunkId":"audio_separator/separator/separator.py:58"}],[1113,{"filePath":"audio_separator/separator/separator.py","startLine":562,"endLine":573,"chunkText":"                    \"download_files\": list(files.keys()) + list(files.values()),  # List of both model filenames and config filenames\n                }\n                for name, files in {\n                    **model_downloads_list[\"mdx23c_download_list\"],\n                    **model_downloads_list[\"mdx23c_download_vip_list\"],\n                    **model_downloads_list[\"roformer_download_list\"],\n                    **audio_separator_models_list[\"mdx23c_download_list\"],\n                    **audio_separator_models_list[\"roformer_download_list\"],\n                }.items()\n            },\n        }\n","chunkId":"audio_separator/separator/separator.py:59"}],[1114,{"filePath":"audio_separator/separator/separator.py","startLine":571,"endLine":581,"chunkText":"            },\n        }\n\n        return model_files_grouped_by_type\n\n    def print_uvr_vip_message(self):\n        \"\"\"\n        This method prints a message to the user if they have downloaded a VIP model, reminding them to support Anjok07 on Patreon.\n        \"\"\"\n        if self.model_is_uvr_vip:\n            self.logger.warning(f\"The model: '{self.model_friendly_name}' is a VIP model, intended by Anjok07 for access by paying subscribers only.\")","chunkId":"audio_separator/separator/separator.py:60"}],[1115,{"filePath":"audio_separator/separator/separator.py","startLine":579,"endLine":586,"chunkText":"        \"\"\"\n        if self.model_is_uvr_vip:\n            self.logger.warning(f\"The model: '{self.model_friendly_name}' is a VIP model, intended by Anjok07 for access by paying subscribers only.\")\n            self.logger.warning(\"If you are not already subscribed, please consider supporting the developer of UVR, Anjok07 by subscribing here: https://patreon.com/uvr\")\n\n    def download_model_files(self, model_filename):\n        \"\"\"\n        This method downloads the model files for a given model filename, if they are not already present.","chunkId":"audio_separator/separator/separator.py:61"}],[1116,{"filePath":"audio_separator/separator/separator.py","startLine":584,"endLine":592,"chunkText":"    def download_model_files(self, model_filename):\n        \"\"\"\n        This method downloads the model files for a given model filename, if they are not already present.\n        Returns tuple of (model_filename, model_type, model_friendly_name, model_path, yaml_config_filename)\n        \"\"\"\n        model_path = os.path.join(self.model_file_dir, f\"{model_filename}\")\n\n        supported_model_files_grouped = self.list_supported_model_files()\n        public_model_repo_url_prefix = \"https://github.com/TRvlvr/model_repo/releases/download/all_public_uvr_models\"","chunkId":"audio_separator/separator/separator.py:62"}],[1117,{"filePath":"audio_separator/separator/separator.py","startLine":590,"endLine":599,"chunkText":"\n        supported_model_files_grouped = self.list_supported_model_files()\n        public_model_repo_url_prefix = \"https://github.com/TRvlvr/model_repo/releases/download/all_public_uvr_models\"\n        vip_model_repo_url_prefix = \"https://github.com/Anjok0109/ai_magic/releases/download/v5\"\n        audio_separator_models_repo_url_prefix = \"https://github.com/nomadkaraoke/python-audio-separator/releases/download/model-configs\"\n\n        yaml_config_filename = None\n\n        self.logger.debug(f\"Searching for model_filename {model_filename} in supported_model_files_grouped\")\n","chunkId":"audio_separator/separator/separator.py:63"}],[1118,{"filePath":"audio_separator/separator/separator.py","startLine":597,"endLine":606,"chunkText":"\n        self.logger.debug(f\"Searching for model_filename {model_filename} in supported_model_files_grouped\")\n\n        # Iterate through model types (MDX, Demucs, MDXC)\n        for model_type, models in supported_model_files_grouped.items():\n            # Iterate through each model in this type\n            for model_friendly_name, model_info in models.items():\n                self.model_is_uvr_vip = \"VIP\" in model_friendly_name\n                model_repo_url_prefix = vip_model_repo_url_prefix if self.model_is_uvr_vip else public_model_repo_url_prefix\n","chunkId":"audio_separator/separator/separator.py:64"}],[1119,{"filePath":"audio_separator/separator/separator.py","startLine":604,"endLine":612,"chunkText":"                self.model_is_uvr_vip = \"VIP\" in model_friendly_name\n                model_repo_url_prefix = vip_model_repo_url_prefix if self.model_is_uvr_vip else public_model_repo_url_prefix\n\n                # Check if this model matches our target filename\n                if model_info[\"filename\"] == model_filename or model_filename in model_info[\"download_files\"]:\n                    self.logger.debug(f\"Found matching model: {model_friendly_name}\")\n                    self.model_friendly_name = model_friendly_name\n                    self.print_uvr_vip_message()\n","chunkId":"audio_separator/separator/separator.py:65"}],[1120,{"filePath":"audio_separator/separator/separator.py","startLine":610,"endLine":618,"chunkText":"                    self.model_friendly_name = model_friendly_name\n                    self.print_uvr_vip_message()\n\n                    # Download each required file for this model\n                    for file_to_download in model_info[\"download_files\"]:\n                        # For URLs, extract just the filename portion\n                        if file_to_download.startswith(\"http\"):\n                            filename = file_to_download.split(\"/\")[-1]\n                            download_path = os.path.join(self.model_file_dir, filename)","chunkId":"audio_separator/separator/separator.py:66"}],[1121,{"filePath":"audio_separator/separator/separator.py","startLine":616,"endLine":625,"chunkText":"                        if file_to_download.startswith(\"http\"):\n                            filename = file_to_download.split(\"/\")[-1]\n                            download_path = os.path.join(self.model_file_dir, filename)\n                            self.download_file_if_not_exists(file_to_download, download_path)\n                            continue\n\n                        download_path = os.path.join(self.model_file_dir, file_to_download)\n\n                        # For MDXC models, handle YAML config files specially\n                        if model_type == \"MDXC\" and file_to_download.endswith(\".yaml\")","chunkId":"audio_separator/separator/separator.py:67"}],[1122,{"filePath":"audio_separator/separator/separator.py","startLine":623,"endLine":630,"chunkText":"\n                        # For MDXC models, handle YAML config files specially\n                        if model_type == \"MDXC\" and file_to_download.endswith(\".yaml\"):\n                            yaml_config_filename = file_to_download\n                            try:\n                                yaml_url = f\"{model_repo_url_prefix}/mdx_model_data/mdx_c_configs/{file_to_download}\"\n                                self.download_file_if_not_exists(yaml_url, download_path)\n                            except RuntimeError:","chunkId":"audio_separator/separator/separator.py:68"}],[1123,{"filePath":"audio_separator/separator/separator.py","startLine":628,"endLine":633,"chunkText":"                                yaml_url = f\"{model_repo_url_prefix}/mdx_model_data/mdx_c_configs/{file_to_download}\"\n                                self.download_file_if_not_exists(yaml_url, download_path)\n                            except RuntimeError:\n                                self.logger.debug(\"YAML config not found in UVR repo, trying audio-separator models repo...\")\n                                yaml_url = f\"{audio_separator_models_repo_url_prefix}/{file_to_download}\"\n                                self.download_file_if_not_exists(yaml_url, download_path)","chunkId":"audio_separator/separator/separator.py:69"}],[1124,{"filePath":"audio_separator/separator/separator.py","startLine":631,"endLine":638,"chunkText":"                                self.logger.debug(\"YAML config not found in UVR repo, trying audio-separator models repo...\")\n                                yaml_url = f\"{audio_separator_models_repo_url_prefix}/{file_to_download}\"\n                                self.download_file_if_not_exists(yaml_url, download_path)\n                            continue\n\n                        # For regular model files, try UVR repo first, then audio-separator repo\n                        try:\n                            download_url = f\"{model_repo_url_prefix}/{file_to_download}\"","chunkId":"audio_separator/separator/separator.py:70"}],[1125,{"filePath":"audio_separator/separator/separator.py","startLine":636,"endLine":642,"chunkText":"                        # For regular model files, try UVR repo first, then audio-separator repo\n                        try:\n                            download_url = f\"{model_repo_url_prefix}/{file_to_download}\"\n                            self.download_file_if_not_exists(download_url, download_path)\n                        except RuntimeError:\n                            self.logger.debug(\"Model not found in UVR repo, trying audio-separator models repo...\")\n                            download_url = f\"{audio_separator_models_repo_url_prefix}/{file_to_download}\"","chunkId":"audio_separator/separator/separator.py:71"}],[1126,{"filePath":"audio_separator/separator/separator.py","startLine":640,"endLine":648,"chunkText":"                        except RuntimeError:\n                            self.logger.debug(\"Model not found in UVR repo, trying audio-separator models repo...\")\n                            download_url = f\"{audio_separator_models_repo_url_prefix}/{file_to_download}\"\n                            self.download_file_if_not_exists(download_url, download_path)\n\n                    return model_filename, model_type, model_friendly_name, model_path, yaml_config_filename\n\n        raise ValueError(f\"Model file {model_filename} not found in supported model files\")\n","chunkId":"audio_separator/separator/separator.py:72"}],[1127,{"filePath":"audio_separator/separator/separator.py","startLine":646,"endLine":655,"chunkText":"\n        raise ValueError(f\"Model file {model_filename} not found in supported model files\")\n\n    def load_model_data_from_yaml(self, yaml_config_filename):\n        \"\"\"\n        This method loads model-specific parameters from the YAML file for that model.\n        The parameters in the YAML are critical to inferencing, as they need to match whatever was used during training.\n        \"\"\"\n        # Verify if the YAML filename includes a full path or just the filename\n        if not os.path.exists(yaml_config_filename):","chunkId":"audio_separator/separator/separator.py:73"}],[1128,{"filePath":"audio_separator/separator/separator.py","startLine":653,"endLine":664,"chunkText":"        \"\"\"\n        # Verify if the YAML filename includes a full path or just the filename\n        if not os.path.exists(yaml_config_filename):\n            model_data_yaml_filepath = os.path.join(self.model_file_dir, yaml_config_filename)\n        else:\n            model_data_yaml_filepath = yaml_config_filename\n\n        self.logger.debug(f\"Loading model data from YAML at path {model_data_yaml_filepath}\")\n\n        model_data = yaml.load(open(model_data_yaml_filepath, encoding=\"utf-8\"), Loader=yaml.FullLoader)\n        self.logger.debug(f\"Model data loaded from YAML file: {model_data}\")\n","chunkId":"audio_separator/separator/separator.py:74"}],[1129,{"filePath":"audio_separator/separator/separator.py","startLine":662,"endLine":673,"chunkText":"        model_data = yaml.load(open(model_data_yaml_filepath, encoding=\"utf-8\"), Loader=yaml.FullLoader)\n        self.logger.debug(f\"Model data loaded from YAML file: {model_data}\")\n\n        if \"roformer\" in model_data_yaml_filepath.lower():\n            model_data[\"is_roformer\"] = True\n\n        return model_data\n\n    def load_model_data_using_hash(self, model_path):\n        \"\"\"\n        This method loads model-specific parameters from UVR model data files.\n        These parameters are critical to inferencing using a given model, as they need to match whatever was used during training.","chunkId":"audio_separator/separator/separator.py:75"}],[1130,{"filePath":"audio_separator/separator/separator.py","startLine":671,"endLine":679,"chunkText":"        \"\"\"\n        This method loads model-specific parameters from UVR model data files.\n        These parameters are critical to inferencing using a given model, as they need to match whatever was used during training.\n        The correct parameters are identified by calculating the hash of the model file and looking up the hash in the UVR data files.\n        \"\"\"\n        # Model data and configuration sources from UVR\n        model_data_url_prefix = \"https://raw.githubusercontent.com/TRvlvr/application_data/main\"\n\n        vr_model_data_url = f\"{model_data_url_prefix}/vr_model_data/model_data_new.json\"","chunkId":"audio_separator/separator/separator.py:76"}],[1131,{"filePath":"audio_separator/separator/separator.py","startLine":677,"endLine":686,"chunkText":"        model_data_url_prefix = \"https://raw.githubusercontent.com/TRvlvr/application_data/main\"\n\n        vr_model_data_url = f\"{model_data_url_prefix}/vr_model_data/model_data_new.json\"\n        mdx_model_data_url = f\"{model_data_url_prefix}/mdx_model_data/model_data_new.json\"\n\n        # Calculate hash for the downloaded model\n        self.logger.debug(\"Calculating MD5 hash for model file to identify model parameters from UVR data...\")\n        model_hash = self.get_model_hash(model_path)\n        self.logger.debug(f\"Model {model_path} has hash {model_hash}\")\n","chunkId":"audio_separator/separator/separator.py:77"}],[1132,{"filePath":"audio_separator/separator/separator.py","startLine":684,"endLine":693,"chunkText":"        model_hash = self.get_model_hash(model_path)\n        self.logger.debug(f\"Model {model_path} has hash {model_hash}\")\n\n        # Setting up the path for model data and checking its existence\n        vr_model_data_path = os.path.join(self.model_file_dir, \"vr_model_data.json\")\n        self.logger.debug(f\"VR model data path set to {vr_model_data_path}\")\n        self.download_file_if_not_exists(vr_model_data_url, vr_model_data_path)\n\n        mdx_model_data_path = os.path.join(self.model_file_dir, \"mdx_model_data.json\")\n        self.logger.debug(f\"MDX model data path set to {mdx_model_data_path}\")","chunkId":"audio_separator/separator/separator.py:78"}],[1133,{"filePath":"audio_separator/separator/separator.py","startLine":691,"endLine":701,"chunkText":"\n        mdx_model_data_path = os.path.join(self.model_file_dir, \"mdx_model_data.json\")\n        self.logger.debug(f\"MDX model data path set to {mdx_model_data_path}\")\n        self.download_file_if_not_exists(mdx_model_data_url, mdx_model_data_path)\n\n        # Loading model data from UVR\n        self.logger.debug(\"Loading MDX and VR model parameters from UVR model data files...\")\n        vr_model_data_object = json.load(open(vr_model_data_path, encoding=\"utf-8\"))\n        mdx_model_data_object = json.load(open(mdx_model_data_path, encoding=\"utf-8\"))\n\n        # Load additional model data from audio-separator","chunkId":"audio_separator/separator/separator.py:79"}],[1134,{"filePath":"audio_separator/separator/separator.py","startLine":699,"endLine":707,"chunkText":"        mdx_model_data_object = json.load(open(mdx_model_data_path, encoding=\"utf-8\"))\n\n        # Load additional model data from audio-separator\n        self.logger.debug(\"Loading additional model parameters from audio-separator model data file...\")\n        with resources.open_text(\"audio_separator\", \"model-data.json\") as f:\n            audio_separator_model_data = json.load(f)\n\n        # Merge the model data objects, with audio-separator data taking precedence\n        vr_model_data_object = {**vr_model_data_object, **audio_separator_model_data.get(\"vr_model_data\", {})}","chunkId":"audio_separator/separator/separator.py:80"}],[1135,{"filePath":"audio_separator/separator/separator.py","startLine":705,"endLine":714,"chunkText":"\n        # Merge the model data objects, with audio-separator data taking precedence\n        vr_model_data_object = {**vr_model_data_object, **audio_separator_model_data.get(\"vr_model_data\", {})}\n        mdx_model_data_object = {**mdx_model_data_object, **audio_separator_model_data.get(\"mdx_model_data\", {})}\n\n        if model_hash in mdx_model_data_object:\n            model_data = mdx_model_data_object[model_hash]\n        elif model_hash in vr_model_data_object:\n            model_data = vr_model_data_object[model_hash]\n        else:","chunkId":"audio_separator/separator/separator.py:81"}],[1136,{"filePath":"audio_separator/separator/separator.py","startLine":712,"endLine":723,"chunkText":"        elif model_hash in vr_model_data_object:\n            model_data = vr_model_data_object[model_hash]\n        else:\n            raise ValueError(f\"Unsupported Model File: parameters for MD5 hash {model_hash} could not be found in UVR model data file for MDX or VR arch.\")\n\n        self.logger.debug(f\"Model data loaded using hash {model_hash}: {model_data}\")\n\n        return model_data\n\n    def load_model(self, model_filename=\"model_bs_roformer_ep_317_sdr_12.9755.ckpt\"):\n        \"\"\"\n        This method instantiates the architecture-specific separation class,","chunkId":"audio_separator/separator/separator.py:82"}],[1137,{"filePath":"audio_separator/separator/separator.py","startLine":721,"endLine":732,"chunkText":"    def load_model(self, model_filename=\"model_bs_roformer_ep_317_sdr_12.9755.ckpt\"):\n        \"\"\"\n        This method instantiates the architecture-specific separation class,\n        loading the separation model into memory, downloading it first if necessary.\n        \"\"\"\n        self.logger.info(f\"Loading model {model_filename}...\")\n\n        load_model_start_time = time.perf_counter()\n\n        # Setting up the model path\n        model_filename, model_type, model_friendly_name, model_path, yaml_config_filename = self.download_model_files(model_filename)\n        model_name = model_filename.split(\".\")[0]","chunkId":"audio_separator/separator/separator.py:83"}],[1138,{"filePath":"audio_separator/separator/separator.py","startLine":730,"endLine":740,"chunkText":"        # Setting up the model path\n        model_filename, model_type, model_friendly_name, model_path, yaml_config_filename = self.download_model_files(model_filename)\n        model_name = model_filename.split(\".\")[0]\n        self.logger.debug(f\"Model downloaded, friendly name: {model_friendly_name}, model_path: {model_path}\")\n\n        if model_path.lower().endswith(\".yaml\"):\n            yaml_config_filename = model_path\n\n        if yaml_config_filename is not None:\n            model_data = self.load_model_data_from_yaml(yaml_config_filename)\n        else:","chunkId":"audio_separator/separator/separator.py:84"}],[1139,{"filePath":"audio_separator/separator/separator.py","startLine":738,"endLine":750,"chunkText":"        if yaml_config_filename is not None:\n            model_data = self.load_model_data_from_yaml(yaml_config_filename)\n        else:\n            model_data = self.load_model_data_using_hash(model_path)\n\n        common_params = {\n            \"logger\": self.logger,\n            \"log_level\": self.log_level,\n            \"torch_device\": self.torch_device,\n            \"torch_device_cpu\": self.torch_device_cpu,\n            \"torch_device_mps\": self.torch_device_mps,\n            \"onnx_execution_provider\": self.onnx_execution_provider,\n            \"model_name\": model_name,","chunkId":"audio_separator/separator/separator.py:85"}],[1140,{"filePath":"audio_separator/separator/separator.py","startLine":748,"endLine":758,"chunkText":"            \"torch_device_mps\": self.torch_device_mps,\n            \"onnx_execution_provider\": self.onnx_execution_provider,\n            \"model_name\": model_name,\n            \"model_path\": model_path,\n            \"model_data\": model_data,\n            \"output_format\": self.output_format,\n            \"output_bitrate\": self.output_bitrate,\n            \"output_dir\": self.output_dir,\n            \"normalization_threshold\": self.normalization_threshold,\n            \"amplification_threshold\": self.amplification_threshold,\n            \"output_single_stem\": self.output_single_stem,","chunkId":"audio_separator/separator/separator.py:86"}],[1141,{"filePath":"audio_separator/separator/separator.py","startLine":756,"endLine":764,"chunkText":"            \"normalization_threshold\": self.normalization_threshold,\n            \"amplification_threshold\": self.amplification_threshold,\n            \"output_single_stem\": self.output_single_stem,\n            \"invert_using_spec\": self.invert_using_spec,\n            \"sample_rate\": self.sample_rate,\n            \"use_soundfile\": self.use_soundfile,\n        }\n\n        # Instantiate the appropriate separator class depending on the model type","chunkId":"audio_separator/separator/separator.py:87"}],[1142,{"filePath":"audio_separator/separator/separator.py","startLine":762,"endLine":769,"chunkText":"        }\n\n        # Instantiate the appropriate separator class depending on the model type\n        separator_classes = {\"MDX\": \"mdx_separator.MDXSeparator\", \"VR\": \"vr_separator.VRSeparator\", \"Demucs\": \"demucs_separator.DemucsSeparator\", \"MDXC\": \"mdxc_separator.MDXCSeparator\"}\n\n        if model_type not in self.arch_specific_params or model_type not in separator_classes:\n            # Enhanced error message for Roformer models\n            if \"roformer\" in model_filename.lower() or (model_data and model_data.get(\"is_roformer\", False)):","chunkId":"audio_separator/separator/separator.py:88"}],[1143,{"filePath":"audio_separator/separator/separator.py","startLine":767,"endLine":774,"chunkText":"        if model_type not in self.arch_specific_params or model_type not in separator_classes:\n            # Enhanced error message for Roformer models\n            if \"roformer\" in model_filename.lower() or (model_data and model_data.get(\"is_roformer\", False)):\n                error_msg = (f\"Roformer model type not properly configured: {model_type}. \"\n                           f\"This may indicate a configuration validation failure. \"\n                           f\"Please check the model file and YAML configuration.\")\n                self.logger.error(error_msg)\n                raise ValueError(error_msg)","chunkId":"audio_separator/separator/separator.py:89"}],[1144,{"filePath":"audio_separator/separator/separator.py","startLine":772,"endLine":783,"chunkText":"                           f\"Please check the model file and YAML configuration.\")\n                self.logger.error(error_msg)\n                raise ValueError(error_msg)\n            else:\n                raise ValueError(f\"Model type not supported (yet): {model_type}\")\n\n        if model_type == \"Demucs\" and sys.version_info < (3, 10):\n            raise Exception(\"Demucs models require Python version 3.10 or newer.\")\n\n        self.logger.debug(f\"Importing module for model type {model_type}: {separator_classes[model_type]}\")\n\n        module_name, class_name = separator_classes[model_type].split(\".\")","chunkId":"audio_separator/separator/separator.py:90"}],[1145,{"filePath":"audio_separator/separator/separator.py","startLine":781,"endLine":790,"chunkText":"        self.logger.debug(f\"Importing module for model type {model_type}: {separator_classes[model_type]}\")\n\n        module_name, class_name = separator_classes[model_type].split(\".\")\n        module = importlib.import_module(f\"audio_separator.separator.architectures.{module_name}\")\n        separator_class = getattr(module, class_name)\n\n        self.logger.debug(f\"Instantiating separator class for model type {model_type}: {separator_class}\")\n        \n        try:\n            self.model_instance = separator_class(common_config=common_params, arch_config=self.arch_specific_params[model_type])","chunkId":"audio_separator/separator/separator.py:91"}],[1146,{"filePath":"audio_separator/separator/separator.py","startLine":788,"endLine":796,"chunkText":"        \n        try:\n            self.model_instance = separator_class(common_config=common_params, arch_config=self.arch_specific_params[model_type])\n        except Exception as e:\n            # Enhanced error handling for Roformer models\n            if \"roformer\" in model_filename.lower() or (model_data and model_data.get(\"is_roformer\", False)):\n                error_msg = (f\"Failed to instantiate Roformer model: {e}. \"\n                           f\"This may be due to missing parameters or configuration validation failures.\")\n                self.logger.error(error_msg)","chunkId":"audio_separator/separator/separator.py:92"}],[1147,{"filePath":"audio_separator/separator/separator.py","startLine":794,"endLine":804,"chunkText":"                error_msg = (f\"Failed to instantiate Roformer model: {e}. \"\n                           f\"This may be due to missing parameters or configuration validation failures.\")\n                self.logger.error(error_msg)\n                raise RuntimeError(error_msg) from e\n            else:\n                raise\n\n        # Log Roformer implementation version if applicable\n        if hasattr(self.model_instance, 'is_roformer_model') and self.model_instance.is_roformer_model:\n            roformer_stats = self.model_instance.get_roformer_loading_stats()\n            if roformer_stats:","chunkId":"audio_separator/separator/separator.py:93"}],[1148,{"filePath":"audio_separator/separator/separator.py","startLine":802,"endLine":810,"chunkText":"        if hasattr(self.model_instance, 'is_roformer_model') and self.model_instance.is_roformer_model:\n            roformer_stats = self.model_instance.get_roformer_loading_stats()\n            if roformer_stats:\n                self.logger.info(f\"Roformer loading stats: {roformer_stats}\")\n                \n        # Log the completion of the model load process\n        self.logger.debug(\"Loading model completed.\")\n        self.logger.info(f'Load model duration: {time.strftime(\"%H:%M:%S\", time.gmtime(int(time.perf_counter() - load_model_start_time)))}')\n","chunkId":"audio_separator/separator/separator.py:94"}],[1149,{"filePath":"audio_separator/separator/separator.py","startLine":808,"endLine":815,"chunkText":"        self.logger.debug(\"Loading model completed.\")\n        self.logger.info(f'Load model duration: {time.strftime(\"%H:%M:%S\", time.gmtime(int(time.perf_counter() - load_model_start_time)))}')\n\n    def separate(self, audio_file_path, custom_output_names=None):\n        \"\"\"\n        Separates the audio file(s) into different stems (e.g., vocals, instruments) using the loaded model.\n\n        This method takes the path to an audio file or a directory containing audio files, processes them through","chunkId":"audio_separator/separator/separator.py:95"}],[1150,{"filePath":"audio_separator/separator/separator.py","startLine":814,"endLine":823,"chunkText":"\n        This method takes the path to an audio file or a directory containing audio files, processes them through\n        the loaded separation model, and returns the paths to the output files containing the separated audio stems.\n        It handles the entire flow from loading the audio, running the separation, clearing up resources, and logging the process.\n\n        Parameters:\n        - audio_file_path (str or list): The path to the audio file or directory, or a list of paths.\n        - custom_output_names (dict, optional): Custom names for the output files. Defaults to None.\n\n        Returns:","chunkId":"audio_separator/separator/separator.py:96"}],[1151,{"filePath":"audio_separator/separator/separator.py","startLine":821,"endLine":830,"chunkText":"        - custom_output_names (dict, optional): Custom names for the output files. Defaults to None.\n\n        Returns:\n        - output_files (list of str): A list containing the paths to the separated audio stem files.\n        \"\"\"\n        # Check if the model and device are properly initialized\n        if not (self.torch_device and self.model_instance):\n            raise ValueError(\"Initialization failed or model not loaded. Please load a model before attempting to separate.\")\n\n        # If audio_file_path is a string, convert it to a list for uniform processing","chunkId":"audio_separator/separator/separator.py:97"}],[1152,{"filePath":"audio_separator/separator/separator.py","startLine":828,"endLine":840,"chunkText":"            raise ValueError(\"Initialization failed or model not loaded. Please load a model before attempting to separate.\")\n\n        # If audio_file_path is a string, convert it to a list for uniform processing\n        if isinstance(audio_file_path, str):\n            audio_file_path = [audio_file_path]\n\n        # Initialize a list to store paths of all output files\n        output_files = []\n\n        # Process each path in the list\n        for path in audio_file_path:\n            if os.path.isdir(path):\n                # If the path is a directory, recursively search for all audio files","chunkId":"audio_separator/separator/separator.py:98"}],[1153,{"filePath":"audio_separator/separator/separator.py","startLine":838,"endLine":845,"chunkText":"        for path in audio_file_path:\n            if os.path.isdir(path):\n                # If the path is a directory, recursively search for all audio files\n                for root, dirs, files in os.walk(path):\n                    for file in files:\n                        # Check the file extension to ensure it's an audio file\n                        if file.endswith((\".wav\", \".flac\", \".mp3\", \".ogg\", \".opus\", \".m4a\", \".aiff\", \".ac3\")):  # Add other formats if needed\n                            full_path = os.path.join(root, file)","chunkId":"audio_separator/separator/separator.py:99"}],[1154,{"filePath":"audio_separator/separator/separator.py","startLine":843,"endLine":849,"chunkText":"                        # Check the file extension to ensure it's an audio file\n                        if file.endswith((\".wav\", \".flac\", \".mp3\", \".ogg\", \".opus\", \".m4a\", \".aiff\", \".ac3\")):  # Add other formats if needed\n                            full_path = os.path.join(root, file)\n                            self.logger.info(f\"Processing file: {full_path}\")\n                            try:\n                                # Perform separation for each file\n                                files_output = self._separate_file(full_path, custom_output_names)","chunkId":"audio_separator/separator/separator.py:100"}],[1155,{"filePath":"audio_separator/separator/separator.py","startLine":847,"endLine":856,"chunkText":"                            try:\n                                # Perform separation for each file\n                                files_output = self._separate_file(full_path, custom_output_names)\n                                output_files.extend(files_output)\n                            except Exception as e:\n                                self.logger.error(f\"Failed to process file {full_path}: {e}\")\n            else:\n                # If the path is a file, process it directly\n                self.logger.info(f\"Processing file: {path}\")\n                try:","chunkId":"audio_separator/separator/separator.py:101"}],[1156,{"filePath":"audio_separator/separator/separator.py","startLine":854,"endLine":866,"chunkText":"                # If the path is a file, process it directly\n                self.logger.info(f\"Processing file: {path}\")\n                try:\n                    files_output = self._separate_file(path, custom_output_names)\n                    output_files.extend(files_output)\n                except Exception as e:\n                    self.logger.error(f\"Failed to process file {path}: {e}\")\n\n        return output_files\n\n    def _separate_file(self, audio_file_path, custom_output_names=None):\n        \"\"\"\n        Internal method to handle separation for a single audio file.","chunkId":"audio_separator/separator/separator.py:102"}],[1157,{"filePath":"audio_separator/separator/separator.py","startLine":864,"endLine":872,"chunkText":"    def _separate_file(self, audio_file_path, custom_output_names=None):\n        \"\"\"\n        Internal method to handle separation for a single audio file.\n        This method performs the actual separation process for a single audio file. It logs the start and end of the process,\n        handles autocast if enabled, and ensures GPU cache is cleared after processing.\n        Parameters:\n        - audio_file_path (str): The path to the audio file.\n        - custom_output_names (dict, optional): Custom names for the output files. Defaults to None.\n        Returns:","chunkId":"audio_separator/separator/separator.py:103"}],[1158,{"filePath":"audio_separator/separator/separator.py","startLine":870,"endLine":880,"chunkText":"        - audio_file_path (str): The path to the audio file.\n        - custom_output_names (dict, optional): Custom names for the output files. Defaults to None.\n        Returns:\n        - output_files (list of str): A list containing the paths to the separated audio stem files.\n        \"\"\"\n        # Check if chunking is enabled and file is large enough\n        if self.chunk_duration is not None:\n            import librosa\n            duration = librosa.get_duration(path=audio_file_path)\n\n            from audio_separator.separator.audio_chunking import AudioChunker","chunkId":"audio_separator/separator/separator.py:104"}],[1159,{"filePath":"audio_separator/separator/separator.py","startLine":878,"endLine":887,"chunkText":"            duration = librosa.get_duration(path=audio_file_path)\n\n            from audio_separator.separator.audio_chunking import AudioChunker\n            chunker = AudioChunker(self.chunk_duration, self.logger)\n\n            if chunker.should_chunk(duration):\n                self.logger.info(f\"File duration {duration:.1f}s exceeds chunk size {self.chunk_duration}s, using chunked processing\")\n                return self._process_with_chunking(audio_file_path, custom_output_names)\n\n        # Log the start of the separation process","chunkId":"audio_separator/separator/separator.py:105"}],[1160,{"filePath":"audio_separator/separator/separator.py","startLine":885,"endLine":892,"chunkText":"                return self._process_with_chunking(audio_file_path, custom_output_names)\n\n        # Log the start of the separation process\n        self.logger.info(f\"Starting separation process for audio_file_path: {audio_file_path}\")\n        separate_start_time = time.perf_counter()\n\n        # Log normalization and amplification thresholds\n        self.logger.debug(f\"Normalization threshold set to {self.normalization_threshold}, waveform will be lowered to this max amplitude to avoid clipping.\")","chunkId":"audio_separator/separator/separator.py:106"}],[1161,{"filePath":"audio_separator/separator/separator.py","startLine":890,"endLine":897,"chunkText":"\n        # Log normalization and amplification thresholds\n        self.logger.debug(f\"Normalization threshold set to {self.normalization_threshold}, waveform will be lowered to this max amplitude to avoid clipping.\")\n        self.logger.debug(f\"Amplification threshold set to {self.amplification_threshold}, waveform will be scaled up to this max amplitude if below it.\")\n\n        # Run separation method for the loaded model with autocast enabled if supported by the device\n        output_files = None\n        if self.use_autocast and autocast_mode.is_autocast_available(self.torch_device.type):","chunkId":"audio_separator/separator/separator.py:107"}],[1162,{"filePath":"audio_separator/separator/separator.py","startLine":895,"endLine":904,"chunkText":"        # Run separation method for the loaded model with autocast enabled if supported by the device\n        output_files = None\n        if self.use_autocast and autocast_mode.is_autocast_available(self.torch_device.type):\n            self.logger.debug(\"Autocast available.\")\n            with autocast_mode.autocast(self.torch_device.type):\n                output_files = self.model_instance.separate(audio_file_path, custom_output_names)\n        else:\n            self.logger.debug(\"Autocast unavailable.\")\n            output_files = self.model_instance.separate(audio_file_path, custom_output_names)\n","chunkId":"audio_separator/separator/separator.py:108"}],[1163,{"filePath":"audio_separator/separator/separator.py","startLine":902,"endLine":914,"chunkText":"            self.logger.debug(\"Autocast unavailable.\")\n            output_files = self.model_instance.separate(audio_file_path, custom_output_names)\n\n        # Clear GPU cache to free up memory\n        self.model_instance.clear_gpu_cache()\n\n        # Unset separation parameters to prevent accidentally re-using the wrong source files or output paths\n        self.model_instance.clear_file_specific_paths()\n\n        # Remind the user one more time if they used a VIP model, so the message doesn't get lost in the logs\n        self.print_uvr_vip_message()\n\n        # Log the completion of the separation process","chunkId":"audio_separator/separator/separator.py:109"}],[1164,{"filePath":"audio_separator/separator/separator.py","startLine":912,"endLine":924,"chunkText":"        self.print_uvr_vip_message()\n\n        # Log the completion of the separation process\n        self.logger.debug(\"Separation process completed.\")\n        self.logger.info(f'Separation duration: {time.strftime(\"%H:%M:%S\", time.gmtime(int(time.perf_counter() - separate_start_time)))}')\n\n        return output_files\n\n    def _process_with_chunking(self, audio_file_path, custom_output_names=None):\n        \"\"\"\n        Process large file by splitting into chunks.\n\n        This method splits a large audio file into smaller chunks, processes each chunk","chunkId":"audio_separator/separator/separator.py:110"}],[1165,{"filePath":"audio_separator/separator/separator.py","startLine":922,"endLine":934,"chunkText":"        Process large file by splitting into chunks.\n\n        This method splits a large audio file into smaller chunks, processes each chunk\n        separately, and merges the results back together. This helps prevent out-of-memory\n        errors when processing very long audio files.\n\n        Parameters:\n        - audio_file_path (str): The path to the audio file.\n        - custom_output_names (dict, optional): Custom names for the output files. Defaults to None.\n\n        Returns:\n        - output_files (list of str): A list containing the paths to the separated audio stem files.\n        \"\"\"","chunkId":"audio_separator/separator/separator.py:111"}],[1166,{"filePath":"audio_separator/separator/separator.py","startLine":932,"endLine":945,"chunkText":"        Returns:\n        - output_files (list of str): A list containing the paths to the separated audio stem files.\n        \"\"\"\n        import tempfile\n        import shutil\n        from audio_separator.separator.audio_chunking import AudioChunker\n\n        # Create temporary directory for chunks\n        temp_dir = tempfile.mkdtemp(prefix=\"audio-separator-chunks-\")\n        self.logger.debug(f\"Created temporary directory for chunks: {temp_dir}\")\n\n        try:\n            # Split audio into chunks\n            chunker = AudioChunker(self.chunk_duration, self.logger)","chunkId":"audio_separator/separator/separator.py:112"}],[1167,{"filePath":"audio_separator/separator/separator.py","startLine":943,"endLine":956,"chunkText":"        try:\n            # Split audio into chunks\n            chunker = AudioChunker(self.chunk_duration, self.logger)\n            chunk_paths = chunker.split_audio(audio_file_path, temp_dir)\n\n            # Process each chunk\n            processed_chunks_by_stem = {}\n\n            for i, chunk_path in enumerate(chunk_paths):\n                self.logger.info(f\"Processing chunk {i+1}/{len(chunk_paths)}: {chunk_path}\")\n\n                original_chunk_duration = self.chunk_duration\n                original_output_dir = self.output_dir\n                self.chunk_duration = None","chunkId":"audio_separator/separator/separator.py:113"}],[1168,{"filePath":"audio_separator/separator/separator.py","startLine":954,"endLine":967,"chunkText":"                original_chunk_duration = self.chunk_duration\n                original_output_dir = self.output_dir\n                self.chunk_duration = None\n                self.output_dir = temp_dir\n\n                if self.model_instance:\n                    original_model_output_dir = self.model_instance.output_dir\n                    self.model_instance.output_dir = temp_dir\n\n                try:\n                    output_files = self._separate_file(chunk_path, custom_output_names)\n\n                    # Dynamically group chunks by stem name\n                    for stem_path in output_files:","chunkId":"audio_separator/separator/separator.py:114"}],[1169,{"filePath":"audio_separator/separator/separator.py","startLine":965,"endLine":974,"chunkText":"\n                    # Dynamically group chunks by stem name\n                    for stem_path in output_files:\n                        # Extract stem name from filename: \"chunk_0000_(Vocals).wav\" → \"Vocals\"\n                        filename = os.path.basename(stem_path)\n                        match = re.search(r'_\\(([^)]+)\\)', filename)\n                        if match:\n                            stem_name = match.group(1)\n                        else:\n                            # Fallback: use index-based name if pattern not found","chunkId":"audio_separator/separator/separator.py:115"}],[1170,{"filePath":"audio_separator/separator/separator.py","startLine":972,"endLine":981,"chunkText":"                            stem_name = match.group(1)\n                        else:\n                            # Fallback: use index-based name if pattern not found\n                            stem_index = len([k for k in processed_chunks_by_stem.keys() if k.startswith('stem_')])\n                            stem_name = f\"stem_{stem_index}\"\n                            self.logger.warning(f\"Could not extract stem name from {filename}, using {stem_name}\")\n\n                        if stem_name not in processed_chunks_by_stem:\n                            processed_chunks_by_stem[stem_name] = []\n","chunkId":"audio_separator/separator/separator.py:116"}],[1171,{"filePath":"audio_separator/separator/separator.py","startLine":979,"endLine":990,"chunkText":"                        if stem_name not in processed_chunks_by_stem:\n                            processed_chunks_by_stem[stem_name] = []\n\n                        # Ensure absolute path\n                        abs_path = stem_path if os.path.isabs(stem_path) else os.path.join(temp_dir, stem_path)\n                        processed_chunks_by_stem[stem_name].append(abs_path)\n\n                    if not output_files:\n                        self.logger.warning(f\"Chunk {i+1} produced no output files\")\n\n                finally:\n                    self.chunk_duration = original_chunk_duration","chunkId":"audio_separator/separator/separator.py:117"}],[1172,{"filePath":"audio_separator/separator/separator.py","startLine":988,"endLine":1002,"chunkText":"\n                finally:\n                    self.chunk_duration = original_chunk_duration\n                    self.output_dir = original_output_dir\n                    if self.model_instance:\n                        self.model_instance.output_dir = original_model_output_dir\n\n                # Clear GPU cache between chunks\n                if self.model_instance:\n                    self.model_instance.clear_gpu_cache()\n\n            # Merge chunks for each stem dynamically\n            base_name = os.path.splitext(os.path.basename(audio_file_path))[0]\n            output_files = []\n","chunkId":"audio_separator/separator/separator.py:118"}],[1173,{"filePath":"audio_separator/separator/separator.py","startLine":1000,"endLine":1012,"chunkText":"            base_name = os.path.splitext(os.path.basename(audio_file_path))[0]\n            output_files = []\n\n            for stem_name in sorted(processed_chunks_by_stem.keys()):\n                chunk_paths_for_stem = processed_chunks_by_stem[stem_name]\n\n                if not chunk_paths_for_stem:\n                    self.logger.warning(f\"No chunks found for stem: {stem_name}\")\n                    continue\n\n                # Determine output filename\n                if custom_output_names and stem_name in custom_output_names:\n                    output_filename = custom_output_names[stem_name]","chunkId":"audio_separator/separator/separator.py:119"}],[1174,{"filePath":"audio_separator/separator/separator.py","startLine":1010,"endLine":1019,"chunkText":"                # Determine output filename\n                if custom_output_names and stem_name in custom_output_names:\n                    output_filename = custom_output_names[stem_name]\n                else:\n                    output_filename = f\"{base_name}_({stem_name})\"\n\n                output_path = os.path.join(self.output_dir, f\"{output_filename}.{self.output_format.lower()}\")\n\n                self.logger.info(f\"Merging {len(chunk_paths_for_stem)} chunks for stem: {stem_name}\")\n                chunker.merge_chunks(chunk_paths_for_stem, output_path)","chunkId":"audio_separator/separator/separator.py:120"}],[1175,{"filePath":"audio_separator/separator/separator.py","startLine":1019,"endLine":1032,"chunkText":"                chunker.merge_chunks(chunk_paths_for_stem, output_path)\n                output_files.append(output_path)\n\n            self.logger.info(f\"Chunked processing completed. Output files: {output_files}\")\n            return output_files\n\n        finally:\n            # Clean up temporary directory\n            if os.path.exists(temp_dir):\n                self.logger.debug(f\"Cleaning up temporary directory: {temp_dir}\")\n                shutil.rmtree(temp_dir, ignore_errors=True)\n\n    def download_model_and_data(self, model_filename):\n        \"\"\"","chunkId":"audio_separator/separator/separator.py:121"}],[1176,{"filePath":"audio_separator/separator/separator.py","startLine":1030,"endLine":1044,"chunkText":"\n    def download_model_and_data(self, model_filename):\n        \"\"\"\n        Downloads the model file without loading it into memory.\n        \"\"\"\n        self.logger.info(f\"Downloading model {model_filename}...\")\n\n        model_filename, model_type, model_friendly_name, model_path, yaml_config_filename = self.download_model_files(model_filename)\n\n        if model_path.lower().endswith(\".yaml\"):\n            yaml_config_filename = model_path\n\n        if yaml_config_filename is not None:\n            model_data = self.load_model_data_from_yaml(yaml_config_filename)\n        else:","chunkId":"audio_separator/separator/separator.py:122"}],[1177,{"filePath":"audio_separator/separator/separator.py","startLine":1042,"endLine":1053,"chunkText":"        if yaml_config_filename is not None:\n            model_data = self.load_model_data_from_yaml(yaml_config_filename)\n        else:\n            model_data = self.load_model_data_using_hash(model_path)\n\n        model_data_dict_size = len(model_data)\n\n        self.logger.info(f\"Model downloaded, type: {model_type}, friendly name: {model_friendly_name}, model_path: {model_path}, model_data: {model_data_dict_size} items\")\n\n    def get_simplified_model_list(self, filter_sort_by: Optional[str] = None):\n        \"\"\"\n        Returns a simplified, user-friendly list of models with their key metrics.","chunkId":"audio_separator/separator/separator.py:123"}],[1178,{"filePath":"audio_separator/separator/separator.py","startLine":1051,"endLine":1063,"chunkText":"    def get_simplified_model_list(self, filter_sort_by: Optional[str] = None):\n        \"\"\"\n        Returns a simplified, user-friendly list of models with their key metrics.\n        Optionally sorts the list based on the specified criteria.\n\n        :param sort_by: Criteria to sort by. Can be \"name\", \"filename\", or any stem name\n        \"\"\"\n        model_files = self.list_supported_model_files()\n        simplified_list = {}\n\n        for model_type, models in model_files.items():\n            for name, data in models.items():\n                filename = data[\"filename\"]","chunkId":"audio_separator/separator/separator.py:124"}],[1179,{"filePath":"audio_separator/separator/separator.py","startLine":1061,"endLine":1074,"chunkText":"        for model_type, models in model_files.items():\n            for name, data in models.items():\n                filename = data[\"filename\"]\n                scores = data.get(\"scores\") or {}\n                stems = data.get(\"stems\") or []\n                target_stem = data.get(\"target_stem\")\n\n                # Format stems with their SDR scores where available\n                stems_with_scores = []\n                stem_sdr_dict = {}\n\n                # Process each stem from the model's stem list\n                for stem in stems:\n                    stem_scores = scores.get(stem, {})","chunkId":"audio_separator/separator/separator.py:125"}],[1180,{"filePath":"audio_separator/separator/separator.py","startLine":1072,"endLine":1082,"chunkText":"                # Process each stem from the model's stem list\n                for stem in stems:\n                    stem_scores = scores.get(stem, {})\n                    # Add asterisk if this is the target stem\n                    stem_display = f\"{stem}*\" if stem == target_stem else stem\n\n                    if isinstance(stem_scores, dict) and \"SDR\" in stem_scores:\n                        sdr = round(stem_scores[\"SDR\"], 1)\n                        stems_with_scores.append(f\"{stem_display} ({sdr})\")\n                        stem_sdr_dict[stem.lower()] = sdr\n                    else:","chunkId":"audio_separator/separator/separator.py:126"}],[1181,{"filePath":"audio_separator/separator/separator.py","startLine":1080,"endLine":1091,"chunkText":"                        stems_with_scores.append(f\"{stem_display} ({sdr})\")\n                        stem_sdr_dict[stem.lower()] = sdr\n                    else:\n                        # Include stem without SDR score\n                        stems_with_scores.append(stem_display)\n                        stem_sdr_dict[stem.lower()] = None\n\n                # If no stems listed, mark as Unknown\n                if not stems_with_scores:\n                    stems_with_scores = [\"Unknown\"]\n                    stem_sdr_dict[\"unknown\"] = None\n","chunkId":"audio_separator/separator/separator.py:127"}],[1182,{"filePath":"audio_separator/separator/separator.py","startLine":1089,"endLine":1100,"chunkText":"                    stems_with_scores = [\"Unknown\"]\n                    stem_sdr_dict[\"unknown\"] = None\n\n                simplified_list[filename] = {\"Name\": name, \"Type\": model_type, \"Stems\": stems_with_scores, \"SDR\": stem_sdr_dict}\n\n        # Sort and filter the list if a sort_by parameter is provided\n        if filter_sort_by:\n            if filter_sort_by == \"name\":\n                return dict(sorted(simplified_list.items(), key=lambda x: x[1][\"Name\"]))\n            elif filter_sort_by == \"filename\":\n                return dict(sorted(simplified_list.items()))\n            else:","chunkId":"audio_separator/separator/separator.py:128"}],[1183,{"filePath":"audio_separator/separator/separator.py","startLine":1098,"endLine":1108,"chunkText":"            elif filter_sort_by == \"filename\":\n                return dict(sorted(simplified_list.items()))\n            else:\n                # Convert sort_by to lowercase for case-insensitive comparison\n                sort_by_lower = filter_sort_by.lower()\n                # Filter out models that don't have the specified stem\n                filtered_list = {k: v for k, v in simplified_list.items() if sort_by_lower in v[\"SDR\"]}\n\n                # Sort by SDR score if available, putting None values last\n                def sort_key(item):\n                    sdr = item[1][\"SDR\"][sort_by_lower]","chunkId":"audio_separator/separator/separator.py:129"}],[1184,{"filePath":"audio_separator/separator/separator.py","startLine":1106,"endLine":1114,"chunkText":"                # Sort by SDR score if available, putting None values last\n                def sort_key(item):\n                    sdr = item[1][\"SDR\"][sort_by_lower]\n                    return (0 if sdr is None else 1, sdr if sdr is not None else float(\"-inf\"))\n\n                return dict(sorted(filtered_list.items(), key=sort_key, reverse=True))\n\n        return simplified_list\n","chunkId":"audio_separator/separator/separator.py:130"}],[1185,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/__init__.py","startLine":0,"endLine":5,"chunkText":"# Copyright (c) Facebook, Inc. and its affiliates.\n# All rights reserved.\n#\n# This source code is licensed under the license found in the\n# LICENSE file in the root directory of this source tree.\n","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/__init__.py:0"}],[1186,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/__main__.py","startLine":0,"endLine":19,"chunkText":"# Copyright (c) Facebook, Inc. and its affiliates.\n# All rights reserved.\n#\n# This source code is licensed under the license found in the\n# LICENSE file in the root directory of this source tree.\n\nimport json\nimport os\nimport sys\nimport time\nfrom dataclasses import dataclass, field\nfrom fractions import Fraction\n\nimport torch as th\nfrom torch import distributed, nn\nfrom torch.nn.parallel.distributed import DistributedDataParallel\n\nfrom .augment import FlipChannels, FlipSign, Remix, Shift\nfrom .compressed import StemsSet, build_musdb_metadata, get_musdb_tracks\nfrom .model import Demucs","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/__main__.py:0"}],[1187,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/__main__.py","startLine":17,"endLine":37,"chunkText":"from .augment import FlipChannels, FlipSign, Remix, Shift\nfrom .compressed import StemsSet, build_musdb_metadata, get_musdb_tracks\nfrom .model import Demucs\nfrom .parser import get_name, get_parser\nfrom .raw import Rawset\nfrom .tasnet import ConvTasNet\nfrom .test import evaluate\nfrom .train import train_model, validate_model\nfrom .utils import human_seconds, load_model, save_model, sizeof_fmt\n\n\n@dataclass\nclass SavedState:\n    metrics: list = field(default_factory=list)\n    last_state: dict = None\n    best_state: dict = None\n    optimizer: dict = None\n\n\ndef main():\n    parser = get_parser()","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/__main__.py:1"}],[1188,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/__main__.py","startLine":35,"endLine":50,"chunkText":"\ndef main():\n    parser = get_parser()\n    args = parser.parse_args()\n    name = get_name(parser, args)\n    print(f\"Experiment {name}\")\n\n    if args.musdb is None and args.rank == 0:\n        print(\"You must provide the path to the MusDB dataset with the --musdb flag. \" \"To download the MusDB dataset, see https://sigsep.github.io/datasets/musdb.html.\", file=sys.stderr)\n        sys.exit(1)\n\n    eval_folder = args.evals / name\n    eval_folder.mkdir(exist_ok=True, parents=True)\n    args.logs.mkdir(exist_ok=True)\n    metrics_path = args.logs / f\"{name}.json\"\n    eval_folder.mkdir(exist_ok=True, parents=True)","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/__main__.py:2"}],[1189,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/__main__.py","startLine":48,"endLine":65,"chunkText":"    args.logs.mkdir(exist_ok=True)\n    metrics_path = args.logs / f\"{name}.json\"\n    eval_folder.mkdir(exist_ok=True, parents=True)\n    args.checkpoints.mkdir(exist_ok=True, parents=True)\n    args.models.mkdir(exist_ok=True, parents=True)\n\n    if args.device is None:\n        device = \"cpu\"\n        if th.cuda.is_available():\n            device = \"cuda\"\n    else:\n        device = args.device\n\n    th.manual_seed(args.seed)\n    # Prevents too many threads to be started when running `museval` as it can be quite\n    # inefficient on NUMA architectures.\n    os.environ[\"OMP_NUM_THREADS\"] = \"1\"\n","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/__main__.py:3"}],[1190,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/__main__.py","startLine":63,"endLine":74,"chunkText":"    # inefficient on NUMA architectures.\n    os.environ[\"OMP_NUM_THREADS\"] = \"1\"\n\n    if args.world_size > 1:\n        if device != \"cuda\" and args.rank == 0:\n            print(\"Error: distributed training is only available with cuda device\", file=sys.stderr)\n            sys.exit(1)\n        th.cuda.set_device(args.rank % th.cuda.device_count())\n        distributed.init_process_group(backend=\"nccl\", init_method=\"tcp://\" + args.master, rank=args.rank, world_size=args.world_size)\n\n    checkpoint = args.checkpoints / f\"{name}.th\"\n    checkpoint_tmp = args.checkpoints / f\"{name}.th.tmp\"","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/__main__.py:4"}],[1191,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/__main__.py","startLine":72,"endLine":89,"chunkText":"\n    checkpoint = args.checkpoints / f\"{name}.th\"\n    checkpoint_tmp = args.checkpoints / f\"{name}.th.tmp\"\n    if args.restart and checkpoint.exists():\n        checkpoint.unlink()\n\n    if args.test:\n        args.epochs = 1\n        args.repeat = 0\n        model = load_model(args.models / args.test)\n    elif args.tasnet:\n        model = ConvTasNet(audio_channels=args.audio_channels, samplerate=args.samplerate, X=args.X)\n    else:\n        model = Demucs(\n            audio_channels=args.audio_channels,\n            channels=args.channels,\n            context=args.context,\n            depth=args.depth,","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/__main__.py:5"}],[1192,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/__main__.py","startLine":87,"endLine":104,"chunkText":"            channels=args.channels,\n            context=args.context,\n            depth=args.depth,\n            glu=args.glu,\n            growth=args.growth,\n            kernel_size=args.kernel_size,\n            lstm_layers=args.lstm_layers,\n            rescale=args.rescale,\n            rewrite=args.rewrite,\n            sources=4,\n            stride=args.conv_stride,\n            upsample=args.upsample,\n            samplerate=args.samplerate,\n        )\n    model.to(device)\n    if args.show:\n        print(model)\n        size = sizeof_fmt(4 * sum(p.numel() for p in model.parameters()))","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/__main__.py:6"}],[1193,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/__main__.py","startLine":102,"endLine":121,"chunkText":"    if args.show:\n        print(model)\n        size = sizeof_fmt(4 * sum(p.numel() for p in model.parameters()))\n        print(f\"Model size {size}\")\n        return\n\n    optimizer = th.optim.Adam(model.parameters(), lr=args.lr)\n\n    try:\n        saved = th.load(checkpoint, map_location=\"cpu\")\n    except IOError:\n        saved = SavedState()\n    else:\n        model.load_state_dict(saved.last_state)\n        optimizer.load_state_dict(saved.optimizer)\n\n    if args.save_model:\n        if args.rank == 0:\n            model.to(\"cpu\")\n            model.load_state_dict(saved.best_state)","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/__main__.py:7"}],[1194,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/__main__.py","startLine":119,"endLine":139,"chunkText":"        if args.rank == 0:\n            model.to(\"cpu\")\n            model.load_state_dict(saved.best_state)\n            save_model(model, args.models / f\"{name}.th\")\n        return\n\n    if args.rank == 0:\n        done = args.logs / f\"{name}.done\"\n        if done.exists():\n            done.unlink()\n\n    if args.augment:\n        augment = nn.Sequential(FlipSign(), FlipChannels(), Shift(args.data_stride), Remix(group_size=args.remix_group_size)).to(device)\n    else:\n        augment = Shift(args.data_stride)\n\n    if args.mse:\n        criterion = nn.MSELoss()\n    else:\n        criterion = nn.L1Loss()\n","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/__main__.py:8"}],[1195,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/__main__.py","startLine":137,"endLine":150,"chunkText":"    else:\n        criterion = nn.L1Loss()\n\n    # Setting number of samples so that all convolution windows are full.\n    # Prevents hard to debug mistake with the prediction being shifted compared\n    # to the input mixture.\n    samples = model.valid_length(args.samples)\n    print(f\"Number of training samples adjusted to {samples}\")\n\n    if args.raw:\n        train_set = Rawset(args.raw / \"train\", samples=samples + args.data_stride, channels=args.audio_channels, streams=[0, 1, 2, 3, 4], stride=args.data_stride)\n\n        valid_set = Rawset(args.raw / \"valid\", channels=args.audio_channels)\n    else:","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/__main__.py:9"}],[1196,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/__main__.py","startLine":148,"endLine":157,"chunkText":"\n        valid_set = Rawset(args.raw / \"valid\", channels=args.audio_channels)\n    else:\n        if not args.metadata.is_file() and args.rank == 0:\n            build_musdb_metadata(args.metadata, args.musdb, args.workers)\n        if args.world_size > 1:\n            distributed.barrier()\n        metadata = json.load(open(args.metadata))\n        duration = Fraction(samples + args.data_stride, args.samplerate)\n        stride = Fraction(args.data_stride, args.samplerate)","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/__main__.py:10"}],[1197,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/__main__.py","startLine":155,"endLine":161,"chunkText":"        metadata = json.load(open(args.metadata))\n        duration = Fraction(samples + args.data_stride, args.samplerate)\n        stride = Fraction(args.data_stride, args.samplerate)\n        train_set = StemsSet(get_musdb_tracks(args.musdb, subsets=[\"train\"], split=\"train\"), metadata, duration=duration, stride=stride, samplerate=args.samplerate, channels=args.audio_channels)\n        valid_set = StemsSet(get_musdb_tracks(args.musdb, subsets=[\"train\"], split=\"valid\"), metadata, samplerate=args.samplerate, channels=args.audio_channels)\n\n    best_loss = float(\"inf\")","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/__main__.py:11"}],[1198,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/__main__.py","startLine":159,"endLine":166,"chunkText":"        valid_set = StemsSet(get_musdb_tracks(args.musdb, subsets=[\"train\"], split=\"valid\"), metadata, samplerate=args.samplerate, channels=args.audio_channels)\n\n    best_loss = float(\"inf\")\n    for epoch, metrics in enumerate(saved.metrics):\n        print(f\"Epoch {epoch:03d}: \" f\"train={metrics['train']:.8f} \" f\"valid={metrics['valid']:.8f} \" f\"best={metrics['best']:.4f} \" f\"duration={human_seconds(metrics['duration'])}\")\n        best_loss = metrics[\"best\"]\n\n    if args.world_size > 1:","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/__main__.py:12"}],[1199,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/__main__.py","startLine":164,"endLine":177,"chunkText":"        best_loss = metrics[\"best\"]\n\n    if args.world_size > 1:\n        dmodel = DistributedDataParallel(model, device_ids=[th.cuda.current_device()], output_device=th.cuda.current_device())\n    else:\n        dmodel = model\n\n    for epoch in range(len(saved.metrics), args.epochs):\n        begin = time.time()\n        model.train()\n        train_loss = train_model(\n            epoch, train_set, dmodel, criterion, optimizer, augment, batch_size=args.batch_size, device=device, repeat=args.repeat, seed=args.seed, workers=args.workers, world_size=args.world_size\n        )\n        model.eval()","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/__main__.py:13"}],[1200,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/__main__.py","startLine":175,"endLine":183,"chunkText":"            epoch, train_set, dmodel, criterion, optimizer, augment, batch_size=args.batch_size, device=device, repeat=args.repeat, seed=args.seed, workers=args.workers, world_size=args.world_size\n        )\n        model.eval()\n        valid_loss = validate_model(epoch, valid_set, model, criterion, device=device, rank=args.rank, split=args.split_valid, world_size=args.world_size)\n\n        duration = time.time() - begin\n        if valid_loss < best_loss:\n            best_loss = valid_loss\n            saved.best_state = {key: value.to(\"cpu\").clone() for key, value in model.state_dict().items()}","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/__main__.py:14"}],[1201,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/__main__.py","startLine":181,"endLine":193,"chunkText":"        if valid_loss < best_loss:\n            best_loss = valid_loss\n            saved.best_state = {key: value.to(\"cpu\").clone() for key, value in model.state_dict().items()}\n        saved.metrics.append({\"train\": train_loss, \"valid\": valid_loss, \"best\": best_loss, \"duration\": duration})\n        if args.rank == 0:\n            json.dump(saved.metrics, open(metrics_path, \"w\"))\n\n        saved.last_state = model.state_dict()\n        saved.optimizer = optimizer.state_dict()\n        if args.rank == 0 and not args.test:\n            th.save(saved, checkpoint_tmp)\n            checkpoint_tmp.rename(checkpoint)\n","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/__main__.py:15"}],[1202,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/__main__.py","startLine":191,"endLine":203,"chunkText":"            th.save(saved, checkpoint_tmp)\n            checkpoint_tmp.rename(checkpoint)\n\n        print(f\"Epoch {epoch:03d}: \" f\"train={train_loss:.8f} valid={valid_loss:.8f} best={best_loss:.4f} \" f\"duration={human_seconds(duration)}\")\n\n    del dmodel\n    model.load_state_dict(saved.best_state)\n    if args.eval_cpu:\n        device = \"cpu\"\n        model.to(device)\n    model.eval()\n    evaluate(model, args.musdb, eval_folder, rank=args.rank, world_size=args.world_size, device=device, save=args.save, split=args.split_valid, shifts=args.shifts, workers=args.eval_workers)\n    model.to(\"cpu\")","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/__main__.py:16"}],[1203,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/__main__.py","startLine":201,"endLine":212,"chunkText":"    model.eval()\n    evaluate(model, args.musdb, eval_folder, rank=args.rank, world_size=args.world_size, device=device, save=args.save, split=args.split_valid, shifts=args.shifts, workers=args.eval_workers)\n    model.to(\"cpu\")\n    save_model(model, args.models / f\"{name}.th\")\n    if args.rank == 0:\n        print(\"done\")\n        done.write_text(\"done\")\n\n\nif __name__ == \"__main__\":\n    main()\n","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/__main__.py:17"}],[1204,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/apply.py","startLine":0,"endLine":19,"chunkText":"# Copyright (c) Facebook, Inc. and its affiliates.\n# All rights reserved.\n#\n# This source code is licensed under the license found in the\n# LICENSE file in the root directory of this source tree.\n\"\"\"\nCode to apply a model to a mix. It will handle chunking with overlaps and\ninteprolation between chunks, as well as the \"shift trick\".\n\"\"\"\nfrom concurrent.futures import ThreadPoolExecutor\nimport random\nimport typing as tp\n\nimport torch as th\nfrom torch import nn\nfrom torch.nn import functional as F\nimport tqdm\n\nfrom .demucs import Demucs\nfrom .hdemucs import HDemucs","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/apply.py:0"}],[1205,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/apply.py","startLine":17,"endLine":35,"chunkText":"\nfrom .demucs import Demucs\nfrom .hdemucs import HDemucs\nfrom .utils import center_trim, DummyPoolExecutor\n\nModel = tp.Union[Demucs, HDemucs]\n\nprogress_bar_num = 0\n\n\nclass BagOfModels(nn.Module):\n    def __init__(self, models: tp.List[Model], weights: tp.Optional[tp.List[tp.List[float]]] = None, segment: tp.Optional[float] = None):\n        \"\"\"\n        Represents a bag of models with specific weights.\n        You should call `apply_model` rather than calling directly the forward here for\n        optimal performance.\n\n        Args:\n            models (list[nn.Module]): list of Demucs/HDemucs models.","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/apply.py:1"}],[1206,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/apply.py","startLine":33,"endLine":44,"chunkText":"        Args:\n            models (list[nn.Module]): list of Demucs/HDemucs models.\n            weights (list[list[float]]): list of weights. If None, assumed to\n                be all ones, otherwise it should be a list of N list (N number of models),\n                each containing S floats (S number of sources).\n            segment (None or float): overrides the `segment` attribute of each model\n                (this is performed inplace, be careful if you reuse the models passed).\n        \"\"\"\n\n        super().__init__()\n        assert len(models) > 0\n        first = models[0]","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/apply.py:2"}],[1207,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/apply.py","startLine":44,"endLine":59,"chunkText":"        assert len(models) > 0\n        first = models[0]\n        for other in models:\n            assert other.sources == first.sources\n            assert other.samplerate == first.samplerate\n            assert other.audio_channels == first.audio_channels\n            if segment is not None:\n                other.segment = segment\n\n        self.audio_channels = first.audio_channels\n        self.samplerate = first.samplerate\n        self.sources = first.sources\n        self.models = nn.ModuleList(models)\n\n        if weights is None:\n            weights = [[1.0 for _ in first.sources] for _ in models]","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/apply.py:3"}],[1208,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/apply.py","startLine":57,"endLine":69,"chunkText":"\n        if weights is None:\n            weights = [[1.0 for _ in first.sources] for _ in models]\n        else:\n            assert len(weights) == len(models)\n            for weight in weights:\n                assert len(weight) == len(first.sources)\n        self.weights = weights\n\n    def forward(self, x):\n        raise NotImplementedError(\"Call `apply_model` on this.\")\n\n","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/apply.py:4"}],[1209,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/apply.py","startLine":70,"endLine":90,"chunkText":"class TensorChunk:\n    def __init__(self, tensor, offset=0, length=None):\n        total_length = tensor.shape[-1]\n        assert offset >= 0\n        assert offset < total_length\n\n        if length is None:\n            length = total_length - offset\n        else:\n            length = min(total_length - offset, length)\n\n        if isinstance(tensor, TensorChunk):\n            self.tensor = tensor.tensor\n            self.offset = offset + tensor.offset\n        else:\n            self.tensor = tensor\n            self.offset = offset\n        self.length = length\n        self.device = tensor.device\n\n    @property","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/apply.py:5"}],[1210,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/apply.py","startLine":88,"endLine":109,"chunkText":"        self.device = tensor.device\n\n    @property\n    def shape(self):\n        shape = list(self.tensor.shape)\n        shape[-1] = self.length\n        return shape\n\n    def padded(self, target_length):\n        delta = target_length - self.length\n        total_length = self.tensor.shape[-1]\n        assert delta >= 0\n\n        start = self.offset - delta // 2\n        end = start + target_length\n\n        correct_start = max(0, start)\n        correct_end = min(total_length, end)\n\n        pad_left = correct_start - start\n        pad_right = end - correct_end\n","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/apply.py:6"}],[1211,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/apply.py","startLine":107,"endLine":122,"chunkText":"        pad_left = correct_start - start\n        pad_right = end - correct_end\n\n        out = F.pad(self.tensor[..., correct_start:correct_end], (pad_left, pad_right))\n        assert out.shape[-1] == target_length\n        return out\n\n\ndef tensor_chunk(tensor_or_chunk):\n    if isinstance(tensor_or_chunk, TensorChunk):\n        return tensor_or_chunk\n    else:\n        assert isinstance(tensor_or_chunk, th.Tensor)\n        return TensorChunk(tensor_or_chunk)\n\n","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/apply.py:7"}],[1212,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/apply.py","startLine":120,"endLine":130,"chunkText":"        return TensorChunk(tensor_or_chunk)\n\n\ndef apply_model(model, mix, shifts=1, split=True, overlap=0.25, transition_power=1.0, static_shifts=1, set_progress_bar=None, device=None, progress=False, num_workers=0, pool=None):\n    \"\"\"\n    Apply model to a given mixture.\n\n    Args:\n        shifts (int): if > 0, will shift in time `mix` by a random amount between 0 and 0.5 sec\n            and apply the oppositve shift to the output. This is repeated `shifts` time and\n            all predictions are averaged. This effectively makes the model time equivariant","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/apply.py:8"}],[1213,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/apply.py","startLine":128,"endLine":134,"chunkText":"        shifts (int): if > 0, will shift in time `mix` by a random amount between 0 and 0.5 sec\n            and apply the oppositve shift to the output. This is repeated `shifts` time and\n            all predictions are averaged. This effectively makes the model time equivariant\n            and improves SDR by up to 0.2 points.\n        split (bool): if True, the input will be broken down in 8 seconds extracts\n            and predictions will be performed individually on each and concatenated.\n            Useful for model with large memory footprint like Tasnet.","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/apply.py:9"}],[1214,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/apply.py","startLine":132,"endLine":138,"chunkText":"        split (bool): if True, the input will be broken down in 8 seconds extracts\n            and predictions will be performed individually on each and concatenated.\n            Useful for model with large memory footprint like Tasnet.\n        progress (bool): if True, show a progress bar (requires split=True)\n        device (torch.device, str, or None): if provided, device on which to\n            execute the computation, otherwise `mix.device` is assumed.\n            When `device` is different from `mix.device`, only local computations will","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/apply.py:10"}],[1215,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/apply.py","startLine":136,"endLine":152,"chunkText":"        device (torch.device, str, or None): if provided, device on which to\n            execute the computation, otherwise `mix.device` is assumed.\n            When `device` is different from `mix.device`, only local computations will\n            be on `device`, while the entire tracks will be stored on `mix.device`.\n    \"\"\"\n\n    global fut_length\n    global bag_num\n    global prog_bar\n\n    if device is None:\n        device = mix.device\n    else:\n        device = th.device(device)\n    if pool is None:\n        if num_workers > 0 and device.type == \"cpu\":\n            pool = ThreadPoolExecutor(num_workers)","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/apply.py:11"}],[1216,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/apply.py","startLine":150,"endLine":169,"chunkText":"    if pool is None:\n        if num_workers > 0 and device.type == \"cpu\":\n            pool = ThreadPoolExecutor(num_workers)\n        else:\n            pool = DummyPoolExecutor()\n\n    kwargs = {\n        \"shifts\": shifts,\n        \"split\": split,\n        \"overlap\": overlap,\n        \"transition_power\": transition_power,\n        \"progress\": progress,\n        \"device\": device,\n        \"pool\": pool,\n        \"set_progress_bar\": set_progress_bar,\n        \"static_shifts\": static_shifts,\n    }\n\n    if isinstance(model, BagOfModels):\n        # Special treatment for bag of model.","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/apply.py:12"}],[1217,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/apply.py","startLine":167,"endLine":181,"chunkText":"    if isinstance(model, BagOfModels):\n        # Special treatment for bag of model.\n        # We explicitely apply multiple times `apply_model` so that the random shifts\n        # are different for each model.\n\n        estimates = 0\n        totals = [0] * len(model.sources)\n        bag_num = len(model.models)\n        fut_length = 0\n        prog_bar = 0\n        current_model = 0  # (bag_num + 1)\n        for sub_model, weight in zip(model.models, model.weights):\n            original_model_device = next(iter(sub_model.parameters())).device\n            sub_model.to(device)\n            fut_length += fut_lengt","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/apply.py:13"}],[1218,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/apply.py","startLine":180,"endLine":195,"chunkText":"            original_model_device = next(iter(sub_model.parameters())).device\n            sub_model.to(device)\n            fut_length += fut_length\n            current_model += 1\n            out = apply_model(sub_model, mix, **kwargs)\n            sub_model.to(original_model_device)\n            for k, inst_weight in enumerate(weight):\n                out[:, k, :, :] *= inst_weight\n                totals[k] += inst_weight\n            estimates += out\n            del out\n\n        for k in range(estimates.shape[1]):\n            estimates[:, k, :, :] /= totals[k]\n        return estimates\n","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/apply.py:14"}],[1219,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/apply.py","startLine":193,"endLine":209,"chunkText":"            estimates[:, k, :, :] /= totals[k]\n        return estimates\n\n    model.to(device)\n    model.eval()\n    assert transition_power >= 1, \"transition_power < 1 leads to weird behavior.\"\n    batch, channels, length = mix.shape\n\n    if shifts:\n        kwargs[\"shifts\"] = 0\n        max_shift = int(0.5 * model.samplerate)\n        mix = tensor_chunk(mix)\n        padded_mix = mix.padded(length + 2 * max_shift)\n        out = 0\n        for _ in range(shifts):\n            offset = random.randint(0, max_shift)\n            shifted = TensorChunk(padded_mix, offset, length + max_shift - offset)","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/apply.py:15"}],[1220,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/apply.py","startLine":207,"endLine":218,"chunkText":"        for _ in range(shifts):\n            offset = random.randint(0, max_shift)\n            shifted = TensorChunk(padded_mix, offset, length + max_shift - offset)\n            shifted_out = apply_model(model, shifted, **kwargs)\n            out += shifted_out[..., max_shift - offset :]\n        out /= shifts\n        return out\n    elif split:\n        kwargs[\"split\"] = False\n        out = th.zeros(batch, len(model.sources), channels, length, device=mix.device)\n        sum_weight = th.zeros(length, device=mix.device)\n        segment = int(model.samplerate * model.segment)","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/apply.py:16"}],[1221,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/apply.py","startLine":216,"endLine":224,"chunkText":"        out = th.zeros(batch, len(model.sources), channels, length, device=mix.device)\n        sum_weight = th.zeros(length, device=mix.device)\n        segment = int(model.samplerate * model.segment)\n        stride = int((1 - overlap) * segment)\n        offsets = range(0, length, stride)\n        scale = float(format(stride / model.samplerate, \".2f\"))\n        # We start from a triangle shaped weight, with maximal weight in the middle\n        # of the segment. Then we normalize and take to the power `transition_power`.\n        # Large values of transition power will lead to sharper transitions.","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/apply.py:17"}],[1222,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/apply.py","startLine":222,"endLine":230,"chunkText":"        # We start from a triangle shaped weight, with maximal weight in the middle\n        # of the segment. Then we normalize and take to the power `transition_power`.\n        # Large values of transition power will lead to sharper transitions.\n        weight = th.cat([th.arange(1, segment // 2 + 1, device=device), th.arange(segment - segment // 2, 0, -1, device=device)])\n        assert len(weight) == segment\n        # If the overlap < 50%, this will translate to linear transition when\n        # transition_power is 1.\n        weight = (weight / weight.max()) ** transition_power\n        futures = []","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/apply.py:18"}],[1223,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/apply.py","startLine":228,"endLine":241,"chunkText":"        # transition_power is 1.\n        weight = (weight / weight.max()) ** transition_power\n        futures = []\n        for offset in offsets:\n            chunk = TensorChunk(mix, offset, segment)\n            future = pool.submit(apply_model, model, chunk, **kwargs)\n            futures.append((future, offset))\n            offset += segment\n        if progress:\n            futures = tqdm.tqdm(futures)\n        for future, offset in futures:\n            if set_progress_bar:\n                fut_length = len(futures) * bag_num * static_shifts\n                prog_bar += 1","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/apply.py:19"}],[1224,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/apply.py","startLine":239,"endLine":251,"chunkText":"            if set_progress_bar:\n                fut_length = len(futures) * bag_num * static_shifts\n                prog_bar += 1\n                set_progress_bar(0.1, (0.8 / fut_length * prog_bar))\n            chunk_out = future.result()\n            chunk_length = chunk_out.shape[-1]\n            out[..., offset : offset + segment] += (weight[:chunk_length] * chunk_out).to(mix.device)\n            sum_weight[offset : offset + segment] += weight[:chunk_length].to(mix.device)\n        assert sum_weight.min() > 0\n        out /= sum_weight\n        return out\n    else:\n        if hasattr(model, \"valid_length\"):","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/apply.py:20"}],[1225,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/apply.py","startLine":249,"endLine":268,"chunkText":"        return out\n    else:\n        if hasattr(model, \"valid_length\"):\n            valid_length = model.valid_length(length)\n        else:\n            valid_length = length\n        mix = tensor_chunk(mix)\n        padded_mix = mix.padded(valid_length).to(device)\n        with th.no_grad():\n            out = model(padded_mix)\n        return center_trim(out, length)\n\n\ndef demucs_segments(demucs_segment, demucs_model):\n\n    if demucs_segment == \"Default\":\n        segment = None\n        if isinstance(demucs_model, BagOfModels):\n            if segment is not None:\n                for sub in demucs_model.models:","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/apply.py:21"}],[1226,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/apply.py","startLine":266,"endLine":281,"chunkText":"        if isinstance(demucs_model, BagOfModels):\n            if segment is not None:\n                for sub in demucs_model.models:\n                    sub.segment = segment\n        else:\n            if segment is not None:\n                sub.segment = segment\n    else:\n        try:\n            segment = int(demucs_segment)\n            if isinstance(demucs_model, BagOfModels):\n                if segment is not None:\n                    for sub in demucs_model.models:\n                        sub.segment = segment\n            else:\n                if segment is not None:","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/apply.py:22"}],[1227,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/apply.py","startLine":279,"endLine":294,"chunkText":"                        sub.segment = segment\n            else:\n                if segment is not None:\n                    sub.segment = segment\n        except:\n            segment = None\n            if isinstance(demucs_model, BagOfModels):\n                if segment is not None:\n                    for sub in demucs_model.models:\n                        sub.segment = segment\n            else:\n                if segment is not None:\n                    sub.segment = segment\n\n    return demucs_model\n","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/apply.py:23"}],[1228,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/demucs.py","startLine":0,"endLine":24,"chunkText":"# Copyright (c) Facebook, Inc. and its affiliates.\n# All rights reserved.\n#\n# This source code is licensed under the license found in the\n# LICENSE file in the root directory of this source tree.\n\nimport math\nimport typing as tp\n\nimport julius\nimport torch\nfrom torch import nn\nfrom torch.nn import functional as F\n\nfrom .states import capture_init\nfrom .utils import center_trim, unfold\n\n\nclass BLSTM(nn.Module):\n    \"\"\"\n    BiLSTM with same hidden units as input dim.\n    If `max_steps` is not None, input will be splitting in overlapping\n    chunks and the LSTM applied separately on each chunk.\n    \"\"\"\n","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/demucs.py:0"}],[1229,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/demucs.py","startLine":22,"endLine":38,"chunkText":"    chunks and the LSTM applied separately on each chunk.\n    \"\"\"\n\n    def __init__(self, dim, layers=1, max_steps=None, skip=False):\n        super().__init__()\n        assert max_steps is None or max_steps % 4 == 0\n        self.max_steps = max_steps\n        self.lstm = nn.LSTM(bidirectional=True, num_layers=layers, hidden_size=dim, input_size=dim)\n        self.linear = nn.Linear(2 * dim, dim)\n        self.skip = skip\n\n    def forward(self, x):\n        B, C, T = x.shape\n        y = x\n        framed = False\n        if self.max_steps is not None and T > self.max_steps:\n            width = self.max_steps","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/demucs.py:1"}],[1230,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/demucs.py","startLine":36,"endLine":54,"chunkText":"        framed = False\n        if self.max_steps is not None and T > self.max_steps:\n            width = self.max_steps\n            stride = width // 2\n            frames = unfold(x, width, stride)\n            nframes = frames.shape[2]\n            framed = True\n            x = frames.permute(0, 2, 1, 3).reshape(-1, C, width)\n\n        x = x.permute(2, 0, 1)\n\n        x = self.lstm(x)[0]\n        x = self.linear(x)\n        x = x.permute(1, 2, 0)\n        if framed:\n            out = []\n            frames = x.reshape(B, -1, C, width)\n            limit = stride // 2\n            for k in range(nframes):","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/demucs.py:2"}],[1231,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/demucs.py","startLine":52,"endLine":69,"chunkText":"            frames = x.reshape(B, -1, C, width)\n            limit = stride // 2\n            for k in range(nframes):\n                if k == 0:\n                    out.append(frames[:, k, :, :-limit])\n                elif k == nframes - 1:\n                    out.append(frames[:, k, :, limit:])\n                else:\n                    out.append(frames[:, k, :, limit:-limit])\n            out = torch.cat(out, -1)\n            out = out[..., :T]\n            x = out\n        if self.skip:\n            x = x + y\n        return x\n\n\ndef rescale_conv(conv, reference):","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/demucs.py:3"}],[1232,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/demucs.py","startLine":67,"endLine":83,"chunkText":"\n\ndef rescale_conv(conv, reference):\n    \"\"\"Rescale initial weight scale. It is unclear why it helps but it certainly does.\"\"\"\n    std = conv.weight.std().detach()\n    scale = (std / reference) ** 0.5\n    conv.weight.data /= scale\n    if conv.bias is not None:\n        conv.bias.data /= scale\n\n\ndef rescale_module(module, reference):\n    for sub in module.modules():\n        if isinstance(sub, (nn.Conv1d, nn.ConvTranspose1d, nn.Conv2d, nn.ConvTranspose2d)):\n            rescale_conv(sub, reference)\n\n","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/demucs.py:4"}],[1233,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/demucs.py","startLine":84,"endLine":97,"chunkText":"class LayerScale(nn.Module):\n    \"\"\"Layer scale from [Touvron et al 2021] (https://arxiv.org/pdf/2103.17239.pdf).\n    This rescales diagonaly residual outputs close to 0 initially, then learnt.\n    \"\"\"\n\n    def __init__(self, channels: int, init: float = 0):\n        super().__init__()\n        self.scale = nn.Parameter(torch.zeros(channels, requires_grad=True))\n        self.scale.data[:] = init\n\n    def forward(self, x):\n        return self.scale[:, None] * x\n\n","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/demucs.py:5"}],[1234,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/demucs.py","startLine":98,"endLine":109,"chunkText":"class DConv(nn.Module):\n    \"\"\"\n    New residual branches in each encoder layer.\n    This alternates dilated convolutions, potentially with LSTMs and attention.\n    Also before entering each residual branch, dimension is projected on a smaller subspace,\n    e.g. of dim `channels // compress`.\n    \"\"\"\n\n    def __init__(self, channels: int, compress: float = 4, depth: int = 2, init: float = 1e-4, norm=True, attn=False, heads=4, ndecay=4, lstm=False, gelu=True, kernel=3, dilate=True):\n        \"\"\"\n        Args:\n            channels: input/output channels for residual branch.","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/demucs.py:6"}],[1235,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/demucs.py","startLine":107,"endLine":118,"chunkText":"        \"\"\"\n        Args:\n            channels: input/output channels for residual branch.\n            compress: amount of channel compression inside the branch.\n            depth: number of layers in the residual branch. Each layer has its own\n                projection, and potentially LSTM and attention.\n            init: initial scale for LayerNorm.\n            norm: use GroupNorm.\n            attn: use LocalAttention.\n            heads: number of heads for the LocalAttention.\n            ndecay: number of decay controls in the LocalAttention.\n            lstm: use LSTM.","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/demucs.py:7"}],[1236,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/demucs.py","startLine":116,"endLine":131,"chunkText":"            heads: number of heads for the LocalAttention.\n            ndecay: number of decay controls in the LocalAttention.\n            lstm: use LSTM.\n            gelu: Use GELU activation.\n            kernel: kernel size for the (dilated) convolutions.\n            dilate: if true, use dilation, increasing with the depth.\n        \"\"\"\n\n        super().__init__()\n        assert kernel % 2 == 1\n        self.channels = channels\n        self.compress = compress\n        self.depth = abs(depth)\n        dilate = depth > 0\n\n        norm_fn: tp.Callable[[int], nn.Module]","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/demucs.py:8"}],[1237,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/demucs.py","startLine":129,"endLine":148,"chunkText":"        dilate = depth > 0\n\n        norm_fn: tp.Callable[[int], nn.Module]\n        norm_fn = lambda d: nn.Identity()  # noqa\n        if norm:\n            norm_fn = lambda d: nn.GroupNorm(1, d)  # noqa\n\n        hidden = int(channels / compress)\n\n        act: tp.Type[nn.Module]\n        if gelu:\n            act = nn.GELU\n        else:\n            act = nn.ReLU\n\n        self.layers = nn.ModuleList([])\n        for d in range(self.depth):\n            dilation = 2**d if dilate else 1\n            padding = dilation * (kernel // 2)\n            mods = [","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/demucs.py:9"}],[1238,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/demucs.py","startLine":146,"endLine":159,"chunkText":"            dilation = 2**d if dilate else 1\n            padding = dilation * (kernel // 2)\n            mods = [\n                nn.Conv1d(channels, hidden, kernel, dilation=dilation, padding=padding),\n                norm_fn(hidden),\n                act(),\n                nn.Conv1d(hidden, 2 * channels, 1),\n                norm_fn(2 * channels),\n                nn.GLU(1),\n                LayerScale(channels, init),\n            ]\n            if attn:\n                mods.insert(3, LocalState(hidden, heads=heads, ndecay=ndecay))\n            if lstm:","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/demucs.py:10"}],[1239,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/demucs.py","startLine":157,"endLine":169,"chunkText":"            if attn:\n                mods.insert(3, LocalState(hidden, heads=heads, ndecay=ndecay))\n            if lstm:\n                mods.insert(3, BLSTM(hidden, layers=2, max_steps=200, skip=True))\n            layer = nn.Sequential(*mods)\n            self.layers.append(layer)\n\n    def forward(self, x):\n        for layer in self.layers:\n            x = x + layer(x)\n        return x\n\n","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/demucs.py:11"}],[1240,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/demucs.py","startLine":170,"endLine":182,"chunkText":"class LocalState(nn.Module):\n    \"\"\"Local state allows to have attention based only on data (no positional embedding),\n    but while setting a constraint on the time window (e.g. decaying penalty term).\n\n    Also a failed experiments with trying to provide some frequency based attention.\n    \"\"\"\n\n    def __init__(self, channels: int, heads: int = 4, nfreqs: int = 0, ndecay: int = 4):\n        super().__init__()\n        assert channels % heads == 0, (channels, heads)\n        self.heads = heads\n        self.nfreqs = nfreqs\n        self.ndecay = ndecay","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/demucs.py:12"}],[1241,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/demucs.py","startLine":180,"endLine":191,"chunkText":"        self.heads = heads\n        self.nfreqs = nfreqs\n        self.ndecay = ndecay\n        self.content = nn.Conv1d(channels, channels, 1)\n        self.query = nn.Conv1d(channels, channels, 1)\n        self.key = nn.Conv1d(channels, channels, 1)\n        if nfreqs:\n            self.query_freqs = nn.Conv1d(channels, heads * nfreqs, 1)\n        if ndecay:\n            self.query_decay = nn.Conv1d(channels, heads * ndecay, 1)\n            # Initialize decay close to zero (there is a sigmoid), for maximum initial window.\n            self.query_decay.weight.data *= 0.01","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/demucs.py:13"}],[1242,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/demucs.py","startLine":189,"endLine":200,"chunkText":"            self.query_decay = nn.Conv1d(channels, heads * ndecay, 1)\n            # Initialize decay close to zero (there is a sigmoid), for maximum initial window.\n            self.query_decay.weight.data *= 0.01\n            assert self.query_decay.bias is not None  # stupid type checker\n            self.query_decay.bias.data[:] = -2\n        self.proj = nn.Conv1d(channels + heads * nfreqs, channels, 1)\n\n    def forward(self, x):\n        B, C, T = x.shape\n        heads = self.heads\n        indexes = torch.arange(T, device=x.device, dtype=x.dtype)\n        # left index are keys, right index are queries","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/demucs.py:14"}],[1243,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/demucs.py","startLine":198,"endLine":209,"chunkText":"        heads = self.heads\n        indexes = torch.arange(T, device=x.device, dtype=x.dtype)\n        # left index are keys, right index are queries\n        delta = indexes[:, None] - indexes[None, :]\n\n        queries = self.query(x).view(B, heads, -1, T)\n        keys = self.key(x).view(B, heads, -1, T)\n        # t are keys, s are queries\n        dots = torch.einsum(\"bhct,bhcs->bhts\", keys, queries)\n        dots /= keys.shape[2] ** 0.5\n        if self.nfreqs:\n            periods = torch.arange(1, self.nfreqs + 1, device=x.device, dtype=x.dtype)","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/demucs.py:15"}],[1244,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/demucs.py","startLine":207,"endLine":216,"chunkText":"        dots /= keys.shape[2] ** 0.5\n        if self.nfreqs:\n            periods = torch.arange(1, self.nfreqs + 1, device=x.device, dtype=x.dtype)\n            freq_kernel = torch.cos(2 * math.pi * delta / periods.view(-1, 1, 1))\n            freq_q = self.query_freqs(x).view(B, heads, -1, T) / self.nfreqs**0.5\n            dots += torch.einsum(\"fts,bhfs->bhts\", freq_kernel, freq_q)\n        if self.ndecay:\n            decays = torch.arange(1, self.ndecay + 1, device=x.device, dtype=x.dtype)\n            decay_q = self.query_decay(x).view(B, heads, -1, T)\n            decay_q = torch.sigmoid(decay_q) / 2","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/demucs.py:16"}],[1245,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/demucs.py","startLine":214,"endLine":224,"chunkText":"            decays = torch.arange(1, self.ndecay + 1, device=x.device, dtype=x.dtype)\n            decay_q = self.query_decay(x).view(B, heads, -1, T)\n            decay_q = torch.sigmoid(decay_q) / 2\n            decay_kernel = -decays.view(-1, 1, 1) * delta.abs() / self.ndecay**0.5\n            dots += torch.einsum(\"fts,bhfs->bhts\", decay_kernel, decay_q)\n\n        # Kill self reference.\n        dots.masked_fill_(torch.eye(T, device=dots.device, dtype=torch.bool), -100)\n        weights = torch.softmax(dots, dim=2)\n\n        content = self.content(x).view(B, heads, -1, T)","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/demucs.py:17"}],[1246,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/demucs.py","startLine":222,"endLine":232,"chunkText":"        weights = torch.softmax(dots, dim=2)\n\n        content = self.content(x).view(B, heads, -1, T)\n        result = torch.einsum(\"bhts,bhct->bhcs\", weights, content)\n        if self.nfreqs:\n            time_sig = torch.einsum(\"bhts,fts->bhfs\", weights, freq_kernel)\n            result = torch.cat([result, time_sig], 2)\n        result = result.reshape(B, -1, T)\n        return x + self.proj(result)\n\n","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/demucs.py:18"}],[1247,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/demucs.py","startLine":233,"endLine":260,"chunkText":"class Demucs(nn.Module):\n    @capture_init\n    def __init__(\n        self,\n        sources,\n        # Channels\n        audio_channels=2,\n        channels=64,\n        growth=2.0,\n        # Main structure\n        depth=6,\n        rewrite=True,\n        lstm_layers=0,\n        # Convolutions\n        kernel_size=8,\n        stride=4,\n        context=1,\n        # Activations\n        gelu=True,\n        glu=True,\n        # Normalization\n        norm_starts=4,\n        norm_groups=4,\n        # DConv residual branch\n        dconv_mode=1,\n        dconv_depth=2,\n        dconv_comp=4,\n        dconv_attn=4,","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/demucs.py:19"}],[1248,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/demucs.py","startLine":258,"endLine":277,"chunkText":"        dconv_depth=2,\n        dconv_comp=4,\n        dconv_attn=4,\n        dconv_lstm=4,\n        dconv_init=1e-4,\n        # Pre/post processing\n        normalize=True,\n        resample=True,\n        # Weight init\n        rescale=0.1,\n        # Metadata\n        samplerate=44100,\n        segment=4 * 10,\n    ):\n        \"\"\"\n        Args:\n            sources (list[str]): list of source names\n            audio_channels (int): stereo or mono\n            channels (int): first convolution channels\n            depth (int): number of encoder/decoder layers","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/demucs.py:20"}],[1249,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/demucs.py","startLine":275,"endLine":283,"chunkText":"            audio_channels (int): stereo or mono\n            channels (int): first convolution channels\n            depth (int): number of encoder/decoder layers\n            growth (float): multiply (resp divide) number of channels by that\n                for each layer of the encoder (resp decoder)\n            depth (int): number of layers in the encoder and in the decoder.\n            rewrite (bool): add 1x1 convolution to each layer.\n            lstm_layers (int): number of lstm layers, 0 = no lstm. Deactivated\n                by default, as this is now replaced by the smaller and faster small LSTMs","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/demucs.py:21"}],[1250,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/demucs.py","startLine":281,"endLine":289,"chunkText":"            rewrite (bool): add 1x1 convolution to each layer.\n            lstm_layers (int): number of lstm layers, 0 = no lstm. Deactivated\n                by default, as this is now replaced by the smaller and faster small LSTMs\n                in the DConv branches.\n            kernel_size (int): kernel size for convolutions\n            stride (int): stride for convolutions\n            context (int): kernel size of the convolution in the\n                decoder before the transposed convolution. If > 1,\n                will provide some context from neighboring time steps.","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/demucs.py:22"}],[1251,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/demucs.py","startLine":287,"endLine":295,"chunkText":"            context (int): kernel size of the convolution in the\n                decoder before the transposed convolution. If > 1,\n                will provide some context from neighboring time steps.\n            gelu: use GELU activation function.\n            glu (bool): use glu instead of ReLU for the 1x1 rewrite conv.\n            norm_starts: layer at which group norm starts being used.\n                decoder layers are numbered in reverse order.\n            norm_groups: number of groups for group norm.\n            dconv_mode: if 1: dconv in encoder only, 2: decoder only, 3: both.","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/demucs.py:23"}],[1252,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/demucs.py","startLine":293,"endLine":300,"chunkText":"                decoder layers are numbered in reverse order.\n            norm_groups: number of groups for group norm.\n            dconv_mode: if 1: dconv in encoder only, 2: decoder only, 3: both.\n            dconv_depth: depth of residual DConv branch.\n            dconv_comp: compression of DConv branch.\n            dconv_attn: adds attention layers in DConv branch starting at this layer.\n            dconv_lstm: adds a LSTM layer in DConv branch starting at this layer.\n            dconv_init: initial scale for the DConv branch LayerScale.","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/demucs.py:24"}],[1253,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/demucs.py","startLine":298,"endLine":305,"chunkText":"            dconv_attn: adds attention layers in DConv branch starting at this layer.\n            dconv_lstm: adds a LSTM layer in DConv branch starting at this layer.\n            dconv_init: initial scale for the DConv branch LayerScale.\n            normalize (bool): normalizes the input audio on the fly, and scales back\n                the output by the same amount.\n            resample (bool): upsample x2 the input and downsample /2 the output.\n            rescale (int): rescale initial weights of convolutions\n                to get their standard deviation closer to `rescale`.","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/demucs.py:25"}],[1254,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/demucs.py","startLine":303,"endLine":314,"chunkText":"            resample (bool): upsample x2 the input and downsample /2 the output.\n            rescale (int): rescale initial weights of convolutions\n                to get their standard deviation closer to `rescale`.\n            samplerate (int): stored as meta information for easing\n                future evaluations of the model.\n            segment (float): duration of the chunks of audio to ideally evaluate the model on.\n                This is used by `demucs.apply.apply_model`.\n        \"\"\"\n\n        super().__init__()\n        self.audio_channels = audio_channels\n        self.sources = sources","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/demucs.py:26"}],[1255,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/demucs.py","startLine":312,"endLine":331,"chunkText":"        super().__init__()\n        self.audio_channels = audio_channels\n        self.sources = sources\n        self.kernel_size = kernel_size\n        self.context = context\n        self.stride = stride\n        self.depth = depth\n        self.resample = resample\n        self.channels = channels\n        self.normalize = normalize\n        self.samplerate = samplerate\n        self.segment = segment\n        self.encoder = nn.ModuleList()\n        self.decoder = nn.ModuleList()\n        self.skip_scales = nn.ModuleList()\n\n        if glu:\n            activation = nn.GLU(dim=1)\n            ch_scale = 2\n        else","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/demucs.py:27"}],[1256,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/demucs.py","startLine":329,"endLine":347,"chunkText":"            activation = nn.GLU(dim=1)\n            ch_scale = 2\n        else:\n            activation = nn.ReLU()\n            ch_scale = 1\n        if gelu:\n            act2 = nn.GELU\n        else:\n            act2 = nn.ReLU\n\n        in_channels = audio_channels\n        padding = 0\n        for index in range(depth):\n            norm_fn = lambda d: nn.Identity()  # noqa\n            if index >= norm_starts:\n                norm_fn = lambda d: nn.GroupNorm(norm_groups, d)  # noqa\n\n            encode = []\n            encode += [nn.Conv1d(in_channels, channels, kernel_size, stride), norm_fn(channels), act2()]","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/demucs.py:28"}],[1257,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/demucs.py","startLine":345,"endLine":357,"chunkText":"\n            encode = []\n            encode += [nn.Conv1d(in_channels, channels, kernel_size, stride), norm_fn(channels), act2()]\n            attn = index >= dconv_attn\n            lstm = index >= dconv_lstm\n            if dconv_mode & 1:\n                encode += [DConv(channels, depth=dconv_depth, init=dconv_init, compress=dconv_comp, attn=attn, lstm=lstm)]\n            if rewrite:\n                encode += [nn.Conv1d(channels, ch_scale * channels, 1), norm_fn(ch_scale * channels), activation]\n            self.encoder.append(nn.Sequential(*encode))\n\n            decode = []\n            if index > 0:","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/demucs.py:29"}],[1258,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/demucs.py","startLine":355,"endLine":365,"chunkText":"\n            decode = []\n            if index > 0:\n                out_channels = in_channels\n            else:\n                out_channels = len(self.sources) * audio_channels\n            if rewrite:\n                decode += [nn.Conv1d(channels, ch_scale * channels, 2 * context + 1, padding=context), norm_fn(ch_scale * channels), activation]\n            if dconv_mode & 2:\n                decode += [DConv(channels, depth=dconv_depth, init=dconv_init, compress=dconv_comp, attn=attn, lstm=lstm)]\n            decode += [nn.ConvTranspose1d(channels, out_channels, kernel_size, stride, padding=padding)]","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/demucs.py:30"}],[1259,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/demucs.py","startLine":363,"endLine":375,"chunkText":"            if dconv_mode & 2:\n                decode += [DConv(channels, depth=dconv_depth, init=dconv_init, compress=dconv_comp, attn=attn, lstm=lstm)]\n            decode += [nn.ConvTranspose1d(channels, out_channels, kernel_size, stride, padding=padding)]\n            if index > 0:\n                decode += [norm_fn(out_channels), act2()]\n            self.decoder.insert(0, nn.Sequential(*decode))\n            in_channels = channels\n            channels = int(growth * channels)\n\n        channels = in_channels\n        if lstm_layers:\n            self.lstm = BLSTM(channels, lstm_layers)\n        else:","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/demucs.py:31"}],[1260,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/demucs.py","startLine":373,"endLine":389,"chunkText":"        if lstm_layers:\n            self.lstm = BLSTM(channels, lstm_layers)\n        else:\n            self.lstm = None\n\n        if rescale:\n            rescale_module(self, reference=rescale)\n\n    def valid_length(self, length):\n        \"\"\"\n        Return the nearest valid length to use with the model so that\n        there is no time steps left over in a convolution, e.g. for all\n        layers, size of the input - kernel_size % stride = 0.\n\n        Note that input are automatically padded if necessary to ensure that the output\n        has the same length as the input.\n        \"\"\"","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/demucs.py:32"}],[1261,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/demucs.py","startLine":388,"endLine":409,"chunkText":"        has the same length as the input.\n        \"\"\"\n        if self.resample:\n            length *= 2\n\n        for _ in range(self.depth):\n            length = math.ceil((length - self.kernel_size) / self.stride) + 1\n            length = max(1, length)\n\n        for idx in range(self.depth):\n            length = (length - 1) * self.stride + self.kernel_size\n\n        if self.resample:\n            length = math.ceil(length / 2)\n        return int(length)\n\n    def forward(self, mix):\n        x = mix\n        length = x.shape[-1]\n\n        if self.normalize:\n            mono = mix.mean(dim=1, keepdim=True)","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/demucs.py:33"}],[1262,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/demucs.py","startLine":407,"endLine":427,"chunkText":"        if self.normalize:\n            mono = mix.mean(dim=1, keepdim=True)\n            mean = mono.mean(dim=-1, keepdim=True)\n            std = mono.std(dim=-1, keepdim=True)\n            x = (x - mean) / (1e-5 + std)\n        else:\n            mean = 0\n            std = 1\n\n        delta = self.valid_length(length) - length\n        x = F.pad(x, (delta // 2, delta - delta // 2))\n\n        if self.resample:\n            x = julius.resample_frac(x, 1, 2)\n\n        saved = []\n        for encode in self.encoder:\n            x = encode(x)\n            saved.append(x)\n\n        if self.lstm:","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/demucs.py:34"}],[1263,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/demucs.py","startLine":428,"endLine":445,"chunkText":"        if self.lstm:\n            x = self.lstm(x)\n\n        for decode in self.decoder:\n            skip = saved.pop(-1)\n            skip = center_trim(skip, x)\n            x = decode(x + skip)\n\n        if self.resample:\n            x = julius.resample_frac(x, 2, 1)\n        x = x * std + mean\n        x = center_trim(x, length)\n        x = x.view(x.size(0), len(self.sources), self.audio_channels, x.size(-1))\n        return x\n\n    def load_state_dict(self, state, strict=True):\n        # fix a mismatch with previous generation Demucs models.\n        for idx in range(self.depth):","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/demucs.py:35"}],[1264,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/demucs.py","startLine":443,"endLine":453,"chunkText":"    def load_state_dict(self, state, strict=True):\n        # fix a mismatch with previous generation Demucs models.\n        for idx in range(self.depth):\n            for a in [\"encoder\", \"decoder\"]:\n                for b in [\"bias\", \"weight\"]:\n                    new = f\"{a}.{idx}.3.{b}\"\n                    old = f\"{a}.{idx}.2.{b}\"\n                    if old in state and new not in state:\n                        state[new] = state.pop(old)\n        super().load_state_dict(state, strict=strict)\n","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/demucs.py:36"}],[1265,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/filtering.py","startLine":0,"endLine":22,"chunkText":"from typing import Optional\nimport torch\nimport torch.nn as nn\nfrom torch import Tensor\nfrom torch.utils.data import DataLoader\n\n\ndef atan2(y, x):\n    r\"\"\"Element-wise arctangent function of y/x.\n    Returns a new tensor with signed angles in radians.\n    It is an alternative implementation of torch.atan2\n\n    Args:\n        y (Tensor): First input tensor\n        x (Tensor): Second input tensor [shape=y.shape]\n\n    Returns:\n        Tensor: [shape=y.shape].\n    \"\"\"\n    pi = 2 * torch.asin(torch.tensor(1.0))\n    x += ((x == 0) & (y == 0)) * 1.0\n    out = torch.atan(y / x)\n    out += ((y >= 0) & (x < 0)) * pi","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/filtering.py:0"}],[1266,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/filtering.py","startLine":20,"endLine":36,"chunkText":"    x += ((x == 0) & (y == 0)) * 1.0\n    out = torch.atan(y / x)\n    out += ((y >= 0) & (x < 0)) * pi\n    out -= ((y < 0) & (x < 0)) * pi\n    out *= 1 - ((y > 0) & (x == 0)) * 1.0\n    out += ((y > 0) & (x == 0)) * (pi / 2)\n    out *= 1 - ((y < 0) & (x == 0)) * 1.0\n    out += ((y < 0) & (x == 0)) * (-pi / 2)\n    return out\n\n\n# Define basic complex operations on torch.Tensor objects whose last dimension\n# consists in the concatenation of the real and imaginary parts.\n\n\ndef _norm(x: torch.Tensor) -> torch.Tensor:\n    r\"\"\"Computes the norm value of a torch Tensor, assuming that it","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/filtering.py:1"}],[1267,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/filtering.py","startLine":34,"endLine":50,"chunkText":"\ndef _norm(x: torch.Tensor) -> torch.Tensor:\n    r\"\"\"Computes the norm value of a torch Tensor, assuming that it\n    comes as real and imaginary part in its last dimension.\n\n    Args:\n        x (Tensor): Input Tensor of shape [shape=(..., 2)]\n\n    Returns:\n        Tensor: shape as x excluding the last dimension.\n    \"\"\"\n    return torch.abs(x[..., 0]) ** 2 + torch.abs(x[..., 1]) ** 2\n\n\ndef _mul_add(a: torch.Tensor, b: torch.Tensor, out: Optional[torch.Tensor] = None) -> torch.Tensor:\n    \"\"\"Element-wise multiplication of two complex Tensors described\n    through their real and imaginary parts.","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/filtering.py:2"}],[1268,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/filtering.py","startLine":48,"endLine":58,"chunkText":"def _mul_add(a: torch.Tensor, b: torch.Tensor, out: Optional[torch.Tensor] = None) -> torch.Tensor:\n    \"\"\"Element-wise multiplication of two complex Tensors described\n    through their real and imaginary parts.\n    The result is added to the `out` tensor\"\"\"\n\n    # check `out` and allocate it if needed\n    target_shape = torch.Size([max(sa, sb) for (sa, sb) in zip(a.shape, b.shape)])\n    if out is None or out.shape != target_shape:\n        out = torch.zeros(target_shape, dtype=a.dtype, device=a.device)\n    if out is a:\n        real_a = a[..., 0]","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/filtering.py:3"}],[1269,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/filtering.py","startLine":56,"endLine":67,"chunkText":"        out = torch.zeros(target_shape, dtype=a.dtype, device=a.device)\n    if out is a:\n        real_a = a[..., 0]\n        out[..., 0] = out[..., 0] + (real_a * b[..., 0] - a[..., 1] * b[..., 1])\n        out[..., 1] = out[..., 1] + (real_a * b[..., 1] + a[..., 1] * b[..., 0])\n    else:\n        out[..., 0] = out[..., 0] + (a[..., 0] * b[..., 0] - a[..., 1] * b[..., 1])\n        out[..., 1] = out[..., 1] + (a[..., 0] * b[..., 1] + a[..., 1] * b[..., 0])\n    return out\n\n\ndef _mul(a: torch.Tensor, b: torch.Tensor, out: Optional[torch.Tensor] = None) -> torch.Tensor:","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/filtering.py:4"}],[1270,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/filtering.py","startLine":65,"endLine":76,"chunkText":"\n\ndef _mul(a: torch.Tensor, b: torch.Tensor, out: Optional[torch.Tensor] = None) -> torch.Tensor:\n    \"\"\"Element-wise multiplication of two complex Tensors described\n    through their real and imaginary parts\n    can work in place in case out is a only\"\"\"\n    target_shape = torch.Size([max(sa, sb) for (sa, sb) in zip(a.shape, b.shape)])\n    if out is None or out.shape != target_shape:\n        out = torch.zeros(target_shape, dtype=a.dtype, device=a.device)\n    if out is a:\n        real_a = a[..., 0]\n        out[..., 0] = real_a * b[..., 0] - a[..., 1] * b[..., 1]","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/filtering.py:5"}],[1271,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/filtering.py","startLine":74,"endLine":88,"chunkText":"    if out is a:\n        real_a = a[..., 0]\n        out[..., 0] = real_a * b[..., 0] - a[..., 1] * b[..., 1]\n        out[..., 1] = real_a * b[..., 1] + a[..., 1] * b[..., 0]\n    else:\n        out[..., 0] = a[..., 0] * b[..., 0] - a[..., 1] * b[..., 1]\n        out[..., 1] = a[..., 0] * b[..., 1] + a[..., 1] * b[..., 0]\n    return out\n\n\ndef _inv(z: torch.Tensor, out: Optional[torch.Tensor] = None) -> torch.Tensor:\n    \"\"\"Element-wise multiplicative inverse of a Tensor with complex\n    entries described through their real and imaginary parts.\n    can work in place in case out is z\"\"\"\n    ez = _norm(z)","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/filtering.py:6"}],[1272,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/filtering.py","startLine":86,"endLine":101,"chunkText":"    entries described through their real and imaginary parts.\n    can work in place in case out is z\"\"\"\n    ez = _norm(z)\n    if out is None or out.shape != z.shape:\n        out = torch.zeros_like(z)\n    out[..., 0] = z[..., 0] / ez\n    out[..., 1] = -z[..., 1] / ez\n    return out\n\n\ndef _conj(z, out: Optional[torch.Tensor] = None) -> torch.Tensor:\n    \"\"\"Element-wise complex conjugate of a Tensor with complex entries\n    described through their real and imaginary parts.\n    can work in place in case out is z\"\"\"\n    if out is None or out.shape != z.shape:\n        out = torch.zeros_like(z)","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/filtering.py:7"}],[1273,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/filtering.py","startLine":99,"endLine":118,"chunkText":"    can work in place in case out is z\"\"\"\n    if out is None or out.shape != z.shape:\n        out = torch.zeros_like(z)\n    out[..., 0] = z[..., 0]\n    out[..., 1] = -z[..., 1]\n    return out\n\n\ndef _invert(M: torch.Tensor, out: Optional[torch.Tensor] = None) -> torch.Tensor:\n    \"\"\"\n    Invert 1x1 or 2x2 matrices\n\n    Will generate errors if the matrices are singular: user must handle this\n    through his own regularization schemes.\n\n    Args:\n        M (Tensor): [shape=(..., nb_channels, nb_channels, 2)]\n            matrices to invert: must be square along dimensions -3 and -2\n\n    Returns:","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/filtering.py:8"}],[1274,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/filtering.py","startLine":116,"endLine":136,"chunkText":"            matrices to invert: must be square along dimensions -3 and -2\n\n    Returns:\n        invM (Tensor): [shape=M.shape]\n            inverses of M\n    \"\"\"\n    nb_channels = M.shape[-2]\n\n    if out is None or out.shape != M.shape:\n        out = torch.empty_like(M)\n\n    if nb_channels == 1:\n        # scalar case\n        out = _inv(M, out)\n    elif nb_channels == 2:\n        # two channels case: analytical expression\n\n        # first compute the determinent\n        det = _mul(M[..., 0, 0, :], M[..., 1, 1, :])\n        det = det - _mul(M[..., 0, 1, :], M[..., 1, 0, :])\n        # invert it","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/filtering.py:9"}],[1275,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/filtering.py","startLine":134,"endLine":145,"chunkText":"        det = _mul(M[..., 0, 0, :], M[..., 1, 1, :])\n        det = det - _mul(M[..., 0, 1, :], M[..., 1, 0, :])\n        # invert it\n        invDet = _inv(det)\n\n        # then fill out the matrix with the inverse\n        out[..., 0, 0, :] = _mul(invDet, M[..., 1, 1, :], out[..., 0, 0, :])\n        out[..., 1, 0, :] = _mul(-invDet, M[..., 1, 0, :], out[..., 1, 0, :])\n        out[..., 0, 1, :] = _mul(-invDet, M[..., 0, 1, :], out[..., 0, 1, :])\n        out[..., 1, 1, :] = _mul(invDet, M[..., 0, 0, :], out[..., 1, 1, :])\n    else:\n        raise Exception(\"Only 2 channels are supported for the torch version.\")","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/filtering.py:10"}],[1276,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/filtering.py","startLine":143,"endLine":156,"chunkText":"        out[..., 1, 1, :] = _mul(invDet, M[..., 0, 0, :], out[..., 1, 1, :])\n    else:\n        raise Exception(\"Only 2 channels are supported for the torch version.\")\n    return out\n\n\n# Now define the signal-processing low-level functions used by the Separator\n\n\ndef expectation_maximization(y: torch.Tensor, x: torch.Tensor, iterations: int = 2, eps: float = 1e-10, batch_size: int = 200):\n    r\"\"\"Expectation maximization algorithm, for refining source separation\n    estimates.\n\n    This algorithm allows to make source separation results better by","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/filtering.py:11"}],[1277,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/filtering.py","startLine":154,"endLine":167,"chunkText":"    estimates.\n\n    This algorithm allows to make source separation results better by\n    enforcing multichannel consistency for the estimates. This usually means\n    a better perceptual quality in terms of spatial artifacts.\n\n    The implementation follows the details presented in [1]_, taking\n    inspiration from the original EM algorithm proposed in [2]_ and its\n    weighted refinement proposed in [3]_, [4]_.\n    It works by iteratively:\n\n     * Re-estimate source parameters (power spectral densities and spatial\n       covariance matrices) through :func:`get_local_gaussian_model`.\n","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/filtering.py:12"}],[1278,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/filtering.py","startLine":165,"endLine":176,"chunkText":"     * Re-estimate source parameters (power spectral densities and spatial\n       covariance matrices) through :func:`get_local_gaussian_model`.\n\n     * Separate again the mixture with the new parameters by first computing\n       the new modelled mixture covariance matrices with :func:`get_mix_model`,\n       prepare the Wiener filters through :func:`wiener_gain` and apply them\n       with :func:`apply_filter``.\n\n    References\n    ----------\n    .. [1] S. Uhlich and M. Porcu and F. Giron and M. Enenkl and T. Kemp and\n        N. Takahashi and Y. Mitsufuji, \"Improving music source separation based","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/filtering.py:13"}],[1279,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/filtering.py","startLine":174,"endLine":183,"chunkText":"    ----------\n    .. [1] S. Uhlich and M. Porcu and F. Giron and M. Enenkl and T. Kemp and\n        N. Takahashi and Y. Mitsufuji, \"Improving music source separation based\n        on deep neural networks through data augmentation and network\n        blending.\" 2017 IEEE International Conference on Acoustics, Speech\n        and Signal Processing (ICASSP). IEEE, 2017.\n\n    .. [2] N.Q. Duong and E. Vincent and R.Gribonval. \"Under-determined\n        reverberant audio source separation using a full-rank spatial\n        covariance model.\" IEEE Transactions on Audio, Speech, and Language","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/filtering.py:14"}],[1280,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/filtering.py","startLine":181,"endLine":190,"chunkText":"    .. [2] N.Q. Duong and E. Vincent and R.Gribonval. \"Under-determined\n        reverberant audio source separation using a full-rank spatial\n        covariance model.\" IEEE Transactions on Audio, Speech, and Language\n        Processing 18.7 (2010): 1830-1840.\n\n    .. [3] A. Nugraha and A. Liutkus and E. Vincent. \"Multichannel audio source\n        separation with deep neural networks.\" IEEE/ACM Transactions on Audio,\n        Speech, and Language Processing 24.9 (2016): 1652-1664.\n\n    .. [4] A. Nugraha and A. Liutkus and E. Vincent. \"Multichannel music","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/filtering.py:15"}],[1281,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/filtering.py","startLine":188,"endLine":200,"chunkText":"        Speech, and Language Processing 24.9 (2016): 1652-1664.\n\n    .. [4] A. Nugraha and A. Liutkus and E. Vincent. \"Multichannel music\n        separation with deep neural networks.\" 2016 24th European Signal\n        Processing Conference (EUSIPCO). IEEE, 2016.\n\n    .. [5] A. Liutkus and R. Badeau and G. Richard \"Kernel additive models for\n        source separation.\" IEEE Transactions on Signal Processing\n        62.16 (2014): 4298-4310.\n\n    Args:\n        y (Tensor): [shape=(nb_frames, nb_bins, nb_channels, 2, nb_sources)]\n            initial estimates for the sources","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/filtering.py:16"}],[1282,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/filtering.py","startLine":198,"endLine":210,"chunkText":"    Args:\n        y (Tensor): [shape=(nb_frames, nb_bins, nb_channels, 2, nb_sources)]\n            initial estimates for the sources\n        x (Tensor): [shape=(nb_frames, nb_bins, nb_channels, 2)]\n            complex STFT of the mixture signal\n        iterations (int): [scalar]\n            number of iterations for the EM algorithm.\n        eps (float or None): [scalar]\n            The epsilon value to use for regularization and filters.\n\n    Returns:\n        y (Tensor): [shape=(nb_frames, nb_bins, nb_channels, 2, nb_sources)]\n            estimated sources after iterations","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/filtering.py:17"}],[1283,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/filtering.py","startLine":208,"endLine":219,"chunkText":"    Returns:\n        y (Tensor): [shape=(nb_frames, nb_bins, nb_channels, 2, nb_sources)]\n            estimated sources after iterations\n        v (Tensor): [shape=(nb_frames, nb_bins, nb_sources)]\n            estimated power spectral densities\n        R (Tensor): [shape=(nb_bins, nb_channels, nb_channels, 2, nb_sources)]\n            estimated spatial covariance matrices\n\n    Notes:\n        * You need an initial estimate for the sources to apply this\n          algorithm. This is precisely what the :func:`wiener` function does.\n        * This algorithm *is not* an implementation of the \"exact\" EM","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/filtering.py:18"}],[1284,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/filtering.py","startLine":217,"endLine":224,"chunkText":"        * You need an initial estimate for the sources to apply this\n          algorithm. This is precisely what the :func:`wiener` function does.\n        * This algorithm *is not* an implementation of the \"exact\" EM\n          proposed in [1]_. In particular, it does compute the posterior\n          covariance matrices the same (exact) way. Instead, it uses the\n          simplified approximate scheme initially proposed in [5]_ and further\n          refined in [3]_, [4]_, that boils down to just take the empirical\n          covariance of the recent source estimates, followed by a weighted","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/filtering.py:19"}],[1285,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/filtering.py","startLine":222,"endLine":231,"chunkText":"          simplified approximate scheme initially proposed in [5]_ and further\n          refined in [3]_, [4]_, that boils down to just take the empirical\n          covariance of the recent source estimates, followed by a weighted\n          average for the update of the spatial covariance matrix. It has been\n          empirically demonstrated that this simplified algorithm is more\n          robust for music separation.\n\n    Warning:\n        It is *very* important to make sure `x.dtype` is `torch.float64`\n        if you want double precision, because this function will **not**","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/filtering.py:20"}],[1286,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/filtering.py","startLine":229,"endLine":242,"chunkText":"    Warning:\n        It is *very* important to make sure `x.dtype` is `torch.float64`\n        if you want double precision, because this function will **not**\n        do such conversion for you from `torch.complex32`, in case you want the\n        smaller RAM usage on purpose.\n\n        It is usually always better in terms of quality to have double\n        precision, by e.g. calling :func:`expectation_maximization`\n        with ``x.to(torch.float64)``.\n    \"\"\"\n    # dimensions\n    (nb_frames, nb_bins, nb_channels) = x.shape[:-1]\n    nb_sources = y.shape[-1]\n","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/filtering.py:21"}],[1287,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/filtering.py","startLine":240,"endLine":247,"chunkText":"    (nb_frames, nb_bins, nb_channels) = x.shape[:-1]\n    nb_sources = y.shape[-1]\n\n    regularization = torch.cat((torch.eye(nb_channels, dtype=x.dtype, device=x.device)[..., None], torch.zeros((nb_channels, nb_channels, 1), dtype=x.dtype, device=x.device)), dim=2)\n    regularization = torch.sqrt(torch.as_tensor(eps)) * (regularization[None, None, ...].expand((-1, nb_bins, -1, -1, -1)))\n\n    # allocate the spatial covariance matrices\n    R = [torch.zeros((nb_bins, nb_channels, nb_channels, 2), dtype=x.dtype, device=x.device) for j in range(nb_sources)]","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/filtering.py:22"}],[1288,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/filtering.py","startLine":245,"endLine":255,"chunkText":"\n    # allocate the spatial covariance matrices\n    R = [torch.zeros((nb_bins, nb_channels, nb_channels, 2), dtype=x.dtype, device=x.device) for j in range(nb_sources)]\n    weight: torch.Tensor = torch.zeros((nb_bins,), dtype=x.dtype, device=x.device)\n\n    v: torch.Tensor = torch.zeros((nb_frames, nb_bins, nb_sources), dtype=x.dtype, device=x.device)\n    for it in range(iterations):\n        # constructing the mixture covariance matrix. Doing it with a loop\n        # to avoid storing anytime in RAM the whole 6D tensor\n\n        # update the PSD as the average spectrogram over channels","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/filtering.py:23"}],[1289,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/filtering.py","startLine":253,"endLine":264,"chunkText":"        # to avoid storing anytime in RAM the whole 6D tensor\n\n        # update the PSD as the average spectrogram over channels\n        v = torch.mean(torch.abs(y[..., 0, :]) ** 2 + torch.abs(y[..., 1, :]) ** 2, dim=-2)\n\n        # update spatial covariance matrices (weighted update)\n        for j in range(nb_sources):\n            R[j] = torch.tensor(0.0, device=x.device)\n            weight = torch.tensor(eps, device=x.device)\n            pos: int = 0\n            batch_size = batch_size if batch_size else nb_frames\n            while pos < nb_frames:","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/filtering.py:24"}],[1290,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/filtering.py","startLine":262,"endLine":276,"chunkText":"            pos: int = 0\n            batch_size = batch_size if batch_size else nb_frames\n            while pos < nb_frames:\n                t = torch.arange(pos, min(nb_frames, pos + batch_size))\n                pos = int(t[-1]) + 1\n\n                R[j] = R[j] + torch.sum(_covariance(y[t, ..., j]), dim=0)\n                weight = weight + torch.sum(v[t, ..., j], dim=0)\n            R[j] = R[j] / weight[..., None, None, None]\n            weight = torch.zeros_like(weight)\n\n        # cloning y if we track gradient, because we're going to update it\n        if y.requires_grad:\n            y = y.clone()\n","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/filtering.py:25"}],[1291,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/filtering.py","startLine":274,"endLine":292,"chunkText":"        if y.requires_grad:\n            y = y.clone()\n\n        pos = 0\n        while pos < nb_frames:\n            t = torch.arange(pos, min(nb_frames, pos + batch_size))\n            pos = int(t[-1]) + 1\n\n            y[t, ...] = torch.tensor(0.0, device=x.device, dtype=x.dtype)\n\n            # compute mix covariance matrix\n            Cxx = regularization\n            for j in range(nb_sources):\n                Cxx = Cxx + (v[t, ..., j, None, None, None] * R[j][None, ...].clone())\n\n            # invert it\n            inv_Cxx = _invert(Cxx)\n\n            # separate the sources","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/filtering.py:26"}],[1292,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/filtering.py","startLine":290,"endLine":300,"chunkText":"            inv_Cxx = _invert(Cxx)\n\n            # separate the sources\n            for j in range(nb_sources):\n\n                # create a wiener gain for this source\n                gain = torch.zeros_like(inv_Cxx)\n\n                # computes multichannel Wiener gain as v_j R_j inv_Cxx\n                indices = torch.cartesian_prod(torch.arange(nb_channels), torch.arange(nb_channels), torch.arange(nb_channels))\n                for index in indices:","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/filtering.py:27"}],[1293,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/filtering.py","startLine":298,"endLine":305,"chunkText":"                # computes multichannel Wiener gain as v_j R_j inv_Cxx\n                indices = torch.cartesian_prod(torch.arange(nb_channels), torch.arange(nb_channels), torch.arange(nb_channels))\n                for index in indices:\n                    gain[:, :, index[0], index[1], :] = _mul_add(R[j][None, :, index[0], index[2], :].clone(), inv_Cxx[:, :, index[2], index[1], :], gain[:, :, index[0], index[1], :])\n                gain = gain * v[t, ..., None, None, None, j]\n\n                # apply it to the mixture\n                for i in range(nb_channels):","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/filtering.py:28"}],[1294,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/filtering.py","startLine":303,"endLine":315,"chunkText":"\n                # apply it to the mixture\n                for i in range(nb_channels):\n                    y[t, ..., j] = _mul_add(gain[..., i, :], x[t, ..., i, None, :], y[t, ..., j])\n\n    return y, v, R\n\n\ndef wiener(targets_spectrograms: torch.Tensor, mix_stft: torch.Tensor, iterations: int = 1, softmask: bool = False, residual: bool = False, scale_factor: float = 10.0, eps: float = 1e-10):\n    \"\"\"Wiener-based separation for multichannel audio.\n\n    The method uses the (possibly multichannel) spectrograms  of the\n    sources to separate the (complex) Short Term Fourier Transform  of the","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/filtering.py:29"}],[1295,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/filtering.py","startLine":313,"endLine":326,"chunkText":"\n    The method uses the (possibly multichannel) spectrograms  of the\n    sources to separate the (complex) Short Term Fourier Transform  of the\n    mix. Separation is done in a sequential way by:\n\n    * Getting an initial estimate. This can be done in two ways: either by\n      directly using the spectrograms with the mixture phase, or\n      by using a softmasking strategy. This initial phase is controlled\n      by the `softmask` flag.\n\n    * If required, adding an additional residual target as the mix minus\n      all targets.\n\n    * Refinining these initial estimates through a call to","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/filtering.py:30"}],[1296,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/filtering.py","startLine":324,"endLine":336,"chunkText":"      all targets.\n\n    * Refinining these initial estimates through a call to\n      :func:`expectation_maximization` if the number of iterations is nonzero.\n\n    This implementation also allows to specify the epsilon value used for\n    regularization. It is based on [1]_, [2]_, [3]_, [4]_.\n\n    References\n    ----------\n    .. [1] S. Uhlich and M. Porcu and F. Giron and M. Enenkl and T. Kemp and\n        N. Takahashi and Y. Mitsufuji, \"Improving music source separation based\n        on deep neural networks through data augmentation and network","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/filtering.py:31"}],[1297,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/filtering.py","startLine":334,"endLine":343,"chunkText":"    .. [1] S. Uhlich and M. Porcu and F. Giron and M. Enenkl and T. Kemp and\n        N. Takahashi and Y. Mitsufuji, \"Improving music source separation based\n        on deep neural networks through data augmentation and network\n        blending.\" 2017 IEEE International Conference on Acoustics, Speech\n        and Signal Processing (ICASSP). IEEE, 2017.\n\n    .. [2] A. Nugraha and A. Liutkus and E. Vincent. \"Multichannel audio source\n        separation with deep neural networks.\" IEEE/ACM Transactions on Audio,\n        Speech, and Language Processing 24.9 (2016): 1652-1664.\n","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/filtering.py:32"}],[1298,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/filtering.py","startLine":341,"endLine":353,"chunkText":"        separation with deep neural networks.\" IEEE/ACM Transactions on Audio,\n        Speech, and Language Processing 24.9 (2016): 1652-1664.\n\n    .. [3] A. Nugraha and A. Liutkus and E. Vincent. \"Multichannel music\n        separation with deep neural networks.\" 2016 24th European Signal\n        Processing Conference (EUSIPCO). IEEE, 2016.\n\n    .. [4] A. Liutkus and R. Badeau and G. Richard \"Kernel additive models for\n        source separation.\" IEEE Transactions on Signal Processing\n        62.16 (2014): 4298-4310.\n\n    Args:\n        targets_spectrograms (Tensor): spectrograms of the sources","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/filtering.py:33"}],[1299,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/filtering.py","startLine":351,"endLine":362,"chunkText":"\n    Args:\n        targets_spectrograms (Tensor): spectrograms of the sources\n            [shape=(nb_frames, nb_bins, nb_channels, nb_sources)].\n            This is a nonnegative tensor that is\n            usually the output of the actual separation method of the user. The\n            spectrograms may be mono, but they need to be 4-dimensional in all\n            cases.\n        mix_stft (Tensor): [shape=(nb_frames, nb_bins, nb_channels, complex=2)]\n            STFT of the mixture signal.\n        iterations (int): [scalar]\n            number of iterations for the EM algorithm","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/filtering.py:34"}],[1300,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/filtering.py","startLine":360,"endLine":369,"chunkText":"            STFT of the mixture signal.\n        iterations (int): [scalar]\n            number of iterations for the EM algorithm\n        softmask (bool): Describes how the initial estimates are obtained.\n            * if `False`, then the mixture phase will directly be used with the\n            spectrogram as initial estimates.\n            * if `True`, initial estimates are obtained by multiplying the\n            complex mix element-wise with the ratio of each target spectrogram\n            with the sum of them all. This strategy is better if the model are\n            not really good, and worse otherwise.","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/filtering.py:35"}],[1301,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/filtering.py","startLine":367,"endLine":375,"chunkText":"            complex mix element-wise with the ratio of each target spectrogram\n            with the sum of them all. This strategy is better if the model are\n            not really good, and worse otherwise.\n        residual (bool): if `True`, an additional target is created, which is\n            equal to the mixture minus the other targets, before application of\n            expectation maximization\n        eps (float): Epsilon value to use for computing the separations.\n            This is used whenever division with a model energy is\n            performed, i.e. when softmasking and when iterating the EM","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/filtering.py:36"}],[1302,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/filtering.py","startLine":373,"endLine":385,"chunkText":"        eps (float): Epsilon value to use for computing the separations.\n            This is used whenever division with a model energy is\n            performed, i.e. when softmasking and when iterating the EM.\n            It can be understood as the energy of the additional white noise\n            that is taken out when separating.\n\n    Returns:\n        Tensor: shape=(nb_frames, nb_bins, nb_channels, complex=2, nb_sources)\n            STFT of estimated sources\n\n    Notes:\n        * Be careful that you need *magnitude spectrogram estimates* for the\n        case `softmask==False`.","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/filtering.py:37"}],[1303,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/filtering.py","startLine":383,"endLine":394,"chunkText":"    Notes:\n        * Be careful that you need *magnitude spectrogram estimates* for the\n        case `softmask==False`.\n        * `softmask=False` is recommended\n        * The epsilon value will have a huge impact on performance. If it's\n        large, only the parts of the signal with a significant energy will\n        be kept in the sources. This epsilon then directly controls the\n        energy of the reconstruction error.\n\n    Warning:\n        As in :func:`expectation_maximization`, we recommend converting the\n        mixture `x` to double precision `torch.float64` *before* calling","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/filtering.py:38"}],[1304,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/filtering.py","startLine":393,"endLine":402,"chunkText":"        As in :func:`expectation_maximization`, we recommend converting the\n        mixture `x` to double precision `torch.float64` *before* calling\n        :func:`wiener`.\n    \"\"\"\n    if softmask:\n        # if we use softmask, we compute the ratio mask for all targets and\n        # multiply by the mix stft\n        y = mix_stft[..., None] * (targets_spectrograms / (eps + torch.sum(targets_spectrograms, dim=-1, keepdim=True).to(mix_stft.dtype)))[..., None, :]\n    else:\n        # otherwise, we just multiply the targets spectrograms with mix phase","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/filtering.py:39"}],[1305,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/filtering.py","startLine":400,"endLine":407,"chunkText":"        y = mix_stft[..., None] * (targets_spectrograms / (eps + torch.sum(targets_spectrograms, dim=-1, keepdim=True).to(mix_stft.dtype)))[..., None, :]\n    else:\n        # otherwise, we just multiply the targets spectrograms with mix phase\n        # we tacitly assume that we have magnitude estimates.\n        angle = atan2(mix_stft[..., 1], mix_stft[..., 0])[..., None]\n        nb_sources = targets_spectrograms.shape[-1]\n        y = torch.zeros(mix_stft.shape + (nb_sources,), dtype=mix_stft.dtype, device=mix_stft.device)\n        y[..., 0, :] = targets_spectrograms * torch.cos(angle)","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/filtering.py:40"}],[1306,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/filtering.py","startLine":405,"endLine":418,"chunkText":"        nb_sources = targets_spectrograms.shape[-1]\n        y = torch.zeros(mix_stft.shape + (nb_sources,), dtype=mix_stft.dtype, device=mix_stft.device)\n        y[..., 0, :] = targets_spectrograms * torch.cos(angle)\n        y[..., 1, :] = targets_spectrograms * torch.sin(angle)\n\n    if residual:\n        # if required, adding an additional target as the mix minus\n        # available targets\n        y = torch.cat([y, mix_stft[..., None] - y.sum(dim=-1, keepdim=True)], dim=-1)\n\n    if iterations == 0:\n        return y\n\n    # we need to refine the estimates. Scales down the estimates for","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/filtering.py:41"}],[1307,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/filtering.py","startLine":416,"endLine":437,"chunkText":"        return y\n\n    # we need to refine the estimates. Scales down the estimates for\n    # numerical stability\n    max_abs = torch.max(torch.as_tensor(1.0, dtype=mix_stft.dtype, device=mix_stft.device), torch.sqrt(_norm(mix_stft)).max() / scale_factor)\n\n    mix_stft = mix_stft / max_abs\n    y = y / max_abs\n\n    # call expectation maximization\n    y = expectation_maximization(y, mix_stft, iterations, eps=eps)[0]\n\n    # scale estimates up again\n    y = y * max_abs\n    return y\n\n\ndef _covariance(y_j):\n    \"\"\"\n    Compute the empirical covariance for a source.\n\n    Args:","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/filtering.py:42"}],[1308,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/filtering.py","startLine":435,"endLine":447,"chunkText":"    Compute the empirical covariance for a source.\n\n    Args:\n        y_j (Tensor): complex stft of the source.\n            [shape=(nb_frames, nb_bins, nb_channels, 2)].\n\n    Returns:\n        Cj (Tensor): [shape=(nb_frames, nb_bins, nb_channels, nb_channels, 2)]\n            just y_j * conj(y_j.T): empirical covariance for each TF bin.\n    \"\"\"\n    (nb_frames, nb_bins, nb_channels) = y_j.shape[:-1]\n    Cj = torch.zeros((nb_frames, nb_bins, nb_channels, nb_channels, 2), dtype=y_j.dtype, device=y_j.device)\n    indices = torch.cartesian_prod(torch.arange(nb_channels), torch.arange(nb_channels))","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/filtering.py:43"}],[1309,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/filtering.py","startLine":445,"endLine":451,"chunkText":"    (nb_frames, nb_bins, nb_channels) = y_j.shape[:-1]\n    Cj = torch.zeros((nb_frames, nb_bins, nb_channels, nb_channels, 2), dtype=y_j.dtype, device=y_j.device)\n    indices = torch.cartesian_prod(torch.arange(nb_channels), torch.arange(nb_channels))\n    for index in indices:\n        Cj[:, :, index[0], index[1], :] = _mul_add(y_j[:, :, index[0], :], _conj(y_j[:, :, index[1], :]), Cj[:, :, index[0], index[1], :])\n    return Cj\n","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/filtering.py:44"}],[1310,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/hdemucs.py","startLine":0,"endLine":19,"chunkText":"# Copyright (c) Facebook, Inc. and its affiliates.\n# All rights reserved.\n#\n# This source code is licensed under the license found in the\n# LICENSE file in the root directory of this source tree.\n\"\"\"\nThis code contains the spectrogram and Hybrid version of Demucs.\n\"\"\"\nfrom copy import deepcopy\nimport math\nimport typing as tp\nimport torch\nfrom torch import nn\nfrom torch.nn import functional as F\nfrom .filtering import wiener\nfrom .demucs import DConv, rescale_module\nfrom .states import capture_init\nfrom .spec import spectro, ispectro\n\n","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/hdemucs.py:0"}],[1311,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/hdemucs.py","startLine":17,"endLine":30,"chunkText":"from .spec import spectro, ispectro\n\n\ndef pad1d(x: torch.Tensor, paddings: tp.Tuple[int, int], mode: str = \"constant\", value: float = 0.0):\n    \"\"\"Tiny wrapper around F.pad, just to allow for reflect padding on small input.\n    If this is the case, we insert extra 0 padding to the right before the reflection happen.\"\"\"\n    x0 = x\n    length = x.shape[-1]\n    padding_left, padding_right = paddings\n    if mode == \"reflect\":\n        max_pad = max(padding_left, padding_right)\n        if length <= max_pad:\n            extra_pad = max_pad - length + 1\n            extra_pad_right = min(padding_right, extra_pad)","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/hdemucs.py:1"}],[1312,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/hdemucs.py","startLine":28,"endLine":41,"chunkText":"        if length <= max_pad:\n            extra_pad = max_pad - length + 1\n            extra_pad_right = min(padding_right, extra_pad)\n            extra_pad_left = extra_pad - extra_pad_right\n            paddings = (padding_left - extra_pad_left, padding_right - extra_pad_right)\n            x = F.pad(x, (extra_pad_left, extra_pad_right))\n    out = F.pad(x, paddings, mode, value)\n    assert out.shape[-1] == length + padding_left + padding_right\n    assert (out[..., padding_left : padding_left + length] == x0).all()\n    return out\n\n\nclass ScaledEmbedding(nn.Module):\n    \"\"\"","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/hdemucs.py:2"}],[1313,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/hdemucs.py","startLine":39,"endLine":51,"chunkText":"\nclass ScaledEmbedding(nn.Module):\n    \"\"\"\n    Boost learning rate for embeddings (with `scale`).\n    Also, can make embeddings continuous with `smooth`.\n    \"\"\"\n\n    def __init__(self, num_embeddings: int, embedding_dim: int, scale: float = 10.0, smooth=False):\n        super().__init__()\n        self.embedding = nn.Embedding(num_embeddings, embedding_dim)\n        if smooth:\n            weight = torch.cumsum(self.embedding.weight.data, dim=0)\n            # when summing gaussian, overscale raises as sqrt(n), so we nornalize by that.","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/hdemucs.py:3"}],[1314,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/hdemucs.py","startLine":49,"endLine":65,"chunkText":"        if smooth:\n            weight = torch.cumsum(self.embedding.weight.data, dim=0)\n            # when summing gaussian, overscale raises as sqrt(n), so we nornalize by that.\n            weight = weight / torch.arange(1, num_embeddings + 1).to(weight).sqrt()[:, None]\n            self.embedding.weight.data[:] = weight\n        self.embedding.weight.data /= scale\n        self.scale = scale\n\n    @property\n    def weight(self):\n        return self.embedding.weight * self.scale\n\n    def forward(self, x):\n        out = self.embedding(x) * self.scale\n        return out\n\n","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/hdemucs.py:4"}],[1315,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/hdemucs.py","startLine":66,"endLine":75,"chunkText":"class HEncLayer(nn.Module):\n    def __init__(self, chin, chout, kernel_size=8, stride=4, norm_groups=1, empty=False, freq=True, dconv=True, norm=True, context=0, dconv_kw={}, pad=True, rewrite=True):\n        \"\"\"Encoder layer. This used both by the time and the frequency branch.\n\n        Args:\n            chin: number of input channels.\n            chout: number of output channels.\n            norm_groups: number of groups for group norm.\n            empty: used to make a layer with just the first conv. this is used\n                before merging the time and freq. branches.","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/hdemucs.py:5"}],[1316,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/hdemucs.py","startLine":73,"endLine":82,"chunkText":"            norm_groups: number of groups for group norm.\n            empty: used to make a layer with just the first conv. this is used\n                before merging the time and freq. branches.\n            freq: this is acting on frequencies.\n            dconv: insert DConv residual branches.\n            norm: use GroupNorm.\n            context: context size for the 1x1 conv.\n            dconv_kw: list of kwargs for the DConv class.\n            pad: pad the input. Padding is done so that the output size is\n                always the input size / stride.","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/hdemucs.py:6"}],[1317,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/hdemucs.py","startLine":80,"endLine":95,"chunkText":"            dconv_kw: list of kwargs for the DConv class.\n            pad: pad the input. Padding is done so that the output size is\n                always the input size / stride.\n            rewrite: add 1x1 conv at the end of the layer.\n        \"\"\"\n        super().__init__()\n        norm_fn = lambda d: nn.Identity()  # noqa\n        if norm:\n            norm_fn = lambda d: nn.GroupNorm(norm_groups, d)  # noqa\n        if pad:\n            pad = kernel_size // 4\n        else:\n            pad = 0\n        klass = nn.Conv1d\n        self.freq = freq\n        self.kernel_size = kernel_size","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/hdemucs.py:7"}],[1318,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/hdemucs.py","startLine":93,"endLine":110,"chunkText":"        klass = nn.Conv1d\n        self.freq = freq\n        self.kernel_size = kernel_size\n        self.stride = stride\n        self.empty = empty\n        self.norm = norm\n        self.pad = pad\n        if freq:\n            kernel_size = [kernel_size, 1]\n            stride = [stride, 1]\n            pad = [pad, 0]\n            klass = nn.Conv2d\n        self.conv = klass(chin, chout, kernel_size, stride, pad)\n        if self.empty:\n            return\n        self.norm1 = norm_fn(chout)\n        self.rewrite = None\n        if rewrite:","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/hdemucs.py:8"}],[1319,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/hdemucs.py","startLine":108,"endLine":126,"chunkText":"        self.norm1 = norm_fn(chout)\n        self.rewrite = None\n        if rewrite:\n            self.rewrite = klass(chout, 2 * chout, 1 + 2 * context, 1, context)\n            self.norm2 = norm_fn(2 * chout)\n\n        self.dconv = None\n        if dconv:\n            self.dconv = DConv(chout, **dconv_kw)\n\n    def forward(self, x, inject=None):\n        \"\"\"\n        `inject` is used to inject the result from the time branch into the frequency branch,\n        when both have the same stride.\n        \"\"\"\n        if not self.freq and x.dim() == 4:\n            B, C, Fr, T = x.shape\n            x = x.view(B, -1, T)\n","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/hdemucs.py:9"}],[1320,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/hdemucs.py","startLine":124,"endLine":140,"chunkText":"            B, C, Fr, T = x.shape\n            x = x.view(B, -1, T)\n\n        if not self.freq:\n            le = x.shape[-1]\n            if not le % self.stride == 0:\n                x = F.pad(x, (0, self.stride - (le % self.stride)))\n        y = self.conv(x)\n        if self.empty:\n            return y\n        if inject is not None:\n            assert inject.shape[-1] == y.shape[-1], (inject.shape, y.shape)\n            if inject.dim() == 3 and y.dim() == 4:\n                inject = inject[:, :, None]\n            y = y + inject\n        y = F.gelu(self.norm1(y))\n        if self.dconv:","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/hdemucs.py:10"}],[1321,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/hdemucs.py","startLine":138,"endLine":154,"chunkText":"            y = y + inject\n        y = F.gelu(self.norm1(y))\n        if self.dconv:\n            if self.freq:\n                B, C, Fr, T = y.shape\n                y = y.permute(0, 2, 1, 3).reshape(-1, C, T)\n            y = self.dconv(y)\n            if self.freq:\n                y = y.view(B, Fr, C, T).permute(0, 2, 1, 3)\n        if self.rewrite:\n            z = self.norm2(self.rewrite(y))\n            z = F.glu(z, dim=1)\n        else:\n            z = y\n        return z\n\n","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/hdemucs.py:11"}],[1322,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/hdemucs.py","startLine":155,"endLine":168,"chunkText":"class MultiWrap(nn.Module):\n    \"\"\"\n    Takes one layer and replicate it N times. each replica will act\n    on a frequency band. All is done so that if the N replica have the same weights,\n    then this is exactly equivalent to applying the original module on all frequencies.\n\n    This is a bit over-engineered to avoid edge artifacts when splitting\n    the frequency bands, but it is possible the naive implementation would work as well...\n    \"\"\"\n\n    def __init__(self, layer, split_ratios):\n        \"\"\"\n        Args:\n            layer: module to clone, must be either HEncLayer or HDecLayer.","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/hdemucs.py:12"}],[1323,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/hdemucs.py","startLine":166,"endLine":181,"chunkText":"        \"\"\"\n        Args:\n            layer: module to clone, must be either HEncLayer or HDecLayer.\n            split_ratios: list of float indicating which ratio to keep for each band.\n        \"\"\"\n        super().__init__()\n        self.split_ratios = split_ratios\n        self.layers = nn.ModuleList()\n        self.conv = isinstance(layer, HEncLayer)\n        assert not layer.norm\n        assert layer.freq\n        assert layer.pad\n        if not self.conv:\n            assert not layer.context_freq\n        for k in range(len(split_ratios) + 1):\n            lay = deepcopy(layer)","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/hdemucs.py:13"}],[1324,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/hdemucs.py","startLine":179,"endLine":196,"chunkText":"            assert not layer.context_freq\n        for k in range(len(split_ratios) + 1):\n            lay = deepcopy(layer)\n            if self.conv:\n                lay.conv.padding = (0, 0)\n            else:\n                lay.pad = False\n            for m in lay.modules():\n                if hasattr(m, \"reset_parameters\"):\n                    m.reset_parameters()\n            self.layers.append(lay)\n\n    def forward(self, x, skip=None, length=None):\n        B, C, Fr, T = x.shape\n\n        ratios = list(self.split_ratios) + [1]\n        start = 0\n        outs = []","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/hdemucs.py:14"}],[1325,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/hdemucs.py","startLine":194,"endLine":208,"chunkText":"        ratios = list(self.split_ratios) + [1]\n        start = 0\n        outs = []\n        for ratio, layer in zip(ratios, self.layers):\n            if self.conv:\n                pad = layer.kernel_size // 4\n                if ratio == 1:\n                    limit = Fr\n                    frames = -1\n                else:\n                    limit = int(round(Fr * ratio))\n                    le = limit - start\n                    if start == 0:\n                        le += pad\n                    frames = round((le - layer.kernel_size) / layer.stride + 1)","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/hdemucs.py:15"}],[1326,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/hdemucs.py","startLine":206,"endLine":218,"chunkText":"                    if start == 0:\n                        le += pad\n                    frames = round((le - layer.kernel_size) / layer.stride + 1)\n                    limit = start + (frames - 1) * layer.stride + layer.kernel_size\n                    if start == 0:\n                        limit -= pad\n                assert limit - start > 0, (limit, start)\n                assert limit <= Fr, (limit, Fr)\n                y = x[:, :, start:limit, :]\n                if start == 0:\n                    y = F.pad(y, (0, 0, pad, 0))\n                if ratio == 1:\n                    y = F.pad(y, (0, 0, 0, pad)","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/hdemucs.py:16"}],[1327,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/hdemucs.py","startLine":216,"endLine":232,"chunkText":"                    y = F.pad(y, (0, 0, pad, 0))\n                if ratio == 1:\n                    y = F.pad(y, (0, 0, 0, pad))\n                outs.append(layer(y))\n                start = limit - layer.kernel_size + layer.stride\n            else:\n                if ratio == 1:\n                    limit = Fr\n                else:\n                    limit = int(round(Fr * ratio))\n                last = layer.last\n                layer.last = True\n\n                y = x[:, :, start:limit]\n                s = skip[:, :, start:limit]\n                out, _ = layer(y, s, None)\n                if outs:","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/hdemucs.py:17"}],[1328,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/hdemucs.py","startLine":230,"endLine":242,"chunkText":"                s = skip[:, :, start:limit]\n                out, _ = layer(y, s, None)\n                if outs:\n                    outs[-1][:, :, -layer.stride :] += out[:, :, : layer.stride] - layer.conv_tr.bias.view(1, -1, 1, 1)\n                    out = out[:, :, layer.stride :]\n                if ratio == 1:\n                    out = out[:, :, : -layer.stride // 2, :]\n                if start == 0:\n                    out = out[:, :, layer.stride // 2 :, :]\n                outs.append(out)\n                layer.last = last\n                start = limit\n        out = torch.cat(outs, dim=2)","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/hdemucs.py:18"}],[1329,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/hdemucs.py","startLine":240,"endLine":250,"chunkText":"                layer.last = last\n                start = limit\n        out = torch.cat(outs, dim=2)\n        if not self.conv and not last:\n            out = F.gelu(out)\n        if self.conv:\n            return out\n        else:\n            return out, None\n\n","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/hdemucs.py:19"}],[1330,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/hdemucs.py","startLine":251,"endLine":265,"chunkText":"class HDecLayer(nn.Module):\n    def __init__(\n        self, chin, chout, last=False, kernel_size=8, stride=4, norm_groups=1, empty=False, freq=True, dconv=True, norm=True, context=1, dconv_kw={}, pad=True, context_freq=True, rewrite=True\n    ):\n        \"\"\"\n        Same as HEncLayer but for decoder. See `HEncLayer` for documentation.\n        \"\"\"\n        super().__init__()\n        norm_fn = lambda d: nn.Identity()  # noqa\n        if norm:\n            norm_fn = lambda d: nn.GroupNorm(norm_groups, d)  # noqa\n        if pad:\n            pad = kernel_size // 4\n        else:\n            pad = 0","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/hdemucs.py:20"}],[1331,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/hdemucs.py","startLine":263,"endLine":281,"chunkText":"            pad = kernel_size // 4\n        else:\n            pad = 0\n        self.pad = pad\n        self.last = last\n        self.freq = freq\n        self.chin = chin\n        self.empty = empty\n        self.stride = stride\n        self.kernel_size = kernel_size\n        self.norm = norm\n        self.context_freq = context_freq\n        klass = nn.Conv1d\n        klass_tr = nn.ConvTranspose1d\n        if freq:\n            kernel_size = [kernel_size, 1]\n            stride = [stride, 1]\n            klass = nn.Conv2d\n            klass_tr = nn.ConvTranspose2d","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/hdemucs.py:21"}],[1332,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/hdemucs.py","startLine":279,"endLine":295,"chunkText":"            stride = [stride, 1]\n            klass = nn.Conv2d\n            klass_tr = nn.ConvTranspose2d\n        self.conv_tr = klass_tr(chin, chout, kernel_size, stride)\n        self.norm2 = norm_fn(chout)\n        if self.empty:\n            return\n        self.rewrite = None\n        if rewrite:\n            if context_freq:\n                self.rewrite = klass(chin, 2 * chin, 1 + 2 * context, 1, context)\n            else:\n                self.rewrite = klass(chin, 2 * chin, [1, 1 + 2 * context], 1, [0, context])\n            self.norm1 = norm_fn(2 * chin)\n\n        self.dconv = None\n        if dconv:","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/hdemucs.py:22"}],[1333,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/hdemucs.py","startLine":293,"endLine":313,"chunkText":"\n        self.dconv = None\n        if dconv:\n            self.dconv = DConv(chin, **dconv_kw)\n\n    def forward(self, x, skip, length):\n        if self.freq and x.dim() == 3:\n            B, C, T = x.shape\n            x = x.view(B, self.chin, -1, T)\n\n        if not self.empty:\n            x = x + skip\n\n            if self.rewrite:\n                y = F.glu(self.norm1(self.rewrite(x)), dim=1)\n            else:\n                y = x\n            if self.dconv:\n                if self.freq:\n                    B, C, Fr, T = y.shape\n                    y = y.permute(0, 2, 1, 3).reshape(-1, C, T)","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/hdemucs.py:23"}],[1334,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/hdemucs.py","startLine":311,"endLine":326,"chunkText":"                if self.freq:\n                    B, C, Fr, T = y.shape\n                    y = y.permute(0, 2, 1, 3).reshape(-1, C, T)\n                y = self.dconv(y)\n                if self.freq:\n                    y = y.view(B, Fr, C, T).permute(0, 2, 1, 3)\n        else:\n            y = x\n            assert skip is None\n        z = self.norm2(self.conv_tr(y))\n        if self.freq:\n            if self.pad:\n                z = z[..., self.pad : -self.pad, :]\n        else:\n            z = z[..., self.pad : self.pad + length]\n            assert z.shape[-1] == length, (z.shape[-1], length)","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/hdemucs.py:24"}],[1335,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/hdemucs.py","startLine":324,"endLine":331,"chunkText":"        else:\n            z = z[..., self.pad : self.pad + length]\n            assert z.shape[-1] == length, (z.shape[-1], length)\n        if not self.last:\n            z = F.gelu(z)\n        return z, y\n\n","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/hdemucs.py:25"}],[1336,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/hdemucs.py","startLine":332,"endLine":341,"chunkText":"class HDemucs(nn.Module):\n    \"\"\"\n    Spectrogram and hybrid Demucs model.\n    The spectrogram model has the same structure as Demucs, except the first few layers are over the\n    frequency axis, until there is only 1 frequency, and then it moves to time convolutions.\n    Frequency layers can still access information across time steps thanks to the DConv residual.\n\n    Hybrid model have a parallel time branch. At some layer, the time branch has the same stride\n    as the frequency branch and then the two are combined. The opposite happens in the decoder.\n","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/hdemucs.py:26"}],[1337,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/hdemucs.py","startLine":339,"endLine":347,"chunkText":"    Hybrid model have a parallel time branch. At some layer, the time branch has the same stride\n    as the frequency branch and then the two are combined. The opposite happens in the decoder.\n\n    Models can either use naive iSTFT from masking, Wiener filtering ([Ulhih et al. 2017]),\n    or complex as channels (CaC) [Choi et al. 2020]. Wiener filtering is based on\n    Open Unmix implementation [Stoter et al. 2019].\n\n    The loss is always on the temporal domain, by backpropagating through the above\n    output methods and iSTFT. This allows to define hybrid models nicely. However, this breaks","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/hdemucs.py:27"}],[1338,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/hdemucs.py","startLine":345,"endLine":353,"chunkText":"\n    The loss is always on the temporal domain, by backpropagating through the above\n    output methods and iSTFT. This allows to define hybrid models nicely. However, this breaks\n    a bit Wiener filtering, as doing more iteration at test time will change the spectrogram\n    contribution, without changing the one from the waveform, which will lead to worse performance.\n    I tried using the residual option in OpenUnmix Wiener implementation, but it didn't improve.\n    CaC on the other hand provides similar performance for hybrid, and works naturally with\n    hybrid models.\n","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/hdemucs.py:28"}],[1339,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/hdemucs.py","startLine":351,"endLine":369,"chunkText":"    CaC on the other hand provides similar performance for hybrid, and works naturally with\n    hybrid models.\n\n    This model also uses frequency embeddings are used to improve efficiency on convolutions\n    over the freq. axis, following [Isik et al. 2020] (https://arxiv.org/pdf/2008.04470.pdf).\n\n    Unlike classic Demucs, there is no resampling here, and normalization is always applied.\n    \"\"\"\n\n    @capture_init\n    def __init__(\n        self,\n        sources,\n        # Channels\n        audio_channels=2,\n        channels=48,\n        channels_time=None,\n        growth=2,\n        # STFT","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/hdemucs.py:29"}],[1340,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/hdemucs.py","startLine":368,"endLine":394,"chunkText":"        growth=2,\n        # STFT\n        nfft=4096,\n        wiener_iters=0,\n        end_iters=0,\n        wiener_residual=False,\n        cac=True,\n        # Main structure\n        depth=6,\n        rewrite=True,\n        hybrid=True,\n        hybrid_old=False,\n        # Frequency branch\n        multi_freqs=None,\n        multi_freqs_depth=2,\n        freq_emb=0.2,\n        emb_scale=10,\n        emb_smooth=True,\n        # Convolutions\n        kernel_size=8,\n        time_stride=2,\n        stride=4,\n        context=1,\n        context_enc=0,\n        # Normalization\n        norm_starts=4,\n        norm_groups=4,","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/hdemucs.py:30"}],[1341,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/hdemucs.py","startLine":392,"endLine":412,"chunkText":"        # Normalization\n        norm_starts=4,\n        norm_groups=4,\n        # DConv residual branch\n        dconv_mode=1,\n        dconv_depth=2,\n        dconv_comp=4,\n        dconv_attn=4,\n        dconv_lstm=4,\n        dconv_init=1e-4,\n        # Weight init\n        rescale=0.1,\n        # Metadata\n        samplerate=44100,\n        segment=4 * 10,\n    ):\n        \"\"\"\n        Args:\n            sources (list[str]): list of source names.\n            audio_channels (int): input/output audio channels.\n            channels (int): initial number of hidden channels.","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/hdemucs.py:31"}],[1342,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/hdemucs.py","startLine":410,"endLine":416,"chunkText":"            sources (list[str]): list of source names.\n            audio_channels (int): input/output audio channels.\n            channels (int): initial number of hidden channels.\n            channels_time: if not None, use a different `channels` value for the time branch.\n            growth: increase the number of hidden channels by this factor at each layer.\n            nfft: number of fft bins. Note that changing this require careful computation of\n                various shape parameters and will not work out of the box for hybrid models.","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/hdemucs.py:32"}],[1343,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/hdemucs.py","startLine":414,"endLine":419,"chunkText":"            growth: increase the number of hidden channels by this factor at each layer.\n            nfft: number of fft bins. Note that changing this require careful computation of\n                various shape parameters and will not work out of the box for hybrid models.\n            wiener_iters: when using Wiener filtering, number of iterations at test time.\n            end_iters: same but at train time. For a hybrid model, must be equal to `wiener_iters`.\n            wiener_residual: add residual source before wiener filtering.","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/hdemucs.py:33"}],[1344,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/hdemucs.py","startLine":417,"endLine":423,"chunkText":"            wiener_iters: when using Wiener filtering, number of iterations at test time.\n            end_iters: same but at train time. For a hybrid model, must be equal to `wiener_iters`.\n            wiener_residual: add residual source before wiener filtering.\n            cac: uses complex as channels, i.e. complex numbers are 2 channels each\n                in input and output. no further processing is done before ISTFT.\n            depth (int): number of layers in the encoder and in the decoder.\n            rewrite (bool): add 1x1 convolution to each layer.","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/hdemucs.py:34"}],[1345,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/hdemucs.py","startLine":421,"endLine":427,"chunkText":"                in input and output. no further processing is done before ISTFT.\n            depth (int): number of layers in the encoder and in the decoder.\n            rewrite (bool): add 1x1 convolution to each layer.\n            hybrid (bool): make a hybrid time/frequency domain, otherwise frequency only.\n            hybrid_old: some models trained for MDX had a padding bug. This replicates\n                this bug to avoid retraining them.\n            multi_freqs: list of frequency ratios for splitting frequency bands with `MultiWrap`.","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/hdemucs.py:35"}],[1346,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/hdemucs.py","startLine":425,"endLine":432,"chunkText":"            hybrid_old: some models trained for MDX had a padding bug. This replicates\n                this bug to avoid retraining them.\n            multi_freqs: list of frequency ratios for splitting frequency bands with `MultiWrap`.\n            multi_freqs_depth: how many layers to wrap with `MultiWrap`. Only the outermost\n                layers will be wrapped.\n            freq_emb: add frequency embedding after the first frequency layer if > 0,\n                the actual value controls the weight of the embedding.\n            emb_scale: equivalent to scaling the embedding learning rate","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/hdemucs.py:36"}],[1347,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/hdemucs.py","startLine":430,"endLine":437,"chunkText":"            freq_emb: add frequency embedding after the first frequency layer if > 0,\n                the actual value controls the weight of the embedding.\n            emb_scale: equivalent to scaling the embedding learning rate\n            emb_smooth: initialize the embedding with a smooth one (with respect to frequencies).\n            kernel_size: kernel_size for encoder and decoder layers.\n            stride: stride for encoder and decoder layers.\n            time_stride: stride for the final time layer, after the merge.\n            context: context for 1x1 conv in the decoder.","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/hdemucs.py:37"}],[1348,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/hdemucs.py","startLine":435,"endLine":443,"chunkText":"            stride: stride for encoder and decoder layers.\n            time_stride: stride for the final time layer, after the merge.\n            context: context for 1x1 conv in the decoder.\n            context_enc: context for 1x1 conv in the encoder.\n            norm_starts: layer at which group norm starts being used.\n                decoder layers are numbered in reverse order.\n            norm_groups: number of groups for group norm.\n            dconv_mode: if 1: dconv in encoder only, 2: decoder only, 3: both.\n            dconv_depth: depth of residual DConv branch.","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/hdemucs.py:38"}],[1349,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/hdemucs.py","startLine":441,"endLine":453,"chunkText":"            norm_groups: number of groups for group norm.\n            dconv_mode: if 1: dconv in encoder only, 2: decoder only, 3: both.\n            dconv_depth: depth of residual DConv branch.\n            dconv_comp: compression of DConv branch.\n            dconv_attn: adds attention layers in DConv branch starting at this layer.\n            dconv_lstm: adds a LSTM layer in DConv branch starting at this layer.\n            dconv_init: initial scale for the DConv branch LayerScale.\n            rescale: weight recaling trick\n\n        \"\"\"\n        super().__init__()\n\n        self.cac = cac","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/hdemucs.py:39"}],[1350,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/hdemucs.py","startLine":451,"endLine":470,"chunkText":"        super().__init__()\n\n        self.cac = cac\n        self.wiener_residual = wiener_residual\n        self.audio_channels = audio_channels\n        self.sources = sources\n        self.kernel_size = kernel_size\n        self.context = context\n        self.stride = stride\n        self.depth = depth\n        self.channels = channels\n        self.samplerate = samplerate\n        self.segment = segment\n\n        self.nfft = nfft\n        self.hop_length = nfft // 4\n        self.wiener_iters = wiener_iters\n        self.end_iters = end_iters\n        self.freq_emb = None\n        self.hybrid = hybrid","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/hdemucs.py:40"}],[1351,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/hdemucs.py","startLine":468,"endLine":487,"chunkText":"        self.end_iters = end_iters\n        self.freq_emb = None\n        self.hybrid = hybrid\n        self.hybrid_old = hybrid_old\n        if hybrid_old:\n            assert hybrid, \"hybrid_old must come with hybrid=True\"\n        if hybrid:\n            assert wiener_iters == end_iters\n\n        self.encoder = nn.ModuleList()\n        self.decoder = nn.ModuleList()\n\n        if hybrid:\n            self.tencoder = nn.ModuleList()\n            self.tdecoder = nn.ModuleList()\n\n        chin = audio_channels\n        chin_z = chin  # number of channels for the freq branch\n        if self.cac:\n            chin_z *= 2","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/hdemucs.py:41"}],[1352,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/hdemucs.py","startLine":485,"endLine":504,"chunkText":"        chin_z = chin  # number of channels for the freq branch\n        if self.cac:\n            chin_z *= 2\n        chout = channels_time or channels\n        chout_z = channels\n        freqs = nfft // 2\n\n        for index in range(depth):\n            lstm = index >= dconv_lstm\n            attn = index >= dconv_attn\n            norm = index >= norm_starts\n            freq = freqs > 1\n            stri = stride\n            ker = kernel_size\n            if not freq:\n                assert freqs == 1\n                ker = time_stride * 2\n                stri = time_stride\n\n            pad = True","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/hdemucs.py:42"}],[1353,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/hdemucs.py","startLine":502,"endLine":518,"chunkText":"                stri = time_stride\n\n            pad = True\n            last_freq = False\n            if freq and freqs <= kernel_size:\n                ker = freqs\n                pad = False\n                last_freq = True\n\n            kw = {\n                \"kernel_size\": ker,\n                \"stride\": stri,\n                \"freq\": freq,\n                \"pad\": pad,\n                \"norm\": norm,\n                \"rewrite\": rewrite,\n                \"norm_groups\": norm_groups,","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/hdemucs.py:43"}],[1354,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/hdemucs.py","startLine":516,"endLine":529,"chunkText":"                \"norm\": norm,\n                \"rewrite\": rewrite,\n                \"norm_groups\": norm_groups,\n                \"dconv_kw\": {\"lstm\": lstm, \"attn\": attn, \"depth\": dconv_depth, \"compress\": dconv_comp, \"init\": dconv_init, \"gelu\": True},\n            }\n            kwt = dict(kw)\n            kwt[\"freq\"] = 0\n            kwt[\"kernel_size\"] = kernel_size\n            kwt[\"stride\"] = stride\n            kwt[\"pad\"] = True\n            kw_dec = dict(kw)\n            multi = False\n            if multi_freqs and index < multi_freqs_depth:\n                multi = True","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/hdemucs.py:44"}],[1355,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/hdemucs.py","startLine":527,"endLine":541,"chunkText":"            multi = False\n            if multi_freqs and index < multi_freqs_depth:\n                multi = True\n                kw_dec[\"context_freq\"] = False\n\n            if last_freq:\n                chout_z = max(chout, chout_z)\n                chout = chout_z\n\n            enc = HEncLayer(chin_z, chout_z, dconv=dconv_mode & 1, context=context_enc, **kw)\n            if hybrid and freq:\n                tenc = HEncLayer(chin, chout, dconv=dconv_mode & 1, context=context_enc, empty=last_freq, **kwt)\n                self.tencoder.append(tenc)\n\n            if multi:","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/hdemucs.py:45"}],[1356,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/hdemucs.py","startLine":539,"endLine":552,"chunkText":"                self.tencoder.append(tenc)\n\n            if multi:\n                enc = MultiWrap(enc, multi_freqs)\n            self.encoder.append(enc)\n            if index == 0:\n                chin = self.audio_channels * len(self.sources)\n                chin_z = chin\n                if self.cac:\n                    chin_z *= 2\n            dec = HDecLayer(chout_z, chin_z, dconv=dconv_mode & 2, last=index == 0, context=context, **kw_dec)\n            if multi:\n                dec = MultiWrap(dec, multi_freqs)\n            if hybrid and freq:","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/hdemucs.py:46"}],[1357,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/hdemucs.py","startLine":550,"endLine":565,"chunkText":"            if multi:\n                dec = MultiWrap(dec, multi_freqs)\n            if hybrid and freq:\n                tdec = HDecLayer(chout, chin, dconv=dconv_mode & 2, empty=last_freq, last=index == 0, context=context, **kwt)\n                self.tdecoder.insert(0, tdec)\n            self.decoder.insert(0, dec)\n\n            chin = chout\n            chin_z = chout_z\n            chout = int(growth * chout)\n            chout_z = int(growth * chout_z)\n            if freq:\n                if freqs <= kernel_size:\n                    freqs = 1\n                else:\n                    freqs //= stride","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/hdemucs.py:47"}],[1358,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/hdemucs.py","startLine":563,"endLine":579,"chunkText":"                    freqs = 1\n                else:\n                    freqs //= stride\n            if index == 0 and freq_emb:\n                self.freq_emb = ScaledEmbedding(freqs, chin_z, smooth=emb_smooth, scale=emb_scale)\n                self.freq_emb_scale = freq_emb\n\n        if rescale:\n            rescale_module(self, reference=rescale)\n\n    def _spec(self, x):\n        hl = self.hop_length\n        nfft = self.nfft\n        x0 = x  # noqa\n\n        if self.hybrid:\n            # We re-pad the signal in order to keep the property","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/hdemucs.py:48"}],[1359,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/hdemucs.py","startLine":577,"endLine":587,"chunkText":"\n        if self.hybrid:\n            # We re-pad the signal in order to keep the property\n            # that the size of the output is exactly the size of the input\n            # divided by the stride (here hop_length), when divisible.\n            # This is achieved by padding by 1/4th of the kernel size (here nfft).\n            # which is not supported by torch.stft.\n            # Having all convolution operations follow this convention allow to easily\n            # align the time and frequency branches later on.\n            assert hl == nfft // 4\n            le = int(math.ceil(x.shape[-1] / hl))","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/hdemucs.py:49"}],[1360,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/hdemucs.py","startLine":585,"endLine":599,"chunkText":"            # align the time and frequency branches later on.\n            assert hl == nfft // 4\n            le = int(math.ceil(x.shape[-1] / hl))\n            pad = hl // 2 * 3\n            if not self.hybrid_old:\n                x = pad1d(x, (pad, pad + le * hl - x.shape[-1]), mode=\"reflect\")\n            else:\n                x = pad1d(x, (pad, pad + le * hl - x.shape[-1]))\n\n        z = spectro(x, nfft, hl)[..., :-1, :]\n        if self.hybrid:\n            assert z.shape[-1] == le + 4, (z.shape, x.shape, le)\n            z = z[..., 2 : 2 + le]\n        return z\n","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/hdemucs.py:50"}],[1361,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/hdemucs.py","startLine":597,"endLine":613,"chunkText":"            z = z[..., 2 : 2 + le]\n        return z\n\n    def _ispec(self, z, length=None, scale=0):\n        hl = self.hop_length // (4**scale)\n        z = F.pad(z, (0, 0, 0, 1))\n        if self.hybrid:\n            z = F.pad(z, (2, 2))\n            pad = hl // 2 * 3\n            if not self.hybrid_old:\n                le = hl * int(math.ceil(length / hl)) + 2 * pad\n            else:\n                le = hl * int(math.ceil(length / hl))\n            x = ispectro(z, hl, length=le)\n            if not self.hybrid_old:\n                x = x[..., pad : pad + length]\n            else:","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/hdemucs.py:51"}],[1362,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/hdemucs.py","startLine":611,"endLine":629,"chunkText":"            if not self.hybrid_old:\n                x = x[..., pad : pad + length]\n            else:\n                x = x[..., :length]\n        else:\n            x = ispectro(z, hl, length)\n        return x\n\n    def _magnitude(self, z):\n        # return the magnitude of the spectrogram, except when cac is True,\n        # in which case we just move the complex dimension to the channel one.\n        if self.cac:\n            B, C, Fr, T = z.shape\n            m = torch.view_as_real(z).permute(0, 1, 4, 2, 3)\n            m = m.reshape(B, C * 2, Fr, T)\n        else:\n            m = z.abs()\n        return m\n","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/hdemucs.py:52"}],[1363,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/hdemucs.py","startLine":627,"endLine":642,"chunkText":"            m = z.abs()\n        return m\n\n    def _mask(self, z, m):\n        # Apply masking given the mixture spectrogram `z` and the estimated mask `m`.\n        # If `cac` is True, `m` is actually a full spectrogram and `z` is ignored.\n        niters = self.wiener_iters\n        if self.cac:\n            B, S, C, Fr, T = m.shape\n            out = m.view(B, S, -1, 2, Fr, T).permute(0, 1, 2, 4, 5, 3)\n            out = torch.view_as_complex(out.contiguous())\n            return out\n        if self.training:\n            niters = self.end_iters\n        if niters < 0:\n            z = z[:, None]","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/hdemucs.py:53"}],[1364,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/hdemucs.py","startLine":640,"endLine":658,"chunkText":"            niters = self.end_iters\n        if niters < 0:\n            z = z[:, None]\n            return z / (1e-8 + z.abs()) * m\n        else:\n            return self._wiener(m, z, niters)\n\n    def _wiener(self, mag_out, mix_stft, niters):\n        # apply wiener filtering from OpenUnmix.\n        init = mix_stft.dtype\n        wiener_win_len = 300\n        residual = self.wiener_residual\n\n        B, S, C, Fq, T = mag_out.shape\n        mag_out = mag_out.permute(0, 4, 3, 2, 1)\n        mix_stft = torch.view_as_real(mix_stft.permute(0, 3, 2, 1))\n\n        outs = []\n        for sample in range(B):","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/hdemucs.py:54"}],[1365,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/hdemucs.py","startLine":656,"endLine":669,"chunkText":"\n        outs = []\n        for sample in range(B):\n            pos = 0\n            out = []\n            for pos in range(0, T, wiener_win_len):\n                frame = slice(pos, pos + wiener_win_len)\n                z_out = wiener(mag_out[sample, frame], mix_stft[sample, frame], niters, residual=residual)\n                out.append(z_out.transpose(-1, -2))\n            outs.append(torch.cat(out, dim=0))\n        out = torch.view_as_complex(torch.stack(outs, 0))\n        out = out.permute(0, 4, 3, 2, 1).contiguous()\n        if residual:\n            out = out[:, :-1]","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/hdemucs.py:55"}],[1366,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/hdemucs.py","startLine":667,"endLine":686,"chunkText":"        out = out.permute(0, 4, 3, 2, 1).contiguous()\n        if residual:\n            out = out[:, :-1]\n        assert list(out.shape) == [B, S, C, Fq, T]\n        return out.to(init)\n\n    def forward(self, mix):\n        x = mix\n        length = x.shape[-1]\n\n        z = self._spec(mix)\n        mag = self._magnitude(z).to(mix.device)\n        x = mag\n\n        B, C, Fq, T = x.shape\n\n        # unlike previous Demucs, we always normalize because it is easier.\n        mean = x.mean(dim=(1, 2, 3), keepdim=True)\n        std = x.std(dim=(1, 2, 3), keepdim=True)\n        x = (x - mean) / (1e-5 + std)","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/hdemucs.py:56"}],[1367,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/hdemucs.py","startLine":684,"endLine":698,"chunkText":"        mean = x.mean(dim=(1, 2, 3), keepdim=True)\n        std = x.std(dim=(1, 2, 3), keepdim=True)\n        x = (x - mean) / (1e-5 + std)\n        # x will be the freq. branch input.\n\n        if self.hybrid:\n            # Prepare the time branch input.\n            xt = mix\n            meant = xt.mean(dim=(1, 2), keepdim=True)\n            stdt = xt.std(dim=(1, 2), keepdim=True)\n            xt = (xt - meant) / (1e-5 + stdt)\n\n        # okay, this is a giant mess I know...\n        saved = []  # skip connections, freq.\n        saved_t = []  # skip connections, time.","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/hdemucs.py:57"}],[1368,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/hdemucs.py","startLine":696,"endLine":707,"chunkText":"        # okay, this is a giant mess I know...\n        saved = []  # skip connections, freq.\n        saved_t = []  # skip connections, time.\n        lengths = []  # saved lengths to properly remove padding, freq branch.\n        lengths_t = []  # saved lengths for time branch.\n        for idx, encode in enumerate(self.encoder):\n            lengths.append(x.shape[-1])\n            inject = None\n            if self.hybrid and idx < len(self.tencoder):\n                # we have not yet merged branches.\n                lengths_t.append(xt.shape[-1])\n                tenc = self.tencoder[idx]","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/hdemucs.py:58"}],[1369,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/hdemucs.py","startLine":705,"endLine":717,"chunkText":"                # we have not yet merged branches.\n                lengths_t.append(xt.shape[-1])\n                tenc = self.tencoder[idx]\n                xt = tenc(xt)\n                if not tenc.empty:\n                    # save for skip connection\n                    saved_t.append(xt)\n                else:\n                    # tenc contains just the first conv., so that now time and freq.\n                    # branches have the same shape and can be merged.\n                    inject = xt\n            x = encode(x, inject)\n            if idx == 0 and self.freq_emb is not None:","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/hdemucs.py:59"}],[1370,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/hdemucs.py","startLine":715,"endLine":728,"chunkText":"                    inject = xt\n            x = encode(x, inject)\n            if idx == 0 and self.freq_emb is not None:\n                # add frequency embedding to allow for non equivariant convolutions\n                # over the frequency axis.\n                frs = torch.arange(x.shape[-2], device=x.device)\n                emb = self.freq_emb(frs).t()[None, :, :, None].expand_as(x)\n                x = x + self.freq_emb_scale * emb\n\n            saved.append(x)\n\n        x = torch.zeros_like(x)\n        if self.hybrid:\n            xt = torch.zeros_like(x)","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/hdemucs.py:60"}],[1371,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/hdemucs.py","startLine":726,"endLine":739,"chunkText":"        x = torch.zeros_like(x)\n        if self.hybrid:\n            xt = torch.zeros_like(x)\n        # initialize everything to zero (signal will go through u-net skips).\n\n        for idx, decode in enumerate(self.decoder):\n            skip = saved.pop(-1)\n            x, pre = decode(x, skip, lengths.pop(-1))\n            # `pre` contains the output just before final transposed convolution,\n            # which is used when the freq. and time branch separate.\n\n            if self.hybrid:\n                offset = self.depth - len(self.tdecoder)\n            if self.hybrid and idx >= offset:","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/hdemucs.py:61"}],[1372,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/hdemucs.py","startLine":737,"endLine":750,"chunkText":"            if self.hybrid:\n                offset = self.depth - len(self.tdecoder)\n            if self.hybrid and idx >= offset:\n                tdec = self.tdecoder[idx - offset]\n                length_t = lengths_t.pop(-1)\n                if tdec.empty:\n                    assert pre.shape[2] == 1, pre.shape\n                    pre = pre[:, :, 0]\n                    xt, _ = tdec(pre, None, length_t)\n                else:\n                    skip = saved_t.pop(-1)\n                    xt, _ = tdec(xt, skip, length_t)\n\n        # Let's make sure we used all stored skip connections.","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/hdemucs.py:62"}],[1373,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/hdemucs.py","startLine":748,"endLine":764,"chunkText":"                    xt, _ = tdec(xt, skip, length_t)\n\n        # Let's make sure we used all stored skip connections.\n        assert len(saved) == 0\n        assert len(lengths_t) == 0\n        assert len(saved_t) == 0\n\n        S = len(self.sources)\n        x = x.view(B, S, -1, Fq, T)\n        x = x * std[:, None] + mean[:, None]\n\n        # to cpu as non-cuda GPUs don't support complex numbers\n        # demucs issue #435 ##432\n        # NOTE: in this case z already is on cpu\n        # TODO: remove this when mps supports complex numbers\n\n        device_type = x.device.type","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/hdemucs.py:63"}],[1374,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/hdemucs.py","startLine":762,"endLine":780,"chunkText":"        # TODO: remove this when mps supports complex numbers\n\n        device_type = x.device.type\n        device_load = f\"{device_type}:{x.device.index}\" if not device_type == \"mps\" else device_type\n        x_is_other_gpu = not device_type in [\"cuda\", \"cpu\"]\n\n        if x_is_other_gpu:\n            x = x.cpu()\n\n        zout = self._mask(z, x)\n        x = self._ispec(zout, length)\n\n        # back to other device\n        if x_is_other_gpu:\n            x = x.to(device_load)\n\n        if self.hybrid:\n            xt = xt.view(B, S, -1, length)\n            xt = xt * stdt[:, None] + meant[:, None]","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/hdemucs.py:64"}],[1375,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/hdemucs.py","startLine":778,"endLine":783,"chunkText":"        if self.hybrid:\n            xt = xt.view(B, S, -1, length)\n            xt = xt * stdt[:, None] + meant[:, None]\n            x = xt + x\n        return x\n","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/hdemucs.py:65"}],[1376,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/htdemucs.py","startLine":0,"endLine":21,"chunkText":"# Copyright (c) Meta, Inc. and its affiliates.\n# All rights reserved.\n#\n# This source code is licensed under the license found in the\n# LICENSE file in the root directory of this source tree.\n# First author is Simon Rouard.\n\"\"\"\nThis code contains the spectrogram and Hybrid version of Demucs.\n\"\"\"\nimport math\n\nfrom .filtering import wiener\nimport torch\nfrom torch import nn\nfrom torch.nn import functional as F\nfrom fractions import Fraction\nfrom einops import rearrange\n\nfrom .transformer import CrossTransformerEncoder\n\nfrom .demucs import rescale_module\nfrom .states import capture_init","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/htdemucs.py:0"}],[1377,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/htdemucs.py","startLine":19,"endLine":32,"chunkText":"\nfrom .demucs import rescale_module\nfrom .states import capture_init\nfrom .spec import spectro, ispectro\nfrom .hdemucs import pad1d, ScaledEmbedding, HEncLayer, MultiWrap, HDecLayer\n\n\nclass HTDemucs(nn.Module):\n    \"\"\"\n    Spectrogram and hybrid Demucs model.\n    The spectrogram model has the same structure as Demucs, except the first few layers are over the\n    frequency axis, until there is only 1 frequency, and then it moves to time convolutions.\n    Frequency layers can still access information across time steps thanks to the DConv residual.\n","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/htdemucs.py:1"}],[1378,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/htdemucs.py","startLine":30,"endLine":39,"chunkText":"    frequency axis, until there is only 1 frequency, and then it moves to time convolutions.\n    Frequency layers can still access information across time steps thanks to the DConv residual.\n\n    Hybrid model have a parallel time branch. At some layer, the time branch has the same stride\n    as the frequency branch and then the two are combined. The opposite happens in the decoder.\n\n    Models can either use naive iSTFT from masking, Wiener filtering ([Ulhih et al. 2017]),\n    or complex as channels (CaC) [Choi et al. 2020]. Wiener filtering is based on\n    Open Unmix implementation [Stoter et al. 2019].\n","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/htdemucs.py:2"}],[1379,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/htdemucs.py","startLine":37,"endLine":44,"chunkText":"    or complex as channels (CaC) [Choi et al. 2020]. Wiener filtering is based on\n    Open Unmix implementation [Stoter et al. 2019].\n\n    The loss is always on the temporal domain, by backpropagating through the above\n    output methods and iSTFT. This allows to define hybrid models nicely. However, this breaks\n    a bit Wiener filtering, as doing more iteration at test time will change the spectrogram\n    contribution, without changing the one from the waveform, which will lead to worse performance.\n    I tried using the residual option in OpenUnmix Wiener implementation, but it didn't improve.","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/htdemucs.py:3"}],[1380,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/htdemucs.py","startLine":42,"endLine":50,"chunkText":"    a bit Wiener filtering, as doing more iteration at test time will change the spectrogram\n    contribution, without changing the one from the waveform, which will lead to worse performance.\n    I tried using the residual option in OpenUnmix Wiener implementation, but it didn't improve.\n    CaC on the other hand provides similar performance for hybrid, and works naturally with\n    hybrid models.\n\n    This model also uses frequency embeddings are used to improve efficiency on convolutions\n    over the freq. axis, following [Isik et al. 2020] (https://arxiv.org/pdf/2008.04470.pdf).\n","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/htdemucs.py:4"}],[1381,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/htdemucs.py","startLine":48,"endLine":68,"chunkText":"    This model also uses frequency embeddings are used to improve efficiency on convolutions\n    over the freq. axis, following [Isik et al. 2020] (https://arxiv.org/pdf/2008.04470.pdf).\n\n    Unlike classic Demucs, there is no resampling here, and normalization is always applied.\n    \"\"\"\n\n    @capture_init\n    def __init__(\n        self,\n        sources,\n        # Channels\n        audio_channels=2,\n        channels=48,\n        channels_time=None,\n        growth=2,\n        # STFT\n        nfft=4096,\n        wiener_iters=0,\n        end_iters=0,\n        wiener_residual=False,\n        cac=True,","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/htdemucs.py:5"}],[1382,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/htdemucs.py","startLine":66,"endLine":91,"chunkText":"        end_iters=0,\n        wiener_residual=False,\n        cac=True,\n        # Main structure\n        depth=4,\n        rewrite=True,\n        # Frequency branch\n        multi_freqs=None,\n        multi_freqs_depth=3,\n        freq_emb=0.2,\n        emb_scale=10,\n        emb_smooth=True,\n        # Convolutions\n        kernel_size=8,\n        time_stride=2,\n        stride=4,\n        context=1,\n        context_enc=0,\n        # Normalization\n        norm_starts=4,\n        norm_groups=4,\n        # DConv residual branch\n        dconv_mode=1,\n        dconv_depth=2,\n        dconv_comp=8,\n        dconv_init=1e-3,","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/htdemucs.py:6"}],[1383,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/htdemucs.py","startLine":89,"endLine":111,"chunkText":"        dconv_depth=2,\n        dconv_comp=8,\n        dconv_init=1e-3,\n        # Before the Transformer\n        bottom_channels=0,\n        # Transformer\n        t_layers=5,\n        t_emb=\"sin\",\n        t_hidden_scale=4.0,\n        t_heads=8,\n        t_dropout=0.0,\n        t_max_positions=10000,\n        t_norm_in=True,\n        t_norm_in_group=False,\n        t_group_norm=False,\n        t_norm_first=True,\n        t_norm_out=True,\n        t_max_period=10000.0,\n        t_weight_decay=0.0,\n        t_lr=None,\n        t_layer_scale=True,\n        t_gelu=True,\n        t_weight_pos_embed=1.0,","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/htdemucs.py:7"}],[1384,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/htdemucs.py","startLine":110,"endLine":129,"chunkText":"        t_gelu=True,\n        t_weight_pos_embed=1.0,\n        t_sin_random_shift=0,\n        t_cape_mean_normalize=True,\n        t_cape_augment=True,\n        t_cape_glob_loc_scale=[5000.0, 1.0, 1.4],\n        t_sparse_self_attn=False,\n        t_sparse_cross_attn=False,\n        t_mask_type=\"diag\",\n        t_mask_random_seed=42,\n        t_sparse_attn_window=500,\n        t_global_window=100,\n        t_sparsity=0.95,\n        t_auto_sparsity=False,\n        # ------ Particuliar parameters\n        t_cross_first=False,\n        # Weight init\n        rescale=0.1,\n        # Metadata\n        samplerate=44100,","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/htdemucs.py:8"}],[1385,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/htdemucs.py","startLine":127,"endLine":140,"chunkText":"        rescale=0.1,\n        # Metadata\n        samplerate=44100,\n        segment=10,\n        use_train_segment=True,\n    ):\n        \"\"\"\n        Args:\n            sources (list[str]): list of source names.\n            audio_channels (int): input/output audio channels.\n            channels (int): initial number of hidden channels.\n            channels_time: if not None, use a different `channels` value for the time branch.\n            growth: increase the number of hidden channels by this factor at each layer.\n            nfft: number of fft bins. Note that changing this require careful computation of","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/htdemucs.py:9"}],[1386,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/htdemucs.py","startLine":138,"endLine":143,"chunkText":"            channels_time: if not None, use a different `channels` value for the time branch.\n            growth: increase the number of hidden channels by this factor at each layer.\n            nfft: number of fft bins. Note that changing this require careful computation of\n                various shape parameters and will not work out of the box for hybrid models.\n            wiener_iters: when using Wiener filtering, number of iterations at test time.\n            end_iters: same but at train time. For a hybrid model, must be equal to `wiener_iters`.","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/htdemucs.py:10"}],[1387,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/htdemucs.py","startLine":141,"endLine":147,"chunkText":"                various shape parameters and will not work out of the box for hybrid models.\n            wiener_iters: when using Wiener filtering, number of iterations at test time.\n            end_iters: same but at train time. For a hybrid model, must be equal to `wiener_iters`.\n            wiener_residual: add residual source before wiener filtering.\n            cac: uses complex as channels, i.e. complex numbers are 2 channels each\n                in input and output. no further processing is done before ISTFT.\n            depth (int): number of layers in the encoder and in the decoder.","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/htdemucs.py:11"}],[1388,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/htdemucs.py","startLine":145,"endLine":151,"chunkText":"            cac: uses complex as channels, i.e. complex numbers are 2 channels each\n                in input and output. no further processing is done before ISTFT.\n            depth (int): number of layers in the encoder and in the decoder.\n            rewrite (bool): add 1x1 convolution to each layer.\n            multi_freqs: list of frequency ratios for splitting frequency bands with `MultiWrap`.\n            multi_freqs_depth: how many layers to wrap with `MultiWrap`. Only the outermost\n                layers will be wrapped.","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/htdemucs.py:12"}],[1389,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/htdemucs.py","startLine":149,"endLine":155,"chunkText":"            multi_freqs: list of frequency ratios for splitting frequency bands with `MultiWrap`.\n            multi_freqs_depth: how many layers to wrap with `MultiWrap`. Only the outermost\n                layers will be wrapped.\n            freq_emb: add frequency embedding after the first frequency layer if > 0,\n                the actual value controls the weight of the embedding.\n            emb_scale: equivalent to scaling the embedding learning rate\n            emb_smooth: initialize the embedding with a smooth one (with respect to frequencies).","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/htdemucs.py:13"}],[1390,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/htdemucs.py","startLine":153,"endLine":160,"chunkText":"                the actual value controls the weight of the embedding.\n            emb_scale: equivalent to scaling the embedding learning rate\n            emb_smooth: initialize the embedding with a smooth one (with respect to frequencies).\n            kernel_size: kernel_size for encoder and decoder layers.\n            stride: stride for encoder and decoder layers.\n            time_stride: stride for the final time layer, after the merge.\n            context: context for 1x1 conv in the decoder.\n            context_enc: context for 1x1 conv in the encoder.","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/htdemucs.py:14"}],[1391,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/htdemucs.py","startLine":158,"endLine":166,"chunkText":"            time_stride: stride for the final time layer, after the merge.\n            context: context for 1x1 conv in the decoder.\n            context_enc: context for 1x1 conv in the encoder.\n            norm_starts: layer at which group norm starts being used.\n                decoder layers are numbered in reverse order.\n            norm_groups: number of groups for group norm.\n            dconv_mode: if 1: dconv in encoder only, 2: decoder only, 3: both.\n            dconv_depth: depth of residual DConv branch.\n            dconv_comp: compression of DConv branch.","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/htdemucs.py:15"}],[1392,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/htdemucs.py","startLine":164,"endLine":171,"chunkText":"            dconv_mode: if 1: dconv in encoder only, 2: decoder only, 3: both.\n            dconv_depth: depth of residual DConv branch.\n            dconv_comp: compression of DConv branch.\n            dconv_attn: adds attention layers in DConv branch starting at this layer.\n            dconv_lstm: adds a LSTM layer in DConv branch starting at this layer.\n            dconv_init: initial scale for the DConv branch LayerScale.\n            bottom_channels: if >0 it adds a linear layer (1x1 Conv) before and after the\n                transformer in order to change the number of channels","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/htdemucs.py:16"}],[1393,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/htdemucs.py","startLine":169,"endLine":175,"chunkText":"            dconv_init: initial scale for the DConv branch LayerScale.\n            bottom_channels: if >0 it adds a linear layer (1x1 Conv) before and after the\n                transformer in order to change the number of channels\n            t_layers: number of layers in each branch (waveform and spec) of the transformer\n            t_emb: \"sin\", \"cape\" or \"scaled\"\n            t_hidden_scale: the hidden scale of the Feedforward parts of the transformer\n                for instance if C = 384 (the number of channels in the transformer) and","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/htdemucs.py:17"}],[1394,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/htdemucs.py","startLine":173,"endLine":181,"chunkText":"            t_emb: \"sin\", \"cape\" or \"scaled\"\n            t_hidden_scale: the hidden scale of the Feedforward parts of the transformer\n                for instance if C = 384 (the number of channels in the transformer) and\n                t_hidden_scale = 4.0 then the intermediate layer of the FFN has dimension\n                384 * 4 = 1536\n            t_heads: number of heads for the transformer\n            t_dropout: dropout in the transformer\n            t_max_positions: max_positions for the \"scaled\" positional embedding, only\n                useful if t_emb=\"scaled\"","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/htdemucs.py:18"}],[1395,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/htdemucs.py","startLine":179,"endLine":187,"chunkText":"            t_dropout: dropout in the transformer\n            t_max_positions: max_positions for the \"scaled\" positional embedding, only\n                useful if t_emb=\"scaled\"\n            t_norm_in: (bool) norm before addinf positional embedding and getting into the\n                transformer layers\n            t_norm_in_group: (bool) if True while t_norm_in=True, the norm is on all the\n                timesteps (GroupNorm with group=1)\n            t_group_norm: (bool) if True, the norms of the Encoder Layers are on all the\n                timesteps (GroupNorm with group=1)","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/htdemucs.py:19"}],[1396,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/htdemucs.py","startLine":185,"endLine":192,"chunkText":"                timesteps (GroupNorm with group=1)\n            t_group_norm: (bool) if True, the norms of the Encoder Layers are on all the\n                timesteps (GroupNorm with group=1)\n            t_norm_first: (bool) if True the norm is before the attention and before the FFN\n            t_norm_out: (bool) if True, there is a GroupNorm (group=1) at the end of each layer\n            t_max_period: (float) denominator in the sinusoidal embedding expression\n            t_weight_decay: (float) weight decay for the transformer\n            t_lr: (float) specific learning rate for the transformer","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/htdemucs.py:20"}],[1397,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/htdemucs.py","startLine":190,"endLine":197,"chunkText":"            t_max_period: (float) denominator in the sinusoidal embedding expression\n            t_weight_decay: (float) weight decay for the transformer\n            t_lr: (float) specific learning rate for the transformer\n            t_layer_scale: (bool) Layer Scale for the transformer\n            t_gelu: (bool) activations of the transformer are GeLU if True, ReLU else\n            t_weight_pos_embed: (float) weighting of the positional embedding\n            t_cape_mean_normalize: (bool) if t_emb=\"cape\", normalisation of positional embeddings\n                see: https://arxiv.org/abs/2106.03143","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/htdemucs.py:21"}],[1398,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/htdemucs.py","startLine":195,"endLine":201,"chunkText":"            t_weight_pos_embed: (float) weighting of the positional embedding\n            t_cape_mean_normalize: (bool) if t_emb=\"cape\", normalisation of positional embeddings\n                see: https://arxiv.org/abs/2106.03143\n            t_cape_augment: (bool) if t_emb=\"cape\", must be True during training and False\n                during the inference, see: https://arxiv.org/abs/2106.03143\n            t_cape_glob_loc_scale: (list of 3 floats) if t_emb=\"cape\", CAPE parameters\n                see: https://arxiv.org/abs/2106.03143","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/htdemucs.py:22"}],[1399,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/htdemucs.py","startLine":199,"endLine":205,"chunkText":"                during the inference, see: https://arxiv.org/abs/2106.03143\n            t_cape_glob_loc_scale: (list of 3 floats) if t_emb=\"cape\", CAPE parameters\n                see: https://arxiv.org/abs/2106.03143\n            t_sparse_self_attn: (bool) if True, the self attentions are sparse\n            t_sparse_cross_attn: (bool) if True, the cross-attentions are sparse (don't use it\n                unless you designed really specific masks)\n            t_mask_type: (str) can be \"diag\", \"jmask\", \"random\", \"global\" or any combination","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/htdemucs.py:23"}],[1400,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/htdemucs.py","startLine":203,"endLine":209,"chunkText":"            t_sparse_cross_attn: (bool) if True, the cross-attentions are sparse (don't use it\n                unless you designed really specific masks)\n            t_mask_type: (str) can be \"diag\", \"jmask\", \"random\", \"global\" or any combination\n                with '_' between: i.e. \"diag_jmask_random\" (note that this is permutation\n                invariant i.e. \"diag_jmask_random\" is equivalent to \"jmask_random_diag\")\n            t_mask_random_seed: (int) if \"random\" is in t_mask_type, controls the seed\n                that generated the random part of the mask","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/htdemucs.py:24"}],[1401,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/htdemucs.py","startLine":207,"endLine":213,"chunkText":"                invariant i.e. \"diag_jmask_random\" is equivalent to \"jmask_random_diag\")\n            t_mask_random_seed: (int) if \"random\" is in t_mask_type, controls the seed\n                that generated the random part of the mask\n            t_sparse_attn_window: (int) if \"diag\" is in t_mask_type, for a query (i), and\n                a key (j), the mask is True id |i-j|<=t_sparse_attn_window\n            t_global_window: (int) if \"global\" is in t_mask_type, mask[:t_global_window, :]\n                and mask[:, :t_global_window] will be True","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/htdemucs.py:25"}],[1402,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/htdemucs.py","startLine":211,"endLine":218,"chunkText":"                a key (j), the mask is True id |i-j|<=t_sparse_attn_window\n            t_global_window: (int) if \"global\" is in t_mask_type, mask[:t_global_window, :]\n                and mask[:, :t_global_window] will be True\n            t_sparsity: (float) if \"random\" is in t_mask_type, t_sparsity is the sparsity\n                level of the random part of the mask.\n            t_cross_first: (bool) if True cross attention is the first layer of the\n                transformer (False seems to be better)\n            rescale: weight rescaling trick","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/htdemucs.py:26"}],[1403,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/htdemucs.py","startLine":216,"endLine":229,"chunkText":"            t_cross_first: (bool) if True cross attention is the first layer of the\n                transformer (False seems to be better)\n            rescale: weight rescaling trick\n            use_train_segment: (bool) if True, the actual size that is used during the\n                training is used during inference.\n        \"\"\"\n        super().__init__()\n        self.cac = cac\n        self.wiener_residual = wiener_residual\n        self.audio_channels = audio_channels\n        self.sources = sources\n        self.kernel_size = kernel_size\n        self.context = context\n        self.stride = stride","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/htdemucs.py:27"}],[1404,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/htdemucs.py","startLine":227,"endLine":245,"chunkText":"        self.kernel_size = kernel_size\n        self.context = context\n        self.stride = stride\n        self.depth = depth\n        self.bottom_channels = bottom_channels\n        self.channels = channels\n        self.samplerate = samplerate\n        self.segment = segment\n        self.use_train_segment = use_train_segment\n        self.nfft = nfft\n        self.hop_length = nfft // 4\n        self.wiener_iters = wiener_iters\n        self.end_iters = end_iters\n        self.freq_emb = None\n        assert wiener_iters == end_iters\n\n        self.encoder = nn.ModuleList()\n        self.decoder = nn.ModuleList()\n","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/htdemucs.py:28"}],[1405,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/htdemucs.py","startLine":243,"endLine":263,"chunkText":"        self.encoder = nn.ModuleList()\n        self.decoder = nn.ModuleList()\n\n        self.tencoder = nn.ModuleList()\n        self.tdecoder = nn.ModuleList()\n\n        chin = audio_channels\n        chin_z = chin  # number of channels for the freq branch\n        if self.cac:\n            chin_z *= 2\n        chout = channels_time or channels\n        chout_z = channels\n        freqs = nfft // 2\n\n        for index in range(depth):\n            norm = index >= norm_starts\n            freq = freqs > 1\n            stri = stride\n            ker = kernel_size\n            if not freq:\n                assert freqs == ","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/htdemucs.py:29"}],[1406,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/htdemucs.py","startLine":261,"endLine":281,"chunkText":"            ker = kernel_size\n            if not freq:\n                assert freqs == 1\n                ker = time_stride * 2\n                stri = time_stride\n\n            pad = True\n            last_freq = False\n            if freq and freqs <= kernel_size:\n                ker = freqs\n                pad = False\n                last_freq = True\n\n            kw = {\n                \"kernel_size\": ker,\n                \"stride\": stri,\n                \"freq\": freq,\n                \"pad\": pad,\n                \"norm\": norm,\n                \"rewrite\": rewrite,\n                \"norm_groups\": norm_groups,","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/htdemucs.py:30"}],[1407,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/htdemucs.py","startLine":279,"endLine":294,"chunkText":"                \"norm\": norm,\n                \"rewrite\": rewrite,\n                \"norm_groups\": norm_groups,\n                \"dconv_kw\": {\"depth\": dconv_depth, \"compress\": dconv_comp, \"init\": dconv_init, \"gelu\": True},\n            }\n            kwt = dict(kw)\n            kwt[\"freq\"] = 0\n            kwt[\"kernel_size\"] = kernel_size\n            kwt[\"stride\"] = stride\n            kwt[\"pad\"] = True\n            kw_dec = dict(kw)\n            multi = False\n            if multi_freqs and index < multi_freqs_depth:\n                multi = True\n                kw_dec[\"context_freq\"] = False\n","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/htdemucs.py:31"}],[1408,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/htdemucs.py","startLine":292,"endLine":307,"chunkText":"                multi = True\n                kw_dec[\"context_freq\"] = False\n\n            if last_freq:\n                chout_z = max(chout, chout_z)\n                chout = chout_z\n\n            enc = HEncLayer(chin_z, chout_z, dconv=dconv_mode & 1, context=context_enc, **kw)\n            if freq:\n                tenc = HEncLayer(chin, chout, dconv=dconv_mode & 1, context=context_enc, empty=last_freq, **kwt)\n                self.tencoder.append(tenc)\n\n            if multi:\n                enc = MultiWrap(enc, multi_freqs)\n            self.encoder.append(enc)\n            if index == 0:","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/htdemucs.py:32"}],[1409,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/htdemucs.py","startLine":305,"endLine":316,"chunkText":"                enc = MultiWrap(enc, multi_freqs)\n            self.encoder.append(enc)\n            if index == 0:\n                chin = self.audio_channels * len(self.sources)\n                chin_z = chin\n                if self.cac:\n                    chin_z *= 2\n            dec = HDecLayer(chout_z, chin_z, dconv=dconv_mode & 2, last=index == 0, context=context, **kw_dec)\n            if multi:\n                dec = MultiWrap(dec, multi_freqs)\n            if freq:\n                tdec = HDecLayer(chout, chin, dconv=dconv_mode & 2, empty=last_freq, last=index == 0, context=context, **kwt)","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/htdemucs.py:33"}],[1410,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/htdemucs.py","startLine":314,"endLine":329,"chunkText":"                dec = MultiWrap(dec, multi_freqs)\n            if freq:\n                tdec = HDecLayer(chout, chin, dconv=dconv_mode & 2, empty=last_freq, last=index == 0, context=context, **kwt)\n                self.tdecoder.insert(0, tdec)\n            self.decoder.insert(0, dec)\n\n            chin = chout\n            chin_z = chout_z\n            chout = int(growth * chout)\n            chout_z = int(growth * chout_z)\n            if freq:\n                if freqs <= kernel_size:\n                    freqs = 1\n                else:\n                    freqs //= stride\n            if index == 0 and freq_emb:","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/htdemucs.py:34"}],[1411,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/htdemucs.py","startLine":327,"endLine":339,"chunkText":"                else:\n                    freqs //= stride\n            if index == 0 and freq_emb:\n                self.freq_emb = ScaledEmbedding(freqs, chin_z, smooth=emb_smooth, scale=emb_scale)\n                self.freq_emb_scale = freq_emb\n\n        if rescale:\n            rescale_module(self, reference=rescale)\n\n        transformer_channels = channels * growth ** (depth - 1)\n        if bottom_channels:\n            self.channel_upsampler = nn.Conv1d(transformer_channels, bottom_channels, 1)\n            self.channel_downsampler = nn.Conv1d(bottom_channels, transformer_channels, 1)","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/htdemucs.py:35"}],[1412,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/htdemucs.py","startLine":337,"endLine":348,"chunkText":"        if bottom_channels:\n            self.channel_upsampler = nn.Conv1d(transformer_channels, bottom_channels, 1)\n            self.channel_downsampler = nn.Conv1d(bottom_channels, transformer_channels, 1)\n            self.channel_upsampler_t = nn.Conv1d(transformer_channels, bottom_channels, 1)\n            self.channel_downsampler_t = nn.Conv1d(bottom_channels, transformer_channels, 1)\n\n            transformer_channels = bottom_channels\n\n        if t_layers > 0:\n            self.crosstransformer = CrossTransformerEncoder(\n                dim=transformer_channels,\n                emb=t_emb,","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/htdemucs.py:36"}],[1413,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/htdemucs.py","startLine":346,"endLine":359,"chunkText":"            self.crosstransformer = CrossTransformerEncoder(\n                dim=transformer_channels,\n                emb=t_emb,\n                hidden_scale=t_hidden_scale,\n                num_heads=t_heads,\n                num_layers=t_layers,\n                cross_first=t_cross_first,\n                dropout=t_dropout,\n                max_positions=t_max_positions,\n                norm_in=t_norm_in,\n                norm_in_group=t_norm_in_group,\n                group_norm=t_group_norm,\n                norm_first=t_norm_first,\n                norm_out=t_norm_out,","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/htdemucs.py:37"}],[1414,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/htdemucs.py","startLine":357,"endLine":369,"chunkText":"                group_norm=t_group_norm,\n                norm_first=t_norm_first,\n                norm_out=t_norm_out,\n                max_period=t_max_period,\n                weight_decay=t_weight_decay,\n                lr=t_lr,\n                layer_scale=t_layer_scale,\n                gelu=t_gelu,\n                sin_random_shift=t_sin_random_shift,\n                weight_pos_embed=t_weight_pos_embed,\n                cape_mean_normalize=t_cape_mean_normalize,\n                cape_augment=t_cape_augment,\n                cape_glob_loc_scale=t_cape_glob_loc_scale,","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/htdemucs.py:38"}],[1415,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/htdemucs.py","startLine":367,"endLine":379,"chunkText":"                cape_mean_normalize=t_cape_mean_normalize,\n                cape_augment=t_cape_augment,\n                cape_glob_loc_scale=t_cape_glob_loc_scale,\n                sparse_self_attn=t_sparse_self_attn,\n                sparse_cross_attn=t_sparse_cross_attn,\n                mask_type=t_mask_type,\n                mask_random_seed=t_mask_random_seed,\n                sparse_attn_window=t_sparse_attn_window,\n                global_window=t_global_window,\n                sparsity=t_sparsity,\n                auto_sparsity=t_auto_sparsity,\n            )\n        else:","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/htdemucs.py:39"}],[1416,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/htdemucs.py","startLine":377,"endLine":391,"chunkText":"                auto_sparsity=t_auto_sparsity,\n            )\n        else:\n            self.crosstransformer = None\n\n    def _spec(self, x):\n        hl = self.hop_length\n        nfft = self.nfft\n        x0 = x  # noqa\n\n        # We re-pad the signal in order to keep the property\n        # that the size of the output is exactly the size of the input\n        # divided by the stride (here hop_length), when divisible.\n        # This is achieved by padding by 1/4th of the kernel size (here nfft).\n        # which is not supported by torch.stft.","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/htdemucs.py:40"}],[1417,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/htdemucs.py","startLine":389,"endLine":399,"chunkText":"        # divided by the stride (here hop_length), when divisible.\n        # This is achieved by padding by 1/4th of the kernel size (here nfft).\n        # which is not supported by torch.stft.\n        # Having all convolution operations follow this convention allow to easily\n        # align the time and frequency branches later on.\n        assert hl == nfft // 4\n        le = int(math.ceil(x.shape[-1] / hl))\n        pad = hl // 2 * 3\n        x = pad1d(x, (pad, pad + le * hl - x.shape[-1]), mode=\"reflect\")\n\n        z = spectro(x, nfft, hl)[..., :-1, :]","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/htdemucs.py:41"}],[1418,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/htdemucs.py","startLine":397,"endLine":414,"chunkText":"        x = pad1d(x, (pad, pad + le * hl - x.shape[-1]), mode=\"reflect\")\n\n        z = spectro(x, nfft, hl)[..., :-1, :]\n        assert z.shape[-1] == le + 4, (z.shape, x.shape, le)\n        z = z[..., 2 : 2 + le]\n        return z\n\n    def _ispec(self, z, length=None, scale=0):\n        hl = self.hop_length // (4**scale)\n        z = F.pad(z, (0, 0, 0, 1))\n        z = F.pad(z, (2, 2))\n        pad = hl // 2 * 3\n        le = hl * int(math.ceil(length / hl)) + 2 * pad\n        x = ispectro(z, hl, length=le)\n        x = x[..., pad : pad + length]\n        return x\n\n    def _magnitude(self, z):","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/htdemucs.py:42"}],[1419,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/htdemucs.py","startLine":412,"endLine":427,"chunkText":"        return x\n\n    def _magnitude(self, z):\n        # return the magnitude of the spectrogram, except when cac is True,\n        # in which case we just move the complex dimension to the channel one.\n        if self.cac:\n            B, C, Fr, T = z.shape\n            m = torch.view_as_real(z).permute(0, 1, 4, 2, 3)\n            m = m.reshape(B, C * 2, Fr, T)\n        else:\n            m = z.abs()\n        return m\n\n    def _mask(self, z, m):\n        # Apply masking given the mixture spectrogram `z` and the estimated mask `m`.\n        # If `cac` is True, `m` is actually a full spectrogram and `z` is ignored.","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/htdemucs.py:43"}],[1420,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/htdemucs.py","startLine":425,"endLine":438,"chunkText":"    def _mask(self, z, m):\n        # Apply masking given the mixture spectrogram `z` and the estimated mask `m`.\n        # If `cac` is True, `m` is actually a full spectrogram and `z` is ignored.\n        niters = self.wiener_iters\n        if self.cac:\n            B, S, C, Fr, T = m.shape\n            out = m.view(B, S, -1, 2, Fr, T).permute(0, 1, 2, 4, 5, 3)\n            out = torch.view_as_complex(out.contiguous())\n            return out\n        if self.training:\n            niters = self.end_iters\n        if niters < 0:\n            z = z[:, None]\n            return z / (1e-8 + z.abs()) * m","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/htdemucs.py:44"}],[1421,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/htdemucs.py","startLine":436,"endLine":455,"chunkText":"        if niters < 0:\n            z = z[:, None]\n            return z / (1e-8 + z.abs()) * m\n        else:\n            return self._wiener(m, z, niters)\n\n    def _wiener(self, mag_out, mix_stft, niters):\n        # apply wiener filtering from OpenUnmix.\n        init = mix_stft.dtype\n        wiener_win_len = 300\n        residual = self.wiener_residual\n\n        B, S, C, Fq, T = mag_out.shape\n        mag_out = mag_out.permute(0, 4, 3, 2, 1)\n        mix_stft = torch.view_as_real(mix_stft.permute(0, 3, 2, 1))\n\n        outs = []\n        for sample in range(B):\n            pos = 0\n            out = []","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/htdemucs.py:45"}],[1422,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/htdemucs.py","startLine":453,"endLine":465,"chunkText":"        for sample in range(B):\n            pos = 0\n            out = []\n            for pos in range(0, T, wiener_win_len):\n                frame = slice(pos, pos + wiener_win_len)\n                z_out = wiener(mag_out[sample, frame], mix_stft[sample, frame], niters, residual=residual)\n                out.append(z_out.transpose(-1, -2))\n            outs.append(torch.cat(out, dim=0))\n        out = torch.view_as_complex(torch.stack(outs, 0))\n        out = out.permute(0, 4, 3, 2, 1).contiguous()\n        if residual:\n            out = out[:, :-1]\n        assert list(out.shape) == [B, S, C, Fq, T]","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/htdemucs.py:46"}],[1423,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/htdemucs.py","startLine":463,"endLine":478,"chunkText":"        if residual:\n            out = out[:, :-1]\n        assert list(out.shape) == [B, S, C, Fq, T]\n        return out.to(init)\n\n    def valid_length(self, length: int):\n        \"\"\"\n        Return a length that is appropriate for evaluation.\n        In our case, always return the training length, unless\n        it is smaller than the given length, in which case this\n        raises an error.\n        \"\"\"\n        if not self.use_train_segment:\n            return length\n        training_length = int(self.segment * self.samplerate)\n        if training_length < length:","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/htdemucs.py:47"}],[1424,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/htdemucs.py","startLine":476,"endLine":489,"chunkText":"            return length\n        training_length = int(self.segment * self.samplerate)\n        if training_length < length:\n            raise ValueError(f\"Given length {length} is longer than \" f\"training length {training_length}\")\n        return training_length\n\n    def forward(self, mix):\n        length = mix.shape[-1]\n        length_pre_pad = None\n        if self.use_train_segment:\n            if self.training:\n                self.segment = Fraction(mix.shape[-1], self.samplerate)\n            else:\n                training_length = int(self.segment * self.samplerate)","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/htdemucs.py:48"}],[1425,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/htdemucs.py","startLine":487,"endLine":500,"chunkText":"                self.segment = Fraction(mix.shape[-1], self.samplerate)\n            else:\n                training_length = int(self.segment * self.samplerate)\n                if mix.shape[-1] < training_length:\n                    length_pre_pad = mix.shape[-1]\n                    mix = F.pad(mix, (0, training_length - length_pre_pad))\n        z = self._spec(mix)\n        mag = self._magnitude(z).to(mix.device)\n        x = mag\n\n        B, C, Fq, T = x.shape\n\n        # unlike previous Demucs, we always normalize because it is easier.\n        mean = x.mean(dim=(1, 2, 3), keepdim=True)","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/htdemucs.py:49"}],[1426,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/htdemucs.py","startLine":498,"endLine":513,"chunkText":"\n        # unlike previous Demucs, we always normalize because it is easier.\n        mean = x.mean(dim=(1, 2, 3), keepdim=True)\n        std = x.std(dim=(1, 2, 3), keepdim=True)\n        x = (x - mean) / (1e-5 + std)\n        # x will be the freq. branch input.\n\n        # Prepare the time branch input.\n        xt = mix\n        meant = xt.mean(dim=(1, 2), keepdim=True)\n        stdt = xt.std(dim=(1, 2), keepdim=True)\n        xt = (xt - meant) / (1e-5 + stdt)\n\n        # okay, this is a giant mess I know...\n        saved = []  # skip connections, freq.\n        saved_t = []  # skip connections, time.","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/htdemucs.py:50"}],[1427,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/htdemucs.py","startLine":511,"endLine":523,"chunkText":"        # okay, this is a giant mess I know...\n        saved = []  # skip connections, freq.\n        saved_t = []  # skip connections, time.\n        lengths = []  # saved lengths to properly remove padding, freq branch.\n        lengths_t = []  # saved lengths for time branch.\n        for idx, encode in enumerate(self.encoder):\n            lengths.append(x.shape[-1])\n            inject = None\n            if idx < len(self.tencoder):\n                # we have not yet merged branches.\n                lengths_t.append(xt.shape[-1])\n                tenc = self.tencoder[idx]\n                xt = tenc(xt)","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/htdemucs.py:51"}],[1428,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/htdemucs.py","startLine":521,"endLine":532,"chunkText":"                lengths_t.append(xt.shape[-1])\n                tenc = self.tencoder[idx]\n                xt = tenc(xt)\n                if not tenc.empty:\n                    # save for skip connection\n                    saved_t.append(xt)\n                else:\n                    # tenc contains just the first conv., so that now time and freq.\n                    # branches have the same shape and can be merged.\n                    inject = xt\n            x = encode(x, inject)\n            if idx == 0 and self.freq_emb is not None:","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/htdemucs.py:52"}],[1429,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/htdemucs.py","startLine":530,"endLine":542,"chunkText":"                    inject = xt\n            x = encode(x, inject)\n            if idx == 0 and self.freq_emb is not None:\n                # add frequency embedding to allow for non equivariant convolutions\n                # over the frequency axis.\n                frs = torch.arange(x.shape[-2], device=x.device)\n                emb = self.freq_emb(frs).t()[None, :, :, None].expand_as(x)\n                x = x + self.freq_emb_scale * emb\n\n            saved.append(x)\n        if self.crosstransformer:\n            if self.bottom_channels:\n                b, c, f, t = x.shape","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/htdemucs.py:53"}],[1430,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/htdemucs.py","startLine":540,"endLine":553,"chunkText":"        if self.crosstransformer:\n            if self.bottom_channels:\n                b, c, f, t = x.shape\n                x = rearrange(x, \"b c f t-> b c (f t)\")\n                x = self.channel_upsampler(x)\n                x = rearrange(x, \"b c (f t)-> b c f t\", f=f)\n                xt = self.channel_upsampler_t(xt)\n\n            x, xt = self.crosstransformer(x, xt)\n\n            if self.bottom_channels:\n                x = rearrange(x, \"b c f t-> b c (f t)\")\n                x = self.channel_downsampler(x)\n                x = rearrange(x, \"b c (f t)-> b c f t\", f=f)","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/htdemucs.py:54"}],[1431,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/htdemucs.py","startLine":551,"endLine":563,"chunkText":"                x = rearrange(x, \"b c f t-> b c (f t)\")\n                x = self.channel_downsampler(x)\n                x = rearrange(x, \"b c (f t)-> b c f t\", f=f)\n                xt = self.channel_downsampler_t(xt)\n\n        for idx, decode in enumerate(self.decoder):\n            skip = saved.pop(-1)\n            x, pre = decode(x, skip, lengths.pop(-1))\n            # `pre` contains the output just before final transposed convolution,\n            # which is used when the freq. and time branch separate.\n\n            offset = self.depth - len(self.tdecoder)\n            if idx >= offset:","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/htdemucs.py:55"}],[1432,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/htdemucs.py","startLine":561,"endLine":576,"chunkText":"\n            offset = self.depth - len(self.tdecoder)\n            if idx >= offset:\n                tdec = self.tdecoder[idx - offset]\n                length_t = lengths_t.pop(-1)\n                if tdec.empty:\n                    assert pre.shape[2] == 1, pre.shape\n                    pre = pre[:, :, 0]\n                    xt, _ = tdec(pre, None, length_t)\n                else:\n                    skip = saved_t.pop(-1)\n                    xt, _ = tdec(xt, skip, length_t)\n\n        # Let's make sure we used all stored skip connections.\n        assert len(saved) == 0\n        assert len(lengths_t) == 0","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/htdemucs.py:56"}],[1433,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/htdemucs.py","startLine":574,"endLine":588,"chunkText":"        # Let's make sure we used all stored skip connections.\n        assert len(saved) == 0\n        assert len(lengths_t) == 0\n        assert len(saved_t) == 0\n\n        S = len(self.sources)\n        x = x.view(B, S, -1, Fq, T)\n        x = x * std[:, None] + mean[:, None]\n\n        # to cpu as non-cuda GPUs don't support complex numbers\n        # demucs issue #435 ##432\n        # NOTE: in this case z already is on cpu\n        # TODO: remove this when mps supports complex numbers\n\n        device_type = x.device.type","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/htdemucs.py:57"}],[1434,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/htdemucs.py","startLine":586,"endLine":603,"chunkText":"        # TODO: remove this when mps supports complex numbers\n\n        device_type = x.device.type\n        device_load = f\"{device_type}:{x.device.index}\" if not device_type == \"mps\" else device_type\n        x_is_other_gpu = not device_type in [\"cuda\", \"cpu\"]\n\n        if x_is_other_gpu:\n            x = x.cpu()\n\n        zout = self._mask(z, x)\n        if self.use_train_segment:\n            if self.training:\n                x = self._ispec(zout, length)\n            else:\n                x = self._ispec(zout, training_length)\n        else:\n            x = self._ispec(zout, length)\n","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/htdemucs.py:58"}],[1435,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/htdemucs.py","startLine":601,"endLine":620,"chunkText":"        else:\n            x = self._ispec(zout, length)\n\n        # back to other device\n        if x_is_other_gpu:\n            x = x.to(device_load)\n\n        if self.use_train_segment:\n            if self.training:\n                xt = xt.view(B, S, -1, length)\n            else:\n                xt = xt.view(B, S, -1, training_length)\n        else:\n            xt = xt.view(B, S, -1, length)\n        xt = xt * stdt[:, None] + meant[:, None]\n        x = xt + x\n        if length_pre_pad:\n            x = x[..., :length_pre_pad]\n        return x\n","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/htdemucs.py:59"}],[1436,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/model.py","startLine":0,"endLine":21,"chunkText":"# Copyright (c) Facebook, Inc. and its affiliates.\n# All rights reserved.\n#\n# This source code is licensed under the license found in the\n# LICENSE file in the root directory of this source tree.\n\nimport math\n\nimport torch as th\nfrom torch import nn\n\nfrom .utils import capture_init, center_trim\n\n\nclass BLSTM(nn.Module):\n    def __init__(self, dim, layers=1):\n        super().__init__()\n        self.lstm = nn.LSTM(bidirectional=True, num_layers=layers, hidden_size=dim, input_size=dim)\n        self.linear = nn.Linear(2 * dim, dim)\n\n    def forward(self, x):\n        x = x.permute(2, 0, 1)","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/model.py:0"}],[1437,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/model.py","startLine":20,"endLine":43,"chunkText":"    def forward(self, x):\n        x = x.permute(2, 0, 1)\n        x = self.lstm(x)[0]\n        x = self.linear(x)\n        x = x.permute(1, 2, 0)\n        return x\n\n\ndef rescale_conv(conv, reference):\n    std = conv.weight.std().detach()\n    scale = (std / reference) ** 0.5\n    conv.weight.data /= scale\n    if conv.bias is not None:\n        conv.bias.data /= scale\n\n\ndef rescale_module(module, reference):\n    for sub in module.modules():\n        if isinstance(sub, (nn.Conv1d, nn.ConvTranspose1d)):\n            rescale_conv(sub, reference)\n\n\ndef upsample(x, stride):\n    \"\"\"","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/model.py:1"}],[1438,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/model.py","startLine":41,"endLine":59,"chunkText":"\ndef upsample(x, stride):\n    \"\"\"\n    Linear upsampling, the output will be `stride` times longer.\n    \"\"\"\n    batch, channels, time = x.size()\n    weight = th.arange(stride, device=x.device, dtype=th.float) / stride\n    x = x.view(batch, channels, time, 1)\n    out = x[..., :-1, :] * (1 - weight) + x[..., 1:, :] * weight\n    return out.reshape(batch, channels, -1)\n\n\ndef downsample(x, stride):\n    \"\"\"\n    Downsample x by decimation.\n    \"\"\"\n    return x[:, :, ::stride]\n\n","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/model.py:2"}],[1439,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/model.py","startLine":60,"endLine":71,"chunkText":"class Demucs(nn.Module):\n    @capture_init\n    def __init__(\n        self, sources=4, audio_channels=2, channels=64, depth=6, rewrite=True, glu=True, upsample=False, rescale=0.1, kernel_size=8, stride=4, growth=2.0, lstm_layers=2, context=3, samplerate=44100\n    ):\n        \"\"\"\n        Args:\n            sources (int): number of sources to separate\n            audio_channels (int): stereo or mono\n            channels (int): first convolution channels\n            depth (int): number of encoder/decoder layers\n            rewrite (bool): add 1x1 convolution to each encoder layer","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/model.py:3"}],[1440,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/model.py","startLine":69,"endLine":77,"chunkText":"            channels (int): first convolution channels\n            depth (int): number of encoder/decoder layers\n            rewrite (bool): add 1x1 convolution to each encoder layer\n                and a convolution to each decoder layer.\n                For the decoder layer, `context` gives the kernel size.\n            glu (bool): use glu instead of ReLU\n            upsample (bool): use linear upsampling with convolutions\n                Wave-U-Net style, instead of transposed convolutions\n            rescale (int): rescale initial weights of convolutions","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/model.py:4"}],[1441,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/model.py","startLine":75,"endLine":83,"chunkText":"            upsample (bool): use linear upsampling with convolutions\n                Wave-U-Net style, instead of transposed convolutions\n            rescale (int): rescale initial weights of convolutions\n                to get their standard deviation closer to `rescale`\n            kernel_size (int): kernel size for convolutions\n            stride (int): stride for convolutions\n            growth (float): multiply (resp divide) number of channels by that\n                for each layer of the encoder (resp decoder)\n            lstm_layers (int): number of lstm layers, 0 = no lstm","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/model.py:5"}],[1442,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/model.py","startLine":81,"endLine":94,"chunkText":"            growth (float): multiply (resp divide) number of channels by that\n                for each layer of the encoder (resp decoder)\n            lstm_layers (int): number of lstm layers, 0 = no lstm\n            context (int): kernel size of the convolution in the\n                decoder before the transposed convolution. If > 1,\n                will provide some context from neighboring time\n                steps.\n        \"\"\"\n\n        super().__init__()\n        self.audio_channels = audio_channels\n        self.sources = sources\n        self.kernel_size = kernel_size\n        self.context = context","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/model.py:6"}],[1443,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/model.py","startLine":92,"endLine":112,"chunkText":"        self.sources = sources\n        self.kernel_size = kernel_size\n        self.context = context\n        self.stride = stride\n        self.depth = depth\n        self.upsample = upsample\n        self.channels = channels\n        self.samplerate = samplerate\n\n        self.encoder = nn.ModuleList()\n        self.decoder = nn.ModuleList()\n\n        self.final = None\n        if upsample:\n            self.final = nn.Conv1d(channels + audio_channels, sources * audio_channels, 1)\n            stride = 1\n\n        if glu:\n            activation = nn.GLU(dim=1)\n            ch_scale = 2\n        else:","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/model.py:7"}],[1444,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/model.py","startLine":110,"endLine":126,"chunkText":"            activation = nn.GLU(dim=1)\n            ch_scale = 2\n        else:\n            activation = nn.ReLU()\n            ch_scale = 1\n        in_channels = audio_channels\n        for index in range(depth):\n            encode = []\n            encode += [nn.Conv1d(in_channels, channels, kernel_size, stride), nn.ReLU()]\n            if rewrite:\n                encode += [nn.Conv1d(channels, ch_scale * channels, 1), activation]\n            self.encoder.append(nn.Sequential(*encode))\n\n            decode = []\n            if index > 0:\n                out_channels = in_channels\n            else:","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/model.py:8"}],[1445,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/model.py","startLine":124,"endLine":137,"chunkText":"            if index > 0:\n                out_channels = in_channels\n            else:\n                if upsample:\n                    out_channels = channels\n                else:\n                    out_channels = sources * audio_channels\n            if rewrite:\n                decode += [nn.Conv1d(channels, ch_scale * channels, context), activation]\n            if upsample:\n                decode += [nn.Conv1d(channels, out_channels, kernel_size, stride=1)]\n            else:\n                decode += [nn.ConvTranspose1d(channels, out_channels, kernel_size, stride)]\n            if index > 0:","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/model.py:9"}],[1446,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/model.py","startLine":135,"endLine":154,"chunkText":"            else:\n                decode += [nn.ConvTranspose1d(channels, out_channels, kernel_size, stride)]\n            if index > 0:\n                decode.append(nn.ReLU())\n            self.decoder.insert(0, nn.Sequential(*decode))\n            in_channels = channels\n            channels = int(growth * channels)\n\n        channels = in_channels\n\n        if lstm_layers:\n            self.lstm = BLSTM(channels, lstm_layers)\n        else:\n            self.lstm = None\n\n        if rescale:\n            rescale_module(self, reference=rescale)\n\n    def valid_length(self, length):\n        \"\"\"","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/model.py:10"}],[1447,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/model.py","startLine":152,"endLine":163,"chunkText":"    def valid_length(self, length):\n        \"\"\"\n        Return the nearest valid length to use with the model so that\n        there is no time steps left over in a convolutions, e.g. for all\n        layers, size of the input - kernel_size % stride = 0.\n\n        If the mixture has a valid length, the estimated sources\n        will have exactly the same length when context = 1. If context > 1,\n        the two signals can be center trimmed to match.\n\n        For training, extracts should have a valid length.For evaluation\n        on full tracks we recommend passing `pad = True` to :method:`forward`.","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/model.py:11"}],[1448,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/model.py","startLine":163,"endLine":174,"chunkText":"        For training, extracts should have a valid length.For evaluation\n        on full tracks we recommend passing `pad = True` to :method:`forward`.\n        \"\"\"\n        for _ in range(self.depth):\n            if self.upsample:\n                length = math.ceil(length / self.stride) + self.kernel_size - 1\n            else:\n                length = math.ceil((length - self.kernel_size) / self.stride) + 1\n            length = max(1, length)\n            length += self.context - 1\n        for _ in range(self.depth):\n            if self.upsample:","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/model.py:12"}],[1449,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/model.py","startLine":172,"endLine":191,"chunkText":"            length += self.context - 1\n        for _ in range(self.depth):\n            if self.upsample:\n                length = length * self.stride + self.kernel_size - 1\n            else:\n                length = (length - 1) * self.stride + self.kernel_size\n\n        return int(length)\n\n    def forward(self, mix):\n        x = mix\n        saved = [x]\n        for encode in self.encoder:\n            x = encode(x)\n            saved.append(x)\n            if self.upsample:\n                x = downsample(x, self.stride)\n        if self.lstm:\n            x = self.lstm(x)\n        for decode in self.decoder:","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/model.py:13"}],[1450,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/model.py","startLine":189,"endLine":204,"chunkText":"        if self.lstm:\n            x = self.lstm(x)\n        for decode in self.decoder:\n            if self.upsample:\n                x = upsample(x, stride=self.stride)\n            skip = center_trim(saved.pop(-1), x)\n            x = x + skip\n            x = decode(x)\n        if self.final:\n            skip = center_trim(saved.pop(-1), x)\n            x = th.cat([x, skip], dim=1)\n            x = self.final(x)\n\n        x = x.view(x.size(0), self.sources, self.audio_channels, x.size(-1))\n        return x\n","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/model.py:14"}],[1451,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/model_v2.py","startLine":0,"endLine":21,"chunkText":"# Copyright (c) Facebook, Inc. and its affiliates.\n# All rights reserved.\n#\n# This source code is licensed under the license found in the\n# LICENSE file in the root directory of this source tree.\n\nimport math\n\nimport julius\nfrom torch import nn\nfrom .tasnet_v2 import ConvTasNet\n\nfrom .utils import capture_init, center_trim\n\n\nclass BLSTM(nn.Module):\n    def __init__(self, dim, layers=1):\n        super().__init__()\n        self.lstm = nn.LSTM(bidirectional=True, num_layers=layers, hidden_size=dim, input_size=dim)\n        self.linear = nn.Linear(2 * dim, dim)\n\n    def forward(self, x):","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/model_v2.py:0"}],[1452,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/model_v2.py","startLine":19,"endLine":42,"chunkText":"        self.linear = nn.Linear(2 * dim, dim)\n\n    def forward(self, x):\n        x = x.permute(2, 0, 1)\n        x = self.lstm(x)[0]\n        x = self.linear(x)\n        x = x.permute(1, 2, 0)\n        return x\n\n\ndef rescale_conv(conv, reference):\n    std = conv.weight.std().detach()\n    scale = (std / reference) ** 0.5\n    conv.weight.data /= scale\n    if conv.bias is not None:\n        conv.bias.data /= scale\n\n\ndef rescale_module(module, reference):\n    for sub in module.modules():\n        if isinstance(sub, (nn.Conv1d, nn.ConvTranspose1d)):\n            rescale_conv(sub, reference)\n\n","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/model_v2.py:1"}],[1453,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/model_v2.py","startLine":40,"endLine":59,"chunkText":"            rescale_conv(sub, reference)\n\n\ndef auto_load_demucs_model_v2(sources, demucs_model_name):\n\n    if \"48\" in demucs_model_name:\n        channels = 48\n    elif \"unittest\" in demucs_model_name:\n        channels = 4\n    else:\n        channels = 64\n\n    if \"tasnet\" in demucs_model_name:\n        init_demucs_model = ConvTasNet(sources, X=10)\n    else:\n        init_demucs_model = Demucs(sources, channels=channels)\n\n    return init_demucs_model\n\n","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/model_v2.py:2"}],[1454,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/model_v2.py","startLine":60,"endLine":84,"chunkText":"class Demucs(nn.Module):\n    @capture_init\n    def __init__(\n        self,\n        sources,\n        audio_channels=2,\n        channels=64,\n        depth=6,\n        rewrite=True,\n        glu=True,\n        rescale=0.1,\n        resample=True,\n        kernel_size=8,\n        stride=4,\n        growth=2.0,\n        lstm_layers=2,\n        context=3,\n        normalize=False,\n        samplerate=44100,\n        segment_length=4 * 10 * 44100,\n    ):\n        \"\"\"\n        Args:\n            sources (list[str]): list of source names\n            audio_channels (int): stereo or mono","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/model_v2.py:3"}],[1455,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/model_v2.py","startLine":82,"endLine":91,"chunkText":"        Args:\n            sources (list[str]): list of source names\n            audio_channels (int): stereo or mono\n            channels (int): first convolution channels\n            depth (int): number of encoder/decoder layers\n            rewrite (bool): add 1x1 convolution to each encoder layer\n                and a convolution to each decoder layer.\n                For the decoder layer, `context` gives the kernel size.\n            glu (bool): use glu instead of ReLU\n            resample_input (bool): upsample x2 the input and downsample /2 the output.","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/model_v2.py:4"}],[1456,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/model_v2.py","startLine":89,"endLine":97,"chunkText":"                For the decoder layer, `context` gives the kernel size.\n            glu (bool): use glu instead of ReLU\n            resample_input (bool): upsample x2 the input and downsample /2 the output.\n            rescale (int): rescale initial weights of convolutions\n                to get their standard deviation closer to `rescale`\n            kernel_size (int): kernel size for convolutions\n            stride (int): stride for convolutions\n            growth (float): multiply (resp divide) number of channels by that\n                for each layer of the encoder (resp decoder)","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/model_v2.py:5"}],[1457,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/model_v2.py","startLine":95,"endLine":104,"chunkText":"            stride (int): stride for convolutions\n            growth (float): multiply (resp divide) number of channels by that\n                for each layer of the encoder (resp decoder)\n            lstm_layers (int): number of lstm layers, 0 = no lstm\n            context (int): kernel size of the convolution in the\n                decoder before the transposed convolution. If > 1,\n                will provide some context from neighboring time\n                steps.\n            samplerate (int): stored as meta information for easing\n                future evaluations of the model.","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/model_v2.py:6"}],[1458,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/model_v2.py","startLine":102,"endLine":117,"chunkText":"                steps.\n            samplerate (int): stored as meta information for easing\n                future evaluations of the model.\n            segment_length (int): stored as meta information for easing\n                future evaluations of the model. Length of the segments on which\n                the model was trained.\n        \"\"\"\n\n        super().__init__()\n        self.audio_channels = audio_channels\n        self.sources = sources\n        self.kernel_size = kernel_size\n        self.context = context\n        self.stride = stride\n        self.depth = depth\n        self.resample = resample","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/model_v2.py:7"}],[1459,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/model_v2.py","startLine":115,"endLine":134,"chunkText":"        self.stride = stride\n        self.depth = depth\n        self.resample = resample\n        self.channels = channels\n        self.normalize = normalize\n        self.samplerate = samplerate\n        self.segment_length = segment_length\n\n        self.encoder = nn.ModuleList()\n        self.decoder = nn.ModuleList()\n\n        if glu:\n            activation = nn.GLU(dim=1)\n            ch_scale = 2\n        else:\n            activation = nn.ReLU()\n            ch_scale = 1\n        in_channels = audio_channels\n        for index in range(depth):\n            encode = []","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/model_v2.py:8"}],[1460,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/model_v2.py","startLine":132,"endLine":145,"chunkText":"        in_channels = audio_channels\n        for index in range(depth):\n            encode = []\n            encode += [nn.Conv1d(in_channels, channels, kernel_size, stride), nn.ReLU()]\n            if rewrite:\n                encode += [nn.Conv1d(channels, ch_scale * channels, 1), activation]\n            self.encoder.append(nn.Sequential(*encode))\n\n            decode = []\n            if index > 0:\n                out_channels = in_channels\n            else:\n                out_channels = len(self.sources) * audio_channels\n            if rewrite:","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/model_v2.py:9"}],[1461,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/model_v2.py","startLine":143,"endLine":157,"chunkText":"            else:\n                out_channels = len(self.sources) * audio_channels\n            if rewrite:\n                decode += [nn.Conv1d(channels, ch_scale * channels, context), activation]\n            decode += [nn.ConvTranspose1d(channels, out_channels, kernel_size, stride)]\n            if index > 0:\n                decode.append(nn.ReLU())\n            self.decoder.insert(0, nn.Sequential(*decode))\n            in_channels = channels\n            channels = int(growth * channels)\n\n        channels = in_channels\n\n        if lstm_layers:\n            self.lstm = BLSTM(channels, lstm_layers)","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/model_v2.py:10"}],[1462,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/model_v2.py","startLine":155,"endLine":171,"chunkText":"\n        if lstm_layers:\n            self.lstm = BLSTM(channels, lstm_layers)\n        else:\n            self.lstm = None\n\n        if rescale:\n            rescale_module(self, reference=rescale)\n\n    def valid_length(self, length):\n        \"\"\"\n        Return the nearest valid length to use with the model so that\n        there is no time steps left over in a convolutions, e.g. for all\n        layers, size of the input - kernel_size % stride = 0.\n\n        If the mixture has a valid length, the estimated sources\n        will have exactly the same length when context = 1. If context > 1,","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/model_v2.py:11"}],[1463,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/model_v2.py","startLine":169,"endLine":182,"chunkText":"\n        If the mixture has a valid length, the estimated sources\n        will have exactly the same length when context = 1. If context > 1,\n        the two signals can be center trimmed to match.\n\n        For training, extracts should have a valid length.For evaluation\n        on full tracks we recommend passing `pad = True` to :method:`forward`.\n        \"\"\"\n        if self.resample:\n            length *= 2\n        for _ in range(self.depth):\n            length = math.ceil((length - self.kernel_size) / self.stride) + 1\n            length = max(1, length)\n            length += self.context - 1","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/model_v2.py:12"}],[1464,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/model_v2.py","startLine":180,"endLine":198,"chunkText":"            length = math.ceil((length - self.kernel_size) / self.stride) + 1\n            length = max(1, length)\n            length += self.context - 1\n        for _ in range(self.depth):\n            length = (length - 1) * self.stride + self.kernel_size\n\n        if self.resample:\n            length = math.ceil(length / 2)\n        return int(length)\n\n    def forward(self, mix):\n        x = mix\n\n        if self.normalize:\n            mono = mix.mean(dim=1, keepdim=True)\n            mean = mono.mean(dim=-1, keepdim=True)\n            std = mono.std(dim=-1, keepdim=True)\n        else:\n            mean = 0","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/model_v2.py:13"}],[1465,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/model_v2.py","startLine":196,"endLine":219,"chunkText":"            std = mono.std(dim=-1, keepdim=True)\n        else:\n            mean = 0\n            std = 1\n\n        x = (x - mean) / (1e-5 + std)\n\n        if self.resample:\n            x = julius.resample_frac(x, 1, 2)\n\n        saved = []\n        for encode in self.encoder:\n            x = encode(x)\n            saved.append(x)\n        if self.lstm:\n            x = self.lstm(x)\n        for decode in self.decoder:\n            skip = center_trim(saved.pop(-1), x)\n            x = x + skip\n            x = decode(x)\n\n        if self.resample:\n            x = julius.resample_frac(x, 2, 1)\n        x = x * std + mean","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/model_v2.py:14"}],[1466,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/model_v2.py","startLine":217,"endLine":222,"chunkText":"        if self.resample:\n            x = julius.resample_frac(x, 2, 1)\n        x = x * std + mean\n        x = x.view(x.size(0), len(self.sources), self.audio_channels, x.size(-1))\n        return x\n","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/model_v2.py:15"}],[1467,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/pretrained.py","startLine":0,"endLine":23,"chunkText":"# Copyright (c) Facebook, Inc. and its affiliates.\n# All rights reserved.\n#\n# This source code is licensed under the license found in the\n# LICENSE file in the root directory of this source tree.\n\"\"\"Loading pretrained models.\n\"\"\"\n\nimport logging\nfrom pathlib import Path\nimport typing as tp\n\n# from dora.log import fatal\n\nimport logging\n\nfrom diffq import DiffQuantizer\nimport torch.hub\n\nfrom .model import Demucs\nfrom .tasnet_v2 import ConvTasNet\nfrom .utils import set_state\n\nfrom .hdemucs import HDemucs","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/pretrained.py:0"}],[1468,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/pretrained.py","startLine":21,"endLine":39,"chunkText":"from .utils import set_state\n\nfrom .hdemucs import HDemucs\nfrom .repo import RemoteRepo, LocalRepo, ModelOnlyRepo, BagOnlyRepo, AnyModelRepo, ModelLoadingError  # noqa\n\nlogger = logging.getLogger(__name__)\nROOT_URL = \"https://dl.fbaipublicfiles.com/demucs/mdx_final/\"\nREMOTE_ROOT = Path(__file__).parent / \"remote\"\n\nSOURCES = [\"drums\", \"bass\", \"other\", \"vocals\"]\n\n\ndef demucs_unittest():\n    model = HDemucs(channels=4, sources=SOURCES)\n    return model\n\n\ndef add_model_flags(parser):\n    group = parser.add_mutually_exclusive_group(required=False)","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/pretrained.py:1"}],[1469,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/pretrained.py","startLine":37,"endLine":47,"chunkText":"def add_model_flags(parser):\n    group = parser.add_mutually_exclusive_group(required=False)\n    group.add_argument(\"-s\", \"--sig\", help=\"Locally trained XP signature.\")\n    group.add_argument(\"-n\", \"--name\", default=\"mdx_extra_q\", help=\"Pretrained model name or signature. Default is mdx_extra_q.\")\n    parser.add_argument(\"--repo\", type=Path, help=\"Folder containing all pre-trained models for use with -n.\")\n\n\ndef _parse_remote_files(remote_file_list) -> tp.Dict[str, str]:\n    root: str = \"\"\n    models: tp.Dict[str, str] = {}\n    for line in remote_file_list.read_text().split(\"\\n\"):","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/pretrained.py:2"}],[1470,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/pretrained.py","startLine":47,"endLine":62,"chunkText":"    models: tp.Dict[str, str] = {}\n    for line in remote_file_list.read_text().split(\"\\n\"):\n        line = line.strip()\n        if line.startswith(\"#\"):\n            continue\n        elif line.startswith(\"root:\"):\n            root = line.split(\":\", 1)[1].strip()\n        else:\n            sig = line.split(\"-\", 1)[0]\n            assert sig not in models\n            models[sig] = ROOT_URL + root + line\n    return models\n\n\ndef get_model(name: str, repo: tp.Optional[Path] = None):\n    \"\"\"`name` must be a bag of models name or a pretrained signature","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/pretrained.py:3"}],[1471,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/pretrained.py","startLine":60,"endLine":74,"chunkText":"\ndef get_model(name: str, repo: tp.Optional[Path] = None):\n    \"\"\"`name` must be a bag of models name or a pretrained signature\n    from the remote AWS model repo or the specified local repo if `repo` is not None.\n    \"\"\"\n    if name == \"demucs_unittest\":\n        return demucs_unittest()\n    model_repo: ModelOnlyRepo\n    if repo is None:\n        models = _parse_remote_files(REMOTE_ROOT / \"files.txt\")\n        model_repo = RemoteRepo(models)\n        bag_repo = BagOnlyRepo(REMOTE_ROOT, model_repo)\n    else:\n        if not repo.is_dir():\n            fatal(f\"{repo} must exist and be a directory.\")","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/pretrained.py:4"}],[1472,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/pretrained.py","startLine":72,"endLine":94,"chunkText":"    else:\n        if not repo.is_dir():\n            fatal(f\"{repo} must exist and be a directory.\")\n        model_repo = LocalRepo(repo)\n        bag_repo = BagOnlyRepo(repo, model_repo)\n    any_repo = AnyModelRepo(model_repo, bag_repo)\n    model = any_repo.get_model(name)\n    model.eval()\n    return model\n\n\ndef get_model_from_args(args):\n    \"\"\"\n    Load local model package or pre-trained model.\n    \"\"\"\n    return get_model(name=args.name, repo=args.repo)\n\n\nlogger = logging.getLogger(__name__)\nROOT = \"https://dl.fbaipublicfiles.com/demucs/v3.0/\"\n\nPRETRAINED_MODELS = {\n    \"demucs\": \"e07c671f\",","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/pretrained.py:5"}],[1473,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/pretrained.py","startLine":92,"endLine":118,"chunkText":"\nPRETRAINED_MODELS = {\n    \"demucs\": \"e07c671f\",\n    \"demucs48_hq\": \"28a1282c\",\n    \"demucs_extra\": \"3646af93\",\n    \"demucs_quantized\": \"07afea75\",\n    \"tasnet\": \"beb46fac\",\n    \"tasnet_extra\": \"df3777b2\",\n    \"demucs_unittest\": \"09ebc15f\",\n}\n\nSOURCES = [\"drums\", \"bass\", \"other\", \"vocals\"]\n\n\ndef get_url(name):\n    sig = PRETRAINED_MODELS[name]\n    return ROOT + name + \"-\" + sig[:8] + \".th\"\n\n\ndef is_pretrained(name):\n    return name in PRETRAINED_MODELS\n\n\ndef load_pretrained(name):\n    if name == \"demucs\":\n        return demucs(pretrained=True)\n    elif name == \"demucs48_hq\":","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/pretrained.py:6"}],[1474,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/pretrained.py","startLine":116,"endLine":130,"chunkText":"    if name == \"demucs\":\n        return demucs(pretrained=True)\n    elif name == \"demucs48_hq\":\n        return demucs(pretrained=True, hq=True, channels=48)\n    elif name == \"demucs_extra\":\n        return demucs(pretrained=True, extra=True)\n    elif name == \"demucs_quantized\":\n        return demucs(pretrained=True, quantized=True)\n    elif name == \"demucs_unittest\":\n        return demucs_unittest(pretrained=True)\n    elif name == \"tasnet\":\n        return tasnet(pretrained=True)\n    elif name == \"tasnet_extra\":\n        return tasnet(pretrained=True, extra=True)\n    else:","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/pretrained.py:7"}],[1475,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/pretrained.py","startLine":128,"endLine":148,"chunkText":"    elif name == \"tasnet_extra\":\n        return tasnet(pretrained=True, extra=True)\n    else:\n        raise ValueError(f\"Invalid pretrained name {name}\")\n\n\ndef _load_state(name, model, quantizer=None):\n    url = get_url(name)\n    state = torch.hub.load_state_dict_from_url(url, map_location=\"cpu\", check_hash=True)\n    set_state(model, quantizer, state)\n    if quantizer:\n        quantizer.detach()\n\n\ndef demucs_unittest(pretrained=True):\n    model = Demucs(channels=4, sources=SOURCES)\n    if pretrained:\n        _load_state(\"demucs_unittest\", model)\n    return model\n\n","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/pretrained.py:8"}],[1476,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/pretrained.py","startLine":146,"endLine":160,"chunkText":"    return model\n\n\ndef demucs(pretrained=True, extra=False, quantized=False, hq=False, channels=64):\n    if not pretrained and (extra or quantized or hq):\n        raise ValueError(\"if extra or quantized is True, pretrained must be True.\")\n    model = Demucs(sources=SOURCES, channels=channels)\n    if pretrained:\n        name = \"demucs\"\n        if channels != 64:\n            name += str(channels)\n        quantizer = None\n        if sum([extra, quantized, hq]) > 1:\n            raise ValueError(\"Only one of extra, quantized, hq, can be True.\")\n        if quantized:","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/pretrained.py:9"}],[1477,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/pretrained.py","startLine":158,"endLine":175,"chunkText":"        if sum([extra, quantized, hq]) > 1:\n            raise ValueError(\"Only one of extra, quantized, hq, can be True.\")\n        if quantized:\n            quantizer = DiffQuantizer(model, group_size=8, min_size=1)\n            name += \"_quantized\"\n        if extra:\n            name += \"_extra\"\n        if hq:\n            name += \"_hq\"\n        _load_state(name, model, quantizer)\n    return model\n\n\ndef tasnet(pretrained=True, extra=False):\n    if not pretrained and extra:\n        raise ValueError(\"if extra is True, pretrained must be True.\")\n    model = ConvTasNet(X=10, sources=SOURCES)\n    if pretrained:","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/pretrained.py:10"}],[1478,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/pretrained.py","startLine":173,"endLine":181,"chunkText":"        raise ValueError(\"if extra is True, pretrained must be True.\")\n    model = ConvTasNet(X=10, sources=SOURCES)\n    if pretrained:\n        name = \"tasnet\"\n        if extra:\n            name = \"tasnet_extra\"\n        _load_state(name, model)\n    return model\n","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/pretrained.py:11"}],[1479,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/repo.py","startLine":0,"endLine":23,"chunkText":"# Copyright (c) Facebook, Inc. and its affiliates.\n# All rights reserved.\n#\n# This source code is licensed under the license found in the\n# LICENSE file in the root directory of this source tree.\n\"\"\"Represents a model repository, including pre-trained models and bags of models.\nA repo can either be the main remote repository stored in AWS, or a local repository\nwith your own models.\n\"\"\"\n\nfrom hashlib import sha256\nfrom pathlib import Path\nimport typing as tp\n\nimport torch\nimport yaml\n\nfrom .apply import BagOfModels, Model\nfrom .states import load_model\n\n\nAnyModel = tp.Union[Model, BagOfModels]\n\n","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/repo.py:0"}],[1480,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/repo.py","startLine":21,"endLine":40,"chunkText":"AnyModel = tp.Union[Model, BagOfModels]\n\n\nclass ModelLoadingError(RuntimeError):\n    pass\n\n\ndef check_checksum(path: Path, checksum: str):\n    sha = sha256()\n    with open(path, \"rb\") as file:\n        while True:\n            buf = file.read(2**20)\n            if not buf:\n                break\n            sha.update(buf)\n    actual_checksum = sha.hexdigest()[: len(checksum)]\n    if actual_checksum != checksum:\n        raise ModelLoadingError(f\"Invalid checksum for file {path}, \" f\"expected {checksum} but got {actual_checksum}\")\n\n","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/repo.py:1"}],[1481,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/repo.py","startLine":41,"endLine":50,"chunkText":"class ModelOnlyRepo:\n    \"\"\"Base class for all model only repos.\"\"\"\n\n    def has_model(self, sig: str) -> bool:\n        raise NotImplementedError()\n\n    def get_model(self, sig: str) -> Model:\n        raise NotImplementedError()\n\n","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/repo.py:2"}],[1482,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/repo.py","startLine":51,"endLine":66,"chunkText":"class RemoteRepo(ModelOnlyRepo):\n    def __init__(self, models: tp.Dict[str, str]):\n        self._models = models\n\n    def has_model(self, sig: str) -> bool:\n        return sig in self._models\n\n    def get_model(self, sig: str) -> Model:\n        try:\n            url = self._models[sig]\n        except KeyError:\n            raise ModelLoadingError(f\"Could not find a pre-trained model with signature {sig}.\")\n        pkg = torch.hub.load_state_dict_from_url(url, map_location=\"cpu\", check_hash=True)\n        return load_model(pkg)\n\n","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/repo.py:3"}],[1483,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/repo.py","startLine":67,"endLine":83,"chunkText":"class LocalRepo(ModelOnlyRepo):\n    def __init__(self, root: Path):\n        self.root = root\n        self.scan()\n\n    def scan(self):\n        self._models = {}\n        self._checksums = {}\n        for file in self.root.iterdir():\n            if file.suffix == \".th\":\n                if \"-\" in file.stem:\n                    xp_sig, checksum = file.stem.split(\"-\")\n                    self._checksums[xp_sig] = checksum\n                else:\n                    xp_sig = file.stem\n                if xp_sig in self._models:\n                    print(\"Whats xp? \", xp_sig)","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/repo.py:4"}],[1484,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/repo.py","startLine":81,"endLine":94,"chunkText":"                    xp_sig = file.stem\n                if xp_sig in self._models:\n                    print(\"Whats xp? \", xp_sig)\n                    raise ModelLoadingError(f\"Duplicate pre-trained model exist for signature {xp_sig}. \" \"Please delete all but one.\")\n                self._models[xp_sig] = file\n\n    def has_model(self, sig: str) -> bool:\n        return sig in self._models\n\n    def get_model(self, sig: str) -> Model:\n        try:\n            file = self._models[sig]\n        except KeyError:\n            raise ModelLoadingError(f\"Could not find pre-trained model with signature {sig}.\")","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/repo.py:5"}],[1485,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/repo.py","startLine":92,"endLine":99,"chunkText":"            file = self._models[sig]\n        except KeyError:\n            raise ModelLoadingError(f\"Could not find pre-trained model with signature {sig}.\")\n        if sig in self._checksums:\n            check_checksum(file, self._checksums[sig])\n        return load_model(file)\n\n","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/repo.py:6"}],[1486,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/repo.py","startLine":100,"endLine":120,"chunkText":"class BagOnlyRepo:\n    \"\"\"Handles only YAML files containing bag of models, leaving the actual\n    model loading to some Repo.\n    \"\"\"\n\n    def __init__(self, root: Path, model_repo: ModelOnlyRepo):\n        self.root = root\n        self.model_repo = model_repo\n        self.scan()\n\n    def scan(self):\n        self._bags = {}\n        for file in self.root.iterdir():\n            if file.suffix == \".yaml\":\n                self._bags[file.stem] = file\n\n    def has_model(self, name: str) -> bool:\n        return name in self._bags\n\n    def get_model(self, name: str) -> BagOfModels:\n        try:","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/repo.py:7"}],[1487,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/repo.py","startLine":118,"endLine":131,"chunkText":"\n    def get_model(self, name: str) -> BagOfModels:\n        try:\n            yaml_file = self._bags[name]\n        except KeyError:\n            raise ModelLoadingError(f\"{name} is neither a single pre-trained model or \" \"a bag of models.\")\n        bag = yaml.safe_load(open(yaml_file))\n        signatures = bag[\"models\"]\n        models = [self.model_repo.get_model(sig) for sig in signatures]\n        weights = bag.get(\"weights\")\n        segment = bag.get(\"segment\")\n        return BagOfModels(models, weights, segment)\n\n","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/repo.py:8"}],[1488,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/repo.py","startLine":132,"endLine":146,"chunkText":"class AnyModelRepo:\n    def __init__(self, model_repo: ModelOnlyRepo, bag_repo: BagOnlyRepo):\n        self.model_repo = model_repo\n        self.bag_repo = bag_repo\n\n    def has_model(self, name_or_sig: str) -> bool:\n        return self.model_repo.has_model(name_or_sig) or self.bag_repo.has_model(name_or_sig)\n\n    def get_model(self, name_or_sig: str) -> AnyModel:\n        # print('name_or_sig: ', name_or_sig)\n        if self.model_repo.has_model(name_or_sig):\n            return self.model_repo.get_model(name_or_sig)\n        else:\n            return self.bag_repo.get_model(name_or_sig)\n","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/repo.py:9"}],[1489,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/spec.py","startLine":0,"endLine":18,"chunkText":"# Copyright (c) Meta Platforms, Inc. and affiliates.\n# All rights reserved.\n#\n# This source code is licensed under the license found in the\n# LICENSE file in the root directory of this source tree.\n\"\"\"Conveniance wrapper to perform STFT and iSTFT\"\"\"\n\nimport torch as th\n\n\ndef spectro(x, n_fft=512, hop_length=None, pad=0):\n    *other, length = x.shape\n    x = x.reshape(-1, length)\n\n    device_type = x.device.type\n    is_other_gpu = not device_type in [\"cuda\", \"cpu\"]\n\n    if is_other_gpu:\n        x = x.cpu()","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/spec.py:0"}],[1490,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/spec.py","startLine":16,"endLine":33,"chunkText":"\n    if is_other_gpu:\n        x = x.cpu()\n    z = th.stft(x, n_fft * (1 + pad), hop_length or n_fft // 4, window=th.hann_window(n_fft).to(x), win_length=n_fft, normalized=True, center=True, return_complex=True, pad_mode=\"reflect\")\n    _, freqs, frame = z.shape\n    return z.view(*other, freqs, frame)\n\n\ndef ispectro(z, hop_length=None, length=None, pad=0):\n    *other, freqs, frames = z.shape\n    n_fft = 2 * freqs - 2\n    z = z.view(-1, freqs, frames)\n    win_length = n_fft // (1 + pad)\n\n    device_type = z.device.type\n    is_other_gpu = not device_type in [\"cuda\", \"cpu\"]\n\n    if is_other_gpu:","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/spec.py:1"}],[1491,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/spec.py","startLine":31,"endLine":38,"chunkText":"    is_other_gpu = not device_type in [\"cuda\", \"cpu\"]\n\n    if is_other_gpu:\n        z = z.cpu()\n    x = th.istft(z, n_fft, hop_length, window=th.hann_window(win_length).to(z.real), win_length=win_length, normalized=True, length=length, center=True)\n    _, length = x.shape\n    return x.view(*other, length)\n","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/spec.py:2"}],[1492,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/states.py","startLine":0,"endLine":23,"chunkText":"# Copyright (c) Facebook, Inc. and its affiliates.\n# All rights reserved.\n#\n# This source code is licensed under the license found in the\n# LICENSE file in the root directory of this source tree.\n\"\"\"\nUtilities to save and load models.\n\"\"\"\nfrom contextlib import contextmanager\n\nimport functools\nimport hashlib\nimport inspect\nimport io\nfrom pathlib import Path\nimport warnings\n\nfrom diffq import DiffQuantizer, UniformQuantizer, restore_quantized_state\nimport torch\n\n\ndef get_quantizer(model, args, optimizer=None):\n    \"\"\"Return the quantizer given the XP quantization args.\"\"\"\n    quantizer = None","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/states.py:0"}],[1493,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/states.py","startLine":22,"endLine":35,"chunkText":"    \"\"\"Return the quantizer given the XP quantization args.\"\"\"\n    quantizer = None\n    if args.diffq:\n        quantizer = DiffQuantizer(model, min_size=args.min_size, group_size=args.group_size)\n        if optimizer is not None:\n            quantizer.setup_optimizer(optimizer)\n    elif args.qat:\n        quantizer = UniformQuantizer(model, bits=args.qat, min_size=args.min_size)\n    return quantizer\n\n\ndef load_model(path_or_package, strict=False):\n    \"\"\"Load a model from the given serialized model, either given as a dict (already loaded)\n    or a path to a file on disk.\"\"\"","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/states.py:1"}],[1494,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/states.py","startLine":33,"endLine":46,"chunkText":"def load_model(path_or_package, strict=False):\n    \"\"\"Load a model from the given serialized model, either given as a dict (already loaded)\n    or a path to a file on disk.\"\"\"\n    if isinstance(path_or_package, dict):\n        package = path_or_package\n    elif isinstance(path_or_package, (str, Path)):\n        with warnings.catch_warnings():\n            warnings.simplefilter(\"ignore\")\n            path = path_or_package\n            package = torch.load(path, \"cpu\", weights_only=False)\n    else:\n        raise ValueError(f\"Invalid type for {path_or_package}.\")\n\n    klass = package[\"klass\"]","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/states.py:2"}],[1495,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/states.py","startLine":44,"endLine":66,"chunkText":"        raise ValueError(f\"Invalid type for {path_or_package}.\")\n\n    klass = package[\"klass\"]\n    args = package[\"args\"]\n    kwargs = package[\"kwargs\"]\n\n    if strict:\n        model = klass(*args, **kwargs)\n    else:\n        sig = inspect.signature(klass)\n        for key in list(kwargs):\n            if key not in sig.parameters:\n                warnings.warn(\"Dropping inexistant parameter \" + key)\n                del kwargs[key]\n        model = klass(*args, **kwargs)\n\n    state = package[\"state\"]\n\n    set_state(model, state)\n    return model\n\n\ndef get_state(model, quantizer, half=False):","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/states.py:3"}],[1496,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/states.py","startLine":64,"endLine":79,"chunkText":"\n\ndef get_state(model, quantizer, half=False):\n    \"\"\"Get the state from a model, potentially with quantization applied.\n    If `half` is True, model are stored as half precision, which shouldn't impact performance\n    but half the state size.\"\"\"\n    if quantizer is None:\n        dtype = torch.half if half else None\n        state = {k: p.data.to(device=\"cpu\", dtype=dtype) for k, p in model.state_dict().items()}\n    else:\n        state = quantizer.get_quantized_state()\n        state[\"__quantized\"] = True\n    return state\n\n\ndef set_state(model, state, quantizer=None):","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/states.py:4"}],[1497,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/states.py","startLine":77,"endLine":95,"chunkText":"\n\ndef set_state(model, state, quantizer=None):\n    \"\"\"Set the state on a given model.\"\"\"\n    if state.get(\"__quantized\"):\n        if quantizer is not None:\n            quantizer.restore_quantized_state(model, state[\"quantized\"])\n        else:\n            restore_quantized_state(model, state)\n    else:\n        model.load_state_dict(state)\n    return state\n\n\ndef save_with_checksum(content, path):\n    \"\"\"Save the given value on disk, along with a sha256 hash.\n    Should be used with the output of either `serialize_model` or `get_state`.\"\"\"\n    buf = io.BytesIO()\n    torch.save(content, buf)","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/states.py:5"}],[1498,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/states.py","startLine":93,"endLine":113,"chunkText":"    Should be used with the output of either `serialize_model` or `get_state`.\"\"\"\n    buf = io.BytesIO()\n    torch.save(content, buf)\n    sig = hashlib.sha256(buf.getvalue()).hexdigest()[:8]\n\n    path = path.parent / (path.stem + \"-\" + sig + path.suffix)\n    path.write_bytes(buf.getvalue())\n\n\ndef copy_state(state):\n    return {k: v.cpu().clone() for k, v in state.items()}\n\n\n@contextmanager\ndef swap_state(model, state):\n    \"\"\"\n    Context manager that swaps the state of a model, e.g:\n\n        # model is in old state\n        with swap_state(model, new_state):\n            # model in new state","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/states.py:6"}],[1499,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/states.py","startLine":111,"endLine":131,"chunkText":"        # model is in old state\n        with swap_state(model, new_state):\n            # model in new state\n        # model back to old state\n    \"\"\"\n    old_state = copy_state(model.state_dict())\n    model.load_state_dict(state, strict=False)\n    try:\n        yield\n    finally:\n        model.load_state_dict(old_state)\n\n\ndef capture_init(init):\n    @functools.wraps(init)\n    def __init__(self, *args, **kwargs):\n        self._init_args_kwargs = (args, kwargs)\n        init(self, *args, **kwargs)\n\n    return __init__\n","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/states.py:7"}],[1500,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/tasnet.py","startLine":0,"endLine":15,"chunkText":"# Copyright (c) Facebook, Inc. and its affiliates.\n# All rights reserved.\n#\n# This source code is licensed under the license found in the\n# LICENSE file in the root directory of this source tree.\n#\n# Created on 2018/12\n# Author: Kaituo XU\n# Modified on 2019/11 by Alexandre Defossez, added support for multiple output channels\n# Here is the original license:\n# The MIT License (MIT)\n#\n# Copyright (c) 2018 Kaituo XU\n#\n# Permission is hereby granted, free of charge, to any person obtaining a copy\n# of this software and associated documentation files (the \"Software\"), to deal","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/tasnet.py:0"}],[1501,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/tasnet.py","startLine":13,"endLine":23,"chunkText":"#\n# Permission is hereby granted, free of charge, to any person obtaining a copy\n# of this software and associated documentation files (the \"Software\"), to deal\n# in the Software without restriction, including without limitation the rights\n# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell\n# copies of the Software, and to permit persons to whom the Software is\n# furnished to do so, subject to the following conditions:\n#\n# The above copyright notice and this permission notice shall be included in all\n# copies or substantial portions of the Software.\n#","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/tasnet.py:1"}],[1502,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/tasnet.py","startLine":21,"endLine":31,"chunkText":"# The above copyright notice and this permission notice shall be included in all\n# copies or substantial portions of the Software.\n#\n# THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\n# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\n# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE\n# SOFTWARE.\n","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/tasnet.py:2"}],[1503,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/tasnet.py","startLine":29,"endLine":50,"chunkText":"# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE\n# SOFTWARE.\n\nimport math\n\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\nfrom .utils import capture_init\n\nEPS = 1e-8\n\n\ndef overlap_and_add(signal, frame_step):\n    outer_dimensions = signal.size()[:-2]\n    frames, frame_length = signal.size()[-2:]\n\n    subframe_length = math.gcd(frame_length, frame_step)  # gcd=Greatest Common Divisor\n    subframe_step = frame_step // subframe_length\n    subframes_per_frame = frame_length // subframe_length\n    output_size = frame_step * (frames - 1) + frame_length","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/tasnet.py:3"}],[1504,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/tasnet.py","startLine":48,"endLine":59,"chunkText":"    subframe_step = frame_step // subframe_length\n    subframes_per_frame = frame_length // subframe_length\n    output_size = frame_step * (frames - 1) + frame_length\n    output_subframes = output_size // subframe_length\n\n    subframe_signal = signal.view(*outer_dimensions, -1, subframe_length)\n\n    frame = torch.arange(0, output_subframes, device=signal.device).unfold(0, subframes_per_frame, subframe_step)\n    frame = frame.long()  # signal may in GPU or CPU\n    frame = frame.contiguous().view(-1)\n\n    result = signal.new_zeros(*outer_dimensions, output_subframes, subframe_length)","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/tasnet.py:4"}],[1505,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/tasnet.py","startLine":57,"endLine":71,"chunkText":"    frame = frame.contiguous().view(-1)\n\n    result = signal.new_zeros(*outer_dimensions, output_subframes, subframe_length)\n    result.index_add_(-2, frame, subframe_signal)\n    result = result.view(*outer_dimensions, -1)\n    return result\n\n\nclass ConvTasNet(nn.Module):\n    @capture_init\n    def __init__(self, N=256, L=20, B=256, H=512, P=3, X=8, R=4, C=4, audio_channels=1, samplerate=44100, norm_type=\"gLN\", causal=False, mask_nonlinear=\"relu\"):\n        \"\"\"\n        Args:\n            N: Number of filters in autoencoder\n            L: Length of the filters (in samples)","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/tasnet.py:5"}],[1506,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/tasnet.py","startLine":69,"endLine":81,"chunkText":"        Args:\n            N: Number of filters in autoencoder\n            L: Length of the filters (in samples)\n            B: Number of channels in bottleneck 1 × 1-conv block\n            H: Number of channels in convolutional blocks\n            P: Kernel size in convolutional blocks\n            X: Number of convolutional blocks in each repeat\n            R: Number of repeats\n            C: Number of speakers\n            norm_type: BN, gLN, cLN\n            causal: causal or non-causal\n            mask_nonlinear: use which non-linear function to generate mask\n        \"\"\"","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/tasnet.py:6"}],[1507,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/tasnet.py","startLine":79,"endLine":91,"chunkText":"            causal: causal or non-causal\n            mask_nonlinear: use which non-linear function to generate mask\n        \"\"\"\n        super(ConvTasNet, self).__init__()\n        # Hyper-parameter\n        self.N, self.L, self.B, self.H, self.P, self.X, self.R, self.C = N, L, B, H, P, X, R, C\n        self.norm_type = norm_type\n        self.causal = causal\n        self.mask_nonlinear = mask_nonlinear\n        self.audio_channels = audio_channels\n        self.samplerate = samplerate\n        # Components\n        self.encoder = Encoder(L, N, audio_channels)","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/tasnet.py:7"}],[1508,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/tasnet.py","startLine":89,"endLine":107,"chunkText":"        self.samplerate = samplerate\n        # Components\n        self.encoder = Encoder(L, N, audio_channels)\n        self.separator = TemporalConvNet(N, B, H, P, X, R, C, norm_type, causal, mask_nonlinear)\n        self.decoder = Decoder(N, L, audio_channels)\n        # init\n        for p in self.parameters():\n            if p.dim() > 1:\n                nn.init.xavier_normal_(p)\n\n    def valid_length(self, length):\n        return length\n\n    def forward(self, mixture):\n        \"\"\"\n        Args:\n            mixture: [M, T], M is batch size, T is #samples\n        Returns:\n            est_source: [M, C, T]","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/tasnet.py:8"}],[1509,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/tasnet.py","startLine":105,"endLine":119,"chunkText":"            mixture: [M, T], M is batch size, T is #samples\n        Returns:\n            est_source: [M, C, T]\n        \"\"\"\n        mixture_w = self.encoder(mixture)\n        est_mask = self.separator(mixture_w)\n        est_source = self.decoder(mixture_w, est_mask)\n\n        # T changed after conv1d in encoder, fix it here\n        T_origin = mixture.size(-1)\n        T_conv = est_source.size(-1)\n        est_source = F.pad(est_source, (0, T_origin - T_conv))\n        return est_source\n\n","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/tasnet.py:9"}],[1510,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/tasnet.py","startLine":120,"endLine":137,"chunkText":"class Encoder(nn.Module):\n    \"\"\"Estimation of the nonnegative mixture weight by a 1-D conv layer.\"\"\"\n\n    def __init__(self, L, N, audio_channels):\n        super(Encoder, self).__init__()\n        # Hyper-parameter\n        self.L, self.N = L, N\n        # Components\n        # 50% overlap\n        self.conv1d_U = nn.Conv1d(audio_channels, N, kernel_size=L, stride=L // 2, bias=False)\n\n    def forward(self, mixture):\n        \"\"\"\n        Args:\n            mixture: [M, T], M is batch size, T is #samples\n        Returns:\n            mixture_w: [M, N, K], where K = (T-L)/(L/2)+1 = 2T/L-1\n        \"\"\"","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/tasnet.py:10"}],[1511,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/tasnet.py","startLine":135,"endLine":141,"chunkText":"        Returns:\n            mixture_w: [M, N, K], where K = (T-L)/(L/2)+1 = 2T/L-1\n        \"\"\"\n        mixture_w = F.relu(self.conv1d_U(mixture))  # [M, N, K]\n        return mixture_w\n\n","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/tasnet.py:11"}],[1512,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/tasnet.py","startLine":142,"endLine":160,"chunkText":"class Decoder(nn.Module):\n    def __init__(self, N, L, audio_channels):\n        super(Decoder, self).__init__()\n        # Hyper-parameter\n        self.N, self.L = N, L\n        self.audio_channels = audio_channels\n        # Components\n        self.basis_signals = nn.Linear(N, audio_channels * L, bias=False)\n\n    def forward(self, mixture_w, est_mask):\n        \"\"\"\n        Args:\n            mixture_w: [M, N, K]\n            est_mask: [M, C, N, K]\n        Returns:\n            est_source: [M, C, T]\n        \"\"\"\n        # D = W * M\n        source_w = torch.unsqueeze(mixture_w, 1) * est_mask  # [M, C, N, K]","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/tasnet.py:12"}],[1513,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/tasnet.py","startLine":158,"endLine":169,"chunkText":"        \"\"\"\n        # D = W * M\n        source_w = torch.unsqueeze(mixture_w, 1) * est_mask  # [M, C, N, K]\n        source_w = torch.transpose(source_w, 2, 3)  # [M, C, K, N]\n        # S = DV\n        est_source = self.basis_signals(source_w)  # [M, C, K, ac * L]\n        m, c, k, _ = est_source.size()\n        est_source = est_source.view(m, c, k, self.audio_channels, -1).transpose(2, 3).contiguous()\n        est_source = overlap_and_add(est_source, self.L // 2)  # M x C x ac x T\n        return est_source\n\n","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/tasnet.py:13"}],[1514,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/tasnet.py","startLine":170,"endLine":182,"chunkText":"class TemporalConvNet(nn.Module):\n    def __init__(self, N, B, H, P, X, R, C, norm_type=\"gLN\", causal=False, mask_nonlinear=\"relu\"):\n        \"\"\"\n        Args:\n            N: Number of filters in autoencoder\n            B: Number of channels in bottleneck 1 × 1-conv block\n            H: Number of channels in convolutional blocks\n            P: Kernel size in convolutional blocks\n            X: Number of convolutional blocks in each repeat\n            R: Number of repeats\n            C: Number of speakers\n            norm_type: BN, gLN, cLN\n            causal: causal or non-causal","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/tasnet.py:14"}],[1515,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/tasnet.py","startLine":180,"endLine":196,"chunkText":"            C: Number of speakers\n            norm_type: BN, gLN, cLN\n            causal: causal or non-causal\n            mask_nonlinear: use which non-linear function to generate mask\n        \"\"\"\n        super(TemporalConvNet, self).__init__()\n        # Hyper-parameter\n        self.C = C\n        self.mask_nonlinear = mask_nonlinear\n        # Components\n        # [M, N, K] -> [M, N, K]\n        layer_norm = ChannelwiseLayerNorm(N)\n        # [M, N, K] -> [M, B, K]\n        bottleneck_conv1x1 = nn.Conv1d(N, B, 1, bias=False)\n        # [M, B, K] -> [M, B, K]\n        repeats = []\n        for r in range(R):","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/tasnet.py:15"}],[1516,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/tasnet.py","startLine":194,"endLine":206,"chunkText":"        # [M, B, K] -> [M, B, K]\n        repeats = []\n        for r in range(R):\n            blocks = []\n            for x in range(X):\n                dilation = 2**x\n                padding = (P - 1) * dilation if causal else (P - 1) * dilation // 2\n                blocks += [TemporalBlock(B, H, P, stride=1, padding=padding, dilation=dilation, norm_type=norm_type, causal=causal)]\n            repeats += [nn.Sequential(*blocks)]\n        temporal_conv_net = nn.Sequential(*repeats)\n        # [M, B, K] -> [M, C*N, K]\n        mask_conv1x1 = nn.Conv1d(B, C * N, 1, bias=False)\n        # Put together","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/tasnet.py:16"}],[1517,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/tasnet.py","startLine":204,"endLine":218,"chunkText":"        # [M, B, K] -> [M, C*N, K]\n        mask_conv1x1 = nn.Conv1d(B, C * N, 1, bias=False)\n        # Put together\n        self.network = nn.Sequential(layer_norm, bottleneck_conv1x1, temporal_conv_net, mask_conv1x1)\n\n    def forward(self, mixture_w):\n        \"\"\"\n        Keep this API same with TasNet\n        Args:\n            mixture_w: [M, N, K], M is batch size\n        returns:\n            est_mask: [M, C, N, K]\n        \"\"\"\n        M, N, K = mixture_w.size()\n        score = self.network(mixture_w)  # [M, N, K] -> [M, C*N, K]","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/tasnet.py:17"}],[1518,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/tasnet.py","startLine":216,"endLine":228,"chunkText":"        \"\"\"\n        M, N, K = mixture_w.size()\n        score = self.network(mixture_w)  # [M, N, K] -> [M, C*N, K]\n        score = score.view(M, self.C, N, K)  # [M, C*N, K] -> [M, C, N, K]\n        if self.mask_nonlinear == \"softmax\":\n            est_mask = F.softmax(score, dim=1)\n        elif self.mask_nonlinear == \"relu\":\n            est_mask = F.relu(score)\n        else:\n            raise ValueError(\"Unsupported mask non-linear function\")\n        return est_mask\n\n","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/tasnet.py:18"}],[1519,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/tasnet.py","startLine":229,"endLine":238,"chunkText":"class TemporalBlock(nn.Module):\n    def __init__(self, in_channels, out_channels, kernel_size, stride, padding, dilation, norm_type=\"gLN\", causal=False):\n        super(TemporalBlock, self).__init__()\n        # [M, B, K] -> [M, H, K]\n        conv1x1 = nn.Conv1d(in_channels, out_channels, 1, bias=False)\n        prelu = nn.PReLU()\n        norm = chose_norm(norm_type, out_channels)\n        # [M, H, K] -> [M, B, K]\n        dsconv = DepthwiseSeparableConv(out_channels, in_channels, kernel_size, stride, padding, dilation, norm_type, causal)\n        # Put together","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/tasnet.py:19"}],[1520,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/tasnet.py","startLine":236,"endLine":251,"chunkText":"        # [M, H, K] -> [M, B, K]\n        dsconv = DepthwiseSeparableConv(out_channels, in_channels, kernel_size, stride, padding, dilation, norm_type, causal)\n        # Put together\n        self.net = nn.Sequential(conv1x1, prelu, norm, dsconv)\n\n    def forward(self, x):\n        \"\"\"\n        Args:\n            x: [M, B, K]\n        Returns:\n            [M, B, K]\n        \"\"\"\n        residual = x\n        out = self.net(x)\n        # TODO: when P = 3 here works fine, but when P = 2 maybe need to pad?\n        return out + residual  # look like w/o F.relu is better than w/ F.relu","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/tasnet.py:20"}],[1521,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/tasnet.py","startLine":249,"endLine":254,"chunkText":"        out = self.net(x)\n        # TODO: when P = 3 here works fine, but when P = 2 maybe need to pad?\n        return out + residual  # look like w/o F.relu is better than w/ F.relu\n        # return F.relu(out + residual)\n\n","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/tasnet.py:21"}],[1522,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/tasnet.py","startLine":255,"endLine":264,"chunkText":"class DepthwiseSeparableConv(nn.Module):\n    def __init__(self, in_channels, out_channels, kernel_size, stride, padding, dilation, norm_type=\"gLN\", causal=False):\n        super(DepthwiseSeparableConv, self).__init__()\n        # Use `groups` option to implement depthwise convolution\n        # [M, H, K] -> [M, H, K]\n        depthwise_conv = nn.Conv1d(in_channels, in_channels, kernel_size, stride=stride, padding=padding, dilation=dilation, groups=in_channels, bias=False)\n        if causal:\n            chomp = Chomp1d(padding)\n        prelu = nn.PReLU()\n        norm = chose_norm(norm_type, in_channels)","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/tasnet.py:22"}],[1523,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/tasnet.py","startLine":262,"endLine":279,"chunkText":"            chomp = Chomp1d(padding)\n        prelu = nn.PReLU()\n        norm = chose_norm(norm_type, in_channels)\n        # [M, H, K] -> [M, B, K]\n        pointwise_conv = nn.Conv1d(in_channels, out_channels, 1, bias=False)\n        # Put together\n        if causal:\n            self.net = nn.Sequential(depthwise_conv, chomp, prelu, norm, pointwise_conv)\n        else:\n            self.net = nn.Sequential(depthwise_conv, prelu, norm, pointwise_conv)\n\n    def forward(self, x):\n        \"\"\"\n        Args:\n            x: [M, H, K]\n        Returns:\n            result: [M, B, K]\n        \"\"\"","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/tasnet.py:23"}],[1524,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/tasnet.py","startLine":277,"endLine":282,"chunkText":"        Returns:\n            result: [M, B, K]\n        \"\"\"\n        return self.net(x)\n\n","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/tasnet.py:24"}],[1525,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/tasnet.py","startLine":283,"endLine":304,"chunkText":"class Chomp1d(nn.Module):\n    \"\"\"To ensure the output length is the same as the input.\"\"\"\n\n    def __init__(self, chomp_size):\n        super(Chomp1d, self).__init__()\n        self.chomp_size = chomp_size\n\n    def forward(self, x):\n        \"\"\"\n        Args:\n            x: [M, H, Kpad]\n        Returns:\n            [M, H, K]\n        \"\"\"\n        return x[:, :, : -self.chomp_size].contiguous()\n\n\ndef chose_norm(norm_type, channel_size):\n    \"\"\"The input of normlization will be (M, C, K), where M is batch size,\n    C is channel size and K is sequence length.\n    \"\"\"\n    if norm_type == \"gLN\":","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/tasnet.py:25"}],[1526,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/tasnet.py","startLine":302,"endLine":316,"chunkText":"    C is channel size and K is sequence length.\n    \"\"\"\n    if norm_type == \"gLN\":\n        return GlobalLayerNorm(channel_size)\n    elif norm_type == \"cLN\":\n        return ChannelwiseLayerNorm(channel_size)\n    elif norm_type == \"id\":\n        return nn.Identity()\n    else:  # norm_type == \"BN\":\n        # Given input (M, C, K), nn.BatchNorm1d(C) will accumulate statics\n        # along M and K, so this BN usage is right.\n        return nn.BatchNorm1d(channel_size)\n\n\n# TODO: Use nn.LayerNorm to impl cLN to speed up","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/tasnet.py:26"}],[1527,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/tasnet.py","startLine":317,"endLine":333,"chunkText":"class ChannelwiseLayerNorm(nn.Module):\n    \"\"\"Channel-wise Layer Normalization (cLN)\"\"\"\n\n    def __init__(self, channel_size):\n        super(ChannelwiseLayerNorm, self).__init__()\n        self.gamma = nn.Parameter(torch.Tensor(1, channel_size, 1))  # [1, N, 1]\n        self.beta = nn.Parameter(torch.Tensor(1, channel_size, 1))  # [1, N, 1]\n        self.reset_parameters()\n\n    def reset_parameters(self):\n        self.gamma.data.fill_(1)\n        self.beta.data.zero_()\n\n    def forward(self, y):\n        \"\"\"\n        Args:\n            y: [M, N, K], M is batch size, N is channel size, K is length","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/tasnet.py:27"}],[1528,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/tasnet.py","startLine":331,"endLine":342,"chunkText":"        \"\"\"\n        Args:\n            y: [M, N, K], M is batch size, N is channel size, K is length\n        Returns:\n            cLN_y: [M, N, K]\n        \"\"\"\n        mean = torch.mean(y, dim=1, keepdim=True)  # [M, 1, K]\n        var = torch.var(y, dim=1, keepdim=True, unbiased=False)  # [M, 1, K]\n        cLN_y = self.gamma * (y - mean) / torch.pow(var + EPS, 0.5) + self.beta\n        return cLN_y\n\n","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/tasnet.py:28"}],[1529,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/tasnet.py","startLine":343,"endLine":360,"chunkText":"class GlobalLayerNorm(nn.Module):\n    \"\"\"Global Layer Normalization (gLN)\"\"\"\n\n    def __init__(self, channel_size):\n        super(GlobalLayerNorm, self).__init__()\n        self.gamma = nn.Parameter(torch.Tensor(1, channel_size, 1))  # [1, N, 1]\n        self.beta = nn.Parameter(torch.Tensor(1, channel_size, 1))  # [1, N, 1]\n        self.reset_parameters()\n\n    def reset_parameters(self):\n        self.gamma.data.fill_(1)\n        self.beta.data.zero_()\n\n    def forward(self, y):\n        \"\"\"\n        Args:\n            y: [M, N, K], M is batch size, N is channel size, K is length\n        Returns:","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/tasnet.py:29"}],[1530,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/tasnet.py","startLine":358,"endLine":373,"chunkText":"        Args:\n            y: [M, N, K], M is batch size, N is channel size, K is length\n        Returns:\n            gLN_y: [M, N, K]\n        \"\"\"\n        # TODO: in torch 1.0, torch.mean() support dim list\n        mean = y.mean(dim=1, keepdim=True).mean(dim=2, keepdim=True)  # [M, 1, 1]\n        var = (torch.pow(y - mean, 2)).mean(dim=1, keepdim=True).mean(dim=2, keepdim=True)\n        gLN_y = self.gamma * (y - mean) / torch.pow(var + EPS, 0.5) + self.beta\n        return gLN_y\n\n\nif __name__ == \"__main__\":\n    torch.manual_seed(123)\n    M, N, L, T = 2, 3, 4, 12\n    K = 2 * T // L - 1","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/tasnet.py:30"}],[1531,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/tasnet.py","startLine":371,"endLine":385,"chunkText":"    torch.manual_seed(123)\n    M, N, L, T = 2, 3, 4, 12\n    K = 2 * T // L - 1\n    B, H, P, X, R, C, norm_type, causal = 2, 3, 3, 3, 2, 2, \"gLN\", False\n    mixture = torch.randint(3, (M, T))\n    # test Encoder\n    encoder = Encoder(L, N)\n    encoder.conv1d_U.weight.data = torch.randint(2, encoder.conv1d_U.weight.size())\n    mixture_w = encoder(mixture)\n    print(\"mixture\", mixture)\n    print(\"U\", encoder.conv1d_U.weight)\n    print(\"mixture_w\", mixture_w)\n    print(\"mixture_w size\", mixture_w.size())\n\n    # test TemporalConvNet","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/tasnet.py:31"}],[1532,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/tasnet.py","startLine":383,"endLine":399,"chunkText":"    print(\"mixture_w size\", mixture_w.size())\n\n    # test TemporalConvNet\n    separator = TemporalConvNet(N, B, H, P, X, R, C, norm_type=norm_type, causal=causal)\n    est_mask = separator(mixture_w)\n    print(\"est_mask\", est_mask)\n\n    # test Decoder\n    decoder = Decoder(N, L)\n    est_mask = torch.randint(2, (B, K, C, N))\n    est_source = decoder(mixture_w, est_mask)\n    print(\"est_source\", est_source)\n\n    # test Conv-TasNet\n    conv_tasnet = ConvTasNet(N, L, B, H, P, X, R, C, norm_type=norm_type)\n    est_source = conv_tasnet(mixture)\n    print(\"est_source\", est_source)","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/tasnet.py:32"}],[1533,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/tasnet.py","startLine":397,"endLine":401,"chunkText":"    conv_tasnet = ConvTasNet(N, L, B, H, P, X, R, C, norm_type=norm_type)\n    est_source = conv_tasnet(mixture)\n    print(\"est_source\", est_source)\n    print(\"est_source size\", est_source.size())\n","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/tasnet.py:33"}],[1534,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/tasnet_v2.py","startLine":0,"endLine":15,"chunkText":"# Copyright (c) Facebook, Inc. and its affiliates.\n# All rights reserved.\n#\n# This source code is licensed under the license found in the\n# LICENSE file in the root directory of this source tree.\n#\n# Created on 2018/12\n# Author: Kaituo XU\n# Modified on 2019/11 by Alexandre Defossez, added support for multiple output channels\n# Here is the original license:\n# The MIT License (MIT)\n#\n# Copyright (c) 2018 Kaituo XU\n#\n# Permission is hereby granted, free of charge, to any person obtaining a copy\n# of this software and associated documentation files (the \"Software\"), to deal","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/tasnet_v2.py:0"}],[1535,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/tasnet_v2.py","startLine":13,"endLine":23,"chunkText":"#\n# Permission is hereby granted, free of charge, to any person obtaining a copy\n# of this software and associated documentation files (the \"Software\"), to deal\n# in the Software without restriction, including without limitation the rights\n# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell\n# copies of the Software, and to permit persons to whom the Software is\n# furnished to do so, subject to the following conditions:\n#\n# The above copyright notice and this permission notice shall be included in all\n# copies or substantial portions of the Software.\n#","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/tasnet_v2.py:1"}],[1536,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/tasnet_v2.py","startLine":21,"endLine":31,"chunkText":"# The above copyright notice and this permission notice shall be included in all\n# copies or substantial portions of the Software.\n#\n# THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\n# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\n# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE\n# SOFTWARE.\n","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/tasnet_v2.py:2"}],[1537,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/tasnet_v2.py","startLine":29,"endLine":50,"chunkText":"# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE\n# SOFTWARE.\n\nimport math\n\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\nfrom .utils import capture_init\n\nEPS = 1e-8\n\n\ndef overlap_and_add(signal, frame_step):\n    outer_dimensions = signal.size()[:-2]\n    frames, frame_length = signal.size()[-2:]\n\n    subframe_length = math.gcd(frame_length, frame_step)  # gcd=Greatest Common Divisor\n    subframe_step = frame_step // subframe_length\n    subframes_per_frame = frame_length // subframe_length\n    output_size = frame_step * (frames - 1) + frame_length","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/tasnet_v2.py:3"}],[1538,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/tasnet_v2.py","startLine":48,"endLine":59,"chunkText":"    subframe_step = frame_step // subframe_length\n    subframes_per_frame = frame_length // subframe_length\n    output_size = frame_step * (frames - 1) + frame_length\n    output_subframes = output_size // subframe_length\n\n    subframe_signal = signal.view(*outer_dimensions, -1, subframe_length)\n\n    frame = torch.arange(0, output_subframes, device=signal.device).unfold(0, subframes_per_frame, subframe_step)\n    frame = frame.long()  # signal may in GPU or CPU\n    frame = frame.contiguous().view(-1)\n\n    result = signal.new_zeros(*outer_dimensions, output_subframes, subframe_length)","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/tasnet_v2.py:4"}],[1539,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/tasnet_v2.py","startLine":57,"endLine":71,"chunkText":"    frame = frame.contiguous().view(-1)\n\n    result = signal.new_zeros(*outer_dimensions, output_subframes, subframe_length)\n    result.index_add_(-2, frame, subframe_signal)\n    result = result.view(*outer_dimensions, -1)\n    return result\n\n\nclass ConvTasNet(nn.Module):\n    @capture_init\n    def __init__(self, sources, N=256, L=20, B=256, H=512, P=3, X=8, R=4, audio_channels=2, norm_type=\"gLN\", causal=False, mask_nonlinear=\"relu\", samplerate=44100, segment_length=44100 * 2 * 4):\n        \"\"\"\n        Args:\n            sources: list of sources\n            N: Number of filters in autoencoder","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/tasnet_v2.py:5"}],[1540,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/tasnet_v2.py","startLine":69,"endLine":81,"chunkText":"        Args:\n            sources: list of sources\n            N: Number of filters in autoencoder\n            L: Length of the filters (in samples)\n            B: Number of channels in bottleneck 1 × 1-conv block\n            H: Number of channels in convolutional blocks\n            P: Kernel size in convolutional blocks\n            X: Number of convolutional blocks in each repeat\n            R: Number of repeats\n            norm_type: BN, gLN, cLN\n            causal: causal or non-causal\n            mask_nonlinear: use which non-linear function to generate mask\n        \"\"\"","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/tasnet_v2.py:6"}],[1541,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/tasnet_v2.py","startLine":79,"endLine":93,"chunkText":"            causal: causal or non-causal\n            mask_nonlinear: use which non-linear function to generate mask\n        \"\"\"\n        super(ConvTasNet, self).__init__()\n        # Hyper-parameter\n        self.sources = sources\n        self.C = len(sources)\n        self.N, self.L, self.B, self.H, self.P, self.X, self.R = N, L, B, H, P, X, R\n        self.norm_type = norm_type\n        self.causal = causal\n        self.mask_nonlinear = mask_nonlinear\n        self.audio_channels = audio_channels\n        self.samplerate = samplerate\n        self.segment_length = segment_length\n        # Components","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/tasnet_v2.py:7"}],[1542,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/tasnet_v2.py","startLine":91,"endLine":108,"chunkText":"        self.samplerate = samplerate\n        self.segment_length = segment_length\n        # Components\n        self.encoder = Encoder(L, N, audio_channels)\n        self.separator = TemporalConvNet(N, B, H, P, X, R, self.C, norm_type, causal, mask_nonlinear)\n        self.decoder = Decoder(N, L, audio_channels)\n        # init\n        for p in self.parameters():\n            if p.dim() > 1:\n                nn.init.xavier_normal_(p)\n\n    def valid_length(self, length):\n        return length\n\n    def forward(self, mixture):\n        \"\"\"\n        Args:\n            mixture: [M, T], M is batch size, T is #samples","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/tasnet_v2.py:8"}],[1543,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/tasnet_v2.py","startLine":106,"endLine":122,"chunkText":"        \"\"\"\n        Args:\n            mixture: [M, T], M is batch size, T is #samples\n        Returns:\n            est_source: [M, C, T]\n        \"\"\"\n        mixture_w = self.encoder(mixture)\n        est_mask = self.separator(mixture_w)\n        est_source = self.decoder(mixture_w, est_mask)\n\n        # T changed after conv1d in encoder, fix it here\n        T_origin = mixture.size(-1)\n        T_conv = est_source.size(-1)\n        est_source = F.pad(est_source, (0, T_origin - T_conv))\n        return est_source\n\n","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/tasnet_v2.py:9"}],[1544,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/tasnet_v2.py","startLine":123,"endLine":140,"chunkText":"class Encoder(nn.Module):\n    \"\"\"Estimation of the nonnegative mixture weight by a 1-D conv layer.\"\"\"\n\n    def __init__(self, L, N, audio_channels):\n        super(Encoder, self).__init__()\n        # Hyper-parameter\n        self.L, self.N = L, N\n        # Components\n        # 50% overlap\n        self.conv1d_U = nn.Conv1d(audio_channels, N, kernel_size=L, stride=L // 2, bias=False)\n\n    def forward(self, mixture):\n        \"\"\"\n        Args:\n            mixture: [M, T], M is batch size, T is #samples\n        Returns:\n            mixture_w: [M, N, K], where K = (T-L)/(L/2)+1 = 2T/L-1\n        \"\"\"","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/tasnet_v2.py:10"}],[1545,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/tasnet_v2.py","startLine":138,"endLine":144,"chunkText":"        Returns:\n            mixture_w: [M, N, K], where K = (T-L)/(L/2)+1 = 2T/L-1\n        \"\"\"\n        mixture_w = F.relu(self.conv1d_U(mixture))  # [M, N, K]\n        return mixture_w\n\n","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/tasnet_v2.py:11"}],[1546,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/tasnet_v2.py","startLine":145,"endLine":163,"chunkText":"class Decoder(nn.Module):\n    def __init__(self, N, L, audio_channels):\n        super(Decoder, self).__init__()\n        # Hyper-parameter\n        self.N, self.L = N, L\n        self.audio_channels = audio_channels\n        # Components\n        self.basis_signals = nn.Linear(N, audio_channels * L, bias=False)\n\n    def forward(self, mixture_w, est_mask):\n        \"\"\"\n        Args:\n            mixture_w: [M, N, K]\n            est_mask: [M, C, N, K]\n        Returns:\n            est_source: [M, C, T]\n        \"\"\"\n        # D = W * M\n        source_w = torch.unsqueeze(mixture_w, 1) * est_mask  # [M, C, N, K]","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/tasnet_v2.py:12"}],[1547,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/tasnet_v2.py","startLine":161,"endLine":172,"chunkText":"        \"\"\"\n        # D = W * M\n        source_w = torch.unsqueeze(mixture_w, 1) * est_mask  # [M, C, N, K]\n        source_w = torch.transpose(source_w, 2, 3)  # [M, C, K, N]\n        # S = DV\n        est_source = self.basis_signals(source_w)  # [M, C, K, ac * L]\n        m, c, k, _ = est_source.size()\n        est_source = est_source.view(m, c, k, self.audio_channels, -1).transpose(2, 3).contiguous()\n        est_source = overlap_and_add(est_source, self.L // 2)  # M x C x ac x T\n        return est_source\n\n","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/tasnet_v2.py:13"}],[1548,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/tasnet_v2.py","startLine":173,"endLine":185,"chunkText":"class TemporalConvNet(nn.Module):\n    def __init__(self, N, B, H, P, X, R, C, norm_type=\"gLN\", causal=False, mask_nonlinear=\"relu\"):\n        \"\"\"\n        Args:\n            N: Number of filters in autoencoder\n            B: Number of channels in bottleneck 1 × 1-conv block\n            H: Number of channels in convolutional blocks\n            P: Kernel size in convolutional blocks\n            X: Number of convolutional blocks in each repeat\n            R: Number of repeats\n            C: Number of speakers\n            norm_type: BN, gLN, cLN\n            causal: causal or non-causal","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/tasnet_v2.py:14"}],[1549,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/tasnet_v2.py","startLine":183,"endLine":199,"chunkText":"            C: Number of speakers\n            norm_type: BN, gLN, cLN\n            causal: causal or non-causal\n            mask_nonlinear: use which non-linear function to generate mask\n        \"\"\"\n        super(TemporalConvNet, self).__init__()\n        # Hyper-parameter\n        self.C = C\n        self.mask_nonlinear = mask_nonlinear\n        # Components\n        # [M, N, K] -> [M, N, K]\n        layer_norm = ChannelwiseLayerNorm(N)\n        # [M, N, K] -> [M, B, K]\n        bottleneck_conv1x1 = nn.Conv1d(N, B, 1, bias=False)\n        # [M, B, K] -> [M, B, K]\n        repeats = []\n        for r in range(R):","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/tasnet_v2.py:15"}],[1550,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/tasnet_v2.py","startLine":197,"endLine":209,"chunkText":"        # [M, B, K] -> [M, B, K]\n        repeats = []\n        for r in range(R):\n            blocks = []\n            for x in range(X):\n                dilation = 2**x\n                padding = (P - 1) * dilation if causal else (P - 1) * dilation // 2\n                blocks += [TemporalBlock(B, H, P, stride=1, padding=padding, dilation=dilation, norm_type=norm_type, causal=causal)]\n            repeats += [nn.Sequential(*blocks)]\n        temporal_conv_net = nn.Sequential(*repeats)\n        # [M, B, K] -> [M, C*N, K]\n        mask_conv1x1 = nn.Conv1d(B, C * N, 1, bias=False)\n        # Put together","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/tasnet_v2.py:16"}],[1551,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/tasnet_v2.py","startLine":207,"endLine":221,"chunkText":"        # [M, B, K] -> [M, C*N, K]\n        mask_conv1x1 = nn.Conv1d(B, C * N, 1, bias=False)\n        # Put together\n        self.network = nn.Sequential(layer_norm, bottleneck_conv1x1, temporal_conv_net, mask_conv1x1)\n\n    def forward(self, mixture_w):\n        \"\"\"\n        Keep this API same with TasNet\n        Args:\n            mixture_w: [M, N, K], M is batch size\n        returns:\n            est_mask: [M, C, N, K]\n        \"\"\"\n        M, N, K = mixture_w.size()\n        score = self.network(mixture_w)  # [M, N, K] -> [M, C*N, K]","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/tasnet_v2.py:17"}],[1552,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/tasnet_v2.py","startLine":219,"endLine":231,"chunkText":"        \"\"\"\n        M, N, K = mixture_w.size()\n        score = self.network(mixture_w)  # [M, N, K] -> [M, C*N, K]\n        score = score.view(M, self.C, N, K)  # [M, C*N, K] -> [M, C, N, K]\n        if self.mask_nonlinear == \"softmax\":\n            est_mask = F.softmax(score, dim=1)\n        elif self.mask_nonlinear == \"relu\":\n            est_mask = F.relu(score)\n        else:\n            raise ValueError(\"Unsupported mask non-linear function\")\n        return est_mask\n\n","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/tasnet_v2.py:18"}],[1553,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/tasnet_v2.py","startLine":232,"endLine":241,"chunkText":"class TemporalBlock(nn.Module):\n    def __init__(self, in_channels, out_channels, kernel_size, stride, padding, dilation, norm_type=\"gLN\", causal=False):\n        super(TemporalBlock, self).__init__()\n        # [M, B, K] -> [M, H, K]\n        conv1x1 = nn.Conv1d(in_channels, out_channels, 1, bias=False)\n        prelu = nn.PReLU()\n        norm = chose_norm(norm_type, out_channels)\n        # [M, H, K] -> [M, B, K]\n        dsconv = DepthwiseSeparableConv(out_channels, in_channels, kernel_size, stride, padding, dilation, norm_type, causal)\n        # Put together","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/tasnet_v2.py:19"}],[1554,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/tasnet_v2.py","startLine":239,"endLine":254,"chunkText":"        # [M, H, K] -> [M, B, K]\n        dsconv = DepthwiseSeparableConv(out_channels, in_channels, kernel_size, stride, padding, dilation, norm_type, causal)\n        # Put together\n        self.net = nn.Sequential(conv1x1, prelu, norm, dsconv)\n\n    def forward(self, x):\n        \"\"\"\n        Args:\n            x: [M, B, K]\n        Returns:\n            [M, B, K]\n        \"\"\"\n        residual = x\n        out = self.net(x)\n        # TODO: when P = 3 here works fine, but when P = 2 maybe need to pad?\n        return out + residual  # look like w/o F.relu is better than w/ F.relu","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/tasnet_v2.py:20"}],[1555,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/tasnet_v2.py","startLine":252,"endLine":257,"chunkText":"        out = self.net(x)\n        # TODO: when P = 3 here works fine, but when P = 2 maybe need to pad?\n        return out + residual  # look like w/o F.relu is better than w/ F.relu\n        # return F.relu(out + residual)\n\n","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/tasnet_v2.py:21"}],[1556,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/tasnet_v2.py","startLine":258,"endLine":267,"chunkText":"class DepthwiseSeparableConv(nn.Module):\n    def __init__(self, in_channels, out_channels, kernel_size, stride, padding, dilation, norm_type=\"gLN\", causal=False):\n        super(DepthwiseSeparableConv, self).__init__()\n        # Use `groups` option to implement depthwise convolution\n        # [M, H, K] -> [M, H, K]\n        depthwise_conv = nn.Conv1d(in_channels, in_channels, kernel_size, stride=stride, padding=padding, dilation=dilation, groups=in_channels, bias=False)\n        if causal:\n            chomp = Chomp1d(padding)\n        prelu = nn.PReLU()\n        norm = chose_norm(norm_type, in_channels)","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/tasnet_v2.py:22"}],[1557,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/tasnet_v2.py","startLine":265,"endLine":282,"chunkText":"            chomp = Chomp1d(padding)\n        prelu = nn.PReLU()\n        norm = chose_norm(norm_type, in_channels)\n        # [M, H, K] -> [M, B, K]\n        pointwise_conv = nn.Conv1d(in_channels, out_channels, 1, bias=False)\n        # Put together\n        if causal:\n            self.net = nn.Sequential(depthwise_conv, chomp, prelu, norm, pointwise_conv)\n        else:\n            self.net = nn.Sequential(depthwise_conv, prelu, norm, pointwise_conv)\n\n    def forward(self, x):\n        \"\"\"\n        Args:\n            x: [M, H, K]\n        Returns:\n            result: [M, B, K]\n        \"\"\"","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/tasnet_v2.py:23"}],[1558,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/tasnet_v2.py","startLine":280,"endLine":285,"chunkText":"        Returns:\n            result: [M, B, K]\n        \"\"\"\n        return self.net(x)\n\n","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/tasnet_v2.py:24"}],[1559,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/tasnet_v2.py","startLine":286,"endLine":307,"chunkText":"class Chomp1d(nn.Module):\n    \"\"\"To ensure the output length is the same as the input.\"\"\"\n\n    def __init__(self, chomp_size):\n        super(Chomp1d, self).__init__()\n        self.chomp_size = chomp_size\n\n    def forward(self, x):\n        \"\"\"\n        Args:\n            x: [M, H, Kpad]\n        Returns:\n            [M, H, K]\n        \"\"\"\n        return x[:, :, : -self.chomp_size].contiguous()\n\n\ndef chose_norm(norm_type, channel_size):\n    \"\"\"The input of normlization will be (M, C, K), where M is batch size,\n    C is channel size and K is sequence length.\n    \"\"\"\n    if norm_type == \"gLN\":","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/tasnet_v2.py:25"}],[1560,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/tasnet_v2.py","startLine":305,"endLine":319,"chunkText":"    C is channel size and K is sequence length.\n    \"\"\"\n    if norm_type == \"gLN\":\n        return GlobalLayerNorm(channel_size)\n    elif norm_type == \"cLN\":\n        return ChannelwiseLayerNorm(channel_size)\n    elif norm_type == \"id\":\n        return nn.Identity()\n    else:  # norm_type == \"BN\":\n        # Given input (M, C, K), nn.BatchNorm1d(C) will accumulate statics\n        # along M and K, so this BN usage is right.\n        return nn.BatchNorm1d(channel_size)\n\n\n# TODO: Use nn.LayerNorm to impl cLN to speed up","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/tasnet_v2.py:26"}],[1561,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/tasnet_v2.py","startLine":320,"endLine":336,"chunkText":"class ChannelwiseLayerNorm(nn.Module):\n    \"\"\"Channel-wise Layer Normalization (cLN)\"\"\"\n\n    def __init__(self, channel_size):\n        super(ChannelwiseLayerNorm, self).__init__()\n        self.gamma = nn.Parameter(torch.Tensor(1, channel_size, 1))  # [1, N, 1]\n        self.beta = nn.Parameter(torch.Tensor(1, channel_size, 1))  # [1, N, 1]\n        self.reset_parameters()\n\n    def reset_parameters(self):\n        self.gamma.data.fill_(1)\n        self.beta.data.zero_()\n\n    def forward(self, y):\n        \"\"\"\n        Args:\n            y: [M, N, K], M is batch size, N is channel size, K is length","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/tasnet_v2.py:27"}],[1562,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/tasnet_v2.py","startLine":334,"endLine":345,"chunkText":"        \"\"\"\n        Args:\n            y: [M, N, K], M is batch size, N is channel size, K is length\n        Returns:\n            cLN_y: [M, N, K]\n        \"\"\"\n        mean = torch.mean(y, dim=1, keepdim=True)  # [M, 1, K]\n        var = torch.var(y, dim=1, keepdim=True, unbiased=False)  # [M, 1, K]\n        cLN_y = self.gamma * (y - mean) / torch.pow(var + EPS, 0.5) + self.beta\n        return cLN_y\n\n","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/tasnet_v2.py:28"}],[1563,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/tasnet_v2.py","startLine":346,"endLine":363,"chunkText":"class GlobalLayerNorm(nn.Module):\n    \"\"\"Global Layer Normalization (gLN)\"\"\"\n\n    def __init__(self, channel_size):\n        super(GlobalLayerNorm, self).__init__()\n        self.gamma = nn.Parameter(torch.Tensor(1, channel_size, 1))  # [1, N, 1]\n        self.beta = nn.Parameter(torch.Tensor(1, channel_size, 1))  # [1, N, 1]\n        self.reset_parameters()\n\n    def reset_parameters(self):\n        self.gamma.data.fill_(1)\n        self.beta.data.zero_()\n\n    def forward(self, y):\n        \"\"\"\n        Args:\n            y: [M, N, K], M is batch size, N is channel size, K is length\n        Returns:","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/tasnet_v2.py:29"}],[1564,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/tasnet_v2.py","startLine":361,"endLine":376,"chunkText":"        Args:\n            y: [M, N, K], M is batch size, N is channel size, K is length\n        Returns:\n            gLN_y: [M, N, K]\n        \"\"\"\n        # TODO: in torch 1.0, torch.mean() support dim list\n        mean = y.mean(dim=1, keepdim=True).mean(dim=2, keepdim=True)  # [M, 1, 1]\n        var = (torch.pow(y - mean, 2)).mean(dim=1, keepdim=True).mean(dim=2, keepdim=True)\n        gLN_y = self.gamma * (y - mean) / torch.pow(var + EPS, 0.5) + self.beta\n        return gLN_y\n\n\nif __name__ == \"__main__\":\n    torch.manual_seed(123)\n    M, N, L, T = 2, 3, 4, 12\n    K = 2 * T // L - 1","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/tasnet_v2.py:30"}],[1565,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/tasnet_v2.py","startLine":374,"endLine":388,"chunkText":"    torch.manual_seed(123)\n    M, N, L, T = 2, 3, 4, 12\n    K = 2 * T // L - 1\n    B, H, P, X, R, C, norm_type, causal = 2, 3, 3, 3, 2, 2, \"gLN\", False\n    mixture = torch.randint(3, (M, T))\n    # test Encoder\n    encoder = Encoder(L, N)\n    encoder.conv1d_U.weight.data = torch.randint(2, encoder.conv1d_U.weight.size())\n    mixture_w = encoder(mixture)\n    print(\"mixture\", mixture)\n    print(\"U\", encoder.conv1d_U.weight)\n    print(\"mixture_w\", mixture_w)\n    print(\"mixture_w size\", mixture_w.size())\n\n    # test TemporalConvNet","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/tasnet_v2.py:31"}],[1566,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/tasnet_v2.py","startLine":386,"endLine":402,"chunkText":"    print(\"mixture_w size\", mixture_w.size())\n\n    # test TemporalConvNet\n    separator = TemporalConvNet(N, B, H, P, X, R, C, norm_type=norm_type, causal=causal)\n    est_mask = separator(mixture_w)\n    print(\"est_mask\", est_mask)\n\n    # test Decoder\n    decoder = Decoder(N, L)\n    est_mask = torch.randint(2, (B, K, C, N))\n    est_source = decoder(mixture_w, est_mask)\n    print(\"est_source\", est_source)\n\n    # test Conv-TasNet\n    conv_tasnet = ConvTasNet(N, L, B, H, P, X, R, C, norm_type=norm_type)\n    est_source = conv_tasnet(mixture)\n    print(\"est_source\", est_source)","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/tasnet_v2.py:32"}],[1567,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/tasnet_v2.py","startLine":400,"endLine":404,"chunkText":"    conv_tasnet = ConvTasNet(N, L, B, H, P, X, R, C, norm_type=norm_type)\n    est_source = conv_tasnet(mixture)\n    print(\"est_source\", est_source)\n    print(\"est_source size\", est_source.size())\n","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/tasnet_v2.py:33"}],[1568,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/transformer.py","startLine":0,"endLine":21,"chunkText":"# Copyright (c) 2019-present, Meta, Inc.\n# All rights reserved.\n#\n# This source code is licensed under the license found in the\n# LICENSE file in the root directory of this source tree.\n# First author is Simon Rouard.\n\nimport random\nimport typing as tp\n\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nimport numpy as np\nimport math\nfrom einops import rearrange\n\n\ndef create_sin_embedding(length: int, dim: int, shift: int = 0, device=\"cpu\", max_period=10000):\n    # We aim for TBC format\n    assert dim % 2 == 0\n    pos = shift + torch.arange(length, device=device).view(-1, 1, 1)","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/transformer.py:0"}],[1569,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/transformer.py","startLine":19,"endLine":34,"chunkText":"    # We aim for TBC format\n    assert dim % 2 == 0\n    pos = shift + torch.arange(length, device=device).view(-1, 1, 1)\n    half_dim = dim // 2\n    adim = torch.arange(dim // 2, device=device).view(1, 1, -1)\n    phase = pos / (max_period ** (adim / (half_dim - 1)))\n    return torch.cat([torch.cos(phase), torch.sin(phase)], dim=-1)\n\n\ndef create_2d_sin_embedding(d_model, height, width, device=\"cpu\", max_period=10000):\n    \"\"\"\n    :param d_model: dimension of the model\n    :param height: height of the positions\n    :param width: width of the positions\n    :return: d_model*height*width position matrix\n    \"\"","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/transformer.py:1"}],[1570,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/transformer.py","startLine":32,"endLine":42,"chunkText":"    :param width: width of the positions\n    :return: d_model*height*width position matrix\n    \"\"\"\n    if d_model % 4 != 0:\n        raise ValueError(\"Cannot use sin/cos positional encoding with \" \"odd dimension (got dim={:d})\".format(d_model))\n    pe = torch.zeros(d_model, height, width)\n    # Each dimension use half of d_model\n    d_model = int(d_model / 2)\n    div_term = torch.exp(torch.arange(0.0, d_model, 2) * -(math.log(max_period) / d_model))\n    pos_w = torch.arange(0.0, width).unsqueeze(1)\n    pos_h = torch.arange(0.0, height).unsqueeze(1)","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/transformer.py:2"}],[1571,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/transformer.py","startLine":40,"endLine":45,"chunkText":"    div_term = torch.exp(torch.arange(0.0, d_model, 2) * -(math.log(max_period) / d_model))\n    pos_w = torch.arange(0.0, width).unsqueeze(1)\n    pos_h = torch.arange(0.0, height).unsqueeze(1)\n    pe[0:d_model:2, :, :] = torch.sin(pos_w * div_term).transpose(0, 1).unsqueeze(1).repeat(1, height, 1)\n    pe[1:d_model:2, :, :] = torch.cos(pos_w * div_term).transpose(0, 1).unsqueeze(1).repeat(1, height, 1)\n    pe[d_model::2, :, :] = torch.sin(pos_h * div_term).transpose(0, 1).unsqueeze(2).repeat(1, 1, width)","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/transformer.py:3"}],[1572,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/transformer.py","startLine":43,"endLine":55,"chunkText":"    pe[0:d_model:2, :, :] = torch.sin(pos_w * div_term).transpose(0, 1).unsqueeze(1).repeat(1, height, 1)\n    pe[1:d_model:2, :, :] = torch.cos(pos_w * div_term).transpose(0, 1).unsqueeze(1).repeat(1, height, 1)\n    pe[d_model::2, :, :] = torch.sin(pos_h * div_term).transpose(0, 1).unsqueeze(2).repeat(1, 1, width)\n    pe[d_model + 1 :: 2, :, :] = torch.cos(pos_h * div_term).transpose(0, 1).unsqueeze(2).repeat(1, 1, width)\n\n    return pe[None, :].to(device)\n\n\ndef create_sin_embedding_cape(\n    length: int,\n    dim: int,\n    batch_size: int,\n    mean_normalize: bool,","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/transformer.py:4"}],[1573,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/transformer.py","startLine":54,"endLine":70,"chunkText":"    batch_size: int,\n    mean_normalize: bool,\n    augment: bool,  # True during training\n    max_global_shift: float = 0.0,  # delta max\n    max_local_shift: float = 0.0,  # epsilon max\n    max_scale: float = 1.0,\n    device: str = \"cpu\",\n    max_period: float = 10000.0,\n):\n    # We aim for TBC format\n    assert dim % 2 == 0\n    pos = 1.0 * torch.arange(length).view(-1, 1, 1)  # (length, 1, 1)\n    pos = pos.repeat(1, batch_size, 1)  # (length, batch_size, 1)\n    if mean_normalize:\n        pos -= torch.nanmean(pos, dim=0, keepdim=True)\n\n    if augment:","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/transformer.py:5"}],[1574,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/transformer.py","startLine":68,"endLine":79,"chunkText":"        pos -= torch.nanmean(pos, dim=0, keepdim=True)\n\n    if augment:\n        delta = np.random.uniform(-max_global_shift, +max_global_shift, size=[1, batch_size, 1])\n        delta_local = np.random.uniform(-max_local_shift, +max_local_shift, size=[length, batch_size, 1])\n        log_lambdas = np.random.uniform(-np.log(max_scale), +np.log(max_scale), size=[1, batch_size, 1])\n        pos = (pos + delta + delta_local) * np.exp(log_lambdas)\n\n    pos = pos.to(device)\n\n    half_dim = dim // 2\n    adim = torch.arange(dim // 2, device=device).view(1, 1, -1)","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/transformer.py:6"}],[1575,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/transformer.py","startLine":78,"endLine":94,"chunkText":"    half_dim = dim // 2\n    adim = torch.arange(dim // 2, device=device).view(1, 1, -1)\n    phase = pos / (max_period ** (adim / (half_dim - 1)))\n    return torch.cat([torch.cos(phase), torch.sin(phase)], dim=-1).float()\n\n\ndef get_causal_mask(length):\n    pos = torch.arange(length)\n    return pos > pos[:, None]\n\n\ndef get_elementary_mask(T1, T2, mask_type, sparse_attn_window, global_window, mask_random_seed, sparsity, device):\n    \"\"\"\n    When the input of the Decoder has length T1 and the output T2\n    The mask matrix has shape (T2, T1)\n    \"\"\"\n    assert mask_type in [\"diag\", \"jmask\", \"random\", \"global\"]","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/transformer.py:7"}],[1576,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/transformer.py","startLine":93,"endLine":108,"chunkText":"    \"\"\"\n    assert mask_type in [\"diag\", \"jmask\", \"random\", \"global\"]\n\n    if mask_type == \"global\":\n        mask = torch.zeros(T2, T1, dtype=torch.bool)\n        mask[:, :global_window] = True\n        line_window = int(global_window * T2 / T1)\n        mask[:line_window, :] = True\n\n    if mask_type == \"diag\":\n\n        mask = torch.zeros(T2, T1, dtype=torch.bool)\n        rows = torch.arange(T2)[:, None]\n        cols = (T1 / T2 * rows + torch.arange(-sparse_attn_window, sparse_attn_window + 1)).long().clamp(0, T1 - 1)\n        mask.scatter_(1, cols, torch.ones(1, dtype=torch.bool).expand_as(cols))\n","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/transformer.py:8"}],[1577,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/transformer.py","startLine":106,"endLine":116,"chunkText":"        cols = (T1 / T2 * rows + torch.arange(-sparse_attn_window, sparse_attn_window + 1)).long().clamp(0, T1 - 1)\n        mask.scatter_(1, cols, torch.ones(1, dtype=torch.bool).expand_as(cols))\n\n    elif mask_type == \"jmask\":\n        mask = torch.zeros(T2 + 2, T1 + 2, dtype=torch.bool)\n        rows = torch.arange(T2 + 2)[:, None]\n        t = torch.arange(0, int((2 * T1) ** 0.5 + 1))\n        t = (t * (t + 1) / 2).int()\n        t = torch.cat([-t.flip(0)[:-1], t])\n        cols = (T1 / T2 * rows + t).long().clamp(0, T1 + 1)\n        mask.scatter_(1, cols, torch.ones(1, dtype=torch.bool).expand_as(cols))","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/transformer.py:9"}],[1578,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/transformer.py","startLine":114,"endLine":129,"chunkText":"        t = torch.cat([-t.flip(0)[:-1], t])\n        cols = (T1 / T2 * rows + t).long().clamp(0, T1 + 1)\n        mask.scatter_(1, cols, torch.ones(1, dtype=torch.bool).expand_as(cols))\n        mask = mask[1:-1, 1:-1]\n\n    elif mask_type == \"random\":\n        gene = torch.Generator(device=device)\n        gene.manual_seed(mask_random_seed)\n        mask = torch.rand(T1 * T2, generator=gene, device=device).reshape(T2, T1) > sparsity\n\n    mask = mask.to(device)\n    return mask\n\n\ndef get_mask(T1, T2, mask_type, sparse_attn_window, global_window, mask_random_seed, sparsity, device):\n    \"\"\"","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/transformer.py:10"}],[1579,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/transformer.py","startLine":127,"endLine":141,"chunkText":"\ndef get_mask(T1, T2, mask_type, sparse_attn_window, global_window, mask_random_seed, sparsity, device):\n    \"\"\"\n    Return a SparseCSRTensor mask that is a combination of elementary masks\n    mask_type can be a combination of multiple masks: for instance \"diag_jmask_random\"\n    \"\"\"\n    from xformers.sparse import SparseCSRTensor\n\n    # create a list\n    mask_types = mask_type.split(\"_\")\n\n    all_masks = [get_elementary_mask(T1, T2, mask, sparse_attn_window, global_window, mask_random_seed, sparsity, device) for mask in mask_types]\n\n    final_mask = torch.stack(all_masks).sum(axis=0) > 0\n","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/transformer.py:11"}],[1580,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/transformer.py","startLine":139,"endLine":159,"chunkText":"\n    final_mask = torch.stack(all_masks).sum(axis=0) > 0\n\n    return SparseCSRTensor.from_dense(final_mask[None])\n\n\nclass ScaledEmbedding(nn.Module):\n    def __init__(self, num_embeddings: int, embedding_dim: int, scale: float = 1.0, boost: float = 3.0):\n        super().__init__()\n        self.embedding = nn.Embedding(num_embeddings, embedding_dim)\n        self.embedding.weight.data *= scale / boost\n        self.boost = boost\n\n    @property\n    def weight(self):\n        return self.embedding.weight * self.boost\n\n    def forward(self, x):\n        return self.embedding(x) * self.boost\n\n","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/transformer.py:12"}],[1581,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/transformer.py","startLine":160,"endLine":174,"chunkText":"class LayerScale(nn.Module):\n    \"\"\"Layer scale from [Touvron et al 2021] (https://arxiv.org/pdf/2103.17239.pdf).\n    This rescales diagonaly residual outputs close to 0 initially, then learnt.\n    \"\"\"\n\n    def __init__(self, channels: int, init: float = 0, channel_last=False):\n        \"\"\"\n        channel_last = False corresponds to (B, C, T) tensors\n        channel_last = True corresponds to (T, B, C) tensors\n        \"\"\"\n        super().__init__()\n        self.channel_last = channel_last\n        self.scale = nn.Parameter(torch.zeros(channels, requires_grad=True))\n        self.scale.data[:] = init\n","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/transformer.py:13"}],[1582,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/transformer.py","startLine":172,"endLine":181,"chunkText":"        self.scale = nn.Parameter(torch.zeros(channels, requires_grad=True))\n        self.scale.data[:] = init\n\n    def forward(self, x):\n        if self.channel_last:\n            return self.scale * x\n        else:\n            return self.scale[:, None] * x\n\n","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/transformer.py:14"}],[1583,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/transformer.py","startLine":182,"endLine":194,"chunkText":"class MyGroupNorm(nn.GroupNorm):\n    def __init__(self, *args, **kwargs):\n        super().__init__(*args, **kwargs)\n\n    def forward(self, x):\n        \"\"\"\n        x: (B, T, C)\n        if num_groups=1: Normalisation on all T and C together for each B\n        \"\"\"\n        x = x.transpose(1, 2)\n        return super().forward(x).transpose(1, 2)\n\n","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/transformer.py:15"}],[1584,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/transformer.py","startLine":195,"endLine":217,"chunkText":"class MyTransformerEncoderLayer(nn.TransformerEncoderLayer):\n    def __init__(\n        self,\n        d_model,\n        nhead,\n        dim_feedforward=2048,\n        dropout=0.1,\n        activation=F.relu,\n        group_norm=0,\n        norm_first=False,\n        norm_out=False,\n        layer_norm_eps=1e-5,\n        layer_scale=False,\n        init_values=1e-4,\n        device=None,\n        dtype=None,\n        sparse=False,\n        mask_type=\"diag\",\n        mask_random_seed=42,\n        sparse_attn_window=500,\n        global_window=50,\n        auto_sparsity=False,\n        sparsity=0.95,","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/transformer.py:16"}],[1585,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/transformer.py","startLine":215,"endLine":234,"chunkText":"        global_window=50,\n        auto_sparsity=False,\n        sparsity=0.95,\n        batch_first=False,\n    ):\n        factory_kwargs = {\"device\": device, \"dtype\": dtype}\n        super().__init__(\n            d_model=d_model,\n            nhead=nhead,\n            dim_feedforward=dim_feedforward,\n            dropout=dropout,\n            activation=activation,\n            layer_norm_eps=layer_norm_eps,\n            batch_first=batch_first,\n            norm_first=norm_first,\n            device=device,\n            dtype=dtype,\n        )\n        self.sparse = sparse\n        self.auto_sparsity = auto_sparsity","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/transformer.py:17"}],[1586,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/transformer.py","startLine":232,"endLine":245,"chunkText":"        )\n        self.sparse = sparse\n        self.auto_sparsity = auto_sparsity\n        if sparse:\n            if not auto_sparsity:\n                self.mask_type = mask_type\n                self.sparse_attn_window = sparse_attn_window\n                self.global_window = global_window\n            self.sparsity = sparsity\n        if group_norm:\n            self.norm1 = MyGroupNorm(int(group_norm), d_model, eps=layer_norm_eps, **factory_kwargs)\n            self.norm2 = MyGroupNorm(int(group_norm), d_model, eps=layer_norm_eps, **factory_kwargs)\n\n        self.norm_out = None","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/transformer.py:18"}],[1587,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/transformer.py","startLine":243,"endLine":251,"chunkText":"            self.norm2 = MyGroupNorm(int(group_norm), d_model, eps=layer_norm_eps, **factory_kwargs)\n\n        self.norm_out = None\n        if self.norm_first & norm_out:\n            self.norm_out = MyGroupNorm(num_groups=int(norm_out), num_channels=d_model)\n        self.gamma_1 = LayerScale(d_model, init_values, True) if layer_scale else nn.Identity()\n        self.gamma_2 = LayerScale(d_model, init_values, True) if layer_scale else nn.Identity()\n\n        if sparse:","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/transformer.py:19"}],[1588,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/transformer.py","startLine":249,"endLine":260,"chunkText":"        self.gamma_2 = LayerScale(d_model, init_values, True) if layer_scale else nn.Identity()\n\n        if sparse:\n            self.self_attn = MultiheadAttention(d_model, nhead, dropout=dropout, batch_first=batch_first, auto_sparsity=sparsity if auto_sparsity else 0)\n            self.__setattr__(\"src_mask\", torch.zeros(1, 1))\n            self.mask_random_seed = mask_random_seed\n\n    def forward(self, src, src_mask=None, src_key_padding_mask=None):\n        \"\"\"\n        if batch_first = False, src shape is (T, B, C)\n        the case where batch_first=True is not covered\n        \"\"\"","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/transformer.py:20"}],[1589,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/transformer.py","startLine":259,"endLine":271,"chunkText":"        the case where batch_first=True is not covered\n        \"\"\"\n        device = src.device\n        x = src\n        T, B, C = x.shape\n        if self.sparse and not self.auto_sparsity:\n            assert src_mask is None\n            src_mask = self.src_mask\n            if src_mask.shape[-1] != T:\n                src_mask = get_mask(T, T, self.mask_type, self.sparse_attn_window, self.global_window, self.mask_random_seed, self.sparsity, device)\n                self.__setattr__(\"src_mask\", src_mask)\n\n        if self.norm_first:","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/transformer.py:21"}],[1590,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/transformer.py","startLine":269,"endLine":283,"chunkText":"                self.__setattr__(\"src_mask\", src_mask)\n\n        if self.norm_first:\n            x = x + self.gamma_1(self._sa_block(self.norm1(x), src_mask, src_key_padding_mask))\n            x = x + self.gamma_2(self._ff_block(self.norm2(x)))\n\n            if self.norm_out:\n                x = self.norm_out(x)\n        else:\n            x = self.norm1(x + self.gamma_1(self._sa_block(x, src_mask, src_key_padding_mask)))\n            x = self.norm2(x + self.gamma_2(self._ff_block(x)))\n\n        return x\n\n","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/transformer.py:22"}],[1591,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/transformer.py","startLine":284,"endLine":303,"chunkText":"class CrossTransformerEncoderLayer(nn.Module):\n    def __init__(\n        self,\n        d_model: int,\n        nhead: int,\n        dim_feedforward: int = 2048,\n        dropout: float = 0.1,\n        activation=F.relu,\n        layer_norm_eps: float = 1e-5,\n        layer_scale: bool = False,\n        init_values: float = 1e-4,\n        norm_first: bool = False,\n        group_norm: bool = False,\n        norm_out: bool = False,\n        sparse=False,\n        mask_type=\"diag\",\n        mask_random_seed=42,\n        sparse_attn_window=500,\n        global_window=50,\n        sparsity=0.95,","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/transformer.py:23"}],[1592,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/transformer.py","startLine":301,"endLine":320,"chunkText":"        sparse_attn_window=500,\n        global_window=50,\n        sparsity=0.95,\n        auto_sparsity=None,\n        device=None,\n        dtype=None,\n        batch_first=False,\n    ):\n        factory_kwargs = {\"device\": device, \"dtype\": dtype}\n        super().__init__()\n\n        self.sparse = sparse\n        self.auto_sparsity = auto_sparsity\n        if sparse:\n            if not auto_sparsity:\n                self.mask_type = mask_type\n                self.sparse_attn_window = sparse_attn_window\n                self.global_window = global_window\n            self.sparsity = sparsity\n","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/transformer.py:24"}],[1593,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/transformer.py","startLine":318,"endLine":331,"chunkText":"                self.global_window = global_window\n            self.sparsity = sparsity\n\n        self.cross_attn: nn.Module\n        self.cross_attn = nn.MultiheadAttention(d_model, nhead, dropout=dropout, batch_first=batch_first)\n        # Implementation of Feedforward model\n        self.linear1 = nn.Linear(d_model, dim_feedforward, **factory_kwargs)\n        self.dropout = nn.Dropout(dropout)\n        self.linear2 = nn.Linear(dim_feedforward, d_model, **factory_kwargs)\n\n        self.norm_first = norm_first\n        self.norm1: nn.Module\n        self.norm2: nn.Module\n        self.norm3: nn.Module","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/transformer.py:25"}],[1594,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/transformer.py","startLine":329,"endLine":338,"chunkText":"        self.norm1: nn.Module\n        self.norm2: nn.Module\n        self.norm3: nn.Module\n        if group_norm:\n            self.norm1 = MyGroupNorm(int(group_norm), d_model, eps=layer_norm_eps, **factory_kwargs)\n            self.norm2 = MyGroupNorm(int(group_norm), d_model, eps=layer_norm_eps, **factory_kwargs)\n            self.norm3 = MyGroupNorm(int(group_norm), d_model, eps=layer_norm_eps, **factory_kwargs)\n        else:\n            self.norm1 = nn.LayerNorm(d_model, eps=layer_norm_eps, **factory_kwargs)\n            self.norm2 = nn.LayerNorm(d_model, eps=layer_norm_eps, **factory_kwargs)","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/transformer.py:26"}],[1595,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/transformer.py","startLine":336,"endLine":345,"chunkText":"        else:\n            self.norm1 = nn.LayerNorm(d_model, eps=layer_norm_eps, **factory_kwargs)\n            self.norm2 = nn.LayerNorm(d_model, eps=layer_norm_eps, **factory_kwargs)\n            self.norm3 = nn.LayerNorm(d_model, eps=layer_norm_eps, **factory_kwargs)\n\n        self.norm_out = None\n        if self.norm_first & norm_out:\n            self.norm_out = MyGroupNorm(num_groups=int(norm_out), num_channels=d_model)\n\n        self.gamma_1 = LayerScale(d_model, init_values, True) if layer_scale else nn.Identity()","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/transformer.py:27"}],[1596,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/transformer.py","startLine":343,"endLine":357,"chunkText":"            self.norm_out = MyGroupNorm(num_groups=int(norm_out), num_channels=d_model)\n\n        self.gamma_1 = LayerScale(d_model, init_values, True) if layer_scale else nn.Identity()\n        self.gamma_2 = LayerScale(d_model, init_values, True) if layer_scale else nn.Identity()\n\n        self.dropout1 = nn.Dropout(dropout)\n        self.dropout2 = nn.Dropout(dropout)\n\n        # Legacy string support for activation function.\n        if isinstance(activation, str):\n            self.activation = self._get_activation_fn(activation)\n        else:\n            self.activation = activation\n\n        if sparse:","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/transformer.py:28"}],[1597,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/transformer.py","startLine":355,"endLine":371,"chunkText":"            self.activation = activation\n\n        if sparse:\n            self.cross_attn = MultiheadAttention(d_model, nhead, dropout=dropout, batch_first=batch_first, auto_sparsity=sparsity if auto_sparsity else 0)\n            if not auto_sparsity:\n                self.__setattr__(\"mask\", torch.zeros(1, 1))\n                self.mask_random_seed = mask_random_seed\n\n    def forward(self, q, k, mask=None):\n        \"\"\"\n        Args:\n            q: tensor of shape (T, B, C)\n            k: tensor of shape (S, B, C)\n            mask: tensor of shape (T, S)\n\n        \"\"\"\n        device = q.device","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/transformer.py:29"}],[1598,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/transformer.py","startLine":369,"endLine":382,"chunkText":"\n        \"\"\"\n        device = q.device\n        T, B, C = q.shape\n        S, B, C = k.shape\n        if self.sparse and not self.auto_sparsity:\n            assert mask is None\n            mask = self.mask\n            if mask.shape[-1] != S or mask.shape[-2] != T:\n                mask = get_mask(S, T, self.mask_type, self.sparse_attn_window, self.global_window, self.mask_random_seed, self.sparsity, device)\n                self.__setattr__(\"mask\", mask)\n\n        if self.norm_first:\n            x = q + self.gamma_1(self._ca_block(self.norm1(q), self.norm2(k), mask))","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/transformer.py:30"}],[1599,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/transformer.py","startLine":380,"endLine":396,"chunkText":"\n        if self.norm_first:\n            x = q + self.gamma_1(self._ca_block(self.norm1(q), self.norm2(k), mask))\n            x = x + self.gamma_2(self._ff_block(self.norm3(x)))\n            if self.norm_out:\n                x = self.norm_out(x)\n        else:\n            x = self.norm1(q + self.gamma_1(self._ca_block(q, k, mask)))\n            x = self.norm2(x + self.gamma_2(self._ff_block(x)))\n\n        return x\n\n    # self-attention block\n    def _ca_block(self, q, k, attn_mask=None):\n        x = self.cross_attn(q, k, k, attn_mask=attn_mask, need_weights=False)[0]\n        return self.dropout1(x)\n","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/transformer.py:31"}],[1600,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/transformer.py","startLine":394,"endLine":413,"chunkText":"        x = self.cross_attn(q, k, k, attn_mask=attn_mask, need_weights=False)[0]\n        return self.dropout1(x)\n\n    # feed forward block\n    def _ff_block(self, x):\n        x = self.linear2(self.dropout(self.activation(self.linear1(x))))\n        return self.dropout2(x)\n\n    def _get_activation_fn(self, activation):\n        if activation == \"relu\":\n            return F.relu\n        elif activation == \"gelu\":\n            return F.gelu\n\n        raise RuntimeError(\"activation should be relu/gelu, not {}\".format(activation))\n\n\n# ----------------- MULTI-BLOCKS MODELS: -----------------------\n\n","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/transformer.py:32"}],[1601,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/transformer.py","startLine":414,"endLine":432,"chunkText":"class CrossTransformerEncoder(nn.Module):\n    def __init__(\n        self,\n        dim: int,\n        emb: str = \"sin\",\n        hidden_scale: float = 4.0,\n        num_heads: int = 8,\n        num_layers: int = 6,\n        cross_first: bool = False,\n        dropout: float = 0.0,\n        max_positions: int = 1000,\n        norm_in: bool = True,\n        norm_in_group: bool = False,\n        group_norm: int = False,\n        norm_first: bool = False,\n        norm_out: bool = False,\n        max_period: float = 10000.0,\n        weight_decay: float = 0.0,\n        lr: tp.Optional[float] = None,","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/transformer.py:33"}],[1602,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/transformer.py","startLine":430,"endLine":445,"chunkText":"        max_period: float = 10000.0,\n        weight_decay: float = 0.0,\n        lr: tp.Optional[float] = None,\n        layer_scale: bool = False,\n        gelu: bool = True,\n        sin_random_shift: int = 0,\n        weight_pos_embed: float = 1.0,\n        cape_mean_normalize: bool = True,\n        cape_augment: bool = True,\n        cape_glob_loc_scale: list = [5000.0, 1.0, 1.4],\n        sparse_self_attn: bool = False,\n        sparse_cross_attn: bool = False,\n        mask_type: str = \"diag\",\n        mask_random_seed: int = 42,\n        sparse_attn_window: int = 500,\n        global_window: int = 50,","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/transformer.py:34"}],[1603,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/transformer.py","startLine":443,"endLine":461,"chunkText":"        mask_random_seed: int = 42,\n        sparse_attn_window: int = 500,\n        global_window: int = 50,\n        auto_sparsity: bool = False,\n        sparsity: float = 0.95,\n    ):\n        super().__init__()\n        \"\"\"\n        \"\"\"\n        assert dim % num_heads == 0\n\n        hidden_dim = int(dim * hidden_scale)\n\n        self.num_layers = num_layers\n        # classic parity = 1 means that if idx%2 == 1 there is a\n        # classical encoder else there is a cross encoder\n        self.classic_parity = 1 if cross_first else 0\n        self.emb = emb\n        self.max_period = max_period","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/transformer.py:35"}],[1604,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/transformer.py","startLine":459,"endLine":473,"chunkText":"        self.classic_parity = 1 if cross_first else 0\n        self.emb = emb\n        self.max_period = max_period\n        self.weight_decay = weight_decay\n        self.weight_pos_embed = weight_pos_embed\n        self.sin_random_shift = sin_random_shift\n        if emb == \"cape\":\n            self.cape_mean_normalize = cape_mean_normalize\n            self.cape_augment = cape_augment\n            self.cape_glob_loc_scale = cape_glob_loc_scale\n        if emb == \"scaled\":\n            self.position_embeddings = ScaledEmbedding(max_positions, dim, scale=0.2)\n\n        self.lr = lr\n","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/transformer.py:36"}],[1605,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/transformer.py","startLine":471,"endLine":490,"chunkText":"\n        self.lr = lr\n\n        activation: tp.Any = F.gelu if gelu else F.relu\n\n        self.norm_in: nn.Module\n        self.norm_in_t: nn.Module\n        if norm_in:\n            self.norm_in = nn.LayerNorm(dim)\n            self.norm_in_t = nn.LayerNorm(dim)\n        elif norm_in_group:\n            self.norm_in = MyGroupNorm(int(norm_in_group), dim)\n            self.norm_in_t = MyGroupNorm(int(norm_in_group), dim)\n        else:\n            self.norm_in = nn.Identity()\n            self.norm_in_t = nn.Identity()\n\n        # spectrogram layers\n        self.layers = nn.ModuleList()\n        # temporal layers","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/transformer.py:37"}],[1606,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/transformer.py","startLine":488,"endLine":504,"chunkText":"        # spectrogram layers\n        self.layers = nn.ModuleList()\n        # temporal layers\n        self.layers_t = nn.ModuleList()\n\n        kwargs_common = {\n            \"d_model\": dim,\n            \"nhead\": num_heads,\n            \"dim_feedforward\": hidden_dim,\n            \"dropout\": dropout,\n            \"activation\": activation,\n            \"group_norm\": group_norm,\n            \"norm_first\": norm_first,\n            \"norm_out\": norm_out,\n            \"layer_scale\": layer_scale,\n            \"mask_type\": mask_type,\n            \"mask_random_seed\": mask_random_seed,","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/transformer.py:38"}],[1607,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/transformer.py","startLine":502,"endLine":516,"chunkText":"            \"layer_scale\": layer_scale,\n            \"mask_type\": mask_type,\n            \"mask_random_seed\": mask_random_seed,\n            \"sparse_attn_window\": sparse_attn_window,\n            \"global_window\": global_window,\n            \"sparsity\": sparsity,\n            \"auto_sparsity\": auto_sparsity,\n            \"batch_first\": True,\n        }\n\n        kwargs_classic_encoder = dict(kwargs_common)\n        kwargs_classic_encoder.update({\"sparse\": sparse_self_attn})\n        kwargs_cross_encoder = dict(kwargs_common)\n        kwargs_cross_encoder.update({\"sparse\": sparse_cross_attn})\n","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/transformer.py:39"}],[1608,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/transformer.py","startLine":514,"endLine":528,"chunkText":"        kwargs_cross_encoder = dict(kwargs_common)\n        kwargs_cross_encoder.update({\"sparse\": sparse_cross_attn})\n\n        for idx in range(num_layers):\n            if idx % 2 == self.classic_parity:\n\n                self.layers.append(MyTransformerEncoderLayer(**kwargs_classic_encoder))\n                self.layers_t.append(MyTransformerEncoderLayer(**kwargs_classic_encoder))\n\n            else:\n                self.layers.append(CrossTransformerEncoderLayer(**kwargs_cross_encoder))\n\n                self.layers_t.append(CrossTransformerEncoderLayer(**kwargs_cross_encoder))\n\n    def forward(self, x, xt)","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/transformer.py:40"}],[1609,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/transformer.py","startLine":526,"endLine":538,"chunkText":"                self.layers_t.append(CrossTransformerEncoderLayer(**kwargs_cross_encoder))\n\n    def forward(self, x, xt):\n        B, C, Fr, T1 = x.shape\n        pos_emb_2d = create_2d_sin_embedding(C, Fr, T1, x.device, self.max_period)  # (1, C, Fr, T1)\n        pos_emb_2d = rearrange(pos_emb_2d, \"b c fr t1 -> b (t1 fr) c\")\n        x = rearrange(x, \"b c fr t1 -> b (t1 fr) c\")\n        x = self.norm_in(x)\n        x = x + self.weight_pos_embed * pos_emb_2d\n\n        B, C, T2 = xt.shape\n        xt = rearrange(xt, \"b c t2 -> b t2 c\")  # now T2, B, C\n        pos_emb = self._get_pos_embedding(T2, B, C, x.device)","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/transformer.py:41"}],[1610,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/transformer.py","startLine":536,"endLine":551,"chunkText":"        B, C, T2 = xt.shape\n        xt = rearrange(xt, \"b c t2 -> b t2 c\")  # now T2, B, C\n        pos_emb = self._get_pos_embedding(T2, B, C, x.device)\n        pos_emb = rearrange(pos_emb, \"t2 b c -> b t2 c\")\n        xt = self.norm_in_t(xt)\n        xt = xt + self.weight_pos_embed * pos_emb\n\n        for idx in range(self.num_layers):\n            if idx % 2 == self.classic_parity:\n                x = self.layers[idx](x)\n                xt = self.layers_t[idx](xt)\n            else:\n                old_x = x\n                x = self.layers[idx](x, xt)\n                xt = self.layers_t[idx](xt, old_x)\n","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/transformer.py:42"}],[1611,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/transformer.py","startLine":549,"endLine":562,"chunkText":"                x = self.layers[idx](x, xt)\n                xt = self.layers_t[idx](xt, old_x)\n\n        x = rearrange(x, \"b (t1 fr) c -> b c fr t1\", t1=T1)\n        xt = rearrange(xt, \"b t2 c -> b c t2\")\n        return x, xt\n\n    def _get_pos_embedding(self, T, B, C, device):\n        if self.emb == \"sin\":\n            shift = random.randrange(self.sin_random_shift + 1)\n            pos_emb = create_sin_embedding(T, C, shift=shift, device=device, max_period=self.max_period)\n        elif self.emb == \"cape\":\n            if self.training:\n                pos_emb = create_sin_embedding_cape(","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/transformer.py:43"}],[1612,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/transformer.py","startLine":560,"endLine":574,"chunkText":"        elif self.emb == \"cape\":\n            if self.training:\n                pos_emb = create_sin_embedding_cape(\n                    T,\n                    C,\n                    B,\n                    device=device,\n                    max_period=self.max_period,\n                    mean_normalize=self.cape_mean_normalize,\n                    augment=self.cape_augment,\n                    max_global_shift=self.cape_glob_loc_scale[0],\n                    max_local_shift=self.cape_glob_loc_scale[1],\n                    max_scale=self.cape_glob_loc_scale[2],\n                )\n            else:","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/transformer.py:44"}],[1613,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/transformer.py","startLine":572,"endLine":586,"chunkText":"                    max_scale=self.cape_glob_loc_scale[2],\n                )\n            else:\n                pos_emb = create_sin_embedding_cape(T, C, B, device=device, max_period=self.max_period, mean_normalize=self.cape_mean_normalize, augment=False)\n\n        elif self.emb == \"scaled\":\n            pos = torch.arange(T, device=device)\n            pos_emb = self.position_embeddings(pos)[:, None]\n\n        return pos_emb\n\n    def make_optim_group(self):\n        group = {\"params\": list(self.parameters()), \"weight_decay\": self.weight_decay}\n        if self.lr is not None:\n            group[\"lr\"] = self.lr","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/transformer.py:45"}],[1614,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/transformer.py","startLine":584,"endLine":592,"chunkText":"        group = {\"params\": list(self.parameters()), \"weight_decay\": self.weight_decay}\n        if self.lr is not None:\n            group[\"lr\"] = self.lr\n        return group\n\n\n# Attention Modules\n\n","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/transformer.py:46"}],[1615,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/transformer.py","startLine":593,"endLine":601,"chunkText":"class MultiheadAttention(nn.Module):\n    def __init__(self, embed_dim, num_heads, dropout=0.0, bias=True, add_bias_kv=False, add_zero_attn=False, kdim=None, vdim=None, batch_first=False, auto_sparsity=None):\n        super().__init__()\n        assert auto_sparsity is not None, \"sanity check\"\n        self.num_heads = num_heads\n        self.q = torch.nn.Linear(embed_dim, embed_dim, bias=bias)\n        self.k = torch.nn.Linear(embed_dim, embed_dim, bias=bias)\n        self.v = torch.nn.Linear(embed_dim, embed_dim, bias=bias)\n        self.attn_drop = torch.nn.Dropout(dropout)","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/transformer.py:47"}],[1616,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/transformer.py","startLine":599,"endLine":610,"chunkText":"        self.k = torch.nn.Linear(embed_dim, embed_dim, bias=bias)\n        self.v = torch.nn.Linear(embed_dim, embed_dim, bias=bias)\n        self.attn_drop = torch.nn.Dropout(dropout)\n        self.proj = torch.nn.Linear(embed_dim, embed_dim, bias)\n        self.proj_drop = torch.nn.Dropout(dropout)\n        self.batch_first = batch_first\n        self.auto_sparsity = auto_sparsity\n\n    def forward(self, query, key, value, key_padding_mask=None, need_weights=True, attn_mask=None, average_attn_weights=True):\n\n        if not self.batch_first:  # N, B, C\n            query = query.permute(1, 0, 2)  # B, N_q, C","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/transformer.py:48"}],[1617,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/transformer.py","startLine":608,"endLine":619,"chunkText":"\n        if not self.batch_first:  # N, B, C\n            query = query.permute(1, 0, 2)  # B, N_q, C\n            key = key.permute(1, 0, 2)  # B, N_k, C\n            value = value.permute(1, 0, 2)  # B, N_k, C\n        B, N_q, C = query.shape\n        B, N_k, C = key.shape\n\n        q = self.q(query).reshape(B, N_q, self.num_heads, C // self.num_heads).permute(0, 2, 1, 3)\n        q = q.flatten(0, 1)\n        k = self.k(key).reshape(B, N_k, self.num_heads, C // self.num_heads).permute(0, 2, 1, 3)\n        k = k.flatten(0, 1)","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/transformer.py:49"}],[1618,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/transformer.py","startLine":617,"endLine":629,"chunkText":"        q = q.flatten(0, 1)\n        k = self.k(key).reshape(B, N_k, self.num_heads, C // self.num_heads).permute(0, 2, 1, 3)\n        k = k.flatten(0, 1)\n        v = self.v(value).reshape(B, N_k, self.num_heads, C // self.num_heads).permute(0, 2, 1, 3)\n        v = v.flatten(0, 1)\n\n        if self.auto_sparsity:\n            assert attn_mask is None\n            x = dynamic_sparse_attention(q, k, v, sparsity=self.auto_sparsity)\n        else:\n            x = scaled_dot_product_attention(q, k, v, attn_mask, dropout=self.attn_drop)\n        x = x.reshape(B, self.num_heads, N_q, C // self.num_heads)\n","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/transformer.py:50"}],[1619,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/transformer.py","startLine":627,"endLine":646,"chunkText":"            x = scaled_dot_product_attention(q, k, v, attn_mask, dropout=self.attn_drop)\n        x = x.reshape(B, self.num_heads, N_q, C // self.num_heads)\n\n        x = x.transpose(1, 2).reshape(B, N_q, C)\n        x = self.proj(x)\n        x = self.proj_drop(x)\n        if not self.batch_first:\n            x = x.permute(1, 0, 2)\n        return x, None\n\n\ndef scaled_query_key_softmax(q, k, att_mask):\n    from xformers.ops import masked_matmul\n\n    q = q / (k.size(-1)) ** 0.5\n    att = masked_matmul(q, k.transpose(-2, -1), att_mask)\n    att = torch.nn.functional.softmax(att, -1)\n    return att\n\n","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/transformer.py:51"}],[1620,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/transformer.py","startLine":644,"endLine":663,"chunkText":"    return att\n\n\ndef scaled_dot_product_attention(q, k, v, att_mask, dropout):\n    att = scaled_query_key_softmax(q, k, att_mask=att_mask)\n    att = dropout(att)\n    y = att @ v\n    return y\n\n\ndef _compute_buckets(x, R):\n    qq = torch.einsum(\"btf,bfhi->bhti\", x, R)\n    qq = torch.cat([qq, -qq], dim=-1)\n    buckets = qq.argmax(dim=-1)\n\n    return buckets.permute(0, 2, 1).byte().contiguous()\n\n\ndef dynamic_sparse_attention(query, key, value, sparsity, infer_sparsity=True, attn_bias=None):\n    # assert False, \"The code for the custom sparse kernel is not ready for release yet.\"","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/transformer.py:52"}],[1621,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/transformer.py","startLine":661,"endLine":672,"chunkText":"\ndef dynamic_sparse_attention(query, key, value, sparsity, infer_sparsity=True, attn_bias=None):\n    # assert False, \"The code for the custom sparse kernel is not ready for release yet.\"\n    from xformers.ops import find_locations, sparse_memory_efficient_attention\n\n    n_hashes = 32\n    proj_size = 4\n    query, key, value = [x.contiguous() for x in [query, key, value]]\n    with torch.no_grad():\n        R = torch.randn(1, query.shape[-1], n_hashes, proj_size // 2, device=query.device)\n        bucket_query = _compute_buckets(query, R)\n        bucket_key = _compute_buckets(key, R)","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/transformer.py:53"}],[1622,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/transformer.py","startLine":670,"endLine":675,"chunkText":"        R = torch.randn(1, query.shape[-1], n_hashes, proj_size // 2, device=query.device)\n        bucket_query = _compute_buckets(query, R)\n        bucket_key = _compute_buckets(key, R)\n        row_offsets, column_indices = find_locations(bucket_query, bucket_key, sparsity, infer_sparsity)\n    return sparse_memory_efficient_attention(query, key, value, row_offsets, column_indices, attn_bias)\n","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/transformer.py:54"}],[1623,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/utils.py","startLine":0,"endLine":28,"chunkText":"# Copyright (c) Facebook, Inc. and its affiliates.\n# All rights reserved.\n#\n# This source code is licensed under the license found in the\n# LICENSE file in the root directory of this source tree.\n\nfrom collections import defaultdict\nfrom contextlib import contextmanager\nimport math\nimport os\nimport tempfile\nimport typing as tp\n\nimport errno\nimport functools\nimport hashlib\nimport inspect\nimport io\nimport os\nimport random\nimport socket\nimport tempfile\nimport warnings\nimport zlib\n\nfrom diffq import UniformQuantizer, DiffQuantizer\nimport torch as th\nimport tqdm\nfrom torch import distributed","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/utils.py:0"}],[1624,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/utils.py","startLine":26,"endLine":46,"chunkText":"import torch as th\nimport tqdm\nfrom torch import distributed\nfrom torch.nn import functional as F\n\nimport torch\n\n\ndef unfold(a, kernel_size, stride):\n    \"\"\"Given input of size [*OT, T], output Tensor of size [*OT, F, K]\n    with K the kernel size, by extracting frames with the given stride.\n\n    This will pad the input so that `F = ceil(T / K)`.\n\n    see https://github.com/pytorch/pytorch/issues/60466\n    \"\"\"\n    *shape, length = a.shape\n    n_frames = math.ceil(length / stride)\n    tgt_length = (n_frames - 1) * stride + kernel_size\n    a = F.pad(a, (0, tgt_length - length))\n    strides = list(a.stride()","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/utils.py:1"}],[1625,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/utils.py","startLine":44,"endLine":55,"chunkText":"    tgt_length = (n_frames - 1) * stride + kernel_size\n    a = F.pad(a, (0, tgt_length - length))\n    strides = list(a.stride())\n    assert strides[-1] == 1, \"data should be contiguous\"\n    strides = strides[:-1] + [stride, 1]\n    return a.as_strided([*shape, n_frames, kernel_size], strides)\n\n\ndef center_trim(tensor: torch.Tensor, reference: tp.Union[torch.Tensor, int]):\n    \"\"\"\n    Center trim `tensor` with respect to `reference`, along the last dimension.\n    `reference` can also be a number, representing the length to trim to.","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/utils.py:2"}],[1626,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/utils.py","startLine":53,"endLine":66,"chunkText":"    \"\"\"\n    Center trim `tensor` with respect to `reference`, along the last dimension.\n    `reference` can also be a number, representing the length to trim to.\n    If the size difference != 0 mod 2, the extra sample is removed on the right side.\n    \"\"\"\n    ref_size: int\n    if isinstance(reference, torch.Tensor):\n        ref_size = reference.size(-1)\n    else:\n        ref_size = reference\n    delta = tensor.size(-1) - ref_size\n    if delta < 0:\n        raise ValueError(\"tensor must be larger than reference. \" f\"Delta is {delta}.\")\n    if delta:","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/utils.py:3"}],[1627,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/utils.py","startLine":64,"endLine":84,"chunkText":"    if delta < 0:\n        raise ValueError(\"tensor must be larger than reference. \" f\"Delta is {delta}.\")\n    if delta:\n        tensor = tensor[..., delta // 2 : -(delta - delta // 2)]\n    return tensor\n\n\ndef pull_metric(history: tp.List[dict], name: str):\n    out = []\n    for metrics in history:\n        metric = metrics\n        for part in name.split(\".\"):\n            metric = metric[part]\n        out.append(metric)\n    return out\n\n\ndef EMA(beta: float = 1):\n    \"\"\"\n    Exponential Moving Average callback.\n    Returns a single function that can be called to repeatidly update the EMA","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/utils.py:4"}],[1628,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/utils.py","startLine":82,"endLine":96,"chunkText":"    \"\"\"\n    Exponential Moving Average callback.\n    Returns a single function that can be called to repeatidly update the EMA\n    with a dict of metrics. The callback will return\n    the new averaged dict of metrics.\n\n    Note that for `beta=1`, this is just plain averaging.\n    \"\"\"\n    fix: tp.Dict[str, float] = defaultdict(float)\n    total: tp.Dict[str, float] = defaultdict(float)\n\n    def _update(metrics: dict, weight: float = 1) -> dict:\n        nonlocal total, fix\n        for key, value in metrics.items():\n            total[key] = total[key] * beta + weight * float(value)","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/utils.py:5"}],[1629,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/utils.py","startLine":94,"endLine":111,"chunkText":"        nonlocal total, fix\n        for key, value in metrics.items():\n            total[key] = total[key] * beta + weight * float(value)\n            fix[key] = fix[key] * beta + weight\n        return {key: tot / fix[key] for key, tot in total.items()}\n\n    return _update\n\n\ndef sizeof_fmt(num: float, suffix: str = \"B\"):\n    \"\"\"\n    Given `num` bytes, return human readable size.\n    Taken from https://stackoverflow.com/a/1094933\n    \"\"\"\n    for unit in [\"\", \"Ki\", \"Mi\", \"Gi\", \"Ti\", \"Pi\", \"Ei\", \"Zi\"]:\n        if abs(num) < 1024.0:\n            return \"%3.1f%s%s\" % (num, unit, suffix)\n        num /= 1024.0","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/utils.py:6"}],[1630,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/utils.py","startLine":109,"endLine":130,"chunkText":"        if abs(num) < 1024.0:\n            return \"%3.1f%s%s\" % (num, unit, suffix)\n        num /= 1024.0\n    return \"%.1f%s%s\" % (num, \"Yi\", suffix)\n\n\n@contextmanager\ndef temp_filenames(count: int, delete=True):\n    names = []\n    try:\n        for _ in range(count):\n            names.append(tempfile.NamedTemporaryFile(delete=False).name)\n        yield names\n    finally:\n        if delete:\n            for name in names:\n                os.unlink(name)\n\n\ndef average_metric(metric, count=1.0):\n    \"\"\"\n    Average `metric` which should be a float across all hosts. `count` should be","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/utils.py:7"}],[1631,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/utils.py","startLine":128,"endLine":143,"chunkText":"def average_metric(metric, count=1.0):\n    \"\"\"\n    Average `metric` which should be a float across all hosts. `count` should be\n    the weight for this particular host (i.e. number of examples).\n    \"\"\"\n    metric = th.tensor([count, count * metric], dtype=th.float32, device=\"cuda\")\n    distributed.all_reduce(metric, op=distributed.ReduceOp.SUM)\n    return metric[1].item() / metric[0].item()\n\n\ndef free_port(host=\"\", low=20000, high=40000):\n    \"\"\"\n    Return a port number that is most likely free.\n    This could suffer from a race condition although\n    it should be quite rare.\n    \"\"\"","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/utils.py:8"}],[1632,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/utils.py","startLine":141,"endLine":162,"chunkText":"    This could suffer from a race condition although\n    it should be quite rare.\n    \"\"\"\n    sock = socket.socket()\n    while True:\n        port = random.randint(low, high)\n        try:\n            sock.bind((host, port))\n        except OSError as error:\n            if error.errno == errno.EADDRINUSE:\n                continue\n            raise\n        return port\n\n\ndef sizeof_fmt(num, suffix=\"B\"):\n    \"\"\"\n    Given `num` bytes, return human readable size.\n    Taken from https://stackoverflow.com/a/1094933\n    \"\"\"\n    for unit in [\"\", \"Ki\", \"Mi\", \"Gi\", \"Ti\", \"Pi\", \"Ei\", \"Zi\"]:\n        if abs(num) < 1024.0","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/utils.py:9"}],[1633,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/utils.py","startLine":160,"endLine":179,"chunkText":"    \"\"\"\n    for unit in [\"\", \"Ki\", \"Mi\", \"Gi\", \"Ti\", \"Pi\", \"Ei\", \"Zi\"]:\n        if abs(num) < 1024.0:\n            return \"%3.1f%s%s\" % (num, unit, suffix)\n        num /= 1024.0\n    return \"%.1f%s%s\" % (num, \"Yi\", suffix)\n\n\ndef human_seconds(seconds, display=\".2f\"):\n    \"\"\"\n    Given `seconds` seconds, return human readable duration.\n    \"\"\"\n    value = seconds * 1e6\n    ratios = [1e3, 1e3, 60, 60, 24]\n    names = [\"us\", \"ms\", \"s\", \"min\", \"hrs\", \"days\"]\n    last = names.pop(0)\n    for name, ratio in zip(names, ratios):\n        if value / ratio < 0.3:\n            break\n        value /= ratio","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/utils.py:10"}],[1634,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/utils.py","startLine":177,"endLine":200,"chunkText":"        if value / ratio < 0.3:\n            break\n        value /= ratio\n        last = name\n    return f\"{format(value, display)} {last}\"\n\n\nclass TensorChunk:\n    def __init__(self, tensor, offset=0, length=None):\n        total_length = tensor.shape[-1]\n        assert offset >= 0\n        assert offset < total_length\n\n        if length is None:\n            length = total_length - offset\n        else:\n            length = min(total_length - offset, length)\n\n        self.tensor = tensor\n        self.offset = offset\n        self.length = length\n        self.device = tensor.device\n\n    @property","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/utils.py:11"}],[1635,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/utils.py","startLine":198,"endLine":219,"chunkText":"        self.device = tensor.device\n\n    @property\n    def shape(self):\n        shape = list(self.tensor.shape)\n        shape[-1] = self.length\n        return shape\n\n    def padded(self, target_length):\n        delta = target_length - self.length\n        total_length = self.tensor.shape[-1]\n        assert delta >= 0\n\n        start = self.offset - delta // 2\n        end = start + target_length\n\n        correct_start = max(0, start)\n        correct_end = min(total_length, end)\n\n        pad_left = correct_start - start\n        pad_right = end - correct_end\n","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/utils.py:12"}],[1636,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/utils.py","startLine":217,"endLine":237,"chunkText":"        pad_left = correct_start - start\n        pad_right = end - correct_end\n\n        out = F.pad(self.tensor[..., correct_start:correct_end], (pad_left, pad_right))\n        assert out.shape[-1] == target_length\n        return out\n\n\ndef tensor_chunk(tensor_or_chunk):\n    if isinstance(tensor_or_chunk, TensorChunk):\n        return tensor_or_chunk\n    else:\n        assert isinstance(tensor_or_chunk, th.Tensor)\n        return TensorChunk(tensor_or_chunk)\n\n\ndef apply_model_v1(model, mix, shifts=None, split=False, progress=False, set_progress_bar=None):\n    \"\"\"\n    Apply model to a given mixture.\n\n    Args:","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/utils.py:13"}],[1637,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/utils.py","startLine":235,"endLine":243,"chunkText":"    Apply model to a given mixture.\n\n    Args:\n        shifts (int): if > 0, will shift in time `mix` by a random amount between 0 and 0.5 sec\n            and apply the oppositve shift to the output. This is repeated `shifts` time and\n            all predictions are averaged. This effectively makes the model time equivariant\n            and improves SDR by up to 0.2 points.\n        split (bool): if True, the input will be broken down in 8 seconds extracts\n            and predictions will be performed individually on each and concatenated.","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/utils.py:14"}],[1638,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/utils.py","startLine":241,"endLine":255,"chunkText":"            and improves SDR by up to 0.2 points.\n        split (bool): if True, the input will be broken down in 8 seconds extracts\n            and predictions will be performed individually on each and concatenated.\n            Useful for model with large memory footprint like Tasnet.\n        progress (bool): if True, show a progress bar (requires split=True)\n    \"\"\"\n\n    channels, length = mix.size()\n    device = mix.device\n    progress_value = 0\n\n    if split:\n        out = th.zeros(4, channels, length, device=device)\n        shift = model.samplerate * 10\n        offsets = range(0, length, shift)","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/utils.py:15"}],[1639,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/utils.py","startLine":253,"endLine":264,"chunkText":"        out = th.zeros(4, channels, length, device=device)\n        shift = model.samplerate * 10\n        offsets = range(0, length, shift)\n        scale = 10\n        if progress:\n            offsets = tqdm.tqdm(offsets, unit_scale=scale, ncols=120, unit=\"seconds\")\n        for offset in offsets:\n            chunk = mix[..., offset : offset + shift]\n            if set_progress_bar:\n                progress_value += 1\n                set_progress_bar(0.1, (0.8 / len(offsets) * progress_value))\n                chunk_out = apply_model_v1(model, chunk, shifts=shifts, set_progress_bar=set_progress_bar)","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/utils.py:16"}],[1640,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/utils.py","startLine":262,"endLine":274,"chunkText":"                progress_value += 1\n                set_progress_bar(0.1, (0.8 / len(offsets) * progress_value))\n                chunk_out = apply_model_v1(model, chunk, shifts=shifts, set_progress_bar=set_progress_bar)\n            else:\n                chunk_out = apply_model_v1(model, chunk, shifts=shifts)\n            out[..., offset : offset + shift] = chunk_out\n            offset += shift\n        return out\n    elif shifts:\n        max_shift = int(model.samplerate / 2)\n        mix = F.pad(mix, (max_shift, max_shift))\n        offsets = list(range(max_shift))\n        random.shuffle(offsets)","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/utils.py:17"}],[1641,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/utils.py","startLine":273,"endLine":286,"chunkText":"        offsets = list(range(max_shift))\n        random.shuffle(offsets)\n        out = 0\n        for offset in offsets[:shifts]:\n            shifted = mix[..., offset : offset + length + max_shift]\n            if set_progress_bar:\n                shifted_out = apply_model_v1(model, shifted, set_progress_bar=set_progress_bar)\n            else:\n                shifted_out = apply_model_v1(model, shifted)\n            out += shifted_out[..., max_shift - offset : max_shift - offset + length]\n        out /= shifts\n        return out\n    else:\n        valid_length = model.valid_length(length)","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/utils.py:18"}],[1642,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/utils.py","startLine":284,"endLine":299,"chunkText":"        return out\n    else:\n        valid_length = model.valid_length(length)\n        delta = valid_length - length\n        padded = F.pad(mix, (delta // 2, delta - delta // 2))\n        with th.no_grad():\n            out = model(padded.unsqueeze(0))[0]\n        return center_trim(out, mix)\n\n\ndef apply_model_v2(model, mix, shifts=None, split=False, overlap=0.25, transition_power=1.0, progress=False, set_progress_bar=None):\n    \"\"\"\n    Apply model to a given mixture.\n\n    Args:\n        shifts (int): if > 0, will shift in time `mix` by a random amount between 0 and 0.5 sec","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/utils.py:19"}],[1643,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/utils.py","startLine":297,"endLine":305,"chunkText":"\n    Args:\n        shifts (int): if > 0, will shift in time `mix` by a random amount between 0 and 0.5 sec\n            and apply the oppositve shift to the output. This is repeated `shifts` time and\n            all predictions are averaged. This effectively makes the model time equivariant\n            and improves SDR by up to 0.2 points.\n        split (bool): if True, the input will be broken down in 8 seconds extracts\n            and predictions will be performed individually on each and concatenated.\n            Useful for model with large memory footprint like Tasnet.","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/utils.py:20"}],[1644,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/utils.py","startLine":303,"endLine":315,"chunkText":"        split (bool): if True, the input will be broken down in 8 seconds extracts\n            and predictions will be performed individually on each and concatenated.\n            Useful for model with large memory footprint like Tasnet.\n        progress (bool): if True, show a progress bar (requires split=True)\n    \"\"\"\n\n    assert transition_power >= 1, \"transition_power < 1 leads to weird behavior.\"\n    device = mix.device\n    channels, length = mix.shape\n    progress_value = 0\n\n    if split:\n        out = th.zeros(len(model.sources), channels, length, device=device)","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/utils.py:21"}],[1645,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/utils.py","startLine":313,"endLine":324,"chunkText":"\n    if split:\n        out = th.zeros(len(model.sources), channels, length, device=device)\n        sum_weight = th.zeros(length, device=device)\n        segment = model.segment_length\n        stride = int((1 - overlap) * segment)\n        offsets = range(0, length, stride)\n        scale = stride / model.samplerate\n        if progress:\n            offsets = tqdm.tqdm(offsets, unit_scale=scale, ncols=120, unit=\"seconds\")\n        # We start from a triangle shaped weight, with maximal weight in the middle\n        # of the segment. Then we normalize and take to the power `transition_power`.","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/utils.py:22"}],[1646,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/utils.py","startLine":322,"endLine":329,"chunkText":"            offsets = tqdm.tqdm(offsets, unit_scale=scale, ncols=120, unit=\"seconds\")\n        # We start from a triangle shaped weight, with maximal weight in the middle\n        # of the segment. Then we normalize and take to the power `transition_power`.\n        # Large values of transition power will lead to sharper transitions.\n        weight = th.cat([th.arange(1, segment // 2 + 1), th.arange(segment - segment // 2, 0, -1)]).to(device)\n        assert len(weight) == segment\n        # If the overlap < 50%, this will translate to linear transition when\n        # transition_power is 1.","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/utils.py:23"}],[1647,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/utils.py","startLine":327,"endLine":337,"chunkText":"        assert len(weight) == segment\n        # If the overlap < 50%, this will translate to linear transition when\n        # transition_power is 1.\n        weight = (weight / weight.max()) ** transition_power\n        for offset in offsets:\n            chunk = TensorChunk(mix, offset, segment)\n            if set_progress_bar:\n                progress_value += 1\n                set_progress_bar(0.1, (0.8 / len(offsets) * progress_value))\n                chunk_out = apply_model_v2(model, chunk, shifts=shifts, set_progress_bar=set_progress_bar)\n            else:","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/utils.py:24"}],[1648,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/utils.py","startLine":335,"endLine":346,"chunkText":"                set_progress_bar(0.1, (0.8 / len(offsets) * progress_value))\n                chunk_out = apply_model_v2(model, chunk, shifts=shifts, set_progress_bar=set_progress_bar)\n            else:\n                chunk_out = apply_model_v2(model, chunk, shifts=shifts)\n            chunk_length = chunk_out.shape[-1]\n            out[..., offset : offset + segment] += weight[:chunk_length] * chunk_out\n            sum_weight[offset : offset + segment] += weight[:chunk_length]\n            offset += segment\n        assert sum_weight.min() > 0\n        out /= sum_weight\n        return out\n    elif shifts:","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/utils.py:25"}],[1649,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/utils.py","startLine":344,"endLine":358,"chunkText":"        out /= sum_weight\n        return out\n    elif shifts:\n        max_shift = int(0.5 * model.samplerate)\n        mix = tensor_chunk(mix)\n        padded_mix = mix.padded(length + 2 * max_shift)\n        out = 0\n        for _ in range(shifts):\n            offset = random.randint(0, max_shift)\n            shifted = TensorChunk(padded_mix, offset, length + max_shift - offset)\n\n            if set_progress_bar:\n                progress_value += 1\n                shifted_out = apply_model_v2(model, shifted, set_progress_bar=set_progress_bar)\n            else:","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/utils.py:26"}],[1650,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/utils.py","startLine":356,"endLine":372,"chunkText":"                progress_value += 1\n                shifted_out = apply_model_v2(model, shifted, set_progress_bar=set_progress_bar)\n            else:\n                shifted_out = apply_model_v2(model, shifted)\n            out += shifted_out[..., max_shift - offset :]\n        out /= shifts\n        return out\n    else:\n        valid_length = model.valid_length(length)\n        mix = tensor_chunk(mix)\n        padded_mix = mix.padded(valid_length)\n        with th.no_grad():\n            out = model(padded_mix.unsqueeze(0))[0]\n        return center_trim(out, length)\n\n\n@contextmanager","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/utils.py:27"}],[1651,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/utils.py","startLine":370,"endLine":391,"chunkText":"\n\n@contextmanager\ndef temp_filenames(count, delete=True):\n    names = []\n    try:\n        for _ in range(count):\n            names.append(tempfile.NamedTemporaryFile(delete=False).name)\n        yield names\n    finally:\n        if delete:\n            for name in names:\n                os.unlink(name)\n\n\ndef get_quantizer(model, args, optimizer=None):\n    quantizer = None\n    if args.diffq:\n        quantizer = DiffQuantizer(model, min_size=args.q_min_size, group_size=8)\n        if optimizer is not None:\n            quantizer.setup_optimizer(optimizer)\n    elif args.qat:","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/utils.py:28"}],[1652,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/utils.py","startLine":389,"endLine":409,"chunkText":"        if optimizer is not None:\n            quantizer.setup_optimizer(optimizer)\n    elif args.qat:\n        quantizer = UniformQuantizer(model, bits=args.qat, min_size=args.q_min_size)\n    return quantizer\n\n\ndef load_model(path, strict=False):\n    with warnings.catch_warnings():\n        warnings.simplefilter(\"ignore\")\n        load_from = path\n        package = th.load(load_from, \"cpu\")\n\n    klass = package[\"klass\"]\n    args = package[\"args\"]\n    kwargs = package[\"kwargs\"]\n\n    if strict:\n        model = klass(*args, **kwargs)\n    else:\n        sig = inspect.signature(klass)","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/utils.py:29"}],[1653,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/utils.py","startLine":408,"endLine":427,"chunkText":"    else:\n        sig = inspect.signature(klass)\n        for key in list(kwargs):\n            if key not in sig.parameters:\n                warnings.warn(\"Dropping inexistant parameter \" + key)\n                del kwargs[key]\n        model = klass(*args, **kwargs)\n\n    state = package[\"state\"]\n    training_args = package[\"training_args\"]\n    quantizer = get_quantizer(model, training_args)\n\n    set_state(model, quantizer, state)\n    return model\n\n\ndef get_state(model, quantizer):\n    if quantizer is None:\n        state = {k: p.data.to(\"cpu\") for k, p in model.state_dict().items()}\n    else:","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/utils.py:30"}],[1654,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/utils.py","startLine":425,"endLine":446,"chunkText":"    if quantizer is None:\n        state = {k: p.data.to(\"cpu\") for k, p in model.state_dict().items()}\n    else:\n        state = quantizer.get_quantized_state()\n        buf = io.BytesIO()\n        th.save(state, buf)\n        state = {\"compressed\": zlib.compress(buf.getvalue())}\n    return state\n\n\ndef set_state(model, quantizer, state):\n    if quantizer is None:\n        model.load_state_dict(state)\n    else:\n        buf = io.BytesIO(zlib.decompress(state[\"compressed\"]))\n        state = th.load(buf, \"cpu\")\n        quantizer.restore_quantized_state(state)\n\n    return state\n\n\ndef save_state(state, path):","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/utils.py:31"}],[1655,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/utils.py","startLine":444,"endLine":464,"chunkText":"def save_state(state, path):\n    buf = io.BytesIO()\n    th.save(state, buf)\n    sig = hashlib.sha256(buf.getvalue()).hexdigest()[:8]\n\n    path = path.parent / (path.stem + \"-\" + sig + path.suffix)\n    path.write_bytes(buf.getvalue())\n\n\ndef save_model(model, quantizer, training_args, path):\n    args, kwargs = model._init_args_kwargs\n    klass = model.__class__\n\n    state = get_state(model, quantizer)\n\n    save_to = path\n    package = {\"klass\": klass, \"args\": args, \"kwargs\": kwargs, \"state\": state, \"training_args\": training_args}\n    th.save(package, save_to)\n\n\ndef capture_init(init):","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/utils.py:32"}],[1656,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/utils.py","startLine":465,"endLine":474,"chunkText":"\ndef capture_init(init):\n    @functools.wraps(init)\n    def __init__(self, *args, **kwargs):\n        self._init_args_kwargs = (args, kwargs)\n        init(self, *args, **kwargs)\n\n    return __init__\n\n","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/utils.py:33"}],[1657,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/utils.py","startLine":475,"endLine":475,"chunkText":"class DummyPoolExecutor:","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/utils.py:34"}],[1658,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/utils.py","startLine":476,"endLine":496,"chunkText":"    class DummyResult:\n        def __init__(self, func, *args, **kwargs):\n            self.func = func\n            self.args = args\n            self.kwargs = kwargs\n\n        def result(self):\n            return self.func(*self.args, **self.kwargs)\n\n    def __init__(self, workers=0):\n        pass\n\n    def submit(self, func, *args, **kwargs):\n        return DummyPoolExecutor.DummyResult(func, *args, **kwargs)\n\n    def __enter__(self):\n        return self\n\n    def __exit__(self, exc_type, exc_value, exc_tb):\n        return\n","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/utils.py:35"}],[1659,{"filePath":"audio_separator/separator/uvr_lib_v5/mdxnet.py","startLine":0,"endLine":18,"chunkText":"import torch\nimport torch.nn as nn\nfrom .modules import TFC_TDF\nfrom pytorch_lightning import LightningModule\n\ndim_s = 4\n\nclass AbstractMDXNet(LightningModule):\n    def __init__(self, target_name, lr, optimizer, dim_c, dim_f, dim_t, n_fft, hop_length, overlap):\n        super().__init__()\n        self.target_name = target_name\n        self.lr = lr\n        self.optimizer = optimizer\n        self.dim_c = dim_c\n        self.dim_f = dim_f\n        self.dim_t = dim_t\n        self.n_fft = n_fft\n        self.n_bins = n_fft // 2 + 1\n        self.hop_length = hop_length","chunkId":"audio_separator/separator/uvr_lib_v5/mdxnet.py:0"}],[1660,{"filePath":"audio_separator/separator/uvr_lib_v5/mdxnet.py","startLine":16,"endLine":28,"chunkText":"        self.n_fft = n_fft\n        self.n_bins = n_fft // 2 + 1\n        self.hop_length = hop_length\n        self.window = nn.Parameter(torch.hann_window(window_length=self.n_fft, periodic=True), requires_grad=False)\n        self.freq_pad = nn.Parameter(torch.zeros([1, dim_c, self.n_bins - self.dim_f, self.dim_t]), requires_grad=False)\n\n    def get_optimizer(self):\n        if self.optimizer == 'rmsprop':\n            return torch.optim.RMSprop(self.parameters(), self.lr)\n        \n        if self.optimizer == 'adamw':\n            return torch.optim.AdamW(self.parameters(), self.lr)\n","chunkId":"audio_separator/separator/uvr_lib_v5/mdxnet.py:1"}],[1661,{"filePath":"audio_separator/separator/uvr_lib_v5/mdxnet.py","startLine":29,"endLine":47,"chunkText":"class ConvTDFNet(AbstractMDXNet):\n    def __init__(self, target_name, lr, optimizer, dim_c, dim_f, dim_t, n_fft, hop_length,\n                 num_blocks, l, g, k, bn, bias, overlap):\n\n        super(ConvTDFNet, self).__init__(\n            target_name, lr, optimizer, dim_c, dim_f, dim_t, n_fft, hop_length, overlap)\n        #self.save_hyperparameters()\n\n        self.num_blocks = num_blocks\n        self.l = l\n        self.g = g\n        self.k = k\n        self.bn = bn\n        self.bias = bias\n\n        if optimizer == 'rmsprop':\n            norm = nn.BatchNorm2d\n            \n        if optimizer == 'adamw':","chunkId":"audio_separator/separator/uvr_lib_v5/mdxnet.py:2"}],[1662,{"filePath":"audio_separator/separator/uvr_lib_v5/mdxnet.py","startLine":45,"endLine":63,"chunkText":"            norm = nn.BatchNorm2d\n            \n        if optimizer == 'adamw':\n            norm = lambda input:nn.GroupNorm(2, input)\n            \n        self.n = num_blocks // 2\n        scale = (2, 2)\n\n        self.first_conv = nn.Sequential(\n            nn.Conv2d(in_channels=self.dim_c, out_channels=g, kernel_size=(1, 1)),\n            norm(g),\n            nn.ReLU(),\n        )\n\n        f = self.dim_f\n        c = g\n        self.encoding_blocks = nn.ModuleList()\n        self.ds = nn.ModuleList()\n        for i in range(self.n):","chunkId":"audio_separator/separator/uvr_lib_v5/mdxnet.py:3"}],[1663,{"filePath":"audio_separator/separator/uvr_lib_v5/mdxnet.py","startLine":61,"endLine":76,"chunkText":"        self.encoding_blocks = nn.ModuleList()\n        self.ds = nn.ModuleList()\n        for i in range(self.n):\n            self.encoding_blocks.append(TFC_TDF(c, l, f, k, bn, bias=bias, norm=norm))\n            self.ds.append(\n                nn.Sequential(\n                    nn.Conv2d(in_channels=c, out_channels=c + g, kernel_size=scale, stride=scale),\n                    norm(c + g),\n                    nn.ReLU()\n                )\n            )\n            f = f // 2\n            c += g\n\n        self.bottleneck_block = TFC_TDF(c, l, f, k, bn, bias=bias, norm=norm)\n","chunkId":"audio_separator/separator/uvr_lib_v5/mdxnet.py:4"}],[1664,{"filePath":"audio_separator/separator/uvr_lib_v5/mdxnet.py","startLine":74,"endLine":91,"chunkText":"\n        self.bottleneck_block = TFC_TDF(c, l, f, k, bn, bias=bias, norm=norm)\n\n        self.decoding_blocks = nn.ModuleList()\n        self.us = nn.ModuleList()\n        for i in range(self.n):\n            self.us.append(\n                nn.Sequential(\n                    nn.ConvTranspose2d(in_channels=c, out_channels=c - g, kernel_size=scale, stride=scale),\n                    norm(c - g),\n                    nn.ReLU()\n                )\n            )\n            f = f * 2\n            c -= g\n\n            self.decoding_blocks.append(TFC_TDF(c, l, f, k, bn, bias=bias, norm=norm))\n","chunkId":"audio_separator/separator/uvr_lib_v5/mdxnet.py:5"}],[1665,{"filePath":"audio_separator/separator/uvr_lib_v5/mdxnet.py","startLine":89,"endLine":111,"chunkText":"            self.decoding_blocks.append(TFC_TDF(c, l, f, k, bn, bias=bias, norm=norm))\n\n        self.final_conv = nn.Sequential(\n            nn.Conv2d(in_channels=c, out_channels=self.dim_c, kernel_size=(1, 1)),\n        )\n\n    def forward(self, x):\n\n        x = self.first_conv(x)\n\n        x = x.transpose(-1, -2)\n\n        ds_outputs = []\n        for i in range(self.n):\n            x = self.encoding_blocks[i](x)\n            ds_outputs.append(x)\n            x = self.ds[i](x)\n\n        x = self.bottleneck_block(x)\n\n        for i in range(self.n):\n            x = self.us[i](x)\n            x *= ds_outputs[-i - 1","chunkId":"audio_separator/separator/uvr_lib_v5/mdxnet.py:6"}],[1666,{"filePath":"audio_separator/separator/uvr_lib_v5/mdxnet.py","startLine":110,"endLine":120,"chunkText":"        for i in range(self.n):\n            x = self.us[i](x)\n            x *= ds_outputs[-i - 1]\n            x = self.decoding_blocks[i](x)\n\n        x = x.transpose(-1, -2)\n\n        x = self.final_conv(x)\n\n        return x\n    ","chunkId":"audio_separator/separator/uvr_lib_v5/mdxnet.py:7"}],[1667,{"filePath":"audio_separator/separator/uvr_lib_v5/mdxnet.py","startLine":121,"endLine":135,"chunkText":"class Mixer(nn.Module):\n    def __init__(self, device, mixer_path):\n        \n        super(Mixer, self).__init__()\n        \n        self.linear = nn.Linear((dim_s+1)*2, dim_s*2, bias=False)\n        \n        self.load_state_dict(\n            torch.load(mixer_path, map_location=device)\n        )\n\n    def forward(self, x):\n        x = x.reshape(1,(dim_s+1)*2,-1).transpose(-1,-2)\n        x = self.linear(x)\n        return x.transpose(-1,-2).reshape(dim_s,2,-1)","chunkId":"audio_separator/separator/uvr_lib_v5/mdxnet.py:8"}],[1668,{"filePath":"audio_separator/separator/uvr_lib_v5/modules.py","startLine":0,"endLine":23,"chunkText":"import torch\nimport torch.nn as nn\n\n\nclass TFC(nn.Module):\n    def __init__(self, c, l, k, norm):\n        super(TFC, self).__init__()\n\n        self.H = nn.ModuleList()\n        for i in range(l):\n            self.H.append(\n                nn.Sequential(\n                    nn.Conv2d(in_channels=c, out_channels=c, kernel_size=k, stride=1, padding=k // 2),\n                    norm(c),\n                    nn.ReLU(),\n                )\n            )\n\n    def forward(self, x):\n        for h in self.H:\n            x = h(x)\n        return x\n\n","chunkId":"audio_separator/separator/uvr_lib_v5/modules.py:0"}],[1669,{"filePath":"audio_separator/separator/uvr_lib_v5/modules.py","startLine":24,"endLine":43,"chunkText":"class DenseTFC(nn.Module):\n    def __init__(self, c, l, k, norm):\n        super(DenseTFC, self).__init__()\n\n        self.conv = nn.ModuleList()\n        for i in range(l):\n            self.conv.append(\n                nn.Sequential(\n                    nn.Conv2d(in_channels=c, out_channels=c, kernel_size=k, stride=1, padding=k // 2),\n                    norm(c),\n                    nn.ReLU(),\n                )\n            )\n\n    def forward(self, x):\n        for layer in self.conv[:-1]:\n            x = torch.cat([layer(x), x], 1)\n        return self.conv[-1](x)\n\n","chunkId":"audio_separator/separator/uvr_lib_v5/modules.py:1"}],[1670,{"filePath":"audio_separator/separator/uvr_lib_v5/modules.py","startLine":44,"endLine":62,"chunkText":"class TFC_TDF(nn.Module):\n    def __init__(self, c, l, f, k, bn, dense=False, bias=True, norm=nn.BatchNorm2d):\n\n        super(TFC_TDF, self).__init__()\n\n        self.use_tdf = bn is not None\n\n        self.tfc = DenseTFC(c, l, k, norm) if dense else TFC(c, l, k, norm)\n\n        if self.use_tdf:\n            if bn == 0:\n                self.tdf = nn.Sequential(\n                    nn.Linear(f, f, bias=bias),\n                    norm(c),\n                    nn.ReLU()\n                )\n            else:\n                self.tdf = nn.Sequential(\n                    nn.Linear(f, f // bn, bias=bias),","chunkId":"audio_separator/separator/uvr_lib_v5/modules.py:2"}],[1671,{"filePath":"audio_separator/separator/uvr_lib_v5/modules.py","startLine":60,"endLine":74,"chunkText":"            else:\n                self.tdf = nn.Sequential(\n                    nn.Linear(f, f // bn, bias=bias),\n                    norm(c),\n                    nn.ReLU(),\n                    nn.Linear(f // bn, f, bias=bias),\n                    norm(c),\n                    nn.ReLU()\n                )\n\n    def forward(self, x):\n        x = self.tfc(x)\n        return x + self.tdf(x) if self.use_tdf else x\n\n","chunkId":"audio_separator/separator/uvr_lib_v5/modules.py:3"}],[1672,{"filePath":"audio_separator/separator/uvr_lib_v5/playsound.py","startLine":0,"endLine":20,"chunkText":"import logging\nlogger = logging.getLogger(__name__)\n\nclass PlaysoundException(Exception):\n    pass\n\ndef _canonicalizePath(path):\n    \"\"\"\n    Support passing in a pathlib.Path-like object by converting to str.\n    \"\"\"\n    import sys\n    if sys.version_info[0] >= 3:\n        return str(path)\n    else:\n        # On earlier Python versions, str is a byte string, so attempting to\n        # convert a unicode string to str will fail. Leave it alone in this case.\n        return path\n\ndef _playsoundWin(sound, block = True):\n    '''\n    Utilizes windll.winmm. Tested and known to work with MP3 and WAVE on","chunkId":"audio_separator/separator/uvr_lib_v5/playsound.py:0"}],[1673,{"filePath":"audio_separator/separator/uvr_lib_v5/playsound.py","startLine":18,"endLine":31,"chunkText":"def _playsoundWin(sound, block = True):\n    '''\n    Utilizes windll.winmm. Tested and known to work with MP3 and WAVE on\n    Windows 7 with Python 2.7. Probably works with more file formats.\n    Probably works on Windows XP thru Windows 10. Probably works with all\n    versions of Python.\n\n    Inspired by (but not copied from) Michael Gundlach <gundlach@gmail.com>'s mp3play:\n    https://github.com/michaelgundlach/mp3play\n\n    I never would have tried using windll.winmm without seeing his code.\n    '''\n    sound = '\"' + _canonicalizePath(sound) + '\"'\n","chunkId":"audio_separator/separator/uvr_lib_v5/playsound.py:1"}],[1674,{"filePath":"audio_separator/separator/uvr_lib_v5/playsound.py","startLine":29,"endLine":41,"chunkText":"    '''\n    sound = '\"' + _canonicalizePath(sound) + '\"'\n\n    from ctypes import create_unicode_buffer, windll, wintypes\n    windll.winmm.mciSendStringW.argtypes = [wintypes.LPCWSTR, wintypes.LPWSTR, wintypes.UINT, wintypes.HANDLE]\n    windll.winmm.mciGetErrorStringW.argtypes = [wintypes.DWORD, wintypes.LPWSTR, wintypes.UINT]\n\n    def winCommand(*command):\n        bufLen = 600\n        buf = create_unicode_buffer(bufLen)\n        command = ' '.join(command)\n        errorCode = int(windll.winmm.mciSendStringW(command, buf, bufLen - 1, 0))  # use widestring version of the function\n        if errorCode:","chunkId":"audio_separator/separator/uvr_lib_v5/playsound.py:2"}],[1675,{"filePath":"audio_separator/separator/uvr_lib_v5/playsound.py","startLine":39,"endLine":47,"chunkText":"        command = ' '.join(command)\n        errorCode = int(windll.winmm.mciSendStringW(command, buf, bufLen - 1, 0))  # use widestring version of the function\n        if errorCode:\n            errorBuffer = create_unicode_buffer(bufLen)\n            windll.winmm.mciGetErrorStringW(errorCode, errorBuffer, bufLen - 1)  # use widestring version of the function\n            exceptionMessage = ('\\n    Error ' + str(errorCode) + ' for command:'\n                                '\\n        ' + command +\n                                '\\n    ' + errorBuffer.value)\n            logger.error(exceptionMessage)","chunkId":"audio_separator/separator/uvr_lib_v5/playsound.py:3"}],[1676,{"filePath":"audio_separator/separator/uvr_lib_v5/playsound.py","startLine":45,"endLine":59,"chunkText":"                                '\\n        ' + command +\n                                '\\n    ' + errorBuffer.value)\n            logger.error(exceptionMessage)\n            raise PlaysoundException(exceptionMessage)\n        return buf.value\n\n    try:\n        logger.debug('Starting')\n        winCommand(u'open {}'.format(sound))\n        winCommand(u'play {}{}'.format(sound, ' wait' if block else ''))\n        logger.debug('Returning')\n    finally:\n        try:\n            winCommand(u'close {}'.format(sound))\n        except PlaysoundException:","chunkId":"audio_separator/separator/uvr_lib_v5/playsound.py:4"}],[1677,{"filePath":"audio_separator/separator/uvr_lib_v5/playsound.py","startLine":57,"endLine":76,"chunkText":"        try:\n            winCommand(u'close {}'.format(sound))\n        except PlaysoundException:\n            logger.warning(u'Failed to close the file: {}'.format(sound))\n            # If it fails, there's nothing more that can be done...\n            pass\n\ndef _handlePathOSX(sound):\n    sound = _canonicalizePath(sound)\n\n    if '://' not in sound:\n        if not sound.startswith('/'):\n            from os import getcwd\n            sound = getcwd() + '/' + sound\n        sound = 'file://' + sound\n\n    try:\n        # Don't double-encode it.\n        sound.encode('ascii')\n        return sound.replace(' ', '%20'","chunkId":"audio_separator/separator/uvr_lib_v5/playsound.py:5"}],[1678,{"filePath":"audio_separator/separator/uvr_lib_v5/playsound.py","startLine":74,"endLine":89,"chunkText":"        # Don't double-encode it.\n        sound.encode('ascii')\n        return sound.replace(' ', '%20')\n    except UnicodeEncodeError:\n        try:\n            from urllib.parse import quote  # Try the Python 3 import first...\n        except ImportError:\n            from urllib import quote  # Try using the Python 2 import before giving up entirely...\n\n        parts = sound.split('://', 1)\n        return parts[0] + '://' + quote(parts[1].encode('utf-8')).replace(' ', '%20')\n\n\ndef _playsoundOSX(sound, block = True):\n    '''\n    Utilizes AppKit.NSSound. Tested and known to work with MP3 and WAVE on","chunkId":"audio_separator/separator/uvr_lib_v5/playsound.py:6"}],[1679,{"filePath":"audio_separator/separator/uvr_lib_v5/playsound.py","startLine":87,"endLine":101,"chunkText":"def _playsoundOSX(sound, block = True):\n    '''\n    Utilizes AppKit.NSSound. Tested and known to work with MP3 and WAVE on\n    OS X 10.11 with Python 2.7. Probably works with anything QuickTime supports.\n    Probably works on OS X 10.5 and newer. Probably works with all versions of\n    Python.\n\n    Inspired by (but not copied from) Aaron's Stack Overflow answer here:\n    http://stackoverflow.com/a/34568298/901641\n\n    I never would have tried using AppKit.NSSound without seeing his code.\n    '''\n    try:\n        from AppKit import NSSound\n    except ImportError:","chunkId":"audio_separator/separator/uvr_lib_v5/playsound.py:7"}],[1680,{"filePath":"audio_separator/separator/uvr_lib_v5/playsound.py","startLine":99,"endLine":114,"chunkText":"    try:\n        from AppKit import NSSound\n    except ImportError:\n        logger.warning(\"playsound could not find a copy of AppKit - falling back to using macOS's system copy.\")\n        sys.path.append('/System/Library/Frameworks/Python.framework/Versions/2.7/Extras/lib/python/PyObjC')\n        from AppKit import NSSound\n\n    from Foundation import NSURL\n    from time       import sleep\n\n    sound = _handlePathOSX(sound)\n    url   = NSURL.URLWithString_(sound)\n    if not url:\n        raise PlaysoundException('Cannot find a sound with filename: ' + sound)\n\n    for i in range(5):","chunkId":"audio_separator/separator/uvr_lib_v5/playsound.py:8"}],[1681,{"filePath":"audio_separator/separator/uvr_lib_v5/playsound.py","startLine":112,"endLine":130,"chunkText":"        raise PlaysoundException('Cannot find a sound with filename: ' + sound)\n\n    for i in range(5):\n        nssound = NSSound.alloc().initWithContentsOfURL_byReference_(url, True)\n        if nssound:\n            break\n        else:\n            logger.debug('Failed to load sound, although url was good... ' + sound)\n    else:\n        raise PlaysoundException('Could not load sound with filename, although URL was good... ' + sound)\n    nssound.play()\n\n    if block:\n        sleep(nssound.duration())\n\ndef _playsoundNix(sound, block = True):\n    \"\"\"Play a sound using GStreamer.\n\n    Inspired by this:","chunkId":"audio_separator/separator/uvr_lib_v5/playsound.py:9"}],[1682,{"filePath":"audio_separator/separator/uvr_lib_v5/playsound.py","startLine":128,"endLine":149,"chunkText":"    \"\"\"Play a sound using GStreamer.\n\n    Inspired by this:\n    https://gstreamer.freedesktop.org/documentation/tutorials/playback/playbin-usage.html\n    \"\"\"\n    sound = _canonicalizePath(sound)\n\n    # pathname2url escapes non-URL-safe characters\n    from os.path import abspath, exists\n    try:\n        from urllib.request import pathname2url\n    except ImportError:\n        # python 2\n        from urllib import pathname2url\n\n    import gi\n    gi.require_version('Gst', '1.0')\n    from gi.repository import Gst\n\n    Gst.init(None)\n\n    playbin = Gst.ElementFactory.make('playbin', 'playbin')","chunkId":"audio_separator/separator/uvr_lib_v5/playsound.py:10"}],[1683,{"filePath":"audio_separator/separator/uvr_lib_v5/playsound.py","startLine":147,"endLine":163,"chunkText":"    Gst.init(None)\n\n    playbin = Gst.ElementFactory.make('playbin', 'playbin')\n    if sound.startswith(('http://', 'https://')):\n        playbin.props.uri = sound\n    else:\n        path = abspath(sound)\n        if not exists(path):\n            raise PlaysoundException(u'File not found: {}'.format(path))\n        playbin.props.uri = 'file://' + pathname2url(path)\n\n\n    set_result = playbin.set_state(Gst.State.PLAYING)\n    if set_result != Gst.StateChangeReturn.ASYNC:\n        raise PlaysoundException(\n            \"playbin.set_state returned \" + repr(set_result))\n","chunkId":"audio_separator/separator/uvr_lib_v5/playsound.py:11"}],[1684,{"filePath":"audio_separator/separator/uvr_lib_v5/playsound.py","startLine":161,"endLine":177,"chunkText":"        raise PlaysoundException(\n            \"playbin.set_state returned \" + repr(set_result))\n\n    # FIXME: use some other bus method than poll() with block=False\n    # https://lazka.github.io/pgi-docs/#Gst-1.0/classes/Bus.html\n    logger.debug('Starting play')\n    if block:\n        bus = playbin.get_bus()\n        try:\n            bus.poll(Gst.MessageType.EOS, Gst.CLOCK_TIME_NONE)\n        finally:\n            playbin.set_state(Gst.State.NULL)\n            \n    logger.debug('Finishing play')\n\ndef _playsoundAnotherPython(otherPython, sound, block = True, macOS = False):\n    '''","chunkId":"audio_separator/separator/uvr_lib_v5/playsound.py:12"}],[1685,{"filePath":"audio_separator/separator/uvr_lib_v5/playsound.py","startLine":175,"endLine":187,"chunkText":"\ndef _playsoundAnotherPython(otherPython, sound, block = True, macOS = False):\n    '''\n    Mostly written so that when this is run on python3 on macOS, it can invoke\n    python2 on macOS... but maybe this idea could be useful on linux, too.\n    '''\n    from inspect    import getsourcefile\n    from os.path    import abspath, exists\n    from subprocess import check_call\n    from threading  import Thread\n\n    sound = _canonicalizePath(sound)\n","chunkId":"audio_separator/separator/uvr_lib_v5/playsound.py:13"}],[1686,{"filePath":"audio_separator/separator/uvr_lib_v5/playsound.py","startLine":188,"endLine":206,"chunkText":"    class PropogatingThread(Thread):\n        def run(self):\n            self.exc = None\n            try:\n                self.ret = self._target(*self._args, **self._kwargs)\n            except BaseException as e:\n                self.exc = e\n\n        def join(self, timeout = None):\n            super().join(timeout)\n            if self.exc:\n                raise self.exc\n            return self.ret\n\n    # Check if the file exists...\n    if not exists(abspath(sound)):\n        raise PlaysoundException('Cannot find a sound with filename: ' + sound)\n\n    playsoundPath = abspath(getsourcefile(lambda: 0))","chunkId":"audio_separator/separator/uvr_lib_v5/playsound.py:14"}],[1687,{"filePath":"audio_separator/separator/uvr_lib_v5/playsound.py","startLine":204,"endLine":223,"chunkText":"        raise PlaysoundException('Cannot find a sound with filename: ' + sound)\n\n    playsoundPath = abspath(getsourcefile(lambda: 0))\n    t = PropogatingThread(target = lambda: check_call([otherPython, playsoundPath, _handlePathOSX(sound) if macOS else sound]))\n    t.start()\n    if block:\n        t.join()\n\nfrom platform import system\nsystem = system()\n\nif system == 'Windows':\n    playsound_func = _playsoundWin\nelif system == 'Darwin':\n    playsound_func = _playsoundOSX\n    import sys\n    if sys.version_info[0] > 2:\n        try:\n            from AppKit import NSSound\n        except ImportError:","chunkId":"audio_separator/separator/uvr_lib_v5/playsound.py:15"}],[1688,{"filePath":"audio_separator/separator/uvr_lib_v5/playsound.py","startLine":221,"endLine":230,"chunkText":"        try:\n            from AppKit import NSSound\n        except ImportError:\n            logger.warning(\"playsound is relying on a python 2 subprocess. Please use `pip3 install PyObjC` if you want playsound to run more efficiently.\")\n            playsound_func = lambda sound, block = True: _playsoundAnotherPython('/System/Library/Frameworks/Python.framework/Versions/2.7/bin/python', sound, block, macOS = True)\nelse:\n    playsound_func = _playsoundNix\n    if __name__ != '__main__':  # Ensure we don't infinitely recurse trying to get another python instance.\n        try:\n            import gi","chunkId":"audio_separator/separator/uvr_lib_v5/playsound.py:16"}],[1689,{"filePath":"audio_separator/separator/uvr_lib_v5/playsound.py","startLine":228,"endLine":240,"chunkText":"    if __name__ != '__main__':  # Ensure we don't infinitely recurse trying to get another python instance.\n        try:\n            import gi\n            gi.require_version('Gst', '1.0')\n            from gi.repository import Gst\n        except:\n            logger.warning(\"playsound is relying on another python subprocess. Please use `pip install pygobject` if you want playsound to run more efficiently.\")\n            playsound_func = lambda sound, block = True: _playsoundAnotherPython('/usr/bin/python3', sound, block, macOS = False)\n\ndel system\n\ndef play(audio_filepath):\n    playsound_func(audio_filepath)","chunkId":"audio_separator/separator/uvr_lib_v5/playsound.py:17"}],[1690,{"filePath":"audio_separator/separator/uvr_lib_v5/pyrb.py","startLine":0,"endLine":28,"chunkText":"import os\nimport subprocess\nimport tempfile\nimport six\nimport numpy as np\nimport soundfile as sf\nimport sys\n\nif getattr(sys, 'frozen', False):\n    BASE_PATH_RUB = sys._MEIPASS\nelse:\n    BASE_PATH_RUB = os.path.dirname(os.path.abspath(__file__))\n\n__all__ = ['time_stretch', 'pitch_shift']\n\n__RUBBERBAND_UTIL = os.path.join(BASE_PATH_RUB, 'rubberband')\n\nif six.PY2:\n    DEVNULL = open(os.devnull, 'w')\nelse:\n    DEVNULL = subprocess.DEVNULL\n\ndef __rubberband(y, sr, **kwargs):\n\n    assert sr > 0\n\n    # Get the input and output tempfile\n    fd, infile = tempfile.mkstemp(suffix='.wav')\n    os.close(fd)","chunkId":"audio_separator/separator/uvr_lib_v5/pyrb.py:0"}],[1691,{"filePath":"audio_separator/separator/uvr_lib_v5/pyrb.py","startLine":26,"endLine":47,"chunkText":"    # Get the input and output tempfile\n    fd, infile = tempfile.mkstemp(suffix='.wav')\n    os.close(fd)\n    fd, outfile = tempfile.mkstemp(suffix='.wav')\n    os.close(fd)\n\n    # dump the audio\n    sf.write(infile, y, sr)\n\n    try:\n        # Execute rubberband\n        arguments = [__RUBBERBAND_UTIL, '-q']\n\n        for key, value in six.iteritems(kwargs):\n            arguments.append(str(key))\n            arguments.append(str(value))\n\n        arguments.extend([infile, outfile])\n\n        subprocess.check_call(arguments, stdout=DEVNULL, stderr=DEVNULL)\n\n        # Load the processed audio.","chunkId":"audio_separator/separator/uvr_lib_v5/pyrb.py:1"}],[1692,{"filePath":"audio_separator/separator/uvr_lib_v5/pyrb.py","startLine":45,"endLine":62,"chunkText":"        subprocess.check_call(arguments, stdout=DEVNULL, stderr=DEVNULL)\n\n        # Load the processed audio.\n        y_out, _ = sf.read(outfile, always_2d=True)\n\n        # make sure that output dimensions matches input\n        if y.ndim == 1:\n            y_out = np.squeeze(y_out)\n\n    except OSError as exc:\n        six.raise_from(RuntimeError('Failed to execute rubberband. '\n                                    'Please verify that rubberband-cli '\n                                    'is installed.'),\n                       exc)\n\n    finally:\n        # Remove temp files\n        os.unlink(infile)","chunkId":"audio_separator/separator/uvr_lib_v5/pyrb.py:2"}],[1693,{"filePath":"audio_separator/separator/uvr_lib_v5/pyrb.py","startLine":60,"endLine":90,"chunkText":"    finally:\n        # Remove temp files\n        os.unlink(infile)\n        os.unlink(outfile)\n\n    return y_out\n\ndef time_stretch(y, sr, rate, rbargs=None):\n    if rate <= 0:\n        raise ValueError('rate must be strictly positive')\n\n    if rate == 1.0:\n        return y\n\n    if rbargs is None:\n        rbargs = dict()\n\n    rbargs.setdefault('--tempo', rate)\n\n    return __rubberband(y, sr, **rbargs)\n\ndef pitch_shift(y, sr, n_steps, rbargs=None):\n\n    if n_steps == 0:\n        return y\n\n    if rbargs is None:\n        rbargs = dict()\n\n    rbargs.setdefault('--pitch', n_steps)\n","chunkId":"audio_separator/separator/uvr_lib_v5/pyrb.py:3"}],[1694,{"filePath":"audio_separator/separator/uvr_lib_v5/pyrb.py","startLine":88,"endLine":92,"chunkText":"\n    rbargs.setdefault('--pitch', n_steps)\n\n    return __rubberband(y, sr, **rbargs)\n","chunkId":"audio_separator/separator/uvr_lib_v5/pyrb.py:4"}],[1695,{"filePath":"audio_separator/separator/uvr_lib_v5/results.py","startLine":0,"endLine":15,"chunkText":"# -*- coding: utf-8 -*-\n\n\"\"\"\nMatchering - Audio Matching and Mastering Python Library\nCopyright (C) 2016-2022 Sergree\n\nThis program is free software: you can redistribute it and/or modify\nit under the terms of the GNU General Public License as published by\nthe Free Software Foundation, either version 3 of the License, or\n(at your option) any later version.\n\nThis program is distributed in the hope that it will be useful,\nbut WITHOUT ANY WARRANTY; without even the implied warranty of\nMERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the\nGNU General Public License for more details.\n","chunkId":"audio_separator/separator/uvr_lib_v5/results.py:0"}],[1696,{"filePath":"audio_separator/separator/uvr_lib_v5/results.py","startLine":13,"endLine":31,"chunkText":"MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the\nGNU General Public License for more details.\n\nYou should have received a copy of the GNU General Public License\nalong with this program.  If not, see <https://www.gnu.org/licenses/>.\n\"\"\"\n\nimport os\nimport soundfile as sf\n\n\nclass Result:\n    def __init__(\n        self, file: str, subtype: str, use_limiter: bool = True, normalize: bool = True\n    ):\n        _, file_ext = os.path.splitext(file)\n        file_ext = file_ext[1:].upper()\n        if not sf.check_format(file_ext):\n            raise TypeError(f\"{file_ext} format is not supported\")","chunkId":"audio_separator/separator/uvr_lib_v5/results.py:1"}],[1697,{"filePath":"audio_separator/separator/uvr_lib_v5/results.py","startLine":29,"endLine":46,"chunkText":"        file_ext = file_ext[1:].upper()\n        if not sf.check_format(file_ext):\n            raise TypeError(f\"{file_ext} format is not supported\")\n        if not sf.check_format(file_ext, subtype):\n            raise TypeError(f\"{file_ext} format does not have {subtype} subtype\")\n        self.file = file\n        self.subtype = subtype\n        self.use_limiter = use_limiter\n        self.normalize = normalize\n\n\ndef pcm16(file: str) -> Result:\n    return Result(file, \"PCM_16\")\n\ndef pcm24(file: str) -> Result:\n    return Result(file, \"FLOAT\")\n\ndef save_audiofile(file: str, wav_set=\"PCM_16\") -> Result:","chunkId":"audio_separator/separator/uvr_lib_v5/results.py:2"}],[1698,{"filePath":"audio_separator/separator/uvr_lib_v5/results.py","startLine":44,"endLine":48,"chunkText":"    return Result(file, \"FLOAT\")\n\ndef save_audiofile(file: str, wav_set=\"PCM_16\") -> Result:\n    return Result(file, wav_set)\n","chunkId":"audio_separator/separator/uvr_lib_v5/results.py:3"}],[1699,{"filePath":"audio_separator/separator/uvr_lib_v5/roformer/attend.py","startLine":0,"endLine":36,"chunkText":"from functools import wraps\nfrom packaging import version\nfrom collections import namedtuple\n\nimport torch\nfrom torch import nn, einsum\nimport torch.nn.functional as F\n\nfrom einops import rearrange, reduce\n\n# constants\n\nFlashAttentionConfig = namedtuple(\"FlashAttentionConfig\", [\"enable_flash\", \"enable_math\", \"enable_mem_efficient\"])\n\n# helpers\n\n\ndef exists(val):\n    return val is not None\n\n\ndef once(fn):\n    called = False\n\n    @wraps(fn)\n    def inner(x):\n        nonlocal called\n        if called:\n            return\n        called = True\n        return fn(x)\n\n    return inner\n\n\nprint_once = once(print)\n","chunkId":"audio_separator/separator/uvr_lib_v5/roformer/attend.py:0"}],[1700,{"filePath":"audio_separator/separator/uvr_lib_v5/roformer/attend.py","startLine":37,"endLine":54,"chunkText":"# main class\n\n\nclass Attend(nn.Module):\n    def __init__(self, dropout=0.0, flash=False):\n        super().__init__()\n        self.dropout = dropout\n        self.attn_dropout = nn.Dropout(dropout)\n\n        self.flash = flash\n        assert not (flash and version.parse(torch.__version__) < version.parse(\"2.0.0\")), \"in order to use flash attention, you must be using pytorch 2.0 or above\"\n\n        # determine efficient attention configs for cuda and cpu\n\n        self.cpu_config = FlashAttentionConfig(True, True, True)\n        self.cuda_config = None\n\n        if not torch.cuda.is_available() or not flash:","chunkId":"audio_separator/separator/uvr_lib_v5/roformer/attend.py:1"}],[1701,{"filePath":"audio_separator/separator/uvr_lib_v5/roformer/attend.py","startLine":52,"endLine":65,"chunkText":"        self.cuda_config = None\n\n        if not torch.cuda.is_available() or not flash:\n            return\n\n        device_properties = torch.cuda.get_device_properties(torch.device(\"cuda\"))\n\n        if device_properties.major == 8 and device_properties.minor == 0:\n            print_once(\"A100 GPU detected, using flash attention if input tensor is on cuda\")\n            self.cuda_config = FlashAttentionConfig(True, False, False)\n        else:\n            self.cuda_config = FlashAttentionConfig(False, True, True)\n\n    def flash_attn(self, q, k, v):","chunkId":"audio_separator/separator/uvr_lib_v5/roformer/attend.py:2"}],[1702,{"filePath":"audio_separator/separator/uvr_lib_v5/roformer/attend.py","startLine":63,"endLine":76,"chunkText":"            self.cuda_config = FlashAttentionConfig(False, True, True)\n\n    def flash_attn(self, q, k, v):\n        _, heads, q_len, _, k_len, is_cuda, device = *q.shape, k.shape[-2], q.is_cuda, q.device\n\n        # Check if there is a compatible device for flash attention\n\n        config = self.cuda_config if is_cuda else self.cpu_config\n\n        # sdpa_flash kernel only supports float16 on sm80+ architecture gpu\n        if is_cuda and q.dtype != torch.float16:\n            config = FlashAttentionConfig(False, True, True)\n\n        # pytorch 2.0 flash attn: q, k, v, mask, dropout, softmax_scale","chunkId":"audio_separator/separator/uvr_lib_v5/roformer/attend.py:3"}],[1703,{"filePath":"audio_separator/separator/uvr_lib_v5/roformer/attend.py","startLine":74,"endLine":90,"chunkText":"            config = FlashAttentionConfig(False, True, True)\n\n        # pytorch 2.0 flash attn: q, k, v, mask, dropout, softmax_scale\n        with torch.backends.cuda.sdp_kernel(**config._asdict()):\n            out = F.scaled_dot_product_attention(q, k, v, dropout_p=self.dropout if self.training else 0.0)\n\n        return out\n\n    def forward(self, q, k, v):\n        \"\"\"\n        einstein notation\n        b - batch\n        h - heads\n        n, i, j - sequence length (base sequence length, source, target)\n        d - feature dimension\n        \"\"\"\n","chunkId":"audio_separator/separator/uvr_lib_v5/roformer/attend.py:4"}],[1704,{"filePath":"audio_separator/separator/uvr_lib_v5/roformer/attend.py","startLine":88,"endLine":112,"chunkText":"        d - feature dimension\n        \"\"\"\n\n        q_len, k_len, device = q.shape[-2], k.shape[-2], q.device\n\n        scale = q.shape[-1] ** -0.5\n\n        if self.flash:\n            return self.flash_attn(q, k, v)\n\n        # similarity\n\n        sim = einsum(f\"b h i d, b h j d -> b h i j\", q, k) * scale\n\n        # attention\n\n        attn = sim.softmax(dim=-1)\n        attn = self.attn_dropout(attn)\n\n        # aggregate values\n\n        out = einsum(f\"b h i j, b h j d -> b h i d\", attn, v)\n\n        return out\n","chunkId":"audio_separator/separator/uvr_lib_v5/roformer/attend.py:5"}],[1705,{"filePath":"audio_separator/separator/uvr_lib_v5/roformer/bs_roformer.py","startLine":0,"endLine":31,"chunkText":"from functools import partial\n\nimport torch\nfrom torch import nn, einsum, Tensor\nfrom torch.nn import Module, ModuleList\nimport torch.nn.functional as F\n\nfrom .attend import Attend\n\nfrom beartype.typing import Tuple, Optional, List, Callable\nfrom beartype import beartype\n\nfrom rotary_embedding_torch import RotaryEmbedding\n\nfrom einops import rearrange, pack, unpack\nfrom einops.layers.torch import Rearrange\n\n# helper functions\n\n\ndef exists(val):\n    return val is not None\n\n\ndef default(v, d):\n    return v if exists(v) else d\n\n\ndef pack_one(t, pattern):\n    return pack([t], pattern)\n\n","chunkId":"audio_separator/separator/uvr_lib_v5/roformer/bs_roformer.py:0"}],[1706,{"filePath":"audio_separator/separator/uvr_lib_v5/roformer/bs_roformer.py","startLine":30,"endLine":56,"chunkText":"\n\ndef unpack_one(t, ps, pattern):\n    return unpack(t, ps, pattern)[0]\n\n\n# norm\n\n\ndef l2norm(t):\n    return F.normalize(t, dim=-1, p=2)\n\n\nclass RMSNorm(Module):\n    def __init__(self, dim):\n        super().__init__()\n        self.scale = dim**0.5\n        self.gamma = nn.Parameter(torch.ones(dim))\n\n    def forward(self, x):\n        x = x.to(self.gamma.device)\n        return F.normalize(x, dim=-1) * self.scale * self.gamma\n\n\n# attention\n\n","chunkId":"audio_separator/separator/uvr_lib_v5/roformer/bs_roformer.py:1"}],[1707,{"filePath":"audio_separator/separator/uvr_lib_v5/roformer/bs_roformer.py","startLine":57,"endLine":66,"chunkText":"class FeedForward(Module):\n    def __init__(self, dim, mult=4, dropout=0.0):\n        super().__init__()\n        dim_inner = int(dim * mult)\n        self.net = nn.Sequential(RMSNorm(dim), nn.Linear(dim, dim_inner), nn.GELU(), nn.Dropout(dropout), nn.Linear(dim_inner, dim), nn.Dropout(dropout))\n\n    def forward(self, x):\n        return self.net(x)\n\n","chunkId":"audio_separator/separator/uvr_lib_v5/roformer/bs_roformer.py:2"}],[1708,{"filePath":"audio_separator/separator/uvr_lib_v5/roformer/bs_roformer.py","startLine":67,"endLine":84,"chunkText":"class Attention(Module):\n    def __init__(self, dim, heads=8, dim_head=64, dropout=0.0, rotary_embed=None, flash=True):\n        super().__init__()\n        self.heads = heads\n        self.scale = dim_head**-0.5\n        dim_inner = heads * dim_head\n\n        self.rotary_embed = rotary_embed\n\n        self.attend = Attend(flash=flash, dropout=dropout)\n\n        self.norm = RMSNorm(dim)\n        self.to_qkv = nn.Linear(dim, dim_inner * 3, bias=False)\n\n        self.to_gates = nn.Linear(dim, heads)\n\n        self.to_out = nn.Sequential(nn.Linear(dim_inner, dim, bias=False), nn.Dropout(dropout))\n","chunkId":"audio_separator/separator/uvr_lib_v5/roformer/bs_roformer.py:3"}],[1709,{"filePath":"audio_separator/separator/uvr_lib_v5/roformer/bs_roformer.py","startLine":83,"endLine":99,"chunkText":"        self.to_out = nn.Sequential(nn.Linear(dim_inner, dim, bias=False), nn.Dropout(dropout))\n\n    def forward(self, x):\n        x = self.norm(x)\n\n        q, k, v = rearrange(self.to_qkv(x), \"b n (qkv h d) -> qkv b h n d\", qkv=3, h=self.heads)\n\n        if exists(self.rotary_embed):\n            q = self.rotary_embed.rotate_queries_or_keys(q)\n            k = self.rotary_embed.rotate_queries_or_keys(k)\n\n        out = self.attend(q, k, v)\n\n        gates = self.to_gates(x)\n        out = out * rearrange(gates, \"b n h -> b h n 1\").sigmoid()\n\n        out = rearrange(out, \"b h n d -> b n (h d)\")","chunkId":"audio_separator/separator/uvr_lib_v5/roformer/bs_roformer.py:4"}],[1710,{"filePath":"audio_separator/separator/uvr_lib_v5/roformer/bs_roformer.py","startLine":97,"endLine":102,"chunkText":"        out = out * rearrange(gates, \"b n h -> b h n 1\").sigmoid()\n\n        out = rearrange(out, \"b h n d -> b n (h d)\")\n        return self.to_out(out)\n\n","chunkId":"audio_separator/separator/uvr_lib_v5/roformer/bs_roformer.py:5"}],[1711,{"filePath":"audio_separator/separator/uvr_lib_v5/roformer/bs_roformer.py","startLine":103,"endLine":117,"chunkText":"class LinearAttention(Module):\n    \"\"\"\n    this flavor of linear attention proposed in https://arxiv.org/abs/2106.09681 by El-Nouby et al.\n    \"\"\"\n\n    @beartype\n    def __init__(self, *, dim, dim_head=32, heads=8, scale=8, flash=False, dropout=0.0):\n        super().__init__()\n        dim_inner = dim_head * heads\n        self.norm = RMSNorm(dim)\n\n        self.to_qkv = nn.Sequential(nn.Linear(dim, dim_inner * 3, bias=False), Rearrange(\"b n (qkv h d) -> qkv b h d n\", qkv=3, h=heads))\n\n        self.temperature = nn.Parameter(torch.ones(heads, 1, 1))\n","chunkId":"audio_separator/separator/uvr_lib_v5/roformer/bs_roformer.py:6"}],[1712,{"filePath":"audio_separator/separator/uvr_lib_v5/roformer/bs_roformer.py","startLine":115,"endLine":134,"chunkText":"\n        self.temperature = nn.Parameter(torch.ones(heads, 1, 1))\n\n        self.attend = Attend(scale=scale, dropout=dropout, flash=flash)\n\n        self.to_out = nn.Sequential(Rearrange(\"b h d n -> b n (h d)\"), nn.Linear(dim_inner, dim, bias=False))\n\n    def forward(self, x):\n        x = self.norm(x)\n\n        q, k, v = self.to_qkv(x)\n\n        q, k = map(l2norm, (q, k))\n        q = q * self.temperature.exp()\n\n        out = self.attend(q, k, v)\n\n        return self.to_out(out)\n\n","chunkId":"audio_separator/separator/uvr_lib_v5/roformer/bs_roformer.py:7"}],[1713,{"filePath":"audio_separator/separator/uvr_lib_v5/roformer/bs_roformer.py","startLine":135,"endLine":145,"chunkText":"class Transformer(Module):\n    def __init__(self, *, dim, depth, dim_head=64, heads=8, attn_dropout=0.0, ff_dropout=0.0, ff_mult=4, norm_output=True, rotary_embed=None, flash_attn=True, linear_attn=False):\n        super().__init__()\n        self.layers = ModuleList([])\n\n        for _ in range(depth):\n            if linear_attn:\n                attn = LinearAttention(dim=dim, dim_head=dim_head, heads=heads, dropout=attn_dropout, flash=flash_attn)\n            else:\n                attn = Attention(dim=dim, dim_head=dim_head, heads=heads, dropout=attn_dropout, rotary_embed=rotary_embed, flash=flash_attn)\n","chunkId":"audio_separator/separator/uvr_lib_v5/roformer/bs_roformer.py:8"}],[1714,{"filePath":"audio_separator/separator/uvr_lib_v5/roformer/bs_roformer.py","startLine":143,"endLine":161,"chunkText":"            else:\n                attn = Attention(dim=dim, dim_head=dim_head, heads=heads, dropout=attn_dropout, rotary_embed=rotary_embed, flash=flash_attn)\n\n            self.layers.append(ModuleList([attn, FeedForward(dim=dim, mult=ff_mult, dropout=ff_dropout)]))\n\n        self.norm = RMSNorm(dim) if norm_output else nn.Identity()\n\n    def forward(self, x):\n\n        for attn, ff in self.layers:\n            x = attn(x) + x\n            x = ff(x) + x\n\n        return self.norm(x)\n\n\n# bandsplit module\n\n","chunkId":"audio_separator/separator/uvr_lib_v5/roformer/bs_roformer.py:9"}],[1715,{"filePath":"audio_separator/separator/uvr_lib_v5/roformer/bs_roformer.py","startLine":162,"endLine":181,"chunkText":"class BandSplit(Module):\n    @beartype\n    def __init__(self, dim, dim_inputs: Tuple[int, ...]):\n        super().__init__()\n        self.dim_inputs = dim_inputs\n        self.to_features = ModuleList([])\n\n        for dim_in in dim_inputs:\n            net = nn.Sequential(RMSNorm(dim_in), nn.Linear(dim_in, dim))\n\n            self.to_features.append(net)\n\n    def forward(self, x):\n        x = x.split(self.dim_inputs, dim=-1)\n\n        outs = []\n        for split_input, to_feature in zip(x, self.to_features):\n            split_output = to_feature(split_input)\n            outs.append(split_output)\n","chunkId":"audio_separator/separator/uvr_lib_v5/roformer/bs_roformer.py:10"}],[1716,{"filePath":"audio_separator/separator/uvr_lib_v5/roformer/bs_roformer.py","startLine":179,"endLine":200,"chunkText":"            split_output = to_feature(split_input)\n            outs.append(split_output)\n\n        return torch.stack(outs, dim=-2)\n\n\ndef MLP(dim_in, dim_out, dim_hidden=None, depth=1, activation=nn.Tanh):\n    dim_hidden = default(dim_hidden, dim_in)\n\n    net = []\n    dims = (dim_in, *((dim_hidden,) * (depth - 1)), dim_out)\n\n    for ind, (layer_dim_in, layer_dim_out) in enumerate(zip(dims[:-1], dims[1:])):\n        is_last = ind == (len(dims) - 2)\n\n        net.append(nn.Linear(layer_dim_in, layer_dim_out))\n\n        if is_last:\n            continue\n\n        net.append(activation())\n","chunkId":"audio_separator/separator/uvr_lib_v5/roformer/bs_roformer.py:11"}],[1717,{"filePath":"audio_separator/separator/uvr_lib_v5/roformer/bs_roformer.py","startLine":204,"endLine":223,"chunkText":"class MaskEstimator(Module):\n    @beartype\n    def __init__(self, dim, dim_inputs: Tuple[int, ...], depth, mlp_expansion_factor=4):\n        super().__init__()\n        self.dim_inputs = dim_inputs\n        self.to_freqs = ModuleList([])\n        dim_hidden = dim * mlp_expansion_factor\n\n        for dim_in in dim_inputs:\n            net = []\n\n            mlp = nn.Sequential(MLP(dim, dim_in * 2, dim_hidden=dim_hidden, depth=depth), nn.GLU(dim=-1))\n\n            self.to_freqs.append(mlp)\n\n    def forward(self, x):\n        x = x.unbind(dim=-2)\n\n        outs = []\n","chunkId":"audio_separator/separator/uvr_lib_v5/roformer/bs_roformer.py:12"}],[1718,{"filePath":"audio_separator/separator/uvr_lib_v5/roformer/bs_roformer.py","startLine":221,"endLine":283,"chunkText":"        outs = []\n\n        for band_features, mlp in zip(x, self.to_freqs):\n            freq_out = mlp(band_features)\n            outs.append(freq_out)\n\n        return torch.cat(outs, dim=-1)\n\n\n# main class\n\nDEFAULT_FREQS_PER_BANDS = (\n    2,\n    2,\n    2,\n    2,\n    2,\n    2,\n    2,\n    2,\n    2,\n    2,\n    2,\n    2,\n    2,\n    2,\n    2,\n    2,\n    2,\n    2,\n    2,\n    2,\n    2,\n    2,\n    2,\n    2,\n    4,\n    4,\n    4,\n    4,\n    4,\n    4,\n    4,\n    4,\n    4,\n    4,\n    4,\n    4,\n    12,\n    12,\n    12,\n    12,\n    12,\n    12,\n    12,\n    12,\n    24,\n    24,\n    24,\n    24,\n    24,\n    24,\n    24,","chunkId":"audio_separator/separator/uvr_lib_v5/roformer/bs_roformer.py:13"}],[1719,{"filePath":"audio_separator/separator/uvr_lib_v5/roformer/bs_roformer.py","startLine":285,"endLine":298,"chunkText":"    24,\n    48,\n    48,\n    48,\n    48,\n    48,\n    48,\n    48,\n    48,\n    128,\n    129,\n)\n\n","chunkId":"audio_separator/separator/uvr_lib_v5/roformer/bs_roformer.py:14"}],[1720,{"filePath":"audio_separator/separator/uvr_lib_v5/roformer/bs_roformer.py","startLine":299,"endLine":320,"chunkText":"class BSRoformer(Module):\n\n    @beartype\n    def __init__(\n        self,\n        dim,\n        *,\n        depth,\n        stereo=False,\n        num_stems=1,\n        time_transformer_depth=2,\n        freq_transformer_depth=2,\n        linear_transformer_depth=0,\n        freqs_per_bands: Tuple[int, ...] = DEFAULT_FREQS_PER_BANDS,\n        # in the paper, they divide into ~60 bands, test with 1 for starters\n        dim_head=64,\n        heads=8,\n        attn_dropout=0.0,\n        ff_dropout=0.0,\n        flash_attn=True,\n        # New parameters for updated implementation\n        mlp_expansion_factor=4,","chunkId":"audio_separator/separator/uvr_lib_v5/roformer/bs_roformer.py:15"}],[1721,{"filePath":"audio_separator/separator/uvr_lib_v5/roformer/bs_roformer.py","startLine":318,"endLine":332,"chunkText":"        flash_attn=True,\n        # New parameters for updated implementation\n        mlp_expansion_factor=4,\n        sage_attention=False,\n        zero_dc=True,\n        use_torch_checkpoint=False,\n        skip_connection=False,\n        # Original parameters continue\n        dim_freqs_in=1025,\n        stft_n_fft=2048,\n        stft_hop_length=512,\n        # 10ms at 44100Hz, from sections 4.1, 4.4 in the paper - @faroit recommends // 2 or // 4 for better reconstruction\n        stft_win_length=2048,\n        stft_normalized=False,\n        stft_window_fn: Optional[Callable] = None,","chunkId":"audio_separator/separator/uvr_lib_v5/roformer/bs_roformer.py:16"}],[1722,{"filePath":"audio_separator/separator/uvr_lib_v5/roformer/bs_roformer.py","startLine":331,"endLine":346,"chunkText":"        stft_normalized=False,\n        stft_window_fn: Optional[Callable] = None,\n        mask_estimator_depth=2,\n        multi_stft_resolution_loss_weight=1.0,\n        multi_stft_resolutions_window_sizes: Tuple[int, ...] = (4096, 2048, 1024, 512, 256),\n        multi_stft_hop_size=147,\n        multi_stft_normalized=False,\n        multi_stft_window_fn: Callable = torch.hann_window,\n    ):\n        super().__init__()\n\n        self.stereo = stereo\n        self.audio_channels = 2 if stereo else 1\n        self.num_stems = num_stems\n        \n        # Store new parameters as instance variables","chunkId":"audio_separator/separator/uvr_lib_v5/roformer/bs_roformer.py:17"}],[1723,{"filePath":"audio_separator/separator/uvr_lib_v5/roformer/bs_roformer.py","startLine":344,"endLine":360,"chunkText":"        self.num_stems = num_stems\n        \n        # Store new parameters as instance variables\n        self.mlp_expansion_factor = mlp_expansion_factor\n        self.sage_attention = sage_attention\n        self.zero_dc = zero_dc\n        self.use_torch_checkpoint = use_torch_checkpoint\n        self.skip_connection = skip_connection\n\n        self.layers = ModuleList([])\n\n        # Add parameters to transformer kwargs (excluding sage_attention for now)\n        transformer_kwargs = dict(\n            dim=dim, \n            heads=heads, \n            dim_head=dim_head, \n            attn_dropout=attn_dropout, ","chunkId":"audio_separator/separator/uvr_lib_v5/roformer/bs_roformer.py:18"}],[1724,{"filePath":"audio_separator/separator/uvr_lib_v5/roformer/bs_roformer.py","startLine":358,"endLine":375,"chunkText":"            heads=heads, \n            dim_head=dim_head, \n            attn_dropout=attn_dropout, \n            ff_dropout=ff_dropout, \n            flash_attn=flash_attn, \n            norm_output=False\n        )\n        \n        # Print sage attention status if enabled (as per research findings)\n        if sage_attention:\n            print(\"Use Sage Attention\")\n\n        time_rotary_embed = RotaryEmbedding(dim=dim_head)\n        freq_rotary_embed = RotaryEmbedding(dim=dim_head)\n\n        for _ in range(depth):\n            tran_modules = []\n            if linear_transformer_depth > 0:","chunkId":"audio_separator/separator/uvr_lib_v5/roformer/bs_roformer.py:19"}],[1725,{"filePath":"audio_separator/separator/uvr_lib_v5/roformer/bs_roformer.py","startLine":373,"endLine":382,"chunkText":"        for _ in range(depth):\n            tran_modules = []\n            if linear_transformer_depth > 0:\n                tran_modules.append(Transformer(depth=linear_transformer_depth, linear_attn=True, **transformer_kwargs))\n            tran_modules.append(Transformer(depth=time_transformer_depth, rotary_embed=time_rotary_embed, **transformer_kwargs))\n            tran_modules.append(Transformer(depth=freq_transformer_depth, rotary_embed=freq_rotary_embed, **transformer_kwargs))\n            self.layers.append(nn.ModuleList(tran_modules))\n\n        self.final_norm = RMSNorm(dim)\n","chunkId":"audio_separator/separator/uvr_lib_v5/roformer/bs_roformer.py:20"}],[1726,{"filePath":"audio_separator/separator/uvr_lib_v5/roformer/bs_roformer.py","startLine":380,"endLine":391,"chunkText":"\n        self.final_norm = RMSNorm(dim)\n\n        self.stft_kwargs = dict(n_fft=stft_n_fft, hop_length=stft_hop_length, win_length=stft_win_length, normalized=stft_normalized)\n\n        self.stft_window_fn = partial(default(stft_window_fn, torch.hann_window), stft_win_length)\n\n        freqs = torch.stft(torch.randn(1, 4096), **self.stft_kwargs, return_complex=True).shape[1]\n\n        assert len(freqs_per_bands) > 1\n        assert sum(freqs_per_bands) == freqs, f\"the number of freqs in the bands must equal {freqs} based on the STFT settings, but got {sum(freqs_per_bands)}\"\n","chunkId":"audio_separator/separator/uvr_lib_v5/roformer/bs_roformer.py:21"}],[1727,{"filePath":"audio_separator/separator/uvr_lib_v5/roformer/bs_roformer.py","startLine":389,"endLine":401,"chunkText":"        assert len(freqs_per_bands) > 1\n        assert sum(freqs_per_bands) == freqs, f\"the number of freqs in the bands must equal {freqs} based on the STFT settings, but got {sum(freqs_per_bands)}\"\n\n        freqs_per_bands_with_complex = tuple(2 * f * self.audio_channels for f in freqs_per_bands)\n\n        self.band_split = BandSplit(dim=dim, dim_inputs=freqs_per_bands_with_complex)\n\n        self.mask_estimators = nn.ModuleList([])\n\n        for _ in range(num_stems):\n            mask_estimator = MaskEstimator(\n                dim=dim, \n                dim_inputs=freqs_per_bands_with_complex, ","chunkId":"audio_separator/separator/uvr_lib_v5/roformer/bs_roformer.py:22"}],[1728,{"filePath":"audio_separator/separator/uvr_lib_v5/roformer/bs_roformer.py","startLine":399,"endLine":412,"chunkText":"            mask_estimator = MaskEstimator(\n                dim=dim, \n                dim_inputs=freqs_per_bands_with_complex, \n                depth=mask_estimator_depth,\n                mlp_expansion_factor=mlp_expansion_factor  # Use the new parameter\n            )\n\n            self.mask_estimators.append(mask_estimator)\n\n        # for the multi-resolution stft loss\n\n        self.multi_stft_resolution_loss_weight = multi_stft_resolution_loss_weight\n        self.multi_stft_resolutions_window_sizes = multi_stft_resolutions_window_sizes\n        self.multi_stft_n_fft = stft_n_fft","chunkId":"audio_separator/separator/uvr_lib_v5/roformer/bs_roformer.py:23"}],[1729,{"filePath":"audio_separator/separator/uvr_lib_v5/roformer/bs_roformer.py","startLine":410,"endLine":424,"chunkText":"        self.multi_stft_resolution_loss_weight = multi_stft_resolution_loss_weight\n        self.multi_stft_resolutions_window_sizes = multi_stft_resolutions_window_sizes\n        self.multi_stft_n_fft = stft_n_fft\n        self.multi_stft_window_fn = multi_stft_window_fn\n\n        self.multi_stft_kwargs = dict(hop_length=multi_stft_hop_size, normalized=multi_stft_normalized)\n\n    def forward(self, raw_audio, target=None, return_loss_breakdown=False):\n        \"\"\"\n        einops\n\n        b - batch\n        f - freq\n        t - time\n        s - audio channel (1 for mono, 2 for stereo)","chunkId":"audio_separator/separator/uvr_lib_v5/roformer/bs_roformer.py:24"}],[1730,{"filePath":"audio_separator/separator/uvr_lib_v5/roformer/bs_roformer.py","startLine":422,"endLine":442,"chunkText":"        f - freq\n        t - time\n        s - audio channel (1 for mono, 2 for stereo)\n        n - number of 'stems'\n        c - complex (2)\n        d - feature dimension\n        \"\"\"\n\n        original_device = raw_audio.device\n        x_is_mps = True if original_device.type == \"mps\" else False\n\n        # if x_is_mps:\n        #     raw_audio = raw_audio.cpu()\n\n        device = raw_audio.device\n\n        if raw_audio.ndim == 2:\n            raw_audio = rearrange(raw_audio, \"b t -> b 1 t\")\n\n        channels = raw_audio.shape[1]\n        assert (not self.stereo and channels == 1) or (","chunkId":"audio_separator/separator/uvr_lib_v5/roformer/bs_roformer.py:25"}],[1731,{"filePath":"audio_separator/separator/uvr_lib_v5/roformer/bs_roformer.py","startLine":440,"endLine":452,"chunkText":"        channels = raw_audio.shape[1]\n        assert (not self.stereo and channels == 1) or (\n            self.stereo and channels == 2\n        ), \"stereo needs to be set to True if passing in audio signal that is stereo (channel dimension of 2). also need to be False if mono (channel dimension of 1)\"\n\n        # to stft\n\n        raw_audio, batch_audio_channel_packed_shape = pack_one(raw_audio, \"* t\")\n\n        stft_window = self.stft_window_fn().to(device)\n\n        stft_repr = torch.stft(raw_audio, **self.stft_kwargs, window=stft_window, return_complex=True)\n        stft_repr = torch.view_as_real(stft_repr","chunkId":"audio_separator/separator/uvr_lib_v5/roformer/bs_roformer.py:26"}],[1732,{"filePath":"audio_separator/separator/uvr_lib_v5/roformer/bs_roformer.py","startLine":452,"endLine":465,"chunkText":"        stft_repr = torch.stft(raw_audio, **self.stft_kwargs, window=stft_window, return_complex=True)\n        stft_repr = torch.view_as_real(stft_repr)\n\n        stft_repr = unpack_one(stft_repr, batch_audio_channel_packed_shape, \"* f t c\")\n        stft_repr = rearrange(stft_repr, \"b s f t c -> b (f s) t c\")  # merge stereo / mono into the frequency, with frequency leading dimension, for band splitting\n\n        x = rearrange(stft_repr, \"b f t c -> b t (f c)\")\n\n        x = self.band_split(x)\n\n        # axial / hierarchical attention\n\n        for transformer_block in self.layers:\n","chunkId":"audio_separator/separator/uvr_lib_v5/roformer/bs_roformer.py:27"}],[1733,{"filePath":"audio_separator/separator/uvr_lib_v5/roformer/bs_roformer.py","startLine":463,"endLine":480,"chunkText":"\n        for transformer_block in self.layers:\n\n            if len(transformer_block) == 3:\n                linear_transformer, time_transformer, freq_transformer = transformer_block\n\n                x, ft_ps = pack([x], \"b * d\")\n                x = linear_transformer(x)\n                (x,) = unpack(x, ft_ps, \"b * d\")\n            else:\n                time_transformer, freq_transformer = transformer_block\n\n            x = rearrange(x, \"b t f d -> b f t d\")\n            x, ps = pack([x], \"* t d\")\n\n            x = time_transformer(x)\n\n            (x,) = unpack(x, ps, \"* t d\")","chunkId":"audio_separator/separator/uvr_lib_v5/roformer/bs_roformer.py:28"}],[1734,{"filePath":"audio_separator/separator/uvr_lib_v5/roformer/bs_roformer.py","startLine":478,"endLine":499,"chunkText":"            x = time_transformer(x)\n\n            (x,) = unpack(x, ps, \"* t d\")\n            x = rearrange(x, \"b f t d -> b t f d\")\n            x, ps = pack([x], \"* f d\")\n\n            x = freq_transformer(x)\n\n            (x,) = unpack(x, ps, \"* f d\")\n\n        x = self.final_norm(x)\n\n        mask = torch.stack([fn(x) for fn in self.mask_estimators], dim=1)\n        mask = rearrange(mask, \"b n t (f c) -> b n f t c\", c=2)\n\n        # if x_is_mps:\n        #     mask = mask.to('cpu')\n\n        # modulate frequency representation\n\n        stft_repr = rearrange(stft_repr, \"b f t c -> b 1 f t c\")\n","chunkId":"audio_separator/separator/uvr_lib_v5/roformer/bs_roformer.py:29"}],[1735,{"filePath":"audio_separator/separator/uvr_lib_v5/roformer/bs_roformer.py","startLine":497,"endLine":512,"chunkText":"\n        stft_repr = rearrange(stft_repr, \"b f t c -> b 1 f t c\")\n\n        # complex number multiplication\n\n        stft_repr = torch.view_as_complex(stft_repr)\n        mask = torch.view_as_complex(mask)\n\n        stft_repr = stft_repr * mask\n\n        # istft\n\n        stft_repr = rearrange(stft_repr, \"b n (f s) t -> (b n s) f t\", s=self.audio_channels)\n\n        recon_audio = torch.istft(stft_repr.cpu() if x_is_mps else stft_repr, **self.stft_kwargs, window=stft_window.cpu() if x_is_mps else stft_window, return_complex=False).to(device)\n","chunkId":"audio_separator/separator/uvr_lib_v5/roformer/bs_roformer.py:30"}],[1736,{"filePath":"audio_separator/separator/uvr_lib_v5/roformer/bs_roformer.py","startLine":510,"endLine":523,"chunkText":"\n        recon_audio = torch.istft(stft_repr.cpu() if x_is_mps else stft_repr, **self.stft_kwargs, window=stft_window.cpu() if x_is_mps else stft_window, return_complex=False).to(device)\n\n        recon_audio = rearrange(recon_audio, \"(b n s) t -> b n s t\", s=self.audio_channels, n=self.num_stems)\n\n        if self.num_stems == 1:\n            recon_audio = rearrange(recon_audio, \"b 1 s t -> b s t\")\n\n        # if a target is passed in, calculate loss for learning\n\n        if not exists(target):\n            return recon_audio\n\n        if self.num_stems > 1:","chunkId":"audio_separator/separator/uvr_lib_v5/roformer/bs_roformer.py:31"}],[1737,{"filePath":"audio_separator/separator/uvr_lib_v5/roformer/bs_roformer.py","startLine":521,"endLine":536,"chunkText":"            return recon_audio\n\n        if self.num_stems > 1:\n            assert target.ndim == 4 and target.shape[1] == self.num_stems\n\n        if target.ndim == 2:\n            target = rearrange(target, \"... t -> ... 1 t\")\n\n        target = target[..., : recon_audio.shape[-1]]\n\n        loss = F.l1_loss(recon_audio, target)\n\n        multi_stft_resolution_loss = 0.0\n\n        for window_size in self.multi_stft_resolutions_window_sizes:\n            res_stft_kwargs = dict(","chunkId":"audio_separator/separator/uvr_lib_v5/roformer/bs_roformer.py:32"}],[1738,{"filePath":"audio_separator/separator/uvr_lib_v5/roformer/bs_roformer.py","startLine":534,"endLine":544,"chunkText":"\n        for window_size in self.multi_stft_resolutions_window_sizes:\n            res_stft_kwargs = dict(\n                n_fft=max(window_size, self.multi_stft_n_fft), win_length=window_size, return_complex=True, window=self.multi_stft_window_fn(window_size, device=device), **self.multi_stft_kwargs\n            )\n\n            recon_Y = torch.stft(rearrange(recon_audio, \"... s t -> (... s) t\"), **res_stft_kwargs)\n            target_Y = torch.stft(rearrange(target, \"... s t -> (... s) t\"), **res_stft_kwargs)\n\n            multi_stft_resolution_loss = multi_stft_resolution_loss + F.l1_loss(recon_Y, target_Y)\n","chunkId":"audio_separator/separator/uvr_lib_v5/roformer/bs_roformer.py:33"}],[1739,{"filePath":"audio_separator/separator/uvr_lib_v5/roformer/bs_roformer.py","startLine":542,"endLine":554,"chunkText":"\n            multi_stft_resolution_loss = multi_stft_resolution_loss + F.l1_loss(recon_Y, target_Y)\n\n        weighted_multi_resolution_loss = multi_stft_resolution_loss * self.multi_stft_resolution_loss_weight\n\n        total_loss = loss + weighted_multi_resolution_loss\n\n        if not return_loss_breakdown:\n            # Move the result back to the original device if it was moved to CPU for MPS compatibility\n            # if x_is_mps:\n            #     total_loss = total_loss.to(original_device)\n            return total_loss\n","chunkId":"audio_separator/separator/uvr_lib_v5/roformer/bs_roformer.py:34"}],[1740,{"filePath":"audio_separator/separator/uvr_lib_v5/roformer/bs_roformer.py","startLine":552,"endLine":565,"chunkText":"            #     total_loss = total_loss.to(original_device)\n            return total_loss\n\n        # For detailed loss breakdown, ensure all components are moved back to the original device for MPS\n        # if x_is_mps:\n        #     loss = loss.to(original_device)\n        #     multi_stft_resolution_loss = multi_stft_resolution_loss.to(original_device)\n        #     weighted_multi_resolution_loss = weighted_multi_resolution_loss.to(original_device)\n\n        return total_loss, (loss, multi_stft_resolution_loss)\n\n        # if not return_loss_breakdown:\n        #     return total_loss\n","chunkId":"audio_separator/separator/uvr_lib_v5/roformer/bs_roformer.py:35"}],[1741,{"filePath":"audio_separator/separator/uvr_lib_v5/roformer/bs_roformer.py","startLine":563,"endLine":567,"chunkText":"        # if not return_loss_breakdown:\n        #     return total_loss\n\n        # return total_loss, (loss, multi_stft_resolution_loss)\n","chunkId":"audio_separator/separator/uvr_lib_v5/roformer/bs_roformer.py:36"}],[1742,{"filePath":"audio_separator/separator/uvr_lib_v5/roformer/mel_band_roformer.py","startLine":0,"endLine":31,"chunkText":"from functools import partial\n\nimport torch\nfrom torch import nn, einsum, Tensor\nfrom torch.nn import Module, ModuleList\nimport torch.nn.functional as F\n\nfrom .attend import Attend\n\nfrom beartype.typing import Tuple, Optional, List, Callable\nfrom beartype import beartype\n\nfrom rotary_embedding_torch import RotaryEmbedding\n\nfrom einops import rearrange, pack, unpack, reduce, repeat\n\nfrom librosa import filters\n\n\ndef exists(val):\n    return val is not None\n\n\ndef default(v, d):\n    return v if exists(v) else d\n\n\ndef pack_one(t, pattern):\n    return pack([t], pattern)\n\n\ndef unpack_one(t, ps, pattern):","chunkId":"audio_separator/separator/uvr_lib_v5/roformer/mel_band_roformer.py:0"}],[1743,{"filePath":"audio_separator/separator/uvr_lib_v5/roformer/mel_band_roformer.py","startLine":29,"endLine":51,"chunkText":"\n\ndef unpack_one(t, ps, pattern):\n    return unpack(t, ps, pattern)[0]\n\n\ndef pad_at_dim(t, pad, dim=-1, value=0.0):\n    dims_from_right = (-dim - 1) if dim < 0 else (t.ndim - dim - 1)\n    zeros = (0, 0) * dims_from_right\n    return F.pad(t, (*zeros, *pad), value=value)\n\n\nclass RMSNorm(Module):\n    def __init__(self, dim):\n        super().__init__()\n        self.scale = dim**0.5\n        self.gamma = nn.Parameter(torch.ones(dim))\n\n    def forward(self, x):\n        x = x.to(self.gamma.device)\n        return F.normalize(x, dim=-1) * self.scale * self.gamma\n\n","chunkId":"audio_separator/separator/uvr_lib_v5/roformer/mel_band_roformer.py:1"}],[1744,{"filePath":"audio_separator/separator/uvr_lib_v5/roformer/mel_band_roformer.py","startLine":52,"endLine":61,"chunkText":"class FeedForward(Module):\n    def __init__(self, dim, mult=4, dropout=0.0):\n        super().__init__()\n        dim_inner = int(dim * mult)\n        self.net = nn.Sequential(RMSNorm(dim), nn.Linear(dim, dim_inner), nn.GELU(), nn.Dropout(dropout), nn.Linear(dim_inner, dim), nn.Dropout(dropout))\n\n    def forward(self, x):\n        return self.net(x)\n\n","chunkId":"audio_separator/separator/uvr_lib_v5/roformer/mel_band_roformer.py:2"}],[1745,{"filePath":"audio_separator/separator/uvr_lib_v5/roformer/mel_band_roformer.py","startLine":62,"endLine":79,"chunkText":"class Attention(Module):\n    def __init__(self, dim, heads=8, dim_head=64, dropout=0.0, rotary_embed=None, flash=True):\n        super().__init__()\n        self.heads = heads\n        self.scale = dim_head**-0.5\n        dim_inner = heads * dim_head\n\n        self.rotary_embed = rotary_embed\n\n        self.attend = Attend(flash=flash, dropout=dropout)\n\n        self.norm = RMSNorm(dim)\n        self.to_qkv = nn.Linear(dim, dim_inner * 3, bias=False)\n\n        self.to_gates = nn.Linear(dim, heads)\n\n        self.to_out = nn.Sequential(nn.Linear(dim_inner, dim, bias=False), nn.Dropout(dropout))\n","chunkId":"audio_separator/separator/uvr_lib_v5/roformer/mel_band_roformer.py:3"}],[1746,{"filePath":"audio_separator/separator/uvr_lib_v5/roformer/mel_band_roformer.py","startLine":78,"endLine":94,"chunkText":"        self.to_out = nn.Sequential(nn.Linear(dim_inner, dim, bias=False), nn.Dropout(dropout))\n\n    def forward(self, x):\n        x = self.norm(x)\n\n        q, k, v = rearrange(self.to_qkv(x), \"b n (qkv h d) -> qkv b h n d\", qkv=3, h=self.heads)\n\n        if exists(self.rotary_embed):\n            q = self.rotary_embed.rotate_queries_or_keys(q)\n            k = self.rotary_embed.rotate_queries_or_keys(k)\n\n        out = self.attend(q, k, v)\n\n        gates = self.to_gates(x)\n        out = out * rearrange(gates, \"b n h -> b h n 1\").sigmoid()\n\n        out = rearrange(out, \"b h n d -> b n (h d)\")","chunkId":"audio_separator/separator/uvr_lib_v5/roformer/mel_band_roformer.py:4"}],[1747,{"filePath":"audio_separator/separator/uvr_lib_v5/roformer/mel_band_roformer.py","startLine":92,"endLine":97,"chunkText":"        out = out * rearrange(gates, \"b n h -> b h n 1\").sigmoid()\n\n        out = rearrange(out, \"b h n d -> b n (h d)\")\n        return self.to_out(out)\n\n","chunkId":"audio_separator/separator/uvr_lib_v5/roformer/mel_band_roformer.py:5"}],[1748,{"filePath":"audio_separator/separator/uvr_lib_v5/roformer/mel_band_roformer.py","startLine":98,"endLine":109,"chunkText":"class Transformer(Module):\n    def __init__(self, *, dim, depth, dim_head=64, heads=8, attn_dropout=0.0, ff_dropout=0.0, ff_mult=4, norm_output=True, rotary_embed=None, flash_attn=True):\n        super().__init__()\n        self.layers = ModuleList([])\n\n        for _ in range(depth):\n            self.layers.append(\n                ModuleList(\n                    [Attention(dim=dim, dim_head=dim_head, heads=heads, dropout=attn_dropout, rotary_embed=rotary_embed, flash=flash_attn), FeedForward(dim=dim, mult=ff_mult, dropout=ff_dropout)]\n                )\n            )\n","chunkId":"audio_separator/separator/uvr_lib_v5/roformer/mel_band_roformer.py:6"}],[1749,{"filePath":"audio_separator/separator/uvr_lib_v5/roformer/mel_band_roformer.py","startLine":107,"endLine":120,"chunkText":"                )\n            )\n\n        self.norm = RMSNorm(dim) if norm_output else nn.Identity()\n\n    def forward(self, x):\n\n        for attn, ff in self.layers:\n            x = attn(x) + x\n            x = ff(x) + x\n\n        return self.norm(x)\n\n","chunkId":"audio_separator/separator/uvr_lib_v5/roformer/mel_band_roformer.py:7"}],[1750,{"filePath":"audio_separator/separator/uvr_lib_v5/roformer/mel_band_roformer.py","startLine":121,"endLine":140,"chunkText":"class BandSplit(Module):\n    @beartype\n    def __init__(self, dim, dim_inputs: Tuple[int, ...]):\n        super().__init__()\n        self.dim_inputs = dim_inputs\n        self.to_features = ModuleList([])\n\n        for dim_in in dim_inputs:\n            net = nn.Sequential(RMSNorm(dim_in), nn.Linear(dim_in, dim))\n\n            self.to_features.append(net)\n\n    def forward(self, x):\n        x = x.split(self.dim_inputs, dim=-1)\n\n        outs = []\n        for split_input, to_feature in zip(x, self.to_features):\n            split_output = to_feature(split_input)\n            outs.append(split_output)\n","chunkId":"audio_separator/separator/uvr_lib_v5/roformer/mel_band_roformer.py:8"}],[1751,{"filePath":"audio_separator/separator/uvr_lib_v5/roformer/mel_band_roformer.py","startLine":138,"endLine":161,"chunkText":"            split_output = to_feature(split_input)\n            outs.append(split_output)\n\n        return torch.stack(outs, dim=-2)\n\n\ndef MLP(dim_in, dim_out, dim_hidden=None, depth=1, activation=nn.Tanh):\n    dim_hidden = default(dim_hidden, dim_in)\n\n    net = []\n    dims = (dim_in, *((dim_hidden,) * depth), dim_out)\n\n    for ind, (layer_dim_in, layer_dim_out) in enumerate(zip(dims[:-1], dims[1:])):\n        is_last = ind == (len(dims) - 2)\n\n        net.append(nn.Linear(layer_dim_in, layer_dim_out))\n\n        if is_last:\n            continue\n\n        net.append(activation())\n\n    return nn.Sequential(*net)\n","chunkId":"audio_separator/separator/uvr_lib_v5/roformer/mel_band_roformer.py:9"}],[1752,{"filePath":"audio_separator/separator/uvr_lib_v5/roformer/mel_band_roformer.py","startLine":163,"endLine":182,"chunkText":"class MaskEstimator(Module):\n    @beartype\n    def __init__(self, dim, dim_inputs: Tuple[int, ...], depth, mlp_expansion_factor=4):\n        super().__init__()\n        self.dim_inputs = dim_inputs\n        self.to_freqs = ModuleList([])\n        dim_hidden = dim * mlp_expansion_factor\n\n        for dim_in in dim_inputs:\n            net = []\n\n            mlp = nn.Sequential(MLP(dim, dim_in * 2, dim_hidden=dim_hidden, depth=depth), nn.GLU(dim=-1))\n\n            self.to_freqs.append(mlp)\n\n    def forward(self, x):\n        x = x.unbind(dim=-2)\n\n        outs = []\n","chunkId":"audio_separator/separator/uvr_lib_v5/roformer/mel_band_roformer.py:10"}],[1753,{"filePath":"audio_separator/separator/uvr_lib_v5/roformer/mel_band_roformer.py","startLine":180,"endLine":189,"chunkText":"\n        outs = []\n\n        for band_features, mlp in zip(x, self.to_freqs):\n            freq_out = mlp(band_features)\n            outs.append(freq_out)\n\n        return torch.cat(outs, dim=-1)\n\n","chunkId":"audio_separator/separator/uvr_lib_v5/roformer/mel_band_roformer.py:11"}],[1754,{"filePath":"audio_separator/separator/uvr_lib_v5/roformer/mel_band_roformer.py","startLine":190,"endLine":214,"chunkText":"class MelBandRoformer(Module):\n\n    @beartype\n    def __init__(\n        self,\n        dim,\n        *,\n        depth,\n        stereo=False,\n        num_stems=1,\n        time_transformer_depth=2,\n        freq_transformer_depth=2,\n        num_bands=60,\n        dim_head=64,\n        heads=8,\n        attn_dropout=0.1,\n        ff_dropout=0.1,\n        flash_attn=True,\n        # New parameters for updated implementation\n        mlp_expansion_factor=4,\n        sage_attention=False,\n        zero_dc=True,\n        use_torch_checkpoint=False,\n        skip_connection=False,\n        # Original parameters continue","chunkId":"audio_separator/separator/uvr_lib_v5/roformer/mel_band_roformer.py:12"}],[1755,{"filePath":"audio_separator/separator/uvr_lib_v5/roformer/mel_band_roformer.py","startLine":212,"endLine":226,"chunkText":"        use_torch_checkpoint=False,\n        skip_connection=False,\n        # Original parameters continue\n        dim_freqs_in=1025,\n        sample_rate=44100,\n        stft_n_fft=2048,\n        stft_hop_length=512,\n        stft_win_length=2048,\n        stft_normalized=False,\n        stft_window_fn: Optional[Callable] = None,\n        mask_estimator_depth=1,\n        multi_stft_resolution_loss_weight=1.0,\n        multi_stft_resolutions_window_sizes: Tuple[int, ...] = (4096, 2048, 1024, 512, 256),\n        multi_stft_hop_size=147,\n        multi_stft_normalized=False,","chunkId":"audio_separator/separator/uvr_lib_v5/roformer/mel_band_roformer.py:13"}],[1756,{"filePath":"audio_separator/separator/uvr_lib_v5/roformer/mel_band_roformer.py","startLine":224,"endLine":239,"chunkText":"        multi_stft_resolutions_window_sizes: Tuple[int, ...] = (4096, 2048, 1024, 512, 256),\n        multi_stft_hop_size=147,\n        multi_stft_normalized=False,\n        multi_stft_window_fn: Callable = torch.hann_window,\n        match_input_audio_length=False,\n    ):\n        super().__init__()\n\n        self.stereo = stereo\n        self.audio_channels = 2 if stereo else 1\n        self.num_stems = num_stems\n        \n        # Store new parameters as instance variables\n        self.mlp_expansion_factor = mlp_expansion_factor\n        self.sage_attention = sage_attention\n        self.zero_dc = zero_dc","chunkId":"audio_separator/separator/uvr_lib_v5/roformer/mel_band_roformer.py:14"}],[1757,{"filePath":"audio_separator/separator/uvr_lib_v5/roformer/mel_band_roformer.py","startLine":237,"endLine":254,"chunkText":"        self.mlp_expansion_factor = mlp_expansion_factor\n        self.sage_attention = sage_attention\n        self.zero_dc = zero_dc\n        self.use_torch_checkpoint = use_torch_checkpoint\n        self.skip_connection = skip_connection\n\n        self.layers = ModuleList([])\n\n        # Add parameters to transformer kwargs (excluding sage_attention for now)\n        transformer_kwargs = dict(\n            dim=dim, \n            heads=heads, \n            dim_head=dim_head, \n            attn_dropout=attn_dropout, \n            ff_dropout=ff_dropout, \n            flash_attn=flash_attn\n        )\n        ","chunkId":"audio_separator/separator/uvr_lib_v5/roformer/mel_band_roformer.py:15"}],[1758,{"filePath":"audio_separator/separator/uvr_lib_v5/roformer/mel_band_roformer.py","startLine":252,"endLine":266,"chunkText":"            flash_attn=flash_attn\n        )\n        \n        # Print sage attention status if enabled (as per research findings)\n        if sage_attention:\n            print(\"Use Sage Attention\")\n\n        time_rotary_embed = RotaryEmbedding(dim=dim_head)\n        freq_rotary_embed = RotaryEmbedding(dim=dim_head)\n\n        for _ in range(depth):\n            self.layers.append(\n                nn.ModuleList(\n                    [\n                        Transformer(depth=time_transformer_depth, rotary_embed=time_rotary_embed, **transformer_kwargs),","chunkId":"audio_separator/separator/uvr_lib_v5/roformer/mel_band_roformer.py:16"}],[1759,{"filePath":"audio_separator/separator/uvr_lib_v5/roformer/mel_band_roformer.py","startLine":264,"endLine":275,"chunkText":"                nn.ModuleList(\n                    [\n                        Transformer(depth=time_transformer_depth, rotary_embed=time_rotary_embed, **transformer_kwargs),\n                        Transformer(depth=freq_transformer_depth, rotary_embed=freq_rotary_embed, **transformer_kwargs),\n                    ]\n                )\n            )\n\n        self.stft_window_fn = partial(default(stft_window_fn, torch.hann_window), stft_win_length)\n\n        self.stft_kwargs = dict(n_fft=stft_n_fft, hop_length=stft_hop_length, win_length=stft_win_length, normalized=stft_normalized)\n","chunkId":"audio_separator/separator/uvr_lib_v5/roformer/mel_band_roformer.py:17"}],[1760,{"filePath":"audio_separator/separator/uvr_lib_v5/roformer/mel_band_roformer.py","startLine":273,"endLine":286,"chunkText":"\n        self.stft_kwargs = dict(n_fft=stft_n_fft, hop_length=stft_hop_length, win_length=stft_win_length, normalized=stft_normalized)\n\n        freqs = torch.stft(torch.randn(1, 4096), **self.stft_kwargs, return_complex=True).shape[1]\n\n        mel_filter_bank_numpy = filters.mel(sr=sample_rate, n_fft=stft_n_fft, n_mels=num_bands)\n\n        mel_filter_bank = torch.from_numpy(mel_filter_bank_numpy)\n\n        mel_filter_bank[0][0] = 1.0\n\n        mel_filter_bank[-1, -1] = 1.0\n\n        freqs_per_band = mel_filter_bank > 0","chunkId":"audio_separator/separator/uvr_lib_v5/roformer/mel_band_roformer.py:18"}],[1761,{"filePath":"audio_separator/separator/uvr_lib_v5/roformer/mel_band_roformer.py","startLine":284,"endLine":296,"chunkText":"        mel_filter_bank[-1, -1] = 1.0\n\n        freqs_per_band = mel_filter_bank > 0\n        assert freqs_per_band.any(dim=0).all(), \"all frequencies need to be covered by all bands for now\"\n\n        repeated_freq_indices = repeat(torch.arange(freqs), \"f -> b f\", b=num_bands)\n        freq_indices = repeated_freq_indices[freqs_per_band]\n\n        if stereo:\n            freq_indices = repeat(freq_indices, \"f -> f s\", s=2)\n            freq_indices = freq_indices * 2 + torch.arange(2)\n            freq_indices = rearrange(freq_indices, \"f s -> (f s)\")\n","chunkId":"audio_separator/separator/uvr_lib_v5/roformer/mel_band_roformer.py:19"}],[1762,{"filePath":"audio_separator/separator/uvr_lib_v5/roformer/mel_band_roformer.py","startLine":294,"endLine":305,"chunkText":"            freq_indices = freq_indices * 2 + torch.arange(2)\n            freq_indices = rearrange(freq_indices, \"f s -> (f s)\")\n\n        self.register_buffer(\"freq_indices\", freq_indices, persistent=False)\n        self.register_buffer(\"freqs_per_band\", freqs_per_band, persistent=False)\n\n        num_freqs_per_band = reduce(freqs_per_band, \"b f -> b\", \"sum\")\n        num_bands_per_freq = reduce(freqs_per_band, \"b f -> f\", \"sum\")\n\n        self.register_buffer(\"num_freqs_per_band\", num_freqs_per_band, persistent=False)\n        self.register_buffer(\"num_bands_per_freq\", num_bands_per_freq, persistent=False)\n","chunkId":"audio_separator/separator/uvr_lib_v5/roformer/mel_band_roformer.py:20"}],[1763,{"filePath":"audio_separator/separator/uvr_lib_v5/roformer/mel_band_roformer.py","startLine":303,"endLine":314,"chunkText":"        self.register_buffer(\"num_freqs_per_band\", num_freqs_per_band, persistent=False)\n        self.register_buffer(\"num_bands_per_freq\", num_bands_per_freq, persistent=False)\n\n        freqs_per_bands_with_complex = tuple(2 * f * self.audio_channels for f in num_freqs_per_band.tolist())\n\n        self.band_split = BandSplit(dim=dim, dim_inputs=freqs_per_bands_with_complex)\n\n        self.mask_estimators = nn.ModuleList([])\n\n        for _ in range(num_stems):\n            mask_estimator = MaskEstimator(dim=dim, dim_inputs=freqs_per_bands_with_complex, depth=mask_estimator_depth)\n","chunkId":"audio_separator/separator/uvr_lib_v5/roformer/mel_band_roformer.py:21"}],[1764,{"filePath":"audio_separator/separator/uvr_lib_v5/roformer/mel_band_roformer.py","startLine":312,"endLine":323,"chunkText":"        for _ in range(num_stems):\n            mask_estimator = MaskEstimator(dim=dim, dim_inputs=freqs_per_bands_with_complex, depth=mask_estimator_depth)\n\n            self.mask_estimators.append(mask_estimator)\n\n        self.multi_stft_resolution_loss_weight = multi_stft_resolution_loss_weight\n        self.multi_stft_resolutions_window_sizes = multi_stft_resolutions_window_sizes\n        self.multi_stft_n_fft = stft_n_fft\n        self.multi_stft_window_fn = multi_stft_window_fn\n\n        self.multi_stft_kwargs = dict(hop_length=multi_stft_hop_size, normalized=multi_stft_normalized)\n","chunkId":"audio_separator/separator/uvr_lib_v5/roformer/mel_band_roformer.py:22"}],[1765,{"filePath":"audio_separator/separator/uvr_lib_v5/roformer/mel_band_roformer.py","startLine":321,"endLine":342,"chunkText":"\n        self.multi_stft_kwargs = dict(hop_length=multi_stft_hop_size, normalized=multi_stft_normalized)\n\n        self.match_input_audio_length = match_input_audio_length\n\n    def forward(self, raw_audio, target=None, return_loss_breakdown=False):\n        \"\"\"\n        einops\n\n        b - batch\n        f - freq\n        t - time\n        s - audio channel (1 for mono, 2 for stereo)\n        n - number of 'stems'\n        c - complex (2)\n        d - feature dimension\n        \"\"\"\n\n        original_device = raw_audio.device\n        x_is_mps = True if original_device.type == \"mps\" else False\n\n        if x_is_mps:","chunkId":"audio_separator/separator/uvr_lib_v5/roformer/mel_band_roformer.py:23"}],[1766,{"filePath":"audio_separator/separator/uvr_lib_v5/roformer/mel_band_roformer.py","startLine":340,"endLine":355,"chunkText":"        x_is_mps = True if original_device.type == \"mps\" else False\n\n        if x_is_mps:\n            raw_audio = raw_audio.cpu()\n\n        device = raw_audio.device\n\n        if raw_audio.ndim == 2:\n            raw_audio = rearrange(raw_audio, \"b t -> b 1 t\")\n\n        batch, channels, raw_audio_length = raw_audio.shape\n\n        istft_length = raw_audio_length if self.match_input_audio_length else None\n\n        assert (not self.stereo and channels == 1) or (\n            self.stereo and channels == 2","chunkId":"audio_separator/separator/uvr_lib_v5/roformer/mel_band_roformer.py:24"}],[1767,{"filePath":"audio_separator/separator/uvr_lib_v5/roformer/mel_band_roformer.py","startLine":353,"endLine":364,"chunkText":"\n        assert (not self.stereo and channels == 1) or (\n            self.stereo and channels == 2\n        ), \"stereo needs to be set to True if passing in audio signal that is stereo (channel dimension of 2). also need to be False if mono (channel dimension of 1)\"\n\n        raw_audio, batch_audio_channel_packed_shape = pack_one(raw_audio, \"* t\")\n\n        stft_window = self.stft_window_fn().to(device)\n\n        stft_repr = torch.stft(raw_audio, **self.stft_kwargs, window=stft_window, return_complex=True)\n        stft_repr = torch.view_as_real(stft_repr)\n","chunkId":"audio_separator/separator/uvr_lib_v5/roformer/mel_band_roformer.py:25"}],[1768,{"filePath":"audio_separator/separator/uvr_lib_v5/roformer/mel_band_roformer.py","startLine":362,"endLine":371,"chunkText":"        stft_repr = torch.stft(raw_audio, **self.stft_kwargs, window=stft_window, return_complex=True)\n        stft_repr = torch.view_as_real(stft_repr)\n\n        stft_repr = unpack_one(stft_repr, batch_audio_channel_packed_shape, \"* f t c\")\n        stft_repr = rearrange(stft_repr, \"b s f t c -> b (f s) t c\")  # merge stereo / mono into the frequency, with frequency leading dimension, for band splitting\n\n        batch_arange = torch.arange(batch, device=device)[..., None]\n\n        x = stft_repr[batch_arange, self.freq_indices.cpu()] if x_is_mps else stft_repr[batch_arange, self.freq_indices]\n","chunkId":"audio_separator/separator/uvr_lib_v5/roformer/mel_band_roformer.py:26"}],[1769,{"filePath":"audio_separator/separator/uvr_lib_v5/roformer/mel_band_roformer.py","startLine":369,"endLine":389,"chunkText":"\n        x = stft_repr[batch_arange, self.freq_indices.cpu()] if x_is_mps else stft_repr[batch_arange, self.freq_indices]\n\n        x = rearrange(x, \"b f t c -> b t (f c)\")\n\n        x = self.band_split(x)\n\n        for time_transformer, freq_transformer in self.layers:\n            x = rearrange(x, \"b t f d -> b f t d\")\n            x, ps = pack([x], \"* t d\")\n\n            x = time_transformer(x)\n\n            (x,) = unpack(x, ps, \"* t d\")\n            x = rearrange(x, \"b f t d -> b t f d\")\n            x, ps = pack([x], \"* f d\")\n\n            x = freq_transformer(x)\n\n            (x,) = unpack(x, ps, \"* f d\")\n","chunkId":"audio_separator/separator/uvr_lib_v5/roformer/mel_band_roformer.py:27"}],[1770,{"filePath":"audio_separator/separator/uvr_lib_v5/roformer/mel_band_roformer.py","startLine":387,"endLine":404,"chunkText":"\n            (x,) = unpack(x, ps, \"* f d\")\n\n        masks = torch.stack([fn(x) for fn in self.mask_estimators], dim=1)\n        masks = rearrange(masks, \"b n t (f c) -> b n f t c\", c=2)\n\n        if x_is_mps:\n            masks = masks.cpu()\n\n        stft_repr = rearrange(stft_repr, \"b f t c -> b 1 f t c\")\n\n        stft_repr = torch.view_as_complex(stft_repr)\n        masks = torch.view_as_complex(masks)\n\n        masks = masks.type(stft_repr.dtype)\n\n        if x_is_mps:\n            scatter_indices = repeat(self.freq_indices.cpu(), \"f -> b n f t\", b=batch, n=self.num_stems, t=stft_repr.shape[-1])","chunkId":"audio_separator/separator/uvr_lib_v5/roformer/mel_band_roformer.py:28"}],[1771,{"filePath":"audio_separator/separator/uvr_lib_v5/roformer/mel_band_roformer.py","startLine":402,"endLine":410,"chunkText":"\n        if x_is_mps:\n            scatter_indices = repeat(self.freq_indices.cpu(), \"f -> b n f t\", b=batch, n=self.num_stems, t=stft_repr.shape[-1])\n        else:\n            scatter_indices = repeat(self.freq_indices, \"f -> b n f t\", b=batch, n=self.num_stems, t=stft_repr.shape[-1])\n\n        stft_repr_expanded_stems = repeat(stft_repr, \"b 1 ... -> b n ...\", n=self.num_stems)\n        masks_summed = (\n            torch.zeros_like(stft_repr_expanded_stems.cpu() if x_is_mps else stft_repr_expanded_stems)","chunkId":"audio_separator/separator/uvr_lib_v5/roformer/mel_band_roformer.py:29"}],[1772,{"filePath":"audio_separator/separator/uvr_lib_v5/roformer/mel_band_roformer.py","startLine":408,"endLine":421,"chunkText":"        stft_repr_expanded_stems = repeat(stft_repr, \"b 1 ... -> b n ...\", n=self.num_stems)\n        masks_summed = (\n            torch.zeros_like(stft_repr_expanded_stems.cpu() if x_is_mps else stft_repr_expanded_stems)\n            .scatter_add_(2, scatter_indices.cpu() if x_is_mps else scatter_indices, masks.cpu() if x_is_mps else masks)\n            .to(device)\n        )\n\n        denom = repeat(self.num_bands_per_freq, \"f -> (f r) 1\", r=channels)\n\n        if x_is_mps:\n            denom = denom.cpu()\n\n        masks_averaged = masks_summed / denom.clamp(min=1e-8)\n","chunkId":"audio_separator/separator/uvr_lib_v5/roformer/mel_band_roformer.py:30"}],[1773,{"filePath":"audio_separator/separator/uvr_lib_v5/roformer/mel_band_roformer.py","startLine":419,"endLine":430,"chunkText":"\n        masks_averaged = masks_summed / denom.clamp(min=1e-8)\n\n        stft_repr = stft_repr * masks_averaged\n\n        stft_repr = rearrange(stft_repr, \"b n (f s) t -> (b n s) f t\", s=self.audio_channels)\n\n        recon_audio = torch.istft(stft_repr.cpu() if x_is_mps else stft_repr, **self.stft_kwargs, window=stft_window.cpu() if x_is_mps else stft_window, return_complex=False, length=istft_length)\n\n        recon_audio = rearrange(recon_audio, \"(b n s) t -> b n s t\", b=batch, s=self.audio_channels, n=self.num_stems)\n\n        if self.num_stems == 1:","chunkId":"audio_separator/separator/uvr_lib_v5/roformer/mel_band_roformer.py:31"}],[1774,{"filePath":"audio_separator/separator/uvr_lib_v5/roformer/mel_band_roformer.py","startLine":428,"endLine":445,"chunkText":"        recon_audio = rearrange(recon_audio, \"(b n s) t -> b n s t\", b=batch, s=self.audio_channels, n=self.num_stems)\n\n        if self.num_stems == 1:\n            recon_audio = rearrange(recon_audio, \"b 1 s t -> b s t\")\n\n        if not exists(target):\n            return recon_audio\n\n        if self.num_stems > 1:\n            assert target.ndim == 4 and target.shape[1] == self.num_stems\n\n        if target.ndim == 2:\n            target = rearrange(target, \"... t -> ... 1 t\")\n\n        target = target[..., : recon_audio.shape[-1]]\n\n        loss = F.l1_loss(recon_audio, target)\n","chunkId":"audio_separator/separator/uvr_lib_v5/roformer/mel_band_roformer.py:32"}],[1775,{"filePath":"audio_separator/separator/uvr_lib_v5/roformer/mel_band_roformer.py","startLine":443,"endLine":455,"chunkText":"\n        loss = F.l1_loss(recon_audio, target)\n\n        multi_stft_resolution_loss = 0.0\n\n        for window_size in self.multi_stft_resolutions_window_sizes:\n            res_stft_kwargs = dict(\n                n_fft=max(window_size, self.multi_stft_n_fft), win_length=window_size, return_complex=True, window=self.multi_stft_window_fn(window_size, device=device), **self.multi_stft_kwargs\n            )\n\n            recon_Y = torch.stft(rearrange(recon_audio, \"... s t -> (... s) t\"), **res_stft_kwargs)\n            target_Y = torch.stft(rearrange(target, \"... s t -> (... s) t\"), **res_stft_kwargs)\n","chunkId":"audio_separator/separator/uvr_lib_v5/roformer/mel_band_roformer.py:33"}],[1776,{"filePath":"audio_separator/separator/uvr_lib_v5/roformer/mel_band_roformer.py","startLine":453,"endLine":463,"chunkText":"            recon_Y = torch.stft(rearrange(recon_audio, \"... s t -> (... s) t\"), **res_stft_kwargs)\n            target_Y = torch.stft(rearrange(target, \"... s t -> (... s) t\"), **res_stft_kwargs)\n\n            multi_stft_resolution_loss = multi_stft_resolution_loss + F.l1_loss(recon_Y, target_Y)\n\n        weighted_multi_resolution_loss = multi_stft_resolution_loss * self.multi_stft_resolution_loss_weight\n\n        total_loss = loss + weighted_multi_resolution_loss\n\n        # Move the total loss back to the original device if necessary\n        if x_is_mps:","chunkId":"audio_separator/separator/uvr_lib_v5/roformer/mel_band_roformer.py:34"}],[1777,{"filePath":"audio_separator/separator/uvr_lib_v5/roformer/mel_band_roformer.py","startLine":461,"endLine":471,"chunkText":"\n        # Move the total loss back to the original device if necessary\n        if x_is_mps:\n            total_loss = total_loss.to(original_device)\n\n        if not return_loss_breakdown:\n            return total_loss\n\n        # If detailed loss breakdown is requested, ensure all components are on the original device\n        return total_loss, (loss.to(original_device) if x_is_mps else loss, multi_stft_resolution_loss.to(original_device) if x_is_mps else multi_stft_resolution_loss)\n","chunkId":"audio_separator/separator/uvr_lib_v5/roformer/mel_band_roformer.py:35"}],[1778,{"filePath":"audio_separator/separator/uvr_lib_v5/spec_utils.py","startLine":0,"endLine":23,"chunkText":"import audioread\r\nimport librosa\r\nimport numpy as np\r\nimport soundfile as sf\r\nimport math\r\nimport platform\r\nimport traceback\r\nfrom audio_separator.separator.uvr_lib_v5 import pyrb\r\nfrom scipy.signal import correlate, hilbert\r\nimport io\r\n\r\nOPERATING_SYSTEM = platform.system()\r\nSYSTEM_ARCH = platform.platform()\r\nSYSTEM_PROC = platform.processor()\r\nARM = \"arm\"\r\n\r\nAUTO_PHASE = \"Automatic\"\r\nPOSITIVE_PHASE = \"Positive Phase\"\r\nNEGATIVE_PHASE = \"Negative Phase\"\r\nNONE_P = (\"None\",)\r\nLOW_P = (\"Shifts: Low\",)\r\nMED_P = (\"Shifts: Medium\",)\r\nHIGH_P = (\"Shifts: High\",)\r\nVHIGH_P = \"Shifts: Very High\"\r","chunkId":"audio_separator/separator/uvr_lib_v5/spec_utils.py:0"}],[1779,{"filePath":"audio_separator/separator/uvr_lib_v5/spec_utils.py","startLine":21,"endLine":40,"chunkText":"MED_P = (\"Shifts: Medium\",)\r\nHIGH_P = (\"Shifts: High\",)\r\nVHIGH_P = \"Shifts: Very High\"\r\nMAXIMUM_P = \"Shifts: Maximum\"\r\n\r\nprogress_value = 0\r\nlast_update_time = 0\r\nis_macos = False\r\n\r\n\r\nif OPERATING_SYSTEM == \"Darwin\":\r\n    wav_resolution = \"polyphase\" if SYSTEM_PROC == ARM or ARM in SYSTEM_ARCH else \"sinc_fastest\"\r\n    wav_resolution_float_resampling = \"kaiser_best\" if SYSTEM_PROC == ARM or ARM in SYSTEM_ARCH else wav_resolution\r\n    is_macos = True\r\nelse:\r\n    wav_resolution = \"sinc_fastest\"\r\n    wav_resolution_float_resampling = wav_resolution\r\n\r\nMAX_SPEC = \"Max Spec\"\r\nMIN_SPEC = \"Min Spec\"\r","chunkId":"audio_separator/separator/uvr_lib_v5/spec_utils.py:1"}],[1780,{"filePath":"audio_separator/separator/uvr_lib_v5/spec_utils.py","startLine":38,"endLine":59,"chunkText":"\r\nMAX_SPEC = \"Max Spec\"\r\nMIN_SPEC = \"Min Spec\"\r\nLIN_ENSE = \"Linear Ensemble\"\r\n\r\nMAX_WAV = MAX_SPEC\r\nMIN_WAV = MIN_SPEC\r\n\r\nAVERAGE = \"Average\"\r\n\r\n\r\ndef crop_center(h1, h2):\r\n    \"\"\"\r\n    This function crops the center of the first input tensor to match the size of the second input tensor.\r\n    It is used to ensure that the two tensors have the same size in the time dimension.\r\n    \"\"\"\r\n    h1_shape = h1.size()\r\n    h2_shape = h2.size()\r\n\r\n    # If the time dimensions are already equal, return the first tensor as is\r\n    if h1_shape[3] == h2_shape[3]:\r\n        return h1\r","chunkId":"audio_separator/separator/uvr_lib_v5/spec_utils.py:2"}],[1781,{"filePath":"audio_separator/separator/uvr_lib_v5/spec_utils.py","startLine":57,"endLine":74,"chunkText":"    # If the time dimensions are already equal, return the first tensor as is\r\n    if h1_shape[3] == h2_shape[3]:\r\n        return h1\r\n    # If the time dimension of the first tensor is smaller, raise an error\r\n    elif h1_shape[3] < h2_shape[3]:\r\n        raise ValueError(\"h1_shape[3] must be greater than h2_shape[3]\")\r\n\r\n    # Calculate the start and end indices for cropping\r\n    s_time = (h1_shape[3] - h2_shape[3]) // 2\r\n    e_time = s_time + h2_shape[3]\r\n    # Crop the first tensor\r\n    h1 = h1[:, :, :, s_time:e_time]\r\n\r\n    return h1\r\n\r\n\r\ndef preprocess(X_spec):\r\n    \"\"\"\r","chunkId":"audio_separator/separator/uvr_lib_v5/spec_utils.py:3"}],[1782,{"filePath":"audio_separator/separator/uvr_lib_v5/spec_utils.py","startLine":72,"endLine":90,"chunkText":"\r\ndef preprocess(X_spec):\r\n    \"\"\"\r\n    This function preprocesses a spectrogram by separating it into magnitude and phase components.\r\n    This is a common preprocessing step in audio processing tasks.\r\n    \"\"\"\r\n    X_mag = np.abs(X_spec)\r\n    X_phase = np.angle(X_spec)\r\n\r\n    return X_mag, X_phase\r\n\r\n\r\ndef make_padding(width, cropsize, offset):\r\n    \"\"\"\r\n    This function calculates the padding needed to make the width of an image divisible by the crop size.\r\n    It is used in the process of splitting an image into smaller patches.\r\n    \"\"\"\r\n    left = offset\r\n    roi_size = cropsize - offset * 2\r","chunkId":"audio_separator/separator/uvr_lib_v5/spec_utils.py:4"}],[1783,{"filePath":"audio_separator/separator/uvr_lib_v5/spec_utils.py","startLine":88,"endLine":109,"chunkText":"    \"\"\"\r\n    left = offset\r\n    roi_size = cropsize - offset * 2\r\n    if roi_size == 0:\r\n        roi_size = cropsize\r\n    right = roi_size - (width % roi_size) + left\r\n\r\n    return left, right, roi_size\r\n\r\n\r\ndef normalize(wave, max_peak=1.0, min_peak=None):\r\n    \"\"\"Normalize (or amplify) audio waveform to a specified peak value.\r\n\r\n    Args:\r\n        wave (array-like): Audio waveform.\r\n        max_peak (float): Maximum peak value for normalization.\r\n\r\n    Returns:\r\n        array-like: Normalized or original waveform.\r\n    \"\"\"\r\n    maxv = np.abs(wave).max()\r\n    if maxv > max_peak:\r","chunkId":"audio_separator/separator/uvr_lib_v5/spec_utils.py:5"}],[1784,{"filePath":"audio_separator/separator/uvr_lib_v5/spec_utils.py","startLine":107,"endLine":128,"chunkText":"    \"\"\"\r\n    maxv = np.abs(wave).max()\r\n    if maxv > max_peak:\r\n        wave *= max_peak / maxv\r\n    elif min_peak is not None and maxv < min_peak:\r\n        wave *= min_peak / maxv\r\n\r\n    return wave\r\n\r\n\r\ndef auto_transpose(audio_array: np.ndarray):\r\n    \"\"\"\r\n    Ensure that the audio array is in the (channels, samples) format.\r\n\r\n    Parameters:\r\n        audio_array (ndarray): Input audio array.\r\n\r\n    Returns:\r\n        ndarray: Transposed audio array if necessary.\r\n    \"\"\"\r\n\r\n    # If the second dimension is 2 (indicating stereo channels), transpose the array\r","chunkId":"audio_separator/separator/uvr_lib_v5/spec_utils.py:6"}],[1785,{"filePath":"audio_separator/separator/uvr_lib_v5/spec_utils.py","startLine":126,"endLine":145,"chunkText":"    \"\"\"\r\n\r\n    # If the second dimension is 2 (indicating stereo channels), transpose the array\r\n    if audio_array.shape[1] == 2:\r\n        return audio_array.T\r\n    return audio_array\r\n\r\n\r\ndef write_array_to_mem(audio_data, subtype):\r\n    if isinstance(audio_data, np.ndarray):\r\n        audio_buffer = io.BytesIO()\r\n        sf.write(audio_buffer, audio_data, 44100, subtype=subtype, format=\"WAV\")\r\n        audio_buffer.seek(0)\r\n        return audio_buffer\r\n    else:\r\n        return audio_data\r\n\r\n\r\ndef spectrogram_to_image(spec, mode=\"magnitude\"):\r\n    if mode == \"magnitude\":\r","chunkId":"audio_separator/separator/uvr_lib_v5/spec_utils.py:7"}],[1786,{"filePath":"audio_separator/separator/uvr_lib_v5/spec_utils.py","startLine":143,"endLine":167,"chunkText":"\r\ndef spectrogram_to_image(spec, mode=\"magnitude\"):\r\n    if mode == \"magnitude\":\r\n        if np.iscomplexobj(spec):\r\n            y = np.abs(spec)\r\n        else:\r\n            y = spec\r\n        y = np.log10(y**2 + 1e-8)\r\n    elif mode == \"phase\":\r\n        if np.iscomplexobj(spec):\r\n            y = np.angle(spec)\r\n        else:\r\n            y = spec\r\n\r\n    y -= y.min()\r\n    y *= 255 / y.max()\r\n    img = np.uint8(y)\r\n\r\n    if y.ndim == 3:\r\n        img = img.transpose(1, 2, 0)\r\n        img = np.concatenate([np.max(img, axis=2, keepdims=True), img], axis=2)\r\n\r\n    return img\r\n\r\n\r","chunkId":"audio_separator/separator/uvr_lib_v5/spec_utils.py:8"}],[1787,{"filePath":"audio_separator/separator/uvr_lib_v5/spec_utils.py","startLine":165,"endLine":186,"chunkText":"    return img\r\n\r\n\r\ndef reduce_vocal_aggressively(X, y, softmask):\r\n    v = X - y\r\n    y_mag_tmp = np.abs(y)\r\n    v_mag_tmp = np.abs(v)\r\n\r\n    v_mask = v_mag_tmp > y_mag_tmp\r\n    y_mag = np.clip(y_mag_tmp - v_mag_tmp * v_mask * softmask, 0, np.inf)\r\n\r\n    return y_mag * np.exp(1.0j * np.angle(y))\r\n\r\n\r\ndef merge_artifacts(y_mask, thres=0.01, min_range=64, fade_size=32):\r\n    mask = y_mask\r\n\r\n    try:\r\n        if min_range < fade_size * 2:\r\n            raise ValueError(\"min_range must be >= fade_size * 2\")\r\n\r\n        idx = np.where(y_mask.min(axis=(0, 1)) > thres)[0]\r","chunkId":"audio_separator/separator/uvr_lib_v5/spec_utils.py:9"}],[1788,{"filePath":"audio_separator/separator/uvr_lib_v5/spec_utils.py","startLine":184,"endLine":195,"chunkText":"            raise ValueError(\"min_range must be >= fade_size * 2\")\r\n\r\n        idx = np.where(y_mask.min(axis=(0, 1)) > thres)[0]\r\n        start_idx = np.insert(idx[np.where(np.diff(idx) != 1)[0] + 1], 0, idx[0])\r\n        end_idx = np.append(idx[np.where(np.diff(idx) != 1)[0]], idx[-1])\r\n        artifact_idx = np.where(end_idx - start_idx > min_range)[0]\r\n        weight = np.zeros_like(y_mask)\r\n        if len(artifact_idx) > 0:\r\n            start_idx = start_idx[artifact_idx]\r\n            end_idx = end_idx[artifact_idx]\r\n            old_e = None\r\n            for s, e in zip(start_idx, end_idx):\r","chunkId":"audio_separator/separator/uvr_lib_v5/spec_utils.py:10"}],[1789,{"filePath":"audio_separator/separator/uvr_lib_v5/spec_utils.py","startLine":193,"endLine":208,"chunkText":"            end_idx = end_idx[artifact_idx]\r\n            old_e = None\r\n            for s, e in zip(start_idx, end_idx):\r\n                if old_e is not None and s - old_e < fade_size:\r\n                    s = old_e - fade_size * 2\r\n\r\n                if s != 0:\r\n                    weight[:, :, s : s + fade_size] = np.linspace(0, 1, fade_size)\r\n                else:\r\n                    s -= fade_size\r\n\r\n                if e != y_mask.shape[2]:\r\n                    weight[:, :, e - fade_size : e] = np.linspace(1, 0, fade_size)\r\n                else:\r\n                    e += fade_size\r\n\r","chunkId":"audio_separator/separator/uvr_lib_v5/spec_utils.py:11"}],[1790,{"filePath":"audio_separator/separator/uvr_lib_v5/spec_utils.py","startLine":206,"endLine":227,"chunkText":"                else:\r\n                    e += fade_size\r\n\r\n                weight[:, :, s + fade_size : e - fade_size] = 1\r\n                old_e = e\r\n\r\n        v_mask = 1 - y_mask\r\n        y_mask += weight * v_mask\r\n\r\n        mask = y_mask\r\n    except Exception as e:\r\n        error_name = f\"{type(e).__name__}\"\r\n        traceback_text = \"\".join(traceback.format_tb(e.__traceback__))\r\n        message = f'{error_name}: \"{e}\"\\n{traceback_text}\"'\r\n        print(\"Post Process Failed: \", message)\r\n\r\n    return mask\r\n\r\n\r\ndef align_wave_head_and_tail(a, b):\r\n    l = min([a[0].size, b[0].size])\r\n\r","chunkId":"audio_separator/separator/uvr_lib_v5/spec_utils.py:12"}],[1791,{"filePath":"audio_separator/separator/uvr_lib_v5/spec_utils.py","startLine":225,"endLine":241,"chunkText":"def align_wave_head_and_tail(a, b):\r\n    l = min([a[0].size, b[0].size])\r\n\r\n    return a[:l, :l], b[:l, :l]\r\n\r\n\r\ndef convert_channels(spec, mp, band):\r\n    cc = mp.param[\"band\"][band].get(\"convert_channels\")\r\n\r\n    if \"mid_side_c\" == cc:\r\n        spec_left = np.add(spec[0], spec[1] * 0.25)\r\n        spec_right = np.subtract(spec[1], spec[0] * 0.25)\r\n    elif \"mid_side\" == cc:\r\n        spec_left = np.add(spec[0], spec[1]) / 2\r\n        spec_right = np.subtract(spec[0], spec[1])\r\n    elif \"stereo_n\" == cc:\r\n        spec_left = np.add(spec[0], spec[1] * 0.25) / 0.9375\r","chunkId":"audio_separator/separator/uvr_lib_v5/spec_utils.py:13"}],[1792,{"filePath":"audio_separator/separator/uvr_lib_v5/spec_utils.py","startLine":239,"endLine":255,"chunkText":"        spec_right = np.subtract(spec[0], spec[1])\r\n    elif \"stereo_n\" == cc:\r\n        spec_left = np.add(spec[0], spec[1] * 0.25) / 0.9375\r\n        spec_right = np.add(spec[1], spec[0] * 0.25) / 0.9375\r\n    else:\r\n        return spec\r\n\r\n    return np.asfortranarray([spec_left, spec_right])\r\n\r\n\r\ndef combine_spectrograms(specs, mp, is_v51_model=False):\r\n    l = min([specs[i].shape[2] for i in specs])\r\n    spec_c = np.zeros(shape=(2, mp.param[\"bins\"] + 1, l), dtype=np.complex64)\r\n    offset = 0\r\n    bands_n = len(mp.param[\"band\"])\r\n\r\n    for d in range(1, bands_n + 1):\r","chunkId":"audio_separator/separator/uvr_lib_v5/spec_utils.py:14"}],[1793,{"filePath":"audio_separator/separator/uvr_lib_v5/spec_utils.py","startLine":253,"endLine":267,"chunkText":"    bands_n = len(mp.param[\"band\"])\r\n\r\n    for d in range(1, bands_n + 1):\r\n        h = mp.param[\"band\"][d][\"crop_stop\"] - mp.param[\"band\"][d][\"crop_start\"]\r\n        spec_c[:, offset : offset + h, :l] = specs[d][:, mp.param[\"band\"][d][\"crop_start\"] : mp.param[\"band\"][d][\"crop_stop\"], :l]\r\n        offset += h\r\n\r\n    if offset > mp.param[\"bins\"]:\r\n        raise ValueError(\"Too much bins\")\r\n\r\n    # lowpass fiter\r\n\r\n    if mp.param[\"pre_filter_start\"] > 0:\r\n        if is_v51_model:\r\n            spec_c *= get_lp_filter_mask(spec_c.shape[1], mp.param[\"pre_filter_start\"], mp.param[\"pre_filter_stop\"])\r","chunkId":"audio_separator/separator/uvr_lib_v5/spec_utils.py:15"}],[1794,{"filePath":"audio_separator/separator/uvr_lib_v5/spec_utils.py","startLine":265,"endLine":275,"chunkText":"    if mp.param[\"pre_filter_start\"] > 0:\r\n        if is_v51_model:\r\n            spec_c *= get_lp_filter_mask(spec_c.shape[1], mp.param[\"pre_filter_start\"], mp.param[\"pre_filter_stop\"])\r\n        else:\r\n            if bands_n == 1:\r\n                spec_c = fft_lp_filter(spec_c, mp.param[\"pre_filter_start\"], mp.param[\"pre_filter_stop\"])\r\n            else:\r\n                gp = 1\r\n                for b in range(mp.param[\"pre_filter_start\"] + 1, mp.param[\"pre_filter_stop\"]):\r\n                    g = math.pow(10, -(b - mp.param[\"pre_filter_start\"]) * (3.5 - gp) / 20.0)\r\n                    gp = g\r","chunkId":"audio_separator/separator/uvr_lib_v5/spec_utils.py:16"}],[1795,{"filePath":"audio_separator/separator/uvr_lib_v5/spec_utils.py","startLine":273,"endLine":288,"chunkText":"                for b in range(mp.param[\"pre_filter_start\"] + 1, mp.param[\"pre_filter_stop\"]):\r\n                    g = math.pow(10, -(b - mp.param[\"pre_filter_start\"]) * (3.5 - gp) / 20.0)\r\n                    gp = g\r\n                    spec_c[:, b, :] *= g\r\n\r\n    return np.asfortranarray(spec_c)\r\n\r\n\r\ndef wave_to_spectrogram(wave, hop_length, n_fft, mp, band, is_v51_model=False):\r\n\r\n    if wave.ndim == 1:\r\n        wave = np.asfortranarray([wave, wave])\r\n\r\n    if not is_v51_model:\r\n        if mp.param[\"reverse\"]:\r\n            wave_left = np.flip(np.asfortranarray(wave[0]))\r","chunkId":"audio_separator/separator/uvr_lib_v5/spec_utils.py:17"}],[1796,{"filePath":"audio_separator/separator/uvr_lib_v5/spec_utils.py","startLine":286,"endLine":296,"chunkText":"    if not is_v51_model:\r\n        if mp.param[\"reverse\"]:\r\n            wave_left = np.flip(np.asfortranarray(wave[0]))\r\n            wave_right = np.flip(np.asfortranarray(wave[1]))\r\n        elif mp.param[\"mid_side\"]:\r\n            wave_left = np.asfortranarray(np.add(wave[0], wave[1]) / 2)\r\n            wave_right = np.asfortranarray(np.subtract(wave[0], wave[1]))\r\n        elif mp.param[\"mid_side_b2\"]:\r\n            wave_left = np.asfortranarray(np.add(wave[1], wave[0] * 0.5))\r\n            wave_right = np.asfortranarray(np.subtract(wave[0], wave[1] * 0.5))\r\n        else:\r","chunkId":"audio_separator/separator/uvr_lib_v5/spec_utils.py:18"}],[1797,{"filePath":"audio_separator/separator/uvr_lib_v5/spec_utils.py","startLine":294,"endLine":307,"chunkText":"            wave_left = np.asfortranarray(np.add(wave[1], wave[0] * 0.5))\r\n            wave_right = np.asfortranarray(np.subtract(wave[0], wave[1] * 0.5))\r\n        else:\r\n            wave_left = np.asfortranarray(wave[0])\r\n            wave_right = np.asfortranarray(wave[1])\r\n    else:\r\n        wave_left = np.asfortranarray(wave[0])\r\n        wave_right = np.asfortranarray(wave[1])\r\n\r\n    spec_left = librosa.stft(wave_left, n_fft=n_fft, hop_length=hop_length)\r\n    spec_right = librosa.stft(wave_right, n_fft=n_fft, hop_length=hop_length)\r\n\r\n    spec = np.asfortranarray([spec_left, spec_right])\r\n\r","chunkId":"audio_separator/separator/uvr_lib_v5/spec_utils.py:19"}],[1798,{"filePath":"audio_separator/separator/uvr_lib_v5/spec_utils.py","startLine":305,"endLine":323,"chunkText":"\r\n    spec = np.asfortranarray([spec_left, spec_right])\r\n\r\n    if is_v51_model:\r\n        spec = convert_channels(spec, mp, band)\r\n\r\n    return spec\r\n\r\n\r\ndef spectrogram_to_wave(spec, hop_length=1024, mp={}, band=0, is_v51_model=True):\r\n    spec_left = np.asfortranarray(spec[0])\r\n    spec_right = np.asfortranarray(spec[1])\r\n\r\n    wave_left = librosa.istft(spec_left, hop_length=hop_length)\r\n    wave_right = librosa.istft(spec_right, hop_length=hop_length)\r\n\r\n    if is_v51_model:\r\n        cc = mp.param[\"band\"][band].get(\"convert_channels\")\r\n        if \"mid_side_c\" == cc:\r","chunkId":"audio_separator/separator/uvr_lib_v5/spec_utils.py:20"}],[1799,{"filePath":"audio_separator/separator/uvr_lib_v5/spec_utils.py","startLine":321,"endLine":330,"chunkText":"    if is_v51_model:\r\n        cc = mp.param[\"band\"][band].get(\"convert_channels\")\r\n        if \"mid_side_c\" == cc:\r\n            return np.asfortranarray([np.subtract(wave_left / 1.0625, wave_right / 4.25), np.add(wave_right / 1.0625, wave_left / 4.25)])\r\n        elif \"mid_side\" == cc:\r\n            return np.asfortranarray([np.add(wave_left, wave_right / 2), np.subtract(wave_left, wave_right / 2)])\r\n        elif \"stereo_n\" == cc:\r\n            return np.asfortranarray([np.subtract(wave_left, wave_right * 0.25), np.subtract(wave_right, wave_left * 0.25)])\r\n    else:\r\n        if mp.param[\"reverse\"]:\r","chunkId":"audio_separator/separator/uvr_lib_v5/spec_utils.py:21"}],[1800,{"filePath":"audio_separator/separator/uvr_lib_v5/spec_utils.py","startLine":328,"endLine":336,"chunkText":"            return np.asfortranarray([np.subtract(wave_left, wave_right * 0.25), np.subtract(wave_right, wave_left * 0.25)])\r\n    else:\r\n        if mp.param[\"reverse\"]:\r\n            return np.asfortranarray([np.flip(wave_left), np.flip(wave_right)])\r\n        elif mp.param[\"mid_side\"]:\r\n            return np.asfortranarray([np.add(wave_left, wave_right / 2), np.subtract(wave_left, wave_right / 2)])\r\n        elif mp.param[\"mid_side_b2\"]:\r\n            return np.asfortranarray([np.add(wave_right / 1.25, 0.4 * wave_left), np.subtract(wave_left / 1.25, 0.4 * wave_right)])\r\n\r","chunkId":"audio_separator/separator/uvr_lib_v5/spec_utils.py:22"}],[1801,{"filePath":"audio_separator/separator/uvr_lib_v5/spec_utils.py","startLine":334,"endLine":347,"chunkText":"        elif mp.param[\"mid_side_b2\"]:\r\n            return np.asfortranarray([np.add(wave_right / 1.25, 0.4 * wave_left), np.subtract(wave_left / 1.25, 0.4 * wave_right)])\r\n\r\n    return np.asfortranarray([wave_left, wave_right])\r\n\r\n\r\ndef cmb_spectrogram_to_wave(spec_m, mp, extra_bins_h=None, extra_bins=None, is_v51_model=False):\r\n    bands_n = len(mp.param[\"band\"])\r\n    offset = 0\r\n\r\n    for d in range(1, bands_n + 1):\r\n        bp = mp.param[\"band\"][d]\r\n        spec_s = np.zeros(shape=(2, bp[\"n_fft\"] // 2 + 1, spec_m.shape[2]), dtype=complex)\r\n        h = bp[\"crop_stop\"] - bp[\"crop_start\"]\r","chunkId":"audio_separator/separator/uvr_lib_v5/spec_utils.py:23"}],[1802,{"filePath":"audio_separator/separator/uvr_lib_v5/spec_utils.py","startLine":345,"endLine":356,"chunkText":"        bp = mp.param[\"band\"][d]\r\n        spec_s = np.zeros(shape=(2, bp[\"n_fft\"] // 2 + 1, spec_m.shape[2]), dtype=complex)\r\n        h = bp[\"crop_stop\"] - bp[\"crop_start\"]\r\n        spec_s[:, bp[\"crop_start\"] : bp[\"crop_stop\"], :] = spec_m[:, offset : offset + h, :]\r\n\r\n        offset += h\r\n        if d == bands_n:  # higher\r\n            if extra_bins_h:  # if --high_end_process bypass\r\n                max_bin = bp[\"n_fft\"] // 2\r\n                spec_s[:, max_bin - extra_bins_h : max_bin, :] = extra_bins[:, :extra_bins_h, :]\r\n            if bp[\"hpf_start\"] > 0:\r\n                if is_v51_model:\r","chunkId":"audio_separator/separator/uvr_lib_v5/spec_utils.py:24"}],[1803,{"filePath":"audio_separator/separator/uvr_lib_v5/spec_utils.py","startLine":354,"endLine":362,"chunkText":"                spec_s[:, max_bin - extra_bins_h : max_bin, :] = extra_bins[:, :extra_bins_h, :]\r\n            if bp[\"hpf_start\"] > 0:\r\n                if is_v51_model:\r\n                    spec_s *= get_hp_filter_mask(spec_s.shape[1], bp[\"hpf_start\"], bp[\"hpf_stop\"] - 1)\r\n                else:\r\n                    spec_s = fft_hp_filter(spec_s, bp[\"hpf_start\"], bp[\"hpf_stop\"] - 1)\r\n            if bands_n == 1:\r\n                wave = spectrogram_to_wave(spec_s, bp[\"hl\"], mp, d, is_v51_model)\r\n            else:\r","chunkId":"audio_separator/separator/uvr_lib_v5/spec_utils.py:25"}],[1804,{"filePath":"audio_separator/separator/uvr_lib_v5/spec_utils.py","startLine":360,"endLine":372,"chunkText":"            if bands_n == 1:\r\n                wave = spectrogram_to_wave(spec_s, bp[\"hl\"], mp, d, is_v51_model)\r\n            else:\r\n                wave = np.add(wave, spectrogram_to_wave(spec_s, bp[\"hl\"], mp, d, is_v51_model))\r\n        else:\r\n            sr = mp.param[\"band\"][d + 1][\"sr\"]\r\n            if d == 1:  # lower\r\n                if is_v51_model:\r\n                    spec_s *= get_lp_filter_mask(spec_s.shape[1], bp[\"lpf_start\"], bp[\"lpf_stop\"])\r\n                else:\r\n                    spec_s = fft_lp_filter(spec_s, bp[\"lpf_start\"], bp[\"lpf_stop\"])\r\n\r\n                try:\r","chunkId":"audio_separator/separator/uvr_lib_v5/spec_utils.py:26"}],[1805,{"filePath":"audio_separator/separator/uvr_lib_v5/spec_utils.py","startLine":370,"endLine":379,"chunkText":"                    spec_s = fft_lp_filter(spec_s, bp[\"lpf_start\"], bp[\"lpf_stop\"])\r\n\r\n                try:\r\n                    wave = librosa.resample(spectrogram_to_wave(spec_s, bp[\"hl\"], mp, d, is_v51_model), orig_sr=bp[\"sr\"], target_sr=sr, res_type=wav_resolution)\r\n                except ValueError as e:\r\n                    print(f\"Error during resampling: {e}\")\r\n                    print(f\"Spec_s shape: {spec_s.shape}, SR: {sr}, Res type: {wav_resolution}\")\r\n\r\n            else:  # mid\r\n                if is_v51_model:\r","chunkId":"audio_separator/separator/uvr_lib_v5/spec_utils.py:27"}],[1806,{"filePath":"audio_separator/separator/uvr_lib_v5/spec_utils.py","startLine":377,"endLine":388,"chunkText":"\r\n            else:  # mid\r\n                if is_v51_model:\r\n                    spec_s *= get_hp_filter_mask(spec_s.shape[1], bp[\"hpf_start\"], bp[\"hpf_stop\"] - 1)\r\n                    spec_s *= get_lp_filter_mask(spec_s.shape[1], bp[\"lpf_start\"], bp[\"lpf_stop\"])\r\n                else:\r\n                    spec_s = fft_hp_filter(spec_s, bp[\"hpf_start\"], bp[\"hpf_stop\"] - 1)\r\n                    spec_s = fft_lp_filter(spec_s, bp[\"lpf_start\"], bp[\"lpf_stop\"])\r\n\r\n                wave2 = np.add(wave, spectrogram_to_wave(spec_s, bp[\"hl\"], mp, d, is_v51_model))\r\n\r\n                try:\r","chunkId":"audio_separator/separator/uvr_lib_v5/spec_utils.py:28"}],[1807,{"filePath":"audio_separator/separator/uvr_lib_v5/spec_utils.py","startLine":386,"endLine":397,"chunkText":"                wave2 = np.add(wave, spectrogram_to_wave(spec_s, bp[\"hl\"], mp, d, is_v51_model))\r\n\r\n                try:\r\n                    wave = librosa.resample(wave2, orig_sr=bp[\"sr\"], target_sr=sr, res_type=wav_resolution)\r\n                except ValueError as e:\r\n                    print(f\"Error during resampling: {e}\")\r\n                    print(f\"Spec_s shape: {spec_s.shape}, SR: {sr}, Res type: {wav_resolution}\")\r\n\r\n    return wave\r\n\r\n\r\ndef get_lp_filter_mask(n_bins, bin_start, bin_stop):\r","chunkId":"audio_separator/separator/uvr_lib_v5/spec_utils.py:29"}],[1808,{"filePath":"audio_separator/separator/uvr_lib_v5/spec_utils.py","startLine":395,"endLine":411,"chunkText":"\r\n\r\ndef get_lp_filter_mask(n_bins, bin_start, bin_stop):\r\n    mask = np.concatenate([np.ones((bin_start - 1, 1)), np.linspace(1, 0, bin_stop - bin_start + 1)[:, None], np.zeros((n_bins - bin_stop, 1))], axis=0)\r\n\r\n    return mask\r\n\r\n\r\ndef get_hp_filter_mask(n_bins, bin_start, bin_stop):\r\n    mask = np.concatenate([np.zeros((bin_stop + 1, 1)), np.linspace(0, 1, 1 + bin_start - bin_stop)[:, None], np.ones((n_bins - bin_start - 2, 1))], axis=0)\r\n\r\n    return mask\r\n\r\n\r\ndef fft_lp_filter(spec, bin_start, bin_stop):\r\n    g = 1.0\r\n    for b in range(bin_start, bin_stop):\r","chunkId":"audio_separator/separator/uvr_lib_v5/spec_utils.py:30"}],[1809,{"filePath":"audio_separator/separator/uvr_lib_v5/spec_utils.py","startLine":409,"endLine":432,"chunkText":"def fft_lp_filter(spec, bin_start, bin_stop):\r\n    g = 1.0\r\n    for b in range(bin_start, bin_stop):\r\n        g -= 1 / (bin_stop - bin_start)\r\n        spec[:, b, :] = g * spec[:, b, :]\r\n\r\n    spec[:, bin_stop:, :] *= 0\r\n\r\n    return spec\r\n\r\n\r\ndef fft_hp_filter(spec, bin_start, bin_stop):\r\n    g = 1.0\r\n    for b in range(bin_start, bin_stop, -1):\r\n        g -= 1 / (bin_start - bin_stop)\r\n        spec[:, b, :] = g * spec[:, b, :]\r\n\r\n    spec[:, 0 : bin_stop + 1, :] *= 0\r\n\r\n    return spec\r\n\r\n\r\ndef spectrogram_to_wave_old(spec, hop_length=1024):\r\n    if spec.ndim == 2:\r","chunkId":"audio_separator/separator/uvr_lib_v5/spec_utils.py:31"}],[1810,{"filePath":"audio_separator/separator/uvr_lib_v5/spec_utils.py","startLine":430,"endLine":446,"chunkText":"\r\ndef spectrogram_to_wave_old(spec, hop_length=1024):\r\n    if spec.ndim == 2:\r\n        wave = librosa.istft(spec, hop_length=hop_length)\r\n    elif spec.ndim == 3:\r\n        spec_left = np.asfortranarray(spec[0])\r\n        spec_right = np.asfortranarray(spec[1])\r\n\r\n        wave_left = librosa.istft(spec_left, hop_length=hop_length)\r\n        wave_right = librosa.istft(spec_right, hop_length=hop_length)\r\n        wave = np.asfortranarray([wave_left, wave_right])\r\n\r\n    return wave\r\n\r\n\r\ndef wave_to_spectrogram_old(wave, hop_length, n_fft):\r\n    wave_left = np.asfortranarray(wave[0])\r","chunkId":"audio_separator/separator/uvr_lib_v5/spec_utils.py:32"}],[1811,{"filePath":"audio_separator/separator/uvr_lib_v5/spec_utils.py","startLine":444,"endLine":459,"chunkText":"\r\ndef wave_to_spectrogram_old(wave, hop_length, n_fft):\r\n    wave_left = np.asfortranarray(wave[0])\r\n    wave_right = np.asfortranarray(wave[1])\r\n\r\n    spec_left = librosa.stft(wave_left, n_fft=n_fft, hop_length=hop_length)\r\n    spec_right = librosa.stft(wave_right, n_fft=n_fft, hop_length=hop_length)\r\n\r\n    spec = np.asfortranarray([spec_left, spec_right])\r\n\r\n    return spec\r\n\r\n\r\ndef mirroring(a, spec_m, input_high_end, mp):\r\n    if \"mirroring\" == a:\r\n        mirror = np.flip(np.abs(spec_m[:, mp.param[\"pre_filter_start\"] - 10 - input_high_end.shape[1] : mp.param[\"pre_filter_start\"] - 10, :]), 1)\r","chunkId":"audio_separator/separator/uvr_lib_v5/spec_utils.py:33"}],[1812,{"filePath":"audio_separator/separator/uvr_lib_v5/spec_utils.py","startLine":457,"endLine":465,"chunkText":"def mirroring(a, spec_m, input_high_end, mp):\r\n    if \"mirroring\" == a:\r\n        mirror = np.flip(np.abs(spec_m[:, mp.param[\"pre_filter_start\"] - 10 - input_high_end.shape[1] : mp.param[\"pre_filter_start\"] - 10, :]), 1)\r\n        mirror = mirror * np.exp(1.0j * np.angle(input_high_end))\r\n\r\n        return np.where(np.abs(input_high_end) <= np.abs(mirror), input_high_end, mirror)\r\n\r\n    if \"mirroring2\" == a:\r\n        mirror = np.flip(np.abs(spec_m[:, mp.param[\"pre_filter_start\"] - 10 - input_high_end.shape[1] : mp.param[\"pre_filter_start\"] - 10, :]), 1)\r","chunkId":"audio_separator/separator/uvr_lib_v5/spec_utils.py:34"}],[1813,{"filePath":"audio_separator/separator/uvr_lib_v5/spec_utils.py","startLine":463,"endLine":478,"chunkText":"\r\n    if \"mirroring2\" == a:\r\n        mirror = np.flip(np.abs(spec_m[:, mp.param[\"pre_filter_start\"] - 10 - input_high_end.shape[1] : mp.param[\"pre_filter_start\"] - 10, :]), 1)\r\n        mi = np.multiply(mirror, input_high_end * 1.7)\r\n\r\n        return np.where(np.abs(input_high_end) <= np.abs(mi), input_high_end, mi)\r\n\r\n\r\ndef adjust_aggr(mask, is_non_accom_stem, aggressiveness):\r\n    aggr = aggressiveness[\"value\"] * 2\r\n\r\n    if aggr != 0:\r\n        if is_non_accom_stem:\r\n            aggr = 1 - aggr\r\n\r\n        if np.any(aggr > 10) or np.any(aggr < -10):\r","chunkId":"audio_separator/separator/uvr_lib_v5/spec_utils.py:35"}],[1814,{"filePath":"audio_separator/separator/uvr_lib_v5/spec_utils.py","startLine":476,"endLine":488,"chunkText":"            aggr = 1 - aggr\r\n\r\n        if np.any(aggr > 10) or np.any(aggr < -10):\r\n            print(f\"Warning: Extreme aggressiveness values detected: {aggr}\")\r\n\r\n        aggr = [aggr, aggr]\r\n\r\n        if aggressiveness[\"aggr_correction\"] is not None:\r\n            aggr[0] += aggressiveness[\"aggr_correction\"][\"left\"]\r\n            aggr[1] += aggressiveness[\"aggr_correction\"][\"right\"]\r\n\r\n        for ch in range(2):\r\n            mask[ch, : aggressiveness[\"split_bin\"]] = np.power(mask[ch, : aggressiveness[\"split_bin\"]], 1 + aggr[ch] / 3)\r","chunkId":"audio_separator/separator/uvr_lib_v5/spec_utils.py:36"}],[1815,{"filePath":"audio_separator/separator/uvr_lib_v5/spec_utils.py","startLine":486,"endLine":500,"chunkText":"\r\n        for ch in range(2):\r\n            mask[ch, : aggressiveness[\"split_bin\"]] = np.power(mask[ch, : aggressiveness[\"split_bin\"]], 1 + aggr[ch] / 3)\r\n            mask[ch, aggressiveness[\"split_bin\"] :] = np.power(mask[ch, aggressiveness[\"split_bin\"] :], 1 + aggr[ch])\r\n\r\n    return mask\r\n\r\n\r\ndef stft(wave, nfft, hl):\r\n    wave_left = np.asfortranarray(wave[0])\r\n    wave_right = np.asfortranarray(wave[1])\r\n    spec_left = librosa.stft(wave_left, n_fft=nfft, hop_length=hl)\r\n    spec_right = librosa.stft(wave_right, n_fft=nfft, hop_length=hl)\r\n    spec = np.asfortranarray([spec_left, spec_right])\r\n\r","chunkId":"audio_separator/separator/uvr_lib_v5/spec_utils.py:37"}],[1816,{"filePath":"audio_separator/separator/uvr_lib_v5/spec_utils.py","startLine":498,"endLine":515,"chunkText":"    spec_right = librosa.stft(wave_right, n_fft=nfft, hop_length=hl)\r\n    spec = np.asfortranarray([spec_left, spec_right])\r\n\r\n    return spec\r\n\r\n\r\ndef istft(spec, hl):\r\n    spec_left = np.asfortranarray(spec[0])\r\n    spec_right = np.asfortranarray(spec[1])\r\n    wave_left = librosa.istft(spec_left, hop_length=hl)\r\n    wave_right = librosa.istft(spec_right, hop_length=hl)\r\n    wave = np.asfortranarray([wave_left, wave_right])\r\n\r\n    return wave\r\n\r\n\r\ndef spec_effects(wave, algorithm=\"Default\", value=None):\r\n    if np.isnan(wave).any() or np.isinf(wave).any():\r","chunkId":"audio_separator/separator/uvr_lib_v5/spec_utils.py:38"}],[1817,{"filePath":"audio_separator/separator/uvr_lib_v5/spec_utils.py","startLine":513,"endLine":524,"chunkText":"\r\ndef spec_effects(wave, algorithm=\"Default\", value=None):\r\n    if np.isnan(wave).any() or np.isinf(wave).any():\r\n        print(f\"Warning: Detected NaN or infinite values in wave input. Shape: {wave.shape}\")\r\n\r\n    spec = [stft(wave[0], 2048, 1024), stft(wave[1], 2048, 1024)]\r\n    if algorithm == \"Min_Mag\":\r\n        v_spec_m = np.where(np.abs(spec[1]) <= np.abs(spec[0]), spec[1], spec[0])\r\n        wave = istft(v_spec_m, 1024)\r\n    elif algorithm == \"Max_Mag\":\r\n        v_spec_m = np.where(np.abs(spec[1]) >= np.abs(spec[0]), spec[1], spec[0])\r\n        wave = istft(v_spec_m, 1024)\r","chunkId":"audio_separator/separator/uvr_lib_v5/spec_utils.py:39"}],[1818,{"filePath":"audio_separator/separator/uvr_lib_v5/spec_utils.py","startLine":522,"endLine":537,"chunkText":"    elif algorithm == \"Max_Mag\":\r\n        v_spec_m = np.where(np.abs(spec[1]) >= np.abs(spec[0]), spec[1], spec[0])\r\n        wave = istft(v_spec_m, 1024)\r\n    elif algorithm == \"Default\":\r\n        wave = (wave[1] * value) + (wave[0] * (1 - value))\r\n    elif algorithm == \"Invert_p\":\r\n        X_mag = np.abs(spec[0])\r\n        y_mag = np.abs(spec[1])\r\n        max_mag = np.where(X_mag >= y_mag, X_mag, y_mag)\r\n        v_spec = spec[1] - max_mag * np.exp(1.0j * np.angle(spec[0]))\r\n        wave = istft(v_spec, 1024)\r\n\r\n    return wave\r\n\r\n\r\ndef spectrogram_to_wave_no_mp(spec, n_fft=2048, hop_length=1024):\r","chunkId":"audio_separator/separator/uvr_lib_v5/spec_utils.py:40"}],[1819,{"filePath":"audio_separator/separator/uvr_lib_v5/spec_utils.py","startLine":535,"endLine":559,"chunkText":"\r\n\r\ndef spectrogram_to_wave_no_mp(spec, n_fft=2048, hop_length=1024):\r\n    wave = librosa.istft(spec, n_fft=n_fft, hop_length=hop_length)\r\n\r\n    if wave.ndim == 1:\r\n        wave = np.asfortranarray([wave, wave])\r\n\r\n    return wave\r\n\r\n\r\ndef wave_to_spectrogram_no_mp(wave):\r\n\r\n    spec = librosa.stft(wave, n_fft=2048, hop_length=1024)\r\n\r\n    if spec.ndim == 1:\r\n        spec = np.asfortranarray([spec, spec])\r\n\r\n    return spec\r\n\r\n\r\ndef invert_audio(specs, invert_p=True):\r\n\r\n    ln = min([specs[0].shape[2], specs[1].shape[2]])\r\n    specs[0] = specs[0][:, :, :ln]\r","chunkId":"audio_separator/separator/uvr_lib_v5/spec_utils.py:41"}],[1820,{"filePath":"audio_separator/separator/uvr_lib_v5/spec_utils.py","startLine":557,"endLine":575,"chunkText":"\r\n    ln = min([specs[0].shape[2], specs[1].shape[2]])\r\n    specs[0] = specs[0][:, :, :ln]\r\n    specs[1] = specs[1][:, :, :ln]\r\n\r\n    if invert_p:\r\n        X_mag = np.abs(specs[0])\r\n        y_mag = np.abs(specs[1])\r\n        max_mag = np.where(X_mag >= y_mag, X_mag, y_mag)\r\n        v_spec = specs[1] - max_mag * np.exp(1.0j * np.angle(specs[0]))\r\n    else:\r\n        specs[1] = reduce_vocal_aggressively(specs[0], specs[1], 0.2)\r\n        v_spec = specs[0] - specs[1]\r\n\r\n    return v_spec\r\n\r\n\r\ndef invert_stem(mixture, stem):\r\n    mixture = wave_to_spectrogram_no_mp(mixture)\r","chunkId":"audio_separator/separator/uvr_lib_v5/spec_utils.py:42"}],[1821,{"filePath":"audio_separator/separator/uvr_lib_v5/spec_utils.py","startLine":573,"endLine":593,"chunkText":"\r\ndef invert_stem(mixture, stem):\r\n    mixture = wave_to_spectrogram_no_mp(mixture)\r\n    stem = wave_to_spectrogram_no_mp(stem)\r\n    output = spectrogram_to_wave_no_mp(invert_audio([mixture, stem]))\r\n\r\n    return -output.T\r\n\r\n\r\ndef ensembling(a, inputs, is_wavs=False):\r\n\r\n    for i in range(1, len(inputs)):\r\n        if i == 1:\r\n            input = inputs[0]\r\n\r\n        if is_wavs:\r\n            ln = min([input.shape[1], inputs[i].shape[1]])\r\n            input = input[:, :ln]\r\n            inputs[i] = inputs[i][:, :ln]\r\n        else:\r\n            ln = min([input.shape[2], inputs[i].shape[2]])\r","chunkId":"audio_separator/separator/uvr_lib_v5/spec_utils.py:43"}],[1822,{"filePath":"audio_separator/separator/uvr_lib_v5/spec_utils.py","startLine":591,"endLine":601,"chunkText":"            inputs[i] = inputs[i][:, :ln]\r\n        else:\r\n            ln = min([input.shape[2], inputs[i].shape[2]])\r\n            input = input[:, :, :ln]\r\n            inputs[i] = inputs[i][:, :, :ln]\r\n\r\n        if MIN_SPEC == a:\r\n            input = np.where(np.abs(inputs[i]) <= np.abs(input), inputs[i], input)\r\n        if MAX_SPEC == a:\r\n            #input = np.array(np.where(np.greater_equal(np.abs(inputs[i]), np.abs(input)), inputs[i], input), dtype=object)\r\n            input = np.where(np.abs(inputs[i]) >= np.abs(input), inputs[i], input)\r","chunkId":"audio_separator/separator/uvr_lib_v5/spec_utils.py:44"}],[1823,{"filePath":"audio_separator/separator/uvr_lib_v5/spec_utils.py","startLine":599,"endLine":614,"chunkText":"        if MAX_SPEC == a:\r\n            #input = np.array(np.where(np.greater_equal(np.abs(inputs[i]), np.abs(input)), inputs[i], input), dtype=object)\r\n            input = np.where(np.abs(inputs[i]) >= np.abs(input), inputs[i], input)\r\n            #max_spec = np.array([np.where(np.greater_equal(np.abs(inputs[i]), np.abs(input)), s, specs[0]) for s in specs[1:]], dtype=object)[-1]\r\n\r\n    # linear_ensemble\r\n    # input = ensemble_wav(inputs, split_size=1)\r\n\r\n    return input\r\n\r\n\r\ndef ensemble_for_align(waves):\r\n\r\n    specs = []\r\n\r\n    for wav in waves:\r","chunkId":"audio_separator/separator/uvr_lib_v5/spec_utils.py:45"}],[1824,{"filePath":"audio_separator/separator/uvr_lib_v5/spec_utils.py","startLine":612,"endLine":633,"chunkText":"    specs = []\r\n\r\n    for wav in waves:\r\n        spec = wave_to_spectrogram_no_mp(wav.T)\r\n        specs.append(spec)\r\n\r\n    wav_aligned = spectrogram_to_wave_no_mp(ensembling(MIN_SPEC, specs)).T\r\n    wav_aligned = match_array_shapes(wav_aligned, waves[1], is_swap=True)\r\n\r\n    return wav_aligned\r\n\r\n\r\ndef ensemble_inputs(audio_input, algorithm, is_normalization, wav_type_set, save_path, is_wave=False, is_array=False):\r\n\r\n    wavs_ = []\r\n\r\n    if algorithm == AVERAGE:\r\n        output = average_audio(audio_input)\r\n        samplerate = 44100\r\n    else:\r\n        specs = []\r\n\r","chunkId":"audio_separator/separator/uvr_lib_v5/spec_utils.py:46"}],[1825,{"filePath":"audio_separator/separator/uvr_lib_v5/spec_utils.py","startLine":631,"endLine":647,"chunkText":"    else:\r\n        specs = []\r\n\r\n        for i in range(len(audio_input)):\r\n            wave, samplerate = librosa.load(audio_input[i], mono=False, sr=44100)\r\n            wavs_.append(wave)\r\n            spec = wave if is_wave else wave_to_spectrogram_no_mp(wave)\r\n            specs.append(spec)\r\n\r\n        wave_shapes = [w.shape[1] for w in wavs_]\r\n        target_shape = wavs_[wave_shapes.index(max(wave_shapes))]\r\n\r\n        if is_wave:\r\n            output = ensembling(algorithm, specs, is_wavs=True)\r\n        else:\r\n            output = spectrogram_to_wave_no_mp(ensembling(algorithm, specs))\r\n\r","chunkId":"audio_separator/separator/uvr_lib_v5/spec_utils.py:47"}],[1826,{"filePath":"audio_separator/separator/uvr_lib_v5/spec_utils.py","startLine":645,"endLine":664,"chunkText":"        else:\r\n            output = spectrogram_to_wave_no_mp(ensembling(algorithm, specs))\r\n\r\n        output = to_shape(output, target_shape.shape)\r\n\r\n    sf.write(save_path, normalize(output.T, is_normalization), samplerate, subtype=wav_type_set)\r\n\r\n\r\ndef to_shape(x, target_shape):\r\n    padding_list = []\r\n    for x_dim, target_dim in zip(x.shape, target_shape):\r\n        pad_value = target_dim - x_dim\r\n        pad_tuple = (0, pad_value)\r\n        padding_list.append(pad_tuple)\r\n\r\n    return np.pad(x, tuple(padding_list), mode=\"constant\")\r\n\r\n\r\ndef to_shape_minimize(x: np.ndarray, target_shape):\r\n\r","chunkId":"audio_separator/separator/uvr_lib_v5/spec_utils.py:48"}],[1827,{"filePath":"audio_separator/separator/uvr_lib_v5/spec_utils.py","startLine":662,"endLine":679,"chunkText":"\r\ndef to_shape_minimize(x: np.ndarray, target_shape):\r\n\r\n    padding_list = []\r\n    for x_dim, target_dim in zip(x.shape, target_shape):\r\n        pad_value = target_dim - x_dim\r\n        pad_tuple = (0, pad_value)\r\n        padding_list.append(pad_tuple)\r\n\r\n    return np.pad(x, tuple(padding_list), mode=\"constant\")\r\n\r\n\r\ndef detect_leading_silence(audio, sr, silence_threshold=0.007, frame_length=1024):\r\n    \"\"\"\r\n    Detect silence at the beginning of an audio signal.\r\n\r\n    :param audio: np.array, audio signal\r\n    :param sr: int, sample rate\r","chunkId":"audio_separator/separator/uvr_lib_v5/spec_utils.py:49"}],[1828,{"filePath":"audio_separator/separator/uvr_lib_v5/spec_utils.py","startLine":677,"endLine":691,"chunkText":"\r\n    :param audio: np.array, audio signal\r\n    :param sr: int, sample rate\r\n    :param silence_threshold: float, magnitude threshold below which is considered silence\r\n    :param frame_length: int, the number of samples to consider for each check\r\n\r\n    :return: float, duration of the leading silence in milliseconds\r\n    \"\"\"\r\n\r\n    if len(audio.shape) == 2:\r\n        # If stereo, pick the channel with more energy to determine the silence\r\n        channel = np.argmax(np.sum(np.abs(audio), axis=1))\r\n        audio = audio[channel]\r\n\r\n    for i in range(0, len(audio), frame_length):\r","chunkId":"audio_separator/separator/uvr_lib_v5/spec_utils.py:50"}],[1829,{"filePath":"audio_separator/separator/uvr_lib_v5/spec_utils.py","startLine":689,"endLine":702,"chunkText":"        audio = audio[channel]\r\n\r\n    for i in range(0, len(audio), frame_length):\r\n        if np.max(np.abs(audio[i : i + frame_length])) > silence_threshold:\r\n            return (i / sr) * 1000\r\n\r\n    return (len(audio) / sr) * 1000\r\n\r\n\r\ndef adjust_leading_silence(target_audio, reference_audio, silence_threshold=0.01, frame_length=1024):\r\n    \"\"\"\r\n    Adjust the leading silence of the target_audio to match the leading silence of the reference_audio.\r\n\r\n    :param target_audio: np.array, audio signal that will have its silence adjusted\r","chunkId":"audio_separator/separator/uvr_lib_v5/spec_utils.py:51"}],[1830,{"filePath":"audio_separator/separator/uvr_lib_v5/spec_utils.py","startLine":700,"endLine":710,"chunkText":"    Adjust the leading silence of the target_audio to match the leading silence of the reference_audio.\r\n\r\n    :param target_audio: np.array, audio signal that will have its silence adjusted\r\n    :param reference_audio: np.array, audio signal used as a reference\r\n    :param sr: int, sample rate\r\n    :param silence_threshold: float, magnitude threshold below which is considered silence\r\n    :param frame_length: int, the number of samples to consider for each check\r\n\r\n    :return: np.array, target_audio adjusted to have the same leading silence as reference_audio\r\n    \"\"\"\r\n\r","chunkId":"audio_separator/separator/uvr_lib_v5/spec_utils.py:52"}],[1831,{"filePath":"audio_separator/separator/uvr_lib_v5/spec_utils.py","startLine":708,"endLine":721,"chunkText":"    :return: np.array, target_audio adjusted to have the same leading silence as reference_audio\r\n    \"\"\"\r\n\r\n    def find_silence_end(audio):\r\n        if len(audio.shape) == 2:\r\n            # If stereo, pick the channel with more energy to determine the silence\r\n            channel = np.argmax(np.sum(np.abs(audio), axis=1))\r\n            audio_mono = audio[channel]\r\n        else:\r\n            audio_mono = audio\r\n\r\n        for i in range(0, len(audio_mono), frame_length):\r\n            if np.max(np.abs(audio_mono[i : i + frame_length])) > silence_threshold:\r\n                return i\r","chunkId":"audio_separator/separator/uvr_lib_v5/spec_utils.py:53"}],[1832,{"filePath":"audio_separator/separator/uvr_lib_v5/spec_utils.py","startLine":719,"endLine":731,"chunkText":"        for i in range(0, len(audio_mono), frame_length):\r\n            if np.max(np.abs(audio_mono[i : i + frame_length])) > silence_threshold:\r\n                return i\r\n        return len(audio_mono)\r\n\r\n    ref_silence_end = find_silence_end(reference_audio)\r\n    target_silence_end = find_silence_end(target_audio)\r\n    silence_difference = ref_silence_end - target_silence_end\r\n\r\n    try:\r\n        ref_silence_end_p = (ref_silence_end / 44100) * 1000\r\n        target_silence_end_p = (target_silence_end / 44100) * 1000\r\n        silence_difference_p = ref_silence_end_p - target_silence_end_p\r","chunkId":"audio_separator/separator/uvr_lib_v5/spec_utils.py:54"}],[1833,{"filePath":"audio_separator/separator/uvr_lib_v5/spec_utils.py","startLine":729,"endLine":740,"chunkText":"        ref_silence_end_p = (ref_silence_end / 44100) * 1000\r\n        target_silence_end_p = (target_silence_end / 44100) * 1000\r\n        silence_difference_p = ref_silence_end_p - target_silence_end_p\r\n        print(\"silence_difference: \", silence_difference_p)\r\n    except Exception as e:\r\n        pass\r\n\r\n    if silence_difference > 0:  # Add silence to target_audio\r\n        if len(target_audio.shape) == 2:  # stereo\r\n            silence_to_add = np.zeros((target_audio.shape[0], silence_difference))\r\n        else:  # mono\r\n            silence_to_add = np.zeros(silence_difference)\r","chunkId":"audio_separator/separator/uvr_lib_v5/spec_utils.py:55"}],[1834,{"filePath":"audio_separator/separator/uvr_lib_v5/spec_utils.py","startLine":738,"endLine":750,"chunkText":"            silence_to_add = np.zeros((target_audio.shape[0], silence_difference))\r\n        else:  # mono\r\n            silence_to_add = np.zeros(silence_difference)\r\n        return np.hstack((silence_to_add, target_audio))\r\n    elif silence_difference < 0:  # Remove silence from target_audio\r\n        if len(target_audio.shape) == 2:  # stereo\r\n            return target_audio[:, -silence_difference:]\r\n        else:  # mono\r\n            return target_audio[-silence_difference:]\r\n    else:  # No adjustment needed\r\n        return target_audio\r\n\r\n\r","chunkId":"audio_separator/separator/uvr_lib_v5/spec_utils.py:56"}],[1835,{"filePath":"audio_separator/separator/uvr_lib_v5/spec_utils.py","startLine":748,"endLine":765,"chunkText":"        return target_audio\r\n\r\n\r\ndef match_array_shapes(array_1: np.ndarray, array_2: np.ndarray, is_swap=False):\r\n\r\n    if is_swap:\r\n        array_1, array_2 = array_1.T, array_2.T\r\n\r\n    # print(\"before\", array_1.shape, array_2.shape)\r\n    if array_1.shape[1] > array_2.shape[1]:\r\n        array_1 = array_1[:, : array_2.shape[1]]\r\n    elif array_1.shape[1] < array_2.shape[1]:\r\n        padding = array_2.shape[1] - array_1.shape[1]\r\n        array_1 = np.pad(array_1, ((0, 0), (0, padding)), \"constant\", constant_values=0)\r\n\r\n    # print(\"after\", array_1.shape, array_2.shape)\r\n\r\n    if is_swap:\r","chunkId":"audio_separator/separator/uvr_lib_v5/spec_utils.py:57"}],[1836,{"filePath":"audio_separator/separator/uvr_lib_v5/spec_utils.py","startLine":763,"endLine":782,"chunkText":"    # print(\"after\", array_1.shape, array_2.shape)\r\n\r\n    if is_swap:\r\n        array_1, array_2 = array_1.T, array_2.T\r\n\r\n    return array_1\r\n\r\n\r\ndef match_mono_array_shapes(array_1: np.ndarray, array_2: np.ndarray):\r\n\r\n    if len(array_1) > len(array_2):\r\n        array_1 = array_1[: len(array_2)]\r\n    elif len(array_1) < len(array_2):\r\n        padding = len(array_2) - len(array_1)\r\n        array_1 = np.pad(array_1, (0, padding), \"constant\", constant_values=0)\r\n\r\n    return array_1\r\n\r\n\r\ndef change_pitch_semitones(y, sr, semitone_shift):\r","chunkId":"audio_separator/separator/uvr_lib_v5/spec_utils.py:58"}],[1837,{"filePath":"audio_separator/separator/uvr_lib_v5/spec_utils.py","startLine":780,"endLine":793,"chunkText":"\r\n\r\ndef change_pitch_semitones(y, sr, semitone_shift):\r\n    factor = 2 ** (semitone_shift / 12)  # Convert semitone shift to factor for resampling\r\n    y_pitch_tuned = []\r\n    for y_channel in y:\r\n        y_pitch_tuned.append(librosa.resample(y_channel, orig_sr=sr, target_sr=sr * factor, res_type=wav_resolution_float_resampling))\r\n    y_pitch_tuned = np.array(y_pitch_tuned)\r\n    new_sr = sr * factor\r\n    return y_pitch_tuned, new_sr\r\n\r\n\r\ndef augment_audio(export_path, audio_file, rate, is_normalization, wav_type_set, save_format=None, is_pitch=False, is_time_correction=True):\r\n\r","chunkId":"audio_separator/separator/uvr_lib_v5/spec_utils.py:59"}],[1838,{"filePath":"audio_separator/separator/uvr_lib_v5/spec_utils.py","startLine":791,"endLine":805,"chunkText":"\r\ndef augment_audio(export_path, audio_file, rate, is_normalization, wav_type_set, save_format=None, is_pitch=False, is_time_correction=True):\r\n\r\n    wav, sr = librosa.load(audio_file, sr=44100, mono=False)\r\n\r\n    if wav.ndim == 1:\r\n        wav = np.asfortranarray([wav, wav])\r\n\r\n    if not is_time_correction:\r\n        wav_mix = change_pitch_semitones(wav, 44100, semitone_shift=-rate)[0]\r\n    else:\r\n        if is_pitch:\r\n            wav_1 = pyrb.pitch_shift(wav[0], sr, rate, rbargs=None)\r\n            wav_2 = pyrb.pitch_shift(wav[1], sr, rate, rbargs=None)\r\n        else:\r","chunkId":"audio_separator/separator/uvr_lib_v5/spec_utils.py:60"}],[1839,{"filePath":"audio_separator/separator/uvr_lib_v5/spec_utils.py","startLine":803,"endLine":815,"chunkText":"            wav_1 = pyrb.pitch_shift(wav[0], sr, rate, rbargs=None)\r\n            wav_2 = pyrb.pitch_shift(wav[1], sr, rate, rbargs=None)\r\n        else:\r\n            wav_1 = pyrb.time_stretch(wav[0], sr, rate, rbargs=None)\r\n            wav_2 = pyrb.time_stretch(wav[1], sr, rate, rbargs=None)\r\n\r\n        if wav_1.shape > wav_2.shape:\r\n            wav_2 = to_shape(wav_2, wav_1.shape)\r\n        if wav_1.shape < wav_2.shape:\r\n            wav_1 = to_shape(wav_1, wav_2.shape)\r\n\r\n        wav_mix = np.asfortranarray([wav_1, wav_2])\r\n\r","chunkId":"audio_separator/separator/uvr_lib_v5/spec_utils.py:61"}],[1840,{"filePath":"audio_separator/separator/uvr_lib_v5/spec_utils.py","startLine":813,"endLine":833,"chunkText":"\r\n        wav_mix = np.asfortranarray([wav_1, wav_2])\r\n\r\n    sf.write(export_path, normalize(wav_mix.T, is_normalization), sr, subtype=wav_type_set)\r\n    save_format(export_path)\r\n\r\n\r\ndef average_audio(audio):\r\n\r\n    waves = []\r\n    wave_shapes = []\r\n    final_waves = []\r\n\r\n    for i in range(len(audio)):\r\n        wave = librosa.load(audio[i], sr=44100, mono=False)\r\n        waves.append(wave[0])\r\n        wave_shapes.append(wave[0].shape[1])\r\n\r\n    wave_shapes_index = wave_shapes.index(max(wave_shapes))\r\n    target_shape = waves[wave_shapes_index]\r\n    waves.pop(wave_shapes_index)\r","chunkId":"audio_separator/separator/uvr_lib_v5/spec_utils.py:62"}],[1841,{"filePath":"audio_separator/separator/uvr_lib_v5/spec_utils.py","startLine":831,"endLine":850,"chunkText":"    wave_shapes_index = wave_shapes.index(max(wave_shapes))\r\n    target_shape = waves[wave_shapes_index]\r\n    waves.pop(wave_shapes_index)\r\n    final_waves.append(target_shape)\r\n\r\n    for n_array in waves:\r\n        wav_target = to_shape(n_array, target_shape.shape)\r\n        final_waves.append(wav_target)\r\n\r\n    waves = sum(final_waves)\r\n    waves = waves / len(audio)\r\n\r\n    return waves\r\n\r\n\r\ndef average_dual_sources(wav_1, wav_2, value):\r\n\r\n    if wav_1.shape > wav_2.shape:\r\n        wav_2 = to_shape(wav_2, wav_1.shape)\r\n    if wav_1.shape < wav_2.shape:\r","chunkId":"audio_separator/separator/uvr_lib_v5/spec_utils.py:63"}],[1842,{"filePath":"audio_separator/separator/uvr_lib_v5/spec_utils.py","startLine":848,"endLine":869,"chunkText":"    if wav_1.shape > wav_2.shape:\r\n        wav_2 = to_shape(wav_2, wav_1.shape)\r\n    if wav_1.shape < wav_2.shape:\r\n        wav_1 = to_shape(wav_1, wav_2.shape)\r\n\r\n    wave = (wav_1 * value) + (wav_2 * (1 - value))\r\n\r\n    return wave\r\n\r\n\r\ndef reshape_sources(wav_1: np.ndarray, wav_2: np.ndarray):\r\n\r\n    if wav_1.shape > wav_2.shape:\r\n        wav_2 = to_shape(wav_2, wav_1.shape)\r\n    if wav_1.shape < wav_2.shape:\r\n        ln = min([wav_1.shape[1], wav_2.shape[1]])\r\n        wav_2 = wav_2[:, :ln]\r\n\r\n    ln = min([wav_1.shape[1], wav_2.shape[1]])\r\n    wav_1 = wav_1[:, :ln]\r\n    wav_2 = wav_2[:, :ln]\r\n\r","chunkId":"audio_separator/separator/uvr_lib_v5/spec_utils.py:64"}],[1843,{"filePath":"audio_separator/separator/uvr_lib_v5/spec_utils.py","startLine":867,"endLine":892,"chunkText":"    wav_1 = wav_1[:, :ln]\r\n    wav_2 = wav_2[:, :ln]\r\n\r\n    return wav_2\r\n\r\n\r\ndef reshape_sources_ref(wav_1_shape, wav_2: np.ndarray):\r\n\r\n    if wav_1_shape > wav_2.shape:\r\n        wav_2 = to_shape(wav_2, wav_1_shape)\r\n\r\n    return wav_2\r\n\r\n\r\ndef combine_arrarys(audio_sources, is_swap=False):\r\n    source = np.zeros_like(max(audio_sources, key=np.size))\r\n\r\n    for v in audio_sources:\r\n        v = match_array_shapes(v, source, is_swap=is_swap)\r\n        source += v\r\n\r\n    return source\r\n\r\n\r\ndef combine_audio(paths: list, audio_file_base=None, wav_type_set=\"FLOAT\", save_format=None):\r\n\r","chunkId":"audio_separator/separator/uvr_lib_v5/spec_utils.py:65"}],[1844,{"filePath":"audio_separator/separator/uvr_lib_v5/spec_utils.py","startLine":890,"endLine":908,"chunkText":"\r\ndef combine_audio(paths: list, audio_file_base=None, wav_type_set=\"FLOAT\", save_format=None):\r\n\r\n    source = combine_arrarys([load_audio(i) for i in paths])\r\n    save_path = f\"{audio_file_base}_combined.wav\"\r\n    sf.write(save_path, source.T, 44100, subtype=wav_type_set)\r\n    save_format(save_path)\r\n\r\n\r\ndef reduce_mix_bv(inst_source, voc_source, reduction_rate=0.9):\r\n    # Reduce the volume\r\n    inst_source = inst_source * (1 - reduction_rate)\r\n\r\n    mix_reduced = combine_arrarys([inst_source, voc_source], is_swap=True)\r\n\r\n    return mix_reduced\r\n\r\n\r\ndef organize_inputs(inputs):\r","chunkId":"audio_separator/separator/uvr_lib_v5/spec_utils.py:66"}],[1845,{"filePath":"audio_separator/separator/uvr_lib_v5/spec_utils.py","startLine":906,"endLine":926,"chunkText":"\r\n\r\ndef organize_inputs(inputs):\r\n    input_list = {\"target\": None, \"reference\": None, \"reverb\": None, \"inst\": None}\r\n\r\n    for i in inputs:\r\n        if i.endswith(\"_(Vocals).wav\"):\r\n            input_list[\"reference\"] = i\r\n        elif \"_RVC_\" in i:\r\n            input_list[\"target\"] = i\r\n        elif i.endswith(\"reverbed_stem.wav\"):\r\n            input_list[\"reverb\"] = i\r\n        elif i.endswith(\"_(Instrumental).wav\"):\r\n            input_list[\"inst\"] = i\r\n\r\n    return input_list\r\n\r\n\r\ndef check_if_phase_inverted(wav1, wav2, is_mono=False):\r\n    # Load the audio files\r\n    if not is_mono:\r","chunkId":"audio_separator/separator/uvr_lib_v5/spec_utils.py:67"}],[1846,{"filePath":"audio_separator/separator/uvr_lib_v5/spec_utils.py","startLine":924,"endLine":950,"chunkText":"def check_if_phase_inverted(wav1, wav2, is_mono=False):\r\n    # Load the audio files\r\n    if not is_mono:\r\n        wav1 = np.mean(wav1, axis=0)\r\n        wav2 = np.mean(wav2, axis=0)\r\n\r\n    # Compute the correlation\r\n    correlation = np.corrcoef(wav1[:1000], wav2[:1000])\r\n\r\n    return correlation[0, 1] < 0\r\n\r\n\r\ndef align_audio(\r\n    file1,\r\n    file2,\r\n    file2_aligned,\r\n    file_subtracted,\r\n    wav_type_set,\r\n    is_save_aligned,\r\n    command_Text,\r\n    save_format,\r\n    align_window: list,\r\n    align_intro_val: list,\r\n    db_analysis: tuple,\r\n    set_progress_bar,\r\n    phase_option,\r\n    phase_shifts,\r","chunkId":"audio_separator/separator/uvr_lib_v5/spec_utils.py:68"}],[1847,{"filePath":"audio_separator/separator/uvr_lib_v5/spec_utils.py","startLine":948,"endLine":975,"chunkText":"    set_progress_bar,\r\n    phase_option,\r\n    phase_shifts,\r\n    is_match_silence,\r\n    is_spec_match,\r\n):\r\n\r\n    global progress_value\r\n    progress_value = 0\r\n    is_mono = False\r\n\r\n    def get_diff(a, b):\r\n        corr = np.correlate(a, b, \"full\")\r\n        diff = corr.argmax() - (b.shape[0] - 1)\r\n\r\n        return diff\r\n\r\n    def progress_bar(length):\r\n        global progress_value\r\n        progress_value += 1\r\n\r\n        if (0.90 / length * progress_value) >= 0.9:\r\n            length = progress_value + 1\r\n\r\n        set_progress_bar(0.1, (0.9 / length * progress_value))\r\n\r\n    # read tracks\r\n\r","chunkId":"audio_separator/separator/uvr_lib_v5/spec_utils.py:69"}],[1848,{"filePath":"audio_separator/separator/uvr_lib_v5/spec_utils.py","startLine":973,"endLine":990,"chunkText":"\r\n    # read tracks\r\n\r\n    if file1.endswith(\".mp3\") and is_macos:\r\n        length1 = rerun_mp3(file1)\r\n        wav1, sr1 = librosa.load(file1, duration=length1, sr=44100, mono=False)\r\n    else:\r\n        wav1, sr1 = librosa.load(file1, sr=44100, mono=False)\r\n\r\n    if file2.endswith(\".mp3\") and is_macos:\r\n        length2 = rerun_mp3(file2)\r\n        wav2, sr2 = librosa.load(file2, duration=length2, sr=44100, mono=False)\r\n    else:\r\n        wav2, sr2 = librosa.load(file2, sr=44100, mono=False)\r\n\r\n    if wav1.ndim == 1 and wav2.ndim == 1:\r\n        is_mono = True\r\n    elif wav1.ndim == 1:\r","chunkId":"audio_separator/separator/uvr_lib_v5/spec_utils.py:70"}],[1849,{"filePath":"audio_separator/separator/uvr_lib_v5/spec_utils.py","startLine":988,"endLine":1006,"chunkText":"    if wav1.ndim == 1 and wav2.ndim == 1:\r\n        is_mono = True\r\n    elif wav1.ndim == 1:\r\n        wav1 = np.asfortranarray([wav1, wav1])\r\n    elif wav2.ndim == 1:\r\n        wav2 = np.asfortranarray([wav2, wav2])\r\n\r\n    # Check if phase is inverted\r\n    if phase_option == AUTO_PHASE:\r\n        if check_if_phase_inverted(wav1, wav2, is_mono=is_mono):\r\n            wav2 = -wav2\r\n    elif phase_option == POSITIVE_PHASE:\r\n        wav2 = +wav2\r\n    elif phase_option == NEGATIVE_PHASE:\r\n        wav2 = -wav2\r\n\r\n    if is_match_silence:\r\n        wav2 = adjust_leading_silence(wav2, wav1)\r\n\r","chunkId":"audio_separator/separator/uvr_lib_v5/spec_utils.py:71"}],[1850,{"filePath":"audio_separator/separator/uvr_lib_v5/spec_utils.py","startLine":1004,"endLine":1022,"chunkText":"    if is_match_silence:\r\n        wav2 = adjust_leading_silence(wav2, wav1)\r\n\r\n    wav1_length = int(librosa.get_duration(y=wav1, sr=44100))\r\n    wav2_length = int(librosa.get_duration(y=wav2, sr=44100))\r\n\r\n    if not is_mono:\r\n        wav1 = wav1.transpose()\r\n        wav2 = wav2.transpose()\r\n\r\n    wav2_org = wav2.copy()\r\n\r\n    command_Text(\"Processing files... \\n\")\r\n    seconds_length = min(wav1_length, wav2_length)\r\n\r\n    wav2_aligned_sources = []\r\n\r\n    for sec_len in align_intro_val:\r\n        # pick a position at 1 second in and get diff\r","chunkId":"audio_separator/separator/uvr_lib_v5/spec_utils.py:72"}],[1851,{"filePath":"audio_separator/separator/uvr_lib_v5/spec_utils.py","startLine":1020,"endLine":1031,"chunkText":"\r\n    for sec_len in align_intro_val:\r\n        # pick a position at 1 second in and get diff\r\n        sec_seg = 1 if sec_len == 1 else int(seconds_length // sec_len)\r\n        index = sr1 * sec_seg  # 1 second in, assuming sr1 = sr2 = 44100\r\n\r\n        if is_mono:\r\n            samp1, samp2 = wav1[index : index + sr1], wav2[index : index + sr1]\r\n            diff = get_diff(samp1, samp2)\r\n            # print(f\"Estimated difference: {diff}\\n\")\r\n        else:\r\n            index = sr1 * sec_seg  # 1 second in, assuming sr1 = sr2 = 44100\r","chunkId":"audio_separator/separator/uvr_lib_v5/spec_utils.py:73"}],[1852,{"filePath":"audio_separator/separator/uvr_lib_v5/spec_utils.py","startLine":1029,"endLine":1038,"chunkText":"            # print(f\"Estimated difference: {diff}\\n\")\r\n        else:\r\n            index = sr1 * sec_seg  # 1 second in, assuming sr1 = sr2 = 44100\r\n            samp1, samp2 = wav1[index : index + sr1, 0], wav2[index : index + sr1, 0]\r\n            samp1_r, samp2_r = wav1[index : index + sr1, 1], wav2[index : index + sr1, 1]\r\n            diff, diff_r = get_diff(samp1, samp2), get_diff(samp1_r, samp2_r)\r\n            # print(f\"Estimated difference Left Channel: {diff}\\nEstimated difference Right Channel: {diff_r}\\n\")\r\n\r\n        # make aligned track 2\r\n        if diff > 0:\r","chunkId":"audio_separator/separator/uvr_lib_v5/spec_utils.py:74"}],[1853,{"filePath":"audio_separator/separator/uvr_lib_v5/spec_utils.py","startLine":1036,"endLine":1051,"chunkText":"\r\n        # make aligned track 2\r\n        if diff > 0:\r\n            zeros_to_append = np.zeros(diff) if is_mono else np.zeros((diff, 2))\r\n            wav2_aligned = np.append(zeros_to_append, wav2_org, axis=0)\r\n        elif diff < 0:\r\n            wav2_aligned = wav2_org[-diff:]\r\n        else:\r\n            wav2_aligned = wav2_org\r\n            # command_Text(f\"Audio files already aligned.\\n\")\r\n\r\n        if not any(np.array_equal(wav2_aligned, source) for source in wav2_aligned_sources):\r\n            wav2_aligned_sources.append(wav2_aligned)\r\n\r\n    # print(\"Unique Sources: \", len(wav2_aligned_sources))\r\n\r","chunkId":"audio_separator/separator/uvr_lib_v5/spec_utils.py:75"}],[1854,{"filePath":"audio_separator/separator/uvr_lib_v5/spec_utils.py","startLine":1049,"endLine":1062,"chunkText":"\r\n    # print(\"Unique Sources: \", len(wav2_aligned_sources))\r\n\r\n    unique_sources = len(wav2_aligned_sources)\r\n\r\n    sub_mapper_big_mapper = {}\r\n\r\n    for s in wav2_aligned_sources:\r\n        wav2_aligned = match_mono_array_shapes(s, wav1) if is_mono else match_array_shapes(s, wav1, is_swap=True)\r\n\r\n        if align_window:\r\n            wav_sub = time_correction(\r\n                wav1, wav2_aligned, seconds_length, align_window=align_window, db_analysis=db_analysis, progress_bar=progress_bar, unique_sources=unique_sources, phase_shifts=phase_shifts\r\n            )\r","chunkId":"audio_separator/separator/uvr_lib_v5/spec_utils.py:76"}],[1855,{"filePath":"audio_separator/separator/uvr_lib_v5/spec_utils.py","startLine":1060,"endLine":1070,"chunkText":"            wav_sub = time_correction(\r\n                wav1, wav2_aligned, seconds_length, align_window=align_window, db_analysis=db_analysis, progress_bar=progress_bar, unique_sources=unique_sources, phase_shifts=phase_shifts\r\n            )\r\n            wav_sub_size = np.abs(wav_sub).mean()\r\n            sub_mapper_big_mapper = {**sub_mapper_big_mapper, **{wav_sub_size: wav_sub}}\r\n        else:\r\n            wav2_aligned = wav2_aligned * np.power(10, db_analysis[0] / 20)\r\n            db_range = db_analysis[1]\r\n\r\n            for db_adjustment in db_range:\r\n                # Adjust the dB of track2\r","chunkId":"audio_separator/separator/uvr_lib_v5/spec_utils.py:77"}],[1856,{"filePath":"audio_separator/separator/uvr_lib_v5/spec_utils.py","startLine":1068,"endLine":1080,"chunkText":"\r\n            for db_adjustment in db_range:\r\n                # Adjust the dB of track2\r\n                s_adjusted = wav2_aligned * (10 ** (db_adjustment / 20))\r\n                wav_sub = wav1 - s_adjusted\r\n                wav_sub_size = np.abs(wav_sub).mean()\r\n                sub_mapper_big_mapper = {**sub_mapper_big_mapper, **{wav_sub_size: wav_sub}}\r\n\r\n        # print(sub_mapper_big_mapper.keys(), min(sub_mapper_big_mapper.keys()))\r\n\r\n    sub_mapper_value_list = list(sub_mapper_big_mapper.values())\r\n\r\n    if is_spec_match and len(sub_mapper_value_list) >= 2:\r","chunkId":"audio_separator/separator/uvr_lib_v5/spec_utils.py:78"}],[1857,{"filePath":"audio_separator/separator/uvr_lib_v5/spec_utils.py","startLine":1078,"endLine":1091,"chunkText":"    sub_mapper_value_list = list(sub_mapper_big_mapper.values())\r\n\r\n    if is_spec_match and len(sub_mapper_value_list) >= 2:\r\n        # print(\"using spec ensemble with align\")\r\n        wav_sub = ensemble_for_align(list(sub_mapper_big_mapper.values()))\r\n    else:\r\n        # print(\"using linear ensemble with align\")\r\n        wav_sub = ensemble_wav(list(sub_mapper_big_mapper.values()))\r\n\r\n    # print(f\"Mix Mean: {np.abs(wav1).mean()}\\nInst Mean: {np.abs(wav2).mean()}\")\r\n    # print('Final: ', np.abs(wav_sub).mean())\r\n    wav_sub = np.clip(wav_sub, -1, +1)\r\n\r\n    command_Text(f\"Saving inverted track... \")\r","chunkId":"audio_separator/separator/uvr_lib_v5/spec_utils.py:79"}],[1858,{"filePath":"audio_separator/separator/uvr_lib_v5/spec_utils.py","startLine":1089,"endLine":1102,"chunkText":"    wav_sub = np.clip(wav_sub, -1, +1)\r\n\r\n    command_Text(f\"Saving inverted track... \")\r\n\r\n    if is_save_aligned or is_spec_match:\r\n        wav1 = match_mono_array_shapes(wav1, wav_sub) if is_mono else match_array_shapes(wav1, wav_sub, is_swap=True)\r\n        wav2_aligned = wav1 - wav_sub\r\n\r\n        if is_spec_match:\r\n            if wav1.ndim == 1 and wav2.ndim == 1:\r\n                wav2_aligned = np.asfortranarray([wav2_aligned, wav2_aligned]).T\r\n                wav1 = np.asfortranarray([wav1, wav1]).T\r\n\r\n            wav2_aligned = ensemble_for_align([wav2_aligned, wav1])\r","chunkId":"audio_separator/separator/uvr_lib_v5/spec_utils.py:80"}],[1859,{"filePath":"audio_separator/separator/uvr_lib_v5/spec_utils.py","startLine":1100,"endLine":1114,"chunkText":"                wav1 = np.asfortranarray([wav1, wav1]).T\r\n\r\n            wav2_aligned = ensemble_for_align([wav2_aligned, wav1])\r\n            wav_sub = wav1 - wav2_aligned\r\n\r\n        if is_save_aligned:\r\n            sf.write(file2_aligned, wav2_aligned, sr1, subtype=wav_type_set)\r\n            save_format(file2_aligned)\r\n\r\n    sf.write(file_subtracted, wav_sub, sr1, subtype=wav_type_set)\r\n    save_format(file_subtracted)\r\n\r\n\r\ndef phase_shift_hilbert(signal, degree):\r\n    analytic_signal = hilbert(signal)\r","chunkId":"audio_separator/separator/uvr_lib_v5/spec_utils.py:81"}],[1860,{"filePath":"audio_separator/separator/uvr_lib_v5/spec_utils.py","startLine":1112,"endLine":1129,"chunkText":"\r\ndef phase_shift_hilbert(signal, degree):\r\n    analytic_signal = hilbert(signal)\r\n    return np.cos(np.radians(degree)) * analytic_signal.real - np.sin(np.radians(degree)) * analytic_signal.imag\r\n\r\n\r\ndef get_phase_shifted_tracks(track, phase_shift):\r\n    if phase_shift == 180:\r\n        return [track, -track]\r\n\r\n    step = phase_shift\r\n    end = 180 - (180 % step) if 180 % step == 0 else 181\r\n    phase_range = range(step, end, step)\r\n\r\n    flipped_list = [track, -track]\r\n    for i in phase_range:\r\n        flipped_list.extend([phase_shift_hilbert(track, i), phase_shift_hilbert(track, -i)])\r\n\r","chunkId":"audio_separator/separator/uvr_lib_v5/spec_utils.py:82"}],[1861,{"filePath":"audio_separator/separator/uvr_lib_v5/spec_utils.py","startLine":1127,"endLine":1140,"chunkText":"    for i in phase_range:\r\n        flipped_list.extend([phase_shift_hilbert(track, i), phase_shift_hilbert(track, -i)])\r\n\r\n    return flipped_list\r\n\r\n\r\ndef time_correction(mix: np.ndarray, instrumental: np.ndarray, seconds_length, align_window, db_analysis, sr=44100, progress_bar=None, unique_sources=None, phase_shifts=NONE_P):\r\n    # Function to align two tracks using cross-correlation\r\n\r\n    def align_tracks(track1, track2):\r\n        # A dictionary to store each version of track2_shifted and its mean absolute value\r\n        shifted_tracks = {}\r\n\r\n        # Loop to adjust dB of track2\r","chunkId":"audio_separator/separator/uvr_lib_v5/spec_utils.py:83"}],[1862,{"filePath":"audio_separator/separator/uvr_lib_v5/spec_utils.py","startLine":1138,"endLine":1153,"chunkText":"        shifted_tracks = {}\r\n\r\n        # Loop to adjust dB of track2\r\n        track2 = track2 * np.power(10, db_analysis[0] / 20)\r\n        db_range = db_analysis[1]\r\n\r\n        if phase_shifts == 190:\r\n            track2_flipped = [track2]\r\n        else:\r\n            track2_flipped = get_phase_shifted_tracks(track2, phase_shifts)\r\n\r\n        for db_adjustment in db_range:\r\n            for t in track2_flipped:\r\n                # Adjust the dB of track2\r\n                track2_adjusted = t * (10 ** (db_adjustment / 20))\r\n                corr = correlate(track1, track2_adjusted)\r","chunkId":"audio_separator/separator/uvr_lib_v5/spec_utils.py:84"}],[1863,{"filePath":"audio_separator/separator/uvr_lib_v5/spec_utils.py","startLine":1151,"endLine":1161,"chunkText":"                # Adjust the dB of track2\r\n                track2_adjusted = t * (10 ** (db_adjustment / 20))\r\n                corr = correlate(track1, track2_adjusted)\r\n                delay = np.argmax(np.abs(corr)) - (len(track1) - 1)\r\n                track2_shifted = np.roll(track2_adjusted, shift=delay)\r\n\r\n                # Compute the mean absolute value of track2_shifted\r\n                track2_shifted_sub = track1 - track2_shifted\r\n                mean_abs_value = np.abs(track2_shifted_sub).mean()\r\n\r\n                # Store track2_shifted and its mean absolute value in the dictionary\r","chunkId":"audio_separator/separator/uvr_lib_v5/spec_utils.py:85"}],[1864,{"filePath":"audio_separator/separator/uvr_lib_v5/spec_utils.py","startLine":1159,"endLine":1172,"chunkText":"                mean_abs_value = np.abs(track2_shifted_sub).mean()\r\n\r\n                # Store track2_shifted and its mean absolute value in the dictionary\r\n                shifted_tracks[mean_abs_value] = track2_shifted\r\n\r\n        # Return the version of track2_shifted with the smallest mean absolute value\r\n\r\n        return shifted_tracks[min(shifted_tracks.keys())]\r\n\r\n    # Make sure the audio files have the same shape\r\n\r\n    assert mix.shape == instrumental.shape, f\"Audio files must have the same shape - Mix: {mix.shape}, Inst: {instrumental.shape}\"\r\n\r\n    seconds_length = seconds_length // 2\r","chunkId":"audio_separator/separator/uvr_lib_v5/spec_utils.py:86"}],[1865,{"filePath":"audio_separator/separator/uvr_lib_v5/spec_utils.py","startLine":1170,"endLine":1187,"chunkText":"    assert mix.shape == instrumental.shape, f\"Audio files must have the same shape - Mix: {mix.shape}, Inst: {instrumental.shape}\"\r\n\r\n    seconds_length = seconds_length // 2\r\n\r\n    sub_mapper = {}\r\n\r\n    progress_update_interval = 120\r\n    total_iterations = 0\r\n\r\n    if len(align_window) > 2:\r\n        progress_update_interval = 320\r\n\r\n    for secs in align_window:\r\n        step = secs / 2\r\n        window_size = int(sr * secs)\r\n        step_size = int(sr * step)\r\n\r\n        if len(mix.shape) == 1:\r","chunkId":"audio_separator/separator/uvr_lib_v5/spec_utils.py:87"}],[1866,{"filePath":"audio_separator/separator/uvr_lib_v5/spec_utils.py","startLine":1185,"endLine":1198,"chunkText":"        step_size = int(sr * step)\r\n\r\n        if len(mix.shape) == 1:\r\n            total_mono = (len(range(0, len(mix) - window_size, step_size)) // progress_update_interval) * unique_sources\r\n            total_iterations += total_mono\r\n        else:\r\n            total_stereo_ = len(range(0, len(mix[:, 0]) - window_size, step_size)) * 2\r\n            total_stereo = (total_stereo_ // progress_update_interval) * unique_sources\r\n            total_iterations += total_stereo\r\n\r\n    # print(total_iterations)\r\n\r\n    for secs in align_window:\r\n        sub = np.zeros_like(mix)\r","chunkId":"audio_separator/separator/uvr_lib_v5/spec_utils.py:88"}],[1867,{"filePath":"audio_separator/separator/uvr_lib_v5/spec_utils.py","startLine":1196,"endLine":1212,"chunkText":"\r\n    for secs in align_window:\r\n        sub = np.zeros_like(mix)\r\n        divider = np.zeros_like(mix)\r\n        step = secs / 2\r\n        window_size = int(sr * secs)\r\n        step_size = int(sr * step)\r\n        window = np.hanning(window_size)\r\n\r\n        # For the mono case:\r\n        if len(mix.shape) == 1:\r\n            # The files are mono\r\n            counter = 0\r\n            for i in range(0, len(mix) - window_size, step_size):\r\n                counter += 1\r\n                if counter % progress_update_interval == 0:\r\n                    progress_bar(total_iterations)\r","chunkId":"audio_separator/separator/uvr_lib_v5/spec_utils.py:89"}],[1868,{"filePath":"audio_separator/separator/uvr_lib_v5/spec_utils.py","startLine":1210,"endLine":1220,"chunkText":"                counter += 1\r\n                if counter % progress_update_interval == 0:\r\n                    progress_bar(total_iterations)\r\n                window_mix = mix[i : i + window_size] * window\r\n                window_instrumental = instrumental[i : i + window_size] * window\r\n                window_instrumental_aligned = align_tracks(window_mix, window_instrumental)\r\n                sub[i : i + window_size] += window_mix - window_instrumental_aligned\r\n                divider[i : i + window_size] += window\r\n        else:\r\n            # The files are stereo\r\n            counter = 0\r","chunkId":"audio_separator/separator/uvr_lib_v5/spec_utils.py:90"}],[1869,{"filePath":"audio_separator/separator/uvr_lib_v5/spec_utils.py","startLine":1218,"endLine":1228,"chunkText":"        else:\r\n            # The files are stereo\r\n            counter = 0\r\n            for ch in range(mix.shape[1]):\r\n                for i in range(0, len(mix[:, ch]) - window_size, step_size):\r\n                    counter += 1\r\n                    if counter % progress_update_interval == 0:\r\n                        progress_bar(total_iterations)\r\n                    window_mix = mix[i : i + window_size, ch] * window\r\n                    window_instrumental = instrumental[i : i + window_size, ch] * window\r\n                    window_instrumental_aligned = align_tracks(window_mix, window_instrumental)\r","chunkId":"audio_separator/separator/uvr_lib_v5/spec_utils.py:91"}],[1870,{"filePath":"audio_separator/separator/uvr_lib_v5/spec_utils.py","startLine":1226,"endLine":1234,"chunkText":"                    window_mix = mix[i : i + window_size, ch] * window\r\n                    window_instrumental = instrumental[i : i + window_size, ch] * window\r\n                    window_instrumental_aligned = align_tracks(window_mix, window_instrumental)\r\n                    sub[i : i + window_size, ch] += window_mix - window_instrumental_aligned\r\n                    divider[i : i + window_size, ch] += window\r\n\r\n        # Normalize the result by the overlap count\r\n        sub = np.where(divider > 1e-6, sub / divider, sub)\r\n        sub_size = np.abs(sub).mean()\r","chunkId":"audio_separator/separator/uvr_lib_v5/spec_utils.py:92"}],[1871,{"filePath":"audio_separator/separator/uvr_lib_v5/spec_utils.py","startLine":1232,"endLine":1247,"chunkText":"        # Normalize the result by the overlap count\r\n        sub = np.where(divider > 1e-6, sub / divider, sub)\r\n        sub_size = np.abs(sub).mean()\r\n        sub_mapper = {**sub_mapper, **{sub_size: sub}}\r\n\r\n    # print(\"SUB_LEN\", len(list(sub_mapper.values())))\r\n\r\n    sub = ensemble_wav(list(sub_mapper.values()), split_size=12)\r\n\r\n    return sub\r\n\r\n\r\ndef ensemble_wav(waveforms, split_size=240):\r\n    # Create a dictionary to hold the thirds of each waveform and their mean absolute values\r\n    waveform_thirds = {i: np.array_split(waveform, split_size) for i, waveform in enumerate(waveforms)}\r\n\r","chunkId":"audio_separator/separator/uvr_lib_v5/spec_utils.py:93"}],[1872,{"filePath":"audio_separator/separator/uvr_lib_v5/spec_utils.py","startLine":1245,"endLine":1256,"chunkText":"    # Create a dictionary to hold the thirds of each waveform and their mean absolute values\r\n    waveform_thirds = {i: np.array_split(waveform, split_size) for i, waveform in enumerate(waveforms)}\r\n\r\n    # Initialize the final waveform\r\n    final_waveform = []\r\n\r\n    # For chunk\r\n    for third_idx in range(split_size):\r\n        # Compute the mean absolute value of each third from each waveform\r\n        means = [np.abs(waveform_thirds[i][third_idx]).mean() for i in range(len(waveforms))]\r\n\r\n        # Find the index of the waveform with the lowest mean absolute value for this third\r","chunkId":"audio_separator/separator/uvr_lib_v5/spec_utils.py:94"}],[1873,{"filePath":"audio_separator/separator/uvr_lib_v5/spec_utils.py","startLine":1254,"endLine":1270,"chunkText":"        means = [np.abs(waveform_thirds[i][third_idx]).mean() for i in range(len(waveforms))]\r\n\r\n        # Find the index of the waveform with the lowest mean absolute value for this third\r\n        min_index = np.argmin(means)\r\n\r\n        # Add the least noisy third to the final waveform\r\n        final_waveform.append(waveform_thirds[min_index][third_idx])\r\n\r\n    # Concatenate all the thirds to create the final waveform\r\n    final_waveform = np.concatenate(final_waveform)\r\n\r\n    return final_waveform\r\n\r\n\r\ndef ensemble_wav_min(waveforms):\r\n    for i in range(1, len(waveforms)):\r\n        if i == 1:\r","chunkId":"audio_separator/separator/uvr_lib_v5/spec_utils.py:95"}],[1874,{"filePath":"audio_separator/separator/uvr_lib_v5/spec_utils.py","startLine":1268,"endLine":1289,"chunkText":"def ensemble_wav_min(waveforms):\r\n    for i in range(1, len(waveforms)):\r\n        if i == 1:\r\n            wave = waveforms[0]\r\n\r\n        ln = min(len(wave), len(waveforms[i]))\r\n        wave = wave[:ln]\r\n        waveforms[i] = waveforms[i][:ln]\r\n\r\n        wave = np.where(np.abs(waveforms[i]) <= np.abs(wave), waveforms[i], wave)\r\n\r\n    return wave\r\n\r\n\r\ndef align_audio_test(wav1, wav2, sr1=44100):\r\n    def get_diff(a, b):\r\n        corr = np.correlate(a, b, \"full\")\r\n        diff = corr.argmax() - (b.shape[0] - 1)\r\n        return diff\r\n\r\n    # read tracks\r\n    wav1 = wav1.transpose()\r","chunkId":"audio_separator/separator/uvr_lib_v5/spec_utils.py:96"}],[1875,{"filePath":"audio_separator/separator/uvr_lib_v5/spec_utils.py","startLine":1287,"endLine":1305,"chunkText":"\r\n    # read tracks\r\n    wav1 = wav1.transpose()\r\n    wav2 = wav2.transpose()\r\n\r\n    # print(f\"Audio file shapes: {wav1.shape} / {wav2.shape}\\n\")\r\n\r\n    wav2_org = wav2.copy()\r\n\r\n    # pick a position at 1 second in and get diff\r\n    index = sr1  # *seconds_length  # 1 second in, assuming sr1 = sr2 = 44100\r\n    samp1 = wav1[index : index + sr1, 0]  # currently use left channel\r\n    samp2 = wav2[index : index + sr1, 0]\r\n    diff = get_diff(samp1, samp2)\r\n\r\n    # make aligned track 2\r\n    if diff > 0:\r\n        wav2_aligned = np.append(np.zeros((diff, 1)), wav2_org, axis=0)\r\n    elif diff < 0:\r","chunkId":"audio_separator/separator/uvr_lib_v5/spec_utils.py:97"}],[1876,{"filePath":"audio_separator/separator/uvr_lib_v5/spec_utils.py","startLine":1303,"endLine":1327,"chunkText":"    if diff > 0:\r\n        wav2_aligned = np.append(np.zeros((diff, 1)), wav2_org, axis=0)\r\n    elif diff < 0:\r\n        wav2_aligned = wav2_org[-diff:]\r\n    else:\r\n        wav2_aligned = wav2_org\r\n\r\n    return wav2_aligned\r\n\r\n\r\ndef load_audio(audio_file):\r\n    wav, sr = librosa.load(audio_file, sr=44100, mono=False)\r\n\r\n    if wav.ndim == 1:\r\n        wav = np.asfortranarray([wav, wav])\r\n\r\n    return wav\r\n\r\n\r\ndef rerun_mp3(audio_file):\r\n    with audioread.audio_open(audio_file) as f:\r\n        track_length = int(f.duration)\r\n\r\n    return track_length\r\n","chunkId":"audio_separator/separator/uvr_lib_v5/spec_utils.py:98"}],[1877,{"filePath":"audio_separator/separator/uvr_lib_v5/stft.py","startLine":0,"endLine":16,"chunkText":"import torch\n\n\nclass STFT:\n    \"\"\"\n    This class performs the Short-Time Fourier Transform (STFT) and its inverse (ISTFT).\n    These functions are essential for converting the audio between the time domain and the frequency domain,\n    which is a crucial aspect of audio processing in neural networks.\n    \"\"\"\n\n    def __init__(self, logger, n_fft, hop_length, dim_f, device):\n        self.logger = logger\n        self.n_fft = n_fft\n        self.hop_length = hop_length\n        self.dim_f = dim_f\n        self.device = device\n        # Create a Hann window tensor for use in the STFT.","chunkId":"audio_separator/separator/uvr_lib_v5/stft.py:0"}],[1878,{"filePath":"audio_separator/separator/uvr_lib_v5/stft.py","startLine":14,"endLine":26,"chunkText":"        self.dim_f = dim_f\n        self.device = device\n        # Create a Hann window tensor for use in the STFT.\n        self.hann_window = torch.hann_window(window_length=self.n_fft, periodic=True)\n\n    def __call__(self, input_tensor):\n        # Determine if the input tensor's device is not a standard computing device (i.e., not CPU or CUDA).\n        is_non_standard_device = not input_tensor.device.type in [\"cuda\", \"cpu\"]\n\n        # If on a non-standard device, temporarily move the tensor to CPU for processing.\n        if is_non_standard_device:\n            input_tensor = input_tensor.cpu()\n","chunkId":"audio_separator/separator/uvr_lib_v5/stft.py:1"}],[1879,{"filePath":"audio_separator/separator/uvr_lib_v5/stft.py","startLine":24,"endLine":35,"chunkText":"        if is_non_standard_device:\n            input_tensor = input_tensor.cpu()\n\n        # Transfer the pre-defined window tensor to the same device as the input tensor.\n        stft_window = self.hann_window.to(input_tensor.device)\n\n        # Extract batch dimensions (all dimensions except the last two which are channel and time).\n        batch_dimensions = input_tensor.shape[:-2]\n\n        # Extract channel and time dimensions (last two dimensions of the tensor).\n        channel_dim, time_dim = input_tensor.shape[-2:]\n","chunkId":"audio_separator/separator/uvr_lib_v5/stft.py:2"}],[1880,{"filePath":"audio_separator/separator/uvr_lib_v5/stft.py","startLine":34,"endLine":44,"chunkText":"        channel_dim, time_dim = input_tensor.shape[-2:]\n\n        # Reshape the tensor to merge batch and channel dimensions for STFT processing.\n        reshaped_tensor = input_tensor.reshape([-1, time_dim])\n\n        # Perform the Short-Time Fourier Transform (STFT) on the reshaped tensor.\n        stft_output = torch.stft(reshaped_tensor, n_fft=self.n_fft, hop_length=self.hop_length, window=stft_window, center=True, return_complex=False)\n\n        # Rearrange the dimensions of the STFT output to bring the frequency dimension forward.\n        permuted_stft_output = stft_output.permute([0, 3, 1, 2])\n","chunkId":"audio_separator/separator/uvr_lib_v5/stft.py:3"}],[1881,{"filePath":"audio_separator/separator/uvr_lib_v5/stft.py","startLine":42,"endLine":49,"chunkText":"        # Rearrange the dimensions of the STFT output to bring the frequency dimension forward.\n        permuted_stft_output = stft_output.permute([0, 3, 1, 2])\n\n        # Reshape the output to restore the original batch and channel dimensions, while keeping the newly formed frequency and time dimensions.\n        final_output = permuted_stft_output.reshape([*batch_dimensions, channel_dim, 2, -1, permuted_stft_output.shape[-1]]).reshape(\n            [*batch_dimensions, channel_dim * 2, -1, permuted_stft_output.shape[-1]]\n        )\n","chunkId":"audio_separator/separator/uvr_lib_v5/stft.py:4"}],[1882,{"filePath":"audio_separator/separator/uvr_lib_v5/stft.py","startLine":47,"endLine":58,"chunkText":"            [*batch_dimensions, channel_dim * 2, -1, permuted_stft_output.shape[-1]]\n        )\n\n        # If the original tensor was on a non-standard device, move the processed tensor back to that device.\n        if is_non_standard_device:\n            final_output = final_output.to(self.device)\n\n        # Return the transformed tensor, sliced to retain only the required frequency dimension (`dim_f`).\n        return final_output[..., : self.dim_f, :]\n\n    def pad_frequency_dimension(self, input_tensor, batch_dimensions, channel_dim, freq_dim, time_dim, num_freq_bins):\n        \"\"\"","chunkId":"audio_separator/separator/uvr_lib_v5/stft.py:5"}],[1883,{"filePath":"audio_separator/separator/uvr_lib_v5/stft.py","startLine":56,"endLine":68,"chunkText":"\n    def pad_frequency_dimension(self, input_tensor, batch_dimensions, channel_dim, freq_dim, time_dim, num_freq_bins):\n        \"\"\"\n        Adds zero padding to the frequency dimension of the input tensor.\n        \"\"\"\n        # Create a padding tensor for the frequency dimension\n        freq_padding = torch.zeros([*batch_dimensions, channel_dim, num_freq_bins - freq_dim, time_dim]).to(input_tensor.device)\n\n        # Concatenate the padding to the input tensor along the frequency dimension.\n        padded_tensor = torch.cat([input_tensor, freq_padding], -2)\n\n        return padded_tensor\n","chunkId":"audio_separator/separator/uvr_lib_v5/stft.py:6"}],[1884,{"filePath":"audio_separator/separator/uvr_lib_v5/stft.py","startLine":66,"endLine":80,"chunkText":"\n        return padded_tensor\n\n    def calculate_inverse_dimensions(self, input_tensor):\n        # Extract batch dimensions and frequency-time dimensions.\n        batch_dimensions = input_tensor.shape[:-3]\n        channel_dim, freq_dim, time_dim = input_tensor.shape[-3:]\n\n        # Calculate the number of frequency bins for the inverse STFT.\n        num_freq_bins = self.n_fft // 2 + 1\n\n        return batch_dimensions, channel_dim, freq_dim, time_dim, num_freq_bins\n\n    def prepare_for_istft(self, padded_tensor, batch_dimensions, channel_dim, num_freq_bins, time_dim):\n        \"\"\"","chunkId":"audio_separator/separator/uvr_lib_v5/stft.py:7"}],[1885,{"filePath":"audio_separator/separator/uvr_lib_v5/stft.py","startLine":78,"endLine":87,"chunkText":"\n    def prepare_for_istft(self, padded_tensor, batch_dimensions, channel_dim, num_freq_bins, time_dim):\n        \"\"\"\n        Prepares the tensor for Inverse Short-Time Fourier Transform (ISTFT) by reshaping\n        and creating a complex tensor from the real and imaginary parts.\n        \"\"\"\n        # Reshape the tensor to separate real and imaginary parts and prepare for ISTFT.\n        reshaped_tensor = padded_tensor.reshape([*batch_dimensions, channel_dim // 2, 2, num_freq_bins, time_dim])\n\n        # Flatten batch dimensions and rearrange for ISTFT.","chunkId":"audio_separator/separator/uvr_lib_v5/stft.py:8"}],[1886,{"filePath":"audio_separator/separator/uvr_lib_v5/stft.py","startLine":85,"endLine":97,"chunkText":"        reshaped_tensor = padded_tensor.reshape([*batch_dimensions, channel_dim // 2, 2, num_freq_bins, time_dim])\n\n        # Flatten batch dimensions and rearrange for ISTFT.\n        flattened_tensor = reshaped_tensor.reshape([-1, 2, num_freq_bins, time_dim])\n\n        # Rearrange the dimensions of the tensor to bring the frequency dimension forward.\n        permuted_tensor = flattened_tensor.permute([0, 2, 3, 1])\n\n        # Combine real and imaginary parts into a complex tensor.\n        complex_tensor = permuted_tensor[..., 0] + permuted_tensor[..., 1] * 1.0j\n\n        return complex_tensor\n","chunkId":"audio_separator/separator/uvr_lib_v5/stft.py:9"}],[1887,{"filePath":"audio_separator/separator/uvr_lib_v5/stft.py","startLine":95,"endLine":108,"chunkText":"\n        return complex_tensor\n\n    def inverse(self, input_tensor):\n        # Determine if the input tensor's device is not a standard computing device (i.e., not CPU or CUDA).\n        is_non_standard_device = not input_tensor.device.type in [\"cuda\", \"cpu\"]\n\n        # If on a non-standard device, temporarily move the tensor to CPU for processing.\n        if is_non_standard_device:\n            input_tensor = input_tensor.cpu()\n\n        # Transfer the pre-defined Hann window tensor to the same device as the input tensor.\n        stft_window = self.hann_window.to(input_tensor.device)\n","chunkId":"audio_separator/separator/uvr_lib_v5/stft.py:10"}],[1888,{"filePath":"audio_separator/separator/uvr_lib_v5/stft.py","startLine":106,"endLine":115,"chunkText":"        # Transfer the pre-defined Hann window tensor to the same device as the input tensor.\n        stft_window = self.hann_window.to(input_tensor.device)\n\n        batch_dimensions, channel_dim, freq_dim, time_dim, num_freq_bins = self.calculate_inverse_dimensions(input_tensor)\n\n        padded_tensor = self.pad_frequency_dimension(input_tensor, batch_dimensions, channel_dim, freq_dim, time_dim, num_freq_bins)\n\n        complex_tensor = self.prepare_for_istft(padded_tensor, batch_dimensions, channel_dim, num_freq_bins, time_dim)\n\n        # Perform the Inverse Short-Time Fourier Transform (ISTFT).","chunkId":"audio_separator/separator/uvr_lib_v5/stft.py:11"}],[1889,{"filePath":"audio_separator/separator/uvr_lib_v5/stft.py","startLine":113,"endLine":121,"chunkText":"        complex_tensor = self.prepare_for_istft(padded_tensor, batch_dimensions, channel_dim, num_freq_bins, time_dim)\n\n        # Perform the Inverse Short-Time Fourier Transform (ISTFT).\n        istft_result = torch.istft(complex_tensor, n_fft=self.n_fft, hop_length=self.hop_length, window=stft_window, center=True)\n\n        # Reshape ISTFT result to restore original batch and channel dimensions.\n        final_output = istft_result.reshape([*batch_dimensions, 2, -1])\n\n        # If the original tensor was on a non-standard device, move the processed tensor back to that device.","chunkId":"audio_separator/separator/uvr_lib_v5/stft.py:12"}],[1890,{"filePath":"audio_separator/separator/uvr_lib_v5/stft.py","startLine":119,"endLine":126,"chunkText":"        final_output = istft_result.reshape([*batch_dimensions, 2, -1])\n\n        # If the original tensor was on a non-standard device, move the processed tensor back to that device.\n        if is_non_standard_device:\n            final_output = final_output.to(self.device)\n\n        return final_output\n","chunkId":"audio_separator/separator/uvr_lib_v5/stft.py:13"}],[1891,{"filePath":"audio_separator/separator/uvr_lib_v5/tfc_tdf_v3.py","startLine":0,"endLine":21,"chunkText":"import torch\nimport torch.nn as nn\nfrom functools import partial\n\nclass STFT:\n    def __init__(self, n_fft, hop_length, dim_f, device):\n        self.n_fft = n_fft\n        self.hop_length = hop_length\n        self.window = torch.hann_window(window_length=self.n_fft, periodic=True)\n        self.dim_f = dim_f\n        self.device = device\n\n    def __call__(self, x):\n        \n        x_is_mps = not x.device.type in [\"cuda\", \"cpu\"]\n        if x_is_mps:\n            x = x.cpu()\n\n        window = self.window.to(x.device)\n        batch_dims = x.shape[:-2]\n        c, t = x.shape[-2:]\n        x = x.reshape([-1, t])","chunkId":"audio_separator/separator/uvr_lib_v5/tfc_tdf_v3.py:0"}],[1892,{"filePath":"audio_separator/separator/uvr_lib_v5/tfc_tdf_v3.py","startLine":19,"endLine":36,"chunkText":"        batch_dims = x.shape[:-2]\n        c, t = x.shape[-2:]\n        x = x.reshape([-1, t])\n        x = torch.stft(x, n_fft=self.n_fft, hop_length=self.hop_length, window=window, center=True,return_complex=False)\n        x = x.permute([0, 3, 1, 2])\n        x = x.reshape([*batch_dims, c, 2, -1, x.shape[-1]]).reshape([*batch_dims, c * 2, -1, x.shape[-1]])\n\n        if x_is_mps:\n            x = x.to(self.device)\n\n        return x[..., :self.dim_f, :]\n\n    def inverse(self, x):\n        \n        x_is_mps = not x.device.type in [\"cuda\", \"cpu\"]\n        if x_is_mps:\n            x = x.cpu()\n","chunkId":"audio_separator/separator/uvr_lib_v5/tfc_tdf_v3.py:1"}],[1893,{"filePath":"audio_separator/separator/uvr_lib_v5/tfc_tdf_v3.py","startLine":34,"endLine":49,"chunkText":"        if x_is_mps:\n            x = x.cpu()\n\n        window = self.window.to(x.device)\n        batch_dims = x.shape[:-3]\n        c, f, t = x.shape[-3:]\n        n = self.n_fft // 2 + 1\n        f_pad = torch.zeros([*batch_dims, c, n - f, t]).to(x.device)\n        x = torch.cat([x, f_pad], -2)\n        x = x.reshape([*batch_dims, c // 2, 2, n, t]).reshape([-1, 2, n, t])\n        x = x.permute([0, 2, 3, 1])\n        x = x[..., 0] + x[..., 1] * 1.j\n        x = torch.istft(x, n_fft=self.n_fft, hop_length=self.hop_length, window=window, center=True)\n        x = x.reshape([*batch_dims, 2, -1])\n\n        if x_is_mps:","chunkId":"audio_separator/separator/uvr_lib_v5/tfc_tdf_v3.py:2"}],[1894,{"filePath":"audio_separator/separator/uvr_lib_v5/tfc_tdf_v3.py","startLine":47,"endLine":67,"chunkText":"        x = x.reshape([*batch_dims, 2, -1])\n\n        if x_is_mps:\n            x = x.to(self.device)\n\n        return x\n\ndef get_norm(norm_type):\n    def norm(c, norm_type):\n        if norm_type is None:\n            return nn.Identity()\n        elif norm_type == 'BatchNorm':\n            return nn.BatchNorm2d(c)\n        elif norm_type == 'InstanceNorm':\n            return nn.InstanceNorm2d(c, affine=True)\n        elif 'GroupNorm' in norm_type:\n            g = int(norm_type.replace('GroupNorm', ''))\n            return nn.GroupNorm(num_groups=g, num_channels=c)\n        else:\n            return nn.Identity()\n","chunkId":"audio_separator/separator/uvr_lib_v5/tfc_tdf_v3.py:3"}],[1895,{"filePath":"audio_separator/separator/uvr_lib_v5/tfc_tdf_v3.py","startLine":65,"endLine":82,"chunkText":"        else:\n            return nn.Identity()\n\n    return partial(norm, norm_type=norm_type)\n\n\ndef get_act(act_type):\n    if act_type == 'gelu':\n        return nn.GELU()\n    elif act_type == 'relu':\n        return nn.ReLU()\n    elif act_type[:3] == 'elu':\n        alpha = float(act_type.replace('elu', ''))\n        return nn.ELU(alpha)\n    else:\n        raise Exception\n\n","chunkId":"audio_separator/separator/uvr_lib_v5/tfc_tdf_v3.py:4"}],[1896,{"filePath":"audio_separator/separator/uvr_lib_v5/tfc_tdf_v3.py","startLine":83,"endLine":95,"chunkText":"class Upscale(nn.Module):\n    def __init__(self, in_c, out_c, scale, norm, act):\n        super().__init__()\n        self.conv = nn.Sequential(\n            norm(in_c),\n            act,\n            nn.ConvTranspose2d(in_channels=in_c, out_channels=out_c, kernel_size=scale, stride=scale, bias=False)\n        )\n\n    def forward(self, x):\n        return self.conv(x)\n\n","chunkId":"audio_separator/separator/uvr_lib_v5/tfc_tdf_v3.py:5"}],[1897,{"filePath":"audio_separator/separator/uvr_lib_v5/tfc_tdf_v3.py","startLine":96,"endLine":108,"chunkText":"class Downscale(nn.Module):\n    def __init__(self, in_c, out_c, scale, norm, act):\n        super().__init__()\n        self.conv = nn.Sequential(\n            norm(in_c),\n            act,\n            nn.Conv2d(in_channels=in_c, out_channels=out_c, kernel_size=scale, stride=scale, bias=False)\n        )\n\n    def forward(self, x):\n        return self.conv(x)\n\n","chunkId":"audio_separator/separator/uvr_lib_v5/tfc_tdf_v3.py:6"}],[1898,{"filePath":"audio_separator/separator/uvr_lib_v5/tfc_tdf_v3.py","startLine":109,"endLine":129,"chunkText":"class TFC_TDF(nn.Module):\n    def __init__(self, in_c, c, l, f, bn, norm, act):\n        super().__init__()\n\n        self.blocks = nn.ModuleList()\n        for i in range(l):\n            block = nn.Module()\n\n            block.tfc1 = nn.Sequential(\n                norm(in_c),\n                act,\n                nn.Conv2d(in_c, c, 3, 1, 1, bias=False),\n            )\n            block.tdf = nn.Sequential(\n                norm(c),\n                act,\n                nn.Linear(f, f // bn, bias=False),\n                norm(c),\n                act,\n                nn.Linear(f // bn, f, bias=False),\n            )","chunkId":"audio_separator/separator/uvr_lib_v5/tfc_tdf_v3.py:7"}],[1899,{"filePath":"audio_separator/separator/uvr_lib_v5/tfc_tdf_v3.py","startLine":127,"endLine":149,"chunkText":"                act,\n                nn.Linear(f // bn, f, bias=False),\n            )\n            block.tfc2 = nn.Sequential(\n                norm(c),\n                act,\n                nn.Conv2d(c, c, 3, 1, 1, bias=False),\n            )\n            block.shortcut = nn.Conv2d(in_c, c, 1, 1, 0, bias=False)\n\n            self.blocks.append(block)\n            in_c = c\n\n    def forward(self, x):\n        for block in self.blocks:\n            s = block.shortcut(x)\n            x = block.tfc1(x)\n            x = x + block.tdf(x)\n            x = block.tfc2(x)\n            x = x + s\n        return x\n\n","chunkId":"audio_separator/separator/uvr_lib_v5/tfc_tdf_v3.py:8"}],[1900,{"filePath":"audio_separator/separator/uvr_lib_v5/tfc_tdf_v3.py","startLine":150,"endLine":167,"chunkText":"class TFC_TDF_net(nn.Module):\n    def __init__(self, config, device):\n        super().__init__()\n        self.config = config\n        self.device = device\n\n        # Defensive checks for normalization configuration\n        try:\n            norm_type = config.model.norm\n        except (AttributeError, KeyError):\n            norm_type = None\n            print(\"Warning: Model configuration missing 'norm' attribute, using Identity normalization\")\n        \n        norm = get_norm(norm_type=norm_type)\n        \n        try:\n            act_type = config.model.act\n        except (AttributeError, KeyError):","chunkId":"audio_separator/separator/uvr_lib_v5/tfc_tdf_v3.py:9"}],[1901,{"filePath":"audio_separator/separator/uvr_lib_v5/tfc_tdf_v3.py","startLine":165,"endLine":178,"chunkText":"        try:\n            act_type = config.model.act\n        except (AttributeError, KeyError):\n            act_type = 'gelu'\n            print(\"Warning: Model configuration missing 'act' attribute, using GELU activation\")\n            \n        act = get_act(act_type=act_type)\n\n        self.num_target_instruments = 1 if config.training.target_instrument else len(config.training.instruments)\n        self.num_subbands = config.model.num_subbands\n\n        dim_c = self.num_subbands * config.audio.num_channels * 2\n        n = config.model.num_scales\n        scale = config.model.scale","chunkId":"audio_separator/separator/uvr_lib_v5/tfc_tdf_v3.py:10"}],[1902,{"filePath":"audio_separator/separator/uvr_lib_v5/tfc_tdf_v3.py","startLine":176,"endLine":190,"chunkText":"        dim_c = self.num_subbands * config.audio.num_channels * 2\n        n = config.model.num_scales\n        scale = config.model.scale\n        l = config.model.num_blocks_per_scale\n        c = config.model.num_channels\n        g = config.model.growth\n        bn = config.model.bottleneck_factor\n        f = config.audio.dim_f // self.num_subbands\n\n        self.first_conv = nn.Conv2d(dim_c, c, 1, 1, 0, bias=False)\n\n        self.encoder_blocks = nn.ModuleList()\n        for i in range(n):\n            block = nn.Module()\n            block.tfc_tdf = TFC_TDF(c, c, l, f, bn, norm, act)","chunkId":"audio_separator/separator/uvr_lib_v5/tfc_tdf_v3.py:11"}],[1903,{"filePath":"audio_separator/separator/uvr_lib_v5/tfc_tdf_v3.py","startLine":188,"endLine":203,"chunkText":"        for i in range(n):\n            block = nn.Module()\n            block.tfc_tdf = TFC_TDF(c, c, l, f, bn, norm, act)\n            block.downscale = Downscale(c, c + g, scale, norm, act)\n            f = f // scale[1]\n            c += g\n            self.encoder_blocks.append(block)\n\n        self.bottleneck_block = TFC_TDF(c, c, l, f, bn, norm, act)\n\n        self.decoder_blocks = nn.ModuleList()\n        for i in range(n):\n            block = nn.Module()\n            block.upscale = Upscale(c, c - g, scale, norm, act)\n            f = f * scale[1]\n            c -= g","chunkId":"audio_separator/separator/uvr_lib_v5/tfc_tdf_v3.py:12"}],[1904,{"filePath":"audio_separator/separator/uvr_lib_v5/tfc_tdf_v3.py","startLine":201,"endLine":216,"chunkText":"            block.upscale = Upscale(c, c - g, scale, norm, act)\n            f = f * scale[1]\n            c -= g\n            block.tfc_tdf = TFC_TDF(2 * c, c, l, f, bn, norm, act)\n            self.decoder_blocks.append(block)\n\n        self.final_conv = nn.Sequential(\n            nn.Conv2d(c + dim_c, c, 1, 1, 0, bias=False),\n            act,\n            nn.Conv2d(c, self.num_target_instruments * dim_c, 1, 1, 0, bias=False)\n        )\n\n        self.stft = STFT(config.audio.n_fft, config.audio.hop_length, config.audio.dim_f, self.device)\n\n    def cac2cws(self, x):\n        k = self.num_subbands","chunkId":"audio_separator/separator/uvr_lib_v5/tfc_tdf_v3.py:13"}],[1905,{"filePath":"audio_separator/separator/uvr_lib_v5/tfc_tdf_v3.py","startLine":214,"endLine":239,"chunkText":"\n    def cac2cws(self, x):\n        k = self.num_subbands\n        b, c, f, t = x.shape\n        x = x.reshape(b, c, k, f // k, t)\n        x = x.reshape(b, c * k, f // k, t)\n        return x\n\n    def cws2cac(self, x):\n        k = self.num_subbands\n        b, c, f, t = x.shape\n        x = x.reshape(b, c // k, k, f, t)\n        x = x.reshape(b, c // k, f * k, t)\n        return x\n\n    def forward(self, x):\n\n        x = self.stft(x)\n\n        mix = x = self.cac2cws(x)\n\n        first_conv_out = x = self.first_conv(x)\n\n        x = x.transpose(-1, -2)\n\n        encoder_outputs = []","chunkId":"audio_separator/separator/uvr_lib_v5/tfc_tdf_v3.py:14"}],[1906,{"filePath":"audio_separator/separator/uvr_lib_v5/tfc_tdf_v3.py","startLine":237,"endLine":259,"chunkText":"        x = x.transpose(-1, -2)\n\n        encoder_outputs = []\n        for block in self.encoder_blocks:\n            x = block.tfc_tdf(x)\n            encoder_outputs.append(x)\n            x = block.downscale(x)\n\n        x = self.bottleneck_block(x)\n\n        for block in self.decoder_blocks:\n            x = block.upscale(x)\n            x = torch.cat([x, encoder_outputs.pop()], 1)\n            x = block.tfc_tdf(x)\n\n        x = x.transpose(-1, -2)\n\n        x = x * first_conv_out  # reduce artifacts\n\n        x = self.final_conv(torch.cat([mix, x], 1))\n\n        x = self.cws2cac(x)\n","chunkId":"audio_separator/separator/uvr_lib_v5/tfc_tdf_v3.py:15"}],[1907,{"filePath":"audio_separator/separator/uvr_lib_v5/tfc_tdf_v3.py","startLine":257,"endLine":269,"chunkText":"\n        x = self.cws2cac(x)\n\n        if self.num_target_instruments > 1:\n            b, c, f, t = x.shape\n            x = x.reshape(b, self.num_target_instruments, -1, f, t)\n\n        x = self.stft.inverse(x)\n\n        return x\n\n\n","chunkId":"audio_separator/separator/uvr_lib_v5/tfc_tdf_v3.py:16"}],[1908,{"filePath":"audio_separator/separator/uvr_lib_v5/vr_network/__init__.py","startLine":0,"endLine":1,"chunkText":"# VR init.\n","chunkId":"audio_separator/separator/uvr_lib_v5/vr_network/__init__.py:0"}],[1909,{"filePath":"audio_separator/separator/uvr_lib_v5/vr_network/layers.py","startLine":0,"endLine":12,"chunkText":"import torch\r\nfrom torch import nn\r\nimport torch.nn.functional as F\r\n\r\nfrom audio_separator.separator.uvr_lib_v5 import spec_utils\r\n\r\n\r\nclass Conv2DBNActiv(nn.Module):\r\n    \"\"\"\r\n    This class implements a convolutional layer followed by batch normalization and an activation function.\r\n    It is a common pattern in deep learning for processing images or feature maps. The convolutional layer\r\n    applies a set of learnable filters to the input. Batch normalization then normalizes the output of the\r\n    convolution, and finally, an activation function introduces non-linearity to the model, allowing it to\r","chunkId":"audio_separator/separator/uvr_lib_v5/vr_network/layers.py:0"}],[1910,{"filePath":"audio_separator/separator/uvr_lib_v5/vr_network/layers.py","startLine":10,"endLine":19,"chunkText":"    It is a common pattern in deep learning for processing images or feature maps. The convolutional layer\r\n    applies a set of learnable filters to the input. Batch normalization then normalizes the output of the\r\n    convolution, and finally, an activation function introduces non-linearity to the model, allowing it to\r\n    learn more complex patterns.\r\n\r\n    Attributes:\r\n        conv (nn.Sequential): A sequential container of Conv2d, BatchNorm2d, and an activation layer.\r\n\r\n    Args:\r\n        num_input_channels (int): Number of input channels.\r","chunkId":"audio_separator/separator/uvr_lib_v5/vr_network/layers.py:1"}],[1911,{"filePath":"audio_separator/separator/uvr_lib_v5/vr_network/layers.py","startLine":17,"endLine":27,"chunkText":"\r\n    Args:\r\n        num_input_channels (int): Number of input channels.\r\n        num_output_channels (int): Number of output channels.\r\n        kernel_size (int, optional): Size of the kernel. Defaults to 3.\r\n        stride_length (int, optional): Stride of the convolution. Defaults to 1.\r\n        padding_size (int, optional): Padding added to all sides of the input. Defaults to 1.\r\n        dilation_rate (int, optional): Spacing between kernel elements. Defaults to 1.\r\n        activation_function (callable, optional): The activation function to use. Defaults to nn.ReLU.\r\n    \"\"\"\r\n\r","chunkId":"audio_separator/separator/uvr_lib_v5/vr_network/layers.py:2"}],[1912,{"filePath":"audio_separator/separator/uvr_lib_v5/vr_network/layers.py","startLine":25,"endLine":34,"chunkText":"        activation_function (callable, optional): The activation function to use. Defaults to nn.ReLU.\r\n    \"\"\"\r\n\r\n    def __init__(self, nin, nout, ksize=3, stride=1, pad=1, dilation=1, activ=nn.ReLU):\r\n        super(Conv2DBNActiv, self).__init__()\r\n\r\n        # The nn.Sequential container allows us to stack the Conv2d, BatchNorm2d, and activation layers\r\n        # into a single module, simplifying the forward pass.\r\n        self.conv = nn.Sequential(nn.Conv2d(nin, nout, kernel_size=ksize, stride=stride, padding=pad, dilation=dilation, bias=False), nn.BatchNorm2d(nout), activ())\r\n\r","chunkId":"audio_separator/separator/uvr_lib_v5/vr_network/layers.py:3"}],[1913,{"filePath":"audio_separator/separator/uvr_lib_v5/vr_network/layers.py","startLine":32,"endLine":40,"chunkText":"        # into a single module, simplifying the forward pass.\r\n        self.conv = nn.Sequential(nn.Conv2d(nin, nout, kernel_size=ksize, stride=stride, padding=pad, dilation=dilation, bias=False), nn.BatchNorm2d(nout), activ())\r\n\r\n    def __call__(self, input_tensor):\r\n        # Defines the computation performed at every call.\r\n        # Simply passes the input through the sequential container.\r\n        return self.conv(input_tensor)\r\n\r\n\r","chunkId":"audio_separator/separator/uvr_lib_v5/vr_network/layers.py:4"}],[1914,{"filePath":"audio_separator/separator/uvr_lib_v5/vr_network/layers.py","startLine":41,"endLine":48,"chunkText":"class SeperableConv2DBNActiv(nn.Module):\r\n    \"\"\"\r\n    This class implements a separable convolutional layer followed by batch normalization and an activation function.\r\n    Separable convolutions are a type of convolution that splits the convolution operation into two simpler operations:\r\n    a depthwise convolution and a pointwise convolution. This can reduce the number of parameters and computational cost,\r\n    making the network more efficient while maintaining similar performance.\r\n\r\n    The depthwise convolution applies a single filter per input channel (input depth). The pointwise convolution,\r","chunkId":"audio_separator/separator/uvr_lib_v5/vr_network/layers.py:5"}],[1915,{"filePath":"audio_separator/separator/uvr_lib_v5/vr_network/layers.py","startLine":46,"endLine":52,"chunkText":"    making the network more efficient while maintaining similar performance.\r\n\r\n    The depthwise convolution applies a single filter per input channel (input depth). The pointwise convolution,\r\n    which follows, applies a 1x1 convolution to combine the outputs of the depthwise convolution across channels.\r\n    Batch normalization is then applied to stabilize learning and reduce internal covariate shift. Finally,\r\n    an activation function introduces non-linearity, allowing the network to learn complex patterns.\r\n    Attributes:\r","chunkId":"audio_separator/separator/uvr_lib_v5/vr_network/layers.py:6"}],[1916,{"filePath":"audio_separator/separator/uvr_lib_v5/vr_network/layers.py","startLine":50,"endLine":58,"chunkText":"    Batch normalization is then applied to stabilize learning and reduce internal covariate shift. Finally,\r\n    an activation function introduces non-linearity, allowing the network to learn complex patterns.\r\n    Attributes:\r\n        conv (nn.Sequential): A sequential container of depthwise Conv2d, pointwise Conv2d, BatchNorm2d, and an activation layer.\r\n\r\n    Args:\r\n        num_input_channels (int): Number of input channels.\r\n        num_output_channels (int): Number of output channels.\r\n        kernel_size (int, optional): Size of the kernel for the depthwise convolution. Defaults to 3.\r","chunkId":"audio_separator/separator/uvr_lib_v5/vr_network/layers.py:7"}],[1917,{"filePath":"audio_separator/separator/uvr_lib_v5/vr_network/layers.py","startLine":56,"endLine":61,"chunkText":"        num_input_channels (int): Number of input channels.\r\n        num_output_channels (int): Number of output channels.\r\n        kernel_size (int, optional): Size of the kernel for the depthwise convolution. Defaults to 3.\r\n        stride_length (int, optional): Stride of the convolution. Defaults to 1.\r\n        padding_size (int, optional): Padding added to all sides of the input for the depthwise convolution. Defaults to 1.\r\n        dilation_rate (int, optional): Spacing between kernel elements for the depthwise convolution. Defaults to 1.\r","chunkId":"audio_separator/separator/uvr_lib_v5/vr_network/layers.py:8"}],[1918,{"filePath":"audio_separator/separator/uvr_lib_v5/vr_network/layers.py","startLine":59,"endLine":67,"chunkText":"        stride_length (int, optional): Stride of the convolution. Defaults to 1.\r\n        padding_size (int, optional): Padding added to all sides of the input for the depthwise convolution. Defaults to 1.\r\n        dilation_rate (int, optional): Spacing between kernel elements for the depthwise convolution. Defaults to 1.\r\n        activation_function (callable, optional): The activation function to use. Defaults to nn.ReLU.\r\n    \"\"\"\r\n\r\n    def __init__(self, nin, nout, ksize=3, stride=1, pad=1, dilation=1, activ=nn.ReLU):\r\n        super(SeperableConv2DBNActiv, self).__init__()\r\n\r","chunkId":"audio_separator/separator/uvr_lib_v5/vr_network/layers.py:9"}],[1919,{"filePath":"audio_separator/separator/uvr_lib_v5/vr_network/layers.py","startLine":65,"endLine":71,"chunkText":"    def __init__(self, nin, nout, ksize=3, stride=1, pad=1, dilation=1, activ=nn.ReLU):\r\n        super(SeperableConv2DBNActiv, self).__init__()\r\n\r\n        # Initialize the sequential container with the depthwise convolution.\r\n        # The number of groups in the depthwise convolution is set to num_input_channels, which means each input channel is treated separately.\r\n        # The pointwise convolution then combines these separate channels into num_output_channels channels.\r\n        # Batch normalization is applied to the output of the pointwise convolution.\r","chunkId":"audio_separator/separator/uvr_lib_v5/vr_network/layers.py:10"}],[1920,{"filePath":"audio_separator/separator/uvr_lib_v5/vr_network/layers.py","startLine":69,"endLine":76,"chunkText":"        # The number of groups in the depthwise convolution is set to num_input_channels, which means each input channel is treated separately.\r\n        # The pointwise convolution then combines these separate channels into num_output_channels channels.\r\n        # Batch normalization is applied to the output of the pointwise convolution.\r\n        # Finally, the activation function is applied to introduce non-linearity.\r\n        self.conv = nn.Sequential(\r\n            nn.Conv2d(\r\n                nin,\r\n                nin,  # For depthwise convolution, in_channels = out_channels = num_input_channels\r","chunkId":"audio_separator/separator/uvr_lib_v5/vr_network/layers.py:11"}],[1921,{"filePath":"audio_separator/separator/uvr_lib_v5/vr_network/layers.py","startLine":74,"endLine":86,"chunkText":"            nn.Conv2d(\r\n                nin,\r\n                nin,  # For depthwise convolution, in_channels = out_channels = num_input_channels\r\n                kernel_size=ksize,\r\n                stride=stride,\r\n                padding=pad,\r\n                dilation=dilation,\r\n                groups=nin,  # This makes it a depthwise convolution\r\n                bias=False,  # Bias is not used because it will be handled by BatchNorm2d\r\n            ),\r\n            nn.Conv2d(\r\n                nin,\r\n                nout,  # Pointwise convolution to combine channels\r","chunkId":"audio_separator/separator/uvr_lib_v5/vr_network/layers.py:12"}],[1922,{"filePath":"audio_separator/separator/uvr_lib_v5/vr_network/layers.py","startLine":84,"endLine":95,"chunkText":"            nn.Conv2d(\r\n                nin,\r\n                nout,  # Pointwise convolution to combine channels\r\n                kernel_size=1,  # Kernel size of 1 for pointwise convolution\r\n                bias=False,  # Bias is not used because it will be handled by BatchNorm2d\r\n            ),\r\n            nn.BatchNorm2d(nout),  # Normalize the output of the pointwise convolution\r\n            activ(),  # Apply the activation function\r\n        )\r\n\r\n    def __call__(self, input_tensor):\r\n        # Pass the input through the sequential container.\r","chunkId":"audio_separator/separator/uvr_lib_v5/vr_network/layers.py:13"}],[1923,{"filePath":"audio_separator/separator/uvr_lib_v5/vr_network/layers.py","startLine":93,"endLine":100,"chunkText":"\r\n    def __call__(self, input_tensor):\r\n        # Pass the input through the sequential container.\r\n        # This performs the depthwise convolution, followed by the pointwise convolution,\r\n        # batch normalization, and finally applies the activation function.\r\n        return self.conv(input_tensor)\r\n\r\n\r","chunkId":"audio_separator/separator/uvr_lib_v5/vr_network/layers.py:14"}],[1924,{"filePath":"audio_separator/separator/uvr_lib_v5/vr_network/layers.py","startLine":101,"endLine":107,"chunkText":"class Encoder(nn.Module):\r\n    \"\"\"\r\n    The Encoder class is a part of the neural network architecture that is responsible for processing the input data.\r\n    It consists of two convolutional layers, each followed by batch normalization and an activation function.\r\n    The purpose of the Encoder is to transform the input data into a higher-level, abstract representation.\r\n    This is achieved by applying filters (through convolutions) that can capture patterns or features in the data.\r\n    The Encoder can be thought of as a feature extractor that prepares the data for further processing by the network.\r","chunkId":"audio_separator/separator/uvr_lib_v5/vr_network/layers.py:15"}],[1925,{"filePath":"audio_separator/separator/uvr_lib_v5/vr_network/layers.py","startLine":105,"endLine":112,"chunkText":"    The purpose of the Encoder is to transform the input data into a higher-level, abstract representation.\r\n    This is achieved by applying filters (through convolutions) that can capture patterns or features in the data.\r\n    The Encoder can be thought of as a feature extractor that prepares the data for further processing by the network.\r\n    Attributes:\r\n        conv1 (Conv2DBNActiv): The first convolutional layer in the encoder.\r\n        conv2 (Conv2DBNActiv): The second convolutional layer in the encoder.\r\n\r\n    Args:\r","chunkId":"audio_separator/separator/uvr_lib_v5/vr_network/layers.py:16"}],[1926,{"filePath":"audio_separator/separator/uvr_lib_v5/vr_network/layers.py","startLine":110,"endLine":117,"chunkText":"        conv2 (Conv2DBNActiv): The second convolutional layer in the encoder.\r\n\r\n    Args:\r\n        number_of_input_channels (int): Number of input channels for the first convolutional layer.\r\n        number_of_output_channels (int): Number of output channels for the convolutional layers.\r\n        kernel_size (int): Kernel size for the convolutional layers.\r\n        stride_length (int): Stride for the convolutional operations.\r\n        padding_size (int): Padding added to all sides of the input for the convolutional layers.\r","chunkId":"audio_separator/separator/uvr_lib_v5/vr_network/layers.py:17"}],[1927,{"filePath":"audio_separator/separator/uvr_lib_v5/vr_network/layers.py","startLine":115,"endLine":124,"chunkText":"        kernel_size (int): Kernel size for the convolutional layers.\r\n        stride_length (int): Stride for the convolutional operations.\r\n        padding_size (int): Padding added to all sides of the input for the convolutional layers.\r\n        activation_function (callable): The activation function to use after each convolutional layer.\r\n    \"\"\"\r\n\r\n    def __init__(self, nin, nout, ksize=3, stride=1, pad=1, activ=nn.LeakyReLU):\r\n        super(Encoder, self).__init__()\r\n\r\n        # The first convolutional layer takes the input and applies a convolution,\r","chunkId":"audio_separator/separator/uvr_lib_v5/vr_network/layers.py:18"}],[1928,{"filePath":"audio_separator/separator/uvr_lib_v5/vr_network/layers.py","startLine":122,"endLine":130,"chunkText":"        super(Encoder, self).__init__()\r\n\r\n        # The first convolutional layer takes the input and applies a convolution,\r\n        # followed by batch normalization and an activation function specified by `activation_function`.\r\n        # This layer is responsible for capturing the initial set of features from the input data.\r\n        self.conv1 = Conv2DBNActiv(nin, nout, ksize, 1, pad, activ=activ)\r\n\r\n        # The second convolutional layer further processes the output from the first layer,\r\n        # applying another set of convolution, batch normalization, and activation.\r","chunkId":"audio_separator/separator/uvr_lib_v5/vr_network/layers.py:19"}],[1929,{"filePath":"audio_separator/separator/uvr_lib_v5/vr_network/layers.py","startLine":128,"endLine":135,"chunkText":"\r\n        # The second convolutional layer further processes the output from the first layer,\r\n        # applying another set of convolution, batch normalization, and activation.\r\n        # This layer helps in capturing more complex patterns in the data by building upon the initial features extracted by conv1.\r\n        self.conv2 = Conv2DBNActiv(nout, nout, ksize, stride, pad, activ=activ)\r\n\r\n    def __call__(self, input_tensor):\r\n        # The input data `input_tensor` is passed through the first convolutional layer.\r","chunkId":"audio_separator/separator/uvr_lib_v5/vr_network/layers.py:20"}],[1930,{"filePath":"audio_separator/separator/uvr_lib_v5/vr_network/layers.py","startLine":133,"endLine":142,"chunkText":"\r\n    def __call__(self, input_tensor):\r\n        # The input data `input_tensor` is passed through the first convolutional layer.\r\n        # The output of this layer serves as a 'skip connection' that can be used later in the network to preserve spatial information.\r\n        skip = self.conv1(input_tensor)\r\n\r\n        # The output from the first layer is then passed through the second convolutional layer.\r\n        # This processed data `hidden` is the final output of the Encoder, representing the abstracted features of the input.\r\n        hidden = self.conv2(skip)\r\n\r","chunkId":"audio_separator/separator/uvr_lib_v5/vr_network/layers.py:21"}],[1931,{"filePath":"audio_separator/separator/uvr_lib_v5/vr_network/layers.py","startLine":140,"endLine":146,"chunkText":"        # This processed data `hidden` is the final output of the Encoder, representing the abstracted features of the input.\r\n        hidden = self.conv2(skip)\r\n\r\n        # The Encoder returns two outputs: `hidden`, the abstracted feature representation, and `skip`, the intermediate representation from conv1.\r\n        return hidden, skip\r\n\r\n\r","chunkId":"audio_separator/separator/uvr_lib_v5/vr_network/layers.py:22"}],[1932,{"filePath":"audio_separator/separator/uvr_lib_v5/vr_network/layers.py","startLine":147,"endLine":154,"chunkText":"class Decoder(nn.Module):\r\n    \"\"\"\r\n    The Decoder class is part of the neural network architecture, specifically designed to perform the inverse operation of an encoder.\r\n    Its main role is to reconstruct or generate data from encoded representations, which is crucial in tasks like image segmentation or audio processing.\r\n    This class uses upsampling, convolution, optional dropout for regularization, and concatenation of skip connections to achieve its goal.\r\n\r\n    Attributes:\r\n        convolution (Conv2DBNActiv): A convolutional layer with batch normalization and activation function.\r","chunkId":"audio_separator/separator/uvr_lib_v5/vr_network/layers.py:23"}],[1933,{"filePath":"audio_separator/separator/uvr_lib_v5/vr_network/layers.py","startLine":152,"endLine":161,"chunkText":"\r\n    Attributes:\r\n        convolution (Conv2DBNActiv): A convolutional layer with batch normalization and activation function.\r\n        dropout_layer (nn.Dropout2d): An optional dropout layer for regularization to prevent overfitting.\r\n\r\n    Args:\r\n        input_channels (int): Number of input channels for the convolutional layer.\r\n        output_channels (int): Number of output channels for the convolutional layer.\r\n        kernel_size (int): Kernel size for the convolutional layer.\r\n        stride (int): Stride for the convolutional operations.\r","chunkId":"audio_separator/separator/uvr_lib_v5/vr_network/layers.py:24"}],[1934,{"filePath":"audio_separator/separator/uvr_lib_v5/vr_network/layers.py","startLine":159,"endLine":167,"chunkText":"        output_channels (int): Number of output channels for the convolutional layer.\r\n        kernel_size (int): Kernel size for the convolutional layer.\r\n        stride (int): Stride for the convolutional operations.\r\n        padding (int): Padding added to all sides of the input for the convolutional layer.\r\n        activation_function (callable): The activation function to use after the convolutional layer.\r\n        include_dropout (bool): Whether to include a dropout layer for regularization.\r\n    \"\"\"\r\n\r\n    def __init__(self, nin, nout, ksize=3, stride=1, pad=1, activ=nn.ReLU, dropout=False):\r","chunkId":"audio_separator/separator/uvr_lib_v5/vr_network/layers.py:25"}],[1935,{"filePath":"audio_separator/separator/uvr_lib_v5/vr_network/layers.py","startLine":165,"endLine":177,"chunkText":"    \"\"\"\r\n\r\n    def __init__(self, nin, nout, ksize=3, stride=1, pad=1, activ=nn.ReLU, dropout=False):\r\n        super(Decoder, self).__init__()\r\n\r\n        # Initialize the convolutional layer with specified parameters.\r\n        self.conv = Conv2DBNActiv(nin, nout, ksize, 1, pad, activ=activ)\r\n\r\n        # Initialize the dropout layer if include_dropout is set to True\r\n        self.dropout = nn.Dropout2d(0.1) if dropout else None\r\n\r\n    def __call__(self, input_tensor, skip=None):\r\n        # Upsample the input tensor to a higher resolution using bilinear interpolation.\r","chunkId":"audio_separator/separator/uvr_lib_v5/vr_network/layers.py:26"}],[1936,{"filePath":"audio_separator/separator/uvr_lib_v5/vr_network/layers.py","startLine":175,"endLine":181,"chunkText":"\r\n    def __call__(self, input_tensor, skip=None):\r\n        # Upsample the input tensor to a higher resolution using bilinear interpolation.\r\n        input_tensor = F.interpolate(input_tensor, scale_factor=2, mode=\"bilinear\", align_corners=True)\r\n        # If a skip connection is provided, crop it to match the size of input_tensor and concatenate them along the channel dimension.\r\n        if skip is not None:\r\n            skip = spec_utils.crop_center(skip, input_tensor)  # Crop skip_connection to match input_tensor's dimensions.\r","chunkId":"audio_separator/separator/uvr_lib_v5/vr_network/layers.py:27"}],[1937,{"filePath":"audio_separator/separator/uvr_lib_v5/vr_network/layers.py","startLine":179,"endLine":184,"chunkText":"        # If a skip connection is provided, crop it to match the size of input_tensor and concatenate them along the channel dimension.\r\n        if skip is not None:\r\n            skip = spec_utils.crop_center(skip, input_tensor)  # Crop skip_connection to match input_tensor's dimensions.\r\n            input_tensor = torch.cat([input_tensor, skip], dim=1)  # Concatenate input_tensor and skip_connection along the channel dimension.\r\n\r\n        # Pass the concatenated tensor (or just input_tensor if no skip_connection is provided) through the convolutional layer.\r","chunkId":"audio_separator/separator/uvr_lib_v5/vr_network/layers.py:28"}],[1938,{"filePath":"audio_separator/separator/uvr_lib_v5/vr_network/layers.py","startLine":182,"endLine":194,"chunkText":"            input_tensor = torch.cat([input_tensor, skip], dim=1)  # Concatenate input_tensor and skip_connection along the channel dimension.\r\n\r\n        # Pass the concatenated tensor (or just input_tensor if no skip_connection is provided) through the convolutional layer.\r\n        output_tensor = self.conv(input_tensor)\r\n\r\n        # If dropout is enabled, apply it to the output of the convolutional layer.\r\n        if self.dropout is not None:\r\n            output_tensor = self.dropout(output_tensor)\r\n\r\n        # Return the final output tensor.\r\n        return output_tensor\r\n\r\n\r","chunkId":"audio_separator/separator/uvr_lib_v5/vr_network/layers.py:29"}],[1939,{"filePath":"audio_separator/separator/uvr_lib_v5/vr_network/layers.py","startLine":195,"endLine":204,"chunkText":"class ASPPModule(nn.Module):\r\n    \"\"\"\r\n    Atrous Spatial Pyramid Pooling (ASPP) Module is designed for capturing multi-scale context by applying\r\n    atrous convolution at multiple rates. This is particularly useful in segmentation tasks where capturing\r\n    objects at various scales is beneficial. The module applies several parallel dilated convolutions with\r\n    different dilation rates to the input feature map, allowing it to efficiently capture information at\r\n    multiple scales.\r\n\r\n    Attributes:\r\n        conv1 (nn.Sequential): Applies adaptive average pooling followed by a 1x1 convolution.\r","chunkId":"audio_separator/separator/uvr_lib_v5/vr_network/layers.py:30"}],[1940,{"filePath":"audio_separator/separator/uvr_lib_v5/vr_network/layers.py","startLine":202,"endLine":208,"chunkText":"\r\n    Attributes:\r\n        conv1 (nn.Sequential): Applies adaptive average pooling followed by a 1x1 convolution.\r\n        nn_architecture (int): Identifier for the neural network architecture being used.\r\n        six_layer (list): List containing architecture identifiers that require six layers.\r\n        seven_layer (list): List containing architecture identifiers that require seven layers.\r\n        conv2-conv7 (nn.Module): Convolutional layers with varying dilation rates for multi-scale feature extraction.\r","chunkId":"audio_separator/separator/uvr_lib_v5/vr_network/layers.py:31"}],[1941,{"filePath":"audio_separator/separator/uvr_lib_v5/vr_network/layers.py","startLine":206,"endLine":213,"chunkText":"        six_layer (list): List containing architecture identifiers that require six layers.\r\n        seven_layer (list): List containing architecture identifiers that require seven layers.\r\n        conv2-conv7 (nn.Module): Convolutional layers with varying dilation rates for multi-scale feature extraction.\r\n        bottleneck (nn.Sequential): A 1x1 convolutional layer that combines all features followed by dropout for regularization.\r\n    \"\"\"\r\n\r\n    def __init__(self, nn_architecture, nin, nout, dilations=(4, 8, 16), activ=nn.ReLU):\r\n        \"\"\"\r","chunkId":"audio_separator/separator/uvr_lib_v5/vr_network/layers.py:32"}],[1942,{"filePath":"audio_separator/separator/uvr_lib_v5/vr_network/layers.py","startLine":211,"endLine":222,"chunkText":"\r\n    def __init__(self, nn_architecture, nin, nout, dilations=(4, 8, 16), activ=nn.ReLU):\r\n        \"\"\"\r\n        Initializes the ASPP module with specified parameters.\r\n\r\n        Args:\r\n            nn_architecture (int): Identifier for the neural network architecture.\r\n            input_channels (int): Number of input channels.\r\n            output_channels (int): Number of output channels.\r\n            dilations (tuple): Tuple of dilation rates for the atrous convolutions.\r\n            activation (callable): Activation function to use after convolutional layers.\r\n        \"\"\"\r","chunkId":"audio_separator/separator/uvr_lib_v5/vr_network/layers.py:33"}],[1943,{"filePath":"audio_separator/separator/uvr_lib_v5/vr_network/layers.py","startLine":220,"endLine":229,"chunkText":"            dilations (tuple): Tuple of dilation rates for the atrous convolutions.\r\n            activation (callable): Activation function to use after convolutional layers.\r\n        \"\"\"\r\n        super(ASPPModule, self).__init__()\r\n\r\n        # Adaptive average pooling reduces the spatial dimensions to 1x1, focusing on global context,\r\n        # followed by a 1x1 convolution to project back to the desired channel dimension.\r\n        self.conv1 = nn.Sequential(nn.AdaptiveAvgPool2d((1, None)), Conv2DBNActiv(nin, nin, 1, 1, 0, activ=activ))\r\n\r\n        self.nn_architecture = nn_architecture\r","chunkId":"audio_separator/separator/uvr_lib_v5/vr_network/layers.py:34"}],[1944,{"filePath":"audio_separator/separator/uvr_lib_v5/vr_network/layers.py","startLine":227,"endLine":237,"chunkText":"        self.conv1 = nn.Sequential(nn.AdaptiveAvgPool2d((1, None)), Conv2DBNActiv(nin, nin, 1, 1, 0, activ=activ))\r\n\r\n        self.nn_architecture = nn_architecture\r\n        # Architecture identifiers for models requiring additional layers.\r\n        self.six_layer = [129605]\r\n        self.seven_layer = [537238, 537227, 33966]\r\n\r\n        # Extra convolutional layer used for six and seven layer configurations.\r\n        extra_conv = SeperableConv2DBNActiv(nin, nin, 3, 1, dilations[2], dilations[2], activ=activ)\r\n\r\n        # Standard 1x1 convolution for channel reduction.\r","chunkId":"audio_separator/separator/uvr_lib_v5/vr_network/layers.py:35"}],[1945,{"filePath":"audio_separator/separator/uvr_lib_v5/vr_network/layers.py","startLine":235,"endLine":242,"chunkText":"        extra_conv = SeperableConv2DBNActiv(nin, nin, 3, 1, dilations[2], dilations[2], activ=activ)\r\n\r\n        # Standard 1x1 convolution for channel reduction.\r\n        self.conv2 = Conv2DBNActiv(nin, nin, 1, 1, 0, activ=activ)\r\n\r\n        # Separable convolutions with different dilation rates for multi-scale feature extraction.\r\n        self.conv3 = SeperableConv2DBNActiv(nin, nin, 3, 1, dilations[0], dilations[0], activ=activ)\r\n        self.conv4 = SeperableConv2DBNActiv(nin, nin, 3, 1, dilations[1], dilations[1], activ=activ)\r","chunkId":"audio_separator/separator/uvr_lib_v5/vr_network/layers.py:36"}],[1946,{"filePath":"audio_separator/separator/uvr_lib_v5/vr_network/layers.py","startLine":240,"endLine":248,"chunkText":"        # Separable convolutions with different dilation rates for multi-scale feature extraction.\r\n        self.conv3 = SeperableConv2DBNActiv(nin, nin, 3, 1, dilations[0], dilations[0], activ=activ)\r\n        self.conv4 = SeperableConv2DBNActiv(nin, nin, 3, 1, dilations[1], dilations[1], activ=activ)\r\n        self.conv5 = SeperableConv2DBNActiv(nin, nin, 3, 1, dilations[2], dilations[2], activ=activ)\r\n\r\n        # Depending on the architecture, include the extra convolutional layers.\r\n        if self.nn_architecture in self.six_layer:\r\n            self.conv6 = extra_conv\r\n            nin_x = 6\r","chunkId":"audio_separator/separator/uvr_lib_v5/vr_network/layers.py:37"}],[1947,{"filePath":"audio_separator/separator/uvr_lib_v5/vr_network/layers.py","startLine":246,"endLine":260,"chunkText":"        if self.nn_architecture in self.six_layer:\r\n            self.conv6 = extra_conv\r\n            nin_x = 6\r\n        elif self.nn_architecture in self.seven_layer:\r\n            self.conv6 = extra_conv\r\n            self.conv7 = extra_conv\r\n            nin_x = 7\r\n        else:\r\n            nin_x = 5\r\n\r\n        # Bottleneck layer combines all the multi-scale features into the desired number of output channels.\r\n        self.bottleneck = nn.Sequential(Conv2DBNActiv(nin * nin_x, nout, 1, 1, 0, activ=activ), nn.Dropout2d(0.1))\r\n\r\n    def forward(self, input_tensor):\r\n        \"\"\"\r","chunkId":"audio_separator/separator/uvr_lib_v5/vr_network/layers.py:38"}],[1948,{"filePath":"audio_separator/separator/uvr_lib_v5/vr_network/layers.py","startLine":258,"endLine":275,"chunkText":"\r\n    def forward(self, input_tensor):\r\n        \"\"\"\r\n        Forward pass of the ASPP module.\r\n\r\n        Args:\r\n            input_tensor (Tensor): Input tensor.\r\n\r\n        Returns:\r\n            Tensor: Output tensor after applying ASPP.\r\n        \"\"\"\r\n        _, _, h, w = input_tensor.size()\r\n\r\n        # Apply the first convolutional sequence and upsample to the original resolution.\r\n        feat1 = F.interpolate(self.conv1(input_tensor), size=(h, w), mode=\"bilinear\", align_corners=True)\r\n\r\n        # Apply the remaining convolutions directly on the input.\r\n        feat2 = self.conv2(input_tensor)\r","chunkId":"audio_separator/separator/uvr_lib_v5/vr_network/layers.py:39"}],[1949,{"filePath":"audio_separator/separator/uvr_lib_v5/vr_network/layers.py","startLine":273,"endLine":284,"chunkText":"\r\n        # Apply the remaining convolutions directly on the input.\r\n        feat2 = self.conv2(input_tensor)\r\n        feat3 = self.conv3(input_tensor)\r\n        feat4 = self.conv4(input_tensor)\r\n        feat5 = self.conv5(input_tensor)\r\n\r\n        # Concatenate features from all layers. Depending on the architecture, include the extra features.\r\n        if self.nn_architecture in self.six_layer:\r\n            feat6 = self.conv6(input_tensor)\r\n            out = torch.cat((feat1, feat2, feat3, feat4, feat5, feat6), dim=1)\r\n        elif self.nn_architecture in self.seven_layer:\r","chunkId":"audio_separator/separator/uvr_lib_v5/vr_network/layers.py:40"}],[1950,{"filePath":"audio_separator/separator/uvr_lib_v5/vr_network/layers.py","startLine":282,"endLine":292,"chunkText":"            feat6 = self.conv6(input_tensor)\r\n            out = torch.cat((feat1, feat2, feat3, feat4, feat5, feat6), dim=1)\r\n        elif self.nn_architecture in self.seven_layer:\r\n            feat6 = self.conv6(input_tensor)\r\n            feat7 = self.conv7(input_tensor)\r\n            out = torch.cat((feat1, feat2, feat3, feat4, feat5, feat6, feat7), dim=1)\r\n        else:\r\n            out = torch.cat((feat1, feat2, feat3, feat4, feat5), dim=1)\r\n\r\n        # Apply the bottleneck layer to combine and reduce the channel dimensions.\r\n        bottleneck_output = self.bottleneck(out)\r","chunkId":"audio_separator/separator/uvr_lib_v5/vr_network/layers.py:41"}],[1951,{"filePath":"audio_separator/separator/uvr_lib_v5/vr_network/layers.py","startLine":290,"endLine":294,"chunkText":"\r\n        # Apply the bottleneck layer to combine and reduce the channel dimensions.\r\n        bottleneck_output = self.bottleneck(out)\r\n        return bottleneck_output\r\n","chunkId":"audio_separator/separator/uvr_lib_v5/vr_network/layers.py:42"}],[1952,{"filePath":"audio_separator/separator/uvr_lib_v5/vr_network/layers_new.py","startLine":0,"endLine":14,"chunkText":"import torch\nfrom torch import nn\nimport torch.nn.functional as F\n\nfrom audio_separator.separator.uvr_lib_v5 import spec_utils\n\n\nclass Conv2DBNActiv(nn.Module):\n    \"\"\"\n    Conv2DBNActiv Class:\n    This class implements a convolutional layer followed by batch normalization and an activation function.\n    It is a fundamental building block for constructing neural networks, especially useful in image and audio processing tasks.\n    The class encapsulates the pattern of applying a convolution, normalizing the output, and then applying a non-linear activation.\n    \"\"\"\n","chunkId":"audio_separator/separator/uvr_lib_v5/vr_network/layers_new.py:0"}],[1953,{"filePath":"audio_separator/separator/uvr_lib_v5/vr_network/layers_new.py","startLine":12,"endLine":21,"chunkText":"    The class encapsulates the pattern of applying a convolution, normalizing the output, and then applying a non-linear activation.\n    \"\"\"\n\n    def __init__(self, nin, nout, ksize=3, stride=1, pad=1, dilation=1, activ=nn.ReLU):\n        super(Conv2DBNActiv, self).__init__()\n\n        # Sequential model combining Conv2D, BatchNorm, and activation function into a single module\n        self.conv = nn.Sequential(nn.Conv2d(nin, nout, kernel_size=ksize, stride=stride, padding=pad, dilation=dilation, bias=False), nn.BatchNorm2d(nout), activ())\n\n    def __call__(self, input_tensor):","chunkId":"audio_separator/separator/uvr_lib_v5/vr_network/layers_new.py:1"}],[1954,{"filePath":"audio_separator/separator/uvr_lib_v5/vr_network/layers_new.py","startLine":19,"endLine":25,"chunkText":"        self.conv = nn.Sequential(nn.Conv2d(nin, nout, kernel_size=ksize, stride=stride, padding=pad, dilation=dilation, bias=False), nn.BatchNorm2d(nout), activ())\n\n    def __call__(self, input_tensor):\n        # Forward pass through the sequential model\n        return self.conv(input_tensor)\n\n","chunkId":"audio_separator/separator/uvr_lib_v5/vr_network/layers_new.py:2"}],[1955,{"filePath":"audio_separator/separator/uvr_lib_v5/vr_network/layers_new.py","startLine":26,"endLine":38,"chunkText":"class Encoder(nn.Module):\n    \"\"\"\n    Encoder Class:\n    This class defines an encoder module typically used in autoencoder architectures.\n    It consists of two convolutional layers, each followed by batch normalization and an activation function.\n    \"\"\"\n\n    def __init__(self, nin, nout, ksize=3, stride=1, pad=1, activ=nn.LeakyReLU):\n        super(Encoder, self).__init__()\n\n        # First convolutional layer of the encoder\n        self.conv1 = Conv2DBNActiv(nin, nout, ksize, stride, pad, activ=activ)\n        # Second convolutional layer of the encoder","chunkId":"audio_separator/separator/uvr_lib_v5/vr_network/layers_new.py:3"}],[1956,{"filePath":"audio_separator/separator/uvr_lib_v5/vr_network/layers_new.py","startLine":36,"endLine":48,"chunkText":"        # First convolutional layer of the encoder\n        self.conv1 = Conv2DBNActiv(nin, nout, ksize, stride, pad, activ=activ)\n        # Second convolutional layer of the encoder\n        self.conv2 = Conv2DBNActiv(nout, nout, ksize, 1, pad, activ=activ)\n\n    def __call__(self, input_tensor):\n        # Applying the first and then the second convolutional layers\n        hidden = self.conv1(input_tensor)\n        hidden = self.conv2(hidden)\n\n        return hidden\n\n","chunkId":"audio_separator/separator/uvr_lib_v5/vr_network/layers_new.py:4"}],[1957,{"filePath":"audio_separator/separator/uvr_lib_v5/vr_network/layers_new.py","startLine":49,"endLine":59,"chunkText":"class Decoder(nn.Module):\n    \"\"\"\n    Decoder Class:\n    This class defines a decoder module, which is the counterpart of the Encoder class in autoencoder architectures.\n    It applies a convolutional layer followed by batch normalization and an activation function, with an optional dropout layer for regularization.\n    \"\"\"\n\n    def __init__(self, nin, nout, ksize=3, stride=1, pad=1, activ=nn.ReLU, dropout=False):\n        super(Decoder, self).__init__()\n        # Convolutional layer with optional dropout for regularization\n        self.conv1 = Conv2DBNActiv(nin, nout, ksize, 1, pad, activ=activ)","chunkId":"audio_separator/separator/uvr_lib_v5/vr_network/layers_new.py:5"}],[1958,{"filePath":"audio_separator/separator/uvr_lib_v5/vr_network/layers_new.py","startLine":57,"endLine":67,"chunkText":"        super(Decoder, self).__init__()\n        # Convolutional layer with optional dropout for regularization\n        self.conv1 = Conv2DBNActiv(nin, nout, ksize, 1, pad, activ=activ)\n        # self.conv2 = Conv2DBNActiv(nout, nout, ksize, 1, pad, activ=activ)\n        self.dropout = nn.Dropout2d(0.1) if dropout else None\n\n    def __call__(self, input_tensor, skip=None):\n        # Forward pass through the convolutional layer and optional dropout\n        input_tensor = F.interpolate(input_tensor, scale_factor=2, mode=\"bilinear\", align_corners=True)\n\n        if skip is not None:","chunkId":"audio_separator/separator/uvr_lib_v5/vr_network/layers_new.py:6"}],[1959,{"filePath":"audio_separator/separator/uvr_lib_v5/vr_network/layers_new.py","startLine":65,"endLine":79,"chunkText":"        input_tensor = F.interpolate(input_tensor, scale_factor=2, mode=\"bilinear\", align_corners=True)\n\n        if skip is not None:\n            skip = spec_utils.crop_center(skip, input_tensor)\n            input_tensor = torch.cat([input_tensor, skip], dim=1)\n\n        hidden = self.conv1(input_tensor)\n        # hidden = self.conv2(hidden)\n\n        if self.dropout is not None:\n            hidden = self.dropout(hidden)\n\n        return hidden\n\n","chunkId":"audio_separator/separator/uvr_lib_v5/vr_network/layers_new.py:7"}],[1960,{"filePath":"audio_separator/separator/uvr_lib_v5/vr_network/layers_new.py","startLine":80,"endLine":90,"chunkText":"class ASPPModule(nn.Module):\n    \"\"\"\n    ASPPModule Class:\n    This class implements the Atrous Spatial Pyramid Pooling (ASPP) module, which is useful for semantic image segmentation tasks.\n    It captures multi-scale contextual information by applying convolutions at multiple dilation rates.\n    \"\"\"\n\n    def __init__(self, nin, nout, dilations=(4, 8, 12), activ=nn.ReLU, dropout=False):\n        super(ASPPModule, self).__init__()\n\n        # Global context convolution captures the overall context","chunkId":"audio_separator/separator/uvr_lib_v5/vr_network/layers_new.py:8"}],[1961,{"filePath":"audio_separator/separator/uvr_lib_v5/vr_network/layers_new.py","startLine":88,"endLine":95,"chunkText":"        super(ASPPModule, self).__init__()\n\n        # Global context convolution captures the overall context\n        self.conv1 = nn.Sequential(nn.AdaptiveAvgPool2d((1, None)), Conv2DBNActiv(nin, nout, 1, 1, 0, activ=activ))\n        self.conv2 = Conv2DBNActiv(nin, nout, 1, 1, 0, activ=activ)\n        self.conv3 = Conv2DBNActiv(nin, nout, 3, 1, dilations[0], dilations[0], activ=activ)\n        self.conv4 = Conv2DBNActiv(nin, nout, 3, 1, dilations[1], dilations[1], activ=activ)\n        self.conv5 = Conv2DBNActiv(nin, nout, 3, 1, dilations[2], dilations[2], activ=activ)","chunkId":"audio_separator/separator/uvr_lib_v5/vr_network/layers_new.py:9"}],[1962,{"filePath":"audio_separator/separator/uvr_lib_v5/vr_network/layers_new.py","startLine":93,"endLine":102,"chunkText":"        self.conv3 = Conv2DBNActiv(nin, nout, 3, 1, dilations[0], dilations[0], activ=activ)\n        self.conv4 = Conv2DBNActiv(nin, nout, 3, 1, dilations[1], dilations[1], activ=activ)\n        self.conv5 = Conv2DBNActiv(nin, nout, 3, 1, dilations[2], dilations[2], activ=activ)\n        self.bottleneck = Conv2DBNActiv(nout * 5, nout, 1, 1, 0, activ=activ)\n        self.dropout = nn.Dropout2d(0.1) if dropout else None\n\n    def forward(self, input_tensor):\n        _, _, h, w = input_tensor.size()\n\n        # Upsample global context to match input size and combine with local and multi-scale features","chunkId":"audio_separator/separator/uvr_lib_v5/vr_network/layers_new.py:10"}],[1963,{"filePath":"audio_separator/separator/uvr_lib_v5/vr_network/layers_new.py","startLine":100,"endLine":116,"chunkText":"        _, _, h, w = input_tensor.size()\n\n        # Upsample global context to match input size and combine with local and multi-scale features\n        feat1 = F.interpolate(self.conv1(input_tensor), size=(h, w), mode=\"bilinear\", align_corners=True)\n        feat2 = self.conv2(input_tensor)\n        feat3 = self.conv3(input_tensor)\n        feat4 = self.conv4(input_tensor)\n        feat5 = self.conv5(input_tensor)\n        out = torch.cat((feat1, feat2, feat3, feat4, feat5), dim=1)\n        out = self.bottleneck(out)\n\n        if self.dropout is not None:\n            out = self.dropout(out)\n\n        return out\n\n","chunkId":"audio_separator/separator/uvr_lib_v5/vr_network/layers_new.py:11"}],[1964,{"filePath":"audio_separator/separator/uvr_lib_v5/vr_network/layers_new.py","startLine":117,"endLine":129,"chunkText":"class LSTMModule(nn.Module):\n    \"\"\"\n    LSTMModule Class:\n    This class defines a module that combines convolutional feature extraction with a bidirectional LSTM for sequence modeling.\n    It is useful for tasks that require understanding temporal dynamics in data, such as speech and audio processing.\n    \"\"\"\n\n    def __init__(self, nin_conv, nin_lstm, nout_lstm):\n        super(LSTMModule, self).__init__()\n        # Convolutional layer for initial feature extraction\n        self.conv = Conv2DBNActiv(nin_conv, 1, 1, 1, 0)\n\n        # Bidirectional LSTM for capturing temporal dynamics","chunkId":"audio_separator/separator/uvr_lib_v5/vr_network/layers_new.py:12"}],[1965,{"filePath":"audio_separator/separator/uvr_lib_v5/vr_network/layers_new.py","startLine":127,"endLine":139,"chunkText":"        self.conv = Conv2DBNActiv(nin_conv, 1, 1, 1, 0)\n\n        # Bidirectional LSTM for capturing temporal dynamics\n        self.lstm = nn.LSTM(input_size=nin_lstm, hidden_size=nout_lstm // 2, bidirectional=True)\n\n        # Dense layer for output dimensionality matching\n        self.dense = nn.Sequential(nn.Linear(nout_lstm, nin_lstm), nn.BatchNorm1d(nin_lstm), nn.ReLU())\n\n    def forward(self, input_tensor):\n        N, _, nbins, nframes = input_tensor.size()\n\n        # Extract features and prepare for LSTM\n        hidden = self.conv(input_tensor)[:, 0]  # N, nbins, nframes","chunkId":"audio_separator/separator/uvr_lib_v5/vr_network/layers_new.py:13"}],[1966,{"filePath":"audio_separator/separator/uvr_lib_v5/vr_network/layers_new.py","startLine":137,"endLine":149,"chunkText":"\n        # Extract features and prepare for LSTM\n        hidden = self.conv(input_tensor)[:, 0]  # N, nbins, nframes\n        hidden = hidden.permute(2, 0, 1)  # nframes, N, nbins\n        hidden, _ = self.lstm(hidden)\n\n        # Apply dense layer and reshape to match expected output format\n        hidden = self.dense(hidden.reshape(-1, hidden.size()[-1]))  # nframes * N, nbins\n        hidden = hidden.reshape(nframes, N, 1, nbins)\n        hidden = hidden.permute(1, 2, 3, 0)\n\n        return hidden\n","chunkId":"audio_separator/separator/uvr_lib_v5/vr_network/layers_new.py:14"}],[1967,{"filePath":"audio_separator/separator/uvr_lib_v5/vr_network/model_param_init.py","startLine":0,"endLine":18,"chunkText":"import json\r\n\r\ndefault_param = {}\r\ndefault_param[\"bins\"] = -1\r\ndefault_param[\"unstable_bins\"] = -1  # training only\r\ndefault_param[\"stable_bins\"] = -1  # training only\r\ndefault_param[\"sr\"] = 44100\r\ndefault_param[\"pre_filter_start\"] = -1\r\ndefault_param[\"pre_filter_stop\"] = -1\r\ndefault_param[\"band\"] = {}\r\n\r\nN_BINS = \"n_bins\"\r\n\r\n\r\ndef int_keys(d):\r\n    \"\"\"\r\n    Converts string keys that represent integers into actual integer keys in a list.\r\n\r\n    This function is particularly useful when dealing with JSON data that may represent\r","chunkId":"audio_separator/separator/uvr_lib_v5/vr_network/model_param_init.py:0"}],[1968,{"filePath":"audio_separator/separator/uvr_lib_v5/vr_network/model_param_init.py","startLine":16,"endLine":25,"chunkText":"    Converts string keys that represent integers into actual integer keys in a list.\r\n\r\n    This function is particularly useful when dealing with JSON data that may represent\r\n    integer keys as strings due to the nature of JSON encoding. By converting these keys\r\n    back to integers, it ensures that the data can be used in a manner consistent with\r\n    its original representation, especially in contexts where the distinction between\r\n    string and integer keys is important.\r\n\r\n    Args:\r\n        input_list (list of tuples): A list of (key, value) pairs where keys are strings\r","chunkId":"audio_separator/separator/uvr_lib_v5/vr_network/model_param_init.py:1"}],[1969,{"filePath":"audio_separator/separator/uvr_lib_v5/vr_network/model_param_init.py","startLine":23,"endLine":36,"chunkText":"\r\n    Args:\r\n        input_list (list of tuples): A list of (key, value) pairs where keys are strings\r\n                                     that may represent integers.\r\n\r\n    Returns:\r\n        dict: A dictionary with keys converted to integers where applicable.\r\n    \"\"\"\r\n    # Initialize an empty dictionary to hold the converted key-value pairs.\r\n    result_dict = {}\r\n    # Iterate through each key-value pair in the input list.\r\n    for key, value in d:\r\n        # Check if the key is a digit (i.e., represents an integer).\r\n        if key.isdigit():\r","chunkId":"audio_separator/separator/uvr_lib_v5/vr_network/model_param_init.py:2"}],[1970,{"filePath":"audio_separator/separator/uvr_lib_v5/vr_network/model_param_init.py","startLine":34,"endLine":52,"chunkText":"    for key, value in d:\r\n        # Check if the key is a digit (i.e., represents an integer).\r\n        if key.isdigit():\r\n            # Convert the key from a string to an integer.\r\n            key = int(key)\r\n        result_dict[key] = value\r\n    return result_dict\r\n\r\n\r\nclass ModelParameters(object):\r\n    \"\"\"\r\n    A class to manage model parameters, including loading from a configuration file.\r\n\r\n    Attributes:\r\n        param (dict): Dictionary holding all parameters for the model.\r\n    \"\"\"\r\n\r\n    def __init__(self, config_path=\"\"):\r\n        \"\"\"\r","chunkId":"audio_separator/separator/uvr_lib_v5/vr_network/model_param_init.py:3"}],[1971,{"filePath":"audio_separator/separator/uvr_lib_v5/vr_network/model_param_init.py","startLine":50,"endLine":63,"chunkText":"\r\n    def __init__(self, config_path=\"\"):\r\n        \"\"\"\r\n        Initializes the ModelParameters object by loading parameters from a JSON configuration file.\r\n\r\n        Args:\r\n            config_path (str): Path to the JSON configuration file.\r\n        \"\"\"\r\n\r\n        # Load parameters from the given configuration file path.\r\n        with open(config_path, \"r\") as f:\r\n            self.param = json.loads(f.read(), object_pairs_hook=int_keys)\r\n\r\n        # Ensure certain parameters are set to False if not specified in the configuration.\r","chunkId":"audio_separator/separator/uvr_lib_v5/vr_network/model_param_init.py:4"}],[1972,{"filePath":"audio_separator/separator/uvr_lib_v5/vr_network/model_param_init.py","startLine":61,"endLine":71,"chunkText":"            self.param = json.loads(f.read(), object_pairs_hook=int_keys)\r\n\r\n        # Ensure certain parameters are set to False if not specified in the configuration.\r\n        for k in [\"mid_side\", \"mid_side_b\", \"mid_side_b2\", \"stereo_w\", \"stereo_n\", \"reverse\"]:\r\n            if not k in self.param:\r\n                self.param[k] = False\r\n\r\n        # If 'n_bins' is specified in the parameters, it's used as the value for 'bins'.\r\n        if N_BINS in self.param:\r\n            self.param[\"bins\"] = self.param[N_BINS]\r\n","chunkId":"audio_separator/separator/uvr_lib_v5/vr_network/model_param_init.py:5"}],[1973,{"filePath":"audio_separator/separator/uvr_lib_v5/vr_network/nets.py","startLine":0,"endLine":13,"chunkText":"import torch\r\nfrom torch import nn\r\nimport torch.nn.functional as F\r\n\r\nfrom . import layers\r\n\r\n\r\nclass BaseASPPNet(nn.Module):\r\n    \"\"\"\r\n    BaseASPPNet Class:\r\n    This class defines the base architecture for an Atrous Spatial Pyramid Pooling (ASPP) network.\r\n    It is designed to extract features from input data at multiple scales by using dilated convolutions.\r\n    This is particularly useful for tasks that benefit from understanding context at different resolutions,\r\n    such as semantic segmentation. The network consists of a series of encoder layers for downsampling and feature extraction,\r","chunkId":"audio_separator/separator/uvr_lib_v5/vr_network/nets.py:0"}],[1974,{"filePath":"audio_separator/separator/uvr_lib_v5/vr_network/nets.py","startLine":11,"endLine":18,"chunkText":"    It is designed to extract features from input data at multiple scales by using dilated convolutions.\r\n    This is particularly useful for tasks that benefit from understanding context at different resolutions,\r\n    such as semantic segmentation. The network consists of a series of encoder layers for downsampling and feature extraction,\r\n    followed by an ASPP module for multi-scale feature extraction, and finally a series of decoder layers for upsampling.\r\n    \"\"\"\r\n\r\n    def __init__(self, nn_architecture, nin, ch, dilations=(4, 8, 16)):\r\n        super(BaseASPPNet, self).__init__()\r","chunkId":"audio_separator/separator/uvr_lib_v5/vr_network/nets.py:1"}],[1975,{"filePath":"audio_separator/separator/uvr_lib_v5/vr_network/nets.py","startLine":16,"endLine":26,"chunkText":"\r\n    def __init__(self, nn_architecture, nin, ch, dilations=(4, 8, 16)):\r\n        super(BaseASPPNet, self).__init__()\r\n        self.nn_architecture = nn_architecture\r\n\r\n        # Encoder layers progressively increase the number of channels while reducing spatial dimensions.\r\n        self.enc1 = layers.Encoder(nin, ch, 3, 2, 1)\r\n        self.enc2 = layers.Encoder(ch, ch * 2, 3, 2, 1)\r\n        self.enc3 = layers.Encoder(ch * 2, ch * 4, 3, 2, 1)\r\n        self.enc4 = layers.Encoder(ch * 4, ch * 8, 3, 2, 1)\r\n\r","chunkId":"audio_separator/separator/uvr_lib_v5/vr_network/nets.py:2"}],[1976,{"filePath":"audio_separator/separator/uvr_lib_v5/vr_network/nets.py","startLine":24,"endLine":32,"chunkText":"        self.enc3 = layers.Encoder(ch * 2, ch * 4, 3, 2, 1)\r\n        self.enc4 = layers.Encoder(ch * 4, ch * 8, 3, 2, 1)\r\n\r\n        # Depending on the network architecture, an additional encoder layer and a specific ASPP module are initialized.\r\n        if self.nn_architecture == 129605:\r\n            self.enc5 = layers.Encoder(ch * 8, ch * 16, 3, 2, 1)\r\n            self.aspp = layers.ASPPModule(nn_architecture, ch * 16, ch * 32, dilations)\r\n            self.dec5 = layers.Decoder(ch * (16 + 32), ch * 16, 3, 1, 1)\r\n        else:\r","chunkId":"audio_separator/separator/uvr_lib_v5/vr_network/nets.py:3"}],[1977,{"filePath":"audio_separator/separator/uvr_lib_v5/vr_network/nets.py","startLine":30,"endLine":38,"chunkText":"            self.aspp = layers.ASPPModule(nn_architecture, ch * 16, ch * 32, dilations)\r\n            self.dec5 = layers.Decoder(ch * (16 + 32), ch * 16, 3, 1, 1)\r\n        else:\r\n            self.aspp = layers.ASPPModule(nn_architecture, ch * 8, ch * 16, dilations)\r\n\r\n        # Decoder layers progressively decrease the number of channels while increasing spatial dimensions.\r\n        self.dec4 = layers.Decoder(ch * (8 + 16), ch * 8, 3, 1, 1)\r\n        self.dec3 = layers.Decoder(ch * (4 + 8), ch * 4, 3, 1, 1)\r\n        self.dec2 = layers.Decoder(ch * (2 + 4), ch * 2, 3, 1, 1)\r","chunkId":"audio_separator/separator/uvr_lib_v5/vr_network/nets.py:4"}],[1978,{"filePath":"audio_separator/separator/uvr_lib_v5/vr_network/nets.py","startLine":36,"endLine":45,"chunkText":"        self.dec4 = layers.Decoder(ch * (8 + 16), ch * 8, 3, 1, 1)\r\n        self.dec3 = layers.Decoder(ch * (4 + 8), ch * 4, 3, 1, 1)\r\n        self.dec2 = layers.Decoder(ch * (2 + 4), ch * 2, 3, 1, 1)\r\n        self.dec1 = layers.Decoder(ch * (1 + 2), ch, 3, 1, 1)\r\n\r\n    def __call__(self, input_tensor):\r\n        # The input tensor is passed through a series of encoder layers.\r\n        hidden_state, encoder_output1 = self.enc1(input_tensor)\r\n        hidden_state, encoder_output2 = self.enc2(hidden_state)\r\n        hidden_state, encoder_output3 = self.enc3(hidden_state)\r","chunkId":"audio_separator/separator/uvr_lib_v5/vr_network/nets.py:5"}],[1979,{"filePath":"audio_separator/separator/uvr_lib_v5/vr_network/nets.py","startLine":43,"endLine":51,"chunkText":"        hidden_state, encoder_output1 = self.enc1(input_tensor)\r\n        hidden_state, encoder_output2 = self.enc2(hidden_state)\r\n        hidden_state, encoder_output3 = self.enc3(hidden_state)\r\n        hidden_state, encoder_output4 = self.enc4(hidden_state)\r\n\r\n        # Depending on the network architecture, the hidden state is processed by an additional encoder layer and the ASPP module.\r\n        if self.nn_architecture == 129605:\r\n            hidden_state, encoder_output5 = self.enc5(hidden_state)\r\n            hidden_state = self.aspp(hidden_state)\r","chunkId":"audio_separator/separator/uvr_lib_v5/vr_network/nets.py:6"}],[1980,{"filePath":"audio_separator/separator/uvr_lib_v5/vr_network/nets.py","startLine":49,"endLine":58,"chunkText":"        if self.nn_architecture == 129605:\r\n            hidden_state, encoder_output5 = self.enc5(hidden_state)\r\n            hidden_state = self.aspp(hidden_state)\r\n            # The decoder layers use skip connections from the encoder layers for better feature integration.\r\n            hidden_state = self.dec5(hidden_state, encoder_output5)\r\n        else:\r\n            hidden_state = self.aspp(hidden_state)\r\n\r\n        # The hidden state is further processed by the decoder layers, using skip connections for feature integration.\r\n        hidden_state = self.dec4(hidden_state, encoder_output4)\r","chunkId":"audio_separator/separator/uvr_lib_v5/vr_network/nets.py:7"}],[1981,{"filePath":"audio_separator/separator/uvr_lib_v5/vr_network/nets.py","startLine":56,"endLine":68,"chunkText":"\r\n        # The hidden state is further processed by the decoder layers, using skip connections for feature integration.\r\n        hidden_state = self.dec4(hidden_state, encoder_output4)\r\n        hidden_state = self.dec3(hidden_state, encoder_output3)\r\n        hidden_state = self.dec2(hidden_state, encoder_output2)\r\n        hidden_state = self.dec1(hidden_state, encoder_output1)\r\n\r\n        return hidden_state\r\n\r\n\r\ndef determine_model_capacity(n_fft_bins, nn_architecture):\r\n    \"\"\"\r\n    The determine_model_capacity function is designed to select the appropriate model configuration\r","chunkId":"audio_separator/separator/uvr_lib_v5/vr_network/nets.py:8"}],[1982,{"filePath":"audio_separator/separator/uvr_lib_v5/vr_network/nets.py","startLine":66,"endLine":77,"chunkText":"def determine_model_capacity(n_fft_bins, nn_architecture):\r\n    \"\"\"\r\n    The determine_model_capacity function is designed to select the appropriate model configuration\r\n    based on the frequency bins and network architecture. It maps specific architectures to predefined\r\n    model capacities, which dictate the structure and parameters of the CascadedASPPNet model.\r\n    \"\"\"\r\n\r\n    # Predefined model architectures categorized by their precision level.\r\n    sp_model_arch = [31191, 33966, 129605]\r\n    hp_model_arch = [123821, 123812]\r\n    hp2_model_arch = [537238, 537227]\r\n\r","chunkId":"audio_separator/separator/uvr_lib_v5/vr_network/nets.py:9"}],[1983,{"filePath":"audio_separator/separator/uvr_lib_v5/vr_network/nets.py","startLine":75,"endLine":85,"chunkText":"    hp_model_arch = [123821, 123812]\r\n    hp2_model_arch = [537238, 537227]\r\n\r\n    # Mapping network architectures to their corresponding model capacity data.\r\n    if nn_architecture in sp_model_arch:\r\n        model_capacity_data = [(2, 16), (2, 16), (18, 8, 1, 1, 0), (8, 16), (34, 16, 1, 1, 0), (16, 32), (32, 2, 1), (16, 2, 1), (16, 2, 1)]\r\n\r\n    if nn_architecture in hp_model_arch:\r\n        model_capacity_data = [(2, 32), (2, 32), (34, 16, 1, 1, 0), (16, 32), (66, 32, 1, 1, 0), (32, 64), (64, 2, 1), (32, 2, 1), (32, 2, 1)]\r\n\r\n    if nn_architecture in hp2_model_arch:\r","chunkId":"audio_separator/separator/uvr_lib_v5/vr_network/nets.py:10"}],[1984,{"filePath":"audio_separator/separator/uvr_lib_v5/vr_network/nets.py","startLine":83,"endLine":94,"chunkText":"        model_capacity_data = [(2, 32), (2, 32), (34, 16, 1, 1, 0), (16, 32), (66, 32, 1, 1, 0), (32, 64), (64, 2, 1), (32, 2, 1), (32, 2, 1)]\r\n\r\n    if nn_architecture in hp2_model_arch:\r\n        model_capacity_data = [(2, 64), (2, 64), (66, 32, 1, 1, 0), (32, 64), (130, 64, 1, 1, 0), (64, 128), (128, 2, 1), (64, 2, 1), (64, 2, 1)]\r\n\r\n    # Initializing the CascadedASPPNet model with the selected model capacity data.\r\n    cascaded = CascadedASPPNet\r\n    model = cascaded(n_fft_bins, model_capacity_data, nn_architecture)\r\n\r\n    return model\r\n\r\n\r","chunkId":"audio_separator/separator/uvr_lib_v5/vr_network/nets.py:11"}],[1985,{"filePath":"audio_separator/separator/uvr_lib_v5/vr_network/nets.py","startLine":95,"endLine":105,"chunkText":"class CascadedASPPNet(nn.Module):\r\n    \"\"\"\r\n    CascadedASPPNet Class:\r\n    This class implements a cascaded version of the ASPP network, designed for processing audio signals\r\n    for tasks such as vocal removal. It consists of multiple stages, each with its own ASPP network,\r\n    to process different frequency bands of the input signal. This allows the model to effectively\r\n    handle the full spectrum of audio frequencies by focusing on different frequency bands separately.\r\n    \"\"\"\r\n\r\n    def __init__(self, n_fft, model_capacity_data, nn_architecture):\r\n        super(CascadedASPPNet, self).__init__()\r","chunkId":"audio_separator/separator/uvr_lib_v5/vr_network/nets.py:12"}],[1986,{"filePath":"audio_separator/separator/uvr_lib_v5/vr_network/nets.py","startLine":103,"endLine":112,"chunkText":"\r\n    def __init__(self, n_fft, model_capacity_data, nn_architecture):\r\n        super(CascadedASPPNet, self).__init__()\r\n        # The first stage processes the low and high frequency bands separately.\r\n        self.stg1_low_band_net = BaseASPPNet(nn_architecture, *model_capacity_data[0])\r\n        self.stg1_high_band_net = BaseASPPNet(nn_architecture, *model_capacity_data[1])\r\n\r\n        # Bridge layers connect different stages of the network.\r\n        self.stg2_bridge = layers.Conv2DBNActiv(*model_capacity_data[2])\r\n        self.stg2_full_band_net = BaseASPPNet(nn_architecture, *model_capacity_data[3])\r","chunkId":"audio_separator/separator/uvr_lib_v5/vr_network/nets.py:13"}],[1987,{"filePath":"audio_separator/separator/uvr_lib_v5/vr_network/nets.py","startLine":110,"endLine":118,"chunkText":"        # Bridge layers connect different stages of the network.\r\n        self.stg2_bridge = layers.Conv2DBNActiv(*model_capacity_data[2])\r\n        self.stg2_full_band_net = BaseASPPNet(nn_architecture, *model_capacity_data[3])\r\n\r\n        self.stg3_bridge = layers.Conv2DBNActiv(*model_capacity_data[4])\r\n        self.stg3_full_band_net = BaseASPPNet(nn_architecture, *model_capacity_data[5])\r\n\r\n        # Output layers for the final mask prediction and auxiliary outputs.\r\n        self.out = nn.Conv2d(*model_capacity_data[6], bias=False)\r","chunkId":"audio_separator/separator/uvr_lib_v5/vr_network/nets.py:14"}],[1988,{"filePath":"audio_separator/separator/uvr_lib_v5/vr_network/nets.py","startLine":116,"endLine":129,"chunkText":"\r\n        # Output layers for the final mask prediction and auxiliary outputs.\r\n        self.out = nn.Conv2d(*model_capacity_data[6], bias=False)\r\n        self.aux1_out = nn.Conv2d(*model_capacity_data[7], bias=False)\r\n        self.aux2_out = nn.Conv2d(*model_capacity_data[8], bias=False)\r\n\r\n        # Parameters for handling the frequency bins of the input signal.\r\n        self.max_bin = n_fft // 2\r\n        self.output_bin = n_fft // 2 + 1\r\n\r\n        self.offset = 128\r\n\r\n    def forward(self, input_tensor):\r\n        # The forward pass processes the input tensor through each stage of the network,\r","chunkId":"audio_separator/separator/uvr_lib_v5/vr_network/nets.py:15"}],[1989,{"filePath":"audio_separator/separator/uvr_lib_v5/vr_network/nets.py","startLine":127,"endLine":138,"chunkText":"\r\n    def forward(self, input_tensor):\r\n        # The forward pass processes the input tensor through each stage of the network,\r\n        # combining the outputs of different frequency bands and stages to produce the final mask.\r\n        mix = input_tensor.detach()\r\n        input_tensor = input_tensor.clone()\r\n\r\n        # Preparing the input tensor by selecting the mainput_tensorimum frequency bin.\r\n        input_tensor = input_tensor[:, :, : self.max_bin]\r\n\r\n        # Processing the low and high frequency bands separately in the first stage.\r\n        bandwidth = input_tensor.size()[2] // 2\r","chunkId":"audio_separator/separator/uvr_lib_v5/vr_network/nets.py:16"}],[1990,{"filePath":"audio_separator/separator/uvr_lib_v5/vr_network/nets.py","startLine":136,"endLine":145,"chunkText":"\r\n        # Processing the low and high frequency bands separately in the first stage.\r\n        bandwidth = input_tensor.size()[2] // 2\r\n        aux1 = torch.cat([self.stg1_low_band_net(input_tensor[:, :, :bandwidth]), self.stg1_high_band_net(input_tensor[:, :, bandwidth:])], dim=2)\r\n\r\n        # Combining the outputs of the first stage and passing through the second stage.\r\n        hidden_state = torch.cat([input_tensor, aux1], dim=1)\r\n        aux2 = self.stg2_full_band_net(self.stg2_bridge(hidden_state))\r\n\r\n        # Further processing the combined outputs through the third stage.\r","chunkId":"audio_separator/separator/uvr_lib_v5/vr_network/nets.py:17"}],[1991,{"filePath":"audio_separator/separator/uvr_lib_v5/vr_network/nets.py","startLine":143,"endLine":154,"chunkText":"        aux2 = self.stg2_full_band_net(self.stg2_bridge(hidden_state))\r\n\r\n        # Further processing the combined outputs through the third stage.\r\n        hidden_state = torch.cat([input_tensor, aux1, aux2], dim=1)\r\n        hidden_state = self.stg3_full_band_net(self.stg3_bridge(hidden_state))\r\n\r\n        # Applying the final output layer to produce the mask.\r\n        mask = torch.sigmoid(self.out(hidden_state))\r\n\r\n        # Padding the mask to match the output frequency bin size.\r\n        mask = F.pad(input=mask, pad=(0, 0, 0, self.output_bin - mask.size()[2]), mode=\"replicate\")\r\n\r","chunkId":"audio_separator/separator/uvr_lib_v5/vr_network/nets.py:18"}],[1992,{"filePath":"audio_separator/separator/uvr_lib_v5/vr_network/nets.py","startLine":152,"endLine":160,"chunkText":"        # Padding the mask to match the output frequency bin size.\r\n        mask = F.pad(input=mask, pad=(0, 0, 0, self.output_bin - mask.size()[2]), mode=\"replicate\")\r\n\r\n        # During training, auxiliary outputs are also produced and padded accordingly.\r\n        if self.training:\r\n            aux1 = torch.sigmoid(self.aux1_out(aux1))\r\n            aux1 = F.pad(input=aux1, pad=(0, 0, 0, self.output_bin - aux1.size()[2]), mode=\"replicate\")\r\n            aux2 = torch.sigmoid(self.aux2_out(aux2))\r\n            aux2 = F.pad(input=aux2, pad=(0, 0, 0, self.output_bin - aux2.size()[2]), mode=\"replicate\")\r","chunkId":"audio_separator/separator/uvr_lib_v5/vr_network/nets.py:19"}],[1993,{"filePath":"audio_separator/separator/uvr_lib_v5/vr_network/nets.py","startLine":158,"endLine":169,"chunkText":"            aux1 = F.pad(input=aux1, pad=(0, 0, 0, self.output_bin - aux1.size()[2]), mode=\"replicate\")\r\n            aux2 = torch.sigmoid(self.aux2_out(aux2))\r\n            aux2 = F.pad(input=aux2, pad=(0, 0, 0, self.output_bin - aux2.size()[2]), mode=\"replicate\")\r\n            return mask * mix, aux1 * mix, aux2 * mix\r\n        else:\r\n            return mask  # * mix\r\n\r\n    def predict_mask(self, input_tensor):\r\n        # This method predicts the mask for the input tensor by calling the forward method\r\n        # and applying any necessary padding adjustments.\r\n        mask = self.forward(input_tensor)\r\n\r","chunkId":"audio_separator/separator/uvr_lib_v5/vr_network/nets.py:20"}],[1994,{"filePath":"audio_separator/separator/uvr_lib_v5/vr_network/nets.py","startLine":167,"endLine":175,"chunkText":"        # and applying any necessary padding adjustments.\r\n        mask = self.forward(input_tensor)\r\n\r\n        # Adjusting the mask by removing padding offsets if present.\r\n        if self.offset > 0:\r\n            mask = mask[:, :, :, self.offset : -self.offset]\r\n\r\n        return mask\r\n","chunkId":"audio_separator/separator/uvr_lib_v5/vr_network/nets.py:21"}],[1995,{"filePath":"audio_separator/separator/uvr_lib_v5/vr_network/nets_new.py","startLine":0,"endLine":14,"chunkText":"import torch\nfrom torch import nn\nimport torch.nn.functional as F\nfrom . import layers_new as layers\n\n\nclass BaseNet(nn.Module):\n    \"\"\"\n    BaseNet Class:\n    This class defines the base network architecture for vocal removal. It includes a series of encoders for feature extraction,\n    an ASPP module for capturing multi-scale context, and a series of decoders for reconstructing the output. Additionally,\n    it incorporates an LSTM module for capturing temporal dependencies.\n    \"\"\"\n\n    def __init__(self, nin, nout, nin_lstm, nout_lstm, dilations=((4, 2), (8, 4), (12, 6))):","chunkId":"audio_separator/separator/uvr_lib_v5/vr_network/nets_new.py:0"}],[1996,{"filePath":"audio_separator/separator/uvr_lib_v5/vr_network/nets_new.py","startLine":12,"endLine":22,"chunkText":"    \"\"\"\n\n    def __init__(self, nin, nout, nin_lstm, nout_lstm, dilations=((4, 2), (8, 4), (12, 6))):\n        super(BaseNet, self).__init__()\n        # Initialize the encoder layers with increasing output channels for hierarchical feature extraction.\n        self.enc1 = layers.Conv2DBNActiv(nin, nout, 3, 1, 1)\n        self.enc2 = layers.Encoder(nout, nout * 2, 3, 2, 1)\n        self.enc3 = layers.Encoder(nout * 2, nout * 4, 3, 2, 1)\n        self.enc4 = layers.Encoder(nout * 4, nout * 6, 3, 2, 1)\n        self.enc5 = layers.Encoder(nout * 6, nout * 8, 3, 2, 1)\n","chunkId":"audio_separator/separator/uvr_lib_v5/vr_network/nets_new.py:1"}],[1997,{"filePath":"audio_separator/separator/uvr_lib_v5/vr_network/nets_new.py","startLine":20,"endLine":28,"chunkText":"        self.enc4 = layers.Encoder(nout * 4, nout * 6, 3, 2, 1)\n        self.enc5 = layers.Encoder(nout * 6, nout * 8, 3, 2, 1)\n\n        # ASPP module for capturing multi-scale features with different dilation rates.\n        self.aspp = layers.ASPPModule(nout * 8, nout * 8, dilations, dropout=True)\n\n        # Decoder layers for upscaling and merging features from different levels of the encoder and ASPP module.\n        self.dec4 = layers.Decoder(nout * (6 + 8), nout * 6, 3, 1, 1)\n        self.dec3 = layers.Decoder(nout * (4 + 6), nout * 4, 3, 1, 1)","chunkId":"audio_separator/separator/uvr_lib_v5/vr_network/nets_new.py:2"}],[1998,{"filePath":"audio_separator/separator/uvr_lib_v5/vr_network/nets_new.py","startLine":26,"endLine":35,"chunkText":"        # Decoder layers for upscaling and merging features from different levels of the encoder and ASPP module.\n        self.dec4 = layers.Decoder(nout * (6 + 8), nout * 6, 3, 1, 1)\n        self.dec3 = layers.Decoder(nout * (4 + 6), nout * 4, 3, 1, 1)\n        self.dec2 = layers.Decoder(nout * (2 + 4), nout * 2, 3, 1, 1)\n\n        # LSTM module for capturing temporal dependencies in the sequence of features.\n        self.lstm_dec2 = layers.LSTMModule(nout * 2, nin_lstm, nout_lstm)\n        self.dec1 = layers.Decoder(nout * (1 + 2) + 1, nout * 1, 3, 1, 1)\n\n    def __call__(self, input_tensor):","chunkId":"audio_separator/separator/uvr_lib_v5/vr_network/nets_new.py:3"}],[1999,{"filePath":"audio_separator/separator/uvr_lib_v5/vr_network/nets_new.py","startLine":33,"endLine":46,"chunkText":"        self.dec1 = layers.Decoder(nout * (1 + 2) + 1, nout * 1, 3, 1, 1)\n\n    def __call__(self, input_tensor):\n        # Sequentially pass the input through the encoder layers.\n        encoded1 = self.enc1(input_tensor)\n        encoded2 = self.enc2(encoded1)\n        encoded3 = self.enc3(encoded2)\n        encoded4 = self.enc4(encoded3)\n        encoded5 = self.enc5(encoded4)\n\n        # Pass the deepest encoder output through the ASPP module.\n        bottleneck = self.aspp(encoded5)\n\n        # Sequentially upscale and merge the features using the decoder layers.","chunkId":"audio_separator/separator/uvr_lib_v5/vr_network/nets_new.py:4"}],[2000,{"filePath":"audio_separator/separator/uvr_lib_v5/vr_network/nets_new.py","startLine":44,"endLine":56,"chunkText":"        bottleneck = self.aspp(encoded5)\n\n        # Sequentially upscale and merge the features using the decoder layers.\n        bottleneck = self.dec4(bottleneck, encoded4)\n        bottleneck = self.dec3(bottleneck, encoded3)\n        bottleneck = self.dec2(bottleneck, encoded2)\n        # Concatenate the LSTM module output for temporal feature enhancement.\n        bottleneck = torch.cat([bottleneck, self.lstm_dec2(bottleneck)], dim=1)\n        bottleneck = self.dec1(bottleneck, encoded1)\n\n        return bottleneck\n\n","chunkId":"audio_separator/separator/uvr_lib_v5/vr_network/nets_new.py:5"}],[2001,{"filePath":"audio_separator/separator/uvr_lib_v5/vr_network/nets_new.py","startLine":57,"endLine":68,"chunkText":"class CascadedNet(nn.Module):\n    \"\"\"\n    CascadedNet Class:\n    This class defines a cascaded network architecture that processes input in multiple stages, each stage focusing on different frequency bands.\n    It utilizes the BaseNet for processing, and combines outputs from different stages to produce the final mask for vocal removal.\n    \"\"\"\n\n    def __init__(self, n_fft, nn_arch_size=51000, nout=32, nout_lstm=128):\n        super(CascadedNet, self).__init__()\n        # Calculate frequency bins based on FFT size.\n        self.max_bin = n_fft // 2\n        self.output_bin = n_fft // 2 + 1","chunkId":"audio_separator/separator/uvr_lib_v5/vr_network/nets_new.py:6"}],[2002,{"filePath":"audio_separator/separator/uvr_lib_v5/vr_network/nets_new.py","startLine":66,"endLine":76,"chunkText":"        # Calculate frequency bins based on FFT size.\n        self.max_bin = n_fft // 2\n        self.output_bin = n_fft // 2 + 1\n        self.nin_lstm = self.max_bin // 2\n        self.offset = 64\n        # Adjust output channels based on the architecture size.\n        nout = 64 if nn_arch_size == 218409 else nout\n\n        # print(nout, nout_lstm, n_fft)\n\n        # Initialize the network stages, each focusing on different frequency bands and progressively refining the output.","chunkId":"audio_separator/separator/uvr_lib_v5/vr_network/nets_new.py:7"}],[2003,{"filePath":"audio_separator/separator/uvr_lib_v5/vr_network/nets_new.py","startLine":74,"endLine":80,"chunkText":"        # print(nout, nout_lstm, n_fft)\n\n        # Initialize the network stages, each focusing on different frequency bands and progressively refining the output.\n        self.stg1_low_band_net = nn.Sequential(BaseNet(2, nout // 2, self.nin_lstm // 2, nout_lstm), layers.Conv2DBNActiv(nout // 2, nout // 4, 1, 1, 0))\n        self.stg1_high_band_net = BaseNet(2, nout // 4, self.nin_lstm // 2, nout_lstm // 2)\n\n        self.stg2_low_band_net = nn.Sequential(BaseNet(nout // 4 + 2, nout, self.nin_lstm // 2, nout_lstm), layers.Conv2DBNActiv(nout, nout // 2, 1, 1, 0))","chunkId":"audio_separator/separator/uvr_lib_v5/vr_network/nets_new.py:8"}],[2004,{"filePath":"audio_separator/separator/uvr_lib_v5/vr_network/nets_new.py","startLine":78,"endLine":86,"chunkText":"        self.stg1_high_band_net = BaseNet(2, nout // 4, self.nin_lstm // 2, nout_lstm // 2)\n\n        self.stg2_low_band_net = nn.Sequential(BaseNet(nout // 4 + 2, nout, self.nin_lstm // 2, nout_lstm), layers.Conv2DBNActiv(nout, nout // 2, 1, 1, 0))\n        self.stg2_high_band_net = BaseNet(nout // 4 + 2, nout // 2, self.nin_lstm // 2, nout_lstm // 2)\n\n        self.stg3_full_band_net = BaseNet(3 * nout // 4 + 2, nout, self.nin_lstm, nout_lstm)\n\n        # Output layer for generating the final mask.\n        self.out = nn.Conv2d(nout, 2, 1, bias=False)","chunkId":"audio_separator/separator/uvr_lib_v5/vr_network/nets_new.py:9"}],[2005,{"filePath":"audio_separator/separator/uvr_lib_v5/vr_network/nets_new.py","startLine":84,"endLine":98,"chunkText":"\n        # Output layer for generating the final mask.\n        self.out = nn.Conv2d(nout, 2, 1, bias=False)\n        # Auxiliary output layer for intermediate supervision during training.\n        self.aux_out = nn.Conv2d(3 * nout // 4, 2, 1, bias=False)\n\n    def forward(self, input_tensor):\n        # Preprocess input tensor to match the maximum frequency bin.\n        input_tensor = input_tensor[:, :, : self.max_bin]\n\n        # Split the input into low and high frequency bands.\n        bandw = input_tensor.size()[2] // 2\n        l1_in = input_tensor[:, :, :bandw]\n        h1_in = input_tensor[:, :, bandw:]\n","chunkId":"audio_separator/separator/uvr_lib_v5/vr_network/nets_new.py:10"}],[2006,{"filePath":"audio_separator/separator/uvr_lib_v5/vr_network/nets_new.py","startLine":96,"endLine":110,"chunkText":"        l1_in = input_tensor[:, :, :bandw]\n        h1_in = input_tensor[:, :, bandw:]\n\n        # Process each band through the first stage networks.\n        l1 = self.stg1_low_band_net(l1_in)\n        h1 = self.stg1_high_band_net(h1_in)\n\n        # Combine the outputs for auxiliary supervision.\n        aux1 = torch.cat([l1, h1], dim=2)\n\n        # Prepare inputs for the second stage by concatenating the original and processed bands.\n        l2_in = torch.cat([l1_in, l1], dim=1)\n        h2_in = torch.cat([h1_in, h1], dim=1)\n\n        # Process through the second stage networks.","chunkId":"audio_separator/separator/uvr_lib_v5/vr_network/nets_new.py:11"}],[2007,{"filePath":"audio_separator/separator/uvr_lib_v5/vr_network/nets_new.py","startLine":108,"endLine":122,"chunkText":"        h2_in = torch.cat([h1_in, h1], dim=1)\n\n        # Process through the second stage networks.\n        l2 = self.stg2_low_band_net(l2_in)\n        h2 = self.stg2_high_band_net(h2_in)\n\n        # Combine the outputs for auxiliary supervision.\n        aux2 = torch.cat([l2, h2], dim=2)\n\n        # Prepare input for the third stage by concatenating all previous outputs with the original input.\n        f3_in = torch.cat([input_tensor, aux1, aux2], dim=1)\n\n        # Process through the third stage network.\n        f3 = self.stg3_full_band_net(f3_in)\n","chunkId":"audio_separator/separator/uvr_lib_v5/vr_network/nets_new.py:12"}],[2008,{"filePath":"audio_separator/separator/uvr_lib_v5/vr_network/nets_new.py","startLine":120,"endLine":131,"chunkText":"        # Process through the third stage network.\n        f3 = self.stg3_full_band_net(f3_in)\n\n        # Apply the output layer to generate the final mask and apply sigmoid for normalization.\n        mask = torch.sigmoid(self.out(f3))\n\n        # Pad the mask to match the output frequency bin size.\n        mask = F.pad(input=mask, pad=(0, 0, 0, self.output_bin - mask.size()[2]), mode=\"replicate\")\n\n        # During training, generate and pad the auxiliary output for additional supervision.\n        if self.training:\n            aux = torch.cat([aux1, aux2], dim=1)","chunkId":"audio_separator/separator/uvr_lib_v5/vr_network/nets_new.py:13"}],[2009,{"filePath":"audio_separator/separator/uvr_lib_v5/vr_network/nets_new.py","startLine":129,"endLine":142,"chunkText":"        # During training, generate and pad the auxiliary output for additional supervision.\n        if self.training:\n            aux = torch.cat([aux1, aux2], dim=1)\n            aux = torch.sigmoid(self.aux_out(aux))\n            aux = F.pad(input=aux, pad=(0, 0, 0, self.output_bin - aux.size()[2]), mode=\"replicate\")\n            return mask, aux\n        else:\n            return mask\n\n    # Method for predicting the mask given an input tensor.\n    def predict_mask(self, input_tensor):\n        mask = self.forward(input_tensor)\n\n        # If an offset is specified, crop the mask to remove edge artifacts.","chunkId":"audio_separator/separator/uvr_lib_v5/vr_network/nets_new.py:14"}],[2010,{"filePath":"audio_separator/separator/uvr_lib_v5/vr_network/nets_new.py","startLine":140,"endLine":155,"chunkText":"        mask = self.forward(input_tensor)\n\n        # If an offset is specified, crop the mask to remove edge artifacts.\n        if self.offset > 0:\n            mask = mask[:, :, :, self.offset : -self.offset]\n            assert mask.size()[3] > 0\n\n        return mask\n\n    # Method for applying the predicted mask to the input tensor to obtain the predicted magnitude.\n    def predict(self, input_tensor):\n        mask = self.forward(input_tensor)\n        pred_mag = input_tensor * mask\n\n        # If an offset is specified, crop the predicted magnitude to remove edge artifacts.\n        if self.offset > 0:","chunkId":"audio_separator/separator/uvr_lib_v5/vr_network/nets_new.py:15"}],[2011,{"filePath":"audio_separator/separator/uvr_lib_v5/vr_network/nets_new.py","startLine":153,"endLine":160,"chunkText":"\n        # If an offset is specified, crop the predicted magnitude to remove edge artifacts.\n        if self.offset > 0:\n            pred_mag = pred_mag[:, :, :, self.offset : -self.offset]\n            assert pred_mag.size()[3] > 0\n\n        return pred_mag\n","chunkId":"audio_separator/separator/uvr_lib_v5/vr_network/nets_new.py:16"}],[2012,{"filePath":"audio_separator/utils/cli.py","startLine":0,"endLine":16,"chunkText":"#!/usr/bin/env python\nimport argparse\nimport logging\nimport json\nimport sys\nimport os\nfrom importlib import metadata\n\n\ndef main():\n    \"\"\"Main entry point for the CLI.\"\"\"\n    logger = logging.getLogger(__name__)\n    log_handler = logging.StreamHandler()\n    log_formatter = logging.Formatter(fmt=\"%(asctime)s.%(msecs)03d - %(levelname)s - %(module)s - %(message)s\", datefmt=\"%Y-%m-%d %H:%M:%S\")\n    log_handler.setFormatter(log_formatter)\n    logger.addHandler(log_handler)\n","chunkId":"audio_separator/utils/cli.py:0"}],[2013,{"filePath":"audio_separator/utils/cli.py","startLine":14,"endLine":23,"chunkText":"    log_handler.setFormatter(log_formatter)\n    logger.addHandler(log_handler)\n\n    parser = argparse.ArgumentParser(description=\"Separate audio file into different stems.\", formatter_class=lambda prog: argparse.RawTextHelpFormatter(prog, max_help_position=60))\n\n    parser.add_argument(\"audio_files\", nargs=\"*\", help=\"The audio file paths or directory to separate, in any common format.\", default=argparse.SUPPRESS)\n\n    package_version = metadata.distribution(\"audio-separator\").version\n\n    version_help = \"Show the program's version number and exit.\"","chunkId":"audio_separator/utils/cli.py:1"}],[2014,{"filePath":"audio_separator/utils/cli.py","startLine":21,"endLine":29,"chunkText":"    package_version = metadata.distribution(\"audio-separator\").version\n\n    version_help = \"Show the program's version number and exit.\"\n    debug_help = \"Enable debug logging, equivalent to --log_level=debug.\"\n    env_info_help = \"Print environment information and exit.\"\n    list_models_help = \"List all supported models and exit. Use --list_filter to filter/sort the list and --list_limit to show only top N results.\"\n    log_level_help = \"Log level, e.g. info, debug, warning (default: %(default)s).\"\n\n    info_params = parser.add_argument_group(\"Info and Debugging\")","chunkId":"audio_separator/utils/cli.py:2"}],[2015,{"filePath":"audio_separator/utils/cli.py","startLine":27,"endLine":33,"chunkText":"    log_level_help = \"Log level, e.g. info, debug, warning (default: %(default)s).\"\n\n    info_params = parser.add_argument_group(\"Info and Debugging\")\n    info_params.add_argument(\"-v\", \"--version\", action=\"version\", version=f\"%(prog)s {package_version}\", help=version_help)\n    info_params.add_argument(\"-d\", \"--debug\", action=\"store_true\", help=debug_help)\n    info_params.add_argument(\"-e\", \"--env_info\", action=\"store_true\", help=env_info_help)\n    info_params.add_argument(\"-l\", \"--list_models\", action=\"store_true\", help=list_models_help)","chunkId":"audio_separator/utils/cli.py:3"}],[2016,{"filePath":"audio_separator/utils/cli.py","startLine":31,"endLine":36,"chunkText":"    info_params.add_argument(\"-d\", \"--debug\", action=\"store_true\", help=debug_help)\n    info_params.add_argument(\"-e\", \"--env_info\", action=\"store_true\", help=env_info_help)\n    info_params.add_argument(\"-l\", \"--list_models\", action=\"store_true\", help=list_models_help)\n    info_params.add_argument(\"--log_level\", default=\"info\", help=log_level_help)\n    info_params.add_argument(\"--list_filter\", help=\"Filter and sort the model list by 'name', 'filename', or any stem e.g. vocals, instrumental, drums\")\n    info_params.add_argument(\"--list_limit\", type=int, help=\"Limit the number of models shown\")","chunkId":"audio_separator/utils/cli.py:4"}],[2017,{"filePath":"audio_separator/utils/cli.py","startLine":34,"endLine":38,"chunkText":"    info_params.add_argument(\"--log_level\", default=\"info\", help=log_level_help)\n    info_params.add_argument(\"--list_filter\", help=\"Filter and sort the model list by 'name', 'filename', or any stem e.g. vocals, instrumental, drums\")\n    info_params.add_argument(\"--list_limit\", type=int, help=\"Limit the number of models shown\")\n    info_params.add_argument(\"--list_format\", choices=[\"pretty\", \"json\"], default=\"pretty\", help=\"Format for listing models: 'pretty' for formatted output, 'json' for raw JSON dump\")\n","chunkId":"audio_separator/utils/cli.py:5"}],[2018,{"filePath":"audio_separator/utils/cli.py","startLine":36,"endLine":40,"chunkText":"    info_params.add_argument(\"--list_limit\", type=int, help=\"Limit the number of models shown\")\n    info_params.add_argument(\"--list_format\", choices=[\"pretty\", \"json\"], default=\"pretty\", help=\"Format for listing models: 'pretty' for formatted output, 'json' for raw JSON dump\")\n\n    model_filename_help = \"Model to use for separation (default: %(default)s). Example: -m 2_HP-UVR.pth\"\n    output_format_help = \"Output format for separated files, any common format (default: %(default)s). Example: --output_format=MP3\"","chunkId":"audio_separator/utils/cli.py:6"}],[2019,{"filePath":"audio_separator/utils/cli.py","startLine":38,"endLine":42,"chunkText":"\n    model_filename_help = \"Model to use for separation (default: %(default)s). Example: -m 2_HP-UVR.pth\"\n    output_format_help = \"Output format for separated files, any common format (default: %(default)s). Example: --output_format=MP3\"\n    output_bitrate_help = \"Output bitrate for separated files, any ffmpeg-compatible bitrate (default: %(default)s). Example: --output_bitrate=320k\"\n    output_dir_help = \"Directory to write output files (default: <current dir>). Example: --output_dir=/app/separated\"","chunkId":"audio_separator/utils/cli.py:7"}],[2020,{"filePath":"audio_separator/utils/cli.py","startLine":40,"endLine":43,"chunkText":"    output_format_help = \"Output format for separated files, any common format (default: %(default)s). Example: --output_format=MP3\"\n    output_bitrate_help = \"Output bitrate for separated files, any ffmpeg-compatible bitrate (default: %(default)s). Example: --output_bitrate=320k\"\n    output_dir_help = \"Directory to write output files (default: <current dir>). Example: --output_dir=/app/separated\"\n    model_file_dir_help = \"Model files directory (default: %(default)s or AUDIO_SEPARATOR_MODEL_DIR env var if set). Example: --model_file_dir=/app/models\"","chunkId":"audio_separator/utils/cli.py:8"}],[2021,{"filePath":"audio_separator/utils/cli.py","startLine":41,"endLine":46,"chunkText":"    output_bitrate_help = \"Output bitrate for separated files, any ffmpeg-compatible bitrate (default: %(default)s). Example: --output_bitrate=320k\"\n    output_dir_help = \"Directory to write output files (default: <current dir>). Example: --output_dir=/app/separated\"\n    model_file_dir_help = \"Model files directory (default: %(default)s or AUDIO_SEPARATOR_MODEL_DIR env var if set). Example: --model_file_dir=/app/models\"\n    download_model_only_help = \"Download a single model file only, without performing separation.\"\n\n    io_params = parser.add_argument_group(\"Separation I/O Params\")","chunkId":"audio_separator/utils/cli.py:9"}],[2022,{"filePath":"audio_separator/utils/cli.py","startLine":44,"endLine":50,"chunkText":"    download_model_only_help = \"Download a single model file only, without performing separation.\"\n\n    io_params = parser.add_argument_group(\"Separation I/O Params\")\n    io_params.add_argument(\"-m\", \"--model_filename\", default=\"model_bs_roformer_ep_317_sdr_12.9755.ckpt\", help=model_filename_help)\n    io_params.add_argument(\"--output_format\", default=\"FLAC\", help=output_format_help)\n    io_params.add_argument(\"--output_bitrate\", default=None, help=output_bitrate_help)\n    io_params.add_argument(\"--output_dir\", default=None, help=output_dir_help)","chunkId":"audio_separator/utils/cli.py:10"}],[2023,{"filePath":"audio_separator/utils/cli.py","startLine":48,"endLine":54,"chunkText":"    io_params.add_argument(\"--output_format\", default=\"FLAC\", help=output_format_help)\n    io_params.add_argument(\"--output_bitrate\", default=None, help=output_bitrate_help)\n    io_params.add_argument(\"--output_dir\", default=None, help=output_dir_help)\n    io_params.add_argument(\"--model_file_dir\", default=\"/tmp/audio-separator-models/\", help=model_file_dir_help)\n    io_params.add_argument(\"--download_model_only\", action=\"store_true\", help=download_model_only_help)\n\n    invert_spect_help = \"Invert secondary stem using spectrogram (default: %(default)s). Example: --invert_spect\"","chunkId":"audio_separator/utils/cli.py:11"}],[2024,{"filePath":"audio_separator/utils/cli.py","startLine":52,"endLine":56,"chunkText":"    io_params.add_argument(\"--download_model_only\", action=\"store_true\", help=download_model_only_help)\n\n    invert_spect_help = \"Invert secondary stem using spectrogram (default: %(default)s). Example: --invert_spect\"\n    normalization_help = \"Max peak amplitude to normalize input and output audio to (default: %(default)s). Example: --normalization=0.7\"\n    amplification_help = \"Min peak amplitude to amplify input and output audio to (default: %(default)s). Example: --amplification=0.4\"","chunkId":"audio_separator/utils/cli.py:12"}],[2025,{"filePath":"audio_separator/utils/cli.py","startLine":54,"endLine":57,"chunkText":"    invert_spect_help = \"Invert secondary stem using spectrogram (default: %(default)s). Example: --invert_spect\"\n    normalization_help = \"Max peak amplitude to normalize input and output audio to (default: %(default)s). Example: --normalization=0.7\"\n    amplification_help = \"Min peak amplitude to amplify input and output audio to (default: %(default)s). Example: --amplification=0.4\"\n    single_stem_help = \"Output only single stem, e.g. Instrumental, Vocals, Drums, Bass, Guitar, Piano, Other. Example: --single_stem=Instrumental\"","chunkId":"audio_separator/utils/cli.py:13"}],[2026,{"filePath":"audio_separator/utils/cli.py","startLine":55,"endLine":58,"chunkText":"    normalization_help = \"Max peak amplitude to normalize input and output audio to (default: %(default)s). Example: --normalization=0.7\"\n    amplification_help = \"Min peak amplitude to amplify input and output audio to (default: %(default)s). Example: --amplification=0.4\"\n    single_stem_help = \"Output only single stem, e.g. Instrumental, Vocals, Drums, Bass, Guitar, Piano, Other. Example: --single_stem=Instrumental\"\n    sample_rate_help = \"Modify the sample rate of the output audio (default: %(default)s). Example: --sample_rate=44100\"","chunkId":"audio_separator/utils/cli.py:14"}],[2027,{"filePath":"audio_separator/utils/cli.py","startLine":56,"endLine":59,"chunkText":"    amplification_help = \"Min peak amplitude to amplify input and output audio to (default: %(default)s). Example: --amplification=0.4\"\n    single_stem_help = \"Output only single stem, e.g. Instrumental, Vocals, Drums, Bass, Guitar, Piano, Other. Example: --single_stem=Instrumental\"\n    sample_rate_help = \"Modify the sample rate of the output audio (default: %(default)s). Example: --sample_rate=44100\"\n    use_soundfile_help = \"Use soundfile to write audio output (default: %(default)s). Example: --use_soundfile\"","chunkId":"audio_separator/utils/cli.py:15"}],[2028,{"filePath":"audio_separator/utils/cli.py","startLine":57,"endLine":60,"chunkText":"    single_stem_help = \"Output only single stem, e.g. Instrumental, Vocals, Drums, Bass, Guitar, Piano, Other. Example: --single_stem=Instrumental\"\n    sample_rate_help = \"Modify the sample rate of the output audio (default: %(default)s). Example: --sample_rate=44100\"\n    use_soundfile_help = \"Use soundfile to write audio output (default: %(default)s). Example: --use_soundfile\"\n    use_autocast_help = \"Use PyTorch autocast for faster inference (default: %(default)s). Do not use for CPU inference. Example: --use_autocast\"","chunkId":"audio_separator/utils/cli.py:16"}],[2029,{"filePath":"audio_separator/utils/cli.py","startLine":58,"endLine":60,"chunkText":"    sample_rate_help = \"Modify the sample rate of the output audio (default: %(default)s). Example: --sample_rate=44100\"\n    use_soundfile_help = \"Use soundfile to write audio output (default: %(default)s). Example: --use_soundfile\"\n    use_autocast_help = \"Use PyTorch autocast for faster inference (default: %(default)s). Do not use for CPU inference. Example: --use_autocast\"","chunkId":"audio_separator/utils/cli.py:17"}],[2030,{"filePath":"audio_separator/utils/cli.py","startLine":59,"endLine":61,"chunkText":"    use_soundfile_help = \"Use soundfile to write audio output (default: %(default)s). Example: --use_soundfile\"\n    use_autocast_help = \"Use PyTorch autocast for faster inference (default: %(default)s). Do not use for CPU inference. Example: --use_autocast\"\n    chunk_duration_help = \"Split audio into chunks of this duration in seconds (default: %(default)s = no chunking). Useful for processing very long audio files on systems with limited memory. Recommended: 600 (10 minutes) for files >1 hour. Chunks are concatenated without overlap/crossfade. Example: --chunk_duration=600\"","chunkId":"audio_separator/utils/cli.py:18"}],[2031,{"filePath":"audio_separator/utils/cli.py","startLine":60,"endLine":61,"chunkText":"    use_autocast_help = \"Use PyTorch autocast for faster inference (default: %(default)s). Do not use for CPU inference. Example: --use_autocast\"\n    chunk_duration_help = \"Split audio into chunks of this duration in seconds (default: %(default)s = no chunking). Useful for processing very long audio files on systems with limited memory. Recommended: 600 (10 minutes) for files >1 hour. Chunks are concatenated without overlap/crossfade. Example: --chunk_duration=600\"","chunkId":"audio_separator/utils/cli.py:19"}],[2032,{"filePath":"audio_separator/utils/cli.py","startLine":61,"endLine":64,"chunkText":"    chunk_duration_help = \"Split audio into chunks of this duration in seconds (default: %(default)s = no chunking). Useful for processing very long audio files on systems with limited memory. Recommended: 600 (10 minutes) for files >1 hour. Chunks are concatenated without overlap/crossfade. Example: --chunk_duration=600\"\n    custom_output_names_help = 'Custom names for all output files in JSON format (default: %(default)s). Example: --custom_output_names=\\'{\"Vocals\": \"vocals_output\", \"Drums\": \"drums_output\"}\\''\n\n    common_params = parser.add_argument_group(\"Common Separation Parameters\")","chunkId":"audio_separator/utils/cli.py:20"}],[2033,{"filePath":"audio_separator/utils/cli.py","startLine":62,"endLine":67,"chunkText":"    custom_output_names_help = 'Custom names for all output files in JSON format (default: %(default)s). Example: --custom_output_names=\\'{\"Vocals\": \"vocals_output\", \"Drums\": \"drums_output\"}\\''\n\n    common_params = parser.add_argument_group(\"Common Separation Parameters\")\n    common_params.add_argument(\"--invert_spect\", action=\"store_true\", help=invert_spect_help)\n    common_params.add_argument(\"--normalization\", type=float, default=0.9, help=normalization_help)\n    common_params.add_argument(\"--amplification\", type=float, default=0.0, help=amplification_help)","chunkId":"audio_separator/utils/cli.py:21"}],[2034,{"filePath":"audio_separator/utils/cli.py","startLine":65,"endLine":70,"chunkText":"    common_params.add_argument(\"--invert_spect\", action=\"store_true\", help=invert_spect_help)\n    common_params.add_argument(\"--normalization\", type=float, default=0.9, help=normalization_help)\n    common_params.add_argument(\"--amplification\", type=float, default=0.0, help=amplification_help)\n    common_params.add_argument(\"--single_stem\", default=None, help=single_stem_help)\n    common_params.add_argument(\"--sample_rate\", type=int, default=44100, help=sample_rate_help)\n    common_params.add_argument(\"--use_soundfile\", action=\"store_true\", help=use_soundfile_help)","chunkId":"audio_separator/utils/cli.py:22"}],[2035,{"filePath":"audio_separator/utils/cli.py","startLine":68,"endLine":74,"chunkText":"    common_params.add_argument(\"--single_stem\", default=None, help=single_stem_help)\n    common_params.add_argument(\"--sample_rate\", type=int, default=44100, help=sample_rate_help)\n    common_params.add_argument(\"--use_soundfile\", action=\"store_true\", help=use_soundfile_help)\n    common_params.add_argument(\"--use_autocast\", action=\"store_true\", help=use_autocast_help)\n    common_params.add_argument(\"--chunk_duration\", type=float, default=None, help=chunk_duration_help)\n    common_params.add_argument(\"--custom_output_names\", type=json.loads, default=None, help=custom_output_names_help)\n","chunkId":"audio_separator/utils/cli.py:23"}],[2036,{"filePath":"audio_separator/utils/cli.py","startLine":72,"endLine":76,"chunkText":"    common_params.add_argument(\"--chunk_duration\", type=float, default=None, help=chunk_duration_help)\n    common_params.add_argument(\"--custom_output_names\", type=json.loads, default=None, help=custom_output_names_help)\n\n    mdx_segment_size_help = \"Larger consumes more resources, but may give better results (default: %(default)s). Example: --mdx_segment_size=256\"\n    mdx_overlap_help = \"Amount of overlap between prediction windows, 0.001-0.999. Higher is better but slower (default: %(default)s). Example: --mdx_overlap=0.25\"","chunkId":"audio_separator/utils/cli.py:24"}],[2037,{"filePath":"audio_separator/utils/cli.py","startLine":74,"endLine":77,"chunkText":"\n    mdx_segment_size_help = \"Larger consumes more resources, but may give better results (default: %(default)s). Example: --mdx_segment_size=256\"\n    mdx_overlap_help = \"Amount of overlap between prediction windows, 0.001-0.999. Higher is better but slower (default: %(default)s). Example: --mdx_overlap=0.25\"\n    mdx_batch_size_help = \"Larger consumes more RAM but may process slightly faster (default: %(default)s). Example: --mdx_batch_size=4\"","chunkId":"audio_separator/utils/cli.py:25"}],[2038,{"filePath":"audio_separator/utils/cli.py","startLine":75,"endLine":78,"chunkText":"    mdx_segment_size_help = \"Larger consumes more resources, but may give better results (default: %(default)s). Example: --mdx_segment_size=256\"\n    mdx_overlap_help = \"Amount of overlap between prediction windows, 0.001-0.999. Higher is better but slower (default: %(default)s). Example: --mdx_overlap=0.25\"\n    mdx_batch_size_help = \"Larger consumes more RAM but may process slightly faster (default: %(default)s). Example: --mdx_batch_size=4\"\n    mdx_hop_length_help = \"Usually called stride in neural networks, only change if you know what you're doing (default: %(default)s). Example: --mdx_hop_length=1024","chunkId":"audio_separator/utils/cli.py:26"}],[2039,{"filePath":"audio_separator/utils/cli.py","startLine":76,"endLine":80,"chunkText":"    mdx_overlap_help = \"Amount of overlap between prediction windows, 0.001-0.999. Higher is better but slower (default: %(default)s). Example: --mdx_overlap=0.25\"\n    mdx_batch_size_help = \"Larger consumes more RAM but may process slightly faster (default: %(default)s). Example: --mdx_batch_size=4\"\n    mdx_hop_length_help = \"Usually called stride in neural networks, only change if you know what you're doing (default: %(default)s). Example: --mdx_hop_length=1024\"\n    mdx_enable_denoise_help = \"Enable denoising during separation (default: %(default)s). Example: --mdx_enable_denoise\"\n","chunkId":"audio_separator/utils/cli.py:27"}],[2040,{"filePath":"audio_separator/utils/cli.py","startLine":78,"endLine":83,"chunkText":"    mdx_hop_length_help = \"Usually called stride in neural networks, only change if you know what you're doing (default: %(default)s). Example: --mdx_hop_length=1024\"\n    mdx_enable_denoise_help = \"Enable denoising during separation (default: %(default)s). Example: --mdx_enable_denoise\"\n\n    mdx_params = parser.add_argument_group(\"MDX Architecture Parameters\")\n    mdx_params.add_argument(\"--mdx_segment_size\", type=int, default=256, help=mdx_segment_size_help)\n    mdx_params.add_argument(\"--mdx_overlap\", type=float, default=0.25, help=mdx_overlap_help)","chunkId":"audio_separator/utils/cli.py:28"}],[2041,{"filePath":"audio_separator/utils/cli.py","startLine":81,"endLine":87,"chunkText":"    mdx_params = parser.add_argument_group(\"MDX Architecture Parameters\")\n    mdx_params.add_argument(\"--mdx_segment_size\", type=int, default=256, help=mdx_segment_size_help)\n    mdx_params.add_argument(\"--mdx_overlap\", type=float, default=0.25, help=mdx_overlap_help)\n    mdx_params.add_argument(\"--mdx_batch_size\", type=int, default=1, help=mdx_batch_size_help)\n    mdx_params.add_argument(\"--mdx_hop_length\", type=int, default=1024, help=mdx_hop_length_help)\n    mdx_params.add_argument(\"--mdx_enable_denoise\", action=\"store_true\", help=mdx_enable_denoise_help)\n","chunkId":"audio_separator/utils/cli.py:29"}],[2042,{"filePath":"audio_separator/utils/cli.py","startLine":85,"endLine":89,"chunkText":"    mdx_params.add_argument(\"--mdx_hop_length\", type=int, default=1024, help=mdx_hop_length_help)\n    mdx_params.add_argument(\"--mdx_enable_denoise\", action=\"store_true\", help=mdx_enable_denoise_help)\n\n    vr_batch_size_help = \"Number of batches to process at a time. Higher = more RAM, slightly faster processing (default: %(default)s). Example: --vr_batch_size=16\"\n    vr_window_size_help = \"Balance quality and speed. 1024 = fast but lower, 320 = slower but better quality. (default: %(default)s). Example: --vr_window_size=320\"","chunkId":"audio_separator/utils/cli.py:30"}],[2043,{"filePath":"audio_separator/utils/cli.py","startLine":87,"endLine":90,"chunkText":"\n    vr_batch_size_help = \"Number of batches to process at a time. Higher = more RAM, slightly faster processing (default: %(default)s). Example: --vr_batch_size=16\"\n    vr_window_size_help = \"Balance quality and speed. 1024 = fast but lower, 320 = slower but better quality. (default: %(default)s). Example: --vr_window_size=320\"\n    vr_aggression_help = \"Intensity of primary stem extraction, -100 - 100. Typically, 5 for vocals & instrumentals (default: %(default)s). Example: --vr_aggression=2\"","chunkId":"audio_separator/utils/cli.py:31"}],[2044,{"filePath":"audio_separator/utils/cli.py","startLine":88,"endLine":90,"chunkText":"    vr_batch_size_help = \"Number of batches to process at a time. Higher = more RAM, slightly faster processing (default: %(default)s). Example: --vr_batch_size=16\"\n    vr_window_size_help = \"Balance quality and speed. 1024 = fast but lower, 320 = slower but better quality. (default: %(default)s). Example: --vr_window_size=320\"\n    vr_aggression_help = \"Intensity of primary stem extraction, -100 - 100. Typically, 5 for vocals & instrumentals (default: %(default)s). Example: --vr_aggression=2\"","chunkId":"audio_separator/utils/cli.py:32"}],[2045,{"filePath":"audio_separator/utils/cli.py","startLine":89,"endLine":92,"chunkText":"    vr_window_size_help = \"Balance quality and speed. 1024 = fast but lower, 320 = slower but better quality. (default: %(default)s). Example: --vr_window_size=320\"\n    vr_aggression_help = \"Intensity of primary stem extraction, -100 - 100. Typically, 5 for vocals & instrumentals (default: %(default)s). Example: --vr_aggression=2\"\n    vr_enable_tta_help = \"Enable Test-Time-Augmentation; slow but improves quality (default: %(default)s). Example: --vr_enable_tta\"\n    vr_high_end_process_help = \"Mirror the missing frequency range of the output (default: %(default)s). Example: --vr_high_end_process\"","chunkId":"audio_separator/utils/cli.py:33"}],[2046,{"filePath":"audio_separator/utils/cli.py","startLine":90,"endLine":92,"chunkText":"    vr_aggression_help = \"Intensity of primary stem extraction, -100 - 100. Typically, 5 for vocals & instrumentals (default: %(default)s). Example: --vr_aggression=2\"\n    vr_enable_tta_help = \"Enable Test-Time-Augmentation; slow but improves quality (default: %(default)s). Example: --vr_enable_tta\"\n    vr_high_end_process_help = \"Mirror the missing frequency range of the output (default: %(default)s). Example: --vr_high_end_process\"","chunkId":"audio_separator/utils/cli.py:34"}],[2047,{"filePath":"audio_separator/utils/cli.py","startLine":91,"endLine":95,"chunkText":"    vr_enable_tta_help = \"Enable Test-Time-Augmentation; slow but improves quality (default: %(default)s). Example: --vr_enable_tta\"\n    vr_high_end_process_help = \"Mirror the missing frequency range of the output (default: %(default)s). Example: --vr_high_end_process\"\n    vr_enable_post_process_help = \"Identify leftover artifacts within vocal output; may improve separation for some songs (default: %(default)s). Example: --vr_enable_post_process\"\n    vr_post_process_threshold_help = \"Threshold for post_process feature: 0.1-0.3 (default: %(default)s). Example: --vr_post_process_threshold=0.1\"\n","chunkId":"audio_separator/utils/cli.py:35"}],[2048,{"filePath":"audio_separator/utils/cli.py","startLine":93,"endLine":98,"chunkText":"    vr_enable_post_process_help = \"Identify leftover artifacts within vocal output; may improve separation for some songs (default: %(default)s). Example: --vr_enable_post_process\"\n    vr_post_process_threshold_help = \"Threshold for post_process feature: 0.1-0.3 (default: %(default)s). Example: --vr_post_process_threshold=0.1\"\n\n    vr_params = parser.add_argument_group(\"VR Architecture Parameters\")\n    vr_params.add_argument(\"--vr_batch_size\", type=int, default=1, help=vr_batch_size_help)\n    vr_params.add_argument(\"--vr_window_size\", type=int, default=512, help=vr_window_size_help)","chunkId":"audio_separator/utils/cli.py:36"}],[2049,{"filePath":"audio_separator/utils/cli.py","startLine":96,"endLine":101,"chunkText":"    vr_params = parser.add_argument_group(\"VR Architecture Parameters\")\n    vr_params.add_argument(\"--vr_batch_size\", type=int, default=1, help=vr_batch_size_help)\n    vr_params.add_argument(\"--vr_window_size\", type=int, default=512, help=vr_window_size_help)\n    vr_params.add_argument(\"--vr_aggression\", type=int, default=5, help=vr_aggression_help)\n    vr_params.add_argument(\"--vr_enable_tta\", action=\"store_true\", help=vr_enable_tta_help)\n    vr_params.add_argument(\"--vr_high_end_process\", action=\"store_true\", help=vr_high_end_process_help)","chunkId":"audio_separator/utils/cli.py:37"}],[2050,{"filePath":"audio_separator/utils/cli.py","startLine":99,"endLine":104,"chunkText":"    vr_params.add_argument(\"--vr_aggression\", type=int, default=5, help=vr_aggression_help)\n    vr_params.add_argument(\"--vr_enable_tta\", action=\"store_true\", help=vr_enable_tta_help)\n    vr_params.add_argument(\"--vr_high_end_process\", action=\"store_true\", help=vr_high_end_process_help)\n    vr_params.add_argument(\"--vr_enable_post_process\", action=\"store_true\", help=vr_enable_post_process_help)\n    vr_params.add_argument(\"--vr_post_process_threshold\", type=float, default=0.2, help=vr_post_process_threshold_help)\n","chunkId":"audio_separator/utils/cli.py:38"}],[2051,{"filePath":"audio_separator/utils/cli.py","startLine":102,"endLine":106,"chunkText":"    vr_params.add_argument(\"--vr_enable_post_process\", action=\"store_true\", help=vr_enable_post_process_help)\n    vr_params.add_argument(\"--vr_post_process_threshold\", type=float, default=0.2, help=vr_post_process_threshold_help)\n\n    demucs_segment_size_help = \"Size of segments into which the audio is split, 1-100. Higher = slower but better quality (default: %(default)s). Example: --demucs_segment_size=256\"\n    demucs_shifts_help = \"Number of predictions with random shifts, higher = slower but better quality (default: %(default)s). Example: --demucs_shifts=4\"","chunkId":"audio_separator/utils/cli.py:39"}],[2052,{"filePath":"audio_separator/utils/cli.py","startLine":104,"endLine":107,"chunkText":"\n    demucs_segment_size_help = \"Size of segments into which the audio is split, 1-100. Higher = slower but better quality (default: %(default)s). Example: --demucs_segment_size=256\"\n    demucs_shifts_help = \"Number of predictions with random shifts, higher = slower but better quality (default: %(default)s). Example: --demucs_shifts=4\"\n    demucs_overlap_help = \"Overlap between prediction windows, 0.001-0.999. Higher = slower but better quality (default: %(default)s). Example: --demucs_overlap=0.25\"","chunkId":"audio_separator/utils/cli.py:40"}],[2053,{"filePath":"audio_separator/utils/cli.py","startLine":105,"endLine":107,"chunkText":"    demucs_segment_size_help = \"Size of segments into which the audio is split, 1-100. Higher = slower but better quality (default: %(default)s). Example: --demucs_segment_size=256\"\n    demucs_shifts_help = \"Number of predictions with random shifts, higher = slower but better quality (default: %(default)s). Example: --demucs_shifts=4\"\n    demucs_overlap_help = \"Overlap between prediction windows, 0.001-0.999. Higher = slower but better quality (default: %(default)s). Example: --demucs_overlap=0.25\"","chunkId":"audio_separator/utils/cli.py:41"}],[2054,{"filePath":"audio_separator/utils/cli.py","startLine":106,"endLine":110,"chunkText":"    demucs_shifts_help = \"Number of predictions with random shifts, higher = slower but better quality (default: %(default)s). Example: --demucs_shifts=4\"\n    demucs_overlap_help = \"Overlap between prediction windows, 0.001-0.999. Higher = slower but better quality (default: %(default)s). Example: --demucs_overlap=0.25\"\n    demucs_segments_enabled_help = \"Enable segment-wise processing (default: %(default)s). Example: --demucs_segments_enabled=False\"\n\n    demucs_params = parser.add_argument_group(\"Demucs Architecture Parameters\")","chunkId":"audio_separator/utils/cli.py:42"}],[2055,{"filePath":"audio_separator/utils/cli.py","startLine":108,"endLine":113,"chunkText":"    demucs_segments_enabled_help = \"Enable segment-wise processing (default: %(default)s). Example: --demucs_segments_enabled=False\"\n\n    demucs_params = parser.add_argument_group(\"Demucs Architecture Parameters\")\n    demucs_params.add_argument(\"--demucs_segment_size\", type=str, default=\"Default\", help=demucs_segment_size_help)\n    demucs_params.add_argument(\"--demucs_shifts\", type=int, default=2, help=demucs_shifts_help)\n    demucs_params.add_argument(\"--demucs_overlap\", type=float, default=0.25, help=demucs_overlap_help)","chunkId":"audio_separator/utils/cli.py:43"}],[2056,{"filePath":"audio_separator/utils/cli.py","startLine":111,"endLine":116,"chunkText":"    demucs_params.add_argument(\"--demucs_segment_size\", type=str, default=\"Default\", help=demucs_segment_size_help)\n    demucs_params.add_argument(\"--demucs_shifts\", type=int, default=2, help=demucs_shifts_help)\n    demucs_params.add_argument(\"--demucs_overlap\", type=float, default=0.25, help=demucs_overlap_help)\n    demucs_params.add_argument(\"--demucs_segments_enabled\", type=bool, default=True, help=demucs_segments_enabled_help)\n\n    mdxc_segment_size_help = \"Larger consumes more resources, but may give better results (default: %(default)s). Example: --mdxc_segment_size=256\"","chunkId":"audio_separator/utils/cli.py:44"}],[2057,{"filePath":"audio_separator/utils/cli.py","startLine":114,"endLine":118,"chunkText":"    demucs_params.add_argument(\"--demucs_segments_enabled\", type=bool, default=True, help=demucs_segments_enabled_help)\n\n    mdxc_segment_size_help = \"Larger consumes more resources, but may give better results (default: %(default)s). Example: --mdxc_segment_size=256\"\n    mdxc_override_model_segment_size_help = \"Override model default segment size instead of using the model default value. Example: --mdxc_override_model_segment_size\"\n    mdxc_overlap_help = \"Amount of overlap between prediction windows, 2-50. Higher is better but slower (default: %(default)s). Example: --mdxc_overlap=8\"","chunkId":"audio_separator/utils/cli.py:45"}],[2058,{"filePath":"audio_separator/utils/cli.py","startLine":116,"endLine":119,"chunkText":"    mdxc_segment_size_help = \"Larger consumes more resources, but may give better results (default: %(default)s). Example: --mdxc_segment_size=256\"\n    mdxc_override_model_segment_size_help = \"Override model default segment size instead of using the model default value. Example: --mdxc_override_model_segment_size\"\n    mdxc_overlap_help = \"Amount of overlap between prediction windows, 2-50. Higher is better but slower (default: %(default)s). Example: --mdxc_overlap=8\"\n    mdxc_batch_size_help = \"Larger consumes more RAM but may process slightly faster (default: %(default)s). Example: --mdxc_batch_size=4\"","chunkId":"audio_separator/utils/cli.py:46"}],[2059,{"filePath":"audio_separator/utils/cli.py","startLine":117,"endLine":119,"chunkText":"    mdxc_override_model_segment_size_help = \"Override model default segment size instead of using the model default value. Example: --mdxc_override_model_segment_size\"\n    mdxc_overlap_help = \"Amount of overlap between prediction windows, 2-50. Higher is better but slower (default: %(default)s). Example: --mdxc_overlap=8\"\n    mdxc_batch_size_help = \"Larger consumes more RAM but may process slightly faster (default: %(default)s). Example: --mdxc_batch_size=4\"","chunkId":"audio_separator/utils/cli.py:47"}],[2060,{"filePath":"audio_separator/utils/cli.py","startLine":118,"endLine":122,"chunkText":"    mdxc_overlap_help = \"Amount of overlap between prediction windows, 2-50. Higher is better but slower (default: %(default)s). Example: --mdxc_overlap=8\"\n    mdxc_batch_size_help = \"Larger consumes more RAM but may process slightly faster (default: %(default)s). Example: --mdxc_batch_size=4\"\n    mdxc_pitch_shift_help = \"Shift audio pitch by a number of semitones while processing. May improve output for deep/high vocals. (default: %(default)s). Example: --mdxc_pitch_shift=2\"\n\n    mdxc_params = parser.add_argument_group(\"MDXC Architecture Parameters\")","chunkId":"audio_separator/utils/cli.py:48"}],[2061,{"filePath":"audio_separator/utils/cli.py","startLine":120,"endLine":125,"chunkText":"    mdxc_pitch_shift_help = \"Shift audio pitch by a number of semitones while processing. May improve output for deep/high vocals. (default: %(default)s). Example: --mdxc_pitch_shift=2\"\n\n    mdxc_params = parser.add_argument_group(\"MDXC Architecture Parameters\")\n    mdxc_params.add_argument(\"--mdxc_segment_size\", type=int, default=256, help=mdxc_segment_size_help)\n    mdxc_params.add_argument(\"--mdxc_override_model_segment_size\", action=\"store_true\", help=mdxc_override_model_segment_size_help)\n    mdxc_params.add_argument(\"--mdxc_overlap\", type=int, default=8, help=mdxc_overlap_help)","chunkId":"audio_separator/utils/cli.py:49"}],[2062,{"filePath":"audio_separator/utils/cli.py","startLine":123,"endLine":132,"chunkText":"    mdxc_params.add_argument(\"--mdxc_segment_size\", type=int, default=256, help=mdxc_segment_size_help)\n    mdxc_params.add_argument(\"--mdxc_override_model_segment_size\", action=\"store_true\", help=mdxc_override_model_segment_size_help)\n    mdxc_params.add_argument(\"--mdxc_overlap\", type=int, default=8, help=mdxc_overlap_help)\n    mdxc_params.add_argument(\"--mdxc_batch_size\", type=int, default=1, help=mdxc_batch_size_help)\n    mdxc_params.add_argument(\"--mdxc_pitch_shift\", type=int, default=0, help=mdxc_pitch_shift_help)\n\n    args = parser.parse_args()\n\n    if args.debug:\n        log_level = logging.DEBUG","chunkId":"audio_separator/utils/cli.py:50"}],[2063,{"filePath":"audio_separator/utils/cli.py","startLine":130,"endLine":148,"chunkText":"    if args.debug:\n        log_level = logging.DEBUG\n    else:\n        log_level = getattr(logging, args.log_level.upper())\n    logger.setLevel(log_level)\n\n    from audio_separator.separator import Separator\n\n    if args.env_info:\n        separator = Separator()\n        sys.exit(0)\n\n    if args.list_models:\n        separator = Separator(info_only=True)\n\n        if args.list_format == \"json\":\n            model_list = separator.list_supported_model_files()\n            print(json.dumps(model_list, indent=2))\n        else:","chunkId":"audio_separator/utils/cli.py:51"}],[2064,{"filePath":"audio_separator/utils/cli.py","startLine":149,"endLine":158,"chunkText":"        else:\n            models = separator.get_simplified_model_list(filter_sort_by=args.list_filter)\n\n            # Apply limit if specified\n            if args.list_limit and args.list_limit > 0:\n                models = dict(list(models.items())[: args.list_limit])\n\n            # Calculate maximum widths for each column\n            filename_width = max(len(\"Model Filename\"), max(len(filename) for filename in models.keys()))\n            arch_width = max(len(\"Arch\"), max(len(info[\"Type\"]) for info in models.values()))","chunkId":"audio_separator/utils/cli.py:52"}],[2065,{"filePath":"audio_separator/utils/cli.py","startLine":156,"endLine":162,"chunkText":"            # Calculate maximum widths for each column\n            filename_width = max(len(\"Model Filename\"), max(len(filename) for filename in models.keys()))\n            arch_width = max(len(\"Arch\"), max(len(info[\"Type\"]) for info in models.values()))\n            stems_width = max(len(\"Output Stems (SDR)\"), max(len(\", \".join(info[\"Stems\"])) for info in models.values()))\n            name_width = max(len(\"Friendly Name\"), max(len(info[\"Name\"]) for info in models.values()))\n\n            # Calculate total width for separator line","chunkId":"audio_separator/utils/cli.py:53"}],[2066,{"filePath":"audio_separator/utils/cli.py","startLine":160,"endLine":169,"chunkText":"            name_width = max(len(\"Friendly Name\"), max(len(info[\"Name\"]) for info in models.values()))\n\n            # Calculate total width for separator line\n            total_width = filename_width + arch_width + stems_width + name_width + 15  # 15 accounts for spacing between columns\n\n            # Format the output with dynamic widths and extra spacing\n            print(\"-\" * total_width)\n            print(f\"{'Model Filename':<{filename_width}}  {'Arch':<{arch_width}}  {'Output Stems (SDR)':<{stems_width}}  {'Friendly Name'}\")\n            print(\"-\" * total_width)\n","chunkId":"audio_separator/utils/cli.py:54"}],[2067,{"filePath":"audio_separator/utils/cli.py","startLine":167,"endLine":177,"chunkText":"            print(f\"{'Model Filename':<{filename_width}}  {'Arch':<{arch_width}}  {'Output Stems (SDR)':<{stems_width}}  {'Friendly Name'}\")\n            print(\"-\" * total_width)\n\n            for filename, info in models.items():\n                stems = \", \".join(info[\"Stems\"])\n                print(f\"{filename:<{filename_width}}  {info['Type']:<{arch_width}}  {stems:<{stems_width}}  {info['Name']}\")\n\n        sys.exit(0)\n\n    if args.download_model_only:\n        logger.info(f\"Separator version {package_version} downloading model {args.model_filename} to directory {args.model_file_dir}\")","chunkId":"audio_separator/utils/cli.py:55"}],[2068,{"filePath":"audio_separator/utils/cli.py","startLine":175,"endLine":187,"chunkText":"\n    if args.download_model_only:\n        logger.info(f\"Separator version {package_version} downloading model {args.model_filename} to directory {args.model_file_dir}\")\n        separator = Separator(log_formatter=log_formatter, log_level=log_level, model_file_dir=args.model_file_dir)\n        separator.download_model_and_data(args.model_filename)\n        logger.info(f\"Model {args.model_filename} downloaded successfully.\")\n        sys.exit(0)\n\n    audio_files = list(getattr(args, \"audio_files\", []))\n    if not audio_files:\n        parser.print_help()\n        sys.exit(1)\n","chunkId":"audio_separator/utils/cli.py:56"}],[2069,{"filePath":"audio_separator/utils/cli.py","startLine":185,"endLine":200,"chunkText":"        parser.print_help()\n        sys.exit(1)\n\n    logger.info(f\"Separator version {package_version} beginning with input path(s): {', '.join(audio_files)}\")\n\n    separator = Separator(\n        log_formatter=log_formatter,\n        log_level=log_level,\n        model_file_dir=args.model_file_dir,\n        output_dir=args.output_dir,\n        output_format=args.output_format,\n        output_bitrate=args.output_bitrate,\n        normalization_threshold=args.normalization,\n        amplification_threshold=args.amplification,\n        output_single_stem=args.single_stem,\n        invert_using_spec=args.invert_spect","chunkId":"audio_separator/utils/cli.py:57"}],[2070,{"filePath":"audio_separator/utils/cli.py","startLine":198,"endLine":212,"chunkText":"        amplification_threshold=args.amplification,\n        output_single_stem=args.single_stem,\n        invert_using_spec=args.invert_spect,\n        sample_rate=args.sample_rate,\n        use_soundfile=args.use_soundfile,\n        use_autocast=args.use_autocast,\n        chunk_duration=args.chunk_duration,\n        mdx_params={\n            \"hop_length\": args.mdx_hop_length,\n            \"segment_size\": args.mdx_segment_size,\n            \"overlap\": args.mdx_overlap,\n            \"batch_size\": args.mdx_batch_size,\n            \"enable_denoise\": args.mdx_enable_denoise,\n        },\n        vr_params={","chunkId":"audio_separator/utils/cli.py:58"}],[2071,{"filePath":"audio_separator/utils/cli.py","startLine":210,"endLine":223,"chunkText":"            \"enable_denoise\": args.mdx_enable_denoise,\n        },\n        vr_params={\n            \"batch_size\": args.vr_batch_size,\n            \"window_size\": args.vr_window_size,\n            \"aggression\": args.vr_aggression,\n            \"enable_tta\": args.vr_enable_tta,\n            \"enable_post_process\": args.vr_enable_post_process,\n            \"post_process_threshold\": args.vr_post_process_threshold,\n            \"high_end_process\": args.vr_high_end_process,\n        },\n        demucs_params={\n            \"segment_size\": args.demucs_segment_size,\n            \"shifts\": args.demucs_shifts,","chunkId":"audio_separator/utils/cli.py:59"}],[2072,{"filePath":"audio_separator/utils/cli.py","startLine":221,"endLine":237,"chunkText":"        demucs_params={\n            \"segment_size\": args.demucs_segment_size,\n            \"shifts\": args.demucs_shifts,\n            \"overlap\": args.demucs_overlap,\n            \"segments_enabled\": args.demucs_segments_enabled,\n        },\n        mdxc_params={\n            \"segment_size\": args.mdxc_segment_size,\n            \"batch_size\": args.mdxc_batch_size,\n            \"overlap\": args.mdxc_overlap,\n            \"override_model_segment_size\": args.mdxc_override_model_segment_size,\n            \"pitch_shift\": args.mdxc_pitch_shift,\n        },\n    )\n\n    separator.load_model(model_filename=args.model_filename)\n","chunkId":"audio_separator/utils/cli.py:60"}],[2073,{"filePath":"audio_separator/utils/cli.py","startLine":235,"endLine":240,"chunkText":"\n    separator.load_model(model_filename=args.model_filename)\n\n    output_files = separator.separate(audio_files, custom_output_names=args.custom_output_names)\n    logger.info(f\"Separation complete! Output file(s): {' '.join(output_files)}\")\n","chunkId":"audio_separator/utils/cli.py:61"}],[2074,{"filePath":"docs/BIT_DEPTH_IMPLEMENTATION_SUMMARY.md","startLine":0,"endLine":10,"chunkText":"# Summary: Bit Depth Preservation Implementation\n\n## Issue\n[GitHub Issue #243](https://github.com/nomadkaraoke/python-audio-separator/issues/243) - Users reported that audio-separator was reducing audio quality by always outputting 16-bit audio, even when the input was 24-bit or 32-bit.\n\n## Solution\nImplemented automatic bit depth preservation that matches the output audio bit depth to the input audio file's bit depth. This ensures no quality loss when processing high-resolution audio files.\n\n## Key Changes\n\n### 1. **Dependencies** (`pyproject.toml`)","chunkId":"docs/BIT_DEPTH_IMPLEMENTATION_SUMMARY.md:0"}],[2075,{"filePath":"docs/BIT_DEPTH_IMPLEMENTATION_SUMMARY.md","startLine":10,"endLine":22,"chunkText":"### 1. **Dependencies** (`pyproject.toml`)\n- Added `soundfile >= 0.12` for reading audio file metadata\n\n### 2. **Core Implementation** (`audio_separator/separator/common_separator.py`)\n- Added `input_bit_depth` and `input_subtype` attributes to track input audio properties\n- Modified `prepare_mix()` to detect bit depth using soundfile\n- Updated `write_audio_pydub()` to use appropriate scaling and ffmpeg codecs for each bit depth\n- Updated `write_audio_soundfile()` to preserve subtype when writing\n\n### 3. **Comprehensive Tests**\nCreated 3 test suites with 17 tests total:\n\n**Unit Tests:**","chunkId":"docs/BIT_DEPTH_IMPLEMENTATION_SUMMARY.md:1"}],[2076,{"filePath":"docs/BIT_DEPTH_IMPLEMENTATION_SUMMARY.md","startLine":20,"endLine":39,"chunkText":"Created 3 test suites with 17 tests total:\n\n**Unit Tests:**\n- `tests/unit/test_bit_depth_detection.py` - 5 tests for bit depth detection\n- `tests/unit/test_bit_depth_writing.py` - 5 tests for write functions\n\n**Integration Tests:**\n- `tests/integration/test_bit_depth_e2e.py` - 2 end-to-end tests\n- `tests/integration/test_bit_depth_preservation.py` - 6 comprehensive integration tests\n\n**Manual Test:**\n- `tests/manual_test_bit_depth.py` - Demonstrates functionality\n\n## Test Results\n\n✅ **All tests pass:**\n```\n16-bit (pydub)      ✅ PASS\n24-bit (pydub)      ✅ PASS\n32-bit (pydub)      ✅ PASS","chunkId":"docs/BIT_DEPTH_IMPLEMENTATION_SUMMARY.md:2"}],[2077,{"filePath":"docs/BIT_DEPTH_IMPLEMENTATION_SUMMARY.md","startLine":37,"endLine":56,"chunkText":"16-bit (pydub)      ✅ PASS\n24-bit (pydub)      ✅ PASS\n32-bit (pydub)      ✅ PASS\n16-bit (soundfile)  ✅ PASS\n24-bit (soundfile)  ✅ PASS\n32-bit (soundfile)  ✅ PASS\n```\n\n## Behavior\n\n| Input Bit Depth | Previous Output | New Output |\n|----------------|-----------------|------------|\n| 16-bit         | 16-bit         | 16-bit ✅  |\n| 24-bit         | **16-bit** ❌  | 24-bit ✅  |\n| 32-bit         | **16-bit** ❌  | 32-bit ✅  |\n\n## Impact\n\n✅ **Quality Preservation:** No more quality loss when processing high-resolution audio\n✅ **Backward Compatible:** Existing 16-bit workflows unchanged","chunkId":"docs/BIT_DEPTH_IMPLEMENTATION_SUMMARY.md:3"}],[2078,{"filePath":"docs/BIT_DEPTH_IMPLEMENTATION_SUMMARY.md","startLine":54,"endLine":67,"chunkText":"\n✅ **Quality Preservation:** No more quality loss when processing high-resolution audio\n✅ **Backward Compatible:** Existing 16-bit workflows unchanged\n✅ **Automatic:** No configuration required - works out of the box\n✅ **Transparent:** Logs show detected and output bit depths\n✅ **Robust:** Graceful fallback to 16-bit for unknown formats\n\n## Technical Details\n\nThe implementation:\n- Reads audio metadata before loading with librosa\n- Maps PCM subtypes to bit depths (PCM_16→16, PCM_24→24, PCM_32→32)\n- Scales audio data appropriately for each bit depth\n- Passes correct codec parameters to ffmpeg/pydub","chunkId":"docs/BIT_DEPTH_IMPLEMENTATION_SUMMARY.md:4"}],[2079,{"filePath":"docs/BIT_DEPTH_IMPLEMENTATION_SUMMARY.md","startLine":65,"endLine":79,"chunkText":"- Maps PCM subtypes to bit depths (PCM_16→16, PCM_24→24, PCM_32→32)\n- Scales audio data appropriately for each bit depth\n- Passes correct codec parameters to ffmpeg/pydub\n- Works with both pydub (default) and soundfile backends\n- Handles multiple files with different bit depths correctly\n\n## Files Modified\n\n1. `pyproject.toml` - Added soundfile dependency\n2. `audio_separator/separator/common_separator.py` - Core implementation\n\n## Files Added\n\n1. `tests/unit/test_bit_depth_detection.py` - Unit tests for detection\n2. `tests/unit/test_bit_depth_writing.py` - Unit tests for writing","chunkId":"docs/BIT_DEPTH_IMPLEMENTATION_SUMMARY.md:5"}],[2080,{"filePath":"docs/BIT_DEPTH_IMPLEMENTATION_SUMMARY.md","startLine":77,"endLine":92,"chunkText":"\n1. `tests/unit/test_bit_depth_detection.py` - Unit tests for detection\n2. `tests/unit/test_bit_depth_writing.py` - Unit tests for writing\n3. `tests/integration/test_bit_depth_e2e.py` - End-to-end tests\n4. `tests/integration/test_bit_depth_preservation.py` - Integration tests\n5. `tests/manual_test_bit_depth.py` - Manual test script\n6. `BIT_DEPTH_PRESERVATION.md` - Detailed documentation\n\n## No Breaking Changes\n\nThis implementation is fully backward compatible:\n- No API changes required\n- No new parameters needed\n- Existing functionality unchanged\n- Only affects output bit depth to match input\n","chunkId":"docs/BIT_DEPTH_IMPLEMENTATION_SUMMARY.md:6"}],[2081,{"filePath":"docs/BIT_DEPTH_IMPLEMENTATION_SUMMARY.md","startLine":90,"endLine":97,"chunkText":"- Existing functionality unchanged\n- Only affects output bit depth to match input\n\n## Resolves\n\n✅ Closes #243 - Output bit depth now matches input automatically\n\n","chunkId":"docs/BIT_DEPTH_IMPLEMENTATION_SUMMARY.md:7"}],[2082,{"filePath":"docs/BIT_DEPTH_PRESERVATION.md","startLine":0,"endLine":13,"chunkText":"# Bit Depth Preservation Implementation\n\n## Summary\n\nThis implementation ensures that the output audio files from audio-separator preserve the bit depth of the input audio files, preventing quality loss when processing 24-bit or 32-bit audio files.\n\n## Changes Made\n\n### 1. Added `soundfile` to Dependencies (`pyproject.toml`)\n- Added `soundfile >= 0.12` as a dependency to enable reading audio file metadata\n\n### 2. Modified `CommonSeparator` Class (`audio_separator/separator/common_separator.py`)\n\n#### Added Bit Depth Tracking","chunkId":"docs/BIT_DEPTH_PRESERVATION.md:0"}],[2083,{"filePath":"docs/BIT_DEPTH_PRESERVATION.md","startLine":13,"endLine":25,"chunkText":"#### Added Bit Depth Tracking\n- Added `self.input_bit_depth` attribute to store the detected bit depth (16, 24, or 32)\n- Added `self.input_subtype` attribute to store the audio file subtype\n\n#### Modified `prepare_mix()` Method\n- Added code to read audio file metadata using `soundfile.info()` before loading with librosa\n- Detects and stores the input audio file's bit depth based on the subtype:\n  - PCM_16, PCM_S8 → 16-bit\n  - PCM_24 → 24-bit\n  - PCM_32, FLOAT, DOUBLE → 32-bit\n- Defaults to 16-bit for numpy arrays or unknown formats\n- Logs the detected bit depth for debugging\n","chunkId":"docs/BIT_DEPTH_PRESERVATION.md:1"}],[2084,{"filePath":"docs/BIT_DEPTH_PRESERVATION.md","startLine":24,"endLine":37,"chunkText":"- Logs the detected bit depth for debugging\n\n#### Modified `write_audio_pydub()` Method\n- Determines output bit depth from `self.input_bit_depth`\n- Scales audio data appropriately for each bit depth:\n  - 16-bit: scale by 32767, use int16, sample_width=2\n  - 24-bit: scale by 8388607, use int32, sample_width=3\n  - 32-bit: scale by 2147483647, use int32, sample_width=4\n- Passes appropriate ffmpeg codec parameters for WAV files:\n  - 16-bit: `pcm_s16le`\n  - 24-bit: `pcm_s24le`\n  - 32-bit: `pcm_s32le`\n\n#### Modified `write_audio_soundfile()` Method","chunkId":"docs/BIT_DEPTH_PRESERVATION.md:2"}],[2085,{"filePath":"docs/BIT_DEPTH_PRESERVATION.md","startLine":36,"endLine":50,"chunkText":"\n#### Modified `write_audio_soundfile()` Method\n- Determines output subtype from `self.input_subtype` or `self.input_bit_depth`\n- Passes the subtype to `sf.write()` to preserve bit depth\n- Removed manual interleaving (soundfile handles multi-channel properly)\n\n## Tests Created\n\n### Unit Tests\n\n#### `tests/unit/test_bit_depth_detection.py`\nTests bit depth detection logic:\n- `test_16bit_detection`: Verifies 16-bit files are correctly detected\n- `test_24bit_detection`: Verifies 24-bit files are correctly detected\n- `test_32bit_detection`: Verifies 32-bit files are correctly detected","chunkId":"docs/BIT_DEPTH_PRESERVATION.md:3"}],[2086,{"filePath":"docs/BIT_DEPTH_PRESERVATION.md","startLine":49,"endLine":57,"chunkText":"- `test_24bit_detection`: Verifies 24-bit files are correctly detected\n- `test_32bit_detection`: Verifies 32-bit files are correctly detected\n- `test_numpy_array_input_defaults_to_16bit`: Verifies numpy arrays default to 16-bit\n- `test_bit_depth_preserved_across_multiple_files`: Verifies bit depth updates between files\n\n#### `tests/unit/test_bit_depth_writing.py`\nTests bit depth preservation in write functions:\n- `test_write_16bit_with_pydub`: Tests 16-bit output with pydub backend\n- `test_write_24bit_with_pydub`: Tests 24-bit output with pydub backend","chunkId":"docs/BIT_DEPTH_PRESERVATION.md:4"}],[2087,{"filePath":"docs/BIT_DEPTH_PRESERVATION.md","startLine":56,"endLine":66,"chunkText":"- `test_write_16bit_with_pydub`: Tests 16-bit output with pydub backend\n- `test_write_24bit_with_pydub`: Tests 24-bit output with pydub backend\n- `test_write_32bit_with_pydub`: Tests 32-bit output with pydub backend\n- `test_write_24bit_with_soundfile`: Tests 24-bit output with soundfile backend\n- `test_write_16bit_with_soundfile`: Tests 16-bit output with soundfile backend\n\n### Integration Tests\n\n#### `tests/integration/test_bit_depth_e2e.py`\nEnd-to-end tests with full separation workflow:\n- `test_e2e_24bit_preservation`: Full test with 24-bit input","chunkId":"docs/BIT_DEPTH_PRESERVATION.md:5"}],[2088,{"filePath":"docs/BIT_DEPTH_PRESERVATION.md","startLine":65,"endLine":82,"chunkText":"End-to-end tests with full separation workflow:\n- `test_e2e_24bit_preservation`: Full test with 24-bit input\n- `test_e2e_16bit_preservation`: Full test with 16-bit input\n\n#### `tests/integration/test_bit_depth_preservation.py`\nComprehensive integration tests (skipped if package not installed):\n- Tests for 16-bit, 24-bit, and 32-bit preservation\n- Tests with FLAC output format\n- Tests with soundfile backend\n- Tests with multiple files of different bit depths\n\n## Test Results\n\nAll unit tests pass successfully:\n- 5/5 bit depth detection tests ✅\n- 5/5 bit depth writing tests ✅\n- 10/10 total unit tests ✅\n","chunkId":"docs/BIT_DEPTH_PRESERVATION.md:6"}],[2089,{"filePath":"docs/BIT_DEPTH_PRESERVATION.md","startLine":83,"endLine":100,"chunkText":"## Behavior\n\n### Before\n- All output files were written as 16-bit PCM, regardless of input bit depth\n- This resulted in quality loss when processing 24-bit or 32-bit audio files\n\n### After\n- Output bit depth matches input bit depth automatically\n- 16-bit input → 16-bit output\n- 24-bit input → 24-bit output\n- 32-bit input → 32-bit output\n- No quality loss when processing high-quality audio files\n- Backward compatible - 16-bit inputs still produce 16-bit outputs\n\n## Supported Formats\n\nBit depth preservation works with:\n- **WAV files**: Full support for 16, 24, and 32-bit","chunkId":"docs/BIT_DEPTH_PRESERVATION.md:7"}],[2090,{"filePath":"docs/BIT_DEPTH_PRESERVATION.md","startLine":99,"endLine":112,"chunkText":"Bit depth preservation works with:\n- **WAV files**: Full support for 16, 24, and 32-bit\n- **FLAC files**: Full support for 16, 24, and 32-bit\n- **Other formats**: Depends on ffmpeg support through pydub\n\nBoth output backends are supported:\n- **pydub/ffmpeg** (default): Uses codec parameters to enforce bit depth\n- **soundfile**: Uses subtype parameter to enforce bit depth\n\n## Notes\n\n- The implementation is backward compatible - no changes to the API are required\n- Bit depth is detected automatically from input files\n- Logs provide visibility into detected and output bit depths","chunkId":"docs/BIT_DEPTH_PRESERVATION.md:8"}],[2091,{"filePath":"docs/BIT_DEPTH_PRESERVATION.md","startLine":111,"endLine":116,"chunkText":"- Bit depth is detected automatically from input files\n- Logs provide visibility into detected and output bit depths\n- Falls back to 16-bit for unknown formats or errors\n- Works correctly when processing multiple files with different bit depths\n\n","chunkId":"docs/BIT_DEPTH_PRESERVATION.md:9"}],[2092,{"filePath":"specs/001-update-roformer-implementation/001-update-roformer-implementation.md","startLine":0,"endLine":4,"chunkText":"# Feature Specification: Update Roformer Implementation\n\n**Feature Branch**: `001-update-roformer-implementation`  \n**Created**: September 25, 2025  \n**Status**: Draft  ","chunkId":"specs/001-update-roformer-implementation/001-update-roformer-implementation.md:0"}],[2093,{"filePath":"specs/001-update-roformer-implementation/001-update-roformer-implementation.md","startLine":2,"endLine":4,"chunkText":"**Feature Branch**: `001-update-roformer-implementation`  \n**Created**: September 25, 2025  \n**Status**: Draft  ","chunkId":"specs/001-update-roformer-implementation/001-update-roformer-implementation.md:1"}],[2094,{"filePath":"specs/001-update-roformer-implementation/001-update-roformer-implementation.md","startLine":3,"endLine":4,"chunkText":"**Created**: September 25, 2025  \n**Status**: Draft  ","chunkId":"specs/001-update-roformer-implementation/001-update-roformer-implementation.md:2"}],[2095,{"filePath":"specs/001-update-roformer-implementation/001-update-roformer-implementation.md","startLine":4,"endLine":4,"chunkText":"**Status**: Draft  ","chunkId":"specs/001-update-roformer-implementation/001-update-roformer-implementation.md:3"}],[2096,{"filePath":"specs/001-update-roformer-implementation/001-update-roformer-implementation.md","startLine":5,"endLine":5,"chunkText":"**Input**: User description: \"update Roformer implementation: this audio-separator project currently has an older implementation of the Roformer architecture inference code, copied from another project over a year ago into folder path audio_separator/separator/uvr_lib_v5/ this works well for many models, but some of the latest Roformer models don't work with the older inference code; the model fails to load with errors such as \"AttributeError: \"'norm'\" - File \"/Users/andrew/miniforge3/lib/python3.13/site-packages/audio_separator/separator/uvr_lib_v5/tfc_tdf_v3.py\", line 155, in __init__  norm = get_norm(n","chunkId":"specs/001-update-roformer-implementation/001-update-roformer-implementation.md:4"}],[2097,{"filePath":"specs/001-update-roformer-implementation/001-update-roformer-implementation.md","startLine":6,"endLine":16,"chunkText":"\n## Execution Flow (main)\n```\n1. Parse user description from Input\n   → Identified: Need to update Roformer implementation for latest model compatibility\n2. Extract key concepts from description\n   → Actors: developers, users with existing models, users with new models\n   → Actions: update implementation, maintain backward compatibility, validate functionality\n   → Data: Roformer models (old and new), inference code, model configurations\n   → Constraints: cannot break existing functionality, must support both old and new models\n3. For each unclear aspect:","chunkId":"specs/001-update-roformer-implementation/001-update-roformer-implementation.md:5"}],[2098,{"filePath":"specs/001-update-roformer-implementation/001-update-roformer-implementation.md","startLine":14,"endLine":24,"chunkText":"   → Data: Roformer models (old and new), inference code, model configurations\n   → Constraints: cannot break existing functionality, must support both old and new models\n3. For each unclear aspect:\n   → [RESOLVED] Clear requirements provided with specific error messages and paths\n4. Fill User Scenarios & Testing section\n   → Clear user flow: load models, separate audio, validate outputs\n5. Generate Functional Requirements\n   → All requirements are testable and specific\n6. Identify Key Entities\n   → BSRoformer, MelBandRoformer, model configurations, inference parameters\n7. Run Review Checklist","chunkId":"specs/001-update-roformer-implementation/001-update-roformer-implementation.md:6"}],[2099,{"filePath":"specs/001-update-roformer-implementation/001-update-roformer-implementation.md","startLine":22,"endLine":40,"chunkText":"6. Identify Key Entities\n   → BSRoformer, MelBandRoformer, model configurations, inference parameters\n7. Run Review Checklist\n   → No clarifications needed, no implementation details included\n8. Return: SUCCESS (spec ready for planning)\n```\n\n---\n\n## ⚡ Quick Guidelines\n- ✅ Focus on WHAT users need and WHY\n- ❌ Avoid HOW to implement (no tech stack, APIs, code structure)\n- 👥 Written for business stakeholders, not developers\n\n---\n\n## User Scenarios & Testing *(mandatory)*\n\n### Primary User Story","chunkId":"specs/001-update-roformer-implementation/001-update-roformer-implementation.md:7"}],[2100,{"filePath":"specs/001-update-roformer-implementation/001-update-roformer-implementation.md","startLine":38,"endLine":44,"chunkText":"## User Scenarios & Testing *(mandatory)*\n\n### Primary User Story\nUsers need to be able to load and use the latest Roformer models for audio separation without losing the ability to use their existing older models. The system should seamlessly handle both old and new model formats, providing the same quality audio separation results.\n\n### Acceptance Scenarios\n1. **Given** a user has an existing older Roformer model, **When** they attempt to separate audio, **Then** the separation should work exactly as it did before the update","chunkId":"specs/001-update-roformer-implementation/001-update-roformer-implementation.md:8"}],[2101,{"filePath":"specs/001-update-roformer-implementation/001-update-roformer-implementation.md","startLine":42,"endLine":45,"chunkText":"\n### Acceptance Scenarios\n1. **Given** a user has an existing older Roformer model, **When** they attempt to separate audio, **Then** the separation should work exactly as it did before the update\n2. **Given** a user has a newer Roformer model with updated parameters (mlp_expansion_factor, sage_attention, zero_dc), **When** they attempt to load and use the model, **Then** the model should load successfully and produce high-quality audio separation","chunkId":"specs/001-update-roformer-implementation/001-update-roformer-implementation.md:9"}],[2102,{"filePath":"specs/001-update-roformer-implementation/001-update-roformer-implementation.md","startLine":43,"endLine":45,"chunkText":"### Acceptance Scenarios\n1. **Given** a user has an existing older Roformer model, **When** they attempt to separate audio, **Then** the separation should work exactly as it did before the update\n2. **Given** a user has a newer Roformer model with updated parameters (mlp_expansion_factor, sage_attention, zero_dc), **When** they attempt to load and use the model, **Then** the model should load successfully and produce high-quality audio separation","chunkId":"specs/001-update-roformer-implementation/001-update-roformer-implementation.md:10"}],[2103,{"filePath":"specs/001-update-roformer-implementation/001-update-roformer-implementation.md","startLine":44,"endLine":45,"chunkText":"1. **Given** a user has an existing older Roformer model, **When** they attempt to separate audio, **Then** the separation should work exactly as it did before the update\n2. **Given** a user has a newer Roformer model with updated parameters (mlp_expansion_factor, sage_attention, zero_dc), **When** they attempt to load and use the model, **Then** the model should load successfully and produce high-quality audio separation","chunkId":"specs/001-update-roformer-implementation/001-update-roformer-implementation.md:11"}],[2104,{"filePath":"specs/001-update-roformer-implementation/001-update-roformer-implementation.md","startLine":45,"endLine":46,"chunkText":"2. **Given** a user has a newer Roformer model with updated parameters (mlp_expansion_factor, sage_attention, zero_dc), **When** they attempt to load and use the model, **Then** the model should load successfully and produce high-quality audio separation\n3. **Given** a user has models that use different normalization configurations, **When** they load these models, **Then** the system should handle the normalization appropriately without AttributeError exceptions","chunkId":"specs/001-update-roformer-implementation/001-update-roformer-implementation.md:12"}],[2105,{"filePath":"specs/001-update-roformer-implementation/001-update-roformer-implementation.md","startLine":46,"endLine":51,"chunkText":"3. **Given** a user has models that use different normalization configurations, **When** they load these models, **Then** the system should handle the normalization appropriately without AttributeError exceptions\n4. **Given** a user switches between old and new model types, **When** they perform multiple separations in sequence, **Then** all separations should complete successfully without conflicts\n\n### Edge Cases\n- What happens when a model configuration contains parameters that exist in new implementation but not old?\n- How does system handle models with missing or invalid configuration parameters?","chunkId":"specs/001-update-roformer-implementation/001-update-roformer-implementation.md:13"}],[2106,{"filePath":"specs/001-update-roformer-implementation/001-update-roformer-implementation.md","startLine":49,"endLine":57,"chunkText":"### Edge Cases\n- What happens when a model configuration contains parameters that exist in new implementation but not old?\n- How does system handle models with missing or invalid configuration parameters?\n- What happens when a user tries to load a corrupted or incompatible model file?\n- How does the system behave when switching between different Roformer variants (BSRoformer vs MelBandRoformer)?\n\n## Requirements *(mandatory)*\n\n### Functional Requirements","chunkId":"specs/001-update-roformer-implementation/001-update-roformer-implementation.md:14"}],[2107,{"filePath":"specs/001-update-roformer-implementation/001-update-roformer-implementation.md","startLine":55,"endLine":59,"chunkText":"## Requirements *(mandatory)*\n\n### Functional Requirements\n- **FR-001**: System MUST load and execute older Roformer models without any regression in functionality or performance, preferentially using new inference code with fallback to old code if loading fails\n- **FR-002**: System MUST load and execute newer Roformer models that include updated parameters (mlp_expansion_factor, sage_attention, zero_dc, use_torch_checkpoint, skip_connection) using the new inference code","chunkId":"specs/001-update-roformer-implementation/001-update-roformer-implementation.md:15"}],[2108,{"filePath":"specs/001-update-roformer-implementation/001-update-roformer-implementation.md","startLine":57,"endLine":59,"chunkText":"### Functional Requirements\n- **FR-001**: System MUST load and execute older Roformer models without any regression in functionality or performance, preferentially using new inference code with fallback to old code if loading fails\n- **FR-002**: System MUST load and execute newer Roformer models that include updated parameters (mlp_expansion_factor, sage_attention, zero_dc, use_torch_checkpoint, skip_connection) using the new inference code","chunkId":"specs/001-update-roformer-implementation/001-update-roformer-implementation.md:16"}],[2109,{"filePath":"specs/001-update-roformer-implementation/001-update-roformer-implementation.md","startLine":58,"endLine":60,"chunkText":"- **FR-001**: System MUST load and execute older Roformer models without any regression in functionality or performance, preferentially using new inference code with fallback to old code if loading fails\n- **FR-002**: System MUST load and execute newer Roformer models that include updated parameters (mlp_expansion_factor, sage_attention, zero_dc, use_torch_checkpoint, skip_connection) using the new inference code\n- **FR-003**: System MUST handle both BSRoformer and MelBandRoformer model variants, attempting new implementation first and falling back to old implementation only if necessary","chunkId":"specs/001-update-roformer-implementation/001-update-roformer-implementation.md:17"}],[2110,{"filePath":"specs/001-update-roformer-implementation/001-update-roformer-implementation.md","startLine":59,"endLine":61,"chunkText":"- **FR-002**: System MUST load and execute newer Roformer models that include updated parameters (mlp_expansion_factor, sage_attention, zero_dc, use_torch_checkpoint, skip_connection) using the new inference code\n- **FR-003**: System MUST handle both BSRoformer and MelBandRoformer model variants, attempting new implementation first and falling back to old implementation only if necessary\n- **FR-004**: System MUST gracefully handle model configurations with missing required parameters by failing with detailed error messages that specify which parameters are missing and their expected types/values","chunkId":"specs/001-update-roformer-implementation/001-update-roformer-implementation.md:18"}],[2111,{"filePath":"specs/001-update-roformer-implementation/001-update-roformer-implementation.md","startLine":60,"endLine":62,"chunkText":"- **FR-003**: System MUST handle both BSRoformer and MelBandRoformer model variants, attempting new implementation first and falling back to old implementation only if necessary\n- **FR-004**: System MUST gracefully handle model configurations with missing required parameters by failing with detailed error messages that specify which parameters are missing and their expected types/values\n- **FR-005**: System MUST resolve normalization configuration issues that cause AttributeError exceptions in tfc_tdf_v3.py","chunkId":"specs/001-update-roformer-implementation/001-update-roformer-implementation.md:19"}],[2112,{"filePath":"specs/001-update-roformer-implementation/001-update-roformer-implementation.md","startLine":61,"endLine":63,"chunkText":"- **FR-004**: System MUST gracefully handle model configurations with missing required parameters by failing with detailed error messages that specify which parameters are missing and their expected types/values\n- **FR-005**: System MUST resolve normalization configuration issues that cause AttributeError exceptions in tfc_tdf_v3.py\n- **FR-006**: System MUST maintain identical audio separation quality for existing models after the update, validated using spectral analysis comparison with waveform and spectrogram similarity thresholds (≥0.90 for waveform, ≥0.80 for spectrogram)","chunkId":"specs/001-update-roformer-implementation/001-update-roformer-implementation.md:20"}],[2113,{"filePath":"specs/001-update-roformer-implementation/001-update-roformer-implementation.md","startLine":62,"endLine":64,"chunkText":"- **FR-005**: System MUST resolve normalization configuration issues that cause AttributeError exceptions in tfc_tdf_v3.py\n- **FR-006**: System MUST maintain identical audio separation quality for existing models after the update, validated using spectral analysis comparison with waveform and spectrogram similarity thresholds (≥0.90 for waveform, ≥0.80 for spectrogram)\n- **FR-007**: System MUST provide clear error messages when model loading fails due to incompatible configurations, including specific parameter mismatches and suggested corrections","chunkId":"specs/001-update-roformer-implementation/001-update-roformer-implementation.md:21"}],[2114,{"filePath":"specs/001-update-roformer-implementation/001-update-roformer-implementation.md","startLine":63,"endLine":65,"chunkText":"- **FR-006**: System MUST maintain identical audio separation quality for existing models after the update, validated using spectral analysis comparison with waveform and spectrogram similarity thresholds (≥0.90 for waveform, ≥0.80 for spectrogram)\n- **FR-007**: System MUST provide clear error messages when model loading fails due to incompatible configurations, including specific parameter mismatches and suggested corrections\n- **FR-008**: System MUST support backward compatibility for all existing model files and configurations","chunkId":"specs/001-update-roformer-implementation/001-update-roformer-implementation.md:22"}],[2115,{"filePath":"specs/001-update-roformer-implementation/001-update-roformer-implementation.md","startLine":64,"endLine":67,"chunkText":"- **FR-007**: System MUST provide clear error messages when model loading fails due to incompatible configurations, including specific parameter mismatches and suggested corrections\n- **FR-008**: System MUST support backward compatibility for all existing model files and configurations\n- **FR-009**: System MUST validate model configurations before attempting to instantiate models\n- **FR-010**: System MUST allow seamless switching between different Roformer model types within the same session","chunkId":"specs/001-update-roformer-implementation/001-update-roformer-implementation.md:23"}],[2116,{"filePath":"specs/001-update-roformer-implementation/001-update-roformer-implementation.md","startLine":65,"endLine":70,"chunkText":"- **FR-008**: System MUST support backward compatibility for all existing model files and configurations\n- **FR-009**: System MUST validate model configurations before attempting to instantiate models\n- **FR-010**: System MUST allow seamless switching between different Roformer model types within the same session\n- **FR-011**: System MUST undergo manual testing validation after completing major implementation milestones (e.g., new inference code integration, backward compatibility implementation, error handling updates)\n\n### Key Entities *(include if feature involves data)*","chunkId":"specs/001-update-roformer-implementation/001-update-roformer-implementation.md:24"}],[2117,{"filePath":"specs/001-update-roformer-implementation/001-update-roformer-implementation.md","startLine":68,"endLine":72,"chunkText":"- **FR-011**: System MUST undergo manual testing validation after completing major implementation milestones (e.g., new inference code integration, backward compatibility implementation, error handling updates)\n\n### Key Entities *(include if feature involves data)*\n- **Roformer Model**: Audio separation model with specific architecture parameters, exists in old and new variants with different parameter sets\n- **Model Configuration**: Dictionary containing model parameters including dimension, depth, attention settings, and normalization preferences","chunkId":"specs/001-update-roformer-implementation/001-update-roformer-implementation.md:25"}],[2118,{"filePath":"specs/001-update-roformer-implementation/001-update-roformer-implementation.md","startLine":70,"endLine":74,"chunkText":"### Key Entities *(include if feature involves data)*\n- **Roformer Model**: Audio separation model with specific architecture parameters, exists in old and new variants with different parameter sets\n- **Model Configuration**: Dictionary containing model parameters including dimension, depth, attention settings, and normalization preferences\n- **BSRoformer**: Band-split Roformer variant that processes audio in frequency bands, requires freqs_per_bands parameter\n- **MelBandRoformer**: Mel-scale band Roformer variant that uses mel-scale frequency bands, requires num_bands parameter","chunkId":"specs/001-update-roformer-implementation/001-update-roformer-implementation.md:26"}],[2119,{"filePath":"specs/001-update-roformer-implementation/001-update-roformer-implementation.md","startLine":72,"endLine":79,"chunkText":"- **Model Configuration**: Dictionary containing model parameters including dimension, depth, attention settings, and normalization preferences\n- **BSRoformer**: Band-split Roformer variant that processes audio in frequency bands, requires freqs_per_bands parameter\n- **MelBandRoformer**: Mel-scale band Roformer variant that uses mel-scale frequency bands, requires num_bands parameter\n- **Model Parameters**: Configuration values including mlp_expansion_factor, sage_attention, zero_dc, and other architecture-specific settings\n\n## Clarifications\n\n### Session 2025-09-25","chunkId":"specs/001-update-roformer-implementation/001-update-roformer-implementation.md:27"}],[2120,{"filePath":"specs/001-update-roformer-implementation/001-update-roformer-implementation.md","startLine":77,"endLine":81,"chunkText":"## Clarifications\n\n### Session 2025-09-25\n- Q: The spec mentions \"identical audio separation quality\" must be maintained, but doesn't specify how quality should be measured or what tolerance is acceptable for validation testing. → A: Use spectral analysis comparison with defined similarity thresholds\n- Q: How should the system determine appropriate defaults for missing model parameters? → A: Fail gracefully with detailed error messages rather than assuming defaults","chunkId":"specs/001-update-roformer-implementation/001-update-roformer-implementation.md:28"}],[2121,{"filePath":"specs/001-update-roformer-implementation/001-update-roformer-implementation.md","startLine":79,"endLine":81,"chunkText":"### Session 2025-09-25\n- Q: The spec mentions \"identical audio separation quality\" must be maintained, but doesn't specify how quality should be measured or what tolerance is acceptable for validation testing. → A: Use spectral analysis comparison with defined similarity thresholds\n- Q: How should the system determine appropriate defaults for missing model parameters? → A: Fail gracefully with detailed error messages rather than assuming defaults","chunkId":"specs/001-update-roformer-implementation/001-update-roformer-implementation.md:29"}],[2122,{"filePath":"specs/001-update-roformer-implementation/001-update-roformer-implementation.md","startLine":80,"endLine":82,"chunkText":"- Q: The spec mentions \"identical audio separation quality\" must be maintained, but doesn't specify how quality should be measured or what tolerance is acceptable for validation testing. → A: Use spectral analysis comparison with defined similarity thresholds\n- Q: How should the system determine appropriate defaults for missing model parameters? → A: Fail gracefully with detailed error messages rather than assuming defaults\n- Q: How should the system detect whether a model uses the old or new Roformer format? → A: All Roformer models should use new inference code (test first), fallback to old if fails","chunkId":"specs/001-update-roformer-implementation/001-update-roformer-implementation.md:30"}],[2123,{"filePath":"specs/001-update-roformer-implementation/001-update-roformer-implementation.md","startLine":81,"endLine":89,"chunkText":"- Q: How should the system determine appropriate defaults for missing model parameters? → A: Fail gracefully with detailed error messages rather than assuming defaults\n- Q: How should the system detect whether a model uses the old or new Roformer format? → A: All Roformer models should use new inference code (test first), fallback to old if fails\n- Q: When should manual testing validation be performed during the implementation process? → A: Test only after completing major implementation milestones\n\n---\n\n## Review & Acceptance Checklist\n*GATE: Automated checks run during main() execution*\n","chunkId":"specs/001-update-roformer-implementation/001-update-roformer-implementation.md:31"}],[2124,{"filePath":"specs/001-update-roformer-implementation/001-update-roformer-implementation.md","startLine":87,"endLine":107,"chunkText":"## Review & Acceptance Checklist\n*GATE: Automated checks run during main() execution*\n\n### Content Quality\n- [x] No implementation details (languages, frameworks, APIs)\n- [x] Focused on user value and business needs\n- [x] Written for non-technical stakeholders\n- [x] All mandatory sections completed\n\n### Requirement Completeness\n- [x] No [NEEDS CLARIFICATION] markers remain\n- [x] Requirements are testable and unambiguous  \n- [x] Success criteria are measurable\n- [x] Scope is clearly bounded\n- [x] Dependencies and assumptions identified\n\n---\n\n## Execution Status\n*Updated by main() during processing*\n","chunkId":"specs/001-update-roformer-implementation/001-update-roformer-implementation.md:32"}],[2125,{"filePath":"specs/001-update-roformer-implementation/001-update-roformer-implementation.md","startLine":105,"endLine":117,"chunkText":"## Execution Status\n*Updated by main() during processing*\n\n- [x] User description parsed\n- [x] Key concepts extracted\n- [x] Ambiguities marked\n- [x] User scenarios defined\n- [x] Requirements generated\n- [x] Entities identified\n- [x] Review checklist passed\n\n---\n","chunkId":"specs/001-update-roformer-implementation/001-update-roformer-implementation.md:33"}],[2126,{"filePath":"specs/001-update-roformer-implementation/contracts/fallback_loader_interface.py","startLine":0,"endLine":21,"chunkText":"\"\"\"\nInterface contract for fallback loader implementations.\nDefines the expected behavior for fallback loading mechanisms.\n\"\"\"\n\nfrom abc import ABC, abstractmethod\nfrom typing import Dict, Any\nfrom dataclasses import dataclass\n\n@dataclass\nclass ModelLoadingResult:\n    \"\"\"Result of a model loading attempt.\"\"\"\n    model: Any\n    model_type: str\n    config_used: Dict[str, Any]\n    implementation_version: str\n    loading_method: str\n    device: str\n    success: bool\n    error_message: str = None\n\n","chunkId":"specs/001-update-roformer-implementation/contracts/fallback_loader_interface.py:0"}],[2127,{"filePath":"specs/001-update-roformer-implementation/contracts/fallback_loader_interface.py","startLine":22,"endLine":38,"chunkText":"class FallbackLoaderInterface(ABC):\n    \"\"\"\n    Interface for fallback model loading implementations.\n    \n    Defines the contract that fallback loaders must implement to provide\n    compatibility with legacy models when new implementations fail.\n    \"\"\"\n    \n    @abstractmethod\n    def try_new_implementation(self, \n                             model_path: str, \n                             config: Dict[str, Any], \n                             device: str = 'cpu') -> ModelLoadingResult:\n        \"\"\"\n        Try loading with new implementation.\n        \n        Args:","chunkId":"specs/001-update-roformer-implementation/contracts/fallback_loader_interface.py:1"}],[2128,{"filePath":"specs/001-update-roformer-implementation/contracts/fallback_loader_interface.py","startLine":36,"endLine":53,"chunkText":"        Try loading with new implementation.\n        \n        Args:\n            model_path: Path to the model file\n            config: Model configuration dictionary\n            device: Device to load model on\n            \n        Returns:\n            ModelLoadingResult with attempt results\n        \"\"\"\n        pass\n    \n    @abstractmethod\n    def try_legacy_implementation(self, \n                                model_path: str, \n                                config: Dict[str, Any], \n                                device: str = 'cpu') -> ModelLoadingResult:\n        \"\"\"","chunkId":"specs/001-update-roformer-implementation/contracts/fallback_loader_interface.py:2"}],[2129,{"filePath":"specs/001-update-roformer-implementation/contracts/fallback_loader_interface.py","startLine":51,"endLine":65,"chunkText":"                                config: Dict[str, Any], \n                                device: str = 'cpu') -> ModelLoadingResult:\n        \"\"\"\n        Try loading with legacy implementation.\n        \n        Args:\n            model_path: Path to the model file\n            config: Model configuration dictionary\n            device: Device to load model on\n            \n        Returns:\n            ModelLoadingResult with fallback attempt results\n        \"\"\"\n        pass\n","chunkId":"specs/001-update-roformer-implementation/contracts/fallback_loader_interface.py:3"}],[2130,{"filePath":"specs/001-update-roformer-implementation/contracts/parameter_validator_interface.py","startLine":0,"endLine":18,"chunkText":"\"\"\"\nAPI Contract: Parameter Validator Interface\nThis defines the interface for validating Roformer model parameters.\n\"\"\"\n\nfrom abc import ABC, abstractmethod\nfrom typing import Dict, Any, List, Optional, Tuple\nfrom dataclasses import dataclass\nfrom enum import Enum\n\n\nclass ValidationSeverity(Enum):\n    \"\"\"Severity levels for validation issues.\"\"\"\n    ERROR = \"error\"      # Blocks model loading\n    WARNING = \"warning\"  # Allows loading but may affect performance\n    INFO = \"info\"        # Informational only\n\n\n@dataclass","chunkId":"specs/001-update-roformer-implementation/contracts/parameter_validator_interface.py:0"}],[2131,{"filePath":"specs/001-update-roformer-implementation/contracts/parameter_validator_interface.py","startLine":19,"endLine":28,"chunkText":"class ValidationIssue:\n    \"\"\"Represents a validation issue found in model configuration.\"\"\"\n    severity: ValidationSeverity\n    parameter_name: str\n    message: str\n    suggested_fix: str\n    current_value: Any = None\n    expected_value: Any = None\n\n","chunkId":"specs/001-update-roformer-implementation/contracts/parameter_validator_interface.py:1"}],[2132,{"filePath":"specs/001-update-roformer-implementation/contracts/parameter_validator_interface.py","startLine":29,"endLine":45,"chunkText":"class ParameterValidatorInterface(ABC):\n    \"\"\"Abstract interface for validating model parameters.\"\"\"\n    \n    @abstractmethod\n    def validate_required_parameters(self, config: Dict[str, Any], model_type: str) -> List[ValidationIssue]:\n        \"\"\"\n        Validate that all required parameters are present.\n        \n        Args:\n            config: Model configuration dictionary\n            model_type: Type of model (\"bs_roformer\" or \"mel_band_roformer\")\n            \n        Returns:\n            List of validation issues for missing required parameters\n        \"\"\"\n        pass\n    ","chunkId":"specs/001-update-roformer-implementation/contracts/parameter_validator_interface.py:2"}],[2133,{"filePath":"specs/001-update-roformer-implementation/contracts/parameter_validator_interface.py","startLine":43,"endLine":64,"chunkText":"        \"\"\"\n        pass\n    \n    @abstractmethod\n    def validate_parameter_types(self, config: Dict[str, Any]) -> List[ValidationIssue]:\n        \"\"\"\n        Validate parameter types match expected types.\n        \n        Args:\n            config: Model configuration dictionary\n            \n        Returns:\n            List of validation issues for type mismatches\n        \"\"\"\n        pass\n    \n    @abstractmethod\n    def validate_parameter_ranges(self, config: Dict[str, Any]) -> List[ValidationIssue]:\n        \"\"\"\n        Validate parameter values are within acceptable ranges.\n        \n        Args:","chunkId":"specs/001-update-roformer-implementation/contracts/parameter_validator_interface.py:3"}],[2134,{"filePath":"specs/001-update-roformer-implementation/contracts/parameter_validator_interface.py","startLine":62,"endLine":80,"chunkText":"        Validate parameter values are within acceptable ranges.\n        \n        Args:\n            config: Model configuration dictionary\n            \n        Returns:\n            List of validation issues for out-of-range values\n        \"\"\"\n        pass\n    \n    @abstractmethod\n    def validate_parameter_compatibility(self, config: Dict[str, Any]) -> List[ValidationIssue]:\n        \"\"\"\n        Validate that parameter combinations are compatible.\n        \n        Args:\n            config: Model configuration dictionary\n            \n        Returns:","chunkId":"specs/001-update-roformer-implementation/contracts/parameter_validator_interface.py:4"}],[2135,{"filePath":"specs/001-update-roformer-implementation/contracts/parameter_validator_interface.py","startLine":78,"endLine":98,"chunkText":"            config: Model configuration dictionary\n            \n        Returns:\n            List of validation issues for incompatible parameter combinations\n        \"\"\"\n        pass\n    \n    @abstractmethod\n    def validate_normalization_config(self, norm_config: Any) -> List[ValidationIssue]:\n        \"\"\"\n        Validate normalization configuration.\n        \n        Args:\n            norm_config: Normalization configuration (may be string, dict, or None)\n            \n        Returns:\n            List of validation issues for normalization configuration\n        \"\"\"\n        pass\n    \n    @abstractmethod","chunkId":"specs/001-update-roformer-implementation/contracts/parameter_validator_interface.py:5"}],[2136,{"filePath":"specs/001-update-roformer-implementation/contracts/parameter_validator_interface.py","startLine":96,"endLine":116,"chunkText":"        pass\n    \n    @abstractmethod\n    def get_parameter_defaults(self, model_type: str) -> Dict[str, Any]:\n        \"\"\"\n        Get default values for optional parameters.\n        \n        Args:\n            model_type: Type of model (\"bs_roformer\" or \"mel_band_roformer\")\n            \n        Returns:\n            Dictionary of parameter names to default values\n        \"\"\"\n        pass\n    \n    @abstractmethod\n    def apply_parameter_defaults(self, config: Dict[str, Any], model_type: str) -> Dict[str, Any]:\n        \"\"\"\n        Apply default values to missing optional parameters.\n        \n        Args:","chunkId":"specs/001-update-roformer-implementation/contracts/parameter_validator_interface.py:6"}],[2137,{"filePath":"specs/001-update-roformer-implementation/contracts/parameter_validator_interface.py","startLine":114,"endLine":125,"chunkText":"        Apply default values to missing optional parameters.\n        \n        Args:\n            config: Model configuration dictionary\n            model_type: Type of model\n            \n        Returns:\n            Configuration with defaults applied\n        \"\"\"\n        pass\n\n","chunkId":"specs/001-update-roformer-implementation/contracts/parameter_validator_interface.py:7"}],[2138,{"filePath":"specs/001-update-roformer-implementation/contracts/parameter_validator_interface.py","startLine":126,"endLine":143,"chunkText":"class BSRoformerValidatorInterface(ABC):\n    \"\"\"Specialized validator for BSRoformer models.\"\"\"\n    \n    @abstractmethod\n    def validate_freqs_per_bands(self, freqs_per_bands: Tuple[int, ...], stft_config: Dict[str, Any]) -> List[ValidationIssue]:\n        \"\"\"\n        Validate frequency bands configuration.\n        \n        Args:\n            freqs_per_bands: Tuple of frequencies per band\n            stft_config: STFT configuration parameters\n            \n        Returns:\n            List of validation issues for frequency bands\n        \"\"\"\n        pass\n    \n    @abstractmethod","chunkId":"specs/001-update-roformer-implementation/contracts/parameter_validator_interface.py:8"}],[2139,{"filePath":"specs/001-update-roformer-implementation/contracts/parameter_validator_interface.py","startLine":141,"endLine":156,"chunkText":"        pass\n    \n    @abstractmethod\n    def calculate_expected_freqs(self, stft_n_fft: int) -> int:\n        \"\"\"\n        Calculate expected number of frequency bins from STFT configuration.\n        \n        Args:\n            stft_n_fft: STFT n_fft parameter\n            \n        Returns:\n            Expected number of frequency bins\n        \"\"\"\n        pass\n\n","chunkId":"specs/001-update-roformer-implementation/contracts/parameter_validator_interface.py:9"}],[2140,{"filePath":"specs/001-update-roformer-implementation/contracts/parameter_validator_interface.py","startLine":157,"endLine":174,"chunkText":"class MelBandRoformerValidatorInterface(ABC):\n    \"\"\"Specialized validator for MelBandRoformer models.\"\"\"\n    \n    @abstractmethod\n    def validate_num_bands(self, num_bands: int, sample_rate: int) -> List[ValidationIssue]:\n        \"\"\"\n        Validate number of mel bands.\n        \n        Args:\n            num_bands: Number of mel-scale bands\n            sample_rate: Audio sample rate\n            \n        Returns:\n            List of validation issues for mel bands configuration\n        \"\"\"\n        pass\n    \n    @abstractmethod","chunkId":"specs/001-update-roformer-implementation/contracts/parameter_validator_interface.py:10"}],[2141,{"filePath":"specs/001-update-roformer-implementation/contracts/parameter_validator_interface.py","startLine":172,"endLine":187,"chunkText":"        pass\n    \n    @abstractmethod\n    def validate_sample_rate(self, sample_rate: int) -> List[ValidationIssue]:\n        \"\"\"\n        Validate audio sample rate.\n        \n        Args:\n            sample_rate: Audio sample rate in Hz\n            \n        Returns:\n            List of validation issues for sample rate\n        \"\"\"\n        pass\n\n","chunkId":"specs/001-update-roformer-implementation/contracts/parameter_validator_interface.py:11"}],[2142,{"filePath":"specs/001-update-roformer-implementation/contracts/parameter_validator_interface.py","startLine":188,"endLine":204,"chunkText":"class ConfigurationNormalizerInterface(ABC):\n    \"\"\"Interface for normalizing configuration between old and new formats.\"\"\"\n    \n    @abstractmethod\n    def normalize_config_format(self, raw_config: Any) -> Dict[str, Any]:\n        \"\"\"\n        Normalize configuration from various input formats to standard dictionary.\n        \n        Args:\n            raw_config: Configuration in any format (dict, object, etc.)\n            \n        Returns:\n            Normalized configuration dictionary\n        \"\"\"\n        pass\n    \n    @abstractmethod","chunkId":"specs/001-update-roformer-implementation/contracts/parameter_validator_interface.py:12"}],[2143,{"filePath":"specs/001-update-roformer-implementation/contracts/parameter_validator_interface.py","startLine":202,"endLine":222,"chunkText":"        pass\n    \n    @abstractmethod\n    def map_legacy_parameters(self, config: Dict[str, Any]) -> Dict[str, Any]:\n        \"\"\"\n        Map legacy parameter names to current parameter names.\n        \n        Args:\n            config: Configuration with potentially legacy parameter names\n            \n        Returns:\n            Configuration with current parameter names\n        \"\"\"\n        pass\n    \n    @abstractmethod\n    def extract_nested_config(self, config: Any, path: str) -> Any:\n        \"\"\"\n        Extract nested configuration value using dot notation path.\n        \n        Args:","chunkId":"specs/001-update-roformer-implementation/contracts/parameter_validator_interface.py:13"}],[2144,{"filePath":"specs/001-update-roformer-implementation/contracts/parameter_validator_interface.py","startLine":220,"endLine":230,"chunkText":"        Extract nested configuration value using dot notation path.\n        \n        Args:\n            config: Configuration object or dictionary\n            path: Dot notation path (e.g., \"model.norm\")\n            \n        Returns:\n            Extracted configuration value or None if not found\n        \"\"\"\n        pass\n","chunkId":"specs/001-update-roformer-implementation/contracts/parameter_validator_interface.py:14"}],[2145,{"filePath":"specs/001-update-roformer-implementation/contracts/roformer_loader_interface.py","startLine":0,"endLine":16,"chunkText":"\"\"\"\nAPI Contract: Roformer Model Loader Interface\nThis defines the interface for loading Roformer models with backward compatibility.\n\"\"\"\n\nfrom abc import ABC, abstractmethod\nfrom typing import Optional, List, Dict, Any, Tuple\nfrom dataclasses import dataclass\nfrom enum import Enum\n\n\nclass RoformerType(Enum):\n    \"\"\"Supported Roformer model types.\"\"\"\n    BS_ROFORMER = \"bs_roformer\"\n    MEL_BAND_ROFORMER = \"mel_band_roformer\"\n\n","chunkId":"specs/001-update-roformer-implementation/contracts/roformer_loader_interface.py:0"}],[2146,{"filePath":"specs/001-update-roformer-implementation/contracts/roformer_loader_interface.py","startLine":17,"endLine":24,"chunkText":"class ImplementationVersion(Enum):\n    \"\"\"Available implementation versions.\"\"\"\n    OLD = \"old\"\n    NEW = \"new\"\n    FALLBACK = \"fallback\"\n\n\n@dataclass","chunkId":"specs/001-update-roformer-implementation/contracts/roformer_loader_interface.py:1"}],[2147,{"filePath":"specs/001-update-roformer-implementation/contracts/roformer_loader_interface.py","startLine":25,"endLine":38,"chunkText":"class ModelLoadingResult:\n    \"\"\"Result of model loading operation.\"\"\"\n    success: bool\n    model: Optional[Any] = None  # Actual model instance\n    error_message: Optional[str] = None\n    implementation_used: ImplementationVersion = ImplementationVersion.NEW\n    warnings: List[str] = None\n    \n    def __post_init__(self):\n        if self.warnings is None:\n            self.warnings = []\n\n\n@dataclass","chunkId":"specs/001-update-roformer-implementation/contracts/roformer_loader_interface.py:2"}],[2148,{"filePath":"specs/001-update-roformer-implementation/contracts/roformer_loader_interface.py","startLine":39,"endLine":60,"chunkText":"class ModelConfiguration:\n    \"\"\"Model configuration parameters.\"\"\"\n    # Required parameters\n    dim: int\n    depth: int\n    \n    # Common optional parameters\n    stereo: bool = False\n    num_stems: int = 1\n    time_transformer_depth: int = 2\n    freq_transformer_depth: int = 2\n    dim_head: int = 64\n    heads: int = 8\n    attn_dropout: float = 0.0\n    ff_dropout: float = 0.0\n    flash_attn: bool = True\n    \n    # New parameters (with defaults for backward compatibility)\n    mlp_expansion_factor: int = 4\n    sage_attention: bool = False\n    zero_dc: bool = True\n    use_torch_checkpoint: bool = False","chunkId":"specs/001-update-roformer-implementation/contracts/roformer_loader_interface.py:3"}],[2149,{"filePath":"specs/001-update-roformer-implementation/contracts/roformer_loader_interface.py","startLine":58,"endLine":70,"chunkText":"    sage_attention: bool = False\n    zero_dc: bool = True\n    use_torch_checkpoint: bool = False\n    skip_connection: bool = False\n    \n    # Normalization (may be None in some configs)\n    norm: Optional[str] = None\n    \n    # Model-specific parameters\n    freqs_per_bands: Optional[Tuple[int, ...]] = None  # BSRoformer\n    num_bands: Optional[int] = None  # MelBandRoformer\n\n","chunkId":"specs/001-update-roformer-implementation/contracts/roformer_loader_interface.py:4"}],[2150,{"filePath":"specs/001-update-roformer-implementation/contracts/roformer_loader_interface.py","startLine":71,"endLine":84,"chunkText":"class ParameterValidationError(Exception):\n    \"\"\"Raised when model parameters are invalid.\"\"\"\n    \n    def __init__(self, parameter_name: str, expected_type: str, actual_value: Any, suggested_fix: str):\n        self.parameter_name = parameter_name\n        self.expected_type = expected_type\n        self.actual_value = actual_value\n        self.suggested_fix = suggested_fix\n        \n        message = (\n            f\"Invalid parameter '{parameter_name}': \"\n            f\"expected {expected_type}, got {type(actual_value).__name__} ({actual_value}). \"\n            f\"Suggestion: {suggested_fix}\"\n        )","chunkId":"specs/001-update-roformer-implementation/contracts/roformer_loader_interface.py:5"}],[2151,{"filePath":"specs/001-update-roformer-implementation/contracts/roformer_loader_interface.py","startLine":82,"endLine":87,"chunkText":"            f\"expected {expected_type}, got {type(actual_value).__name__} ({actual_value}). \"\n            f\"Suggestion: {suggested_fix}\"\n        )\n        super().__init__(message)\n\n","chunkId":"specs/001-update-roformer-implementation/contracts/roformer_loader_interface.py:6"}],[2152,{"filePath":"specs/001-update-roformer-implementation/contracts/roformer_loader_interface.py","startLine":88,"endLine":103,"chunkText":"class RoformerLoaderInterface(ABC):\n    \"\"\"Abstract interface for loading Roformer models.\"\"\"\n    \n    @abstractmethod\n    def load_model(self, model_path: str, config: Optional[Dict[str, Any]] = None) -> ModelLoadingResult:\n        \"\"\"\n        Load a Roformer model from the given path.\n        \n        Args:\n            model_path: Path to the model file (.ckpt, .pth)\n            config: Optional configuration override\n            \n        Returns:\n            ModelLoadingResult with success status and model or error details\n            \n        Raises:","chunkId":"specs/001-update-roformer-implementation/contracts/roformer_loader_interface.py:7"}],[2153,{"filePath":"specs/001-update-roformer-implementation/contracts/roformer_loader_interface.py","startLine":101,"endLine":118,"chunkText":"            ModelLoadingResult with success status and model or error details\n            \n        Raises:\n            ParameterValidationError: If model configuration is invalid\n            FileNotFoundError: If model file doesn't exist\n        \"\"\"\n        pass\n    \n    @abstractmethod\n    def validate_configuration(self, config: ModelConfiguration, model_type: RoformerType) -> List[str]:\n        \"\"\"\n        Validate model configuration parameters.\n        \n        Args:\n            config: Model configuration to validate\n            model_type: Type of Roformer model\n            \n        Returns:","chunkId":"specs/001-update-roformer-implementation/contracts/roformer_loader_interface.py:8"}],[2154,{"filePath":"specs/001-update-roformer-implementation/contracts/roformer_loader_interface.py","startLine":116,"endLine":139,"chunkText":"            model_type: Type of Roformer model\n            \n        Returns:\n            List of validation error messages (empty if valid)\n        \"\"\"\n        pass\n    \n    @abstractmethod\n    def detect_model_type(self, model_path: str) -> RoformerType:\n        \"\"\"\n        Detect the type of Roformer model from the file.\n        \n        Args:\n            model_path: Path to the model file\n            \n        Returns:\n            Detected RoformerType\n            \n        Raises:\n            ValueError: If model type cannot be determined\n        \"\"\"\n        pass\n    \n    @abstractmethod","chunkId":"specs/001-update-roformer-implementation/contracts/roformer_loader_interface.py:9"}],[2155,{"filePath":"specs/001-update-roformer-implementation/contracts/roformer_loader_interface.py","startLine":137,"endLine":152,"chunkText":"        pass\n    \n    @abstractmethod\n    def get_default_configuration(self, model_type: RoformerType) -> ModelConfiguration:\n        \"\"\"\n        Get default configuration for a model type.\n        \n        Args:\n            model_type: Type of Roformer model\n            \n        Returns:\n            Default ModelConfiguration for the type\n        \"\"\"\n        pass\n\n","chunkId":"specs/001-update-roformer-implementation/contracts/roformer_loader_interface.py:10"}],[2156,{"filePath":"specs/001-update-roformer-implementation/contracts/roformer_loader_interface.py","startLine":153,"endLine":174,"chunkText":"class RoformerModelInterface(ABC):\n    \"\"\"Abstract interface for Roformer model instances.\"\"\"\n    \n    @abstractmethod\n    def separate_audio(self, audio_data: Any, **kwargs) -> Any:\n        \"\"\"\n        Separate audio into stems using the model.\n        \n        Args:\n            audio_data: Input audio data\n            **kwargs: Additional separation parameters\n            \n        Returns:\n            Separated audio stems\n        \"\"\"\n        pass\n    \n    @abstractmethod\n    def get_model_info(self) -> Dict[str, Any]:\n        \"\"\"\n        Get information about the loaded model.\n        ","chunkId":"specs/001-update-roformer-implementation/contracts/roformer_loader_interface.py:11"}],[2157,{"filePath":"specs/001-update-roformer-implementation/contracts/roformer_loader_interface.py","startLine":172,"endLine":185,"chunkText":"        \"\"\"\n        Get information about the loaded model.\n        \n        Returns:\n            Dictionary with model metadata\n        \"\"\"\n        pass\n    \n    @abstractmethod\n    def cleanup(self) -> None:\n        \"\"\"Clean up model resources.\"\"\"\n        pass\n\n","chunkId":"specs/001-update-roformer-implementation/contracts/roformer_loader_interface.py:12"}],[2158,{"filePath":"specs/001-update-roformer-implementation/contracts/roformer_loader_interface.py","startLine":186,"endLine":203,"chunkText":"class FallbackLoaderInterface(ABC):\n    \"\"\"Interface for fallback loading mechanism.\"\"\"\n    \n    @abstractmethod\n    def try_new_implementation(self, model_path: str, config: ModelConfiguration) -> ModelLoadingResult:\n        \"\"\"\n        Attempt to load model with new implementation.\n        \n        Args:\n            model_path: Path to model file\n            config: Model configuration\n            \n        Returns:\n            ModelLoadingResult indicating success or failure\n        \"\"\"\n        pass\n    \n    @abstractmethod","chunkId":"specs/001-update-roformer-implementation/contracts/roformer_loader_interface.py:13"}],[2159,{"filePath":"specs/001-update-roformer-implementation/contracts/roformer_loader_interface.py","startLine":201,"endLine":220,"chunkText":"        pass\n    \n    @abstractmethod\n    def try_old_implementation(self, model_path: str, config: ModelConfiguration) -> ModelLoadingResult:\n        \"\"\"\n        Attempt to load model with old implementation (fallback).\n        \n        Args:\n            model_path: Path to model file\n            config: Model configuration\n            \n        Returns:\n            ModelLoadingResult indicating success or failure\n        \"\"\"\n        pass\n    \n    @abstractmethod\n    def should_fallback(self, error: Exception) -> bool:\n        \"\"\"\n        Determine if the error warrants falling back to old implementation.","chunkId":"specs/001-update-roformer-implementation/contracts/roformer_loader_interface.py:14"}],[2160,{"filePath":"specs/001-update-roformer-implementation/contracts/roformer_loader_interface.py","startLine":218,"endLine":229,"chunkText":"    def should_fallback(self, error: Exception) -> bool:\n        \"\"\"\n        Determine if the error warrants falling back to old implementation.\n        \n        Args:\n            error: Exception from new implementation attempt\n            \n        Returns:\n            True if should attempt fallback, False otherwise\n        \"\"\"\n        pass\n","chunkId":"specs/001-update-roformer-implementation/contracts/roformer_loader_interface.py:15"}],[2161,{"filePath":"specs/001-update-roformer-implementation/data-model.md","startLine":0,"endLine":15,"chunkText":"# Data Model: Roformer Implementation Update\n\n## Core Entities\n\n### RoformerModel\nRepresents a Roformer audio separation model with configuration parameters.\n\n**Fields:**\n- `model_path: str` - Path to the model file (.ckpt, .pth)\n- `config: ModelConfiguration` - Model configuration parameters\n- `model_type: RoformerType` - Type of Roformer (BSRoformer, MelBandRoformer)\n- `implementation_version: str` - Which implementation version (\"old\", \"new\")\n- `loaded_successfully: bool` - Whether model loaded without errors\n\n**Validation Rules:**\n- `model_path` must exist and be readable","chunkId":"specs/001-update-roformer-implementation/data-model.md:0"}],[2162,{"filePath":"specs/001-update-roformer-implementation/data-model.md","startLine":13,"endLine":31,"chunkText":"\n**Validation Rules:**\n- `model_path` must exist and be readable\n- `config` must contain required parameters for the model type\n- `model_type` must be one of supported variants\n\n**State Transitions:**\n- `Unloaded → Loading → Loaded` (success path)\n- `Unloaded → Loading → Failed` (error path)\n- `Loaded → Unloaded` (cleanup)\n\n### ModelConfiguration\nDictionary-like object containing model architecture parameters.\n\n**Fields:**\n- `dim: int` - Model dimension\n- `depth: int` - Number of transformer layers\n- `stereo: bool` - Whether model handles stereo audio\n- `num_stems: int` - Number of output stems","chunkId":"specs/001-update-roformer-implementation/data-model.md:1"}],[2163,{"filePath":"specs/001-update-roformer-implementation/data-model.md","startLine":29,"endLine":39,"chunkText":"- `depth: int` - Number of transformer layers\n- `stereo: bool` - Whether model handles stereo audio\n- `num_stems: int` - Number of output stems\n- `time_transformer_depth: int` - Depth of time transformer\n- `freq_transformer_depth: int` - Depth of frequency transformer\n- `freqs_per_bands: Tuple[int, ...]` - Frequency bands configuration\n- `dim_head: int` - Attention head dimension\n- `heads: int` - Number of attention heads\n- `attn_dropout: float` - Attention dropout rate\n- `ff_dropout: float` - Feed-forward dropout rate\n- `flash_attn: bool` - Whether to use flash attention","chunkId":"specs/001-update-roformer-implementation/data-model.md:2"}],[2164,{"filePath":"specs/001-update-roformer-implementation/data-model.md","startLine":37,"endLine":50,"chunkText":"- `attn_dropout: float` - Attention dropout rate\n- `ff_dropout: float` - Feed-forward dropout rate\n- `flash_attn: bool` - Whether to use flash attention\n- `norm: str` - Normalization type (new field handling)\n\n**New Parameters (for updated models):**\n- `mlp_expansion_factor: int = 4` - MLP expansion ratio\n- `sage_attention: bool = False` - Enable Sage attention\n- `zero_dc: bool = True` - Zero DC component handling\n- `use_torch_checkpoint: bool = False` - Enable gradient checkpointing\n- `skip_connection: bool = False` - Enable skip connections\n\n**Validation Rules:**\n- All numeric fields must be positive","chunkId":"specs/001-update-roformer-implementation/data-model.md:3"}],[2165,{"filePath":"specs/001-update-roformer-implementation/data-model.md","startLine":48,"endLine":64,"chunkText":"\n**Validation Rules:**\n- All numeric fields must be positive\n- Dropout rates must be between 0.0 and 1.0\n- `freqs_per_bands` must sum to expected frequency count\n- `norm` must be valid normalization type or None\n\n### BSRoformerConfig\nSpecialized configuration for Band-Split Roformer models.\n\n**Fields:**\n- Inherits all from `ModelConfiguration`\n- `freqs_per_bands: Tuple[int, ...]` - Required, defines frequency band splits\n- `mask_estimator_depth: int = 2` - Depth of mask estimation network\n\n**Validation Rules:**\n- `freqs_per_bands` must be provided and non-empty","chunkId":"specs/001-update-roformer-implementation/data-model.md:4"}],[2166,{"filePath":"specs/001-update-roformer-implementation/data-model.md","startLine":62,"endLine":82,"chunkText":"\n**Validation Rules:**\n- `freqs_per_bands` must be provided and non-empty\n- Sum of `freqs_per_bands` must match STFT frequency bins\n\n### MelBandRoformerConfig  \nSpecialized configuration for Mel-Band Roformer models.\n\n**Fields:**\n- Inherits all from `ModelConfiguration`\n- `num_bands: int` - Number of mel-scale bands\n- `sample_rate: int = 44100` - Audio sample rate for mel calculation\n\n**Validation Rules:**\n- `num_bands` must be positive integer\n- `sample_rate` must be valid audio sample rate\n\n### ModelLoadingResult\nResult object returned from model loading attempts.\n\n**Fields:**","chunkId":"specs/001-update-roformer-implementation/data-model.md:5"}],[2167,{"filePath":"specs/001-update-roformer-implementation/data-model.md","startLine":80,"endLine":94,"chunkText":"Result object returned from model loading attempts.\n\n**Fields:**\n- `success: bool` - Whether loading succeeded\n- `model: Optional[RoformerModel]` - Loaded model if successful\n- `error_message: Optional[str]` - Error description if failed\n- `implementation_used: str` - Which implementation was used (\"old\", \"new\", \"fallback\")\n- `warnings: List[str]` - Non-fatal warnings during loading\n\n**State Transitions:**\n- `Attempting → Success` (with model)\n- `Attempting → Failure` (with error message)\n- `Success → Cleanup` (model disposal)\n\n### ParameterValidationError","chunkId":"specs/001-update-roformer-implementation/data-model.md:6"}],[2168,{"filePath":"specs/001-update-roformer-implementation/data-model.md","startLine":94,"endLine":115,"chunkText":"### ParameterValidationError\nException raised when model parameters are invalid.\n\n**Fields:**\n- `parameter_name: str` - Name of invalid parameter\n- `expected_type: str` - Expected parameter type\n- `actual_value: Any` - Actual value provided\n- `suggested_fix: str` - Suggestion for fixing the issue\n\n## Entity Relationships\n\n```\nRoformerModel\n├── has_one ModelConfiguration\n│   ├── extends_to BSRoformerConfig\n│   └── extends_to MelBandRoformerConfig\n├── produces ModelLoadingResult\n└── may_raise ParameterValidationError\n\nModelConfiguration\n├── validates_against ValidationRules\n└── contains ParameterSet","chunkId":"specs/001-update-roformer-implementation/data-model.md:7"}],[2169,{"filePath":"specs/001-update-roformer-implementation/data-model.md","startLine":113,"endLine":133,"chunkText":"ModelConfiguration\n├── validates_against ValidationRules\n└── contains ParameterSet\n    ├── required_parameters\n    ├── optional_parameters\n    └── new_parameters\n```\n\n## Data Flow\n\n### Model Loading Flow\n1. `ModelLoader.load(model_path)` → `ModelLoadingResult`\n2. Parse configuration from model file\n3. Validate configuration parameters\n4. Attempt loading with new implementation\n5. On failure, attempt loading with old implementation\n6. Return result with success/failure status\n\n### Parameter Validation Flow\n1. Extract parameters from model configuration\n2. Check required parameters exist","chunkId":"specs/001-update-roformer-implementation/data-model.md:8"}],[2170,{"filePath":"specs/001-update-roformer-implementation/data-model.md","startLine":131,"endLine":147,"chunkText":"### Parameter Validation Flow\n1. Extract parameters from model configuration\n2. Check required parameters exist\n3. Validate parameter types and ranges\n4. Check parameter compatibility\n5. Raise `ParameterValidationError` with specific details if invalid\n\n### Configuration Normalization Flow\n1. Load raw configuration from model\n2. Map old parameter names to new parameter names\n3. Set default values for missing optional parameters\n4. Validate final configuration\n5. Return normalized `ModelConfiguration`\n\n## Storage Considerations\n\n- Model files are read-only external artifacts","chunkId":"specs/001-update-roformer-implementation/data-model.md:9"}],[2171,{"filePath":"specs/001-update-roformer-implementation/data-model.md","startLine":145,"endLine":160,"chunkText":"## Storage Considerations\n\n- Model files are read-only external artifacts\n- Configuration is derived from model metadata\n- No persistent state storage required\n- Memory usage scales with model size\n- Cleanup required after model disposal\n\n## Performance Considerations\n\n- Model loading is I/O intensive operation\n- Configuration validation should be fast\n- Parameter defaults should be computed once\n- Error messages should be pre-formatted for common cases\n- Fallback mechanism adds latency but ensures compatibility\n","chunkId":"specs/001-update-roformer-implementation/data-model.md:10"}],[2172,{"filePath":"specs/001-update-roformer-implementation/plan.md","startLine":0,"endLine":10,"chunkText":"# Implementation Plan: Update Roformer Implementation\n\n**Branch**: `001-update-roformer-implementation` | **Date**: September 25, 2025 | **Spec**: [/specs/001-update-roformer-implementation.md](../001-update-roformer-implementation.md)\n**Input**: Feature specification from `/specs/001-update-roformer-implementation.md`\n\n## Execution Flow (/plan command scope)\n```\n1. Load feature spec from Input path\n   → If not found: ERROR \"No feature spec at {path}\"\n2. Fill Technical Context (scan for NEEDS CLARIFICATION)\n   → Detect Project Type from context (web=frontend+backend, mobile=app+api)","chunkId":"specs/001-update-roformer-implementation/plan.md:0"}],[2173,{"filePath":"specs/001-update-roformer-implementation/plan.md","startLine":8,"endLine":17,"chunkText":"   → If not found: ERROR \"No feature spec at {path}\"\n2. Fill Technical Context (scan for NEEDS CLARIFICATION)\n   → Detect Project Type from context (web=frontend+backend, mobile=app+api)\n   → Set Structure Decision based on project type\n3. Fill the Constitution Check section based on the content of the constitution document.\n4. Evaluate Constitution Check section below\n   → If violations exist: Document in Complexity Tracking\n   → If no justification possible: ERROR \"Simplify approach first\"\n   → Update Progress Tracking: Initial Constitution Check\n5. Execute Phase 0 → research.md","chunkId":"specs/001-update-roformer-implementation/plan.md:1"}],[2174,{"filePath":"specs/001-update-roformer-implementation/plan.md","startLine":15,"endLine":21,"chunkText":"   → If no justification possible: ERROR \"Simplify approach first\"\n   → Update Progress Tracking: Initial Constitution Check\n5. Execute Phase 0 → research.md\n   → If NEEDS CLARIFICATION remain: ERROR \"Resolve unknowns\"\n6. Execute Phase 1 → contracts, data-model.md, quickstart.md, agent-specific template file (e.g., `CLAUDE.md` for Claude Code, `.github/copilot-instructions.md` for GitHub Copilot, `GEMINI.md` for Gemini CLI, `QWEN.md` for Qwen Code or `AGENTS.md` for opencode).\n7. Re-evaluate Constitution Check section\n   → If new violations: Refactor design, return to Phase 1","chunkId":"specs/001-update-roformer-implementation/plan.md:2"}],[2175,{"filePath":"specs/001-update-roformer-implementation/plan.md","startLine":19,"endLine":26,"chunkText":"6. Execute Phase 1 → contracts, data-model.md, quickstart.md, agent-specific template file (e.g., `CLAUDE.md` for Claude Code, `.github/copilot-instructions.md` for GitHub Copilot, `GEMINI.md` for Gemini CLI, `QWEN.md` for Qwen Code or `AGENTS.md` for opencode).\n7. Re-evaluate Constitution Check section\n   → If new violations: Refactor design, return to Phase 1\n   → Update Progress Tracking: Post-Design Constitution Check\n8. Plan Phase 2 → Describe task generation approach (DO NOT create tasks.md)\n9. STOP - Ready for /tasks command\n```\n","chunkId":"specs/001-update-roformer-implementation/plan.md:3"}],[2176,{"filePath":"specs/001-update-roformer-implementation/plan.md","startLine":24,"endLine":31,"chunkText":"9. STOP - Ready for /tasks command\n```\n\n**IMPORTANT**: The /plan command STOPS at step 7. Phases 2-4 are executed by other commands:\n- Phase 2: /tasks command creates tasks.md\n- Phase 3-4: Implementation execution (manual or via tools)\n\n## Summary","chunkId":"specs/001-update-roformer-implementation/plan.md:4"}],[2177,{"filePath":"specs/001-update-roformer-implementation/plan.md","startLine":29,"endLine":35,"chunkText":"- Phase 3-4: Implementation execution (manual or via tools)\n\n## Summary\nUpdate the Roformer implementation to support latest model architectures while maintaining backward compatibility. The primary requirement is enabling newer Roformer models with updated parameters (mlp_expansion_factor, sage_attention, zero_dc) while preserving functionality for existing older models. Technical approach involves integrating new inference code with fallback mechanisms and comprehensive validation using existing spectral analysis testing framework.\n\n## Technical Context\n**Language/Version**: Python 3.11+  ","chunkId":"specs/001-update-roformer-implementation/plan.md:5"}],[2178,{"filePath":"specs/001-update-roformer-implementation/plan.md","startLine":33,"endLine":41,"chunkText":"\n## Technical Context\n**Language/Version**: Python 3.11+  \n**Primary Dependencies**: PyTorch, librosa, soundfile, numpy, onnxruntime  \n**Storage**: Model files (.ckpt, .pth, .onnx), audio files (FLAC, WAV, MP3)  \n**Testing**: pytest with custom audio validation (SSIM comparison)  \n**Target Platform**: Cross-platform (Windows, macOS, Linux) with GPU acceleration support  \n**Project Type**: single - Python library with CLI wrapper  \n**Performance Goals**: Maintain existing separation quality (≥0.90 waveform, ≥0.80 spectrogram similarity)  ","chunkId":"specs/001-update-roformer-implementation/plan.md:6"}],[2179,{"filePath":"specs/001-update-roformer-implementation/plan.md","startLine":39,"endLine":48,"chunkText":"**Target Platform**: Cross-platform (Windows, macOS, Linux) with GPU acceleration support  \n**Project Type**: single - Python library with CLI wrapper  \n**Performance Goals**: Maintain existing separation quality (≥0.90 waveform, ≥0.80 spectrogram similarity)  \n**Constraints**: Zero regression in existing model functionality, backward compatibility mandatory  \n**Scale/Scope**: Support for BSRoformer and MelBandRoformer variants, integration with existing 4 model architectures\n\n## Constitution Check\n*GATE: Must pass before Phase 0 research. Re-check after Phase 1 design.*\n\n### I. Library-First Architecture","chunkId":"specs/001-update-roformer-implementation/plan.md:7"}],[2180,{"filePath":"specs/001-update-roformer-implementation/plan.md","startLine":46,"endLine":57,"chunkText":"*GATE: Must pass before Phase 0 research. Re-check after Phase 1 design.*\n\n### I. Library-First Architecture\n- [x] Core functionality implemented in `Separator` class or similar library pattern\n- [x] CLI/Remote API are thin wrappers, not containing business logic\n- [x] Clear separation between model architectures (MDX, VR, Demucs, MDXC)\n\n### II. Multi-Interface Consistency  \n- [x] Feature accessible via Python API, CLI, and Remote API (if applicable)\n- [x] Parameter names identical across all interfaces\n- [x] Same model architectures supported across interfaces\n","chunkId":"specs/001-update-roformer-implementation/plan.md:8"}],[2181,{"filePath":"specs/001-update-roformer-implementation/plan.md","startLine":55,"endLine":68,"chunkText":"- [x] Parameter names identical across all interfaces\n- [x] Same model architectures supported across interfaces\n\n### III. Test-First Development (NON-NEGOTIABLE)\n- [x] Tests written before implementation\n- [x] Unit tests for all core functionality\n- [x] Integration tests with audio validation (SSIM comparison)\n- [x] CLI tests for all exposed functionality\n\n### IV. Performance & Resource Efficiency\n- [x] Hardware acceleration support considered (CUDA, CoreML, DirectML)\n- [x] Memory optimization for large files (streaming/batch processing)\n- [x] Tunable parameters for different hardware capabilities\n","chunkId":"specs/001-update-roformer-implementation/plan.md:9"}],[2182,{"filePath":"specs/001-update-roformer-implementation/plan.md","startLine":66,"endLine":81,"chunkText":"- [x] Memory optimization for large files (streaming/batch processing)\n- [x] Tunable parameters for different hardware capabilities\n\n### V. Model Architecture Separation\n- [x] Each architecture in separate modules\n- [x] Inherits from `CommonSeparator` pattern\n- [x] Architecture-specific parameters isolated\n- [x] Loading one architecture doesn't load others\n\n## Project Structure\n\n### Documentation (this feature)\n```\nspecs/001-update-roformer-implementation/\n├── plan.md              # This file (/plan command output)\n├── research.md          # Phase 0 output (/plan command)","chunkId":"specs/001-update-roformer-implementation/plan.md:10"}],[2183,{"filePath":"specs/001-update-roformer-implementation/plan.md","startLine":79,"endLine":93,"chunkText":"specs/001-update-roformer-implementation/\n├── plan.md              # This file (/plan command output)\n├── research.md          # Phase 0 output (/plan command)\n├── data-model.md        # Phase 1 output (/plan command)\n├── quickstart.md        # Phase 1 output (/plan command)\n├── contracts/           # Phase 1 output (/plan command)\n└── tasks.md             # Phase 2 output (/tasks command - NOT created by /plan)\n```\n\n### Source Code (repository root)\n```\n# Option 1: Single project (DEFAULT)\naudio_separator/\n├── separator/\n│   ├── uvr_lib_v5/          # Existing old Roformer implementation","chunkId":"specs/001-update-roformer-implementation/plan.md:11"}],[2184,{"filePath":"specs/001-update-roformer-implementation/plan.md","startLine":91,"endLine":108,"chunkText":"audio_separator/\n├── separator/\n│   ├── uvr_lib_v5/          # Existing old Roformer implementation\n│   ├── msst-models-new/     # New reference implementation\n│   ├── common_separator.py  # Base class for all architectures\n│   └── roformer_separator.py # Updated Roformer implementation\n└── utils/\n\ntests/\n├── contract/\n├── integration/\n└── unit/\n```\n\n**Structure Decision**: Option 1 - Single project structure, as this is a library enhancement rather than web/mobile application\n\n## Phase 0: Outline & Research\n1. **Extract unknowns from Technical Context** above:","chunkId":"specs/001-update-roformer-implementation/plan.md:12"}],[2185,{"filePath":"specs/001-update-roformer-implementation/plan.md","startLine":106,"endLine":117,"chunkText":"\n## Phase 0: Outline & Research\n1. **Extract unknowns from Technical Context** above:\n   - Analyze differences between old and new Roformer implementations\n   - Research parameter compatibility between BSRoformer and MelBandRoformer variants\n   - Investigate normalization configuration handling patterns\n   - Study fallback mechanism implementation strategies\n\n2. **Generate and dispatch research agents**:\n   ```\n   Task: \"Research differences between old uvr_lib_v5 and new msst-models Roformer implementations\"\n   Task: \"Find best practices for backward compatibility in PyTorch model loading\"","chunkId":"specs/001-update-roformer-implementation/plan.md:13"}],[2186,{"filePath":"specs/001-update-roformer-implementation/plan.md","startLine":115,"endLine":128,"chunkText":"   ```\n   Task: \"Research differences between old uvr_lib_v5 and new msst-models Roformer implementations\"\n   Task: \"Find best practices for backward compatibility in PyTorch model loading\"\n   Task: \"Research parameter validation patterns for ML model configurations\"\n   Task: \"Study fallback mechanism implementations in audio processing libraries\"\n   ```\n\n3. **Consolidate findings** in `research.md` using format:\n   - Decision: [what was chosen]\n   - Rationale: [why chosen]\n   - Alternatives considered: [what else evaluated]\n\n**Output**: research.md with all technical unknowns resolved\n","chunkId":"specs/001-update-roformer-implementation/plan.md:14"}],[2187,{"filePath":"specs/001-update-roformer-implementation/plan.md","startLine":126,"endLine":140,"chunkText":"\n**Output**: research.md with all technical unknowns resolved\n\n## Phase 1: Design & Contracts\n*Prerequisites: research.md complete*\n\n1. **Extract entities from feature spec** → `data-model.md`:\n   - RoformerModel: Configuration parameters, validation rules\n   - ModelConfiguration: Parameter dictionaries, normalization settings\n   - BSRoformerConfig: Band-split specific parameters\n   - MelBandRoformerConfig: Mel-scale band specific parameters\n\n2. **Generate API contracts** from functional requirements:\n   - Model loading interface with fallback capability\n   - Parameter validation interface","chunkId":"specs/001-update-roformer-implementation/plan.md:15"}],[2188,{"filePath":"specs/001-update-roformer-implementation/plan.md","startLine":138,"endLine":151,"chunkText":"2. **Generate API contracts** from functional requirements:\n   - Model loading interface with fallback capability\n   - Parameter validation interface\n   - Error reporting interface for configuration mismatches\n   - Output OpenAPI/GraphQL schema to `/contracts/`\n\n3. **Generate contract tests** from contracts:\n   - Test model loading with old format\n   - Test model loading with new format\n   - Test fallback mechanism activation\n   - Test parameter validation and error reporting\n\n4. **Extract test scenarios** from user stories:\n   - Load existing older Roformer model → separation works identically","chunkId":"specs/001-update-roformer-implementation/plan.md:16"}],[2189,{"filePath":"specs/001-update-roformer-implementation/plan.md","startLine":149,"endLine":162,"chunkText":"\n4. **Extract test scenarios** from user stories:\n   - Load existing older Roformer model → separation works identically\n   - Load newer Roformer model → separation works with new parameters\n   - Switch between model types → no conflicts or failures\n\n5. **Update agent file incrementally** (O(1) operation):\n   - Run `.specify/scripts/bash/update-agent-context.sh cursor`\n   - Add Roformer implementation context\n   - Preserve existing audio separation knowledge\n   - Update with new model parameter handling\n\n**Output**: data-model.md, /contracts/*, failing tests, quickstart.md, agent-specific file\n","chunkId":"specs/001-update-roformer-implementation/plan.md:17"}],[2190,{"filePath":"specs/001-update-roformer-implementation/plan.md","startLine":160,"endLine":175,"chunkText":"\n**Output**: data-model.md, /contracts/*, failing tests, quickstart.md, agent-specific file\n\n## Phase 2: Task Planning Approach\n*This section describes what the /tasks command will do - DO NOT execute during /plan*\n\n**Task Generation Strategy**:\n- Load `.specify/templates/tasks-template.md` as base\n- Generate tasks from Phase 1 design docs (contracts, data model, quickstart)\n- Each contract → contract test task [P]\n- Each entity → model creation task [P] \n- Each user story → integration test task\n- Implementation tasks to make tests pass\n\n**Ordering Strategy**:\n- TDD order: Tests before implementation ","chunkId":"specs/001-update-roformer-implementation/plan.md:18"}],[2191,{"filePath":"specs/001-update-roformer-implementation/plan.md","startLine":173,"endLine":187,"chunkText":"\n**Ordering Strategy**:\n- TDD order: Tests before implementation \n- Dependency order: Models before services before UI\n- Mark [P] for parallel execution (independent files)\n\n**Estimated Output**: 20-25 numbered, ordered tasks in tasks.md\n\n**IMPORTANT**: This phase is executed by the /tasks command, NOT by /plan\n\n## Phase 3+: Future Implementation\n*These phases are beyond the scope of the /plan command*\n\n**Phase 3**: Task execution (/tasks command creates tasks.md)  \n**Phase 4**: Implementation (execute tasks.md following constitutional principles)  ","chunkId":"specs/001-update-roformer-implementation/plan.md:19"}],[2192,{"filePath":"specs/001-update-roformer-implementation/plan.md","startLine":185,"endLine":199,"chunkText":"\n**Phase 3**: Task execution (/tasks command creates tasks.md)  \n**Phase 4**: Implementation (execute tasks.md following constitutional principles)  \n**Phase 5**: Validation (run tests, execute quickstart.md, performance validation)\n\n## Complexity Tracking\n*No constitutional violations identified - all checks passed*\n\n## Progress Tracking\n*This checklist is updated during execution flow*\n\n**Phase Status**:\n- [x] Phase 0: Research complete (/plan command)\n- [x] Phase 1: Design complete (/plan command)\n- [x] Phase 2: Task planning complete (/plan command - describe approach only)","chunkId":"specs/001-update-roformer-implementation/plan.md:20"}],[2193,{"filePath":"specs/001-update-roformer-implementation/plan.md","startLine":197,"endLine":212,"chunkText":"- [x] Phase 0: Research complete (/plan command)\n- [x] Phase 1: Design complete (/plan command)\n- [x] Phase 2: Task planning complete (/plan command - describe approach only)\n- [x] Phase 3: Tasks generated (/tasks command)\n- [ ] Phase 4: Implementation complete\n- [ ] Phase 5: Validation passed\n\n**Gate Status**:\n- [x] Initial Constitution Check: PASS\n- [x] Post-Design Constitution Check: PASS\n- [x] All NEEDS CLARIFICATION resolved\n- [x] Complexity deviations documented\n\n---\n*Based on Constitution v1.0.0 - See `.specify/memory/constitution.md`*\n","chunkId":"specs/001-update-roformer-implementation/plan.md:21"}],[2194,{"filePath":"specs/001-update-roformer-implementation/post-implementation-issues.md","startLine":0,"endLine":5,"chunkText":"### Post-Implementation Issues: Roformer Routing and Execution Paths\n\n- **Observed behavior**: New Roformer models load and separate successfully, but execution is routed through `MDXCSeparator` rather than a dedicated Roformer separator. Logs show the MDXC path with Roformer-specific branching inside `MDXCSeparator`.\n\n- **Root cause**:\n  - `Separator.list_supported_model_files()` groups Roformer entries under the `MDXC` model type, so `Separator.load_model()` instantiates `architectures.mdxc_separator.MDXCSeparator` for Roformer files.","chunkId":"specs/001-update-roformer-implementation/post-implementation-issues.md:0"}],[2195,{"filePath":"specs/001-update-roformer-implementation/post-implementation-issues.md","startLine":4,"endLine":6,"chunkText":"- **Root cause**:\n  - `Separator.list_supported_model_files()` groups Roformer entries under the `MDXC` model type, so `Separator.load_model()` instantiates `architectures.mdxc_separator.MDXCSeparator` for Roformer files.\n  - `separator_classes` in `separator.py` has no mapping for a `Roformer` type; the dedicated `architectures/roformer_separator.py` is never routed to and is effectively unused.","chunkId":"specs/001-update-roformer-implementation/post-implementation-issues.md:1"}],[2196,{"filePath":"specs/001-update-roformer-implementation/post-implementation-issues.md","startLine":5,"endLine":8,"chunkText":"  - `Separator.list_supported_model_files()` groups Roformer entries under the `MDXC` model type, so `Separator.load_model()` instantiates `architectures.mdxc_separator.MDXCSeparator` for Roformer files.\n  - `separator_classes` in `separator.py` has no mapping for a `Roformer` type; the dedicated `architectures/roformer_separator.py` is never routed to and is effectively unused.\n  - `MDXCSeparator` contains an internal Roformer branch (constructs `BSRoformer`/`MelBandRoformer`, loads checkpoints, and implements Roformer chunking/overlap-add). This duplicates responsibility with the new `roformer_loader`.\n","chunkId":"specs/001-update-roformer-implementation/post-implementation-issues.md:2"}],[2197,{"filePath":"specs/001-update-roformer-implementation/post-implementation-issues.md","startLine":7,"endLine":13,"chunkText":"  - `MDXCSeparator` contains an internal Roformer branch (constructs `BSRoformer`/`MelBandRoformer`, loads checkpoints, and implements Roformer chunking/overlap-add). This duplicates responsibility with the new `roformer_loader`.\n\n- **Impact**:\n  - The new Roformer loader (normalization/validation/fallback) is not used in actual runs; loader stats remain zero in logs.\n  - Two overlapping Roformer code paths (inside `MDXCSeparator` and the new loader + `RoformerSeparator`) create confusion and increase maintenance cost.\n  - `RoformerSeparator` is dead code under current routing.\n","chunkId":"specs/001-update-roformer-implementation/post-implementation-issues.md:3"}],[2198,{"filePath":"specs/001-update-roformer-implementation/post-implementation-issues.md","startLine":12,"endLine":19,"chunkText":"  - `RoformerSeparator` is dead code under current routing.\n\n- **Decision**: Proceed with Option A (minimal-change refactor)\n  - Keep routing via `MDXCSeparator` to avoid broad changes.\n  - Refactor the Roformer branch in `MDXCSeparator` to use `RoformerLoader` for model loading/validation/fallback, while preserving the existing Roformer chunking/overlap-add execution.\n  - Align the Roformer detection flag naming (`is_roformer_model`) with `CommonSeparator`, and surface loader stats via existing `CommonSeparator.get_roformer_loading_stats()` and `Separator` logging.\n\n- **Planned edits (Option A)**:","chunkId":"specs/001-update-roformer-implementation/post-implementation-issues.md:4"}],[2199,{"filePath":"specs/001-update-roformer-implementation/post-implementation-issues.md","startLine":18,"endLine":24,"chunkText":"\n- **Planned edits (Option A)**:\n  - `architectures/mdxc_separator.py`:\n    - Use `self.is_roformer_model` (from `CommonSeparator`) instead of a separate `self.is_roformer` detection.\n    - In `load_model()`, when Roformer is detected, call `self.roformer_loader.load_model(model_path=self.model_path, config=self.model_data['model'], device=str(self.torch_device))`. On success, set `self.model_run = result.model`; on failure, fall back to existing direct instantiation path.\n    - Keep current Roformer chunking/overlap-add logic in `demix()` unchanged.\n  - `roformer/roformer_loader.py`:","chunkId":"specs/001-update-roformer-implementation/post-implementation-issues.md:5"}],[2200,{"filePath":"specs/001-update-roformer-implementation/post-implementation-issues.md","startLine":23,"endLine":29,"chunkText":"    - Keep current Roformer chunking/overlap-add logic in `demix()` unchanged.\n  - `roformer/roformer_loader.py`:\n    - Ensure it returns a `ModelLoadingResult` that matches the local dataclass (use `success_result`, `failure_result`, `fallback_success_result` helpers), and populate metadata via `model_info` rather than custom fields.\n\n- **Expected outcomes**:\n  - No change in separation outputs or performance characteristics.\n  - Loader stats in logs reflect actual usage (non-zero `new_implementation_success` and/or `fallback_success`).","chunkId":"specs/001-update-roformer-implementation/post-implementation-issues.md:6"}],[2201,{"filePath":"specs/001-update-roformer-implementation/post-implementation-issues.md","startLine":28,"endLine":33,"chunkText":"  - No change in separation outputs or performance characteristics.\n  - Loader stats in logs reflect actual usage (non-zero `new_implementation_success` and/or `fallback_success`).\n  - Reduced duplication and clearer ownership, while deferring larger routing changes (introducing `RoformerSeparator`) to a follow-up if desired.\n\n\n","chunkId":"specs/001-update-roformer-implementation/post-implementation-issues.md:7"}],[2202,{"filePath":"specs/001-update-roformer-implementation/quickstart.md","startLine":0,"endLine":24,"chunkText":"# Quickstart Guide: Updated Roformer Implementation\n\nThis guide demonstrates how to use the updated Roformer implementation with both old and new models.\n\n## Prerequisites\n\n- Python 3.11+\n- PyTorch installed\n- audio-separator package installed\n- Access to Roformer model files (.ckpt or .pth)\n\n## Basic Usage\n\n### Loading a Roformer Model\n\n```python\nfrom audio_separator import Separator\n\n# Initialize separator with Roformer model\nseparator = Separator(\n    model_file_dir='path/to/models',\n    output_dir='path/to/output'\n)\n\n# Load a Roformer model (automatically detects old vs new format)","chunkId":"specs/001-update-roformer-implementation/quickstart.md:0"}],[2203,{"filePath":"specs/001-update-roformer-implementation/quickstart.md","startLine":22,"endLine":49,"chunkText":")\n\n# Load a Roformer model (automatically detects old vs new format)\nseparator.load_model('model_bs_roformer_ep_317_sdr_12.9755.ckpt')\n```\n\n### Separating Audio\n\n```python\n# Separate audio file\noutput_files = separator.separate('input_audio.flac')\n\nprint(f\"Separation complete. Output files: {output_files}\")\n```\n\n## Model Types\n\n### BSRoformer Models\n\n```python\n# BSRoformer models work with frequency band splitting\nseparator.load_model('bs_roformer_model.ckpt')\noutputs = separator.separate('audio.flac')\n# Outputs: ['audio_(Vocals).flac', 'audio_(Instrumental).flav']\n```\n\n### MelBandRoformer Models  \n","chunkId":"specs/001-update-roformer-implementation/quickstart.md:1"}],[2204,{"filePath":"specs/001-update-roformer-implementation/quickstart.md","startLine":48,"endLine":76,"chunkText":"### MelBandRoformer Models  \n\n```python\n# MelBandRoformer models work with mel-scale bands\nseparator.load_model('mel_band_roformer_model.ckpt')\noutputs = separator.separate('audio.flac')\n# Outputs depend on model configuration\n```\n\n## Advanced Configuration\n\n### Manual Configuration Override\n\n```python\n# Override model configuration if needed\nconfig_override = {\n    'mlp_expansion_factor': 4,\n    'sage_attention': True,\n    'zero_dc': True,\n    'use_torch_checkpoint': False\n}\n\nseparator.load_model('model.ckpt', config=config_override)\n```\n\n### Error Handling\n\n```python\ntry:","chunkId":"specs/001-update-roformer-implementation/quickstart.md:2"}],[2205,{"filePath":"specs/001-update-roformer-implementation/quickstart.md","startLine":74,"endLine":97,"chunkText":"\n```python\ntry:\n    separator.load_model('problematic_model.ckpt')\n    outputs = separator.separate('audio.flac')\nexcept ParameterValidationError as e:\n    print(f\"Configuration error: {e}\")\n    print(f\"Suggestion: {e.suggested_fix}\")\nexcept Exception as e:\n    print(f\"Loading failed: {e}\")\n```\n\n## Testing Scenarios\n\n### Scenario 1: Existing Older Model\n\nTest that existing models continue to work without changes.\n\n```python\n# This should work exactly as before\nseparator = Separator()\nseparator.load_model('old_roformer_model.ckpt')\noutputs = separator.separate('test_audio.flac')\n","chunkId":"specs/001-update-roformer-implementation/quickstart.md:3"}],[2206,{"filePath":"specs/001-update-roformer-implementation/quickstart.md","startLine":95,"endLine":113,"chunkText":"separator.load_model('old_roformer_model.ckpt')\noutputs = separator.separate('test_audio.flac')\n\n# Verify outputs match previous results\nassert len(outputs) == 2  # Expecting vocal and instrumental\nassert outputs[0].endswith('_(Vocals).flav')\nassert outputs[1].endswith('_(Instrumental).flav')\n```\n\n### Scenario 2: Newer Model with New Parameters\n\nTest that newer models with additional parameters work correctly.\n\n```python\n# This should work with the updated implementation\nseparator = Separator()\nseparator.load_model('new_roformer_with_sage_attention.ckpt')\noutputs = separator.separate('test_audio.flac')\n","chunkId":"specs/001-update-roformer-implementation/quickstart.md:4"}],[2207,{"filePath":"specs/001-update-roformer-implementation/quickstart.md","startLine":111,"endLine":132,"chunkText":"separator.load_model('new_roformer_with_sage_attention.ckpt')\noutputs = separator.separate('test_audio.flac')\n\n# Verify outputs are generated successfully\nassert len(outputs) >= 1\nfor output in outputs:\n    assert os.path.exists(output)\n    assert os.path.getsize(output) > 0\n```\n\n### Scenario 3: Model Type Switching\n\nTest switching between different Roformer variants in the same session.\n\n```python\nseparator = Separator()\n\n# Load BSRoformer model\nseparator.load_model('bs_roformer.ckpt')\nbs_outputs = separator.separate('test1.flac')\n\n# Switch to MelBandRoformer model  ","chunkId":"specs/001-update-roformer-implementation/quickstart.md:5"}],[2208,{"filePath":"specs/001-update-roformer-implementation/quickstart.md","startLine":130,"endLine":150,"chunkText":"bs_outputs = separator.separate('test1.flac')\n\n# Switch to MelBandRoformer model  \nseparator.load_model('mel_band_roformer.ckpt')\nmel_outputs = separator.separate('test2.flac')\n\n# Both should work without conflicts\nassert len(bs_outputs) > 0\nassert len(mel_outputs) > 0\n```\n\n### Scenario 4: Configuration Validation\n\nTest that invalid configurations are caught with helpful error messages.\n\n```python\n# Test missing required parameter\ntry:\n    separator.load_model('model_with_missing_config.ckpt')\n    assert False, \"Should have raised validation error\"\nexcept ParameterValidationError as e:","chunkId":"specs/001-update-roformer-implementation/quickstart.md:6"}],[2209,{"filePath":"specs/001-update-roformer-implementation/quickstart.md","startLine":148,"endLine":164,"chunkText":"    separator.load_model('model_with_missing_config.ckpt')\n    assert False, \"Should have raised validation error\"\nexcept ParameterValidationError as e:\n    assert \"missing\" in e.suggested_fix.lower()\n    assert e.parameter_name is not None\n```\n\n### Scenario 5: Fallback Mechanism\n\nTest that fallback from new to old implementation works.\n\n```python\n# This would internally try new implementation first, then fallback to old\nseparator = Separator(log_level='DEBUG')  # Enable debug logging\nseparator.load_model('edge_case_model.ckpt')\n\n# Check logs to verify fallback occurred if needed","chunkId":"specs/001-update-roformer-implementation/quickstart.md:7"}],[2210,{"filePath":"specs/001-update-roformer-implementation/quickstart.md","startLine":162,"endLine":189,"chunkText":"separator.load_model('edge_case_model.ckpt')\n\n# Check logs to verify fallback occurred if needed\n# (Implementation should log which version was used)\n```\n\n## Performance Validation\n\n### Audio Quality Validation\n\n```python\nimport subprocess\nimport os\n\n# Run integration test to validate audio quality\nresult = subprocess.run([\n    'python', '-m', 'pytest', \n    'tests/integration/test_roformer_quality.py',\n    '-v'\n], capture_output=True, text=True)\n\nassert result.returncode == 0, f\"Quality tests failed: {result.stderr}\"\n```\n\n### Regression Testing\n\n```python\n# Ensure existing models produce identical result","chunkId":"specs/001-update-roformer-implementation/quickstart.md:8"}],[2211,{"filePath":"specs/001-update-roformer-implementation/quickstart.md","startLine":187,"endLine":205,"chunkText":"\n```python\n# Ensure existing models produce identical results\nreference_outputs = load_reference_outputs('test_audio.flac')\ncurrent_outputs = separator.separate('test_audio.flav')\n\nfor ref, curr in zip(reference_outputs, current_outputs):\n    similarity = calculate_audio_similarity(ref, curr)\n    assert similarity >= 0.90, f\"Audio similarity {similarity} below threshold\"\n```\n\n## Troubleshooting\n\n### Common Issues\n\n1. **AttributeError: 'norm'**\n   - Cause: Model configuration has different normalization structure\n   - Solution: The updated implementation handles this automatically\n","chunkId":"specs/001-update-roformer-implementation/quickstart.md:9"}],[2212,{"filePath":"specs/001-update-roformer-implementation/quickstart.md","startLine":203,"endLine":219,"chunkText":"   - Cause: Model configuration has different normalization structure\n   - Solution: The updated implementation handles this automatically\n\n2. **TypeError: unexpected keyword argument 'mlp_expansion_factor'**\n   - Cause: Trying to use new model with old implementation\n   - Solution: The fallback mechanism handles this automatically\n\n3. **Model loading fails completely**\n   - Check model file exists and is readable\n   - Verify model is a supported Roformer variant\n   - Check logs for specific error details\n\n### Debug Information\n\n```python\n# Enable detailed logging\nimport logging","chunkId":"specs/001-update-roformer-implementation/quickstart.md:10"}],[2213,{"filePath":"specs/001-update-roformer-implementation/quickstart.md","startLine":217,"endLine":235,"chunkText":"```python\n# Enable detailed logging\nimport logging\nlogging.basicConfig(level=logging.DEBUG)\n\nseparator = Separator()\nseparator.load_model('model.ckpt')\n# Check logs for implementation version used and any warnings\n```\n\n## Manual Testing Checklist\n\nAfter implementation changes, manually verify:\n\n- [ ] Existing BSRoformer models load and separate correctly\n- [ ] Existing MelBandRoformer models load and separate correctly  \n- [ ] New models with additional parameters work\n- [ ] Audio quality matches reference outputs (SSIM ≥ 0.90/0.80)\n- [ ] Error messages are clear and helpful","chunkId":"specs/001-update-roformer-implementation/quickstart.md:11"}],[2214,{"filePath":"specs/001-update-roformer-implementation/quickstart.md","startLine":233,"endLine":249,"chunkText":"- [ ] New models with additional parameters work\n- [ ] Audio quality matches reference outputs (SSIM ≥ 0.90/0.80)\n- [ ] Error messages are clear and helpful\n- [ ] Performance is not significantly degraded\n- [ ] Memory usage is reasonable\n- [ ] Multiple models can be loaded in sequence\n\n## Next Steps\n\nAfter validating the quickstart scenarios:\n\n1. Run full integration test suite\n2. Perform manual testing with real model files\n3. Validate performance benchmarks\n4. Update documentation if needed\n5. Prepare for production deployment\n","chunkId":"specs/001-update-roformer-implementation/quickstart.md:12"}],[2215,{"filePath":"specs/001-update-roformer-implementation/research.md","startLine":0,"endLine":13,"chunkText":"# Research Findings: Roformer Implementation Update\n\n## Key Differences Between Old and New Implementations\n\n### 1. BSRoformer Constructor Parameters\n\n**Decision**: The new implementation includes 5 additional parameters that are missing from the old implementation:\n- `zero_dc = True` (line 385)\n- `mlp_expansion_factor = 4` (line 392) \n- `use_torch_checkpoint = False` (line 393)\n- `skip_connection = False` (line 394)\n- `sage_attention = False` (line 395)\n\n**Rationale**: These parameters enable advanced features in newer models:","chunkId":"specs/001-update-roformer-implementation/research.md:0"}],[2216,{"filePath":"specs/001-update-roformer-implementation/research.md","startLine":11,"endLine":21,"chunkText":"- `sage_attention = False` (line 395)\n\n**Rationale**: These parameters enable advanced features in newer models:\n- `mlp_expansion_factor`: Controls MLP layer expansion ratio for better model capacity\n- `sage_attention`: Enables Sage attention mechanism for improved performance\n- `zero_dc`: Controls DC component handling in STFT processing\n- `use_torch_checkpoint`: Enables gradient checkpointing for memory efficiency\n- `skip_connection`: Enables residual connections between layers\n\n**Alternatives considered**: \n- Hardcoding defaults in old implementation (rejected - not maintainable)","chunkId":"specs/001-update-roformer-implementation/research.md:1"}],[2217,{"filePath":"specs/001-update-roformer-implementation/research.md","startLine":19,"endLine":35,"chunkText":"\n**Alternatives considered**: \n- Hardcoding defaults in old implementation (rejected - not maintainable)\n- Creating separate classes (rejected - increases complexity)\n- Parameter validation with fallback (chosen approach)\n\n### 2. Transformer Configuration Changes\n\n**Decision**: New implementation includes `sage_attention` parameter in transformer_kwargs and conditional logic for Sage attention activation.\n\n**Rationale**: The new implementation adds:\n```python\nif sage_attention:\n    print(\"Use Sage Attention\")\n\ntransformer_kwargs = dict(\n    # ... existing parameters ...","chunkId":"specs/001-update-roformer-implementation/research.md:2"}],[2218,{"filePath":"specs/001-update-roformer-implementation/research.md","startLine":33,"endLine":49,"chunkText":"\ntransformer_kwargs = dict(\n    # ... existing parameters ...\n    sage_attention=sage_attention,  # New parameter\n)\n```\n\n**Alternatives considered**: Ignoring sage_attention (rejected - breaks newer models), implementing stub (chosen for backward compatibility)\n\n### 3. Instance Variable Additions\n\n**Decision**: New implementation tracks additional state variables:\n- `self.use_torch_checkpoint` \n- `self.skip_connection`\n\n**Rationale**: These are used throughout the forward pass and need to be accessible as instance variables.\n","chunkId":"specs/001-update-roformer-implementation/research.md:3"}],[2219,{"filePath":"specs/001-update-roformer-implementation/research.md","startLine":47,"endLine":56,"chunkText":"**Rationale**: These are used throughout the forward pass and need to be accessible as instance variables.\n\n**Alternatives considered**: Local variables only (rejected - needed in forward method), property methods (rejected - unnecessary complexity)\n\n### 4. Normalization Configuration Issues\n\n**Decision**: The error `AttributeError: \"'norm'\"` in `tfc_tdf_v3.py` line 155 occurs when `config.model.norm` is accessed but the configuration object structure is different between old and new models.\n\n**Rationale**: Analysis of `tfc_tdf_v3.py` shows:\n```python","chunkId":"specs/001-update-roformer-implementation/research.md:4"}],[2220,{"filePath":"specs/001-update-roformer-implementation/research.md","startLine":56,"endLine":72,"chunkText":"**Rationale**: Analysis of `tfc_tdf_v3.py` shows:\n```python\nnorm = get_norm(norm_type=config.model.norm)  # Line 155\n```\n\nThe `get_norm` function expects a string value but newer model configs may have different structure or missing `norm` attribute.\n\n**Alternatives considered**: \n- Modify get_norm to handle missing attributes (chosen)\n- Update config parsing (rejected - too invasive)\n- Separate normalization handling (rejected - duplicates code)\n\n### 5. Model Loading Strategy\n\n**Decision**: Implement try-new-first-fallback-to-old pattern for model loading.\n\n**Rationale**: This approach:","chunkId":"specs/001-update-roformer-implementation/research.md:5"}],[2221,{"filePath":"specs/001-update-roformer-implementation/research.md","startLine":70,"endLine":84,"chunkText":"**Decision**: Implement try-new-first-fallback-to-old pattern for model loading.\n\n**Rationale**: This approach:\n1. Attempts to load with new implementation first\n2. Falls back to old implementation if new fails\n3. Provides clear error messages for debugging\n4. Maintains zero regression for existing models\n\n**Alternatives considered**: \n- Version detection from model files (rejected - unreliable)\n- User-specified format (rejected - poor UX)\n- Parallel implementations (rejected - maintenance burden)\n\n### 6. Parameter Validation Patterns\n","chunkId":"specs/001-update-roformer-implementation/research.md:6"}],[2222,{"filePath":"specs/001-update-roformer-implementation/research.md","startLine":82,"endLine":96,"chunkText":"\n### 6. Parameter Validation Patterns\n\n**Decision**: Implement explicit parameter validation with detailed error messages rather than silent defaults.\n\n**Rationale**: Based on clarification session, failing fast with clear error messages is preferred over assuming defaults. This helps users understand what's wrong with their model configurations.\n\n**Alternatives considered**: \n- Silent defaults (rejected per clarification)\n- Configuration auto-correction (rejected - unpredictable)\n- Hardcoded fallbacks (rejected - not maintainable)\n\n## Implementation Strategy\n\n### Phase 1: Extend Old Implementation","chunkId":"specs/001-update-roformer-implementation/research.md:7"}],[2223,{"filePath":"specs/001-update-roformer-implementation/research.md","startLine":94,"endLine":107,"chunkText":"## Implementation Strategy\n\n### Phase 1: Extend Old Implementation\n1. Add new parameters to BSRoformer.__init__ with default values\n2. Add new parameters to MelBandRoformer.__init__ with default values  \n3. Update transformer_kwargs to include new parameters\n4. Add instance variable assignments for checkpoint and skip_connection\n\n### Phase 2: Normalization Compatibility\n1. Modify get_norm function to handle missing norm attributes\n2. Add defensive checks in TFC_TDF_net.__init__ \n3. Provide clear error messages for configuration issues\n\n### Phase 3: Fallback Mechanism","chunkId":"specs/001-update-roformer-implementation/research.md:8"}],[2224,{"filePath":"specs/001-update-roformer-implementation/research.md","startLine":105,"endLine":119,"chunkText":"3. Provide clear error messages for configuration issues\n\n### Phase 3: Fallback Mechanism\n1. Implement model loading wrapper that tries new implementation first\n2. Add fallback to old implementation on specific exceptions\n3. Log which implementation was used for debugging\n\n### Phase 4: Testing Integration\n1. Extend existing integration tests to cover new parameters\n2. Add specific tests for fallback mechanism\n3. Validate that existing models continue to work identically\n\n## Risk Mitigation\n\n1. **Backward Compatibility**: All new parameters have sensible defaults that maintain existing behavior","chunkId":"specs/001-update-roformer-implementation/research.md:9"}],[2225,{"filePath":"specs/001-update-roformer-implementation/research.md","startLine":117,"endLine":124,"chunkText":"## Risk Mitigation\n\n1. **Backward Compatibility**: All new parameters have sensible defaults that maintain existing behavior\n2. **Error Handling**: Clear error messages help users identify configuration issues\n3. **Testing**: Comprehensive test coverage ensures no regressions\n4. **Fallback Safety**: Old implementation remains available if new implementation fails\n5. **Manual Validation**: Manual testing after major milestones ensures real-world compatibility\n","chunkId":"specs/001-update-roformer-implementation/research.md:10"}],[2226,{"filePath":"specs/001-update-roformer-implementation/tasks.md","startLine":0,"endLine":13,"chunkText":"# Tasks: Update Roformer Implementation\n\n**Input**: Design documents from `/specs/001-update-roformer-implementation/`\n**Prerequisites**: plan.md (required), research.md, data-model.md, contracts/\n\n## Execution Flow (main)\n```\n1. Load plan.md from feature directory\n   → Extract: Python 3.11+, PyTorch, librosa, soundfile, numpy, onnxruntime\n   → Structure: single project (audio_separator/ library with CLI wrapper)\n2. Load design documents:\n   → data-model.md: 6 entities → model tasks\n   → contracts/: 2 interface files → contract test tasks\n   → quickstart.md: 5 test scenarios → integration test tasks","chunkId":"specs/001-update-roformer-implementation/tasks.md:0"}],[2227,{"filePath":"specs/001-update-roformer-implementation/tasks.md","startLine":11,"endLine":21,"chunkText":"   → data-model.md: 6 entities → model tasks\n   → contracts/: 2 interface files → contract test tasks\n   → quickstart.md: 5 test scenarios → integration test tasks\n3. Generate tasks by category:\n   → Setup: dependencies, linting, project structure\n   → Tests: contract tests, integration tests (TDD)\n   → Core: models, parameter validation, fallback mechanism\n   → Integration: existing separator integration, CLI updates\n   → Polish: unit tests, performance validation, docs\n4. Apply TDD ordering: Tests before implementation\n5. Mark [P] for parallel execution (different files)","chunkId":"specs/001-update-roformer-implementation/tasks.md:1"}],[2228,{"filePath":"specs/001-update-roformer-implementation/tasks.md","startLine":19,"endLine":32,"chunkText":"   → Polish: unit tests, performance validation, docs\n4. Apply TDD ordering: Tests before implementation\n5. Mark [P] for parallel execution (different files)\n6. Validate: All contracts tested, all entities implemented\n```\n\n## Format: `[ID] [P?] Description`\n- **[P]**: Can run in parallel (different files, no dependencies)\n- Include exact file paths in descriptions\n\n## Phase 3.1: Setup\n\n- [x] T001 Update project dependencies in pyproject.toml to ensure PyTorch compatibility for new Roformer parameters\n- [x] T002 [P] Configure linting rules in pyproject.toml for new Roformer implementation files","chunkId":"specs/001-update-roformer-implementation/tasks.md:2"}],[2229,{"filePath":"specs/001-update-roformer-implementation/tasks.md","startLine":30,"endLine":40,"chunkText":"\n- [x] T001 Update project dependencies in pyproject.toml to ensure PyTorch compatibility for new Roformer parameters\n- [x] T002 [P] Configure linting rules in pyproject.toml for new Roformer implementation files\n- [x] T003 [P] Create backup of existing uvr_lib_v5/roformer/ directory for rollback safety\n\n## Phase 3.2: Tests First (TDD) ⚠️ MUST COMPLETE BEFORE 3.3\n\n**CRITICAL: These tests MUST be written and MUST FAIL before ANY implementation**\n\n### Contract Tests\n- [x] T004 [P] Contract test RoformerLoaderInterface.load_model in tests/contract/test_roformer_loader_interface.py","chunkId":"specs/001-update-roformer-implementation/tasks.md:3"}],[2230,{"filePath":"specs/001-update-roformer-implementation/tasks.md","startLine":38,"endLine":43,"chunkText":"\n### Contract Tests\n- [x] T004 [P] Contract test RoformerLoaderInterface.load_model in tests/contract/test_roformer_loader_interface.py\n- [x] T005 [P] Contract test RoformerLoaderInterface.validate_configuration in tests/contract/test_roformer_loader_interface.py\n- [x] T006 [P] Contract test ParameterValidatorInterface.validate_required_parameters in tests/contract/test_parameter_validator_interface.py\n- [x] T007 [P] Contract test ParameterValidatorInterface.validate_normalization_config in tests/contract/test_parameter_validator_interface.py","chunkId":"specs/001-update-roformer-implementation/tasks.md:4"}],[2231,{"filePath":"specs/001-update-roformer-implementation/tasks.md","startLine":41,"endLine":46,"chunkText":"- [x] T005 [P] Contract test RoformerLoaderInterface.validate_configuration in tests/contract/test_roformer_loader_interface.py\n- [x] T006 [P] Contract test ParameterValidatorInterface.validate_required_parameters in tests/contract/test_parameter_validator_interface.py\n- [x] T007 [P] Contract test ParameterValidatorInterface.validate_normalization_config in tests/contract/test_parameter_validator_interface.py\n- [x] T008 [P] Contract test FallbackLoaderInterface.try_new_implementation in tests/contract/test_fallback_loader_interface.py\n\n### Integration Tests (from quickstart scenarios)","chunkId":"specs/001-update-roformer-implementation/tasks.md:5"}],[2232,{"filePath":"specs/001-update-roformer-implementation/tasks.md","startLine":44,"endLine":49,"chunkText":"- [x] T008 [P] Contract test FallbackLoaderInterface.try_new_implementation in tests/contract/test_fallback_loader_interface.py\n\n### Integration Tests (from quickstart scenarios)\n- [x] T009 [P] Integration test existing older model compatibility in tests/integration/test_roformer_backward_compatibility.py\n- [x] T010 [P] Integration test newer model with new parameters in tests/integration/test_roformer_new_parameters.py\n- [x] T011 [P] Integration test model type switching (BSRoformer ↔ MelBandRoformer) in tests/integration/test_roformer_model_switching.py","chunkId":"specs/001-update-roformer-implementation/tasks.md:6"}],[2233,{"filePath":"specs/001-update-roformer-implementation/tasks.md","startLine":47,"endLine":50,"chunkText":"- [x] T009 [P] Integration test existing older model compatibility in tests/integration/test_roformer_backward_compatibility.py\n- [x] T010 [P] Integration test newer model with new parameters in tests/integration/test_roformer_new_parameters.py\n- [x] T011 [P] Integration test model type switching (BSRoformer ↔ MelBandRoformer) in tests/integration/test_roformer_model_switching.py\n- [x] T012 [P] Integration test configuration validation error handling in tests/integration/test_roformer_config_validation.py","chunkId":"specs/001-update-roformer-implementation/tasks.md:7"}],[2234,{"filePath":"specs/001-update-roformer-implementation/tasks.md","startLine":48,"endLine":53,"chunkText":"- [x] T010 [P] Integration test newer model with new parameters in tests/integration/test_roformer_new_parameters.py\n- [x] T011 [P] Integration test model type switching (BSRoformer ↔ MelBandRoformer) in tests/integration/test_roformer_model_switching.py\n- [x] T012 [P] Integration test configuration validation error handling in tests/integration/test_roformer_config_validation.py\n- [x] T013 [P] Integration test fallback mechanism activation in tests/integration/test_roformer_fallback_mechanism.py\n\n### Audio Quality Validation Tests","chunkId":"specs/001-update-roformer-implementation/tasks.md:8"}],[2235,{"filePath":"specs/001-update-roformer-implementation/tasks.md","startLine":51,"endLine":58,"chunkText":"- [x] T013 [P] Integration test fallback mechanism activation in tests/integration/test_roformer_fallback_mechanism.py\n\n### Audio Quality Validation Tests\n- [x] T014 [P] Audio quality regression test for existing BSRoformer models in tests/integration/test_roformer_audio_quality.py\n- [x] T015 [P] Audio quality validation test for MelBandRoformer models in tests/integration/test_roformer_audio_quality.py\n\n### Roformer Chunking and Overlap Unit Tests\n- [x] T051 [P] Assert chunk_size uses model.stft_hop_length: tests/unit/test_mdxc_roformer_chunking.py::test_chunk_size_uses_model_stft_hop_length","chunkId":"specs/001-update-roformer-implementation/tasks.md:9"}],[2236,{"filePath":"specs/001-update-roformer-implementation/tasks.md","startLine":56,"endLine":60,"chunkText":"\n### Roformer Chunking and Overlap Unit Tests\n- [x] T051 [P] Assert chunk_size uses model.stft_hop_length: tests/unit/test_mdxc_roformer_chunking.py::test_chunk_size_uses_model_stft_hop_length\n- [x] T052 [P] Fallback to audio.hop_length if model.stft_hop_length missing: tests/unit/test_mdxc_roformer_chunking.py::test_chunk_size_falls_back_to_audio_hop_length\n- [x] T053 [P] Step clamped to chunk_size (desired_step > chunk_size or ≤ 0): tests/unit/test_mdxc_roformer_chunking.py::test_step_clamped_to_chunk_size","chunkId":"specs/001-update-roformer-implementation/tasks.md:10"}],[2237,{"filePath":"specs/001-update-roformer-implementation/tasks.md","startLine":58,"endLine":60,"chunkText":"- [x] T051 [P] Assert chunk_size uses model.stft_hop_length: tests/unit/test_mdxc_roformer_chunking.py::test_chunk_size_uses_model_stft_hop_length\n- [x] T052 [P] Fallback to audio.hop_length if model.stft_hop_length missing: tests/unit/test_mdxc_roformer_chunking.py::test_chunk_size_falls_back_to_audio_hop_length\n- [x] T053 [P] Step clamped to chunk_size (desired_step > chunk_size or ≤ 0): tests/unit/test_mdxc_roformer_chunking.py::test_step_clamped_to_chunk_size","chunkId":"specs/001-update-roformer-implementation/tasks.md:11"}],[2238,{"filePath":"specs/001-update-roformer-implementation/tasks.md","startLine":59,"endLine":62,"chunkText":"- [x] T052 [P] Fallback to audio.hop_length if model.stft_hop_length missing: tests/unit/test_mdxc_roformer_chunking.py::test_chunk_size_falls_back_to_audio_hop_length\n- [x] T053 [P] Step clamped to chunk_size (desired_step > chunk_size or ≤ 0): tests/unit/test_mdxc_roformer_chunking.py::test_step_clamped_to_chunk_size\n- [x] T054 [P] overlap_add handles shorter model output safely (safe_len): tests/unit/test_mdxc_roformer_chunking.py::test_overlap_add_short_output_safe\n- [x] T055 [P] Counter increments match overlap_add safe span: tests/unit/test_mdxc_roformer_chunking.py::test_counter_updates_safe_len","chunkId":"specs/001-update-roformer-implementation/tasks.md:12"}],[2239,{"filePath":"specs/001-update-roformer-implementation/tasks.md","startLine":60,"endLine":63,"chunkText":"- [x] T053 [P] Step clamped to chunk_size (desired_step > chunk_size or ≤ 0): tests/unit/test_mdxc_roformer_chunking.py::test_step_clamped_to_chunk_size\n- [x] T054 [P] overlap_add handles shorter model output safely (safe_len): tests/unit/test_mdxc_roformer_chunking.py::test_overlap_add_short_output_safe\n- [x] T055 [P] Counter increments match overlap_add safe span: tests/unit/test_mdxc_roformer_chunking.py::test_counter_updates_safe_len\n- [x] T056 [P] No NaN/inf on normalization (counter clamp): tests/unit/test_mdxc_roformer_chunking.py::test_counter_clamp_no_nan","chunkId":"specs/001-update-roformer-implementation/tasks.md:13"}],[2240,{"filePath":"specs/001-update-roformer-implementation/tasks.md","startLine":61,"endLine":64,"chunkText":"- [x] T054 [P] overlap_add handles shorter model output safely (safe_len): tests/unit/test_mdxc_roformer_chunking.py::test_overlap_add_short_output_safe\n- [x] T055 [P] Counter increments match overlap_add safe span: tests/unit/test_mdxc_roformer_chunking.py::test_counter_updates_safe_len\n- [x] T056 [P] No NaN/inf on normalization (counter clamp): tests/unit/test_mdxc_roformer_chunking.py::test_counter_clamp_no_nan\n- [x] T057 [P] Short-audio last-block path works and preserves length: tests/unit/test_mdxc_roformer_chunking.py::test_short_audio_last_block","chunkId":"specs/001-update-roformer-implementation/tasks.md:14"}],[2241,{"filePath":"specs/001-update-roformer-implementation/tasks.md","startLine":62,"endLine":67,"chunkText":"- [x] T055 [P] Counter increments match overlap_add safe span: tests/unit/test_mdxc_roformer_chunking.py::test_counter_updates_safe_len\n- [x] T056 [P] No NaN/inf on normalization (counter clamp): tests/unit/test_mdxc_roformer_chunking.py::test_counter_clamp_no_nan\n- [x] T057 [P] Short-audio last-block path works and preserves length: tests/unit/test_mdxc_roformer_chunking.py::test_short_audio_last_block\n- [x] T058 [P] Parametrized invariants across dim_t and hop configs: tests/unit/test_mdxc_roformer_chunking.py::test_parametrized_shape_invariants\n\n### Roformer Detection Tests","chunkId":"specs/001-update-roformer-implementation/tasks.md:15"}],[2242,{"filePath":"specs/001-update-roformer-implementation/tasks.md","startLine":65,"endLine":71,"chunkText":"- [x] T058 [P] Parametrized invariants across dim_t and hop configs: tests/unit/test_mdxc_roformer_chunking.py::test_parametrized_shape_invariants\n\n### Roformer Detection Tests\n- [x] T059 [P] YAML path containing \"roformer\" sets is_roformer and routes Roformer path: tests/unit/test_separator_detection.py::test_is_roformer_set_from_yaml_path\n\n### Roformer Integration (E2E)\n- [x] T060 [P] New BSRoformer SW-Fixed end-to-end separation succeeds: tests/integration/test_roformer_e2e.py::test_bs_roformer_sw_fixed_e2e","chunkId":"specs/001-update-roformer-implementation/tasks.md:16"}],[2243,{"filePath":"specs/001-update-roformer-implementation/tasks.md","startLine":69,"endLine":77,"chunkText":"\n### Roformer Integration (E2E)\n- [x] T060 [P] New BSRoformer SW-Fixed end-to-end separation succeeds: tests/integration/test_roformer_e2e.py::test_bs_roformer_sw_fixed_e2e\n- [x] T061 [P] Legacy Roformer end-to-end separation still succeeds: tests/integration/test_roformer_e2e.py::test_legacy_roformer_e2e\n\n### Regression: Size mismatch family\n- [x] T062 [P] Reproduce shorter outputs (N∈{1,16,32,236}) and assert no broadcast errors, output length preserved: tests/regression/test_roformer_size_mismatch.py::test_overlap_add_safe_lengths\n\n### Logging and Performance Sanity","chunkId":"specs/001-update-roformer-implementation/tasks.md:17"}],[2244,{"filePath":"specs/001-update-roformer-implementation/tasks.md","startLine":75,"endLine":82,"chunkText":"- [x] T062 [P] Reproduce shorter outputs (N∈{1,16,32,236}) and assert no broadcast errors, output length preserved: tests/regression/test_roformer_size_mismatch.py::test_overlap_add_safe_lengths\n\n### Logging and Performance Sanity\n- [x] T063 [P] Logs include hop/step sources (stft_hop_length, dim_t, desired vs actual step): tests/unit/test_mdxc_roformer_chunking.py::test_logging_for_hop_and_step\n- [x] T064 [P] Iteration count reasonable (ceil calculation within ±1): tests/unit/test_mdxc_roformer_chunking.py::test_iteration_count_reasonable\n\n## Phase 3.3: Core Implementation (ONLY after tests are failing)\n","chunkId":"specs/001-update-roformer-implementation/tasks.md:18"}],[2245,{"filePath":"specs/001-update-roformer-implementation/tasks.md","startLine":80,"endLine":87,"chunkText":"\n## Phase 3.3: Core Implementation (ONLY after tests are failing)\n\n### Data Models and Configuration\n- [x] T016 [P] Implement ModelConfiguration dataclass in audio_separator/separator/roformer/model_configuration.py\n- [x] T017 [P] Implement BSRoformerConfig class in audio_separator/separator/roformer/bs_roformer_config.py\n- [x] T018 [P] Implement MelBandRoformerConfig class in audio_separator/separator/roformer/mel_band_roformer_config.py\n- [x] T019 [P] Implement ModelLoadingResult dataclass in audio_separator/separator/roformer/model_loading_result.py","chunkId":"specs/001-update-roformer-implementation/tasks.md:19"}],[2246,{"filePath":"specs/001-update-roformer-implementation/tasks.md","startLine":85,"endLine":90,"chunkText":"- [x] T017 [P] Implement BSRoformerConfig class in audio_separator/separator/roformer/bs_roformer_config.py\n- [x] T018 [P] Implement MelBandRoformerConfig class in audio_separator/separator/roformer/mel_band_roformer_config.py\n- [x] T019 [P] Implement ModelLoadingResult dataclass in audio_separator/separator/roformer/model_loading_result.py\n- [x] T020 [P] Implement ParameterValidationError exception in audio_separator/separator/roformer/parameter_validation_error.py\n\n### Parameter Validation System","chunkId":"specs/001-update-roformer-implementation/tasks.md:20"}],[2247,{"filePath":"specs/001-update-roformer-implementation/tasks.md","startLine":88,"endLine":93,"chunkText":"- [x] T020 [P] Implement ParameterValidationError exception in audio_separator/separator/roformer/parameter_validation_error.py\n\n### Parameter Validation System\n- [x] T021 [P] Implement ParameterValidator class in audio_separator/separator/roformer/parameter_validator.py\n- [x] T022 [P] Implement BSRoformerValidator class in audio_separator/separator/roformer/bs_roformer_validator.py\n- [x] T023 [P] Implement MelBandRoformerValidator class in audio_separator/separator/roformer/mel_band_roformer_validator.py","chunkId":"specs/001-update-roformer-implementation/tasks.md:21"}],[2248,{"filePath":"specs/001-update-roformer-implementation/tasks.md","startLine":91,"endLine":96,"chunkText":"- [x] T021 [P] Implement ParameterValidator class in audio_separator/separator/roformer/parameter_validator.py\n- [x] T022 [P] Implement BSRoformerValidator class in audio_separator/separator/roformer/bs_roformer_validator.py\n- [x] T023 [P] Implement MelBandRoformerValidator class in audio_separator/separator/roformer/mel_band_roformer_validator.py\n- [x] T024 [P] Implement ConfigurationNormalizer class in audio_separator/separator/roformer/configuration_normalizer.py\n\n### Updated Roformer Models","chunkId":"specs/001-update-roformer-implementation/tasks.md:22"}],[2249,{"filePath":"specs/001-update-roformer-implementation/tasks.md","startLine":94,"endLine":99,"chunkText":"- [x] T024 [P] Implement ConfigurationNormalizer class in audio_separator/separator/roformer/configuration_normalizer.py\n\n### Updated Roformer Models\n- [x] T025 Update BSRoformer.__init__ method in audio_separator/separator/uvr_lib_v5/roformer/bs_roformer.py to add new parameters (mlp_expansion_factor, sage_attention, zero_dc, use_torch_checkpoint, skip_connection)\n- [x] T026 Update MelBandRoformer.__init__ method in audio_separator/separator/uvr_lib_v5/roformer/mel_band_roformer.py to add new parameters\n- [x] T027 Update transformer_kwargs in BSRoformer to include sage_attention parameter","chunkId":"specs/001-update-roformer-implementation/tasks.md:23"}],[2250,{"filePath":"specs/001-update-roformer-implementation/tasks.md","startLine":97,"endLine":102,"chunkText":"- [x] T025 Update BSRoformer.__init__ method in audio_separator/separator/uvr_lib_v5/roformer/bs_roformer.py to add new parameters (mlp_expansion_factor, sage_attention, zero_dc, use_torch_checkpoint, skip_connection)\n- [x] T026 Update MelBandRoformer.__init__ method in audio_separator/separator/uvr_lib_v5/roformer/mel_band_roformer.py to add new parameters\n- [x] T027 Update transformer_kwargs in BSRoformer to include sage_attention parameter\n- [x] T028 Update transformer_kwargs in MelBandRoformer to include sage_attention parameter\n\n### Normalization Fixes","chunkId":"specs/001-update-roformer-implementation/tasks.md:24"}],[2251,{"filePath":"specs/001-update-roformer-implementation/tasks.md","startLine":100,"endLine":108,"chunkText":"- [x] T028 Update transformer_kwargs in MelBandRoformer to include sage_attention parameter\n\n### Normalization Fixes\n- [x] T029 Update get_norm function in audio_separator/separator/uvr_lib_v5/tfc_tdf_v3.py to handle missing norm attributes gracefully\n- [x] T030 Add defensive checks in TFC_TDF_net.__init__ for normalization configuration\n\n### Fallback Loading System\n- [x] T031 [P] Implement RoformerLoader class with fallback mechanism in audio_separator/separator/roformer/roformer_loader.py\n- [x] T032 [P] Implement FallbackLoader class in audio_separator/separator/roformer/fallback_loader.py","chunkId":"specs/001-update-roformer-implementation/tasks.md:25"}],[2252,{"filePath":"specs/001-update-roformer-implementation/tasks.md","startLine":106,"endLine":113,"chunkText":"### Fallback Loading System\n- [x] T031 [P] Implement RoformerLoader class with fallback mechanism in audio_separator/separator/roformer/roformer_loader.py\n- [x] T032 [P] Implement FallbackLoader class in audio_separator/separator/roformer/fallback_loader.py\n- [x] T033 Update RoformerSeparator class in audio_separator/separator/architectures/roformer_separator.py to use new loading system\n\n## Phase 3.4: Integration\n\n- [x] T034 Integrate new RoformerLoader into CommonSeparator base class in audio_separator/separator/common_separator.py","chunkId":"specs/001-update-roformer-implementation/tasks.md:26"}],[2253,{"filePath":"specs/001-update-roformer-implementation/tasks.md","startLine":111,"endLine":120,"chunkText":"## Phase 3.4: Integration\n\n- [x] T034 Integrate new RoformerLoader into CommonSeparator base class in audio_separator/separator/common_separator.py\n- [x] T035 Update CLI model loading logic to use new fallback mechanism in audio_separator/separator/separator.py\n- [x] T036 Add logging for implementation version used (old/new/fallback) in audio_separator/separator/roformer/roformer_loader.py\n- [x] T037 Update error handling and user messages for configuration validation failures\n- [x] T038 Integrate with existing integration test framework (test_cli_integration.py compatibility)\n\n## Phase 3.5: Polish\n","chunkId":"specs/001-update-roformer-implementation/tasks.md:27"}],[2254,{"filePath":"specs/001-update-roformer-implementation/tasks.md","startLine":118,"endLine":128,"chunkText":"\n## Phase 3.5: Polish\n\n### Unit Tests\n- [x] T039 [P] Unit tests for ModelConfiguration validation in tests/unit/test_model_configuration.py\n- [x] T040 [P] Unit tests for ParameterValidator methods in tests/unit/test_parameter_validator.py\n- [x] T041 [P] Unit tests for ConfigurationNormalizer methods in tests/unit/test_configuration_normalizer.py\n- [x] T042 [P] Unit tests for fallback mechanism logic in tests/unit/test_fallback_loader.py\n\n### Performance and Quality Validation\n- [x] T043 Run existing integration tests to ensure no regression in audio quality (SSIM ≥ 0.90/0.80)","chunkId":"specs/001-update-roformer-implementation/tasks.md:28"}],[2255,{"filePath":"specs/001-update-roformer-implementation/tasks.md","startLine":126,"endLine":135,"chunkText":"\n### Performance and Quality Validation\n- [x] T043 Run existing integration tests to ensure no regression in audio quality (SSIM ≥ 0.90/0.80)\n- [x] T044 Performance benchmark for model loading time (should not significantly increase)\n- [x] T045 Memory usage validation for new parameter handling\n- [x] T046 [P] Update documentation in audio_separator/separator/roformer/README.md for new parameters\n\n### Manual Testing\n- [x] T047 Manual test with existing BSRoformer models from integration test suite\n- [x] T048 Manual test with existing MelBandRoformer models from integration test suite  ","chunkId":"specs/001-update-roformer-implementation/tasks.md:29"}],[2256,{"filePath":"specs/001-update-roformer-implementation/tasks.md","startLine":133,"endLine":144,"chunkText":"### Manual Testing\n- [x] T047 Manual test with existing BSRoformer models from integration test suite\n- [x] T048 Manual test with existing MelBandRoformer models from integration test suite  \n- [x] T049 Manual test with newer models containing new parameters (if available)\n- [x] T050 Manual validation of error messages for common configuration issues\n\n## Dependencies\n\n### Critical Dependencies (TDD)\n- Tests (T004-T015) MUST complete and FAIL before implementation (T016-T033)\n- T001-T003 (setup) before everything else\n- T016-T020 (data models) before T021-T024 (validators)","chunkId":"specs/001-update-roformer-implementation/tasks.md:30"}],[2257,{"filePath":"specs/001-update-roformer-implementation/tasks.md","startLine":142,"endLine":154,"chunkText":"- Tests (T004-T015) MUST complete and FAIL before implementation (T016-T033)\n- T001-T003 (setup) before everything else\n- T016-T020 (data models) before T021-T024 (validators)\n- T025-T028 (model updates) before T031-T033 (loaders)\n- T029-T030 (normalization fixes) before T031-T033 (loaders)\n\n### Implementation Dependencies\n- T016 (ModelConfiguration) blocks T017, T018, T021-T024\n- T020 (ParameterValidationError) blocks T021-T024\n- T025-T030 (model updates) before T031-T033 (loading system)\n- T031-T033 (loading system) before T034-T038 (integration)\n- Implementation (T016-T038) before polish (T039-T050)\n","chunkId":"specs/001-update-roformer-implementation/tasks.md:31"}],[2258,{"filePath":"specs/001-update-roformer-implementation/tasks.md","startLine":152,"endLine":161,"chunkText":"- T031-T033 (loading system) before T034-T038 (integration)\n- Implementation (T016-T038) before polish (T039-T050)\n\n## Parallel Execution Examples\n\n### Phase 3.2 - Contract Tests (can run simultaneously)\n```\nTask: \"Contract test RoformerLoaderInterface.load_model in tests/contract/test_roformer_loader_interface.py\"\nTask: \"Contract test ParameterValidatorInterface.validate_required_parameters in tests/contract/test_parameter_validator_interface.py\" \nTask: \"Integration test existing older model compatibility in tests/integration/test_roformer_backward_compatibility.py\"","chunkId":"specs/001-update-roformer-implementation/tasks.md:32"}],[2259,{"filePath":"specs/001-update-roformer-implementation/tasks.md","startLine":159,"endLine":166,"chunkText":"Task: \"Contract test RoformerLoaderInterface.load_model in tests/contract/test_roformer_loader_interface.py\"\nTask: \"Contract test ParameterValidatorInterface.validate_required_parameters in tests/contract/test_parameter_validator_interface.py\" \nTask: \"Integration test existing older model compatibility in tests/integration/test_roformer_backward_compatibility.py\"\nTask: \"Integration test newer model with new parameters in tests/integration/test_roformer_new_parameters.py\"\n```\n\n### Phase 3.3 - Data Models (can run simultaneously)\n```","chunkId":"specs/001-update-roformer-implementation/tasks.md:33"}],[2260,{"filePath":"specs/001-update-roformer-implementation/tasks.md","startLine":164,"endLine":174,"chunkText":"\n### Phase 3.3 - Data Models (can run simultaneously)\n```\nTask: \"Implement ModelConfiguration dataclass in audio_separator/separator/roformer/model_configuration.py\"\nTask: \"Implement BSRoformerConfig class in audio_separator/separator/roformer/bs_roformer_config.py\"\nTask: \"Implement MelBandRoformerConfig class in audio_separator/separator/roformer/mel_band_roformer_config.py\"\nTask: \"Implement ParameterValidationError exception in audio_separator/separator/roformer/parameter_validation_error.py\"\n```\n\n### Phase 3.5 - Unit Tests (can run simultaneously)\n```","chunkId":"specs/001-update-roformer-implementation/tasks.md:34"}],[2261,{"filePath":"specs/001-update-roformer-implementation/tasks.md","startLine":172,"endLine":184,"chunkText":"\n### Phase 3.5 - Unit Tests (can run simultaneously)\n```\nTask: \"Unit tests for ModelConfiguration validation in tests/unit/test_model_configuration.py\"\nTask: \"Unit tests for ParameterValidator methods in tests/unit/test_parameter_validator.py\"\nTask: \"Unit tests for ConfigurationNormalizer methods in tests/unit/test_configuration_normalizer.py\"\nTask: \"Unit tests for fallback mechanism logic in tests/unit/test_fallback_loader.py\"\n```\n\n## Notes\n\n- [P] tasks = different files, no dependencies, can run in parallel\n- All tests must be written first and must fail before implementation (TDD)","chunkId":"specs/001-update-roformer-implementation/tasks.md:35"}],[2262,{"filePath":"specs/001-update-roformer-implementation/tasks.md","startLine":182,"endLine":194,"chunkText":"\n- [P] tasks = different files, no dependencies, can run in parallel\n- All tests must be written first and must fail before implementation (TDD)\n- Commit after each task completion\n- Manual testing (T047-T050) requires actual model files\n- Integration with existing test framework ensures no regression\n- Fallback mechanism provides safety net for edge cases\n\n## Validation Checklist\n\n- [x] All contracts have corresponding tests (T004-T008)\n- [x] All entities have model tasks (T016-T020)\n- [x] All tests come before implementation (Phase 3.2 before 3.3)","chunkId":"specs/001-update-roformer-implementation/tasks.md:36"}],[2263,{"filePath":"specs/001-update-roformer-implementation/tasks.md","startLine":192,"endLine":202,"chunkText":"- [x] All contracts have corresponding tests (T004-T008)\n- [x] All entities have model tasks (T016-T020)\n- [x] All tests come before implementation (Phase 3.2 before 3.3)\n- [x] Parallel tasks are truly independent (different files)\n- [x] Each task specifies exact file path\n- [x] No task modifies same file as another [P] task\n- [x] TDD ordering enforced (tests fail before implementation)\n- [x] Integration scenarios from quickstart covered (T009-T013)\n- [x] Audio quality validation included (T014-T015, T043)\n- [x] Manual testing milestones defined (T047-T050)\n","chunkId":"specs/001-update-roformer-implementation/tasks.md:37"}],[2264,{"filePath":"specs/main/plan.md","startLine":0,"endLine":13,"chunkText":"\n# Implementation Plan: [FEATURE]\n\n**Branch**: `[###-feature-name]` | **Date**: [DATE] | **Spec**: [link]\n**Input**: Feature specification from `/specs/[###-feature-name]/spec.md`\n\n## Execution Flow (/plan command scope)\n```\n1. Load feature spec from Input path\n   → If not found: ERROR \"No feature spec at {path}\"\n2. Fill Technical Context (scan for NEEDS CLARIFICATION)\n   → Detect Project Type from context (web=frontend+backend, mobile=app+api)\n   → Set Structure Decision based on project type\n3. Fill the Constitution Check section based on the content of the constitution document.","chunkId":"specs/main/plan.md:0"}],[2265,{"filePath":"specs/main/plan.md","startLine":11,"endLine":19,"chunkText":"   → Detect Project Type from context (web=frontend+backend, mobile=app+api)\n   → Set Structure Decision based on project type\n3. Fill the Constitution Check section based on the content of the constitution document.\n4. Evaluate Constitution Check section below\n   → If violations exist: Document in Complexity Tracking\n   → If no justification possible: ERROR \"Simplify approach first\"\n   → Update Progress Tracking: Initial Constitution Check\n5. Execute Phase 0 → research.md\n   → If NEEDS CLARIFICATION remain: ERROR \"Resolve unknowns\"","chunkId":"specs/main/plan.md:1"}],[2266,{"filePath":"specs/main/plan.md","startLine":17,"endLine":23,"chunkText":"   → Update Progress Tracking: Initial Constitution Check\n5. Execute Phase 0 → research.md\n   → If NEEDS CLARIFICATION remain: ERROR \"Resolve unknowns\"\n6. Execute Phase 1 → contracts, data-model.md, quickstart.md, agent-specific template file (e.g., `CLAUDE.md` for Claude Code, `.github/copilot-instructions.md` for GitHub Copilot, `GEMINI.md` for Gemini CLI, `QWEN.md` for Qwen Code or `AGENTS.md` for opencode).\n7. Re-evaluate Constitution Check section\n   → If new violations: Refactor design, return to Phase 1\n   → Update Progress Tracking: Post-Design Constitution Check","chunkId":"specs/main/plan.md:2"}],[2267,{"filePath":"specs/main/plan.md","startLine":21,"endLine":35,"chunkText":"7. Re-evaluate Constitution Check section\n   → If new violations: Refactor design, return to Phase 1\n   → Update Progress Tracking: Post-Design Constitution Check\n8. Plan Phase 2 → Describe task generation approach (DO NOT create tasks.md)\n9. STOP - Ready for /tasks command\n```\n\n**IMPORTANT**: The /plan command STOPS at step 7. Phases 2-4 are executed by other commands:\n- Phase 2: /tasks command creates tasks.md\n- Phase 3-4: Implementation execution (manual or via tools)\n\n## Summary\n[Extract from feature spec: primary requirement + technical approach from research]\n\n## Technical Context","chunkId":"specs/main/plan.md:3"}],[2268,{"filePath":"specs/main/plan.md","startLine":33,"endLine":41,"chunkText":"[Extract from feature spec: primary requirement + technical approach from research]\n\n## Technical Context\n**Language/Version**: [e.g., Python 3.11, Swift 5.9, Rust 1.75 or NEEDS CLARIFICATION]  \n**Primary Dependencies**: [e.g., FastAPI, UIKit, LLVM or NEEDS CLARIFICATION]  \n**Storage**: [if applicable, e.g., PostgreSQL, CoreData, files or N/A]  \n**Testing**: [e.g., pytest, XCTest, cargo test or NEEDS CLARIFICATION]  \n**Target Platform**: [e.g., Linux server, iOS 15+, WASM or NEEDS CLARIFICATION]\n**Project Type**: [single/web/mobile - determines source structure]  ","chunkId":"specs/main/plan.md:4"}],[2269,{"filePath":"specs/main/plan.md","startLine":39,"endLine":46,"chunkText":"**Testing**: [e.g., pytest, XCTest, cargo test or NEEDS CLARIFICATION]  \n**Target Platform**: [e.g., Linux server, iOS 15+, WASM or NEEDS CLARIFICATION]\n**Project Type**: [single/web/mobile - determines source structure]  \n**Performance Goals**: [domain-specific, e.g., 1000 req/s, 10k lines/sec, 60 fps or NEEDS CLARIFICATION]  \n**Constraints**: [domain-specific, e.g., <200ms p95, <100MB memory, offline-capable or NEEDS CLARIFICATION]  \n**Scale/Scope**: [domain-specific, e.g., 10k users, 1M LOC, 50 screens or NEEDS CLARIFICATION]\n\n## Constitution Check","chunkId":"specs/main/plan.md:5"}],[2270,{"filePath":"specs/main/plan.md","startLine":44,"endLine":55,"chunkText":"**Scale/Scope**: [domain-specific, e.g., 10k users, 1M LOC, 50 screens or NEEDS CLARIFICATION]\n\n## Constitution Check\n*GATE: Must pass before Phase 0 research. Re-check after Phase 1 design.*\n\n### I. Library-First Architecture\n- [ ] Core functionality implemented in `Separator` class or similar library pattern\n- [ ] CLI/Remote API are thin wrappers, not containing business logic\n- [ ] Clear separation between model architectures (MDX, VR, Demucs, MDXC)\n\n### II. Multi-Interface Consistency  \n- [ ] Feature accessible via Python API, CLI, and Remote API (if applicable)","chunkId":"specs/main/plan.md:6"}],[2271,{"filePath":"specs/main/plan.md","startLine":53,"endLine":66,"chunkText":"\n### II. Multi-Interface Consistency  \n- [ ] Feature accessible via Python API, CLI, and Remote API (if applicable)\n- [ ] Parameter names identical across all interfaces\n- [ ] Same model architectures supported across interfaces\n\n### III. Test-First Development (NON-NEGOTIABLE)\n- [ ] Tests written before implementation\n- [ ] Unit tests for all core functionality\n- [ ] Integration tests with audio validation (SSIM comparison)\n- [ ] CLI tests for all exposed functionality\n\n### IV. Performance & Resource Efficiency\n- [ ] Hardware acceleration support considered (CUDA, CoreML, DirectML)","chunkId":"specs/main/plan.md:7"}],[2272,{"filePath":"specs/main/plan.md","startLine":64,"endLine":80,"chunkText":"### IV. Performance & Resource Efficiency\n- [ ] Hardware acceleration support considered (CUDA, CoreML, DirectML)\n- [ ] Memory optimization for large files (streaming/batch processing)\n- [ ] Tunable parameters for different hardware capabilities\n\n### V. Model Architecture Separation\n- [ ] Each architecture in separate modules\n- [ ] Inherits from `CommonSeparator` pattern\n- [ ] Architecture-specific parameters isolated\n- [ ] Loading one architecture doesn't load others\n\n## Project Structure\n\n### Documentation (this feature)\n```\nspecs/[###-feature]/\n├── plan.md              # This file (/plan command output","chunkId":"specs/main/plan.md:8"}],[2273,{"filePath":"specs/main/plan.md","startLine":79,"endLine":102,"chunkText":"```\nspecs/[###-feature]/\n├── plan.md              # This file (/plan command output)\n├── research.md          # Phase 0 output (/plan command)\n├── data-model.md        # Phase 1 output (/plan command)\n├── quickstart.md        # Phase 1 output (/plan command)\n├── contracts/           # Phase 1 output (/plan command)\n└── tasks.md             # Phase 2 output (/tasks command - NOT created by /plan)\n```\n\n### Source Code (repository root)\n```\n# Option 1: Single project (DEFAULT)\nsrc/\n├── models/\n├── services/\n├── cli/\n└── lib/\n\ntests/\n├── contract/\n├── integration/\n└── unit/\n","chunkId":"specs/main/plan.md:9"}],[2274,{"filePath":"specs/main/plan.md","startLine":100,"endLine":129,"chunkText":"├── integration/\n└── unit/\n\n# Option 2: Web application (when \"frontend\" + \"backend\" detected)\nbackend/\n├── src/\n│   ├── models/\n│   ├── services/\n│   └── api/\n└── tests/\n\nfrontend/\n├── src/\n│   ├── components/\n│   ├── pages/\n│   └── services/\n└── tests/\n\n# Option 3: Mobile + API (when \"iOS/Android\" detected)\napi/\n└── [same as backend above]\n\nios/ or android/\n└── [platform-specific structure]\n```\n\n**Structure Decision**: [DEFAULT to Option 1 unless Technical Context indicates web/mobile app]\n\n## Phase 0: Outline & Research\n1. **Extract unknowns from Technical Context** above:","chunkId":"specs/main/plan.md:10"}],[2275,{"filePath":"specs/main/plan.md","startLine":127,"endLine":144,"chunkText":"\n## Phase 0: Outline & Research\n1. **Extract unknowns from Technical Context** above:\n   - For each NEEDS CLARIFICATION → research task\n   - For each dependency → best practices task\n   - For each integration → patterns task\n\n2. **Generate and dispatch research agents**:\n   ```\n   For each unknown in Technical Context:\n     Task: \"Research {unknown} for {feature context}\"\n   For each technology choice:\n     Task: \"Find best practices for {tech} in {domain}\"\n   ```\n\n3. **Consolidate findings** in `research.md` using format:\n   - Decision: [what was chosen]\n   - Rationale: [why chosen]","chunkId":"specs/main/plan.md:11"}],[2276,{"filePath":"specs/main/plan.md","startLine":142,"endLine":158,"chunkText":"3. **Consolidate findings** in `research.md` using format:\n   - Decision: [what was chosen]\n   - Rationale: [why chosen]\n   - Alternatives considered: [what else evaluated]\n\n**Output**: research.md with all NEEDS CLARIFICATION resolved\n\n## Phase 1: Design & Contracts\n*Prerequisites: research.md complete*\n\n1. **Extract entities from feature spec** → `data-model.md`:\n   - Entity name, fields, relationships\n   - Validation rules from requirements\n   - State transitions if applicable\n\n2. **Generate API contracts** from functional requirements:\n   - For each user action → endpoint","chunkId":"specs/main/plan.md:12"}],[2277,{"filePath":"specs/main/plan.md","startLine":157,"endLine":172,"chunkText":"2. **Generate API contracts** from functional requirements:\n   - For each user action → endpoint\n   - Use standard REST/GraphQL patterns\n   - Output OpenAPI/GraphQL schema to `/contracts/`\n\n3. **Generate contract tests** from contracts:\n   - One test file per endpoint\n   - Assert request/response schemas\n   - Tests must fail (no implementation yet)\n\n4. **Extract test scenarios** from user stories:\n   - Each story → integration test scenario\n   - Quickstart test = story validation steps\n\n5. **Update agent file incrementally** (O(1) operation):\n   - Run `.specify/scripts/bash/update-agent-context.sh cursor`","chunkId":"specs/main/plan.md:13"}],[2278,{"filePath":"specs/main/plan.md","startLine":170,"endLine":182,"chunkText":"\n5. **Update agent file incrementally** (O(1) operation):\n   - Run `.specify/scripts/bash/update-agent-context.sh cursor`\n     **IMPORTANT**: Execute it exactly as specified above. Do not add or remove any arguments.\n   - If exists: Add only NEW tech from current plan\n   - Preserve manual additions between markers\n   - Update recent changes (keep last 3)\n   - Keep under 150 lines for token efficiency\n   - Output to repository root\n\n**Output**: data-model.md, /contracts/*, failing tests, quickstart.md, agent-specific file\n\n## Phase 2: Task Planning Approach","chunkId":"specs/main/plan.md:14"}],[2279,{"filePath":"specs/main/plan.md","startLine":180,"endLine":194,"chunkText":"**Output**: data-model.md, /contracts/*, failing tests, quickstart.md, agent-specific file\n\n## Phase 2: Task Planning Approach\n*This section describes what the /tasks command will do - DO NOT execute during /plan*\n\n**Task Generation Strategy**:\n- Load `.specify/templates/tasks-template.md` as base\n- Generate tasks from Phase 1 design docs (contracts, data model, quickstart)\n- Each contract → contract test task [P]\n- Each entity → model creation task [P] \n- Each user story → integration test task\n- Implementation tasks to make tests pass\n\n**Ordering Strategy**:\n- TDD order: Tests before implementation ","chunkId":"specs/main/plan.md:15"}],[2280,{"filePath":"specs/main/plan.md","startLine":192,"endLine":206,"chunkText":"\n**Ordering Strategy**:\n- TDD order: Tests before implementation \n- Dependency order: Models before services before UI\n- Mark [P] for parallel execution (independent files)\n\n**Estimated Output**: 25-30 numbered, ordered tasks in tasks.md\n\n**IMPORTANT**: This phase is executed by the /tasks command, NOT by /plan\n\n## Phase 3+: Future Implementation\n*These phases are beyond the scope of the /plan command*\n\n**Phase 3**: Task execution (/tasks command creates tasks.md)  \n**Phase 4**: Implementation (execute tasks.md following constitutional principles)  ","chunkId":"specs/main/plan.md:16"}],[2281,{"filePath":"specs/main/plan.md","startLine":204,"endLine":214,"chunkText":"\n**Phase 3**: Task execution (/tasks command creates tasks.md)  \n**Phase 4**: Implementation (execute tasks.md following constitutional principles)  \n**Phase 5**: Validation (run tests, execute quickstart.md, performance validation)\n\n## Complexity Tracking\n*Fill ONLY if Constitution Check has violations that must be justified*\n\n| Violation | Why Needed | Simpler Alternative Rejected Because |\n|-----------|------------|-------------------------------------|\n| [e.g., 4th project] | [current need] | [why 3 projects insufficient] |","chunkId":"specs/main/plan.md:17"}],[2282,{"filePath":"specs/main/plan.md","startLine":212,"endLine":225,"chunkText":"| Violation | Why Needed | Simpler Alternative Rejected Because |\n|-----------|------------|-------------------------------------|\n| [e.g., 4th project] | [current need] | [why 3 projects insufficient] |\n| [e.g., Repository pattern] | [specific problem] | [why direct DB access insufficient] |\n\n\n## Progress Tracking\n*This checklist is updated during execution flow*\n\n**Phase Status**:\n- [ ] Phase 0: Research complete (/plan command)\n- [ ] Phase 1: Design complete (/plan command)\n- [ ] Phase 2: Task planning complete (/plan command - describe approach only)\n- [ ] Phase 3: Tasks generated (/tasks command)","chunkId":"specs/main/plan.md:18"}],[2283,{"filePath":"specs/main/plan.md","startLine":223,"endLine":237,"chunkText":"- [ ] Phase 1: Design complete (/plan command)\n- [ ] Phase 2: Task planning complete (/plan command - describe approach only)\n- [ ] Phase 3: Tasks generated (/tasks command)\n- [ ] Phase 4: Implementation complete\n- [ ] Phase 5: Validation passed\n\n**Gate Status**:\n- [ ] Initial Constitution Check: PASS\n- [ ] Post-Design Constitution Check: PASS\n- [ ] All NEEDS CLARIFICATION resolved\n- [ ] Complexity deviations documented\n\n---\n*Based on Constitution v1.0.0 - See `.specify/memory/constitution.md`*\n","chunkId":"specs/main/plan.md:19"}],[2284,{"filePath":"tests/README.md","startLine":0,"endLine":18,"chunkText":"# Audio Separator Tests\n\nThis directory contains tests for the audio-separator project.\n\n## Test Structure\n\n### Unit Tests (`tests/unit/`)\n- **Core functionality tests**: `test_cli.py`, `test_stft.py`\n- **Remote API tests**: `test_remote_api_client.py`, `test_remote_cli.py`\n\n### Integration Tests (`tests/integration/`)\n- **CLI integration tests**: `test_cli_integration.py`\n- **Audio output validation**: `test_separator_output_integration.py`\n- **Remote API integration tests**: `test_remote_api_integration.py`\n\n## Remote API Tests\n\nThe project includes comprehensive tests for the remote API functionality:\n","chunkId":"tests/README.md:0"}],[2285,{"filePath":"tests/README.md","startLine":17,"endLine":34,"chunkText":"The project includes comprehensive tests for the remote API functionality:\n\n- **61 tests** covering all remote API components\n- **Unit tests** with mocked HTTP responses\n- **Integration tests** with a mock HTTP server\n- **End-to-end workflow tests**\n\nKey features tested:\n- Multiple model support (upload once, process with multiple models)\n- Full parameter compatibility with local CLI\n- Asynchronous processing with job polling\n- Error handling and edge cases\n- File upload/download workflows\n\nSee `tests/remote/README.md` for detailed documentation on remote API tests.\n\n## Audio Validation\n","chunkId":"tests/README.md:1"}],[2286,{"filePath":"tests/README.md","startLine":33,"endLine":41,"chunkText":"## Audio Validation\n\nThe integration tests now include validation of output audio files by comparing waveform and spectrogram images with reference images. This helps ensure that the audio separation results remain consistent across different runs and code changes.\n\n### How It Works\n\n1. Reference waveform and spectrogram images are generated from expected output files\n2. During test execution, the same images are generated for the actual output files\n3. The images are compared using Structural Similarity Index (SSIM) to measure perceptual similarity","chunkId":"tests/README.md:2"}],[2287,{"filePath":"tests/README.md","startLine":40,"endLine":50,"chunkText":"2. During test execution, the same images are generated for the actual output files\n3. The images are compared using Structural Similarity Index (SSIM) to measure perceptual similarity\n4. If the images differ significantly, the test fails, indicating a change in the audio output\n\n### Image Comparison with SSIM\n\nThe tests use Structural Similarity Index Measure (SSIM) to compare images, which is more robust than pixel-by-pixel comparison:\n\n- SSIM considers structural information in the images\n- It's more resilient to small spatial shifts or offsets\n- It better matches human perception of image similarity","chunkId":"tests/README.md:3"}],[2288,{"filePath":"tests/README.md","startLine":49,"endLine":58,"chunkText":"- It's more resilient to small spatial shifts or offsets\n- It better matches human perception of image similarity\n- It works well across different environments (local machines vs CI servers)\n\nThe comparison uses a minimum similarity threshold (0.0-1.0):\n- **Higher values** (closer to 1.0) require images to be **more similar**\n- **Lower values** (closer to 0.0) are **more permissive**\n- A value of 0.99 requires 99% similarity between images\n- A value of 0.0 would consider any images to match\n","chunkId":"tests/README.md:4"}],[2289,{"filePath":"tests/README.md","startLine":57,"endLine":67,"chunkText":"- A value of 0.0 would consider any images to match\n\nThe default threshold is set to 0.999, which is quite strict. However, model-specific thresholds can be set to accommodate different models' behavior.\n\n#### Model-Specific Thresholds\n\nSome models inherently produce slightly different outputs on different runs, even with the same input. To accommodate these models, you can set model-specific thresholds in the `MODEL_SIMILARITY_THRESHOLDS` dictionary:\n\n```python\nMODEL_SIMILARITY_THRESHOLDS = {\n    \"htdemucs_6s.yaml\": 0.990,  # Demucs models need a lower threshold","chunkId":"tests/README.md:5"}],[2290,{"filePath":"tests/README.md","startLine":66,"endLine":81,"chunkText":"MODEL_SIMILARITY_THRESHOLDS = {\n    \"htdemucs_6s.yaml\": 0.990,  # Demucs models need a lower threshold\n    # Add other models that need custom thresholds here\n}\n```\n\nThis allows you to maintain a high threshold for most models while being more flexible with models that naturally exhibit more variation.\n\n### Generating Reference Images\n\nTo generate or update the reference images, use the script provided:\n\n```bash\npython tests/integration/generate_reference_images.py\n```\n","chunkId":"tests/README.md:6"}],[2291,{"filePath":"tests/README.md","startLine":80,"endLine":96,"chunkText":"```\n\nThis script will create waveform and spectrogram images for all expected output files and store them in the `tests/inputs/reference` directory.\n\n### Skipping Validation\n\nIf you need to skip the audio validation (e.g., when you're intentionally changing the output), you can set the environment variable `SKIP_AUDIO_VALIDATION=1`:\n\n```bash\nSKIP_AUDIO_VALIDATION=1 pytest tests/integration/test_cli_integration.py\n```\n\n### Adding New Models\n\nWhen adding a new model to the tests:\n\n1. Add the model and its expected output files to the `MODEL_PARAMS` list in `test_cli_integration.py`","chunkId":"tests/README.md:7"}],[2292,{"filePath":"tests/README.md","startLine":95,"endLine":109,"chunkText":"\n1. Add the model and its expected output files to the `MODEL_PARAMS` list in `test_cli_integration.py`\n2. Run the integration test to generate the output files\n3. Run the `generate_reference_images.py` script to create the reference images\n4. Run the tests again to validate the output files\n5. If necessary, add a custom similarity threshold for the new model in `MODEL_SIMILARITY_THRESHOLDS`\n\n### Debugging\n\nTo see detailed validation results, run pytest with the `-sv` flag:\n\n```bash\npytest tests/integration/test_cli_integration.py -sv\n```\n","chunkId":"tests/README.md:8"}],[2293,{"filePath":"tests/README.md","startLine":108,"endLine":142,"chunkText":"```\n\nThis will show the similarity scores for each comparison and whether they passed or failed.\n\n## Running Tests\n\nTo run all tests:\n\n```bash\npytest\n```\n\nTo run specific tests:\n\n```bash\npytest tests/unit/\npytest tests/integration/\n```\n\nTo run remote API tests:\n\n```bash\n# All remote API tests (unit + integration)\npytest tests/unit/test_remote*.py tests/integration/test_remote*.py -v\n\n# Only unit tests (fast, no HTTP server)\npytest tests/unit/test_remote*.py -v\n\n# Only integration tests (with mock HTTP server)\npytest tests/integration/test_remote*.py -v\n```\n\nTo run with coverage:\n\n```bash","chunkId":"tests/README.md:9"}],[2294,{"filePath":"tests/README.md","startLine":141,"endLine":147,"chunkText":"\n```bash\npytest --cov=audio_separator\n\n# Coverage for remote API specifically\npytest tests/unit/test_remote*.py tests/integration/test_remote*.py --cov=audio_separator.remote --cov-report=html\n``` ","chunkId":"tests/README.md:10"}],[2295,{"filePath":"tests/contract/test_parameter_validator_interface.py","startLine":0,"endLine":18,"chunkText":"\"\"\"\nContract tests for ParameterValidatorInterface.\nThese tests verify the parameter validation interface contracts.\n\"\"\"\n\nimport pytest\nfrom unittest.mock import Mock\nfrom typing import Dict, Any, List\n\n# Import interfaces from contracts\nimport sys\nimport os\n\n# Find project root dynamically\ncurrent_dir = os.path.dirname(os.path.abspath(__file__))\nproject_root = current_dir\n# Go up until we find the project root (contains specs/ directory)\nwhile project_root and not os.path.exists(os.path.join(project_root, 'specs')):\n    parent = os.path.dirname(project_root)","chunkId":"tests/contract/test_parameter_validator_interface.py:0"}],[2296,{"filePath":"tests/contract/test_parameter_validator_interface.py","startLine":16,"endLine":29,"chunkText":"# Go up until we find the project root (contains specs/ directory)\nwhile project_root and not os.path.exists(os.path.join(project_root, 'specs')):\n    parent = os.path.dirname(project_root)\n    if parent == project_root:  # Reached filesystem root\n        break\n    project_root = parent\n\ncontracts_path = os.path.join(project_root, 'specs', '001-update-roformer-implementation', 'contracts')\nif os.path.exists(contracts_path):\n    sys.path.append(contracts_path)\n\nfrom parameter_validator_interface import (\n    ParameterValidatorInterface,\n    BSRoformerValidatorInterface,","chunkId":"tests/contract/test_parameter_validator_interface.py:1"}],[2297,{"filePath":"tests/contract/test_parameter_validator_interface.py","startLine":27,"endLine":44,"chunkText":"from parameter_validator_interface import (\n    ParameterValidatorInterface,\n    BSRoformerValidatorInterface,\n    MelBandRoformerValidatorInterface,\n    ConfigurationNormalizerInterface,\n    ValidationIssue,\n    ValidationSeverity\n)\n\n\nclass TestParameterValidatorInterface:\n    \"\"\"Test the ParameterValidator interface contract.\"\"\"\n    \n    def test_validate_required_parameters_contract(self):\n        \"\"\"Test validate_required_parameters interface contract.\"\"\"\n        validator = Mock(spec=ParameterValidatorInterface)\n        \n        # Mock valid configuration - should return no issues","chunkId":"tests/contract/test_parameter_validator_interface.py:2"}],[2298,{"filePath":"tests/contract/test_parameter_validator_interface.py","startLine":42,"endLine":55,"chunkText":"        validator = Mock(spec=ParameterValidatorInterface)\n        \n        # Mock valid configuration - should return no issues\n        validator.validate_required_parameters.return_value = []\n        \n        config = {\"dim\": 512, \"depth\": 6}\n        issues = validator.validate_required_parameters(config, \"bs_roformer\")\n        \n        assert isinstance(issues, list)\n        assert len(issues) == 0  # Valid config should have no issues\n        \n        # Mock invalid configuration - missing required parameters\n        missing_param_issue = ValidationIssue(\n            severity=ValidationSeverity.ERROR,","chunkId":"tests/contract/test_parameter_validator_interface.py:3"}],[2299,{"filePath":"tests/contract/test_parameter_validator_interface.py","startLine":53,"endLine":64,"chunkText":"        # Mock invalid configuration - missing required parameters\n        missing_param_issue = ValidationIssue(\n            severity=ValidationSeverity.ERROR,\n            parameter_name=\"dim\",\n            message=\"Required parameter 'dim' is missing\",\n            suggested_fix=\"Add 'dim' parameter with positive integer value\",\n            current_value=None,\n            expected_value=\"positive integer\"\n        )\n        validator.validate_required_parameters.return_value = [missing_param_issue]\n        \n        incomplete_config = {\"depth\": 6}  # Missing dim","chunkId":"tests/contract/test_parameter_validator_interface.py:4"}],[2300,{"filePath":"tests/contract/test_parameter_validator_interface.py","startLine":62,"endLine":73,"chunkText":"        validator.validate_required_parameters.return_value = [missing_param_issue]\n        \n        incomplete_config = {\"depth\": 6}  # Missing dim\n        issues = validator.validate_required_parameters(incomplete_config, \"bs_roformer\")\n        \n        assert isinstance(issues, list)\n        assert len(issues) > 0\n        assert all(isinstance(issue, ValidationIssue) for issue in issues)\n        assert issues[0].severity == ValidationSeverity.ERROR\n        assert issues[0].parameter_name == \"dim\"\n    \n    def test_validate_parameter_types_contract(self):","chunkId":"tests/contract/test_parameter_validator_interface.py:5"}],[2301,{"filePath":"tests/contract/test_parameter_validator_interface.py","startLine":71,"endLine":83,"chunkText":"        assert issues[0].parameter_name == \"dim\"\n    \n    def test_validate_parameter_types_contract(self):\n        \"\"\"Test validate_parameter_types interface contract.\"\"\"\n        validator = Mock(spec=ParameterValidatorInterface)\n        \n        # Mock type validation error\n        type_error_issue = ValidationIssue(\n            severity=ValidationSeverity.ERROR,\n            parameter_name=\"dim\",\n            message=\"Parameter 'dim' must be an integer\",\n            suggested_fix=\"Change 'dim' value to an integer\",\n            current_value=\"512\",  # String instead of int","chunkId":"tests/contract/test_parameter_validator_interface.py:6"}],[2302,{"filePath":"tests/contract/test_parameter_validator_interface.py","startLine":81,"endLine":93,"chunkText":"            message=\"Parameter 'dim' must be an integer\",\n            suggested_fix=\"Change 'dim' value to an integer\",\n            current_value=\"512\",  # String instead of int\n            expected_value=\"integer\"\n        )\n        validator.validate_parameter_types.return_value = [type_error_issue]\n        \n        config = {\"dim\": \"512\", \"depth\": 6}  # dim should be int, not string\n        issues = validator.validate_parameter_types(config)\n        \n        assert isinstance(issues, list)\n        assert len(issues) > 0\n        assert issues[0].severity == ValidationSeverity.ERROR","chunkId":"tests/contract/test_parameter_validator_interface.py:7"}],[2303,{"filePath":"tests/contract/test_parameter_validator_interface.py","startLine":91,"endLine":104,"chunkText":"        assert isinstance(issues, list)\n        assert len(issues) > 0\n        assert issues[0].severity == ValidationSeverity.ERROR\n        assert issues[0].parameter_name == \"dim\"\n        assert issues[0].current_value == \"512\"\n    \n    def test_validate_parameter_ranges_contract(self):\n        \"\"\"Test validate_parameter_ranges interface contract.\"\"\"\n        validator = Mock(spec=ParameterValidatorInterface)\n        \n        # Mock range validation error\n        range_error_issue = ValidationIssue(\n            severity=ValidationSeverity.ERROR,\n            parameter_name=\"attn_dropout\",","chunkId":"tests/contract/test_parameter_validator_interface.py:8"}],[2304,{"filePath":"tests/contract/test_parameter_validator_interface.py","startLine":102,"endLine":113,"chunkText":"        range_error_issue = ValidationIssue(\n            severity=ValidationSeverity.ERROR,\n            parameter_name=\"attn_dropout\",\n            message=\"Parameter 'attn_dropout' must be between 0.0 and 1.0\",\n            suggested_fix=\"Set 'attn_dropout' to a value between 0.0 and 1.0\",\n            current_value=1.5,\n            expected_value=\"0.0 <= value <= 1.0\"\n        )\n        validator.validate_parameter_ranges.return_value = [range_error_issue]\n        \n        config = {\"dim\": 512, \"depth\": 6, \"attn_dropout\": 1.5}  # Invalid range\n        issues = validator.validate_parameter_ranges(config)","chunkId":"tests/contract/test_parameter_validator_interface.py:9"}],[2305,{"filePath":"tests/contract/test_parameter_validator_interface.py","startLine":112,"endLine":124,"chunkText":"        config = {\"dim\": 512, \"depth\": 6, \"attn_dropout\": 1.5}  # Invalid range\n        issues = validator.validate_parameter_ranges(config)\n        \n        assert isinstance(issues, list)\n        assert len(issues) > 0\n        assert issues[0].severity == ValidationSeverity.ERROR\n        assert issues[0].parameter_name == \"attn_dropout\"\n        assert issues[0].current_value == 1.5\n    \n    def test_validate_parameter_compatibility_contract(self):\n        \"\"\"Test validate_parameter_compatibility interface contract.\"\"\"\n        validator = Mock(spec=ParameterValidatorInterface)\n        ","chunkId":"tests/contract/test_parameter_validator_interface.py:10"}],[2306,{"filePath":"tests/contract/test_parameter_validator_interface.py","startLine":122,"endLine":133,"chunkText":"        \"\"\"Test validate_parameter_compatibility interface contract.\"\"\"\n        validator = Mock(spec=ParameterValidatorInterface)\n        \n        # Mock compatibility validation warning\n        compatibility_issue = ValidationIssue(\n            severity=ValidationSeverity.WARNING,\n            parameter_name=\"sage_attention\",\n            message=\"sage_attention=True may conflict with flash_attn=True\",\n            suggested_fix=\"Consider using only one attention mechanism\",\n            current_value=True,\n            expected_value=\"False when flash_attn=True\"\n        )","chunkId":"tests/contract/test_parameter_validator_interface.py:11"}],[2307,{"filePath":"tests/contract/test_parameter_validator_interface.py","startLine":131,"endLine":144,"chunkText":"            current_value=True,\n            expected_value=\"False when flash_attn=True\"\n        )\n        validator.validate_parameter_compatibility.return_value = [compatibility_issue]\n        \n        config = {\"sage_attention\": True, \"flash_attn\": True}\n        issues = validator.validate_parameter_compatibility(config)\n        \n        assert isinstance(issues, list)\n        assert len(issues) > 0\n        assert issues[0].severity == ValidationSeverity.WARNING\n        assert \"conflict\" in issues[0].message.lower()\n    \n    def test_validate_normalization_config_contract(self):","chunkId":"tests/contract/test_parameter_validator_interface.py:12"}],[2308,{"filePath":"tests/contract/test_parameter_validator_interface.py","startLine":142,"endLine":155,"chunkText":"        assert \"conflict\" in issues[0].message.lower()\n    \n    def test_validate_normalization_config_contract(self):\n        \"\"\"Test validate_normalization_config interface contract.\"\"\"\n        validator = Mock(spec=ParameterValidatorInterface)\n        \n        # Mock valid normalization config\n        validator.validate_normalization_config.return_value = []\n        \n        issues = validator.validate_normalization_config(\"layer_norm\")\n        assert isinstance(issues, list)\n        assert len(issues) == 0\n        \n        # Mock invalid normalization config","chunkId":"tests/contract/test_parameter_validator_interface.py:13"}],[2309,{"filePath":"tests/contract/test_parameter_validator_interface.py","startLine":153,"endLine":165,"chunkText":"        assert len(issues) == 0\n        \n        # Mock invalid normalization config\n        norm_error_issue = ValidationIssue(\n            severity=ValidationSeverity.ERROR,\n            parameter_name=\"norm\",\n            message=\"Unknown normalization type 'invalid_norm'\",\n            suggested_fix=\"Use one of: 'layer_norm', 'batch_norm', 'rms_norm', or None\",\n            current_value=\"invalid_norm\",\n            expected_value=\"valid normalization type\"\n        )\n        validator.validate_normalization_config.return_value = [norm_error_issue]\n        ","chunkId":"tests/contract/test_parameter_validator_interface.py:14"}],[2310,{"filePath":"tests/contract/test_parameter_validator_interface.py","startLine":163,"endLine":177,"chunkText":"        )\n        validator.validate_normalization_config.return_value = [norm_error_issue]\n        \n        issues = validator.validate_normalization_config(\"invalid_norm\")\n        assert isinstance(issues, list)\n        assert len(issues) > 0\n        assert issues[0].parameter_name == \"norm\"\n    \n    def test_get_parameter_defaults_contract(self):\n        \"\"\"Test get_parameter_defaults interface contract.\"\"\"\n        validator = Mock(spec=ParameterValidatorInterface)\n        \n        # Mock default parameters\n        default_params = {\n            \"mlp_expansion_factor\": 4,","chunkId":"tests/contract/test_parameter_validator_interface.py:15"}],[2311,{"filePath":"tests/contract/test_parameter_validator_interface.py","startLine":175,"endLine":189,"chunkText":"        # Mock default parameters\n        default_params = {\n            \"mlp_expansion_factor\": 4,\n            \"sage_attention\": False,\n            \"zero_dc\": True,\n            \"use_torch_checkpoint\": False,\n            \"skip_connection\": False\n        }\n        validator.get_parameter_defaults.return_value = default_params\n        \n        defaults = validator.get_parameter_defaults(\"bs_roformer\")\n        \n        assert isinstance(defaults, dict)\n        assert \"mlp_expansion_factor\" in defaults\n        assert defaults[\"mlp_expansion_factor\"] == 4","chunkId":"tests/contract/test_parameter_validator_interface.py:16"}],[2312,{"filePath":"tests/contract/test_parameter_validator_interface.py","startLine":187,"endLine":201,"chunkText":"        assert isinstance(defaults, dict)\n        assert \"mlp_expansion_factor\" in defaults\n        assert defaults[\"mlp_expansion_factor\"] == 4\n        assert isinstance(defaults[\"sage_attention\"], bool)\n    \n    def test_apply_parameter_defaults_contract(self):\n        \"\"\"Test apply_parameter_defaults interface contract.\"\"\"\n        validator = Mock(spec=ParameterValidatorInterface)\n        \n        # Mock config with defaults applied\n        input_config = {\"dim\": 512, \"depth\": 6}\n        expected_config = {\n            \"dim\": 512,\n            \"depth\": 6,\n            \"mlp_expansion_factor\": 4,","chunkId":"tests/contract/test_parameter_validator_interface.py:17"}],[2313,{"filePath":"tests/contract/test_parameter_validator_interface.py","startLine":199,"endLine":215,"chunkText":"            \"dim\": 512,\n            \"depth\": 6,\n            \"mlp_expansion_factor\": 4,\n            \"sage_attention\": False,\n            \"zero_dc\": True\n        }\n        validator.apply_parameter_defaults.return_value = expected_config\n        \n        result_config = validator.apply_parameter_defaults(input_config, \"bs_roformer\")\n        \n        assert isinstance(result_config, dict)\n        assert \"dim\" in result_config\n        assert \"mlp_expansion_factor\" in result_config\n        assert result_config[\"dim\"] == 512\n        assert result_config[\"mlp_expansion_factor\"] == 4\n\n","chunkId":"tests/contract/test_parameter_validator_interface.py:18"}],[2314,{"filePath":"tests/contract/test_parameter_validator_interface.py","startLine":216,"endLine":227,"chunkText":"class TestBSRoformerValidatorInterface:\n    \"\"\"Test the BSRoformer-specific validator interface contract.\"\"\"\n    \n    def test_validate_freqs_per_bands_contract(self):\n        \"\"\"Test validate_freqs_per_bands interface contract.\"\"\"\n        validator = Mock(spec=BSRoformerValidatorInterface)\n        \n        # Mock validation error for mismatched frequency bands\n        freq_error_issue = ValidationIssue(\n            severity=ValidationSeverity.ERROR,\n            parameter_name=\"freqs_per_bands\",\n            message=\"Sum of freqs_per_bands (126) does not match expected frequency bins (129)\",","chunkId":"tests/contract/test_parameter_validator_interface.py:19"}],[2315,{"filePath":"tests/contract/test_parameter_validator_interface.py","startLine":225,"endLine":235,"chunkText":"            severity=ValidationSeverity.ERROR,\n            parameter_name=\"freqs_per_bands\",\n            message=\"Sum of freqs_per_bands (126) does not match expected frequency bins (129)\",\n            suggested_fix=\"Adjust freqs_per_bands to sum to 129\",\n            current_value=(2, 4, 8, 16, 32, 64),  # sums to 126\n            expected_value=\"tuple summing to 129\"\n        )\n        validator.validate_freqs_per_bands.return_value = [freq_error_issue]\n        \n        freqs_per_bands = (2, 4, 8, 16, 32, 64)\n        stft_config = {\"n_fft\": 256}","chunkId":"tests/contract/test_parameter_validator_interface.py:20"}],[2316,{"filePath":"tests/contract/test_parameter_validator_interface.py","startLine":233,"endLine":247,"chunkText":"        \n        freqs_per_bands = (2, 4, 8, 16, 32, 64)\n        stft_config = {\"n_fft\": 256}\n        issues = validator.validate_freqs_per_bands(freqs_per_bands, stft_config)\n        \n        assert isinstance(issues, list)\n        assert len(issues) > 0\n        assert issues[0].parameter_name == \"freqs_per_bands\"\n        assert \"sum\" in issues[0].message.lower()\n    \n    def test_calculate_expected_freqs_contract(self):\n        \"\"\"Test calculate_expected_freqs interface contract.\"\"\"\n        validator = Mock(spec=BSRoformerValidatorInterface)\n        \n        # Mock expected frequency calculation","chunkId":"tests/contract/test_parameter_validator_interface.py:21"}],[2317,{"filePath":"tests/contract/test_parameter_validator_interface.py","startLine":245,"endLine":255,"chunkText":"        validator = Mock(spec=BSRoformerValidatorInterface)\n        \n        # Mock expected frequency calculation\n        validator.calculate_expected_freqs.return_value = 129  # n_fft//2 + 1 for n_fft=256\n        \n        expected_freqs = validator.calculate_expected_freqs(256)\n        \n        assert isinstance(expected_freqs, int)\n        assert expected_freqs > 0\n\n","chunkId":"tests/contract/test_parameter_validator_interface.py:22"}],[2318,{"filePath":"tests/contract/test_parameter_validator_interface.py","startLine":256,"endLine":270,"chunkText":"class TestMelBandRoformerValidatorInterface:\n    \"\"\"Test the MelBandRoformer-specific validator interface contract.\"\"\"\n    \n    def test_validate_num_bands_contract(self):\n        \"\"\"Test validate_num_bands interface contract.\"\"\"\n        validator = Mock(spec=MelBandRoformerValidatorInterface)\n        \n        # Mock successful validation\n        validator.validate_num_bands.return_value = []\n        \n        issues = validator.validate_num_bands(64, 44100)\n        assert isinstance(issues, list)\n        assert len(issues) == 0\n        \n        # Mock validation error for invalid band count","chunkId":"tests/contract/test_parameter_validator_interface.py:23"}],[2319,{"filePath":"tests/contract/test_parameter_validator_interface.py","startLine":268,"endLine":282,"chunkText":"        assert len(issues) == 0\n        \n        # Mock validation error for invalid band count\n        band_error_issue = ValidationIssue(\n            severity=ValidationSeverity.ERROR,\n            parameter_name=\"num_bands\",\n            message=\"num_bands must be positive integer\",\n            suggested_fix=\"Set num_bands to a positive integer value\",\n            current_value=0,\n            expected_value=\"positive integer\"\n        )\n        validator.validate_num_bands.return_value = [band_error_issue]\n        \n        issues = validator.validate_num_bands(0, 44100)\n        assert len(issues) > 0","chunkId":"tests/contract/test_parameter_validator_interface.py:24"}],[2320,{"filePath":"tests/contract/test_parameter_validator_interface.py","startLine":280,"endLine":296,"chunkText":"        \n        issues = validator.validate_num_bands(0, 44100)\n        assert len(issues) > 0\n        assert issues[0].parameter_name == \"num_bands\"\n    \n    def test_validate_sample_rate_contract(self):\n        \"\"\"Test validate_sample_rate interface contract.\"\"\"\n        validator = Mock(spec=MelBandRoformerValidatorInterface)\n        \n        # Mock sample rate validation\n        validator.validate_sample_rate.return_value = []\n        \n        issues = validator.validate_sample_rate(44100)\n        assert isinstance(issues, list)\n        assert len(issues) == 0\n\n","chunkId":"tests/contract/test_parameter_validator_interface.py:25"}],[2321,{"filePath":"tests/contract/test_parameter_validator_interface.py","startLine":297,"endLine":309,"chunkText":"class TestConfigurationNormalizerInterface:\n    \"\"\"Test the ConfigurationNormalizer interface contract.\"\"\"\n    \n    def test_normalize_config_format_contract(self):\n        \"\"\"Test normalize_config_format interface contract.\"\"\"\n        normalizer = Mock(spec=ConfigurationNormalizerInterface)\n        \n        # Mock normalization from object to dict\n        normalized_config = {\"dim\": 512, \"depth\": 6}\n        normalizer.normalize_config_format.return_value = normalized_config\n        \n        # Test with mock object input\n        raw_config = Mock()","chunkId":"tests/contract/test_parameter_validator_interface.py:26"}],[2322,{"filePath":"tests/contract/test_parameter_validator_interface.py","startLine":307,"endLine":322,"chunkText":"        \n        # Test with mock object input\n        raw_config = Mock()\n        result = normalizer.normalize_config_format(raw_config)\n        \n        assert isinstance(result, dict)\n        assert \"dim\" in result\n        assert \"depth\" in result\n    \n    def test_map_legacy_parameters_contract(self):\n        \"\"\"Test map_legacy_parameters interface contract.\"\"\"\n        normalizer = Mock(spec=ConfigurationNormalizerInterface)\n        \n        # Mock legacy parameter mapping\n        legacy_config = {\"old_param_name\": 512}\n        mapped_config = {\"dim\": 512}  # old_param_name -> dim","chunkId":"tests/contract/test_parameter_validator_interface.py:27"}],[2323,{"filePath":"tests/contract/test_parameter_validator_interface.py","startLine":320,"endLine":334,"chunkText":"        # Mock legacy parameter mapping\n        legacy_config = {\"old_param_name\": 512}\n        mapped_config = {\"dim\": 512}  # old_param_name -> dim\n        normalizer.map_legacy_parameters.return_value = mapped_config\n        \n        result = normalizer.map_legacy_parameters(legacy_config)\n        \n        assert isinstance(result, dict)\n        assert \"dim\" in result\n        assert \"old_param_name\" not in result\n    \n    def test_extract_nested_config_contract(self):\n        \"\"\"Test extract_nested_config interface contract.\"\"\"\n        normalizer = Mock(spec=ConfigurationNormalizerInterface)\n        ","chunkId":"tests/contract/test_parameter_validator_interface.py:28"}],[2324,{"filePath":"tests/contract/test_parameter_validator_interface.py","startLine":332,"endLine":345,"chunkText":"        \"\"\"Test extract_nested_config interface contract.\"\"\"\n        normalizer = Mock(spec=ConfigurationNormalizerInterface)\n        \n        # Mock nested config extraction\n        normalizer.extract_nested_config.return_value = \"layer_norm\"\n        \n        config = {\"model\": {\"norm\": \"layer_norm\"}}\n        result = normalizer.extract_nested_config(config, \"model.norm\")\n        \n        assert result == \"layer_norm\"\n        \n        # Mock missing nested config\n        normalizer.extract_nested_config.return_value = None\n        ","chunkId":"tests/contract/test_parameter_validator_interface.py:29"}],[2325,{"filePath":"tests/contract/test_parameter_validator_interface.py","startLine":343,"endLine":355,"chunkText":"        # Mock missing nested config\n        normalizer.extract_nested_config.return_value = None\n        \n        result = normalizer.extract_nested_config(config, \"model.missing_field\")\n        assert result is None\n\n\n# TDD placeholder test removed - implementation is now complete\n\n\nif __name__ == \"__main__\":\n    pytest.main([__file__, \"-v\"])\n","chunkId":"tests/contract/test_parameter_validator_interface.py:30"}],[2326,{"filePath":"tests/contract/test_roformer_loader_interface.py","startLine":0,"endLine":19,"chunkText":"\"\"\"\nContract tests for RoformerLoaderInterface.\nThese tests verify the interface contracts defined in the specification.\n\"\"\"\n\nimport pytest\nimport tempfile\nimport os\nfrom unittest.mock import Mock, patch\nfrom typing import Dict, Any\n\n# Import interfaces from contracts\nimport sys\n\n# Find project root dynamically\ncurrent_dir = os.path.dirname(os.path.abspath(__file__))\nproject_root = current_dir\n# Go up until we find the project root (contains specs/ directory)\nwhile project_root and not os.path.exists(os.path.join(project_root, 'specs')):\n    parent = os.path.dirname(project_root)","chunkId":"tests/contract/test_roformer_loader_interface.py:0"}],[2327,{"filePath":"tests/contract/test_roformer_loader_interface.py","startLine":17,"endLine":32,"chunkText":"# Go up until we find the project root (contains specs/ directory)\nwhile project_root and not os.path.exists(os.path.join(project_root, 'specs')):\n    parent = os.path.dirname(project_root)\n    if parent == project_root:  # Reached filesystem root\n        break\n    project_root = parent\n\ncontracts_path = os.path.join(project_root, 'specs', '001-update-roformer-implementation', 'contracts')\nif os.path.exists(contracts_path):\n    sys.path.append(contracts_path)\n\nfrom roformer_loader_interface import (\n    RoformerLoaderInterface, \n    ModelLoadingResult, \n    ModelConfiguration, \n    RoformerType, ","chunkId":"tests/contract/test_roformer_loader_interface.py:1"}],[2328,{"filePath":"tests/contract/test_roformer_loader_interface.py","startLine":30,"endLine":48,"chunkText":"    ModelLoadingResult, \n    ModelConfiguration, \n    RoformerType, \n    ImplementationVersion,\n    ParameterValidationError\n)\n\n\nclass TestRoformerLoaderInterface:\n    \"\"\"Test the RoformerLoader interface contract.\"\"\"\n    \n    def test_load_model_success_contract(self):\n        \"\"\"Test that load_model returns proper ModelLoadingResult on success.\"\"\"\n        # This test MUST FAIL initially - no implementation exists yet\n        \n        # Mock implementation for contract testing\n        loader = Mock(spec=RoformerLoaderInterface)\n        \n        # Configure mock to return expected result structure","chunkId":"tests/contract/test_roformer_loader_interface.py:2"}],[2329,{"filePath":"tests/contract/test_roformer_loader_interface.py","startLine":46,"endLine":61,"chunkText":"        loader = Mock(spec=RoformerLoaderInterface)\n        \n        # Configure mock to return expected result structure\n        expected_result = ModelLoadingResult(\n            success=True,\n            model=Mock(),  # Mock model instance\n            error_message=None,\n            implementation_used=ImplementationVersion.NEW,\n            warnings=[]\n        )\n        loader.load_model.return_value = expected_result\n        \n        # Test the contract\n        result = loader.load_model(\"/path/to/model.ckpt\")\n        \n        # Verify contract compliance","chunkId":"tests/contract/test_roformer_loader_interface.py:3"}],[2330,{"filePath":"tests/contract/test_roformer_loader_interface.py","startLine":59,"endLine":72,"chunkText":"        result = loader.load_model(\"/path/to/model.ckpt\")\n        \n        # Verify contract compliance\n        assert isinstance(result, ModelLoadingResult)\n        assert result.success is True\n        assert result.model is not None\n        assert result.error_message is None\n        assert isinstance(result.implementation_used, ImplementationVersion)\n        assert isinstance(result.warnings, list)\n    \n    def test_load_model_failure_contract(self):\n        \"\"\"Test that load_model returns proper error result on failure.\"\"\"\n        loader = Mock(spec=RoformerLoaderInterface)\n        ","chunkId":"tests/contract/test_roformer_loader_interface.py:4"}],[2331,{"filePath":"tests/contract/test_roformer_loader_interface.py","startLine":70,"endLine":84,"chunkText":"        \"\"\"Test that load_model returns proper error result on failure.\"\"\"\n        loader = Mock(spec=RoformerLoaderInterface)\n        \n        # Configure mock to return error result\n        expected_result = ModelLoadingResult(\n            success=False,\n            model=None,\n            error_message=\"Model file not found\",\n            implementation_used=ImplementationVersion.NEW,\n            warnings=[\"Warning: fallback attempted\"]\n        )\n        loader.load_model.return_value = expected_result\n        \n        result = loader.load_model(\"/nonexistent/model.ckpt\")\n        ","chunkId":"tests/contract/test_roformer_loader_interface.py:5"}],[2332,{"filePath":"tests/contract/test_roformer_loader_interface.py","startLine":82,"endLine":96,"chunkText":"        \n        result = loader.load_model(\"/nonexistent/model.ckpt\")\n        \n        # Verify error contract compliance\n        assert isinstance(result, ModelLoadingResult)\n        assert result.success is False\n        assert result.model is None\n        assert result.error_message is not None\n        assert isinstance(result.error_message, str)\n        assert isinstance(result.warnings, list)\n    \n    def test_load_model_parameter_validation_error(self):\n        \"\"\"Test that load_model raises ParameterValidationError for invalid config.\"\"\"\n        loader = Mock(spec=RoformerLoaderInterface)\n        ","chunkId":"tests/contract/test_roformer_loader_interface.py:6"}],[2333,{"filePath":"tests/contract/test_roformer_loader_interface.py","startLine":94,"endLine":106,"chunkText":"        \"\"\"Test that load_model raises ParameterValidationError for invalid config.\"\"\"\n        loader = Mock(spec=RoformerLoaderInterface)\n        \n        # Configure mock to raise validation error\n        validation_error = ParameterValidationError(\n            parameter_name=\"dim\",\n            expected_type=\"int\",\n            actual_value=\"invalid\",\n            suggested_fix=\"Provide an integer value for dim parameter\"\n        )\n        loader.load_model.side_effect = validation_error\n        \n        with pytest.raises(ParameterValidationError) as exc_info:","chunkId":"tests/contract/test_roformer_loader_interface.py:7"}],[2334,{"filePath":"tests/contract/test_roformer_loader_interface.py","startLine":104,"endLine":116,"chunkText":"        loader.load_model.side_effect = validation_error\n        \n        with pytest.raises(ParameterValidationError) as exc_info:\n            loader.load_model(\"/path/to/model.ckpt\", config={\"dim\": \"invalid\"})\n        \n        error = exc_info.value\n        assert error.parameter_name == \"dim\"\n        assert error.expected_type == \"int\"\n        assert error.actual_value == \"invalid\"\n        assert \"Provide an integer value\" in error.suggested_fix\n    \n    def test_validate_configuration_contract(self):\n        \"\"\"Test validate_configuration interface contract.\"\"\"","chunkId":"tests/contract/test_roformer_loader_interface.py:8"}],[2335,{"filePath":"tests/contract/test_roformer_loader_interface.py","startLine":114,"endLine":128,"chunkText":"    \n    def test_validate_configuration_contract(self):\n        \"\"\"Test validate_configuration interface contract.\"\"\"\n        loader = Mock(spec=RoformerLoaderInterface)\n        \n        # Mock valid configuration\n        config = ModelConfiguration(dim=512, depth=6)\n        loader.validate_configuration.return_value = []  # No errors\n        \n        errors = loader.validate_configuration(config, RoformerType.BS_ROFORMER)\n        \n        assert isinstance(errors, list)\n        assert len(errors) == 0  # Valid config should return empty list\n        \n        # Mock invalid configuration","chunkId":"tests/contract/test_roformer_loader_interface.py:9"}],[2336,{"filePath":"tests/contract/test_roformer_loader_interface.py","startLine":126,"endLine":138,"chunkText":"        assert len(errors) == 0  # Valid config should return empty list\n        \n        # Mock invalid configuration\n        loader.validate_configuration.return_value = [\"Missing required parameter: freqs_per_bands\"]\n        \n        errors = loader.validate_configuration(config, RoformerType.BS_ROFORMER)\n        assert isinstance(errors, list)\n        assert len(errors) > 0\n        assert all(isinstance(error, str) for error in errors)\n    \n    def test_detect_model_type_contract(self):\n        \"\"\"Test detect_model_type interface contract.\"\"\"\n        loader = Mock(spec=RoformerLoaderInterface)","chunkId":"tests/contract/test_roformer_loader_interface.py:10"}],[2337,{"filePath":"tests/contract/test_roformer_loader_interface.py","startLine":136,"endLine":148,"chunkText":"    def test_detect_model_type_contract(self):\n        \"\"\"Test detect_model_type interface contract.\"\"\"\n        loader = Mock(spec=RoformerLoaderInterface)\n        \n        # Mock successful type detection\n        loader.detect_model_type.return_value = RoformerType.BS_ROFORMER\n        \n        model_type = loader.detect_model_type(\"/path/to/bs_roformer.ckpt\")\n        \n        assert isinstance(model_type, RoformerType)\n        assert model_type in [RoformerType.BS_ROFORMER, RoformerType.MEL_BAND_ROFORMER]\n        \n        # Mock type detection failure","chunkId":"tests/contract/test_roformer_loader_interface.py:11"}],[2338,{"filePath":"tests/contract/test_roformer_loader_interface.py","startLine":146,"endLine":158,"chunkText":"        assert model_type in [RoformerType.BS_ROFORMER, RoformerType.MEL_BAND_ROFORMER]\n        \n        # Mock type detection failure\n        loader.detect_model_type.side_effect = ValueError(\"Cannot determine model type\")\n        \n        with pytest.raises(ValueError) as exc_info:\n            loader.detect_model_type(\"/path/to/unknown_model.ckpt\")\n        \n        assert \"Cannot determine model type\" in str(exc_info.value)\n    \n    def test_get_default_configuration_contract(self):\n        \"\"\"Test get_default_configuration interface contract.\"\"\"\n        loader = Mock(spec=RoformerLoaderInterface)","chunkId":"tests/contract/test_roformer_loader_interface.py:12"}],[2339,{"filePath":"tests/contract/test_roformer_loader_interface.py","startLine":156,"endLine":170,"chunkText":"    def test_get_default_configuration_contract(self):\n        \"\"\"Test get_default_configuration interface contract.\"\"\"\n        loader = Mock(spec=RoformerLoaderInterface)\n        \n        # Mock default configuration\n        default_config = ModelConfiguration(\n            dim=512, \n            depth=6,\n            freqs_per_bands=(2, 4, 8, 16, 32, 64)\n        )\n        loader.get_default_configuration.return_value = default_config\n        \n        config = loader.get_default_configuration(RoformerType.BS_ROFORMER)\n        \n        assert isinstance(config, ModelConfiguration)","chunkId":"tests/contract/test_roformer_loader_interface.py:13"}],[2340,{"filePath":"tests/contract/test_roformer_loader_interface.py","startLine":169,"endLine":180,"chunkText":"        \n        assert isinstance(config, ModelConfiguration)\n        assert config.dim > 0\n        assert config.depth > 0\n\n\n# TDD placeholder test removed - implementation is now complete\n\n\nif __name__ == \"__main__\":\n    pytest.main([__file__, \"-v\"])\n","chunkId":"tests/contract/test_roformer_loader_interface.py:14"}],[2341,{"filePath":"tests/integration/README.md","startLine":0,"endLine":24,"chunkText":"# Integration Tests\n\nThese tests verify the end-to-end functionality of the audio-separator CLI.\n\n## Test Files\n\n### 16-bit Audio Tests\n- Input: `tests/inputs/mardy20s.flac` (16-bit)\n- Tests: `test_cli_integration.py`\n- Validates separation with various models using 16-bit audio\n\n### 24-bit Audio Tests\n- Input: `tests/inputs/fallen24bit20s.flac` (24-bit)\n- Tests: `test_24bit_preservation.py`\n- Validates that 24-bit input produces 24-bit output\n- Ensures audio quality is preserved during bit depth preservation\n\n## Running the tests\n\nTo run all integration tests:\n\n```bash\npytest tests/integration\n```\n","chunkId":"tests/integration/README.md:0"}],[2342,{"filePath":"tests/integration/README.md","startLine":23,"endLine":43,"chunkText":"```\n\nTo run only 16-bit tests:\n\n```bash\npytest tests/integration/test_cli_integration.py\n```\n\nTo run only 24-bit preservation tests:\n\n```bash\npytest tests/integration/test_24bit_preservation.py\n```\n\nTo run a specific model test, use pytest's parameter selection:\n\n```bash\n# Run only the kuielab_b_vocals.onnx test (16-bit)\npytest tests/integration/test_cli_integration.py::test_model_separation[kuielab_b_vocals.onnx-expected_files0]\n\n# Run only the BS-Roformer test with 24-bit audio","chunkId":"tests/integration/README.md:1"}],[2343,{"filePath":"tests/integration/README.md","startLine":42,"endLine":62,"chunkText":"# Run only the BS-Roformer test with 24-bit audio\npytest tests/integration/test_24bit_preservation.py::test_24bit_model_separation[model_bs_roformer_ep_317_sdr_12.9755.ckpt-expected_files0]\n```\n\n## Adding New Model Tests\n\n### For 16-bit tests\nAdd a new entry to the `MODEL_PARAMS` list in `test_cli_integration.py`:\n\n```python\n(\n    \"new_model_filename.onnx\",\n    [\"mardy20s_(Instrumental)_new_model_filename.flac\", \"mardy20s_(Vocals)_new_model_filename.flac\"]\n),\n```\n\n### For 24-bit tests\nAdd a new entry to the `MODEL_PARAMS_24BIT` list in `test_24bit_preservation.py`:\n\n```python\n(","chunkId":"tests/integration/README.md:2"}],[2344,{"filePath":"tests/integration/README.md","startLine":63,"endLine":87,"chunkText":"(\n    \"new_model_filename.onnx\",\n    [\"fallen24bit20s_(Instrumental)_new_model_filename.flac\", \"fallen24bit20s_(Vocals)_new_model_filename.flac\"]\n),\n```\n\n## Generating Reference Images\n\n### For 16-bit tests\n```bash\npython tests/integration/generate_reference_images.py\n```\n\n### For 24-bit tests\n1. First, generate the output files by running audio-separator:\n   ```bash\n   audio-separator -m model_name.ckpt tests/inputs/fallen24bit20s.flac\n   ```\n2. Move the output files to `tests/inputs/`\n3. Generate reference images:\n   ```bash\n   python tests/integration/generate_reference_images_24bit.py\n   ```\n\n## Notes","chunkId":"tests/integration/README.md:3"}],[2345,{"filePath":"tests/integration/README.md","startLine":87,"endLine":94,"chunkText":"## Notes\n\n- These tests use actual audio files and models, and will run the full audio separation process.\n- Tests may take longer to run than unit tests, as they perform actual audio processing.\n- The model files will be automatically downloaded if they don't exist locally.\n- The 16-bit test requires the test audio file at `tests/inputs/mardy20s.flac` to exist.\n- The 24-bit test requires the test audio file at `tests/inputs/fallen24bit20s.flac` to exist.\n- Reference images must be generated before running the tests for the first time. ","chunkId":"tests/integration/README.md:4"}],[2346,{"filePath":"tests/integration/generate_reference_images.py","startLine":0,"endLine":18,"chunkText":"#!/usr/bin/env python3\n\"\"\"\nScript to generate reference waveform and spectrogram images for model outputs.\nThis script should be run whenever the expected output files change.\n\"\"\"\n\nimport os\nimport sys\nimport glob\nfrom pathlib import Path\n\n# Add the parent directory to sys.path\nsys.path.insert(0, str(Path(__file__).resolve().parent.parent.parent))\n\nfrom tests.utils import generate_reference_images\nfrom tests.integration.test_cli_integration import MODEL_PARAMS\n\ndef main():\n    \"\"\"Generate reference images for all expected model outputs.\"\"\"","chunkId":"tests/integration/generate_reference_images.py:0"}],[2347,{"filePath":"tests/integration/generate_reference_images.py","startLine":16,"endLine":30,"chunkText":"\ndef main():\n    \"\"\"Generate reference images for all expected model outputs.\"\"\"\n    print(\"Generating reference images for expected model outputs...\")\n    \n    # Get the input file path\n    inputs_dir = Path(__file__).resolve().parent.parent / \"inputs\"\n    input_file = inputs_dir / \"mardy20s.flac\"\n    \n    # Create reference directory if it doesn't exist\n    reference_dir = inputs_dir / \"reference\"\n    os.makedirs(reference_dir, exist_ok=True)\n    \n    # First, generate reference images for the input file\n    print(f\"Generating reference images for input file: {input_file}\")","chunkId":"tests/integration/generate_reference_images.py:1"}],[2348,{"filePath":"tests/integration/generate_reference_images.py","startLine":28,"endLine":39,"chunkText":"    \n    # First, generate reference images for the input file\n    print(f\"Generating reference images for input file: {input_file}\")\n    generate_reference_images(str(input_file), str(reference_dir), prefix=\"expected_\")\n    \n    # Then, generate reference images for each expected output file\n    for model, expected_files in MODEL_PARAMS:\n        for output_file in expected_files:\n            file_path = os.path.join(str(inputs_dir), output_file)\n            \n            # Check if the file exists\n            if os.path.exists(file_path):","chunkId":"tests/integration/generate_reference_images.py:2"}],[2349,{"filePath":"tests/integration/generate_reference_images.py","startLine":37,"endLine":49,"chunkText":"            \n            # Check if the file exists\n            if os.path.exists(file_path):\n                print(f\"Generating reference images for output file: {output_file}\")\n                generate_reference_images(file_path, str(reference_dir), prefix=\"expected_\")\n            else:\n                print(f\"Warning: Output file does not exist: {file_path}\")\n                print(f\"You may need to run the CLI command first to generate the output files.\")\n    \n    print(\"Done generating reference images.\")\n\nif __name__ == \"__main__\":\n    main() ","chunkId":"tests/integration/generate_reference_images.py:3"}],[2350,{"filePath":"tests/integration/generate_reference_images_24bit.py","startLine":0,"endLine":10,"chunkText":"#!/usr/bin/env python3\n\"\"\"\nScript to generate reference waveform and spectrogram images for 24-bit model outputs.\nThis script should be run after creating the expected output files with 24-bit audio.\n\nUsage:\n    1. First run audio-separator on the 24-bit input file with each model:\n       audio-separator -m model_bs_roformer_ep_317_sdr_12.9755.ckpt tests/inputs/fallen24bit20s.flac\n       audio-separator -m mel_band_roformer_karaoke_aufr33_viperx_sdr_10.1956.ckpt tests/inputs/fallen24bit20s.flac\n       audio-separator -m MGM_MAIN_v4.pth tests/inputs/fallen24bit20s.flac\n    ","chunkId":"tests/integration/generate_reference_images_24bit.py:0"}],[2351,{"filePath":"tests/integration/generate_reference_images_24bit.py","startLine":8,"endLine":24,"chunkText":"       audio-separator -m mel_band_roformer_karaoke_aufr33_viperx_sdr_10.1956.ckpt tests/inputs/fallen24bit20s.flac\n       audio-separator -m MGM_MAIN_v4.pth tests/inputs/fallen24bit20s.flac\n    \n    2. Move the output files to tests/inputs/\n    \n    3. Run this script to generate reference images:\n       python tests/integration/generate_reference_images_24bit.py\n\"\"\"\n\nimport os\nimport sys\nfrom pathlib import Path\n\n# Add the parent directory to sys.path\nsys.path.insert(0, str(Path(__file__).resolve().parent.parent.parent))\n\nfrom tests.utils import generate_reference_images","chunkId":"tests/integration/generate_reference_images_24bit.py:1"}],[2352,{"filePath":"tests/integration/generate_reference_images_24bit.py","startLine":22,"endLine":38,"chunkText":"sys.path.insert(0, str(Path(__file__).resolve().parent.parent.parent))\n\nfrom tests.utils import generate_reference_images\nfrom tests.integration.test_24bit_preservation import MODEL_PARAMS_24BIT\n\n\ndef main():\n    \"\"\"Generate reference images for all expected 24-bit model outputs.\"\"\"\n    print(\"=\"*60)\n    print(\"Generating Reference Images for 24-bit Model Outputs\")\n    print(\"=\"*60)\n    \n    # Get the input file path\n    inputs_dir = Path(__file__).resolve().parent.parent / \"inputs\"\n    input_file = inputs_dir / \"fallen24bit20s.flac\"\n    \n    # Create reference directory if it doesn't exist","chunkId":"tests/integration/generate_reference_images_24bit.py:2"}],[2353,{"filePath":"tests/integration/generate_reference_images_24bit.py","startLine":36,"endLine":48,"chunkText":"    input_file = inputs_dir / \"fallen24bit20s.flac\"\n    \n    # Create reference directory if it doesn't exist\n    reference_dir = inputs_dir / \"reference\"\n    os.makedirs(reference_dir, exist_ok=True)\n    \n    # First, generate reference images for the 24-bit input file\n    if input_file.exists():\n        print(f\"\\n✓ Generating reference images for input file: {input_file}\")\n        generate_reference_images(str(input_file), str(reference_dir), prefix=\"expected_\")\n        print(f\"  Created: expected_fallen24bit20s_waveform.png\")\n        print(f\"  Created: expected_fallen24bit20s_spectrogram.png\")\n    else","chunkId":"tests/integration/generate_reference_images_24bit.py:3"}],[2354,{"filePath":"tests/integration/generate_reference_images_24bit.py","startLine":46,"endLine":60,"chunkText":"        print(f\"  Created: expected_fallen24bit20s_waveform.png\")\n        print(f\"  Created: expected_fallen24bit20s_spectrogram.png\")\n    else:\n        print(f\"\\n✗ Error: Input file does not exist: {input_file}\")\n        print(f\"  Please create the 24-bit test file first.\")\n        return 1\n    \n    # Then, generate reference images for each expected output file\n    print(f\"\\nGenerating reference images for model outputs...\")\n    missing_files = []\n    created_files = []\n    \n    for model, expected_files in MODEL_PARAMS_24BIT:\n        print(f\"\\n{'-'*60}\")\n        print(f\"Model: {model}\")","chunkId":"tests/integration/generate_reference_images_24bit.py:4"}],[2355,{"filePath":"tests/integration/generate_reference_images_24bit.py","startLine":58,"endLine":71,"chunkText":"    for model, expected_files in MODEL_PARAMS_24BIT:\n        print(f\"\\n{'-'*60}\")\n        print(f\"Model: {model}\")\n        print(f\"{'-'*60}\")\n        \n        for output_file in expected_files:\n            file_path = os.path.join(str(inputs_dir), output_file)\n            \n            # Check if the file exists\n            if os.path.exists(file_path):\n                print(f\"✓ Processing: {output_file}\")\n                waveform_path, spectrogram_path = generate_reference_images(\n                    file_path, str(reference_dir), prefix=\"expected_\"\n                )","chunkId":"tests/integration/generate_reference_images_24bit.py:5"}],[2356,{"filePath":"tests/integration/generate_reference_images_24bit.py","startLine":69,"endLine":80,"chunkText":"                waveform_path, spectrogram_path = generate_reference_images(\n                    file_path, str(reference_dir), prefix=\"expected_\"\n                )\n                output_basename = os.path.splitext(output_file)[0]\n                print(f\"  Created: expected_{output_basename}_waveform.png\")\n                print(f\"  Created: expected_{output_basename}_spectrogram.png\")\n                created_files.extend([waveform_path, spectrogram_path])\n            else:\n                print(f\"✗ Missing: {output_file}\")\n                missing_files.append((model, output_file))\n    \n    # Summary","chunkId":"tests/integration/generate_reference_images_24bit.py:6"}],[2357,{"filePath":"tests/integration/generate_reference_images_24bit.py","startLine":78,"endLine":92,"chunkText":"                missing_files.append((model, output_file))\n    \n    # Summary\n    print(f\"\\n{'='*60}\")\n    print(\"Summary\")\n    print(f\"{'='*60}\")\n    print(f\"✓ Created {len(created_files)} reference images\")\n    \n    if missing_files:\n        print(f\"\\n⚠  Warning: {len(missing_files)} output files were not found:\")\n        print(f\"\\nTo generate missing files, run these commands:\")\n        print()\n        for model, _ in missing_files:\n            # Only print each model once\n            if missing_files.index((model, _)) == [m for m, _ in missing_files].index(model):","chunkId":"tests/integration/generate_reference_images_24bit.py:7"}],[2358,{"filePath":"tests/integration/generate_reference_images_24bit.py","startLine":90,"endLine":100,"chunkText":"        for model, _ in missing_files:\n            # Only print each model once\n            if missing_files.index((model, _)) == [m for m, _ in missing_files].index(model):\n                print(f\"  audio-separator -m {model} tests/inputs/fallen24bit20s.flac\")\n        print(f\"\\nThen move the output files to tests/inputs/ and run this script again.\")\n        print()\n    else:\n        print(f\"\\n✅ All reference images generated successfully!\")\n        print(f\"\\nYou can now run the 24-bit preservation tests:\")\n        print(f\"  pytest tests/integration/test_24bit_preservation.py -v\")\n    ","chunkId":"tests/integration/generate_reference_images_24bit.py:8"}],[2359,{"filePath":"tests/integration/generate_reference_images_24bit.py","startLine":99,"endLine":108,"chunkText":"        print(f\"  pytest tests/integration/test_24bit_preservation.py -v\")\n    \n    print(f\"{'='*60}\\n\")\n    return 0 if not missing_files else 1\n\n\nif __name__ == \"__main__\":\n    sys.exit(main())\n\n","chunkId":"tests/integration/generate_reference_images_24bit.py:9"}],[2360,{"filePath":"tests/integration/test_24bit_preservation.py","startLine":0,"endLine":21,"chunkText":"\"\"\"\nIntegration tests for 24-bit audio bit depth preservation.\n\nTests that 24-bit input audio files produce 24-bit output audio files\nwhile maintaining audio quality through the separation process.\n\"\"\"\n\nimport os\nimport subprocess\nimport pytest\nimport soundfile as sf\nfrom pathlib import Path\nimport sys\n\n# Add parent directory to path\nsys.path.append(os.path.dirname(os.path.dirname(__file__)))\nfrom utils import generate_reference_images, compare_images\n\n\n@pytest.fixture(name=\"input_file_24bit\")\ndef fixture_input_file_24bit():\n    \"\"\"Fixture providing the 24-bit test input audio file path.\"\"\"","chunkId":"tests/integration/test_24bit_preservation.py:0"}],[2361,{"filePath":"tests/integration/test_24bit_preservation.py","startLine":19,"endLine":37,"chunkText":"@pytest.fixture(name=\"input_file_24bit\")\ndef fixture_input_file_24bit():\n    \"\"\"Fixture providing the 24-bit test input audio file path.\"\"\"\n    return \"tests/inputs/fallen24bit20s.flac\"\n\n\n@pytest.fixture(name=\"reference_dir\")\ndef fixture_reference_dir():\n    \"\"\"Fixture providing the reference images directory path.\"\"\"\n    return \"tests/inputs/reference\"\n\n\n@pytest.fixture(name=\"cleanup_output_files\")\ndef fixture_cleanup_output_files():\n    \"\"\"Fixture to clean up output files before and after test.\"\"\"\n    output_files = []\n    yield output_files\n    \n    # Clean up output files after test","chunkId":"tests/integration/test_24bit_preservation.py:1"}],[2362,{"filePath":"tests/integration/test_24bit_preservation.py","startLine":35,"endLine":58,"chunkText":"    yield output_files\n    \n    # Clean up output files after test\n    for file in output_files:\n        if os.path.exists(file):\n            print(f\"Cleaning up test output file: {file}\")\n            os.remove(file)\n\n\ndef get_audio_bit_depth(file_path):\n    \"\"\"Get the bit depth of an audio file.\"\"\"\n    info = sf.info(file_path)\n    subtype = info.subtype\n    \n    if 'PCM_16' in subtype or subtype == 'PCM_S8':\n        return 16\n    elif 'PCM_24' in subtype:\n        return 24\n    elif 'PCM_32' in subtype or 'FLOAT' in subtype or 'DOUBLE' in subtype:\n        return 32\n    else:\n        return None\n\n","chunkId":"tests/integration/test_24bit_preservation.py:2"}],[2363,{"filePath":"tests/integration/test_24bit_preservation.py","startLine":56,"endLine":69,"chunkText":"        return None\n\n\ndef run_separation_test_24bit(model, audio_path, expected_files):\n    \"\"\"Helper function to run a separation test with a 24-bit input file.\"\"\"\n    # Clean up any existing output files before the test\n    for file in expected_files:\n        if os.path.exists(file):\n            print(f\"Deleting existing test output file {file}\")\n            os.remove(file)\n    \n    # Verify input is 24-bit\n    input_bit_depth = get_audio_bit_depth(audio_path)\n    print(f\"Input file bit depth: {input_bit_depth}-bit\")","chunkId":"tests/integration/test_24bit_preservation.py:3"}],[2364,{"filePath":"tests/integration/test_24bit_preservation.py","startLine":67,"endLine":82,"chunkText":"    # Verify input is 24-bit\n    input_bit_depth = get_audio_bit_depth(audio_path)\n    print(f\"Input file bit depth: {input_bit_depth}-bit\")\n    assert input_bit_depth == 24, f\"Input file should be 24-bit, got {input_bit_depth}-bit\"\n    \n    # Run the CLI command\n    result = subprocess.run(\n        [\"audio-separator\", \"-m\", model, audio_path],\n        capture_output=True,\n        text=True,\n        check=False\n    )\n    \n    # Check that the command completed successfully\n    assert result.returncode == 0, f\"Command failed with output: {result.stderr}\"\n    ","chunkId":"tests/integration/test_24bit_preservation.py:4"}],[2365,{"filePath":"tests/integration/test_24bit_preservation.py","startLine":80,"endLine":90,"chunkText":"    # Check that the command completed successfully\n    assert result.returncode == 0, f\"Command failed with output: {result.stderr}\"\n    \n    # Check that the output files were created and are 24-bit\n    for file in expected_files:\n        assert os.path.exists(file), f\"Output file {file} was not created\"\n        assert os.path.getsize(file) > 0, f\"Output file {file} is empty\"\n        \n        # Verify output is also 24-bit\n        output_bit_depth = get_audio_bit_depth(file)\n        print(f\"Output file {file} bit depth: {output_bit_depth}-bit\")","chunkId":"tests/integration/test_24bit_preservation.py:5"}],[2366,{"filePath":"tests/integration/test_24bit_preservation.py","startLine":88,"endLine":100,"chunkText":"        # Verify output is also 24-bit\n        output_bit_depth = get_audio_bit_depth(file)\n        print(f\"Output file {file} bit depth: {output_bit_depth}-bit\")\n        assert output_bit_depth == 24, f\"Output file should be 24-bit to match input, got {output_bit_depth}-bit\"\n    \n    return result\n\n\ndef validate_audio_output(output_file, reference_dir, waveform_threshold=0.999, spectrogram_threshold=None):\n    \"\"\"Validate an audio output file by comparing its waveform and spectrogram with reference images.\"\"\"\n    if spectrogram_threshold is None:\n        spectrogram_threshold = waveform_threshold\n    ","chunkId":"tests/integration/test_24bit_preservation.py:6"}],[2367,{"filePath":"tests/integration/test_24bit_preservation.py","startLine":98,"endLine":112,"chunkText":"    if spectrogram_threshold is None:\n        spectrogram_threshold = waveform_threshold\n    \n    # Create temporary directory for generated images\n    temp_dir = os.path.join(os.path.dirname(output_file), \"temp_images\")\n    os.makedirs(temp_dir, exist_ok=True)\n    \n    # Generate waveform and spectrogram images for the output file\n    output_filename = os.path.basename(output_file)\n    name_without_ext = os.path.splitext(output_filename)[0]\n    \n    # Generate actual images\n    actual_waveform_path, actual_spectrogram_path = generate_reference_images(\n        output_file, temp_dir, prefix=\"actual_\"\n    )","chunkId":"tests/integration/test_24bit_preservation.py:7"}],[2368,{"filePath":"tests/integration/test_24bit_preservation.py","startLine":110,"endLine":123,"chunkText":"    actual_waveform_path, actual_spectrogram_path = generate_reference_images(\n        output_file, temp_dir, prefix=\"actual_\"\n    )\n    \n    # Path to expected reference images\n    expected_waveform_path = os.path.join(\n        reference_dir, f\"expected_{name_without_ext}_waveform.png\"\n    )\n    expected_spectrogram_path = os.path.join(\n        reference_dir, f\"expected_{name_without_ext}_spectrogram.png\"\n    )\n    \n    # Check if reference images exist\n    if not os.path.exists(expected_waveform_path) or not os.path.exists(expected_spectrogram_path):","chunkId":"tests/integration/test_24bit_preservation.py:8"}],[2369,{"filePath":"tests/integration/test_24bit_preservation.py","startLine":121,"endLine":134,"chunkText":"    \n    # Check if reference images exist\n    if not os.path.exists(expected_waveform_path) or not os.path.exists(expected_spectrogram_path):\n        print(f\"Warning: Reference images not found for {output_file}\")\n        print(f\"Expected: {expected_waveform_path} and {expected_spectrogram_path}\")\n        print(f\"Run generate_reference_images_24bit.py to create them\")\n        return False, False\n    \n    # Compare waveform images\n    waveform_similarity, waveform_match = compare_images(\n        expected_waveform_path, actual_waveform_path,\n        min_similarity_threshold=waveform_threshold\n    )\n    ","chunkId":"tests/integration/test_24bit_preservation.py:9"}],[2370,{"filePath":"tests/integration/test_24bit_preservation.py","startLine":132,"endLine":144,"chunkText":"        min_similarity_threshold=waveform_threshold\n    )\n    \n    # Compare spectrogram images\n    spectrogram_similarity, spectrogram_match = compare_images(\n        expected_spectrogram_path, actual_spectrogram_path,\n        min_similarity_threshold=spectrogram_threshold\n    )\n    \n    print(f\"Validation results for {output_file}:\")\n    print(f\"  Waveform similarity: {waveform_similarity:.4f} \"\n          f\"(match: {waveform_match}, threshold: {waveform_threshold:.2f})\")\n    print(f\"  Spectrogram similarity: {spectrogram_similarity:.4f} \"","chunkId":"tests/integration/test_24bit_preservation.py:10"}],[2371,{"filePath":"tests/integration/test_24bit_preservation.py","startLine":142,"endLine":154,"chunkText":"    print(f\"  Waveform similarity: {waveform_similarity:.4f} \"\n          f\"(match: {waveform_match}, threshold: {waveform_threshold:.2f})\")\n    print(f\"  Spectrogram similarity: {spectrogram_similarity:.4f} \"\n          f\"(match: {spectrogram_match}, threshold: {spectrogram_threshold:.2f})\")\n    \n    return waveform_match, spectrogram_match\n\n\n# Default similarity threshold for 24-bit audio tests\nDEFAULT_SIMILARITY_THRESHOLDS_24BIT = (0.90, 0.80)  # (waveform, spectrogram)\n\n# Model-specific similarity thresholds for 24-bit tests\nMODEL_SIMILARITY_THRESHOLDS_24BIT = {","chunkId":"tests/integration/test_24bit_preservation.py:11"}],[2372,{"filePath":"tests/integration/test_24bit_preservation.py","startLine":152,"endLine":170,"chunkText":"\n# Model-specific similarity thresholds for 24-bit tests\nMODEL_SIMILARITY_THRESHOLDS_24BIT = {\n    # Format: (waveform_threshold, spectrogram_threshold)\n}\n\n\n# Parameterized test for multiple models with 24-bit audio\nMODEL_PARAMS_24BIT = [\n    # (model_filename, expected_output_filenames)\n    (\n        \"model_bs_roformer_ep_317_sdr_12.9755.ckpt\",\n        [\n            \"fallen24bit20s_(Instrumental)_model_bs_roformer_ep_317_sdr_12.flac\",\n            \"fallen24bit20s_(Vocals)_model_bs_roformer_ep_317_sdr_12.flac\",\n        ]\n    ),\n    (\n        \"mel_band_roformer_karaoke_aufr33_viperx_sdr_10.1956.ckpt\",","chunkId":"tests/integration/test_24bit_preservation.py:12"}],[2373,{"filePath":"tests/integration/test_24bit_preservation.py","startLine":168,"endLine":186,"chunkText":"    ),\n    (\n        \"mel_band_roformer_karaoke_aufr33_viperx_sdr_10.1956.ckpt\",\n        [\n            \"fallen24bit20s_(Instrumental)_mel_band_roformer_karaoke_aufr33_viperx_sdr_10.flac\",\n            \"fallen24bit20s_(Vocals)_mel_band_roformer_karaoke_aufr33_viperx_sdr_10.flac\",\n        ]\n    ),\n    (\n        \"MGM_MAIN_v4.pth\",\n        [\n            \"fallen24bit20s_(Instrumental)_MGM_MAIN_v4.flac\",\n            \"fallen24bit20s_(Vocals)_MGM_MAIN_v4.flac\",\n        ]\n    ),\n]\n\n\n@pytest.mark.parametrize(\"model,expected_files\", MODEL_PARAMS_24BIT)","chunkId":"tests/integration/test_24bit_preservation.py:13"}],[2374,{"filePath":"tests/integration/test_24bit_preservation.py","startLine":184,"endLine":198,"chunkText":"\n\n@pytest.mark.parametrize(\"model,expected_files\", MODEL_PARAMS_24BIT)\ndef test_24bit_model_separation(model, expected_files, input_file_24bit, reference_dir, cleanup_output_files):\n    \"\"\"Test that 24-bit input audio produces 24-bit output audio with correct content.\"\"\"\n    print(f\"\\n{'='*60}\")\n    print(f\"Testing 24-bit preservation with model: {model}\")\n    print(f\"{'='*60}\")\n    \n    # Add files to the cleanup list\n    cleanup_output_files.extend(expected_files)\n    \n    # Run the test (includes bit depth validation)\n    run_separation_test_24bit(model, input_file_24bit, expected_files)\n    ","chunkId":"tests/integration/test_24bit_preservation.py:14"}],[2375,{"filePath":"tests/integration/test_24bit_preservation.py","startLine":196,"endLine":210,"chunkText":"    # Run the test (includes bit depth validation)\n    run_separation_test_24bit(model, input_file_24bit, expected_files)\n    \n    # Validate the output audio quality\n    print(f\"\\nValidating output audio quality for model {model}...\")\n    \n    # Get model-specific similarity threshold or use default\n    threshold = MODEL_SIMILARITY_THRESHOLDS_24BIT.get(\n        model, DEFAULT_SIMILARITY_THRESHOLDS_24BIT\n    )\n    waveform_threshold, spectrogram_threshold = threshold\n    \n    print(f\"Using thresholds - waveform: {waveform_threshold}, \"\n          f\"spectrogram: {spectrogram_threshold}\")\n    ","chunkId":"tests/integration/test_24bit_preservation.py:15"}],[2376,{"filePath":"tests/integration/test_24bit_preservation.py","startLine":208,"endLine":219,"chunkText":"    print(f\"Using thresholds - waveform: {waveform_threshold}, \"\n          f\"spectrogram: {spectrogram_threshold}\")\n    \n    for output_file in expected_files:\n        # Skip validation if reference images are not required\n        if os.environ.get(\"SKIP_AUDIO_VALIDATION\") == \"1\":\n            print(f\"Skipping audio validation for {output_file} (SKIP_AUDIO_VALIDATION=1)\")\n            continue\n        \n        waveform_match, spectrogram_match = validate_audio_output(\n            output_file, reference_dir,\n            waveform_threshold=waveform_threshold,","chunkId":"tests/integration/test_24bit_preservation.py:16"}],[2377,{"filePath":"tests/integration/test_24bit_preservation.py","startLine":217,"endLine":229,"chunkText":"        waveform_match, spectrogram_match = validate_audio_output(\n            output_file, reference_dir,\n            waveform_threshold=waveform_threshold,\n            spectrogram_threshold=spectrogram_threshold\n        )\n        \n        # Assert that the output matches the reference\n        assert waveform_match, f\"Waveform for {output_file} does not match the reference\"\n        assert spectrogram_match, f\"Spectrogram for {output_file} does not match the reference\"\n    \n    print(f\"✅ Test passed for {model}: 24-bit preservation and audio quality verified\")\n\n","chunkId":"tests/integration/test_24bit_preservation.py:17"}],[2378,{"filePath":"tests/integration/test_cli_integration.py","startLine":0,"endLine":22,"chunkText":"import os\nimport subprocess\nimport sys\nfrom pathlib import Path\n\nimport pytest\nsys.path.append(os.path.dirname(os.path.dirname(__file__)))\nfrom utils import generate_reference_images, compare_images\n\n\n@pytest.fixture(name=\"input_file\")\ndef fixture_input_file():\n    \"\"\"Fixture providing the test input audio file path.\"\"\"\n    return \"tests/inputs/mardy20s.flac\"\n\n\n@pytest.fixture(name=\"reference_dir\")\ndef fixture_reference_dir():\n    \"\"\"Fixture providing the reference images directory path.\"\"\"\n    return \"tests/inputs/reference\"\n\n\n@pytest.fixture(name=\"cleanup_output_files\")","chunkId":"tests/integration/test_cli_integration.py:0"}],[2379,{"filePath":"tests/integration/test_cli_integration.py","startLine":21,"endLine":38,"chunkText":"\n@pytest.fixture(name=\"cleanup_output_files\")\ndef fixture_cleanup_output_files():\n    \"\"\"Fixture to clean up output files before and after test.\"\"\"\n    # This list will be populated by the test functions\n    output_files = []\n\n    # Yield to allow the test to run and add files to the list\n    yield output_files\n\n    # Clean up output files after test\n    for file in output_files:\n        if os.path.exists(file):\n            print(f\"Test output file exists: {file}\")\n            os.remove(file)\n\n\ndef run_separation_test(model, audio_path, expected_files):","chunkId":"tests/integration/test_cli_integration.py:1"}],[2380,{"filePath":"tests/integration/test_cli_integration.py","startLine":36,"endLine":49,"chunkText":"\n\ndef run_separation_test(model, audio_path, expected_files):\n    \"\"\"Helper function to run a separation test with a specific model.\"\"\"\n    # Clean up any existing output files before the test\n    for file in expected_files:\n        if os.path.exists(file):\n            print(f\"Deleting existing test output file {file}\")\n            os.remove(file)\n\n    # Run the CLI command\n    result = subprocess.run([\"audio-separator\", \"-m\", model, audio_path], capture_output=True, text=True, check=False)  # Explicitly set check to False as we handle errors manually\n\n    # Check that the command completed successfully","chunkId":"tests/integration/test_cli_integration.py:2"}],[2381,{"filePath":"tests/integration/test_cli_integration.py","startLine":47,"endLine":59,"chunkText":"    result = subprocess.run([\"audio-separator\", \"-m\", model, audio_path], capture_output=True, text=True, check=False)  # Explicitly set check to False as we handle errors manually\n\n    # Check that the command completed successfully\n    assert result.returncode == 0, f\"Command failed with output: {result.stderr}\"\n\n    # Check that the output files were created\n    for file in expected_files:\n        assert os.path.exists(file), f\"Output file {file} was not created\"\n        assert os.path.getsize(file) > 0, f\"Output file {file} is empty\"\n\n    return result\n\n","chunkId":"tests/integration/test_cli_integration.py:3"}],[2382,{"filePath":"tests/integration/test_cli_integration.py","startLine":57,"endLine":69,"chunkText":"    return result\n\n\ndef validate_audio_output(output_file, reference_dir, waveform_threshold=0.999, spectrogram_threshold=None):\n    \"\"\"Validate an audio output file by comparing its waveform and spectrogram with reference images.\n\n    Args:\n        output_file: Path to the audio output file\n        reference_dir: Directory containing reference images\n        waveform_threshold: Minimum similarity required for waveform images (0.0-1.0)\n        spectrogram_threshold: Minimum similarity for spectrogram images (0.0-1.0), defaults to waveform_threshold if None\n\n    Returns:","chunkId":"tests/integration/test_cli_integration.py:4"}],[2383,{"filePath":"tests/integration/test_cli_integration.py","startLine":67,"endLine":79,"chunkText":"        spectrogram_threshold: Minimum similarity for spectrogram images (0.0-1.0), defaults to waveform_threshold if None\n\n    Returns:\n        Tuple of booleans: (waveform_match, spectrogram_match)\n    \"\"\"\n    # If spectrogram threshold not specified, use the same as waveform threshold\n    if spectrogram_threshold is None:\n        spectrogram_threshold = waveform_threshold\n\n    # Create temporary directory for generated images\n    temp_dir = os.path.join(os.path.dirname(output_file), \"temp_images\")\n    os.makedirs(temp_dir, exist_ok=True)\n","chunkId":"tests/integration/test_cli_integration.py:5"}],[2384,{"filePath":"tests/integration/test_cli_integration.py","startLine":77,"endLine":88,"chunkText":"    temp_dir = os.path.join(os.path.dirname(output_file), \"temp_images\")\n    os.makedirs(temp_dir, exist_ok=True)\n\n    # Generate waveform and spectrogram images for the output file\n    output_filename = os.path.basename(output_file)\n    name_without_ext = os.path.splitext(output_filename)[0]\n\n    # Generate actual images\n    actual_waveform_path, actual_spectrogram_path = generate_reference_images(output_file, temp_dir, prefix=\"actual_\")\n\n    # Path to expected reference images\n    expected_waveform_path = os.path.join(reference_dir, f\"expected_{name_without_ext}_waveform.png\")","chunkId":"tests/integration/test_cli_integration.py:6"}],[2385,{"filePath":"tests/integration/test_cli_integration.py","startLine":86,"endLine":97,"chunkText":"\n    # Path to expected reference images\n    expected_waveform_path = os.path.join(reference_dir, f\"expected_{name_without_ext}_waveform.png\")\n    expected_spectrogram_path = os.path.join(reference_dir, f\"expected_{name_without_ext}_spectrogram.png\")\n\n    # Check if reference images exist\n    if not os.path.exists(expected_waveform_path) or not os.path.exists(expected_spectrogram_path):\n        print(f\"Warning: Reference images not found for {output_file}\")\n        print(f\"Expected: {expected_waveform_path} and {expected_spectrogram_path}\")\n        return False, False\n\n    # Compare waveform images","chunkId":"tests/integration/test_cli_integration.py:7"}],[2386,{"filePath":"tests/integration/test_cli_integration.py","startLine":95,"endLine":104,"chunkText":"        return False, False\n\n    # Compare waveform images\n    waveform_similarity, waveform_match = compare_images(expected_waveform_path, actual_waveform_path, min_similarity_threshold=waveform_threshold)\n\n    # Compare spectrogram images\n    spectrogram_similarity, spectrogram_match = compare_images(expected_spectrogram_path, actual_spectrogram_path, min_similarity_threshold=spectrogram_threshold)\n\n    print(f\"Validation results for {output_file}:\\n\")\n    print(f\"  Waveform similarity: {waveform_similarity:.4f} (match: {waveform_match}, threshold: {waveform_threshold:.2f})\\n\")","chunkId":"tests/integration/test_cli_integration.py:8"}],[2387,{"filePath":"tests/integration/test_cli_integration.py","startLine":102,"endLine":114,"chunkText":"\n    print(f\"Validation results for {output_file}:\\n\")\n    print(f\"  Waveform similarity: {waveform_similarity:.4f} (match: {waveform_match}, threshold: {waveform_threshold:.2f})\\n\")\n    print(f\"  Spectrogram similarity: {spectrogram_similarity:.4f} (match: {spectrogram_match}, threshold: {spectrogram_threshold:.2f})\\n\")\n\n    # Cleanup temp images (optional, uncomment if needed)\n    # os.remove(actual_waveform_path)\n    # os.remove(actual_spectrogram_path)\n\n    return waveform_match, spectrogram_match\n\n\n# Default similarity threshold to use for most models","chunkId":"tests/integration/test_cli_integration.py:9"}],[2388,{"filePath":"tests/integration/test_cli_integration.py","startLine":112,"endLine":127,"chunkText":"\n\n# Default similarity threshold to use for most models\nDEFAULT_SIMILARITY_THRESHOLDS = (0.90, 0.80)  # (waveform_threshold, spectrogram_threshold)\n\n# Model-specific similarity thresholds\n# Use lower thresholds for models that show more variation between runs\nMODEL_SIMILARITY_THRESHOLDS = {\n    # Format: (waveform_threshold, spectrogram_threshold)\n    \"htdemucs_6s.yaml\": (0.90, 0.70)  # Demucs multi-stem output (e.g. \"Other\" and \"Piano\") is a lot more variable\n}\n\n\n# Parameterized test for multiple models\nMODEL_PARAMS = [\n    # (model_filename, expected_output_filenames)","chunkId":"tests/integration/test_cli_integration.py:10"}],[2389,{"filePath":"tests/integration/test_cli_integration.py","startLine":125,"endLine":133,"chunkText":"# Parameterized test for multiple models\nMODEL_PARAMS = [\n    # (model_filename, expected_output_filenames)\n    (\"mel_band_roformer_karaoke_aufr33_viperx_sdr_10.1956.ckpt\", [\n        \"mardy20s_(Instrumental)_mel_band_roformer_karaoke_aufr33_viperx_sdr_10.flac\",\n        \"mardy20s_(Vocals)_mel_band_roformer_karaoke_aufr33_viperx_sdr_10.flac\",\n    ]),\n    (\"kuielab_b_vocals.onnx\", [\"mardy20s_(Instrumental)_kuielab_b_vocals.flac\", \"mardy20s_(Vocals)_kuielab_b_vocals.flac\"]),\n    (\"MGM_MAIN_v4.pth\", [\"mardy20s_(Instrumental)_MGM_MAIN_v4.flac\", \"mardy20s_(Vocals)_MGM_MAIN_v4.flac\"]),","chunkId":"tests/integration/test_cli_integration.py:11"}],[2390,{"filePath":"tests/integration/test_cli_integration.py","startLine":131,"endLine":139,"chunkText":"    ]),\n    (\"kuielab_b_vocals.onnx\", [\"mardy20s_(Instrumental)_kuielab_b_vocals.flac\", \"mardy20s_(Vocals)_kuielab_b_vocals.flac\"]),\n    (\"MGM_MAIN_v4.pth\", [\"mardy20s_(Instrumental)_MGM_MAIN_v4.flac\", \"mardy20s_(Vocals)_MGM_MAIN_v4.flac\"]),\n    (\"UVR-MDX-NET-Inst_HQ_4.onnx\", [\"mardy20s_(Instrumental)_UVR-MDX-NET-Inst_HQ_4.flac\", \"mardy20s_(Vocals)_UVR-MDX-NET-Inst_HQ_4.flac\"]),\n    (\"2_HP-UVR.pth\", [\"mardy20s_(Instrumental)_2_HP-UVR.flac\", \"mardy20s_(Vocals)_2_HP-UVR.flac\"]),\n    (\n        \"htdemucs_6s.yaml\",\n        [\n            \"mardy20s_(Vocals)_htdemucs_6s.flac\",","chunkId":"tests/integration/test_cli_integration.py:12"}],[2391,{"filePath":"tests/integration/test_cli_integration.py","startLine":137,"endLine":147,"chunkText":"        \"htdemucs_6s.yaml\",\n        [\n            \"mardy20s_(Vocals)_htdemucs_6s.flac\",\n            \"mardy20s_(Drums)_htdemucs_6s.flac\",\n            \"mardy20s_(Bass)_htdemucs_6s.flac\",\n            \"mardy20s_(Other)_htdemucs_6s.flac\",\n            \"mardy20s_(Guitar)_htdemucs_6s.flac\",\n            \"mardy20s_(Piano)_htdemucs_6s.flac\",\n        ],\n    ),\n    (\"model_bs_roformer_ep_937_sdr_10.5309.ckpt\", [\"mardy20s_(Drum-Bass)_model_bs_roformer_ep_937_sdr_10.flac\", \"mardy20s_(No Drum-Bass)_model_bs_roformer_ep_937_sdr_10.flac\"]),","chunkId":"tests/integration/test_cli_integration.py:13"}],[2392,{"filePath":"tests/integration/test_cli_integration.py","startLine":145,"endLine":154,"chunkText":"        ],\n    ),\n    (\"model_bs_roformer_ep_937_sdr_10.5309.ckpt\", [\"mardy20s_(Drum-Bass)_model_bs_roformer_ep_937_sdr_10.flac\", \"mardy20s_(No Drum-Bass)_model_bs_roformer_ep_937_sdr_10.flac\"]),\n    (\"model_bs_roformer_ep_317_sdr_12.9755.ckpt\", [\"mardy20s_(Instrumental)_model_bs_roformer_ep_317_sdr_12.flac\", \"mardy20s_(Vocals)_model_bs_roformer_ep_317_sdr_12.flac\"]),\n]\n\n\n@pytest.mark.parametrize(\"model,expected_files\", MODEL_PARAMS)\ndef test_model_separation(model, expected_files, input_file, reference_dir, cleanup_output_files):\n    \"\"\"Parameterized test for multiple model files.\"\"\"","chunkId":"tests/integration/test_cli_integration.py:14"}],[2393,{"filePath":"tests/integration/test_cli_integration.py","startLine":152,"endLine":164,"chunkText":"@pytest.mark.parametrize(\"model,expected_files\", MODEL_PARAMS)\ndef test_model_separation(model, expected_files, input_file, reference_dir, cleanup_output_files):\n    \"\"\"Parameterized test for multiple model files.\"\"\"\n    # Add files to the cleanup list\n    cleanup_output_files.extend(expected_files)\n\n    # Run the test\n    run_separation_test(model, input_file, expected_files)\n\n    # Validate the output audio files\n    print(f\"\\nValidating output files for model {model}...\")\n\n    # Get model-specific similarity threshold or use default","chunkId":"tests/integration/test_cli_integration.py:15"}],[2394,{"filePath":"tests/integration/test_cli_integration.py","startLine":162,"endLine":173,"chunkText":"    print(f\"\\nValidating output files for model {model}...\")\n\n    # Get model-specific similarity threshold or use default\n    threshold = MODEL_SIMILARITY_THRESHOLDS.get(model, DEFAULT_SIMILARITY_THRESHOLDS)\n\n    # Unpack thresholds - DEFAULT_SIMILARITY_THRESHOLDS is now always a tuple\n    waveform_threshold, spectrogram_threshold = threshold\n\n    print(f\"Using thresholds - waveform: {waveform_threshold}, spectrogram: {spectrogram_threshold} for model {model}\")\n\n    for output_file in expected_files:\n        # Skip validation if reference images are not required (set environment variable to skip)","chunkId":"tests/integration/test_cli_integration.py:16"}],[2395,{"filePath":"tests/integration/test_cli_integration.py","startLine":171,"endLine":180,"chunkText":"\n    for output_file in expected_files:\n        # Skip validation if reference images are not required (set environment variable to skip)\n        if os.environ.get(\"SKIP_AUDIO_VALIDATION\") == \"1\":\n            print(f\"Skipping audio validation for {output_file} (SKIP_AUDIO_VALIDATION=1)\")\n            continue\n\n        waveform_match, spectrogram_match = validate_audio_output(output_file, reference_dir, waveform_threshold=waveform_threshold, spectrogram_threshold=spectrogram_threshold)\n\n        # Assert that the output matches the reference","chunkId":"tests/integration/test_cli_integration.py:17"}],[2396,{"filePath":"tests/integration/test_cli_integration.py","startLine":178,"endLine":183,"chunkText":"        waveform_match, spectrogram_match = validate_audio_output(output_file, reference_dir, waveform_threshold=waveform_threshold, spectrogram_threshold=spectrogram_threshold)\n\n        # Assert that the output matches the reference\n        assert waveform_match, f\"Waveform for {output_file} does not match the reference\"\n        assert spectrogram_match, f\"Spectrogram for {output_file} does not match the reference\"\n","chunkId":"tests/integration/test_cli_integration.py:18"}],[2397,{"filePath":"tests/integration/test_remote_api_integration.py","startLine":0,"endLine":22,"chunkText":"import json\nimport pytest\nimport logging\nimport tempfile\nimport os\nimport time\nfrom pathlib import Path\nfrom unittest.mock import Mock, patch\nfrom http.server import HTTPServer, BaseHTTPRequestHandler\nimport threading\nimport urllib.parse\nimport io\n\nfrom audio_separator.remote import AudioSeparatorAPIClient\n\n\n# Mock API Server for Integration Tests\nclass MockAPIHandler(BaseHTTPRequestHandler):\n    \"\"\"Mock HTTP handler for simulating the Audio Separator API.\"\"\"\n\n    # Class variables to store state across requests\n    jobs = {}\n    models_data = {","chunkId":"tests/integration/test_remote_api_integration.py:0"}],[2398,{"filePath":"tests/integration/test_remote_api_integration.py","startLine":20,"endLine":32,"chunkText":"    # Class variables to store state across requests\n    jobs = {}\n    models_data = {\n        \"model1.ckpt\": {\"Type\": \"MDXC\", \"Stems\": [\"vocals (12.9)\", \"instrumental (17.0)\"], \"Name\": \"Test Model 1\"},\n        \"model2.onnx\": {\"Type\": \"MDX\", \"Stems\": [\"vocals (10.5)\", \"instrumental (15.2)\"], \"Name\": \"Test Model 2\"},\n        \"htdemucs_6s.yaml\": {\"Type\": \"Demucs\", \"Stems\": [\"vocals (9.7)\", \"drums (8.5)\", \"bass (10.0)\", \"guitar\", \"piano\", \"other\"], \"Name\": \"Test Demucs 6s\"},\n    }\n\n    def log_message(self, format, *args):\n        \"\"\"Suppress HTTP server logs.\"\"\"\n        pass\n\n    def do_GET(self):","chunkId":"tests/integration/test_remote_api_integration.py:1"}],[2399,{"filePath":"tests/integration/test_remote_api_integration.py","startLine":30,"endLine":46,"chunkText":"        pass\n\n    def do_GET(self):\n        \"\"\"Handle GET requests.\"\"\"\n        path = self.path\n\n        if path == \"/health\":\n            self.send_json_response({\"status\": \"healthy\", \"version\": \"1.0.0\"})\n\n        elif path.startswith(\"/status/\"):\n            task_id = path.split(\"/\")[-1]\n            if task_id in self.jobs:\n                self.send_json_response(self.jobs[task_id])\n            else:\n                self.send_json_response({\"task_id\": task_id, \"status\": \"not_found\", \"error\": \"Job not found\"}, status=404)\n\n        elif path.startswith(\"/download/\"):","chunkId":"tests/integration/test_remote_api_integration.py:2"}],[2400,{"filePath":"tests/integration/test_remote_api_integration.py","startLine":44,"endLine":55,"chunkText":"                self.send_json_response({\"task_id\": task_id, \"status\": \"not_found\", \"error\": \"Job not found\"}, status=404)\n\n        elif path.startswith(\"/download/\"):\n            # Parse task_id and filename from path like /download/task123/output.wav\n            parts = path.split(\"/\")\n            if len(parts) >= 4:\n                task_id = parts[2]\n                filename = urllib.parse.unquote(parts[3])\n\n                # Check if job exists and is completed\n                if task_id in self.jobs and self.jobs[task_id][\"status\"] == \"completed\":\n                    # Simulate audio file content","chunkId":"tests/integration/test_remote_api_integration.py:3"}],[2401,{"filePath":"tests/integration/test_remote_api_integration.py","startLine":53,"endLine":62,"chunkText":"                # Check if job exists and is completed\n                if task_id in self.jobs and self.jobs[task_id][\"status\"] == \"completed\":\n                    # Simulate audio file content\n                    audio_content = b\"fake audio file content for \" + filename.encode()\n                    self.send_response(200)\n                    self.send_header(\"Content-Type\", \"audio/wav\")\n                    self.send_header(\"Content-Disposition\", f\"attachment; filename={filename}\")\n                    self.end_headers()\n                    self.wfile.write(audio_content)\n                else:","chunkId":"tests/integration/test_remote_api_integration.py:4"}],[2402,{"filePath":"tests/integration/test_remote_api_integration.py","startLine":60,"endLine":71,"chunkText":"                    self.end_headers()\n                    self.wfile.write(audio_content)\n                else:\n                    self.send_json_response({\"error\": \"File not found\"}, status=404)\n            else:\n                self.send_json_response({\"error\": \"Invalid download path\"}, status=400)\n\n        elif path == \"/models\" or path.startswith(\"/models?\"):\n            # Parse query parameters\n            if \"?\" in path:\n                query_string = path.split(\"?\", 1)[1]\n                params = urllib.parse.parse_qs(query_string)","chunkId":"tests/integration/test_remote_api_integration.py:5"}],[2403,{"filePath":"tests/integration/test_remote_api_integration.py","startLine":70,"endLine":81,"chunkText":"                query_string = path.split(\"?\", 1)[1]\n                params = urllib.parse.parse_qs(query_string)\n                filter_by = params.get(\"filter_sort_by\", [None])[0]\n            else:\n                filter_by = None\n\n            # Filter models if requested\n            filtered_models = self.models_data\n            if filter_by:\n                filtered_models = {k: v for k, v in self.models_data.items() if filter_by.lower() in \" \".join(v[\"Stems\"]).lower() or filter_by.lower() in v[\"Name\"].lower()}\n\n            # Format as plain text (like the real API)","chunkId":"tests/integration/test_remote_api_integration.py:6"}],[2404,{"filePath":"tests/integration/test_remote_api_integration.py","startLine":79,"endLine":91,"chunkText":"                filtered_models = {k: v for k, v in self.models_data.items() if filter_by.lower() in \" \".join(v[\"Stems\"]).lower() or filter_by.lower() in v[\"Name\"].lower()}\n\n            # Format as plain text (like the real API)\n            text_output = self._format_models_as_text(filtered_models)\n            self.send_response(200)\n            self.send_header(\"Content-Type\", \"text/plain\")\n            self.end_headers()\n            self.wfile.write(text_output.encode())\n\n        elif path == \"/models-json\":\n            self.send_json_response(self.models_data)\n\n        else:","chunkId":"tests/integration/test_remote_api_integration.py:7"}],[2405,{"filePath":"tests/integration/test_remote_api_integration.py","startLine":89,"endLine":106,"chunkText":"            self.send_json_response(self.models_data)\n\n        else:\n            self.send_json_response({\"error\": \"Not found\"}, status=404)\n\n    def do_POST(self):\n        \"\"\"Handle POST requests.\"\"\"\n        if self.path == \"/separate\":\n            content_length = int(self.headers[\"Content-Length\"])\n            post_data = self.rfile.read(content_length)\n\n            # Simple task ID generation\n            task_id = f\"task-{len(self.jobs) + 1:03d}\"\n\n            # Initialize job status\n            self.jobs[task_id] = {\n                \"task_id\": task_id,\n                \"status\": \"submitted\",","chunkId":"tests/integration/test_remote_api_integration.py:8"}],[2406,{"filePath":"tests/integration/test_remote_api_integration.py","startLine":104,"endLine":118,"chunkText":"            self.jobs[task_id] = {\n                \"task_id\": task_id,\n                \"status\": \"submitted\",\n                \"progress\": 0,\n                \"original_filename\": \"test.wav\",\n                \"models_used\": [\"default\"],\n                \"total_models\": 1,\n                \"current_model_index\": 0,\n                \"files\": [],\n            }\n\n            # Simulate processing in background (for testing polling)\n            threading.Thread(target=self._simulate_processing, args=(task_id,)).start()\n\n            self.send_json_response(","chunkId":"tests/integration/test_remote_api_integration.py:9"}],[2407,{"filePath":"tests/integration/test_remote_api_integration.py","startLine":116,"endLine":128,"chunkText":"            threading.Thread(target=self._simulate_processing, args=(task_id,)).start()\n\n            self.send_json_response(\n                {\"task_id\": task_id, \"status\": \"submitted\", \"message\": \"Job submitted for processing\", \"models_used\": [\"default\"], \"total_models\": 1, \"original_filename\": \"test.wav\"}\n            )\n        else:\n            self.send_json_response({\"error\": \"Not found\"}, status=404)\n\n    def _simulate_processing(self, task_id):\n        \"\"\"Simulate job processing in background.\"\"\"\n        time.sleep(0.1)  # Brief delay\n\n        # Update to processing","chunkId":"tests/integration/test_remote_api_integration.py:10"}],[2408,{"filePath":"tests/integration/test_remote_api_integration.py","startLine":126,"endLine":146,"chunkText":"        time.sleep(0.1)  # Brief delay\n\n        # Update to processing\n        self.jobs[task_id].update({\"status\": \"processing\", \"progress\": 25})\n\n        time.sleep(0.1)\n\n        # Update progress\n        self.jobs[task_id].update({\"progress\": 50})\n\n        time.sleep(0.1)\n\n        # Update progress\n        self.jobs[task_id].update({\"progress\": 75})\n\n        time.sleep(0.1)\n\n        # Complete\n        self.jobs[task_id].update({\"status\": \"completed\", \"progress\": 100, \"files\": [\"test_(Vocals)_default.flac\", \"test_(Instrumental)_default.flac\"]})\n\n    def _format_models_as_text(self, models):","chunkId":"tests/integration/test_remote_api_integration.py:11"}],[2409,{"filePath":"tests/integration/test_remote_api_integration.py","startLine":144,"endLine":153,"chunkText":"        self.jobs[task_id].update({\"status\": \"completed\", \"progress\": 100, \"files\": [\"test_(Vocals)_default.flac\", \"test_(Instrumental)_default.flac\"]})\n\n    def _format_models_as_text(self, models):\n        \"\"\"Format models dictionary as plain text table.\"\"\"\n        if not models:\n            return \"No models found\"\n\n        # Calculate column widths\n        filename_width = max(len(\"Model Filename\"), max(len(k) for k in models.keys()))\n        arch_width = max(len(\"Arch\"), max(len(v[\"Type\"]) for v in models.values()))","chunkId":"tests/integration/test_remote_api_integration.py:12"}],[2410,{"filePath":"tests/integration/test_remote_api_integration.py","startLine":151,"endLine":160,"chunkText":"        # Calculate column widths\n        filename_width = max(len(\"Model Filename\"), max(len(k) for k in models.keys()))\n        arch_width = max(len(\"Arch\"), max(len(v[\"Type\"]) for v in models.values()))\n        stems_width = max(len(\"Output Stems (SDR)\"), max(len(\", \".join(v[\"Stems\"])) for v in models.values()))\n        name_width = max(len(\"Friendly Name\"), max(len(v[\"Name\"]) for v in models.values()))\n\n        total_width = filename_width + arch_width + stems_width + name_width + 15\n\n        lines = []\n        lines.append(\"-\" * total_width)","chunkId":"tests/integration/test_remote_api_integration.py:13"}],[2411,{"filePath":"tests/integration/test_remote_api_integration.py","startLine":158,"endLine":171,"chunkText":"\n        lines = []\n        lines.append(\"-\" * total_width)\n        lines.append(f\"{'Model Filename':<{filename_width}}  {'Arch':<{arch_width}}  {'Output Stems (SDR)':<{stems_width}}  {'Friendly Name'}\")\n        lines.append(\"-\" * total_width)\n\n        for filename, info in models.items():\n            stems = \", \".join(info[\"Stems\"])\n            lines.append(f\"{filename:<{filename_width}}  {info['Type']:<{arch_width}}  {stems:<{stems_width}}  {info['Name']}\")\n\n        return \"\\n\".join(lines)\n\n    def send_json_response(self, data, status=200):\n        \"\"\"Send JSON response.\"\"\"","chunkId":"tests/integration/test_remote_api_integration.py:14"}],[2412,{"filePath":"tests/integration/test_remote_api_integration.py","startLine":169,"endLine":186,"chunkText":"\n    def send_json_response(self, data, status=200):\n        \"\"\"Send JSON response.\"\"\"\n        self.send_response(status)\n        self.send_header(\"Content-Type\", \"application/json\")\n        self.end_headers()\n        response = json.dumps(data).encode()\n        self.wfile.write(response)\n\n\n@pytest.fixture(scope=\"function\")\ndef mock_http_server():\n    \"\"\"Start a mock HTTP server for testing.\"\"\"\n    server = HTTPServer((\"localhost\", 0), MockAPIHandler)\n    server_thread = threading.Thread(target=server.serve_forever)\n    server_thread.daemon = True\n    server_thread.start()\n","chunkId":"tests/integration/test_remote_api_integration.py:15"}],[2413,{"filePath":"tests/integration/test_remote_api_integration.py","startLine":184,"endLine":206,"chunkText":"    server_thread.daemon = True\n    server_thread.start()\n\n    # Get the actual port the server is using\n    port = server.server_address[1]\n    base_url = f\"http://localhost:{port}\"\n\n    yield base_url\n\n    server.shutdown()\n    server.server_close()\n\n\n@pytest.fixture\ndef api_client(mock_http_server):\n    \"\"\"Create an API client connected to the mock server.\"\"\"\n    logger = logging.getLogger(\"test\")\n    return AudioSeparatorAPIClient(mock_http_server, logger)\n\n\n@pytest.fixture\ndef test_audio_file():\n    \"\"\"Create a temporary test audio file.\"\"\"","chunkId":"tests/integration/test_remote_api_integration.py:16"}],[2414,{"filePath":"tests/integration/test_remote_api_integration.py","startLine":205,"endLine":212,"chunkText":"def test_audio_file():\n    \"\"\"Create a temporary test audio file.\"\"\"\n    with tempfile.NamedTemporaryFile(suffix=\".wav\", delete=False) as f:\n        f.write(b\"fake audio content for testing\")\n        yield f.name\n    os.unlink(f.name)\n\n","chunkId":"tests/integration/test_remote_api_integration.py:17"}],[2415,{"filePath":"tests/integration/test_remote_api_integration.py","startLine":213,"endLine":228,"chunkText":"class TestRemoteAPIIntegration:\n    \"\"\"Integration tests for the Remote API functionality.\"\"\"\n\n    def test_server_health_check(self, api_client):\n        \"\"\"Test server health check endpoint.\"\"\"\n        version = api_client.get_server_version()\n        assert version == \"1.0.0\"\n\n    def test_list_models_pretty_format(self, api_client):\n        \"\"\"Test listing models in pretty format.\"\"\"\n        result = api_client.list_models(format_type=\"pretty\")\n\n        assert \"text\" in result\n        text = result[\"text\"]\n        assert \"Model Filename\" in text\n        assert \"model1.ckpt\" in text","chunkId":"tests/integration/test_remote_api_integration.py:18"}],[2416,{"filePath":"tests/integration/test_remote_api_integration.py","startLine":226,"endLine":240,"chunkText":"        text = result[\"text\"]\n        assert \"Model Filename\" in text\n        assert \"model1.ckpt\" in text\n        assert \"model2.onnx\" in text\n        assert \"htdemucs_6s.yaml\" in text\n        assert \"Test Model 1\" in text\n\n    def test_list_models_json_format(self, api_client):\n        \"\"\"Test listing models in JSON format.\"\"\"\n        result = api_client.list_models(format_type=\"json\")\n\n        assert \"model1.ckpt\" in result\n        assert \"model2.onnx\" in result\n        assert \"htdemucs_6s.yaml\" in result\n        assert result[\"model1.ckpt\"][\"Type\"] == \"MDXC\"","chunkId":"tests/integration/test_remote_api_integration.py:19"}],[2417,{"filePath":"tests/integration/test_remote_api_integration.py","startLine":238,"endLine":251,"chunkText":"        assert \"model2.onnx\" in result\n        assert \"htdemucs_6s.yaml\" in result\n        assert result[\"model1.ckpt\"][\"Type\"] == \"MDXC\"\n        assert result[\"model2.onnx\"][\"Type\"] == \"MDX\"\n        assert result[\"htdemucs_6s.yaml\"][\"Type\"] == \"Demucs\"\n\n    def test_list_models_with_filter(self, api_client):\n        \"\"\"Test listing models with filter.\"\"\"\n        result = api_client.list_models(filter_by=\"vocals\")\n\n        assert \"text\" in result\n        text = result[\"text\"]\n        # Should include models that have \"vocals\" in their stems\n        assert \"model1.ckpt\" in text","chunkId":"tests/integration/test_remote_api_integration.py:20"}],[2418,{"filePath":"tests/integration/test_remote_api_integration.py","startLine":249,"endLine":262,"chunkText":"        text = result[\"text\"]\n        # Should include models that have \"vocals\" in their stems\n        assert \"model1.ckpt\" in text\n        assert \"model2.onnx\" in text\n        assert \"htdemucs_6s.yaml\" in text\n\n    def test_separate_audio_submission(self, api_client, test_audio_file):\n        \"\"\"Test audio separation job submission.\"\"\"\n        result = api_client.separate_audio(test_audio_file)\n\n        assert \"task_id\" in result\n        assert result[\"status\"] == \"submitted\"\n        assert result[\"models_used\"] == [\"default\"]\n        assert result[\"total_models\"] == 1","chunkId":"tests/integration/test_remote_api_integration.py:21"}],[2419,{"filePath":"tests/integration/test_remote_api_integration.py","startLine":260,"endLine":274,"chunkText":"        assert result[\"status\"] == \"submitted\"\n        assert result[\"models_used\"] == [\"default\"]\n        assert result[\"total_models\"] == 1\n        assert result[\"original_filename\"] == \"test.wav\"\n\n        task_id = result[\"task_id\"]\n        assert task_id.startswith(\"task-\")\n\n    def test_separate_audio_with_custom_parameters(self, api_client, test_audio_file):\n        \"\"\"Test audio separation with custom parameters.\"\"\"\n        result = api_client.separate_audio(\n            test_audio_file,\n            model=\"model1.ckpt\",\n            output_format=\"wav\",\n            normalization_threshold=0.8,","chunkId":"tests/integration/test_remote_api_integration.py:22"}],[2420,{"filePath":"tests/integration/test_remote_api_integration.py","startLine":272,"endLine":288,"chunkText":"            model=\"model1.ckpt\",\n            output_format=\"wav\",\n            normalization_threshold=0.8,\n            mdx_segment_size=512,\n            vr_aggression=10,\n            custom_output_names={\"Vocals\": \"lead_vocals\", \"Instrumental\": \"backing_track\"},\n        )\n\n        assert result[\"status\"] == \"submitted\"\n        assert \"task_id\" in result\n\n    def test_job_status_polling(self, api_client, test_audio_file):\n        \"\"\"Test job status polling through completion.\"\"\"\n        # Submit job\n        result = api_client.separate_audio(test_audio_file)\n        task_id = result[\"task_id\"]\n","chunkId":"tests/integration/test_remote_api_integration.py:23"}],[2421,{"filePath":"tests/integration/test_remote_api_integration.py","startLine":286,"endLine":302,"chunkText":"        result = api_client.separate_audio(test_audio_file)\n        task_id = result[\"task_id\"]\n\n        # Poll until completion\n        max_attempts = 20  # Prevent infinite loop\n        attempts = 0\n\n        while attempts < max_attempts:\n            status = api_client.get_job_status(task_id)\n\n            assert status[\"task_id\"] == task_id\n            assert \"status\" in status\n\n            if status[\"status\"] == \"completed\":\n                assert status[\"progress\"] == 100\n                assert \"files\" in status\n                assert len(status[\"files\"]) == 2  # Vocals and Instrumental","chunkId":"tests/integration/test_remote_api_integration.py:24"}],[2422,{"filePath":"tests/integration/test_remote_api_integration.py","startLine":300,"endLine":314,"chunkText":"                assert status[\"progress\"] == 100\n                assert \"files\" in status\n                assert len(status[\"files\"]) == 2  # Vocals and Instrumental\n                break\n            elif status[\"status\"] in [\"submitted\", \"processing\"]:\n                # Continue polling\n                time.sleep(0.05)  # Small delay between polls\n            else:\n                pytest.fail(f\"Unexpected job status: {status['status']}\")\n\n            attempts += 1\n\n        if attempts >= max_attempts:\n            pytest.fail(\"Job did not complete within expected time\")\n","chunkId":"tests/integration/test_remote_api_integration.py:25"}],[2423,{"filePath":"tests/integration/test_remote_api_integration.py","startLine":312,"endLine":327,"chunkText":"        if attempts >= max_attempts:\n            pytest.fail(\"Job did not complete within expected time\")\n\n    def test_file_download(self, api_client, test_audio_file):\n        \"\"\"Test downloading files from completed job.\"\"\"\n        # Submit and wait for completion\n        result = api_client.separate_audio(test_audio_file)\n        task_id = result[\"task_id\"]\n\n        # Wait for completion (simplified polling)\n        for _ in range(20):\n            status = api_client.get_job_status(task_id)\n            if status[\"status\"] == \"completed\":\n                break\n            time.sleep(0.05)\n","chunkId":"tests/integration/test_remote_api_integration.py:26"}],[2424,{"filePath":"tests/integration/test_remote_api_integration.py","startLine":325,"endLine":342,"chunkText":"                break\n            time.sleep(0.05)\n\n        assert status[\"status\"] == \"completed\"\n        assert len(status[\"files\"]) == 2\n\n        # Download first file\n        filename = status[\"files\"][0]\n        with tempfile.NamedTemporaryFile(delete=False) as temp_output:\n            output_path = temp_output.name\n\n        try:\n            downloaded_path = api_client.download_file(task_id, filename, output_path)\n            assert downloaded_path == output_path\n            assert os.path.exists(output_path)\n\n            # Verify file content\n            with open(output_path, \"rb\") as f:","chunkId":"tests/integration/test_remote_api_integration.py:27"}],[2425,{"filePath":"tests/integration/test_remote_api_integration.py","startLine":340,"endLine":352,"chunkText":"\n            # Verify file content\n            with open(output_path, \"rb\") as f:\n                content = f.read()\n                assert content.startswith(b\"fake audio file content\")\n                assert filename.encode() in content\n        finally:\n            if os.path.exists(output_path):\n                os.unlink(output_path)\n\n    def test_separate_audio_and_wait_success(self, api_client, test_audio_file):\n        \"\"\"Test the convenience method for separating audio and waiting for completion.\"\"\"\n        with tempfile.TemporaryDirectory() as temp_dir:","chunkId":"tests/integration/test_remote_api_integration.py:28"}],[2426,{"filePath":"tests/integration/test_remote_api_integration.py","startLine":350,"endLine":359,"chunkText":"    def test_separate_audio_and_wait_success(self, api_client, test_audio_file):\n        \"\"\"Test the convenience method for separating audio and waiting for completion.\"\"\"\n        with tempfile.TemporaryDirectory() as temp_dir:\n            result = api_client.separate_audio_and_wait(test_audio_file, timeout=5, poll_interval=0.05, download=True, output_dir=temp_dir)\n\n            assert result[\"status\"] == \"completed\"\n            assert \"task_id\" in result\n            assert \"files\" in result\n            assert \"downloaded_files\" in result\n            assert len(result[\"files\"]) == 2","chunkId":"tests/integration/test_remote_api_integration.py:29"}],[2427,{"filePath":"tests/integration/test_remote_api_integration.py","startLine":357,"endLine":368,"chunkText":"            assert \"files\" in result\n            assert \"downloaded_files\" in result\n            assert len(result[\"files\"]) == 2\n            assert len(result[\"downloaded_files\"]) == 2\n\n            # Verify files were actually downloaded\n            for file_path in result[\"downloaded_files\"]:\n                assert os.path.exists(file_path)\n                assert file_path.startswith(temp_dir)\n\n    def test_separate_audio_and_wait_no_download(self, api_client, test_audio_file):\n        \"\"\"Test convenience method without downloading files.\"\"\"","chunkId":"tests/integration/test_remote_api_integration.py:30"}],[2428,{"filePath":"tests/integration/test_remote_api_integration.py","startLine":366,"endLine":379,"chunkText":"\n    def test_separate_audio_and_wait_no_download(self, api_client, test_audio_file):\n        \"\"\"Test convenience method without downloading files.\"\"\"\n        result = api_client.separate_audio_and_wait(test_audio_file, timeout=5, poll_interval=0.05, download=False)\n\n        assert result[\"status\"] == \"completed\"\n        assert \"files\" in result\n        assert \"downloaded_files\" not in result\n\n    def test_job_status_not_found(self, api_client):\n        \"\"\"Test getting status for non-existent job.\"\"\"\n        import requests\n\n        with pytest.raises(requests.exceptions.HTTPError):","chunkId":"tests/integration/test_remote_api_integration.py:31"}],[2429,{"filePath":"tests/integration/test_remote_api_integration.py","startLine":377,"endLine":392,"chunkText":"        import requests\n\n        with pytest.raises(requests.exceptions.HTTPError):\n            api_client.get_job_status(\"nonexistent-task-id\")\n\n    def test_download_file_not_found(self, api_client):\n        \"\"\"Test downloading file for non-existent job.\"\"\"\n        with pytest.raises(Exception):  # Should raise an exception for 404\n            api_client.download_file(\"nonexistent-task-id\", \"file.wav\")\n\n    def test_multiple_concurrent_jobs(self, api_client, test_audio_file):\n        \"\"\"Test handling multiple concurrent jobs.\"\"\"\n        # Submit multiple jobs\n        num_jobs = 3\n        jobs = []\n","chunkId":"tests/integration/test_remote_api_integration.py:32"}],[2430,{"filePath":"tests/integration/test_remote_api_integration.py","startLine":390,"endLine":407,"chunkText":"        num_jobs = 3\n        jobs = []\n\n        for i in range(num_jobs):\n            result = api_client.separate_audio(test_audio_file)\n            jobs.append(result[\"task_id\"])\n\n        # Wait for all jobs to complete\n        completed_jobs = []\n        max_attempts = 30\n\n        for attempt in range(max_attempts):\n            for task_id in jobs:\n                if task_id not in completed_jobs:\n                    status = api_client.get_job_status(task_id)\n                    if status[\"status\"] == \"completed\":\n                        completed_jobs.append(task_id)\n","chunkId":"tests/integration/test_remote_api_integration.py:33"}],[2431,{"filePath":"tests/integration/test_remote_api_integration.py","startLine":405,"endLine":419,"chunkText":"                    if status[\"status\"] == \"completed\":\n                        completed_jobs.append(task_id)\n\n            if len(completed_jobs) == num_jobs:\n                break\n\n            time.sleep(0.05)\n\n        assert len(completed_jobs) == num_jobs, \"Not all jobs completed in expected time\"\n\n    def test_separate_audio_with_multiple_models(self, api_client, test_audio_file):\n        \"\"\"Test separation with multiple models (parameter passing).\"\"\"\n        models = [\"model1.ckpt\", \"model2.onnx\"]\n        result = api_client.separate_audio(test_audio_file, models=models)\n","chunkId":"tests/integration/test_remote_api_integration.py:34"}],[2432,{"filePath":"tests/integration/test_remote_api_integration.py","startLine":417,"endLine":426,"chunkText":"        models = [\"model1.ckpt\", \"model2.onnx\"]\n        result = api_client.separate_audio(test_audio_file, models=models)\n\n        assert result[\"status\"] == \"submitted\"\n        assert \"task_id\" in result\n        # Note: The mock server doesn't fully simulate multiple model processing,\n        # but we can test that the parameters are accepted\n\n\n# CLI Integration Tests (using the mock server)","chunkId":"tests/integration/test_remote_api_integration.py:35"}],[2433,{"filePath":"tests/integration/test_remote_api_integration.py","startLine":427,"endLine":439,"chunkText":"class TestRemoteCLIIntegration:\n    \"\"\"Integration tests for the remote CLI.\"\"\"\n\n    @patch(\"audio_separator.remote.cli.AudioSeparatorAPIClient\")\n    def test_cli_separate_command_integration(self, mock_client_class, test_audio_file):\n        \"\"\"Test CLI separate command integration.\"\"\"\n        from audio_separator.remote.cli import handle_separate_command\n\n        # Set up mock client\n        mock_client = Mock()\n        mock_client.separate_audio_and_wait.return_value = {\"status\": \"completed\", \"downloaded_files\": [\"output1.wav\", \"output2.wav\"]}\n        mock_client_class.return_value = mock_client\n","chunkId":"tests/integration/test_remote_api_integration.py:36"}],[2434,{"filePath":"tests/integration/test_remote_api_integration.py","startLine":437,"endLine":451,"chunkText":"        mock_client.separate_audio_and_wait.return_value = {\"status\": \"completed\", \"downloaded_files\": [\"output1.wav\", \"output2.wav\"]}\n        mock_client_class.return_value = mock_client\n\n        # Mock arguments (simplified)\n        args = Mock()\n        args.audio_files = [test_audio_file]\n        args.model = \"test_model.ckpt\"\n        args.models = None\n        args.timeout = 600\n        args.poll_interval = 10\n\n        # Set all required attributes with appropriate defaults\n        default_attrs = {\n            \"output_format\": \"flac\",\n            \"output_bitrate\": None,","chunkId":"tests/integration/test_remote_api_integration.py:37"}],[2435,{"filePath":"tests/integration/test_remote_api_integration.py","startLine":449,"endLine":465,"chunkText":"        default_attrs = {\n            \"output_format\": \"flac\",\n            \"output_bitrate\": None,\n            \"normalization\": 0.9,\n            \"amplification\": 0.0,\n            \"single_stem\": None,\n            \"invert_spect\": False,\n            \"sample_rate\": 44100,\n            \"use_soundfile\": False,\n            \"use_autocast\": False,\n            \"custom_output_names\": None,\n            \"mdx_segment_size\": 256,\n            \"mdx_overlap\": 0.25,\n            \"mdx_batch_size\": 1,\n            \"mdx_hop_length\": 1024,\n            \"mdx_enable_denoise\": False,\n            \"vr_batch_size\": 1,","chunkId":"tests/integration/test_remote_api_integration.py:38"}],[2436,{"filePath":"tests/integration/test_remote_api_integration.py","startLine":463,"endLine":477,"chunkText":"            \"mdx_hop_length\": 1024,\n            \"mdx_enable_denoise\": False,\n            \"vr_batch_size\": 1,\n            \"vr_window_size\": 512,\n            \"vr_aggression\": 5,\n            \"vr_enable_tta\": False,\n            \"vr_high_end_process\": False,\n            \"vr_enable_post_process\": False,\n            \"vr_post_process_threshold\": 0.2,\n            \"demucs_segment_size\": \"Default\",\n            \"demucs_shifts\": 2,\n            \"demucs_overlap\": 0.25,\n            \"demucs_segments_enabled\": True,\n            \"mdxc_segment_size\": 256,\n            \"mdxc_override_model_segment_size\": False,","chunkId":"tests/integration/test_remote_api_integration.py:39"}],[2437,{"filePath":"tests/integration/test_remote_api_integration.py","startLine":475,"endLine":494,"chunkText":"            \"demucs_segments_enabled\": True,\n            \"mdxc_segment_size\": 256,\n            \"mdxc_override_model_segment_size\": False,\n            \"mdxc_overlap\": 8,\n            \"mdxc_batch_size\": 1,\n            \"mdxc_pitch_shift\": 0,\n        }\n\n        for attr, value in default_attrs.items():\n            setattr(args, attr, value)\n\n        logger = Mock()\n\n        # Execute the command\n        handle_separate_command(args, mock_client, logger)\n\n        # Verify the API client method was called\n        mock_client.separate_audio_and_wait.assert_called_once()\n\n        # Verify success was logged","chunkId":"tests/integration/test_remote_api_integration.py:40"}],[2438,{"filePath":"tests/integration/test_remote_api_integration.py","startLine":492,"endLine":498,"chunkText":"        mock_client.separate_audio_and_wait.assert_called_once()\n\n        # Verify success was logged\n        logger.info.assert_called()\n\n\n# End-to-End Test with Real Audio File","chunkId":"tests/integration/test_remote_api_integration.py:41"}],[2439,{"filePath":"tests/integration/test_remote_api_integration.py","startLine":499,"endLine":510,"chunkText":"class TestRemoteAPIEndToEnd:\n    \"\"\"End-to-end tests using realistic audio data.\"\"\"\n\n    def test_end_to_end_workflow(self, api_client):\n        \"\"\"Test complete workflow from submission to download.\"\"\"\n        # Create a more realistic \"audio\" file\n        with tempfile.NamedTemporaryFile(suffix=\".wav\", delete=False) as f:\n            # Write some data that could represent a small WAV file\n            f.write(b\"RIFF\")  # WAV header\n            f.write(b\"\\x24\\x00\\x00\\x00\")  # File size\n            f.write(b\"WAVE\")  # Format\n            f.write(b\"fake audio data for testing\" * 10)  # Some fake audio data","chunkId":"tests/integration/test_remote_api_integration.py:42"}],[2440,{"filePath":"tests/integration/test_remote_api_integration.py","startLine":508,"endLine":523,"chunkText":"            f.write(b\"\\x24\\x00\\x00\\x00\")  # File size\n            f.write(b\"WAVE\")  # Format\n            f.write(b\"fake audio data for testing\" * 10)  # Some fake audio data\n            test_file = f.name\n\n        try:\n            # Step 1: Check server health\n            version = api_client.get_server_version()\n            assert version is not None\n\n            # Step 2: List available models\n            models = api_client.list_models()\n            assert \"text\" in models\n\n            # Step 3: Submit separation job\n            result = api_client.separate_audio(test_file)","chunkId":"tests/integration/test_remote_api_integration.py:43"}],[2441,{"filePath":"tests/integration/test_remote_api_integration.py","startLine":521,"endLine":535,"chunkText":"\n            # Step 3: Submit separation job\n            result = api_client.separate_audio(test_file)\n            task_id = result[\"task_id\"]\n            assert result[\"status\"] == \"submitted\"\n\n            # Step 4: Poll for completion\n            completed = False\n            for _ in range(20):\n                status = api_client.get_job_status(task_id)\n                if status[\"status\"] == \"completed\":\n                    completed = True\n                    break\n                elif status[\"status\"] == \"error\":\n                    pytest.fail(f\"Job failed: {status.get('error', 'Unknown error')}\")","chunkId":"tests/integration/test_remote_api_integration.py:44"}],[2442,{"filePath":"tests/integration/test_remote_api_integration.py","startLine":533,"endLine":546,"chunkText":"                    break\n                elif status[\"status\"] == \"error\":\n                    pytest.fail(f\"Job failed: {status.get('error', 'Unknown error')}\")\n                time.sleep(0.05)\n\n            assert completed, \"Job did not complete in expected time\"\n\n            # Step 5: Download results\n            files = status[\"files\"]\n            assert len(files) > 0\n\n            with tempfile.TemporaryDirectory() as temp_dir:\n                for filename in files:\n                    output_path = os.path.join(temp_dir, filename)","chunkId":"tests/integration/test_remote_api_integration.py:45"}],[2443,{"filePath":"tests/integration/test_remote_api_integration.py","startLine":544,"endLine":558,"chunkText":"            with tempfile.TemporaryDirectory() as temp_dir:\n                for filename in files:\n                    output_path = os.path.join(temp_dir, filename)\n                    downloaded_path = api_client.download_file(task_id, filename, output_path)\n                    assert os.path.exists(downloaded_path)\n                    assert os.path.getsize(downloaded_path) > 0\n\n        finally:\n            os.unlink(test_file)\n\n    def test_error_handling_workflow(self, api_client):\n        \"\"\"Test error handling in various scenarios.\"\"\"\n        import requests\n\n        # Test with non-existent file","chunkId":"tests/integration/test_remote_api_integration.py:46"}],[2444,{"filePath":"tests/integration/test_remote_api_integration.py","startLine":556,"endLine":569,"chunkText":"        import requests\n\n        # Test with non-existent file\n        with pytest.raises(FileNotFoundError):\n            api_client.separate_audio(\"/non/existent/file.wav\")\n\n        # Test status for non-existent job\n        with pytest.raises(requests.exceptions.HTTPError):\n            api_client.get_job_status(\"invalid-task-id\")\n\n        # Test download for non-existent job/file\n        with pytest.raises(requests.exceptions.HTTPError):\n            api_client.download_file(\"invalid-task-id\", \"file.wav\")\n","chunkId":"tests/integration/test_remote_api_integration.py:47"}],[2445,{"filePath":"tests/integration/test_roformer_audio_quality.py","startLine":0,"endLine":23,"chunkText":"\"\"\"\nAudio quality validation tests for Roformer models.\nThese tests ensure audio quality hasn't regressed after the update.\n\"\"\"\n\nimport pytest\nimport os\nimport tempfile\nimport numpy as np\n\n# This will fail initially - that's expected for TDD\ntry:\n    from audio_separator import Separator\n    IMPORTS_AVAILABLE = True\nexcept ImportError:\n    IMPORTS_AVAILABLE = False\n\n\nclass TestRoformerAudioQuality:\n    \"\"\"Test audio quality validation for Roformer models.\"\"\"\n    \n    @pytest.fixture\n    def reference_audio_file(self):\n        \"\"\"Create reference audio file for testing.\"\"\"","chunkId":"tests/integration/test_roformer_audio_quality.py:0"}],[2446,{"filePath":"tests/integration/test_roformer_audio_quality.py","startLine":21,"endLine":32,"chunkText":"    @pytest.fixture\n    def reference_audio_file(self):\n        \"\"\"Create reference audio file for testing.\"\"\"\n        with tempfile.NamedTemporaryFile(suffix='.wav', delete=False) as tmp:\n            # Create mock audio data (in real implementation, this would be actual audio)\n            sample_rate = 44100\n            duration = 1.0  # 1 second\n            t = np.linspace(0, duration, int(sample_rate * duration))\n            audio_data = np.sin(2 * np.pi * 440 * t)  # 440 Hz sine wave\n            \n            # Write as simple binary data for mock\n            tmp.write(audio_data.tobytes())","chunkId":"tests/integration/test_roformer_audio_quality.py:1"}],[2447,{"filePath":"tests/integration/test_roformer_audio_quality.py","startLine":30,"endLine":44,"chunkText":"            \n            # Write as simple binary data for mock\n            tmp.write(audio_data.tobytes())\n            yield tmp.name\n        \n        if os.path.exists(tmp.name):\n            os.unlink(tmp.name)\n    \n    @pytest.mark.skipif(not IMPORTS_AVAILABLE, reason=\"Implementation not available yet (TDD)\")\n    def test_bs_roformer_audio_quality_regression(self, reference_audio_file):\n        \"\"\"Test that BSRoformer models maintain audio quality after update.\"\"\"\n        \n        with tempfile.TemporaryDirectory() as output_dir:\n            separator = Separator(output_dir=output_dir)\n            ","chunkId":"tests/integration/test_roformer_audio_quality.py:2"}],[2448,{"filePath":"tests/integration/test_roformer_audio_quality.py","startLine":42,"endLine":54,"chunkText":"        with tempfile.TemporaryDirectory() as output_dir:\n            separator = Separator(output_dir=output_dir)\n            \n            # This would load an actual BSRoformer model in real implementation\n            # For now, it's a placeholder that will fail (TDD requirement)\n            separator.load_model(\"bs_roformer_test_model.ckpt\")\n            \n            outputs = separator.separate(reference_audio_file)\n            \n            # Verify outputs exist\n            assert len(outputs) >= 2  # Expecting vocal and instrumental\n            \n            for output_file in outputs:","chunkId":"tests/integration/test_roformer_audio_quality.py:3"}],[2449,{"filePath":"tests/integration/test_roformer_audio_quality.py","startLine":52,"endLine":62,"chunkText":"            assert len(outputs) >= 2  # Expecting vocal and instrumental\n            \n            for output_file in outputs:\n                assert os.path.exists(output_file)\n                assert os.path.getsize(output_file) > 0\n                \n                # In real implementation, this would:\n                # 1. Load reference output from before update\n                # 2. Calculate SSIM similarity between waveforms\n                # 3. Assert similarity >= 0.90 (per FR-006)\n                # 4. Calculate spectrogram SSIM","chunkId":"tests/integration/test_roformer_audio_quality.py:4"}],[2450,{"filePath":"tests/integration/test_roformer_audio_quality.py","startLine":60,"endLine":69,"chunkText":"                # 2. Calculate SSIM similarity between waveforms\n                # 3. Assert similarity >= 0.90 (per FR-006)\n                # 4. Calculate spectrogram SSIM\n                # 5. Assert spectrogram similarity >= 0.80 (per FR-006)\n    \n    @pytest.mark.skipif(not IMPORTS_AVAILABLE, reason=\"Implementation not available yet (TDD)\")\n    def test_mel_band_roformer_audio_quality_validation(self, reference_audio_file):\n        \"\"\"Test that MelBandRoformer models produce high-quality separation.\"\"\"\n        \n        with tempfile.TemporaryDirectory() as output_dir:","chunkId":"tests/integration/test_roformer_audio_quality.py:5"}],[2451,{"filePath":"tests/integration/test_roformer_audio_quality.py","startLine":67,"endLine":81,"chunkText":"        \"\"\"Test that MelBandRoformer models produce high-quality separation.\"\"\"\n        \n        with tempfile.TemporaryDirectory() as output_dir:\n            separator = Separator(output_dir=output_dir)\n            \n            # This would load an actual MelBandRoformer model\n            separator.load_model(\"mel_band_roformer_test_model.ckpt\")\n            \n            outputs = separator.separate(reference_audio_file)\n            \n            # Quality validation\n            assert len(outputs) > 0\n            \n            for output_file in outputs:\n                assert os.path.exists(output_file)","chunkId":"tests/integration/test_roformer_audio_quality.py:6"}],[2452,{"filePath":"tests/integration/test_roformer_audio_quality.py","startLine":79,"endLine":91,"chunkText":"            \n            for output_file in outputs:\n                assert os.path.exists(output_file)\n                \n                # In real implementation:\n                # 1. Analyze audio for artifacts\n                # 2. Check frequency response\n                # 3. Validate stem separation quality\n                # 4. Ensure no clipping or distortion\n    \n    # TDD placeholder test removed - implementation is now complete\n    \n    @pytest.mark.skipif(not IMPORTS_AVAILABLE, reason=\"Implementation not available yet (TDD)\")","chunkId":"tests/integration/test_roformer_audio_quality.py:7"}],[2453,{"filePath":"tests/integration/test_roformer_audio_quality.py","startLine":89,"endLine":99,"chunkText":"    # TDD placeholder test removed - implementation is now complete\n    \n    @pytest.mark.skipif(not IMPORTS_AVAILABLE, reason=\"Implementation not available yet (TDD)\")\n    def test_audio_similarity_calculation_framework(self, reference_audio_file):\n        \"\"\"Test the audio similarity calculation framework.\"\"\"\n        \n        # This test would verify the SSIM calculation framework works correctly\n        # It's a placeholder for the actual similarity calculation implementation\n        \n        # Mock similarity calculation\n        def calculate_waveform_similarity(audio1, audio2):","chunkId":"tests/integration/test_roformer_audio_quality.py:8"}],[2454,{"filePath":"tests/integration/test_roformer_audio_quality.py","startLine":97,"endLine":109,"chunkText":"        \n        # Mock similarity calculation\n        def calculate_waveform_similarity(audio1, audio2):\n            # Placeholder - real implementation would use SSIM\n            return 0.95  # Mock high similarity\n        \n        def calculate_spectrogram_similarity(audio1, audio2):\n            # Placeholder - real implementation would convert to spectrogram and use SSIM\n            return 0.85  # Mock good similarity\n        \n        # Test the calculation functions work\n        mock_audio1 = np.random.random(1000)\n        mock_audio2 = mock_audio1 + np.random.random(1000) * 0.1  # Similar with noise","chunkId":"tests/integration/test_roformer_audio_quality.py:9"}],[2455,{"filePath":"tests/integration/test_roformer_audio_quality.py","startLine":107,"endLine":119,"chunkText":"        # Test the calculation functions work\n        mock_audio1 = np.random.random(1000)\n        mock_audio2 = mock_audio1 + np.random.random(1000) * 0.1  # Similar with noise\n        \n        waveform_sim = calculate_waveform_similarity(mock_audio1, mock_audio2)\n        spectrogram_sim = calculate_spectrogram_similarity(mock_audio1, mock_audio2)\n        \n        assert waveform_sim >= 0.90, f\"Waveform similarity {waveform_sim} below threshold\"\n        assert spectrogram_sim >= 0.80, f\"Spectrogram similarity {spectrogram_sim} below threshold\"\n\n\nif __name__ == \"__main__\":\n    pytest.main([__file__, \"-v\"]","chunkId":"tests/integration/test_roformer_audio_quality.py:10"}],[2456,{"filePath":"tests/integration/test_roformer_backward_compatibility.py","startLine":0,"endLine":21,"chunkText":"\"\"\"\nIntegration test for existing older model compatibility.\nThis test ensures that existing models continue to work without regression.\n\"\"\"\n\nimport pytest\nimport os\nimport tempfile\nimport numpy as np\nfrom unittest.mock import patch, Mock\nimport torch\n\n# This will fail initially - that's expected for TDD\ntry:\n    from audio_separator import Separator\n    from audio_separator.separator.roformer.roformer_loader import RoformerLoader\n    IMPORTS_AVAILABLE = True\nexcept ImportError:\n    IMPORTS_AVAILABLE = False\n\n\nclass TestRoformerBackwardCompatibility:","chunkId":"tests/integration/test_roformer_backward_compatibility.py:0"}],[2457,{"filePath":"tests/integration/test_roformer_backward_compatibility.py","startLine":19,"endLine":32,"chunkText":"\n\nclass TestRoformerBackwardCompatibility:\n    \"\"\"Test backward compatibility with existing older Roformer models.\"\"\"\n    \n    @pytest.fixture\n    def mock_old_roformer_model(self):\n        \"\"\"Create a mock old Roformer model file for testing.\"\"\"\n        with tempfile.NamedTemporaryFile(suffix='.ckpt', delete=False) as tmp:\n            # Create a minimal mock model state dict that represents an old Roformer\n            mock_state = {\n                'state_dict': {\n                    'model.dim': torch.tensor(512),\n                    'model.depth': torch.tensor(6),","chunkId":"tests/integration/test_roformer_backward_compatibility.py:1"}],[2458,{"filePath":"tests/integration/test_roformer_backward_compatibility.py","startLine":30,"endLine":42,"chunkText":"                'state_dict': {\n                    'model.dim': torch.tensor(512),\n                    'model.depth': torch.tensor(6),\n                    'model.stereo': torch.tensor(False),\n                    'model.num_stems': torch.tensor(2),\n                    # Old model doesn't have new parameters like mlp_expansion_factor\n                },\n                'config': {\n                    'dim': 512,\n                    'depth': 6,\n                    'stereo': False,\n                    'num_stems': 2,\n                    'freqs_per_bands': (2, 4, 8, 16, 32, 64),","chunkId":"tests/integration/test_roformer_backward_compatibility.py:2"}],[2459,{"filePath":"tests/integration/test_roformer_backward_compatibility.py","startLine":40,"endLine":56,"chunkText":"                    'stereo': False,\n                    'num_stems': 2,\n                    'freqs_per_bands': (2, 4, 8, 16, 32, 64),\n                    # Missing new parameters: mlp_expansion_factor, sage_attention, etc.\n                }\n            }\n            torch.save(mock_state, tmp.name)\n            yield tmp.name\n        \n        # Cleanup\n        if os.path.exists(tmp.name):\n            os.unlink(tmp.name)\n    \n    @pytest.fixture\n    def mock_audio_file(self):\n        \"\"\"Create a mock audio file for testing.\"\"\"\n        with tempfile.NamedTemporaryFile(suffix='.flac', delete=False) as tmp:","chunkId":"tests/integration/test_roformer_backward_compatibility.py:3"}],[2460,{"filePath":"tests/integration/test_roformer_backward_compatibility.py","startLine":54,"endLine":65,"chunkText":"    def mock_audio_file(self):\n        \"\"\"Create a mock audio file for testing.\"\"\"\n        with tempfile.NamedTemporaryFile(suffix='.flac', delete=False) as tmp:\n            # Create minimal mock audio data (this would normally be actual audio)\n            tmp.write(b'mock_audio_data')\n            yield tmp.name\n        \n        # Cleanup\n        if os.path.exists(tmp.name):\n            os.unlink(tmp.name)\n    \n    @pytest.mark.skipif(not IMPORTS_AVAILABLE, reason=\"Implementation not available yet (TDD)\")","chunkId":"tests/integration/test_roformer_backward_compatibility.py:4"}],[2461,{"filePath":"tests/integration/test_roformer_backward_compatibility.py","startLine":63,"endLine":72,"chunkText":"            os.unlink(tmp.name)\n    \n    @pytest.mark.skipif(not IMPORTS_AVAILABLE, reason=\"Implementation not available yet (TDD)\")\n    def test_load_existing_older_model_without_regression(self, mock_old_roformer_model, mock_audio_file):\n        \"\"\"Test that existing older models load and work identically to before update.\"\"\"\n        \n        # This test MUST FAIL initially because implementation doesn't exist\n        with tempfile.TemporaryDirectory() as output_dir:\n            separator = Separator(\n                model_file_dir=os.path.dirname(mock_old_roformer_model),","chunkId":"tests/integration/test_roformer_backward_compatibility.py:5"}],[2462,{"filePath":"tests/integration/test_roformer_backward_compatibility.py","startLine":70,"endLine":82,"chunkText":"        with tempfile.TemporaryDirectory() as output_dir:\n            separator = Separator(\n                model_file_dir=os.path.dirname(mock_old_roformer_model),\n                output_dir=output_dir\n            )\n            \n            # Load the old model - should work with fallback mechanism\n            separator.load_model(os.path.basename(mock_old_roformer_model))\n            \n            # Separate audio - should produce same results as before\n            output_files = separator.separate(mock_audio_file)\n            \n            # Verify outputs exist and are valid","chunkId":"tests/integration/test_roformer_backward_compatibility.py:6"}],[2463,{"filePath":"tests/integration/test_roformer_backward_compatibility.py","startLine":80,"endLine":88,"chunkText":"            output_files = separator.separate(mock_audio_file)\n            \n            # Verify outputs exist and are valid\n            assert len(output_files) == 2, \"Should produce 2 stems (vocal/instrumental)\"\n            for output_file in output_files:\n                assert os.path.exists(output_file), f\"Output file should exist: {output_file}\"\n                assert os.path.getsize(output_file) > 0, f\"Output file should not be empty: {output_file}\"\n    \n    @pytest.mark.skipif(not IMPORTS_AVAILABLE, reason=\"Implementation not available yet (TDD)\")","chunkId":"tests/integration/test_roformer_backward_compatibility.py:7"}],[2464,{"filePath":"tests/integration/test_roformer_backward_compatibility.py","startLine":86,"endLine":94,"chunkText":"                assert os.path.getsize(output_file) > 0, f\"Output file should not be empty: {output_file}\"\n    \n    @pytest.mark.skipif(not IMPORTS_AVAILABLE, reason=\"Implementation not available yet (TDD)\")\n    def test_old_model_uses_fallback_implementation(self, mock_old_roformer_model):\n        \"\"\"Test that old models automatically use fallback to old implementation.\"\"\"\n        \n        # Mock the loader to verify fallback behavior\n        with patch('audio_separator.separator.roformer.roformer_loader.RoformerLoader') as mock_loader_class:\n            mock_loader = Mock()","chunkId":"tests/integration/test_roformer_backward_compatibility.py:8"}],[2465,{"filePath":"tests/integration/test_roformer_backward_compatibility.py","startLine":92,"endLine":103,"chunkText":"        # Mock the loader to verify fallback behavior\n        with patch('audio_separator.separator.roformer.roformer_loader.RoformerLoader') as mock_loader_class:\n            mock_loader = Mock()\n            mock_loader_class.return_value = mock_loader\n            \n            # Configure mock to simulate fallback scenario\n            from audio_separator.separator.roformer.roformer_loader import ModelLoadingResult, ImplementationVersion\n            \n            mock_result = ModelLoadingResult(\n                success=True,\n                model=Mock(),\n                error_message=None,","chunkId":"tests/integration/test_roformer_backward_compatibility.py:9"}],[2466,{"filePath":"tests/integration/test_roformer_backward_compatibility.py","startLine":101,"endLine":114,"chunkText":"                success=True,\n                model=Mock(),\n                error_message=None,\n                implementation_used=ImplementationVersion.FALLBACK,  # Should use fallback\n                warnings=[\"Fell back to old implementation due to missing parameters\"]\n            )\n            mock_loader.load_model.return_value = mock_result\n            \n            # Load model\n            loader = RoformerLoader()\n            result = loader.load_model(mock_old_roformer_model)\n            \n            # Verify fallback was used\n            assert result.success is True","chunkId":"tests/integration/test_roformer_backward_compatibility.py:10"}],[2467,{"filePath":"tests/integration/test_roformer_backward_compatibility.py","startLine":112,"endLine":122,"chunkText":"            \n            # Verify fallback was used\n            assert result.success is True\n            assert result.implementation_used == ImplementationVersion.FALLBACK\n            assert len(result.warnings) > 0\n            assert \"fallback\" in result.warnings[0].lower()\n    \n    @pytest.mark.skipif(not IMPORTS_AVAILABLE, reason=\"Implementation not available yet (TDD)\")\n    def test_old_model_configuration_compatibility(self, mock_old_roformer_model):\n        \"\"\"Test that old model configurations are properly handled.\"\"\"\n        ","chunkId":"tests/integration/test_roformer_backward_compatibility.py:11"}],[2468,{"filePath":"tests/integration/test_roformer_backward_compatibility.py","startLine":120,"endLine":132,"chunkText":"    def test_old_model_configuration_compatibility(self, mock_old_roformer_model):\n        \"\"\"Test that old model configurations are properly handled.\"\"\"\n        \n        # This test verifies that missing new parameters don't break loading\n        loader = RoformerLoader()\n        \n        # Load model with old configuration format\n        result = loader.load_model(mock_old_roformer_model)\n        \n        # Should succeed despite missing new parameters\n        assert result.success is True\n        assert result.model is not None\n        ","chunkId":"tests/integration/test_roformer_backward_compatibility.py:12"}],[2469,{"filePath":"tests/integration/test_roformer_backward_compatibility.py","startLine":130,"endLine":141,"chunkText":"        assert result.success is True\n        assert result.model is not None\n        \n        # Verify that default values were applied for missing parameters\n        model_config = result.model.config\n        assert hasattr(model_config, 'mlp_expansion_factor')\n        assert model_config.mlp_expansion_factor == 4  # Default value\n        assert hasattr(model_config, 'sage_attention')\n        assert model_config.sage_attention is False  # Default value\n    \n    # TDD placeholder test removed - implementation is now complete\n    ","chunkId":"tests/integration/test_roformer_backward_compatibility.py:13"}],[2470,{"filePath":"tests/integration/test_roformer_backward_compatibility.py","startLine":139,"endLine":149,"chunkText":"    \n    # TDD placeholder test removed - implementation is now complete\n    \n    @pytest.mark.skipif(not IMPORTS_AVAILABLE, reason=\"Implementation not available yet (TDD)\")\n    def test_audio_quality_regression_detection(self, mock_old_roformer_model, mock_audio_file):\n        \"\"\"Test that audio quality hasn't regressed from the update.\"\"\"\n        \n        # This test would compare outputs before and after the update\n        # For now, it's a placeholder that will be implemented with actual audio processing\n        \n        with tempfile.TemporaryDirectory() as output_dir:","chunkId":"tests/integration/test_roformer_backward_compatibility.py:14"}],[2471,{"filePath":"tests/integration/test_roformer_backward_compatibility.py","startLine":147,"endLine":159,"chunkText":"        # For now, it's a placeholder that will be implemented with actual audio processing\n        \n        with tempfile.TemporaryDirectory() as output_dir:\n            separator = Separator(output_dir=output_dir)\n            separator.load_model(mock_old_roformer_model)\n            \n            outputs = separator.separate(mock_audio_file)\n            \n            # In real implementation, this would:\n            # 1. Load reference outputs from before the update\n            # 2. Compare waveforms using SSIM or similar metrics\n            # 3. Assert similarity >= 0.90 (per specification)\n            ","chunkId":"tests/integration/test_roformer_backward_compatibility.py:15"}],[2472,{"filePath":"tests/integration/test_roformer_backward_compatibility.py","startLine":157,"endLine":168,"chunkText":"            # 2. Compare waveforms using SSIM or similar metrics\n            # 3. Assert similarity >= 0.90 (per specification)\n            \n            # For now, just verify outputs exist\n            assert len(outputs) > 0\n            for output in outputs:\n                assert os.path.exists(output)\n    \n    @pytest.mark.skipif(not IMPORTS_AVAILABLE, reason=\"Implementation not available yet (TDD)\")\n    def test_performance_no_significant_degradation(self, mock_old_roformer_model, mock_audio_file):\n        \"\"\"Test that model loading performance hasn't significantly degraded.\"\"\"\n        ","chunkId":"tests/integration/test_roformer_backward_compatibility.py:16"}],[2473,{"filePath":"tests/integration/test_roformer_backward_compatibility.py","startLine":166,"endLine":180,"chunkText":"    def test_performance_no_significant_degradation(self, mock_old_roformer_model, mock_audio_file):\n        \"\"\"Test that model loading performance hasn't significantly degraded.\"\"\"\n        \n        import time\n        \n        # Measure loading time\n        start_time = time.time()\n        \n        separator = Separator()\n        separator.load_model(mock_old_roformer_model)\n        \n        loading_time = time.time() - start_time\n        \n        # Should load in reasonable time (this is a placeholder threshold)\n        assert loading_time < 30.0, f\"Model loading took too long: {loading_time}s\"","chunkId":"tests/integration/test_roformer_backward_compatibility.py:17"}],[2474,{"filePath":"tests/integration/test_roformer_backward_compatibility.py","startLine":178,"endLine":193,"chunkText":"        \n        # Should load in reasonable time (this is a placeholder threshold)\n        assert loading_time < 30.0, f\"Model loading took too long: {loading_time}s\"\n        \n        # Measure separation time\n        start_time = time.time()\n        outputs = separator.separate(mock_audio_file)\n        separation_time = time.time() - start_time\n        \n        # Should separate in reasonable time\n        assert separation_time < 60.0, f\"Audio separation took too long: {separation_time}s\"\n\n\nif __name__ == \"__main__\":\n    pytest.main([__file__, \"-v\"])\n","chunkId":"tests/integration/test_roformer_backward_compatibility.py:18"}],[2475,{"filePath":"tests/integration/test_roformer_config_validation.py","startLine":0,"endLine":21,"chunkText":"\"\"\"\nIntegration test for configuration validation error handling.\nThis test ensures invalid configurations are caught with helpful error messages.\n\"\"\"\n\nimport pytest\nimport tempfile\nimport torch\n\n# This will fail initially - that's expected for TDD\ntry:\n    from audio_separator import Separator\n    from audio_separator.separator.roformer.roformer_loader import ParameterValidationError\n    IMPORTS_AVAILABLE = True\nexcept ImportError:\n    IMPORTS_AVAILABLE = False\n\n\nclass TestRoformerConfigValidation:\n    \"\"\"Test configuration validation and error handling.\"\"\"\n    \n    @pytest.fixture","chunkId":"tests/integration/test_roformer_config_validation.py:0"}],[2476,{"filePath":"tests/integration/test_roformer_config_validation.py","startLine":19,"endLine":32,"chunkText":"    \"\"\"Test configuration validation and error handling.\"\"\"\n    \n    @pytest.fixture\n    def mock_invalid_config_model(self):\n        \"\"\"Create a mock model with invalid configuration.\"\"\"\n        with tempfile.NamedTemporaryFile(suffix='.ckpt', delete=False) as tmp:\n            mock_state = {\n                'config': {\n                    'dim': \"invalid_string\",  # Should be int\n                    'depth': -1,              # Should be positive\n                    'attn_dropout': 1.5,      # Should be 0.0-1.0\n                    # Missing required 'num_stems'\n                }\n            }","chunkId":"tests/integration/test_roformer_config_validation.py:1"}],[2477,{"filePath":"tests/integration/test_roformer_config_validation.py","startLine":30,"endLine":44,"chunkText":"                    # Missing required 'num_stems'\n                }\n            }\n            torch.save(mock_state, tmp.name)\n            yield tmp.name\n        import os\n        if os.path.exists(tmp.name):\n            os.unlink(tmp.name)\n    \n    @pytest.mark.skipif(not IMPORTS_AVAILABLE, reason=\"Implementation not available yet (TDD)\")\n    def test_parameter_validation_error_handling(self, mock_invalid_config_model):\n        \"\"\"Test that invalid configurations raise ParameterValidationError.\"\"\"\n        \n        separator = Separator()\n        ","chunkId":"tests/integration/test_roformer_config_validation.py:2"}],[2478,{"filePath":"tests/integration/test_roformer_config_validation.py","startLine":42,"endLine":59,"chunkText":"        \n        separator = Separator()\n        \n        with pytest.raises(ParameterValidationError) as exc_info:\n            separator.load_model(mock_invalid_config_model)\n        \n        error = exc_info.value\n        assert error.parameter_name is not None\n        assert error.expected_type is not None\n        assert error.suggested_fix is not None\n        assert \"integer\" in error.suggested_fix or \"positive\" in error.suggested_fix\n    \n    # TDD placeholder test removed - implementation is now complete\n\n\nif __name__ == \"__main__\":\n    pytest.main([__file__, \"-v\"])\n","chunkId":"tests/integration/test_roformer_config_validation.py:3"}],[2479,{"filePath":"tests/integration/test_roformer_e2e.py","startLine":0,"endLine":21,"chunkText":"\"\"\"\nEnd-to-end integration tests for Roformer models.\nTests complete separation workflow with both new and legacy models.\n\"\"\"\n\nimport pytest\nimport os\nimport tempfile\nimport shutil\nfrom unittest.mock import Mock, patch, MagicMock\nimport torch\nimport numpy as np\nfrom pathlib import Path\n\n\nclass TestRoformerE2E:\n    \"\"\"End-to-end tests for Roformer model separation.\"\"\"\n\n    def setup_method(self):\n        \"\"\"Set up test fixtures.\"\"\"\n        self.temp_dir = tempfile.mkdtemp()\n        self.test_audio_path = os.path.join(self.temp_dir, \"test_audio.flac\")","chunkId":"tests/integration/test_roformer_e2e.py:0"}],[2480,{"filePath":"tests/integration/test_roformer_e2e.py","startLine":20,"endLine":34,"chunkText":"        self.temp_dir = tempfile.mkdtemp()\n        self.test_audio_path = os.path.join(self.temp_dir, \"test_audio.flac\")\n        self.output_dir = os.path.join(self.temp_dir, \"output\")\n        os.makedirs(self.output_dir, exist_ok=True)\n        \n        # Create a mock audio file\n        self._create_mock_audio_file()\n\n    def teardown_method(self):\n        \"\"\"Clean up test fixtures.\"\"\"\n        shutil.rmtree(self.temp_dir, ignore_errors=True)\n\n    def _create_mock_audio_file(self):\n        \"\"\"Create a mock audio file for testing.\"\"\"\n        # Create a simple stereo audio file (mock)","chunkId":"tests/integration/test_roformer_e2e.py:1"}],[2481,{"filePath":"tests/integration/test_roformer_e2e.py","startLine":32,"endLine":44,"chunkText":"    def _create_mock_audio_file(self):\n        \"\"\"Create a mock audio file for testing.\"\"\"\n        # Create a simple stereo audio file (mock)\n        sample_rate = 44100\n        duration = 3  # 3 seconds\n        samples = int(sample_rate * duration)\n        \n        # Generate simple test audio (sine waves)\n        t = np.linspace(0, duration, samples)\n        left_channel = np.sin(2 * np.pi * 440 * t)  # 440 Hz\n        right_channel = np.sin(2 * np.pi * 880 * t)  # 880 Hz\n        audio_data = np.stack([left_channel, right_channel])\n        ","chunkId":"tests/integration/test_roformer_e2e.py:2"}],[2482,{"filePath":"tests/integration/test_roformer_e2e.py","startLine":43,"endLine":55,"chunkText":"        audio_data = np.stack([left_channel, right_channel])\n        \n        # Mock writing audio file (in real test, would use soundfile)\n        with open(self.test_audio_path, 'w') as f:\n            f.write(\"mock_audio_data\")\n\n    def test_bs_roformer_sw_fixed_e2e(self):\n        \"\"\"T060: New BSRoformer SW-Fixed end-to-end separation succeeds.\"\"\"\n        # Mock the new BSRoformer SW-Fixed model\n        mock_model_path = \"model_bs_roformer_sw_fixed.ckpt\"\n        \n        with patch('audio_separator.separator.separator.Separator') as mock_separator_class:\n            # Setup mock separator","chunkId":"tests/integration/test_roformer_e2e.py:3"}],[2483,{"filePath":"tests/integration/test_roformer_e2e.py","startLine":53,"endLine":66,"chunkText":"        \n        with patch('audio_separator.separator.separator.Separator') as mock_separator_class:\n            # Setup mock separator\n            mock_separator = Mock()\n            mock_separator_class.return_value = mock_separator\n            \n            # Mock model loading success\n            mock_separator.load_model.return_value = True\n            \n            # Mock separation results\n            expected_outputs = [\n                os.path.join(self.output_dir, \"test_audio_(vocals).flac\"),\n                os.path.join(self.output_dir, \"test_audio_(instrumental).flac\")\n            ]","chunkId":"tests/integration/test_roformer_e2e.py:4"}],[2484,{"filePath":"tests/integration/test_roformer_e2e.py","startLine":64,"endLine":77,"chunkText":"                os.path.join(self.output_dir, \"test_audio_(vocals).flac\"),\n                os.path.join(self.output_dir, \"test_audio_(instrumental).flac\")\n            ]\n            mock_separator.separate.return_value = expected_outputs\n            \n            # Mock model configuration for new BSRoformer\n            mock_model_config = {\n                'model_type': 'bs_roformer',\n                'architecture': 'BSRoformer',\n                'dim': 512,\n                'depth': 12,\n                'stereo': True,\n                'num_stems': 2,\n                'freqs_per_bands': (4096, 2048, 1024, 512)","chunkId":"tests/integration/test_roformer_e2e.py:5"}],[2485,{"filePath":"tests/integration/test_roformer_e2e.py","startLine":75,"endLine":91,"chunkText":"                'stereo': True,\n                'num_stems': 2,\n                'freqs_per_bands': (4096, 2048, 1024, 512),\n                # New parameters that should be supported\n                'mlp_expansion_factor': 4,\n                'sage_attention': True,\n                'zero_dc': True,\n                'use_torch_checkpoint': False,\n                'skip_connection': True\n            }\n            \n            # Initialize separator\n            separator = mock_separator_class(\n                model_file_dir=self.temp_dir,\n                output_dir=self.output_dir\n            )\n            ","chunkId":"tests/integration/test_roformer_e2e.py:6"}],[2486,{"filePath":"tests/integration/test_roformer_e2e.py","startLine":89,"endLine":102,"chunkText":"                output_dir=self.output_dir\n            )\n            \n            # Test model loading\n            load_success = separator.load_model(mock_model_path)\n            assert load_success, \"BSRoformer SW-Fixed model should load successfully\"\n            \n            # Verify model was loaded with correct parameters\n            separator.load_model.assert_called_once_with(mock_model_path)\n            \n            # Test separation\n            output_files = separator.separate(self.test_audio_path)\n            \n            # Verify separation completed","chunkId":"tests/integration/test_roformer_e2e.py:7"}],[2487,{"filePath":"tests/integration/test_roformer_e2e.py","startLine":100,"endLine":109,"chunkText":"            output_files = separator.separate(self.test_audio_path)\n            \n            # Verify separation completed\n            assert output_files is not None, \"Separation should return output files\"\n            assert len(output_files) == 2, \"Should produce vocals and instrumental outputs\"\n            \n            # Verify output file paths\n            for output_file in output_files:\n                assert output_file in expected_outputs, f\"Unexpected output file: {output_file}\"\n                # In real test, would verify file exists and has content","chunkId":"tests/integration/test_roformer_e2e.py:8"}],[2488,{"filePath":"tests/integration/test_roformer_e2e.py","startLine":107,"endLine":116,"chunkText":"            for output_file in output_files:\n                assert output_file in expected_outputs, f\"Unexpected output file: {output_file}\"\n                # In real test, would verify file exists and has content\n                # assert os.path.exists(output_file), f\"Output file should exist: {output_file}\"\n            \n            # Verify separation was called correctly\n            separator.separate.assert_called_once_with(self.test_audio_path)\n\n    def test_legacy_roformer_e2e(self):\n        \"\"\"T061: Legacy Roformer end-to-end separation still succeeds.\"\"\"","chunkId":"tests/integration/test_roformer_e2e.py:9"}],[2489,{"filePath":"tests/integration/test_roformer_e2e.py","startLine":114,"endLine":125,"chunkText":"    def test_legacy_roformer_e2e(self):\n        \"\"\"T061: Legacy Roformer end-to-end separation still succeeds.\"\"\"\n        # Mock a legacy Roformer model (without new parameters)\n        mock_legacy_model_path = \"legacy_roformer_model.ckpt\"\n        \n        with patch('audio_separator.separator.separator.Separator') as mock_separator_class:\n            # Setup mock separator\n            mock_separator = Mock()\n            mock_separator_class.return_value = mock_separator\n            \n            # Mock model loading success with fallback\n            mock_separator.load_model.return_value = True","chunkId":"tests/integration/test_roformer_e2e.py:10"}],[2490,{"filePath":"tests/integration/test_roformer_e2e.py","startLine":125,"endLine":137,"chunkText":"            # Mock model loading success with fallback\n            mock_separator.load_model.return_value = True\n            \n            # Mock separation results\n            expected_outputs = [\n                os.path.join(self.output_dir, \"test_audio_(vocals).flac\"),\n                os.path.join(self.output_dir, \"test_audio_(instrumental).flac\")\n            ]\n            mock_separator.separate.return_value = expected_outputs\n            \n            # Mock legacy model configuration (without new parameters)\n            mock_legacy_config = {\n                'model_type': 'bs_roformer',","chunkId":"tests/integration/test_roformer_e2e.py:11"}],[2491,{"filePath":"tests/integration/test_roformer_e2e.py","startLine":135,"endLine":149,"chunkText":"            # Mock legacy model configuration (without new parameters)\n            mock_legacy_config = {\n                'model_type': 'bs_roformer',\n                'architecture': 'BSRoformer',\n                'dim': 384,\n                'depth': 8,\n                'stereo': True,\n                'num_stems': 2,\n                'freqs_per_bands': (2048, 1024, 512, 256),\n                # Legacy config - missing new parameters\n                # Should fall back to old implementation\n            }\n            \n            # Initialize separator\n            separator = mock_separator_class(","chunkId":"tests/integration/test_roformer_e2e.py:12"}],[2492,{"filePath":"tests/integration/test_roformer_e2e.py","startLine":147,"endLine":160,"chunkText":"            \n            # Initialize separator\n            separator = mock_separator_class(\n                model_file_dir=self.temp_dir,\n                output_dir=self.output_dir\n            )\n            \n            # Test model loading (should use fallback mechanism)\n            load_success = separator.load_model(mock_legacy_model_path)\n            assert load_success, \"Legacy Roformer model should load successfully via fallback\"\n            \n            # Verify model was loaded\n            separator.load_model.assert_called_once_with(mock_legacy_model_path)\n            ","chunkId":"tests/integration/test_roformer_e2e.py:13"}],[2493,{"filePath":"tests/integration/test_roformer_e2e.py","startLine":158,"endLine":169,"chunkText":"            # Verify model was loaded\n            separator.load_model.assert_called_once_with(mock_legacy_model_path)\n            \n            # Test separation\n            output_files = separator.separate(self.test_audio_path)\n            \n            # Verify separation completed\n            assert output_files is not None, \"Legacy separation should return output files\"\n            assert len(output_files) == 2, \"Should produce vocals and instrumental outputs\"\n            \n            # Verify output file paths match expected\n            for output_file in output_files:","chunkId":"tests/integration/test_roformer_e2e.py:14"}],[2494,{"filePath":"tests/integration/test_roformer_e2e.py","startLine":167,"endLine":178,"chunkText":"            \n            # Verify output file paths match expected\n            for output_file in output_files:\n                assert output_file in expected_outputs, f\"Unexpected output file: {output_file}\"\n            \n            # Verify separation was called correctly\n            separator.separate.assert_called_once_with(self.test_audio_path)\n\n    def test_mel_band_roformer_e2e(self):\n        \"\"\"Test MelBandRoformer end-to-end separation.\"\"\"\n        mock_mel_model_path = \"mel_band_roformer_model.ckpt\"\n        ","chunkId":"tests/integration/test_roformer_e2e.py:15"}],[2495,{"filePath":"tests/integration/test_roformer_e2e.py","startLine":177,"endLine":189,"chunkText":"        mock_mel_model_path = \"mel_band_roformer_model.ckpt\"\n        \n        with patch('audio_separator.separator.separator.Separator') as mock_separator_class:\n            # Setup mock separator\n            mock_separator = Mock()\n            mock_separator_class.return_value = mock_separator\n            \n            # Mock model loading success\n            mock_separator.load_model.return_value = True\n            \n            # Mock separation results for MelBandRoformer\n            expected_outputs = [\n                os.path.join(self.output_dir, \"test_audio_(vocals).flac\"),","chunkId":"tests/integration/test_roformer_e2e.py:16"}],[2496,{"filePath":"tests/integration/test_roformer_e2e.py","startLine":187,"endLine":200,"chunkText":"            # Mock separation results for MelBandRoformer\n            expected_outputs = [\n                os.path.join(self.output_dir, \"test_audio_(vocals).flac\"),\n                os.path.join(self.output_dir, \"test_audio_(accompaniment).flac\")\n            ]\n            mock_separator.separate.return_value = expected_outputs\n            \n            # Mock MelBandRoformer configuration\n            mock_mel_config = {\n                'model_type': 'mel_band_roformer',\n                'architecture': 'MelBandRoformer',\n                'dim': 256,\n                'depth': 6,\n                'stereo': True,","chunkId":"tests/integration/test_roformer_e2e.py:17"}],[2497,{"filePath":"tests/integration/test_roformer_e2e.py","startLine":198,"endLine":216,"chunkText":"                'dim': 256,\n                'depth': 6,\n                'stereo': True,\n                'num_stems': 2,\n                'num_bands': 64,\n                'sample_rate': 44100,\n                # New parameters\n                'mlp_expansion_factor': 4,\n                'sage_attention': False,\n                'zero_dc': True\n            }\n            \n            # Initialize separator\n            separator = mock_separator_class(\n                model_file_dir=self.temp_dir,\n                output_dir=self.output_dir\n            )\n            \n            # Test model loading","chunkId":"tests/integration/test_roformer_e2e.py:18"}],[2498,{"filePath":"tests/integration/test_roformer_e2e.py","startLine":214,"endLine":227,"chunkText":"            )\n            \n            # Test model loading\n            load_success = separator.load_model(mock_mel_model_path)\n            assert load_success, \"MelBandRoformer model should load successfully\"\n            \n            # Test separation\n            output_files = separator.separate(self.test_audio_path)\n            \n            # Verify separation completed\n            assert output_files is not None, \"MelBandRoformer separation should return output files\"\n            assert len(output_files) == 2, \"Should produce two output stems\"\n            \n            # Verify output file paths","chunkId":"tests/integration/test_roformer_e2e.py:19"}],[2499,{"filePath":"tests/integration/test_roformer_e2e.py","startLine":225,"endLine":236,"chunkText":"            assert len(output_files) == 2, \"Should produce two output stems\"\n            \n            # Verify output file paths\n            for output_file in output_files:\n                assert output_file in expected_outputs, f\"Unexpected output file: {output_file}\"\n\n    def test_roformer_e2e_with_different_audio_formats(self):\n        \"\"\"Test E2E separation with different audio formats.\"\"\"\n        audio_formats = ['.flac', '.wav', '.mp3', '.m4a']\n        mock_model_path = \"model_bs_roformer_test.ckpt\"\n        \n        with patch('audio_separator.separator.separator.Separator') as mock_separator_class","chunkId":"tests/integration/test_roformer_e2e.py:20"}],[2500,{"filePath":"tests/integration/test_roformer_e2e.py","startLine":234,"endLine":244,"chunkText":"        mock_model_path = \"model_bs_roformer_test.ckpt\"\n        \n        with patch('audio_separator.separator.separator.Separator') as mock_separator_class:\n            mock_separator = Mock()\n            mock_separator_class.return_value = mock_separator\n            mock_separator.load_model.return_value = True\n            \n            for audio_format in audio_formats:\n                # Create mock audio file with different format\n                test_audio = os.path.join(self.temp_dir, f\"test_audio{audio_format}\")\n                with open(test_audio, 'w') as f:","chunkId":"tests/integration/test_roformer_e2e.py:21"}],[2501,{"filePath":"tests/integration/test_roformer_e2e.py","startLine":242,"endLine":251,"chunkText":"                # Create mock audio file with different format\n                test_audio = os.path.join(self.temp_dir, f\"test_audio{audio_format}\")\n                with open(test_audio, 'w') as f:\n                    f.write(\"mock_audio_data\")\n                \n                # Mock separation results\n                expected_outputs = [\n                    os.path.join(self.output_dir, f\"test_audio_(vocals){audio_format}\"),\n                    os.path.join(self.output_dir, f\"test_audio_(instrumental){audio_format}\")\n                ]","chunkId":"tests/integration/test_roformer_e2e.py:22"}],[2502,{"filePath":"tests/integration/test_roformer_e2e.py","startLine":249,"endLine":260,"chunkText":"                    os.path.join(self.output_dir, f\"test_audio_(vocals){audio_format}\"),\n                    os.path.join(self.output_dir, f\"test_audio_(instrumental){audio_format}\")\n                ]\n                mock_separator.separate.return_value = expected_outputs\n                \n                # Initialize separator\n                separator = mock_separator_class(\n                    model_file_dir=self.temp_dir,\n                    output_dir=self.output_dir\n                )\n                \n                # Load model","chunkId":"tests/integration/test_roformer_e2e.py:23"}],[2503,{"filePath":"tests/integration/test_roformer_e2e.py","startLine":258,"endLine":269,"chunkText":"                )\n                \n                # Load model\n                load_success = separator.load_model(mock_model_path)\n                assert load_success, f\"Model should load for {audio_format} format\"\n                \n                # Test separation\n                output_files = separator.separate(test_audio)\n                assert output_files is not None, f\"Separation should work with {audio_format} format\"\n                assert len(output_files) == 2, f\"Should produce 2 outputs for {audio_format}\"\n\n    def test_roformer_e2e_error_handling(self):","chunkId":"tests/integration/test_roformer_e2e.py:24"}],[2504,{"filePath":"tests/integration/test_roformer_e2e.py","startLine":267,"endLine":279,"chunkText":"                assert len(output_files) == 2, f\"Should produce 2 outputs for {audio_format}\"\n\n    def test_roformer_e2e_error_handling(self):\n        \"\"\"Test E2E error handling scenarios.\"\"\"\n        mock_model_path = \"problematic_model.ckpt\"\n        \n        with patch('audio_separator.separator.separator.Separator') as mock_separator_class:\n            mock_separator = Mock()\n            mock_separator_class.return_value = mock_separator\n            \n            # Test model loading failure\n            mock_separator.load_model.return_value = False\n            ","chunkId":"tests/integration/test_roformer_e2e.py:25"}],[2505,{"filePath":"tests/integration/test_roformer_e2e.py","startLine":277,"endLine":289,"chunkText":"            # Test model loading failure\n            mock_separator.load_model.return_value = False\n            \n            separator = mock_separator_class(\n                model_file_dir=self.temp_dir,\n                output_dir=self.output_dir\n            )\n            \n            load_success = separator.load_model(mock_model_path)\n            assert not load_success, \"Should handle model loading failure gracefully\"\n            \n            # Test separation failure\n            mock_separator.load_model.return_value = True  # Model loads","chunkId":"tests/integration/test_roformer_e2e.py:26"}],[2506,{"filePath":"tests/integration/test_roformer_e2e.py","startLine":287,"endLine":300,"chunkText":"            \n            # Test separation failure\n            mock_separator.load_model.return_value = True  # Model loads\n            mock_separator.separate.side_effect = Exception(\"Separation failed\")\n            \n            separator = mock_separator_class(\n                model_file_dir=self.temp_dir,\n                output_dir=self.output_dir\n            )\n            \n            separator.load_model(mock_model_path)\n            \n            # Should handle separation exception\n            with pytest.raises(Exception, match=\"Separation failed\"):","chunkId":"tests/integration/test_roformer_e2e.py:27"}],[2507,{"filePath":"tests/integration/test_roformer_e2e.py","startLine":300,"endLine":311,"chunkText":"            with pytest.raises(Exception, match=\"Separation failed\"):\n                separator.separate(self.test_audio_path)\n\n    def test_roformer_e2e_performance_validation(self):\n        \"\"\"Test E2E performance characteristics.\"\"\"\n        mock_model_path = \"performance_test_model.ckpt\"\n        \n        with patch('audio_separator.separator.separator.Separator') as mock_separator_class:\n            mock_separator = Mock()\n            mock_separator_class.return_value = mock_separator\n            mock_separator.load_model.return_value = True\n            ","chunkId":"tests/integration/test_roformer_e2e.py:28"}],[2508,{"filePath":"tests/integration/test_roformer_e2e.py","startLine":309,"endLine":323,"chunkText":"            mock_separator_class.return_value = mock_separator\n            mock_separator.load_model.return_value = True\n            \n            # Mock timing for performance validation\n            import time\n            \n            def mock_separate_with_timing(audio_path):\n                # Simulate processing time\n                start_time = time.time()\n                time.sleep(0.1)  # Simulate 100ms processing\n                end_time = time.time()\n                \n                processing_time = end_time - start_time\n                \n                # Return mock results with timing info","chunkId":"tests/integration/test_roformer_e2e.py:29"}],[2509,{"filePath":"tests/integration/test_roformer_e2e.py","startLine":321,"endLine":333,"chunkText":"                processing_time = end_time - start_time\n                \n                # Return mock results with timing info\n                return [\n                    os.path.join(self.output_dir, \"test_audio_(vocals).flac\"),\n                    os.path.join(self.output_dir, \"test_audio_(instrumental).flac\")\n                ], processing_time\n            \n            mock_separator.separate.side_effect = lambda x: mock_separate_with_timing(x)[0]\n            \n            separator = mock_separator_class(\n                model_file_dir=self.temp_dir,\n                output_dir=self.output_dir","chunkId":"tests/integration/test_roformer_e2e.py:30"}],[2510,{"filePath":"tests/integration/test_roformer_e2e.py","startLine":331,"endLine":346,"chunkText":"            separator = mock_separator_class(\n                model_file_dir=self.temp_dir,\n                output_dir=self.output_dir\n            )\n            \n            separator.load_model(mock_model_path)\n            \n            # Measure separation time\n            start_time = time.time()\n            output_files = separator.separate(self.test_audio_path)\n            end_time = time.time()\n            \n            processing_time = end_time - start_time\n            \n            # Verify performance characteristics\n            assert output_files is not None, \"Should produce outputs\"","chunkId":"tests/integration/test_roformer_e2e.py:31"}],[2511,{"filePath":"tests/integration/test_roformer_e2e.py","startLine":344,"endLine":355,"chunkText":"            \n            # Verify performance characteristics\n            assert output_files is not None, \"Should produce outputs\"\n            assert processing_time < 10.0, \"Processing should complete in reasonable time\"\n            assert len(output_files) == 2, \"Should produce expected number of outputs\"\n\n    def test_roformer_e2e_memory_usage(self):\n        \"\"\"Test E2E memory usage characteristics.\"\"\"\n        mock_model_path = \"memory_test_model.ckpt\"\n        \n        with patch('audio_separator.separator.separator.Separator') as mock_separator_class:\n            mock_separator = Mock()","chunkId":"tests/integration/test_roformer_e2e.py:32"}],[2512,{"filePath":"tests/integration/test_roformer_e2e.py","startLine":353,"endLine":364,"chunkText":"        \n        with patch('audio_separator.separator.separator.Separator') as mock_separator_class:\n            mock_separator = Mock()\n            mock_separator_class.return_value = mock_separator\n            mock_separator.load_model.return_value = True\n            \n            # Mock memory usage tracking\n            def mock_separate_with_memory_tracking(audio_path):\n                # Simulate memory usage\n                mock_memory_usage = {\n                    'peak_memory_mb': 1024,  # 1GB peak\n                    'current_memory_mb': 512,  # 512MB current","chunkId":"tests/integration/test_roformer_e2e.py:33"}],[2513,{"filePath":"tests/integration/test_roformer_e2e.py","startLine":362,"endLine":373,"chunkText":"                mock_memory_usage = {\n                    'peak_memory_mb': 1024,  # 1GB peak\n                    'current_memory_mb': 512,  # 512MB current\n                    'gpu_memory_mb': 2048 if torch.cuda.is_available() else 0\n                }\n                \n                return [\n                    os.path.join(self.output_dir, \"test_audio_(vocals).flac\"),\n                    os.path.join(self.output_dir, \"test_audio_(instrumental).flac\")\n                ], mock_memory_usage\n            \n            mock_separator.separate.side_effect = lambda x: mock_separate_with_memory_tracking(x)[0]","chunkId":"tests/integration/test_roformer_e2e.py:34"}],[2514,{"filePath":"tests/integration/test_roformer_e2e.py","startLine":371,"endLine":384,"chunkText":"                ], mock_memory_usage\n            \n            mock_separator.separate.side_effect = lambda x: mock_separate_with_memory_tracking(x)[0]\n            \n            separator = mock_separator_class(\n                model_file_dir=self.temp_dir,\n                output_dir=self.output_dir\n            )\n            \n            separator.load_model(mock_model_path)\n            output_files = separator.separate(self.test_audio_path)\n            \n            # Verify memory usage is reasonable\n            assert output_files is not None, \"Should produce outputs despite memory constraints\"","chunkId":"tests/integration/test_roformer_e2e.py:35"}],[2515,{"filePath":"tests/integration/test_roformer_e2e.py","startLine":382,"endLine":394,"chunkText":"            \n            # Verify memory usage is reasonable\n            assert output_files is not None, \"Should produce outputs despite memory constraints\"\n            # In real implementation, would check actual memory usage\n            # assert peak_memory < threshold, \"Memory usage should be within limits\"\n\n    def test_roformer_e2e_batch_processing(self):\n        \"\"\"Test E2E batch processing of multiple files.\"\"\"\n        mock_model_path = \"batch_test_model.ckpt\"\n        \n        # Create multiple test audio files\n        test_files = []\n        for i in range(3):","chunkId":"tests/integration/test_roformer_e2e.py:36"}],[2516,{"filePath":"tests/integration/test_roformer_e2e.py","startLine":392,"endLine":404,"chunkText":"        # Create multiple test audio files\n        test_files = []\n        for i in range(3):\n            test_file = os.path.join(self.temp_dir, f\"test_audio_{i}.flac\")\n            with open(test_file, 'w') as f:\n                f.write(f\"mock_audio_data_{i}\")\n            test_files.append(test_file)\n        \n        with patch('audio_separator.separator.separator.Separator') as mock_separator_class:\n            mock_separator = Mock()\n            mock_separator_class.return_value = mock_separator\n            mock_separator.load_model.return_value = True\n            ","chunkId":"tests/integration/test_roformer_e2e.py:37"}],[2517,{"filePath":"tests/integration/test_roformer_e2e.py","startLine":402,"endLine":414,"chunkText":"            mock_separator_class.return_value = mock_separator\n            mock_separator.load_model.return_value = True\n            \n            # Mock batch separation results\n            def mock_batch_separate(audio_path):\n                basename = os.path.splitext(os.path.basename(audio_path))[0]\n                return [\n                    os.path.join(self.output_dir, f\"{basename}_(vocals).flac\"),\n                    os.path.join(self.output_dir, f\"{basename}_(instrumental).flac\")\n                ]\n            \n            mock_separator.separate.side_effect = mock_batch_separate\n            ","chunkId":"tests/integration/test_roformer_e2e.py:38"}],[2518,{"filePath":"tests/integration/test_roformer_e2e.py","startLine":412,"endLine":428,"chunkText":"            \n            mock_separator.separate.side_effect = mock_batch_separate\n            \n            separator = mock_separator_class(\n                model_file_dir=self.temp_dir,\n                output_dir=self.output_dir\n            )\n            \n            separator.load_model(mock_model_path)\n            \n            # Process all files\n            all_outputs = []\n            for test_file in test_files:\n                outputs = separator.separate(test_file)\n                all_outputs.extend(outputs)\n            \n            # Verify batch processing","chunkId":"tests/integration/test_roformer_e2e.py:39"}],[2519,{"filePath":"tests/integration/test_roformer_e2e.py","startLine":426,"endLine":435,"chunkText":"                all_outputs.extend(outputs)\n            \n            # Verify batch processing\n            expected_total_outputs = len(test_files) * 2  # 2 outputs per input\n            assert len(all_outputs) == expected_total_outputs, \"Should process all files\"\n            \n            # Verify each file was processed\n            for i in range(len(test_files)):\n                expected_vocals = os.path.join(self.output_dir, f\"test_audio_{i}_(vocals).flac\")\n                expected_instrumental = os.path.join(self.output_dir, f\"test_audio_{i}_(instrumental).flac\")","chunkId":"tests/integration/test_roformer_e2e.py:40"}],[2520,{"filePath":"tests/integration/test_roformer_e2e.py","startLine":433,"endLine":438,"chunkText":"            for i in range(len(test_files)):\n                expected_vocals = os.path.join(self.output_dir, f\"test_audio_{i}_(vocals).flac\")\n                expected_instrumental = os.path.join(self.output_dir, f\"test_audio_{i}_(instrumental).flac\")\n                assert expected_vocals in all_outputs, f\"Missing vocals output for file {i}\"\n                assert expected_instrumental in all_outputs, f\"Missing instrumental output for file {i}\"\n","chunkId":"tests/integration/test_roformer_e2e.py:41"}],[2521,{"filePath":"tests/integration/test_roformer_fallback_mechanism.py","startLine":0,"endLine":22,"chunkText":"\"\"\"\nIntegration test for fallback mechanism activation.\nThis test ensures fallback from new to old implementation works correctly.\n\"\"\"\n\nimport pytest\nimport tempfile\nimport torch\n\n# This will fail initially - that's expected for TDD\ntry:\n    from audio_separator import Separator\n    from audio_separator.separator.roformer.roformer_loader import RoformerLoader, ImplementationVersion\n    IMPORTS_AVAILABLE = True\nexcept ImportError:\n    IMPORTS_AVAILABLE = False\n\n\nclass TestRoformerFallbackMechanism:\n    \"\"\"Test fallback mechanism activation.\"\"\"\n    \n    @pytest.fixture\n    def mock_edge_case_model(self):","chunkId":"tests/integration/test_roformer_fallback_mechanism.py:0"}],[2522,{"filePath":"tests/integration/test_roformer_fallback_mechanism.py","startLine":20,"endLine":33,"chunkText":"    \n    @pytest.fixture\n    def mock_edge_case_model(self):\n        \"\"\"Create a mock model that triggers fallback.\"\"\"\n        with tempfile.NamedTemporaryFile(suffix='.ckpt', delete=False) as tmp:\n            # Model that might cause new implementation to fail\n            mock_state = {\n                'config': {\n                    'dim': 512, 'depth': 6, 'num_stems': 2,\n                    'legacy_parameter': True,  # Unknown to new implementation\n                    'freqs_per_bands': (2, 4, 8, 16, 32, 64)\n                }\n            }\n            torch.save(mock_state, tmp.name)","chunkId":"tests/integration/test_roformer_fallback_mechanism.py:1"}],[2523,{"filePath":"tests/integration/test_roformer_fallback_mechanism.py","startLine":31,"endLine":46,"chunkText":"                }\n            }\n            torch.save(mock_state, tmp.name)\n            yield tmp.name\n        import os\n        if os.path.exists(tmp.name):\n            os.unlink(tmp.name)\n    \n    @pytest.mark.skipif(not IMPORTS_AVAILABLE, reason=\"Implementation not available yet (TDD)\")\n    def test_fallback_mechanism_activation(self, mock_edge_case_model):\n        \"\"\"Test that fallback mechanism activates when new implementation fails.\"\"\"\n        \n        loader = RoformerLoader()\n        result = loader.load_model(mock_edge_case_model)\n        \n        # Should succeed using fallback","chunkId":"tests/integration/test_roformer_fallback_mechanism.py:2"}],[2524,{"filePath":"tests/integration/test_roformer_fallback_mechanism.py","startLine":44,"endLine":57,"chunkText":"        result = loader.load_model(mock_edge_case_model)\n        \n        # Should succeed using fallback\n        assert result.success is True\n        assert result.implementation_used == ImplementationVersion.FALLBACK\n        assert len(result.warnings) > 0\n        assert \"fallback\" in result.warnings[0].lower()\n    \n    # TDD placeholder test removed - implementation is now complete\n\n\nif __name__ == \"__main__\":\n    pytest.main([__file__, \"-v\"])\n","chunkId":"tests/integration/test_roformer_fallback_mechanism.py:3"}],[2525,{"filePath":"tests/integration/test_roformer_model_switching.py","startLine":0,"endLine":23,"chunkText":"\"\"\"\nIntegration test for model type switching (BSRoformer ↔ MelBandRoformer).\nThis test ensures seamless switching between different Roformer model types.\n\"\"\"\n\nimport pytest\nimport os\nimport tempfile\nimport torch\n\n# This will fail initially - that's expected for TDD\ntry:\n    from audio_separator import Separator\n    IMPORTS_AVAILABLE = True\nexcept ImportError:\n    IMPORTS_AVAILABLE = False\n\n\nclass TestRoformerModelSwitching:\n    \"\"\"Test switching between different Roformer model types.\"\"\"\n    \n    @pytest.fixture\n    def mock_bs_roformer_model(self):\n        \"\"\"Create a mock BSRoformer model.\"\"\"","chunkId":"tests/integration/test_roformer_model_switching.py:0"}],[2526,{"filePath":"tests/integration/test_roformer_model_switching.py","startLine":21,"endLine":36,"chunkText":"    @pytest.fixture\n    def mock_bs_roformer_model(self):\n        \"\"\"Create a mock BSRoformer model.\"\"\"\n        with tempfile.NamedTemporaryFile(suffix='.ckpt', delete=False) as tmp:\n            mock_state = {\n                'config': {\n                    'dim': 512, 'depth': 6, 'num_stems': 2,\n                    'freqs_per_bands': (2, 4, 8, 16, 32, 64),  # BSRoformer specific\n                    'model_type': 'bs_roformer'\n                }\n            }\n            torch.save(mock_state, tmp.name)\n            yield tmp.name\n        if os.path.exists(tmp.name):\n            os.unlink(tmp.name)\n    ","chunkId":"tests/integration/test_roformer_model_switching.py:1"}],[2527,{"filePath":"tests/integration/test_roformer_model_switching.py","startLine":34,"endLine":48,"chunkText":"        if os.path.exists(tmp.name):\n            os.unlink(tmp.name)\n    \n    @pytest.fixture\n    def mock_mel_band_roformer_model(self):\n        \"\"\"Create a mock MelBandRoformer model.\"\"\"\n        with tempfile.NamedTemporaryFile(suffix='.ckpt', delete=False) as tmp:\n            mock_state = {\n                'config': {\n                    'dim': 384, 'depth': 8, 'num_stems': 4,\n                    'num_bands': 64,  # MelBandRoformer specific\n                    'sample_rate': 44100,\n                    'model_type': 'mel_band_roformer'\n                }\n            }","chunkId":"tests/integration/test_roformer_model_switching.py:2"}],[2528,{"filePath":"tests/integration/test_roformer_model_switching.py","startLine":46,"endLine":58,"chunkText":"                    'model_type': 'mel_band_roformer'\n                }\n            }\n            torch.save(mock_state, tmp.name)\n            yield tmp.name\n        if os.path.exists(tmp.name):\n            os.unlink(tmp.name)\n    \n    @pytest.mark.skipif(not IMPORTS_AVAILABLE, reason=\"Implementation not available yet (TDD)\")\n    def test_switch_bs_to_mel_band_roformer(self, mock_bs_roformer_model, mock_mel_band_roformer_model):\n        \"\"\"Test switching from BSRoformer to MelBandRoformer.\"\"\"\n        \n        with tempfile.TemporaryDirectory() as output_dir:","chunkId":"tests/integration/test_roformer_model_switching.py:3"}],[2529,{"filePath":"tests/integration/test_roformer_model_switching.py","startLine":56,"endLine":69,"chunkText":"        \"\"\"Test switching from BSRoformer to MelBandRoformer.\"\"\"\n        \n        with tempfile.TemporaryDirectory() as output_dir:\n            separator = Separator(output_dir=output_dir)\n            \n            # Load BSRoformer first\n            separator.load_model(mock_bs_roformer_model)\n            assert separator.model_name == \"bs_roformer\"\n            \n            # Switch to MelBandRoformer\n            separator.load_model(mock_mel_band_roformer_model)\n            assert separator.model_name == \"mel_band_roformer\"\n            \n            # Both should work without conflicts","chunkId":"tests/integration/test_roformer_model_switching.py:4"}],[2530,{"filePath":"tests/integration/test_roformer_model_switching.py","startLine":67,"endLine":77,"chunkText":"            assert separator.model_name == \"mel_band_roformer\"\n            \n            # Both should work without conflicts\n            assert separator.model is not None\n    \n    # TDD placeholder test removed - implementation is now complete\n\n\nif __name__ == \"__main__\":\n    pytest.main([__file__, \"-v\"])\n","chunkId":"tests/integration/test_roformer_model_switching.py:5"}],[2531,{"filePath":"tests/integration/test_roformer_new_parameters.py","startLine":0,"endLine":22,"chunkText":"\"\"\"\nIntegration test for newer models with new parameters.\nThis test ensures that newer Roformer models with additional parameters work correctly.\n\"\"\"\n\nimport pytest\nimport os\nimport tempfile\nimport torch\nfrom unittest.mock import patch, Mock\n\n# This will fail initially - that's expected for TDD\ntry:\n    from audio_separator import Separator\n    from audio_separator.separator.roformer.roformer_loader import RoformerLoader\n    IMPORTS_AVAILABLE = True\nexcept ImportError:\n    IMPORTS_AVAILABLE = False\n\n\nclass TestRoformerNewParameters:\n    \"\"\"Test newer Roformer models with new parameters.\"\"\"\n    ","chunkId":"tests/integration/test_roformer_new_parameters.py:0"}],[2532,{"filePath":"tests/integration/test_roformer_new_parameters.py","startLine":20,"endLine":32,"chunkText":"class TestRoformerNewParameters:\n    \"\"\"Test newer Roformer models with new parameters.\"\"\"\n    \n    @pytest.fixture\n    def mock_new_roformer_model(self):\n        \"\"\"Create a mock new Roformer model with additional parameters.\"\"\"\n        with tempfile.NamedTemporaryFile(suffix='.ckpt', delete=False) as tmp:\n            # Create mock model state dict with new parameters\n            mock_state = {\n                'state_dict': {\n                    'model.dim': torch.tensor(512),\n                    'model.depth': torch.tensor(6),\n                    'model.stereo': torch.tensor(False),","chunkId":"tests/integration/test_roformer_new_parameters.py:1"}],[2533,{"filePath":"tests/integration/test_roformer_new_parameters.py","startLine":30,"endLine":42,"chunkText":"                    'model.dim': torch.tensor(512),\n                    'model.depth': torch.tensor(6),\n                    'model.stereo': torch.tensor(False),\n                    'model.num_stems': torch.tensor(2),\n                },\n                'config': {\n                    'dim': 512,\n                    'depth': 6,\n                    'stereo': False,\n                    'num_stems': 2,\n                    'freqs_per_bands': (2, 4, 8, 16, 32, 64),\n                    # New parameters that should be supported\n                    'mlp_expansion_factor': 6,  # Non-default value","chunkId":"tests/integration/test_roformer_new_parameters.py:2"}],[2534,{"filePath":"tests/integration/test_roformer_new_parameters.py","startLine":40,"endLine":53,"chunkText":"                    'freqs_per_bands': (2, 4, 8, 16, 32, 64),\n                    # New parameters that should be supported\n                    'mlp_expansion_factor': 6,  # Non-default value\n                    'sage_attention': True,     # Enabled\n                    'zero_dc': False,           # Non-default value\n                    'use_torch_checkpoint': True,\n                    'skip_connection': True,\n                }\n            }\n            torch.save(mock_state, tmp.name)\n            yield tmp.name\n        \n        # Cleanup\n        if os.path.exists(tmp.name):","chunkId":"tests/integration/test_roformer_new_parameters.py:3"}],[2535,{"filePath":"tests/integration/test_roformer_new_parameters.py","startLine":51,"endLine":66,"chunkText":"        \n        # Cleanup\n        if os.path.exists(tmp.name):\n            os.unlink(tmp.name)\n    \n    @pytest.fixture\n    def mock_audio_file(self):\n        \"\"\"Create a mock audio file for testing.\"\"\"\n        with tempfile.NamedTemporaryFile(suffix='.flac', delete=False) as tmp:\n            tmp.write(b'mock_audio_data')\n            yield tmp.name\n        \n        if os.path.exists(tmp.name):\n            os.unlink(tmp.name)\n    \n    @pytest.mark.skipif(not IMPORTS_AVAILABLE, reason=\"Implementation not available yet (TDD)\")","chunkId":"tests/integration/test_roformer_new_parameters.py:4"}],[2536,{"filePath":"tests/integration/test_roformer_new_parameters.py","startLine":64,"endLine":74,"chunkText":"            os.unlink(tmp.name)\n    \n    @pytest.mark.skipif(not IMPORTS_AVAILABLE, reason=\"Implementation not available yet (TDD)\")\n    def test_load_newer_model_with_new_parameters(self, mock_new_roformer_model, mock_audio_file):\n        \"\"\"Test that newer models with additional parameters load successfully.\"\"\"\n        \n        # This test MUST FAIL initially because implementation doesn't exist\n        with tempfile.TemporaryDirectory() as output_dir:\n            separator = Separator(\n                model_file_dir=os.path.dirname(mock_new_roformer_model),\n                output_dir=output_dir","chunkId":"tests/integration/test_roformer_new_parameters.py:5"}],[2537,{"filePath":"tests/integration/test_roformer_new_parameters.py","startLine":72,"endLine":84,"chunkText":"            separator = Separator(\n                model_file_dir=os.path.dirname(mock_new_roformer_model),\n                output_dir=output_dir\n            )\n            \n            # Load the new model - should work with new implementation\n            separator.load_model(os.path.basename(mock_new_roformer_model))\n            \n            # Separate audio - should work with new parameters\n            output_files = separator.separate(mock_audio_file)\n            \n            # Verify outputs exist and are valid\n            assert len(output_files) >= 1, \"Should produce audio outputs\"","chunkId":"tests/integration/test_roformer_new_parameters.py:6"}],[2538,{"filePath":"tests/integration/test_roformer_new_parameters.py","startLine":82,"endLine":90,"chunkText":"            \n            # Verify outputs exist and are valid\n            assert len(output_files) >= 1, \"Should produce audio outputs\"\n            for output_file in output_files:\n                assert os.path.exists(output_file), f\"Output file should exist: {output_file}\"\n                assert os.path.getsize(output_file) > 0, f\"Output file should not be empty: {output_file}\"\n    \n    @pytest.mark.skipif(not IMPORTS_AVAILABLE, reason=\"Implementation not available yet (TDD)\")\n    def test_new_model_uses_new_implementation(self, mock_new_roformer_model):","chunkId":"tests/integration/test_roformer_new_parameters.py:7"}],[2539,{"filePath":"tests/integration/test_roformer_new_parameters.py","startLine":88,"endLine":97,"chunkText":"    \n    @pytest.mark.skipif(not IMPORTS_AVAILABLE, reason=\"Implementation not available yet (TDD)\")\n    def test_new_model_uses_new_implementation(self, mock_new_roformer_model):\n        \"\"\"Test that new models use the new implementation (not fallback).\"\"\"\n        \n        # Mock the loader to verify new implementation is used\n        with patch('audio_separator.separator.roformer.roformer_loader.RoformerLoader') as mock_loader_class:\n            mock_loader = Mock()\n            mock_loader_class.return_value = mock_loader\n            ","chunkId":"tests/integration/test_roformer_new_parameters.py:8"}],[2540,{"filePath":"tests/integration/test_roformer_new_parameters.py","startLine":95,"endLine":109,"chunkText":"            mock_loader = Mock()\n            mock_loader_class.return_value = mock_loader\n            \n            from audio_separator.separator.roformer.roformer_loader import ModelLoadingResult, ImplementationVersion\n            \n            mock_result = ModelLoadingResult(\n                success=True,\n                model=Mock(),\n                error_message=None,\n                implementation_used=ImplementationVersion.NEW,  # Should use new implementation\n                warnings=[]\n            )\n            mock_loader.load_model.return_value = mock_result\n            \n            # Load model","chunkId":"tests/integration/test_roformer_new_parameters.py:9"}],[2541,{"filePath":"tests/integration/test_roformer_new_parameters.py","startLine":107,"endLine":118,"chunkText":"            mock_loader.load_model.return_value = mock_result\n            \n            # Load model\n            loader = RoformerLoader()\n            result = loader.load_model(mock_new_roformer_model)\n            \n            # Verify new implementation was used\n            assert result.success is True\n            assert result.implementation_used == ImplementationVersion.NEW\n            assert len(result.warnings) == 0  # No fallback warnings\n    \n    @pytest.mark.skipif(not IMPORTS_AVAILABLE, reason=\"Implementation not available yet (TDD)\")","chunkId":"tests/integration/test_roformer_new_parameters.py:10"}],[2542,{"filePath":"tests/integration/test_roformer_new_parameters.py","startLine":116,"endLine":128,"chunkText":"            assert len(result.warnings) == 0  # No fallback warnings\n    \n    @pytest.mark.skipif(not IMPORTS_AVAILABLE, reason=\"Implementation not available yet (TDD)\")\n    def test_new_parameters_are_properly_handled(self, mock_new_roformer_model):\n        \"\"\"Test that new parameters are properly loaded and used.\"\"\"\n        \n        loader = RoformerLoader()\n        result = loader.load_model(mock_new_roformer_model)\n        \n        assert result.success is True\n        model_config = result.model.config\n        \n        # Verify new parameters are loaded correctly","chunkId":"tests/integration/test_roformer_new_parameters.py:11"}],[2543,{"filePath":"tests/integration/test_roformer_new_parameters.py","startLine":126,"endLine":135,"chunkText":"        model_config = result.model.config\n        \n        # Verify new parameters are loaded correctly\n        assert model_config.mlp_expansion_factor == 6  # From mock config\n        assert model_config.sage_attention is True     # From mock config\n        assert model_config.zero_dc is False          # From mock config\n        assert model_config.use_torch_checkpoint is True\n        assert model_config.skip_connection is True\n    \n    @pytest.mark.skipif(not IMPORTS_AVAILABLE, reason=\"Implementation not available yet (TDD)\")","chunkId":"tests/integration/test_roformer_new_parameters.py:12"}],[2544,{"filePath":"tests/integration/test_roformer_new_parameters.py","startLine":133,"endLine":144,"chunkText":"        assert model_config.skip_connection is True\n    \n    @pytest.mark.skipif(not IMPORTS_AVAILABLE, reason=\"Implementation not available yet (TDD)\")\n    def test_sage_attention_parameter_integration(self, mock_new_roformer_model):\n        \"\"\"Test that sage_attention parameter is properly integrated.\"\"\"\n        \n        # This test verifies that sage_attention=True is handled correctly\n        # and doesn't cause the AttributeError that was mentioned in the spec\n        \n        loader = RoformerLoader()\n        result = loader.load_model(mock_new_roformer_model)\n        ","chunkId":"tests/integration/test_roformer_new_parameters.py:13"}],[2545,{"filePath":"tests/integration/test_roformer_new_parameters.py","startLine":142,"endLine":155,"chunkText":"        loader = RoformerLoader()\n        result = loader.load_model(mock_new_roformer_model)\n        \n        assert result.success is True\n        \n        # Verify that sage_attention is passed to transformer_kwargs\n        model = result.model\n        assert hasattr(model, 'sage_attention')\n        assert model.sage_attention is True\n        \n        # Verify no AttributeError is raised during model initialization\n        # (This would be caught in the loading process)\n        assert result.error_message is None\n    ","chunkId":"tests/integration/test_roformer_new_parameters.py:14"}],[2546,{"filePath":"tests/integration/test_roformer_new_parameters.py","startLine":153,"endLine":165,"chunkText":"        # (This would be caught in the loading process)\n        assert result.error_message is None\n    \n    @pytest.mark.skipif(not IMPORTS_AVAILABLE, reason=\"Implementation not available yet (TDD)\")\n    def test_mlp_expansion_factor_parameter_handling(self, mock_new_roformer_model):\n        \"\"\"Test that mlp_expansion_factor parameter is handled correctly.\"\"\"\n        \n        # This test verifies that the mlp_expansion_factor parameter\n        # doesn't cause the TypeError mentioned in the spec\n        \n        loader = RoformerLoader()\n        result = loader.load_model(mock_new_roformer_model)\n        ","chunkId":"tests/integration/test_roformer_new_parameters.py:15"}],[2547,{"filePath":"tests/integration/test_roformer_new_parameters.py","startLine":163,"endLine":176,"chunkText":"        loader = RoformerLoader()\n        result = loader.load_model(mock_new_roformer_model)\n        \n        assert result.success is True\n        \n        # Verify parameter is properly set\n        model = result.model\n        assert hasattr(model, 'mlp_expansion_factor')\n        assert model.mlp_expansion_factor == 6  # From mock config\n        \n        # Verify no TypeError during initialization\n        assert result.error_message is None\n    \n    @pytest.mark.skipif(not IMPORTS_AVAILABLE, reason=\"Implementation not available yet (TDD)\")","chunkId":"tests/integration/test_roformer_new_parameters.py:16"}],[2548,{"filePath":"tests/integration/test_roformer_new_parameters.py","startLine":174,"endLine":187,"chunkText":"        assert result.error_message is None\n    \n    @pytest.mark.skipif(not IMPORTS_AVAILABLE, reason=\"Implementation not available yet (TDD)\")\n    def test_torch_checkpoint_parameter_integration(self, mock_new_roformer_model):\n        \"\"\"Test that use_torch_checkpoint parameter works correctly.\"\"\"\n        \n        loader = RoformerLoader()\n        result = loader.load_model(mock_new_roformer_model)\n        \n        assert result.success is True\n        model = result.model\n        \n        # Verify checkpoint parameter is set\n        assert hasattr(model, 'use_torch_checkpoint')","chunkId":"tests/integration/test_roformer_new_parameters.py:17"}],[2549,{"filePath":"tests/integration/test_roformer_new_parameters.py","startLine":185,"endLine":197,"chunkText":"        \n        # Verify checkpoint parameter is set\n        assert hasattr(model, 'use_torch_checkpoint')\n        assert model.use_torch_checkpoint is True\n        \n        # This parameter affects memory usage during forward pass\n        # In real implementation, this would be tested during audio separation\n    \n    @pytest.mark.skipif(not IMPORTS_AVAILABLE, reason=\"Implementation not available yet (TDD)\")\n    def test_skip_connection_parameter_integration(self, mock_new_roformer_model):\n        \"\"\"Test that skip_connection parameter works correctly.\"\"\"\n        \n        loader = RoformerLoader()","chunkId":"tests/integration/test_roformer_new_parameters.py:18"}],[2550,{"filePath":"tests/integration/test_roformer_new_parameters.py","startLine":195,"endLine":208,"chunkText":"        \"\"\"Test that skip_connection parameter works correctly.\"\"\"\n        \n        loader = RoformerLoader()\n        result = loader.load_model(mock_new_roformer_model)\n        \n        assert result.success is True\n        model = result.model\n        \n        # Verify skip connection parameter is set\n        assert hasattr(model, 'skip_connection')\n        assert model.skip_connection is True\n    \n    @pytest.mark.skipif(not IMPORTS_AVAILABLE, reason=\"Implementation not available yet (TDD)\")\n    def test_zero_dc_parameter_handling(self, mock_new_roformer_model):","chunkId":"tests/integration/test_roformer_new_parameters.py:19"}],[2551,{"filePath":"tests/integration/test_roformer_new_parameters.py","startLine":206,"endLine":220,"chunkText":"    \n    @pytest.mark.skipif(not IMPORTS_AVAILABLE, reason=\"Implementation not available yet (TDD)\")\n    def test_zero_dc_parameter_handling(self, mock_new_roformer_model):\n        \"\"\"Test that zero_dc parameter is handled correctly.\"\"\"\n        \n        loader = RoformerLoader()\n        result = loader.load_model(mock_new_roformer_model)\n        \n        assert result.success is True\n        model = result.model\n        \n        # Verify zero_dc parameter is set to non-default value\n        assert hasattr(model, 'zero_dc')\n        assert model.zero_dc is False  # Non-default value from mock\n    ","chunkId":"tests/integration/test_roformer_new_parameters.py:20"}],[2552,{"filePath":"tests/integration/test_roformer_new_parameters.py","startLine":218,"endLine":228,"chunkText":"        assert hasattr(model, 'zero_dc')\n        assert model.zero_dc is False  # Non-default value from mock\n    \n    # TDD placeholder test removed - implementation is now complete\n    \n    @pytest.mark.skipif(not IMPORTS_AVAILABLE, reason=\"Implementation not available yet (TDD)\")\n    def test_new_model_audio_quality_validation(self, mock_new_roformer_model, mock_audio_file):\n        \"\"\"Test that new models produce high-quality audio separation.\"\"\"\n        \n        with tempfile.TemporaryDirectory() as output_dir:\n            separator = Separator(output_dir=output_dir)","chunkId":"tests/integration/test_roformer_new_parameters.py:21"}],[2553,{"filePath":"tests/integration/test_roformer_new_parameters.py","startLine":226,"endLine":239,"chunkText":"        \n        with tempfile.TemporaryDirectory() as output_dir:\n            separator = Separator(output_dir=output_dir)\n            separator.load_model(mock_new_roformer_model)\n            \n            outputs = separator.separate(mock_audio_file)\n            \n            # Verify outputs exist and meet quality standards\n            assert len(outputs) > 0\n            for output in outputs:\n                assert os.path.exists(output)\n                \n                # In real implementation, this would:\n                # 1. Load and analyze the audio","chunkId":"tests/integration/test_roformer_new_parameters.py:22"}],[2554,{"filePath":"tests/integration/test_roformer_new_parameters.py","startLine":237,"endLine":247,"chunkText":"                \n                # In real implementation, this would:\n                # 1. Load and analyze the audio\n                # 2. Check for artifacts or quality issues\n                # 3. Compare against reference outputs if available\n                # 4. Verify SSIM >= 0.80 for spectrograms (per spec)\n\n\nif __name__ == \"__main__\":\n    pytest.main([__file__, \"-v\"])\n","chunkId":"tests/integration/test_roformer_new_parameters.py:23"}],[2555,{"filePath":"tests/integration/test_separator_output_integration.py","startLine":0,"endLine":23,"chunkText":"import os\nimport pytest\nimport tempfile\nimport shutil\nfrom pathlib import Path\n\nfrom audio_separator.separator import Separator\n\n\n@pytest.fixture(name=\"input_file\")\ndef fixture_input_file():\n    \"\"\"Fixture providing the test input audio file path.\"\"\"\n    return \"tests/inputs/mardy20s.flac\"\n\n\n@pytest.fixture(name=\"temp_output_dir\")\ndef fixture_temp_output_dir():\n    \"\"\"Fixture providing a temporary directory for output files.\"\"\"\n    temp_dir = tempfile.mkdtemp()\n    yield temp_dir\n    # Clean up after test\n    shutil.rmtree(temp_dir)\n\n","chunkId":"tests/integration/test_separator_output_integration.py:0"}],[2556,{"filePath":"tests/integration/test_separator_output_integration.py","startLine":22,"endLine":35,"chunkText":"\n\ndef test_separator_output_dir_and_custom_names(input_file, temp_output_dir):\n    \"\"\"Test that Separator respects output_dir and custom_output_names parameters.\"\"\"\n    print(\"\\n>>> TEST: Checking output_dir with custom output names\")\n    \n    # Define custom output filenames\n    vocal_output_filename = \"custom_vocals_output\"\n    instrumental_output_filename = \"custom_instrumental_output\"\n\n    # Create output name mapping\n    custom_output_names = {\"Vocals\": vocal_output_filename, \"Instrumental\": instrumental_output_filename}\n\n    # Initialize separator with specified output directory","chunkId":"tests/integration/test_separator_output_integration.py:1"}],[2557,{"filePath":"tests/integration/test_separator_output_integration.py","startLine":33,"endLine":44,"chunkText":"    custom_output_names = {\"Vocals\": vocal_output_filename, \"Instrumental\": instrumental_output_filename}\n\n    # Initialize separator with specified output directory\n    print(f\"Creating Separator with output_dir: {temp_output_dir}\")\n    separator = Separator(output_dir=temp_output_dir, log_level=20)  # INFO level\n\n    # Load model\n    print(\"Loading model: MGM_MAIN_v4.pth\")\n    separator.load_model(model_filename=\"MGM_MAIN_v4.pth\")\n\n    # Run separation with custom output names\n    print(f\"Running separation with custom_output_names: {custom_output_names}\")","chunkId":"tests/integration/test_separator_output_integration.py:2"}],[2558,{"filePath":"tests/integration/test_separator_output_integration.py","startLine":42,"endLine":52,"chunkText":"\n    # Run separation with custom output names\n    print(f\"Running separation with custom_output_names: {custom_output_names}\")\n    output_files = separator.separate(input_file, custom_output_names=custom_output_names)\n    print(f\"Separator.separate() returned: {output_files}\")\n\n    # The separator adds .wav extension since the default output format is WAV\n    expected_vocal_filename = vocal_output_filename + \".wav\"\n    expected_instrumental_filename = instrumental_output_filename + \".wav\"\n    \n    # Check that the returned filenames match the expected names","chunkId":"tests/integration/test_separator_output_integration.py:3"}],[2559,{"filePath":"tests/integration/test_separator_output_integration.py","startLine":50,"endLine":59,"chunkText":"    expected_instrumental_filename = instrumental_output_filename + \".wav\"\n    \n    # Check that the returned filenames match the expected names\n    output_filenames = [os.path.basename(f) for f in output_files]\n    print(f\"Extracted filenames from output_files: {output_filenames}\")\n    \n    # NOTE: The Separator class returns only the filenames, not the full paths with output_dir\n    print(\"EXPECTED BEHAVIOR: The Separator.separate() method returns filenames without the output_dir path\")\n    print(f\"Expected filenames (without path): {expected_vocal_filename} and {expected_instrumental_filename}\")\n    ","chunkId":"tests/integration/test_separator_output_integration.py:4"}],[2560,{"filePath":"tests/integration/test_separator_output_integration.py","startLine":57,"endLine":63,"chunkText":"    print(\"EXPECTED BEHAVIOR: The Separator.separate() method returns filenames without the output_dir path\")\n    print(f\"Expected filenames (without path): {expected_vocal_filename} and {expected_instrumental_filename}\")\n    \n    assert expected_vocal_filename in output_filenames, f\"Expected {expected_vocal_filename} in output files\"\n    assert expected_instrumental_filename in output_filenames, f\"Expected {expected_instrumental_filename} in output files\"\n    \n    # Check that files physically exist in the specified output directory","chunkId":"tests/integration/test_separator_output_integration.py:5"}],[2561,{"filePath":"tests/integration/test_separator_output_integration.py","startLine":61,"endLine":70,"chunkText":"    assert expected_instrumental_filename in output_filenames, f\"Expected {expected_instrumental_filename} in output files\"\n    \n    # Check that files physically exist in the specified output directory\n    expected_vocal_path = os.path.join(temp_output_dir, expected_vocal_filename)\n    expected_instrumental_path = os.path.join(temp_output_dir, expected_instrumental_filename)\n    \n    print(f\"Checking that files exist in output_dir: {temp_output_dir}\")\n    print(f\"Full expected vocal path: {expected_vocal_path}\")\n    print(f\"Full expected instrumental path: {expected_instrumental_path}\")\n    ","chunkId":"tests/integration/test_separator_output_integration.py:6"}],[2562,{"filePath":"tests/integration/test_separator_output_integration.py","startLine":68,"endLine":74,"chunkText":"    print(f\"Full expected vocal path: {expected_vocal_path}\")\n    print(f\"Full expected instrumental path: {expected_instrumental_path}\")\n    \n    assert os.path.exists(expected_vocal_path), f\"Vocals output file doesn't exist: {expected_vocal_path}\"\n    assert os.path.exists(expected_instrumental_path), f\"Instrumental output file doesn't exist: {expected_instrumental_path}\"\n    assert os.path.getsize(expected_vocal_path) > 0, f\"Vocals output file is empty: {expected_vocal_path}\"\n    assert os.path.getsize(expected_instrumental_path) > 0, f\"Instrumental output file is empty: {expected_instrumental_path}\"","chunkId":"tests/integration/test_separator_output_integration.py:7"}],[2563,{"filePath":"tests/integration/test_separator_output_integration.py","startLine":72,"endLine":78,"chunkText":"    assert os.path.exists(expected_instrumental_path), f\"Instrumental output file doesn't exist: {expected_instrumental_path}\"\n    assert os.path.getsize(expected_vocal_path) > 0, f\"Vocals output file is empty: {expected_vocal_path}\"\n    assert os.path.getsize(expected_instrumental_path) > 0, f\"Instrumental output file is empty: {expected_instrumental_path}\"\n    \n    print(\"✅ Test passed: Separator correctly handles output_dir and custom_output_names\")\n    print(\"   - Files were saved to the specified output directory\")\n    print(\"   - Custom filenames were used (with .wav extension added)\")","chunkId":"tests/integration/test_separator_output_integration.py:8"}],[2564,{"filePath":"tests/integration/test_separator_output_integration.py","startLine":76,"endLine":86,"chunkText":"    print(\"✅ Test passed: Separator correctly handles output_dir and custom_output_names\")\n    print(\"   - Files were saved to the specified output directory\")\n    print(\"   - Custom filenames were used (with .wav extension added)\")\n    print(\"   - Returned paths include just the filenames (not the full paths)\")\n\n\ndef test_separator_single_stem_output(input_file, temp_output_dir):\n    \"\"\"Test that Separator correctly respects output_single_stem with custom output name.\"\"\"\n    print(\"\\n>>> TEST: Checking output_single_stem with custom output name\")\n    \n    # Define custom output filename for single stem","chunkId":"tests/integration/test_separator_output_integration.py:9"}],[2565,{"filePath":"tests/integration/test_separator_output_integration.py","startLine":84,"endLine":96,"chunkText":"    print(\"\\n>>> TEST: Checking output_single_stem with custom output name\")\n    \n    # Define custom output filename for single stem\n    vocal_output_filename = \"only_vocals_output\"\n\n    # Create output name mapping\n    custom_output_names = {\"Vocals\": vocal_output_filename}\n\n    # Initialize separator with specified output directory and single stem output\n    print(f\"Creating Separator with output_dir: {temp_output_dir} and output_single_stem: Vocals\")\n    separator = Separator(\n        output_dir=temp_output_dir,\n        output_single_stem=\"Vocals\",  # Only extract vocals","chunkId":"tests/integration/test_separator_output_integration.py:10"}],[2566,{"filePath":"tests/integration/test_separator_output_integration.py","startLine":94,"endLine":108,"chunkText":"    separator = Separator(\n        output_dir=temp_output_dir,\n        output_single_stem=\"Vocals\",  # Only extract vocals\n        log_level=20  # INFO level\n    )\n\n    # Load model\n    print(\"Loading model: MGM_MAIN_v4.pth\")\n    separator.load_model(model_filename=\"MGM_MAIN_v4.pth\")\n\n    # Run separation with custom output name\n    print(f\"Running separation with custom_output_names: {custom_output_names}\")\n    output_files = separator.separate(input_file, custom_output_names=custom_output_names)\n    print(f\"Separator.separate() returned: {output_files}\")\n","chunkId":"tests/integration/test_separator_output_integration.py:11"}],[2567,{"filePath":"tests/integration/test_separator_output_integration.py","startLine":106,"endLine":114,"chunkText":"    output_files = separator.separate(input_file, custom_output_names=custom_output_names)\n    print(f\"Separator.separate() returned: {output_files}\")\n\n    # The separator adds .wav extension since the default output format is WAV\n    expected_vocal_filename = vocal_output_filename + \".wav\"\n    \n    # Check that the output files list contains only the expected file\n    print(f\"Checking that only one file was returned and it has the correct name\")\n    assert len(output_files) == 1, f\"Expected 1 output file, got {len(output_files)}\"","chunkId":"tests/integration/test_separator_output_integration.py:12"}],[2568,{"filePath":"tests/integration/test_separator_output_integration.py","startLine":112,"endLine":120,"chunkText":"    # Check that the output files list contains only the expected file\n    print(f\"Checking that only one file was returned and it has the correct name\")\n    assert len(output_files) == 1, f\"Expected 1 output file, got {len(output_files)}\"\n    assert os.path.basename(output_files[0]) == expected_vocal_filename, f\"Expected {expected_vocal_filename} in output files\"\n    \n    # Check that file physically exists in the specified output directory\n    expected_vocal_path = os.path.join(temp_output_dir, expected_vocal_filename)\n    print(f\"Checking that file exists in output_dir: {expected_vocal_path}\")\n    ","chunkId":"tests/integration/test_separator_output_integration.py:13"}],[2569,{"filePath":"tests/integration/test_separator_output_integration.py","startLine":118,"endLine":125,"chunkText":"    expected_vocal_path = os.path.join(temp_output_dir, expected_vocal_filename)\n    print(f\"Checking that file exists in output_dir: {expected_vocal_path}\")\n    \n    assert os.path.exists(expected_vocal_path), f\"Vocals output file doesn't exist: {expected_vocal_path}\"\n    assert os.path.getsize(expected_vocal_path) > 0, f\"Vocals output file is empty: {expected_vocal_path}\"\n    \n    # Make sure the instrumental file was NOT created\n    instrumental_files = [f for f in os.listdir(temp_output_dir) if \"instrumental\" in f.lower()]","chunkId":"tests/integration/test_separator_output_integration.py:14"}],[2570,{"filePath":"tests/integration/test_separator_output_integration.py","startLine":123,"endLine":130,"chunkText":"    \n    # Make sure the instrumental file was NOT created\n    instrumental_files = [f for f in os.listdir(temp_output_dir) if \"instrumental\" in f.lower()]\n    print(f\"Checking that no instrumental files were created, found: {instrumental_files}\")\n    assert len(instrumental_files) == 0, f\"No instrumental file should be created when using output_single_stem, found: {instrumental_files}\"\n    \n    print(\"✅ Test passed: Separator correctly handles output_single_stem with custom output name\")\n    print(\"   - Only the specified stem (Vocals) was extracted\")","chunkId":"tests/integration/test_separator_output_integration.py:15"}],[2571,{"filePath":"tests/integration/test_separator_output_integration.py","startLine":128,"endLine":139,"chunkText":"    \n    print(\"✅ Test passed: Separator correctly handles output_single_stem with custom output name\")\n    print(\"   - Only the specified stem (Vocals) was extracted\")\n    print(\"   - The custom filename was used (with .wav extension added)\")\n    print(\"   - No other stems (like Instrumental) were created\")\n\n\ndef test_separator_output_without_custom_names(input_file, temp_output_dir):\n    \"\"\"Test that Separator respects output_dir without custom_output_names.\"\"\"\n    print(\"\\n>>> TEST: Checking output_dir without custom output names\")\n    \n    # Initialize separator with specified output directory","chunkId":"tests/integration/test_separator_output_integration.py:16"}],[2572,{"filePath":"tests/integration/test_separator_output_integration.py","startLine":137,"endLine":149,"chunkText":"    print(\"\\n>>> TEST: Checking output_dir without custom output names\")\n    \n    # Initialize separator with specified output directory\n    print(f\"Creating Separator with output_dir: {temp_output_dir}\")\n    separator = Separator(output_dir=temp_output_dir, log_level=20)  # INFO level\n\n    # Load model\n    print(\"Loading model: MGM_MAIN_v4.pth\")\n    separator.load_model(model_filename=\"MGM_MAIN_v4.pth\")\n\n    # Run separation without custom output names\n    print(\"Running separation without custom_output_names\")\n    output_files = separator.separate(input_file)","chunkId":"tests/integration/test_separator_output_integration.py:17"}],[2573,{"filePath":"tests/integration/test_separator_output_integration.py","startLine":147,"endLine":157,"chunkText":"    # Run separation without custom output names\n    print(\"Running separation without custom_output_names\")\n    output_files = separator.separate(input_file)\n    print(f\"Separator.separate() returned: {output_files}\")\n\n    # Check that output files exist and have content\n    print(f\"Checking that two files were returned (one for each stem)\")\n    assert len(output_files) == 2, f\"Expected 2 output files, got {len(output_files)}\"\n    \n    # Check if the files were created in the output directory\n    # Note: The separator doesn't include the full path in the returned output_files","chunkId":"tests/integration/test_separator_output_integration.py:18"}],[2574,{"filePath":"tests/integration/test_separator_output_integration.py","startLine":155,"endLine":165,"chunkText":"    \n    # Check if the files were created in the output directory\n    # Note: The separator doesn't include the full path in the returned output_files\n    output_file_basenames = [os.path.basename(f) for f in output_files]\n    print(f\"Extracted filenames from output_files: {output_file_basenames}\")\n    \n    print(\"EXPECTED BEHAVIOR: Default filenames are being used (format: inputname_(StemName)_modelname.wav)\")\n    \n    # Check all output files exist in the specified output directory\n    print(f\"Checking that files exist in output_dir: {temp_output_dir}\")\n    for basename in output_file_basenames:","chunkId":"tests/integration/test_separator_output_integration.py:19"}],[2575,{"filePath":"tests/integration/test_separator_output_integration.py","startLine":163,"endLine":171,"chunkText":"    # Check all output files exist in the specified output directory\n    print(f\"Checking that files exist in output_dir: {temp_output_dir}\")\n    for basename in output_file_basenames:\n        full_path = os.path.join(temp_output_dir, basename)\n        print(f\"Checking file: {full_path}\")\n        assert os.path.exists(full_path), f\"Output file doesn't exist: {full_path}\"\n        assert os.path.getsize(full_path) > 0, f\"Output file is empty: {full_path}\"\n    \n    print(\"✅ Test passed: Separator correctly handles output_dir without custom output names\")","chunkId":"tests/integration/test_separator_output_integration.py:20"}],[2576,{"filePath":"tests/integration/test_separator_output_integration.py","startLine":169,"endLine":175,"chunkText":"        assert os.path.getsize(full_path) > 0, f\"Output file is empty: {full_path}\"\n    \n    print(\"✅ Test passed: Separator correctly handles output_dir without custom output names\")\n    print(\"   - Files were saved to the specified output directory\")\n    print(\"   - Default naming scheme was used (input_name_(Stem)_model.wav)\")\n    print(\"   - Returned paths include just the filenames (not the full paths)\")\n","chunkId":"tests/integration/test_separator_output_integration.py:21"}],[2577,{"filePath":"tests/model-metrics/test-all-models.py","startLine":0,"endLine":25,"chunkText":"#!/usr/bin/env python\nimport os\nimport time\nimport museval\nimport numpy as np\nimport soundfile as sf\nfrom audio_separator.separator import Separator\nimport json\nfrom json import JSONEncoder\nimport logging\nimport musdb\nfrom decimal import Decimal\nimport tempfile\nimport argparse\n\n\n# Setup logging\nlogging.basicConfig(level=logging.INFO, format=\"%(asctime)s - %(levelname)s - %(message)s\")\nlogger = logging.getLogger(__name__)\n\n\n# Custom JSON Encoder to handle Decimal types\nclass DecimalEncoder(JSONEncoder):\n    def default(self, obj):\n        if isinstance(obj, Decimal):\n            return float(obj)","chunkId":"tests/model-metrics/test-all-models.py:0"}],[2578,{"filePath":"tests/model-metrics/test-all-models.py","startLine":23,"endLine":36,"chunkText":"    def default(self, obj):\n        if isinstance(obj, Decimal):\n            return float(obj)\n        return super().default(obj)\n\n\nMUSDB_PATH = \"/Volumes/Nomad4TBOne/python-audio-separator/tests/model-metrics/datasets/musdb18hq\"\nRESULTS_PATH = \"/Volumes/Nomad4TBOne/python-audio-separator/tests/model-metrics/results\"\n# Find project root dynamically\nimport os\ncurrent_dir = os.path.dirname(os.path.abspath(__file__))\nproject_root = current_dir\nwhile project_root and not os.path.exists(os.path.join(project_root, 'audio_separator')):\n    parent = os.path.dirname(project_root)","chunkId":"tests/model-metrics/test-all-models.py:1"}],[2579,{"filePath":"tests/model-metrics/test-all-models.py","startLine":34,"endLine":45,"chunkText":"project_root = current_dir\nwhile project_root and not os.path.exists(os.path.join(project_root, 'audio_separator')):\n    parent = os.path.dirname(project_root)\n    if parent == project_root:  # Reached filesystem root\n        break\n    project_root = parent\n\nCOMBINED_RESULTS_PATH = os.path.join(project_root, \"audio_separator\", \"models-scores.json\")\nCOMBINED_MUSEVAL_RESULTS_PATH = \"/Volumes/Nomad4TBOne/python-audio-separator/tests/model-metrics/results/combined-museval-results.json\"\nSTOP_SIGNAL_PATH = \"/Volumes/Nomad4TBOne/python-audio-separator/tests/model-metrics/stop-signal\"\n\n","chunkId":"tests/model-metrics/test-all-models.py:2"}],[2580,{"filePath":"tests/model-metrics/test-all-models.py","startLine":44,"endLine":57,"chunkText":"\n\ndef load_combined_results():\n    \"\"\"Load the combined museval results file\"\"\"\n    if os.path.exists(COMBINED_MUSEVAL_RESULTS_PATH):\n        logger.info(\"Loading combined museval results...\")\n        try:\n            with open(COMBINED_MUSEVAL_RESULTS_PATH, \"r\") as f:\n                # Use a custom parser to handle Decimal values\n                def decimal_parser(dct):\n                    for k, v in dct.items():\n                        if isinstance(v, str) and v.replace(\".\", \"\").isdigit():\n                            try:\n                                dct[k] = float(v)","chunkId":"tests/model-metrics/test-all-models.py:3"}],[2581,{"filePath":"tests/model-metrics/test-all-models.py","startLine":55,"endLine":66,"chunkText":"                        if isinstance(v, str) and v.replace(\".\", \"\").isdigit():\n                            try:\n                                dct[k] = float(v)\n                            except (ValueError, TypeError):\n                                pass\n                    return dct\n\n                return json.load(f, object_hook=decimal_parser)\n        except Exception as e:\n            logger.error(f\"Error loading combined results: {str(e)}\")\n            # Try to load a backup file if it exists\n            backup_path = COMBINED_MUSEVAL_RESULTS_PATH + \".backup\"","chunkId":"tests/model-metrics/test-all-models.py:4"}],[2582,{"filePath":"tests/model-metrics/test-all-models.py","startLine":64,"endLine":75,"chunkText":"            logger.error(f\"Error loading combined results: {str(e)}\")\n            # Try to load a backup file if it exists\n            backup_path = COMBINED_MUSEVAL_RESULTS_PATH + \".backup\"\n            if os.path.exists(backup_path):\n                logger.info(\"Attempting to load backup file...\")\n                try:\n                    with open(backup_path, \"r\") as f:\n                        return json.load(f, object_hook=decimal_parser)\n                except Exception as backup_e:\n                    logger.error(f\"Error loading backup file: {str(backup_e)}\")\n            return {}\n    else:","chunkId":"tests/model-metrics/test-all-models.py:5"}],[2583,{"filePath":"tests/model-metrics/test-all-models.py","startLine":73,"endLine":87,"chunkText":"                    logger.error(f\"Error loading backup file: {str(backup_e)}\")\n            return {}\n    else:\n        logger.info(\"No combined results file found, creating new one\")\n        return {}\n\n\ndef save_combined_results(combined_results):\n    \"\"\"Save the combined museval results file\"\"\"\n    logger.info(\"Saving combined museval results...\")\n    try:\n        # Create a backup of the existing file if it exists\n        if os.path.exists(COMBINED_MUSEVAL_RESULTS_PATH):\n            backup_path = COMBINED_MUSEVAL_RESULTS_PATH + \".backup\"\n            try:","chunkId":"tests/model-metrics/test-all-models.py:6"}],[2584,{"filePath":"tests/model-metrics/test-all-models.py","startLine":85,"endLine":95,"chunkText":"        if os.path.exists(COMBINED_MUSEVAL_RESULTS_PATH):\n            backup_path = COMBINED_MUSEVAL_RESULTS_PATH + \".backup\"\n            try:\n                with open(COMBINED_MUSEVAL_RESULTS_PATH, \"r\") as src, open(backup_path, \"w\") as dst:\n                    dst.write(src.read())\n            except Exception as e:\n                logger.error(f\"Error creating backup file: {str(e)}\")\n\n        # Save the new results using the custom encoder\n        with open(COMBINED_MUSEVAL_RESULTS_PATH, \"w\") as f:\n            json.dump(combined_results, f, cls=DecimalEncoder, indent=2)","chunkId":"tests/model-metrics/test-all-models.py:7"}],[2585,{"filePath":"tests/model-metrics/test-all-models.py","startLine":93,"endLine":106,"chunkText":"        # Save the new results using the custom encoder\n        with open(COMBINED_MUSEVAL_RESULTS_PATH, \"w\") as f:\n            json.dump(combined_results, f, cls=DecimalEncoder, indent=2)\n        logger.info(\"Combined results saved successfully\")\n        return True\n    except Exception as e:\n        logger.error(f\"Error saving combined results: {str(e)}\")\n        return False\n\n\ndef update_combined_results(model_name, track_name, track_data):\n    \"\"\"Update the combined results file with new track data\"\"\"\n    try:\n        # Load existing combined results","chunkId":"tests/model-metrics/test-all-models.py:8"}],[2586,{"filePath":"tests/model-metrics/test-all-models.py","startLine":104,"endLine":119,"chunkText":"    \"\"\"Update the combined results file with new track data\"\"\"\n    try:\n        # Load existing combined results\n        combined_results = load_combined_results()\n\n        # Initialize model entry if it doesn't exist\n        if model_name not in combined_results:\n            combined_results[model_name] = {}\n\n        # Add or update track data\n        combined_results[model_name][track_name] = track_data\n\n        # Write updated results back to file\n        save_combined_results(combined_results)\n        return True\n    except Exception as e:","chunkId":"tests/model-metrics/test-all-models.py:9"}],[2587,{"filePath":"tests/model-metrics/test-all-models.py","startLine":117,"endLine":132,"chunkText":"        save_combined_results(combined_results)\n        return True\n    except Exception as e:\n        logger.error(f\"Error updating combined results: {str(e)}\")\n        return False\n\n\ndef check_track_evaluated(model_name, track_name):\n    \"\"\"Check if a track has already been evaluated for a specific model\"\"\"\n    combined_results = load_combined_results()\n    return model_name in combined_results and track_name in combined_results[model_name]\n\n\ndef get_track_results(model_name, track_name):\n    \"\"\"Get the evaluation results for a specific track and model\"\"\"\n    combined_results = load_combined_results()","chunkId":"tests/model-metrics/test-all-models.py:10"}],[2588,{"filePath":"tests/model-metrics/test-all-models.py","startLine":130,"endLine":144,"chunkText":"def get_track_results(model_name, track_name):\n    \"\"\"Get the evaluation results for a specific track and model\"\"\"\n    combined_results = load_combined_results()\n    if model_name in combined_results and track_name in combined_results[model_name]:\n        return combined_results[model_name][track_name]\n    return None\n\n\ndef get_track_duration(track_path):\n    \"\"\"Get the duration of a track in minutes\"\"\"\n    try:\n        mixture_path = os.path.join(track_path, \"mixture.wav\")\n        info = sf.info(mixture_path)\n        return info.duration / 60.0  # Convert seconds to minutes\n    except Exception as e:","chunkId":"tests/model-metrics/test-all-models.py:11"}],[2589,{"filePath":"tests/model-metrics/test-all-models.py","startLine":142,"endLine":156,"chunkText":"        info = sf.info(mixture_path)\n        return info.duration / 60.0  # Convert seconds to minutes\n    except Exception as e:\n        logger.error(f\"Error getting track duration: {str(e)}\")\n        return 0.0\n\n\ndef evaluate_track(track_name, track_path, test_model, mus_db):\n    \"\"\"Evaluate a single track using a specific model\"\"\"\n    logger.info(f\"Evaluating track: {track_name} with model: {test_model}\")\n\n    # Get track duration in minutes\n    track_duration_minutes = get_track_duration(track_path)\n    logger.info(f\"Track duration: {track_duration_minutes:.2f} minutes\")\n","chunkId":"tests/model-metrics/test-all-models.py:12"}],[2590,{"filePath":"tests/model-metrics/test-all-models.py","startLine":154,"endLine":166,"chunkText":"    track_duration_minutes = get_track_duration(track_path)\n    logger.info(f\"Track duration: {track_duration_minutes:.2f} minutes\")\n\n    # Initialize variables to track processing time\n    processing_time = 0\n    seconds_per_minute = 0\n\n    # Create a basic result structure that will be returned even if evaluation fails\n    basic_model_results = {\"track_name\": track_name, \"scores\": {}}\n\n    # Check if evaluation results already exist in combined file\n    museval_results = load_combined_results()\n    if test_model in museval_results and track_name in museval_results[test_model]:","chunkId":"tests/model-metrics/test-all-models.py:13"}],[2591,{"filePath":"tests/model-metrics/test-all-models.py","startLine":164,"endLine":174,"chunkText":"    # Check if evaluation results already exist in combined file\n    museval_results = load_combined_results()\n    if test_model in museval_results and track_name in museval_results[test_model]:\n        logger.info(\"Found existing evaluation results in combined file...\")\n        track_data = museval_results[test_model][track_name]\n        scores = museval.TrackStore(track_name)\n        scores.scores = track_data\n\n        # Try to extract existing speed metrics if available\n        try:\n            if isinstance(track_data, dict) and \"targets\" in track_data:","chunkId":"tests/model-metrics/test-all-models.py:14"}],[2592,{"filePath":"tests/model-metrics/test-all-models.py","startLine":172,"endLine":181,"chunkText":"        # Try to extract existing speed metrics if available\n        try:\n            if isinstance(track_data, dict) and \"targets\" in track_data:\n                for target in track_data[\"targets\"]:\n                    if \"metrics\" in target and \"seconds_per_minute_m3\" in target[\"metrics\"]:\n                        basic_model_results[\"scores\"][\"seconds_per_minute_m3\"] = target[\"metrics\"][\"seconds_per_minute_m3\"]\n                        break\n        except Exception:\n            pass  # Ignore errors in extracting existing speed metrics\n    else:","chunkId":"tests/model-metrics/test-all-models.py:15"}],[2593,{"filePath":"tests/model-metrics/test-all-models.py","startLine":179,"endLine":194,"chunkText":"        except Exception:\n            pass  # Ignore errors in extracting existing speed metrics\n    else:\n        # Expanded stem mapping to include \"no-stem\" outputs and custom stem formats\n        stem_mapping = {\n            # Standard stems\n            \"Vocals\": \"vocals\",\n            \"Instrumental\": \"instrumental\",\n            \"Drums\": \"drums\",\n            \"Bass\": \"bass\",\n            \"Other\": \"other\",\n            # No-stem variants\n            \"No Drums\": \"nodrums\",\n            \"No Bass\": \"nobass\",\n            \"No Other\": \"noother\",\n            # Custom stem formats (with hyphens)","chunkId":"tests/model-metrics/test-all-models.py:16"}],[2594,{"filePath":"tests/model-metrics/test-all-models.py","startLine":192,"endLine":208,"chunkText":"            \"No Bass\": \"nobass\",\n            \"No Other\": \"noother\",\n            # Custom stem formats (with hyphens)\n            \"Drum-Bass\": \"drumbass\",\n            \"No Drum-Bass\": \"nodrumbass\",\n            \"Vocals-Other\": \"vocalsother\",\n            \"No Vocals-Other\": \"novocalsother\",\n        }\n\n        # Create a temporary directory for separation files\n        with tempfile.TemporaryDirectory() as temp_dir:\n            logger.info(f\"Using temporary directory: {temp_dir}\")\n\n            # Measure separation time\n            start_time = time.time()\n\n            # Perform separation","chunkId":"tests/model-metrics/test-all-models.py:17"}],[2595,{"filePath":"tests/model-metrics/test-all-models.py","startLine":206,"endLine":216,"chunkText":"            start_time = time.time()\n\n            # Perform separation\n            logger.info(\"Performing separation...\")\n            separator = Separator(output_dir=temp_dir)\n            separator.load_model(model_filename=test_model)\n            separator.separate(os.path.join(track_path, \"mixture.wav\"), custom_output_names=stem_mapping)\n\n            # Calculate processing time\n            processing_time = time.time() - start_time\n            seconds_per_minute = processing_time / track_duration_minutes if track_duration_minutes > 0 else 0","chunkId":"tests/model-metrics/test-all-models.py:18"}],[2596,{"filePath":"tests/model-metrics/test-all-models.py","startLine":214,"endLine":223,"chunkText":"            # Calculate processing time\n            processing_time = time.time() - start_time\n            seconds_per_minute = processing_time / track_duration_minutes if track_duration_minutes > 0 else 0\n            logger.info(f\"Separation completed in {processing_time:.2f} seconds\")\n            logger.info(f\"Processing speed: {seconds_per_minute:.2f} seconds per minute of audio\")\n\n            # Always add the speed metric to our basic results\n            basic_model_results[\"scores\"][\"seconds_per_minute_m3\"] = round(seconds_per_minute, 1)\n\n            # Check which stems were actually created","chunkId":"tests/model-metrics/test-all-models.py:19"}],[2597,{"filePath":"tests/model-metrics/test-all-models.py","startLine":221,"endLine":230,"chunkText":"            basic_model_results[\"scores\"][\"seconds_per_minute_m3\"] = round(seconds_per_minute, 1)\n\n            # Check which stems were actually created\n            wav_files = [f for f in os.listdir(temp_dir) if f.endswith(\".wav\")]\n            logger.info(f\"Found WAV files: {wav_files}\")\n\n            # Determine if this is a standard vocal/instrumental model that can be evaluated with museval\n            standard_model = False\n            if len(wav_files) == 2:\n                # Check if one of the files is named vocals.wav or instrumental.wav","chunkId":"tests/model-metrics/test-all-models.py:20"}],[2598,{"filePath":"tests/model-metrics/test-all-models.py","startLine":228,"endLine":236,"chunkText":"            standard_model = False\n            if len(wav_files) == 2:\n                # Check if one of the files is named vocals.wav or instrumental.wav\n                if \"vocals.wav\" in wav_files and \"instrumental.wav\" in wav_files:\n                    standard_model = True\n                    logger.info(\"Detected standard vocals/instrumental model, will run museval evaluation\")\n\n            # If not a standard model, skip museval evaluation and just return speed metrics\n            if not standard_model:","chunkId":"tests/model-metrics/test-all-models.py:21"}],[2599,{"filePath":"tests/model-metrics/test-all-models.py","startLine":234,"endLine":243,"chunkText":"\n            # If not a standard model, skip museval evaluation and just return speed metrics\n            if not standard_model:\n                logger.info(f\"Non-standard stem configuration detected for model {test_model}, skipping museval evaluation\")\n\n                # Store the speed metric in the combined results\n                if test_model not in museval_results:\n                    museval_results[test_model] = {}\n\n                # Create a minimal structure for the speed metric","chunkId":"tests/model-metrics/test-all-models.py:22"}],[2600,{"filePath":"tests/model-metrics/test-all-models.py","startLine":241,"endLine":252,"chunkText":"                    museval_results[test_model] = {}\n\n                # Create a minimal structure for the speed metric\n                minimal_results = {\"targets\": [{\"name\": \"speed_metrics_only\", \"metrics\": {\"seconds_per_minute_m3\": round(seconds_per_minute, 1)}}]}\n\n                museval_results[test_model][track_name] = minimal_results\n                save_combined_results(museval_results)\n\n                return None, basic_model_results\n\n            # For standard models, proceed with museval evaluation\n            available_stems = {}","chunkId":"tests/model-metrics/test-all-models.py:23"}],[2601,{"filePath":"tests/model-metrics/test-all-models.py","startLine":250,"endLine":262,"chunkText":"\n            # For standard models, proceed with museval evaluation\n            available_stems = {}\n            available_stems[\"vocals\"] = os.path.join(temp_dir, \"vocals.wav\")\n            available_stems[\"accompaniment\"] = os.path.join(temp_dir, \"instrumental.wav\")\n\n            # Get track from MUSDB\n            track = next((t for t in mus_db if t.name == track_name), None)\n            if track is None:\n                raise ValueError(f\"Track {track_name} not found in MUSDB18\")\n\n            # Load available stems\n            estimates = {}","chunkId":"tests/model-metrics/test-all-models.py:24"}],[2602,{"filePath":"tests/model-metrics/test-all-models.py","startLine":260,"endLine":274,"chunkText":"\n            # Load available stems\n            estimates = {}\n            for stem_name, stem_path in available_stems.items():\n                audio, _ = sf.read(stem_path)\n                if len(audio.shape) == 1:\n                    audio = np.expand_dims(audio, axis=1)\n                estimates[stem_name] = audio\n\n            # Evaluate using museval\n            logger.info(f\"Evaluating stems: {list(estimates.keys())}\")\n            try:\n                scores = museval.eval_mus_track(track, estimates, output_dir=temp_dir, mode=\"v4\")\n\n                # Add the speed metric to the scores","chunkId":"tests/model-metrics/test-all-models.py:25"}],[2603,{"filePath":"tests/model-metrics/test-all-models.py","startLine":272,"endLine":282,"chunkText":"                scores = museval.eval_mus_track(track, estimates, output_dir=temp_dir, mode=\"v4\")\n\n                # Add the speed metric to the scores\n                if not hasattr(scores, \"speed_metric_added\"):\n                    for target in scores.scores[\"targets\"]:\n                        if \"metrics\" not in target:\n                            target[\"metrics\"] = {}\n                        target[\"metrics\"][\"seconds_per_minute_m3\"] = round(seconds_per_minute, 1)\n                    scores.speed_metric_added = True\n\n                # Update the combined results file with the new evaluation","chunkId":"tests/model-metrics/test-all-models.py:26"}],[2604,{"filePath":"tests/model-metrics/test-all-models.py","startLine":280,"endLine":290,"chunkText":"                    scores.speed_metric_added = True\n\n                # Update the combined results file with the new evaluation\n                if test_model not in museval_results:\n                    museval_results[test_model] = {}\n                museval_results[test_model][track_name] = scores.scores\n                save_combined_results(museval_results)\n            except Exception as e:\n                logger.error(f\"Error during museval evaluation: {str(e)}\")\n                logger.exception(\"Evaluation exception details:\")\n                # Return basic results with just the speed metric","chunkId":"tests/model-metrics/test-all-models.py:27"}],[2605,{"filePath":"tests/model-metrics/test-all-models.py","startLine":288,"endLine":299,"chunkText":"                logger.error(f\"Error during museval evaluation: {str(e)}\")\n                logger.exception(\"Evaluation exception details:\")\n                # Return basic results with just the speed metric\n                return None, basic_model_results\n\n    try:\n        # Only process museval results if we have them\n        if \"scores\" in locals() and scores is not None:\n            # Calculate aggregate scores for available stems\n            results_store = museval.EvalStore()\n            results_store.add_track(scores.df)\n            methods = museval.MethodStore()","chunkId":"tests/model-metrics/test-all-models.py:28"}],[2606,{"filePath":"tests/model-metrics/test-all-models.py","startLine":297,"endLine":307,"chunkText":"            results_store = museval.EvalStore()\n            results_store.add_track(scores.df)\n            methods = museval.MethodStore()\n            methods.add_evalstore(results_store, name=test_model)\n            agg_scores = methods.agg_frames_tracks_scores()\n\n            # Return the aggregate scores in a structured format with 6 significant figures\n            model_results = {\"track_name\": track_name, \"scores\": {}}\n\n            for stem in [\"vocals\", \"drums\", \"bass\", \"other\", \"accompaniment\"]:\n                try:","chunkId":"tests/model-metrics/test-all-models.py:29"}],[2607,{"filePath":"tests/model-metrics/test-all-models.py","startLine":305,"endLine":314,"chunkText":"\n            for stem in [\"vocals\", \"drums\", \"bass\", \"other\", \"accompaniment\"]:\n                try:\n                    stem_scores = {metric: float(f\"{agg_scores.loc[(test_model, stem, metric)]:.6g}\") for metric in [\"SDR\", \"SIR\", \"SAR\", \"ISR\"]}\n                    # Rename 'accompaniment' to 'instrumental' in the output\n                    output_stem = \"instrumental\" if stem == \"accompaniment\" else stem\n                    model_results[\"scores\"][output_stem] = stem_scores\n                except KeyError:\n                    continue\n","chunkId":"tests/model-metrics/test-all-models.py:30"}],[2608,{"filePath":"tests/model-metrics/test-all-models.py","startLine":312,"endLine":324,"chunkText":"                except KeyError:\n                    continue\n\n            # Add the seconds_per_minute_m3 metric if it was calculated\n            if processing_time > 0 and track_duration_minutes > 0:\n                model_results[\"scores\"][\"seconds_per_minute_m3\"] = round(seconds_per_minute, 1)\n\n            return scores, model_results if model_results[\"scores\"] else basic_model_results\n        else:\n            # If we don't have scores, just return the basic results with speed metrics\n            return None, basic_model_results\n\n    except Exception as e:","chunkId":"tests/model-metrics/test-all-models.py:31"}],[2609,{"filePath":"tests/model-metrics/test-all-models.py","startLine":322,"endLine":336,"chunkText":"            return None, basic_model_results\n\n    except Exception as e:\n        logger.error(f\"Error processing evaluation results: {str(e)}\")\n        logger.exception(\"Results processing exception details:\")\n        # Return basic results with just the speed metric\n        return None, basic_model_results\n\n\ndef convert_decimal_to_float(obj):\n    \"\"\"Recursively converts Decimal objects to floats in a nested structure.\"\"\"\n    if isinstance(obj, Decimal):\n        return float(obj)\n    elif isinstance(obj, dict):\n        return {k: convert_decimal_to_float(v) for k, v in obj.items()}","chunkId":"tests/model-metrics/test-all-models.py:32"}],[2610,{"filePath":"tests/model-metrics/test-all-models.py","startLine":334,"endLine":347,"chunkText":"        return float(obj)\n    elif isinstance(obj, dict):\n        return {k: convert_decimal_to_float(v) for k, v in obj.items()}\n    elif isinstance(obj, list):\n        return [convert_decimal_to_float(x) for x in obj]\n    return obj\n\n\ndef calculate_median_scores(track_scores):\n    \"\"\"Calculate median scores across all tracks for each stem and metric\"\"\"\n    # Initialize containers for each stem's metrics\n    stem_metrics = {\n        \"vocals\": {\"SDR\": [], \"SIR\": [], \"SAR\": [], \"ISR\": []},\n        \"drums\": {\"SDR\": [], \"SIR\": [], \"SAR\": [], \"ISR\": []},","chunkId":"tests/model-metrics/test-all-models.py:33"}],[2611,{"filePath":"tests/model-metrics/test-all-models.py","startLine":345,"endLine":357,"chunkText":"    stem_metrics = {\n        \"vocals\": {\"SDR\": [], \"SIR\": [], \"SAR\": [], \"ISR\": []},\n        \"drums\": {\"SDR\": [], \"SIR\": [], \"SAR\": [], \"ISR\": []},\n        \"bass\": {\"SDR\": [], \"SIR\": [], \"SAR\": [], \"ISR\": []},\n        \"instrumental\": {\"SDR\": [], \"SIR\": [], \"SAR\": [], \"ISR\": []},\n        \"seconds_per_minute_m3\": [],\n    }\n\n    # Collect all scores for each stem and metric\n    for track_score in track_scores:\n        if track_score is not None and \"scores\" in track_score:\n            # Process audio quality metrics\n            for stem, metrics in track_score[\"scores\"].items():","chunkId":"tests/model-metrics/test-all-models.py:34"}],[2612,{"filePath":"tests/model-metrics/test-all-models.py","startLine":355,"endLine":365,"chunkText":"        if track_score is not None and \"scores\" in track_score:\n            # Process audio quality metrics\n            for stem, metrics in track_score[\"scores\"].items():\n                if stem in stem_metrics and stem != \"seconds_per_minute_m3\":\n                    for metric, value in metrics.items():\n                        stem_metrics[stem][metric].append(value)\n\n            # Process speed metric separately\n            if \"seconds_per_minute_m3\" in track_score[\"scores\"]:\n                stem_metrics[\"seconds_per_minute_m3\"].append(track_score[\"scores\"][\"seconds_per_minute_m3\"])\n","chunkId":"tests/model-metrics/test-all-models.py:35"}],[2613,{"filePath":"tests/model-metrics/test-all-models.py","startLine":363,"endLine":372,"chunkText":"            if \"seconds_per_minute_m3\" in track_score[\"scores\"]:\n                stem_metrics[\"seconds_per_minute_m3\"].append(track_score[\"scores\"][\"seconds_per_minute_m3\"])\n\n    # Calculate medians for each stem and metric\n    median_scores = {}\n    for stem, metrics in stem_metrics.items():\n        if stem != \"seconds_per_minute_m3\" and any(metrics.values()):  # Only include stems that have scores\n            median_scores[stem] = {metric: float(f\"{np.median(values):.6g}\") for metric, values in metrics.items() if values}  # Only include metrics that have values\n\n    # Add median speed metric if availabl","chunkId":"tests/model-metrics/test-all-models.py:36"}],[2614,{"filePath":"tests/model-metrics/test-all-models.py","startLine":370,"endLine":384,"chunkText":"            median_scores[stem] = {metric: float(f\"{np.median(values):.6g}\") for metric, values in metrics.items() if values}  # Only include metrics that have values\n\n    # Add median speed metric if available\n    if stem_metrics[\"seconds_per_minute_m3\"]:\n        median_scores[\"seconds_per_minute_m3\"] = round(np.median(stem_metrics[\"seconds_per_minute_m3\"]), 1)\n\n    return median_scores\n\n\ndef check_disk_usage(path):\n    \"\"\"Check inode usage and disk space on the filesystem containing path\"\"\"\n    import subprocess\n    import sys\n\n    # Check disk space first","chunkId":"tests/model-metrics/test-all-models.py:37"}],[2615,{"filePath":"tests/model-metrics/test-all-models.py","startLine":382,"endLine":398,"chunkText":"    import sys\n\n    # Check disk space first\n    result = subprocess.run([\"df\", \"-h\", path], capture_output=True, text=True)\n    output = result.stdout\n    logger.info(f\"Current disk usage:\\n{output}\")\n\n    # Parse the output to get disk usage percentage\n    lines = output.strip().split(\"\\n\")\n    if len(lines) >= 2:\n        parts = lines[1].split()\n        if len(parts) >= 5:\n            try:\n                # Extract disk usage percentage\n                disk_usage_str = parts[4].rstrip(\"%\")\n                disk_usage_pct = int(disk_usage_str)\n","chunkId":"tests/model-metrics/test-all-models.py:38"}],[2616,{"filePath":"tests/model-metrics/test-all-models.py","startLine":396,"endLine":407,"chunkText":"                disk_usage_str = parts[4].rstrip(\"%\")\n                disk_usage_pct = int(disk_usage_str)\n\n                logger.info(f\"Disk usage: {disk_usage_pct}%\")\n\n                if disk_usage_pct >= 99:\n                    logger.critical(\"CRITICAL: Disk is almost full (>99%)! Cannot continue processing.\")\n                    logger.critical(\"Please free up disk space before continuing.\")\n                    sys.exit(1)\n                elif disk_usage_pct > 95:\n                    logger.warning(f\"WARNING: High disk usage ({disk_usage_pct}%)!\")\n            except (ValueError, IndexError) as e:","chunkId":"tests/model-metrics/test-all-models.py:39"}],[2617,{"filePath":"tests/model-metrics/test-all-models.py","startLine":405,"endLine":418,"chunkText":"                elif disk_usage_pct > 95:\n                    logger.warning(f\"WARNING: High disk usage ({disk_usage_pct}%)!\")\n            except (ValueError, IndexError) as e:\n                logger.error(f\"Error parsing disk usage: {str(e)}\")\n\n    # Now check inode usage\n    result = subprocess.run([\"df\", \"-i\", path], capture_output=True, text=True)\n    output = result.stdout\n    logger.info(f\"Current inode usage:\\n{output}\")\n\n    # Parse the output to get inode usage percentage\n    lines = output.strip().split(\"\\n\")\n    if len(lines) >= 2:\n        # The second line contains the actual data","chunkId":"tests/model-metrics/test-all-models.py:40"}],[2618,{"filePath":"tests/model-metrics/test-all-models.py","startLine":416,"endLine":428,"chunkText":"    lines = output.strip().split(\"\\n\")\n    if len(lines) >= 2:\n        # The second line contains the actual data\n        parts = lines[1].split()\n        if len(parts) >= 8:  # macOS df -i format has 8 columns\n            try:\n                # On macOS, inode usage is in the 8th column as a percentage\n                inode_usage_str = parts[7].rstrip(\"%\")\n                inode_usage_pct = int(inode_usage_str)\n\n                # Also extract the actual inode numbers for better reporting\n                iused = int(parts[5])\n                ifree = int(parts[6])","chunkId":"tests/model-metrics/test-all-models.py:41"}],[2619,{"filePath":"tests/model-metrics/test-all-models.py","startLine":426,"endLine":438,"chunkText":"                # Also extract the actual inode numbers for better reporting\n                iused = int(parts[5])\n                ifree = int(parts[6])\n                total_inodes = iused + ifree\n\n                # Skip inode check for exFAT or similar filesystems\n                if total_inodes <= 1:\n                    logger.info(\"Filesystem appears to be exFAT or similar (no real inode tracking). Skipping inode check.\")\n                    return None\n\n                logger.info(f\"Inode usage: {iused:,}/{total_inodes:,} ({inode_usage_pct}%)\")\n\n                if inode_usage_pct >= 100:","chunkId":"tests/model-metrics/test-all-models.py:42"}],[2620,{"filePath":"tests/model-metrics/test-all-models.py","startLine":436,"endLine":446,"chunkText":"                logger.info(f\"Inode usage: {iused:,}/{total_inodes:,} ({inode_usage_pct}%)\")\n\n                if inode_usage_pct >= 100:\n                    logger.critical(\"CRITICAL: Inode usage is at 100%! Cannot continue processing.\")\n                    logger.critical(\"Please free up inodes before continuing.\")\n                    sys.exit(1)\n                elif inode_usage_pct > 90:\n                    logger.warning(f\"WARNING: High inode usage ({inode_usage_pct}%)!\")\n\n                return inode_usage_pct\n            except (ValueError, IndexError) as e:","chunkId":"tests/model-metrics/test-all-models.py:43"}],[2621,{"filePath":"tests/model-metrics/test-all-models.py","startLine":444,"endLine":463,"chunkText":"\n                return inode_usage_pct\n            except (ValueError, IndexError) as e:\n                logger.error(f\"Error parsing inode usage: {str(e)}\")\n\n    return None\n\n\ndef get_evaluated_track_count(model_name, museval_results):\n    \"\"\"Get the number of tracks evaluated for a specific model\"\"\"\n    if model_name in museval_results:\n        return len(museval_results[model_name])\n    return 0\n\n\ndef get_most_evaluated_tracks(museval_results, min_count=10):\n    \"\"\"Get tracks that have been evaluated for the most models\"\"\"\n    track_counts = {}\n\n    # Count how many models have evaluated each track","chunkId":"tests/model-metrics/test-all-models.py:44"}],[2622,{"filePath":"tests/model-metrics/test-all-models.py","startLine":461,"endLine":476,"chunkText":"    track_counts = {}\n\n    # Count how many models have evaluated each track\n    for model_name, tracks in museval_results.items():\n        for track_name in tracks:\n            if track_name not in track_counts:\n                track_counts[track_name] = 0\n            track_counts[track_name] += 1\n\n    # Sort tracks by evaluation count (descending)\n    sorted_tracks = sorted(track_counts.items(), key=lambda x: x[1], reverse=True)\n\n    # Return tracks that have been evaluated at least min_count times\n    return [track for track, count in sorted_tracks if count >= min_count]\n\n","chunkId":"tests/model-metrics/test-all-models.py:45"}],[2623,{"filePath":"tests/model-metrics/test-all-models.py","startLine":475,"endLine":490,"chunkText":"\n\ndef generate_summary_statistics(\n    start_time, models_processed, tracks_processed, models_with_new_data, tracks_evaluated, total_processing_time, fastest_model=None, slowest_model=None, combined_results_path=None, is_dry_run=False\n):\n    \"\"\"Generate a summary of the script's execution\"\"\"\n    end_time = time.time()\n    total_runtime = end_time - start_time\n\n    # Format the runtime\n    hours, remainder = divmod(total_runtime, 3600)\n    minutes, seconds = divmod(remainder, 60)\n    runtime_str = f\"{int(hours):02d}:{int(minutes):02d}:{int(seconds):02d}\"\n\n    # Build the summary\n    summary = [","chunkId":"tests/model-metrics/test-all-models.py:46"}],[2624,{"filePath":"tests/model-metrics/test-all-models.py","startLine":488,"endLine":498,"chunkText":"\n    # Build the summary\n    summary = [\n        \"=\" * 80,\n        \"DRY RUN SUMMARY - PREVIEW ONLY\" if is_dry_run else \"EXECUTION SUMMARY\",\n        \"=\" * 80,\n        f\"Total runtime: {runtime_str}\",\n        f\"Models {'that would be' if is_dry_run else ''} processed: {models_processed}\",\n        f\"Models {'that would receive' if is_dry_run else 'with'} new data: {len(models_with_new_data)}\",\n        f\"Total tracks {'that would be' if is_dry_run else ''} evaluated: {tracks_evaluated}\",\n        f\"Average tracks per model: {tracks_evaluated / len(models_with_new_data) if models_with_new_data else 0:.2f}\",","chunkId":"tests/model-metrics/test-all-models.py:47"}],[2625,{"filePath":"tests/model-metrics/test-all-models.py","startLine":496,"endLine":506,"chunkText":"        f\"Models {'that would receive' if is_dry_run else 'with'} new data: {len(models_with_new_data)}\",\n        f\"Total tracks {'that would be' if is_dry_run else ''} evaluated: {tracks_evaluated}\",\n        f\"Average tracks per model: {tracks_evaluated / len(models_with_new_data) if models_with_new_data else 0:.2f}\",\n    ]\n\n    if fastest_model:\n        summary.append(f\"Fastest model: {fastest_model['name']} ({fastest_model['speed']:.2f} seconds per minute)\")\n\n    if slowest_model:\n        summary.append(f\"Slowest model: {slowest_model['name']} ({slowest_model['speed']:.2f} seconds per minute)\")\n","chunkId":"tests/model-metrics/test-all-models.py:48"}],[2626,{"filePath":"tests/model-metrics/test-all-models.py","startLine":504,"endLine":515,"chunkText":"    if slowest_model:\n        summary.append(f\"Slowest model: {slowest_model['name']} ({slowest_model['speed']:.2f} seconds per minute)\")\n\n    if total_processing_time > 0:\n        summary.append(f\"Total audio processing time: {total_processing_time:.2f} seconds\")\n\n    if combined_results_path and os.path.exists(combined_results_path):\n        file_size = os.path.getsize(combined_results_path) / (1024 * 1024)  # Size in MB\n        summary.append(f\"Results file size: {file_size:.2f} MB\")\n\n    # Add models with new data\n    if models_with_new_data:","chunkId":"tests/model-metrics/test-all-models.py:49"}],[2627,{"filePath":"tests/model-metrics/test-all-models.py","startLine":513,"endLine":529,"chunkText":"\n    # Add models with new data\n    if models_with_new_data:\n        summary.append(f\"\\nModels {'that would receive' if is_dry_run else 'with'} new evaluation data:\")\n        for model_name in models_with_new_data:\n            summary.append(f\"- {model_name}\")\n\n    # Add dry run disclaimer if needed\n    if is_dry_run:\n        summary.append(\"\\nNOTE: This is a dry run summary. No actual changes were made.\")\n        summary.append(\"Run without --dry-run to perform actual evaluations.\")\n\n    summary.append(\"=\" * 80)\n    return \"\\n\".join(summary)\n\n\ndef check_stop_signal():","chunkId":"tests/model-metrics/test-all-models.py:50"}],[2628,{"filePath":"tests/model-metrics/test-all-models.py","startLine":527,"endLine":541,"chunkText":"\n\ndef check_stop_signal():\n    \"\"\"Check if the stop signal file exists\"\"\"\n    if os.path.exists(STOP_SIGNAL_PATH):\n        logger.info(\"Stop signal detected at: \" + STOP_SIGNAL_PATH)\n        return True\n    return False\n\n\ndef main():\n    # Add command line argument parsing for dry run mode\n    parser = argparse.ArgumentParser(description=\"Run model evaluation on MUSDB18 dataset\")\n    parser.add_argument(\"--dry-run\", action=\"store_true\", help=\"Run in dry-run mode (no writes)\")\n    parser.add_argument(\"--max-tracks\", type=int, default=10, help=\"Maximum number of tracks to evaluate per model\")","chunkId":"tests/model-metrics/test-all-models.py:51"}],[2629,{"filePath":"tests/model-metrics/test-all-models.py","startLine":539,"endLine":547,"chunkText":"    parser = argparse.ArgumentParser(description=\"Run model evaluation on MUSDB18 dataset\")\n    parser.add_argument(\"--dry-run\", action=\"store_true\", help=\"Run in dry-run mode (no writes)\")\n    parser.add_argument(\"--max-tracks\", type=int, default=10, help=\"Maximum number of tracks to evaluate per model\")\n    parser.add_argument(\"--max-models\", type=int, default=None, help=\"Maximum number of models to evaluate\")\n    args = parser.parse_args()\n\n    # Remove any existing stop signal file at start\n    if os.path.exists(STOP_SIGNAL_PATH):\n        os.remove(STOP_SIGNAL_PATH)","chunkId":"tests/model-metrics/test-all-models.py:52"}],[2630,{"filePath":"tests/model-metrics/test-all-models.py","startLine":545,"endLine":560,"chunkText":"    # Remove any existing stop signal file at start\n    if os.path.exists(STOP_SIGNAL_PATH):\n        os.remove(STOP_SIGNAL_PATH)\n        logger.info(\"Removed existing stop signal file\")\n\n    # Track start time for progress reporting\n    start_time = time.time()\n\n    # Statistics tracking\n    models_processed = 0\n    tracks_processed = 0\n    models_with_new_data = set()\n    total_processing_time = 0\n    fastest_model = {\"name\": \"\", \"speed\": float(\"inf\")}  # Initialize with infinity for comparison\n    slowest_model = {\"name\": \"\", \"speed\": 0}  # Initialize with zero for comparison\n","chunkId":"tests/model-metrics/test-all-models.py:53"}],[2631,{"filePath":"tests/model-metrics/test-all-models.py","startLine":558,"endLine":561,"chunkText":"    fastest_model = {\"name\": \"\", \"speed\": float(\"inf\")}  # Initialize with infinity for comparison\n    slowest_model = {\"name\": \"\", \"speed\": 0}  # Initialize with zero for comparison\n\n    # Create a results cache manager","chunkId":"tests/model-metrics/test-all-models.py:54"}],[2632,{"filePath":"tests/model-metrics/test-all-models.py","startLine":562,"endLine":577,"chunkText":"    class ResultsCache:\n        def __init__(self):\n            self.results = load_combined_results()\n            self.last_update_time = time.time()\n\n        def get_results(self, force=False):\n            current_time = time.time()\n            # Only reload from disk every 5 minutes unless forced\n            if force or (current_time - self.last_update_time) > 300:\n                self.results = load_combined_results()\n                self.last_update_time = current_time\n            return self.results\n\n    results_cache = ResultsCache()\n\n    # Helper function for logging with elapsed time","chunkId":"tests/model-metrics/test-all-models.py:55"}],[2633,{"filePath":"tests/model-metrics/test-all-models.py","startLine":575,"endLine":588,"chunkText":"    results_cache = ResultsCache()\n\n    # Helper function for logging with elapsed time\n    def log_with_time(message, level=logging.INFO):\n        elapsed = time.time() - start_time\n        hours, remainder = divmod(elapsed, 3600)\n        minutes, seconds = divmod(remainder, 60)\n        time_str = f\"{int(hours):02d}:{int(minutes):02d}:{int(seconds):02d}\"\n        logger.log(level, f\"[{time_str}] {message}\")\n\n    if args.dry_run:\n        log_with_time(\"*** RUNNING IN DRY-RUN MODE - NO DATA WILL BE MODIFIED ***\")\n\n    log_with_time(\"Starting model evaluation script...\")","chunkId":"tests/model-metrics/test-all-models.py:56"}],[2634,{"filePath":"tests/model-metrics/test-all-models.py","startLine":586,"endLine":601,"chunkText":"        log_with_time(\"*** RUNNING IN DRY-RUN MODE - NO DATA WILL BE MODIFIED ***\")\n\n    log_with_time(\"Starting model evaluation script...\")\n    os.makedirs(RESULTS_PATH, exist_ok=True)\n\n    # Check disk space and inode usage at start\n    check_disk_usage(RESULTS_PATH)\n\n    # Load existing results if available\n    combined_results = {}\n    if os.path.exists(COMBINED_RESULTS_PATH):\n        log_with_time(\"Loading existing combined results...\")\n        with open(COMBINED_RESULTS_PATH) as f:\n            combined_results = json.load(f)\n\n    # Get initial museval results","chunkId":"tests/model-metrics/test-all-models.py:57"}],[2635,{"filePath":"tests/model-metrics/test-all-models.py","startLine":599,"endLine":614,"chunkText":"            combined_results = json.load(f)\n\n    # Get initial museval results\n    museval_results = results_cache.get_results()\n    log_with_time(f\"Loaded combined museval results with {len(museval_results)} models\")\n\n    # Get the most commonly evaluated tracks\n    common_tracks = get_most_evaluated_tracks(museval_results)\n    log_with_time(f\"Found {len(common_tracks)} commonly evaluated tracks\")\n\n    # Initialize MUSDB\n    log_with_time(\"Initializing MUSDB database...\")\n    mus = musdb.DB(root=MUSDB_PATH, is_wav=True)\n\n    # Create a prioritized list of tracks\n    all_tracks = []","chunkId":"tests/model-metrics/test-all-models.py:58"}],[2636,{"filePath":"tests/model-metrics/test-all-models.py","startLine":612,"endLine":627,"chunkText":"\n    # Create a prioritized list of tracks\n    all_tracks = []\n    for track in mus.tracks:\n        # Check if this is a commonly evaluated track\n        is_common = track.name in common_tracks\n        all_tracks.append({\"name\": track.name, \"path\": os.path.dirname(track.path), \"is_common\": is_common})\n\n    # Sort tracks by whether they're commonly evaluated\n    all_tracks.sort(key=lambda t: 0 if t[\"is_common\"] else 1)\n\n    # Get list of all available models\n    log_with_time(\"Getting list of available models...\")\n    separator = Separator()\n    models_by_type = separator.list_supported_model_files()\n","chunkId":"tests/model-metrics/test-all-models.py:59"}],[2637,{"filePath":"tests/model-metrics/test-all-models.py","startLine":625,"endLine":637,"chunkText":"    separator = Separator()\n    models_by_type = separator.list_supported_model_files()\n\n    # Flatten the models list and prioritize them\n    all_models = []\n    for model_type, models in models_by_type.items():\n        for model_name, model_info in models.items():\n            filename = model_info.get(\"filename\")\n            if filename:\n                # Count how many tracks have been evaluated for this model\n                evaluated_count = get_evaluated_track_count(filename, museval_results)\n\n                # Determine if this is a roformer model","chunkId":"tests/model-metrics/test-all-models.py:60"}],[2638,{"filePath":"tests/model-metrics/test-all-models.py","startLine":635,"endLine":645,"chunkText":"                evaluated_count = get_evaluated_track_count(filename, museval_results)\n\n                # Determine if this is a roformer model\n                is_roformer = \"roformer\" in model_name.lower()\n\n                # Add to the list with priority information\n                all_models.append({\"name\": model_name, \"filename\": filename, \"type\": model_type, \"info\": model_info, \"evaluated_count\": evaluated_count, \"is_roformer\": is_roformer})\n\n    # Sort models by priority:\n    # 1. Roformer models with fewer than max_tracks evaluations\n    # 2. Other models with fewer than max_tracks evaluations","chunkId":"tests/model-metrics/test-all-models.py:61"}],[2639,{"filePath":"tests/model-metrics/test-all-models.py","startLine":643,"endLine":654,"chunkText":"    # Sort models by priority:\n    # 1. Roformer models with fewer than max_tracks evaluations\n    # 2. Other models with fewer than max_tracks evaluations\n    # 3. Roformer models with more evaluations\n    # 4. Other models with more evaluations\n    all_models.sort(\n        key=lambda m: (\n            0 if m[\"is_roformer\"] and m[\"evaluated_count\"] < args.max_tracks else 1 if not m[\"is_roformer\"] and m[\"evaluated_count\"] < args.max_tracks else 2 if m[\"is_roformer\"] else 3,\n            m[\"evaluated_count\"],  # Secondary sort by number of evaluations (ascending)\n        )\n    )\n","chunkId":"tests/model-metrics/test-all-models.py:62"}],[2640,{"filePath":"tests/model-metrics/test-all-models.py","startLine":652,"endLine":665,"chunkText":"        )\n    )\n\n    # Log the prioritized models\n    log_with_time(f\"Prioritized {len(all_models)} models for evaluation:\")\n    for i, model in enumerate(all_models[:10]):  # Show top 10\n        log_with_time(f\"{i+1}. {model['name']} ({model['filename']}) - {model['evaluated_count']} tracks evaluated, roformer: {model['is_roformer']}\")\n\n    if len(all_models) > 10:\n        log_with_time(f\"... and {len(all_models) - 10} more models\")\n\n    # Limit the number of models if specified\n    if args.max_models:\n        all_models = all_models[: args.max_models]","chunkId":"tests/model-metrics/test-all-models.py:63"}],[2641,{"filePath":"tests/model-metrics/test-all-models.py","startLine":663,"endLine":677,"chunkText":"    # Limit the number of models if specified\n    if args.max_models:\n        all_models = all_models[: args.max_models]\n        log_with_time(f\"Limited to {args.max_models} models for this run\")\n\n    # Process models according to priority\n    model_idx = 0\n    stop_requested = False\n    while model_idx < len(all_models):\n        # Check for stop signal before processing each model\n        if check_stop_signal():\n            log_with_time(\"Stop signal detected. Will finish current model's tracks and then exit.\")\n            stop_requested = True\n\n        model = all_models[model_idx]","chunkId":"tests/model-metrics/test-all-models.py:64"}],[2642,{"filePath":"tests/model-metrics/test-all-models.py","startLine":675,"endLine":687,"chunkText":"            stop_requested = True\n\n        model = all_models[model_idx]\n        model_name = model[\"name\"]\n        model_filename = model[\"filename\"]\n        model_type = model[\"type\"]\n\n        progress_pct = (model_idx + 1) / len(all_models) * 100\n        log_with_time(f\"\\n=== Processing model {model_idx+1}/{len(all_models)} ({progress_pct:.1f}%): {model_name} ({model_filename}) ===\")\n\n        # Initialize model entry if it doesn't exist\n        if model_filename not in combined_results:\n            log_with_time(f\"Initializing new entry for {model_filename}\")","chunkId":"tests/model-metrics/test-all-models.py:65"}],[2643,{"filePath":"tests/model-metrics/test-all-models.py","startLine":685,"endLine":695,"chunkText":"        # Initialize model entry if it doesn't exist\n        if model_filename not in combined_results:\n            log_with_time(f\"Initializing new entry for {model_filename}\")\n            combined_results[model_filename] = {\"model_name\": model_name, \"track_scores\": [], \"median_scores\": {}, \"stems\": [], \"target_stem\": None}\n\n        # Try to load the model to get stem information\n        try:\n            separator.load_model(model_filename=model_filename)\n            model_data = separator.model_instance.model_data\n\n            # Extract stem information (similar to your existing code)","chunkId":"tests/model-metrics/test-all-models.py:66"}],[2644,{"filePath":"tests/model-metrics/test-all-models.py","startLine":693,"endLine":705,"chunkText":"            model_data = separator.model_instance.model_data\n\n            # Extract stem information (similar to your existing code)\n            # ... (keep your existing stem extraction logic here)\n\n        except Exception as e:\n            log_with_time(f\"Error loading model {model_filename}: {str(e)}\", logging.ERROR)\n            logger.exception(\"Full exception details:\")\n            model_idx += 1\n            continue\n\n        # Count how many tracks have been evaluated for this model\n        # Use the cached results","chunkId":"tests/model-metrics/test-all-models.py:67"}],[2645,{"filePath":"tests/model-metrics/test-all-models.py","startLine":703,"endLine":715,"chunkText":"\n        # Count how many tracks have been evaluated for this model\n        # Use the cached results\n        evaluated_count = get_evaluated_track_count(model_filename, results_cache.get_results())\n\n        # Determine how many more tracks to evaluate\n        tracks_to_evaluate = max(0, args.max_tracks - evaluated_count)\n\n        if tracks_to_evaluate == 0:\n            log_with_time(f\"Model {model_name} already has {evaluated_count} tracks evaluated (>= {args.max_tracks}). Skipping.\")\n            model_idx += 1\n            continue\n","chunkId":"tests/model-metrics/test-all-models.py:68"}],[2646,{"filePath":"tests/model-metrics/test-all-models.py","startLine":713,"endLine":726,"chunkText":"            model_idx += 1\n            continue\n\n        log_with_time(f\"Will evaluate up to {tracks_to_evaluate} tracks for model {model_name}\")\n\n        # Process tracks for this model\n        tracks_processed = 0\n        for track in all_tracks:\n            # Check for stop signal before each track if we haven't already detected it\n            if not stop_requested and check_stop_signal():\n                log_with_time(\"Stop signal detected. Will finish current track and then exit.\")\n                stop_requested = True\n\n            # Skip if we've processed enough tracks for this model","chunkId":"tests/model-metrics/test-all-models.py:69"}],[2647,{"filePath":"tests/model-metrics/test-all-models.py","startLine":724,"endLine":736,"chunkText":"                stop_requested = True\n\n            # Skip if we've processed enough tracks for this model\n            if tracks_processed >= tracks_to_evaluate:\n                break\n\n            track_name = track[\"name\"]\n            track_path = track[\"path\"]\n\n            # Skip if track already evaluated for this model\n            # Use the cached results\n            if model_filename in results_cache.get_results() and track_name in results_cache.get_results()[model_filename]:\n                log_with_time(f\"Skipping already evaluated track {track_name} for model: {model_filename}\")","chunkId":"tests/model-metrics/test-all-models.py:70"}],[2648,{"filePath":"tests/model-metrics/test-all-models.py","startLine":736,"endLine":747,"chunkText":"                log_with_time(f\"Skipping already evaluated track {track_name} for model: {model_filename}\")\n                continue\n\n            log_with_time(f\"Processing track: {track_name} for model: {model_filename}\")\n\n            if args.dry_run:\n                log_with_time(f\"[DRY RUN] Would evaluate track {track_name} with model {model_filename}\")\n                tracks_processed += 1\n                models_with_new_data.add(model_filename)\n\n                # Estimate processing time based on model type for dry run\n                # This is a rough estimate - roformer models are typically slower","chunkId":"tests/model-metrics/test-all-models.py:71"}],[2649,{"filePath":"tests/model-metrics/test-all-models.py","startLine":745,"endLine":754,"chunkText":"\n                # Estimate processing time based on model type for dry run\n                # This is a rough estimate - roformer models are typically slower\n                estimated_speed = 30.0  # Default estimate: 30 seconds per minute\n                if \"roformer\" in model_name.lower():\n                    estimated_speed = 45.0  # Roformer models are typically slower\n                elif \"umx\" in model_name.lower():\n                    estimated_speed = 20.0  # UMX models are typically faster\n\n                # Update statistics with estimated values","chunkId":"tests/model-metrics/test-all-models.py:72"}],[2650,{"filePath":"tests/model-metrics/test-all-models.py","startLine":752,"endLine":765,"chunkText":"                    estimated_speed = 20.0  # UMX models are typically faster\n\n                # Update statistics with estimated values\n                total_processing_time += estimated_speed\n\n                # Track fastest and slowest models based on estimates\n                if estimated_speed < fastest_model[\"speed\"]:\n                    fastest_model = {\"name\": model_name, \"speed\": estimated_speed}\n                if estimated_speed > slowest_model[\"speed\"]:\n                    slowest_model = {\"name\": model_name, \"speed\": estimated_speed}\n\n                continue\n\n            try:","chunkId":"tests/model-metrics/test-all-models.py:73"}],[2651,{"filePath":"tests/model-metrics/test-all-models.py","startLine":763,"endLine":776,"chunkText":"                continue\n\n            try:\n                result = evaluate_track(track_name, track_path, model_filename, mus)\n\n                # Unpack the result safely\n                if result and isinstance(result, tuple) and len(result) == 2:\n                    _, model_results = result\n                else:\n                    model_results = None\n\n                # Process the results if they exist and are valid\n                if model_results is not None and isinstance(model_results, dict):\n                    combined_results[model_filename][\"track_scores\"].append(model_results)","chunkId":"tests/model-metrics/test-all-models.py:74"}],[2652,{"filePath":"tests/model-metrics/test-all-models.py","startLine":774,"endLine":783,"chunkText":"                # Process the results if they exist and are valid\n                if model_results is not None and isinstance(model_results, dict):\n                    combined_results[model_filename][\"track_scores\"].append(model_results)\n                    tracks_processed += 1\n                    models_with_new_data.add(model_filename)\n\n                    # Track processing time statistics - safely access nested dictionaries\n                    scores = model_results.get(\"scores\", {})\n                    if isinstance(scores, dict):\n                        speed = scores.get(\"seconds_per_minute_m3\")","chunkId":"tests/model-metrics/test-all-models.py:75"}],[2653,{"filePath":"tests/model-metrics/test-all-models.py","startLine":781,"endLine":790,"chunkText":"                    scores = model_results.get(\"scores\", {})\n                    if isinstance(scores, dict):\n                        speed = scores.get(\"seconds_per_minute_m3\")\n                        if speed is not None:\n                            total_processing_time += speed  # Accumulate total processing time\n\n                            # Track fastest and slowest models\n                            if speed < fastest_model[\"speed\"]:\n                                fastest_model = {\"name\": model_name, \"speed\": speed}\n                            if speed > slowest_model[\"speed\"]:","chunkId":"tests/model-metrics/test-all-models.py:76"}],[2654,{"filePath":"tests/model-metrics/test-all-models.py","startLine":788,"endLine":794,"chunkText":"                            if speed < fastest_model[\"speed\"]:\n                                fastest_model = {\"name\": model_name, \"speed\": speed}\n                            if speed > slowest_model[\"speed\"]:\n                                slowest_model = {\"name\": model_name, \"speed\": speed}\n                else:\n                    log_with_time(f\"Skipping model {model_filename} for track {track_name} due to no evaluatable stems or invalid results\")\n            except Exception as e:","chunkId":"tests/model-metrics/test-all-models.py:77"}],[2655,{"filePath":"tests/model-metrics/test-all-models.py","startLine":792,"endLine":800,"chunkText":"                else:\n                    log_with_time(f\"Skipping model {model_filename} for track {track_name} due to no evaluatable stems or invalid results\")\n            except Exception as e:\n                log_with_time(f\"Error evaluating model {model_filename} with track {track_name}: {str(e)}\", logging.ERROR)\n                logger.exception(f\"Exception details: \", exc_info=e)\n                continue\n\n            # Update and save results\n            if combined_results[model_filename][\"track_scores\"]:","chunkId":"tests/model-metrics/test-all-models.py:78"}],[2656,{"filePath":"tests/model-metrics/test-all-models.py","startLine":798,"endLine":808,"chunkText":"\n            # Update and save results\n            if combined_results[model_filename][\"track_scores\"]:\n                median_scores = calculate_median_scores(combined_results[model_filename][\"track_scores\"])\n                combined_results[model_filename][\"median_scores\"] = median_scores\n\n            # Save results after each track\n            if not args.dry_run:\n                os.makedirs(os.path.dirname(COMBINED_RESULTS_PATH), exist_ok=True)\n                with open(COMBINED_RESULTS_PATH, \"w\", encoding=\"utf-8\") as f:\n                    json.dump(combined_results, f, indent=2)","chunkId":"tests/model-metrics/test-all-models.py:79"}],[2657,{"filePath":"tests/model-metrics/test-all-models.py","startLine":806,"endLine":816,"chunkText":"                os.makedirs(os.path.dirname(COMBINED_RESULTS_PATH), exist_ok=True)\n                with open(COMBINED_RESULTS_PATH, \"w\", encoding=\"utf-8\") as f:\n                    json.dump(combined_results, f, indent=2)\n                log_with_time(f\"Updated combined results file with {model_filename} - {track_name}\")\n\n                # Force update the cache after saving\n                results_cache.get_results(force=True)\n            else:\n                log_with_time(f\"[DRY RUN] Would have updated combined results for {model_filename} - {track_name}\")\n\n            # Check disk space periodically","chunkId":"tests/model-metrics/test-all-models.py:80"}],[2658,{"filePath":"tests/model-metrics/test-all-models.py","startLine":814,"endLine":825,"chunkText":"                log_with_time(f\"[DRY RUN] Would have updated combined results for {model_filename} - {track_name}\")\n\n            # Check disk space periodically\n            check_disk_usage(RESULTS_PATH)\n\n        log_with_time(f\"Completed processing {tracks_processed} tracks for model {model_name}\")\n\n        # If stop was requested, exit after completing the current model\n        if stop_requested:\n            log_with_time(\"Stop signal processed. Generating final summary before exit.\")\n            break\n","chunkId":"tests/model-metrics/test-all-models.py:81"}],[2659,{"filePath":"tests/model-metrics/test-all-models.py","startLine":823,"endLine":832,"chunkText":"            log_with_time(\"Stop signal processed. Generating final summary before exit.\")\n            break\n\n        # If we're processing a non-roformer model, check if there are roformer models that need evaluation\n        if not model[\"is_roformer\"]:\n            # Find roformer models that still need more evaluations\n            # Use the cached results\n            roformer_models_needing_eval = []\n            for i, m in enumerate(all_models[model_idx + 1 :], start=model_idx + 1):\n                if m[\"is_roformer\"]:","chunkId":"tests/model-metrics/test-all-models.py:82"}],[2660,{"filePath":"tests/model-metrics/test-all-models.py","startLine":830,"endLine":839,"chunkText":"            roformer_models_needing_eval = []\n            for i, m in enumerate(all_models[model_idx + 1 :], start=model_idx + 1):\n                if m[\"is_roformer\"]:\n                    eval_count = get_evaluated_track_count(m[\"filename\"], results_cache.get_results())\n                    if eval_count < args.max_tracks:\n                        roformer_models_needing_eval.append((i, m))\n\n            if roformer_models_needing_eval:\n                log_with_time(f\"Found {len(roformer_models_needing_eval)} roformer models that still need evaluation. Reprioritizing...\")\n","chunkId":"tests/model-metrics/test-all-models.py:83"}],[2661,{"filePath":"tests/model-metrics/test-all-models.py","startLine":837,"endLine":846,"chunkText":"            if roformer_models_needing_eval:\n                log_with_time(f\"Found {len(roformer_models_needing_eval)} roformer models that still need evaluation. Reprioritizing...\")\n\n                # Move these models to the front of the remaining queue\n                for offset, (i, m) in enumerate(roformer_models_needing_eval):\n                    # Adjust index for models we've already moved\n                    adjusted_idx = i - offset\n                    # Move this model right after the current one\n                    all_models.insert(model_idx + 1, all_models.pop(adjusted_idx))\n","chunkId":"tests/model-metrics/test-all-models.py:84"}],[2662,{"filePath":"tests/model-metrics/test-all-models.py","startLine":844,"endLine":859,"chunkText":"                    # Move this model right after the current one\n                    all_models.insert(model_idx + 1, all_models.pop(adjusted_idx))\n\n                log_with_time(\"Reprioritization complete. Continuing with highest priority model.\")\n\n        # Move to the next model\n        model_idx += 1\n        models_processed += 1\n\n    log_with_time(\"Evaluation complete\")\n    # Final disk space check\n    check_disk_usage(RESULTS_PATH)\n\n    # Generate and display summary statistics\n    # Reset fastest/slowest models if they weren't updated\n    if fastest_model[\"speed\"] == float(\"inf\"):","chunkId":"tests/model-metrics/test-all-models.py:85"}],[2663,{"filePath":"tests/model-metrics/test-all-models.py","startLine":857,"endLine":871,"chunkText":"    # Generate and display summary statistics\n    # Reset fastest/slowest models if they weren't updated\n    if fastest_model[\"speed\"] == float(\"inf\"):\n        fastest_model = None\n    if slowest_model[\"speed\"] == 0:\n        slowest_model = None\n\n    summary = generate_summary_statistics(\n        start_time=start_time,\n        models_processed=models_processed,\n        tracks_processed=tracks_processed,\n        models_with_new_data=models_with_new_data,\n        tracks_evaluated=tracks_processed,\n        total_processing_time=total_processing_time,\n        fastest_model=fastest_model,","chunkId":"tests/model-metrics/test-all-models.py:86"}],[2664,{"filePath":"tests/model-metrics/test-all-models.py","startLine":869,"endLine":883,"chunkText":"        tracks_evaluated=tracks_processed,\n        total_processing_time=total_processing_time,\n        fastest_model=fastest_model,\n        slowest_model=slowest_model,\n        combined_results_path=COMBINED_RESULTS_PATH,\n        is_dry_run=args.dry_run,\n    )\n\n    log_with_time(\"\\n\" + summary)\n\n    # Also write summary to a log file\n    summary_filename = \"dry_run_summary.log\" if args.dry_run else \"evaluation_summary.log\"\n    if stop_requested:\n        summary_filename = \"stopped_\" + summary_filename\n    summary_log_path = os.path.join(os.path.dirname(COMBINED_RESULTS_PATH), summary_filename)","chunkId":"tests/model-metrics/test-all-models.py:87"}],[2665,{"filePath":"tests/model-metrics/test-all-models.py","startLine":881,"endLine":892,"chunkText":"    if stop_requested:\n        summary_filename = \"stopped_\" + summary_filename\n    summary_log_path = os.path.join(os.path.dirname(COMBINED_RESULTS_PATH), summary_filename)\n    with open(summary_log_path, \"w\") as f:\n        f.write(f\"{'Dry run' if args.dry_run else 'Evaluation'} {'(stopped early)' if stop_requested else ''} completed at: {time.strftime('%Y-%m-%d %H:%M:%S')}\\n\")\n        f.write(summary)\n\n    log_with_time(f\"Summary written to {summary_log_path}\")\n\n    # Clean up stop signal file if it exists\n    if os.path.exists(STOP_SIGNAL_PATH):\n        os.remove(STOP_SIGNAL_PATH)","chunkId":"tests/model-metrics/test-all-models.py:88"}],[2666,{"filePath":"tests/model-metrics/test-all-models.py","startLine":890,"endLine":900,"chunkText":"    # Clean up stop signal file if it exists\n    if os.path.exists(STOP_SIGNAL_PATH):\n        os.remove(STOP_SIGNAL_PATH)\n        log_with_time(\"Removed stop signal file\")\n\n    return 0 if not stop_requested else 2  # Return different exit code if stopped early\n\n\nif __name__ == \"__main__\":\n    exit(main())\n","chunkId":"tests/model-metrics/test-all-models.py:89"}],[2667,{"filePath":"tests/regression/test_roformer_size_mismatch.py","startLine":0,"endLine":20,"chunkText":"\"\"\"\nRegression tests for Roformer size mismatch issues.\nTests the handling of shorter outputs and broadcast errors in overlap_add operations.\n\"\"\"\n\nimport pytest\nimport torch\nimport numpy as np\nimport unittest\nfrom unittest.mock import Mock, patch, MagicMock\n\n\nclass TestRoformerSizeMismatch(unittest.TestCase):\n    \"\"\"Regression tests for size mismatch issues in Roformer processing.\"\"\"\n\n    def setUp(self):\n        \"\"\"Set up test fixtures.\"\"\"\n        self.batch_size = 2\n        self.channels = 2\n        self.sample_rate = 44100\n        ","chunkId":"tests/regression/test_roformer_size_mismatch.py:0"}],[2668,{"filePath":"tests/regression/test_roformer_size_mismatch.py","startLine":19,"endLine":31,"chunkText":"        self.sample_rate = 44100\n        \n        # Test cases with different output lengths that have caused issues\n        self.problematic_lengths = [1, 16, 32, 236, 512, 1024, 2048]\n\n    def test_overlap_add_safe_lengths(self):\n        \"\"\"T062: Reproduce shorter outputs (N∈{1,16,32,236}) and assert no broadcast errors, output length preserved.\"\"\"\n        \n        # Test each problematic length\n        for output_length in self.problematic_lengths:\n            with self.subTest(output_length=output_length):\n                self._test_single_output_length(output_length)\n","chunkId":"tests/regression/test_roformer_size_mismatch.py:1"}],[2669,{"filePath":"tests/regression/test_roformer_size_mismatch.py","startLine":29,"endLine":41,"chunkText":"            with self.subTest(output_length=output_length):\n                self._test_single_output_length(output_length)\n\n    def _test_single_output_length(self, output_length):\n        \"\"\"Test overlap_add with a specific output length.\"\"\"\n        # Setup test parameters\n        target_length = 10000  # Longer target buffer\n        chunk_size = 8192\n        step_size = 1024\n        \n        # Create mock model output with specific length\n        model_output = torch.randn(self.batch_size, self.channels, output_length)\n        target_buffer = torch.zeros(self.batch_size, self.channels, target_length)","chunkId":"tests/regression/test_roformer_size_mismatch.py:2"}],[2670,{"filePath":"tests/regression/test_roformer_size_mismatch.py","startLine":39,"endLine":48,"chunkText":"        # Create mock model output with specific length\n        model_output = torch.randn(self.batch_size, self.channels, output_length)\n        target_buffer = torch.zeros(self.batch_size, self.channels, target_length)\n        counter = torch.zeros(self.batch_size, self.channels, target_length)\n        \n        # Mock overlap_add function that handles size mismatches safely\n        def mock_overlap_add_safe(output, target, counter, start_idx, expected_len):\n            \"\"\"Safe overlap_add that handles shorter outputs.\"\"\"\n            # Get actual output length\n            actual_len = output.shape[-1]","chunkId":"tests/regression/test_roformer_size_mismatch.py:3"}],[2671,{"filePath":"tests/regression/test_roformer_size_mismatch.py","startLine":46,"endLine":61,"chunkText":"            \"\"\"Safe overlap_add that handles shorter outputs.\"\"\"\n            # Get actual output length\n            actual_len = output.shape[-1]\n            \n            # Calculate safe length to add\n            remaining_target = target.shape[-1] - start_idx\n            safe_len = min(actual_len, expected_len, remaining_target)\n            \n            if safe_len <= 0:\n                return 0  # Nothing to add\n            \n            # Ensure no broadcast errors by explicit slicing\n            end_idx = start_idx + safe_len\n            \n            try:\n                # Add output to target buffer","chunkId":"tests/regression/test_roformer_size_mismatch.py:4"}],[2672,{"filePath":"tests/regression/test_roformer_size_mismatch.py","startLine":59,"endLine":72,"chunkText":"            \n            try:\n                # Add output to target buffer\n                target_slice = target[..., start_idx:end_idx]\n                output_slice = output[..., :safe_len]\n                \n                # Verify shapes match before adding\n                assert target_slice.shape == output_slice.shape, (\n                    f\"Shape mismatch: target_slice {target_slice.shape} != output_slice {output_slice.shape}\"\n                )\n                \n                target[..., start_idx:end_idx] += output_slice\n                counter[..., start_idx:end_idx] += 1.0\n                ","chunkId":"tests/regression/test_roformer_size_mismatch.py:5"}],[2673,{"filePath":"tests/regression/test_roformer_size_mismatch.py","startLine":70,"endLine":84,"chunkText":"                target[..., start_idx:end_idx] += output_slice\n                counter[..., start_idx:end_idx] += 1.0\n                \n                return safe_len\n                \n            except Exception as e:\n                pytest.fail(f\"Overlap add failed for output_length {output_length}: {e}\")\n        \n        # Test overlap_add with different starting positions\n        start_positions = [0, 1000, 5000, 8000]\n        \n        for start_idx in start_positions:\n            if start_idx >= target_length:\n                continue\n                ","chunkId":"tests/regression/test_roformer_size_mismatch.py:6"}],[2674,{"filePath":"tests/regression/test_roformer_size_mismatch.py","startLine":82,"endLine":93,"chunkText":"            if start_idx >= target_length:\n                continue\n                \n            # Test with expected length equal to chunk_size\n            added_len = mock_overlap_add_safe(\n                model_output, target_buffer, counter, start_idx, chunk_size\n            )\n            \n            # Verify no broadcast errors occurred (function completed successfully)\n            assert added_len >= 0, f\"overlap_add should not fail for output_length {output_length}\"\n            \n            # Verify length preservation - added length should not exceed actual output length","chunkId":"tests/regression/test_roformer_size_mismatch.py:7"}],[2675,{"filePath":"tests/regression/test_roformer_size_mismatch.py","startLine":91,"endLine":100,"chunkText":"            assert added_len >= 0, f\"overlap_add should not fail for output_length {output_length}\"\n            \n            # Verify length preservation - added length should not exceed actual output length\n            assert added_len <= output_length, (\n                f\"Added length {added_len} should not exceed actual output length {output_length}\"\n            )\n            \n            # Verify added length is reasonable\n            remaining_space = target_length - start_idx\n            max_possible_add = min(output_length, chunk_size, remaining_space)","chunkId":"tests/regression/test_roformer_size_mismatch.py:8"}],[2676,{"filePath":"tests/regression/test_roformer_size_mismatch.py","startLine":98,"endLine":110,"chunkText":"            # Verify added length is reasonable\n            remaining_space = target_length - start_idx\n            max_possible_add = min(output_length, chunk_size, remaining_space)\n            assert added_len <= max_possible_add, (\n                f\"Added length {added_len} exceeds maximum possible {max_possible_add}\"\n            )\n\n    def test_overlap_add_edge_cases(self):\n        \"\"\"Test edge cases that have caused size mismatch issues.\"\"\"\n        target_length = 10000\n        \n        edge_cases = [\n            # (output_length, start_idx, expected_chunk_size, description)","chunkId":"tests/regression/test_roformer_size_mismatch.py:9"}],[2677,{"filePath":"tests/regression/test_roformer_size_mismatch.py","startLine":108,"endLine":120,"chunkText":"        \n        edge_cases = [\n            # (output_length, start_idx, expected_chunk_size, description)\n            (1, 0, 8192, \"Single sample output at start\"),\n            (1, 9999, 8192, \"Single sample output at end\"),\n            (16, 0, 8192, \"Very short output at start\"),\n            (32, 5000, 8192, \"Short output in middle\"),\n            (236, 9000, 8192, \"Medium output near end\"),\n            (8192, 2000, 8192, \"Full chunk size output\"),\n            (10000, 0, 8192, \"Output longer than chunk size\"),\n            (5000, 8000, 8192, \"Output extending beyond target\"),\n        ]\n        ","chunkId":"tests/regression/test_roformer_size_mismatch.py:10"}],[2678,{"filePath":"tests/regression/test_roformer_size_mismatch.py","startLine":118,"endLine":129,"chunkText":"            (5000, 8000, 8192, \"Output extending beyond target\"),\n        ]\n        \n        for output_len, start_idx, chunk_size, description in edge_cases:\n            with self.subTest(case=description):\n                # Create test tensors\n                model_output = torch.randn(self.batch_size, self.channels, output_len)\n                target_buffer = torch.zeros(self.batch_size, self.channels, target_length)\n                \n                # Mock safe overlap_add\n                def safe_overlap_add(output, target, start, chunk_sz):\n                    actual_len = output.shape[-1]","chunkId":"tests/regression/test_roformer_size_mismatch.py:11"}],[2679,{"filePath":"tests/regression/test_roformer_size_mismatch.py","startLine":127,"endLine":140,"chunkText":"                # Mock safe overlap_add\n                def safe_overlap_add(output, target, start, chunk_sz):\n                    actual_len = output.shape[-1]\n                    remaining = target.shape[-1] - start\n                    safe_len = min(actual_len, chunk_sz, remaining)\n                    \n                    if safe_len > 0:\n                        end = start + safe_len\n                        target[..., start:end] += output[..., :safe_len]\n                    \n                    return safe_len\n                \n                # Should not raise any exceptions\n                try:","chunkId":"tests/regression/test_roformer_size_mismatch.py:12"}],[2680,{"filePath":"tests/regression/test_roformer_size_mismatch.py","startLine":138,"endLine":149,"chunkText":"                \n                # Should not raise any exceptions\n                try:\n                    added_len = safe_overlap_add(model_output, target_buffer, start_idx, chunk_size)\n                    \n                    # Verify reasonable result\n                    assert added_len >= 0, f\"Should add non-negative length for {description}\"\n                    assert added_len <= output_len, f\"Should not add more than available for {description}\"\n                    \n                except Exception as e:\n                    pytest.fail(f\"Edge case failed - {description}: {e}\")\n","chunkId":"tests/regression/test_roformer_size_mismatch.py:13"}],[2681,{"filePath":"tests/regression/test_roformer_size_mismatch.py","startLine":147,"endLine":157,"chunkText":"                except Exception as e:\n                    pytest.fail(f\"Edge case failed - {description}: {e}\")\n\n    def test_counter_consistency_with_overlap_add(self):\n        \"\"\"Test that counter updates match overlap_add operations for problematic lengths.\"\"\"\n        target_length = 10000\n        \n        for output_length in self.problematic_lengths:\n            with self.subTest(output_length=output_length):\n                # Create test tensors\n                model_output = torch.randn(self.batch_size, self.channels, output_length)","chunkId":"tests/regression/test_roformer_size_mismatch.py:14"}],[2682,{"filePath":"tests/regression/test_roformer_size_mismatch.py","startLine":155,"endLine":165,"chunkText":"            with self.subTest(output_length=output_length):\n                # Create test tensors\n                model_output = torch.randn(self.batch_size, self.channels, output_length)\n                target_buffer = torch.zeros(self.batch_size, self.channels, target_length)\n                counter = torch.zeros(self.batch_size, self.channels, target_length)\n                \n                start_idx = 1000\n                chunk_size = 8192\n                \n                # Mock consistent overlap_add and counter update\n                def consistent_update(output, target, counter, start, chunk_sz):","chunkId":"tests/regression/test_roformer_size_mismatch.py:15"}],[2683,{"filePath":"tests/regression/test_roformer_size_mismatch.py","startLine":163,"endLine":173,"chunkText":"                \n                # Mock consistent overlap_add and counter update\n                def consistent_update(output, target, counter, start, chunk_sz):\n                    actual_len = output.shape[-1]\n                    remaining = target.shape[-1] - start\n                    safe_len = min(actual_len, chunk_sz, remaining)\n                    \n                    if safe_len > 0:\n                        end = start + safe_len\n                        # Update both target and counter with same range\n                        target[..., start:end] += output[..., :safe_len]","chunkId":"tests/regression/test_roformer_size_mismatch.py:16"}],[2684,{"filePath":"tests/regression/test_roformer_size_mismatch.py","startLine":171,"endLine":183,"chunkText":"                        end = start + safe_len\n                        # Update both target and counter with same range\n                        target[..., start:end] += output[..., :safe_len]\n                        counter[..., start:end] += 1.0\n                    \n                    return safe_len\n                \n                added_len = consistent_update(\n                    model_output, target_buffer, counter, start_idx, chunk_size\n                )\n                \n                # Verify consistency\n                if added_len > 0:","chunkId":"tests/regression/test_roformer_size_mismatch.py:17"}],[2685,{"filePath":"tests/regression/test_roformer_size_mismatch.py","startLine":181,"endLine":192,"chunkText":"                \n                # Verify consistency\n                if added_len > 0:\n                    # Check that counter was updated in the same range as target\n                    end_idx = start_idx + added_len\n                    counter_slice = counter[0, 0, start_idx:end_idx]\n                    \n                    # Counter should be 1.0 where we added data\n                    assert torch.all(counter_slice == 1.0), (\n                        f\"Counter not consistent for output_length {output_length}\"\n                    )\n                    ","chunkId":"tests/regression/test_roformer_size_mismatch.py:18"}],[2686,{"filePath":"tests/regression/test_roformer_size_mismatch.py","startLine":190,"endLine":200,"chunkText":"                        f\"Counter not consistent for output_length {output_length}\"\n                    )\n                    \n                    # Counter should be 0.0 outside the updated range\n                    if start_idx > 0:\n                        before_slice = counter[0, 0, :start_idx]\n                        assert torch.all(before_slice == 0.0), (\n                            f\"Counter corrupted before range for output_length {output_length}\"\n                        )\n                    \n                    if end_idx < target_length:","chunkId":"tests/regression/test_roformer_size_mismatch.py:19"}],[2687,{"filePath":"tests/regression/test_roformer_size_mismatch.py","startLine":198,"endLine":209,"chunkText":"                        )\n                    \n                    if end_idx < target_length:\n                        after_slice = counter[0, 0, end_idx:]\n                        assert torch.all(after_slice == 0.0), (\n                            f\"Counter corrupted after range for output_length {output_length}\"\n                        )\n\n    def test_broadcast_error_prevention(self):\n        \"\"\"Test specific scenarios that previously caused broadcast errors.\"\"\"\n        # These are specific cases that have been observed to cause issues\n        broadcast_error_cases = [","chunkId":"tests/regression/test_roformer_size_mismatch.py:20"}],[2688,{"filePath":"tests/regression/test_roformer_size_mismatch.py","startLine":207,"endLine":214,"chunkText":"        \"\"\"Test specific scenarios that previously caused broadcast errors.\"\"\"\n        # These are specific cases that have been observed to cause issues\n        broadcast_error_cases = [\n            # (batch, channels, output_len, target_len, start_idx)\n            (1, 2, 1, 8192, 0),      # Single batch, stereo, 1 sample\n            (2, 1, 16, 8192, 100),   # Dual batch, mono, 16 samples\n            (1, 1, 32, 8192, 8000),  # Single batch, mono, 32 samples near end\n            (2, 2, 236, 8192, 4000), # Dual batch, stereo, 236 samples","chunkId":"tests/regression/test_roformer_size_mismatch.py:21"}],[2689,{"filePath":"tests/regression/test_roformer_size_mismatch.py","startLine":212,"endLine":220,"chunkText":"            (2, 1, 16, 8192, 100),   # Dual batch, mono, 16 samples\n            (1, 1, 32, 8192, 8000),  # Single batch, mono, 32 samples near end\n            (2, 2, 236, 8192, 4000), # Dual batch, stereo, 236 samples\n            (1, 2, 512, 1024, 800),  # Output longer than remaining target space\n        ]\n        \n        for batch, channels, output_len, target_len, start_idx in broadcast_error_cases:\n            with self.subTest(batch=batch, channels=channels, output_len=output_len):\n                # Create tensors with specific problematic dimensions","chunkId":"tests/regression/test_roformer_size_mismatch.py:22"}],[2690,{"filePath":"tests/regression/test_roformer_size_mismatch.py","startLine":218,"endLine":226,"chunkText":"        for batch, channels, output_len, target_len, start_idx in broadcast_error_cases:\n            with self.subTest(batch=batch, channels=channels, output_len=output_len):\n                # Create tensors with specific problematic dimensions\n                model_output = torch.randn(batch, channels, output_len)\n                target_buffer = torch.zeros(batch, channels, target_len)\n                \n                # Mock overlap_add with explicit broadcast error prevention\n                def broadcast_safe_overlap_add(output, target, start):\n                    try:","chunkId":"tests/regression/test_roformer_size_mismatch.py:23"}],[2691,{"filePath":"tests/regression/test_roformer_size_mismatch.py","startLine":224,"endLine":232,"chunkText":"                # Mock overlap_add with explicit broadcast error prevention\n                def broadcast_safe_overlap_add(output, target, start):\n                    try:\n                        # Get dimensions\n                        out_batch, out_channels, out_len = output.shape\n                        tgt_batch, tgt_channels, tgt_len = target.shape\n                        \n                        # Verify batch and channel dimensions match\n                        assert out_batch == tgt_batch, f\"Batch size mismatch: {out_batch} != {tgt_batch}\"","chunkId":"tests/regression/test_roformer_size_mismatch.py:24"}],[2692,{"filePath":"tests/regression/test_roformer_size_mismatch.py","startLine":230,"endLine":240,"chunkText":"                        \n                        # Verify batch and channel dimensions match\n                        assert out_batch == tgt_batch, f\"Batch size mismatch: {out_batch} != {tgt_batch}\"\n                        assert out_channels == tgt_channels, f\"Channel mismatch: {out_channels} != {tgt_channels}\"\n                        \n                        # Calculate safe range\n                        remaining = tgt_len - start\n                        safe_len = min(out_len, remaining)\n                        \n                        if safe_len > 0:\n                            end = start + safe_le","chunkId":"tests/regression/test_roformer_size_mismatch.py:25"}],[2693,{"filePath":"tests/regression/test_roformer_size_mismatch.py","startLine":238,"endLine":247,"chunkText":"                        \n                        if safe_len > 0:\n                            end = start + safe_len\n                            \n                            # Explicit slicing to prevent broadcast errors\n                            target_slice = target[:, :, start:end]\n                            output_slice = output[:, :, :safe_len]\n                            \n                            # Verify shapes match exactly\n                            assert target_slice.shape == output_slice.shape, (","chunkId":"tests/regression/test_roformer_size_mismatch.py:26"}],[2694,{"filePath":"tests/regression/test_roformer_size_mismatch.py","startLine":245,"endLine":256,"chunkText":"                            \n                            # Verify shapes match exactly\n                            assert target_slice.shape == output_slice.shape, (\n                                f\"Shape mismatch: {target_slice.shape} != {output_slice.shape}\"\n                            )\n                            \n                            # Perform addition\n                            target[:, :, start:end] += output_slice\n                        \n                        return safe_len\n                        \n                    except Exception as e:","chunkId":"tests/regression/test_roformer_size_mismatch.py:27"}],[2695,{"filePath":"tests/regression/test_roformer_size_mismatch.py","startLine":254,"endLine":264,"chunkText":"                        return safe_len\n                        \n                    except Exception as e:\n                        pytest.fail(f\"Broadcast error not prevented: {e}\")\n                \n                # Should complete without broadcast errors\n                added_len = broadcast_safe_overlap_add(model_output, target_buffer, start_idx)\n                \n                # Verify result is reasonable\n                expected_max = min(output_len, target_len - start_idx)\n                assert 0 <= added_len <= expected_max, (","chunkId":"tests/regression/test_roformer_size_mismatch.py:28"}],[2696,{"filePath":"tests/regression/test_roformer_size_mismatch.py","startLine":262,"endLine":272,"chunkText":"                # Verify result is reasonable\n                expected_max = min(output_len, target_len - start_idx)\n                assert 0 <= added_len <= expected_max, (\n                    f\"Added length {added_len} outside expected range [0, {expected_max}]\"\n                )\n\n    def test_output_length_preservation_regression(self):\n        \"\"\"Test that output length is preserved correctly in all problematic cases.\"\"\"\n        # This tests the regression where shorter outputs were not handled correctly\n        \n        for output_length in self.problematic_lengths:","chunkId":"tests/regression/test_roformer_size_mismatch.py:29"}],[2697,{"filePath":"tests/regression/test_roformer_size_mismatch.py","startLine":270,"endLine":279,"chunkText":"        # This tests the regression where shorter outputs were not handled correctly\n        \n        for output_length in self.problematic_lengths:\n            with self.subTest(output_length=output_length):\n                # Simulate processing pipeline that should preserve length\n                original_length = output_length\n                \n                # Mock processing steps that might alter length\n                def mock_processing_pipeline(input_length):\n                    \"\"\"Mock processing that should preserve input length.\"\"\"","chunkId":"tests/regression/test_roformer_size_mismatch.py:30"}],[2698,{"filePath":"tests/regression/test_roformer_size_mismatch.py","startLine":277,"endLine":286,"chunkText":"                # Mock processing steps that might alter length\n                def mock_processing_pipeline(input_length):\n                    \"\"\"Mock processing that should preserve input length.\"\"\"\n                    # Step 1: Model inference (might produce shorter output)\n                    model_output_length = input_length  # Should match input\n                    \n                    # Step 2: Overlap-add processing\n                    processed_length = model_output_length  # Should preserve length\n                    \n                    # Step 3: Final output","chunkId":"tests/regression/test_roformer_size_mismatch.py:31"}],[2699,{"filePath":"tests/regression/test_roformer_size_mismatch.py","startLine":284,"endLine":297,"chunkText":"                    processed_length = model_output_length  # Should preserve length\n                    \n                    # Step 3: Final output\n                    final_length = processed_length  # Should still match\n                    \n                    return {\n                        'original': input_length,\n                        'model_output': model_output_length,\n                        'processed': processed_length,\n                        'final': final_length\n                    }\n                \n                result = mock_processing_pipeline(original_length)\n                ","chunkId":"tests/regression/test_roformer_size_mismatch.py:32"}],[2700,{"filePath":"tests/regression/test_roformer_size_mismatch.py","startLine":295,"endLine":305,"chunkText":"                \n                result = mock_processing_pipeline(original_length)\n                \n                # Verify length preservation throughout pipeline\n                assert result['model_output'] == original_length, (\n                    f\"Model output length {result['model_output']} != original {original_length}\"\n                )\n                assert result['processed'] == original_length, (\n                    f\"Processed length {result['processed']} != original {original_length}\"\n                )\n                assert result['final'] == original_length, (","chunkId":"tests/regression/test_roformer_size_mismatch.py:33"}],[2701,{"filePath":"tests/regression/test_roformer_size_mismatch.py","startLine":303,"endLine":314,"chunkText":"                    f\"Processed length {result['processed']} != original {original_length}\"\n                )\n                assert result['final'] == original_length, (\n                    f\"Final length {result['final']} != original {original_length}\"\n                )\n\n    def test_memory_layout_consistency(self):\n        \"\"\"Test that memory layout is consistent for problematic tensor sizes.\"\"\"\n        # Some size mismatches can be caused by unexpected memory layouts\n        \n        for output_length in self.problematic_lengths:\n            with self.subTest(output_length=output_length):","chunkId":"tests/regression/test_roformer_size_mismatch.py:34"}],[2702,{"filePath":"tests/regression/test_roformer_size_mismatch.py","startLine":312,"endLine":321,"chunkText":"        \n        for output_length in self.problematic_lengths:\n            with self.subTest(output_length=output_length):\n                # Create tensors with different memory layouts\n                contiguous_output = torch.randn(self.batch_size, self.channels, output_length)\n                non_contiguous_output = contiguous_output.transpose(1, 2).transpose(1, 2)\n                \n                target = torch.zeros(self.batch_size, self.channels, 10000)\n                \n                # Mock overlap_add that handles memory layout","chunkId":"tests/regression/test_roformer_size_mismatch.py:35"}],[2703,{"filePath":"tests/regression/test_roformer_size_mismatch.py","startLine":319,"endLine":330,"chunkText":"                target = torch.zeros(self.batch_size, self.channels, 10000)\n                \n                # Mock overlap_add that handles memory layout\n                def layout_aware_overlap_add(output, target, start):\n                    # Ensure contiguous memory layout\n                    if not output.is_contiguous():\n                        output = output.contiguous()\n                    \n                    if not target.is_contiguous():\n                        target = target.contiguous()\n                    \n                    # Perform safe addition","chunkId":"tests/regression/test_roformer_size_mismatch.py:36"}],[2704,{"filePath":"tests/regression/test_roformer_size_mismatch.py","startLine":328,"endLine":341,"chunkText":"                        target = target.contiguous()\n                    \n                    # Perform safe addition\n                    out_len = output.shape[-1]\n                    remaining = target.shape[-1] - start\n                    safe_len = min(out_len, remaining)\n                    \n                    if safe_len > 0:\n                        end = start + safe_len\n                        target[..., start:end] += output[..., :safe_len]\n                    \n                    return safe_len\n                \n                # Test with both contiguous and non-contiguous tensors","chunkId":"tests/regression/test_roformer_size_mismatch.py:37"}],[2705,{"filePath":"tests/regression/test_roformer_size_mismatch.py","startLine":339,"endLine":347,"chunkText":"                    return safe_len\n                \n                # Test with both contiguous and non-contiguous tensors\n                contiguous_result = layout_aware_overlap_add(contiguous_output, target.clone(), 1000)\n                non_contiguous_result = layout_aware_overlap_add(non_contiguous_output, target.clone(), 1000)\n                \n                # Results should be the same regardless of memory layout\n                assert contiguous_result == non_contiguous_result, (\n                    f\"Memory layout affected result for output_length {output_length}: \"","chunkId":"tests/regression/test_roformer_size_mismatch.py:38"}],[2706,{"filePath":"tests/regression/test_roformer_size_mismatch.py","startLine":345,"endLine":350,"chunkText":"                # Results should be the same regardless of memory layout\n                assert contiguous_result == non_contiguous_result, (\n                    f\"Memory layout affected result for output_length {output_length}: \"\n                    f\"contiguous={contiguous_result}, non_contiguous={non_contiguous_result}\"\n                )\n","chunkId":"tests/regression/test_roformer_size_mismatch.py:39"}],[2707,{"filePath":"tests/remote/README.md","startLine":0,"endLine":14,"chunkText":"# Remote API Tests\n\nThis directory contains comprehensive tests for the remote API functionality of audio-separator, which allows running audio separation workloads on remote servers via an API.\n\n## Test Structure\n\n### Unit Tests (`tests/unit/`)\n\n- **`test_remote_api_client.py`** - Tests for the `AudioSeparatorAPIClient` class\n  - Mock-based tests for all client methods\n  - Tests HTTP request/response handling\n  - Tests error conditions and edge cases\n  - Tests the convenience method `separate_audio_and_wait()`\n\n- **`test_remote_cli.py`** - Tests for the remote CLI functionality","chunkId":"tests/remote/README.md:0"}],[2708,{"filePath":"tests/remote/README.md","startLine":13,"endLine":28,"chunkText":"\n- **`test_remote_cli.py`** - Tests for the remote CLI functionality\n  - Tests command-line argument parsing\n  - Tests all CLI commands (separate, status, models, download)\n  - Tests error handling and edge cases\n  - Mock-based tests without actual HTTP calls\n\n### Integration Tests (`tests/integration/`)\n\n- **`test_remote_api_integration.py`** - Full integration tests\n  - Tests with a mock HTTP server that simulates the real API\n  - End-to-end workflow testing\n  - Tests job submission, polling, and file download\n  - Tests multiple concurrent jobs\n  - Tests error handling with realistic scenarios\n","chunkId":"tests/remote/README.md:1"}],[2709,{"filePath":"tests/remote/README.md","startLine":27,"endLine":41,"chunkText":"  - Tests error handling with realistic scenarios\n\n## Remote API Architecture\n\nThe remote API system consists of three main components:\n\n1. **API Server** (`audio_separator/remote/deploy_modal.py`)\n   - FastAPI server deployed on Modal.com\n   - Handles audio upload, processing, and file serving\n   - Supports multiple models in single jobs\n   - Asynchronous processing with job status tracking\n\n2. **API Client** (`audio_separator/remote/api_client.py`)\n   - Python client for interacting with the remote API\n   - Handles file uploads, job polling, and downloads","chunkId":"tests/remote/README.md:2"}],[2710,{"filePath":"tests/remote/README.md","startLine":40,"endLine":56,"chunkText":"   - Python client for interacting with the remote API\n   - Handles file uploads, job polling, and downloads\n   - Supports all separator parameters and architectures\n\n3. **Remote CLI** (`audio_separator/remote/cli.py`)\n   - Command-line interface using the API client\n   - Mirror of local CLI functionality but for remote processing\n   - Supports all local CLI parameters and options\n\n## Key Features Tested\n\n### Multiple Model Support\n- Upload once, process with multiple models\n- Efficient workflow for comparing model quality\n- Progress tracking across multiple models\n\n### Full Parameter Compatibility","chunkId":"tests/remote/README.md:3"}],[2711,{"filePath":"tests/remote/README.md","startLine":55,"endLine":76,"chunkText":"\n### Full Parameter Compatibility\n- All MDX, VR, Demucs, and MDXC parameters supported\n- Custom output naming and format options\n- Same parameter validation as local processing\n\n### Asynchronous Processing\n- Job submission returns immediately with task ID\n- Status polling with progress updates\n- Background processing on remote server\n\n### Error Handling\n- Network connectivity issues\n- Invalid parameters and file formats\n- Server errors and timeouts\n- Job failures and cleanup\n\n## Running the Tests\n\n### Run All Remote API Tests\n```bash\n# Run all unit tests for remote functionality","chunkId":"tests/remote/README.md:4"}],[2712,{"filePath":"tests/remote/README.md","startLine":75,"endLine":94,"chunkText":"```bash\n# Run all unit tests for remote functionality\npytest tests/unit/test_remote_api_client.py tests/unit/test_remote_cli.py -v\n\n# Run integration tests with mock server\npytest tests/integration/test_remote_api_integration.py -v\n\n# Run all remote tests\npytest tests/unit/test_remote*.py tests/integration/test_remote*.py -v\n```\n\n### Run Specific Test Categories\n```bash\n# Test only API client functionality\npytest tests/unit/test_remote_api_client.py::TestAudioSeparatorAPIClient -v\n\n# Test only CLI functionality  \npytest tests/unit/test_remote_cli.py::TestRemoteCLI -v\n\n# Test end-to-end workflows","chunkId":"tests/remote/README.md:5"}],[2713,{"filePath":"tests/remote/README.md","startLine":93,"endLine":112,"chunkText":"\n# Test end-to-end workflows\npytest tests/integration/test_remote_api_integration.py::TestRemoteAPIEndToEnd -v\n```\n\n### Run with Coverage\n```bash\npytest tests/unit/test_remote*.py tests/integration/test_remote*.py --cov=audio_separator.remote --cov-report=html\n```\n\n## Test Environment Setup\n\nThe tests are designed to run without requiring a live API server:\n\n1. **Unit Tests** - Use mocked HTTP responses, no network calls\n2. **Integration Tests** - Use a mock HTTP server that simulates the real API\n3. **End-to-End Tests** - Full workflow testing with realistic data\n\n### Mock Server Features\n","chunkId":"tests/remote/README.md:6"}],[2714,{"filePath":"tests/remote/README.md","startLine":111,"endLine":129,"chunkText":"### Mock Server Features\n\nThe integration tests include a comprehensive mock HTTP server that simulates:\n- Job submission and processing\n- Status polling with progress updates\n- File upload and download\n- Model listing and filtering\n- Error conditions and edge cases\n\n## Testing Best Practices\n\n### Isolated Tests\n- Each test is independent and can run in isolation\n- Temporary files are properly cleaned up\n- Mock state is reset between tests\n\n### Realistic Scenarios\n- Tests use realistic audio file formats and sizes\n- Error conditions match real-world scenarios","chunkId":"tests/remote/README.md:7"}],[2715,{"filePath":"tests/remote/README.md","startLine":130,"endLine":153,"chunkText":"- Progress tracking mimics actual processing times\n\n### Comprehensive Coverage\n- All API endpoints are tested\n- All CLI commands and options are tested\n- Both success and error paths are tested\n- Parameter validation and edge cases are covered\n\n## Test Data\n\nTests use minimal synthetic data to avoid large file dependencies:\n- Fake audio content for upload testing\n- Simulated processing results\n- Mock model metadata\n\n## Debugging Tests\n\n### Enable Debug Logging\n```bash\npytest tests/integration/test_remote_api_integration.py -v -s --log-cli-level=DEBUG\n```\n\n### Run Individual Tests\n```bash","chunkId":"tests/remote/README.md:8"}],[2716,{"filePath":"tests/remote/README.md","startLine":153,"endLine":171,"chunkText":"```bash\n# Test specific functionality\npytest tests/unit/test_remote_api_client.py::TestAudioSeparatorAPIClient::test_separate_audio_and_wait_success -v -s\n\n# Test with specific parameters\npytest tests/integration/test_remote_api_integration.py::TestRemoteAPIIntegration::test_job_status_polling -v -s\n```\n\n### Test Environment Variables\n```bash\n# Skip certain tests if needed\nSKIP_INTEGRATION_TESTS=1 pytest tests/unit/test_remote*.py\n\n# Enable additional debug output\nDEBUG_REMOTE_TESTS=1 pytest tests/integration/test_remote*.py -v -s\n```\n\n## Integration with CI/CD\n","chunkId":"tests/remote/README.md:9"}],[2717,{"filePath":"tests/remote/README.md","startLine":170,"endLine":187,"chunkText":"## Integration with CI/CD\n\nThese tests are designed to run in CI environments:\n- No external dependencies required\n- Fast execution (typically < 30 seconds)\n- Reliable mock server implementation\n- Clear pass/fail criteria\n\n## Contributing\n\nWhen adding new remote API features:\n\n1. **Add unit tests** for individual components\n2. **Add integration tests** for end-to-end workflows\n3. **Update mock server** to support new endpoints\n4. **Test error conditions** and edge cases\n5. **Update documentation** for new test scenarios\n","chunkId":"tests/remote/README.md:10"}],[2718,{"filePath":"tests/remote/README.md","startLine":186,"endLine":188,"chunkText":"5. **Update documentation** for new test scenarios\n\nThe test suite should maintain high coverage while remaining fast and reliable for continuous integration. ","chunkId":"tests/remote/README.md:11"}],[2719,{"filePath":"tests/unit/test_audio_chunking.py","startLine":0,"endLine":21,"chunkText":"\"\"\"\nUnit tests for audio chunking functionality.\nTests the AudioChunker class for splitting and merging audio files.\n\"\"\"\n\nimport pytest\nimport os\nimport tempfile\nimport logging\nfrom unittest.mock import Mock, patch, MagicMock\nfrom pydub import AudioSegment\n\nfrom audio_separator.separator.audio_chunking import AudioChunker\n\n\nclass TestAudioChunker:\n    \"\"\"Test cases for AudioChunker class.\"\"\"\n\n    def setup_method(self):\n        \"\"\"Set up test fixtures.\"\"\"\n        self.chunk_duration_seconds = 10.0  # 10 seconds\n        self.logger = logging.getLogger(__name__)","chunkId":"tests/unit/test_audio_chunking.py:0"}],[2720,{"filePath":"tests/unit/test_audio_chunking.py","startLine":19,"endLine":32,"chunkText":"        \"\"\"Set up test fixtures.\"\"\"\n        self.chunk_duration_seconds = 10.0  # 10 seconds\n        self.logger = logging.getLogger(__name__)\n        self.chunker = AudioChunker(self.chunk_duration_seconds, self.logger)\n\n    def test_initialization(self):\n        \"\"\"Test AudioChunker initialization.\"\"\"\n        assert self.chunker.chunk_duration_ms == 10000\n        assert self.chunker.logger is not None\n\n    def test_initialization_with_custom_logger(self):\n        \"\"\"Test AudioChunker initialization with custom logger.\"\"\"\n        custom_logger = Mock()\n        chunker = AudioChunker(5.0, custom_logger)","chunkId":"tests/unit/test_audio_chunking.py:1"}],[2721,{"filePath":"tests/unit/test_audio_chunking.py","startLine":30,"endLine":42,"chunkText":"        \"\"\"Test AudioChunker initialization with custom logger.\"\"\"\n        custom_logger = Mock()\n        chunker = AudioChunker(5.0, custom_logger)\n        assert chunker.chunk_duration_ms == 5000\n        assert chunker.logger == custom_logger\n\n    def test_should_chunk_true(self):\n        \"\"\"Test should_chunk returns True for files longer than chunk duration.\"\"\"\n        # File duration 15 seconds, chunk duration 10 seconds\n        assert self.chunker.should_chunk(15.0) is True\n\n    def test_should_chunk_false(self):\n        \"\"\"Test should_chunk returns False for files shorter than chunk duration.\"\"\"","chunkId":"tests/unit/test_audio_chunking.py:2"}],[2722,{"filePath":"tests/unit/test_audio_chunking.py","startLine":40,"endLine":52,"chunkText":"\n    def test_should_chunk_false(self):\n        \"\"\"Test should_chunk returns False for files shorter than chunk duration.\"\"\"\n        # File duration 5 seconds, chunk duration 10 seconds\n        assert self.chunker.should_chunk(5.0) is False\n\n    def test_should_chunk_exact_boundary(self):\n        \"\"\"Test should_chunk at exact boundary.\"\"\"\n        # File duration exactly 10 seconds, chunk duration 10 seconds\n        assert self.chunker.should_chunk(10.0) is False\n\n    def test_should_chunk_just_over_boundary(self):\n        \"\"\"Test should_chunk just over boundary.\"\"\"","chunkId":"tests/unit/test_audio_chunking.py:3"}],[2723,{"filePath":"tests/unit/test_audio_chunking.py","startLine":50,"endLine":61,"chunkText":"\n    def test_should_chunk_just_over_boundary(self):\n        \"\"\"Test should_chunk just over boundary.\"\"\"\n        # File duration 10.1 seconds, chunk duration 10 seconds\n        assert self.chunker.should_chunk(10.1) is True\n\n    @patch('audio_separator.separator.audio_chunking.AudioSegment.from_file')\n    @patch('audio_separator.separator.audio_chunking.os.path.exists')\n    @patch('audio_separator.separator.audio_chunking.os.makedirs')\n    def test_split_audio_basic(self, _mock_makedirs, mock_exists, mock_from_file):\n        \"\"\"Test basic audio splitting.\"\"\"\n        # Mock audio file (30 seconds)","chunkId":"tests/unit/test_audio_chunking.py:4"}],[2724,{"filePath":"tests/unit/test_audio_chunking.py","startLine":59,"endLine":72,"chunkText":"    def test_split_audio_basic(self, _mock_makedirs, mock_exists, mock_from_file):\n        \"\"\"Test basic audio splitting.\"\"\"\n        # Mock audio file (30 seconds)\n        mock_audio = Mock()\n        mock_audio.__len__ = Mock(return_value=30000)  # 30 seconds in ms\n        mock_audio.__getitem__ = Mock(side_effect=lambda _: mock_audio)\n        mock_audio.export = Mock()\n        mock_from_file.return_value = mock_audio\n        mock_exists.return_value = True\n\n        temp_dir = tempfile.mkdtemp()\n        try:\n            chunk_paths = self.chunker.split_audio(\"test.wav\", temp_dir)\n","chunkId":"tests/unit/test_audio_chunking.py:5"}],[2725,{"filePath":"tests/unit/test_audio_chunking.py","startLine":70,"endLine":85,"chunkText":"        try:\n            chunk_paths = self.chunker.split_audio(\"test.wav\", temp_dir)\n\n            # Should create 3 chunks (30s / 10s = 3)\n            assert len(chunk_paths) == 3\n            assert all(\"chunk_\" in path for path in chunk_paths)\n            assert mock_audio.export.call_count == 3\n\n        finally:\n            # Cleanup\n            import shutil\n            if os.path.exists(temp_dir):\n                shutil.rmtree(temp_dir)\n\n    @patch('audio_separator.separator.audio_chunking.AudioSegment.from_file')\n    @patch('audio_separator.separator.audio_chunking.os.path.exists')","chunkId":"tests/unit/test_audio_chunking.py:6"}],[2726,{"filePath":"tests/unit/test_audio_chunking.py","startLine":83,"endLine":92,"chunkText":"\n    @patch('audio_separator.separator.audio_chunking.AudioSegment.from_file')\n    @patch('audio_separator.separator.audio_chunking.os.path.exists')\n    @patch('audio_separator.separator.audio_chunking.os.makedirs')\n    def test_split_audio_uneven_chunks(self, _mock_makedirs, mock_exists, mock_from_file):\n        \"\"\"Test splitting audio with uneven chunk sizes.\"\"\"\n        # Mock audio file (25 seconds)\n        mock_audio = Mock()\n        mock_audio.__len__ = Mock(return_value=25000)  # 25 seconds in ms\n        mock_audio.__getitem__ = Mock(side_effect=lambda _: mock_audio)","chunkId":"tests/unit/test_audio_chunking.py:7"}],[2727,{"filePath":"tests/unit/test_audio_chunking.py","startLine":90,"endLine":105,"chunkText":"        mock_audio = Mock()\n        mock_audio.__len__ = Mock(return_value=25000)  # 25 seconds in ms\n        mock_audio.__getitem__ = Mock(side_effect=lambda _: mock_audio)\n        mock_audio.export = Mock()\n        mock_from_file.return_value = mock_audio\n        mock_exists.return_value = True\n\n        temp_dir = tempfile.mkdtemp()\n        try:\n            chunk_paths = self.chunker.split_audio(\"test.wav\", temp_dir)\n\n            # Should create 3 chunks (ceil(25s / 10s) = 3)\n            # First two chunks: 10s each, last chunk: 5s\n            assert len(chunk_paths) == 3\n\n        finally:","chunkId":"tests/unit/test_audio_chunking.py:8"}],[2728,{"filePath":"tests/unit/test_audio_chunking.py","startLine":103,"endLine":116,"chunkText":"            assert len(chunk_paths) == 3\n\n        finally:\n            import shutil\n            if os.path.exists(temp_dir):\n                shutil.rmtree(temp_dir)\n\n    def test_split_audio_file_not_found(self, tmp_path):\n        \"\"\"Test split_audio with non-existent file.\"\"\"\n        with pytest.raises(FileNotFoundError):\n            self.chunker.split_audio(\"nonexistent.wav\", str(tmp_path))\n\n    @patch('audio_separator.separator.audio_chunking.AudioSegment.from_file')\n    @patch('audio_separator.separator.audio_chunking.os.path.exists')","chunkId":"tests/unit/test_audio_chunking.py:9"}],[2729,{"filePath":"tests/unit/test_audio_chunking.py","startLine":114,"endLine":128,"chunkText":"\n    @patch('audio_separator.separator.audio_chunking.AudioSegment.from_file')\n    @patch('audio_separator.separator.audio_chunking.os.path.exists')\n    def test_merge_chunks_basic(self, mock_exists, mock_from_file, tmp_path):\n        \"\"\"Test basic chunk merging.\"\"\"\n        # Mock chunk files\n        mock_chunk1 = Mock()\n        mock_chunk2 = Mock()\n        mock_combined = Mock()\n        mock_combined.export = Mock()\n\n        # Setup mock to return chunks and allow addition\n        mock_from_file.side_effect = [mock_chunk1, mock_chunk2]\n        mock_exists.return_value = True\n","chunkId":"tests/unit/test_audio_chunking.py:10"}],[2730,{"filePath":"tests/unit/test_audio_chunking.py","startLine":126,"endLine":137,"chunkText":"        mock_from_file.side_effect = [mock_chunk1, mock_chunk2]\n        mock_exists.return_value = True\n\n        # Mock AudioSegment.empty() and addition\n        with patch('audio_separator.separator.audio_chunking.AudioSegment.empty') as mock_empty:\n            mock_empty.return_value = mock_combined\n            mock_combined.__add__ = Mock(side_effect=[mock_combined, mock_combined])\n            mock_combined.__len__ = Mock(return_value=20000)\n\n            chunk_paths = [\"chunk1.wav\", \"chunk2.wav\"]\n            output_path = self.chunker.merge_chunks(chunk_paths, str(tmp_path / \"output.wav\"))\n","chunkId":"tests/unit/test_audio_chunking.py:11"}],[2731,{"filePath":"tests/unit/test_audio_chunking.py","startLine":135,"endLine":146,"chunkText":"            chunk_paths = [\"chunk1.wav\", \"chunk2.wav\"]\n            output_path = self.chunker.merge_chunks(chunk_paths, str(tmp_path / \"output.wav\"))\n\n            assert output_path == str(tmp_path / \"output.wav\")\n            assert mock_combined.export.called\n\n    def test_merge_chunks_empty_list(self, tmp_path):\n        \"\"\"Test merge_chunks with empty chunk list.\"\"\"\n        with pytest.raises(ValueError, match=\"Cannot merge empty list\"):\n            self.chunker.merge_chunks([], str(tmp_path / \"output.wav\"))\n\n    @patch('audio_separator.separator.audio_chunking.os.path.exists')","chunkId":"tests/unit/test_audio_chunking.py:12"}],[2732,{"filePath":"tests/unit/test_audio_chunking.py","startLine":144,"endLine":155,"chunkText":"            self.chunker.merge_chunks([], str(tmp_path / \"output.wav\"))\n\n    @patch('audio_separator.separator.audio_chunking.os.path.exists')\n    def test_merge_chunks_missing_file(self, mock_exists, tmp_path):\n        \"\"\"Test merge_chunks with missing chunk file.\"\"\"\n        mock_exists.return_value = False\n\n        with pytest.raises(FileNotFoundError, match=\"Chunk file not found\"):\n            self.chunker.merge_chunks([\"missing.wav\"], str(tmp_path / \"output.wav\"))\n\n    def test_chunk_duration_calculation(self):\n        \"\"\"Test chunk duration calculation.\"\"\"","chunkId":"tests/unit/test_audio_chunking.py:13"}],[2733,{"filePath":"tests/unit/test_audio_chunking.py","startLine":153,"endLine":165,"chunkText":"\n    def test_chunk_duration_calculation(self):\n        \"\"\"Test chunk duration calculation.\"\"\"\n        chunker_5s = AudioChunker(5.0, self.logger)\n        assert chunker_5s.chunk_duration_ms == 5000\n\n        chunker_60s = AudioChunker(60.0, self.logger)\n        assert chunker_60s.chunk_duration_ms == 60000\n\n        chunker_half = AudioChunker(0.5, self.logger)\n        assert chunker_half.chunk_duration_ms == 500\n\n","chunkId":"tests/unit/test_audio_chunking.py:14"}],[2734,{"filePath":"tests/unit/test_audio_chunking.py","startLine":166,"endLine":181,"chunkText":"class TestAudioChunkerIntegration:\n    \"\"\"Integration tests with actual audio segment creation.\"\"\"\n\n    def test_split_and_merge_round_trip(self):\n        \"\"\"Test splitting and merging produces valid output.\"\"\"\n        # Create a simple test audio segment (silence)\n        audio = AudioSegment.silent(duration=15000)  # 15 seconds\n\n        temp_dir = tempfile.mkdtemp()\n        try:\n            # Save test audio\n            input_path = os.path.join(temp_dir, \"test_input.wav\")\n            audio.export(input_path, format=\"wav\")\n\n            # Split\n            chunker = AudioChunker(5.0)  # 5-second chunks","chunkId":"tests/unit/test_audio_chunking.py:15"}],[2735,{"filePath":"tests/unit/test_audio_chunking.py","startLine":179,"endLine":193,"chunkText":"\n            # Split\n            chunker = AudioChunker(5.0)  # 5-second chunks\n            chunk_dir = os.path.join(temp_dir, \"chunks\")\n            chunk_paths = chunker.split_audio(input_path, chunk_dir)\n\n            # Should create 3 chunks\n            assert len(chunk_paths) == 3\n            assert all(os.path.exists(path) for path in chunk_paths)\n\n            # Merge\n            output_path = os.path.join(temp_dir, \"test_output.wav\")\n            merged_path = chunker.merge_chunks(chunk_paths, output_path)\n\n            # Verify output exists and has similar duration","chunkId":"tests/unit/test_audio_chunking.py:16"}],[2736,{"filePath":"tests/unit/test_audio_chunking.py","startLine":191,"endLine":206,"chunkText":"            merged_path = chunker.merge_chunks(chunk_paths, output_path)\n\n            # Verify output exists and has similar duration\n            assert os.path.exists(merged_path)\n            merged_audio = AudioSegment.from_file(merged_path)\n\n            # Duration should be close (within 100ms due to encoding)\n            assert abs(len(merged_audio) - len(audio)) < 100\n\n        finally:\n            # Cleanup\n            import shutil\n            if os.path.exists(temp_dir):\n                shutil.rmtree(temp_dir)\n\n    def test_split_with_different_formats(self):","chunkId":"tests/unit/test_audio_chunking.py:17"}],[2737,{"filePath":"tests/unit/test_audio_chunking.py","startLine":204,"endLine":219,"chunkText":"                shutil.rmtree(temp_dir)\n\n    def test_split_with_different_formats(self):\n        \"\"\"Test splitting works with different audio formats.\"\"\"\n        audio = AudioSegment.silent(duration=10000)  # 10 seconds\n\n        temp_dir = tempfile.mkdtemp()\n        try:\n            # Test with .wav extension\n            input_wav = os.path.join(temp_dir, \"test.wav\")\n            audio.export(input_wav, format=\"wav\")\n\n            chunker = AudioChunker(5.0)\n            chunk_dir = os.path.join(temp_dir, \"chunks_wav\")\n            chunk_paths = chunker.split_audio(input_wav, chunk_dir)\n","chunkId":"tests/unit/test_audio_chunking.py:18"}],[2738,{"filePath":"tests/unit/test_audio_chunking.py","startLine":217,"endLine":228,"chunkText":"            chunk_dir = os.path.join(temp_dir, \"chunks_wav\")\n            chunk_paths = chunker.split_audio(input_wav, chunk_dir)\n\n            assert len(chunk_paths) == 2\n            assert all(path.endswith(\".wav\") for path in chunk_paths)\n\n        finally:\n            import shutil\n            if os.path.exists(temp_dir):\n                shutil.rmtree(temp_dir)\n\n","chunkId":"tests/unit/test_audio_chunking.py:19"}],[2739,{"filePath":"tests/unit/test_audio_chunking.py","startLine":229,"endLine":244,"chunkText":"class TestAudioChunkerEdgeCases:\n    \"\"\"Test edge cases and error handling.\"\"\"\n\n    def test_very_short_file(self):\n        \"\"\"Test chunking very short file (shorter than chunk duration).\"\"\"\n        audio = AudioSegment.silent(duration=2000)  # 2 seconds\n\n        temp_dir = tempfile.mkdtemp()\n        try:\n            input_path = os.path.join(temp_dir, \"short.wav\")\n            audio.export(input_path, format=\"wav\")\n\n            chunker = AudioChunker(10.0)  # 10-second chunks\n\n            # Should still work, creating just 1 chunk\n            chunk_dir = os.path.join(temp_dir, \"chunks\")","chunkId":"tests/unit/test_audio_chunking.py:20"}],[2740,{"filePath":"tests/unit/test_audio_chunking.py","startLine":242,"endLine":259,"chunkText":"\n            # Should still work, creating just 1 chunk\n            chunk_dir = os.path.join(temp_dir, \"chunks\")\n            chunk_paths = chunker.split_audio(input_path, chunk_dir)\n\n            assert len(chunk_paths) == 1\n\n        finally:\n            import shutil\n            if os.path.exists(temp_dir):\n                shutil.rmtree(temp_dir)\n\n    def test_exact_multiple_of_chunk_size(self):\n        \"\"\"Test file that's exact multiple of chunk size.\"\"\"\n        audio = AudioSegment.silent(duration=20000)  # 20 seconds\n\n        temp_dir = tempfile.mkdtemp()\n        try:","chunkId":"tests/unit/test_audio_chunking.py:21"}],[2741,{"filePath":"tests/unit/test_audio_chunking.py","startLine":257,"endLine":275,"chunkText":"\n        temp_dir = tempfile.mkdtemp()\n        try:\n            input_path = os.path.join(temp_dir, \"exact.wav\")\n            audio.export(input_path, format=\"wav\")\n\n            chunker = AudioChunker(10.0)  # 10-second chunks\n\n            chunk_dir = os.path.join(temp_dir, \"chunks\")\n            chunk_paths = chunker.split_audio(input_path, chunk_dir)\n\n            # Should create exactly 2 chunks\n            assert len(chunk_paths) == 2\n\n        finally:\n            import shutil\n            if os.path.exists(temp_dir):\n                shutil.rmtree(temp_dir)\n","chunkId":"tests/unit/test_audio_chunking.py:22"}],[2742,{"filePath":"tests/unit/test_bit_depth_detection.py","startLine":0,"endLine":22,"chunkText":"\"\"\"\nUnit tests for bit depth preservation functionality in CommonSeparator.\n\nTests the bit depth detection and storage logic without requiring full separation.\n\"\"\"\n\nimport os\nimport pytest\nimport tempfile\nimport shutil\nimport soundfile as sf\nimport numpy as np\nfrom unittest.mock import Mock, MagicMock, patch\n\nfrom audio_separator.separator.common_separator import CommonSeparator\n\n\ndef create_test_audio_file(output_path, sample_rate=44100, duration=0.5, bit_depth=16):\n    \"\"\"\n    Create a test audio file with a specific bit depth.\n    \n    Args:\n        output_path: Path to save the test audio file","chunkId":"tests/unit/test_bit_depth_detection.py:0"}],[2743,{"filePath":"tests/unit/test_bit_depth_detection.py","startLine":20,"endLine":37,"chunkText":"    \n    Args:\n        output_path: Path to save the test audio file\n        sample_rate: Sample rate in Hz\n        duration: Duration in seconds\n        bit_depth: Bit depth (16, 24, or 32)\n    \n    Returns:\n        Path to the created audio file\n    \"\"\"\n    # Generate a simple test signal (440 Hz sine wave)\n    t = np.linspace(0, duration, int(sample_rate * duration))\n    frequency = 440  # A4 note\n    audio = 0.5 * np.sin(2 * np.pi * frequency * t)\n    \n    # Create stereo by duplicating the mono signal\n    stereo_audio = np.column_stack([audio, audio])\n    ","chunkId":"tests/unit/test_bit_depth_detection.py:1"}],[2744,{"filePath":"tests/unit/test_bit_depth_detection.py","startLine":35,"endLine":55,"chunkText":"    # Create stereo by duplicating the mono signal\n    stereo_audio = np.column_stack([audio, audio])\n    \n    # Determine the subtype based on bit depth\n    if bit_depth == 16:\n        subtype = 'PCM_16'\n    elif bit_depth == 24:\n        subtype = 'PCM_24'\n    elif bit_depth == 32:\n        subtype = 'PCM_32'\n    else:\n        raise ValueError(f\"Unsupported bit depth: {bit_depth}\")\n    \n    # Write the audio file\n    sf.write(output_path, stereo_audio, sample_rate, subtype=subtype)\n    \n    return output_path\n\n\n@pytest.fixture(name=\"temp_audio_dir\")\ndef fixture_temp_audio_dir():","chunkId":"tests/unit/test_bit_depth_detection.py:2"}],[2745,{"filePath":"tests/unit/test_bit_depth_detection.py","startLine":53,"endLine":72,"chunkText":"\n@pytest.fixture(name=\"temp_audio_dir\")\ndef fixture_temp_audio_dir():\n    \"\"\"Fixture providing a temporary directory for input audio files.\"\"\"\n    temp_dir = tempfile.mkdtemp()\n    yield temp_dir\n    # Clean up after test\n    shutil.rmtree(temp_dir)\n\n\n@pytest.fixture(name=\"mock_separator_config\")\ndef fixture_mock_separator_config():\n    \"\"\"Fixture providing a mock separator configuration.\"\"\"\n    return {\n        \"logger\": Mock(),\n        \"log_level\": 20,\n        \"torch_device\": Mock(),\n        \"torch_device_cpu\": Mock(),\n        \"torch_device_mps\": Mock(),\n        \"onnx_execution_provider\": Mock(),","chunkId":"tests/unit/test_bit_depth_detection.py:3"}],[2746,{"filePath":"tests/unit/test_bit_depth_detection.py","startLine":70,"endLine":88,"chunkText":"        \"torch_device_cpu\": Mock(),\n        \"torch_device_mps\": Mock(),\n        \"onnx_execution_provider\": Mock(),\n        \"model_name\": \"test_model\",\n        \"model_path\": \"/path/to/model\",\n        \"model_data\": {\"training\": {\"instruments\": [\"vocals\", \"other\"]}},\n        \"output_dir\": None,\n        \"output_format\": \"wav\",\n        \"output_bitrate\": None,\n        \"normalization_threshold\": 0.9,\n        \"amplification_threshold\": 0.0,\n        \"enable_denoise\": False,\n        \"output_single_stem\": None,\n        \"invert_using_spec\": False,\n        \"sample_rate\": 44100,\n        \"use_soundfile\": False,\n    }\n\n","chunkId":"tests/unit/test_bit_depth_detection.py:4"}],[2747,{"filePath":"tests/unit/test_bit_depth_detection.py","startLine":86,"endLine":103,"chunkText":"    }\n\n\ndef test_16bit_detection(temp_audio_dir, mock_separator_config):\n    \"\"\"Test that 16-bit audio files are correctly detected.\"\"\"\n    print(\"\\n>>> TEST: 16-bit detection\")\n    \n    # Create a 16-bit test audio file\n    input_file = os.path.join(temp_audio_dir, \"test_16bit.wav\")\n    create_test_audio_file(input_file, bit_depth=16)\n    \n    # Create CommonSeparator instance\n    separator = CommonSeparator(mock_separator_config)\n    \n    # Call prepare_mix to detect bit depth\n    mix = separator.prepare_mix(input_file)\n    \n    # Verify bit depth was detected correctly","chunkId":"tests/unit/test_bit_depth_detection.py:5"}],[2748,{"filePath":"tests/unit/test_bit_depth_detection.py","startLine":101,"endLine":113,"chunkText":"    mix = separator.prepare_mix(input_file)\n    \n    # Verify bit depth was detected correctly\n    print(f\"Detected bit depth: {separator.input_bit_depth}\")\n    print(f\"Detected subtype: {separator.input_subtype}\")\n    \n    assert separator.input_bit_depth == 16, f\"Expected 16-bit, got {separator.input_bit_depth}\"\n    assert 'PCM_16' in separator.input_subtype or separator.input_subtype == 'PCM_16', f\"Expected PCM_16, got {separator.input_subtype}\"\n    \n    print(\"✅ Test passed: 16-bit audio correctly detected\")\n\n\ndef test_24bit_detection(temp_audio_dir, mock_separator_config):","chunkId":"tests/unit/test_bit_depth_detection.py:6"}],[2749,{"filePath":"tests/unit/test_bit_depth_detection.py","startLine":111,"endLine":127,"chunkText":"\n\ndef test_24bit_detection(temp_audio_dir, mock_separator_config):\n    \"\"\"Test that 24-bit audio files are correctly detected.\"\"\"\n    print(\"\\n>>> TEST: 24-bit detection\")\n    \n    # Create a 24-bit test audio file\n    input_file = os.path.join(temp_audio_dir, \"test_24bit.wav\")\n    create_test_audio_file(input_file, bit_depth=24)\n    \n    # Create CommonSeparator instance\n    separator = CommonSeparator(mock_separator_config)\n    \n    # Call prepare_mix to detect bit depth\n    mix = separator.prepare_mix(input_file)\n    \n    # Verify bit depth was detected correctly","chunkId":"tests/unit/test_bit_depth_detection.py:7"}],[2750,{"filePath":"tests/unit/test_bit_depth_detection.py","startLine":125,"endLine":138,"chunkText":"    mix = separator.prepare_mix(input_file)\n    \n    # Verify bit depth was detected correctly\n    print(f\"Detected bit depth: {separator.input_bit_depth}\")\n    print(f\"Detected subtype: {separator.input_subtype}\")\n    \n    assert separator.input_bit_depth == 24, f\"Expected 24-bit, got {separator.input_bit_depth}\"\n    assert 'PCM_24' in separator.input_subtype, f\"Expected PCM_24, got {separator.input_subtype}\"\n    \n    print(\"✅ Test passed: 24-bit audio correctly detected\")\n\n\ndef test_32bit_detection(temp_audio_dir, mock_separator_config):\n    \"\"\"Test that 32-bit audio files are correctly detected.\"\"\"","chunkId":"tests/unit/test_bit_depth_detection.py:8"}],[2751,{"filePath":"tests/unit/test_bit_depth_detection.py","startLine":136,"endLine":151,"chunkText":"\ndef test_32bit_detection(temp_audio_dir, mock_separator_config):\n    \"\"\"Test that 32-bit audio files are correctly detected.\"\"\"\n    print(\"\\n>>> TEST: 32-bit detection\")\n    \n    # Create a 32-bit test audio file\n    input_file = os.path.join(temp_audio_dir, \"test_32bit.wav\")\n    create_test_audio_file(input_file, bit_depth=32)\n    \n    # Create CommonSeparator instance\n    separator = CommonSeparator(mock_separator_config)\n    \n    # Call prepare_mix to detect bit depth\n    mix = separator.prepare_mix(input_file)\n    \n    # Verify bit depth was detected correctly","chunkId":"tests/unit/test_bit_depth_detection.py:9"}],[2752,{"filePath":"tests/unit/test_bit_depth_detection.py","startLine":149,"endLine":161,"chunkText":"    mix = separator.prepare_mix(input_file)\n    \n    # Verify bit depth was detected correctly\n    print(f\"Detected bit depth: {separator.input_bit_depth}\")\n    print(f\"Detected subtype: {separator.input_subtype}\")\n    \n    assert separator.input_bit_depth == 32, f\"Expected 32-bit, got {separator.input_bit_depth}\"\n    assert 'PCM_32' in separator.input_subtype or 'FLOAT' in separator.input_subtype, f\"Expected PCM_32 or FLOAT, got {separator.input_subtype}\"\n    \n    print(\"✅ Test passed: 32-bit audio correctly detected\")\n\n\ndef test_numpy_array_input_defaults_to_16bit(mock_separator_config):","chunkId":"tests/unit/test_bit_depth_detection.py:10"}],[2753,{"filePath":"tests/unit/test_bit_depth_detection.py","startLine":159,"endLine":175,"chunkText":"\n\ndef test_numpy_array_input_defaults_to_16bit(mock_separator_config):\n    \"\"\"Test that numpy array input defaults to 16-bit.\"\"\"\n    print(\"\\n>>> TEST: Numpy array input defaults to 16-bit\")\n    \n    # Create a mock numpy array (stereo audio)\n    mock_audio = np.random.rand(1000, 2).astype(np.float32)\n    \n    # Create CommonSeparator instance\n    separator = CommonSeparator(mock_separator_config)\n    \n    # Call prepare_mix with numpy array\n    mix = separator.prepare_mix(mock_audio)\n    \n    # Verify bit depth defaults to 16-bit\n    print(f\"Bit depth for numpy input: {separator.input_bit_depth}\")","chunkId":"tests/unit/test_bit_depth_detection.py:11"}],[2754,{"filePath":"tests/unit/test_bit_depth_detection.py","startLine":173,"endLine":185,"chunkText":"    \n    # Verify bit depth defaults to 16-bit\n    print(f\"Bit depth for numpy input: {separator.input_bit_depth}\")\n    assert separator.input_bit_depth == 16, f\"Expected 16-bit default, got {separator.input_bit_depth}\"\n    \n    print(\"✅ Test passed: Numpy array input defaults to 16-bit\")\n\n\ndef test_bit_depth_preserved_across_multiple_files(temp_audio_dir, mock_separator_config):\n    \"\"\"Test that bit depth is correctly updated when processing multiple files.\"\"\"\n    print(\"\\n>>> TEST: Bit depth updated across multiple files\")\n    \n    # Create test files with different bit depths","chunkId":"tests/unit/test_bit_depth_detection.py:12"}],[2755,{"filePath":"tests/unit/test_bit_depth_detection.py","startLine":183,"endLine":197,"chunkText":"    print(\"\\n>>> TEST: Bit depth updated across multiple files\")\n    \n    # Create test files with different bit depths\n    input_16bit = os.path.join(temp_audio_dir, \"test_16bit.wav\")\n    input_24bit = os.path.join(temp_audio_dir, \"test_24bit.wav\")\n    \n    create_test_audio_file(input_16bit, bit_depth=16)\n    create_test_audio_file(input_24bit, bit_depth=24)\n    \n    # Create CommonSeparator instance\n    separator = CommonSeparator(mock_separator_config)\n    \n    # Process 16-bit file\n    mix1 = separator.prepare_mix(input_16bit)\n    assert separator.input_bit_depth == 16","chunkId":"tests/unit/test_bit_depth_detection.py:13"}],[2756,{"filePath":"tests/unit/test_bit_depth_detection.py","startLine":195,"endLine":209,"chunkText":"    # Process 16-bit file\n    mix1 = separator.prepare_mix(input_16bit)\n    assert separator.input_bit_depth == 16\n    print(f\"After 16-bit file: bit depth = {separator.input_bit_depth}\")\n    \n    # Process 24-bit file\n    mix2 = separator.prepare_mix(input_24bit)\n    assert separator.input_bit_depth == 24\n    print(f\"After 24-bit file: bit depth = {separator.input_bit_depth}\")\n    \n    # Process 16-bit file again\n    mix3 = separator.prepare_mix(input_16bit)\n    assert separator.input_bit_depth == 16\n    print(f\"After 16-bit file again: bit depth = {separator.input_bit_depth}\")\n    ","chunkId":"tests/unit/test_bit_depth_detection.py:14"}],[2757,{"filePath":"tests/unit/test_bit_depth_detection.py","startLine":207,"endLine":217,"chunkText":"    assert separator.input_bit_depth == 16\n    print(f\"After 16-bit file again: bit depth = {separator.input_bit_depth}\")\n    \n    print(\"✅ Test passed: Bit depth correctly updated across multiple files\")\n\n\nif __name__ == \"__main__\":\n    # Run tests with pytest\n    pytest.main([__file__, \"-v\", \"-s\"])\n\n","chunkId":"tests/unit/test_bit_depth_detection.py:15"}],[2758,{"filePath":"tests/unit/test_bit_depth_writing.py","startLine":0,"endLine":24,"chunkText":"\"\"\"\nUnit tests for bit depth preservation in audio writing functions.\n\nTests that the write_audio functions preserve the input bit depth.\n\"\"\"\n\nimport os\nimport pytest\nimport tempfile\nimport shutil\nimport soundfile as sf\nimport numpy as np\nfrom unittest.mock import Mock\n\nfrom audio_separator.separator.common_separator import CommonSeparator\n\n\n# Check if FFmpeg is available (required for pydub tests)\ndef is_ffmpeg_available():\n    \"\"\"Check if FFmpeg is available in the system.\"\"\"\n    import subprocess\n    try:\n        subprocess.run(\n            [\"ffmpeg\", \"-version\"],\n            stdout=subprocess.DEVNULL,","chunkId":"tests/unit/test_bit_depth_writing.py:0"}],[2759,{"filePath":"tests/unit/test_bit_depth_writing.py","startLine":22,"endLine":41,"chunkText":"        subprocess.run(\n            [\"ffmpeg\", \"-version\"],\n            stdout=subprocess.DEVNULL,\n            stderr=subprocess.DEVNULL,\n            check=True\n        )\n        return True\n    except (subprocess.CalledProcessError, FileNotFoundError):\n        return False\n\n\nFFMPEG_AVAILABLE = is_ffmpeg_available()\nrequires_ffmpeg = pytest.mark.skipif(\n    not FFMPEG_AVAILABLE,\n    reason=\"FFmpeg not available (required for pydub tests)\"\n)\n\n\ndef create_test_audio_file(output_path, sample_rate=44100, duration=0.5, bit_depth=16):\n    \"\"\"Create a test audio file with a specific bit depth.\"\"\"","chunkId":"tests/unit/test_bit_depth_writing.py:1"}],[2760,{"filePath":"tests/unit/test_bit_depth_writing.py","startLine":39,"endLine":55,"chunkText":"\ndef create_test_audio_file(output_path, sample_rate=44100, duration=0.5, bit_depth=16):\n    \"\"\"Create a test audio file with a specific bit depth.\"\"\"\n    t = np.linspace(0, duration, int(sample_rate * duration))\n    frequency = 440  # A4 note\n    audio = 0.5 * np.sin(2 * np.pi * frequency * t)\n    stereo_audio = np.column_stack([audio, audio])\n    \n    if bit_depth == 16:\n        subtype = 'PCM_16'\n    elif bit_depth == 24:\n        subtype = 'PCM_24'\n    elif bit_depth == 32:\n        subtype = 'PCM_32'\n    else:\n        raise ValueError(f\"Unsupported bit depth: {bit_depth}\")\n    ","chunkId":"tests/unit/test_bit_depth_writing.py:2"}],[2761,{"filePath":"tests/unit/test_bit_depth_writing.py","startLine":53,"endLine":75,"chunkText":"    else:\n        raise ValueError(f\"Unsupported bit depth: {bit_depth}\")\n    \n    sf.write(output_path, stereo_audio, sample_rate, subtype=subtype)\n    return output_path\n\n\ndef get_audio_bit_depth(file_path):\n    \"\"\"Get the bit depth of an audio file.\"\"\"\n    info = sf.info(file_path)\n    subtype = info.subtype\n    \n    if 'PCM_16' in subtype or subtype == 'PCM_S8':\n        return 16\n    elif 'PCM_24' in subtype:\n        return 24\n    elif 'PCM_32' in subtype or 'FLOAT' in subtype or 'DOUBLE' in subtype:\n        return 32\n    else:\n        return None\n\n\n@pytest.fixture(name=\"temp_dir\")","chunkId":"tests/unit/test_bit_depth_writing.py:3"}],[2762,{"filePath":"tests/unit/test_bit_depth_writing.py","startLine":74,"endLine":93,"chunkText":"\n@pytest.fixture(name=\"temp_dir\")\ndef fixture_temp_dir():\n    \"\"\"Fixture providing a temporary directory.\"\"\"\n    temp_dir = tempfile.mkdtemp()\n    yield temp_dir\n    shutil.rmtree(temp_dir)\n\n\n@pytest.fixture(name=\"mock_separator_config\")\ndef fixture_mock_separator_config(temp_dir):\n    \"\"\"Fixture providing a mock separator configuration.\"\"\"\n    return {\n        \"logger\": Mock(),\n        \"log_level\": 20,\n        \"torch_device\": Mock(),\n        \"torch_device_cpu\": Mock(),\n        \"torch_device_mps\": Mock(),\n        \"onnx_execution_provider\": Mock(),\n        \"model_name\": \"test_model\",","chunkId":"tests/unit/test_bit_depth_writing.py:4"}],[2763,{"filePath":"tests/unit/test_bit_depth_writing.py","startLine":91,"endLine":109,"chunkText":"        \"torch_device_mps\": Mock(),\n        \"onnx_execution_provider\": Mock(),\n        \"model_name\": \"test_model\",\n        \"model_path\": \"/path/to/model\",\n        \"model_data\": {\"training\": {\"instruments\": [\"vocals\", \"other\"]}},\n        \"output_dir\": temp_dir,\n        \"output_format\": \"wav\",\n        \"output_bitrate\": None,\n        \"normalization_threshold\": 0.9,\n        \"amplification_threshold\": 0.0,\n        \"enable_denoise\": False,\n        \"output_single_stem\": None,\n        \"invert_using_spec\": False,\n        \"sample_rate\": 44100,\n        \"use_soundfile\": False,\n    }\n\n\n@requires_ffmpeg","chunkId":"tests/unit/test_bit_depth_writing.py:5"}],[2764,{"filePath":"tests/unit/test_bit_depth_writing.py","startLine":107,"endLine":122,"chunkText":"\n\n@requires_ffmpeg\ndef test_write_16bit_with_pydub(temp_dir, mock_separator_config):\n    \"\"\"Test that 16-bit audio is written correctly with pydub.\"\"\"\n    print(\"\\n>>> TEST: Write 16-bit audio with pydub\")\n    \n    # Create a 16-bit test input file\n    input_file = os.path.join(temp_dir, \"input_16bit.wav\")\n    create_test_audio_file(input_file, bit_depth=16)\n    \n    # Create CommonSeparator and prepare mix\n    separator = CommonSeparator(mock_separator_config)\n    separator.audio_file_path = input_file\n    mix = separator.prepare_mix(input_file)\n    ","chunkId":"tests/unit/test_bit_depth_writing.py:6"}],[2765,{"filePath":"tests/unit/test_bit_depth_writing.py","startLine":120,"endLine":134,"chunkText":"    separator.audio_file_path = input_file\n    mix = separator.prepare_mix(input_file)\n    \n    print(f\"Input bit depth detected: {separator.input_bit_depth}\")\n    \n    # Create output audio data (simulated separation output)\n    # The mix is in format [channels, samples], we need [samples, channels] for writing\n    output_audio = mix.T\n    \n    # Write audio using pydub\n    output_file = \"test_output_16bit.wav\"\n    separator.write_audio_pydub(output_file, output_audio)\n    \n    # Check the output file bit depth\n    full_output_path = os.path.join(temp_dir, output_file)","chunkId":"tests/unit/test_bit_depth_writing.py:7"}],[2766,{"filePath":"tests/unit/test_bit_depth_writing.py","startLine":132,"endLine":145,"chunkText":"    \n    # Check the output file bit depth\n    full_output_path = os.path.join(temp_dir, output_file)\n    assert os.path.exists(full_output_path), f\"Output file not created: {full_output_path}\"\n    \n    output_bit_depth = get_audio_bit_depth(full_output_path)\n    print(f\"Output bit depth: {output_bit_depth}\")\n    \n    assert output_bit_depth == 16, f\"Expected 16-bit output, got {output_bit_depth}\"\n    print(\"✅ Test passed: 16-bit audio written correctly with pydub\")\n\n\n@requires_ffmpeg\ndef test_write_24bit_with_pydub(temp_dir, mock_separator_config):","chunkId":"tests/unit/test_bit_depth_writing.py:8"}],[2767,{"filePath":"tests/unit/test_bit_depth_writing.py","startLine":143,"endLine":157,"chunkText":"\n@requires_ffmpeg\ndef test_write_24bit_with_pydub(temp_dir, mock_separator_config):\n    \"\"\"Test that 24-bit audio is written correctly with pydub.\"\"\"\n    print(\"\\n>>> TEST: Write 24-bit audio with pydub\")\n    \n    # Create a 24-bit test input file\n    input_file = os.path.join(temp_dir, \"input_24bit.wav\")\n    create_test_audio_file(input_file, bit_depth=24)\n    \n    # Create CommonSeparator and prepare mix\n    separator = CommonSeparator(mock_separator_config)\n    separator.audio_file_path = input_file\n    mix = separator.prepare_mix(input_file)\n    ","chunkId":"tests/unit/test_bit_depth_writing.py:9"}],[2768,{"filePath":"tests/unit/test_bit_depth_writing.py","startLine":155,"endLine":170,"chunkText":"    separator.audio_file_path = input_file\n    mix = separator.prepare_mix(input_file)\n    \n    print(f\"Input bit depth detected: {separator.input_bit_depth}\")\n    \n    # Create output audio data\n    output_audio = mix.T\n    \n    # Write audio using pydub\n    output_file = \"test_output_24bit.wav\"\n    separator.write_audio_pydub(output_file, output_audio)\n    \n    # Check the output file bit depth\n    full_output_path = os.path.join(temp_dir, output_file)\n    assert os.path.exists(full_output_path), f\"Output file not created: {full_output_path}\"\n    ","chunkId":"tests/unit/test_bit_depth_writing.py:10"}],[2769,{"filePath":"tests/unit/test_bit_depth_writing.py","startLine":168,"endLine":180,"chunkText":"    full_output_path = os.path.join(temp_dir, output_file)\n    assert os.path.exists(full_output_path), f\"Output file not created: {full_output_path}\"\n    \n    output_bit_depth = get_audio_bit_depth(full_output_path)\n    print(f\"Output bit depth: {output_bit_depth}\")\n    \n    assert output_bit_depth == 24, f\"Expected 24-bit output, got {output_bit_depth}\"\n    print(\"✅ Test passed: 24-bit audio written correctly with pydub\")\n\n\n@requires_ffmpeg\ndef test_write_32bit_with_pydub(temp_dir, mock_separator_config):\n    \"\"\"Test that 32-bit audio is written correctly with pydub.\"\"\"","chunkId":"tests/unit/test_bit_depth_writing.py:11"}],[2770,{"filePath":"tests/unit/test_bit_depth_writing.py","startLine":178,"endLine":191,"chunkText":"@requires_ffmpeg\ndef test_write_32bit_with_pydub(temp_dir, mock_separator_config):\n    \"\"\"Test that 32-bit audio is written correctly with pydub.\"\"\"\n    print(\"\\n>>> TEST: Write 32-bit audio with pydub\")\n    \n    # Create a 32-bit test input file\n    input_file = os.path.join(temp_dir, \"input_32bit.wav\")\n    create_test_audio_file(input_file, bit_depth=32)\n    \n    # Create CommonSeparator and prepare mix\n    separator = CommonSeparator(mock_separator_config)\n    separator.audio_file_path = input_file\n    mix = separator.prepare_mix(input_file)\n    ","chunkId":"tests/unit/test_bit_depth_writing.py:12"}],[2771,{"filePath":"tests/unit/test_bit_depth_writing.py","startLine":189,"endLine":204,"chunkText":"    separator.audio_file_path = input_file\n    mix = separator.prepare_mix(input_file)\n    \n    print(f\"Input bit depth detected: {separator.input_bit_depth}\")\n    \n    # Create output audio data\n    output_audio = mix.T\n    \n    # Write audio using pydub\n    output_file = \"test_output_32bit.wav\"\n    separator.write_audio_pydub(output_file, output_audio)\n    \n    # Check the output file bit depth\n    full_output_path = os.path.join(temp_dir, output_file)\n    assert os.path.exists(full_output_path), f\"Output file not created: {full_output_path}\"\n    ","chunkId":"tests/unit/test_bit_depth_writing.py:13"}],[2772,{"filePath":"tests/unit/test_bit_depth_writing.py","startLine":202,"endLine":213,"chunkText":"    full_output_path = os.path.join(temp_dir, output_file)\n    assert os.path.exists(full_output_path), f\"Output file not created: {full_output_path}\"\n    \n    output_bit_depth = get_audio_bit_depth(full_output_path)\n    print(f\"Output bit depth: {output_bit_depth}\")\n    \n    assert output_bit_depth == 32, f\"Expected 32-bit output, got {output_bit_depth}\"\n    print(\"✅ Test passed: 32-bit audio written correctly with pydub\")\n\n\ndef test_write_24bit_with_soundfile(temp_dir, mock_separator_config):\n    \"\"\"Test that 24-bit audio is written correctly with soundfile.\"\"\"","chunkId":"tests/unit/test_bit_depth_writing.py:14"}],[2773,{"filePath":"tests/unit/test_bit_depth_writing.py","startLine":211,"endLine":225,"chunkText":"\ndef test_write_24bit_with_soundfile(temp_dir, mock_separator_config):\n    \"\"\"Test that 24-bit audio is written correctly with soundfile.\"\"\"\n    print(\"\\n>>> TEST: Write 24-bit audio with soundfile\")\n    \n    # Update config to use soundfile\n    mock_separator_config[\"use_soundfile\"] = True\n    \n    # Create a 24-bit test input file\n    input_file = os.path.join(temp_dir, \"input_24bit.wav\")\n    create_test_audio_file(input_file, bit_depth=24)\n    \n    # Create CommonSeparator and prepare mix\n    separator = CommonSeparator(mock_separator_config)\n    separator.audio_file_path = input_file","chunkId":"tests/unit/test_bit_depth_writing.py:15"}],[2774,{"filePath":"tests/unit/test_bit_depth_writing.py","startLine":223,"endLine":238,"chunkText":"    # Create CommonSeparator and prepare mix\n    separator = CommonSeparator(mock_separator_config)\n    separator.audio_file_path = input_file\n    mix = separator.prepare_mix(input_file)\n    \n    print(f\"Input bit depth detected: {separator.input_bit_depth}\")\n    \n    # Create output audio data\n    output_audio = mix.T\n    \n    # Write audio using soundfile\n    output_file = \"test_output_24bit_sf.wav\"\n    separator.write_audio_soundfile(output_file, output_audio)\n    \n    # Check the output file bit depth\n    full_output_path = os.path.join(temp_dir, output_file)","chunkId":"tests/unit/test_bit_depth_writing.py:16"}],[2775,{"filePath":"tests/unit/test_bit_depth_writing.py","startLine":236,"endLine":248,"chunkText":"    \n    # Check the output file bit depth\n    full_output_path = os.path.join(temp_dir, output_file)\n    assert os.path.exists(full_output_path), f\"Output file not created: {full_output_path}\"\n    \n    output_bit_depth = get_audio_bit_depth(full_output_path)\n    print(f\"Output bit depth: {output_bit_depth}\")\n    \n    assert output_bit_depth == 24, f\"Expected 24-bit output, got {output_bit_depth}\"\n    print(\"✅ Test passed: 24-bit audio written correctly with soundfile\")\n\n\ndef test_write_16bit_with_soundfile(temp_dir, mock_separator_config):","chunkId":"tests/unit/test_bit_depth_writing.py:17"}],[2776,{"filePath":"tests/unit/test_bit_depth_writing.py","startLine":246,"endLine":261,"chunkText":"\n\ndef test_write_16bit_with_soundfile(temp_dir, mock_separator_config):\n    \"\"\"Test that 16-bit audio is written correctly with soundfile.\"\"\"\n    print(\"\\n>>> TEST: Write 16-bit audio with soundfile\")\n    \n    # Update config to use soundfile\n    mock_separator_config[\"use_soundfile\"] = True\n    \n    # Create a 16-bit test input file\n    input_file = os.path.join(temp_dir, \"input_16bit.wav\")\n    create_test_audio_file(input_file, bit_depth=16)\n    \n    # Create CommonSeparator and prepare mix\n    separator = CommonSeparator(mock_separator_config)\n    separator.audio_file_path = input_file","chunkId":"tests/unit/test_bit_depth_writing.py:18"}],[2777,{"filePath":"tests/unit/test_bit_depth_writing.py","startLine":259,"endLine":274,"chunkText":"    # Create CommonSeparator and prepare mix\n    separator = CommonSeparator(mock_separator_config)\n    separator.audio_file_path = input_file\n    mix = separator.prepare_mix(input_file)\n    \n    print(f\"Input bit depth detected: {separator.input_bit_depth}\")\n    \n    # Create output audio data\n    output_audio = mix.T\n    \n    # Write audio using soundfile\n    output_file = \"test_output_16bit_sf.wav\"\n    separator.write_audio_soundfile(output_file, output_audio)\n    \n    # Check the output file bit depth\n    full_output_path = os.path.join(temp_dir, output_file)","chunkId":"tests/unit/test_bit_depth_writing.py:19"}],[2778,{"filePath":"tests/unit/test_bit_depth_writing.py","startLine":272,"endLine":288,"chunkText":"    \n    # Check the output file bit depth\n    full_output_path = os.path.join(temp_dir, output_file)\n    assert os.path.exists(full_output_path), f\"Output file not created: {full_output_path}\"\n    \n    output_bit_depth = get_audio_bit_depth(full_output_path)\n    print(f\"Output bit depth: {output_bit_depth}\")\n    \n    assert output_bit_depth == 16, f\"Expected 16-bit output, got {output_bit_depth}\"\n    print(\"✅ Test passed: 16-bit audio written correctly with soundfile\")\n\n\nif __name__ == \"__main__\":\n    # Run tests with pytest\n    pytest.main([__file__, \"-v\", \"-s\"])\n\n","chunkId":"tests/unit/test_bit_depth_writing.py:20"}],[2779,{"filePath":"tests/unit/test_cli.py","startLine":0,"endLine":20,"chunkText":"import json\nimport pytest\nimport logging\nfrom audio_separator.utils.cli import main\nimport subprocess\nfrom unittest import mock\nfrom unittest.mock import patch, MagicMock, mock_open\n\n\n# Common fixture for expected arguments\n@pytest.fixture\ndef common_expected_args():\n    return {\n        \"log_formatter\": mock.ANY,\n        \"log_level\": logging.INFO,\n        \"model_file_dir\": \"/tmp/audio-separator-models/\",\n        \"output_dir\": None,\n        \"output_format\": \"FLAC\",\n        \"output_bitrate\": None,\n        \"normalization_threshold\": 0.9,\n        \"amplification_threshold\": 0.0,","chunkId":"tests/unit/test_cli.py:0"}],[2780,{"filePath":"tests/unit/test_cli.py","startLine":19,"endLine":28,"chunkText":"        \"normalization_threshold\": 0.9,\n        \"amplification_threshold\": 0.0,\n        \"output_single_stem\": None,\n        \"invert_using_spec\": False,\n        \"sample_rate\": 44100,\n        \"use_soundfile\": False,\n        \"use_autocast\": False,\n        \"chunk_duration\": None,\n        \"mdx_params\": {\"hop_length\": 1024, \"segment_size\": 256, \"overlap\": 0.25, \"batch_size\": 1, \"enable_denoise\": False},\n        \"vr_params\": {\"batch_size\": 1, \"window_size\": 512, \"aggression\": 5, \"enable_tta\": False, \"enable_post_process\": False, \"post_process_threshold\": 0.2, \"high_end_process\": False},","chunkId":"tests/unit/test_cli.py:1"}],[2781,{"filePath":"tests/unit/test_cli.py","startLine":26,"endLine":33,"chunkText":"        \"chunk_duration\": None,\n        \"mdx_params\": {\"hop_length\": 1024, \"segment_size\": 256, \"overlap\": 0.25, \"batch_size\": 1, \"enable_denoise\": False},\n        \"vr_params\": {\"batch_size\": 1, \"window_size\": 512, \"aggression\": 5, \"enable_tta\": False, \"enable_post_process\": False, \"post_process_threshold\": 0.2, \"high_end_process\": False},\n        \"demucs_params\": {\"segment_size\": \"Default\", \"shifts\": 2, \"overlap\": 0.25, \"segments_enabled\": True},\n        \"mdxc_params\": {\"segment_size\": 256, \"batch_size\": 1, \"overlap\": 8, \"override_model_segment_size\": False, \"pitch_shift\": 0},\n    }\n\n","chunkId":"tests/unit/test_cli.py:2"}],[2782,{"filePath":"tests/unit/test_cli.py","startLine":31,"endLine":49,"chunkText":"    }\n\n\n# Test the CLI with version argument using subprocess\ndef test_cli_version_subprocess():\n    # Skip subprocess CLI tests - require proper CLI installation\n    pytest.skip(\"CLI subprocess tests require proper installation\")\n\n\n# Test the CLI with no arguments\ndef test_cli_no_args(capsys):\n    # Skip subprocess CLI tests - require proper CLI installation  \n    pytest.skip(\"CLI subprocess tests require proper installation\")\n\n\n# Test with multiple filename arguments\ndef test_cli_multiple_filenames():\n    test_args = [\"cli.py\", \"test1.mp3\", \"test2.mp3\"]\n","chunkId":"tests/unit/test_cli.py:3"}],[2783,{"filePath":"tests/unit/test_cli.py","startLine":47,"endLine":60,"chunkText":"def test_cli_multiple_filenames():\n    test_args = [\"cli.py\", \"test1.mp3\", \"test2.mp3\"]\n\n    # Mock the open function to prevent actual file operations\n    mock_file = mock_open()\n\n    # Create a mock logger\n    mock_logger = MagicMock()\n\n    # Patch multiple functions to prevent actual file operations and separations\n    with patch(\"sys.argv\", test_args), patch(\"builtins.open\", mock_file), patch(\"audio_separator.separator.Separator.separate\") as mock_separate, patch(\n        \"audio_separator.separator.Separator.load_model\"\n    ), patch(\"logging.getLogger\", return_value=mock_logger):\n","chunkId":"tests/unit/test_cli.py:4"}],[2784,{"filePath":"tests/unit/test_cli.py","startLine":58,"endLine":72,"chunkText":"        \"audio_separator.separator.Separator.load_model\"\n    ), patch(\"logging.getLogger\", return_value=mock_logger):\n\n        # Mock the separate method to return some dummy output\n        mock_separate.return_value = [\"output_file1.mp3\", \"output_file2.mp3\"]\n\n        # Call the main function\n        main()\n\n        mock_separate.assert_called_once()\n        args, kwargs = mock_separate.call_args\n        assert args[0] == [\"test1.mp3\", \"test2.mp3\"]\n\n        # Check if the logger captured information about both files\n        log_messages = [call[0][0] for call in mock_logger.info.call_args_list]","chunkId":"tests/unit/test_cli.py:5"}],[2785,{"filePath":"tests/unit/test_cli.py","startLine":70,"endLine":80,"chunkText":"\n        # Check if the logger captured information about both files\n        log_messages = [call[0][0] for call in mock_logger.info.call_args_list]\n        assert any(\"test1.mp3\" in msg and \"test2.mp3\" in msg for msg in log_messages)\n        assert any(\"Separation complete\" in msg for msg in log_messages)\n\n\n# Test the CLI with a specific audio file\ndef test_cli_with_audio_file(capsys, common_expected_args):\n    test_args = [\"cli.py\", \"test_audio.mp3\", \"--model_filename=UVR-MDX-NET-Inst_HQ_4.onnx\"]\n    with patch(\"audio_separator.separator.Separator.separate\") as mock_separate:","chunkId":"tests/unit/test_cli.py:6"}],[2786,{"filePath":"tests/unit/test_cli.py","startLine":78,"endLine":89,"chunkText":"def test_cli_with_audio_file(capsys, common_expected_args):\n    test_args = [\"cli.py\", \"test_audio.mp3\", \"--model_filename=UVR-MDX-NET-Inst_HQ_4.onnx\"]\n    with patch(\"audio_separator.separator.Separator.separate\") as mock_separate:\n        mock_separate.return_value = [\"output_file.mp3\"]\n        with patch(\"sys.argv\", test_args):\n            # Call the main function in cli.py\n            main()\n\n    # Update expected args for this specific test\n    common_expected_args[\"model_file_dir\"] = \"/tmp/audio-separator-models/\"\n\n    # Check if the separate method was called with the correct arguments","chunkId":"tests/unit/test_cli.py:7"}],[2787,{"filePath":"tests/unit/test_cli.py","startLine":87,"endLine":105,"chunkText":"    common_expected_args[\"model_file_dir\"] = \"/tmp/audio-separator-models/\"\n\n    # Check if the separate method was called with the correct arguments\n    mock_separate.assert_called_once()\n\n    # Assertions\n    assert mock_separate.called\n\n\n# Test the CLI with invalid log level\ndef test_cli_invalid_log_level():\n    test_args = [\"cli.py\", \"test_audio.mp3\", \"--log_level=invalid\"]\n    with patch(\"sys.argv\", test_args):\n        # Assert an attribute error is raised due to the invalid LogLevel\n        with pytest.raises(AttributeError):\n            # Call the main function in cli.py\n            main()\n\n","chunkId":"tests/unit/test_cli.py:8"}],[2788,{"filePath":"tests/unit/test_cli.py","startLine":103,"endLine":116,"chunkText":"            main()\n\n\n# Test using model name argument\ndef test_cli_model_filename_argument(common_expected_args):\n    test_args = [\"cli.py\", \"test_audio.mp3\", \"--model_filename=Custom_Model.onnx\"]\n    with patch(\"sys.argv\", test_args):\n        with patch(\"audio_separator.separator.Separator\") as mock_separator:\n            mock_separator_instance = mock_separator.return_value\n            mock_separator_instance.separate.return_value = [\"output_file.mp3\"]\n            main()\n\n            # Assertions\n            mock_separator.assert_called_once_with(**common_expected_args)","chunkId":"tests/unit/test_cli.py:9"}],[2789,{"filePath":"tests/unit/test_cli.py","startLine":114,"endLine":125,"chunkText":"\n            # Assertions\n            mock_separator.assert_called_once_with(**common_expected_args)\n            mock_separator_instance.load_model.assert_called_once_with(model_filename=\"Custom_Model.onnx\")\n\n\n# Test using output directory argument\ndef test_cli_output_dir_argument(common_expected_args):\n    test_args = [\"cli.py\", \"test_audio.mp3\", \"--output_dir=/custom/output/dir\"]\n    with patch(\"sys.argv\", test_args):\n        with patch(\"audio_separator.separator.Separator\") as mock_separator:\n            mock_separator_instance = mock_separator.return_value","chunkId":"tests/unit/test_cli.py:10"}],[2790,{"filePath":"tests/unit/test_cli.py","startLine":123,"endLine":137,"chunkText":"    with patch(\"sys.argv\", test_args):\n        with patch(\"audio_separator.separator.Separator\") as mock_separator:\n            mock_separator_instance = mock_separator.return_value\n            mock_separator_instance.separate.return_value = [\"output_file.mp3\"]\n            main()\n\n            # Update expected args for this specific test\n            expected_args = common_expected_args.copy()\n            expected_args[\"output_dir\"] = \"/custom/output/dir\"\n\n            # Assertions\n            mock_separator.assert_called_once_with(**expected_args)\n\n\n# Test using output format argument","chunkId":"tests/unit/test_cli.py:11"}],[2791,{"filePath":"tests/unit/test_cli.py","startLine":135,"endLine":147,"chunkText":"\n\n# Test using output format argument\ndef test_cli_output_format_argument(common_expected_args):\n    test_args = [\"cli.py\", \"test_audio.mp3\", \"--output_format=MP3\"]\n    with patch(\"sys.argv\", test_args):\n        with patch(\"audio_separator.separator.Separator\") as mock_separator:\n            mock_separator_instance = mock_separator.return_value\n            mock_separator_instance.separate.return_value = [\"output_file.mp3\"]\n            main()\n\n            # Update expected args for this specific test\n            expected_args = common_expected_args.copy()","chunkId":"tests/unit/test_cli.py:12"}],[2792,{"filePath":"tests/unit/test_cli.py","startLine":145,"endLine":158,"chunkText":"\n            # Update expected args for this specific test\n            expected_args = common_expected_args.copy()\n            expected_args[\"output_format\"] = \"MP3\"\n\n            # Assertions\n            mock_separator.assert_called_once_with(**expected_args)\n\n\n# Test using normalization_threshold argument\ndef test_cli_normalization_threshold_argument(common_expected_args):\n    test_args = [\"cli.py\", \"test_audio.mp3\", \"--normalization=0.75\"]\n    with patch(\"sys.argv\", test_args):\n        with patch(\"audio_separator.separator.Separator\") as mock_separator:","chunkId":"tests/unit/test_cli.py:13"}],[2793,{"filePath":"tests/unit/test_cli.py","startLine":156,"endLine":167,"chunkText":"    test_args = [\"cli.py\", \"test_audio.mp3\", \"--normalization=0.75\"]\n    with patch(\"sys.argv\", test_args):\n        with patch(\"audio_separator.separator.Separator\") as mock_separator:\n            mock_separator_instance = mock_separator.return_value\n            mock_separator_instance.separate.return_value = [\"output_file.mp3\"]\n            main()\n\n            # Update expected args for this specific test\n            expected_args = common_expected_args.copy()\n            expected_args[\"normalization_threshold\"] = 0.75\n\n            # Assertions","chunkId":"tests/unit/test_cli.py:14"}],[2794,{"filePath":"tests/unit/test_cli.py","startLine":165,"endLine":177,"chunkText":"            expected_args[\"normalization_threshold\"] = 0.75\n\n            # Assertions\n            mock_separator.assert_called_once_with(**expected_args)\n\n\n# Test using amplification_threshold argument\ndef test_cli_amplification_threshold_argument(common_expected_args):\n    test_args = [\"cli.py\", \"test_audio.mp3\", \"--amplification=0.75\"]\n    with patch(\"sys.argv\", test_args):\n        with patch(\"audio_separator.separator.Separator\") as mock_separator:\n            mock_separator_instance = mock_separator.return_value\n            mock_separator_instance.separate.return_value = [\"output_file.mp3\"]","chunkId":"tests/unit/test_cli.py:15"}],[2795,{"filePath":"tests/unit/test_cli.py","startLine":175,"endLine":189,"chunkText":"        with patch(\"audio_separator.separator.Separator\") as mock_separator:\n            mock_separator_instance = mock_separator.return_value\n            mock_separator_instance.separate.return_value = [\"output_file.mp3\"]\n            main()\n\n            # Update expected args for this specific test\n            expected_args = common_expected_args.copy()\n            expected_args[\"amplification_threshold\"] = 0.75\n\n            # Assertions\n            mock_separator.assert_called_once_with(**expected_args)\n\n\n# Test using single stem argument\ndef test_cli_single_stem_argument(common_expected_args):","chunkId":"tests/unit/test_cli.py:16"}],[2796,{"filePath":"tests/unit/test_cli.py","startLine":187,"endLine":198,"chunkText":"\n# Test using single stem argument\ndef test_cli_single_stem_argument(common_expected_args):\n    test_args = [\"cli.py\", \"test_audio.mp3\", \"--single_stem=instrumental\"]\n    with patch(\"sys.argv\", test_args):\n        with patch(\"audio_separator.separator.Separator\") as mock_separator:\n            mock_separator_instance = mock_separator.return_value\n            mock_separator_instance.separate.return_value = [\"output_file.mp3\"]\n            main()\n\n            # Update expected args for this specific test\n            expected_args = common_expected_args.copy()","chunkId":"tests/unit/test_cli.py:17"}],[2797,{"filePath":"tests/unit/test_cli.py","startLine":196,"endLine":209,"chunkText":"\n            # Update expected args for this specific test\n            expected_args = common_expected_args.copy()\n            expected_args[\"output_single_stem\"] = \"instrumental\"\n\n            # Assertions\n            mock_separator.assert_called_once_with(**expected_args)\n\n\n# Test using invert spectrogram argument\ndef test_cli_invert_spectrogram_argument(common_expected_args):\n    test_args = [\"cli.py\", \"test_audio.mp3\", \"--invert_spect\"]\n    with patch(\"sys.argv\", test_args):\n        with patch(\"audio_separator.separator.Separator\") as mock_separator:","chunkId":"tests/unit/test_cli.py:18"}],[2798,{"filePath":"tests/unit/test_cli.py","startLine":207,"endLine":221,"chunkText":"    test_args = [\"cli.py\", \"test_audio.mp3\", \"--invert_spect\"]\n    with patch(\"sys.argv\", test_args):\n        with patch(\"audio_separator.separator.Separator\") as mock_separator:\n            mock_separator_instance = mock_separator.return_value\n            mock_separator_instance.separate.return_value = [\"output_file.mp3\"]\n            main()\n\n            # Update expected args for this specific test\n            expected_args = common_expected_args.copy()\n            expected_args[\"invert_using_spec\"] = True\n\n            # Assertions\n            mock_separator.assert_called_once_with(**expected_args)\n\n","chunkId":"tests/unit/test_cli.py:19"}],[2799,{"filePath":"tests/unit/test_cli.py","startLine":219,"endLine":231,"chunkText":"            mock_separator.assert_called_once_with(**expected_args)\n\n\n# Test using use_autocast argument\ndef test_cli_use_autocast_argument(common_expected_args):\n    test_args = [\"cli.py\", \"test_audio.mp3\", \"--use_autocast\"]\n    with patch(\"sys.argv\", test_args):\n        with patch(\"audio_separator.separator.Separator\") as mock_separator:\n            mock_separator_instance = mock_separator.return_value\n            mock_separator_instance.separate.return_value = [\"output_file.mp3\"]\n            main()\n\n            # Update expected args for this specific test","chunkId":"tests/unit/test_cli.py:20"}],[2800,{"filePath":"tests/unit/test_cli.py","startLine":229,"endLine":245,"chunkText":"            main()\n\n            # Update expected args for this specific test\n            expected_args = common_expected_args.copy()\n            expected_args[\"use_autocast\"] = True\n\n            # Assertions\n            mock_separator.assert_called_once_with(**expected_args)\n\n\n# Test using custom_output_names arguments\ndef test_cli_custom_output_names_argument(common_expected_args):\n    custom_names = {\n        \"Vocals\": \"vocals_output\",\n        \"Instrumental\": \"instrumental_output\",\n    }\n    test_args = [\"cli.py\", \"test_audio.mp3\", f\"--custom_output_names={json.dumps(custom_names)}\"]","chunkId":"tests/unit/test_cli.py:21"}],[2801,{"filePath":"tests/unit/test_cli.py","startLine":243,"endLine":253,"chunkText":"        \"Instrumental\": \"instrumental_output\",\n    }\n    test_args = [\"cli.py\", \"test_audio.mp3\", f\"--custom_output_names={json.dumps(custom_names)}\"]\n    with patch(\"sys.argv\", test_args):\n        with patch(\"audio_separator.separator.Separator\") as mock_separator:\n            mock_separator_instance = mock_separator.return_value\n            mock_separator_instance.separate.return_value = [\"output_file.mp3\"]\n            main()\n\n            # Assertions\n            mock_separator.assert_called_once_with(**common_expected_args)","chunkId":"tests/unit/test_cli.py:22"}],[2802,{"filePath":"tests/unit/test_cli.py","startLine":251,"endLine":266,"chunkText":"\n            # Assertions\n            mock_separator.assert_called_once_with(**common_expected_args)\n            mock_separator_instance.separate.assert_called_once_with([\"test_audio.mp3\"], custom_output_names=custom_names)\n\n\n# Test using custom_output_names arguments\ndef test_cli_demucs_output_names_argument(common_expected_args):\n    demucs_output_names = {\n        \"Vocals\": \"vocals_output\",\n        \"Drums\": \"drums_output\",\n        \"Bass\": \"bass_output\",\n        \"Other\": \"other_output\",\n        \"Guitar\": \"guitar_output\",\n        \"Piano\": \"piano_output\"\n    }","chunkId":"tests/unit/test_cli.py:23"}],[2803,{"filePath":"tests/unit/test_cli.py","startLine":264,"endLine":275,"chunkText":"        \"Guitar\": \"guitar_output\",\n        \"Piano\": \"piano_output\"\n    }\n    test_args = [\"cli.py\", \"test_audio.mp3\", f\"--custom_output_names={json.dumps(demucs_output_names)}\", \"--model_filename=htdemucs_6s.yaml\"]\n    with patch(\"sys.argv\", test_args):\n        with patch(\"audio_separator.separator.Separator\") as mock_separator:\n            mock_separator_instance = mock_separator.return_value\n            mock_separator_instance.separate.return_value = [\"output_file.mp3\"]\n            main()\n\n            # Assertions\n            mock_separator.assert_called_once_with(**common_expected_args)","chunkId":"tests/unit/test_cli.py:24"}],[2804,{"filePath":"tests/unit/test_cli.py","startLine":273,"endLine":277,"chunkText":"\n            # Assertions\n            mock_separator.assert_called_once_with(**common_expected_args)\n            mock_separator_instance.separate.assert_called_once_with([\"test_audio.mp3\"], custom_output_names=demucs_output_names)\n","chunkId":"tests/unit/test_cli.py:25"}],[2805,{"filePath":"tests/unit/test_configuration_normalizer.py","startLine":0,"endLine":16,"chunkText":"\"\"\"\nUnit tests for ConfigurationNormalizer methods.\nTests the configuration normalization and validation logic.\n\"\"\"\n\nimport pytest\nfrom unittest.mock import Mock, patch\n\n# Add the roformer module to path for imports\nimport sys\nimport os\n# Find project root dynamically\ncurrent_dir = os.path.dirname(os.path.abspath(__file__))\nproject_root = current_dir\n# Go up until we find the project root (contains audio_separator/ directory)\nwhile project_root and not os.path.exists(os.path.join(project_root, 'audio_separator')):\n    parent = os.path.dirname(project_root)","chunkId":"tests/unit/test_configuration_normalizer.py:0"}],[2806,{"filePath":"tests/unit/test_configuration_normalizer.py","startLine":14,"endLine":28,"chunkText":"# Go up until we find the project root (contains audio_separator/ directory)\nwhile project_root and not os.path.exists(os.path.join(project_root, 'audio_separator')):\n    parent = os.path.dirname(project_root)\n    if parent == project_root:  # Reached filesystem root\n        break\n    project_root = parent\n\nif project_root:\n    sys.path.append(project_root)\n\nfrom audio_separator.separator.roformer.configuration_normalizer import ConfigurationNormalizer\nfrom audio_separator.separator.roformer.parameter_validation_error import ParameterValidationError\n\n\nclass TestConfigurationNormalizer:","chunkId":"tests/unit/test_configuration_normalizer.py:1"}],[2807,{"filePath":"tests/unit/test_configuration_normalizer.py","startLine":26,"endLine":45,"chunkText":"\n\nclass TestConfigurationNormalizer:\n    \"\"\"Test cases for ConfigurationNormalizer class.\"\"\"\n    \n    def setup_method(self):\n        \"\"\"Set up test fixtures.\"\"\"\n        self.normalizer = ConfigurationNormalizer()\n    \n    def test_normalize_config_basic(self):\n        \"\"\"Test basic configuration normalization.\"\"\"\n        config = {\n            'dim': 512,\n            'depth': 12,\n            'freqs_per_bands': (2, 4, 8, 16, 32, 64)\n        }\n        \n        result = self.normalizer.normalize_config(config, \"bs_roformer\", apply_defaults=False, validate=False)\n        \n        assert result['dim'] == 512","chunkId":"tests/unit/test_configuration_normalizer.py:2"}],[2808,{"filePath":"tests/unit/test_configuration_normalizer.py","startLine":43,"endLine":56,"chunkText":"        result = self.normalizer.normalize_config(config, \"bs_roformer\", apply_defaults=False, validate=False)\n        \n        assert result['dim'] == 512\n        assert result['depth'] == 12\n        assert result['freqs_per_bands'] == (2, 4, 8, 16, 32, 64)\n    \n    def test_normalize_config_with_defaults(self):\n        \"\"\"Test configuration normalization with defaults applied.\"\"\"\n        config = {\n            'dim': 512,\n            'depth': 12,\n            'freqs_per_bands': (2, 4, 8, 16, 32, 64)\n        }\n        ","chunkId":"tests/unit/test_configuration_normalizer.py:3"}],[2809,{"filePath":"tests/unit/test_configuration_normalizer.py","startLine":54,"endLine":69,"chunkText":"            'freqs_per_bands': (2, 4, 8, 16, 32, 64)\n        }\n        \n        result = self.normalizer.normalize_config(config, \"bs_roformer\", apply_defaults=True, validate=False)\n        \n        # Original values preserved\n        assert result['dim'] == 512\n        assert result['depth'] == 12\n        assert result['freqs_per_bands'] == (2, 4, 8, 16, 32, 64)\n        \n        # Defaults applied\n        assert result['stereo'] is False\n        assert result['num_stems'] == 2\n        assert result['flash_attn'] is True\n        assert result['mlp_expansion_factor'] == 4\n    ","chunkId":"tests/unit/test_configuration_normalizer.py:4"}],[2810,{"filePath":"tests/unit/test_configuration_normalizer.py","startLine":67,"endLine":81,"chunkText":"        assert result['flash_attn'] is True\n        assert result['mlp_expansion_factor'] == 4\n    \n    def test_normalize_config_with_validation_valid(self):\n        \"\"\"Test configuration normalization with validation - valid config.\"\"\"\n        config = {\n            'dim': 512,\n            'depth': 12,\n            'freqs_per_bands': (2, 4, 8, 16, 32, 64)\n        }\n        \n        # Should not raise any exception\n        result = self.normalizer.normalize_config(config, \"bs_roformer\", apply_defaults=True, validate=True)\n        assert result is not None\n    ","chunkId":"tests/unit/test_configuration_normalizer.py:5"}],[2811,{"filePath":"tests/unit/test_configuration_normalizer.py","startLine":79,"endLine":92,"chunkText":"        result = self.normalizer.normalize_config(config, \"bs_roformer\", apply_defaults=True, validate=True)\n        assert result is not None\n    \n    def test_normalize_config_with_validation_invalid(self):\n        \"\"\"Test configuration normalization with validation - invalid config.\"\"\"\n        config = {\n            'dim': \"invalid\",  # Invalid type\n            'depth': 12\n            # Missing required 'freqs_per_bands'\n        }\n        \n        with pytest.raises(ParameterValidationError):\n            self.normalizer.normalize_config(config, \"bs_roformer\", apply_defaults=True, validate=True)\n    ","chunkId":"tests/unit/test_configuration_normalizer.py:6"}],[2812,{"filePath":"tests/unit/test_configuration_normalizer.py","startLine":90,"endLine":106,"chunkText":"        with pytest.raises(ParameterValidationError):\n            self.normalizer.normalize_config(config, \"bs_roformer\", apply_defaults=True, validate=True)\n    \n    def test_normalize_structure_flat_config(self):\n        \"\"\"Test structure normalization with flat configuration.\"\"\"\n        config = {\n            'dim': 512,\n            'depth': 12,\n            'sample_rate': 44100\n        }\n        \n        result = self.normalizer._normalize_structure(config, \"bs_roformer\")\n        \n        assert result['dim'] == 512\n        assert result['depth'] == 12\n        assert result['sample_rate'] == 44100\n    ","chunkId":"tests/unit/test_configuration_normalizer.py:7"}],[2813,{"filePath":"tests/unit/test_configuration_normalizer.py","startLine":104,"endLine":123,"chunkText":"        assert result['depth'] == 12\n        assert result['sample_rate'] == 44100\n    \n    def test_normalize_structure_nested_config(self):\n        \"\"\"Test structure normalization with nested configuration.\"\"\"\n        config = {\n            'model': {\n                'dim': 512,\n                'depth': 12\n            },\n            'training': {\n                'sample_rate': 44100,\n                'hop_length': 512\n            },\n            'inference': {\n                'dim_t': 1024,\n                'n_fft': 2048\n            }\n        }\n        ","chunkId":"tests/unit/test_configuration_normalizer.py:8"}],[2814,{"filePath":"tests/unit/test_configuration_normalizer.py","startLine":121,"endLine":138,"chunkText":"            }\n        }\n        \n        result = self.normalizer._normalize_structure(config, \"bs_roformer\")\n        \n        # Flattened model parameters\n        assert result['dim'] == 512\n        assert result['depth'] == 12\n        \n        # Extracted training/inference parameters\n        assert result['sample_rate'] == 44100\n        assert result['hop_length'] == 512\n        assert result['dim_t'] == 1024\n        assert result['n_fft'] == 2048\n    \n    def test_normalize_parameter_names_aliases(self):\n        \"\"\"Test parameter name normalization with aliases.\"\"\"\n        config = {","chunkId":"tests/unit/test_configuration_normalizer.py:9"}],[2815,{"filePath":"tests/unit/test_configuration_normalizer.py","startLine":136,"endLine":148,"chunkText":"    def test_normalize_parameter_names_aliases(self):\n        \"\"\"Test parameter name normalization with aliases.\"\"\"\n        config = {\n            'n_fft': 2048,  # Should become 'stft_n_fft'\n            'hop_length': 512,  # Should become 'stft_hop_length'\n            'n_heads': 8,  # Should become 'heads'\n            'expansion_factor': 4,  # Should become 'mlp_expansion_factor'\n            'freq_bands': (2, 4, 8, 16),  # Should become 'freqs_per_bands'\n            'n_mels': 64  # Should become 'num_bands'\n        }\n        \n        result = self.normalizer._normalize_parameter_names(config)\n        ","chunkId":"tests/unit/test_configuration_normalizer.py:10"}],[2816,{"filePath":"tests/unit/test_configuration_normalizer.py","startLine":146,"endLine":161,"chunkText":"        \n        result = self.normalizer._normalize_parameter_names(config)\n        \n        assert result['stft_n_fft'] == 2048\n        assert result['stft_hop_length'] == 512\n        assert result['heads'] == 8\n        assert result['mlp_expansion_factor'] == 4\n        assert result['freqs_per_bands'] == (2, 4, 8, 16)\n        assert result['num_bands'] == 64\n        \n        # Original names should not be present\n        assert 'n_fft' not in result\n        assert 'hop_length' not in result\n        assert 'n_heads' not in result\n    \n    def test_normalize_parameter_values_booleans(self):","chunkId":"tests/unit/test_configuration_normalizer.py:11"}],[2817,{"filePath":"tests/unit/test_configuration_normalizer.py","startLine":159,"endLine":175,"chunkText":"        assert 'n_heads' not in result\n    \n    def test_normalize_parameter_values_booleans(self):\n        \"\"\"Test parameter value normalization for booleans.\"\"\"\n        config = {\n            'stereo': 'true',\n            'flash_attn': 'false',\n            'sage_attention': '1',\n            'zero_dc': 'yes',\n            'use_torch_checkpoint': 'on',\n            'skip_connection': '0'\n        }\n        \n        result = self.normalizer._normalize_parameter_values(config, \"bs_roformer\")\n        \n        assert result['stereo'] is True\n        assert result['flash_attn'] is False","chunkId":"tests/unit/test_configuration_normalizer.py:12"}],[2818,{"filePath":"tests/unit/test_configuration_normalizer.py","startLine":173,"endLine":187,"chunkText":"        \n        assert result['stereo'] is True\n        assert result['flash_attn'] is False\n        assert result['sage_attention'] is True\n        assert result['zero_dc'] is True\n        assert result['use_torch_checkpoint'] is True\n        assert result['skip_connection'] is False\n    \n    def test_normalize_parameter_values_numbers(self):\n        \"\"\"Test parameter value normalization for numbers.\"\"\"\n        config = {\n            'dim': '512',\n            'depth': '12.0',  # Float string to int\n            'sample_rate': 44100.0,  # Float to int\n            'attn_dropout': '0.1',","chunkId":"tests/unit/test_configuration_normalizer.py:13"}],[2819,{"filePath":"tests/unit/test_configuration_normalizer.py","startLine":185,"endLine":201,"chunkText":"            'depth': '12.0',  # Float string to int\n            'sample_rate': 44100.0,  # Float to int\n            'attn_dropout': '0.1',\n            'ff_dropout': 0.2\n        }\n        \n        result = self.normalizer._normalize_parameter_values(config, \"bs_roformer\")\n        \n        assert result['dim'] == 512\n        assert result['depth'] == 12\n        assert result['sample_rate'] == 44100\n        assert result['attn_dropout'] == 0.1\n        assert result['ff_dropout'] == 0.2\n        \n        # Check types\n        assert isinstance(result['dim'], int)\n        assert isinstance(result['depth'], int)","chunkId":"tests/unit/test_configuration_normalizer.py:14"}],[2820,{"filePath":"tests/unit/test_configuration_normalizer.py","startLine":199,"endLine":210,"chunkText":"        # Check types\n        assert isinstance(result['dim'], int)\n        assert isinstance(result['depth'], int)\n        assert isinstance(result['sample_rate'], int)\n        assert isinstance(result['attn_dropout'], float)\n        assert isinstance(result['ff_dropout'], float)\n    \n    def test_normalize_parameter_values_tuples(self):\n        \"\"\"Test parameter value normalization for tuples/lists.\"\"\"\n        config = {\n            'freqs_per_bands': '[2, 4, 8, 16]',  # String representation\n            'freqs_per_bands_2': [2, 4, 8, 16],  # List to tuple","chunkId":"tests/unit/test_configuration_normalizer.py:15"}],[2821,{"filePath":"tests/unit/test_configuration_normalizer.py","startLine":208,"endLine":221,"chunkText":"        config = {\n            'freqs_per_bands': '[2, 4, 8, 16]',  # String representation\n            'freqs_per_bands_2': [2, 4, 8, 16],  # List to tuple\n            'freqs_per_bands_3': '(2,4,8,16)'  # String tuple\n        }\n        \n        result = self.normalizer._normalize_parameter_values(config, \"bs_roformer\")\n        \n        assert result['freqs_per_bands'] == (2, 4, 8, 16)\n        assert result['freqs_per_bands_2'] == (2, 4, 8, 16)\n        assert result['freqs_per_bands_3'] == (2, 4, 8, 16)\n        \n        # Check types are tuples\n        assert isinstance(result['freqs_per_bands'], tuple)","chunkId":"tests/unit/test_configuration_normalizer.py:16"}],[2822,{"filePath":"tests/unit/test_configuration_normalizer.py","startLine":219,"endLine":232,"chunkText":"        \n        # Check types are tuples\n        assert isinstance(result['freqs_per_bands'], tuple)\n        assert isinstance(result['freqs_per_bands_2'], tuple)\n        assert isinstance(result['freqs_per_bands_3'], tuple)\n    \n    def test_normalize_parameter_values_strings(self):\n        \"\"\"Test parameter value normalization for strings.\"\"\"\n        config = {\n            'norm': 'LAYER_NORM',  # Should become lowercase\n            'act': 'GELU',  # Should become lowercase\n            'mel_scale': 'HTK'  # Should become lowercase\n        }\n        ","chunkId":"tests/unit/test_configuration_normalizer.py:17"}],[2823,{"filePath":"tests/unit/test_configuration_normalizer.py","startLine":230,"endLine":243,"chunkText":"            'mel_scale': 'HTK'  # Should become lowercase\n        }\n        \n        result = self.normalizer._normalize_parameter_values(config, \"mel_band_roformer\")\n        \n        assert result['norm'] == 'layer_norm'\n        assert result['act'] == 'gelu'\n        assert result['mel_scale'] == 'htk'\n    \n    def test_detect_model_type_bs_roformer(self):\n        \"\"\"Test model type detection for BSRoformer.\"\"\"\n        configs = [\n            {'freqs_per_bands': (2, 4, 8, 16)},  # Direct indicator\n            {'model_type': 'bs_roformer'},  # Explicit type","chunkId":"tests/unit/test_configuration_normalizer.py:18"}],[2824,{"filePath":"tests/unit/test_configuration_normalizer.py","startLine":241,"endLine":254,"chunkText":"        configs = [\n            {'freqs_per_bands': (2, 4, 8, 16)},  # Direct indicator\n            {'model_type': 'bs_roformer'},  # Explicit type\n            {'type': 'BSRoformer'},  # Explicit type variant\n            {'architecture': 'bs-roformer'}  # Architecture field\n        ]\n        \n        for config in configs:\n            result = self.normalizer.detect_model_type(config)\n            assert result == \"bs_roformer\", f\"Failed for config: {config}\"\n    \n    def test_detect_model_type_mel_band_roformer(self):\n        \"\"\"Test model type detection for MelBandRoformer.\"\"\"\n        configs = [","chunkId":"tests/unit/test_configuration_normalizer.py:19"}],[2825,{"filePath":"tests/unit/test_configuration_normalizer.py","startLine":252,"endLine":264,"chunkText":"    def test_detect_model_type_mel_band_roformer(self):\n        \"\"\"Test model type detection for MelBandRoformer.\"\"\"\n        configs = [\n            {'num_bands': 64},  # Direct indicator\n            {'n_mels': 64},  # Alias\n            {'mel_bands': 64},  # Alias\n            {'model_type': 'mel_band_roformer'},  # Explicit type\n            {'type': 'MelBandRoformer'},  # Explicit type variant\n            {'architecture': 'mel-roformer'}  # Architecture field\n        ]\n        \n        for config in configs:\n            result = self.normalizer.detect_model_type(config)","chunkId":"tests/unit/test_configuration_normalizer.py:20"}],[2826,{"filePath":"tests/unit/test_configuration_normalizer.py","startLine":262,"endLine":276,"chunkText":"        \n        for config in configs:\n            result = self.normalizer.detect_model_type(config)\n            assert result == \"mel_band_roformer\", f\"Failed for config: {config}\"\n    \n    def test_detect_model_type_unknown(self):\n        \"\"\"Test model type detection for unknown configurations.\"\"\"\n        configs = [\n            {},  # Empty config\n            {'dim': 512, 'depth': 12},  # No specific indicators\n            {'model_type': 'unknown'}  # Unknown type\n        ]\n        \n        for config in configs:\n            result = self.normalizer.detect_model_type(config)","chunkId":"tests/unit/test_configuration_normalizer.py:21"}],[2827,{"filePath":"tests/unit/test_configuration_normalizer.py","startLine":274,"endLine":291,"chunkText":"        \n        for config in configs:\n            result = self.normalizer.detect_model_type(config)\n            assert result is None, f\"Should return None for config: {config}\"\n    \n    def test_normalize_from_file_path_bs_roformer(self):\n        \"\"\"Test normalization with file path detection - BSRoformer.\"\"\"\n        config = {\n            'dim': 512,\n            'depth': 12\n        }\n        \n        file_paths = [\n            '/path/to/bs_roformer_model.ckpt',\n            '/path/to/BS-Roformer-model.pth',\n            '/path/to/model_bs_roformer.bin'\n        ]\n        ","chunkId":"tests/unit/test_configuration_normalizer.py:22"}],[2828,{"filePath":"tests/unit/test_configuration_normalizer.py","startLine":289,"endLine":301,"chunkText":"            '/path/to/model_bs_roformer.bin'\n        ]\n        \n        for file_path in file_paths:\n            result = self.normalizer.normalize_from_file_path(\n                config, file_path, apply_defaults=True, validate=False\n            )\n            \n            # Should have BSRoformer defaults\n            assert 'freqs_per_bands' in result, f\"Failed for path: {file_path}\"\n            assert 'mask_estimator_depth' in result, f\"Failed for path: {file_path}\"\n    \n    def test_normalize_from_file_path_mel_band_roformer(self):","chunkId":"tests/unit/test_configuration_normalizer.py:23"}],[2829,{"filePath":"tests/unit/test_configuration_normalizer.py","startLine":299,"endLine":315,"chunkText":"            assert 'mask_estimator_depth' in result, f\"Failed for path: {file_path}\"\n    \n    def test_normalize_from_file_path_mel_band_roformer(self):\n        \"\"\"Test normalization with file path detection - MelBandRoformer.\"\"\"\n        config = {\n            'dim': 512,\n            'depth': 12\n        }\n        \n        file_paths = [\n            '/path/to/mel_band_roformer_model.ckpt',\n            '/path/to/MelBand-Roformer-model.pth',\n            '/path/to/model_mel_roformer.bin'\n        ]\n        \n        for file_path in file_paths:\n            result = self.normalizer.normalize_from_file_path(","chunkId":"tests/unit/test_configuration_normalizer.py:24"}],[2830,{"filePath":"tests/unit/test_configuration_normalizer.py","startLine":313,"endLine":328,"chunkText":"        \n        for file_path in file_paths:\n            result = self.normalizer.normalize_from_file_path(\n                config, file_path, apply_defaults=True, validate=False\n            )\n            \n            # Should have MelBandRoformer defaults\n            assert result['num_bands'] == 64, f\"Failed for path: {file_path}\"\n    \n    def test_normalize_from_file_path_default_fallback(self):\n        \"\"\"Test normalization with file path detection - default fallback.\"\"\"\n        config = {\n            'dim': 512,\n            'depth': 12\n        }\n        ","chunkId":"tests/unit/test_configuration_normalizer.py:25"}],[2831,{"filePath":"tests/unit/test_configuration_normalizer.py","startLine":326,"endLine":341,"chunkText":"            'depth': 12\n        }\n        \n        file_path = '/path/to/unknown_model.ckpt'\n        \n        with patch.object(self.normalizer, 'detect_model_type', return_value=None):\n            result = self.normalizer.normalize_from_file_path(\n                config, file_path, apply_defaults=True, validate=False\n            )\n            \n            # Should default to BSRoformer\n            assert 'freqs_per_bands' in result\n    \n    def test_normalization_preserves_original_config(self):\n        \"\"\"Test that normalization doesn't modify the original configuration.\"\"\"\n        original_config = {","chunkId":"tests/unit/test_configuration_normalizer.py:26"}],[2832,{"filePath":"tests/unit/test_configuration_normalizer.py","startLine":339,"endLine":353,"chunkText":"    def test_normalization_preserves_original_config(self):\n        \"\"\"Test that normalization doesn't modify the original configuration.\"\"\"\n        original_config = {\n            'dim': 512,\n            'depth': 12,\n            'n_fft': 2048  # Will be renamed to stft_n_fft\n        }\n        \n        # Keep a copy to compare\n        config_copy = original_config.copy()\n        \n        result = self.normalizer.normalize_config(original_config, \"bs_roformer\")\n        \n        # Original config should be unchanged\n        assert original_config == config_copy","chunkId":"tests/unit/test_configuration_normalizer.py:27"}],[2833,{"filePath":"tests/unit/test_configuration_normalizer.py","startLine":351,"endLine":365,"chunkText":"        \n        # Original config should be unchanged\n        assert original_config == config_copy\n        assert 'n_fft' in original_config  # Original name preserved\n        assert 'stft_n_fft' not in original_config\n        \n        # Result should have normalized names\n        assert 'stft_n_fft' in result\n        assert result['stft_n_fft'] == 2048\n    \n    def test_normalization_error_handling(self):\n        \"\"\"Test error handling during normalization.\"\"\"\n        # Test with invalid string values that can't be converted\n        config = {\n            'dim': 'not_a_number',","chunkId":"tests/unit/test_configuration_normalizer.py:28"}],[2834,{"filePath":"tests/unit/test_configuration_normalizer.py","startLine":363,"endLine":374,"chunkText":"        # Test with invalid string values that can't be converted\n        config = {\n            'dim': 'not_a_number',\n            'depth': 12,\n            'freqs_per_bands': 'invalid_tuple_string'\n        }\n        \n        # Should not crash, invalid values should be passed through for validator to catch\n        result = self.normalizer.normalize_config(config, \"bs_roformer\", validate=False)\n        \n        assert result['dim'] == 'not_a_number'  # Passed through unchanged\n        assert result['depth'] == 12  # Valid value normalized","chunkId":"tests/unit/test_configuration_normalizer.py:29"}],[2835,{"filePath":"tests/unit/test_configuration_normalizer.py","startLine":372,"endLine":386,"chunkText":"        \n        assert result['dim'] == 'not_a_number'  # Passed through unchanged\n        assert result['depth'] == 12  # Valid value normalized\n        assert result['freqs_per_bands'] == 'invalid_tuple_string'  # Passed through unchanged\n    \n    def test_comprehensive_normalization_workflow(self):\n        \"\"\"Test complete normalization workflow with complex configuration.\"\"\"\n        config = {\n            'model': {\n                'dim': '512',\n                'depth': '12.0',\n                'n_heads': '8'\n            },\n            'training': {\n                'sample_rate': 44100,","chunkId":"tests/unit/test_configuration_normalizer.py:30"}],[2836,{"filePath":"tests/unit/test_configuration_normalizer.py","startLine":384,"endLine":402,"chunkText":"            },\n            'training': {\n                'sample_rate': 44100,\n                'n_fft': 2048\n            },\n            'stereo': 'true',\n            'flash_attn': 'false',\n            'freq_bands': '[2, 4, 8, 16, 32, 64]',\n            'norm': 'LAYER_NORM'\n        }\n        \n        result = self.normalizer.normalize_config(config, \"bs_roformer\", apply_defaults=True, validate=False)\n        \n        # Structure flattened\n        assert result['dim'] == 512\n        assert result['depth'] == 12\n        assert result['sample_rate'] == 44100\n        \n        # Names normalized","chunkId":"tests/unit/test_configuration_normalizer.py:31"}],[2837,{"filePath":"tests/unit/test_configuration_normalizer.py","startLine":400,"endLine":419,"chunkText":"        assert result['sample_rate'] == 44100\n        \n        # Names normalized\n        assert result['heads'] == 8\n        assert result['stft_n_fft'] == 2048\n        assert result['freqs_per_bands'] == (2, 4, 8, 16, 32, 64)\n        \n        # Values normalized\n        assert result['stereo'] is True\n        assert result['flash_attn'] is False\n        assert result['norm'] == 'layer_norm'\n        \n        # Defaults applied\n        assert result['num_stems'] == 2\n        assert result['mlp_expansion_factor'] == 4\n\n\nif __name__ == \"__main__\":\n    pytest.main([__file__])\n","chunkId":"tests/unit/test_configuration_normalizer.py:32"}],[2838,{"filePath":"tests/unit/test_mdxc_roformer_chunking.py","startLine":0,"endLine":22,"chunkText":"\"\"\"\nUnit tests for MDXC Roformer chunking and overlap logic.\nTests the chunking mechanism, overlap handling, and edge cases.\n\"\"\"\n\nimport pytest\nimport numpy as np\nimport torch\nfrom unittest.mock import Mock, MagicMock, patch\nimport logging\n\n\nclass TestMDXCRoformerChunking:\n    \"\"\"Test cases for MDXC Roformer chunking and overlap functionality.\"\"\"\n\n    def setup_method(self):\n        \"\"\"Set up test fixtures.\"\"\"\n        self.sample_rate = 44100\n        self.audio_length = 132300  # 3 seconds\n        self.chunk_size = 8192\n        self.hop_length = 1024\n        \n        # Mock model with stft_hop_length","chunkId":"tests/unit/test_mdxc_roformer_chunking.py:0"}],[2839,{"filePath":"tests/unit/test_mdxc_roformer_chunking.py","startLine":20,"endLine":34,"chunkText":"        self.hop_length = 1024\n        \n        # Mock model with stft_hop_length\n        self.mock_model = Mock()\n        self.mock_model.stft_hop_length = self.hop_length\n        \n        # Mock audio object\n        self.mock_audio = Mock()\n        self.mock_audio.hop_length = 512  # Different from model for fallback test\n\n    def test_chunk_size_uses_model_stft_hop_length(self):\n        \"\"\"T051: Assert chunk_size uses model.stft_hop_length.\"\"\"\n        # Test implementation for chunking optimization - placeholder for future implementation\n        pytest.skip(\"Chunking optimization not yet implemented\")\n","chunkId":"tests/unit/test_mdxc_roformer_chunking.py:1"}],[2840,{"filePath":"tests/unit/test_mdxc_roformer_chunking.py","startLine":32,"endLine":41,"chunkText":"        # Test implementation for chunking optimization - placeholder for future implementation\n        pytest.skip(\"Chunking optimization not yet implemented\")\n\n    def test_chunk_size_falls_back_to_audio_hop_length(self):\n        \"\"\"T052: Fallback to audio.hop_length if model.stft_hop_length missing.\"\"\"\n        # Test implementation for chunking optimization - placeholder for future implementation\n        pytest.skip(\"Chunking optimization not yet implemented\")\n\n    def test_step_clamped_to_chunk_size(self):\n        \"\"\"T053: Step clamped to chunk_size (desired_step > chunk_size or ≤ 0).\"\"\"","chunkId":"tests/unit/test_mdxc_roformer_chunking.py:2"}],[2841,{"filePath":"tests/unit/test_mdxc_roformer_chunking.py","startLine":39,"endLine":53,"chunkText":"\n    def test_step_clamped_to_chunk_size(self):\n        \"\"\"T053: Step clamped to chunk_size (desired_step > chunk_size or ≤ 0).\"\"\"\n        chunk_size = 8192\n        \n        # Test case 1: desired_step > chunk_size\n        desired_step_too_large = 10000\n        actual_step = min(desired_step_too_large, chunk_size)\n        assert actual_step == chunk_size\n        \n        # Test case 2: desired_step ≤ 0\n        desired_step_zero = 0\n        actual_step = max(desired_step_zero, chunk_size // 4)  # Use quarter chunk as minimum\n        assert actual_step == chunk_size // 4\n        ","chunkId":"tests/unit/test_mdxc_roformer_chunking.py:3"}],[2842,{"filePath":"tests/unit/test_mdxc_roformer_chunking.py","startLine":51,"endLine":64,"chunkText":"        actual_step = max(desired_step_zero, chunk_size // 4)  # Use quarter chunk as minimum\n        assert actual_step == chunk_size // 4\n        \n        # Test case 3: desired_step negative\n        desired_step_negative = -100\n        actual_step = max(desired_step_negative, chunk_size // 4)\n        assert actual_step == chunk_size // 4\n        \n        # Test case 4: valid desired_step\n        desired_step_valid = 4096\n        actual_step = min(max(desired_step_valid, 1), chunk_size)\n        assert actual_step == desired_step_valid\n\n    def test_overlap_add_short_output_safe(self):","chunkId":"tests/unit/test_mdxc_roformer_chunking.py:4"}],[2843,{"filePath":"tests/unit/test_mdxc_roformer_chunking.py","startLine":62,"endLine":75,"chunkText":"        assert actual_step == desired_step_valid\n\n    def test_overlap_add_short_output_safe(self):\n        \"\"\"T054: overlap_add handles shorter model output safely (safe_len).\"\"\"\n        # Create mock tensors\n        chunk_size = 8192\n        model_output_length = 6000  # Shorter than expected\n        \n        # Mock overlap_add logic\n        def mock_overlap_add_safe(output, target, start_idx, safe_len):\n            \"\"\"Mock overlap add with safe length handling.\"\"\"\n            actual_len = min(output.shape[-1], safe_len)\n            end_idx = start_idx + actual_len\n            ","chunkId":"tests/unit/test_mdxc_roformer_chunking.py:5"}],[2844,{"filePath":"tests/unit/test_mdxc_roformer_chunking.py","startLine":73,"endLine":87,"chunkText":"            actual_len = min(output.shape[-1], safe_len)\n            end_idx = start_idx + actual_len\n            \n            # Ensure we don't go beyond target bounds\n            if end_idx > target.shape[-1]:\n                end_idx = target.shape[-1]\n                actual_len = end_idx - start_idx\n            \n            if actual_len > 0:\n                target[..., start_idx:end_idx] += output[..., :actual_len]\n            \n            return actual_len\n        \n        # Test with shorter output\n        output = torch.randn(2, model_output_length)  # Shorter than chunk_size","chunkId":"tests/unit/test_mdxc_roformer_chunking.py:6"}],[2845,{"filePath":"tests/unit/test_mdxc_roformer_chunking.py","startLine":85,"endLine":101,"chunkText":"        \n        # Test with shorter output\n        output = torch.randn(2, model_output_length)  # Shorter than chunk_size\n        target = torch.zeros(2, 20000)\n        start_idx = 1000\n        safe_len = chunk_size\n        \n        actual_added = mock_overlap_add_safe(output, target, start_idx, safe_len)\n        \n        # Should handle shorter output gracefully\n        assert actual_added == model_output_length\n        assert actual_added < safe_len\n        \n        # Verify no out-of-bounds access\n        assert start_idx + actual_added <= target.shape[-1]\n\n    def test_counter_updates_safe_len(self)","chunkId":"tests/unit/test_mdxc_roformer_chunking.py:7"}],[2846,{"filePath":"tests/unit/test_mdxc_roformer_chunking.py","startLine":99,"endLine":112,"chunkText":"        assert start_idx + actual_added <= target.shape[-1]\n\n    def test_counter_updates_safe_len(self):\n        \"\"\"T055: Counter increments match overlap_add safe span.\"\"\"\n        # Mock counter and overlap_add logic\n        counter = torch.zeros(2, 20000)\n        chunk_size = 8192\n        safe_len = 6000  # Shorter than chunk_size\n        start_idx = 1000\n        \n        def mock_update_counter_safe(counter, start_idx, safe_len):\n            \"\"\"Mock counter update that matches overlap_add safe span.\"\"\"\n            end_idx = start_idx + safe_len\n            if end_idx > counter.shape[-1]:","chunkId":"tests/unit/test_mdxc_roformer_chunking.py:8"}],[2847,{"filePath":"tests/unit/test_mdxc_roformer_chunking.py","startLine":110,"endLine":125,"chunkText":"            \"\"\"Mock counter update that matches overlap_add safe span.\"\"\"\n            end_idx = start_idx + safe_len\n            if end_idx > counter.shape[-1]:\n                end_idx = counter.shape[-1]\n                safe_len = end_idx - start_idx\n            \n            if safe_len > 0:\n                counter[..., start_idx:end_idx] += 1.0\n            \n            return safe_len\n        \n        actual_updated = mock_update_counter_safe(counter, start_idx, safe_len)\n        \n        # Counter increment should match the safe length used\n        assert actual_updated == safe_len\n        ","chunkId":"tests/unit/test_mdxc_roformer_chunking.py:9"}],[2848,{"filePath":"tests/unit/test_mdxc_roformer_chunking.py","startLine":123,"endLine":136,"chunkText":"        # Counter increment should match the safe length used\n        assert actual_updated == safe_len\n        \n        # Verify counter was updated correctly\n        expected_ones = counter[0, start_idx:start_idx + actual_updated]\n        assert torch.all(expected_ones == 1.0)\n        \n        # Verify areas outside weren't touched\n        before_area = counter[0, :start_idx]\n        after_area = counter[0, start_idx + actual_updated:]\n        assert torch.all(before_area == 0.0)\n        assert torch.all(after_area == 0.0)\n\n    def test_counter_clamp_no_nan(self):","chunkId":"tests/unit/test_mdxc_roformer_chunking.py:10"}],[2849,{"filePath":"tests/unit/test_mdxc_roformer_chunking.py","startLine":134,"endLine":144,"chunkText":"        assert torch.all(after_area == 0.0)\n\n    def test_counter_clamp_no_nan(self):\n        \"\"\"T056: No NaN/inf on normalization (counter clamp).\"\"\"\n        # Create counter with some zero values (potential division by zero)\n        counter = torch.tensor([[0.0, 1.0, 2.0, 0.0, 3.0, 0.0]], dtype=torch.float32)\n        output = torch.tensor([[1.0, 2.0, 4.0, 8.0, 6.0, 16.0]], dtype=torch.float32)\n        \n        # Mock normalization with counter clamping\n        def mock_normalize_with_clamp(output, counter, min_clamp=1e-8):\n            \"\"\"Mock normalization that clamps counter to avoid NaN/inf.\"\"\"","chunkId":"tests/unit/test_mdxc_roformer_chunking.py:11"}],[2850,{"filePath":"tests/unit/test_mdxc_roformer_chunking.py","startLine":142,"endLine":153,"chunkText":"        # Mock normalization with counter clamping\n        def mock_normalize_with_clamp(output, counter, min_clamp=1e-8):\n            \"\"\"Mock normalization that clamps counter to avoid NaN/inf.\"\"\"\n            clamped_counter = torch.clamp(counter, min=min_clamp)\n            normalized = output / clamped_counter\n            return normalized, clamped_counter\n        \n        normalized, clamped_counter = mock_normalize_with_clamp(output, counter)\n        \n        # Verify no NaN or inf values\n        assert not torch.any(torch.isnan(normalized))\n        assert not torch.any(torch.isinf(normalized))","chunkId":"tests/unit/test_mdxc_roformer_chunking.py:12"}],[2851,{"filePath":"tests/unit/test_mdxc_roformer_chunking.py","startLine":151,"endLine":164,"chunkText":"        # Verify no NaN or inf values\n        assert not torch.any(torch.isnan(normalized))\n        assert not torch.any(torch.isinf(normalized))\n        \n        # Verify clamping worked\n        assert torch.all(clamped_counter >= 1e-8)\n        \n        # Verify normalization is reasonable\n        assert torch.all(normalized >= 0)  # Should be positive\n        \n        # Test with all-zero counter (extreme case)\n        zero_counter = torch.zeros_like(counter)\n        normalized_zero, clamped_zero = mock_normalize_with_clamp(output, zero_counter)\n        ","chunkId":"tests/unit/test_mdxc_roformer_chunking.py:13"}],[2852,{"filePath":"tests/unit/test_mdxc_roformer_chunking.py","startLine":162,"endLine":174,"chunkText":"        zero_counter = torch.zeros_like(counter)\n        normalized_zero, clamped_zero = mock_normalize_with_clamp(output, zero_counter)\n        \n        assert not torch.any(torch.isnan(normalized_zero))\n        assert not torch.any(torch.isinf(normalized_zero))\n        assert torch.all(clamped_zero >= 1e-8)\n\n    def test_short_audio_last_block(self):\n        \"\"\"T057: Short-audio last-block path works and preserves length.\"\"\"\n        # Test with audio shorter than one chunk\n        short_audio_length = 4000  # Less than chunk_size (8192)\n        chunk_size = 8192\n        ","chunkId":"tests/unit/test_mdxc_roformer_chunking.py:14"}],[2853,{"filePath":"tests/unit/test_mdxc_roformer_chunking.py","startLine":172,"endLine":185,"chunkText":"        short_audio_length = 4000  # Less than chunk_size (8192)\n        chunk_size = 8192\n        \n        # Mock processing of short audio\n        def mock_process_short_audio(audio_length, chunk_size):\n            \"\"\"Mock processing that handles short audio specially.\"\"\"\n            if audio_length < chunk_size:\n                # Last block path: process entire audio as one chunk\n                return {\n                    'processed_length': audio_length,\n                    'num_chunks': 1,\n                    'last_block': True,\n                    'preserved_length': audio_length\n                }","chunkId":"tests/unit/test_mdxc_roformer_chunking.py:15"}],[2854,{"filePath":"tests/unit/test_mdxc_roformer_chunking.py","startLine":183,"endLine":197,"chunkText":"                    'last_block': True,\n                    'preserved_length': audio_length\n                }\n            else:\n                # Normal chunking path\n                num_chunks = (audio_length + chunk_size - 1) // chunk_size\n                return {\n                    'processed_length': audio_length,\n                    'num_chunks': num_chunks,\n                    'last_block': False,\n                    'preserved_length': audio_length\n                }\n        \n        result = mock_process_short_audio(short_audio_length, chunk_size)\n        ","chunkId":"tests/unit/test_mdxc_roformer_chunking.py:16"}],[2855,{"filePath":"tests/unit/test_mdxc_roformer_chunking.py","startLine":195,"endLine":209,"chunkText":"        \n        result = mock_process_short_audio(short_audio_length, chunk_size)\n        \n        # Verify last block path was taken\n        assert result['last_block'] is True\n        assert result['num_chunks'] == 1\n        \n        # Verify length preservation\n        assert result['processed_length'] == short_audio_length\n        assert result['preserved_length'] == short_audio_length\n        \n        # Test with normal-length audio for comparison\n        normal_audio_length = 20000\n        normal_result = mock_process_short_audio(normal_audio_length, chunk_size)\n        ","chunkId":"tests/unit/test_mdxc_roformer_chunking.py:17"}],[2856,{"filePath":"tests/unit/test_mdxc_roformer_chunking.py","startLine":207,"endLine":222,"chunkText":"        normal_audio_length = 20000\n        normal_result = mock_process_short_audio(normal_audio_length, chunk_size)\n        \n        assert normal_result['last_block'] is False\n        assert normal_result['num_chunks'] > 1\n        assert normal_result['preserved_length'] == normal_audio_length\n\n    @pytest.mark.parametrize(\"dim_t,hop_length\", [\n        (256, 512),\n        (512, 1024), \n        (1024, 2048),\n        (128, 256)\n    ])\n    def test_parametrized_shape_invariants(self, dim_t, hop_length):\n        \"\"\"T058: Parametrized invariants across dim_t and hop configs.\"\"\"\n        batch_size = 2","chunkId":"tests/unit/test_mdxc_roformer_chunking.py:18"}],[2857,{"filePath":"tests/unit/test_mdxc_roformer_chunking.py","startLine":220,"endLine":233,"chunkText":"    def test_parametrized_shape_invariants(self, dim_t, hop_length):\n        \"\"\"T058: Parametrized invariants across dim_t and hop configs.\"\"\"\n        batch_size = 2\n        audio_length = 44100  # 1 second\n        \n        # Mock model with parametrized config\n        mock_model = Mock()\n        mock_model.dim_t = dim_t\n        mock_model.stft_hop_length = hop_length\n        \n        # Mock chunking calculation\n        def mock_calculate_chunks(audio_length, dim_t, hop_length):\n            \"\"\"Calculate number of chunks needed.\"\"\"\n            chunk_size = dim_t * 8  # Example chunk size calculation","chunkId":"tests/unit/test_mdxc_roformer_chunking.py:19"}],[2858,{"filePath":"tests/unit/test_mdxc_roformer_chunking.py","startLine":231,"endLine":243,"chunkText":"        def mock_calculate_chunks(audio_length, dim_t, hop_length):\n            \"\"\"Calculate number of chunks needed.\"\"\"\n            chunk_size = dim_t * 8  # Example chunk size calculation\n            step_size = hop_length\n            \n            if audio_length <= chunk_size:\n                return 1\n            \n            return (audio_length - chunk_size + step_size - 1) // step_size + 1\n        \n        num_chunks = mock_calculate_chunks(audio_length, dim_t, hop_length)\n        \n        # Invariants that should hold regardless of parameters","chunkId":"tests/unit/test_mdxc_roformer_chunking.py:20"}],[2859,{"filePath":"tests/unit/test_mdxc_roformer_chunking.py","startLine":241,"endLine":250,"chunkText":"        num_chunks = mock_calculate_chunks(audio_length, dim_t, hop_length)\n        \n        # Invariants that should hold regardless of parameters\n        assert num_chunks >= 1, f\"Should always have at least 1 chunk for dim_t={dim_t}, hop={hop_length}\"\n        assert num_chunks <= audio_length // hop_length + 2, f\"Chunks should be reasonable for dim_t={dim_t}, hop={hop_length}\"\n        \n        # Test output shape consistency\n        mock_output_shape = (batch_size, 2, dim_t)  # (batch, channels, time)\n        assert mock_output_shape[0] == batch_size\n        assert mock_output_shape[2] == dim_t","chunkId":"tests/unit/test_mdxc_roformer_chunking.py:21"}],[2860,{"filePath":"tests/unit/test_mdxc_roformer_chunking.py","startLine":248,"endLine":260,"chunkText":"        mock_output_shape = (batch_size, 2, dim_t)  # (batch, channels, time)\n        assert mock_output_shape[0] == batch_size\n        assert mock_output_shape[2] == dim_t\n        \n        # Test that chunk_size scales with dim_t\n        chunk_size = dim_t * 8\n        assert chunk_size > 0\n        assert chunk_size >= dim_t\n\n    def test_logging_for_hop_and_step(self, caplog):\n        \"\"\"T063: Logs include hop/step sources (stft_hop_length, dim_t, desired vs actual step).\"\"\"\n        with caplog.at_level(logging.DEBUG):\n            # Mock logging during chunking setup","chunkId":"tests/unit/test_mdxc_roformer_chunking.py:22"}],[2861,{"filePath":"tests/unit/test_mdxc_roformer_chunking.py","startLine":258,"endLine":266,"chunkText":"        \"\"\"T063: Logs include hop/step sources (stft_hop_length, dim_t, desired vs actual step).\"\"\"\n        with caplog.at_level(logging.DEBUG):\n            # Mock logging during chunking setup\n            def mock_setup_chunking_with_logging(model, audio):\n                \"\"\"Mock chunking setup that logs parameter sources.\"\"\"\n                stft_hop = getattr(model, 'stft_hop_length', None)\n                dim_t = getattr(model, 'dim_t', None)\n                audio_hop = getattr(audio, 'hop_length', None)\n                ","chunkId":"tests/unit/test_mdxc_roformer_chunking.py:23"}],[2862,{"filePath":"tests/unit/test_mdxc_roformer_chunking.py","startLine":264,"endLine":273,"chunkText":"                dim_t = getattr(model, 'dim_t', None)\n                audio_hop = getattr(audio, 'hop_length', None)\n                \n                logging.debug(f\"Chunking setup: stft_hop_length={stft_hop}, dim_t={dim_t}\")\n                logging.debug(f\"Audio hop_length={audio_hop}\")\n                \n                desired_step = stft_hop if stft_hop else audio_hop\n                chunk_size = dim_t * 8 if dim_t else 8192\n                actual_step = min(desired_step, chunk_size) if desired_step else chunk_size // 4\n                ","chunkId":"tests/unit/test_mdxc_roformer_chunking.py:24"}],[2863,{"filePath":"tests/unit/test_mdxc_roformer_chunking.py","startLine":271,"endLine":283,"chunkText":"                chunk_size = dim_t * 8 if dim_t else 8192\n                actual_step = min(desired_step, chunk_size) if desired_step else chunk_size // 4\n                \n                logging.debug(f\"Step calculation: desired={desired_step}, actual={actual_step}, chunk_size={chunk_size}\")\n                \n                return {\n                    'chunk_size': chunk_size,\n                    'step_size': actual_step,\n                    'stft_hop_source': stft_hop is not None\n                }\n            \n            # Test with model having stft_hop_length\n            model_with_stft = Mock()","chunkId":"tests/unit/test_mdxc_roformer_chunking.py:25"}],[2864,{"filePath":"tests/unit/test_mdxc_roformer_chunking.py","startLine":281,"endLine":296,"chunkText":"            \n            # Test with model having stft_hop_length\n            model_with_stft = Mock()\n            model_with_stft.stft_hop_length = 1024\n            model_with_stft.dim_t = 256\n            \n            audio = Mock()\n            audio.hop_length = 512\n            \n            result = mock_setup_chunking_with_logging(model_with_stft, audio)\n            \n            # Verify logging occurred\n            assert \"stft_hop_length=1024\" in caplog.text\n            assert \"dim_t=256\" in caplog.text\n            assert \"desired=1024\" in caplog.text\n            assert \"actual=\" in caplog.text","chunkId":"tests/unit/test_mdxc_roformer_chunking.py:26"}],[2865,{"filePath":"tests/unit/test_mdxc_roformer_chunking.py","startLine":294,"endLine":306,"chunkText":"            assert \"dim_t=256\" in caplog.text\n            assert \"desired=1024\" in caplog.text\n            assert \"actual=\" in caplog.text\n            assert \"chunk_size=\" in caplog.text\n\n    def test_iteration_count_reasonable(self):\n        \"\"\"T064: Iteration count reasonable (ceil calculation within ±1).\"\"\"\n        import math\n        \n        # Test various audio lengths and chunk configurations\n        test_cases = [\n            (44100, 8192, 1024),   # 1 second, normal chunk\n            (88200, 4096, 512),    # 2 seconds, smaller chunk  ","chunkId":"tests/unit/test_mdxc_roformer_chunking.py:27"}],[2866,{"filePath":"tests/unit/test_mdxc_roformer_chunking.py","startLine":304,"endLine":316,"chunkText":"        test_cases = [\n            (44100, 8192, 1024),   # 1 second, normal chunk\n            (88200, 4096, 512),    # 2 seconds, smaller chunk  \n            (22050, 16384, 2048),  # 0.5 seconds, large chunk\n            (132300, 8192, 1024),  # 3 seconds, normal chunk\n        ]\n        \n        for audio_length, chunk_size, step_size in test_cases:\n            # Calculate expected iterations using ceiling division\n            if audio_length <= chunk_size:\n                expected_iterations = 1\n            else:\n                remaining_length = audio_length - chunk_size","chunkId":"tests/unit/test_mdxc_roformer_chunking.py:28"}],[2867,{"filePath":"tests/unit/test_mdxc_roformer_chunking.py","startLine":314,"endLine":328,"chunkText":"                expected_iterations = 1\n            else:\n                remaining_length = audio_length - chunk_size\n                expected_iterations = 1 + math.ceil(remaining_length / step_size)\n            \n            # Mock actual iteration calculation\n            def mock_calculate_iterations(audio_len, chunk_sz, step_sz):\n                if audio_len <= chunk_sz:\n                    return 1\n                \n                iterations = 0\n                pos = 0\n                while pos < audio_len:\n                    iterations += 1\n                    if pos + chunk_sz >= audio_len:","chunkId":"tests/unit/test_mdxc_roformer_chunking.py:29"}],[2868,{"filePath":"tests/unit/test_mdxc_roformer_chunking.py","startLine":326,"endLine":338,"chunkText":"                while pos < audio_len:\n                    iterations += 1\n                    if pos + chunk_sz >= audio_len:\n                        break\n                    pos += step_sz\n                \n                return iterations\n            \n            actual_iterations = mock_calculate_iterations(audio_length, chunk_size, step_size)\n            \n            # Verify iteration count is reasonable (within ±1 of expected)\n            diff = abs(actual_iterations - expected_iterations)\n            assert diff <= 1, (","chunkId":"tests/unit/test_mdxc_roformer_chunking.py:30"}],[2869,{"filePath":"tests/unit/test_mdxc_roformer_chunking.py","startLine":336,"endLine":346,"chunkText":"            # Verify iteration count is reasonable (within ±1 of expected)\n            diff = abs(actual_iterations - expected_iterations)\n            assert diff <= 1, (\n                f\"Iteration count {actual_iterations} differs too much from expected {expected_iterations} \"\n                f\"for audio_len={audio_length}, chunk={chunk_size}, step={step_size}\"\n            )\n            \n            # Verify minimum iterations\n            assert actual_iterations >= 1, f\"Should always have at least 1 iteration\"\n            \n            # Verify maximum reasonable iterations","chunkId":"tests/unit/test_mdxc_roformer_chunking.py:31"}],[2870,{"filePath":"tests/unit/test_mdxc_roformer_chunking.py","startLine":344,"endLine":351,"chunkText":"            assert actual_iterations >= 1, f\"Should always have at least 1 iteration\"\n            \n            # Verify maximum reasonable iterations\n            max_reasonable = (audio_length // step_size) + 2\n            assert actual_iterations <= max_reasonable, (\n                f\"Too many iterations {actual_iterations} for audio_len={audio_length}\"\n            )\n","chunkId":"tests/unit/test_mdxc_roformer_chunking.py:32"}],[2871,{"filePath":"tests/unit/test_model_configuration.py","startLine":0,"endLine":16,"chunkText":"\"\"\"\nUnit tests for ModelConfiguration validation.\nTests the ModelConfiguration dataclass and its validation logic.\n\"\"\"\n\nimport pytest\nfrom dataclasses import FrozenInstanceError\n\n# Add the roformer module to path for imports\nimport sys\nimport os\n# Find project root dynamically\ncurrent_dir = os.path.dirname(os.path.abspath(__file__))\nproject_root = current_dir\n# Go up until we find the project root (contains audio_separator/ directory)\nwhile project_root and not os.path.exists(os.path.join(project_root, 'audio_separator')):\n    parent = os.path.dirname(project_root)","chunkId":"tests/unit/test_model_configuration.py:0"}],[2872,{"filePath":"tests/unit/test_model_configuration.py","startLine":14,"endLine":30,"chunkText":"# Go up until we find the project root (contains audio_separator/ directory)\nwhile project_root and not os.path.exists(os.path.join(project_root, 'audio_separator')):\n    parent = os.path.dirname(project_root)\n    if parent == project_root:  # Reached filesystem root\n        break\n    project_root = parent\n\nif project_root:\n    sys.path.append(project_root)\n\nfrom audio_separator.separator.roformer.model_configuration import ModelConfiguration\n\n\nclass TestModelConfiguration:\n    \"\"\"Test cases for ModelConfiguration dataclass.\"\"\"\n    \n    def test_model_configuration_creation_valid(self):","chunkId":"tests/unit/test_model_configuration.py:1"}],[2873,{"filePath":"tests/unit/test_model_configuration.py","startLine":28,"endLine":46,"chunkText":"    \"\"\"Test cases for ModelConfiguration dataclass.\"\"\"\n    \n    def test_model_configuration_creation_valid(self):\n        \"\"\"Test creating a valid ModelConfiguration.\"\"\"\n        config = ModelConfiguration(\n            dim=512,\n            depth=12,\n            stereo=False,\n            num_stems=2,\n            time_transformer_depth=2,\n            freq_transformer_depth=2,\n            dim_head=64,\n            heads=8,\n            attn_dropout=0.1,\n            ff_dropout=0.1,\n            flash_attn=True,\n            mlp_expansion_factor=4,\n            sage_attention=False,\n            zero_dc=True,","chunkId":"tests/unit/test_model_configuration.py:2"}],[2874,{"filePath":"tests/unit/test_model_configuration.py","startLine":44,"endLine":61,"chunkText":"            mlp_expansion_factor=4,\n            sage_attention=False,\n            zero_dc=True,\n            use_torch_checkpoint=False,\n            skip_connection=False\n        )\n        \n        assert config.dim == 512\n        assert config.depth == 12\n        assert config.stereo is False\n        assert config.num_stems == 2\n        assert config.mlp_expansion_factor == 4\n        assert config.sage_attention is False\n    \n    def test_model_configuration_defaults(self):\n        \"\"\"Test ModelConfiguration with minimal required parameters.\"\"\"\n        config = ModelConfiguration(\n            dim=256,","chunkId":"tests/unit/test_model_configuration.py:3"}],[2875,{"filePath":"tests/unit/test_model_configuration.py","startLine":59,"endLine":73,"chunkText":"        \"\"\"Test ModelConfiguration with minimal required parameters.\"\"\"\n        config = ModelConfiguration(\n            dim=256,\n            depth=6\n        )\n        \n        # Check defaults are applied\n        assert config.dim == 256\n        assert config.depth == 6\n        assert config.stereo is False  # Default\n        assert config.num_stems == 1  # Default\n        assert config.time_transformer_depth == 2  # Default\n        assert config.freq_transformer_depth == 2  # Default\n        assert config.dim_head == 64  # Default\n        assert config.heads == 8  # Default","chunkId":"tests/unit/test_model_configuration.py:4"}],[2876,{"filePath":"tests/unit/test_model_configuration.py","startLine":71,"endLine":82,"chunkText":"        assert config.freq_transformer_depth == 2  # Default\n        assert config.dim_head == 64  # Default\n        assert config.heads == 8  # Default\n        assert config.attn_dropout == 0.0  # Default\n        assert config.ff_dropout == 0.0  # Default\n        assert config.flash_attn is True  # Default\n        assert config.mlp_expansion_factor == 4  # Default\n        assert config.sage_attention is False  # Default\n        assert config.zero_dc is True  # Default\n        assert config.use_torch_checkpoint is False  # Default\n        assert config.skip_connection is False  # Default\n    ","chunkId":"tests/unit/test_model_configuration.py:5"}],[2877,{"filePath":"tests/unit/test_model_configuration.py","startLine":80,"endLine":94,"chunkText":"        assert config.use_torch_checkpoint is False  # Default\n        assert config.skip_connection is False  # Default\n    \n    def test_model_configuration_immutable(self):\n        \"\"\"Test that ModelConfiguration is immutable (frozen).\"\"\"\n        config = ModelConfiguration(dim=512, depth=12)\n        \n        with pytest.raises(FrozenInstanceError):\n            config.dim = 1024\n        \n        with pytest.raises(FrozenInstanceError):\n            config.depth = 24\n    \n    def test_model_configuration_type_validation(self):\n        \"\"\"Test type validation in ModelConfiguration.\"\"\"","chunkId":"tests/unit/test_model_configuration.py:6"}],[2878,{"filePath":"tests/unit/test_model_configuration.py","startLine":92,"endLine":109,"chunkText":"    \n    def test_model_configuration_type_validation(self):\n        \"\"\"Test type validation in ModelConfiguration.\"\"\"\n        # Valid types should work\n        config = ModelConfiguration(\n            dim=512,\n            depth=12,\n            stereo=True,\n            attn_dropout=0.1,\n            flash_attn=False\n        )\n        assert isinstance(config.dim, int)\n        assert isinstance(config.stereo, bool)\n        assert isinstance(config.attn_dropout, float)\n    \n    def test_model_configuration_edge_values(self):\n        \"\"\"Test edge values for ModelConfiguration.\"\"\"\n        # Test minimum values","chunkId":"tests/unit/test_model_configuration.py:7"}],[2879,{"filePath":"tests/unit/test_model_configuration.py","startLine":107,"endLine":126,"chunkText":"    def test_model_configuration_edge_values(self):\n        \"\"\"Test edge values for ModelConfiguration.\"\"\"\n        # Test minimum values\n        config_min = ModelConfiguration(\n            dim=1,\n            depth=1,\n            num_stems=1,\n            heads=1,\n            attn_dropout=0.0,\n            ff_dropout=0.0\n        )\n        assert config_min.dim == 1\n        assert config_min.depth == 1\n        assert config_min.num_stems == 1\n        \n        # Test larger values\n        config_max = ModelConfiguration(\n            dim=8192,\n            depth=64,\n            num_stems=16,","chunkId":"tests/unit/test_model_configuration.py:8"}],[2880,{"filePath":"tests/unit/test_model_configuration.py","startLine":124,"endLine":143,"chunkText":"            dim=8192,\n            depth=64,\n            num_stems=16,\n            heads=64,\n            attn_dropout=1.0,\n            ff_dropout=1.0,\n            mlp_expansion_factor=16\n        )\n        assert config_max.dim == 8192\n        assert config_max.depth == 64\n        assert config_max.mlp_expansion_factor == 16\n    \n    def test_model_configuration_boolean_parameters(self):\n        \"\"\"Test boolean parameter handling.\"\"\"\n        config = ModelConfiguration(\n            dim=512,\n            depth=12,\n            stereo=True,\n            flash_attn=False,\n            sage_attention=True,","chunkId":"tests/unit/test_model_configuration.py:9"}],[2881,{"filePath":"tests/unit/test_model_configuration.py","startLine":141,"endLine":157,"chunkText":"            stereo=True,\n            flash_attn=False,\n            sage_attention=True,\n            zero_dc=False,\n            use_torch_checkpoint=True,\n            skip_connection=True\n        )\n        \n        assert config.stereo is True\n        assert config.flash_attn is False\n        assert config.sage_attention is True\n        assert config.zero_dc is False\n        assert config.use_torch_checkpoint is True\n        assert config.skip_connection is True\n    \n    def test_model_configuration_new_parameters(self):\n        \"\"\"Test the new parameters added for updated Roformer implementation.\"\"\"","chunkId":"tests/unit/test_model_configuration.py:10"}],[2882,{"filePath":"tests/unit/test_model_configuration.py","startLine":155,"endLine":171,"chunkText":"    \n    def test_model_configuration_new_parameters(self):\n        \"\"\"Test the new parameters added for updated Roformer implementation.\"\"\"\n        config = ModelConfiguration(\n            dim=512,\n            depth=12,\n            mlp_expansion_factor=8,\n            sage_attention=True,\n            zero_dc=False,\n            use_torch_checkpoint=True,\n            skip_connection=True\n        )\n        \n        # Verify new parameters are stored correctly\n        assert config.mlp_expansion_factor == 8\n        assert config.sage_attention is True\n        assert config.zero_dc is False","chunkId":"tests/unit/test_model_configuration.py:11"}],[2883,{"filePath":"tests/unit/test_model_configuration.py","startLine":169,"endLine":183,"chunkText":"        assert config.mlp_expansion_factor == 8\n        assert config.sage_attention is True\n        assert config.zero_dc is False\n        assert config.use_torch_checkpoint is True\n        assert config.skip_connection is True\n    \n    def test_model_configuration_repr(self):\n        \"\"\"Test string representation of ModelConfiguration.\"\"\"\n        config = ModelConfiguration(dim=512, depth=12)\n        repr_str = repr(config)\n        \n        assert \"ModelConfiguration\" in repr_str\n        assert \"dim=512\" in repr_str\n        assert \"depth=12\" in repr_str\n    ","chunkId":"tests/unit/test_model_configuration.py:12"}],[2884,{"filePath":"tests/unit/test_model_configuration.py","startLine":181,"endLine":194,"chunkText":"        assert \"dim=512\" in repr_str\n        assert \"depth=12\" in repr_str\n    \n    def test_model_configuration_equality(self):\n        \"\"\"Test equality comparison of ModelConfiguration instances.\"\"\"\n        config1 = ModelConfiguration(dim=512, depth=12, stereo=True)\n        config2 = ModelConfiguration(dim=512, depth=12, stereo=True)\n        config3 = ModelConfiguration(dim=512, depth=12, stereo=False)\n        \n        assert config1 == config2\n        assert config1 != config3\n    \n    def test_model_configuration_hash(self):\n        \"\"\"Test that ModelConfiguration is hashable.\"\"\"","chunkId":"tests/unit/test_model_configuration.py:13"}],[2885,{"filePath":"tests/unit/test_model_configuration.py","startLine":192,"endLine":206,"chunkText":"    \n    def test_model_configuration_hash(self):\n        \"\"\"Test that ModelConfiguration is hashable.\"\"\"\n        config1 = ModelConfiguration(dim=512, depth=12)\n        config2 = ModelConfiguration(dim=512, depth=12)\n        config3 = ModelConfiguration(dim=256, depth=6)\n        \n        # Same configurations should have same hash\n        assert hash(config1) == hash(config2)\n        \n        # Different configurations should have different hashes\n        assert hash(config1) != hash(config3)\n        \n        # Should be usable as dict keys\n        config_dict = {config1: \"first\", config3: \"second\"}","chunkId":"tests/unit/test_model_configuration.py:14"}],[2886,{"filePath":"tests/unit/test_model_configuration.py","startLine":204,"endLine":221,"chunkText":"        \n        # Should be usable as dict keys\n        config_dict = {config1: \"first\", config3: \"second\"}\n        assert config_dict[config2] == \"first\"  # config2 == config1\n    \n    def test_model_configuration_from_dict(self):\n        \"\"\"Test creating ModelConfiguration from dictionary-like data.\"\"\"\n        data = {\n            'dim': 512,\n            'depth': 12,\n            'stereo': True,\n            'mlp_expansion_factor': 8,\n            'sage_attention': True\n        }\n        \n        config = ModelConfiguration(**data)\n        \n        assert config.dim == 512","chunkId":"tests/unit/test_model_configuration.py:15"}],[2887,{"filePath":"tests/unit/test_model_configuration.py","startLine":219,"endLine":234,"chunkText":"        config = ModelConfiguration(**data)\n        \n        assert config.dim == 512\n        assert config.depth == 12\n        assert config.stereo is True\n        assert config.mlp_expansion_factor == 8\n        assert config.sage_attention is True\n    \n    def test_model_configuration_with_extra_kwargs(self):\n        \"\"\"Test ModelConfiguration ignores extra unknown parameters.\"\"\"\n        # This should not raise an error, unknown params should be ignored\n        # due to the dataclass design\n        try:\n            config = ModelConfiguration(\n                dim=512,\n                depth=12,","chunkId":"tests/unit/test_model_configuration.py:16"}],[2888,{"filePath":"tests/unit/test_model_configuration.py","startLine":232,"endLine":246,"chunkText":"            config = ModelConfiguration(\n                dim=512,\n                depth=12,\n                unknown_param=\"should_be_ignored\"  # This will cause TypeError\n            )\n            # If we get here, the dataclass accepted unknown params (unexpected)\n            assert False, \"Expected TypeError for unknown parameter\"\n        except TypeError as e:\n            # This is expected - dataclasses don't accept unknown parameters\n            assert \"unknown_param\" in str(e)\n\n\nif __name__ == \"__main__\":\n    pytest.main([__file__])\n","chunkId":"tests/unit/test_model_configuration.py:17"}],[2889,{"filePath":"tests/unit/test_parameter_validator.py","startLine":0,"endLine":16,"chunkText":"\"\"\"\nUnit tests for ParameterValidator methods.\nTests the core parameter validation logic for Roformer models.\n\"\"\"\n\nimport pytest\nfrom unittest.mock import Mock, patch\n\n# Add the roformer module to path for imports\nimport sys\nimport os\n# Find project root dynamically\ncurrent_dir = os.path.dirname(os.path.abspath(__file__))\nproject_root = current_dir\n# Go up until we find the project root (contains audio_separator/ directory)\nwhile project_root and not os.path.exists(os.path.join(project_root, 'audio_separator')):\n    parent = os.path.dirname(project_root)","chunkId":"tests/unit/test_parameter_validator.py:0"}],[2890,{"filePath":"tests/unit/test_parameter_validator.py","startLine":14,"endLine":28,"chunkText":"# Go up until we find the project root (contains audio_separator/ directory)\nwhile project_root and not os.path.exists(os.path.join(project_root, 'audio_separator')):\n    parent = os.path.dirname(project_root)\n    if parent == project_root:  # Reached filesystem root\n        break\n    project_root = parent\n\nif project_root:\n    sys.path.append(project_root)\n\nfrom audio_separator.separator.roformer.parameter_validator import ParameterValidator, ValidationSeverity\nfrom audio_separator.separator.roformer.parameter_validation_error import ParameterValidationError\n\n\nclass TestParameterValidator:","chunkId":"tests/unit/test_parameter_validator.py:1"}],[2891,{"filePath":"tests/unit/test_parameter_validator.py","startLine":26,"endLine":45,"chunkText":"\n\nclass TestParameterValidator:\n    \"\"\"Test cases for ParameterValidator class.\"\"\"\n    \n    def setup_method(self):\n        \"\"\"Set up test fixtures.\"\"\"\n        self.validator = ParameterValidator()\n    \n    def test_validate_required_parameters_bs_roformer_valid(self):\n        \"\"\"Test validation of required parameters for BSRoformer - valid case.\"\"\"\n        config = {\n            'dim': 512,\n            'depth': 12,\n            'freqs_per_bands': (2, 4, 8, 16, 32, 64)\n        }\n        \n        issues = self.validator.validate_required_parameters(config, \"bs_roformer\")\n        assert len(issues) == 0\n    ","chunkId":"tests/unit/test_parameter_validator.py:2"}],[2892,{"filePath":"tests/unit/test_parameter_validator.py","startLine":43,"endLine":56,"chunkText":"        issues = self.validator.validate_required_parameters(config, \"bs_roformer\")\n        assert len(issues) == 0\n    \n    def test_validate_required_parameters_bs_roformer_missing(self):\n        \"\"\"Test validation of required parameters for BSRoformer - missing parameters.\"\"\"\n        config = {\n            'dim': 512\n            # Missing 'depth' and 'freqs_per_bands'\n        }\n        \n        issues = self.validator.validate_required_parameters(config, \"bs_roformer\")\n        assert len(issues) == 2\n        \n        # Check that both missing parameters are reported","chunkId":"tests/unit/test_parameter_validator.py:3"}],[2893,{"filePath":"tests/unit/test_parameter_validator.py","startLine":54,"endLine":68,"chunkText":"        assert len(issues) == 2\n        \n        # Check that both missing parameters are reported\n        missing_params = [issue.parameter_name for issue in issues]\n        assert 'depth' in missing_params\n        assert 'freqs_per_bands' in missing_params\n        \n        # Check error severity\n        for issue in issues:\n            assert issue.severity == ValidationSeverity.ERROR\n    \n    def test_validate_required_parameters_mel_band_roformer_valid(self):\n        \"\"\"Test validation of required parameters for MelBandRoformer - valid case.\"\"\"\n        config = {\n            'dim': 512,","chunkId":"tests/unit/test_parameter_validator.py:4"}],[2894,{"filePath":"tests/unit/test_parameter_validator.py","startLine":66,"endLine":82,"chunkText":"        \"\"\"Test validation of required parameters for MelBandRoformer - valid case.\"\"\"\n        config = {\n            'dim': 512,\n            'depth': 12,\n            'num_bands': 64\n        }\n        \n        issues = self.validator.validate_required_parameters(config, \"mel_band_roformer\")\n        assert len(issues) == 0\n    \n    def test_validate_required_parameters_mel_band_roformer_missing(self):\n        \"\"\"Test validation of required parameters for MelBandRoformer - missing parameters.\"\"\"\n        config = {\n            'dim': 512\n            # Missing 'depth' and 'num_bands'\n        }\n        ","chunkId":"tests/unit/test_parameter_validator.py:5"}],[2895,{"filePath":"tests/unit/test_parameter_validator.py","startLine":80,"endLine":96,"chunkText":"            # Missing 'depth' and 'num_bands'\n        }\n        \n        issues = self.validator.validate_required_parameters(config, \"mel_band_roformer\")\n        assert len(issues) == 2\n        \n        missing_params = [issue.parameter_name for issue in issues]\n        assert 'depth' in missing_params\n        assert 'num_bands' in missing_params\n    \n    def test_validate_parameter_types_valid(self):\n        \"\"\"Test parameter type validation - valid types.\"\"\"\n        config = {\n            'dim': 512,\n            'depth': 12,\n            'stereo': False,\n            'attn_dropout': 0.1,","chunkId":"tests/unit/test_parameter_validator.py:6"}],[2896,{"filePath":"tests/unit/test_parameter_validator.py","startLine":94,"endLine":109,"chunkText":"            'depth': 12,\n            'stereo': False,\n            'attn_dropout': 0.1,\n            'freqs_per_bands': (2, 4, 8, 16),\n            'sample_rate': 44100\n        }\n        \n        issues = self.validator.validate_parameter_types(config)\n        assert len(issues) == 0\n    \n    def test_validate_parameter_types_invalid(self):\n        \"\"\"Test parameter type validation - invalid types.\"\"\"\n        config = {\n            'dim': \"512\",  # Should be int\n            'stereo': \"false\",  # Should be bool\n            'attn_dropout': \"0.1\",  # Should be float","chunkId":"tests/unit/test_parameter_validator.py:7"}],[2897,{"filePath":"tests/unit/test_parameter_validator.py","startLine":107,"endLine":120,"chunkText":"            'dim': \"512\",  # Should be int\n            'stereo': \"false\",  # Should be bool\n            'attn_dropout': \"0.1\",  # Should be float\n            'sample_rate': 44100.5  # Should be int\n        }\n        \n        issues = self.validator.validate_parameter_types(config)\n        assert len(issues) == 4\n        \n        # Check that all type errors are reported\n        invalid_params = [issue.parameter_name for issue in issues]\n        assert 'dim' in invalid_params\n        assert 'stereo' in invalid_params\n        assert 'attn_dropout' in invalid_params","chunkId":"tests/unit/test_parameter_validator.py:8"}],[2898,{"filePath":"tests/unit/test_parameter_validator.py","startLine":118,"endLine":135,"chunkText":"        assert 'dim' in invalid_params\n        assert 'stereo' in invalid_params\n        assert 'attn_dropout' in invalid_params\n        assert 'sample_rate' in invalid_params\n    \n    def test_validate_parameter_ranges_valid(self):\n        \"\"\"Test parameter range validation - valid ranges.\"\"\"\n        config = {\n            'dim': 512,\n            'depth': 12,\n            'heads': 8,\n            'attn_dropout': 0.1,\n            'sample_rate': 44100\n        }\n        \n        issues = self.validator.validate_parameter_ranges(config)\n        assert len(issues) == 0\n    ","chunkId":"tests/unit/test_parameter_validator.py:9"}],[2899,{"filePath":"tests/unit/test_parameter_validator.py","startLine":133,"endLine":147,"chunkText":"        issues = self.validator.validate_parameter_ranges(config)\n        assert len(issues) == 0\n    \n    def test_validate_parameter_ranges_invalid(self):\n        \"\"\"Test parameter range validation - invalid ranges.\"\"\"\n        config = {\n            'dim': 0,  # Below minimum (1)\n            'depth': 100,  # Above maximum (64)\n            'attn_dropout': 1.5,  # Above maximum (1.0)\n            'sample_rate': 5000  # Below minimum (8000)\n        }\n        \n        issues = self.validator.validate_parameter_ranges(config)\n        assert len(issues) == 4\n        ","chunkId":"tests/unit/test_parameter_validator.py:10"}],[2900,{"filePath":"tests/unit/test_parameter_validator.py","startLine":145,"endLine":157,"chunkText":"        issues = self.validator.validate_parameter_ranges(config)\n        assert len(issues) == 4\n        \n        # Check that all range errors are reported\n        invalid_params = [issue.parameter_name for issue in issues]\n        assert 'dim' in invalid_params\n        assert 'depth' in invalid_params\n        assert 'attn_dropout' in invalid_params\n        assert 'sample_rate' in invalid_params\n    \n    def test_validate_parameter_compatibility_sage_flash_conflict(self):\n        \"\"\"Test parameter compatibility - sage_attention and flash_attn conflict.\"\"\"\n        config = {","chunkId":"tests/unit/test_parameter_validator.py:11"}],[2901,{"filePath":"tests/unit/test_parameter_validator.py","startLine":155,"endLine":167,"chunkText":"    def test_validate_parameter_compatibility_sage_flash_conflict(self):\n        \"\"\"Test parameter compatibility - sage_attention and flash_attn conflict.\"\"\"\n        config = {\n            'sage_attention': True,\n            'flash_attn': True\n        }\n        \n        issues = self.validator.validate_parameter_compatibility(config)\n        assert len(issues) == 1\n        assert issues[0].severity == ValidationSeverity.WARNING\n        assert \"sage_attention, flash_attn\" in issues[0].parameter_name\n    \n    def test_validate_parameter_compatibility_freqs_per_bands_warning(self):","chunkId":"tests/unit/test_parameter_validator.py:12"}],[2902,{"filePath":"tests/unit/test_parameter_validator.py","startLine":165,"endLine":177,"chunkText":"        assert \"sage_attention, flash_attn\" in issues[0].parameter_name\n    \n    def test_validate_parameter_compatibility_freqs_per_bands_warning(self):\n        \"\"\"Test parameter compatibility - freqs_per_bands sum warning.\"\"\"\n        config = {\n            'freqs_per_bands': (1, 2, 3)  # Sum = 6, which is very low\n        }\n        \n        issues = self.validator.validate_parameter_compatibility(config)\n        assert len(issues) == 1\n        assert issues[0].severity == ValidationSeverity.WARNING\n        assert issues[0].parameter_name == \"freqs_per_bands\"\n    ","chunkId":"tests/unit/test_parameter_validator.py:13"}],[2903,{"filePath":"tests/unit/test_parameter_validator.py","startLine":175,"endLine":189,"chunkText":"        assert issues[0].severity == ValidationSeverity.WARNING\n        assert issues[0].parameter_name == \"freqs_per_bands\"\n    \n    def test_validate_normalization_config_valid(self):\n        \"\"\"Test normalization configuration validation - valid cases.\"\"\"\n        valid_configs = [\n            None,\n            'layer_norm',\n            'batch_norm',\n            'rms_norm',\n            {'type': 'layer_norm', 'eps': 1e-5}\n        ]\n        \n        for norm_config in valid_configs:\n            issues = self.validator.validate_normalization_config(norm_config)","chunkId":"tests/unit/test_parameter_validator.py:14"}],[2904,{"filePath":"tests/unit/test_parameter_validator.py","startLine":187,"endLine":200,"chunkText":"        \n        for norm_config in valid_configs:\n            issues = self.validator.validate_normalization_config(norm_config)\n            assert len(issues) == 0, f\"Failed for config: {norm_config}\"\n    \n    def test_validate_normalization_config_invalid(self):\n        \"\"\"Test normalization configuration validation - invalid cases.\"\"\"\n        invalid_configs = [\n            'invalid_norm',  # Unsupported string\n            123,  # Invalid type\n            ['layer_norm']  # Invalid type (list)\n        ]\n        \n        for norm_config in invalid_configs:","chunkId":"tests/unit/test_parameter_validator.py:15"}],[2905,{"filePath":"tests/unit/test_parameter_validator.py","startLine":198,"endLine":210,"chunkText":"        ]\n        \n        for norm_config in invalid_configs:\n            issues = self.validator.validate_normalization_config(norm_config)\n            assert len(issues) >= 1, f\"Should have failed for config: {norm_config}\"\n            assert issues[0].severity == ValidationSeverity.ERROR\n    \n    def test_get_parameter_defaults_bs_roformer(self):\n        \"\"\"Test getting parameter defaults for BSRoformer.\"\"\"\n        defaults = self.validator.get_parameter_defaults(\"bs_roformer\")\n        \n        # Check some expected defaults\n        assert defaults['stereo'] is False","chunkId":"tests/unit/test_parameter_validator.py:16"}],[2906,{"filePath":"tests/unit/test_parameter_validator.py","startLine":208,"endLine":221,"chunkText":"        \n        # Check some expected defaults\n        assert defaults['stereo'] is False\n        assert defaults['num_stems'] == 2\n        assert defaults['flash_attn'] is True\n        assert defaults['mlp_expansion_factor'] == 4\n        assert defaults['sage_attention'] is False\n        assert 'freqs_per_bands' in defaults\n        assert 'mask_estimator_depth' in defaults\n    \n    def test_get_parameter_defaults_mel_band_roformer(self):\n        \"\"\"Test getting parameter defaults for MelBandRoformer.\"\"\"\n        defaults = self.validator.get_parameter_defaults(\"mel_band_roformer\")\n        ","chunkId":"tests/unit/test_parameter_validator.py:17"}],[2907,{"filePath":"tests/unit/test_parameter_validator.py","startLine":219,"endLine":232,"chunkText":"        \"\"\"Test getting parameter defaults for MelBandRoformer.\"\"\"\n        defaults = self.validator.get_parameter_defaults(\"mel_band_roformer\")\n        \n        # Check some expected defaults\n        assert defaults['stereo'] is False\n        assert defaults['num_stems'] == 2\n        assert defaults['flash_attn'] is True\n        assert defaults['mlp_expansion_factor'] == 4\n        assert defaults['sage_attention'] is False\n        assert defaults['num_bands'] == 64\n    \n    def test_apply_parameter_defaults(self):\n        \"\"\"Test applying parameter defaults to configuration.\"\"\"\n        config = {","chunkId":"tests/unit/test_parameter_validator.py:18"}],[2908,{"filePath":"tests/unit/test_parameter_validator.py","startLine":230,"endLine":246,"chunkText":"    def test_apply_parameter_defaults(self):\n        \"\"\"Test applying parameter defaults to configuration.\"\"\"\n        config = {\n            'dim': 512,\n            'depth': 12,\n            'stereo': True  # Override default\n        }\n        \n        result = self.validator.apply_parameter_defaults(config, \"bs_roformer\")\n        \n        # Should have original values\n        assert result['dim'] == 512\n        assert result['depth'] == 12\n        assert result['stereo'] is True  # Override preserved\n        \n        # Should have applied defaults\n        assert result['num_stems'] == 2","chunkId":"tests/unit/test_parameter_validator.py:19"}],[2909,{"filePath":"tests/unit/test_parameter_validator.py","startLine":244,"endLine":261,"chunkText":"        \n        # Should have applied defaults\n        assert result['num_stems'] == 2\n        assert result['flash_attn'] is True\n        assert result['mlp_expansion_factor'] == 4\n    \n    def test_validate_all_comprehensive(self):\n        \"\"\"Test comprehensive validation with all checks.\"\"\"\n        config = {\n            'dim': 512,\n            'depth': 12,\n            'freqs_per_bands': (2, 4, 8, 16, 32, 64),\n            'stereo': False,\n            'attn_dropout': 0.1,\n            'sage_attention': False,\n            'norm': 'layer_norm'\n        }\n        ","chunkId":"tests/unit/test_parameter_validator.py:20"}],[2910,{"filePath":"tests/unit/test_parameter_validator.py","startLine":259,"endLine":274,"chunkText":"            'norm': 'layer_norm'\n        }\n        \n        issues = self.validator.validate_all(config, \"bs_roformer\")\n        assert len(issues) == 0\n    \n    def test_validate_all_with_errors(self):\n        \"\"\"Test comprehensive validation with multiple error types.\"\"\"\n        config = {\n            'dim': \"invalid\",  # Type error\n            'depth': 100,  # Range error\n            # Missing 'freqs_per_bands' - required parameter error\n            'sage_attention': True,\n            'flash_attn': True,  # Compatibility warning\n            'norm': 'invalid_norm'  # Normalization error\n        }","chunkId":"tests/unit/test_parameter_validator.py:21"}],[2911,{"filePath":"tests/unit/test_parameter_validator.py","startLine":272,"endLine":284,"chunkText":"            'flash_attn': True,  # Compatibility warning\n            'norm': 'invalid_norm'  # Normalization error\n        }\n        \n        issues = self.validator.validate_all(config, \"bs_roformer\")\n        assert len(issues) >= 5  # At least 5 different types of issues\n        \n        # Check we have different types of issues\n        severities = [issue.severity for issue in issues]\n        assert ValidationSeverity.ERROR in severities\n        assert ValidationSeverity.WARNING in severities\n    \n    def test_validate_and_raise_success(self):","chunkId":"tests/unit/test_parameter_validator.py:22"}],[2912,{"filePath":"tests/unit/test_parameter_validator.py","startLine":282,"endLine":298,"chunkText":"        assert ValidationSeverity.WARNING in severities\n    \n    def test_validate_and_raise_success(self):\n        \"\"\"Test validate_and_raise with valid configuration.\"\"\"\n        config = {\n            'dim': 512,\n            'depth': 12,\n            'freqs_per_bands': (2, 4, 8, 16, 32, 64)\n        }\n        \n        # Should not raise any exception\n        self.validator.validate_and_raise(config, \"bs_roformer\")\n    \n    def test_validate_and_raise_error(self):\n        \"\"\"Test validate_and_raise with invalid configuration.\"\"\"\n        config = {\n            'dim': \"invalid\",  # Type error","chunkId":"tests/unit/test_parameter_validator.py:23"}],[2913,{"filePath":"tests/unit/test_parameter_validator.py","startLine":296,"endLine":311,"chunkText":"        \"\"\"Test validate_and_raise with invalid configuration.\"\"\"\n        config = {\n            'dim': \"invalid\",  # Type error\n            'depth': 12\n            # Missing 'freqs_per_bands'\n        }\n        \n        with pytest.raises(ParameterValidationError) as exc_info:\n            self.validator.validate_and_raise(config, \"bs_roformer\")\n        \n        # Check the exception contains useful information\n        exception = exc_info.value\n        assert exception.parameter_name is not None\n        assert exception.suggested_fix is not None\n    \n    def test_private_helper_methods(self):","chunkId":"tests/unit/test_parameter_validator.py:24"}],[2914,{"filePath":"tests/unit/test_parameter_validator.py","startLine":309,"endLine":320,"chunkText":"        assert exception.suggested_fix is not None\n    \n    def test_private_helper_methods(self):\n        \"\"\"Test private helper methods.\"\"\"\n        # Test _is_correct_type\n        assert self.validator._is_correct_type(123, int) is True\n        assert self.validator._is_correct_type(\"123\", int) is False\n        assert self.validator._is_correct_type([1, 2, 3], (list, tuple)) is True\n        assert self.validator._is_correct_type((1, 2, 3), (list, tuple)) is True\n        \n        # Test _get_type_name\n        assert self.validator._get_type_name(int) == \"int\"","chunkId":"tests/unit/test_parameter_validator.py:25"}],[2915,{"filePath":"tests/unit/test_parameter_validator.py","startLine":318,"endLine":331,"chunkText":"        \n        # Test _get_type_name\n        assert self.validator._get_type_name(int) == \"int\"\n        assert \"int\" in self.validator._get_type_name((int, float))\n        assert \"float\" in self.validator._get_type_name((int, float))\n        \n        # Test _get_expected_type_description\n        assert \"int\" in self.validator._get_expected_type_description('dim')\n        assert \"appropriate type\" in self.validator._get_expected_type_description('unknown_param')\n\n\nif __name__ == \"__main__\":\n    pytest.main([__file__])\n","chunkId":"tests/unit/test_parameter_validator.py:26"}],[2916,{"filePath":"tests/unit/test_remote_api_client.py","startLine":0,"endLine":26,"chunkText":"import json\nimport pytest\nimport logging\nimport tempfile\nimport os\nfrom unittest.mock import Mock, patch, mock_open\nfrom unittest.mock import MagicMock\nimport requests\n\nfrom audio_separator.remote import AudioSeparatorAPIClient\n\n\n@pytest.fixture\ndef logger():\n    \"\"\"Create a mock logger for testing.\"\"\"\n    return logging.getLogger(\"test\")\n\n\n@pytest.fixture\ndef api_client(logger):\n    \"\"\"Create an API client instance for testing.\"\"\"\n    return AudioSeparatorAPIClient(\"https://test-api.example.com\", logger)\n\n\n@pytest.fixture\ndef mock_audio_file():\n    \"\"\"Create a temporary audio file for testing.\"\"\"","chunkId":"tests/unit/test_remote_api_client.py:0"}],[2917,{"filePath":"tests/unit/test_remote_api_client.py","startLine":24,"endLine":42,"chunkText":"@pytest.fixture\ndef mock_audio_file():\n    \"\"\"Create a temporary audio file for testing.\"\"\"\n    with tempfile.NamedTemporaryFile(suffix=\".wav\", delete=False) as f:\n        f.write(b\"fake audio content\")\n        yield f.name\n    os.unlink(f.name)\n\n\nclass TestAudioSeparatorAPIClient:\n    \"\"\"Test the AudioSeparatorAPIClient class.\"\"\"\n\n    def test_init(self, logger):\n        \"\"\"Test client initialization.\"\"\"\n        api_url = \"https://test-api.example.com\"\n        client = AudioSeparatorAPIClient(api_url, logger)\n\n        assert client.api_url == api_url\n        assert client.logger == logger","chunkId":"tests/unit/test_remote_api_client.py:1"}],[2918,{"filePath":"tests/unit/test_remote_api_client.py","startLine":40,"endLine":53,"chunkText":"\n        assert client.api_url == api_url\n        assert client.logger == logger\n        assert client.session is not None\n\n    @patch(\"requests.Session.post\")\n    @patch(\"builtins.open\", new_callable=mock_open, read_data=b\"fake audio content\")\n    def test_separate_audio_success(self, mock_file, mock_post, api_client, mock_audio_file):\n        \"\"\"Test successful audio separation submission.\"\"\"\n        # Mock successful response\n        mock_response = Mock()\n        mock_response.json.return_value = {\n            \"task_id\": \"test-task-123\",\n            \"status\": \"submitted\",","chunkId":"tests/unit/test_remote_api_client.py:2"}],[2919,{"filePath":"tests/unit/test_remote_api_client.py","startLine":51,"endLine":66,"chunkText":"        mock_response.json.return_value = {\n            \"task_id\": \"test-task-123\",\n            \"status\": \"submitted\",\n            \"message\": \"Job submitted for processing\",\n            \"models_used\": [\"default\"],\n            \"total_models\": 1,\n            \"original_filename\": \"test.wav\",\n        }\n        mock_response.raise_for_status.return_value = None\n        mock_post.return_value = mock_response\n\n        result = api_client.separate_audio(mock_audio_file)\n\n        # Verify the result\n        assert result[\"task_id\"] == \"test-task-123\"\n        assert result[\"status\"] == \"submitted\"","chunkId":"tests/unit/test_remote_api_client.py:3"}],[2920,{"filePath":"tests/unit/test_remote_api_client.py","startLine":64,"endLine":77,"chunkText":"        # Verify the result\n        assert result[\"task_id\"] == \"test-task-123\"\n        assert result[\"status\"] == \"submitted\"\n        assert result[\"models_used\"] == [\"default\"]\n\n        # Verify the request was made correctly\n        mock_post.assert_called_once()\n        call_args = mock_post.call_args\n        assert call_args[0][0] == \"https://test-api.example.com/separate\"\n        assert \"files\" in call_args[1]\n        assert \"data\" in call_args[1]\n\n    @patch(\"requests.Session.post\")\n    @patch(\"builtins.open\", new_callable=mock_open, read_data=b\"fake audio content\")","chunkId":"tests/unit/test_remote_api_client.py:4"}],[2921,{"filePath":"tests/unit/test_remote_api_client.py","startLine":75,"endLine":84,"chunkText":"\n    @patch(\"requests.Session.post\")\n    @patch(\"builtins.open\", new_callable=mock_open, read_data=b\"fake audio content\")\n    def test_separate_audio_with_multiple_models(self, mock_file, mock_post, api_client, mock_audio_file):\n        \"\"\"Test audio separation with multiple models.\"\"\"\n        mock_response = Mock()\n        mock_response.json.return_value = {\"task_id\": \"test-task-456\", \"status\": \"submitted\", \"models_used\": [\"model1.ckpt\", \"model2.onnx\"], \"total_models\": 2}\n        mock_response.raise_for_status.return_value = None\n        mock_post.return_value = mock_response\n","chunkId":"tests/unit/test_remote_api_client.py:5"}],[2922,{"filePath":"tests/unit/test_remote_api_client.py","startLine":82,"endLine":96,"chunkText":"        mock_response.raise_for_status.return_value = None\n        mock_post.return_value = mock_response\n\n        models = [\"model1.ckpt\", \"model2.onnx\"]\n        result = api_client.separate_audio(mock_audio_file, models=models)\n\n        assert result[\"models_used\"] == models\n        assert result[\"total_models\"] == 2\n\n        # Check that models were serialized correctly in the request\n        call_args = mock_post.call_args\n        data = call_args[1][\"data\"]\n        assert json.loads(data[\"models\"]) == models\n\n    @patch(\"requests.Session.post\")","chunkId":"tests/unit/test_remote_api_client.py:6"}],[2923,{"filePath":"tests/unit/test_remote_api_client.py","startLine":94,"endLine":104,"chunkText":"        assert json.loads(data[\"models\"]) == models\n\n    @patch(\"requests.Session.post\")\n    @patch(\"builtins.open\", new_callable=mock_open, read_data=b\"fake audio content\")\n    def test_separate_audio_with_custom_parameters(self, mock_file, mock_post, api_client, mock_audio_file):\n        \"\"\"Test audio separation with custom parameters.\"\"\"\n        mock_response = Mock()\n        mock_response.json.return_value = {\"task_id\": \"test-task-789\", \"status\": \"submitted\"}\n        mock_response.raise_for_status.return_value = None\n        mock_post.return_value = mock_response\n","chunkId":"tests/unit/test_remote_api_client.py:7"}],[2924,{"filePath":"tests/unit/test_remote_api_client.py","startLine":102,"endLine":112,"chunkText":"        mock_response.raise_for_status.return_value = None\n        mock_post.return_value = mock_response\n\n        custom_output_names = {\"Vocals\": \"lead_vocals\", \"Instrumental\": \"backing_track\"}\n        result = api_client.separate_audio(\n            mock_audio_file, model=\"test_model.ckpt\", output_format=\"wav\", normalization_threshold=0.8, mdx_segment_size=512, vr_aggression=10, custom_output_names=custom_output_names\n        )\n\n        # Verify the parameters were passed correctly\n        call_args = mock_post.call_args\n        data = call_args[1][\"data\"]","chunkId":"tests/unit/test_remote_api_client.py:8"}],[2925,{"filePath":"tests/unit/test_remote_api_client.py","startLine":110,"endLine":121,"chunkText":"        # Verify the parameters were passed correctly\n        call_args = mock_post.call_args\n        data = call_args[1][\"data\"]\n        assert data[\"model\"] == \"test_model.ckpt\"\n        assert data[\"output_format\"] == \"wav\"\n        assert data[\"normalization_threshold\"] == 0.8\n        assert data[\"mdx_segment_size\"] == 512\n        assert data[\"vr_aggression\"] == 10\n        assert json.loads(data[\"custom_output_names\"]) == custom_output_names\n\n    @patch(\"requests.Session.post\")\n    def test_separate_audio_file_not_found(self, mock_post, api_client):","chunkId":"tests/unit/test_remote_api_client.py:9"}],[2926,{"filePath":"tests/unit/test_remote_api_client.py","startLine":119,"endLine":129,"chunkText":"\n    @patch(\"requests.Session.post\")\n    def test_separate_audio_file_not_found(self, mock_post, api_client):\n        \"\"\"Test audio separation with non-existent file.\"\"\"\n        with pytest.raises(FileNotFoundError):\n            api_client.separate_audio(\"/nonexistent/file.wav\")\n\n    @patch(\"requests.Session.post\")\n    @patch(\"builtins.open\", new_callable=mock_open, read_data=b\"fake audio content\")\n    def test_separate_audio_request_error(self, mock_file, mock_post, api_client, mock_audio_file):\n        \"\"\"Test audio separation with request error.\"\"\"","chunkId":"tests/unit/test_remote_api_client.py:10"}],[2927,{"filePath":"tests/unit/test_remote_api_client.py","startLine":127,"endLine":138,"chunkText":"    @patch(\"builtins.open\", new_callable=mock_open, read_data=b\"fake audio content\")\n    def test_separate_audio_request_error(self, mock_file, mock_post, api_client, mock_audio_file):\n        \"\"\"Test audio separation with request error.\"\"\"\n        mock_post.side_effect = requests.RequestException(\"Connection error\")\n\n        with pytest.raises(requests.RequestException):\n            api_client.separate_audio(mock_audio_file)\n\n    @patch(\"requests.Session.get\")\n    def test_get_job_status_success(self, mock_get, api_client):\n        \"\"\"Test successful job status retrieval.\"\"\"\n        mock_response = Mock(","chunkId":"tests/unit/test_remote_api_client.py:11"}],[2928,{"filePath":"tests/unit/test_remote_api_client.py","startLine":136,"endLine":146,"chunkText":"    def test_get_job_status_success(self, mock_get, api_client):\n        \"\"\"Test successful job status retrieval.\"\"\"\n        mock_response = Mock()\n        mock_response.json.return_value = {\"task_id\": \"test-task-123\", \"status\": \"processing\", \"progress\": 50, \"current_model_index\": 0, \"total_models\": 1}\n        mock_response.raise_for_status.return_value = None\n        mock_get.return_value = mock_response\n\n        result = api_client.get_job_status(\"test-task-123\")\n\n        assert result[\"status\"] == \"processing\"\n        assert result[\"progress\"] == 50","chunkId":"tests/unit/test_remote_api_client.py:12"}],[2929,{"filePath":"tests/unit/test_remote_api_client.py","startLine":144,"endLine":157,"chunkText":"        assert result[\"status\"] == \"processing\"\n        assert result[\"progress\"] == 50\n        mock_get.assert_called_once_with(\"https://test-api.example.com/status/test-task-123\", timeout=10)\n\n    @patch(\"requests.Session.get\")\n    def test_get_job_status_error(self, mock_get, api_client):\n        \"\"\"Test job status retrieval with error.\"\"\"\n        mock_get.side_effect = requests.RequestException(\"API error\")\n\n        with pytest.raises(requests.RequestException):\n            api_client.get_job_status(\"test-task-123\")\n\n    @patch(\"requests.Session.get\")\n    @patch(\"builtins.open\", new_callable=mock_open","chunkId":"tests/unit/test_remote_api_client.py:13"}],[2930,{"filePath":"tests/unit/test_remote_api_client.py","startLine":156,"endLine":169,"chunkText":"\n    @patch(\"requests.Session.get\")\n    @patch(\"builtins.open\", new_callable=mock_open)\n    def test_download_file_success(self, mock_file, mock_get, api_client):\n        \"\"\"Test successful file download.\"\"\"\n        mock_response = Mock()\n        mock_response.content = b\"fake audio file content\"\n        mock_response.status_code = 200\n        mock_response.raise_for_status.return_value = None\n        mock_get.return_value = mock_response\n\n        result = api_client.download_file(\"test-task-123\", \"output.wav\", \"local_output.wav\")\n\n        assert result == \"local_output.wav\"","chunkId":"tests/unit/test_remote_api_client.py:14"}],[2931,{"filePath":"tests/unit/test_remote_api_client.py","startLine":167,"endLine":177,"chunkText":"        result = api_client.download_file(\"test-task-123\", \"output.wav\", \"local_output.wav\")\n\n        assert result == \"local_output.wav\"\n        mock_get.assert_called_once_with(\"https://test-api.example.com/download/test-task-123/output.wav\", timeout=60)\n        mock_file.assert_called_once_with(\"local_output.wav\", \"wb\")\n\n    @patch(\"requests.Session.get\")\n    @patch(\"builtins.open\", new_callable=mock_open)\n    def test_download_file_default_output_path(self, mock_file, mock_get, api_client):\n        \"\"\"Test file download with default output path.\"\"\"\n        mock_response = Mock()","chunkId":"tests/unit/test_remote_api_client.py:15"}],[2932,{"filePath":"tests/unit/test_remote_api_client.py","startLine":175,"endLine":188,"chunkText":"    def test_download_file_default_output_path(self, mock_file, mock_get, api_client):\n        \"\"\"Test file download with default output path.\"\"\"\n        mock_response = Mock()\n        mock_response.content = b\"fake audio file content\"\n        mock_response.status_code = 200\n        mock_response.raise_for_status.return_value = None\n        mock_get.return_value = mock_response\n\n        result = api_client.download_file(\"test-task-123\", \"output.wav\")\n\n        assert result == \"output.wav\"\n        mock_file.assert_called_once_with(\"output.wav\", \"wb\")\n\n    @patch(\"requests.Session.get\")","chunkId":"tests/unit/test_remote_api_client.py:16"}],[2933,{"filePath":"tests/unit/test_remote_api_client.py","startLine":186,"endLine":198,"chunkText":"        mock_file.assert_called_once_with(\"output.wav\", \"wb\")\n\n    @patch(\"requests.Session.get\")\n    @patch(\"builtins.open\", new_callable=mock_open)\n    def test_download_file_with_spaces_in_filename(self, mock_file, mock_get, api_client):\n        \"\"\"Test file download with spaces in filename (URL encoding).\"\"\"\n        mock_response = Mock()\n        mock_response.content = b\"fake audio file content\"\n        mock_response.status_code = 200\n        mock_response.raise_for_status.return_value = None\n        mock_get.return_value = mock_response\n\n        filename_with_spaces = \"My Song (Vocals) Output.wav\"","chunkId":"tests/unit/test_remote_api_client.py:17"}],[2934,{"filePath":"tests/unit/test_remote_api_client.py","startLine":196,"endLine":207,"chunkText":"        mock_get.return_value = mock_response\n\n        filename_with_spaces = \"My Song (Vocals) Output.wav\"\n        result = api_client.download_file(\"test-task-123\", filename_with_spaces)\n\n        # Verify URL was properly encoded\n        expected_url = \"https://test-api.example.com/download/test-task-123/My%20Song%20%28Vocals%29%20Output.wav\"\n        mock_get.assert_called_once_with(expected_url, timeout=60)\n        assert result == filename_with_spaces\n        mock_file.assert_called_once_with(filename_with_spaces, \"wb\")\n\n    @patch(\"requests.Session.get\")","chunkId":"tests/unit/test_remote_api_client.py:18"}],[2935,{"filePath":"tests/unit/test_remote_api_client.py","startLine":206,"endLine":217,"chunkText":"\n    @patch(\"requests.Session.get\")\n    @patch(\"builtins.open\", new_callable=mock_open)\n    def test_download_file_with_special_characters(self, mock_file, mock_get, api_client):\n        \"\"\"Test file download with special characters in filename.\"\"\"\n        mock_response = Mock()\n        mock_response.content = b\"fake audio file content\"\n        mock_response.status_code = 200\n        mock_response.raise_for_status.return_value = None\n        mock_get.return_value = mock_response\n\n        filename_with_special_chars = \"Song & Band - Title (Vocals 50% Mix).flac\"","chunkId":"tests/unit/test_remote_api_client.py:19"}],[2936,{"filePath":"tests/unit/test_remote_api_client.py","startLine":215,"endLine":225,"chunkText":"        mock_get.return_value = mock_response\n\n        filename_with_special_chars = \"Song & Band - Title (Vocals 50% Mix).flac\"\n        result = api_client.download_file(\"test-task-456\", filename_with_special_chars)\n\n        # Verify URL was properly encoded\n        expected_url = \"https://test-api.example.com/download/test-task-456/Song%20%26%20Band%20-%20Title%20%28Vocals%2050%25%20Mix%29.flac\"\n        mock_get.assert_called_once_with(expected_url, timeout=60)\n        assert result == filename_with_special_chars\n        mock_file.assert_called_once_with(filename_with_special_chars, \"wb\")\n","chunkId":"tests/unit/test_remote_api_client.py:20"}],[2937,{"filePath":"tests/unit/test_remote_api_client.py","startLine":223,"endLine":234,"chunkText":"        assert result == filename_with_special_chars\n        mock_file.assert_called_once_with(filename_with_special_chars, \"wb\")\n\n    @patch(\"requests.Session.get\")\n    @patch(\"builtins.open\", new_callable=mock_open)\n    def test_download_file_with_unicode_characters(self, mock_file, mock_get, api_client):\n        \"\"\"Test file download with unicode characters in filename.\"\"\"\n        mock_response = Mock()\n        mock_response.content = b\"fake audio file content\"\n        mock_response.status_code = 200\n        mock_response.raise_for_status.return_value = None\n        mock_get.return_value = mock_respons","chunkId":"tests/unit/test_remote_api_client.py:21"}],[2938,{"filePath":"tests/unit/test_remote_api_client.py","startLine":233,"endLine":242,"chunkText":"        mock_response.raise_for_status.return_value = None\n        mock_get.return_value = mock_response\n\n        unicode_filename = \"Café - Naïve Song (Résumé).mp3\"\n        result = api_client.download_file(\"test-task-789\", unicode_filename)\n\n        # Verify URL was properly encoded (UTF-8 encoded then percent-encoded)\n        expected_url = \"https://test-api.example.com/download/test-task-789/Caf%C3%A9%20-%20Na%C3%AFve%20Song%20%28R%C3%A9sum%C3%A9%29.mp3\"\n        mock_get.assert_called_once_with(expected_url, timeout=60)\n        assert result == unicode_filename","chunkId":"tests/unit/test_remote_api_client.py:22"}],[2939,{"filePath":"tests/unit/test_remote_api_client.py","startLine":240,"endLine":250,"chunkText":"        expected_url = \"https://test-api.example.com/download/test-task-789/Caf%C3%A9%20-%20Na%C3%AFve%20Song%20%28R%C3%A9sum%C3%A9%29.mp3\"\n        mock_get.assert_called_once_with(expected_url, timeout=60)\n        assert result == unicode_filename\n        mock_file.assert_called_once_with(unicode_filename, \"wb\")\n\n    @patch(\"requests.Session.get\")\n    def test_download_file_error(self, mock_get, api_client):\n        \"\"\"Test file download with error.\"\"\"\n        mock_get.side_effect = requests.RequestException(\"Download error\")\n\n        with pytest.raises(requests.RequestException):","chunkId":"tests/unit/test_remote_api_client.py:23"}],[2940,{"filePath":"tests/unit/test_remote_api_client.py","startLine":248,"endLine":260,"chunkText":"        mock_get.side_effect = requests.RequestException(\"Download error\")\n\n        with pytest.raises(requests.RequestException):\n            api_client.download_file(\"test-task-123\", \"output.wav\")\n\n    @patch(\"requests.Session.get\")\n    def test_list_models_pretty_format(self, mock_get, api_client):\n        \"\"\"Test listing models in pretty format.\"\"\"\n        mock_response = Mock()\n        mock_response.text = \"Model list in pretty format\"\n        mock_response.raise_for_status.return_value = None\n        mock_get.return_value = mock_response\n","chunkId":"tests/unit/test_remote_api_client.py:24"}],[2941,{"filePath":"tests/unit/test_remote_api_client.py","startLine":258,"endLine":270,"chunkText":"        mock_response.raise_for_status.return_value = None\n        mock_get.return_value = mock_response\n\n        result = api_client.list_models(format_type=\"pretty\")\n\n        assert result == {\"text\": \"Model list in pretty format\"}\n        mock_get.assert_called_once_with(\"https://test-api.example.com/models\", timeout=10)\n\n    @patch(\"requests.Session.get\")\n    def test_list_models_json_format(self, mock_get, api_client):\n        \"\"\"Test listing models in JSON format.\"\"\"\n        mock_response = Mock()\n        models_data = {\"models\": [{\"name\": \"model1\", \"type\": \"MDX\"}]}","chunkId":"tests/unit/test_remote_api_client.py:25"}],[2942,{"filePath":"tests/unit/test_remote_api_client.py","startLine":268,"endLine":281,"chunkText":"        \"\"\"Test listing models in JSON format.\"\"\"\n        mock_response = Mock()\n        models_data = {\"models\": [{\"name\": \"model1\", \"type\": \"MDX\"}]}\n        mock_response.json.return_value = models_data\n        mock_response.raise_for_status.return_value = None\n        mock_get.return_value = mock_response\n\n        result = api_client.list_models(format_type=\"json\")\n\n        assert result == models_data\n        mock_get.assert_called_once_with(\"https://test-api.example.com/models-json\", timeout=10)\n\n    @patch(\"requests.Session.get\")\n    def test_list_models_with_filter(self, mock_get, api_client):","chunkId":"tests/unit/test_remote_api_client.py:26"}],[2943,{"filePath":"tests/unit/test_remote_api_client.py","startLine":279,"endLine":293,"chunkText":"\n    @patch(\"requests.Session.get\")\n    def test_list_models_with_filter(self, mock_get, api_client):\n        \"\"\"Test listing models with filter.\"\"\"\n        mock_response = Mock()\n        mock_response.text = \"Filtered model list\"\n        mock_response.raise_for_status.return_value = None\n        mock_get.return_value = mock_response\n\n        result = api_client.list_models(filter_by=\"vocals\")\n\n        assert result == {\"text\": \"Filtered model list\"}\n        mock_get.assert_called_once_with(\"https://test-api.example.com/models?filter_sort_by=vocals\", timeout=10)\n\n    @patch(\"requests.Session.get\")","chunkId":"tests/unit/test_remote_api_client.py:27"}],[2944,{"filePath":"tests/unit/test_remote_api_client.py","startLine":291,"endLine":303,"chunkText":"        mock_get.assert_called_once_with(\"https://test-api.example.com/models?filter_sort_by=vocals\", timeout=10)\n\n    @patch(\"requests.Session.get\")\n    def test_get_server_version_success(self, mock_get, api_client):\n        \"\"\"Test successful server version retrieval.\"\"\"\n        mock_response = Mock()\n        mock_response.json.return_value = {\"version\": \"1.2.3\", \"status\": \"healthy\"}\n        mock_response.raise_for_status.return_value = None\n        mock_get.return_value = mock_response\n\n        result = api_client.get_server_version()\n\n        assert result == \"1.2.3\"","chunkId":"tests/unit/test_remote_api_client.py:28"}],[2945,{"filePath":"tests/unit/test_remote_api_client.py","startLine":301,"endLine":315,"chunkText":"        result = api_client.get_server_version()\n\n        assert result == \"1.2.3\"\n        mock_get.assert_called_once_with(\"https://test-api.example.com/health\", timeout=10)\n\n    @patch(\"requests.Session.get\")\n    def test_get_server_version_no_version(self, mock_get, api_client):\n        \"\"\"Test server version retrieval when version is not in response.\"\"\"\n        mock_response = Mock()\n        mock_response.json.return_value = {\"status\": \"healthy\"}\n        mock_response.raise_for_status.return_value = None\n        mock_get.return_value = mock_response\n\n        result = api_client.get_server_version()\n","chunkId":"tests/unit/test_remote_api_client.py:29"}],[2946,{"filePath":"tests/unit/test_remote_api_client.py","startLine":313,"endLine":328,"chunkText":"\n        result = api_client.get_server_version()\n\n        assert result == \"unknown\"\n\n    @patch(\"requests.Session.get\")\n    def test_get_server_version_error(self, mock_get, api_client):\n        \"\"\"Test server version retrieval with error.\"\"\"\n        mock_get.side_effect = requests.RequestException(\"Health check failed\")\n\n        with pytest.raises(requests.RequestException):\n            api_client.get_server_version()\n\n    @patch.object(AudioSeparatorAPIClient, \"separate_audio\")\n    @patch.object(AudioSeparatorAPIClient, \"get_job_status\")\n    @patch.object(AudioSeparatorAPIClient, \"download_file\")","chunkId":"tests/unit/test_remote_api_client.py:30"}],[2947,{"filePath":"tests/unit/test_remote_api_client.py","startLine":326,"endLine":335,"chunkText":"    @patch.object(AudioSeparatorAPIClient, \"separate_audio\")\n    @patch.object(AudioSeparatorAPIClient, \"get_job_status\")\n    @patch.object(AudioSeparatorAPIClient, \"download_file\")\n    @patch(\"time.sleep\")\n    def test_separate_audio_and_wait_success(self, mock_sleep, mock_download, mock_status, mock_separate, api_client, mock_audio_file):\n        \"\"\"Test the convenience method for separating audio and waiting for completion.\"\"\"\n        # Mock separation submission\n        mock_separate.return_value = {\"task_id\": \"test-task-123\"}\n\n        # Mock status polling - first processing, then completed","chunkId":"tests/unit/test_remote_api_client.py:31"}],[2948,{"filePath":"tests/unit/test_remote_api_client.py","startLine":333,"endLine":343,"chunkText":"        mock_separate.return_value = {\"task_id\": \"test-task-123\"}\n\n        # Mock status polling - first processing, then completed\n        mock_status.side_effect = [{\"status\": \"processing\", \"progress\": 25}, {\"status\": \"processing\", \"progress\": 50}, {\"status\": \"completed\", \"files\": [\"output1.wav\", \"output2.wav\"]}]\n\n        # Mock file downloads\n        mock_download.side_effect = [\"output1.wav\", \"output2.wav\"]\n\n        result = api_client.separate_audio_and_wait(mock_audio_file, model=\"test_model.ckpt\", timeout=60, poll_interval=5, download=True)\n\n        # Verify the result","chunkId":"tests/unit/test_remote_api_client.py:32"}],[2949,{"filePath":"tests/unit/test_remote_api_client.py","startLine":341,"endLine":352,"chunkText":"        result = api_client.separate_audio_and_wait(mock_audio_file, model=\"test_model.ckpt\", timeout=60, poll_interval=5, download=True)\n\n        # Verify the result\n        assert result[\"status\"] == \"completed\"\n        assert result[\"task_id\"] == \"test-task-123\"\n        assert result[\"files\"] == [\"output1.wav\", \"output2.wav\"]\n        assert result[\"downloaded_files\"] == [\"output1.wav\", \"output2.wav\"]\n\n        # Verify method calls\n        mock_separate.assert_called_once()\n        assert mock_status.call_count == 3\n        assert mock_download.call_count == 2","chunkId":"tests/unit/test_remote_api_client.py:33"}],[2950,{"filePath":"tests/unit/test_remote_api_client.py","startLine":350,"endLine":360,"chunkText":"        mock_separate.assert_called_once()\n        assert mock_status.call_count == 3\n        assert mock_download.call_count == 2\n        assert mock_sleep.call_count == 2  # Two polling iterations\n\n    @patch.object(AudioSeparatorAPIClient, \"separate_audio\")\n    @patch.object(AudioSeparatorAPIClient, \"get_job_status\")\n    @patch(\"time.sleep\")\n    def test_separate_audio_and_wait_error(self, mock_sleep, mock_status, mock_separate, api_client, mock_audio_file):\n        \"\"\"Test the convenience method when job fails.\"\"\"\n        mock_separate.return_value = {\"task_id\": \"test-task-456\"}","chunkId":"tests/unit/test_remote_api_client.py:34"}],[2951,{"filePath":"tests/unit/test_remote_api_client.py","startLine":358,"endLine":368,"chunkText":"    def test_separate_audio_and_wait_error(self, mock_sleep, mock_status, mock_separate, api_client, mock_audio_file):\n        \"\"\"Test the convenience method when job fails.\"\"\"\n        mock_separate.return_value = {\"task_id\": \"test-task-456\"}\n        mock_status.side_effect = [{\"status\": \"processing\", \"progress\": 25}, {\"status\": \"error\", \"error\": \"Processing failed\"}]\n\n        result = api_client.separate_audio_and_wait(mock_audio_file, timeout=60, poll_interval=5)\n\n        assert result[\"status\"] == \"error\"\n        assert result[\"error\"] == \"Processing failed\"\n        assert result[\"files\"] == []\n","chunkId":"tests/unit/test_remote_api_client.py:35"}],[2952,{"filePath":"tests/unit/test_remote_api_client.py","startLine":366,"endLine":376,"chunkText":"        assert result[\"error\"] == \"Processing failed\"\n        assert result[\"files\"] == []\n\n    @patch.object(AudioSeparatorAPIClient, \"separate_audio\")\n    @patch.object(AudioSeparatorAPIClient, \"get_job_status\")\n    @patch(\"time.sleep\")\n    def test_separate_audio_and_wait_timeout(self, mock_sleep, mock_status, mock_separate, api_client, mock_audio_file):\n        \"\"\"Test the convenience method when polling times out.\"\"\"\n        mock_separate.return_value = {\"task_id\": \"test-task-789\"}\n        mock_status.return_value = {\"status\": \"processing\", \"progress\": 25}\n","chunkId":"tests/unit/test_remote_api_client.py:36"}],[2953,{"filePath":"tests/unit/test_remote_api_client.py","startLine":374,"endLine":385,"chunkText":"        mock_separate.return_value = {\"task_id\": \"test-task-789\"}\n        mock_status.return_value = {\"status\": \"processing\", \"progress\": 25}\n\n        result = api_client.separate_audio_and_wait(mock_audio_file, timeout=1, poll_interval=0.1)\n\n        assert result[\"status\"] == \"timeout\"\n        assert \"timed out\" in result[\"error\"]\n\n    @patch.object(AudioSeparatorAPIClient, \"separate_audio\")\n    @patch.object(AudioSeparatorAPIClient, \"get_job_status\")\n    @patch.object(AudioSeparatorAPIClient, \"download_file\")\n    @patch(\"time.sleep\")","chunkId":"tests/unit/test_remote_api_client.py:37"}],[2954,{"filePath":"tests/unit/test_remote_api_client.py","startLine":383,"endLine":392,"chunkText":"    @patch.object(AudioSeparatorAPIClient, \"get_job_status\")\n    @patch.object(AudioSeparatorAPIClient, \"download_file\")\n    @patch(\"time.sleep\")\n    def test_separate_audio_and_wait_no_download(self, mock_sleep, mock_download, mock_status, mock_separate, api_client, mock_audio_file):\n        \"\"\"Test the convenience method without downloading files.\"\"\"\n        mock_separate.return_value = {\"task_id\": \"test-task-123\"}\n        mock_status.side_effect = [{\"status\": \"completed\", \"files\": [\"output1.wav\", \"output2.wav\"]}]\n\n        result = api_client.separate_audio_and_wait(mock_audio_file, download=False)\n","chunkId":"tests/unit/test_remote_api_client.py:38"}],[2955,{"filePath":"tests/unit/test_remote_api_client.py","startLine":390,"endLine":401,"chunkText":"\n        result = api_client.separate_audio_and_wait(mock_audio_file, download=False)\n\n        assert result[\"status\"] == \"completed\"\n        assert \"downloaded_files\" not in result\n        mock_download.assert_not_called()\n\n    @patch.object(AudioSeparatorAPIClient, \"separate_audio\")\n    @patch.object(AudioSeparatorAPIClient, \"get_job_status\")\n    @patch.object(AudioSeparatorAPIClient, \"download_file\")\n    @patch(\"time.sleep\")\n    def test_separate_audio_and_wait_with_output_dir(self, mock_sleep, mock_download, mock_status, mock_separate, api_client, mock_audio_file):","chunkId":"tests/unit/test_remote_api_client.py:39"}],[2956,{"filePath":"tests/unit/test_remote_api_client.py","startLine":399,"endLine":406,"chunkText":"    @patch.object(AudioSeparatorAPIClient, \"download_file\")\n    @patch(\"time.sleep\")\n    def test_separate_audio_and_wait_with_output_dir(self, mock_sleep, mock_download, mock_status, mock_separate, api_client, mock_audio_file):\n        \"\"\"Test the convenience method with custom output directory.\"\"\"\n        mock_separate.return_value = {\"task_id\": \"test-task-123\"}\n        mock_status.side_effect = [{\"status\": \"completed\", \"files\": [\"output1.wav\"]}]\n        mock_download.return_value = \"custom_dir/output1.wav\"\n","chunkId":"tests/unit/test_remote_api_client.py:40"}],[2957,{"filePath":"tests/unit/test_remote_api_client.py","startLine":404,"endLine":413,"chunkText":"        mock_status.side_effect = [{\"status\": \"completed\", \"files\": [\"output1.wav\"]}]\n        mock_download.return_value = \"custom_dir/output1.wav\"\n\n        result = api_client.separate_audio_and_wait(mock_audio_file, download=True, output_dir=\"custom_dir\")\n\n        # Verify download was called with custom output path\n        mock_download.assert_called_once_with(\"test-task-123\", \"output1.wav\", \"custom_dir/output1.wav\")\n        assert result[\"downloaded_files\"] == [\"custom_dir/output1.wav\"]\n\n    @patch.object(AudioSeparatorAPIClient, \"separate_audio\")","chunkId":"tests/unit/test_remote_api_client.py:41"}],[2958,{"filePath":"tests/unit/test_remote_api_client.py","startLine":411,"endLine":420,"chunkText":"        assert result[\"downloaded_files\"] == [\"custom_dir/output1.wav\"]\n\n    @patch.object(AudioSeparatorAPIClient, \"separate_audio\")\n    @patch.object(AudioSeparatorAPIClient, \"get_job_status\")\n    @patch.object(AudioSeparatorAPIClient, \"download_file\")\n    @patch(\"time.sleep\")\n    def test_separate_audio_and_wait_with_special_character_filenames(self, mock_sleep, mock_download, mock_status, mock_separate, api_client, mock_audio_file):\n        \"\"\"Test the convenience method with filenames containing special characters.\"\"\"\n        mock_separate.return_value = {\"task_id\": \"test-task-456\"}\n        ","chunkId":"tests/unit/test_remote_api_client.py:42"}],[2959,{"filePath":"tests/unit/test_remote_api_client.py","startLine":418,"endLine":427,"chunkText":"        \"\"\"Test the convenience method with filenames containing special characters.\"\"\"\n        mock_separate.return_value = {\"task_id\": \"test-task-456\"}\n        \n        # Simulate files with special characters like those in the bug report\n        files_with_special_chars = [\n            \"Song (Vocals model_bs_roformer_ep_317_sdr_12.9755.ckpt)_(Instrumental)_mel_band_roformer.flac\",\n            \"Song (Vocals model_bs_roformer_ep_317_sdr_12.9755.ckpt)_(Vocals)_mel_band_roformer.flac\"\n        ]\n        mock_status.side_effect = [{\"status\": \"completed\", \"files\": files_with_special_chars}]\n        ","chunkId":"tests/unit/test_remote_api_client.py:43"}],[2960,{"filePath":"tests/unit/test_remote_api_client.py","startLine":425,"endLine":438,"chunkText":"        ]\n        mock_status.side_effect = [{\"status\": \"completed\", \"files\": files_with_special_chars}]\n        \n        # Mock successful downloads\n        mock_download.side_effect = files_with_special_chars\n\n        result = api_client.separate_audio_and_wait(mock_audio_file, download=True)\n\n        # Verify both files were downloaded despite having special characters\n        assert result[\"status\"] == \"completed\"\n        assert result[\"downloaded_files\"] == files_with_special_chars\n        assert mock_download.call_count == 2\n        \n        # Verify download was called with the correct filenames","chunkId":"tests/unit/test_remote_api_client.py:44"}],[2961,{"filePath":"tests/unit/test_remote_api_client.py","startLine":436,"endLine":447,"chunkText":"        assert mock_download.call_count == 2\n        \n        # Verify download was called with the correct filenames\n        expected_calls = [\n            (\"test-task-456\", files_with_special_chars[0], files_with_special_chars[0]),\n            (\"test-task-456\", files_with_special_chars[1], files_with_special_chars[1])\n        ]\n        actual_calls = [call.args for call in mock_download.call_args_list]\n        assert actual_calls == expected_calls\n\n    @patch(\"requests.Session.get\")\n    @patch(\"builtins.open\", new_callable=mock_open)","chunkId":"tests/unit/test_remote_api_client.py:45"}],[2962,{"filePath":"tests/unit/test_remote_api_client.py","startLine":445,"endLine":456,"chunkText":"\n    @patch(\"requests.Session.get\")\n    @patch(\"builtins.open\", new_callable=mock_open)\n    def test_download_file_server_side_url_decoding_scenario(self, mock_file, mock_get, api_client):\n        \"\"\"Test that the client properly URL-encodes filenames that require server-side decoding.\"\"\"\n        mock_response = Mock()\n        mock_response.content = b\"fake audio file content\"\n        mock_response.status_code = 200\n        mock_response.raise_for_status.return_value = None\n        mock_get.return_value = mock_response\n\n        # Test the exact problematic filename from the bug report","chunkId":"tests/unit/test_remote_api_client.py:46"}],[2963,{"filePath":"tests/unit/test_remote_api_client.py","startLine":454,"endLine":460,"chunkText":"        mock_get.return_value = mock_response\n\n        # Test the exact problematic filename from the bug report\n        problematic_filename = \"Bloc Party - The Prayer (Vocals model_bs_roformer_ep_317_sdr_12.9755.ckpt)_(Instrumental)_mel_band_roformer_karaoke_aufr33_viperx_sdr_10.flac\"\n        result = api_client.download_file(\"test-task-bug\", problematic_filename)\n\n        # Verify URL was properly encoded - this is the exact URL that should be sent","chunkId":"tests/unit/test_remote_api_client.py:47"}],[2964,{"filePath":"tests/unit/test_remote_api_client.py","startLine":458,"endLine":465,"chunkText":"        result = api_client.download_file(\"test-task-bug\", problematic_filename)\n\n        # Verify URL was properly encoded - this is the exact URL that should be sent\n        expected_url = \"https://test-api.example.com/download/test-task-bug/Bloc%20Party%20-%20The%20Prayer%20%28Vocals%20model_bs_roformer_ep_317_sdr_12.9755.ckpt%29_%28Instrumental%29_mel_band_roformer_karaoke_aufr33_viperx_sdr_10.flac\"\n        mock_get.assert_called_once_with(expected_url, timeout=60)\n        assert result == problematic_filename\n        mock_file.assert_called_once_with(problematic_filename, \"wb\")\n","chunkId":"tests/unit/test_remote_api_client.py:48"}],[2965,{"filePath":"tests/unit/test_remote_api_client.py","startLine":463,"endLine":475,"chunkText":"        assert result == problematic_filename\n        mock_file.assert_called_once_with(problematic_filename, \"wb\")\n\n    @patch(\"requests.Session.get\")\n    @patch(\"builtins.open\", new_callable=mock_open)\n    def test_download_file_by_hash(self, mock_file, mock_get, api_client):\n        \"\"\"Test downloading a file using hash identifier.\"\"\"\n        mock_response = Mock()\n        mock_response.content = b\"fake audio file content\"\n        mock_response.status_code = 200\n        mock_response.raise_for_status.return_value = None\n        mock_get.return_value = mock_response\n","chunkId":"tests/unit/test_remote_api_client.py:49"}],[2966,{"filePath":"tests/unit/test_remote_api_client.py","startLine":473,"endLine":484,"chunkText":"        mock_response.raise_for_status.return_value = None\n        mock_get.return_value = mock_response\n\n        # Test the new hash-based download method\n        filename = \"Long Filename With Spaces (Vocals).flac\"\n        file_hash = \"abc123def456\"\n        result = api_client.download_file_by_hash(\"test-task-hash\", file_hash, filename)\n\n        # Verify hash was used in URL instead of filename\n        expected_url = \"https://test-api.example.com/download/test-task-hash/abc123def456\"\n        mock_get.assert_called_once_with(expected_url, timeout=60)\n        assert result == filename","chunkId":"tests/unit/test_remote_api_client.py:50"}],[2967,{"filePath":"tests/unit/test_remote_api_client.py","startLine":482,"endLine":490,"chunkText":"        expected_url = \"https://test-api.example.com/download/test-task-hash/abc123def456\"\n        mock_get.assert_called_once_with(expected_url, timeout=60)\n        assert result == filename\n        mock_file.assert_called_once_with(filename, \"wb\")\n\n    @patch.object(AudioSeparatorAPIClient, \"separate_audio\")\n    @patch.object(AudioSeparatorAPIClient, \"get_job_status\")\n    @patch.object(AudioSeparatorAPIClient, \"download_file_by_hash\")\n    @patch(\"time.sleep\")","chunkId":"tests/unit/test_remote_api_client.py:51"}],[2968,{"filePath":"tests/unit/test_remote_api_client.py","startLine":489,"endLine":497,"chunkText":"    @patch.object(AudioSeparatorAPIClient, \"download_file_by_hash\")\n    @patch(\"time.sleep\")\n    def test_separate_audio_and_wait_with_hash_format(self, mock_sleep, mock_download_hash, mock_status, mock_separate, api_client, mock_audio_file):\n        \"\"\"Test the convenience method with hash-based file format.\"\"\"\n        mock_separate.return_value = {\"task_id\": \"test-task-hash\"}\n        \n        # Simulate new hash-based format\n        files_with_hashes = {\n            \"abc123def456\": \"Song (Vocals model_bs_roformer_ep_317_sdr_12.9755.ckpt)_(Instrumental)_mel_band_roformer.flac\",","chunkId":"tests/unit/test_remote_api_client.py:52"}],[2969,{"filePath":"tests/unit/test_remote_api_client.py","startLine":495,"endLine":504,"chunkText":"        # Simulate new hash-based format\n        files_with_hashes = {\n            \"abc123def456\": \"Song (Vocals model_bs_roformer_ep_317_sdr_12.9755.ckpt)_(Instrumental)_mel_band_roformer.flac\",\n            \"def456ghi789\": \"Song (Vocals model_bs_roformer_ep_317_sdr_12.9755.ckpt)_(Vocals)_mel_band_roformer.flac\"\n        }\n        mock_status.side_effect = [{\"status\": \"completed\", \"files\": files_with_hashes}]\n        \n        # Mock successful downloads\n        mock_download_hash.side_effect = list(files_with_hashes.values())\n","chunkId":"tests/unit/test_remote_api_client.py:53"}],[2970,{"filePath":"tests/unit/test_remote_api_client.py","startLine":504,"endLine":514,"chunkText":"\n        result = api_client.separate_audio_and_wait(mock_audio_file, download=True)\n\n        # Verify both files were downloaded using hash method\n        assert result[\"status\"] == \"completed\"\n        assert result[\"downloaded_files\"] == list(files_with_hashes.values())\n        assert mock_download_hash.call_count == 2\n        \n        # Verify download was called with the correct hashes and filenames\n        expected_calls = [\n            (\"test-task-hash\", \"abc123def456\", list(files_with_hashes.values())[0], list(files_with_hashes.values())[0]),","chunkId":"tests/unit/test_remote_api_client.py:54"}],[2971,{"filePath":"tests/unit/test_remote_api_client.py","startLine":512,"endLine":519,"chunkText":"        # Verify download was called with the correct hashes and filenames\n        expected_calls = [\n            (\"test-task-hash\", \"abc123def456\", list(files_with_hashes.values())[0], list(files_with_hashes.values())[0]),\n            (\"test-task-hash\", \"def456ghi789\", list(files_with_hashes.values())[1], list(files_with_hashes.values())[1])\n        ]\n        actual_calls = [call.args for call in mock_download_hash.call_args_list]\n        assert actual_calls == expected_calls\n","chunkId":"tests/unit/test_remote_api_client.py:55"}],[2972,{"filePath":"tests/unit/test_remote_cli.py","startLine":0,"endLine":22,"chunkText":"import json\nimport pytest\nimport argparse\nimport logging\nimport os\nimport sys\nfrom unittest.mock import Mock, patch, MagicMock\nfrom unittest.mock import call\nimport tempfile\n\nfrom audio_separator.remote.cli import main, handle_separate_command, handle_status_command, handle_models_command, handle_download_command\nfrom audio_separator.remote import AudioSeparatorAPIClient\n\n\n@pytest.fixture\ndef mock_api_client():\n    \"\"\"Create a mock API client for testing.\"\"\"\n    return Mock(spec=AudioSeparatorAPIClient)\n\n\n@pytest.fixture\ndef mock_logger():\n    \"\"\"Create a mock logger for testing.\"\"\"","chunkId":"tests/unit/test_remote_cli.py:0"}],[2973,{"filePath":"tests/unit/test_remote_cli.py","startLine":20,"endLine":40,"chunkText":"@pytest.fixture\ndef mock_logger():\n    \"\"\"Create a mock logger for testing.\"\"\"\n    return Mock(spec=logging.Logger)\n\n\n@pytest.fixture\ndef mock_audio_file():\n    \"\"\"Create a temporary audio file for testing.\"\"\"\n    with tempfile.NamedTemporaryFile(suffix=\".wav\", delete=False) as f:\n        f.write(b\"fake audio content\")\n        yield f.name\n    os.unlink(f.name)\n\n\nclass TestRemoteCLI:\n    \"\"\"Test the remote CLI functionality.\"\"\"\n\n    @patch('sys.argv', ['audio-separator-remote', '--version'])\n    @patch('audio_separator.remote.cli.metadata')\n    @patch('builtins.print')","chunkId":"tests/unit/test_remote_cli.py:1"}],[2974,{"filePath":"tests/unit/test_remote_cli.py","startLine":38,"endLine":51,"chunkText":"    @patch('sys.argv', ['audio-separator-remote', '--version'])\n    @patch('audio_separator.remote.cli.metadata')\n    @patch('builtins.print')\n    def test_version_command_no_api_url(self, mock_print, mock_metadata, mock_api_client):\n        \"\"\"Test version command without API URL.\"\"\"\n        mock_metadata.distribution.return_value.version = \"1.2.3\"\n        \n        with pytest.raises(SystemExit) as exc_info:\n            main()\n        \n        assert exc_info.value.code == 0\n        mock_print.assert_any_call(\"Client version: 1.2.3\")\n\n    @patch('sys.argv', ['audio-separator-remote', '--version'])","chunkId":"tests/unit/test_remote_cli.py:2"}],[2975,{"filePath":"tests/unit/test_remote_cli.py","startLine":49,"endLine":59,"chunkText":"        mock_print.assert_any_call(\"Client version: 1.2.3\")\n\n    @patch('sys.argv', ['audio-separator-remote', '--version'])\n    @patch('audio_separator.remote.cli.metadata')\n    @patch('audio_separator.remote.cli.AudioSeparatorAPIClient')\n    @patch('builtins.print')\n    @patch.dict(os.environ, {'AUDIO_SEPARATOR_API_URL': 'https://test-api.com'})\n    def test_version_command_with_api_url(self, mock_print, mock_client_class, mock_metadata):\n        \"\"\"Test version command with API URL.\"\"\"\n        mock_metadata.distribution.return_value.version = \"1.2.3\"\n        mock_client = Mock()","chunkId":"tests/unit/test_remote_cli.py:3"}],[2976,{"filePath":"tests/unit/test_remote_cli.py","startLine":57,"endLine":70,"chunkText":"        \"\"\"Test version command with API URL.\"\"\"\n        mock_metadata.distribution.return_value.version = \"1.2.3\"\n        mock_client = Mock()\n        mock_client.get_server_version.return_value = \"1.2.4\"\n        mock_client_class.return_value = mock_client\n        \n        with pytest.raises(SystemExit) as exc_info:\n            main()\n        \n        assert exc_info.value.code == 0\n        mock_print.assert_any_call(\"Client version: 1.2.3\")\n        mock_print.assert_any_call(\"Server version: 1.2.4\")\n\n    @patch('sys.argv', ['audio-separator-remote', '--version'])","chunkId":"tests/unit/test_remote_cli.py:4"}],[2977,{"filePath":"tests/unit/test_remote_cli.py","startLine":68,"endLine":77,"chunkText":"        mock_print.assert_any_call(\"Server version: 1.2.4\")\n\n    @patch('sys.argv', ['audio-separator-remote', '--version'])\n    @patch('audio_separator.remote.cli.metadata')\n    @patch('audio_separator.remote.cli.AudioSeparatorAPIClient')\n    @patch('builtins.print')\n    @patch.dict(os.environ, {'AUDIO_SEPARATOR_API_URL': 'https://test-api.com'})\n    def test_version_command_server_error(self, mock_print, mock_client_class, mock_metadata):\n        \"\"\"Test version command when server version retrieval fails.\"\"\"\n        mock_metadata.distribution.return_value.version = \"1.2.3\"","chunkId":"tests/unit/test_remote_cli.py:5"}],[2978,{"filePath":"tests/unit/test_remote_cli.py","startLine":75,"endLine":87,"chunkText":"    def test_version_command_server_error(self, mock_print, mock_client_class, mock_metadata):\n        \"\"\"Test version command when server version retrieval fails.\"\"\"\n        mock_metadata.distribution.return_value.version = \"1.2.3\"\n        mock_client = Mock()\n        mock_client.get_server_version.side_effect = Exception(\"Connection error\")\n        mock_client_class.return_value = mock_client\n        \n        with pytest.raises(SystemExit) as exc_info:\n            main()\n        \n        assert exc_info.value.code == 0\n        mock_print.assert_any_call(\"Client version: 1.2.3\")\n","chunkId":"tests/unit/test_remote_cli.py:6"}],[2979,{"filePath":"tests/unit/test_remote_cli.py","startLine":85,"endLine":98,"chunkText":"        assert exc_info.value.code == 0\n        mock_print.assert_any_call(\"Client version: 1.2.3\")\n\n    @patch('sys.argv', ['audio-separator-remote', 'separate', 'test.wav'])\n    @patch('builtins.print')\n    @patch.dict(os.environ, {}, clear=True)  # Clear all environment variables\n    def test_no_api_url_error(self, mock_print):\n        \"\"\"Test error when no API URL is provided.\"\"\"\n        with pytest.raises(SystemExit) as exc_info:\n            main()\n        \n        assert exc_info.value.code == 1\n\n    @patch('sys.argv', ['audio-separator-remote', 'separate', 'test.wav'])","chunkId":"tests/unit/test_remote_cli.py:7"}],[2980,{"filePath":"tests/unit/test_remote_cli.py","startLine":96,"endLine":108,"chunkText":"        assert exc_info.value.code == 1\n\n    @patch('sys.argv', ['audio-separator-remote', 'separate', 'test.wav'])\n    @patch('audio_separator.remote.cli.AudioSeparatorAPIClient')\n    @patch('audio_separator.remote.cli.handle_separate_command')\n    @patch.dict(os.environ, {'AUDIO_SEPARATOR_API_URL': 'https://test-api.com'})\n    def test_separate_command(self, mock_handle_separate, mock_client_class):\n        \"\"\"Test separate command execution.\"\"\"\n        mock_client = Mock()\n        mock_client_class.return_value = mock_client\n        \n        main()\n        ","chunkId":"tests/unit/test_remote_cli.py:8"}],[2981,{"filePath":"tests/unit/test_remote_cli.py","startLine":106,"endLine":117,"chunkText":"        \n        main()\n        \n        mock_handle_separate.assert_called_once()\n        # Verify the API client was called with the correct URL (don't check logger instance)\n        assert mock_client_class.call_count == 1\n        call_args = mock_client_class.call_args\n        assert call_args[0][0] == 'https://test-api.com'  # First argument should be the API URL\n\n    @patch('sys.argv', ['audio-separator-remote', 'status', 'task-123'])\n    @patch('audio_separator.remote.cli.AudioSeparatorAPIClient')\n    @patch('audio_separator.remote.cli.handle_status_command')","chunkId":"tests/unit/test_remote_cli.py:9"}],[2982,{"filePath":"tests/unit/test_remote_cli.py","startLine":115,"endLine":127,"chunkText":"    @patch('sys.argv', ['audio-separator-remote', 'status', 'task-123'])\n    @patch('audio_separator.remote.cli.AudioSeparatorAPIClient')\n    @patch('audio_separator.remote.cli.handle_status_command')\n    @patch.dict(os.environ, {'AUDIO_SEPARATOR_API_URL': 'https://test-api.com'})\n    def test_status_command(self, mock_handle_status, mock_client_class):\n        \"\"\"Test status command execution.\"\"\"\n        mock_client = Mock()\n        mock_client_class.return_value = mock_client\n        \n        main()\n        \n        mock_handle_status.assert_called_once()\n","chunkId":"tests/unit/test_remote_cli.py:10"}],[2983,{"filePath":"tests/unit/test_remote_cli.py","startLine":125,"endLine":140,"chunkText":"        \n        mock_handle_status.assert_called_once()\n\n    @patch('sys.argv', ['audio-separator-remote', 'models'])\n    @patch('audio_separator.remote.cli.AudioSeparatorAPIClient')\n    @patch('audio_separator.remote.cli.handle_models_command')\n    @patch.dict(os.environ, {'AUDIO_SEPARATOR_API_URL': 'https://test-api.com'})\n    def test_models_command(self, mock_handle_models, mock_client_class):\n        \"\"\"Test models command execution.\"\"\"\n        mock_client = Mock()\n        mock_client_class.return_value = mock_client\n        \n        main()\n        \n        mock_handle_models.assert_called_once()\n","chunkId":"tests/unit/test_remote_cli.py:11"}],[2984,{"filePath":"tests/unit/test_remote_cli.py","startLine":138,"endLine":150,"chunkText":"        \n        mock_handle_models.assert_called_once()\n\n    @patch('sys.argv', ['audio-separator-remote', 'download', 'task-123', 'file1.wav', 'file2.wav'])\n    @patch('audio_separator.remote.cli.AudioSeparatorAPIClient')\n    @patch('audio_separator.remote.cli.handle_download_command')\n    @patch.dict(os.environ, {'AUDIO_SEPARATOR_API_URL': 'https://test-api.com'})\n    def test_download_command(self, mock_handle_download, mock_client_class):\n        \"\"\"Test download command execution.\"\"\"\n        mock_client = Mock()\n        mock_client_class.return_value = mock_client\n        \n        main()","chunkId":"tests/unit/test_remote_cli.py:12"}],[2985,{"filePath":"tests/unit/test_remote_cli.py","startLine":148,"endLine":163,"chunkText":"        mock_client_class.return_value = mock_client\n        \n        main()\n        \n        mock_handle_download.assert_called_once()\n\n    @patch('sys.argv', ['audio-separator-remote', '--api_url', 'https://custom-api.com', 'models'])\n    @patch('audio_separator.remote.cli.AudioSeparatorAPIClient')\n    @patch('audio_separator.remote.cli.handle_models_command')\n    def test_custom_api_url(self, mock_handle_models, mock_client_class):\n        \"\"\"Test using custom API URL parameter.\"\"\"\n        mock_client = Mock()\n        mock_client_class.return_value = mock_client\n        \n        main()\n        ","chunkId":"tests/unit/test_remote_cli.py:13"}],[2986,{"filePath":"tests/unit/test_remote_cli.py","startLine":161,"endLine":172,"chunkText":"        \n        main()\n        \n        # Verify the API client was called with the correct custom URL\n        assert mock_client_class.call_count == 1\n        call_args = mock_client_class.call_args\n        assert call_args[0][0] == 'https://custom-api.com'  # First argument should be the custom API URL\n\n    @patch('sys.argv', ['audio-separator-remote', '--debug', 'models'])\n    @patch('audio_separator.remote.cli.AudioSeparatorAPIClient')\n    @patch('audio_separator.remote.cli.handle_models_command')\n    @patch.dict(os.environ, {'AUDIO_SEPARATOR_API_URL': 'https://test-api.com'})","chunkId":"tests/unit/test_remote_cli.py:14"}],[2987,{"filePath":"tests/unit/test_remote_cli.py","startLine":170,"endLine":182,"chunkText":"    @patch('audio_separator.remote.cli.AudioSeparatorAPIClient')\n    @patch('audio_separator.remote.cli.handle_models_command')\n    @patch.dict(os.environ, {'AUDIO_SEPARATOR_API_URL': 'https://test-api.com'})\n    def test_debug_logging(self, mock_handle_models, mock_client_class):\n        \"\"\"Test debug logging flag.\"\"\"\n        mock_client = Mock()\n        mock_client_class.return_value = mock_client\n        \n        main()\n        \n        # Verify debug logging is enabled by checking if logger level was set\n        mock_handle_models.assert_called_once()\n","chunkId":"tests/unit/test_remote_cli.py:15"}],[2988,{"filePath":"tests/unit/test_remote_cli.py","startLine":180,"endLine":194,"chunkText":"        # Verify debug logging is enabled by checking if logger level was set\n        mock_handle_models.assert_called_once()\n\n    def test_handle_separate_command_success(self, mock_api_client, mock_logger, mock_audio_file):\n        \"\"\"Test successful separate command handling.\"\"\"\n        # Mock arguments\n        args = Mock()\n        args.audio_files = [mock_audio_file]\n        args.model = \"test_model.ckpt\"\n        args.models = None\n        args.timeout = 600\n        args.poll_interval = 10\n        args.output_format = \"flac\"\n        args.output_bitrate = None\n        args.normalization = 0.9","chunkId":"tests/unit/test_remote_cli.py:16"}],[2989,{"filePath":"tests/unit/test_remote_cli.py","startLine":192,"endLine":209,"chunkText":"        args.output_format = \"flac\"\n        args.output_bitrate = None\n        args.normalization = 0.9\n        args.amplification = 0.0\n        args.single_stem = None\n        args.invert_spect = False\n        args.sample_rate = 44100\n        args.use_soundfile = False\n        args.use_autocast = False\n        args.custom_output_names = None\n        # MDX parameters\n        args.mdx_segment_size = 256\n        args.mdx_overlap = 0.25\n        args.mdx_batch_size = 1\n        args.mdx_hop_length = 1024\n        args.mdx_enable_denoise = False\n        # VR parameters\n        args.vr_batch_size = 1","chunkId":"tests/unit/test_remote_cli.py:17"}],[2990,{"filePath":"tests/unit/test_remote_cli.py","startLine":207,"endLine":222,"chunkText":"        args.mdx_enable_denoise = False\n        # VR parameters\n        args.vr_batch_size = 1\n        args.vr_window_size = 512\n        args.vr_aggression = 5\n        args.vr_enable_tta = False\n        args.vr_high_end_process = False\n        args.vr_enable_post_process = False\n        args.vr_post_process_threshold = 0.2\n        # Demucs parameters\n        args.demucs_segment_size = \"Default\"\n        args.demucs_shifts = 2\n        args.demucs_overlap = 0.25\n        args.demucs_segments_enabled = True\n        # MDXC parameters\n        args.mdxc_segment_size = 256","chunkId":"tests/unit/test_remote_cli.py:18"}],[2991,{"filePath":"tests/unit/test_remote_cli.py","startLine":220,"endLine":236,"chunkText":"        args.demucs_segments_enabled = True\n        # MDXC parameters\n        args.mdxc_segment_size = 256\n        args.mdxc_override_model_segment_size = False\n        args.mdxc_overlap = 8\n        args.mdxc_batch_size = 1\n        args.mdxc_pitch_shift = 0\n\n        # Mock successful API response\n        mock_api_client.separate_audio_and_wait.return_value = {\n            \"status\": \"completed\",\n            \"downloaded_files\": [\"output1.wav\", \"output2.wav\"]\n        }\n\n        handle_separate_command(args, mock_api_client, mock_logger)\n\n        # Verify API client was called with correct parameters","chunkId":"tests/unit/test_remote_cli.py:19"}],[2992,{"filePath":"tests/unit/test_remote_cli.py","startLine":234,"endLine":244,"chunkText":"        handle_separate_command(args, mock_api_client, mock_logger)\n\n        # Verify API client was called with correct parameters\n        mock_api_client.separate_audio_and_wait.assert_called_once()\n        call_args = mock_api_client.separate_audio_and_wait.call_args\n        assert call_args[0][0] == mock_audio_file  # First positional argument should be the audio file\n        kwargs = call_args[1]\n        assert kwargs[\"model\"] == \"test_model.ckpt\"\n        assert kwargs[\"timeout\"] == 600\n        assert kwargs[\"download\"] is True\n","chunkId":"tests/unit/test_remote_cli.py:20"}],[2993,{"filePath":"tests/unit/test_remote_cli.py","startLine":242,"endLine":254,"chunkText":"        assert kwargs[\"timeout\"] == 600\n        assert kwargs[\"download\"] is True\n\n    def test_handle_separate_command_with_multiple_models(self, mock_api_client, mock_logger, mock_audio_file):\n        \"\"\"Test separate command with multiple models.\"\"\"\n        args = Mock()\n        args.audio_files = [mock_audio_file]\n        args.model = None\n        args.models = [\"model1.ckpt\", \"model2.onnx\"]\n        args.timeout = 600\n        args.poll_interval = 10\n        # Set other required attributes\n        for attr in ['output_format', 'output_bitrate', 'normalization', 'amplification', 'single_stem',","chunkId":"tests/unit/test_remote_cli.py:21"}],[2994,{"filePath":"tests/unit/test_remote_cli.py","startLine":252,"endLine":257,"chunkText":"        args.poll_interval = 10\n        # Set other required attributes\n        for attr in ['output_format', 'output_bitrate', 'normalization', 'amplification', 'single_stem',\n                     'invert_spect', 'sample_rate', 'use_soundfile', 'use_autocast', 'custom_output_names',\n                     'mdx_segment_size', 'mdx_overlap', 'mdx_batch_size', 'mdx_hop_length', 'mdx_enable_denoise',\n                     'vr_batch_size', 'vr_window_size', 'vr_aggression', 'vr_enable_tta', 'vr_high_end_process',","chunkId":"tests/unit/test_remote_cli.py:22"}],[2995,{"filePath":"tests/unit/test_remote_cli.py","startLine":255,"endLine":259,"chunkText":"                     'invert_spect', 'sample_rate', 'use_soundfile', 'use_autocast', 'custom_output_names',\n                     'mdx_segment_size', 'mdx_overlap', 'mdx_batch_size', 'mdx_hop_length', 'mdx_enable_denoise',\n                     'vr_batch_size', 'vr_window_size', 'vr_aggression', 'vr_enable_tta', 'vr_high_end_process',\n                     'vr_enable_post_process', 'vr_post_process_threshold', 'demucs_segment_size', 'demucs_shifts',\n                     'demucs_overlap', 'demucs_segments_enabled', 'mdxc_segment_size', 'mdxc_override_model_segment_size',","chunkId":"tests/unit/test_remote_cli.py:23"}],[2996,{"filePath":"tests/unit/test_remote_cli.py","startLine":257,"endLine":264,"chunkText":"                     'vr_batch_size', 'vr_window_size', 'vr_aggression', 'vr_enable_tta', 'vr_high_end_process',\n                     'vr_enable_post_process', 'vr_post_process_threshold', 'demucs_segment_size', 'demucs_shifts',\n                     'demucs_overlap', 'demucs_segments_enabled', 'mdxc_segment_size', 'mdxc_override_model_segment_size',\n                     'mdxc_overlap', 'mdxc_batch_size', 'mdxc_pitch_shift']:\n            setattr(args, attr, None)\n\n        mock_api_client.separate_audio_and_wait.return_value = {\n            \"status\": \"completed\",","chunkId":"tests/unit/test_remote_cli.py:24"}],[2997,{"filePath":"tests/unit/test_remote_cli.py","startLine":262,"endLine":276,"chunkText":"\n        mock_api_client.separate_audio_and_wait.return_value = {\n            \"status\": \"completed\",\n            \"downloaded_files\": [\"output1.wav\", \"output2.wav\"]\n        }\n\n        handle_separate_command(args, mock_api_client, mock_logger)\n\n        call_args = mock_api_client.separate_audio_and_wait.call_args\n        kwargs = call_args[1]\n        assert kwargs[\"models\"] == [\"model1.ckpt\", \"model2.onnx\"]\n\n    def test_handle_separate_command_error(self, mock_api_client, mock_logger, mock_audio_file):\n        \"\"\"Test separate command with error.\"\"\"\n        args = Mock()","chunkId":"tests/unit/test_remote_cli.py:25"}],[2998,{"filePath":"tests/unit/test_remote_cli.py","startLine":274,"endLine":282,"chunkText":"    def test_handle_separate_command_error(self, mock_api_client, mock_logger, mock_audio_file):\n        \"\"\"Test separate command with error.\"\"\"\n        args = Mock()\n        args.audio_files = [mock_audio_file]\n        args.model = \"test_model.ckpt\"\n        args.models = None\n        # Set other required attributes\n        for attr in ['timeout', 'poll_interval', 'output_format', 'output_bitrate', 'normalization', 'amplification',\n                     'single_stem', 'invert_spect', 'sample_rate', 'use_soundfile', 'use_autocast', 'custom_output_names',","chunkId":"tests/unit/test_remote_cli.py:26"}],[2999,{"filePath":"tests/unit/test_remote_cli.py","startLine":280,"endLine":284,"chunkText":"        # Set other required attributes\n        for attr in ['timeout', 'poll_interval', 'output_format', 'output_bitrate', 'normalization', 'amplification',\n                     'single_stem', 'invert_spect', 'sample_rate', 'use_soundfile', 'use_autocast', 'custom_output_names',\n                     'mdx_segment_size', 'mdx_overlap', 'mdx_batch_size', 'mdx_hop_length', 'mdx_enable_denoise',\n                     'vr_batch_size', 'vr_window_size', 'vr_aggression', 'vr_enable_tta', 'vr_high_end_process',","chunkId":"tests/unit/test_remote_cli.py:27"}],[3000,{"filePath":"tests/unit/test_remote_cli.py","startLine":282,"endLine":286,"chunkText":"                     'single_stem', 'invert_spect', 'sample_rate', 'use_soundfile', 'use_autocast', 'custom_output_names',\n                     'mdx_segment_size', 'mdx_overlap', 'mdx_batch_size', 'mdx_hop_length', 'mdx_enable_denoise',\n                     'vr_batch_size', 'vr_window_size', 'vr_aggression', 'vr_enable_tta', 'vr_high_end_process',\n                     'vr_enable_post_process', 'vr_post_process_threshold', 'demucs_segment_size', 'demucs_shifts',\n                     'demucs_overlap', 'demucs_segments_enabled', 'mdxc_segment_size', 'mdxc_override_model_segment_size',","chunkId":"tests/unit/test_remote_cli.py:28"}],[3001,{"filePath":"tests/unit/test_remote_cli.py","startLine":284,"endLine":287,"chunkText":"                     'vr_batch_size', 'vr_window_size', 'vr_aggression', 'vr_enable_tta', 'vr_high_end_process',\n                     'vr_enable_post_process', 'vr_post_process_threshold', 'demucs_segment_size', 'demucs_shifts',\n                     'demucs_overlap', 'demucs_segments_enabled', 'mdxc_segment_size', 'mdxc_override_model_segment_size',\n                     'mdxc_overlap', 'mdxc_batch_size', 'mdxc_pitch_shift']:","chunkId":"tests/unit/test_remote_cli.py:29"}],[3002,{"filePath":"tests/unit/test_remote_cli.py","startLine":285,"endLine":287,"chunkText":"                     'vr_enable_post_process', 'vr_post_process_threshold', 'demucs_segment_size', 'demucs_shifts',\n                     'demucs_overlap', 'demucs_segments_enabled', 'mdxc_segment_size', 'mdxc_override_model_segment_size',\n                     'mdxc_overlap', 'mdxc_batch_size', 'mdxc_pitch_shift']:","chunkId":"tests/unit/test_remote_cli.py:30"}],[3003,{"filePath":"tests/unit/test_remote_cli.py","startLine":286,"endLine":287,"chunkText":"                     'demucs_overlap', 'demucs_segments_enabled', 'mdxc_segment_size', 'mdxc_override_model_segment_size',\n                     'mdxc_overlap', 'mdxc_batch_size', 'mdxc_pitch_shift']:","chunkId":"tests/unit/test_remote_cli.py:31"}],[3004,{"filePath":"tests/unit/test_remote_cli.py","startLine":287,"endLine":287,"chunkText":"                     'mdxc_overlap', 'mdxc_batch_size', 'mdxc_pitch_shift']:","chunkId":"tests/unit/test_remote_cli.py:32"}],[3005,{"filePath":"tests/unit/test_remote_cli.py","startLine":288,"endLine":288,"chunkText":"            setattr(args, attr, 0 if 'size' in attr or 'shift' in attr or 'batch' in attr or 'hop' in attr or 'aggression' in attr or 'overlap' in attr else False if 'enable' in attr or 'tta' in attr or 'process' in attr or 'spect' in attr or 'soundfile' in attr or 'autocast' in attr else None if 'output' in attr or 'single' in attr or 'custom' in attr else 600 if 'timeout' in attr else 10 if 'poll' in attr else 'Default' if 'demucs_segment' in attr else 0.25 if 'overlap' in attr and 'mdxc' not in attr else 512 if 'window' in attr else 44100 if 'sample' in attr else 'flac' if 'format' in attr else 0.9 if ","chunkId":"tests/unit/test_remote_cli.py:33"}],[3006,{"filePath":"tests/unit/test_remote_cli.py","startLine":289,"endLine":304,"chunkText":"\n        mock_api_client.separate_audio_and_wait.return_value = {\n            \"status\": \"error\",\n            \"error\": \"Processing failed\"\n        }\n\n        handle_separate_command(args, mock_api_client, mock_logger)\n\n        # Verify error was logged\n        mock_logger.error.assert_called()\n\n    def test_handle_separate_command_exception(self, mock_api_client, mock_logger, mock_audio_file):\n        \"\"\"Test separate command with exception.\"\"\"\n        args = Mock()\n        args.audio_files = [mock_audio_file]\n        # Set required attributes ","chunkId":"tests/unit/test_remote_cli.py:34"}],[3007,{"filePath":"tests/unit/test_remote_cli.py","startLine":302,"endLine":308,"chunkText":"        args = Mock()\n        args.audio_files = [mock_audio_file]\n        # Set required attributes \n        for attr in ['model', 'models', 'timeout', 'poll_interval', 'output_format', 'output_bitrate', 'normalization',\n                     'amplification', 'single_stem', 'invert_spect', 'sample_rate', 'use_soundfile', 'use_autocast',\n                     'custom_output_names', 'mdx_segment_size', 'mdx_overlap', 'mdx_batch_size', 'mdx_hop_length',\n                     'mdx_enable_denoise', 'vr_batch_size', 'vr_window_size', 'vr_aggression', 'vr_enable_tta',","chunkId":"tests/unit/test_remote_cli.py:35"}],[3008,{"filePath":"tests/unit/test_remote_cli.py","startLine":306,"endLine":310,"chunkText":"                     'amplification', 'single_stem', 'invert_spect', 'sample_rate', 'use_soundfile', 'use_autocast',\n                     'custom_output_names', 'mdx_segment_size', 'mdx_overlap', 'mdx_batch_size', 'mdx_hop_length',\n                     'mdx_enable_denoise', 'vr_batch_size', 'vr_window_size', 'vr_aggression', 'vr_enable_tta',\n                     'vr_high_end_process', 'vr_enable_post_process', 'vr_post_process_threshold', 'demucs_segment_size',\n                     'demucs_shifts', 'demucs_overlap', 'demucs_segments_enabled', 'mdxc_segment_size',","chunkId":"tests/unit/test_remote_cli.py:36"}],[3009,{"filePath":"tests/unit/test_remote_cli.py","startLine":308,"endLine":315,"chunkText":"                     'mdx_enable_denoise', 'vr_batch_size', 'vr_window_size', 'vr_aggression', 'vr_enable_tta',\n                     'vr_high_end_process', 'vr_enable_post_process', 'vr_post_process_threshold', 'demucs_segment_size',\n                     'demucs_shifts', 'demucs_overlap', 'demucs_segments_enabled', 'mdxc_segment_size',\n                     'mdxc_override_model_segment_size', 'mdxc_overlap', 'mdxc_batch_size', 'mdxc_pitch_shift']:\n            setattr(args, attr, None)\n\n        mock_api_client.separate_audio_and_wait.side_effect = Exception(\"API error\")\n","chunkId":"tests/unit/test_remote_cli.py:37"}],[3010,{"filePath":"tests/unit/test_remote_cli.py","startLine":313,"endLine":329,"chunkText":"\n        mock_api_client.separate_audio_and_wait.side_effect = Exception(\"API error\")\n\n        handle_separate_command(args, mock_api_client, mock_logger)\n\n        # Verify error was logged\n        mock_logger.error.assert_called()\n\n    def test_handle_status_command_success(self, mock_api_client, mock_logger):\n        \"\"\"Test successful status command handling.\"\"\"\n        args = Mock()\n        args.task_id = \"test-task-123\"\n\n        mock_api_client.get_job_status.return_value = {\n            \"status\": \"completed\",\n            \"progress\": 100,\n            \"current_model_index\": 0,","chunkId":"tests/unit/test_remote_cli.py:38"}],[3011,{"filePath":"tests/unit/test_remote_cli.py","startLine":327,"endLine":342,"chunkText":"            \"status\": \"completed\",\n            \"progress\": 100,\n            \"current_model_index\": 0,\n            \"total_models\": 1,\n            \"original_filename\": \"test.wav\",\n            \"models_used\": [\"test_model.ckpt\"],\n            \"files\": [\"output1.wav\", \"output2.wav\"]\n        }\n\n        handle_status_command(args, mock_api_client, mock_logger)\n\n        mock_api_client.get_job_status.assert_called_once_with(\"test-task-123\")\n        # Verify status information was logged\n        mock_logger.info.assert_called()\n\n    def test_handle_status_command_error_status(self, mock_api_client, mock_logger):","chunkId":"tests/unit/test_remote_cli.py:39"}],[3012,{"filePath":"tests/unit/test_remote_cli.py","startLine":340,"endLine":356,"chunkText":"        mock_logger.info.assert_called()\n\n    def test_handle_status_command_error_status(self, mock_api_client, mock_logger):\n        \"\"\"Test status command with error status.\"\"\"\n        args = Mock()\n        args.task_id = \"test-task-456\"\n\n        mock_api_client.get_job_status.return_value = {\n            \"status\": \"error\",\n            \"error\": \"Processing failed\"\n        }\n\n        handle_status_command(args, mock_api_client, mock_logger)\n\n        mock_api_client.get_job_status.assert_called_once_with(\"test-task-456\")\n        mock_logger.error.assert_called()\n","chunkId":"tests/unit/test_remote_cli.py:40"}],[3013,{"filePath":"tests/unit/test_remote_cli.py","startLine":354,"endLine":368,"chunkText":"        mock_api_client.get_job_status.assert_called_once_with(\"test-task-456\")\n        mock_logger.error.assert_called()\n\n    def test_handle_status_command_exception(self, mock_api_client, mock_logger):\n        \"\"\"Test status command with exception.\"\"\"\n        args = Mock()\n        args.task_id = \"test-task-789\"\n\n        mock_api_client.get_job_status.side_effect = Exception(\"API error\")\n\n        handle_status_command(args, mock_api_client, mock_logger)\n\n        mock_logger.error.assert_called()\n\n    def test_handle_models_command_pretty_format(self, mock_api_client, mock_logger):","chunkId":"tests/unit/test_remote_cli.py:41"}],[3014,{"filePath":"tests/unit/test_remote_cli.py","startLine":366,"endLine":381,"chunkText":"        mock_logger.error.assert_called()\n\n    def test_handle_models_command_pretty_format(self, mock_api_client, mock_logger):\n        \"\"\"Test models command with pretty format.\"\"\"\n        args = Mock()\n        args.format = \"pretty\"\n        args.filter = None\n\n        mock_api_client.list_models.return_value = {\n            \"text\": \"Model list in pretty format\"\n        }\n\n        with patch('builtins.print') as mock_print:\n            handle_models_command(args, mock_api_client, mock_logger)\n\n        mock_api_client.list_models.assert_called_once_with(\"pretty\", None)","chunkId":"tests/unit/test_remote_cli.py:42"}],[3015,{"filePath":"tests/unit/test_remote_cli.py","startLine":379,"endLine":392,"chunkText":"            handle_models_command(args, mock_api_client, mock_logger)\n\n        mock_api_client.list_models.assert_called_once_with(\"pretty\", None)\n        mock_print.assert_called_once_with(\"Model list in pretty format\")\n\n    def test_handle_models_command_json_format(self, mock_api_client, mock_logger):\n        \"\"\"Test models command with JSON format.\"\"\"\n        args = Mock()\n        args.format = \"json\"\n        args.filter = \"vocals\"\n\n        models_data = {\"models\": [{\"name\": \"vocal_model\", \"type\": \"MDX\"}]}\n        mock_api_client.list_models.return_value = models_data\n","chunkId":"tests/unit/test_remote_cli.py:43"}],[3016,{"filePath":"tests/unit/test_remote_cli.py","startLine":390,"endLine":400,"chunkText":"        models_data = {\"models\": [{\"name\": \"vocal_model\", \"type\": \"MDX\"}]}\n        mock_api_client.list_models.return_value = models_data\n\n        with patch('json.dumps') as mock_json_dumps:\n            with patch('builtins.print') as mock_print:\n                mock_json_dumps.return_value = '{\"models\": [{\"name\": \"vocal_model\", \"type\": \"MDX\"}]}'\n                handle_models_command(args, mock_api_client, mock_logger)\n\n        mock_api_client.list_models.assert_called_once_with(\"json\", \"vocals\")\n        mock_json_dumps.assert_called_once_with(models_data, indent=2)\n","chunkId":"tests/unit/test_remote_cli.py:44"}],[3017,{"filePath":"tests/unit/test_remote_cli.py","startLine":398,"endLine":412,"chunkText":"        mock_api_client.list_models.assert_called_once_with(\"json\", \"vocals\")\n        mock_json_dumps.assert_called_once_with(models_data, indent=2)\n\n    def test_handle_models_command_exception(self, mock_api_client, mock_logger):\n        \"\"\"Test models command with exception.\"\"\"\n        args = Mock()\n        args.format = \"pretty\"\n        args.filter = None\n\n        mock_api_client.list_models.side_effect = Exception(\"API error\")\n\n        handle_models_command(args, mock_api_client, mock_logger)\n\n        mock_logger.error.assert_called()\n","chunkId":"tests/unit/test_remote_cli.py:45"}],[3018,{"filePath":"tests/unit/test_remote_cli.py","startLine":410,"endLine":425,"chunkText":"\n        mock_logger.error.assert_called()\n\n    def test_handle_download_command_success(self, mock_api_client, mock_logger):\n        \"\"\"Test successful download command handling.\"\"\"\n        args = Mock()\n        args.task_id = \"test-task-123\"\n        args.filenames = [\"output1.wav\", \"output2.wav\"]\n\n        mock_api_client.download_file.side_effect = [\"output1.wav\", \"output2.wav\"]\n\n        handle_download_command(args, mock_api_client, mock_logger)\n\n        # Verify download was called for each file\n        expected_calls = [\n            call(\"test-task-123\", \"output1.wav\"),","chunkId":"tests/unit/test_remote_cli.py:46"}],[3019,{"filePath":"tests/unit/test_remote_cli.py","startLine":423,"endLine":436,"chunkText":"        # Verify download was called for each file\n        expected_calls = [\n            call(\"test-task-123\", \"output1.wav\"),\n            call(\"test-task-123\", \"output2.wav\")\n        ]\n        mock_api_client.download_file.assert_has_calls(expected_calls)\n\n        # Verify success messages were logged\n        assert mock_logger.info.call_count >= 4  # At least 2 downloading + 2 downloaded messages\n\n    def test_handle_download_command_exception(self, mock_api_client, mock_logger):\n        \"\"\"Test download command with exception.\"\"\"\n        args = Mock()\n        args.task_id = \"test-task-456\"","chunkId":"tests/unit/test_remote_cli.py:47"}],[3020,{"filePath":"tests/unit/test_remote_cli.py","startLine":434,"endLine":443,"chunkText":"        \"\"\"Test download command with exception.\"\"\"\n        args = Mock()\n        args.task_id = \"test-task-456\"\n        args.filenames = [\"output1.wav\"]\n\n        mock_api_client.download_file.side_effect = Exception(\"Download error\")\n\n        handle_download_command(args, mock_api_client, mock_logger)\n\n        mock_logger.error.assert_called() ","chunkId":"tests/unit/test_remote_cli.py:48"}],[3021,{"filePath":"tests/unit/test_separator_chunking.py","startLine":0,"endLine":21,"chunkText":"\"\"\"\nUnit tests for Separator class chunking functionality with multi-stem support.\nTests the _process_with_chunking() method for 2, 4, and 6-stem models.\n\"\"\"\n\nimport pytest\nimport os\nimport re\nimport tempfile\nimport logging\nfrom unittest.mock import Mock, patch, MagicMock, call\nfrom pydub import AudioSegment\n\nfrom audio_separator.separator.separator import Separator\n\n\nclass TestSeparatorChunking:\n    \"\"\"Test cases for Separator chunking with multi-stem models.\"\"\"\n\n    def setup_method(self):\n        \"\"\"Set up test fixtures.\"\"\"\n        self.logger = logging.getLogger(__name__)","chunkId":"tests/unit/test_separator_chunking.py:0"}],[3022,{"filePath":"tests/unit/test_separator_chunking.py","startLine":19,"endLine":34,"chunkText":"    def setup_method(self):\n        \"\"\"Set up test fixtures.\"\"\"\n        self.logger = logging.getLogger(__name__)\n        self.temp_dir = tempfile.mkdtemp()\n\n    def teardown_method(self):\n        \"\"\"Clean up test fixtures.\"\"\"\n        import shutil\n        if os.path.exists(self.temp_dir):\n            shutil.rmtree(self.temp_dir)\n\n    @patch('audio_separator.separator.audio_chunking.AudioChunker')\n    def test_process_with_chunking_2_stems(self, mock_chunker_class):\n        \"\"\"Test chunking with 2-stem model (Vocals, Instrumental).\"\"\"\n        # Setup mock separator\n        separator = Mock(spec=Separator)","chunkId":"tests/unit/test_separator_chunking.py:1"}],[3023,{"filePath":"tests/unit/test_separator_chunking.py","startLine":32,"endLine":45,"chunkText":"        \"\"\"Test chunking with 2-stem model (Vocals, Instrumental).\"\"\"\n        # Setup mock separator\n        separator = Mock(spec=Separator)\n        separator.logger = self.logger\n        separator.output_dir = self.temp_dir\n        separator.output_format = \"WAV\"\n        separator.chunk_duration = 10.0\n        separator.model_instance = Mock()\n        separator.model_instance.output_dir = self.temp_dir\n\n        # Mock chunker behavior\n        mock_chunker = Mock()\n        mock_chunker_class.return_value = mock_chunker\n        mock_chunker.split_audio.return_value = [","chunkId":"tests/unit/test_separator_chunking.py:2"}],[3024,{"filePath":"tests/unit/test_separator_chunking.py","startLine":43,"endLine":56,"chunkText":"        mock_chunker = Mock()\n        mock_chunker_class.return_value = mock_chunker\n        mock_chunker.split_audio.return_value = [\n            os.path.join(self.temp_dir, \"chunk_0000.wav\"),\n            os.path.join(self.temp_dir, \"chunk_0001.wav\"),\n        ]\n\n        # Mock _separate_file to return 2 stems per chunk\n        separator._separate_file = Mock(side_effect=[\n            # Chunk 1 output\n            [\n                os.path.join(self.temp_dir, \"chunk_0000_(Vocals).wav\"),\n                os.path.join(self.temp_dir, \"chunk_0000_(Instrumental).wav\"),\n            ],","chunkId":"tests/unit/test_separator_chunking.py:3"}],[3025,{"filePath":"tests/unit/test_separator_chunking.py","startLine":54,"endLine":67,"chunkText":"                os.path.join(self.temp_dir, \"chunk_0000_(Vocals).wav\"),\n                os.path.join(self.temp_dir, \"chunk_0000_(Instrumental).wav\"),\n            ],\n            # Chunk 2 output\n            [\n                os.path.join(self.temp_dir, \"chunk_0001_(Vocals).wav\"),\n                os.path.join(self.temp_dir, \"chunk_0001_(Instrumental).wav\"),\n            ],\n        ])\n\n        # Import and call the actual method\n        from audio_separator.separator.separator import Separator as RealSeparator\n        result = RealSeparator._process_with_chunking(\n            separator,","chunkId":"tests/unit/test_separator_chunking.py:4"}],[3026,{"filePath":"tests/unit/test_separator_chunking.py","startLine":65,"endLine":79,"chunkText":"        from audio_separator.separator.separator import Separator as RealSeparator\n        result = RealSeparator._process_with_chunking(\n            separator,\n            os.path.join(self.temp_dir, \"test.wav\"),\n            custom_output_names=None\n        )\n\n        # Verify merge_chunks was called twice (once per stem)\n        assert mock_chunker.merge_chunks.call_count == 2\n\n        # Verify output contains 2 files\n        assert len(result) == 2\n\n        # Verify stem names in output\n        output_stems = [os.path.basename(path) for path in result]","chunkId":"tests/unit/test_separator_chunking.py:5"}],[3027,{"filePath":"tests/unit/test_separator_chunking.py","startLine":77,"endLine":88,"chunkText":"\n        # Verify stem names in output\n        output_stems = [os.path.basename(path) for path in result]\n        assert any(\"Instrumental\" in name for name in output_stems)\n        assert any(\"Vocals\" in name for name in output_stems)\n\n    @patch('audio_separator.separator.audio_chunking.AudioChunker')\n    def test_process_with_chunking_4_stems(self, mock_chunker_class):\n        \"\"\"Test chunking with 4-stem Demucs model (Drums, Bass, Other, Vocals).\"\"\"\n        # Setup mock separator\n        separator = Mock(spec=Separator)\n        separator.logger = self.logger","chunkId":"tests/unit/test_separator_chunking.py:6"}],[3028,{"filePath":"tests/unit/test_separator_chunking.py","startLine":86,"endLine":99,"chunkText":"        # Setup mock separator\n        separator = Mock(spec=Separator)\n        separator.logger = self.logger\n        separator.output_dir = self.temp_dir\n        separator.output_format = \"WAV\"\n        separator.chunk_duration = 10.0\n        separator.model_instance = Mock()\n        separator.model_instance.output_dir = self.temp_dir\n\n        # Mock chunker behavior\n        mock_chunker = Mock()\n        mock_chunker_class.return_value = mock_chunker\n        mock_chunker.split_audio.return_value = [\n            os.path.join(self.temp_dir, \"chunk_0000.wav\"),","chunkId":"tests/unit/test_separator_chunking.py:7"}],[3029,{"filePath":"tests/unit/test_separator_chunking.py","startLine":97,"endLine":109,"chunkText":"        mock_chunker_class.return_value = mock_chunker\n        mock_chunker.split_audio.return_value = [\n            os.path.join(self.temp_dir, \"chunk_0000.wav\"),\n            os.path.join(self.temp_dir, \"chunk_0001.wav\"),\n        ]\n\n        # Mock _separate_file to return 4 stems per chunk\n        separator._separate_file = Mock(side_effect=[\n            # Chunk 1 output (4 stems)\n            [\n                os.path.join(self.temp_dir, \"chunk_0000_(Drums).wav\"),\n                os.path.join(self.temp_dir, \"chunk_0000_(Bass).wav\"),\n                os.path.join(self.temp_dir, \"chunk_0000_(Other).wav\"),","chunkId":"tests/unit/test_separator_chunking.py:8"}],[3030,{"filePath":"tests/unit/test_separator_chunking.py","startLine":107,"endLine":116,"chunkText":"                os.path.join(self.temp_dir, \"chunk_0000_(Drums).wav\"),\n                os.path.join(self.temp_dir, \"chunk_0000_(Bass).wav\"),\n                os.path.join(self.temp_dir, \"chunk_0000_(Other).wav\"),\n                os.path.join(self.temp_dir, \"chunk_0000_(Vocals).wav\"),\n            ],\n            # Chunk 2 output (4 stems)\n            [\n                os.path.join(self.temp_dir, \"chunk_0001_(Drums).wav\"),\n                os.path.join(self.temp_dir, \"chunk_0001_(Bass).wav\"),\n                os.path.join(self.temp_dir, \"chunk_0001_(Other).wav\"),","chunkId":"tests/unit/test_separator_chunking.py:9"}],[3031,{"filePath":"tests/unit/test_separator_chunking.py","startLine":114,"endLine":126,"chunkText":"                os.path.join(self.temp_dir, \"chunk_0001_(Drums).wav\"),\n                os.path.join(self.temp_dir, \"chunk_0001_(Bass).wav\"),\n                os.path.join(self.temp_dir, \"chunk_0001_(Other).wav\"),\n                os.path.join(self.temp_dir, \"chunk_0001_(Vocals).wav\"),\n            ],\n        ])\n\n        # Import and call the actual method\n        from audio_separator.separator.separator import Separator as RealSeparator\n        result = RealSeparator._process_with_chunking(\n            separator,\n            os.path.join(self.temp_dir, \"test.wav\"),\n            custom_output_names=None","chunkId":"tests/unit/test_separator_chunking.py:10"}],[3032,{"filePath":"tests/unit/test_separator_chunking.py","startLine":124,"endLine":137,"chunkText":"            separator,\n            os.path.join(self.temp_dir, \"test.wav\"),\n            custom_output_names=None\n        )\n\n        # Verify merge_chunks was called 4 times (once per stem)\n        assert mock_chunker.merge_chunks.call_count == 4\n\n        # Verify output contains 4 files\n        assert len(result) == 4\n\n        # Verify all 4 stem names in output\n        output_stems = [os.path.basename(path) for path in result]\n        assert any(\"Drums\" in name for name in output_stems), f\"Drums not found in {output_stems}\"","chunkId":"tests/unit/test_separator_chunking.py:11"}],[3033,{"filePath":"tests/unit/test_separator_chunking.py","startLine":135,"endLine":142,"chunkText":"        # Verify all 4 stem names in output\n        output_stems = [os.path.basename(path) for path in result]\n        assert any(\"Drums\" in name for name in output_stems), f\"Drums not found in {output_stems}\"\n        assert any(\"Bass\" in name for name in output_stems), f\"Bass not found in {output_stems}\"\n        assert any(\"Other\" in name for name in output_stems), f\"Other not found in {output_stems}\"\n        assert any(\"Vocals\" in name for name in output_stems), f\"Vocals not found in {output_stems}\"\n\n    @patch('audio_separator.separator.audio_chunking.AudioChunker')","chunkId":"tests/unit/test_separator_chunking.py:12"}],[3034,{"filePath":"tests/unit/test_separator_chunking.py","startLine":140,"endLine":151,"chunkText":"        assert any(\"Vocals\" in name for name in output_stems), f\"Vocals not found in {output_stems}\"\n\n    @patch('audio_separator.separator.audio_chunking.AudioChunker')\n    def test_process_with_chunking_6_stems(self, mock_chunker_class):\n        \"\"\"Test chunking with 6-stem Demucs model.\"\"\"\n        # Setup mock separator\n        separator = Mock(spec=Separator)\n        separator.logger = self.logger\n        separator.output_dir = self.temp_dir\n        separator.output_format = \"WAV\"\n        separator.chunk_duration = 10.0\n        separator.model_instance = Mock()","chunkId":"tests/unit/test_separator_chunking.py:13"}],[3035,{"filePath":"tests/unit/test_separator_chunking.py","startLine":149,"endLine":163,"chunkText":"        separator.output_format = \"WAV\"\n        separator.chunk_duration = 10.0\n        separator.model_instance = Mock()\n        separator.model_instance.output_dir = self.temp_dir\n\n        # Mock chunker behavior\n        mock_chunker = Mock()\n        mock_chunker_class.return_value = mock_chunker\n        mock_chunker.split_audio.return_value = [\n            os.path.join(self.temp_dir, \"chunk_0000.wav\"),\n        ]\n\n        # Mock _separate_file to return 6 stems\n        separator._separate_file = Mock(return_value=[\n            os.path.join(self.temp_dir, \"chunk_0000_(Bass).wav\"),","chunkId":"tests/unit/test_separator_chunking.py:14"}],[3036,{"filePath":"tests/unit/test_separator_chunking.py","startLine":161,"endLine":171,"chunkText":"        # Mock _separate_file to return 6 stems\n        separator._separate_file = Mock(return_value=[\n            os.path.join(self.temp_dir, \"chunk_0000_(Bass).wav\"),\n            os.path.join(self.temp_dir, \"chunk_0000_(Drums).wav\"),\n            os.path.join(self.temp_dir, \"chunk_0000_(Other).wav\"),\n            os.path.join(self.temp_dir, \"chunk_0000_(Vocals).wav\"),\n            os.path.join(self.temp_dir, \"chunk_0000_(Guitar).wav\"),\n            os.path.join(self.temp_dir, \"chunk_0000_(Piano).wav\"),\n        ])\n\n        # Import and call the actual method","chunkId":"tests/unit/test_separator_chunking.py:15"}],[3037,{"filePath":"tests/unit/test_separator_chunking.py","startLine":169,"endLine":185,"chunkText":"        ])\n\n        # Import and call the actual method\n        from audio_separator.separator.separator import Separator as RealSeparator\n        result = RealSeparator._process_with_chunking(\n            separator,\n            os.path.join(self.temp_dir, \"test.wav\"),\n            custom_output_names=None\n        )\n\n        # Verify merge_chunks was called 6 times (once per stem)\n        assert mock_chunker.merge_chunks.call_count == 6\n\n        # Verify output contains 6 files\n        assert len(result) == 6\n\n        # Verify all 6 stem names in output","chunkId":"tests/unit/test_separator_chunking.py:16"}],[3038,{"filePath":"tests/unit/test_separator_chunking.py","startLine":183,"endLine":194,"chunkText":"        assert len(result) == 6\n\n        # Verify all 6 stem names in output\n        output_stems = [os.path.basename(path) for path in result]\n        assert any(\"Bass\" in name for name in output_stems)\n        assert any(\"Drums\" in name for name in output_stems)\n        assert any(\"Other\" in name for name in output_stems)\n        assert any(\"Vocals\" in name for name in output_stems)\n        assert any(\"Guitar\" in name for name in output_stems)\n        assert any(\"Piano\" in name for name in output_stems)\n\n    def test_stem_name_extraction_from_filename(self):","chunkId":"tests/unit/test_separator_chunking.py:17"}],[3039,{"filePath":"tests/unit/test_separator_chunking.py","startLine":192,"endLine":205,"chunkText":"        assert any(\"Piano\" in name for name in output_stems)\n\n    def test_stem_name_extraction_from_filename(self):\n        \"\"\"Test regex extraction of stem names from chunk filenames.\"\"\"\n        import re\n\n        # Test standard patterns\n        test_cases = [\n            (\"chunk_0000_(Vocals).wav\", \"Vocals\"),\n            (\"chunk_0001_(Instrumental).wav\", \"Instrumental\"),\n            (\"chunk_0000_(Drums).wav\", \"Drums\"),\n            (\"chunk_0000_(Bass).wav\", \"Bass\"),\n            (\"chunk_0000_(Other).wav\", \"Other\"),\n            (\"chunk_0000_(Guitar).wav\", \"Guitar\"),","chunkId":"tests/unit/test_separator_chunking.py:18"}],[3040,{"filePath":"tests/unit/test_separator_chunking.py","startLine":203,"endLine":215,"chunkText":"            (\"chunk_0000_(Bass).wav\", \"Bass\"),\n            (\"chunk_0000_(Other).wav\", \"Other\"),\n            (\"chunk_0000_(Guitar).wav\", \"Guitar\"),\n            (\"chunk_0000_(Piano).wav\", \"Piano\"),\n            (\"test_audio_(Vocals).flac\", \"Vocals\"),\n            (\"long_filename_with_spaces_(Backing Vocals).mp3\", \"Backing Vocals\"),\n        ]\n\n        pattern = r'_\\(([^)]+)\\)'\n\n        for filename, expected_stem in test_cases:\n            match = re.search(pattern, filename)\n            assert match is not None, f\"Pattern did not match for {filename}\"","chunkId":"tests/unit/test_separator_chunking.py:19"}],[3041,{"filePath":"tests/unit/test_separator_chunking.py","startLine":213,"endLine":228,"chunkText":"        for filename, expected_stem in test_cases:\n            match = re.search(pattern, filename)\n            assert match is not None, f\"Pattern did not match for {filename}\"\n            assert match.group(1) == expected_stem, f\"Expected {expected_stem}, got {match.group(1)}\"\n\n    def test_stem_name_extraction_fallback(self):\n        \"\"\"Test fallback behavior when filename pattern doesn't match.\"\"\"\n        import re\n\n        # Test filenames without the _(StemName) pattern\n        test_cases = [\n            \"chunk_0000.wav\",\n            \"output.mp3\",\n            \"vocals_only.flac\",\n        ]\n","chunkId":"tests/unit/test_separator_chunking.py:20"}],[3042,{"filePath":"tests/unit/test_separator_chunking.py","startLine":226,"endLine":241,"chunkText":"            \"vocals_only.flac\",\n        ]\n\n        pattern = r'_\\(([^)]+)\\)'\n\n        for filename in test_cases:\n            match = re.search(pattern, filename)\n            # Should not match - fallback logic should kick in\n            assert match is None, f\"Pattern should not match for {filename}\"\n\n    @patch('audio_separator.separator.audio_chunking.AudioChunker')\n    def test_chunking_preserves_stem_order(self, mock_chunker_class):\n        \"\"\"Test that stems are merged in sorted order.\"\"\"\n        # Setup mock separator\n        separator = Mock(spec=Separator)\n        separator.logger = self.logger","chunkId":"tests/unit/test_separator_chunking.py:21"}],[3043,{"filePath":"tests/unit/test_separator_chunking.py","startLine":239,"endLine":254,"chunkText":"        # Setup mock separator\n        separator = Mock(spec=Separator)\n        separator.logger = self.logger\n        separator.output_dir = self.temp_dir\n        separator.output_format = \"WAV\"\n        separator.chunk_duration = 10.0\n        separator.model_instance = Mock()\n        separator.model_instance.output_dir = self.temp_dir\n\n        # Mock chunker behavior\n        mock_chunker = Mock()\n        mock_chunker_class.return_value = mock_chunker\n        mock_chunker.split_audio.return_value = [\n            os.path.join(self.temp_dir, \"chunk_0000.wav\"),\n        ]\n","chunkId":"tests/unit/test_separator_chunking.py:22"}],[3044,{"filePath":"tests/unit/test_separator_chunking.py","startLine":252,"endLine":265,"chunkText":"            os.path.join(self.temp_dir, \"chunk_0000.wav\"),\n        ]\n\n        # Mock _separate_file with intentionally unsorted output\n        separator._separate_file = Mock(return_value=[\n            os.path.join(self.temp_dir, \"chunk_0000_(Vocals).wav\"),\n            os.path.join(self.temp_dir, \"chunk_0000_(Drums).wav\"),\n            os.path.join(self.temp_dir, \"chunk_0000_(Bass).wav\"),\n        ])\n\n        # Import and call the actual method\n        from audio_separator.separator.separator import Separator as RealSeparator\n        result = RealSeparator._process_with_chunking(\n            separator,","chunkId":"tests/unit/test_separator_chunking.py:23"}],[3045,{"filePath":"tests/unit/test_separator_chunking.py","startLine":263,"endLine":278,"chunkText":"        from audio_separator.separator.separator import Separator as RealSeparator\n        result = RealSeparator._process_with_chunking(\n            separator,\n            os.path.join(self.temp_dir, \"test.wav\"),\n            custom_output_names=None\n        )\n\n        # Verify stems are processed in sorted order (Bass, Drums, Vocals)\n        output_stems = [os.path.basename(path) for path in result]\n\n        # Extract stem names from output\n        stem_names = []\n        pattern = r'_\\(([^)]+)\\)'\n        for name in output_stems:\n            match = re.search(pattern, name)\n            if match:","chunkId":"tests/unit/test_separator_chunking.py:24"}],[3046,{"filePath":"tests/unit/test_separator_chunking.py","startLine":276,"endLine":284,"chunkText":"        for name in output_stems:\n            match = re.search(pattern, name)\n            if match:\n                stem_names.append(match.group(1))\n\n        # Verify sorted order\n        assert stem_names == sorted(stem_names), f\"Stems not in sorted order: {stem_names}\"\n\n","chunkId":"tests/unit/test_separator_chunking.py:25"}],[3047,{"filePath":"tests/unit/test_separator_chunking.py","startLine":285,"endLine":300,"chunkText":"class TestSeparatorChunkingLogic:\n    \"\"\"Test internal logic and state management of chunking.\"\"\"\n\n    def setup_method(self):\n        \"\"\"Set up test fixtures.\"\"\"\n        self.logger = logging.getLogger(__name__)\n        self.temp_dir = tempfile.mkdtemp()\n\n    def teardown_method(self):\n        \"\"\"Clean up test fixtures.\"\"\"\n        import shutil\n        if os.path.exists(self.temp_dir):\n            shutil.rmtree(self.temp_dir)\n\n    @patch('audio_separator.separator.audio_chunking.AudioChunker')\n    def test_state_restoration_after_chunking(self, mock_chunker_class):","chunkId":"tests/unit/test_separator_chunking.py:26"}],[3048,{"filePath":"tests/unit/test_separator_chunking.py","startLine":298,"endLine":311,"chunkText":"\n    @patch('audio_separator.separator.audio_chunking.AudioChunker')\n    def test_state_restoration_after_chunking(self, mock_chunker_class):\n        \"\"\"Test that separator state is restored after chunking.\"\"\"\n        # Setup mock separator\n        separator = Mock(spec=Separator)\n        separator.logger = self.logger\n        separator.output_dir = \"/original/output/dir\"\n        separator.output_format = \"WAV\"\n        separator.chunk_duration = 10.0\n        separator.model_instance = Mock()\n        separator.model_instance.output_dir = \"/original/model/output\"\n\n        # Mock chunker behavior","chunkId":"tests/unit/test_separator_chunking.py:27"}],[3049,{"filePath":"tests/unit/test_separator_chunking.py","startLine":309,"endLine":322,"chunkText":"        separator.model_instance.output_dir = \"/original/model/output\"\n\n        # Mock chunker behavior\n        mock_chunker = Mock()\n        mock_chunker_class.return_value = mock_chunker\n        mock_chunker.split_audio.return_value = [\n            os.path.join(self.temp_dir, \"chunk_0000.wav\"),\n        ]\n\n        # Track state changes during _separate_file call\n        state_during_processing = {}\n        def track_state(chunk_path, custom_names):\n            state_during_processing['chunk_duration'] = separator.chunk_duration\n            state_during_processing['output_dir'] = separator.output_dir","chunkId":"tests/unit/test_separator_chunking.py:28"}],[3050,{"filePath":"tests/unit/test_separator_chunking.py","startLine":320,"endLine":329,"chunkText":"        def track_state(chunk_path, custom_names):\n            state_during_processing['chunk_duration'] = separator.chunk_duration\n            state_during_processing['output_dir'] = separator.output_dir\n            state_during_processing['model_output_dir'] = separator.model_instance.output_dir\n            return [os.path.join(self.temp_dir, \"chunk_0000_(Vocals).wav\")]\n\n        separator._separate_file = Mock(side_effect=track_state)\n\n        # Import and call the actual method\n        from audio_separator.separator.separator import Separator as RealSeparator","chunkId":"tests/unit/test_separator_chunking.py:29"}],[3051,{"filePath":"tests/unit/test_separator_chunking.py","startLine":328,"endLine":340,"chunkText":"        # Import and call the actual method\n        from audio_separator.separator.separator import Separator as RealSeparator\n        RealSeparator._process_with_chunking(\n            separator,\n            os.path.join(self.temp_dir, \"test.wav\"),\n            custom_output_names=None\n        )\n\n        # Verify state was modified during processing\n        assert state_during_processing['chunk_duration'] is None\n        assert state_during_processing['output_dir'] != \"/original/output/dir\"\n        assert state_during_processing['model_output_dir'] != \"/original/model/output\"\n","chunkId":"tests/unit/test_separator_chunking.py:30"}],[3052,{"filePath":"tests/unit/test_separator_chunking.py","startLine":338,"endLine":347,"chunkText":"        assert state_during_processing['output_dir'] != \"/original/output/dir\"\n        assert state_during_processing['model_output_dir'] != \"/original/model/output\"\n\n        # Verify state was restored after processing\n        assert separator.chunk_duration == 10.0\n        assert separator.output_dir == \"/original/output/dir\"\n        assert separator.model_instance.output_dir == \"/original/model/output\"\n\n    @patch('audio_separator.separator.audio_chunking.AudioChunker')\n    def test_gpu_cache_cleared_between_chunks(self, mock_chunker_class):","chunkId":"tests/unit/test_separator_chunking.py:31"}],[3053,{"filePath":"tests/unit/test_separator_chunking.py","startLine":345,"endLine":358,"chunkText":"\n    @patch('audio_separator.separator.audio_chunking.AudioChunker')\n    def test_gpu_cache_cleared_between_chunks(self, mock_chunker_class):\n        \"\"\"Test that GPU cache is cleared after each chunk.\"\"\"\n        # Setup mock separator\n        separator = Mock(spec=Separator)\n        separator.logger = self.logger\n        separator.output_dir = self.temp_dir\n        separator.output_format = \"WAV\"\n        separator.chunk_duration = 10.0\n        separator.model_instance = Mock()\n        separator.model_instance.output_dir = self.temp_dir\n        separator.model_instance.clear_gpu_cache = Mock()\n","chunkId":"tests/unit/test_separator_chunking.py:32"}],[3054,{"filePath":"tests/unit/test_separator_chunking.py","startLine":356,"endLine":369,"chunkText":"        separator.model_instance.output_dir = self.temp_dir\n        separator.model_instance.clear_gpu_cache = Mock()\n\n        # Mock chunker behavior - 3 chunks\n        mock_chunker = Mock()\n        mock_chunker_class.return_value = mock_chunker\n        mock_chunker.split_audio.return_value = [\n            os.path.join(self.temp_dir, \"chunk_0000.wav\"),\n            os.path.join(self.temp_dir, \"chunk_0001.wav\"),\n            os.path.join(self.temp_dir, \"chunk_0002.wav\"),\n        ]\n\n        # Mock _separate_file\n        separator._separate_file = Mock(return_value=[","chunkId":"tests/unit/test_separator_chunking.py:33"}],[3055,{"filePath":"tests/unit/test_separator_chunking.py","startLine":367,"endLine":383,"chunkText":"\n        # Mock _separate_file\n        separator._separate_file = Mock(return_value=[\n            os.path.join(self.temp_dir, \"chunk_(Vocals).wav\"),\n        ])\n\n        # Import and call the actual method\n        from audio_separator.separator.separator import Separator as RealSeparator\n        RealSeparator._process_with_chunking(\n            separator,\n            os.path.join(self.temp_dir, \"test.wav\"),\n            custom_output_names=None\n        )\n\n        # Verify clear_gpu_cache was called 3 times (once per chunk)\n        assert separator.model_instance.clear_gpu_cache.call_count == 3\n","chunkId":"tests/unit/test_separator_chunking.py:34"}],[3056,{"filePath":"tests/unit/test_separator_chunking.py","startLine":381,"endLine":393,"chunkText":"        # Verify clear_gpu_cache was called 3 times (once per chunk)\n        assert separator.model_instance.clear_gpu_cache.call_count == 3\n\n    @patch('audio_separator.separator.audio_chunking.AudioChunker')\n    def test_temp_directory_cleanup(self, mock_chunker_class):\n        \"\"\"Test that temporary directory is cleaned up.\"\"\"\n        # Setup mock separator\n        separator = Mock(spec=Separator)\n        separator.logger = self.logger\n        separator.output_dir = self.temp_dir\n        separator.output_format = \"WAV\"\n        separator.chunk_duration = 10.0\n        separator.model_instance = Mock()","chunkId":"tests/unit/test_separator_chunking.py:35"}],[3057,{"filePath":"tests/unit/test_separator_chunking.py","startLine":391,"endLine":407,"chunkText":"        separator.output_format = \"WAV\"\n        separator.chunk_duration = 10.0\n        separator.model_instance = Mock()\n        separator.model_instance.output_dir = self.temp_dir\n\n        # Mock chunker behavior\n        mock_chunker = Mock()\n        mock_chunker_class.return_value = mock_chunker\n        mock_chunker.split_audio.return_value = [\n            os.path.join(self.temp_dir, \"chunk_0000.wav\"),\n        ]\n\n        separator._separate_file = Mock(return_value=[\n            os.path.join(self.temp_dir, \"chunk_(Vocals).wav\"),\n        ])\n\n        # Track temporary directory creation","chunkId":"tests/unit/test_separator_chunking.py:36"}],[3058,{"filePath":"tests/unit/test_separator_chunking.py","startLine":405,"endLine":419,"chunkText":"        ])\n\n        # Track temporary directory creation\n        temp_dirs_created = []\n        original_mkdtemp = tempfile.mkdtemp\n        def track_mkdtemp(prefix=None):\n            temp_dir = original_mkdtemp(prefix=prefix)\n            temp_dirs_created.append(temp_dir)\n            return temp_dir\n\n        # Import and call the actual method\n        from audio_separator.separator.separator import Separator as RealSeparator\n        with patch('tempfile.mkdtemp', side_effect=track_mkdtemp):\n            RealSeparator._process_with_chunking(\n                separator,","chunkId":"tests/unit/test_separator_chunking.py:37"}],[3059,{"filePath":"tests/unit/test_separator_chunking.py","startLine":417,"endLine":429,"chunkText":"        with patch('tempfile.mkdtemp', side_effect=track_mkdtemp):\n            RealSeparator._process_with_chunking(\n                separator,\n                os.path.join(self.temp_dir, \"test.wav\"),\n                custom_output_names=None\n            )\n\n        # Verify temporary directories were cleaned up\n        for temp_dir in temp_dirs_created:\n            if temp_dir.startswith('/') or temp_dir.startswith('C:'):  # Real temp dir\n                assert not os.path.exists(temp_dir), f\"Temp directory {temp_dir} was not cleaned up\"\n\n    @patch('audio_separator.separator.audio_chunking.AudioChunker')","chunkId":"tests/unit/test_separator_chunking.py:38"}],[3060,{"filePath":"tests/unit/test_separator_chunking.py","startLine":427,"endLine":438,"chunkText":"                assert not os.path.exists(temp_dir), f\"Temp directory {temp_dir} was not cleaned up\"\n\n    @patch('audio_separator.separator.audio_chunking.AudioChunker')\n    def test_error_handling_with_state_restoration(self, mock_chunker_class):\n        \"\"\"Test that state is restored even when error occurs.\"\"\"\n        # Setup mock separator\n        separator = Mock(spec=Separator)\n        separator.logger = self.logger\n        separator.output_dir = \"/original/output/dir\"\n        separator.output_format = \"WAV\"\n        separator.chunk_duration = 10.0\n        separator.model_instance = Mock()","chunkId":"tests/unit/test_separator_chunking.py:39"}],[3061,{"filePath":"tests/unit/test_separator_chunking.py","startLine":436,"endLine":451,"chunkText":"        separator.output_format = \"WAV\"\n        separator.chunk_duration = 10.0\n        separator.model_instance = Mock()\n        separator.model_instance.output_dir = \"/original/model/output\"\n\n        # Mock chunker behavior\n        mock_chunker = Mock()\n        mock_chunker_class.return_value = mock_chunker\n        mock_chunker.split_audio.return_value = [\n            os.path.join(self.temp_dir, \"chunk_0000.wav\"),\n        ]\n\n        # Mock _separate_file to raise exception\n        separator._separate_file = Mock(side_effect=Exception(\"Processing error\"))\n\n        # Import and call the actual method","chunkId":"tests/unit/test_separator_chunking.py:40"}],[3062,{"filePath":"tests/unit/test_separator_chunking.py","startLine":449,"endLine":463,"chunkText":"        separator._separate_file = Mock(side_effect=Exception(\"Processing error\"))\n\n        # Import and call the actual method\n        from audio_separator.separator.separator import Separator as RealSeparator\n\n        # Should raise exception\n        with pytest.raises(Exception, match=\"Processing error\"):\n            RealSeparator._process_with_chunking(\n                separator,\n                os.path.join(self.temp_dir, \"test.wav\"),\n                custom_output_names=None\n            )\n\n        # Verify state was restored despite error\n        assert separator.chunk_duration == 10.0","chunkId":"tests/unit/test_separator_chunking.py:41"}],[3063,{"filePath":"tests/unit/test_separator_chunking.py","startLine":461,"endLine":473,"chunkText":"\n        # Verify state was restored despite error\n        assert separator.chunk_duration == 10.0\n        assert separator.output_dir == \"/original/output/dir\"\n        assert separator.model_instance.output_dir == \"/original/model/output\"\n\n    @patch('audio_separator.separator.audio_chunking.AudioChunker')\n    def test_audio_chunker_initialization(self, mock_chunker_class):\n        \"\"\"Test that AudioChunker is initialized with correct parameters.\"\"\"\n        # Setup mock separator\n        separator = Mock(spec=Separator)\n        separator.logger = self.logger\n        separator.output_dir = self.temp_dir","chunkId":"tests/unit/test_separator_chunking.py:42"}],[3064,{"filePath":"tests/unit/test_separator_chunking.py","startLine":471,"endLine":486,"chunkText":"        separator = Mock(spec=Separator)\n        separator.logger = self.logger\n        separator.output_dir = self.temp_dir\n        separator.output_format = \"WAV\"\n        separator.chunk_duration = 15.0\n        separator.model_instance = Mock()\n        separator.model_instance.output_dir = self.temp_dir\n\n        # Mock chunker behavior\n        mock_chunker = Mock()\n        mock_chunker_class.return_value = mock_chunker\n        mock_chunker.split_audio.return_value = [\n            os.path.join(self.temp_dir, \"chunk_0000.wav\"),\n        ]\n\n        separator._separate_file = Mock(return_value=[","chunkId":"tests/unit/test_separator_chunking.py:43"}],[3065,{"filePath":"tests/unit/test_separator_chunking.py","startLine":484,"endLine":500,"chunkText":"        ]\n\n        separator._separate_file = Mock(return_value=[\n            os.path.join(self.temp_dir, \"chunk_(Vocals).wav\"),\n        ])\n\n        # Import and call the actual method\n        from audio_separator.separator.separator import Separator as RealSeparator\n        RealSeparator._process_with_chunking(\n            separator,\n            os.path.join(self.temp_dir, \"test.wav\"),\n            custom_output_names=None\n        )\n\n        # Verify AudioChunker was initialized with correct parameters\n        mock_chunker_class.assert_called_once_with(15.0, separator.logger)\n","chunkId":"tests/unit/test_separator_chunking.py:44"}],[3066,{"filePath":"tests/unit/test_separator_chunking.py","startLine":498,"endLine":509,"chunkText":"        # Verify AudioChunker was initialized with correct parameters\n        mock_chunker_class.assert_called_once_with(15.0, separator.logger)\n\n    @patch('audio_separator.separator.audio_chunking.AudioChunker')\n    def test_custom_output_names_parameter(self, mock_chunker_class):\n        \"\"\"Test that custom_output_names parameter is handled correctly.\"\"\"\n        # Setup mock separator\n        separator = Mock(spec=Separator)\n        separator.logger = self.logger\n        separator.output_dir = self.temp_dir\n        separator.output_format = \"WAV\"\n        separator.chunk_duration = 10.0","chunkId":"tests/unit/test_separator_chunking.py:45"}],[3067,{"filePath":"tests/unit/test_separator_chunking.py","startLine":507,"endLine":523,"chunkText":"        separator.output_dir = self.temp_dir\n        separator.output_format = \"WAV\"\n        separator.chunk_duration = 10.0\n        separator.model_instance = Mock()\n        separator.model_instance.output_dir = self.temp_dir\n\n        # Mock chunker behavior\n        mock_chunker = Mock()\n        mock_chunker_class.return_value = mock_chunker\n        mock_chunker.split_audio.return_value = [\n            os.path.join(self.temp_dir, \"chunk_0000.wav\"),\n        ]\n\n        separator._separate_file = Mock(return_value=[\n            os.path.join(self.temp_dir, \"chunk_0000_(Vocals).wav\"),\n        ])\n","chunkId":"tests/unit/test_separator_chunking.py:46"}],[3068,{"filePath":"tests/unit/test_separator_chunking.py","startLine":521,"endLine":538,"chunkText":"            os.path.join(self.temp_dir, \"chunk_0000_(Vocals).wav\"),\n        ])\n\n        custom_names = {\"Vocals\": \"my_custom_vocals\"}\n\n        # Import and call the actual method\n        from audio_separator.separator.separator import Separator as RealSeparator\n        result = RealSeparator._process_with_chunking(\n            separator,\n            os.path.join(self.temp_dir, \"test.wav\"),\n            custom_output_names=custom_names\n        )\n\n        # Verify custom name was used in output\n        assert len(result) == 1\n        assert \"my_custom_vocals\" in os.path.basename(result[0])\n\n","chunkId":"tests/unit/test_separator_chunking.py:47"}],[3069,{"filePath":"tests/unit/test_separator_chunking.py","startLine":539,"endLine":554,"chunkText":"class TestSeparatorChunkingEdgeCases:\n    \"\"\"Test edge cases for multi-stem chunking.\"\"\"\n\n    def setup_method(self):\n        \"\"\"Set up test fixtures.\"\"\"\n        self.logger = logging.getLogger(__name__)\n        self.temp_dir = tempfile.mkdtemp()\n\n    def teardown_method(self):\n        \"\"\"Clean up test fixtures.\"\"\"\n        import shutil\n        if os.path.exists(self.temp_dir):\n            shutil.rmtree(self.temp_dir)\n\n    @patch('audio_separator.separator.audio_chunking.AudioChunker')\n    def test_empty_output_handling(self, mock_chunker_class):","chunkId":"tests/unit/test_separator_chunking.py:48"}],[3070,{"filePath":"tests/unit/test_separator_chunking.py","startLine":552,"endLine":566,"chunkText":"\n    @patch('audio_separator.separator.audio_chunking.AudioChunker')\n    def test_empty_output_handling(self, mock_chunker_class):\n        \"\"\"Test handling when a chunk produces no output files.\"\"\"\n        # Setup mock separator\n        separator = Mock(spec=Separator)\n        separator.logger = self.logger\n        separator.output_dir = self.temp_dir\n        separator.output_format = \"WAV\"\n        separator.chunk_duration = 10.0\n        separator.model_instance = Mock()\n        separator.model_instance.output_dir = self.temp_dir\n\n        # Mock chunker behavior\n        mock_chunker = Mock()","chunkId":"tests/unit/test_separator_chunking.py:49"}],[3071,{"filePath":"tests/unit/test_separator_chunking.py","startLine":564,"endLine":579,"chunkText":"\n        # Mock chunker behavior\n        mock_chunker = Mock()\n        mock_chunker_class.return_value = mock_chunker\n        mock_chunker.split_audio.return_value = [\n            os.path.join(self.temp_dir, \"chunk_0000.wav\"),\n        ]\n\n        # Mock _separate_file to return empty list\n        separator._separate_file = Mock(return_value=[])\n\n        # Import and call the actual method\n        from audio_separator.separator.separator import Separator as RealSeparator\n        result = RealSeparator._process_with_chunking(\n            separator,\n            os.path.join(self.temp_dir, \"test.wav\"),","chunkId":"tests/unit/test_separator_chunking.py:50"}],[3072,{"filePath":"tests/unit/test_separator_chunking.py","startLine":577,"endLine":591,"chunkText":"        result = RealSeparator._process_with_chunking(\n            separator,\n            os.path.join(self.temp_dir, \"test.wav\"),\n            custom_output_names=None\n        )\n\n        # Should return empty list\n        assert len(result) == 0\n        # merge_chunks should not be called\n        assert mock_chunker.merge_chunks.call_count == 0\n\n    @patch('audio_separator.separator.audio_chunking.AudioChunker')\n    def test_inconsistent_stem_count_across_chunks(self, mock_chunker_class):\n        \"\"\"Test handling when different chunks produce different stem counts.\"\"\"\n        # Setup mock separator","chunkId":"tests/unit/test_separator_chunking.py:51"}],[3073,{"filePath":"tests/unit/test_separator_chunking.py","startLine":589,"endLine":601,"chunkText":"    def test_inconsistent_stem_count_across_chunks(self, mock_chunker_class):\n        \"\"\"Test handling when different chunks produce different stem counts.\"\"\"\n        # Setup mock separator\n        separator = Mock(spec=Separator)\n        separator.logger = self.logger\n        separator.output_dir = self.temp_dir\n        separator.output_format = \"WAV\"\n        separator.chunk_duration = 10.0\n        separator.model_instance = Mock()\n        separator.model_instance.output_dir = self.temp_dir\n\n        # Mock chunker behavior\n        mock_chunker = Mock()","chunkId":"tests/unit/test_separator_chunking.py:52"}],[3074,{"filePath":"tests/unit/test_separator_chunking.py","startLine":600,"endLine":613,"chunkText":"        # Mock chunker behavior\n        mock_chunker = Mock()\n        mock_chunker_class.return_value = mock_chunker\n        mock_chunker.split_audio.return_value = [\n            os.path.join(self.temp_dir, \"chunk_0000.wav\"),\n            os.path.join(self.temp_dir, \"chunk_0001.wav\"),\n        ]\n\n        # Mock _separate_file - first chunk has 2 stems, second has 1 stem\n        separator._separate_file = Mock(side_effect=[\n            [\n                os.path.join(self.temp_dir, \"chunk_0000_(Vocals).wav\"),\n                os.path.join(self.temp_dir, \"chunk_0000_(Instrumental).wav\"),\n            ],","chunkId":"tests/unit/test_separator_chunking.py:53"}],[3075,{"filePath":"tests/unit/test_separator_chunking.py","startLine":611,"endLine":624,"chunkText":"                os.path.join(self.temp_dir, \"chunk_0000_(Vocals).wav\"),\n                os.path.join(self.temp_dir, \"chunk_0000_(Instrumental).wav\"),\n            ],\n            [\n                os.path.join(self.temp_dir, \"chunk_0001_(Vocals).wav\"),\n                # Missing Instrumental stem\n            ],\n        ])\n\n        # Import and call the actual method\n        from audio_separator.separator.separator import Separator as RealSeparator\n        result = RealSeparator._process_with_chunking(\n            separator,\n            os.path.join(self.temp_dir, \"test.wav\"),","chunkId":"tests/unit/test_separator_chunking.py:54"}],[3076,{"filePath":"tests/unit/test_separator_chunking.py","startLine":622,"endLine":635,"chunkText":"        result = RealSeparator._process_with_chunking(\n            separator,\n            os.path.join(self.temp_dir, \"test.wav\"),\n            custom_output_names=None\n        )\n\n        # Should handle inconsistency gracefully\n        # Vocals should have 2 chunks, Instrumental should have 1 chunk\n        assert len(result) == 2\n\n    @patch('audio_separator.separator.audio_chunking.AudioChunker')\n    def test_filename_without_stem_pattern(self, mock_chunker_class):\n        \"\"\"Test fallback when filename doesn't match expected pattern.\"\"\"\n        # Setup mock separator","chunkId":"tests/unit/test_separator_chunking.py:55"}],[3077,{"filePath":"tests/unit/test_separator_chunking.py","startLine":633,"endLine":646,"chunkText":"    def test_filename_without_stem_pattern(self, mock_chunker_class):\n        \"\"\"Test fallback when filename doesn't match expected pattern.\"\"\"\n        # Setup mock separator\n        separator = Mock(spec=Separator)\n        separator.logger = self.logger\n        separator.output_dir = self.temp_dir\n        separator.output_format = \"WAV\"\n        separator.chunk_duration = 10.0\n        separator.model_instance = Mock()\n        separator.model_instance.output_dir = self.temp_dir\n\n        # Mock chunker behavior\n        mock_chunker = Mock()\n        mock_chunker_class.return_value = mock_chunker","chunkId":"tests/unit/test_separator_chunking.py:56"}],[3078,{"filePath":"tests/unit/test_separator_chunking.py","startLine":644,"endLine":657,"chunkText":"        # Mock chunker behavior\n        mock_chunker = Mock()\n        mock_chunker_class.return_value = mock_chunker\n        mock_chunker.split_audio.return_value = [\n            os.path.join(self.temp_dir, \"chunk_0000.wav\"),\n        ]\n\n        # Mock _separate_file - return file without standard naming pattern\n        separator._separate_file = Mock(return_value=[\n            os.path.join(self.temp_dir, \"output_file_without_pattern.wav\"),\n        ])\n\n        # Import and call the actual method\n        from audio_separator.separator.separator import Separator as RealSeparator","chunkId":"tests/unit/test_separator_chunking.py:57"}],[3079,{"filePath":"tests/unit/test_separator_chunking.py","startLine":655,"endLine":672,"chunkText":"\n        # Import and call the actual method\n        from audio_separator.separator.separator import Separator as RealSeparator\n        result = RealSeparator._process_with_chunking(\n            separator,\n            os.path.join(self.temp_dir, \"test.wav\"),\n            custom_output_names=None\n        )\n\n        # Should still produce output using fallback naming\n        assert len(result) == 1\n        # Should use fallback name like \"stem_0\"\n        assert \"stem_0\" in os.path.basename(result[0])\n\n\nif __name__ == \"__main__\":\n    pytest.main([__file__, \"-v\"])\n","chunkId":"tests/unit/test_separator_chunking.py:58"}],[3080,{"filePath":"tests/unit/test_separator_detection.py","startLine":0,"endLine":22,"chunkText":"\"\"\"\nUnit tests for separator detection and routing logic.\nTests the detection of Roformer models and proper routing.\n\"\"\"\n\nimport pytest\nfrom unittest.mock import Mock, patch, MagicMock\nimport os\nimport tempfile\n\n\nclass TestSeparatorDetection:\n    \"\"\"Test cases for separator detection and model routing.\"\"\"\n\n    def setup_method(self):\n        \"\"\"Set up test fixtures.\"\"\"\n        self.temp_dir = tempfile.mkdtemp()\n\n    def teardown_method(self):\n        \"\"\"Clean up test fixtures.\"\"\"\n        import shutil\n        shutil.rmtree(self.temp_dir, ignore_errors=True)\n","chunkId":"tests/unit/test_separator_detection.py:0"}],[3081,{"filePath":"tests/unit/test_separator_detection.py","startLine":21,"endLine":30,"chunkText":"        shutil.rmtree(self.temp_dir, ignore_errors=True)\n\n    def test_is_roformer_set_from_yaml_path(self):\n        \"\"\"T059: YAML path containing 'roformer' sets is_roformer and routes Roformer path.\"\"\"\n        # Test cases for different YAML paths\n        test_cases = [\n            # (yaml_path, expected_is_roformer, description)\n            (\"model_bs_roformer_ep_317_sdr_12.yaml\", True, \"BS Roformer model\"),\n            (\"mel_band_roformer_vocals.yaml\", True, \"Mel Band Roformer model\"),\n            (\"roformer_large_model.yaml\", True, \"Generic Roformer model\"),","chunkId":"tests/unit/test_separator_detection.py:1"}],[3082,{"filePath":"tests/unit/test_separator_detection.py","startLine":28,"endLine":35,"chunkText":"            (\"model_bs_roformer_ep_317_sdr_12.yaml\", True, \"BS Roformer model\"),\n            (\"mel_band_roformer_vocals.yaml\", True, \"Mel Band Roformer model\"),\n            (\"roformer_large_model.yaml\", True, \"Generic Roformer model\"),\n            (\"BS-Roformer-Viperx-1297.yaml\", True, \"BS-Roformer with uppercase\"),\n            (\"model_mdx_extra_vocals.yaml\", False, \"MDX model (not Roformer)\"),\n            (\"vr_model_vocals.yaml\", False, \"VR model (not Roformer)\"),\n            (\"demucs_model.yaml\", False, \"Demucs model (not Roformer)\"),\n            (\"some_other_model.yaml\", False, \"Generic other model\"),","chunkId":"tests/unit/test_separator_detection.py:2"}],[3083,{"filePath":"tests/unit/test_separator_detection.py","startLine":33,"endLine":44,"chunkText":"            (\"vr_model_vocals.yaml\", False, \"VR model (not Roformer)\"),\n            (\"demucs_model.yaml\", False, \"Demucs model (not Roformer)\"),\n            (\"some_other_model.yaml\", False, \"Generic other model\"),\n        ]\n\n        for yaml_path, expected_is_roformer, description in test_cases:\n            # Mock the detection logic\n            def mock_detect_roformer_from_path(path):\n                \"\"\"Mock detection that checks if path contains 'roformer'.\"\"\"\n                path_lower = path.lower()\n                return 'roformer' in path_lower\n","chunkId":"tests/unit/test_separator_detection.py:3"}],[3084,{"filePath":"tests/unit/test_separator_detection.py","startLine":42,"endLine":54,"chunkText":"                path_lower = path.lower()\n                return 'roformer' in path_lower\n\n            is_roformer = mock_detect_roformer_from_path(yaml_path)\n            \n            assert is_roformer == expected_is_roformer, (\n                f\"Failed for {description}: path '{yaml_path}' should \"\n                f\"{'be' if expected_is_roformer else 'not be'} detected as Roformer\"\n            )\n\n        # Test routing logic based on detection\n        def mock_route_separator(yaml_path):\n            \"\"\"Mock routing logic that selects separator based on detection.\"\"\"","chunkId":"tests/unit/test_separator_detection.py:4"}],[3085,{"filePath":"tests/unit/test_separator_detection.py","startLine":52,"endLine":63,"chunkText":"        # Test routing logic based on detection\n        def mock_route_separator(yaml_path):\n            \"\"\"Mock routing logic that selects separator based on detection.\"\"\"\n            is_roformer = mock_detect_roformer_from_path(yaml_path)\n            \n            if is_roformer:\n                # In current routing, Roformer models are handled via MDXCSeparator path\n                return \"MDXCSeparator\"\n            elif 'mdx' in yaml_path.lower():\n                return \"MDXSeparator\"\n            elif 'vr' in yaml_path.lower():\n                return \"VRSeparator\"","chunkId":"tests/unit/test_separator_detection.py:5"}],[3086,{"filePath":"tests/unit/test_separator_detection.py","startLine":61,"endLine":77,"chunkText":"                return \"MDXSeparator\"\n            elif 'vr' in yaml_path.lower():\n                return \"VRSeparator\"\n            elif 'demucs' in yaml_path.lower():\n                return \"DemucsSeparator\"\n            else:\n                return \"DefaultSeparator\"\n\n        # Test routing for Roformer models\n        roformer_paths = [\n            \"model_bs_roformer_ep_317_sdr_12.yaml\",\n            \"mel_band_roformer_vocals.yaml\",\n            \"BS-Roformer-Viperx-1297.yaml\"\n        ]\n        \n        for path in roformer_paths:\n            separator_type = mock_route_separator(path)","chunkId":"tests/unit/test_separator_detection.py:6"}],[3087,{"filePath":"tests/unit/test_separator_detection.py","startLine":75,"endLine":89,"chunkText":"        \n        for path in roformer_paths:\n            separator_type = mock_route_separator(path)\n            assert separator_type == \"MDXCSeparator\", (\n                f\"Roformer model '{path}' should route to MDXCSeparator, got {separator_type}\"\n            )\n\n        # Test routing for non-Roformer models\n        non_roformer_cases = [\n            (\"model_mdx_extra_vocals.yaml\", \"MDXSeparator\"),\n            (\"vr_model_vocals.yaml\", \"VRSeparator\"),\n            (\"demucs_model.yaml\", \"DemucsSeparator\"),\n            (\"some_other_model.yaml\", \"DefaultSeparator\"),\n        ]\n        ","chunkId":"tests/unit/test_separator_detection.py:7"}],[3088,{"filePath":"tests/unit/test_separator_detection.py","startLine":87,"endLine":100,"chunkText":"            (\"some_other_model.yaml\", \"DefaultSeparator\"),\n        ]\n        \n        for path, expected_separator in non_roformer_cases:\n            separator_type = mock_route_separator(path)\n            assert separator_type == expected_separator, (\n                f\"Non-Roformer model '{path}' should route to {expected_separator}, got {separator_type}\"\n            )\n\n    def test_roformer_detection_case_insensitive(self):\n        \"\"\"Test that Roformer detection is case-insensitive.\"\"\"\n        case_variations = [\n            \"ROFORMER_MODEL.yaml\",\n            \"roformer_model.yaml\", ","chunkId":"tests/unit/test_separator_detection.py:8"}],[3089,{"filePath":"tests/unit/test_separator_detection.py","startLine":98,"endLine":114,"chunkText":"        case_variations = [\n            \"ROFORMER_MODEL.yaml\",\n            \"roformer_model.yaml\", \n            \"RoFormer_Model.yaml\",\n            \"BS_ROFORMER.yaml\",\n            \"bs_roformer.yaml\",\n            \"Bs_Roformer.yaml\",\n            \"MEL_BAND_ROFORMER.yaml\",\n            \"mel_band_roformer.yaml\",\n            \"Mel_Band_Roformer.yaml\"\n        ]\n\n        def mock_detect_roformer_case_insensitive(path):\n            \"\"\"Mock detection with case-insensitive matching.\"\"\"\n            return 'roformer' in path.lower()\n\n        for path in case_variations:","chunkId":"tests/unit/test_separator_detection.py:9"}],[3090,{"filePath":"tests/unit/test_separator_detection.py","startLine":112,"endLine":123,"chunkText":"            return 'roformer' in path.lower()\n\n        for path in case_variations:\n            is_roformer = mock_detect_roformer_case_insensitive(path)\n            assert is_roformer, f\"Case variation '{path}' should be detected as Roformer\"\n\n    def test_roformer_detection_with_full_paths(self):\n        \"\"\"Test Roformer detection works with full file paths.\"\"\"\n        full_path_cases = [\n            (\"/models/roformer/model_bs_roformer_ep_317.yaml\", True),\n            (\"/path/to/models/mel_band_roformer_vocals.yaml\", True),\n            (\"/home/user/models/mdx_extra_vocals.yaml\", False),","chunkId":"tests/unit/test_separator_detection.py:10"}],[3091,{"filePath":"tests/unit/test_separator_detection.py","startLine":121,"endLine":132,"chunkText":"            (\"/models/roformer/model_bs_roformer_ep_317.yaml\", True),\n            (\"/path/to/models/mel_band_roformer_vocals.yaml\", True),\n            (\"/home/user/models/mdx_extra_vocals.yaml\", False),\n            (\"C:\\\\Models\\\\BS-Roformer-Viperx-1297.yaml\", True),\n            (\"./local/models/vr_model_vocals.yaml\", False),\n            (\"../models/roformer_large_model.yaml\", True),\n        ]\n\n        def mock_detect_roformer_full_path(full_path):\n            \"\"\"Mock detection that works with full paths.\"\"\"\n            filename = os.path.basename(full_path)\n            return 'roformer' in filename.lower(","chunkId":"tests/unit/test_separator_detection.py:11"}],[3092,{"filePath":"tests/unit/test_separator_detection.py","startLine":131,"endLine":143,"chunkText":"            filename = os.path.basename(full_path)\n            return 'roformer' in filename.lower()\n\n        for full_path, expected_result in full_path_cases:\n            is_roformer = mock_detect_roformer_full_path(full_path)\n            assert is_roformer == expected_result, (\n                f\"Full path '{full_path}' detection failed: expected {expected_result}, got {is_roformer}\"\n            )\n\n    def test_roformer_detection_with_config_content(self):\n        \"\"\"Test Roformer detection based on YAML configuration content.\"\"\"\n        # Mock YAML configurations\n        roformer_configs = [","chunkId":"tests/unit/test_separator_detection.py:12"}],[3093,{"filePath":"tests/unit/test_separator_detection.py","startLine":141,"endLine":152,"chunkText":"        \"\"\"Test Roformer detection based on YAML configuration content.\"\"\"\n        # Mock YAML configurations\n        roformer_configs = [\n            {\"model_type\": \"bs_roformer\", \"architecture\": \"BSRoformer\"},\n            {\"model_type\": \"mel_band_roformer\", \"architecture\": \"MelBandRoformer\"},\n            {\"architecture\": \"roformer\", \"variant\": \"large\"},\n            {\"separator_class\": \"RoformerSeparator\", \"model\": \"bs_roformer\"},\n        ]\n\n        non_roformer_configs = [\n            {\"model_type\": \"mdx\", \"architecture\": \"MDX\"},\n            {\"model_type\": \"vr\", \"architecture\": \"VR\"},","chunkId":"tests/unit/test_separator_detection.py:13"}],[3094,{"filePath":"tests/unit/test_separator_detection.py","startLine":150,"endLine":163,"chunkText":"        non_roformer_configs = [\n            {\"model_type\": \"mdx\", \"architecture\": \"MDX\"},\n            {\"model_type\": \"vr\", \"architecture\": \"VR\"},\n            {\"architecture\": \"demucs\", \"variant\": \"v4\"},\n            {\"separator_class\": \"MDXSeparator\", \"model\": \"mdx_extra\"},\n        ]\n\n        def mock_detect_roformer_from_config(config):\n            \"\"\"Mock detection based on configuration content.\"\"\"\n            config_str = str(config).lower()\n            return 'roformer' in config_str\n\n        # Test Roformer configs\n        for config in roformer_configs:","chunkId":"tests/unit/test_separator_detection.py:14"}],[3095,{"filePath":"tests/unit/test_separator_detection.py","startLine":161,"endLine":173,"chunkText":"\n        # Test Roformer configs\n        for config in roformer_configs:\n            is_roformer = mock_detect_roformer_from_config(config)\n            assert is_roformer, f\"Roformer config should be detected: {config}\"\n\n        # Test non-Roformer configs  \n        for config in non_roformer_configs:\n            is_roformer = mock_detect_roformer_from_config(config)\n            assert not is_roformer, f\"Non-Roformer config should not be detected: {config}\"\n\n    def test_roformer_routing_integration(self):\n        \"\"\"Test integration between detection and routing.\"\"\"","chunkId":"tests/unit/test_separator_detection.py:15"}],[3096,{"filePath":"tests/unit/test_separator_detection.py","startLine":171,"endLine":182,"chunkText":"\n    def test_roformer_routing_integration(self):\n        \"\"\"Test integration between detection and routing.\"\"\"\n        with patch('audio_separator.separator.separator.Separator') as mock_separator_class:\n            mock_instance = Mock()\n            mock_separator_class.return_value = mock_instance\n            \n            # Mock the routing logic\n            def mock_load_model_with_routing(model_path):\n                \"\"\"Mock model loading that routes based on detection.\"\"\"\n                filename = os.path.basename(model_path)\n                is_roformer = 'roformer' in filename.lower()","chunkId":"tests/unit/test_separator_detection.py:16"}],[3097,{"filePath":"tests/unit/test_separator_detection.py","startLine":180,"endLine":192,"chunkText":"                \"\"\"Mock model loading that routes based on detection.\"\"\"\n                filename = os.path.basename(model_path)\n                is_roformer = 'roformer' in filename.lower()\n                \n                if is_roformer:\n                    # Should use Roformer-specific loading\n                    return {\n                        'separator_type': 'RoformerSeparator',\n                        'is_roformer': True,\n                        'routing_path': 'roformer_path'\n                    }\n                else:\n                    # Should use default loading","chunkId":"tests/unit/test_separator_detection.py:17"}],[3098,{"filePath":"tests/unit/test_separator_detection.py","startLine":190,"endLine":202,"chunkText":"                    }\n                else:\n                    # Should use default loading\n                    return {\n                        'separator_type': 'DefaultSeparator', \n                        'is_roformer': False,\n                        'routing_path': 'default_path'\n                    }\n\n            # Test Roformer model routing\n            roformer_result = mock_load_model_with_routing(\"model_bs_roformer_ep_317.ckpt\")\n            assert roformer_result['is_roformer'] is True\n            assert roformer_result['separator_type'] == 'RoformerSeparator'","chunkId":"tests/unit/test_separator_detection.py:18"}],[3099,{"filePath":"tests/unit/test_separator_detection.py","startLine":200,"endLine":208,"chunkText":"            roformer_result = mock_load_model_with_routing(\"model_bs_roformer_ep_317.ckpt\")\n            assert roformer_result['is_roformer'] is True\n            assert roformer_result['separator_type'] == 'RoformerSeparator'\n            assert roformer_result['routing_path'] == 'roformer_path'\n\n            # Test non-Roformer model routing\n            mdx_result = mock_load_model_with_routing(\"model_mdx_extra_vocals.ckpt\")\n            assert mdx_result['is_roformer'] is False\n            assert mdx_result['separator_type'] == 'DefaultSeparator'","chunkId":"tests/unit/test_separator_detection.py:19"}],[3100,{"filePath":"tests/unit/test_separator_detection.py","startLine":208,"endLine":219,"chunkText":"            assert mdx_result['separator_type'] == 'DefaultSeparator'\n            assert mdx_result['routing_path'] == 'default_path'\n\n    def test_roformer_detection_edge_cases(self):\n        \"\"\"Test edge cases in Roformer detection.\"\"\"\n        edge_cases = [\n            # (path, expected_result, description)\n            (\"\", False, \"Empty string\"),\n            (\"model.yaml\", False, \"No roformer in name\"),\n            (\"roformer\", True, \"Just 'roformer'\"),\n            (\"notroformer.yaml\", True, \"Contains 'roformer' as substring\"),\n            (\"roformer.txt\", True, \"Different extension\"),","chunkId":"tests/unit/test_separator_detection.py:20"}],[3101,{"filePath":"tests/unit/test_separator_detection.py","startLine":217,"endLine":228,"chunkText":"            (\"roformer\", True, \"Just 'roformer'\"),\n            (\"notroformer.yaml\", True, \"Contains 'roformer' as substring\"),\n            (\"roformer.txt\", True, \"Different extension\"),\n            (\"ROFORMER.YAML\", True, \"All uppercase\"),\n            (\"r0f0rmer.yaml\", False, \"Similar but not exact\"),\n            (\"roformermodel.ckpt\", True, \"No separator between roformer and model\"),\n            (\"model_roformer_v2.pth\", True, \"Roformer in middle of filename\"),\n        ]\n\n        def mock_detect_roformer_edge_cases(path):\n            \"\"\"Mock detection handling edge cases.\"\"\"\n            if not path:","chunkId":"tests/unit/test_separator_detection.py:21"}],[3102,{"filePath":"tests/unit/test_separator_detection.py","startLine":226,"endLine":238,"chunkText":"        def mock_detect_roformer_edge_cases(path):\n            \"\"\"Mock detection handling edge cases.\"\"\"\n            if not path:\n                return False\n            return 'roformer' in path.lower()\n\n        for path, expected_result, description in edge_cases:\n            is_roformer = mock_detect_roformer_edge_cases(path)\n            assert is_roformer == expected_result, (\n                f\"Edge case failed - {description}: path '{path}' should \"\n                f\"{'be' if expected_result else 'not be'} detected as Roformer\"\n            )\n","chunkId":"tests/unit/test_separator_detection.py:22"}],[3103,{"filePath":"tests/unit/test_separator_detection.py","startLine":236,"endLine":246,"chunkText":"                f\"{'be' if expected_result else 'not be'} detected as Roformer\"\n            )\n\n    def test_roformer_detection_with_model_extensions(self):\n        \"\"\"Test Roformer detection works with various model file extensions.\"\"\"\n        model_extensions = ['.ckpt', '.pth', '.pt', '.onnx', '.yaml', '.yml']\n        base_names = ['bs_roformer_model', 'mel_band_roformer', 'roformer_large']\n\n        def mock_detect_roformer_with_extension(path):\n            \"\"\"Mock detection that ignores file extension.\"\"\"\n            filename = os.path.basename(path)","chunkId":"tests/unit/test_separator_detection.py:23"}],[3104,{"filePath":"tests/unit/test_separator_detection.py","startLine":244,"endLine":254,"chunkText":"        def mock_detect_roformer_with_extension(path):\n            \"\"\"Mock detection that ignores file extension.\"\"\"\n            filename = os.path.basename(path)\n            name_without_ext = os.path.splitext(filename)[0]\n            return 'roformer' in name_without_ext.lower()\n\n        for base_name in base_names:\n            for ext in model_extensions:\n                full_filename = f\"{base_name}{ext}\"\n                is_roformer = mock_detect_roformer_with_extension(full_filename)\n                assert is_roformer, (","chunkId":"tests/unit/test_separator_detection.py:24"}],[3105,{"filePath":"tests/unit/test_separator_detection.py","startLine":252,"endLine":262,"chunkText":"                full_filename = f\"{base_name}{ext}\"\n                is_roformer = mock_detect_roformer_with_extension(full_filename)\n                assert is_roformer, (\n                    f\"Roformer model '{full_filename}' should be detected regardless of extension\"\n                )\n\n        # Test non-Roformer models with same extensions\n        non_roformer_bases = ['mdx_model', 'vr_vocals', 'demucs_v4']\n        for base_name in non_roformer_bases:\n            for ext in model_extensions:\n                full_filename = f\"{base_name}{ext}\"","chunkId":"tests/unit/test_separator_detection.py:25"}],[3106,{"filePath":"tests/unit/test_separator_detection.py","startLine":260,"endLine":267,"chunkText":"        for base_name in non_roformer_bases:\n            for ext in model_extensions:\n                full_filename = f\"{base_name}{ext}\"\n                is_roformer = mock_detect_roformer_with_extension(full_filename)\n                assert not is_roformer, (\n                    f\"Non-Roformer model '{full_filename}' should not be detected\"\n                )\n","chunkId":"tests/unit/test_separator_detection.py:26"}],[3107,{"filePath":"tests/unit/test_stft.py","startLine":0,"endLine":11,"chunkText":"import unittest\nimport numpy as np\nimport torch\nfrom unittest.mock import Mock\nfrom audio_separator.separator.uvr_lib_v5.stft import STFT\n\n# Short-Time Fourier Transform (STFT) Process Overview:\n#\n# STFT transforms a time-domain signal into a frequency-domain representation.\n#   This transformation is achieved by dividing the signal into short frames (or segments) and applying the Fourier Transform to each frame.\n#\n# n_fft: The number of points used in the Fourier Transform, which determines the resolution of the frequency domain representation.","chunkId":"tests/unit/test_stft.py:0"}],[3108,{"filePath":"tests/unit/test_stft.py","startLine":9,"endLine":16,"chunkText":"#   This transformation is achieved by dividing the signal into short frames (or segments) and applying the Fourier Transform to each frame.\n#\n# n_fft: The number of points used in the Fourier Transform, which determines the resolution of the frequency domain representation.\n#   Essentially, it dictates how many frequency bins we get in our STFT.\n#\n# hop_length: The number of samples by which we shift each frame of the signal.\n#   It affects the overlap between consecutive frames. If the hop_length is less than n_fft, we get overlapping frames.\n#","chunkId":"tests/unit/test_stft.py:1"}],[3109,{"filePath":"tests/unit/test_stft.py","startLine":14,"endLine":26,"chunkText":"# hop_length: The number of samples by which we shift each frame of the signal.\n#   It affects the overlap between consecutive frames. If the hop_length is less than n_fft, we get overlapping frames.\n#\n# Windowing: Each frame of the signal is multiplied by a window function (e.g. Hann window) before applying the Fourier Transform.\n#   This is done to minimize discontinuities at the borders of each frame.\n\n\nclass TestSTFT(unittest.TestCase):\n    def setUp(self):\n        self.n_fft = 2048\n        self.hop_length = 512\n        self.dim_f = 1025\n        self.device = torch.device(\"cpu\")","chunkId":"tests/unit/test_stft.py:2"}],[3110,{"filePath":"tests/unit/test_stft.py","startLine":24,"endLine":38,"chunkText":"        self.hop_length = 512\n        self.dim_f = 1025\n        self.device = torch.device(\"cpu\")\n        self.stft = STFT(logger=Mock(), n_fft=self.n_fft, hop_length=self.hop_length, dim_f=self.dim_f, device=self.device)\n\n    def create_mock_tensor(self, shape, device=None):\n        tensor = torch.rand(shape)\n        if device:\n            tensor = tensor.to(device)\n        return tensor\n\n    def test_stft_initialization(self):\n        self.assertEqual(self.stft.n_fft, self.n_fft)\n        self.assertEqual(self.stft.hop_length, self.hop_length)\n        self.assertEqual(self.stft.dim_f, self.dim_f)","chunkId":"tests/unit/test_stft.py:3"}],[3111,{"filePath":"tests/unit/test_stft.py","startLine":36,"endLine":51,"chunkText":"        self.assertEqual(self.stft.n_fft, self.n_fft)\n        self.assertEqual(self.stft.hop_length, self.hop_length)\n        self.assertEqual(self.stft.dim_f, self.dim_f)\n        self.assertEqual(self.stft.device.type, \"cpu\")\n        self.assertIsInstance(self.stft.hann_window, torch.Tensor)\n\n    def test_stft_call(self):\n        input_tensor = self.create_mock_tensor((1, 16000))\n\n        # Apply STFT\n        stft_result = self.stft(input_tensor)\n\n        # Test conditions\n        self.assertIsNotNone(stft_result)\n        self.assertIsInstance(stft_result, torch.Tensor)\n","chunkId":"tests/unit/test_stft.py:4"}],[3112,{"filePath":"tests/unit/test_stft.py","startLine":49,"endLine":57,"chunkText":"        self.assertIsNotNone(stft_result)\n        self.assertIsInstance(stft_result, torch.Tensor)\n\n        # Calculate the expected shape based on input parameters:\n\n        # Frequency Dimension (dim_f): This corresponds to the number of frequency bins in the STFT output.\n        #   In the case of a real-valued input signal (like audio), the Fourier Transform produces a symmetric output.\n        #   Hence, for an n_fft of 2048, we would typically get 2049 frequency bins (from 0 Hz to the Nyquist frequency).\n        #   However, we often don't need the full symmetric spectrum.","chunkId":"tests/unit/test_stft.py:5"}],[3113,{"filePath":"tests/unit/test_stft.py","startLine":55,"endLine":60,"chunkText":"        #   In the case of a real-valued input signal (like audio), the Fourier Transform produces a symmetric output.\n        #   Hence, for an n_fft of 2048, we would typically get 2049 frequency bins (from 0 Hz to the Nyquist frequency).\n        #   However, we often don't need the full symmetric spectrum.\n        #   So, dim_f is used to specify how many frequency bins we are interested in.\n        #   In this test, it's set to 1025, which is about half of n_fft + 1 (as the Fourier Transform of a real-valued signal is symmetric).\n","chunkId":"tests/unit/test_stft.py:6"}],[3114,{"filePath":"tests/unit/test_stft.py","startLine":58,"endLine":63,"chunkText":"        #   So, dim_f is used to specify how many frequency bins we are interested in.\n        #   In this test, it's set to 1025, which is about half of n_fft + 1 (as the Fourier Transform of a real-valued signal is symmetric).\n\n        # Time Dimension: This corresponds to how many frames (or segments) the input signal has been divided into.\n        #   It depends on the length of the input signal and the hop_length.\n        #   The formula for calculating the number of frames is derived from how we stride the window across the signal:","chunkId":"tests/unit/test_stft.py:7"}],[3115,{"filePath":"tests/unit/test_stft.py","startLine":61,"endLine":64,"chunkText":"        # Time Dimension: This corresponds to how many frames (or segments) the input signal has been divided into.\n        #   It depends on the length of the input signal and the hop_length.\n        #   The formula for calculating the number of frames is derived from how we stride the window across the signal:\n        #     Length of Input Signal: Let's denote it as L. In this test, the input tensor has a shape of [1, 16000], so L is 16000 (ignoring the batch dimension for simplicity).","chunkId":"tests/unit/test_stft.py:8"}],[3116,{"filePath":"tests/unit/test_stft.py","startLine":62,"endLine":65,"chunkText":"        #   It depends on the length of the input signal and the hop_length.\n        #   The formula for calculating the number of frames is derived from how we stride the window across the signal:\n        #     Length of Input Signal: Let's denote it as L. In this test, the input tensor has a shape of [1, 16000], so L is 16000 (ignoring the batch dimension for simplicity).\n        #     Number of Frames: The number of frames depends on how we stride the window across the signal. For each frame, we move the window by hop_length samples.","chunkId":"tests/unit/test_stft.py:9"}],[3117,{"filePath":"tests/unit/test_stft.py","startLine":63,"endLine":66,"chunkText":"        #   The formula for calculating the number of frames is derived from how we stride the window across the signal:\n        #     Length of Input Signal: Let's denote it as L. In this test, the input tensor has a shape of [1, 16000], so L is 16000 (ignoring the batch dimension for simplicity).\n        #     Number of Frames: The number of frames depends on how we stride the window across the signal. For each frame, we move the window by hop_length samples.\n        #     Therefore, the number of frames N_frames can be roughly estimated by dividing the length of the signal by the hop_length.","chunkId":"tests/unit/test_stft.py:10"}],[3118,{"filePath":"tests/unit/test_stft.py","startLine":64,"endLine":66,"chunkText":"        #     Length of Input Signal: Let's denote it as L. In this test, the input tensor has a shape of [1, 16000], so L is 16000 (ignoring the batch dimension for simplicity).\n        #     Number of Frames: The number of frames depends on how we stride the window across the signal. For each frame, we move the window by hop_length samples.\n        #     Therefore, the number of frames N_frames can be roughly estimated by dividing the length of the signal by the hop_length.","chunkId":"tests/unit/test_stft.py:11"}],[3119,{"filePath":"tests/unit/test_stft.py","startLine":65,"endLine":69,"chunkText":"        #     Number of Frames: The number of frames depends on how we stride the window across the signal. For each frame, we move the window by hop_length samples.\n        #     Therefore, the number of frames N_frames can be roughly estimated by dividing the length of the signal by the hop_length.\n        #     However, since the window overlaps the signal, we add an extra frame to account for the last segment of the signal. This gives us N_frames = (L // hop_length) + 1.\n\n        # Putting It All Together","chunkId":"tests/unit/test_stft.py:12"}],[3120,{"filePath":"tests/unit/test_stft.py","startLine":67,"endLine":77,"chunkText":"        #     However, since the window overlaps the signal, we add an extra frame to account for the last segment of the signal. This gives us N_frames = (L // hop_length) + 1.\n\n        # Putting It All Together\n        #   expected_shape thus becomes (dim_f, N_frames), which is (1025, (16000 // 512) + 1) in this test case.\n\n        expected_shape = (self.dim_f, (input_tensor.shape[1] // self.hop_length) + 1)\n\n        self.assertEqual(stft_result.shape[-2:], expected_shape)\n\n    def test_calculate_inverse_dimensions(self):\n        # Create a sample input tensor","chunkId":"tests/unit/test_stft.py:13"}],[3121,{"filePath":"tests/unit/test_stft.py","startLine":75,"endLine":88,"chunkText":"\n    def test_calculate_inverse_dimensions(self):\n        # Create a sample input tensor\n        sample_input = torch.randn(1, 2, 500, 32)  # Batch, Channel, Frequency, Time dimensions\n        batch_dims, channel_dim, freq_dim, time_dim, num_freq_bins = self.stft.calculate_inverse_dimensions(sample_input)\n\n        # Expected values\n        expected_num_freq_bins = self.n_fft // 2 + 1\n\n        # Assertions\n        self.assertEqual(batch_dims, sample_input.shape[:-3])\n        self.assertEqual(channel_dim, 2)\n        self.assertEqual(freq_dim, 500)\n        self.assertEqual(time_dim, 32)","chunkId":"tests/unit/test_stft.py:14"}],[3122,{"filePath":"tests/unit/test_stft.py","startLine":86,"endLine":96,"chunkText":"        self.assertEqual(channel_dim, 2)\n        self.assertEqual(freq_dim, 500)\n        self.assertEqual(time_dim, 32)\n        self.assertEqual(num_freq_bins, expected_num_freq_bins)\n\n    def test_pad_frequency_dimension(self):\n        # Create a sample input tensor\n        sample_input = torch.randn(1, 2, 500, 32)  # Batch, Channel, Frequency, Time dimensions\n        batch_dims, channel_dim, freq_dim, time_dim, num_freq_bins = self.stft.calculate_inverse_dimensions(sample_input)\n\n        # Apply padding","chunkId":"tests/unit/test_stft.py:15"}],[3123,{"filePath":"tests/unit/test_stft.py","startLine":94,"endLine":106,"chunkText":"        batch_dims, channel_dim, freq_dim, time_dim, num_freq_bins = self.stft.calculate_inverse_dimensions(sample_input)\n\n        # Apply padding\n        padded_output = self.stft.pad_frequency_dimension(sample_input, batch_dims, channel_dim, freq_dim, time_dim, num_freq_bins)\n\n        # Expected frequency dimension after padding\n        expected_freq_dim = num_freq_bins\n\n        # Assertions\n        self.assertEqual(padded_output.shape[-2], expected_freq_dim)\n\n    def test_prepare_for_istft(self):\n        # Create a sample input tensor","chunkId":"tests/unit/test_stft.py:16"}],[3124,{"filePath":"tests/unit/test_stft.py","startLine":104,"endLine":113,"chunkText":"\n    def test_prepare_for_istft(self):\n        # Create a sample input tensor\n        sample_input = torch.randn(1, 2, 500, 32)  # Batch, Channel, Frequency, Time dimensions\n        batch_dims, channel_dim, freq_dim, time_dim, num_freq_bins = self.stft.calculate_inverse_dimensions(sample_input)\n        padded_output = self.stft.pad_frequency_dimension(sample_input, batch_dims, channel_dim, freq_dim, time_dim, num_freq_bins)\n\n        # Apply prepare_for_istft\n        complex_tensor = self.stft.prepare_for_istft(padded_output, batch_dims, channel_dim, num_freq_bins, time_dim)\n","chunkId":"tests/unit/test_stft.py:17"}],[3125,{"filePath":"tests/unit/test_stft.py","startLine":111,"endLine":123,"chunkText":"        # Apply prepare_for_istft\n        complex_tensor = self.stft.prepare_for_istft(padded_output, batch_dims, channel_dim, num_freq_bins, time_dim)\n\n        # Calculate the expected flattened batch size (flattening batch and channel dimensions)\n        expected_flattened_batch_size = batch_dims[0] * (channel_dim // 2)\n\n        # Expected shape of the complex tensor\n        expected_shape = (expected_flattened_batch_size, num_freq_bins, time_dim)\n\n        # Assertions\n        self.assertEqual(complex_tensor.shape, expected_shape)\n\n    def test_inverse_stft(self):","chunkId":"tests/unit/test_stft.py:18"}],[3126,{"filePath":"tests/unit/test_stft.py","startLine":121,"endLine":135,"chunkText":"        self.assertEqual(complex_tensor.shape, expected_shape)\n\n    def test_inverse_stft(self):\n        # Create a mock tensor with the correct input shape\n        input_tensor = torch.rand(1, 2, 1025, 32)  # shape matching output of STFT\n\n        # Apply inverse STFT\n        output_tensor = self.stft.inverse(input_tensor)\n\n        # Check if the output tensor is on the CPU\n        self.assertEqual(output_tensor.device.type, \"cpu\")\n\n        # Expected output shape: (Batch size, Channel dimension, Time dimension)\n        expected_shape = (1, 2, 7936)  # Calculated based on STFT parameters\n","chunkId":"tests/unit/test_stft.py:19"}],[3127,{"filePath":"tests/unit/test_stft.py","startLine":133,"endLine":144,"chunkText":"        # Expected output shape: (Batch size, Channel dimension, Time dimension)\n        expected_shape = (1, 2, 7936)  # Calculated based on STFT parameters\n\n        # Check if the output tensor has the expected shape\n        self.assertEqual(output_tensor.shape, expected_shape)\n\n    @unittest.skipIf(not torch.backends.mps.is_available(), \"MPS not available\")\n    def test_stft_with_mps_device(self):\n        mps_device = torch.device(\"mps\")\n        self.stft.device = mps_device\n        input_tensor = self.create_mock_tensor((1, 16000), device=mps_device)\n        stft_result = self.stft(input_tensor)","chunkId":"tests/unit/test_stft.py:20"}],[3128,{"filePath":"tests/unit/test_stft.py","startLine":142,"endLine":153,"chunkText":"        self.stft.device = mps_device\n        input_tensor = self.create_mock_tensor((1, 16000), device=mps_device)\n        stft_result = self.stft(input_tensor)\n        self.assertIsNotNone(stft_result)\n        self.assertIsInstance(stft_result, torch.Tensor)\n\n    @unittest.skipIf(not torch.backends.mps.is_available(), \"MPS not available\")\n    def test_inverse_with_mps_device(self):\n        mps_device = torch.device(\"mps\")\n        self.stft.device = mps_device\n        input_tensor = self.create_mock_tensor((1, 2, 1025, 32), device=mps_device)\n        istft_result = self.stft.inverse(input_tensor)","chunkId":"tests/unit/test_stft.py:21"}],[3129,{"filePath":"tests/unit/test_stft.py","startLine":151,"endLine":158,"chunkText":"        self.stft.device = mps_device\n        input_tensor = self.create_mock_tensor((1, 2, 1025, 32), device=mps_device)\n        istft_result = self.stft.inverse(input_tensor)\n        self.assertIsNotNone(istft_result)\n        self.assertIsInstance(istft_result, torch.Tensor)\n\n\n# Mock logger to use in tests","chunkId":"tests/unit/test_stft.py:22"}],[3130,{"filePath":"tests/unit/test_stft.py","startLine":159,"endLine":166,"chunkText":"class MockLogger:\n    def debug(self, message):\n        pass\n\n\nif __name__ == \"__main__\":\n    unittest.main()\n","chunkId":"tests/unit/test_stft.py:23"}],[3131,{"filePath":"tests/utils.py","startLine":0,"endLine":20,"chunkText":"import os\nimport numpy as np\nimport librosa\nimport librosa.display\nimport matplotlib.pyplot as plt\nfrom PIL import Image\nfrom io import BytesIO\nimport soundfile as sf\nfrom pathlib import Path\nfrom skimage.metrics import structural_similarity as ssim\n\n\ndef generate_waveform_image(audio_path, output_path=None, fig_size=(10, 4)):\n    \"\"\"Generate a waveform image from an audio file.\n    \n    Args:\n        audio_path: Path to the audio file\n        output_path: Path to save the generated image (optional)\n        fig_size: Size of the figure (width, height)\n        \n    Returns:","chunkId":"tests/utils.py:0"}],[3132,{"filePath":"tests/utils.py","startLine":18,"endLine":35,"chunkText":"        fig_size: Size of the figure (width, height)\n        \n    Returns:\n        BytesIO object containing the image if output_path is None, otherwise saves to output_path\n    \"\"\"\n    # Load audio file\n    y, sr = librosa.load(audio_path, sr=None, mono=False)\n    \n    # If mono, convert to stereo-like format for consistent plotting\n    if y.ndim == 1:\n        y = np.array([y, y])\n    \n    plt.figure(figsize=fig_size)\n    \n    # Plot waveform for each channel with fixed Y-axis scale\n    plt.subplot(2, 1, 1)\n    plt.plot(y[0])\n    plt.title('Channel 1')","chunkId":"tests/utils.py:1"}],[3133,{"filePath":"tests/utils.py","startLine":33,"endLine":56,"chunkText":"    plt.subplot(2, 1, 1)\n    plt.plot(y[0])\n    plt.title('Channel 1')\n    plt.ylim([-1.0, 1.0])  # Fixed Y-axis scale for all waveforms\n    \n    plt.subplot(2, 1, 2)\n    plt.plot(y[1])\n    plt.title('Channel 2')\n    plt.ylim([-1.0, 1.0])  # Fixed Y-axis scale for all waveforms\n    \n    plt.tight_layout()\n    \n    if output_path:\n        plt.savefig(output_path)\n        plt.close()\n        return output_path\n    else:\n        buf = BytesIO()\n        plt.savefig(buf, format='png')\n        plt.close()\n        buf.seek(0)\n        return buf\n\n","chunkId":"tests/utils.py:2"}],[3134,{"filePath":"tests/utils.py","startLine":54,"endLine":70,"chunkText":"        return buf\n\n\ndef generate_spectrogram_image(audio_path, output_path=None, fig_size=(10, 8)):\n    \"\"\"Generate a spectrogram image from an audio file.\n    \n    Args:\n        audio_path: Path to the audio file\n        output_path: Path to save the generated image (optional)\n        fig_size: Size of the figure (width, height)\n        \n    Returns:\n        BytesIO object containing the image if output_path is None, otherwise saves to output_path\n    \"\"\"\n    # Load audio file\n    y, sr = librosa.load(audio_path, sr=None, mono=False)\n    ","chunkId":"tests/utils.py:3"}],[3135,{"filePath":"tests/utils.py","startLine":68,"endLine":86,"chunkText":"    # Load audio file\n    y, sr = librosa.load(audio_path, sr=None, mono=False)\n    \n    # If mono, convert to stereo-like format for consistent plotting\n    if y.ndim == 1:\n        y = np.array([y, y])\n    \n    plt.figure(figsize=fig_size)\n    \n    # Set fixed min and max values for spectrogram color scale\n    vmin = -80  # dB\n    vmax = 0    # dB\n    \n    # Generate spectrograms for each channel\n    for i in range(2):\n        # Compute spectrogram\n        S = librosa.amplitude_to_db(np.abs(librosa.stft(y[i])), ref=np.max)\n        \n        plt.subplot(2, 1, i+1)","chunkId":"tests/utils.py:4"}],[3136,{"filePath":"tests/utils.py","startLine":84,"endLine":101,"chunkText":"        S = librosa.amplitude_to_db(np.abs(librosa.stft(y[i])), ref=np.max)\n        \n        plt.subplot(2, 1, i+1)\n        # Use fixed frequency range and consistent color scaling\n        librosa.display.specshow(\n            S, \n            sr=sr, \n            x_axis='time', \n            y_axis='log',\n            vmin=vmin,\n            vmax=vmax\n        )\n        plt.colorbar(format='%+2.0f dB')\n        plt.title(f'Channel {i+1} Spectrogram')\n        \n        # Set frequency range (y-axis) - typically up to Nyquist frequency (sr/2)\n        plt.ylim([20, sr/2])  # From 20Hz to Nyquist frequency\n    ","chunkId":"tests/utils.py:5"}],[3137,{"filePath":"tests/utils.py","startLine":99,"endLine":119,"chunkText":"        # Set frequency range (y-axis) - typically up to Nyquist frequency (sr/2)\n        plt.ylim([20, sr/2])  # From 20Hz to Nyquist frequency\n    \n    plt.tight_layout()\n    \n    if output_path:\n        plt.savefig(output_path)\n        plt.close()\n        return output_path\n    else:\n        buf = BytesIO()\n        plt.savefig(buf, format='png')\n        plt.close()\n        buf.seek(0)\n        return buf\n\n\ndef compare_images(image1_path, image2_path, min_similarity_threshold=0.999):\n    \"\"\"Compare two images using Structural Similarity Index (SSIM) which is robust to small shifts.\n    \n    Args:","chunkId":"tests/utils.py:6"}],[3138,{"filePath":"tests/utils.py","startLine":117,"endLine":128,"chunkText":"    \"\"\"Compare two images using Structural Similarity Index (SSIM) which is robust to small shifts.\n    \n    Args:\n        image1_path: Path to the first image\n        image2_path: Path to the second image\n        min_similarity_threshold: Minimum similarity required for images to be considered matching (0.0-1.0)\n            - Higher values (closer to 1.0) require images to be more similar\n            - Lower values (closer to 0.0) are more permissive\n            - A value of 0.99 requires 99% similarity between images\n            - A value of 0.0 would consider any images to match\n        \n    Returns:","chunkId":"tests/utils.py:7"}],[3139,{"filePath":"tests/utils.py","startLine":126,"endLine":142,"chunkText":"            - A value of 0.0 would consider any images to match\n        \n    Returns:\n        Tuple of (similarity_score, is_match)\n        - similarity_score: Value between 0.0 and 1.0, where 1.0 means identical images\n        - is_match: Boolean indicating if similarity_score >= min_similarity_threshold\n    \"\"\"\n    # Open images\n    img1 = Image.open(image1_path).convert('RGB')\n    img2 = Image.open(image2_path).convert('RGB')\n    \n    # Ensure same size for comparison\n    if img1.size != img2.size:\n        img2 = img2.resize(img1.size)\n    \n    # Convert to numpy arrays\n    arr1 = np.array(img1)","chunkId":"tests/utils.py:8"}],[3140,{"filePath":"tests/utils.py","startLine":140,"endLine":159,"chunkText":"    \n    # Convert to numpy arrays\n    arr1 = np.array(img1)\n    arr2 = np.array(img2)\n    \n    # Calculate SSIM for each color channel\n    similarity_scores = []\n    for channel in range(3):  # RGB channels\n        score = ssim(arr1[:,:,channel], arr2[:,:,channel], data_range=255)\n        similarity_scores.append(score)\n    \n    # Calculate average SSIM across channels\n    similarity_score = np.mean(similarity_scores)\n    \n    # Determine if images match by comparing similarity to threshold\n    is_match = similarity_score >= min_similarity_threshold\n    \n    return (similarity_score, is_match)\n\n","chunkId":"tests/utils.py:9"}],[3141,{"filePath":"tests/utils.py","startLine":157,"endLine":174,"chunkText":"    return (similarity_score, is_match)\n\n\ndef generate_reference_images(input_path, output_dir=None, prefix=\"\"):\n    \"\"\"Generate reference waveform and spectrogram images for an audio file.\n    \n    Args:\n        input_path: Path to the audio file\n        output_dir: Directory to save the generated images (optional)\n        prefix: Prefix to add to the output image filenames\n        \n    Returns:\n        Tuple of (waveform_path, spectrogram_path)\n    \"\"\"\n    if output_dir is None:\n        output_dir = os.path.dirname(input_path)\n    \n    # Create output directory if it doesn't exist","chunkId":"tests/utils.py:10"}],[3142,{"filePath":"tests/utils.py","startLine":172,"endLine":185,"chunkText":"        output_dir = os.path.dirname(input_path)\n    \n    # Create output directory if it doesn't exist\n    os.makedirs(output_dir, exist_ok=True)\n    \n    input_filename = os.path.basename(input_path)\n    name_without_ext = os.path.splitext(input_filename)[0]\n    \n    # Generate waveform image\n    waveform_path = os.path.join(output_dir, f\"{prefix}{name_without_ext}_waveform.png\")\n    generate_waveform_image(input_path, waveform_path)\n    \n    # Generate spectrogram image\n    spectrogram_path = os.path.join(output_dir, f\"{prefix}{name_without_ext}_spectrogram.png\")","chunkId":"tests/utils.py:11"}],[3143,{"filePath":"tests/utils.py","startLine":183,"endLine":188,"chunkText":"    \n    # Generate spectrogram image\n    spectrogram_path = os.path.join(output_dir, f\"{prefix}{name_without_ext}_spectrogram.png\")\n    generate_spectrogram_image(input_path, spectrogram_path)\n    \n    return (waveform_path, spectrogram_path) ","chunkId":"tests/utils.py:12"}],[3144,{"filePath":"tools/calculate-model-hashes.py","startLine":0,"endLine":18,"chunkText":"#!/usr/bin/env python3\n\nimport os\nimport sys\nimport json\nimport hashlib\nimport requests\n\nMODEL_CACHE_PATH = \"/tmp/audio-separator-models\"\nVR_MODEL_DATA_LOCAL_PATH = f\"{MODEL_CACHE_PATH}/vr_model_data.json\"\nMDX_MODEL_DATA_LOCAL_PATH = f\"{MODEL_CACHE_PATH}/mdx_model_data.json\"\n\nMODEL_DATA_URL_PREFIX = \"https://raw.githubusercontent.com/TRvlvr/application_data/main\"\nVR_MODEL_DATA_URL = f\"{MODEL_DATA_URL_PREFIX}/vr_model_data/model_data_new.json\"\nMDX_MODEL_DATA_URL = f\"{MODEL_DATA_URL_PREFIX}/mdx_model_data/model_data_new.json\"\n\nOUTPUT_PATH = f\"{MODEL_CACHE_PATH}/model_hashes.json\"\n\n","chunkId":"tools/calculate-model-hashes.py:0"}],[3145,{"filePath":"tools/calculate-model-hashes.py","startLine":16,"endLine":32,"chunkText":"OUTPUT_PATH = f\"{MODEL_CACHE_PATH}/model_hashes.json\"\n\n\ndef get_model_hash(model_path):\n    \"\"\"\n    Get the hash of a model file\n    \"\"\"\n    # print(f\"Getting hash for model at {model_path}\")\n    try:\n        with open(model_path, \"rb\") as f:\n            f.seek(-10000 * 1024, 2)  # Move the file pointer 10MB before the end of the file\n            hash_result = hashlib.md5(f.read()).hexdigest()\n            # print(f\"Hash for {model_path}: {hash_result}\")\n            return hash_result\n    except IOError:\n        with open(model_path, \"rb\") as f:\n            hash_result = hashlib.md5(f.read()).hexdigest()","chunkId":"tools/calculate-model-hashes.py:1"}],[3146,{"filePath":"tools/calculate-model-hashes.py","startLine":30,"endLine":44,"chunkText":"    except IOError:\n        with open(model_path, \"rb\") as f:\n            hash_result = hashlib.md5(f.read()).hexdigest()\n            # print(f\"IOError encountered, hash for {model_path}: {hash_result}\")\n            return hash_result\n\n\ndef download_file_if_missing(url, local_path):\n    \"\"\"\n    Download a file from a URL if it doesn't exist locally\n    \"\"\"\n    print(f\"Checking if {local_path} needs to be downloaded from {url}\")\n    if not os.path.exists(local_path):\n        print(f\"Downloading {url} to {local_path}\")\n        with requests.get(url, stream=True, timeout=10) as r:","chunkId":"tools/calculate-model-hashes.py:2"}],[3147,{"filePath":"tools/calculate-model-hashes.py","startLine":42,"endLine":59,"chunkText":"    if not os.path.exists(local_path):\n        print(f\"Downloading {url} to {local_path}\")\n        with requests.get(url, stream=True, timeout=10) as r:\n            r.raise_for_status()\n            with open(local_path, \"wb\") as f:\n                for chunk in r.iter_content(chunk_size=8192):\n                    f.write(chunk)\n        print(f\"Downloaded {url} to {local_path}\")\n    else:\n        print(f\"{local_path} already exists. Skipping download.\")\n\n\ndef load_json_data(file_path):\n    \"\"\"\n    Load JSON data from a file\n    \"\"\"\n    print(f\"Loading JSON data from {file_path}\")\n    try:","chunkId":"tools/calculate-model-hashes.py:3"}],[3148,{"filePath":"tools/calculate-model-hashes.py","startLine":57,"endLine":73,"chunkText":"    \"\"\"\n    print(f\"Loading JSON data from {file_path}\")\n    try:\n        with open(file_path, \"r\", encoding=\"utf-8\") as file:\n            data = json.load(file)\n            print(f\"Loaded JSON data successfully from {file_path}\")\n            return data\n    except FileNotFoundError:\n        print(f\"{file_path} not found.\")\n        sys.exit(1)\n\n\ndef iterate_and_hash(directory):\n    \"\"\"\n    Iterate through a directory and hash all model files\n    \"\"\"\n    print(f\"Iterating through directory {directory} to hash model files\")","chunkId":"tools/calculate-model-hashes.py:4"}],[3149,{"filePath":"tools/calculate-model-hashes.py","startLine":71,"endLine":82,"chunkText":"    Iterate through a directory and hash all model files\n    \"\"\"\n    print(f\"Iterating through directory {directory} to hash model files\")\n    model_files = [(file, os.path.join(root, file)) for root, _, files in os.walk(directory) for file in files if file.endswith((\".pth\", \".onnx\"))]\n\n    download_file_if_missing(VR_MODEL_DATA_URL, VR_MODEL_DATA_LOCAL_PATH)\n    download_file_if_missing(MDX_MODEL_DATA_URL, MDX_MODEL_DATA_LOCAL_PATH)\n\n    vr_model_data = load_json_data(VR_MODEL_DATA_LOCAL_PATH)\n    mdx_model_data = load_json_data(MDX_MODEL_DATA_LOCAL_PATH)\n\n    combined_model_params = {","chunkId":"tools/calculate-model-hashes.py:5"}],[3150,{"filePath":"tools/calculate-model-hashes.py","startLine":81,"endLine":99,"chunkText":"\n    combined_model_params = {\n        **vr_model_data,\n        **mdx_model_data,\n    }\n\n    model_info_list = []\n    for file, file_path in sorted(model_files):\n        file_hash = get_model_hash(file_path)\n        model_info = {\n            \"file\": file,\n            \"hash\": file_hash,\n            \"params\": combined_model_params.get(file_hash, \"Parameters not found\"),\n        }\n        model_info_list.append(model_info)\n\n    print(f\"Writing model info list to {OUTPUT_PATH}\")\n    with open(OUTPUT_PATH, \"w\", encoding=\"utf-8\") as json_file:\n        json.dump(model_info_list, json_file, indent=4)","chunkId":"tools/calculate-model-hashes.py:6"}],[3151,{"filePath":"tools/calculate-model-hashes.py","startLine":97,"endLine":105,"chunkText":"    print(f\"Writing model info list to {OUTPUT_PATH}\")\n    with open(OUTPUT_PATH, \"w\", encoding=\"utf-8\") as json_file:\n        json.dump(model_info_list, json_file, indent=4)\n        print(f\"Successfully wrote model info list to {OUTPUT_PATH}\")\n\n\nif __name__ == \"__main__\":\n    iterate_and_hash(MODEL_CACHE_PATH)\n","chunkId":"tools/calculate-model-hashes.py:7"}],[3152,{"filePath":"tools/sync-to-github.py","startLine":0,"endLine":18,"chunkText":"#! /usr/bin/env python3\nimport os\nimport requests\nimport hashlib\nfrom typing import List, Dict\nimport sys\n\n# Configuration\nGITHUB_TOKEN = os.getenv(\"GITHUB_TOKEN\", \"\").strip()  # Add .strip() to remove whitespace\nREPO_OWNER = \"nomadkaraoke\"\nREPO_NAME = \"python-audio-separator\"\nRELEASE_TAG = \"model-configs\"\n\nHEADERS = {\"Authorization\": f\"Bearer {GITHUB_TOKEN}\", \"Accept\": \"application/vnd.github.v3+json\"}\n\n\ndef debug_request(url: str, headers: dict, response: requests.Response):\n    \"\"\"Debug helper to print request and response details.\"\"\"\n    print(\"\\n=== Debug Information ===\")","chunkId":"tools/sync-to-github.py:0"}],[3153,{"filePath":"tools/sync-to-github.py","startLine":17,"endLine":29,"chunkText":"    \"\"\"Debug helper to print request and response details.\"\"\"\n    print(\"\\n=== Debug Information ===\")\n    print(f\"Request URL: {url}\")\n    print(f\"Request Headers: {headers}\")\n    print(f\"Response Status: {response.status_code}\")\n    print(f\"Response Headers: {dict(response.headers)}\")\n    print(f\"Response Body: {response.text[:500]}...\")  # First 500 chars of response\n    print(\"=======================\\n\")\n\n\ndef get_release_assets() -> List[Dict]:\n    \"\"\"Get all assets from the specified release.\"\"\"\n    url = f\"https://api.github.com/repos/{REPO_OWNER}/{REPO_NAME}/releases/tags/{RELEASE_TAG}\"","chunkId":"tools/sync-to-github.py:1"}],[3154,{"filePath":"tools/sync-to-github.py","startLine":27,"endLine":40,"chunkText":"def get_release_assets() -> List[Dict]:\n    \"\"\"Get all assets from the specified release.\"\"\"\n    url = f\"https://api.github.com/repos/{REPO_OWNER}/{REPO_NAME}/releases/tags/{RELEASE_TAG}\"\n    print(f\"\\nDebug: Using token: {GITHUB_TOKEN[:4]}...{GITHUB_TOKEN[-4:]}\")  # Show first/last 4 chars\n    print(f\"Debug: Requesting URL: {url}\")\n    print(f\"Debug: Headers: {HEADERS}\")\n\n    response = requests.get(url, headers=HEADERS)\n    debug_request(url, HEADERS, response)\n\n    if response.status_code != 200:\n        print(f\"Error getting release: {response.status_code}\")\n        return []\n","chunkId":"tools/sync-to-github.py:2"}],[3155,{"filePath":"tools/sync-to-github.py","startLine":38,"endLine":55,"chunkText":"        print(f\"Error getting release: {response.status_code}\")\n        return []\n\n    release_data = response.json()\n    return release_data.get(\"assets\", [])\n\n\ndef list_local_files() -> List[str]:\n    \"\"\"List all files in the current directory (excluding directories).\"\"\"\n    return [f for f in os.listdir(\".\") if os.path.isfile(f)]\n\n\ndef calculate_file_hash(filepath: str) -> str:\n    \"\"\"Calculate SHA256 hash of a file.\"\"\"\n    sha256_hash = hashlib.sha256()\n    with open(filepath, \"rb\") as f:\n        for byte_block in iter(lambda: f.read(4096), b\"\"):\n            sha256_hash.update(byte_block)","chunkId":"tools/sync-to-github.py:3"}],[3156,{"filePath":"tools/sync-to-github.py","startLine":53,"endLine":68,"chunkText":"    with open(filepath, \"rb\") as f:\n        for byte_block in iter(lambda: f.read(4096), b\"\"):\n            sha256_hash.update(byte_block)\n    return sha256_hash.hexdigest()\n\n\ndef upload_asset(release_id: int, filepath: str):\n    \"\"\"Upload a file as a release asset.\"\"\"\n    upload_url = f\"https://uploads.github.com/repos/{REPO_OWNER}/{REPO_NAME}/releases/{release_id}/assets\"\n\n    filename = os.path.basename(filepath)\n    headers = {\"Authorization\": f\"Bearer {GITHUB_TOKEN}\", \"Content-Type\": \"application/octet-stream\"}\n\n    params = {\"name\": filename}\n\n    with open(filepath, \"rb\") as f:","chunkId":"tools/sync-to-github.py:4"}],[3157,{"filePath":"tools/sync-to-github.py","startLine":66,"endLine":83,"chunkText":"    params = {\"name\": filename}\n\n    with open(filepath, \"rb\") as f:\n        response = requests.post(upload_url, headers=headers, params=params, data=f)\n\n    if response.status_code == 201:\n        print(f\"Successfully uploaded {filename}\")\n    else:\n        print(f\"Failed to upload {filename}: {response.status_code}\")\n        print(response.text)\n\n\ndef download_asset(asset: Dict):\n    \"\"\"Download a release asset to the local directory.\"\"\"\n    filename = asset[\"name\"]\n    download_url = asset[\"browser_download_url\"]\n\n    print(f\"Downloading {filename}...\")","chunkId":"tools/sync-to-github.py:5"}],[3158,{"filePath":"tools/sync-to-github.py","startLine":81,"endLine":99,"chunkText":"    download_url = asset[\"browser_download_url\"]\n\n    print(f\"Downloading {filename}...\")\n    response = requests.get(download_url, headers=HEADERS, stream=True)\n\n    if response.status_code == 200:\n        with open(filename, \"wb\") as f:\n            for chunk in response.iter_content(chunk_size=8192):\n                f.write(chunk)\n        print(f\"Successfully downloaded {filename}\")\n    else:\n        print(f\"Failed to download {filename}: {response.status_code}\")\n\n\ndef main():\n    if not GITHUB_TOKEN:\n        print(\"Please set GITHUB_TOKEN environment variable\")\n        sys.exit(1)\n","chunkId":"tools/sync-to-github.py:6"}],[3159,{"filePath":"tools/sync-to-github.py","startLine":97,"endLine":110,"chunkText":"        print(\"Please set GITHUB_TOKEN environment variable\")\n        sys.exit(1)\n\n    print(f\"Debug: Script starting with token length: {len(GITHUB_TOKEN)}\")\n    print(f\"Debug: Token characters: {[ord(c) for c in GITHUB_TOKEN]}\")\n    print(f\"Debug: Token first/last chars: {GITHUB_TOKEN[:4]}...{GITHUB_TOKEN[-4:]}\")\n\n    # Get release ID first\n    url = f\"https://api.github.com/repos/{REPO_OWNER}/{REPO_NAME}/releases/tags/{RELEASE_TAG}\"\n    response = requests.get(url, headers=HEADERS)\n    if response.status_code != 200:\n        print(f\"Error getting release: {response.status_code}\")\n        return\n","chunkId":"tools/sync-to-github.py:7"}],[3160,{"filePath":"tools/sync-to-github.py","startLine":108,"endLine":129,"chunkText":"        print(f\"Error getting release: {response.status_code}\")\n        return\n\n    release_id = response.json()[\"id\"]\n\n    # Get existing assets\n    existing_assets = get_release_assets()\n    existing_filenames = {asset[\"name\"] for asset in existing_assets}\n\n    # Get local files\n    local_files = list_local_files()\n\n    print(\"\\nExisting release assets:\")\n    for asset in existing_assets:\n        print(f\"- {asset['name']} ({asset['size']} bytes)\")\n\n    # Add download option\n    print(\"\\nOptions:\")\n    print(\"1. Upload new files\")\n    print(\"2. Download all missing files\")\n    print(\"3. Exit\")\n","chunkId":"tools/sync-to-github.py:8"}],[3161,{"filePath":"tools/sync-to-github.py","startLine":127,"endLine":144,"chunkText":"    print(\"2. Download all missing files\")\n    print(\"3. Exit\")\n\n    choice = input(\"\\nEnter your choice (1-3): \")\n\n    if choice == \"1\":\n        # Original upload logic\n        files_to_upload = []\n        for local_file in local_files:\n            if local_file not in existing_filenames:\n                print(f\"- {local_file}\")\n                files_to_upload.append(local_file)\n\n        if files_to_upload:\n            files_with_size = [(f, os.path.getsize(f)) for f in files_to_upload]\n            files_with_size.sort(key=lambda x: x[1])\n\n            print(\"\\nFiles to upload (in order):\")","chunkId":"tools/sync-to-github.py:9"}],[3162,{"filePath":"tools/sync-to-github.py","startLine":142,"endLine":157,"chunkText":"            files_with_size.sort(key=lambda x: x[1])\n\n            print(\"\\nFiles to upload (in order):\")\n            for file, size in files_with_size:\n                print(f\"- {file} ({size / 1024 / 1024:.2f} MB)\")\n\n            response = input(\"\\nDo you want to upload these files? (y/n): \")\n            if response.lower() == \"y\":\n                for file, _ in files_with_size:\n                    upload_asset(release_id, file)\n        else:\n            print(\"\\nNo new files to upload.\")\n\n    elif choice == \"2\":\n        # Download missing files\n        files_to_download = []","chunkId":"tools/sync-to-github.py:10"}],[3163,{"filePath":"tools/sync-to-github.py","startLine":155,"endLine":169,"chunkText":"    elif choice == \"2\":\n        # Download missing files\n        files_to_download = []\n        for asset in existing_assets:\n            if asset[\"name\"] not in local_files:\n                files_to_download.append(asset)\n\n        if files_to_download:\n            print(\"\\nFiles to download:\")\n            for asset in files_to_download:\n                print(f\"- {asset['name']} ({asset['size'] / 1024 / 1024:.2f} MB)\")\n\n            response = input(\"\\nDo you want to download these files? (y/n): \")\n            if response.lower() == \"y\":\n                for asset in files_to_download:","chunkId":"tools/sync-to-github.py:11"}],[3164,{"filePath":"tools/sync-to-github.py","startLine":167,"endLine":177,"chunkText":"            response = input(\"\\nDo you want to download these files? (y/n): \")\n            if response.lower() == \"y\":\n                for asset in files_to_download:\n                    download_asset(asset)\n        else:\n            print(\"\\nNo files to download. Local directory is in sync.\")\n\n\nif __name__ == \"__main__\":\n    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literal 0
HcmV?d00001

diff --git a/Dockerfile.rocm b/Dockerfile.rocm
new file mode 100644
index 0000000..41420f9
--- /dev/null
+++ b/Dockerfile.rocm
@@ -0,0 +1,26 @@
+# Use ROCm base image with Python
+FROM rocm/dev-ubuntu-22.04:5.7.1-complete
+
+# Set the working directory in the container
+WORKDIR /workdir
+
+# Install necessary packages
+RUN apt-get update && apt-get install -y \
+    ffmpeg \
+    build-essential \
+    python3 \
+    python3-pip \
+    && rm -rf /var/lib/apt/lists/*
+
+RUN python3 -m pip install --upgrade pip
+
+# Install PyTorch with ROCm support and audio-separator with ROCm extra
+RUN --mount=type=cache,target=/root/.cache \
+    pip3 install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/rocm5.7
+
+# Install audio-separator with ROCm support
+RUN --mount=type=cache,target=/root/.cache \
+    pip3 install "audio-separator[rocm]"
+
+# Run audio-separator when the container launches
+ENTRYPOINT ["audio-separator"]
\ No newline at end of file
diff --git a/README.md b/README.md
index c6b8057..5a1c1b5 100644
--- a/README.md
+++ b/README.md
@@ -112,6 +112,26 @@ Docker:
 beveradb/audio-separator:gpu
 ```
 
+### 🖥️ AMD GPU with ROCm (Linux)
+
+**Supported ROCm Versions:** 5.7+
+
+💬 If successfully configured, you should see this log message when running `audio-separator --env_info`:
+ `ONNXruntime has ROCMExecutionProvider available, enabling acceleration`
+
+Pip:
+```sh
+pip install "audio-separator[rocm]"
+```
+
+Note: For ROCm support, you need to have ROCm installed on your system first. PyTorch with ROCm support should be installed separately using the official PyTorch installation instructions for ROCm.
+
+Docker (build from source):
+```sh
+docker build -f Dockerfile.rocm -t audio-separator:rocm .
+docker run -it --device=/dev/kfd --device=/dev/dri --group-add=video -v `pwd`:/workdir audio-separator:rocm input.wav
+```
+
 ###  Apple Silicon, macOS Sonoma+ with M1 or newer CPU (CoreML acceleration)
 
 💬 If successfully configured, you should see this log message when running `audio-separator --env_info`:
@@ -157,11 +177,11 @@ apt-get update; apt-get install -y ffmpeg
 brew update; brew install ffmpeg
 ```
 
-## GPU / CUDA specific installation steps with Pip
+## GPU specific installation steps with Pip (CUDA and ROCm)
 
-In theory, all you should need to do to get `audio-separator` working with a GPU is install it with the `[gpu]` extra as above.
+In theory, all you should need to do to get `audio-separator` working with a GPU is install it with the appropriate extra (`[gpu]` for CUDA/NVIDIA or `[rocm]` for ROCm/AMD) as above.
 
-However, sometimes getting both PyTorch and ONNX Runtime working with CUDA support can be a bit tricky so it may not work that easily.
+However, sometimes getting both PyTorch and ONNX Runtime working with GPU support can be a bit tricky so it may not work that easily.
 
 You may need to reinstall both packages directly, allowing pip to calculate the right versions for your platform, for example:
 
@@ -197,6 +217,22 @@ You can resolve this by running the following command:
 python -m pip install ort-nightly-gpu --index-url=https://aiinfra.pkgs.visualstudio.com/PublicPackages/_packaging/ort-cuda-12-nightly/pypi/simple/
 ```
 
+### ROCm specific troubleshooting
+
+For ROCm (AMD GPU) support, make sure you have:
+1. ROCm installed on your system (typically version 5.7+)
+2. PyTorch with ROCm support installed (check PyTorch website for ROCm installation)
+3. `onnxruntime-rocm` package installed
+
+If you encounter issues with ROCm detection, try reinstalling the packages:
+```sh
+pip uninstall torch onnxruntime
+pip cache purge
+# Install PyTorch with ROCm support (check https://pytorch.org for the correct command)
+pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/rocm5.7
+pip install onnxruntime-rocm
+```
+
 > Note: if anyone knows how to make this cleaner so we can support both different platform-specific dependencies for hardware acceleration without a separate installation process for each, please let me know or raise a PR!
 
 ## Usage 🚀
diff --git a/audio_separator/separator/separator.py b/audio_separator/separator/separator.py
index 9a8e42f..166357e 100644
--- a/audio_separator/separator/separator.py
+++ b/audio_separator/separator/separator.py
@@ -256,6 +256,7 @@ def log_onnxruntime_packages(self):
         onnxruntime_silicon_package = self.get_package_distribution("onnxruntime-silicon")
         onnxruntime_cpu_package = self.get_package_distribution("onnxruntime")
         onnxruntime_dml_package = self.get_package_distribution("onnxruntime-directml")
+        onnxruntime_rocm_package = self.get_package_distribution("onnxruntime-rocm")
 
         if onnxruntime_gpu_package is not None:
             self.logger.info(f"ONNX Runtime GPU package installed with version: {onnxruntime_gpu_package.version}")
@@ -265,6 +266,8 @@ def log_onnxruntime_packages(self):
             self.logger.info(f"ONNX Runtime CPU package installed with version: {onnxruntime_cpu_package.version}")
         if onnxruntime_dml_package is not None:
             self.logger.info(f"ONNX Runtime DirectML package installed with version: {onnxruntime_dml_package.version}")
+        if onnxruntime_rocm_package is not None:
+            self.logger.info(f"ONNX Runtime ROCm package installed with version: {onnxruntime_rocm_package.version}")
 
     def setup_torch_device(self, system_info):
         """
@@ -277,8 +280,14 @@ def setup_torch_device(self, system_info):
         self.torch_device_cpu = torch.device("cpu")
 
         if torch.cuda.is_available():
-            self.configure_cuda(ort_providers)
-            hardware_acceleration_enabled = True
+            # Check if ROCm is available (AMD GPUs)
+            # We check ROCMExecutionProvider first since if it's available, we're likely on ROCm
+            if "ROCMExecutionProvider" in ort_providers:
+                self.configure_rocm(ort_providers)
+                hardware_acceleration_enabled = True
+            else:
+                self.configure_cuda(ort_providers)
+                hardware_acceleration_enabled = True
         elif hasattr(torch.backends, "mps") and torch.backends.mps.is_available() and system_info.processor == "arm":
             self.configure_mps(ort_providers)
             hardware_acceleration_enabled = True
@@ -305,6 +314,18 @@ def configure_cuda(self, ort_providers):
         else:
             self.logger.warning("CUDAExecutionProvider not available in ONNXruntime, so acceleration will NOT be enabled")
 
+    def configure_rocm(self, ort_providers):
+        """
+        This method configures the ROCm device for PyTorch and ONNX Runtime, if available.
+        """
+        self.logger.info("ROCm (AMD GPU) is available, setting Torch device to CUDA (ROCm presents as CUDA)")
+        self.torch_device = torch.device("cuda")
+        if "ROCMExecutionProvider" in ort_providers:
+            self.logger.info("ONNXruntime has ROCMExecutionProvider available, enabling acceleration")
+            self.onnx_execution_provider = ["ROCMExecutionProvider"]
+        else:
+            self.logger.warning("ROCMExecutionProvider not available in ONNXruntime, so acceleration will NOT be enabled")
+
     def configure_mps(self, ort_providers):
         """
         This method configures the Apple Silicon MPS/CoreML device for PyTorch and ONNX Runtime, if available.
diff --git a/pyproject.toml b/pyproject.toml
index b5d56a9..30d3665 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -62,6 +62,7 @@ soundfile = ">=0.12"
 cpu = ["onnxruntime"]
 gpu = ["onnxruntime-gpu"]
 dml = ["onnxruntime-directml", "torch_directml"]
+rocm = ["onnxruntime-rocm"]
 
 [tool.poetry.scripts]
 audio-separator = 'audio_separator.utils.cli:main'

From 2d7af4b6ed708168f6aa464cc8deb6bac7cc152b Mon Sep 17 00:00:00 2001
From: keybangz <wyatt.wheeler@protonmail.com>
Date: Tue, 17 Mar 2026 03:51:56 +1300
Subject: [PATCH 02/13] Improve ROCm installation process and add PyTorch
 version checks

- Add detailed ROCm installation instructions with exact PyTorch commands
- Add PyTorch version detection in device setup
- Add warning when ROCm ExecutionProvider detected but PyTorch has CUDA support
- Improve documentation for proper ROCm setup process
- Maintain backward compatibility while providing better user guidance

This fixes the issue where 'audio-separator[rocm]' installs CPU/default PyTorch
instead of ROCm-compatible PyTorch, and helps users properly configure ROCm.
---
 README.md                              | 14 ++++++++++++--
 audio_separator/separator/separator.py | 23 +++++++++++++++++++++--
 pyproject.toml                         |  1 +
 3 files changed, 34 insertions(+), 4 deletions(-)

diff --git a/README.md b/README.md
index 5a1c1b5..a90e315 100644
--- a/README.md
+++ b/README.md
@@ -119,12 +119,22 @@ beveradb/audio-separator:gpu
 💬 If successfully configured, you should see this log message when running `audio-separator --env_info`:
  `ONNXruntime has ROCMExecutionProvider available, enabling acceleration`
 
-Pip:
+Pip (complete installation):
 ```sh
+# First install PyTorch with ROCm support
+pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/rocm5.7
+
+# Then install audio-separator with ROCm support
 pip install "audio-separator[rocm]"
 ```
 
-Note: For ROCm support, you need to have ROCm installed on your system first. PyTorch with ROCm support should be installed separately using the official PyTorch installation instructions for ROCm.
+**Important:** You must install PyTorch with ROCm support BEFORE installing audio-separator. If you already have PyTorch with CUDA support installed, uninstall it first:
+```sh
+pip uninstall torch torchvision torchaudio
+pip cache purge
+pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/rocm5.7
+pip install "audio-separator[rocm]"
+```
 
 Docker (build from source):
 ```sh
diff --git a/audio_separator/separator/separator.py b/audio_separator/separator/separator.py
index 166357e..f543b80 100644
--- a/audio_separator/separator/separator.py
+++ b/audio_separator/separator/separator.py
@@ -279,6 +279,10 @@ def setup_torch_device(self, system_info):
 
         self.torch_device_cpu = torch.device("cpu")
 
+        # Check for PyTorch version compatibility
+        torch_version = torch.__version__
+        self.logger.debug(f"PyTorch version: {torch_version}")
+        
         if torch.cuda.is_available():
             # Check if ROCm is available (AMD GPUs)
             # We check ROCMExecutionProvider first since if it's available, we're likely on ROCm
@@ -286,8 +290,15 @@ def setup_torch_device(self, system_info):
                 self.configure_rocm(ort_providers)
                 hardware_acceleration_enabled = True
             else:
-                self.configure_cuda(ort_providers)
-                hardware_acceleration_enabled = True
+                # Check if CUDA version includes CUDA (should be +cu***)
+                if "+cu" in torch_version or "cpu" not in torch_version.lower():
+                    self.logger.info("PyTorch with CUDA support detected")
+                    self.configure_cuda(ort_providers)
+                    hardware_acceleration_enabled = True
+                else:
+                    self.logger.warning("ROCm ExecutionProvider not available but PyTorch appears to be CUDA version. Consider installing PyTorch with ROCm support.")
+                    self.configure_cuda(ort_providers)
+                    hardware_acceleration_enabled = True
         elif hasattr(torch.backends, "mps") and torch.backends.mps.is_available() and system_info.processor == "arm":
             self.configure_mps(ort_providers)
             hardware_acceleration_enabled = True
@@ -318,6 +329,14 @@ def configure_rocm(self, ort_providers):
         """
         This method configures the ROCm device for PyTorch and ONNX Runtime, if available.
         """
+        torch_version = torch.__version__
+        if "+cu" in torch_version:
+            self.logger.warning("ROCm ExecutionProvider detected, but PyTorch appears to have CUDA support instead of ROCm support.")
+            self.logger.warning("For optimal AMD GPU performance, consider reinstalling PyTorch with ROCm support:")
+            self.logger.warning("pip uninstall torch torchvision torchaudio")
+            self.logger.warning("pip cache purge") 
+            self.logger.warning("pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/rocm5.7")
+        
         self.logger.info("ROCm (AMD GPU) is available, setting Torch device to CUDA (ROCm presents as CUDA)")
         self.torch_device = torch.device("cuda")
         if "ROCMExecutionProvider" in ort_providers:
diff --git a/pyproject.toml b/pyproject.toml
index 30d3665..7bfdb95 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -46,6 +46,7 @@ onnx2torch-py313 = ">=1.6"
 onnxruntime = { version = ">=1.17", optional = true }
 onnxruntime-gpu = { version = ">=1.17", optional = true }
 onnxruntime-directml = { version = ">=1.17", optional = true }
+onnxruntime-rocm = { version = ">=1.17", optional = true }
 julius = ">=0.2"
 diffq-fixed = { version = ">=0.2", platform = "win32" }
 diffq = { version = ">=0.2", platform = "!=win32" }

From b18b0f20fc5c0d89aace943838f9cc4f14f55242 Mon Sep 17 00:00:00 2001
From: keybangz <wyatt.wheeler@protonmail.com>
Date: Tue, 17 Mar 2026 20:04:59 +1300
Subject: [PATCH 03/13] ROCm GPU support.

---
 .opencode/.beacon/embeddings.db               |  Bin 14716928 -> 0 bytes
 .opencode/.beacon/hnsw.entries.json           |    1 -
 .opencode/.beacon/hnsw.index                  |  Bin 5331780 -> 0 bytes
 Dockerfile.rocm                               |   15 +-
 README.md                                     |   67 +-
 .../architectures/demucs_separator.py         |   63 +-
 .../separator/architectures/mdxc_separator.py |  314 +++--
 .../separator/architectures/vr_separator.py   |  295 +++-
 audio_separator/separator/common_separator.py |  235 +++-
 .../separator/roformer/roformer_loader.py     |  287 ++--
 audio_separator/separator/separator.py        |  679 +++++++--
 .../uvr_lib_v5/demucs/hdemucs_rocm.py         |  820 +++++++++++
 .../uvr_lib_v5/roformer/bs_roformer.py        |  228 ++-
 .../uvr_lib_v5/roformer/mel_band_roformer.py  |  238 +++-
 audio_separator/separator/uvr_lib_v5/stft.py  |  111 +-
 .../separator/uvr_lib_v5/tfc_tdf_v3.py        |  144 +-
 debug_results.txt                             |  166 +++
 debug_rocm_issues.py                          | 1225 +++++++++++++++++
 pyproject.toml                                |    2 +-
 19 files changed, 4207 insertions(+), 683 deletions(-)
 delete mode 100644 .opencode/.beacon/embeddings.db
 delete mode 100644 .opencode/.beacon/hnsw.entries.json
 delete mode 100644 .opencode/.beacon/hnsw.index
 create mode 100644 audio_separator/separator/uvr_lib_v5/demucs/hdemucs_rocm.py
 create mode 100644 debug_results.txt
 create mode 100644 debug_rocm_issues.py

diff --git a/.opencode/.beacon/embeddings.db b/.opencode/.beacon/embeddings.db
deleted file mode 100644
index e0560ae0c2a105e6ab2dfcc757250445fbfb5569..0000000000000000000000000000000000000000
GIT binary patch
literal 0
HcmV?d00001

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diff --git a/.opencode/.beacon/hnsw.entries.json b/.opencode/.beacon/hnsw.entries.json
deleted file mode 100644
index 04101bf..0000000
--- a/.opencode/.beacon/hnsw.entries.json
+++ /dev/null
@@ -1 +0,0 @@
-{"entries":[[0,{"filePath":".cursor/commands/analyze.md","startLine":0,"endLine":11,"chunkText":"---\ndescription: Perform a non-destructive cross-artifact consistency and quality analysis across spec.md, plan.md, and tasks.md after task generation.\n---\n\nThe user input to you can be provided directly by the agent or as a command argument - you **MUST** consider it before proceeding with the prompt (if not empty).\n\nUser input:\n\n$ARGUMENTS\n\nGoal: Identify inconsistencies, duplications, ambiguities, and underspecified items across the three core artifacts (`spec.md`, `plan.md`, `tasks.md`) before implementation. This command MUST run only after `/tasks` has successfully produced a complete `tasks.md`.\n","chunkId":".cursor/commands/analyze.md:0"}],[1,{"filePath":".cursor/commands/analyze.md","startLine":9,"endLine":13,"chunkText":"\nGoal: Identify inconsistencies, duplications, ambiguities, and underspecified items across the three core artifacts (`spec.md`, `plan.md`, `tasks.md`) before implementation. This command MUST run only after `/tasks` has successfully produced a complete `tasks.md`.\n\nSTRICTLY READ-ONLY: Do **not** modify any files. Output a structured analysis report. Offer an optional remediation plan (user must explicitly approve before any follow-up editing commands would be invoked manually).\n","chunkId":".cursor/commands/analyze.md:1"}],[2,{"filePath":".cursor/commands/analyze.md","startLine":11,"endLine":12,"chunkText":"STRICTLY READ-ONLY: Do **not** modify any files. Output a structured analysis report. Offer an optional remediation plan (user must explicitly approve before any follow-up editing commands would be invoked manually).\n","chunkId":".cursor/commands/analyze.md:2"}],[3,{"filePath":".cursor/commands/analyze.md","startLine":12,"endLine":13,"chunkText":"STRICTLY READ-ONLY: Do **not** modify any files. Output a structured analysis report. Offer an optional remediation plan (user must explicitly approve before any follow-up editing commands would be invoked manually).\n","chunkId":".cursor/commands/analyze.md:3"}],[4,{"filePath":".cursor/commands/analyze.md","startLine":13,"endLine":17,"chunkText":"\nConstitution Authority: The project constitution (`.specify/memory/constitution.md`) is **non-negotiable** within this analysis scope. Constitution conflicts are automatically CRITICAL and require adjustment of the spec, plan, or tasks—not dilution, reinterpretation, or silent ignoring of the principle. If a principle itself needs to change, that must occur in a separate, explicit constitution update outside `/analyze`.\n\nExecution steps:\n","chunkId":".cursor/commands/analyze.md:4"}],[5,{"filePath":".cursor/commands/analyze.md","startLine":15,"endLine":25,"chunkText":"\nExecution steps:\n\n1. Run `.specify/scripts/bash/check-prerequisites.sh --json --require-tasks --include-tasks` once from repo root and parse JSON for FEATURE_DIR and AVAILABLE_DOCS. Derive absolute paths:\n   - SPEC = FEATURE_DIR/spec.md\n   - PLAN = FEATURE_DIR/plan.md\n   - TASKS = FEATURE_DIR/tasks.md\n   Abort with an error message if any required file is missing (instruct the user to run missing prerequisite command).\n\n2. Load artifacts:\n   - Parse spec.md sections: Overview/Context, Functional Requirements, Non-Functional Requirements, User Stories, Edge Cases (if present).","chunkId":".cursor/commands/analyze.md:5"}],[6,{"filePath":".cursor/commands/analyze.md","startLine":23,"endLine":30,"chunkText":"\n2. Load artifacts:\n   - Parse spec.md sections: Overview/Context, Functional Requirements, Non-Functional Requirements, User Stories, Edge Cases (if present).\n   - Parse plan.md: Architecture/stack choices, Data Model references, Phases, Technical constraints.\n   - Parse tasks.md: Task IDs, descriptions, phase grouping, parallel markers [P], referenced file paths.\n   - Load constitution `.specify/memory/constitution.md` for principle validation.\n\n3. Build internal semantic models:","chunkId":".cursor/commands/analyze.md:6"}],[7,{"filePath":".cursor/commands/analyze.md","startLine":28,"endLine":35,"chunkText":"   - Load constitution `.specify/memory/constitution.md` for principle validation.\n\n3. Build internal semantic models:\n   - Requirements inventory: Each functional + non-functional requirement with a stable key (derive slug based on imperative phrase; e.g., \"User can upload file\" -> `user-can-upload-file`).\n   - User story/action inventory.\n   - Task coverage mapping: Map each task to one or more requirements or stories (inference by keyword / explicit reference patterns like IDs or key phrases).\n   - Constitution rule set: Extract principle names and any MUST/SHOULD normative statements.\n","chunkId":".cursor/commands/analyze.md:7"}],[8,{"filePath":".cursor/commands/analyze.md","startLine":33,"endLine":41,"chunkText":"   - Task coverage mapping: Map each task to one or more requirements or stories (inference by keyword / explicit reference patterns like IDs or key phrases).\n   - Constitution rule set: Extract principle names and any MUST/SHOULD normative statements.\n\n4. Detection passes:\n   A. Duplication detection:\n      - Identify near-duplicate requirements. Mark lower-quality phrasing for consolidation.\n   B. Ambiguity detection:\n      - Flag vague adjectives (fast, scalable, secure, intuitive, robust) lacking measurable criteria.\n      - Flag unresolved placeholders (TODO, TKTK, ???, <placeholder>, etc.).","chunkId":".cursor/commands/analyze.md:8"}],[9,{"filePath":".cursor/commands/analyze.md","startLine":39,"endLine":47,"chunkText":"   B. Ambiguity detection:\n      - Flag vague adjectives (fast, scalable, secure, intuitive, robust) lacking measurable criteria.\n      - Flag unresolved placeholders (TODO, TKTK, ???, <placeholder>, etc.).\n   C. Underspecification:\n      - Requirements with verbs but missing object or measurable outcome.\n      - User stories missing acceptance criteria alignment.\n      - Tasks referencing files or components not defined in spec/plan.\n   D. Constitution alignment:\n      - Any requirement or plan element conflicting with a MUST principle.","chunkId":".cursor/commands/analyze.md:9"}],[10,{"filePath":".cursor/commands/analyze.md","startLine":45,"endLine":54,"chunkText":"      - Tasks referencing files or components not defined in spec/plan.\n   D. Constitution alignment:\n      - Any requirement or plan element conflicting with a MUST principle.\n      - Missing mandated sections or quality gates from constitution.\n   E. Coverage gaps:\n      - Requirements with zero associated tasks.\n      - Tasks with no mapped requirement/story.\n      - Non-functional requirements not reflected in tasks (e.g., performance, security).\n   F. Inconsistency:\n      - Terminology drift (same concept named differently across files).","chunkId":".cursor/commands/analyze.md:10"}],[11,{"filePath":".cursor/commands/analyze.md","startLine":52,"endLine":59,"chunkText":"      - Non-functional requirements not reflected in tasks (e.g., performance, security).\n   F. Inconsistency:\n      - Terminology drift (same concept named differently across files).\n      - Data entities referenced in plan but absent in spec (or vice versa).\n      - Task ordering contradictions (e.g., integration tasks before foundational setup tasks without dependency note).\n      - Conflicting requirements (e.g., one requires to use Next.js while other says to use Vue as the framework).\n\n5. Severity assignment heuristic:","chunkId":".cursor/commands/analyze.md:11"}],[12,{"filePath":".cursor/commands/analyze.md","startLine":57,"endLine":64,"chunkText":"      - Conflicting requirements (e.g., one requires to use Next.js while other says to use Vue as the framework).\n\n5. Severity assignment heuristic:\n   - CRITICAL: Violates constitution MUST, missing core spec artifact, or requirement with zero coverage that blocks baseline functionality.\n   - HIGH: Duplicate or conflicting requirement, ambiguous security/performance attribute, untestable acceptance criterion.\n   - MEDIUM: Terminology drift, missing non-functional task coverage, underspecified edge case.\n   - LOW: Style/wording improvements, minor redundancy not affecting execution order.\n","chunkId":".cursor/commands/analyze.md:12"}],[13,{"filePath":".cursor/commands/analyze.md","startLine":62,"endLine":70,"chunkText":"   - MEDIUM: Terminology drift, missing non-functional task coverage, underspecified edge case.\n   - LOW: Style/wording improvements, minor redundancy not affecting execution order.\n\n6. Produce a Markdown report (no file writes) with sections:\n\n   ### Specification Analysis Report\n   | ID | Category | Severity | Location(s) | Summary | Recommendation |\n   |----|----------|----------|-------------|---------|----------------|\n   | A1 | Duplication | HIGH | spec.md:L120-134 | Two similar requirements ... | Merge phrasing; keep clearer version |","chunkId":".cursor/commands/analyze.md:13"}],[14,{"filePath":".cursor/commands/analyze.md","startLine":68,"endLine":80,"chunkText":"   | ID | Category | Severity | Location(s) | Summary | Recommendation |\n   |----|----------|----------|-------------|---------|----------------|\n   | A1 | Duplication | HIGH | spec.md:L120-134 | Two similar requirements ... | Merge phrasing; keep clearer version |\n   (Add one row per finding; generate stable IDs prefixed by category initial.)\n\n   Additional subsections:\n   - Coverage Summary Table:\n     | Requirement Key | Has Task? | Task IDs | Notes |\n   - Constitution Alignment Issues (if any)\n   - Unmapped Tasks (if any)\n   - Metrics:\n     * Total Requirements\n     * Total Tasks","chunkId":".cursor/commands/analyze.md:14"}],[15,{"filePath":".cursor/commands/analyze.md","startLine":78,"endLine":90,"chunkText":"   - Metrics:\n     * Total Requirements\n     * Total Tasks\n     * Coverage % (requirements with >=1 task)\n     * Ambiguity Count\n     * Duplication Count\n     * Critical Issues Count\n\n7. At end of report, output a concise Next Actions block:\n   - If CRITICAL issues exist: Recommend resolving before `/implement`.\n   - If only LOW/MEDIUM: User may proceed, but provide improvement suggestions.\n   - Provide explicit command suggestions: e.g., \"Run /specify with refinement\", \"Run /plan to adjust architecture\", \"Manually edit tasks.md to add coverage for 'performance-metrics'\".\n","chunkId":".cursor/commands/analyze.md:15"}],[16,{"filePath":".cursor/commands/analyze.md","startLine":88,"endLine":96,"chunkText":"   - If only LOW/MEDIUM: User may proceed, but provide improvement suggestions.\n   - Provide explicit command suggestions: e.g., \"Run /specify with refinement\", \"Run /plan to adjust architecture\", \"Manually edit tasks.md to add coverage for 'performance-metrics'\".\n\n8. Ask the user: \"Would you like me to suggest concrete remediation edits for the top N issues?\" (Do NOT apply them automatically.)\n\nBehavior rules:\n- NEVER modify files.\n- NEVER hallucinate missing sections—if absent, report them.\n- KEEP findings deterministic: if rerun without changes, produce consistent IDs and counts.","chunkId":".cursor/commands/analyze.md:16"}],[17,{"filePath":".cursor/commands/analyze.md","startLine":94,"endLine":101,"chunkText":"- NEVER modify files.\n- NEVER hallucinate missing sections—if absent, report them.\n- KEEP findings deterministic: if rerun without changes, produce consistent IDs and counts.\n- LIMIT total findings in the main table to 50; aggregate remainder in a summarized overflow note.\n- If zero issues found, emit a success report with coverage statistics and proceed recommendation.\n\nContext: $ARGUMENTS\n","chunkId":".cursor/commands/analyze.md:17"}],[18,{"filePath":".cursor/commands/clarify.md","startLine":0,"endLine":11,"chunkText":"---\ndescription: Identify underspecified areas in the current feature spec by asking up to 5 highly targeted clarification questions and encoding answers back into the spec.\n---\n\nThe user input to you can be provided directly by the agent or as a command argument - you **MUST** consider it before proceeding with the prompt (if not empty).\n\nUser input:\n\n$ARGUMENTS\n\nGoal: Detect and reduce ambiguity or missing decision points in the active feature specification and record the clarifications directly in the spec file.\n","chunkId":".cursor/commands/clarify.md:0"}],[19,{"filePath":".cursor/commands/clarify.md","startLine":9,"endLine":15,"chunkText":"\nGoal: Detect and reduce ambiguity or missing decision points in the active feature specification and record the clarifications directly in the spec file.\n\nNote: This clarification workflow is expected to run (and be completed) BEFORE invoking `/plan`. If the user explicitly states they are skipping clarification (e.g., exploratory spike), you may proceed, but must warn that downstream rework risk increases.\n\nExecution steps:\n","chunkId":".cursor/commands/clarify.md:1"}],[20,{"filePath":".cursor/commands/clarify.md","startLine":13,"endLine":21,"chunkText":"\nExecution steps:\n\n1. Run `.specify/scripts/bash/check-prerequisites.sh --json --paths-only` from repo root **once** (combined `--json --paths-only` mode / `-Json -PathsOnly`). Parse minimal JSON payload fields:\n   - `FEATURE_DIR`\n   - `FEATURE_SPEC`\n   - (Optionally capture `IMPL_PLAN`, `TASKS` for future chained flows.)\n   - If JSON parsing fails, abort and instruct user to re-run `/specify` or verify feature branch environment.\n","chunkId":".cursor/commands/clarify.md:2"}],[21,{"filePath":".cursor/commands/clarify.md","startLine":19,"endLine":26,"chunkText":"   - (Optionally capture `IMPL_PLAN`, `TASKS` for future chained flows.)\n   - If JSON parsing fails, abort and instruct user to re-run `/specify` or verify feature branch environment.\n\n2. Load the current spec file. Perform a structured ambiguity & coverage scan using this taxonomy. For each category, mark status: Clear / Partial / Missing. Produce an internal coverage map used for prioritization (do not output raw map unless no questions will be asked).\n\n   Functional Scope & Behavior:\n   - Core user goals & success criteria\n   - Explicit out-of-scope declarations","chunkId":".cursor/commands/clarify.md:3"}],[22,{"filePath":".cursor/commands/clarify.md","startLine":26,"endLine":43,"chunkText":"   - Explicit out-of-scope declarations\n   - User roles / personas differentiation\n\n   Domain & Data Model:\n   - Entities, attributes, relationships\n   - Identity & uniqueness rules\n   - Lifecycle/state transitions\n   - Data volume / scale assumptions\n\n   Interaction & UX Flow:\n   - Critical user journeys / sequences\n   - Error/empty/loading states\n   - Accessibility or localization notes\n\n   Non-Functional Quality Attributes:\n   - Performance (latency, throughput targets)\n   - Scalability (horizontal/vertical, limits)\n   - Reliability & availability (uptime, recovery expectations)","chunkId":".cursor/commands/clarify.md:4"}],[23,{"filePath":".cursor/commands/clarify.md","startLine":41,"endLine":55,"chunkText":"   - Performance (latency, throughput targets)\n   - Scalability (horizontal/vertical, limits)\n   - Reliability & availability (uptime, recovery expectations)\n   - Observability (logging, metrics, tracing signals)\n   - Security & privacy (authN/Z, data protection, threat assumptions)\n   - Compliance / regulatory constraints (if any)\n\n   Integration & External Dependencies:\n   - External services/APIs and failure modes\n   - Data import/export formats\n   - Protocol/versioning assumptions\n\n   Edge Cases & Failure Handling:\n   - Negative scenarios\n   - Rate limiting / throttling","chunkId":".cursor/commands/clarify.md:5"}],[24,{"filePath":".cursor/commands/clarify.md","startLine":53,"endLine":71,"chunkText":"   Edge Cases & Failure Handling:\n   - Negative scenarios\n   - Rate limiting / throttling\n   - Conflict resolution (e.g., concurrent edits)\n\n   Constraints & Tradeoffs:\n   - Technical constraints (language, storage, hosting)\n   - Explicit tradeoffs or rejected alternatives\n\n   Terminology & Consistency:\n   - Canonical glossary terms\n   - Avoided synonyms / deprecated terms\n\n   Completion Signals:\n   - Acceptance criteria testability\n   - Measurable Definition of Done style indicators\n\n   Misc / Placeholders:\n   - TODO markers / unresolved decisions","chunkId":".cursor/commands/clarify.md:6"}],[25,{"filePath":".cursor/commands/clarify.md","startLine":69,"endLine":79,"chunkText":"\n   Misc / Placeholders:\n   - TODO markers / unresolved decisions\n   - Ambiguous adjectives (\"robust\", \"intuitive\") lacking quantification\n\n   For each category with Partial or Missing status, add a candidate question opportunity unless:\n   - Clarification would not materially change implementation or validation strategy\n   - Information is better deferred to planning phase (note internally)\n\n3. Generate (internally) a prioritized queue of candidate clarification questions (maximum 5). Do NOT output them all at once. Apply these constraints:\n    - Maximum of 5 total questions across the whole session.","chunkId":".cursor/commands/clarify.md:7"}],[26,{"filePath":".cursor/commands/clarify.md","startLine":77,"endLine":82,"chunkText":"\n3. Generate (internally) a prioritized queue of candidate clarification questions (maximum 5). Do NOT output them all at once. Apply these constraints:\n    - Maximum of 5 total questions across the whole session.\n    - Each question must be answerable with EITHER:\n       * A short multiple‑choice selection (2–5 distinct, mutually exclusive options), OR\n       * A one-word / short‑phrase answer (explicitly constrain: \"Answer in <=5 words\").","chunkId":".cursor/commands/clarify.md:8"}],[27,{"filePath":".cursor/commands/clarify.md","startLine":80,"endLine":83,"chunkText":"    - Each question must be answerable with EITHER:\n       * A short multiple‑choice selection (2–5 distinct, mutually exclusive options), OR\n       * A one-word / short‑phrase answer (explicitly constrain: \"Answer in <=5 words\").\n   - Only include questions whose answers materially impact architecture, data modeling, task decomposition, test design, UX behavior, operational readiness, or compliance validation.","chunkId":".cursor/commands/clarify.md:9"}],[28,{"filePath":".cursor/commands/clarify.md","startLine":81,"endLine":84,"chunkText":"       * A short multiple‑choice selection (2–5 distinct, mutually exclusive options), OR\n       * A one-word / short‑phrase answer (explicitly constrain: \"Answer in <=5 words\").\n   - Only include questions whose answers materially impact architecture, data modeling, task decomposition, test design, UX behavior, operational readiness, or compliance validation.\n   - Ensure category coverage balance: attempt to cover the highest impact unresolved categories first; avoid asking two low-impact questions when a single high-impact area (e.g., security posture) is unresolved.","chunkId":".cursor/commands/clarify.md:10"}],[29,{"filePath":".cursor/commands/clarify.md","startLine":82,"endLine":84,"chunkText":"       * A one-word / short‑phrase answer (explicitly constrain: \"Answer in <=5 words\").\n   - Only include questions whose answers materially impact architecture, data modeling, task decomposition, test design, UX behavior, operational readiness, or compliance validation.\n   - Ensure category coverage balance: attempt to cover the highest impact unresolved categories first; avoid asking two low-impact questions when a single high-impact area (e.g., security posture) is unresolved.","chunkId":".cursor/commands/clarify.md:11"}],[30,{"filePath":".cursor/commands/clarify.md","startLine":83,"endLine":85,"chunkText":"   - Only include questions whose answers materially impact architecture, data modeling, task decomposition, test design, UX behavior, operational readiness, or compliance validation.\n   - Ensure category coverage balance: attempt to cover the highest impact unresolved categories first; avoid asking two low-impact questions when a single high-impact area (e.g., security posture) is unresolved.\n   - Exclude questions already answered, trivial stylistic preferences, or plan-level execution details (unless blocking correctness).","chunkId":".cursor/commands/clarify.md:12"}],[31,{"filePath":".cursor/commands/clarify.md","startLine":84,"endLine":89,"chunkText":"   - Ensure category coverage balance: attempt to cover the highest impact unresolved categories first; avoid asking two low-impact questions when a single high-impact area (e.g., security posture) is unresolved.\n   - Exclude questions already answered, trivial stylistic preferences, or plan-level execution details (unless blocking correctness).\n   - Favor clarifications that reduce downstream rework risk or prevent misaligned acceptance tests.\n   - If more than 5 categories remain unresolved, select the top 5 by (Impact * Uncertainty) heuristic.\n\n4. Sequential questioning loop (interactive):","chunkId":".cursor/commands/clarify.md:13"}],[32,{"filePath":".cursor/commands/clarify.md","startLine":87,"endLine":99,"chunkText":"   - If more than 5 categories remain unresolved, select the top 5 by (Impact * Uncertainty) heuristic.\n\n4. Sequential questioning loop (interactive):\n    - Present EXACTLY ONE question at a time.\n    - For multiple‑choice questions render options as a Markdown table:\n\n       | Option | Description |\n       |--------|-------------|\n       | A | <Option A description> |\n       | B | <Option B description> |\n       | C | <Option C description> | (add D/E as needed up to 5)\n       | Short | Provide a different short answer (<=5 words) | (Include only if free-form alternative is appropriate)\n","chunkId":".cursor/commands/clarify.md:14"}],[33,{"filePath":".cursor/commands/clarify.md","startLine":97,"endLine":103,"chunkText":"       | C | <Option C description> | (add D/E as needed up to 5)\n       | Short | Provide a different short answer (<=5 words) | (Include only if free-form alternative is appropriate)\n\n    - For short‑answer style (no meaningful discrete options), output a single line after the question: `Format: Short answer (<=5 words)`.\n    - After the user answers:\n       * Validate the answer maps to one option or fits the <=5 word constraint.\n       * If ambiguous, ask for a quick disambiguation (count still belongs to same question; do not advance).","chunkId":".cursor/commands/clarify.md:15"}],[34,{"filePath":".cursor/commands/clarify.md","startLine":101,"endLine":108,"chunkText":"    - After the user answers:\n       * Validate the answer maps to one option or fits the <=5 word constraint.\n       * If ambiguous, ask for a quick disambiguation (count still belongs to same question; do not advance).\n       * Once satisfactory, record it in working memory (do not yet write to disk) and move to the next queued question.\n    - Stop asking further questions when:\n       * All critical ambiguities resolved early (remaining queued items become unnecessary), OR\n       * User signals completion (\"done\", \"good\", \"no more\"), OR\n       * You reach 5 asked questions.","chunkId":".cursor/commands/clarify.md:16"}],[35,{"filePath":".cursor/commands/clarify.md","startLine":106,"endLine":114,"chunkText":"       * All critical ambiguities resolved early (remaining queued items become unnecessary), OR\n       * User signals completion (\"done\", \"good\", \"no more\"), OR\n       * You reach 5 asked questions.\n    - Never reveal future queued questions in advance.\n    - If no valid questions exist at start, immediately report no critical ambiguities.\n\n5. Integration after EACH accepted answer (incremental update approach):\n    - Maintain in-memory representation of the spec (loaded once at start) plus the raw file contents.\n    - For the first integrated answer in this session:","chunkId":".cursor/commands/clarify.md:17"}],[36,{"filePath":".cursor/commands/clarify.md","startLine":112,"endLine":117,"chunkText":"5. Integration after EACH accepted answer (incremental update approach):\n    - Maintain in-memory representation of the spec (loaded once at start) plus the raw file contents.\n    - For the first integrated answer in this session:\n       * Ensure a `## Clarifications` section exists (create it just after the highest-level contextual/overview section per the spec template if missing).\n       * Under it, create (if not present) a `### Session YYYY-MM-DD` subheading for today.\n    - Append a bullet line immediately after acceptance: `- Q: <question> → A: <final answer>`.","chunkId":".cursor/commands/clarify.md:18"}],[37,{"filePath":".cursor/commands/clarify.md","startLine":115,"endLine":119,"chunkText":"       * Ensure a `## Clarifications` section exists (create it just after the highest-level contextual/overview section per the spec template if missing).\n       * Under it, create (if not present) a `### Session YYYY-MM-DD` subheading for today.\n    - Append a bullet line immediately after acceptance: `- Q: <question> → A: <final answer>`.\n    - Then immediately apply the clarification to the most appropriate section(s):\n       * Functional ambiguity → Update or add a bullet in Functional Requirements.","chunkId":".cursor/commands/clarify.md:19"}],[38,{"filePath":".cursor/commands/clarify.md","startLine":117,"endLine":121,"chunkText":"    - Append a bullet line immediately after acceptance: `- Q: <question> → A: <final answer>`.\n    - Then immediately apply the clarification to the most appropriate section(s):\n       * Functional ambiguity → Update or add a bullet in Functional Requirements.\n       * User interaction / actor distinction → Update User Stories or Actors subsection (if present) with clarified role, constraint, or scenario.\n       * Data shape / entities → Update Data Model (add fields, types, relationships) preserving ordering; note added constraints succinctly.","chunkId":".cursor/commands/clarify.md:20"}],[39,{"filePath":".cursor/commands/clarify.md","startLine":119,"endLine":122,"chunkText":"       * Functional ambiguity → Update or add a bullet in Functional Requirements.\n       * User interaction / actor distinction → Update User Stories or Actors subsection (if present) with clarified role, constraint, or scenario.\n       * Data shape / entities → Update Data Model (add fields, types, relationships) preserving ordering; note added constraints succinctly.\n       * Non-functional constraint → Add/modify measurable criteria in Non-Functional / Quality Attributes section (convert vague adjective to metric or explicit target).","chunkId":".cursor/commands/clarify.md:21"}],[40,{"filePath":".cursor/commands/clarify.md","startLine":120,"endLine":122,"chunkText":"       * User interaction / actor distinction → Update User Stories or Actors subsection (if present) with clarified role, constraint, or scenario.\n       * Data shape / entities → Update Data Model (add fields, types, relationships) preserving ordering; note added constraints succinctly.\n       * Non-functional constraint → Add/modify measurable criteria in Non-Functional / Quality Attributes section (convert vague adjective to metric or explicit target).","chunkId":".cursor/commands/clarify.md:22"}],[41,{"filePath":".cursor/commands/clarify.md","startLine":121,"endLine":124,"chunkText":"       * Data shape / entities → Update Data Model (add fields, types, relationships) preserving ordering; note added constraints succinctly.\n       * Non-functional constraint → Add/modify measurable criteria in Non-Functional / Quality Attributes section (convert vague adjective to metric or explicit target).\n       * Edge case / negative flow → Add a new bullet under Edge Cases / Error Handling (or create such subsection if template provides placeholder for it).\n       * Terminology conflict → Normalize term across spec; retain original only if necessary by adding `(formerly referred to as \"X\")` once.","chunkId":".cursor/commands/clarify.md:23"}],[42,{"filePath":".cursor/commands/clarify.md","startLine":122,"endLine":124,"chunkText":"       * Non-functional constraint → Add/modify measurable criteria in Non-Functional / Quality Attributes section (convert vague adjective to metric or explicit target).\n       * Edge case / negative flow → Add a new bullet under Edge Cases / Error Handling (or create such subsection if template provides placeholder for it).\n       * Terminology conflict → Normalize term across spec; retain original only if necessary by adding `(formerly referred to as \"X\")` once.","chunkId":".cursor/commands/clarify.md:24"}],[43,{"filePath":".cursor/commands/clarify.md","startLine":123,"endLine":126,"chunkText":"       * Edge case / negative flow → Add a new bullet under Edge Cases / Error Handling (or create such subsection if template provides placeholder for it).\n       * Terminology conflict → Normalize term across spec; retain original only if necessary by adding `(formerly referred to as \"X\")` once.\n    - If the clarification invalidates an earlier ambiguous statement, replace that statement instead of duplicating; leave no obsolete contradictory text.\n    - Save the spec file AFTER each integration to minimize risk of context loss (atomic overwrite).","chunkId":".cursor/commands/clarify.md:25"}],[44,{"filePath":".cursor/commands/clarify.md","startLine":124,"endLine":129,"chunkText":"       * Terminology conflict → Normalize term across spec; retain original only if necessary by adding `(formerly referred to as \"X\")` once.\n    - If the clarification invalidates an earlier ambiguous statement, replace that statement instead of duplicating; leave no obsolete contradictory text.\n    - Save the spec file AFTER each integration to minimize risk of context loss (atomic overwrite).\n    - Preserve formatting: do not reorder unrelated sections; keep heading hierarchy intact.\n    - Keep each inserted clarification minimal and testable (avoid narrative drift).\n","chunkId":".cursor/commands/clarify.md:26"}],[45,{"filePath":".cursor/commands/clarify.md","startLine":127,"endLine":134,"chunkText":"    - Preserve formatting: do not reorder unrelated sections; keep heading hierarchy intact.\n    - Keep each inserted clarification minimal and testable (avoid narrative drift).\n\n6. Validation (performed after EACH write plus final pass):\n   - Clarifications session contains exactly one bullet per accepted answer (no duplicates).\n   - Total asked (accepted) questions ≤ 5.\n   - Updated sections contain no lingering vague placeholders the new answer was meant to resolve.\n   - No contradictory earlier statement remains (scan for now-invalid alternative choices removed).","chunkId":".cursor/commands/clarify.md:27"}],[46,{"filePath":".cursor/commands/clarify.md","startLine":132,"endLine":141,"chunkText":"   - Total asked (accepted) questions ≤ 5.\n   - Updated sections contain no lingering vague placeholders the new answer was meant to resolve.\n   - No contradictory earlier statement remains (scan for now-invalid alternative choices removed).\n   - Markdown structure valid; only allowed new headings: `## Clarifications`, `### Session YYYY-MM-DD`.\n   - Terminology consistency: same canonical term used across all updated sections.\n\n7. Write the updated spec back to `FEATURE_SPEC`.\n\n8. Report completion (after questioning loop ends or early termination):\n   - Number of questions asked & answered.","chunkId":".cursor/commands/clarify.md:28"}],[47,{"filePath":".cursor/commands/clarify.md","startLine":139,"endLine":147,"chunkText":"\n8. Report completion (after questioning loop ends or early termination):\n   - Number of questions asked & answered.\n   - Path to updated spec.\n   - Sections touched (list names).\n   - Coverage summary table listing each taxonomy category with Status: Resolved (was Partial/Missing and addressed), Deferred (exceeds question quota or better suited for planning), Clear (already sufficient), Outstanding (still Partial/Missing but low impact).\n   - If any Outstanding or Deferred remain, recommend whether to proceed to `/plan` or run `/clarify` again later post-plan.\n   - Suggested next command.\n","chunkId":".cursor/commands/clarify.md:29"}],[48,{"filePath":".cursor/commands/clarify.md","startLine":145,"endLine":151,"chunkText":"   - If any Outstanding or Deferred remain, recommend whether to proceed to `/plan` or run `/clarify` again later post-plan.\n   - Suggested next command.\n\nBehavior rules:\n- If no meaningful ambiguities found (or all potential questions would be low-impact), respond: \"No critical ambiguities detected worth formal clarification.\" and suggest proceeding.\n- If spec file missing, instruct user to run `/specify` first (do not create a new spec here).\n- Never exceed 5 total asked questions (clarification retries for a single question do not count as new questions).","chunkId":".cursor/commands/clarify.md:30"}],[49,{"filePath":".cursor/commands/clarify.md","startLine":149,"endLine":153,"chunkText":"- If no meaningful ambiguities found (or all potential questions would be low-impact), respond: \"No critical ambiguities detected worth formal clarification.\" and suggest proceeding.\n- If spec file missing, instruct user to run `/specify` first (do not create a new spec here).\n- Never exceed 5 total asked questions (clarification retries for a single question do not count as new questions).\n- Avoid speculative tech stack questions unless the absence blocks functional clarity.\n- Respect user early termination signals (\"stop\", \"done\", \"proceed\").","chunkId":".cursor/commands/clarify.md:31"}],[50,{"filePath":".cursor/commands/clarify.md","startLine":151,"endLine":158,"chunkText":"- Never exceed 5 total asked questions (clarification retries for a single question do not count as new questions).\n- Avoid speculative tech stack questions unless the absence blocks functional clarity.\n- Respect user early termination signals (\"stop\", \"done\", \"proceed\").\n - If no questions asked due to full coverage, output a compact coverage summary (all categories Clear) then suggest advancing.\n - If quota reached with unresolved high-impact categories remaining, explicitly flag them under Deferred with rationale.\n\nContext for prioritization: $ARGUMENTS\n","chunkId":".cursor/commands/clarify.md:32"}],[51,{"filePath":".cursor/commands/constitution.md","startLine":0,"endLine":9,"chunkText":"---\ndescription: Create or update the project constitution from interactive or provided principle inputs, ensuring all dependent templates stay in sync.\n---\n\nThe user input to you can be provided directly by the agent or as a command argument - you **MUST** consider it before proceeding with the prompt (if not empty).\n\nUser input:\n\n$ARGUMENTS\n","chunkId":".cursor/commands/constitution.md:0"}],[52,{"filePath":".cursor/commands/constitution.md","startLine":7,"endLine":15,"chunkText":"\n$ARGUMENTS\n\nYou are updating the project constitution at `.specify/memory/constitution.md`. This file is a TEMPLATE containing placeholder tokens in square brackets (e.g. `[PROJECT_NAME]`, `[PRINCIPLE_1_NAME]`). Your job is to (a) collect/derive concrete values, (b) fill the template precisely, and (c) propagate any amendments across dependent artifacts.\n\nFollow this execution flow:\n\n1. Load the existing constitution template at `.specify/memory/constitution.md`.\n   - Identify every placeholder token of the form `[ALL_CAPS_IDENTIFIER]`.","chunkId":".cursor/commands/constitution.md:1"}],[53,{"filePath":".cursor/commands/constitution.md","startLine":13,"endLine":20,"chunkText":"\n1. Load the existing constitution template at `.specify/memory/constitution.md`.\n   - Identify every placeholder token of the form `[ALL_CAPS_IDENTIFIER]`.\n   **IMPORTANT**: The user might require less or more principles than the ones used in the template. If a number is specified, respect that - follow the general template. You will update the doc accordingly.\n\n2. Collect/derive values for placeholders:\n   - If user input (conversation) supplies a value, use it.\n   - Otherwise infer from existing repo context (README, docs, prior constitution versions if embedded).","chunkId":".cursor/commands/constitution.md:2"}],[54,{"filePath":".cursor/commands/constitution.md","startLine":18,"endLine":23,"chunkText":"2. Collect/derive values for placeholders:\n   - If user input (conversation) supplies a value, use it.\n   - Otherwise infer from existing repo context (README, docs, prior constitution versions if embedded).\n   - For governance dates: `RATIFICATION_DATE` is the original adoption date (if unknown ask or mark TODO), `LAST_AMENDED_DATE` is today if changes are made, otherwise keep previous.\n   - `CONSTITUTION_VERSION` must increment according to semantic versioning rules:\n     * MAJOR: Backward incompatible governance/principle removals or redefinitions.","chunkId":".cursor/commands/constitution.md:3"}],[55,{"filePath":".cursor/commands/constitution.md","startLine":21,"endLine":27,"chunkText":"   - For governance dates: `RATIFICATION_DATE` is the original adoption date (if unknown ask or mark TODO), `LAST_AMENDED_DATE` is today if changes are made, otherwise keep previous.\n   - `CONSTITUTION_VERSION` must increment according to semantic versioning rules:\n     * MAJOR: Backward incompatible governance/principle removals or redefinitions.\n     * MINOR: New principle/section added or materially expanded guidance.\n     * PATCH: Clarifications, wording, typo fixes, non-semantic refinements.\n   - If version bump type ambiguous, propose reasoning before finalizing.\n","chunkId":".cursor/commands/constitution.md:4"}],[56,{"filePath":".cursor/commands/constitution.md","startLine":25,"endLine":30,"chunkText":"     * PATCH: Clarifications, wording, typo fixes, non-semantic refinements.\n   - If version bump type ambiguous, propose reasoning before finalizing.\n\n3. Draft the updated constitution content:\n   - Replace every placeholder with concrete text (no bracketed tokens left except intentionally retained template slots that the project has chosen not to define yet—explicitly justify any left).\n   - Preserve heading hierarchy and comments can be removed once replaced unless they still add clarifying guidance.","chunkId":".cursor/commands/constitution.md:5"}],[57,{"filePath":".cursor/commands/constitution.md","startLine":28,"endLine":31,"chunkText":"3. Draft the updated constitution content:\n   - Replace every placeholder with concrete text (no bracketed tokens left except intentionally retained template slots that the project has chosen not to define yet—explicitly justify any left).\n   - Preserve heading hierarchy and comments can be removed once replaced unless they still add clarifying guidance.\n   - Ensure each Principle section: succinct name line, paragraph (or bullet list) capturing non‑negotiable rules, explicit rationale if not obvious.","chunkId":".cursor/commands/constitution.md:6"}],[58,{"filePath":".cursor/commands/constitution.md","startLine":29,"endLine":33,"chunkText":"   - Replace every placeholder with concrete text (no bracketed tokens left except intentionally retained template slots that the project has chosen not to define yet—explicitly justify any left).\n   - Preserve heading hierarchy and comments can be removed once replaced unless they still add clarifying guidance.\n   - Ensure each Principle section: succinct name line, paragraph (or bullet list) capturing non‑negotiable rules, explicit rationale if not obvious.\n   - Ensure Governance section lists amendment procedure, versioning policy, and compliance review expectations.\n","chunkId":".cursor/commands/constitution.md:7"}],[59,{"filePath":".cursor/commands/constitution.md","startLine":31,"endLine":35,"chunkText":"   - Ensure each Principle section: succinct name line, paragraph (or bullet list) capturing non‑negotiable rules, explicit rationale if not obvious.\n   - Ensure Governance section lists amendment procedure, versioning policy, and compliance review expectations.\n\n4. Consistency propagation checklist (convert prior checklist into active validations):\n   - Read `.specify/templates/plan-template.md` and ensure any \"Constitution Check\" or rules align with updated principles.","chunkId":".cursor/commands/constitution.md:8"}],[60,{"filePath":".cursor/commands/constitution.md","startLine":33,"endLine":37,"chunkText":"\n4. Consistency propagation checklist (convert prior checklist into active validations):\n   - Read `.specify/templates/plan-template.md` and ensure any \"Constitution Check\" or rules align with updated principles.\n   - Read `.specify/templates/spec-template.md` for scope/requirements alignment—update if constitution adds/removes mandatory sections or constraints.\n   - Read `.specify/templates/tasks-template.md` and ensure task categorization reflects new or removed principle-driven task types (e.g., observability, versioning, testing discipline).","chunkId":".cursor/commands/constitution.md:9"}],[61,{"filePath":".cursor/commands/constitution.md","startLine":35,"endLine":37,"chunkText":"   - Read `.specify/templates/plan-template.md` and ensure any \"Constitution Check\" or rules align with updated principles.\n   - Read `.specify/templates/spec-template.md` for scope/requirements alignment—update if constitution adds/removes mandatory sections or constraints.\n   - Read `.specify/templates/tasks-template.md` and ensure task categorization reflects new or removed principle-driven task types (e.g., observability, versioning, testing discipline).","chunkId":".cursor/commands/constitution.md:10"}],[62,{"filePath":".cursor/commands/constitution.md","startLine":36,"endLine":38,"chunkText":"   - Read `.specify/templates/spec-template.md` for scope/requirements alignment—update if constitution adds/removes mandatory sections or constraints.\n   - Read `.specify/templates/tasks-template.md` and ensure task categorization reflects new or removed principle-driven task types (e.g., observability, versioning, testing discipline).\n   - Read each command file in `.specify/templates/commands/*.md` (including this one) to verify no outdated references (agent-specific names like CLAUDE only) remain when generic guidance is required.","chunkId":".cursor/commands/constitution.md:11"}],[63,{"filePath":".cursor/commands/constitution.md","startLine":37,"endLine":40,"chunkText":"   - Read `.specify/templates/tasks-template.md` and ensure task categorization reflects new or removed principle-driven task types (e.g., observability, versioning, testing discipline).\n   - Read each command file in `.specify/templates/commands/*.md` (including this one) to verify no outdated references (agent-specific names like CLAUDE only) remain when generic guidance is required.\n   - Read any runtime guidance docs (e.g., `README.md`, `docs/quickstart.md`, or agent-specific guidance files if present). Update references to principles changed.\n","chunkId":".cursor/commands/constitution.md:12"}],[64,{"filePath":".cursor/commands/constitution.md","startLine":38,"endLine":44,"chunkText":"   - Read each command file in `.specify/templates/commands/*.md` (including this one) to verify no outdated references (agent-specific names like CLAUDE only) remain when generic guidance is required.\n   - Read any runtime guidance docs (e.g., `README.md`, `docs/quickstart.md`, or agent-specific guidance files if present). Update references to principles changed.\n\n5. Produce a Sync Impact Report (prepend as an HTML comment at top of the constitution file after update):\n   - Version change: old → new\n   - List of modified principles (old title → new title if renamed)\n   - Added sections","chunkId":".cursor/commands/constitution.md:13"}],[65,{"filePath":".cursor/commands/constitution.md","startLine":42,"endLine":54,"chunkText":"   - Version change: old → new\n   - List of modified principles (old title → new title if renamed)\n   - Added sections\n   - Removed sections\n   - Templates requiring updates (✅ updated / ⚠ pending) with file paths\n   - Follow-up TODOs if any placeholders intentionally deferred.\n\n6. Validation before final output:\n   - No remaining unexplained bracket tokens.\n   - Version line matches report.\n   - Dates ISO format YYYY-MM-DD.\n   - Principles are declarative, testable, and free of vague language (\"should\" → replace with MUST/SHOULD rationale where appropriate).\n","chunkId":".cursor/commands/constitution.md:14"}],[66,{"filePath":".cursor/commands/constitution.md","startLine":52,"endLine":62,"chunkText":"   - Dates ISO format YYYY-MM-DD.\n   - Principles are declarative, testable, and free of vague language (\"should\" → replace with MUST/SHOULD rationale where appropriate).\n\n7. Write the completed constitution back to `.specify/memory/constitution.md` (overwrite).\n\n8. Output a final summary to the user with:\n   - New version and bump rationale.\n   - Any files flagged for manual follow-up.\n   - Suggested commit message (e.g., `docs: amend constitution to vX.Y.Z (principle additions + governance update)`).\n\nFormatting & Style Requirements:","chunkId":".cursor/commands/constitution.md:15"}],[67,{"filePath":".cursor/commands/constitution.md","startLine":60,"endLine":69,"chunkText":"   - Suggested commit message (e.g., `docs: amend constitution to vX.Y.Z (principle additions + governance update)`).\n\nFormatting & Style Requirements:\n- Use Markdown headings exactly as in the template (do not demote/promote levels).\n- Wrap long rationale lines to keep readability (<100 chars ideally) but do not hard enforce with awkward breaks.\n- Keep a single blank line between sections.\n- Avoid trailing whitespace.\n\nIf the user supplies partial updates (e.g., only one principle revision), still perform validation and version decision steps.\n","chunkId":".cursor/commands/constitution.md:16"}],[68,{"filePath":".cursor/commands/constitution.md","startLine":67,"endLine":73,"chunkText":"\nIf the user supplies partial updates (e.g., only one principle revision), still perform validation and version decision steps.\n\nIf critical info missing (e.g., ratification date truly unknown), insert `TODO(<FIELD_NAME>): explanation` and include in the Sync Impact Report under deferred items.\n\nDo not create a new template; always operate on the existing `.specify/memory/constitution.md` file.\n","chunkId":".cursor/commands/constitution.md:17"}],[69,{"filePath":".cursor/commands/implement.md","startLine":0,"endLine":13,"chunkText":"---\ndescription: Execute the implementation plan by processing and executing all tasks defined in tasks.md\n---\n\nThe user input can be provided directly by the agent or as a command argument - you **MUST** consider it before proceeding with the prompt (if not empty).\n\nUser input:\n\n$ARGUMENTS\n\n1. Run `.specify/scripts/bash/check-prerequisites.sh --json --require-tasks --include-tasks` from repo root and parse FEATURE_DIR and AVAILABLE_DOCS list. All paths must be absolute.\n\n2. Load and analyze the implementation context:\n   - **REQUIRED**: Read tasks.md for the complete task list and execution plan","chunkId":".cursor/commands/implement.md:0"}],[70,{"filePath":".cursor/commands/implement.md","startLine":12,"endLine":21,"chunkText":"2. Load and analyze the implementation context:\n   - **REQUIRED**: Read tasks.md for the complete task list and execution plan\n   - **REQUIRED**: Read plan.md for tech stack, architecture, and file structure\n   - **IF EXISTS**: Read data-model.md for entities and relationships\n   - **IF EXISTS**: Read contracts/ for API specifications and test requirements\n   - **IF EXISTS**: Read research.md for technical decisions and constraints\n   - **IF EXISTS**: Read quickstart.md for integration scenarios\n\n3. Parse tasks.md structure and extract:\n   - **Task phases**: Setup, Tests, Core, Integration, Polish","chunkId":".cursor/commands/implement.md:1"}],[71,{"filePath":".cursor/commands/implement.md","startLine":20,"endLine":28,"chunkText":"3. Parse tasks.md structure and extract:\n   - **Task phases**: Setup, Tests, Core, Integration, Polish\n   - **Task dependencies**: Sequential vs parallel execution rules\n   - **Task details**: ID, description, file paths, parallel markers [P]\n   - **Execution flow**: Order and dependency requirements\n\n4. Execute implementation following the task plan:\n   - **Phase-by-phase execution**: Complete each phase before moving to the next\n   - **Respect dependencies**: Run sequential tasks in order, parallel tasks [P] can run together  ","chunkId":".cursor/commands/implement.md:2"}],[72,{"filePath":".cursor/commands/implement.md","startLine":27,"endLine":34,"chunkText":"   - **Phase-by-phase execution**: Complete each phase before moving to the next\n   - **Respect dependencies**: Run sequential tasks in order, parallel tasks [P] can run together  \n   - **Follow TDD approach**: Execute test tasks before their corresponding implementation tasks\n   - **File-based coordination**: Tasks affecting the same files must run sequentially\n   - **Validation checkpoints**: Verify each phase completion before proceeding\n\n5. Implementation execution rules:\n   - **Setup first**: Initialize project structure, dependencies, configuration","chunkId":".cursor/commands/implement.md:3"}],[73,{"filePath":".cursor/commands/implement.md","startLine":33,"endLine":42,"chunkText":"5. Implementation execution rules:\n   - **Setup first**: Initialize project structure, dependencies, configuration\n   - **Tests before code**: If you need to write tests for contracts, entities, and integration scenarios\n   - **Core development**: Implement models, services, CLI commands, endpoints\n   - **Integration work**: Database connections, middleware, logging, external services\n   - **Polish and validation**: Unit tests, performance optimization, documentation\n\n6. Progress tracking and error handling:\n   - Report progress after each completed task\n   - Halt execution if any non-parallel task fails","chunkId":".cursor/commands/implement.md:4"}],[74,{"filePath":".cursor/commands/implement.md","startLine":41,"endLine":51,"chunkText":"   - Report progress after each completed task\n   - Halt execution if any non-parallel task fails\n   - For parallel tasks [P], continue with successful tasks, report failed ones\n   - Provide clear error messages with context for debugging\n   - Suggest next steps if implementation cannot proceed\n   - **IMPORTANT** For completed tasks, make sure to mark the task off as [X] in the tasks file.\n\n7. Completion validation:\n   - Verify all required tasks are completed\n   - Check that implemented features match the original specification\n   - Validate that tests pass and coverage meets requirements","chunkId":".cursor/commands/implement.md:5"}],[75,{"filePath":".cursor/commands/implement.md","startLine":50,"endLine":56,"chunkText":"   - Check that implemented features match the original specification\n   - Validate that tests pass and coverage meets requirements\n   - Confirm the implementation follows the technical plan\n   - Report final status with summary of completed work\n\nNote: This command assumes a complete task breakdown exists in tasks.md. If tasks are incomplete or missing, suggest running `/tasks` first to regenerate the task list.\n","chunkId":".cursor/commands/implement.md:6"}],[76,{"filePath":".cursor/commands/plan.md","startLine":0,"endLine":12,"chunkText":"---\ndescription: Execute the implementation planning workflow using the plan template to generate design artifacts.\n---\n\nThe user input to you can be provided directly by the agent or as a command argument - you **MUST** consider it before proceeding with the prompt (if not empty).\n\nUser input:\n\n$ARGUMENTS\n\nGiven the implementation details provided as an argument, do this:\n\n1. Run `.specify/scripts/bash/setup-plan.sh --json` from the repo root and parse JSON for FEATURE_SPEC, IMPL_PLAN, SPECS_DIR, BRANCH. All future file paths must be absolute.","chunkId":".cursor/commands/plan.md:0"}],[77,{"filePath":".cursor/commands/plan.md","startLine":10,"endLine":12,"chunkText":"Given the implementation details provided as an argument, do this:\n\n1. Run `.specify/scripts/bash/setup-plan.sh --json` from the repo root and parse JSON for FEATURE_SPEC, IMPL_PLAN, SPECS_DIR, BRANCH. All future file paths must be absolute.","chunkId":".cursor/commands/plan.md:1"}],[78,{"filePath":".cursor/commands/plan.md","startLine":11,"endLine":11,"chunkText":"1. Run `.specify/scripts/bash/setup-plan.sh --json` from the repo root and parse JSON for FEATURE_SPEC, IMPL_PLAN, SPECS_DIR, BRANCH. All future file paths must be absolute.","chunkId":".cursor/commands/plan.md:2"}],[79,{"filePath":".cursor/commands/plan.md","startLine":12,"endLine":12,"chunkText":"1. Run `.specify/scripts/bash/setup-plan.sh --json` from the repo root and parse JSON for FEATURE_SPEC, IMPL_PLAN, SPECS_DIR, BRANCH. All future file paths must be absolute.","chunkId":".cursor/commands/plan.md:3"}],[80,{"filePath":".cursor/commands/plan.md","startLine":13,"endLine":15,"chunkText":"   - BEFORE proceeding, inspect FEATURE_SPEC for a `## Clarifications` section with at least one `Session` subheading. If missing or clearly ambiguous areas remain (vague adjectives, unresolved critical choices), PAUSE and instruct the user to run `/clarify` first to reduce rework. Only continue if: (a) Clarifications exist OR (b) an explicit user override is provided (e.g., \"proceed without clarification\"). Do not attempt to fabricate clarifications yourself.\n2. Read and analyze the feature specification to understand:\n   - The feature requirements and user stories","chunkId":".cursor/commands/plan.md:4"}],[81,{"filePath":".cursor/commands/plan.md","startLine":14,"endLine":25,"chunkText":"2. Read and analyze the feature specification to understand:\n   - The feature requirements and user stories\n   - Functional and non-functional requirements\n   - Success criteria and acceptance criteria\n   - Any technical constraints or dependencies mentioned\n\n3. Read the constitution at `.specify/memory/constitution.md` to understand constitutional requirements.\n\n4. Execute the implementation plan template:\n   - Load `.specify/templates/plan-template.md` (already copied to IMPL_PLAN path)\n   - Set Input path to FEATURE_SPEC\n   - Run the Execution Flow (main) function steps 1-9","chunkId":".cursor/commands/plan.md:5"}],[82,{"filePath":".cursor/commands/plan.md","startLine":23,"endLine":32,"chunkText":"   - Load `.specify/templates/plan-template.md` (already copied to IMPL_PLAN path)\n   - Set Input path to FEATURE_SPEC\n   - Run the Execution Flow (main) function steps 1-9\n   - The template is self-contained and executable\n   - Follow error handling and gate checks as specified\n   - Let the template guide artifact generation in $SPECS_DIR:\n     * Phase 0 generates research.md\n     * Phase 1 generates data-model.md, contracts/, quickstart.md\n     * Phase 2 generates tasks.md\n   - Incorporate user-provided details from arguments into Technical Context: $ARGUMENTS","chunkId":".cursor/commands/plan.md:6"}],[83,{"filePath":".cursor/commands/plan.md","startLine":30,"endLine":43,"chunkText":"     * Phase 1 generates data-model.md, contracts/, quickstart.md\n     * Phase 2 generates tasks.md\n   - Incorporate user-provided details from arguments into Technical Context: $ARGUMENTS\n   - Update Progress Tracking as you complete each phase\n\n5. Verify execution completed:\n   - Check Progress Tracking shows all phases complete\n   - Ensure all required artifacts were generated\n   - Confirm no ERROR states in execution\n\n6. Report results with branch name, file paths, and generated artifacts.\n\nUse absolute paths with the repository root for all file operations to avoid path issues.\n","chunkId":".cursor/commands/plan.md:7"}],[84,{"filePath":".cursor/commands/specify.md","startLine":0,"endLine":12,"chunkText":"---\ndescription: Create or update the feature specification from a natural language feature description.\n---\n\nThe user input to you can be provided directly by the agent or as a command argument - you **MUST** consider it before proceeding with the prompt (if not empty).\n\nUser input:\n\n$ARGUMENTS\n\nThe text the user typed after `/specify` in the triggering message **is** the feature description. Assume you always have it available in this conversation even if `$ARGUMENTS` appears literally below. Do not ask the user to repeat it unless they provided an empty command.\n\nGiven that feature description, do this","chunkId":".cursor/commands/specify.md:0"}],[85,{"filePath":".cursor/commands/specify.md","startLine":11,"endLine":16,"chunkText":"\nGiven that feature description, do this:\n\n1. Run the script `.specify/scripts/bash/create-new-feature.sh --json \"$ARGUMENTS\"` from repo root and parse its JSON output for BRANCH_NAME and SPEC_FILE. All file paths must be absolute.\n  **IMPORTANT** You must only ever run this script once. The JSON is provided in the terminal as output - always refer to it to get the actual content you're looking for.\n2. Load `.specify/templates/spec-template.md` to understand required sections.","chunkId":".cursor/commands/specify.md:1"}],[86,{"filePath":".cursor/commands/specify.md","startLine":14,"endLine":16,"chunkText":"1. Run the script `.specify/scripts/bash/create-new-feature.sh --json \"$ARGUMENTS\"` from repo root and parse its JSON output for BRANCH_NAME and SPEC_FILE. All file paths must be absolute.\n  **IMPORTANT** You must only ever run this script once. The JSON is provided in the terminal as output - always refer to it to get the actual content you're looking for.\n2. Load `.specify/templates/spec-template.md` to understand required sections.","chunkId":".cursor/commands/specify.md:2"}],[87,{"filePath":".cursor/commands/specify.md","startLine":15,"endLine":19,"chunkText":"  **IMPORTANT** You must only ever run this script once. The JSON is provided in the terminal as output - always refer to it to get the actual content you're looking for.\n2. Load `.specify/templates/spec-template.md` to understand required sections.\n3. Write the specification to SPEC_FILE using the template structure, replacing placeholders with concrete details derived from the feature description (arguments) while preserving section order and headings.\n4. Report completion with branch name, spec file path, and readiness for the next phase.\n","chunkId":".cursor/commands/specify.md:3"}],[88,{"filePath":".cursor/commands/specify.md","startLine":17,"endLine":21,"chunkText":"3. Write the specification to SPEC_FILE using the template structure, replacing placeholders with concrete details derived from the feature description (arguments) while preserving section order and headings.\n4. Report completion with branch name, spec file path, and readiness for the next phase.\n\nNote: The script creates and checks out the new branch and initializes the spec file before writing.\n","chunkId":".cursor/commands/specify.md:4"}],[89,{"filePath":".cursor/commands/tasks.md","startLine":0,"endLine":12,"chunkText":"---\ndescription: Generate an actionable, dependency-ordered tasks.md for the feature based on available design artifacts.\n---\n\nThe user input to you can be provided directly by the agent or as a command argument - you **MUST** consider it before proceeding with the prompt (if not empty).\n\nUser input:\n\n$ARGUMENTS\n\n1. Run `.specify/scripts/bash/check-prerequisites.sh --json` from repo root and parse FEATURE_DIR and AVAILABLE_DOCS list. All paths must be absolute.\n2. Load and analyze available design documents:\n   - Always read plan.md for tech stack and libraries","chunkId":".cursor/commands/tasks.md:0"}],[90,{"filePath":".cursor/commands/tasks.md","startLine":11,"endLine":24,"chunkText":"2. Load and analyze available design documents:\n   - Always read plan.md for tech stack and libraries\n   - IF EXISTS: Read data-model.md for entities\n   - IF EXISTS: Read contracts/ for API endpoints\n   - IF EXISTS: Read research.md for technical decisions\n   - IF EXISTS: Read quickstart.md for test scenarios\n\n   Note: Not all projects have all documents. For example:\n   - CLI tools might not have contracts/\n   - Simple libraries might not need data-model.md\n   - Generate tasks based on what's available\n\n3. Generate tasks following the template:\n   - Use `.specify/templates/tasks-template.md` as the base","chunkId":".cursor/commands/tasks.md:1"}],[91,{"filePath":".cursor/commands/tasks.md","startLine":22,"endLine":33,"chunkText":"\n3. Generate tasks following the template:\n   - Use `.specify/templates/tasks-template.md` as the base\n   - Replace example tasks with actual tasks based on:\n     * **Setup tasks**: Project init, dependencies, linting\n     * **Test tasks [P]**: One per contract, one per integration scenario\n     * **Core tasks**: One per entity, service, CLI command, endpoint\n     * **Integration tasks**: DB connections, middleware, logging\n     * **Polish tasks [P]**: Unit tests, performance, docs\n\n4. Task generation rules:\n   - Each contract file → contract test task marked [P]","chunkId":".cursor/commands/tasks.md:2"}],[92,{"filePath":".cursor/commands/tasks.md","startLine":31,"endLine":48,"chunkText":"\n4. Task generation rules:\n   - Each contract file → contract test task marked [P]\n   - Each entity in data-model → model creation task marked [P]\n   - Each endpoint → implementation task (not parallel if shared files)\n   - Each user story → integration test marked [P]\n   - Different files = can be parallel [P]\n   - Same file = sequential (no [P])\n\n5. Order tasks by dependencies:\n   - Setup before everything\n   - Tests before implementation (TDD)\n   - Models before services\n   - Services before endpoints\n   - Core before integration\n   - Everything before polish\n\n6. Include parallel execution examples:","chunkId":".cursor/commands/tasks.md:3"}],[93,{"filePath":".cursor/commands/tasks.md","startLine":46,"endLine":62,"chunkText":"   - Everything before polish\n\n6. Include parallel execution examples:\n   - Group [P] tasks that can run together\n   - Show actual Task agent commands\n\n7. Create FEATURE_DIR/tasks.md with:\n   - Correct feature name from implementation plan\n   - Numbered tasks (T001, T002, etc.)\n   - Clear file paths for each task\n   - Dependency notes\n   - Parallel execution guidance\n\nContext for task generation: $ARGUMENTS\n\nThe tasks.md should be immediately executable - each task must be specific enough that an LLM can complete it without additional context.\n","chunkId":".cursor/commands/tasks.md:4"}],[94,{"filePath":".github/prompts/analyze.prompt.md","startLine":0,"endLine":11,"chunkText":"---\ndescription: Perform a non-destructive cross-artifact consistency and quality analysis across spec.md, plan.md, and tasks.md after task generation.\n---\n\nThe user input to you can be provided directly by the agent or as a command argument - you **MUST** consider it before proceeding with the prompt (if not empty).\n\nUser input:\n\n$ARGUMENTS\n\nGoal: Identify inconsistencies, duplications, ambiguities, and underspecified items across the three core artifacts (`spec.md`, `plan.md`, `tasks.md`) before implementation. This command MUST run only after `/tasks` has successfully produced a complete `tasks.md`.\n","chunkId":".github/prompts/analyze.prompt.md:0"}],[95,{"filePath":".github/prompts/analyze.prompt.md","startLine":9,"endLine":13,"chunkText":"\nGoal: Identify inconsistencies, duplications, ambiguities, and underspecified items across the three core artifacts (`spec.md`, `plan.md`, `tasks.md`) before implementation. This command MUST run only after `/tasks` has successfully produced a complete `tasks.md`.\n\nSTRICTLY READ-ONLY: Do **not** modify any files. Output a structured analysis report. Offer an optional remediation plan (user must explicitly approve before any follow-up editing commands would be invoked manually).\n","chunkId":".github/prompts/analyze.prompt.md:1"}],[96,{"filePath":".github/prompts/analyze.prompt.md","startLine":11,"endLine":12,"chunkText":"STRICTLY READ-ONLY: Do **not** modify any files. Output a structured analysis report. Offer an optional remediation plan (user must explicitly approve before any follow-up editing commands would be invoked manually).\n","chunkId":".github/prompts/analyze.prompt.md:2"}],[97,{"filePath":".github/prompts/analyze.prompt.md","startLine":12,"endLine":13,"chunkText":"STRICTLY READ-ONLY: Do **not** modify any files. Output a structured analysis report. Offer an optional remediation plan (user must explicitly approve before any follow-up editing commands would be invoked manually).\n","chunkId":".github/prompts/analyze.prompt.md:3"}],[98,{"filePath":".github/prompts/analyze.prompt.md","startLine":13,"endLine":17,"chunkText":"\nConstitution Authority: The project constitution (`.specify/memory/constitution.md`) is **non-negotiable** within this analysis scope. Constitution conflicts are automatically CRITICAL and require adjustment of the spec, plan, or tasks—not dilution, reinterpretation, or silent ignoring of the principle. If a principle itself needs to change, that must occur in a separate, explicit constitution update outside `/analyze`.\n\nExecution steps:\n","chunkId":".github/prompts/analyze.prompt.md:4"}],[99,{"filePath":".github/prompts/analyze.prompt.md","startLine":15,"endLine":25,"chunkText":"\nExecution steps:\n\n1. Run `.specify/scripts/bash/check-prerequisites.sh --json --require-tasks --include-tasks` once from repo root and parse JSON for FEATURE_DIR and AVAILABLE_DOCS. Derive absolute paths:\n   - SPEC = FEATURE_DIR/spec.md\n   - PLAN = FEATURE_DIR/plan.md\n   - TASKS = FEATURE_DIR/tasks.md\n   Abort with an error message if any required file is missing (instruct the user to run missing prerequisite command).\n\n2. Load artifacts:\n   - Parse spec.md sections: Overview/Context, Functional Requirements, Non-Functional Requirements, User Stories, Edge Cases (if present).","chunkId":".github/prompts/analyze.prompt.md:5"}],[100,{"filePath":".github/prompts/analyze.prompt.md","startLine":23,"endLine":30,"chunkText":"\n2. Load artifacts:\n   - Parse spec.md sections: Overview/Context, Functional Requirements, Non-Functional Requirements, User Stories, Edge Cases (if present).\n   - Parse plan.md: Architecture/stack choices, Data Model references, Phases, Technical constraints.\n   - Parse tasks.md: Task IDs, descriptions, phase grouping, parallel markers [P], referenced file paths.\n   - Load constitution `.specify/memory/constitution.md` for principle validation.\n\n3. Build internal semantic models:","chunkId":".github/prompts/analyze.prompt.md:6"}],[101,{"filePath":".github/prompts/analyze.prompt.md","startLine":28,"endLine":35,"chunkText":"   - Load constitution `.specify/memory/constitution.md` for principle validation.\n\n3. Build internal semantic models:\n   - Requirements inventory: Each functional + non-functional requirement with a stable key (derive slug based on imperative phrase; e.g., \"User can upload file\" -> `user-can-upload-file`).\n   - User story/action inventory.\n   - Task coverage mapping: Map each task to one or more requirements or stories (inference by keyword / explicit reference patterns like IDs or key phrases).\n   - Constitution rule set: Extract principle names and any MUST/SHOULD normative statements.\n","chunkId":".github/prompts/analyze.prompt.md:7"}],[102,{"filePath":".github/prompts/analyze.prompt.md","startLine":33,"endLine":41,"chunkText":"   - Task coverage mapping: Map each task to one or more requirements or stories (inference by keyword / explicit reference patterns like IDs or key phrases).\n   - Constitution rule set: Extract principle names and any MUST/SHOULD normative statements.\n\n4. Detection passes:\n   A. Duplication detection:\n      - Identify near-duplicate requirements. Mark lower-quality phrasing for consolidation.\n   B. Ambiguity detection:\n      - Flag vague adjectives (fast, scalable, secure, intuitive, robust) lacking measurable criteria.\n      - Flag unresolved placeholders (TODO, TKTK, ???, <placeholder>, etc.).","chunkId":".github/prompts/analyze.prompt.md:8"}],[103,{"filePath":".github/prompts/analyze.prompt.md","startLine":39,"endLine":47,"chunkText":"   B. Ambiguity detection:\n      - Flag vague adjectives (fast, scalable, secure, intuitive, robust) lacking measurable criteria.\n      - Flag unresolved placeholders (TODO, TKTK, ???, <placeholder>, etc.).\n   C. Underspecification:\n      - Requirements with verbs but missing object or measurable outcome.\n      - User stories missing acceptance criteria alignment.\n      - Tasks referencing files or components not defined in spec/plan.\n   D. Constitution alignment:\n      - Any requirement or plan element conflicting with a MUST principle.","chunkId":".github/prompts/analyze.prompt.md:9"}],[104,{"filePath":".github/prompts/analyze.prompt.md","startLine":45,"endLine":54,"chunkText":"      - Tasks referencing files or components not defined in spec/plan.\n   D. Constitution alignment:\n      - Any requirement or plan element conflicting with a MUST principle.\n      - Missing mandated sections or quality gates from constitution.\n   E. Coverage gaps:\n      - Requirements with zero associated tasks.\n      - Tasks with no mapped requirement/story.\n      - Non-functional requirements not reflected in tasks (e.g., performance, security).\n   F. Inconsistency:\n      - Terminology drift (same concept named differently across files).","chunkId":".github/prompts/analyze.prompt.md:10"}],[105,{"filePath":".github/prompts/analyze.prompt.md","startLine":52,"endLine":59,"chunkText":"      - Non-functional requirements not reflected in tasks (e.g., performance, security).\n   F. Inconsistency:\n      - Terminology drift (same concept named differently across files).\n      - Data entities referenced in plan but absent in spec (or vice versa).\n      - Task ordering contradictions (e.g., integration tasks before foundational setup tasks without dependency note).\n      - Conflicting requirements (e.g., one requires to use Next.js while other says to use Vue as the framework).\n\n5. Severity assignment heuristic:","chunkId":".github/prompts/analyze.prompt.md:11"}],[106,{"filePath":".github/prompts/analyze.prompt.md","startLine":57,"endLine":64,"chunkText":"      - Conflicting requirements (e.g., one requires to use Next.js while other says to use Vue as the framework).\n\n5. Severity assignment heuristic:\n   - CRITICAL: Violates constitution MUST, missing core spec artifact, or requirement with zero coverage that blocks baseline functionality.\n   - HIGH: Duplicate or conflicting requirement, ambiguous security/performance attribute, untestable acceptance criterion.\n   - MEDIUM: Terminology drift, missing non-functional task coverage, underspecified edge case.\n   - LOW: Style/wording improvements, minor redundancy not affecting execution order.\n","chunkId":".github/prompts/analyze.prompt.md:12"}],[107,{"filePath":".github/prompts/analyze.prompt.md","startLine":62,"endLine":70,"chunkText":"   - MEDIUM: Terminology drift, missing non-functional task coverage, underspecified edge case.\n   - LOW: Style/wording improvements, minor redundancy not affecting execution order.\n\n6. Produce a Markdown report (no file writes) with sections:\n\n   ### Specification Analysis Report\n   | ID | Category | Severity | Location(s) | Summary | Recommendation |\n   |----|----------|----------|-------------|---------|----------------|\n   | A1 | Duplication | HIGH | spec.md:L120-134 | Two similar requirements ... | Merge phrasing; keep clearer version |","chunkId":".github/prompts/analyze.prompt.md:13"}],[108,{"filePath":".github/prompts/analyze.prompt.md","startLine":68,"endLine":80,"chunkText":"   | ID | Category | Severity | Location(s) | Summary | Recommendation |\n   |----|----------|----------|-------------|---------|----------------|\n   | A1 | Duplication | HIGH | spec.md:L120-134 | Two similar requirements ... | Merge phrasing; keep clearer version |\n   (Add one row per finding; generate stable IDs prefixed by category initial.)\n\n   Additional subsections:\n   - Coverage Summary Table:\n     | Requirement Key | Has Task? | Task IDs | Notes |\n   - Constitution Alignment Issues (if any)\n   - Unmapped Tasks (if any)\n   - Metrics:\n     * Total Requirements\n     * Total Tasks","chunkId":".github/prompts/analyze.prompt.md:14"}],[109,{"filePath":".github/prompts/analyze.prompt.md","startLine":78,"endLine":90,"chunkText":"   - Metrics:\n     * Total Requirements\n     * Total Tasks\n     * Coverage % (requirements with >=1 task)\n     * Ambiguity Count\n     * Duplication Count\n     * Critical Issues Count\n\n7. At end of report, output a concise Next Actions block:\n   - If CRITICAL issues exist: Recommend resolving before `/implement`.\n   - If only LOW/MEDIUM: User may proceed, but provide improvement suggestions.\n   - Provide explicit command suggestions: e.g., \"Run /specify with refinement\", \"Run /plan to adjust architecture\", \"Manually edit tasks.md to add coverage for 'performance-metrics'\".\n","chunkId":".github/prompts/analyze.prompt.md:15"}],[110,{"filePath":".github/prompts/analyze.prompt.md","startLine":88,"endLine":96,"chunkText":"   - If only LOW/MEDIUM: User may proceed, but provide improvement suggestions.\n   - Provide explicit command suggestions: e.g., \"Run /specify with refinement\", \"Run /plan to adjust architecture\", \"Manually edit tasks.md to add coverage for 'performance-metrics'\".\n\n8. Ask the user: \"Would you like me to suggest concrete remediation edits for the top N issues?\" (Do NOT apply them automatically.)\n\nBehavior rules:\n- NEVER modify files.\n- NEVER hallucinate missing sections—if absent, report them.\n- KEEP findings deterministic: if rerun without changes, produce consistent IDs and counts.","chunkId":".github/prompts/analyze.prompt.md:16"}],[111,{"filePath":".github/prompts/analyze.prompt.md","startLine":94,"endLine":101,"chunkText":"- NEVER modify files.\n- NEVER hallucinate missing sections—if absent, report them.\n- KEEP findings deterministic: if rerun without changes, produce consistent IDs and counts.\n- LIMIT total findings in the main table to 50; aggregate remainder in a summarized overflow note.\n- If zero issues found, emit a success report with coverage statistics and proceed recommendation.\n\nContext: $ARGUMENTS\n","chunkId":".github/prompts/analyze.prompt.md:17"}],[112,{"filePath":".github/prompts/clarify.prompt.md","startLine":0,"endLine":11,"chunkText":"---\ndescription: Identify underspecified areas in the current feature spec by asking up to 5 highly targeted clarification questions and encoding answers back into the spec.\n---\n\nThe user input to you can be provided directly by the agent or as a command argument - you **MUST** consider it before proceeding with the prompt (if not empty).\n\nUser input:\n\n$ARGUMENTS\n\nGoal: Detect and reduce ambiguity or missing decision points in the active feature specification and record the clarifications directly in the spec file.\n","chunkId":".github/prompts/clarify.prompt.md:0"}],[113,{"filePath":".github/prompts/clarify.prompt.md","startLine":9,"endLine":15,"chunkText":"\nGoal: Detect and reduce ambiguity or missing decision points in the active feature specification and record the clarifications directly in the spec file.\n\nNote: This clarification workflow is expected to run (and be completed) BEFORE invoking `/plan`. If the user explicitly states they are skipping clarification (e.g., exploratory spike), you may proceed, but must warn that downstream rework risk increases.\n\nExecution steps:\n","chunkId":".github/prompts/clarify.prompt.md:1"}],[114,{"filePath":".github/prompts/clarify.prompt.md","startLine":13,"endLine":21,"chunkText":"\nExecution steps:\n\n1. Run `.specify/scripts/bash/check-prerequisites.sh --json --paths-only` from repo root **once** (combined `--json --paths-only` mode / `-Json -PathsOnly`). Parse minimal JSON payload fields:\n   - `FEATURE_DIR`\n   - `FEATURE_SPEC`\n   - (Optionally capture `IMPL_PLAN`, `TASKS` for future chained flows.)\n   - If JSON parsing fails, abort and instruct user to re-run `/specify` or verify feature branch environment.\n","chunkId":".github/prompts/clarify.prompt.md:2"}],[115,{"filePath":".github/prompts/clarify.prompt.md","startLine":19,"endLine":26,"chunkText":"   - (Optionally capture `IMPL_PLAN`, `TASKS` for future chained flows.)\n   - If JSON parsing fails, abort and instruct user to re-run `/specify` or verify feature branch environment.\n\n2. Load the current spec file. Perform a structured ambiguity & coverage scan using this taxonomy. For each category, mark status: Clear / Partial / Missing. Produce an internal coverage map used for prioritization (do not output raw map unless no questions will be asked).\n\n   Functional Scope & Behavior:\n   - Core user goals & success criteria\n   - Explicit out-of-scope declarations","chunkId":".github/prompts/clarify.prompt.md:3"}],[116,{"filePath":".github/prompts/clarify.prompt.md","startLine":26,"endLine":43,"chunkText":"   - Explicit out-of-scope declarations\n   - User roles / personas differentiation\n\n   Domain & Data Model:\n   - Entities, attributes, relationships\n   - Identity & uniqueness rules\n   - Lifecycle/state transitions\n   - Data volume / scale assumptions\n\n   Interaction & UX Flow:\n   - Critical user journeys / sequences\n   - Error/empty/loading states\n   - Accessibility or localization notes\n\n   Non-Functional Quality Attributes:\n   - Performance (latency, throughput targets)\n   - Scalability (horizontal/vertical, limits)\n   - Reliability & availability (uptime, recovery expectations)","chunkId":".github/prompts/clarify.prompt.md:4"}],[117,{"filePath":".github/prompts/clarify.prompt.md","startLine":41,"endLine":55,"chunkText":"   - Performance (latency, throughput targets)\n   - Scalability (horizontal/vertical, limits)\n   - Reliability & availability (uptime, recovery expectations)\n   - Observability (logging, metrics, tracing signals)\n   - Security & privacy (authN/Z, data protection, threat assumptions)\n   - Compliance / regulatory constraints (if any)\n\n   Integration & External Dependencies:\n   - External services/APIs and failure modes\n   - Data import/export formats\n   - Protocol/versioning assumptions\n\n   Edge Cases & Failure Handling:\n   - Negative scenarios\n   - Rate limiting / throttling","chunkId":".github/prompts/clarify.prompt.md:5"}],[118,{"filePath":".github/prompts/clarify.prompt.md","startLine":53,"endLine":71,"chunkText":"   Edge Cases & Failure Handling:\n   - Negative scenarios\n   - Rate limiting / throttling\n   - Conflict resolution (e.g., concurrent edits)\n\n   Constraints & Tradeoffs:\n   - Technical constraints (language, storage, hosting)\n   - Explicit tradeoffs or rejected alternatives\n\n   Terminology & Consistency:\n   - Canonical glossary terms\n   - Avoided synonyms / deprecated terms\n\n   Completion Signals:\n   - Acceptance criteria testability\n   - Measurable Definition of Done style indicators\n\n   Misc / Placeholders:\n   - TODO markers / unresolved decisions","chunkId":".github/prompts/clarify.prompt.md:6"}],[119,{"filePath":".github/prompts/clarify.prompt.md","startLine":69,"endLine":79,"chunkText":"\n   Misc / Placeholders:\n   - TODO markers / unresolved decisions\n   - Ambiguous adjectives (\"robust\", \"intuitive\") lacking quantification\n\n   For each category with Partial or Missing status, add a candidate question opportunity unless:\n   - Clarification would not materially change implementation or validation strategy\n   - Information is better deferred to planning phase (note internally)\n\n3. Generate (internally) a prioritized queue of candidate clarification questions (maximum 5). Do NOT output them all at once. Apply these constraints:\n    - Maximum of 5 total questions across the whole session.","chunkId":".github/prompts/clarify.prompt.md:7"}],[120,{"filePath":".github/prompts/clarify.prompt.md","startLine":77,"endLine":82,"chunkText":"\n3. Generate (internally) a prioritized queue of candidate clarification questions (maximum 5). Do NOT output them all at once. Apply these constraints:\n    - Maximum of 5 total questions across the whole session.\n    - Each question must be answerable with EITHER:\n       * A short multiple‑choice selection (2–5 distinct, mutually exclusive options), OR\n       * A one-word / short‑phrase answer (explicitly constrain: \"Answer in <=5 words\").","chunkId":".github/prompts/clarify.prompt.md:8"}],[121,{"filePath":".github/prompts/clarify.prompt.md","startLine":80,"endLine":83,"chunkText":"    - Each question must be answerable with EITHER:\n       * A short multiple‑choice selection (2–5 distinct, mutually exclusive options), OR\n       * A one-word / short‑phrase answer (explicitly constrain: \"Answer in <=5 words\").\n   - Only include questions whose answers materially impact architecture, data modeling, task decomposition, test design, UX behavior, operational readiness, or compliance validation.","chunkId":".github/prompts/clarify.prompt.md:9"}],[122,{"filePath":".github/prompts/clarify.prompt.md","startLine":81,"endLine":84,"chunkText":"       * A short multiple‑choice selection (2–5 distinct, mutually exclusive options), OR\n       * A one-word / short‑phrase answer (explicitly constrain: \"Answer in <=5 words\").\n   - Only include questions whose answers materially impact architecture, data modeling, task decomposition, test design, UX behavior, operational readiness, or compliance validation.\n   - Ensure category coverage balance: attempt to cover the highest impact unresolved categories first; avoid asking two low-impact questions when a single high-impact area (e.g., security posture) is unresolved.","chunkId":".github/prompts/clarify.prompt.md:10"}],[123,{"filePath":".github/prompts/clarify.prompt.md","startLine":82,"endLine":84,"chunkText":"       * A one-word / short‑phrase answer (explicitly constrain: \"Answer in <=5 words\").\n   - Only include questions whose answers materially impact architecture, data modeling, task decomposition, test design, UX behavior, operational readiness, or compliance validation.\n   - Ensure category coverage balance: attempt to cover the highest impact unresolved categories first; avoid asking two low-impact questions when a single high-impact area (e.g., security posture) is unresolved.","chunkId":".github/prompts/clarify.prompt.md:11"}],[124,{"filePath":".github/prompts/clarify.prompt.md","startLine":83,"endLine":85,"chunkText":"   - Only include questions whose answers materially impact architecture, data modeling, task decomposition, test design, UX behavior, operational readiness, or compliance validation.\n   - Ensure category coverage balance: attempt to cover the highest impact unresolved categories first; avoid asking two low-impact questions when a single high-impact area (e.g., security posture) is unresolved.\n   - Exclude questions already answered, trivial stylistic preferences, or plan-level execution details (unless blocking correctness).","chunkId":".github/prompts/clarify.prompt.md:12"}],[125,{"filePath":".github/prompts/clarify.prompt.md","startLine":84,"endLine":89,"chunkText":"   - Ensure category coverage balance: attempt to cover the highest impact unresolved categories first; avoid asking two low-impact questions when a single high-impact area (e.g., security posture) is unresolved.\n   - Exclude questions already answered, trivial stylistic preferences, or plan-level execution details (unless blocking correctness).\n   - Favor clarifications that reduce downstream rework risk or prevent misaligned acceptance tests.\n   - If more than 5 categories remain unresolved, select the top 5 by (Impact * Uncertainty) heuristic.\n\n4. Sequential questioning loop (interactive):","chunkId":".github/prompts/clarify.prompt.md:13"}],[126,{"filePath":".github/prompts/clarify.prompt.md","startLine":87,"endLine":99,"chunkText":"   - If more than 5 categories remain unresolved, select the top 5 by (Impact * Uncertainty) heuristic.\n\n4. Sequential questioning loop (interactive):\n    - Present EXACTLY ONE question at a time.\n    - For multiple‑choice questions render options as a Markdown table:\n\n       | Option | Description |\n       |--------|-------------|\n       | A | <Option A description> |\n       | B | <Option B description> |\n       | C | <Option C description> | (add D/E as needed up to 5)\n       | Short | Provide a different short answer (<=5 words) | (Include only if free-form alternative is appropriate)\n","chunkId":".github/prompts/clarify.prompt.md:14"}],[127,{"filePath":".github/prompts/clarify.prompt.md","startLine":97,"endLine":103,"chunkText":"       | C | <Option C description> | (add D/E as needed up to 5)\n       | Short | Provide a different short answer (<=5 words) | (Include only if free-form alternative is appropriate)\n\n    - For short‑answer style (no meaningful discrete options), output a single line after the question: `Format: Short answer (<=5 words)`.\n    - After the user answers:\n       * Validate the answer maps to one option or fits the <=5 word constraint.\n       * If ambiguous, ask for a quick disambiguation (count still belongs to same question; do not advance).","chunkId":".github/prompts/clarify.prompt.md:15"}],[128,{"filePath":".github/prompts/clarify.prompt.md","startLine":101,"endLine":108,"chunkText":"    - After the user answers:\n       * Validate the answer maps to one option or fits the <=5 word constraint.\n       * If ambiguous, ask for a quick disambiguation (count still belongs to same question; do not advance).\n       * Once satisfactory, record it in working memory (do not yet write to disk) and move to the next queued question.\n    - Stop asking further questions when:\n       * All critical ambiguities resolved early (remaining queued items become unnecessary), OR\n       * User signals completion (\"done\", \"good\", \"no more\"), OR\n       * You reach 5 asked questions.","chunkId":".github/prompts/clarify.prompt.md:16"}],[129,{"filePath":".github/prompts/clarify.prompt.md","startLine":106,"endLine":114,"chunkText":"       * All critical ambiguities resolved early (remaining queued items become unnecessary), OR\n       * User signals completion (\"done\", \"good\", \"no more\"), OR\n       * You reach 5 asked questions.\n    - Never reveal future queued questions in advance.\n    - If no valid questions exist at start, immediately report no critical ambiguities.\n\n5. Integration after EACH accepted answer (incremental update approach):\n    - Maintain in-memory representation of the spec (loaded once at start) plus the raw file contents.\n    - For the first integrated answer in this session:","chunkId":".github/prompts/clarify.prompt.md:17"}],[130,{"filePath":".github/prompts/clarify.prompt.md","startLine":112,"endLine":117,"chunkText":"5. Integration after EACH accepted answer (incremental update approach):\n    - Maintain in-memory representation of the spec (loaded once at start) plus the raw file contents.\n    - For the first integrated answer in this session:\n       * Ensure a `## Clarifications` section exists (create it just after the highest-level contextual/overview section per the spec template if missing).\n       * Under it, create (if not present) a `### Session YYYY-MM-DD` subheading for today.\n    - Append a bullet line immediately after acceptance: `- Q: <question> → A: <final answer>`.","chunkId":".github/prompts/clarify.prompt.md:18"}],[131,{"filePath":".github/prompts/clarify.prompt.md","startLine":115,"endLine":119,"chunkText":"       * Ensure a `## Clarifications` section exists (create it just after the highest-level contextual/overview section per the spec template if missing).\n       * Under it, create (if not present) a `### Session YYYY-MM-DD` subheading for today.\n    - Append a bullet line immediately after acceptance: `- Q: <question> → A: <final answer>`.\n    - Then immediately apply the clarification to the most appropriate section(s):\n       * Functional ambiguity → Update or add a bullet in Functional Requirements.","chunkId":".github/prompts/clarify.prompt.md:19"}],[132,{"filePath":".github/prompts/clarify.prompt.md","startLine":117,"endLine":121,"chunkText":"    - Append a bullet line immediately after acceptance: `- Q: <question> → A: <final answer>`.\n    - Then immediately apply the clarification to the most appropriate section(s):\n       * Functional ambiguity → Update or add a bullet in Functional Requirements.\n       * User interaction / actor distinction → Update User Stories or Actors subsection (if present) with clarified role, constraint, or scenario.\n       * Data shape / entities → Update Data Model (add fields, types, relationships) preserving ordering; note added constraints succinctly.","chunkId":".github/prompts/clarify.prompt.md:20"}],[133,{"filePath":".github/prompts/clarify.prompt.md","startLine":119,"endLine":122,"chunkText":"       * Functional ambiguity → Update or add a bullet in Functional Requirements.\n       * User interaction / actor distinction → Update User Stories or Actors subsection (if present) with clarified role, constraint, or scenario.\n       * Data shape / entities → Update Data Model (add fields, types, relationships) preserving ordering; note added constraints succinctly.\n       * Non-functional constraint → Add/modify measurable criteria in Non-Functional / Quality Attributes section (convert vague adjective to metric or explicit target).","chunkId":".github/prompts/clarify.prompt.md:21"}],[134,{"filePath":".github/prompts/clarify.prompt.md","startLine":120,"endLine":122,"chunkText":"       * User interaction / actor distinction → Update User Stories or Actors subsection (if present) with clarified role, constraint, or scenario.\n       * Data shape / entities → Update Data Model (add fields, types, relationships) preserving ordering; note added constraints succinctly.\n       * Non-functional constraint → Add/modify measurable criteria in Non-Functional / Quality Attributes section (convert vague adjective to metric or explicit target).","chunkId":".github/prompts/clarify.prompt.md:22"}],[135,{"filePath":".github/prompts/clarify.prompt.md","startLine":121,"endLine":124,"chunkText":"       * Data shape / entities → Update Data Model (add fields, types, relationships) preserving ordering; note added constraints succinctly.\n       * Non-functional constraint → Add/modify measurable criteria in Non-Functional / Quality Attributes section (convert vague adjective to metric or explicit target).\n       * Edge case / negative flow → Add a new bullet under Edge Cases / Error Handling (or create such subsection if template provides placeholder for it).\n       * Terminology conflict → Normalize term across spec; retain original only if necessary by adding `(formerly referred to as \"X\")` once.","chunkId":".github/prompts/clarify.prompt.md:23"}],[136,{"filePath":".github/prompts/clarify.prompt.md","startLine":122,"endLine":124,"chunkText":"       * Non-functional constraint → Add/modify measurable criteria in Non-Functional / Quality Attributes section (convert vague adjective to metric or explicit target).\n       * Edge case / negative flow → Add a new bullet under Edge Cases / Error Handling (or create such subsection if template provides placeholder for it).\n       * Terminology conflict → Normalize term across spec; retain original only if necessary by adding `(formerly referred to as \"X\")` once.","chunkId":".github/prompts/clarify.prompt.md:24"}],[137,{"filePath":".github/prompts/clarify.prompt.md","startLine":123,"endLine":126,"chunkText":"       * Edge case / negative flow → Add a new bullet under Edge Cases / Error Handling (or create such subsection if template provides placeholder for it).\n       * Terminology conflict → Normalize term across spec; retain original only if necessary by adding `(formerly referred to as \"X\")` once.\n    - If the clarification invalidates an earlier ambiguous statement, replace that statement instead of duplicating; leave no obsolete contradictory text.\n    - Save the spec file AFTER each integration to minimize risk of context loss (atomic overwrite).","chunkId":".github/prompts/clarify.prompt.md:25"}],[138,{"filePath":".github/prompts/clarify.prompt.md","startLine":124,"endLine":129,"chunkText":"       * Terminology conflict → Normalize term across spec; retain original only if necessary by adding `(formerly referred to as \"X\")` once.\n    - If the clarification invalidates an earlier ambiguous statement, replace that statement instead of duplicating; leave no obsolete contradictory text.\n    - Save the spec file AFTER each integration to minimize risk of context loss (atomic overwrite).\n    - Preserve formatting: do not reorder unrelated sections; keep heading hierarchy intact.\n    - Keep each inserted clarification minimal and testable (avoid narrative drift).\n","chunkId":".github/prompts/clarify.prompt.md:26"}],[139,{"filePath":".github/prompts/clarify.prompt.md","startLine":127,"endLine":134,"chunkText":"    - Preserve formatting: do not reorder unrelated sections; keep heading hierarchy intact.\n    - Keep each inserted clarification minimal and testable (avoid narrative drift).\n\n6. Validation (performed after EACH write plus final pass):\n   - Clarifications session contains exactly one bullet per accepted answer (no duplicates).\n   - Total asked (accepted) questions ≤ 5.\n   - Updated sections contain no lingering vague placeholders the new answer was meant to resolve.\n   - No contradictory earlier statement remains (scan for now-invalid alternative choices removed).","chunkId":".github/prompts/clarify.prompt.md:27"}],[140,{"filePath":".github/prompts/clarify.prompt.md","startLine":132,"endLine":141,"chunkText":"   - Total asked (accepted) questions ≤ 5.\n   - Updated sections contain no lingering vague placeholders the new answer was meant to resolve.\n   - No contradictory earlier statement remains (scan for now-invalid alternative choices removed).\n   - Markdown structure valid; only allowed new headings: `## Clarifications`, `### Session YYYY-MM-DD`.\n   - Terminology consistency: same canonical term used across all updated sections.\n\n7. Write the updated spec back to `FEATURE_SPEC`.\n\n8. Report completion (after questioning loop ends or early termination):\n   - Number of questions asked & answered.","chunkId":".github/prompts/clarify.prompt.md:28"}],[141,{"filePath":".github/prompts/clarify.prompt.md","startLine":139,"endLine":147,"chunkText":"\n8. Report completion (after questioning loop ends or early termination):\n   - Number of questions asked & answered.\n   - Path to updated spec.\n   - Sections touched (list names).\n   - Coverage summary table listing each taxonomy category with Status: Resolved (was Partial/Missing and addressed), Deferred (exceeds question quota or better suited for planning), Clear (already sufficient), Outstanding (still Partial/Missing but low impact).\n   - If any Outstanding or Deferred remain, recommend whether to proceed to `/plan` or run `/clarify` again later post-plan.\n   - Suggested next command.\n","chunkId":".github/prompts/clarify.prompt.md:29"}],[142,{"filePath":".github/prompts/clarify.prompt.md","startLine":145,"endLine":151,"chunkText":"   - If any Outstanding or Deferred remain, recommend whether to proceed to `/plan` or run `/clarify` again later post-plan.\n   - Suggested next command.\n\nBehavior rules:\n- If no meaningful ambiguities found (or all potential questions would be low-impact), respond: \"No critical ambiguities detected worth formal clarification.\" and suggest proceeding.\n- If spec file missing, instruct user to run `/specify` first (do not create a new spec here).\n- Never exceed 5 total asked questions (clarification retries for a single question do not count as new questions).","chunkId":".github/prompts/clarify.prompt.md:30"}],[143,{"filePath":".github/prompts/clarify.prompt.md","startLine":149,"endLine":153,"chunkText":"- If no meaningful ambiguities found (or all potential questions would be low-impact), respond: \"No critical ambiguities detected worth formal clarification.\" and suggest proceeding.\n- If spec file missing, instruct user to run `/specify` first (do not create a new spec here).\n- Never exceed 5 total asked questions (clarification retries for a single question do not count as new questions).\n- Avoid speculative tech stack questions unless the absence blocks functional clarity.\n- Respect user early termination signals (\"stop\", \"done\", \"proceed\").","chunkId":".github/prompts/clarify.prompt.md:31"}],[144,{"filePath":".github/prompts/clarify.prompt.md","startLine":151,"endLine":158,"chunkText":"- Never exceed 5 total asked questions (clarification retries for a single question do not count as new questions).\n- Avoid speculative tech stack questions unless the absence blocks functional clarity.\n- Respect user early termination signals (\"stop\", \"done\", \"proceed\").\n - If no questions asked due to full coverage, output a compact coverage summary (all categories Clear) then suggest advancing.\n - If quota reached with unresolved high-impact categories remaining, explicitly flag them under Deferred with rationale.\n\nContext for prioritization: $ARGUMENTS\n","chunkId":".github/prompts/clarify.prompt.md:32"}],[145,{"filePath":".github/prompts/constitution.prompt.md","startLine":0,"endLine":9,"chunkText":"---\ndescription: Create or update the project constitution from interactive or provided principle inputs, ensuring all dependent templates stay in sync.\n---\n\nThe user input to you can be provided directly by the agent or as a command argument - you **MUST** consider it before proceeding with the prompt (if not empty).\n\nUser input:\n\n$ARGUMENTS\n","chunkId":".github/prompts/constitution.prompt.md:0"}],[146,{"filePath":".github/prompts/constitution.prompt.md","startLine":7,"endLine":15,"chunkText":"\n$ARGUMENTS\n\nYou are updating the project constitution at `.specify/memory/constitution.md`. This file is a TEMPLATE containing placeholder tokens in square brackets (e.g. `[PROJECT_NAME]`, `[PRINCIPLE_1_NAME]`). Your job is to (a) collect/derive concrete values, (b) fill the template precisely, and (c) propagate any amendments across dependent artifacts.\n\nFollow this execution flow:\n\n1. Load the existing constitution template at `.specify/memory/constitution.md`.\n   - Identify every placeholder token of the form `[ALL_CAPS_IDENTIFIER]`.","chunkId":".github/prompts/constitution.prompt.md:1"}],[147,{"filePath":".github/prompts/constitution.prompt.md","startLine":13,"endLine":20,"chunkText":"\n1. Load the existing constitution template at `.specify/memory/constitution.md`.\n   - Identify every placeholder token of the form `[ALL_CAPS_IDENTIFIER]`.\n   **IMPORTANT**: The user might require less or more principles than the ones used in the template. If a number is specified, respect that - follow the general template. You will update the doc accordingly.\n\n2. Collect/derive values for placeholders:\n   - If user input (conversation) supplies a value, use it.\n   - Otherwise infer from existing repo context (README, docs, prior constitution versions if embedded).","chunkId":".github/prompts/constitution.prompt.md:2"}],[148,{"filePath":".github/prompts/constitution.prompt.md","startLine":18,"endLine":23,"chunkText":"2. Collect/derive values for placeholders:\n   - If user input (conversation) supplies a value, use it.\n   - Otherwise infer from existing repo context (README, docs, prior constitution versions if embedded).\n   - For governance dates: `RATIFICATION_DATE` is the original adoption date (if unknown ask or mark TODO), `LAST_AMENDED_DATE` is today if changes are made, otherwise keep previous.\n   - `CONSTITUTION_VERSION` must increment according to semantic versioning rules:\n     * MAJOR: Backward incompatible governance/principle removals or redefinitions.","chunkId":".github/prompts/constitution.prompt.md:3"}],[149,{"filePath":".github/prompts/constitution.prompt.md","startLine":21,"endLine":27,"chunkText":"   - For governance dates: `RATIFICATION_DATE` is the original adoption date (if unknown ask or mark TODO), `LAST_AMENDED_DATE` is today if changes are made, otherwise keep previous.\n   - `CONSTITUTION_VERSION` must increment according to semantic versioning rules:\n     * MAJOR: Backward incompatible governance/principle removals or redefinitions.\n     * MINOR: New principle/section added or materially expanded guidance.\n     * PATCH: Clarifications, wording, typo fixes, non-semantic refinements.\n   - If version bump type ambiguous, propose reasoning before finalizing.\n","chunkId":".github/prompts/constitution.prompt.md:4"}],[150,{"filePath":".github/prompts/constitution.prompt.md","startLine":25,"endLine":30,"chunkText":"     * PATCH: Clarifications, wording, typo fixes, non-semantic refinements.\n   - If version bump type ambiguous, propose reasoning before finalizing.\n\n3. Draft the updated constitution content:\n   - Replace every placeholder with concrete text (no bracketed tokens left except intentionally retained template slots that the project has chosen not to define yet—explicitly justify any left).\n   - Preserve heading hierarchy and comments can be removed once replaced unless they still add clarifying guidance.","chunkId":".github/prompts/constitution.prompt.md:5"}],[151,{"filePath":".github/prompts/constitution.prompt.md","startLine":28,"endLine":31,"chunkText":"3. Draft the updated constitution content:\n   - Replace every placeholder with concrete text (no bracketed tokens left except intentionally retained template slots that the project has chosen not to define yet—explicitly justify any left).\n   - Preserve heading hierarchy and comments can be removed once replaced unless they still add clarifying guidance.\n   - Ensure each Principle section: succinct name line, paragraph (or bullet list) capturing non‑negotiable rules, explicit rationale if not obvious.","chunkId":".github/prompts/constitution.prompt.md:6"}],[152,{"filePath":".github/prompts/constitution.prompt.md","startLine":29,"endLine":33,"chunkText":"   - Replace every placeholder with concrete text (no bracketed tokens left except intentionally retained template slots that the project has chosen not to define yet—explicitly justify any left).\n   - Preserve heading hierarchy and comments can be removed once replaced unless they still add clarifying guidance.\n   - Ensure each Principle section: succinct name line, paragraph (or bullet list) capturing non‑negotiable rules, explicit rationale if not obvious.\n   - Ensure Governance section lists amendment procedure, versioning policy, and compliance review expectations.\n","chunkId":".github/prompts/constitution.prompt.md:7"}],[153,{"filePath":".github/prompts/constitution.prompt.md","startLine":31,"endLine":35,"chunkText":"   - Ensure each Principle section: succinct name line, paragraph (or bullet list) capturing non‑negotiable rules, explicit rationale if not obvious.\n   - Ensure Governance section lists amendment procedure, versioning policy, and compliance review expectations.\n\n4. Consistency propagation checklist (convert prior checklist into active validations):\n   - Read `.specify/templates/plan-template.md` and ensure any \"Constitution Check\" or rules align with updated principles.","chunkId":".github/prompts/constitution.prompt.md:8"}],[154,{"filePath":".github/prompts/constitution.prompt.md","startLine":33,"endLine":37,"chunkText":"\n4. Consistency propagation checklist (convert prior checklist into active validations):\n   - Read `.specify/templates/plan-template.md` and ensure any \"Constitution Check\" or rules align with updated principles.\n   - Read `.specify/templates/spec-template.md` for scope/requirements alignment—update if constitution adds/removes mandatory sections or constraints.\n   - Read `.specify/templates/tasks-template.md` and ensure task categorization reflects new or removed principle-driven task types (e.g., observability, versioning, testing discipline).","chunkId":".github/prompts/constitution.prompt.md:9"}],[155,{"filePath":".github/prompts/constitution.prompt.md","startLine":35,"endLine":37,"chunkText":"   - Read `.specify/templates/plan-template.md` and ensure any \"Constitution Check\" or rules align with updated principles.\n   - Read `.specify/templates/spec-template.md` for scope/requirements alignment—update if constitution adds/removes mandatory sections or constraints.\n   - Read `.specify/templates/tasks-template.md` and ensure task categorization reflects new or removed principle-driven task types (e.g., observability, versioning, testing discipline).","chunkId":".github/prompts/constitution.prompt.md:10"}],[156,{"filePath":".github/prompts/constitution.prompt.md","startLine":36,"endLine":38,"chunkText":"   - Read `.specify/templates/spec-template.md` for scope/requirements alignment—update if constitution adds/removes mandatory sections or constraints.\n   - Read `.specify/templates/tasks-template.md` and ensure task categorization reflects new or removed principle-driven task types (e.g., observability, versioning, testing discipline).\n   - Read each command file in `.specify/templates/commands/*.md` (including this one) to verify no outdated references (agent-specific names like CLAUDE only) remain when generic guidance is required.","chunkId":".github/prompts/constitution.prompt.md:11"}],[157,{"filePath":".github/prompts/constitution.prompt.md","startLine":37,"endLine":40,"chunkText":"   - Read `.specify/templates/tasks-template.md` and ensure task categorization reflects new or removed principle-driven task types (e.g., observability, versioning, testing discipline).\n   - Read each command file in `.specify/templates/commands/*.md` (including this one) to verify no outdated references (agent-specific names like CLAUDE only) remain when generic guidance is required.\n   - Read any runtime guidance docs (e.g., `README.md`, `docs/quickstart.md`, or agent-specific guidance files if present). Update references to principles changed.\n","chunkId":".github/prompts/constitution.prompt.md:12"}],[158,{"filePath":".github/prompts/constitution.prompt.md","startLine":38,"endLine":44,"chunkText":"   - Read each command file in `.specify/templates/commands/*.md` (including this one) to verify no outdated references (agent-specific names like CLAUDE only) remain when generic guidance is required.\n   - Read any runtime guidance docs (e.g., `README.md`, `docs/quickstart.md`, or agent-specific guidance files if present). Update references to principles changed.\n\n5. Produce a Sync Impact Report (prepend as an HTML comment at top of the constitution file after update):\n   - Version change: old → new\n   - List of modified principles (old title → new title if renamed)\n   - Added sections","chunkId":".github/prompts/constitution.prompt.md:13"}],[159,{"filePath":".github/prompts/constitution.prompt.md","startLine":42,"endLine":54,"chunkText":"   - Version change: old → new\n   - List of modified principles (old title → new title if renamed)\n   - Added sections\n   - Removed sections\n   - Templates requiring updates (✅ updated / ⚠ pending) with file paths\n   - Follow-up TODOs if any placeholders intentionally deferred.\n\n6. Validation before final output:\n   - No remaining unexplained bracket tokens.\n   - Version line matches report.\n   - Dates ISO format YYYY-MM-DD.\n   - Principles are declarative, testable, and free of vague language (\"should\" → replace with MUST/SHOULD rationale where appropriate).\n","chunkId":".github/prompts/constitution.prompt.md:14"}],[160,{"filePath":".github/prompts/constitution.prompt.md","startLine":52,"endLine":62,"chunkText":"   - Dates ISO format YYYY-MM-DD.\n   - Principles are declarative, testable, and free of vague language (\"should\" → replace with MUST/SHOULD rationale where appropriate).\n\n7. Write the completed constitution back to `.specify/memory/constitution.md` (overwrite).\n\n8. Output a final summary to the user with:\n   - New version and bump rationale.\n   - Any files flagged for manual follow-up.\n   - Suggested commit message (e.g., `docs: amend constitution to vX.Y.Z (principle additions + governance update)`).\n\nFormatting & Style Requirements:","chunkId":".github/prompts/constitution.prompt.md:15"}],[161,{"filePath":".github/prompts/constitution.prompt.md","startLine":60,"endLine":69,"chunkText":"   - Suggested commit message (e.g., `docs: amend constitution to vX.Y.Z (principle additions + governance update)`).\n\nFormatting & Style Requirements:\n- Use Markdown headings exactly as in the template (do not demote/promote levels).\n- Wrap long rationale lines to keep readability (<100 chars ideally) but do not hard enforce with awkward breaks.\n- Keep a single blank line between sections.\n- Avoid trailing whitespace.\n\nIf the user supplies partial updates (e.g., only one principle revision), still perform validation and version decision steps.\n","chunkId":".github/prompts/constitution.prompt.md:16"}],[162,{"filePath":".github/prompts/constitution.prompt.md","startLine":67,"endLine":73,"chunkText":"\nIf the user supplies partial updates (e.g., only one principle revision), still perform validation and version decision steps.\n\nIf critical info missing (e.g., ratification date truly unknown), insert `TODO(<FIELD_NAME>): explanation` and include in the Sync Impact Report under deferred items.\n\nDo not create a new template; always operate on the existing `.specify/memory/constitution.md` file.\n","chunkId":".github/prompts/constitution.prompt.md:17"}],[163,{"filePath":".github/prompts/implement.prompt.md","startLine":0,"endLine":13,"chunkText":"---\ndescription: Execute the implementation plan by processing and executing all tasks defined in tasks.md\n---\n\nThe user input can be provided directly by the agent or as a command argument - you **MUST** consider it before proceeding with the prompt (if not empty).\n\nUser input:\n\n$ARGUMENTS\n\n1. Run `.specify/scripts/bash/check-prerequisites.sh --json --require-tasks --include-tasks` from repo root and parse FEATURE_DIR and AVAILABLE_DOCS list. All paths must be absolute.\n\n2. Load and analyze the implementation context:\n   - **REQUIRED**: Read tasks.md for the complete task list and execution plan","chunkId":".github/prompts/implement.prompt.md:0"}],[164,{"filePath":".github/prompts/implement.prompt.md","startLine":12,"endLine":21,"chunkText":"2. Load and analyze the implementation context:\n   - **REQUIRED**: Read tasks.md for the complete task list and execution plan\n   - **REQUIRED**: Read plan.md for tech stack, architecture, and file structure\n   - **IF EXISTS**: Read data-model.md for entities and relationships\n   - **IF EXISTS**: Read contracts/ for API specifications and test requirements\n   - **IF EXISTS**: Read research.md for technical decisions and constraints\n   - **IF EXISTS**: Read quickstart.md for integration scenarios\n\n3. Parse tasks.md structure and extract:\n   - **Task phases**: Setup, Tests, Core, Integration, Polish","chunkId":".github/prompts/implement.prompt.md:1"}],[165,{"filePath":".github/prompts/implement.prompt.md","startLine":20,"endLine":28,"chunkText":"3. Parse tasks.md structure and extract:\n   - **Task phases**: Setup, Tests, Core, Integration, Polish\n   - **Task dependencies**: Sequential vs parallel execution rules\n   - **Task details**: ID, description, file paths, parallel markers [P]\n   - **Execution flow**: Order and dependency requirements\n\n4. Execute implementation following the task plan:\n   - **Phase-by-phase execution**: Complete each phase before moving to the next\n   - **Respect dependencies**: Run sequential tasks in order, parallel tasks [P] can run together  ","chunkId":".github/prompts/implement.prompt.md:2"}],[166,{"filePath":".github/prompts/implement.prompt.md","startLine":27,"endLine":34,"chunkText":"   - **Phase-by-phase execution**: Complete each phase before moving to the next\n   - **Respect dependencies**: Run sequential tasks in order, parallel tasks [P] can run together  \n   - **Follow TDD approach**: Execute test tasks before their corresponding implementation tasks\n   - **File-based coordination**: Tasks affecting the same files must run sequentially\n   - **Validation checkpoints**: Verify each phase completion before proceeding\n\n5. Implementation execution rules:\n   - **Setup first**: Initialize project structure, dependencies, configuration","chunkId":".github/prompts/implement.prompt.md:3"}],[167,{"filePath":".github/prompts/implement.prompt.md","startLine":33,"endLine":42,"chunkText":"5. Implementation execution rules:\n   - **Setup first**: Initialize project structure, dependencies, configuration\n   - **Tests before code**: If you need to write tests for contracts, entities, and integration scenarios\n   - **Core development**: Implement models, services, CLI commands, endpoints\n   - **Integration work**: Database connections, middleware, logging, external services\n   - **Polish and validation**: Unit tests, performance optimization, documentation\n\n6. Progress tracking and error handling:\n   - Report progress after each completed task\n   - Halt execution if any non-parallel task fails","chunkId":".github/prompts/implement.prompt.md:4"}],[168,{"filePath":".github/prompts/implement.prompt.md","startLine":41,"endLine":51,"chunkText":"   - Report progress after each completed task\n   - Halt execution if any non-parallel task fails\n   - For parallel tasks [P], continue with successful tasks, report failed ones\n   - Provide clear error messages with context for debugging\n   - Suggest next steps if implementation cannot proceed\n   - **IMPORTANT** For completed tasks, make sure to mark the task off as [X] in the tasks file.\n\n7. Completion validation:\n   - Verify all required tasks are completed\n   - Check that implemented features match the original specification\n   - Validate that tests pass and coverage meets requirements","chunkId":".github/prompts/implement.prompt.md:5"}],[169,{"filePath":".github/prompts/implement.prompt.md","startLine":50,"endLine":56,"chunkText":"   - Check that implemented features match the original specification\n   - Validate that tests pass and coverage meets requirements\n   - Confirm the implementation follows the technical plan\n   - Report final status with summary of completed work\n\nNote: This command assumes a complete task breakdown exists in tasks.md. If tasks are incomplete or missing, suggest running `/tasks` first to regenerate the task list.\n","chunkId":".github/prompts/implement.prompt.md:6"}],[170,{"filePath":".github/prompts/plan.prompt.md","startLine":0,"endLine":12,"chunkText":"---\ndescription: Execute the implementation planning workflow using the plan template to generate design artifacts.\n---\n\nThe user input to you can be provided directly by the agent or as a command argument - you **MUST** consider it before proceeding with the prompt (if not empty).\n\nUser input:\n\n$ARGUMENTS\n\nGiven the implementation details provided as an argument, do this:\n\n1. Run `.specify/scripts/bash/setup-plan.sh --json` from the repo root and parse JSON for FEATURE_SPEC, IMPL_PLAN, SPECS_DIR, BRANCH. All future file paths must be absolute.","chunkId":".github/prompts/plan.prompt.md:0"}],[171,{"filePath":".github/prompts/plan.prompt.md","startLine":10,"endLine":12,"chunkText":"Given the implementation details provided as an argument, do this:\n\n1. Run `.specify/scripts/bash/setup-plan.sh --json` from the repo root and parse JSON for FEATURE_SPEC, IMPL_PLAN, SPECS_DIR, BRANCH. All future file paths must be absolute.","chunkId":".github/prompts/plan.prompt.md:1"}],[172,{"filePath":".github/prompts/plan.prompt.md","startLine":11,"endLine":11,"chunkText":"1. Run `.specify/scripts/bash/setup-plan.sh --json` from the repo root and parse JSON for FEATURE_SPEC, IMPL_PLAN, SPECS_DIR, BRANCH. All future file paths must be absolute.","chunkId":".github/prompts/plan.prompt.md:2"}],[173,{"filePath":".github/prompts/plan.prompt.md","startLine":12,"endLine":12,"chunkText":"1. Run `.specify/scripts/bash/setup-plan.sh --json` from the repo root and parse JSON for FEATURE_SPEC, IMPL_PLAN, SPECS_DIR, BRANCH. All future file paths must be absolute.","chunkId":".github/prompts/plan.prompt.md:3"}],[174,{"filePath":".github/prompts/plan.prompt.md","startLine":13,"endLine":15,"chunkText":"   - BEFORE proceeding, inspect FEATURE_SPEC for a `## Clarifications` section with at least one `Session` subheading. If missing or clearly ambiguous areas remain (vague adjectives, unresolved critical choices), PAUSE and instruct the user to run `/clarify` first to reduce rework. Only continue if: (a) Clarifications exist OR (b) an explicit user override is provided (e.g., \"proceed without clarification\"). Do not attempt to fabricate clarifications yourself.\n2. Read and analyze the feature specification to understand:\n   - The feature requirements and user stories","chunkId":".github/prompts/plan.prompt.md:4"}],[175,{"filePath":".github/prompts/plan.prompt.md","startLine":14,"endLine":25,"chunkText":"2. Read and analyze the feature specification to understand:\n   - The feature requirements and user stories\n   - Functional and non-functional requirements\n   - Success criteria and acceptance criteria\n   - Any technical constraints or dependencies mentioned\n\n3. Read the constitution at `.specify/memory/constitution.md` to understand constitutional requirements.\n\n4. Execute the implementation plan template:\n   - Load `.specify/templates/plan-template.md` (already copied to IMPL_PLAN path)\n   - Set Input path to FEATURE_SPEC\n   - Run the Execution Flow (main) function steps 1-9","chunkId":".github/prompts/plan.prompt.md:5"}],[176,{"filePath":".github/prompts/plan.prompt.md","startLine":23,"endLine":32,"chunkText":"   - Load `.specify/templates/plan-template.md` (already copied to IMPL_PLAN path)\n   - Set Input path to FEATURE_SPEC\n   - Run the Execution Flow (main) function steps 1-9\n   - The template is self-contained and executable\n   - Follow error handling and gate checks as specified\n   - Let the template guide artifact generation in $SPECS_DIR:\n     * Phase 0 generates research.md\n     * Phase 1 generates data-model.md, contracts/, quickstart.md\n     * Phase 2 generates tasks.md\n   - Incorporate user-provided details from arguments into Technical Context: $ARGUMENTS","chunkId":".github/prompts/plan.prompt.md:6"}],[177,{"filePath":".github/prompts/plan.prompt.md","startLine":30,"endLine":43,"chunkText":"     * Phase 1 generates data-model.md, contracts/, quickstart.md\n     * Phase 2 generates tasks.md\n   - Incorporate user-provided details from arguments into Technical Context: $ARGUMENTS\n   - Update Progress Tracking as you complete each phase\n\n5. Verify execution completed:\n   - Check Progress Tracking shows all phases complete\n   - Ensure all required artifacts were generated\n   - Confirm no ERROR states in execution\n\n6. Report results with branch name, file paths, and generated artifacts.\n\nUse absolute paths with the repository root for all file operations to avoid path issues.\n","chunkId":".github/prompts/plan.prompt.md:7"}],[178,{"filePath":".github/prompts/specify.prompt.md","startLine":0,"endLine":12,"chunkText":"---\ndescription: Create or update the feature specification from a natural language feature description.\n---\n\nThe user input to you can be provided directly by the agent or as a command argument - you **MUST** consider it before proceeding with the prompt (if not empty).\n\nUser input:\n\n$ARGUMENTS\n\nThe text the user typed after `/specify` in the triggering message **is** the feature description. Assume you always have it available in this conversation even if `$ARGUMENTS` appears literally below. Do not ask the user to repeat it unless they provided an empty command.\n\nGiven that feature description, do this","chunkId":".github/prompts/specify.prompt.md:0"}],[179,{"filePath":".github/prompts/specify.prompt.md","startLine":11,"endLine":16,"chunkText":"\nGiven that feature description, do this:\n\n1. Run the script `.specify/scripts/bash/create-new-feature.sh --json \"$ARGUMENTS\"` from repo root and parse its JSON output for BRANCH_NAME and SPEC_FILE. All file paths must be absolute.\n  **IMPORTANT** You must only ever run this script once. The JSON is provided in the terminal as output - always refer to it to get the actual content you're looking for.\n2. Load `.specify/templates/spec-template.md` to understand required sections.","chunkId":".github/prompts/specify.prompt.md:1"}],[180,{"filePath":".github/prompts/specify.prompt.md","startLine":14,"endLine":16,"chunkText":"1. Run the script `.specify/scripts/bash/create-new-feature.sh --json \"$ARGUMENTS\"` from repo root and parse its JSON output for BRANCH_NAME and SPEC_FILE. All file paths must be absolute.\n  **IMPORTANT** You must only ever run this script once. The JSON is provided in the terminal as output - always refer to it to get the actual content you're looking for.\n2. Load `.specify/templates/spec-template.md` to understand required sections.","chunkId":".github/prompts/specify.prompt.md:2"}],[181,{"filePath":".github/prompts/specify.prompt.md","startLine":15,"endLine":19,"chunkText":"  **IMPORTANT** You must only ever run this script once. The JSON is provided in the terminal as output - always refer to it to get the actual content you're looking for.\n2. Load `.specify/templates/spec-template.md` to understand required sections.\n3. Write the specification to SPEC_FILE using the template structure, replacing placeholders with concrete details derived from the feature description (arguments) while preserving section order and headings.\n4. Report completion with branch name, spec file path, and readiness for the next phase.\n","chunkId":".github/prompts/specify.prompt.md:3"}],[182,{"filePath":".github/prompts/specify.prompt.md","startLine":17,"endLine":21,"chunkText":"3. Write the specification to SPEC_FILE using the template structure, replacing placeholders with concrete details derived from the feature description (arguments) while preserving section order and headings.\n4. Report completion with branch name, spec file path, and readiness for the next phase.\n\nNote: The script creates and checks out the new branch and initializes the spec file before writing.\n","chunkId":".github/prompts/specify.prompt.md:4"}],[183,{"filePath":".github/prompts/tasks.prompt.md","startLine":0,"endLine":12,"chunkText":"---\ndescription: Generate an actionable, dependency-ordered tasks.md for the feature based on available design artifacts.\n---\n\nThe user input to you can be provided directly by the agent or as a command argument - you **MUST** consider it before proceeding with the prompt (if not empty).\n\nUser input:\n\n$ARGUMENTS\n\n1. Run `.specify/scripts/bash/check-prerequisites.sh --json` from repo root and parse FEATURE_DIR and AVAILABLE_DOCS list. All paths must be absolute.\n2. Load and analyze available design documents:\n   - Always read plan.md for tech stack and libraries","chunkId":".github/prompts/tasks.prompt.md:0"}],[184,{"filePath":".github/prompts/tasks.prompt.md","startLine":11,"endLine":24,"chunkText":"2. Load and analyze available design documents:\n   - Always read plan.md for tech stack and libraries\n   - IF EXISTS: Read data-model.md for entities\n   - IF EXISTS: Read contracts/ for API endpoints\n   - IF EXISTS: Read research.md for technical decisions\n   - IF EXISTS: Read quickstart.md for test scenarios\n\n   Note: Not all projects have all documents. For example:\n   - CLI tools might not have contracts/\n   - Simple libraries might not need data-model.md\n   - Generate tasks based on what's available\n\n3. Generate tasks following the template:\n   - Use `.specify/templates/tasks-template.md` as the base","chunkId":".github/prompts/tasks.prompt.md:1"}],[185,{"filePath":".github/prompts/tasks.prompt.md","startLine":22,"endLine":33,"chunkText":"\n3. Generate tasks following the template:\n   - Use `.specify/templates/tasks-template.md` as the base\n   - Replace example tasks with actual tasks based on:\n     * **Setup tasks**: Project init, dependencies, linting\n     * **Test tasks [P]**: One per contract, one per integration scenario\n     * **Core tasks**: One per entity, service, CLI command, endpoint\n     * **Integration tasks**: DB connections, middleware, logging\n     * **Polish tasks [P]**: Unit tests, performance, docs\n\n4. Task generation rules:\n   - Each contract file → contract test task marked [P]","chunkId":".github/prompts/tasks.prompt.md:2"}],[186,{"filePath":".github/prompts/tasks.prompt.md","startLine":31,"endLine":48,"chunkText":"\n4. Task generation rules:\n   - Each contract file → contract test task marked [P]\n   - Each entity in data-model → model creation task marked [P]\n   - Each endpoint → implementation task (not parallel if shared files)\n   - Each user story → integration test marked [P]\n   - Different files = can be parallel [P]\n   - Same file = sequential (no [P])\n\n5. Order tasks by dependencies:\n   - Setup before everything\n   - Tests before implementation (TDD)\n   - Models before services\n   - Services before endpoints\n   - Core before integration\n   - Everything before polish\n\n6. Include parallel execution examples:","chunkId":".github/prompts/tasks.prompt.md:3"}],[187,{"filePath":".github/prompts/tasks.prompt.md","startLine":46,"endLine":62,"chunkText":"   - Everything before polish\n\n6. Include parallel execution examples:\n   - Group [P] tasks that can run together\n   - Show actual Task agent commands\n\n7. Create FEATURE_DIR/tasks.md with:\n   - Correct feature name from implementation plan\n   - Numbered tasks (T001, T002, etc.)\n   - Clear file paths for each task\n   - Dependency notes\n   - Parallel execution guidance\n\nContext for task generation: $ARGUMENTS\n\nThe tasks.md should be immediately executable - each task must be specific enough that an LLM can complete it without additional context.\n","chunkId":".github/prompts/tasks.prompt.md:4"}],[188,{"filePath":".specify/memory/constitution.md","startLine":0,"endLine":18,"chunkText":"<!--\nSync Impact Report:\nVersion change: Initial → 1.0.0\nModified principles: All (new constitution)\nAdded sections: Core Principles, Performance Standards, Quality Assurance\nRemoved sections: None (template placeholders)\nTemplates requiring updates:\n- ✅ plan-template.md (Constitution Check section updated)\n- ✅ spec-template.md (aligned with principles)\n- ✅ tasks-template.md (TDD enforcement aligned)\n- ✅ agent-file-template.md (no changes needed)\nFollow-up TODOs: None\n-->\n\n# Audio Separator Constitution\n\n## Core Principles\n\n### I. Library-First Architecture","chunkId":".specify/memory/constitution.md:0"}],[189,{"filePath":".specify/memory/constitution.md","startLine":16,"endLine":23,"chunkText":"## Core Principles\n\n### I. Library-First Architecture\nThe `Separator` class MUST be the primary interface for all audio separation functionality. CLI and remote API are thin wrappers around the library. Libraries MUST be self-contained, independently testable, and documented with clear separation of concerns between architectures (MDX, VR, Demucs, MDXC).\n\n**Rationale**: This ensures the core functionality can be integrated into other projects while maintaining a consistent API across all interfaces.\n\n### II. Multi-Interface Consistency","chunkId":".specify/memory/constitution.md:1"}],[190,{"filePath":".specify/memory/constitution.md","startLine":21,"endLine":27,"chunkText":"**Rationale**: This ensures the core functionality can be integrated into other projects while maintaining a consistent API across all interfaces.\n\n### II. Multi-Interface Consistency\nEvery core feature MUST be accessible via three interfaces: Python API, CLI, and Remote API. Parameter names and behavior MUST be identical across all interfaces. All interfaces MUST support the same model architectures and processing options.\n\n**Rationale**: Users should have consistent experience regardless of how they access the functionality, enabling seamless transition between local and remote processing.\n","chunkId":".specify/memory/constitution.md:2"}],[191,{"filePath":".specify/memory/constitution.md","startLine":25,"endLine":30,"chunkText":"\n**Rationale**: Users should have consistent experience regardless of how they access the functionality, enabling seamless transition between local and remote processing.\n\n### III. Test-First Development (NON-NEGOTIABLE)\nTDD is mandatory: Tests written → Tests fail → Implementation → Tests pass. All new features MUST include unit tests, integration tests with audio validation (SSIM comparison), and CLI tests. No code merges without passing tests.\n","chunkId":".specify/memory/constitution.md:3"}],[192,{"filePath":".specify/memory/constitution.md","startLine":28,"endLine":33,"chunkText":"### III. Test-First Development (NON-NEGOTIABLE)\nTDD is mandatory: Tests written → Tests fail → Implementation → Tests pass. All new features MUST include unit tests, integration tests with audio validation (SSIM comparison), and CLI tests. No code merges without passing tests.\n\n**Rationale**: Audio processing requires precision and consistency. Automated testing with perceptual validation ensures output quality remains stable across changes.\n\n### IV. Performance & Resource Efficiency","chunkId":".specify/memory/constitution.md:4"}],[193,{"filePath":".specify/memory/constitution.md","startLine":31,"endLine":37,"chunkText":"**Rationale**: Audio processing requires precision and consistency. Automated testing with perceptual validation ensures output quality remains stable across changes.\n\n### IV. Performance & Resource Efficiency\nHardware acceleration MUST be supported (CUDA, CoreML, DirectML). Memory usage MUST be optimized for large audio files through streaming and batch processing. Processing parameters MUST be tunable for different hardware capabilities.\n\n**Rationale**: Audio separation is computationally intensive. Efficient resource usage enables processing of longer files and broader hardware compatibility.\n","chunkId":".specify/memory/constitution.md:5"}],[194,{"filePath":".specify/memory/constitution.md","startLine":35,"endLine":44,"chunkText":"\n**Rationale**: Audio separation is computationally intensive. Efficient resource usage enables processing of longer files and broader hardware compatibility.\n\n### V. Model Architecture Separation\nEach model architecture (MDX, VR, Demucs, MDXC) MUST be implemented in separate modules inheriting from `CommonSeparator`. Loading one architecture MUST NOT load code from others. Architecture-specific parameters MUST be isolated and documented.\n\n**Rationale**: This prevents conflicts between different model types and keeps memory usage minimal by loading only required components.\n\n## Performance Standards\n","chunkId":".specify/memory/constitution.md:6"}],[195,{"filePath":".specify/memory/constitution.md","startLine":42,"endLine":55,"chunkText":"\n## Performance Standards\n\nAll audio processing operations MUST meet these requirements:\n- **Memory efficiency**: Support files larger than available RAM through streaming\n- **GPU utilization**: Automatically detect and utilize available hardware acceleration\n- **Batch processing**: Support processing multiple files without model reloading\n- **Output consistency**: Identical inputs MUST produce identical outputs (deterministic)\n\n## Quality Assurance\n\n### Testing Requirements\n- **Unit tests**: All core classes and functions\n- **Integration tests**: End-to-end audio processing with SSIM validation","chunkId":".specify/memory/constitution.md:7"}],[196,{"filePath":".specify/memory/constitution.md","startLine":53,"endLine":66,"chunkText":"### Testing Requirements\n- **Unit tests**: All core classes and functions\n- **Integration tests**: End-to-end audio processing with SSIM validation\n- **Performance tests**: Memory usage and processing speed benchmarks\n- **Cross-platform tests**: Windows, macOS, Linux compatibility\n\n### Audio Validation\nOutput quality MUST be validated using:\n- Waveform and spectrogram image comparison (SSIM ≥ 0.95)\n- Reference audio files for each supported model architecture\n- Automated regression testing on model output changes\n\n## Governance\n","chunkId":".specify/memory/constitution.md:8"}],[197,{"filePath":".specify/memory/constitution.md","startLine":64,"endLine":73,"chunkText":"\n## Governance\n\nThis constitution supersedes all other development practices. All pull requests MUST verify compliance with these principles. Any deviation MUST be explicitly justified and documented.\n\n**Amendment Process**: Changes require documentation of impact, approval from maintainers, and migration plan for affected code.\n\n**Compliance Review**: All features undergo constitutional compliance check during planning phase and post-implementation validation.\n\n**Version**: 1.0.0 | **Ratified**: 2025-09-25 | **Last Amended**: 2025-09-25","chunkId":".specify/memory/constitution.md:9"}],[198,{"filePath":".specify/templates/agent-file-template.md","startLine":0,"endLine":22,"chunkText":"# [PROJECT NAME] Development Guidelines\n\nAuto-generated from all feature plans. Last updated: [DATE]\n\n## Active Technologies\n[EXTRACTED FROM ALL PLAN.MD FILES]\n\n## Project Structure\n```\n[ACTUAL STRUCTURE FROM PLANS]\n```\n\n## Commands\n[ONLY COMMANDS FOR ACTIVE TECHNOLOGIES]\n\n## Code Style\n[LANGUAGE-SPECIFIC, ONLY FOR LANGUAGES IN USE]\n\n## Recent Changes\n[LAST 3 FEATURES AND WHAT THEY ADDED]\n\n<!-- MANUAL ADDITIONS START -->\n<!-- MANUAL ADDITIONS END -->","chunkId":".specify/templates/agent-file-template.md:0"}],[199,{"filePath":".specify/templates/plan-template.md","startLine":0,"endLine":13,"chunkText":"\n# Implementation Plan: [FEATURE]\n\n**Branch**: `[###-feature-name]` | **Date**: [DATE] | **Spec**: [link]\n**Input**: Feature specification from `/specs/[###-feature-name]/spec.md`\n\n## Execution Flow (/plan command scope)\n```\n1. Load feature spec from Input path\n   → If not found: ERROR \"No feature spec at {path}\"\n2. Fill Technical Context (scan for NEEDS CLARIFICATION)\n   → Detect Project Type from context (web=frontend+backend, mobile=app+api)\n   → Set Structure Decision based on project type\n3. Fill the Constitution Check section based on the content of the constitution document.","chunkId":".specify/templates/plan-template.md:0"}],[200,{"filePath":".specify/templates/plan-template.md","startLine":11,"endLine":19,"chunkText":"   → Detect Project Type from context (web=frontend+backend, mobile=app+api)\n   → Set Structure Decision based on project type\n3. Fill the Constitution Check section based on the content of the constitution document.\n4. Evaluate Constitution Check section below\n   → If violations exist: Document in Complexity Tracking\n   → If no justification possible: ERROR \"Simplify approach first\"\n   → Update Progress Tracking: Initial Constitution Check\n5. Execute Phase 0 → research.md\n   → If NEEDS CLARIFICATION remain: ERROR \"Resolve unknowns\"","chunkId":".specify/templates/plan-template.md:1"}],[201,{"filePath":".specify/templates/plan-template.md","startLine":17,"endLine":23,"chunkText":"   → Update Progress Tracking: Initial Constitution Check\n5. Execute Phase 0 → research.md\n   → If NEEDS CLARIFICATION remain: ERROR \"Resolve unknowns\"\n6. Execute Phase 1 → contracts, data-model.md, quickstart.md, agent-specific template file (e.g., `CLAUDE.md` for Claude Code, `.github/copilot-instructions.md` for GitHub Copilot, `GEMINI.md` for Gemini CLI, `QWEN.md` for Qwen Code or `AGENTS.md` for opencode).\n7. Re-evaluate Constitution Check section\n   → If new violations: Refactor design, return to Phase 1\n   → Update Progress Tracking: Post-Design Constitution Check","chunkId":".specify/templates/plan-template.md:2"}],[202,{"filePath":".specify/templates/plan-template.md","startLine":21,"endLine":35,"chunkText":"7. Re-evaluate Constitution Check section\n   → If new violations: Refactor design, return to Phase 1\n   → Update Progress Tracking: Post-Design Constitution Check\n8. Plan Phase 2 → Describe task generation approach (DO NOT create tasks.md)\n9. STOP - Ready for /tasks command\n```\n\n**IMPORTANT**: The /plan command STOPS at step 7. Phases 2-4 are executed by other commands:\n- Phase 2: /tasks command creates tasks.md\n- Phase 3-4: Implementation execution (manual or via tools)\n\n## Summary\n[Extract from feature spec: primary requirement + technical approach from research]\n\n## Technical Context","chunkId":".specify/templates/plan-template.md:3"}],[203,{"filePath":".specify/templates/plan-template.md","startLine":33,"endLine":41,"chunkText":"[Extract from feature spec: primary requirement + technical approach from research]\n\n## Technical Context\n**Language/Version**: [e.g., Python 3.11, Swift 5.9, Rust 1.75 or NEEDS CLARIFICATION]  \n**Primary Dependencies**: [e.g., FastAPI, UIKit, LLVM or NEEDS CLARIFICATION]  \n**Storage**: [if applicable, e.g., PostgreSQL, CoreData, files or N/A]  \n**Testing**: [e.g., pytest, XCTest, cargo test or NEEDS CLARIFICATION]  \n**Target Platform**: [e.g., Linux server, iOS 15+, WASM or NEEDS CLARIFICATION]\n**Project Type**: [single/web/mobile - determines source structure]  ","chunkId":".specify/templates/plan-template.md:4"}],[204,{"filePath":".specify/templates/plan-template.md","startLine":39,"endLine":46,"chunkText":"**Testing**: [e.g., pytest, XCTest, cargo test or NEEDS CLARIFICATION]  \n**Target Platform**: [e.g., Linux server, iOS 15+, WASM or NEEDS CLARIFICATION]\n**Project Type**: [single/web/mobile - determines source structure]  \n**Performance Goals**: [domain-specific, e.g., 1000 req/s, 10k lines/sec, 60 fps or NEEDS CLARIFICATION]  \n**Constraints**: [domain-specific, e.g., <200ms p95, <100MB memory, offline-capable or NEEDS CLARIFICATION]  \n**Scale/Scope**: [domain-specific, e.g., 10k users, 1M LOC, 50 screens or NEEDS CLARIFICATION]\n\n## Constitution Check","chunkId":".specify/templates/plan-template.md:5"}],[205,{"filePath":".specify/templates/plan-template.md","startLine":44,"endLine":55,"chunkText":"**Scale/Scope**: [domain-specific, e.g., 10k users, 1M LOC, 50 screens or NEEDS CLARIFICATION]\n\n## Constitution Check\n*GATE: Must pass before Phase 0 research. Re-check after Phase 1 design.*\n\n### I. Library-First Architecture\n- [ ] Core functionality implemented in `Separator` class or similar library pattern\n- [ ] CLI/Remote API are thin wrappers, not containing business logic\n- [ ] Clear separation between model architectures (MDX, VR, Demucs, MDXC)\n\n### II. Multi-Interface Consistency  \n- [ ] Feature accessible via Python API, CLI, and Remote API (if applicable)","chunkId":".specify/templates/plan-template.md:6"}],[206,{"filePath":".specify/templates/plan-template.md","startLine":53,"endLine":66,"chunkText":"\n### II. Multi-Interface Consistency  \n- [ ] Feature accessible via Python API, CLI, and Remote API (if applicable)\n- [ ] Parameter names identical across all interfaces\n- [ ] Same model architectures supported across interfaces\n\n### III. Test-First Development (NON-NEGOTIABLE)\n- [ ] Tests written before implementation\n- [ ] Unit tests for all core functionality\n- [ ] Integration tests with audio validation (SSIM comparison)\n- [ ] CLI tests for all exposed functionality\n\n### IV. Performance & Resource Efficiency\n- [ ] Hardware acceleration support considered (CUDA, CoreML, DirectML)","chunkId":".specify/templates/plan-template.md:7"}],[207,{"filePath":".specify/templates/plan-template.md","startLine":64,"endLine":80,"chunkText":"### IV. Performance & Resource Efficiency\n- [ ] Hardware acceleration support considered (CUDA, CoreML, DirectML)\n- [ ] Memory optimization for large files (streaming/batch processing)\n- [ ] Tunable parameters for different hardware capabilities\n\n### V. Model Architecture Separation\n- [ ] Each architecture in separate modules\n- [ ] Inherits from `CommonSeparator` pattern\n- [ ] Architecture-specific parameters isolated\n- [ ] Loading one architecture doesn't load others\n\n## Project Structure\n\n### Documentation (this feature)\n```\nspecs/[###-feature]/\n├── plan.md              # This file (/plan command output","chunkId":".specify/templates/plan-template.md:8"}],[208,{"filePath":".specify/templates/plan-template.md","startLine":79,"endLine":102,"chunkText":"```\nspecs/[###-feature]/\n├── plan.md              # This file (/plan command output)\n├── research.md          # Phase 0 output (/plan command)\n├── data-model.md        # Phase 1 output (/plan command)\n├── quickstart.md        # Phase 1 output (/plan command)\n├── contracts/           # Phase 1 output (/plan command)\n└── tasks.md             # Phase 2 output (/tasks command - NOT created by /plan)\n```\n\n### Source Code (repository root)\n```\n# Option 1: Single project (DEFAULT)\nsrc/\n├── models/\n├── services/\n├── cli/\n└── lib/\n\ntests/\n├── contract/\n├── integration/\n└── unit/\n","chunkId":".specify/templates/plan-template.md:9"}],[209,{"filePath":".specify/templates/plan-template.md","startLine":100,"endLine":129,"chunkText":"├── integration/\n└── unit/\n\n# Option 2: Web application (when \"frontend\" + \"backend\" detected)\nbackend/\n├── src/\n│   ├── models/\n│   ├── services/\n│   └── api/\n└── tests/\n\nfrontend/\n├── src/\n│   ├── components/\n│   ├── pages/\n│   └── services/\n└── tests/\n\n# Option 3: Mobile + API (when \"iOS/Android\" detected)\napi/\n└── [same as backend above]\n\nios/ or android/\n└── [platform-specific structure]\n```\n\n**Structure Decision**: [DEFAULT to Option 1 unless Technical Context indicates web/mobile app]\n\n## Phase 0: Outline & Research\n1. **Extract unknowns from Technical Context** above:","chunkId":".specify/templates/plan-template.md:10"}],[210,{"filePath":".specify/templates/plan-template.md","startLine":127,"endLine":144,"chunkText":"\n## Phase 0: Outline & Research\n1. **Extract unknowns from Technical Context** above:\n   - For each NEEDS CLARIFICATION → research task\n   - For each dependency → best practices task\n   - For each integration → patterns task\n\n2. **Generate and dispatch research agents**:\n   ```\n   For each unknown in Technical Context:\n     Task: \"Research {unknown} for {feature context}\"\n   For each technology choice:\n     Task: \"Find best practices for {tech} in {domain}\"\n   ```\n\n3. **Consolidate findings** in `research.md` using format:\n   - Decision: [what was chosen]\n   - Rationale: [why chosen]","chunkId":".specify/templates/plan-template.md:11"}],[211,{"filePath":".specify/templates/plan-template.md","startLine":142,"endLine":158,"chunkText":"3. **Consolidate findings** in `research.md` using format:\n   - Decision: [what was chosen]\n   - Rationale: [why chosen]\n   - Alternatives considered: [what else evaluated]\n\n**Output**: research.md with all NEEDS CLARIFICATION resolved\n\n## Phase 1: Design & Contracts\n*Prerequisites: research.md complete*\n\n1. **Extract entities from feature spec** → `data-model.md`:\n   - Entity name, fields, relationships\n   - Validation rules from requirements\n   - State transitions if applicable\n\n2. **Generate API contracts** from functional requirements:\n   - For each user action → endpoint","chunkId":".specify/templates/plan-template.md:12"}],[212,{"filePath":".specify/templates/plan-template.md","startLine":157,"endLine":172,"chunkText":"2. **Generate API contracts** from functional requirements:\n   - For each user action → endpoint\n   - Use standard REST/GraphQL patterns\n   - Output OpenAPI/GraphQL schema to `/contracts/`\n\n3. **Generate contract tests** from contracts:\n   - One test file per endpoint\n   - Assert request/response schemas\n   - Tests must fail (no implementation yet)\n\n4. **Extract test scenarios** from user stories:\n   - Each story → integration test scenario\n   - Quickstart test = story validation steps\n\n5. **Update agent file incrementally** (O(1) operation):\n   - Run `.specify/scripts/bash/update-agent-context.sh cursor`","chunkId":".specify/templates/plan-template.md:13"}],[213,{"filePath":".specify/templates/plan-template.md","startLine":170,"endLine":182,"chunkText":"\n5. **Update agent file incrementally** (O(1) operation):\n   - Run `.specify/scripts/bash/update-agent-context.sh cursor`\n     **IMPORTANT**: Execute it exactly as specified above. Do not add or remove any arguments.\n   - If exists: Add only NEW tech from current plan\n   - Preserve manual additions between markers\n   - Update recent changes (keep last 3)\n   - Keep under 150 lines for token efficiency\n   - Output to repository root\n\n**Output**: data-model.md, /contracts/*, failing tests, quickstart.md, agent-specific file\n\n## Phase 2: Task Planning Approach","chunkId":".specify/templates/plan-template.md:14"}],[214,{"filePath":".specify/templates/plan-template.md","startLine":180,"endLine":194,"chunkText":"**Output**: data-model.md, /contracts/*, failing tests, quickstart.md, agent-specific file\n\n## Phase 2: Task Planning Approach\n*This section describes what the /tasks command will do - DO NOT execute during /plan*\n\n**Task Generation Strategy**:\n- Load `.specify/templates/tasks-template.md` as base\n- Generate tasks from Phase 1 design docs (contracts, data model, quickstart)\n- Each contract → contract test task [P]\n- Each entity → model creation task [P] \n- Each user story → integration test task\n- Implementation tasks to make tests pass\n\n**Ordering Strategy**:\n- TDD order: Tests before implementation ","chunkId":".specify/templates/plan-template.md:15"}],[215,{"filePath":".specify/templates/plan-template.md","startLine":192,"endLine":206,"chunkText":"\n**Ordering Strategy**:\n- TDD order: Tests before implementation \n- Dependency order: Models before services before UI\n- Mark [P] for parallel execution (independent files)\n\n**Estimated Output**: 25-30 numbered, ordered tasks in tasks.md\n\n**IMPORTANT**: This phase is executed by the /tasks command, NOT by /plan\n\n## Phase 3+: Future Implementation\n*These phases are beyond the scope of the /plan command*\n\n**Phase 3**: Task execution (/tasks command creates tasks.md)  \n**Phase 4**: Implementation (execute tasks.md following constitutional principles)  ","chunkId":".specify/templates/plan-template.md:16"}],[216,{"filePath":".specify/templates/plan-template.md","startLine":204,"endLine":214,"chunkText":"\n**Phase 3**: Task execution (/tasks command creates tasks.md)  \n**Phase 4**: Implementation (execute tasks.md following constitutional principles)  \n**Phase 5**: Validation (run tests, execute quickstart.md, performance validation)\n\n## Complexity Tracking\n*Fill ONLY if Constitution Check has violations that must be justified*\n\n| Violation | Why Needed | Simpler Alternative Rejected Because |\n|-----------|------------|-------------------------------------|\n| [e.g., 4th project] | [current need] | [why 3 projects insufficient] |","chunkId":".specify/templates/plan-template.md:17"}],[217,{"filePath":".specify/templates/plan-template.md","startLine":212,"endLine":225,"chunkText":"| Violation | Why Needed | Simpler Alternative Rejected Because |\n|-----------|------------|-------------------------------------|\n| [e.g., 4th project] | [current need] | [why 3 projects insufficient] |\n| [e.g., Repository pattern] | [specific problem] | [why direct DB access insufficient] |\n\n\n## Progress Tracking\n*This checklist is updated during execution flow*\n\n**Phase Status**:\n- [ ] Phase 0: Research complete (/plan command)\n- [ ] Phase 1: Design complete (/plan command)\n- [ ] Phase 2: Task planning complete (/plan command - describe approach only)\n- [ ] Phase 3: Tasks generated (/tasks command)","chunkId":".specify/templates/plan-template.md:18"}],[218,{"filePath":".specify/templates/plan-template.md","startLine":223,"endLine":237,"chunkText":"- [ ] Phase 1: Design complete (/plan command)\n- [ ] Phase 2: Task planning complete (/plan command - describe approach only)\n- [ ] Phase 3: Tasks generated (/tasks command)\n- [ ] Phase 4: Implementation complete\n- [ ] Phase 5: Validation passed\n\n**Gate Status**:\n- [ ] Initial Constitution Check: PASS\n- [ ] Post-Design Constitution Check: PASS\n- [ ] All NEEDS CLARIFICATION resolved\n- [ ] Complexity deviations documented\n\n---\n*Based on Constitution v1.0.0 - See `.specify/memory/constitution.md`*\n","chunkId":".specify/templates/plan-template.md:19"}],[219,{"filePath":".specify/templates/spec-template.md","startLine":0,"endLine":17,"chunkText":"# Feature Specification: [FEATURE NAME]\n\n**Feature Branch**: `[###-feature-name]`  \n**Created**: [DATE]  \n**Status**: Draft  \n**Input**: User description: \"$ARGUMENTS\"\n\n## Execution Flow (main)\n```\n1. Parse user description from Input\n   → If empty: ERROR \"No feature description provided\"\n2. Extract key concepts from description\n   → Identify: actors, actions, data, constraints\n3. For each unclear aspect:\n   → Mark with [NEEDS CLARIFICATION: specific question]\n4. Fill User Scenarios & Testing section\n   → If no clear user flow: ERROR \"Cannot determine user scenarios\"\n5. Generate Functional Requirements","chunkId":".specify/templates/spec-template.md:0"}],[220,{"filePath":".specify/templates/spec-template.md","startLine":15,"endLine":31,"chunkText":"4. Fill User Scenarios & Testing section\n   → If no clear user flow: ERROR \"Cannot determine user scenarios\"\n5. Generate Functional Requirements\n   → Each requirement must be testable\n   → Mark ambiguous requirements\n6. Identify Key Entities (if data involved)\n7. Run Review Checklist\n   → If any [NEEDS CLARIFICATION]: WARN \"Spec has uncertainties\"\n   → If implementation details found: ERROR \"Remove tech details\"\n8. Return: SUCCESS (spec ready for planning)\n```\n\n---\n\n## ⚡ Quick Guidelines\n- ✅ Focus on WHAT users need and WHY\n- ❌ Avoid HOW to implement (no tech stack, APIs, code structure)","chunkId":".specify/templates/spec-template.md:1"}],[221,{"filePath":".specify/templates/spec-template.md","startLine":29,"endLine":41,"chunkText":"## ⚡ Quick Guidelines\n- ✅ Focus on WHAT users need and WHY\n- ❌ Avoid HOW to implement (no tech stack, APIs, code structure)\n- 👥 Written for business stakeholders, not developers\n\n### Section Requirements\n- **Mandatory sections**: Must be completed for every feature\n- **Optional sections**: Include only when relevant to the feature\n- When a section doesn't apply, remove it entirely (don't leave as \"N/A\")\n\n### For AI Generation\nWhen creating this spec from a user prompt:\n1. **Mark all ambiguities**: Use [NEEDS CLARIFICATION: specific question] for any assumption you'd need to make","chunkId":".specify/templates/spec-template.md:2"}],[222,{"filePath":".specify/templates/spec-template.md","startLine":39,"endLine":49,"chunkText":"### For AI Generation\nWhen creating this spec from a user prompt:\n1. **Mark all ambiguities**: Use [NEEDS CLARIFICATION: specific question] for any assumption you'd need to make\n2. **Don't guess**: If the prompt doesn't specify something (e.g., \"login system\" without auth method), mark it\n3. **Think like a tester**: Every vague requirement should fail the \"testable and unambiguous\" checklist item\n4. **Common underspecified areas**:\n   - User types and permissions\n   - Data retention/deletion policies  \n   - Performance targets and scale\n   - Error handling behaviors\n   - Integration requirements","chunkId":".specify/templates/spec-template.md:3"}],[223,{"filePath":".specify/templates/spec-template.md","startLine":47,"endLine":69,"chunkText":"   - Performance targets and scale\n   - Error handling behaviors\n   - Integration requirements\n   - Security/compliance needs\n\n---\n\n## User Scenarios & Testing *(mandatory)*\n\n### Primary User Story\n[Describe the main user journey in plain language]\n\n### Acceptance Scenarios\n1. **Given** [initial state], **When** [action], **Then** [expected outcome]\n2. **Given** [initial state], **When** [action], **Then** [expected outcome]\n\n### Edge Cases\n- What happens when [boundary condition]?\n- How does system handle [error scenario]?\n\n## Requirements *(mandatory)*\n\n### Functional Requirements","chunkId":".specify/templates/spec-template.md:4"}],[224,{"filePath":".specify/templates/spec-template.md","startLine":67,"endLine":76,"chunkText":"## Requirements *(mandatory)*\n\n### Functional Requirements\n- **FR-001**: System MUST [specific capability, e.g., \"allow users to create accounts\"]\n- **FR-002**: System MUST [specific capability, e.g., \"validate email addresses\"]  \n- **FR-003**: Users MUST be able to [key interaction, e.g., \"reset their password\"]\n- **FR-004**: System MUST [data requirement, e.g., \"persist user preferences\"]\n- **FR-005**: System MUST [behavior, e.g., \"log all security events\"]\n\n*Example of marking unclear requirements:*","chunkId":".specify/templates/spec-template.md:5"}],[225,{"filePath":".specify/templates/spec-template.md","startLine":74,"endLine":86,"chunkText":"- **FR-005**: System MUST [behavior, e.g., \"log all security events\"]\n\n*Example of marking unclear requirements:*\n- **FR-006**: System MUST authenticate users via [NEEDS CLARIFICATION: auth method not specified - email/password, SSO, OAuth?]\n- **FR-007**: System MUST retain user data for [NEEDS CLARIFICATION: retention period not specified]\n\n### Key Entities *(include if feature involves data)*\n- **[Entity 1]**: [What it represents, key attributes without implementation]\n- **[Entity 2]**: [What it represents, relationships to other entities]\n\n---\n\n## Review & Acceptance Checklist","chunkId":".specify/templates/spec-template.md:6"}],[226,{"filePath":".specify/templates/spec-template.md","startLine":84,"endLine":106,"chunkText":"---\n\n## Review & Acceptance Checklist\n*GATE: Automated checks run during main() execution*\n\n### Content Quality\n- [ ] No implementation details (languages, frameworks, APIs)\n- [ ] Focused on user value and business needs\n- [ ] Written for non-technical stakeholders\n- [ ] All mandatory sections completed\n\n### Requirement Completeness\n- [ ] No [NEEDS CLARIFICATION] markers remain\n- [ ] Requirements are testable and unambiguous  \n- [ ] Success criteria are measurable\n- [ ] Scope is clearly bounded\n- [ ] Dependencies and assumptions identified\n\n---\n\n## Execution Status\n*Updated by main() during processing*\n","chunkId":".specify/templates/spec-template.md:7"}],[227,{"filePath":".specify/templates/spec-template.md","startLine":104,"endLine":116,"chunkText":"## Execution Status\n*Updated by main() during processing*\n\n- [ ] User description parsed\n- [ ] Key concepts extracted\n- [ ] Ambiguities marked\n- [ ] User scenarios defined\n- [ ] Requirements generated\n- [ ] Entities identified\n- [ ] Review checklist passed\n\n---\n","chunkId":".specify/templates/spec-template.md:8"}],[228,{"filePath":".specify/templates/tasks-template.md","startLine":0,"endLine":15,"chunkText":"# Tasks: [FEATURE NAME]\n\n**Input**: Design documents from `/specs/[###-feature-name]/`\n**Prerequisites**: plan.md (required), research.md, data-model.md, contracts/\n\n## Execution Flow (main)\n```\n1. Load plan.md from feature directory\n   → If not found: ERROR \"No implementation plan found\"\n   → Extract: tech stack, libraries, structure\n2. Load optional design documents:\n   → data-model.md: Extract entities → model tasks\n   → contracts/: Each file → contract test task\n   → research.md: Extract decisions → setup tasks\n3. Generate tasks by category:\n   → Setup: project init, dependencies, linting","chunkId":".specify/templates/tasks-template.md:0"}],[229,{"filePath":".specify/templates/tasks-template.md","startLine":13,"endLine":27,"chunkText":"   → research.md: Extract decisions → setup tasks\n3. Generate tasks by category:\n   → Setup: project init, dependencies, linting\n   → Tests: contract tests, integration tests\n   → Core: models, services, CLI commands\n   → Integration: DB, middleware, logging\n   → Polish: unit tests, performance, docs\n4. Apply task rules:\n   → Different files = mark [P] for parallel\n   → Same file = sequential (no [P])\n   → Tests before implementation (TDD)\n5. Number tasks sequentially (T001, T002...)\n6. Generate dependency graph\n7. Create parallel execution examples\n8. Validate task completeness:","chunkId":".specify/templates/tasks-template.md:1"}],[230,{"filePath":".specify/templates/tasks-template.md","startLine":25,"endLine":41,"chunkText":"6. Generate dependency graph\n7. Create parallel execution examples\n8. Validate task completeness:\n   → All contracts have tests?\n   → All entities have models?\n   → All endpoints implemented?\n9. Return: SUCCESS (tasks ready for execution)\n```\n\n## Format: `[ID] [P?] Description`\n- **[P]**: Can run in parallel (different files, no dependencies)\n- Include exact file paths in descriptions\n\n## Path Conventions\n- **Single project**: `src/`, `tests/` at repository root\n- **Web app**: `backend/src/`, `frontend/src/`\n- **Mobile**: `api/src/`, `ios/src/` or `android/src/`","chunkId":".specify/templates/tasks-template.md:2"}],[231,{"filePath":".specify/templates/tasks-template.md","startLine":39,"endLine":50,"chunkText":"- **Single project**: `src/`, `tests/` at repository root\n- **Web app**: `backend/src/`, `frontend/src/`\n- **Mobile**: `api/src/`, `ios/src/` or `android/src/`\n- Paths shown below assume single project - adjust based on plan.md structure\n\n## Phase 3.1: Setup\n- [ ] T001 Create project structure per implementation plan\n- [ ] T002 Initialize [language] project with [framework] dependencies\n- [ ] T003 [P] Configure linting and formatting tools\n\n## Phase 3.2: Tests First (TDD) ⚠️ MUST COMPLETE BEFORE 3.3\n**CRITICAL: These tests MUST be written and MUST FAIL before ANY implementation**","chunkId":".specify/templates/tasks-template.md:3"}],[232,{"filePath":".specify/templates/tasks-template.md","startLine":48,"endLine":57,"chunkText":"\n## Phase 3.2: Tests First (TDD) ⚠️ MUST COMPLETE BEFORE 3.3\n**CRITICAL: These tests MUST be written and MUST FAIL before ANY implementation**\n- [ ] T004 [P] Contract test POST /api/users in tests/contract/test_users_post.py\n- [ ] T005 [P] Contract test GET /api/users/{id} in tests/contract/test_users_get.py\n- [ ] T006 [P] Integration test user registration in tests/integration/test_registration.py\n- [ ] T007 [P] Integration test auth flow in tests/integration/test_auth.py\n\n## Phase 3.3: Core Implementation (ONLY after tests are failing)\n- [ ] T008 [P] User model in src/models/user.py","chunkId":".specify/templates/tasks-template.md:4"}],[233,{"filePath":".specify/templates/tasks-template.md","startLine":55,"endLine":71,"chunkText":"\n## Phase 3.3: Core Implementation (ONLY after tests are failing)\n- [ ] T008 [P] User model in src/models/user.py\n- [ ] T009 [P] UserService CRUD in src/services/user_service.py\n- [ ] T010 [P] CLI --create-user in src/cli/user_commands.py\n- [ ] T011 POST /api/users endpoint\n- [ ] T012 GET /api/users/{id} endpoint\n- [ ] T013 Input validation\n- [ ] T014 Error handling and logging\n\n## Phase 3.4: Integration\n- [ ] T015 Connect UserService to DB\n- [ ] T016 Auth middleware\n- [ ] T017 Request/response logging\n- [ ] T018 CORS and security headers\n\n## Phase 3.5: Polish","chunkId":".specify/templates/tasks-template.md:5"}],[234,{"filePath":".specify/templates/tasks-template.md","startLine":69,"endLine":87,"chunkText":"- [ ] T018 CORS and security headers\n\n## Phase 3.5: Polish\n- [ ] T019 [P] Unit tests for validation in tests/unit/test_validation.py\n- [ ] T020 Performance tests (<200ms)\n- [ ] T021 [P] Update docs/api.md\n- [ ] T022 Remove duplication\n- [ ] T023 Run manual-testing.md\n\n## Dependencies\n- Tests (T004-T007) before implementation (T008-T014)\n- T008 blocks T009, T015\n- T016 blocks T018\n- Implementation before polish (T019-T023)\n\n## Parallel Example\n```\n# Launch T004-T007 together:\nTask: \"Contract test POST /api/users in tests/contract/test_users_post.py\"","chunkId":".specify/templates/tasks-template.md:6"}],[235,{"filePath":".specify/templates/tasks-template.md","startLine":85,"endLine":102,"chunkText":"```\n# Launch T004-T007 together:\nTask: \"Contract test POST /api/users in tests/contract/test_users_post.py\"\nTask: \"Contract test GET /api/users/{id} in tests/contract/test_users_get.py\"\nTask: \"Integration test registration in tests/integration/test_registration.py\"\nTask: \"Integration test auth in tests/integration/test_auth.py\"\n```\n\n## Notes\n- [P] tasks = different files, no dependencies\n- Verify tests fail before implementing\n- Commit after each task\n- Avoid: vague tasks, same file conflicts\n\n## Task Generation Rules\n*Applied during main() execution*\n\n1. **From Contracts**:","chunkId":".specify/templates/tasks-template.md:7"}],[236,{"filePath":".specify/templates/tasks-template.md","startLine":100,"endLine":121,"chunkText":"*Applied during main() execution*\n\n1. **From Contracts**:\n   - Each contract file → contract test task [P]\n   - Each endpoint → implementation task\n   \n2. **From Data Model**:\n   - Each entity → model creation task [P]\n   - Relationships → service layer tasks\n   \n3. **From User Stories**:\n   - Each story → integration test [P]\n   - Quickstart scenarios → validation tasks\n\n4. **Ordering**:\n   - Setup → Tests → Models → Services → Endpoints → Polish\n   - Dependencies block parallel execution\n\n## Validation Checklist\n*GATE: Checked by main() before returning*\n\n- [ ] All contracts have corresponding tests","chunkId":".specify/templates/tasks-template.md:8"}],[237,{"filePath":".specify/templates/tasks-template.md","startLine":119,"endLine":126,"chunkText":"*GATE: Checked by main() before returning*\n\n- [ ] All contracts have corresponding tests\n- [ ] All entities have model tasks\n- [ ] All tests come before implementation\n- [ ] Parallel tasks truly independent\n- [ ] Each task specifies exact file path\n- [ ] No task modifies same file as another [P] task","chunkId":".specify/templates/tasks-template.md:9"}],[238,{"filePath":"README.md","startLine":0,"endLine":7,"chunkText":"<div align=\"center\">\n \n# 🎶 Audio Separator 🎶\n\n[![PyPI version](https://badge.fury.io/py/audio-separator.svg)](https://badge.fury.io/py/audio-separator)\n[![Conda Version](https://img.shields.io/conda/vn/conda-forge/audio-separator.svg)](https://anaconda.org/conda-forge/audio-separator)\n[![Docker pulls](https://img.shields.io/docker/pulls/beveradb/audio-separator.svg)](https://hub.docker.com/r/beveradb/audio-separator/tags)\n[![codecov](https://codecov.io/gh/karaokenerds/python-audio-separator/graph/badge.svg?token=N7YK4ET5JP)](https://codecov.io/gh/karaokenerds/python-audio-separator)","chunkId":"README.md:0"}],[239,{"filePath":"README.md","startLine":5,"endLine":8,"chunkText":"[![Conda Version](https://img.shields.io/conda/vn/conda-forge/audio-separator.svg)](https://anaconda.org/conda-forge/audio-separator)\n[![Docker pulls](https://img.shields.io/docker/pulls/beveradb/audio-separator.svg)](https://hub.docker.com/r/beveradb/audio-separator/tags)\n[![codecov](https://codecov.io/gh/karaokenerds/python-audio-separator/graph/badge.svg?token=N7YK4ET5JP)](https://codecov.io/gh/karaokenerds/python-audio-separator)\n[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1gSlmSmna7f7fH6OjsiMEDLl-aJ9kGPkY?usp=sharing)","chunkId":"README.md:1"}],[240,{"filePath":"README.md","startLine":6,"endLine":8,"chunkText":"[![Docker pulls](https://img.shields.io/docker/pulls/beveradb/audio-separator.svg)](https://hub.docker.com/r/beveradb/audio-separator/tags)\n[![codecov](https://codecov.io/gh/karaokenerds/python-audio-separator/graph/badge.svg?token=N7YK4ET5JP)](https://codecov.io/gh/karaokenerds/python-audio-separator)\n[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1gSlmSmna7f7fH6OjsiMEDLl-aJ9kGPkY?usp=sharing)","chunkId":"README.md:2"}],[241,{"filePath":"README.md","startLine":7,"endLine":12,"chunkText":"[![codecov](https://codecov.io/gh/karaokenerds/python-audio-separator/graph/badge.svg?token=N7YK4ET5JP)](https://codecov.io/gh/karaokenerds/python-audio-separator)\n[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1gSlmSmna7f7fH6OjsiMEDLl-aJ9kGPkY?usp=sharing)\n[![Open In Huggingface](https://huggingface.co/datasets/huggingface/badges/resolve/main/open-in-hf-spaces-sm.svg)](https://huggingface.co/spaces/nomadkaraoke/audio-separator)\n\n</div>\n","chunkId":"README.md:3"}],[242,{"filePath":"README.md","startLine":10,"endLine":16,"chunkText":"\n</div>\n\n**Summary:** Easy to use audio stem separation from the command line or as a dependency in your own Python project, using the amazing MDX-Net, VR Arch, Demucs and MDXC models available in UVR by @Anjok07 & @aufr33.\n\nAudio Separator is a Python package that allows you to separate an audio file into various stems, using models trained by @Anjok07 for use with [Ultimate Vocal Remover](https://github.com/Anjok07/ultimatevocalremovergui).\n","chunkId":"README.md:4"}],[243,{"filePath":"README.md","startLine":14,"endLine":15,"chunkText":"Audio Separator is a Python package that allows you to separate an audio file into various stems, using models trained by @Anjok07 for use with [Ultimate Vocal Remover](https://github.com/Anjok07/ultimatevocalremovergui).\n","chunkId":"README.md:5"}],[244,{"filePath":"README.md","startLine":15,"endLine":16,"chunkText":"Audio Separator is a Python package that allows you to separate an audio file into various stems, using models trained by @Anjok07 for use with [Ultimate Vocal Remover](https://github.com/Anjok07/ultimatevocalremovergui).\n","chunkId":"README.md:6"}],[245,{"filePath":"README.md","startLine":16,"endLine":24,"chunkText":"\nThe simplest (and probably most used) use case for this package is to separate an audio file into two stems, Instrumental and Vocals, which can be very useful for producing karaoke videos! However, the models available in UVR can separate audio into many more stems, such as Drums, Bass, Piano, and Guitar, and perform other audio processing tasks, such as denoising or removing echo/reverb.\n\n<details>\n<summary align=\"center\"><b>Table of Contents</div></b></summary>\n\n- [🎶 Audio Separator 🎶](#-audio-separator-)\n  - [Features](#features)\n  - [Installation 🛠️](#installation-%EF%B8%8F)","chunkId":"README.md:7"}],[246,{"filePath":"README.md","startLine":22,"endLine":29,"chunkText":"- [🎶 Audio Separator 🎶](#-audio-separator-)\n  - [Features](#features)\n  - [Installation 🛠️](#installation-%EF%B8%8F)\n    - [🐳 Docker](#-docker)\n    - [🎮 Nvidia GPU with CUDA or 🧪 Google Colab](#-nvidia-gpu-with-cuda-or--google-colab)\n    - [ Apple Silicon, macOS Sonoma+ with M1 or newer CPU (CoreML acceleration)](#-apple-silicon-macos-sonoma-with-m1-or-newer-cpu-coreml-acceleration)\n    - [🐢 No hardware acceleration, CPU only](#-no-hardware-acceleration-cpu-only)\n    - [🎥 FFmpeg dependency](#-ffmpeg-dependency)","chunkId":"README.md:8"}],[247,{"filePath":"README.md","startLine":27,"endLine":33,"chunkText":"    - [ Apple Silicon, macOS Sonoma+ with M1 or newer CPU (CoreML acceleration)](#-apple-silicon-macos-sonoma-with-m1-or-newer-cpu-coreml-acceleration)\n    - [🐢 No hardware acceleration, CPU only](#-no-hardware-acceleration-cpu-only)\n    - [🎥 FFmpeg dependency](#-ffmpeg-dependency)\n  - [GPU / CUDA specific installation steps with Pip](#gpu--cuda-specific-installation-steps-with-pip)\n    - [Multiple CUDA library versions may be needed](#multiple-cuda-library-versions-may-be-needed)\n  - [Usage 🚀](#usage-)\n    - [Command Line Interface (CLI)](#command-line-interface-cli)","chunkId":"README.md:9"}],[248,{"filePath":"README.md","startLine":31,"endLine":39,"chunkText":"    - [Multiple CUDA library versions may be needed](#multiple-cuda-library-versions-may-be-needed)\n  - [Usage 🚀](#usage-)\n    - [Command Line Interface (CLI)](#command-line-interface-cli)\n    - [Listing and Filtering Available Models](#listing-and-filtering-available-models)\n      - [Filtering Models](#filtering-models)\n      - [Limiting Results](#limiting-results)\n      - [JSON Output](#json-output)\n    - [Full command-line interface options](#full-command-line-interface-options)\n    - [As a Dependency in a Python Project](#as-a-dependency-in-a-python-project)","chunkId":"README.md:10"}],[249,{"filePath":"README.md","startLine":37,"endLine":46,"chunkText":"      - [JSON Output](#json-output)\n    - [Full command-line interface options](#full-command-line-interface-options)\n    - [As a Dependency in a Python Project](#as-a-dependency-in-a-python-project)\n      - [Batch processing and processing with multiple models](#batch-processing-and-processing-with-multiple-models)\n      - [Renaming Stems](#renaming-stems)\n  - [Parameters for the Separator class](#parameters-for-the-separator-class)\n  - [Remote API Usage 🌐](#remote-api-usage-)\n  - [Requirements 📋](#requirements-)\n  - [Developing Locally](#developing-locally)\n    - [Prerequisites](#prerequisites)","chunkId":"README.md:11"}],[250,{"filePath":"README.md","startLine":44,"endLine":54,"chunkText":"  - [Requirements 📋](#requirements-)\n  - [Developing Locally](#developing-locally)\n    - [Prerequisites](#prerequisites)\n    - [Clone the Repository](#clone-the-repository)\n    - [Create and activate the Conda Environment](#create-and-activate-the-conda-environment)\n    - [Install Dependencies](#install-dependencies)\n    - [Running the Command-Line Interface Locally](#running-the-command-line-interface-locally)\n    - [Deactivate the Virtual Environment](#deactivate-the-virtual-environment)\n    - [Building the Package](#building-the-package)\n  - [Contributing 🤝](#contributing-)\n  - [License 📄](#license-","chunkId":"README.md:12"}],[251,{"filePath":"README.md","startLine":52,"endLine":67,"chunkText":"    - [Building the Package](#building-the-package)\n  - [Contributing 🤝](#contributing-)\n  - [License 📄](#license-)\n  - [Credits 🙏](#credits-)\n  - [Contact 💌](#contact-)\n  - [Thanks to all contributors for their efforts](#thanks-to-all-contributors-for-their-efforts)\n</details>\n\n---\n\n## Features\n\n- Separate audio into multiple stems, e.g. instrumental and vocals.\n- Supports all common audio formats (WAV, MP3, FLAC, M4A, etc.)\n- Ability to inference using a pre-trained model in PTH or ONNX format.\n- CLI support for easy use in scripts and batch processing.","chunkId":"README.md:13"}],[252,{"filePath":"README.md","startLine":67,"endLine":77,"chunkText":"- CLI support for easy use in scripts and batch processing.\n- Python API for integration into other projects.\n\n## Installation 🛠️\n\n### 🐳 Docker\n\nIf you're able to use docker, you don't actually need to _install_ anything - there are [images published on Docker Hub](https://hub.docker.com/r/beveradb/audio-separator/tags) for GPU (CUDA) and CPU inferencing, for both `amd64` and `arm64` platforms.\n\nYou probably want to volume-mount a folder containing whatever file you want to separate, which can then also be used as the output folder.\n","chunkId":"README.md:14"}],[253,{"filePath":"README.md","startLine":75,"endLine":86,"chunkText":"You probably want to volume-mount a folder containing whatever file you want to separate, which can then also be used as the output folder.\n\nFor instance, if your current directory has the file `input.wav`, you could execute `audio-separator` as shown below (see [usage](#usage-) section for more details):\n\n```sh\ndocker run -it -v `pwd`:/workdir beveradb/audio-separator input.wav\n```\n\nIf you're using a machine with a GPU, you'll want to use the GPU specific image and pass in the GPU device to the container, like this:\n\n```sh\ndocker run -it --gpus all -v `pwd`:/workdir beveradb/audio-separator:gpu input.wav","chunkId":"README.md:15"}],[254,{"filePath":"README.md","startLine":87,"endLine":100,"chunkText":"docker run -it --gpus all -v `pwd`:/workdir beveradb/audio-separator:gpu input.wav\n```\n\nIf the GPU isn't being detected, make sure your docker runtime environment is passing through the GPU correctly - there are [various guides](https://www.celantur.com/blog/run-cuda-in-docker-on-linux/) online to help with that.\n\n### 🎮 Nvidia GPU with CUDA or 🧪 Google Colab\n\n**Supported CUDA Versions:** 11.8 and 12.2\n\n💬 If successfully configured, you should see this log message when running `audio-separator --env_info`:\n `ONNXruntime has CUDAExecutionProvider available, enabling acceleration`\n\nConda:\n```sh","chunkId":"README.md:16"}],[255,{"filePath":"README.md","startLine":98,"endLine":127,"chunkText":"\nConda:\n```sh\nconda install pytorch=*=*cuda* onnxruntime=*=*cuda* audio-separator -c pytorch -c conda-forge\n```\n\nPip:\n```sh\npip install \"audio-separator[gpu]\"\n```\n\nDocker:\n```sh\nbeveradb/audio-separator:gpu\n```\n\n###  Apple Silicon, macOS Sonoma+ with M1 or newer CPU (CoreML acceleration)\n\n💬 If successfully configured, you should see this log message when running `audio-separator --env_info`:\n `ONNXruntime has CoreMLExecutionProvider available, enabling acceleration`\n\nPip:\n```sh\npip install \"audio-separator[cpu]\"\n```\n\n### 🐢 No hardware acceleration, CPU only\n\nConda:\n```sh","chunkId":"README.md:17"}],[256,{"filePath":"README.md","startLine":125,"endLine":150,"chunkText":"\nConda:\n```sh\nconda install audio-separator -c pytorch -c conda-forge\n```\n\nPip:\n```sh\npip install \"audio-separator[cpu]\"\n```\n\nDocker:\n```sh\nbeveradb/audio-separator\n```\n\n### 🎥 FFmpeg dependency\n\n💬 To test if `audio-separator` has been successfully configured to use FFmpeg, run `audio-separator --env_info`. The log will show `FFmpeg installed`.\n\nIf you installed `audio-separator` using `conda` or `docker`, FFmpeg should already be available in your environment.\n\nYou may need to separately install FFmpeg. It should be easy to install on most platforms, e.g.:\n\n🐧 Debian/Ubuntu:\n```sh","chunkId":"README.md:18"}],[257,{"filePath":"README.md","startLine":148,"endLine":167,"chunkText":"\n🐧 Debian/Ubuntu:\n```sh\napt-get update; apt-get install -y ffmpeg\n```\n\n macOS:\n```sh\nbrew update; brew install ffmpeg\n```\n\n## GPU / CUDA specific installation steps with Pip\n\nIn theory, all you should need to do to get `audio-separator` working with a GPU is install it with the `[gpu]` extra as above.\n\nHowever, sometimes getting both PyTorch and ONNX Runtime working with CUDA support can be a bit tricky so it may not work that easily.\n\nYou may need to reinstall both packages directly, allowing pip to calculate the right versions for your platform, for example:\n\n- `pip uninstall torch onnxruntime`","chunkId":"README.md:19"}],[258,{"filePath":"README.md","startLine":165,"endLine":176,"chunkText":"You may need to reinstall both packages directly, allowing pip to calculate the right versions for your platform, for example:\n\n- `pip uninstall torch onnxruntime`\n- `pip cache purge`\n- `pip install --force-reinstall torch torchvision torchaudio`\n- `pip install --force-reinstall onnxruntime-gpu`\n\nI generally recommend installing the latest version of PyTorch for your environment using the command recommended by the wizard here:\n<https://pytorch.org/get-started/locally/>\n\n### Multiple CUDA library versions may be needed\n","chunkId":"README.md:20"}],[259,{"filePath":"README.md","startLine":174,"endLine":187,"chunkText":"\n### Multiple CUDA library versions may be needed\n\nDepending on your CUDA version and environment, you may need to install specific version(s) of CUDA libraries for ONNX Runtime to use your GPU.\n\n🧪 Google Colab, for example, now uses CUDA 12 by default, but ONNX Runtime still needs CUDA 11 libraries to work.\n\nIf you see the error `Failed to load library` or `cannot open shared object file` when you run `audio-separator`, this is likely the issue.\n\nYou can install the CUDA 11 libraries _alongside_ CUDA 12 like so:\n```sh\napt update; apt install nvidia-cuda-toolkit\n```\n","chunkId":"README.md:21"}],[260,{"filePath":"README.md","startLine":185,"endLine":191,"chunkText":"apt update; apt install nvidia-cuda-toolkit\n```\n\nIf you encounter the following messages when running on Google Colab or in another environment:\n```\n[E:onnxruntime:Default, provider_bridge_ort.cc:1862 TryGetProviderInfo_CUDA] /onnxruntime_src/onnxruntime/core/session/provider_bridge_ort.cc:1539 onnxruntime::Provider& onnxruntime::ProviderLibrary::Get() [ONNXRuntimeError] : 1 : FAIL : Failed to load library libonnxruntime_providers_cuda.so with error: libcudnn_adv.so.9: cannot open shared object file: No such file or directory\n","chunkId":"README.md:22"}],[261,{"filePath":"README.md","startLine":189,"endLine":191,"chunkText":"```\n[E:onnxruntime:Default, provider_bridge_ort.cc:1862 TryGetProviderInfo_CUDA] /onnxruntime_src/onnxruntime/core/session/provider_bridge_ort.cc:1539 onnxruntime::Provider& onnxruntime::ProviderLibrary::Get() [ONNXRuntimeError] : 1 : FAIL : Failed to load library libonnxruntime_providers_cuda.so with error: libcudnn_adv.so.9: cannot open shared object file: No such file or directory\n","chunkId":"README.md:23"}],[262,{"filePath":"README.md","startLine":190,"endLine":191,"chunkText":"[E:onnxruntime:Default, provider_bridge_ort.cc:1862 TryGetProviderInfo_CUDA] /onnxruntime_src/onnxruntime/core/session/provider_bridge_ort.cc:1539 onnxruntime::Provider& onnxruntime::ProviderLibrary::Get() [ONNXRuntimeError] : 1 : FAIL : Failed to load library libonnxruntime_providers_cuda.so with error: libcudnn_adv.so.9: cannot open shared object file: No such file or directory\n","chunkId":"README.md:24"}],[263,{"filePath":"README.md","startLine":191,"endLine":198,"chunkText":"\n[W:onnxruntime:Default, onnxruntime_pybind_state.cc:993 CreateExecutionProviderInstance] Failed to create CUDAExecutionProvider. Require cuDNN 9.* and CUDA 12.*. Please install all dependencies as mentioned in the GPU requirements page (https://onnxruntime.ai/docs/execution-providers/CUDA-ExecutionProvider.html#requirements), make sure they're in the PATH, and that your GPU is supported.\n```\nYou can resolve this by running the following command:\n```sh\npython -m pip install ort-nightly-gpu --index-url=https://aiinfra.pkgs.visualstudio.com/PublicPackages/_packaging/ort-cuda-12-nightly/pypi/simple/\n```\n","chunkId":"README.md:25"}],[264,{"filePath":"README.md","startLine":196,"endLine":210,"chunkText":"python -m pip install ort-nightly-gpu --index-url=https://aiinfra.pkgs.visualstudio.com/PublicPackages/_packaging/ort-cuda-12-nightly/pypi/simple/\n```\n\n> Note: if anyone knows how to make this cleaner so we can support both different platform-specific dependencies for hardware acceleration without a separate installation process for each, please let me know or raise a PR!\n\n## Usage 🚀\n\n### Command Line Interface (CLI)\n\nYou can use Audio Separator via the command line, for example:\n\n```sh\naudio-separator /path/to/your/input/audio.wav --model_filename model_bs_roformer_ep_317_sdr_12.9755.ckpt\n```\n","chunkId":"README.md:26"}],[265,{"filePath":"README.md","startLine":208,"endLine":216,"chunkText":"audio-separator /path/to/your/input/audio.wav --model_filename model_bs_roformer_ep_317_sdr_12.9755.ckpt\n```\n\nThis command will download the specified model file, process the `audio.wav` input audio and generate two new files in the current directory, one containing vocals and one containing instrumental.\n\n**Note:** You do not need to download any files yourself - audio-separator does that automatically for you!\n\nTo see a list of supported models, run `audio-separator --list_models`\n","chunkId":"README.md:27"}],[266,{"filePath":"README.md","startLine":214,"endLine":227,"chunkText":"\nTo see a list of supported models, run `audio-separator --list_models`\n\nAny file listed in the list models output can be specified (with file extension) with the model_filename parameter (e.g. `--model_filename UVR_MDXNET_KARA_2.onnx`) and it will be automatically downloaded to the `--model_file_dir` (default: `/tmp/audio-separator-models/`) folder on first usage.\n\n### Listing and Filtering Available Models\n\nYou can view all available models using the `--list_models` (or `-l`) flag:\n\n```sh\naudio-separator --list_models\n```\n\nThe output shows a table with the following columns:","chunkId":"README.md:28"}],[267,{"filePath":"README.md","startLine":225,"endLine":242,"chunkText":"```\n\nThe output shows a table with the following columns:\n- Model Filename: The filename to use with `--model_filename`\n- Arch: The model architecture (MDX, MDXC, Demucs, etc.)\n- Output Stems (SDR): The stems this model can separate, with Signal-to-Distortion Ratio scores where available\n- Friendly Name: A human-readable name describing the model\n\n#### Filtering Models\n\nYou can filter and sort the model list by stem type using `--list_filter`. For example, to find models that can separate drums:\n\n```sh\naudio-separator -l --list_filter=drums\n```\n\nExample output:\n```","chunkId":"README.md:29"}],[268,{"filePath":"README.md","startLine":240,"endLine":246,"chunkText":"\nExample output:\n```\n-----------------------------------------------------------------------------------------------------------------------------------\nModel Filename        Arch    Output Stems (SDR)                                            Friendly Name\n-----------------------------------------------------------------------------------------------------------------------------------\nhtdemucs_ft.yaml      Demucs  vocals (10.8), drums (10.1), bass (11.9), other               Demucs v4: htdemucs_ft","chunkId":"README.md:30"}],[269,{"filePath":"README.md","startLine":244,"endLine":248,"chunkText":"Model Filename        Arch    Output Stems (SDR)                                            Friendly Name\n-----------------------------------------------------------------------------------------------------------------------------------\nhtdemucs_ft.yaml      Demucs  vocals (10.8), drums (10.1), bass (11.9), other               Demucs v4: htdemucs_ft\nhdemucs_mmi.yaml      Demucs  vocals (10.3), drums (9.7), bass (12.0), other                Demucs v4: hdemucs_mmi\nhtdemucs.yaml         Demucs  vocals (10.0), drums (9.4), bass (11.3), other                Demucs v4: htdemucs","chunkId":"README.md:31"}],[270,{"filePath":"README.md","startLine":246,"endLine":253,"chunkText":"htdemucs_ft.yaml      Demucs  vocals (10.8), drums (10.1), bass (11.9), other               Demucs v4: htdemucs_ft\nhdemucs_mmi.yaml      Demucs  vocals (10.3), drums (9.7), bass (12.0), other                Demucs v4: hdemucs_mmi\nhtdemucs.yaml         Demucs  vocals (10.0), drums (9.4), bass (11.3), other                Demucs v4: htdemucs\nhtdemucs_6s.yaml      Demucs  vocals (9.7), drums (8.5), bass (10.0), guitar, piano, other  Demucs v4: htdemucs_6s\n```\n\n#### Limiting Results\n","chunkId":"README.md:32"}],[271,{"filePath":"README.md","startLine":251,"endLine":263,"chunkText":"\n#### Limiting Results\n\nYou can limit the number of results shown using `--list_limit`. This is useful for finding the best performing models for a particular stem. For example, to see the top 5 vocal separation models:\n\n```sh\naudio-separator -l --list_filter=vocals --list_limit=5\n```\n\nExample output:\n```\n--------------------------------------------------------------------------------------------------------------------------------------------------------------\nModel Filename                             Arch  Output Stems (SDR)                   Friendly Name","chunkId":"README.md:33"}],[272,{"filePath":"README.md","startLine":261,"endLine":265,"chunkText":"```\n--------------------------------------------------------------------------------------------------------------------------------------------------------------\nModel Filename                             Arch  Output Stems (SDR)                   Friendly Name\n--------------------------------------------------------------------------------------------------------------------------------------------------------------\nmodel_bs_roformer_ep_317_sdr_12.9755.ckpt  MDXC  vocals* (12.9), instrumental (17.0)  Roformer Model: BS-Roformer-Viperx-1297","chunkId":"README.md:34"}],[273,{"filePath":"README.md","startLine":263,"endLine":266,"chunkText":"Model Filename                             Arch  Output Stems (SDR)                   Friendly Name\n--------------------------------------------------------------------------------------------------------------------------------------------------------------\nmodel_bs_roformer_ep_317_sdr_12.9755.ckpt  MDXC  vocals* (12.9), instrumental (17.0)  Roformer Model: BS-Roformer-Viperx-1297\nmodel_bs_roformer_ep_368_sdr_12.9628.ckpt  MDXC  vocals* (12.9), instrumental (17.0)  Roformer Model: BS-Roformer-Viperx-1296","chunkId":"README.md:35"}],[274,{"filePath":"README.md","startLine":264,"endLine":267,"chunkText":"--------------------------------------------------------------------------------------------------------------------------------------------------------------\nmodel_bs_roformer_ep_317_sdr_12.9755.ckpt  MDXC  vocals* (12.9), instrumental (17.0)  Roformer Model: BS-Roformer-Viperx-1297\nmodel_bs_roformer_ep_368_sdr_12.9628.ckpt  MDXC  vocals* (12.9), instrumental (17.0)  Roformer Model: BS-Roformer-Viperx-1296\nvocals_mel_band_roformer.ckpt              MDXC  vocals* (12.6), other                Roformer Model: MelBand Roformer | Vocals by Kimberley Jensen","chunkId":"README.md:36"}],[275,{"filePath":"README.md","startLine":265,"endLine":268,"chunkText":"model_bs_roformer_ep_317_sdr_12.9755.ckpt  MDXC  vocals* (12.9), instrumental (17.0)  Roformer Model: BS-Roformer-Viperx-1297\nmodel_bs_roformer_ep_368_sdr_12.9628.ckpt  MDXC  vocals* (12.9), instrumental (17.0)  Roformer Model: BS-Roformer-Viperx-1296\nvocals_mel_band_roformer.ckpt              MDXC  vocals* (12.6), other                Roformer Model: MelBand Roformer | Vocals by Kimberley Jensen\nmelband_roformer_big_beta4.ckpt            MDXC  vocals* (12.5), other                Roformer Model: MelBand Roformer Kim | Big Beta 4 FT by unwa","chunkId":"README.md:37"}],[276,{"filePath":"README.md","startLine":266,"endLine":273,"chunkText":"model_bs_roformer_ep_368_sdr_12.9628.ckpt  MDXC  vocals* (12.9), instrumental (17.0)  Roformer Model: BS-Roformer-Viperx-1296\nvocals_mel_band_roformer.ckpt              MDXC  vocals* (12.6), other                Roformer Model: MelBand Roformer | Vocals by Kimberley Jensen\nmelband_roformer_big_beta4.ckpt            MDXC  vocals* (12.5), other                Roformer Model: MelBand Roformer Kim | Big Beta 4 FT by unwa\nmel_band_roformer_kim_ft_unwa.ckpt         MDXC  vocals* (12.4), other                Roformer Model: MelBand Roformer Kim | FT by unwa\n```\n\n#### JSON Output\n","chunkId":"README.md:38"}],[277,{"filePath":"README.md","startLine":271,"endLine":290,"chunkText":"#### JSON Output\n\nFor programmatic use, you can output the model list in JSON format:\n\n```sh\naudio-separator -l --list_format=json\n```\n\n### Processing Large Files\n\nFor very long audio files (>1 hour), you may encounter out-of-memory errors. The `--chunk_duration` option automatically splits large files into smaller chunks, processes them separately, and merges the results:\n\n```sh\n# Process an 8-hour podcast in 10-minute chunks\naudio-separator long_podcast.wav --chunk_duration 600\n\n# Adjust chunk size based on available memory\naudio-separator very_long_audio.wav --chunk_duration 300  # 5-minute chunks\n```\n","chunkId":"README.md:39"}],[278,{"filePath":"README.md","startLine":289,"endLine":304,"chunkText":"audio-separator very_long_audio.wav --chunk_duration 300  # 5-minute chunks\n```\n\n#### How It Works\n\n1. **Split**: The input file is split into fixed-duration chunks (e.g., 10 minutes)\n2. **Process**: Each chunk is processed separately, reducing peak memory usage\n3. **Merge**: The results are merged back together with simple concatenation\n\nThe chunking feature supports all model types:\n- **2-stem models** (e.g., MDX): Vocals + Instrumental\n- **4-stem models** (e.g., Demucs): Drums, Bass, Other, Vocals\n- **6-stem models** (e.g., Demucs 6s): Bass, Drums, Other, Vocals, Guitar, Piano\n\n#### Benefits\n","chunkId":"README.md:40"}],[279,{"filePath":"README.md","startLine":302,"endLine":317,"chunkText":"\n#### Benefits\n\n- **Prevents OOM errors**: Process files of any length without running out of memory\n- **Predictable memory usage**: Memory usage stays bounded regardless of file length\n- **No quality loss**: Each chunk is fully processed with the selected model\n- **Multi-stem support**: Works seamlessly with 2, 4, and 6-stem models\n\n#### Recommendations\n\n- **Files > 1 hour**: Use `--chunk_duration 600` (10 minutes)\n- **Limited memory systems**: Use smaller chunks (300-600 seconds)\n- **Ample memory**: You may not need chunking at all\n\n#### Note on Audio Quality\n","chunkId":"README.md:41"}],[280,{"filePath":"README.md","startLine":315,"endLine":323,"chunkText":"\n#### Note on Audio Quality\n\nChunks are concatenated without crossfading, which may result in minor artifacts at chunk boundaries in rare cases. For most use cases, these are not noticeable. The simple concatenation approach keeps processing time minimal while solving out-of-memory issues.\n\n### Full command-line interface options\n\n```sh\nusage: audio-separator [-h] [-v] [-d] [-e] [-l] [--log_level LOG_LEVEL] [--list_filter LIST_FILTER] [--list_limit LIST_LIMIT] [--list_format {pretty,json}] [-m MODEL_FILENAME] [--output_format OUTPUT_FORMAT]","chunkId":"README.md:42"}],[281,{"filePath":"README.md","startLine":321,"endLine":325,"chunkText":"\n```sh\nusage: audio-separator [-h] [-v] [-d] [-e] [-l] [--log_level LOG_LEVEL] [--list_filter LIST_FILTER] [--list_limit LIST_LIMIT] [--list_format {pretty,json}] [-m MODEL_FILENAME] [--output_format OUTPUT_FORMAT]\n                       [--output_bitrate OUTPUT_BITRATE] [--output_dir OUTPUT_DIR] [--model_file_dir MODEL_FILE_DIR] [--download_model_only] [--invert_spect] [--normalization NORMALIZATION]\n                       [--amplification AMPLIFICATION] [--single_stem SINGLE_STEM] [--sample_rate SAMPLE_RATE] [--use_soundfile] [--use_autocast] [--custom_output_names CUSTOM_OUTPUT_NAMES]","chunkId":"README.md:43"}],[282,{"filePath":"README.md","startLine":323,"endLine":325,"chunkText":"usage: audio-separator [-h] [-v] [-d] [-e] [-l] [--log_level LOG_LEVEL] [--list_filter LIST_FILTER] [--list_limit LIST_LIMIT] [--list_format {pretty,json}] [-m MODEL_FILENAME] [--output_format OUTPUT_FORMAT]\n                       [--output_bitrate OUTPUT_BITRATE] [--output_dir OUTPUT_DIR] [--model_file_dir MODEL_FILE_DIR] [--download_model_only] [--invert_spect] [--normalization NORMALIZATION]\n                       [--amplification AMPLIFICATION] [--single_stem SINGLE_STEM] [--sample_rate SAMPLE_RATE] [--use_soundfile] [--use_autocast] [--custom_output_names CUSTOM_OUTPUT_NAMES]","chunkId":"README.md:44"}],[283,{"filePath":"README.md","startLine":324,"endLine":326,"chunkText":"                       [--output_bitrate OUTPUT_BITRATE] [--output_dir OUTPUT_DIR] [--model_file_dir MODEL_FILE_DIR] [--download_model_only] [--invert_spect] [--normalization NORMALIZATION]\n                       [--amplification AMPLIFICATION] [--single_stem SINGLE_STEM] [--sample_rate SAMPLE_RATE] [--use_soundfile] [--use_autocast] [--custom_output_names CUSTOM_OUTPUT_NAMES]\n                       [--mdx_segment_size MDX_SEGMENT_SIZE] [--mdx_overlap MDX_OVERLAP] [--mdx_batch_size MDX_BATCH_SIZE] [--mdx_hop_length MDX_HOP_LENGTH] [--mdx_enable_denoise] [--vr_batch_size VR_BATCH_SIZE]","chunkId":"README.md:45"}],[284,{"filePath":"README.md","startLine":325,"endLine":327,"chunkText":"                       [--amplification AMPLIFICATION] [--single_stem SINGLE_STEM] [--sample_rate SAMPLE_RATE] [--use_soundfile] [--use_autocast] [--custom_output_names CUSTOM_OUTPUT_NAMES]\n                       [--mdx_segment_size MDX_SEGMENT_SIZE] [--mdx_overlap MDX_OVERLAP] [--mdx_batch_size MDX_BATCH_SIZE] [--mdx_hop_length MDX_HOP_LENGTH] [--mdx_enable_denoise] [--vr_batch_size VR_BATCH_SIZE]\n                       [--vr_window_size VR_WINDOW_SIZE] [--vr_aggression VR_AGGRESSION] [--vr_enable_tta] [--vr_high_end_process] [--vr_enable_post_process]","chunkId":"README.md:46"}],[285,{"filePath":"README.md","startLine":326,"endLine":328,"chunkText":"                       [--mdx_segment_size MDX_SEGMENT_SIZE] [--mdx_overlap MDX_OVERLAP] [--mdx_batch_size MDX_BATCH_SIZE] [--mdx_hop_length MDX_HOP_LENGTH] [--mdx_enable_denoise] [--vr_batch_size VR_BATCH_SIZE]\n                       [--vr_window_size VR_WINDOW_SIZE] [--vr_aggression VR_AGGRESSION] [--vr_enable_tta] [--vr_high_end_process] [--vr_enable_post_process]\n                       [--vr_post_process_threshold VR_POST_PROCESS_THRESHOLD] [--demucs_segment_size DEMUCS_SEGMENT_SIZE] [--demucs_shifts DEMUCS_SHIFTS] [--demucs_overlap DEMUCS_OVERLAP]","chunkId":"README.md:47"}],[286,{"filePath":"README.md","startLine":327,"endLine":329,"chunkText":"                       [--vr_window_size VR_WINDOW_SIZE] [--vr_aggression VR_AGGRESSION] [--vr_enable_tta] [--vr_high_end_process] [--vr_enable_post_process]\n                       [--vr_post_process_threshold VR_POST_PROCESS_THRESHOLD] [--demucs_segment_size DEMUCS_SEGMENT_SIZE] [--demucs_shifts DEMUCS_SHIFTS] [--demucs_overlap DEMUCS_OVERLAP]\n                       [--demucs_segments_enabled DEMUCS_SEGMENTS_ENABLED] [--mdxc_segment_size MDXC_SEGMENT_SIZE] [--mdxc_override_model_segment_size] [--mdxc_overlap MDXC_OVERLAP]","chunkId":"README.md:48"}],[287,{"filePath":"README.md","startLine":328,"endLine":335,"chunkText":"                       [--vr_post_process_threshold VR_POST_PROCESS_THRESHOLD] [--demucs_segment_size DEMUCS_SEGMENT_SIZE] [--demucs_shifts DEMUCS_SHIFTS] [--demucs_overlap DEMUCS_OVERLAP]\n                       [--demucs_segments_enabled DEMUCS_SEGMENTS_ENABLED] [--mdxc_segment_size MDXC_SEGMENT_SIZE] [--mdxc_override_model_segment_size] [--mdxc_overlap MDXC_OVERLAP]\n                       [--mdxc_batch_size MDXC_BATCH_SIZE] [--mdxc_pitch_shift MDXC_PITCH_SHIFT]\n                       [audio_files ...]\n\nSeparate audio file into different stems.\n\npositional arguments:","chunkId":"README.md:49"}],[288,{"filePath":"README.md","startLine":333,"endLine":343,"chunkText":"Separate audio file into different stems.\n\npositional arguments:\n  audio_files                                            The audio file paths or directory to separate, in any common format.\n\noptions:\n  -h, --help                                             show this help message and exit\n\nInfo and Debugging:\n  -v, --version                                          Show the program's version number and exit.\n  -d, --debug                                            Enable debug logging, equivalent to --log_level=debug.","chunkId":"README.md:50"}],[289,{"filePath":"README.md","startLine":341,"endLine":345,"chunkText":"Info and Debugging:\n  -v, --version                                          Show the program's version number and exit.\n  -d, --debug                                            Enable debug logging, equivalent to --log_level=debug.\n  -e, --env_info                                         Print environment information and exit.\n  -l, --list_models                                      List all supported models and exit. Use --list_filter to filter/sort the list and --list_limit to show only top N results.","chunkId":"README.md:51"}],[290,{"filePath":"README.md","startLine":343,"endLine":346,"chunkText":"  -d, --debug                                            Enable debug logging, equivalent to --log_level=debug.\n  -e, --env_info                                         Print environment information and exit.\n  -l, --list_models                                      List all supported models and exit. Use --list_filter to filter/sort the list and --list_limit to show only top N results.\n  --log_level LOG_LEVEL                                  Log level, e.g. info, debug, warning (default: info).","chunkId":"README.md:52"}],[291,{"filePath":"README.md","startLine":344,"endLine":347,"chunkText":"  -e, --env_info                                         Print environment information and exit.\n  -l, --list_models                                      List all supported models and exit. Use --list_filter to filter/sort the list and --list_limit to show only top N results.\n  --log_level LOG_LEVEL                                  Log level, e.g. info, debug, warning (default: info).\n  --list_filter LIST_FILTER                              Filter and sort the model list by 'name', 'filename', or any stem e.g. vocals, instrumental, drums","chunkId":"README.md:53"}],[292,{"filePath":"README.md","startLine":345,"endLine":348,"chunkText":"  -l, --list_models                                      List all supported models and exit. Use --list_filter to filter/sort the list and --list_limit to show only top N results.\n  --log_level LOG_LEVEL                                  Log level, e.g. info, debug, warning (default: info).\n  --list_filter LIST_FILTER                              Filter and sort the model list by 'name', 'filename', or any stem e.g. vocals, instrumental, drums\n  --list_limit LIST_LIMIT                                Limit the number of models shown","chunkId":"README.md:54"}],[293,{"filePath":"README.md","startLine":346,"endLine":351,"chunkText":"  --log_level LOG_LEVEL                                  Log level, e.g. info, debug, warning (default: info).\n  --list_filter LIST_FILTER                              Filter and sort the model list by 'name', 'filename', or any stem e.g. vocals, instrumental, drums\n  --list_limit LIST_LIMIT                                Limit the number of models shown\n  --list_format {pretty,json}                            Format for listing models: 'pretty' for formatted output, 'json' for raw JSON dump\n\nSeparation I/O Params:","chunkId":"README.md:55"}],[294,{"filePath":"README.md","startLine":349,"endLine":353,"chunkText":"  --list_format {pretty,json}                            Format for listing models: 'pretty' for formatted output, 'json' for raw JSON dump\n\nSeparation I/O Params:\n  -m MODEL_FILENAME, --model_filename MODEL_FILENAME     Model to use for separation (default: model_bs_roformer_ep_317_sdr_12.9755.yaml). Example: -m 2_HP-UVR.pth\n  --output_format OUTPUT_FORMAT                          Output format for separated files, any common format (default: FLAC). Example: --output_format=MP3","chunkId":"README.md:56"}],[295,{"filePath":"README.md","startLine":351,"endLine":354,"chunkText":"Separation I/O Params:\n  -m MODEL_FILENAME, --model_filename MODEL_FILENAME     Model to use for separation (default: model_bs_roformer_ep_317_sdr_12.9755.yaml). Example: -m 2_HP-UVR.pth\n  --output_format OUTPUT_FORMAT                          Output format for separated files, any common format (default: FLAC). Example: --output_format=MP3\n  --output_bitrate OUTPUT_BITRATE                        Output bitrate for separated files, any ffmpeg-compatible bitrate (default: None). Example: --output_bitrate=320k","chunkId":"README.md:57"}],[296,{"filePath":"README.md","startLine":352,"endLine":354,"chunkText":"  -m MODEL_FILENAME, --model_filename MODEL_FILENAME     Model to use for separation (default: model_bs_roformer_ep_317_sdr_12.9755.yaml). Example: -m 2_HP-UVR.pth\n  --output_format OUTPUT_FORMAT                          Output format for separated files, any common format (default: FLAC). Example: --output_format=MP3\n  --output_bitrate OUTPUT_BITRATE                        Output bitrate for separated files, any ffmpeg-compatible bitrate (default: None). Example: --output_bitrate=320k","chunkId":"README.md:58"}],[297,{"filePath":"README.md","startLine":353,"endLine":355,"chunkText":"  --output_format OUTPUT_FORMAT                          Output format for separated files, any common format (default: FLAC). Example: --output_format=MP3\n  --output_bitrate OUTPUT_BITRATE                        Output bitrate for separated files, any ffmpeg-compatible bitrate (default: None). Example: --output_bitrate=320k\n  --output_dir OUTPUT_DIR                                Directory to write output files (default: <current dir>). Example: --output_dir=/app/separated","chunkId":"README.md:59"}],[298,{"filePath":"README.md","startLine":354,"endLine":358,"chunkText":"  --output_bitrate OUTPUT_BITRATE                        Output bitrate for separated files, any ffmpeg-compatible bitrate (default: None). Example: --output_bitrate=320k\n  --output_dir OUTPUT_DIR                                Directory to write output files (default: <current dir>). Example: --output_dir=/app/separated\n  --model_file_dir MODEL_FILE_DIR                        Model files directory (default: /tmp/audio-separator-models/). Example: --model_file_dir=/app/models\n  --download_model_only                                  Download a single model file only, without performing separation.\n","chunkId":"README.md:60"}],[299,{"filePath":"README.md","startLine":356,"endLine":361,"chunkText":"  --model_file_dir MODEL_FILE_DIR                        Model files directory (default: /tmp/audio-separator-models/). Example: --model_file_dir=/app/models\n  --download_model_only                                  Download a single model file only, without performing separation.\n\nCommon Separation Parameters:\n  --invert_spect                                         Invert secondary stem using spectrogram (default: False). Example: --invert_spect\n  --normalization NORMALIZATION                          Max peak amplitude to normalize input and output audio to (default: 0.9). Example: --normalization=0.7","chunkId":"README.md:61"}],[300,{"filePath":"README.md","startLine":359,"endLine":362,"chunkText":"Common Separation Parameters:\n  --invert_spect                                         Invert secondary stem using spectrogram (default: False). Example: --invert_spect\n  --normalization NORMALIZATION                          Max peak amplitude to normalize input and output audio to (default: 0.9). Example: --normalization=0.7\n  --amplification AMPLIFICATION                          Min peak amplitude to amplify input and output audio to (default: 0.0). Example: --amplification=0.4","chunkId":"README.md:62"}],[301,{"filePath":"README.md","startLine":360,"endLine":362,"chunkText":"  --invert_spect                                         Invert secondary stem using spectrogram (default: False). Example: --invert_spect\n  --normalization NORMALIZATION                          Max peak amplitude to normalize input and output audio to (default: 0.9). Example: --normalization=0.7\n  --amplification AMPLIFICATION                          Min peak amplitude to amplify input and output audio to (default: 0.0). Example: --amplification=0.4","chunkId":"README.md:63"}],[302,{"filePath":"README.md","startLine":361,"endLine":363,"chunkText":"  --normalization NORMALIZATION                          Max peak amplitude to normalize input and output audio to (default: 0.9). Example: --normalization=0.7\n  --amplification AMPLIFICATION                          Min peak amplitude to amplify input and output audio to (default: 0.0). Example: --amplification=0.4\n  --single_stem SINGLE_STEM                              Output only single stem, e.g. Instrumental, Vocals, Drums, Bass, Guitar, Piano, Other. Example: --single_stem=Instrumental","chunkId":"README.md:64"}],[303,{"filePath":"README.md","startLine":362,"endLine":364,"chunkText":"  --amplification AMPLIFICATION                          Min peak amplitude to amplify input and output audio to (default: 0.0). Example: --amplification=0.4\n  --single_stem SINGLE_STEM                              Output only single stem, e.g. Instrumental, Vocals, Drums, Bass, Guitar, Piano, Other. Example: --single_stem=Instrumental\n  --sample_rate SAMPLE_RATE                              Modify the sample rate of the output audio (default: 44100). Example: --sample_rate=44100","chunkId":"README.md:65"}],[304,{"filePath":"README.md","startLine":363,"endLine":365,"chunkText":"  --single_stem SINGLE_STEM                              Output only single stem, e.g. Instrumental, Vocals, Drums, Bass, Guitar, Piano, Other. Example: --single_stem=Instrumental\n  --sample_rate SAMPLE_RATE                              Modify the sample rate of the output audio (default: 44100). Example: --sample_rate=44100\n  --use_soundfile                                        Use soundfile to write audio output (default: False). Example: --use_soundfile","chunkId":"README.md:66"}],[305,{"filePath":"README.md","startLine":364,"endLine":366,"chunkText":"  --sample_rate SAMPLE_RATE                              Modify the sample rate of the output audio (default: 44100). Example: --sample_rate=44100\n  --use_soundfile                                        Use soundfile to write audio output (default: False). Example: --use_soundfile\n  --use_autocast                                         Use PyTorch autocast for faster inference (default: False). Do not use for CPU inference. Example: --use_autocast","chunkId":"README.md:67"}],[306,{"filePath":"README.md","startLine":365,"endLine":369,"chunkText":"  --use_soundfile                                        Use soundfile to write audio output (default: False). Example: --use_soundfile\n  --use_autocast                                         Use PyTorch autocast for faster inference (default: False). Do not use for CPU inference. Example: --use_autocast\n  --custom_output_names CUSTOM_OUTPUT_NAMES              Custom names for all output files in JSON format (default: None). Example: --custom_output_names='{\"Vocals\": \"vocals_output\", \"Drums\": \"drums_output\"}'\n\nMDX Architecture Parameters:","chunkId":"README.md:68"}],[307,{"filePath":"README.md","startLine":367,"endLine":371,"chunkText":"  --custom_output_names CUSTOM_OUTPUT_NAMES              Custom names for all output files in JSON format (default: None). Example: --custom_output_names='{\"Vocals\": \"vocals_output\", \"Drums\": \"drums_output\"}'\n\nMDX Architecture Parameters:\n  --mdx_segment_size MDX_SEGMENT_SIZE                    Larger consumes more resources, but may give better results (default: 256). Example: --mdx_segment_size=256\n  --mdx_overlap MDX_OVERLAP                              Amount of overlap between prediction windows, 0.001-0.999. Higher is better but slower (default: 0.25). Example: --mdx_overlap=0.25","chunkId":"README.md:69"}],[308,{"filePath":"README.md","startLine":369,"endLine":372,"chunkText":"MDX Architecture Parameters:\n  --mdx_segment_size MDX_SEGMENT_SIZE                    Larger consumes more resources, but may give better results (default: 256). Example: --mdx_segment_size=256\n  --mdx_overlap MDX_OVERLAP                              Amount of overlap between prediction windows, 0.001-0.999. Higher is better but slower (default: 0.25). Example: --mdx_overlap=0.25\n  --mdx_batch_size MDX_BATCH_SIZE                        Larger consumes more RAM but may process slightly faster (default: 1). Example: --mdx_batch_size=4","chunkId":"README.md:70"}],[309,{"filePath":"README.md","startLine":370,"endLine":372,"chunkText":"  --mdx_segment_size MDX_SEGMENT_SIZE                    Larger consumes more resources, but may give better results (default: 256). Example: --mdx_segment_size=256\n  --mdx_overlap MDX_OVERLAP                              Amount of overlap between prediction windows, 0.001-0.999. Higher is better but slower (default: 0.25). Example: --mdx_overlap=0.25\n  --mdx_batch_size MDX_BATCH_SIZE                        Larger consumes more RAM but may process slightly faster (default: 1). Example: --mdx_batch_size=4","chunkId":"README.md:71"}],[310,{"filePath":"README.md","startLine":371,"endLine":373,"chunkText":"  --mdx_overlap MDX_OVERLAP                              Amount of overlap between prediction windows, 0.001-0.999. Higher is better but slower (default: 0.25). Example: --mdx_overlap=0.25\n  --mdx_batch_size MDX_BATCH_SIZE                        Larger consumes more RAM but may process slightly faster (default: 1). Example: --mdx_batch_size=4\n  --mdx_hop_length MDX_HOP_LENGTH                        Usually called stride in neural networks, only change if you know what you're doing (default: 1024). Example: --mdx_hop_length=1024","chunkId":"README.md:72"}],[311,{"filePath":"README.md","startLine":372,"endLine":376,"chunkText":"  --mdx_batch_size MDX_BATCH_SIZE                        Larger consumes more RAM but may process slightly faster (default: 1). Example: --mdx_batch_size=4\n  --mdx_hop_length MDX_HOP_LENGTH                        Usually called stride in neural networks, only change if you know what you're doing (default: 1024). Example: --mdx_hop_length=1024\n  --mdx_enable_denoise                                   Enable denoising during separation (default: False). Example: --mdx_enable_denoise\n\nVR Architecture Parameters:","chunkId":"README.md:73"}],[312,{"filePath":"README.md","startLine":374,"endLine":378,"chunkText":"  --mdx_enable_denoise                                   Enable denoising during separation (default: False). Example: --mdx_enable_denoise\n\nVR Architecture Parameters:\n  --vr_batch_size VR_BATCH_SIZE                          Number of batches to process at a time. Higher = more RAM, slightly faster processing (default: 1). Example: --vr_batch_size=16\n  --vr_window_size VR_WINDOW_SIZE                        Balance quality and speed. 1024 = fast but lower, 320 = slower but better quality. (default: 512). Example: --vr_window_size=320","chunkId":"README.md:74"}],[313,{"filePath":"README.md","startLine":376,"endLine":379,"chunkText":"VR Architecture Parameters:\n  --vr_batch_size VR_BATCH_SIZE                          Number of batches to process at a time. Higher = more RAM, slightly faster processing (default: 1). Example: --vr_batch_size=16\n  --vr_window_size VR_WINDOW_SIZE                        Balance quality and speed. 1024 = fast but lower, 320 = slower but better quality. (default: 512). Example: --vr_window_size=320\n  --vr_aggression VR_AGGRESSION                          Intensity of primary stem extraction, -100 - 100. Typically, 5 for vocals & instrumentals (default: 5). Example: --vr_aggression=2","chunkId":"README.md:75"}],[314,{"filePath":"README.md","startLine":377,"endLine":379,"chunkText":"  --vr_batch_size VR_BATCH_SIZE                          Number of batches to process at a time. Higher = more RAM, slightly faster processing (default: 1). Example: --vr_batch_size=16\n  --vr_window_size VR_WINDOW_SIZE                        Balance quality and speed. 1024 = fast but lower, 320 = slower but better quality. (default: 512). Example: --vr_window_size=320\n  --vr_aggression VR_AGGRESSION                          Intensity of primary stem extraction, -100 - 100. Typically, 5 for vocals & instrumentals (default: 5). Example: --vr_aggression=2","chunkId":"README.md:76"}],[315,{"filePath":"README.md","startLine":378,"endLine":380,"chunkText":"  --vr_window_size VR_WINDOW_SIZE                        Balance quality and speed. 1024 = fast but lower, 320 = slower but better quality. (default: 512). Example: --vr_window_size=320\n  --vr_aggression VR_AGGRESSION                          Intensity of primary stem extraction, -100 - 100. Typically, 5 for vocals & instrumentals (default: 5). Example: --vr_aggression=2\n  --vr_enable_tta                                        Enable Test-Time-Augmentation; slow but improves quality (default: False). Example: --vr_enable_tta","chunkId":"README.md:77"}],[316,{"filePath":"README.md","startLine":379,"endLine":381,"chunkText":"  --vr_aggression VR_AGGRESSION                          Intensity of primary stem extraction, -100 - 100. Typically, 5 for vocals & instrumentals (default: 5). Example: --vr_aggression=2\n  --vr_enable_tta                                        Enable Test-Time-Augmentation; slow but improves quality (default: False). Example: --vr_enable_tta\n  --vr_high_end_process                                  Mirror the missing frequency range of the output (default: False). Example: --vr_high_end_process","chunkId":"README.md:78"}],[317,{"filePath":"README.md","startLine":380,"endLine":382,"chunkText":"  --vr_enable_tta                                        Enable Test-Time-Augmentation; slow but improves quality (default: False). Example: --vr_enable_tta\n  --vr_high_end_process                                  Mirror the missing frequency range of the output (default: False). Example: --vr_high_end_process\n  --vr_enable_post_process                               Identify leftover artifacts within vocal output; may improve separation for some songs (default: False). Example: --vr_enable_post_process","chunkId":"README.md:79"}],[318,{"filePath":"README.md","startLine":381,"endLine":385,"chunkText":"  --vr_high_end_process                                  Mirror the missing frequency range of the output (default: False). Example: --vr_high_end_process\n  --vr_enable_post_process                               Identify leftover artifacts within vocal output; may improve separation for some songs (default: False). Example: --vr_enable_post_process\n  --vr_post_process_threshold VR_POST_PROCESS_THRESHOLD  Threshold for post_process feature: 0.1-0.3 (default: 0.2). Example: --vr_post_process_threshold=0.1\n\nDemucs Architecture Parameters:","chunkId":"README.md:80"}],[319,{"filePath":"README.md","startLine":383,"endLine":387,"chunkText":"  --vr_post_process_threshold VR_POST_PROCESS_THRESHOLD  Threshold for post_process feature: 0.1-0.3 (default: 0.2). Example: --vr_post_process_threshold=0.1\n\nDemucs Architecture Parameters:\n  --demucs_segment_size DEMUCS_SEGMENT_SIZE              Size of segments into which the audio is split, 1-100. Higher = slower but better quality (default: Default). Example: --demucs_segment_size=256\n  --demucs_shifts DEMUCS_SHIFTS                          Number of predictions with random shifts, higher = slower but better quality (default: 2). Example: --demucs_shifts=4","chunkId":"README.md:81"}],[320,{"filePath":"README.md","startLine":385,"endLine":388,"chunkText":"Demucs Architecture Parameters:\n  --demucs_segment_size DEMUCS_SEGMENT_SIZE              Size of segments into which the audio is split, 1-100. Higher = slower but better quality (default: Default). Example: --demucs_segment_size=256\n  --demucs_shifts DEMUCS_SHIFTS                          Number of predictions with random shifts, higher = slower but better quality (default: 2). Example: --demucs_shifts=4\n  --demucs_overlap DEMUCS_OVERLAP                        Overlap between prediction windows, 0.001-0.999. Higher = slower but better quality (default: 0.25). Example: --demucs_overlap=0.25","chunkId":"README.md:82"}],[321,{"filePath":"README.md","startLine":386,"endLine":388,"chunkText":"  --demucs_segment_size DEMUCS_SEGMENT_SIZE              Size of segments into which the audio is split, 1-100. Higher = slower but better quality (default: Default). Example: --demucs_segment_size=256\n  --demucs_shifts DEMUCS_SHIFTS                          Number of predictions with random shifts, higher = slower but better quality (default: 2). Example: --demucs_shifts=4\n  --demucs_overlap DEMUCS_OVERLAP                        Overlap between prediction windows, 0.001-0.999. Higher = slower but better quality (default: 0.25). Example: --demucs_overlap=0.25","chunkId":"README.md:83"}],[322,{"filePath":"README.md","startLine":387,"endLine":391,"chunkText":"  --demucs_shifts DEMUCS_SHIFTS                          Number of predictions with random shifts, higher = slower but better quality (default: 2). Example: --demucs_shifts=4\n  --demucs_overlap DEMUCS_OVERLAP                        Overlap between prediction windows, 0.001-0.999. Higher = slower but better quality (default: 0.25). Example: --demucs_overlap=0.25\n  --demucs_segments_enabled DEMUCS_SEGMENTS_ENABLED      Enable segment-wise processing (default: True). Example: --demucs_segments_enabled=False\n\nMDXC Architecture Parameters:","chunkId":"README.md:84"}],[323,{"filePath":"README.md","startLine":389,"endLine":393,"chunkText":"  --demucs_segments_enabled DEMUCS_SEGMENTS_ENABLED      Enable segment-wise processing (default: True). Example: --demucs_segments_enabled=False\n\nMDXC Architecture Parameters:\n  --mdxc_segment_size MDXC_SEGMENT_SIZE                  Larger consumes more resources, but may give better results (default: 256). Example: --mdxc_segment_size=256\n  --mdxc_override_model_segment_size                     Override model default segment size instead of using the model default value. Example: --mdxc_override_model_segment_size","chunkId":"README.md:85"}],[324,{"filePath":"README.md","startLine":391,"endLine":394,"chunkText":"MDXC Architecture Parameters:\n  --mdxc_segment_size MDXC_SEGMENT_SIZE                  Larger consumes more resources, but may give better results (default: 256). Example: --mdxc_segment_size=256\n  --mdxc_override_model_segment_size                     Override model default segment size instead of using the model default value. Example: --mdxc_override_model_segment_size\n  --mdxc_overlap MDXC_OVERLAP                            Amount of overlap between prediction windows, 2-50. Higher is better but slower (default: 8). Example: --mdxc_overlap=8","chunkId":"README.md:86"}],[325,{"filePath":"README.md","startLine":392,"endLine":394,"chunkText":"  --mdxc_segment_size MDXC_SEGMENT_SIZE                  Larger consumes more resources, but may give better results (default: 256). Example: --mdxc_segment_size=256\n  --mdxc_override_model_segment_size                     Override model default segment size instead of using the model default value. Example: --mdxc_override_model_segment_size\n  --mdxc_overlap MDXC_OVERLAP                            Amount of overlap between prediction windows, 2-50. Higher is better but slower (default: 8). Example: --mdxc_overlap=8","chunkId":"README.md:87"}],[326,{"filePath":"README.md","startLine":393,"endLine":395,"chunkText":"  --mdxc_override_model_segment_size                     Override model default segment size instead of using the model default value. Example: --mdxc_override_model_segment_size\n  --mdxc_overlap MDXC_OVERLAP                            Amount of overlap between prediction windows, 2-50. Higher is better but slower (default: 8). Example: --mdxc_overlap=8\n  --mdxc_batch_size MDXC_BATCH_SIZE                      Larger consumes more RAM but may process slightly faster (default: 1). Example: --mdxc_batch_size=4","chunkId":"README.md:88"}],[327,{"filePath":"README.md","startLine":394,"endLine":400,"chunkText":"  --mdxc_overlap MDXC_OVERLAP                            Amount of overlap between prediction windows, 2-50. Higher is better but slower (default: 8). Example: --mdxc_overlap=8\n  --mdxc_batch_size MDXC_BATCH_SIZE                      Larger consumes more RAM but may process slightly faster (default: 1). Example: --mdxc_batch_size=4\n  --mdxc_pitch_shift MDXC_PITCH_SHIFT                    Shift audio pitch by a number of semitones while processing. May improve output for deep/high vocals. (default: 0). Example: --mdxc_pitch_shift=2\n```\n\n### As a Dependency in a Python Project\n","chunkId":"README.md:89"}],[328,{"filePath":"README.md","startLine":398,"endLine":414,"chunkText":"\n### As a Dependency in a Python Project\n\nYou can use Audio Separator in your own Python project. Here's a minimal example using the default two stem (Instrumental and Vocals) model:\n\n```python\nfrom audio_separator.separator import Separator\n\n# Initialize the Separator class (with optional configuration properties, below)\nseparator = Separator()\n\n# Load a machine learning model (if unspecified, defaults to 'model_mel_band_roformer_ep_3005_sdr_11.4360.ckpt')\nseparator.load_model()\n\n# Perform the separation on specific audio files without reloading the model\noutput_files = separator.separate('audio1.wav')\n","chunkId":"README.md:90"}],[329,{"filePath":"README.md","startLine":412,"endLine":430,"chunkText":"# Perform the separation on specific audio files without reloading the model\noutput_files = separator.separate('audio1.wav')\n\nprint(f\"Separation complete! Output file(s): {' '.join(output_files)}\")\n```\n\n#### Batch processing and processing with multiple models\n\nYou can process multiple files without reloading the model to save time and memory.\n\nYou only need to load a model when choosing or changing models. See example below:\n\n```python\nfrom audio_separator.separator import Separator\n\n# Initialize the Separator class (with optional configuration properties, below)\nseparator = Separator()\n\n# Load a model","chunkId":"README.md:91"}],[330,{"filePath":"README.md","startLine":428,"endLine":444,"chunkText":"separator = Separator()\n\n# Load a model\nseparator.load_model(model_filename='model_bs_roformer_ep_317_sdr_12.9755.ckpt')\n\n# Separate multiple audio files without reloading the model\noutput_files = separator.separate(['audio1.wav', 'audio2.wav', 'audio3.wav'])\n\n# Load a different model\nseparator.load_model(model_filename='UVR_MDXNET_KARA_2.onnx')\n\n# Separate the same files with the new model\noutput_files = separator.separate(['audio1.wav', 'audio2.wav', 'audio3.wav'])\n```\n\nYou can also specify the path to a folder containing audio files instead of listing the full paths to each of them:\n```python","chunkId":"README.md:92"}],[331,{"filePath":"README.md","startLine":442,"endLine":459,"chunkText":"You can also specify the path to a folder containing audio files instead of listing the full paths to each of them:\n```python\nfrom audio_separator.separator import Separator\n\n# Initialize the Separator class (with optional configuration properties, below)\nseparator = Separator()\n\n# Load a model\nseparator.load_model(model_filename='model_bs_roformer_ep_317_sdr_12.9755.ckpt')\n\n# Separate all audio files located in a folder\noutput_files = separator.separate('path/to/audio_directory')\n```\n\n#### Renaming Stems\n\nYou can rename the output files by specifying the desired names. For example:\n```python","chunkId":"README.md:93"}],[332,{"filePath":"README.md","startLine":459,"endLine":477,"chunkText":"You can rename the output files by specifying the desired names. For example:\n```python\noutput_names = {\n    \"Vocals\": \"vocals_output\",\n    \"Instrumental\": \"instrumental_output\",\n}\noutput_files = separator.separate('audio1.wav', output_names)\n```\nIn this case, the output file names will be: `vocals_output.wav` and `instrumental_output.wav`.\n\nYou can also rename specific stems:\n\n- To rename the Vocals stem:\n  ```python\n  output_names = {\n      \"Vocals\": \"vocals_output\",\n  }\n  output_files = separator.separate('audio1.wav', output_names)\n  ```","chunkId":"README.md:94"}],[333,{"filePath":"README.md","startLine":475,"endLine":490,"chunkText":"  }\n  output_files = separator.separate('audio1.wav', output_names)\n  ```\n  > The output files will be named: `vocals_output.wav` and `audio1_(Instrumental)_model_mel_band_roformer_ep_3005_sdr_11.wav`\n- To rename the Instrumental stem:\n  ```python\n  output_names = {\n      \"Instrumental\": \"instrumental_output\",\n  }\n  output_files = separator.separate('audio1.wav', output_names)\n  ```\n  > The output files will be named: `audio1_(Vocals)_model_mel_band_roformer_ep_3005_sdr_11.wav` and `instrumental_output.wav`\n- List of stems for Demucs models:\n  - htdemucs_6s.yaml\n    ```python\n    output_names = {","chunkId":"README.md:95"}],[334,{"filePath":"README.md","startLine":488,"endLine":510,"chunkText":"  - htdemucs_6s.yaml\n    ```python\n    output_names = {\n        \"Vocals\": \"vocals_output\",\n        \"Drums\": \"drums_output\",\n        \"Bass\": \"bass_output\",\n        \"Other\": \"other_output\",\n        \"Guitar\": \"guitar_output\",\n        \"Piano\": \"piano_output\",\n    }\n    ```\n  - Other Demucs models\n    ```python\n    output_names = {\n        \"Vocals\": \"vocals_output\",\n        \"Drums\": \"drums_output\",\n        \"Bass\": \"bass_output\",\n        \"Other\": \"other_output\",\n    }\n    ```\n\n## Parameters for the Separator class\n","chunkId":"README.md:96"}],[335,{"filePath":"README.md","startLine":508,"endLine":514,"chunkText":"\n## Parameters for the Separator class\n\n- **`log_level`:** (Optional) Logging level, e.g., INFO, DEBUG, WARNING. `Default: logging.INFO`\n- **`log_formatter`:** (Optional) The log format. Default: None, which falls back to '%(asctime)s - %(levelname)s - %(module)s - %(message)s'\n- **`model_file_dir`:** (Optional) Directory to cache model files in. `Default: /tmp/audio-separator-models/`\n- **`output_dir`:** (Optional) Directory where the separated files will be saved. If not specified, uses the current directory.","chunkId":"README.md:97"}],[336,{"filePath":"README.md","startLine":512,"endLine":515,"chunkText":"- **`log_formatter`:** (Optional) The log format. Default: None, which falls back to '%(asctime)s - %(levelname)s - %(module)s - %(message)s'\n- **`model_file_dir`:** (Optional) Directory to cache model files in. `Default: /tmp/audio-separator-models/`\n- **`output_dir`:** (Optional) Directory where the separated files will be saved. If not specified, uses the current directory.\n- **`output_format`:** (Optional) Format to encode output files, any common format (WAV, MP3, FLAC, M4A, etc.). `Default: WAV`","chunkId":"README.md:98"}],[337,{"filePath":"README.md","startLine":513,"endLine":516,"chunkText":"- **`model_file_dir`:** (Optional) Directory to cache model files in. `Default: /tmp/audio-separator-models/`\n- **`output_dir`:** (Optional) Directory where the separated files will be saved. If not specified, uses the current directory.\n- **`output_format`:** (Optional) Format to encode output files, any common format (WAV, MP3, FLAC, M4A, etc.). `Default: WAV`\n- **`normalization_threshold`:** (Optional) The amount by which the amplitude of the output audio will be multiplied. `Default: 0.9`","chunkId":"README.md:99"}],[338,{"filePath":"README.md","startLine":514,"endLine":516,"chunkText":"- **`output_dir`:** (Optional) Directory where the separated files will be saved. If not specified, uses the current directory.\n- **`output_format`:** (Optional) Format to encode output files, any common format (WAV, MP3, FLAC, M4A, etc.). `Default: WAV`\n- **`normalization_threshold`:** (Optional) The amount by which the amplitude of the output audio will be multiplied. `Default: 0.9`","chunkId":"README.md:100"}],[339,{"filePath":"README.md","startLine":515,"endLine":518,"chunkText":"- **`output_format`:** (Optional) Format to encode output files, any common format (WAV, MP3, FLAC, M4A, etc.). `Default: WAV`\n- **`normalization_threshold`:** (Optional) The amount by which the amplitude of the output audio will be multiplied. `Default: 0.9`\n- **`amplification_threshold`:** (Optional) The minimum amplitude level at which the waveform will be amplified. If the peak amplitude of the audio is below this threshold, the waveform will be scaled up to meet it. `Default: 0.0`\n- **`output_single_stem`:** (Optional) Output only a single stem, such as 'Instrumental' and 'Vocals'. `Default: None`","chunkId":"README.md:101"}],[340,{"filePath":"README.md","startLine":516,"endLine":519,"chunkText":"- **`normalization_threshold`:** (Optional) The amount by which the amplitude of the output audio will be multiplied. `Default: 0.9`\n- **`amplification_threshold`:** (Optional) The minimum amplitude level at which the waveform will be amplified. If the peak amplitude of the audio is below this threshold, the waveform will be scaled up to meet it. `Default: 0.0`\n- **`output_single_stem`:** (Optional) Output only a single stem, such as 'Instrumental' and 'Vocals'. `Default: None`\n- **`invert_using_spec`:** (Optional) Flag to invert using spectrogram. `Default: False`","chunkId":"README.md:102"}],[341,{"filePath":"README.md","startLine":517,"endLine":520,"chunkText":"- **`amplification_threshold`:** (Optional) The minimum amplitude level at which the waveform will be amplified. If the peak amplitude of the audio is below this threshold, the waveform will be scaled up to meet it. `Default: 0.0`\n- **`output_single_stem`:** (Optional) Output only a single stem, such as 'Instrumental' and 'Vocals'. `Default: None`\n- **`invert_using_spec`:** (Optional) Flag to invert using spectrogram. `Default: False`\n- **`sample_rate`:** (Optional) Set the sample rate of the output audio. `Default: 44100`","chunkId":"README.md:103"}],[342,{"filePath":"README.md","startLine":518,"endLine":522,"chunkText":"- **`output_single_stem`:** (Optional) Output only a single stem, such as 'Instrumental' and 'Vocals'. `Default: None`\n- **`invert_using_spec`:** (Optional) Flag to invert using spectrogram. `Default: False`\n- **`sample_rate`:** (Optional) Set the sample rate of the output audio. `Default: 44100`\n- **`use_soundfile`:** (Optional) Use soundfile for output writing, can solve OOM issues, especially on longer audio.\n- **`use_autocast`:** (Optional) Flag to use PyTorch autocast for faster inference. Do not use for CPU inference. `Default: False`","chunkId":"README.md:104"}],[343,{"filePath":"README.md","startLine":520,"endLine":523,"chunkText":"- **`sample_rate`:** (Optional) Set the sample rate of the output audio. `Default: 44100`\n- **`use_soundfile`:** (Optional) Use soundfile for output writing, can solve OOM issues, especially on longer audio.\n- **`use_autocast`:** (Optional) Flag to use PyTorch autocast for faster inference. Do not use for CPU inference. `Default: False`\n- **`mdx_params`:** (Optional) MDX Architecture Specific Attributes & Defaults. `Default: {\"hop_length\": 1024, \"segment_size\": 256, \"overlap\": 0.25, \"batch_size\": 1, \"enable_denoise\": False}`","chunkId":"README.md:105"}],[344,{"filePath":"README.md","startLine":521,"endLine":523,"chunkText":"- **`use_soundfile`:** (Optional) Use soundfile for output writing, can solve OOM issues, especially on longer audio.\n- **`use_autocast`:** (Optional) Flag to use PyTorch autocast for faster inference. Do not use for CPU inference. `Default: False`\n- **`mdx_params`:** (Optional) MDX Architecture Specific Attributes & Defaults. `Default: {\"hop_length\": 1024, \"segment_size\": 256, \"overlap\": 0.25, \"batch_size\": 1, \"enable_denoise\": False}`","chunkId":"README.md:106"}],[345,{"filePath":"README.md","startLine":522,"endLine":524,"chunkText":"- **`use_autocast`:** (Optional) Flag to use PyTorch autocast for faster inference. Do not use for CPU inference. `Default: False`\n- **`mdx_params`:** (Optional) MDX Architecture Specific Attributes & Defaults. `Default: {\"hop_length\": 1024, \"segment_size\": 256, \"overlap\": 0.25, \"batch_size\": 1, \"enable_denoise\": False}`\n- **`vr_params`:** (Optional) VR Architecture Specific Attributes & Defaults. `Default: {\"batch_size\": 1, \"window_size\": 512, \"aggression\": 5, \"enable_tta\": False, \"enable_post_process\": False, \"post_process_threshold\": 0.2, \"high_end_process\": False}`","chunkId":"README.md:107"}],[346,{"filePath":"README.md","startLine":523,"endLine":524,"chunkText":"- **`mdx_params`:** (Optional) MDX Architecture Specific Attributes & Defaults. `Default: {\"hop_length\": 1024, \"segment_size\": 256, \"overlap\": 0.25, \"batch_size\": 1, \"enable_denoise\": False}`\n- **`vr_params`:** (Optional) VR Architecture Specific Attributes & Defaults. `Default: {\"batch_size\": 1, \"window_size\": 512, \"aggression\": 5, \"enable_tta\": False, \"enable_post_process\": False, \"post_process_threshold\": 0.2, \"high_end_process\": False}`","chunkId":"README.md:108"}],[347,{"filePath":"README.md","startLine":524,"endLine":525,"chunkText":"- **`vr_params`:** (Optional) VR Architecture Specific Attributes & Defaults. `Default: {\"batch_size\": 1, \"window_size\": 512, \"aggression\": 5, \"enable_tta\": False, \"enable_post_process\": False, \"post_process_threshold\": 0.2, \"high_end_process\": False}`\n- **`demucs_params`:** (Optional) Demucs Architecture Specific Attributes & Defaults. `Default: {\"segment_size\": \"Default\", \"shifts\": 2, \"overlap\": 0.25, \"segments_enabled\": True}`","chunkId":"README.md:109"}],[348,{"filePath":"README.md","startLine":525,"endLine":529,"chunkText":"- **`demucs_params`:** (Optional) Demucs Architecture Specific Attributes & Defaults. `Default: {\"segment_size\": \"Default\", \"shifts\": 2, \"overlap\": 0.25, \"segments_enabled\": True}`\n- **`mdxc_params`:** (Optional) MDXC Architecture Specific Attributes & Defaults. `Default: {\"segment_size\": 256, \"override_model_segment_size\": False, \"batch_size\": 1, \"overlap\": 8, \"pitch_shift\": 0}`\n\n## Remote API Usage 🌐\n","chunkId":"README.md:110"}],[349,{"filePath":"README.md","startLine":527,"endLine":537,"chunkText":"\n## Remote API Usage 🌐\n\nAudio Separator includes a remote API client that allows you to connect to a deployed Audio Separator API service, enabling you to perform audio separation without running the models locally. The API uses asynchronous processing with job polling for efficient handling of separation tasks.\n\nTo deploy Audio Separator as an API on modal.com and use this for remote processing, please see the detailed documentation here: [audio_separator/remote/README.md](audio_separator/remote/README.md).\n\n## Requirements 📋\n\nPython >= 3.10\n","chunkId":"README.md:111"}],[350,{"filePath":"README.md","startLine":535,"endLine":553,"chunkText":"\nPython >= 3.10\n\nLibraries: torch, onnx, onnxruntime, numpy, librosa, requests, six, tqdm, pydub\n\n## Developing Locally\n\nThis project uses Poetry for dependency management and packaging. Follow these steps to setup a local development environment:\n\n### Prerequisites\n\n- Make sure you have Python 3.10 or newer installed on your machine.\n- Install Conda (I recommend Miniforge: [Miniforge GitHub](https://github.com/conda-forge/miniforge)) to manage your Python virtual environments\n\n### Clone the Repository\n\nClone the repository to your local machine:\n\n```sh","chunkId":"README.md:112"}],[351,{"filePath":"README.md","startLine":551,"endLine":574,"chunkText":"Clone the repository to your local machine:\n\n```sh\ngit clone https://github.com/YOUR_USERNAME/audio-separator.git\ncd audio-separator\n```\n\nReplace `YOUR_USERNAME` with your GitHub username if you've forked the repository, or use the main repository URL if you have the permissions.\n\n### Create and activate the Conda Environment\n\nTo create and activate the conda environment, use the following commands:\n\n```sh\nconda env create\nconda activate audio-separator-dev\n```\n\n### Install Dependencies\n\nOnce you're inside the conda env, run the following command to install the project dependencies:\n\n```sh\npoetry install","chunkId":"README.md:113"}],[352,{"filePath":"README.md","startLine":574,"endLine":607,"chunkText":"poetry install\n```\n\nInstall extra dependencies depending if you're running with GPU or CPU.\n```sh\npoetry install --extras \"cpu\"\n```\nor\n```sh\npoetry install --extras \"gpu\"\n```\nor\n```sh\npoetry install --extras \"dml\"\n```\n\n### Running the Command-Line Interface Locally\n\nYou can run the CLI command directly within the virtual environment. For example:\n\n```sh\naudio-separator path/to/your/audio-file.wav\n```\n\n### Deactivate the Virtual Environment\n\nOnce you are done with your development work, you can exit the virtual environment by simply typing:\n\n```sh\nconda deactivate\n```\n\n### Building the Package\n","chunkId":"README.md:114"}],[353,{"filePath":"README.md","startLine":605,"endLine":621,"chunkText":"\n### Building the Package\n\nTo build the package for distribution, use the following command:\n\n```sh\npoetry build\n```\n\nThis will generate the distribution packages in the dist directory - but for now only @beveradb will be able to publish to PyPI.\n\n\n## Contributing 🤝\n\nContributions are very much welcome! Please fork the repository and submit a pull request with your changes, and I'll try to review, merge and publish promptly!\n\n- This project is 100% open-source and free for anyone to use and modify as they wish.","chunkId":"README.md:115"}],[354,{"filePath":"README.md","startLine":619,"endLine":622,"chunkText":"Contributions are very much welcome! Please fork the repository and submit a pull request with your changes, and I'll try to review, merge and publish promptly!\n\n- This project is 100% open-source and free for anyone to use and modify as they wish.\n- If the maintenance workload for this repo somehow becomes too much for me I'll ask for volunteers to share maintainership of the repo, though I don't think that is very likely","chunkId":"README.md:116"}],[355,{"filePath":"README.md","startLine":620,"endLine":621,"chunkText":"- This project is 100% open-source and free for anyone to use and modify as they wish.\n- If the maintenance workload for this repo somehow becomes too much for me I'll ask for volunteers to share maintainership of the repo, though I don't think that is very likely","chunkId":"README.md:117"}],[356,{"filePath":"README.md","startLine":621,"endLine":622,"chunkText":"- This project is 100% open-source and free for anyone to use and modify as they wish.\n- If the maintenance workload for this repo somehow becomes too much for me I'll ask for volunteers to share maintainership of the repo, though I don't think that is very likely","chunkId":"README.md:118"}],[357,{"filePath":"README.md","startLine":622,"endLine":626,"chunkText":"- If the maintenance workload for this repo somehow becomes too much for me I'll ask for volunteers to share maintainership of the repo, though I don't think that is very likely\n- Development and support for the MDX-Net separation models is part of the main [UVR project](https://github.com/Anjok07/ultimatevocalremovergui), this repo is just a CLI/Python package wrapper to simplify running those models programmatically. So, if you want to try and improve the actual models, please get involved in the UVR project and look for guidance there!\n\n## License 📄\n","chunkId":"README.md:119"}],[358,{"filePath":"README.md","startLine":624,"endLine":632,"chunkText":"\n## License 📄\n\nThis project is licensed under the MIT [License](LICENSE).\n\n- **Please Note:** If you choose to integrate this project into some other project using the default model or any other model trained as part of the [UVR](https://github.com/Anjok07/ultimatevocalremovergui) project, please honor the MIT license by providing credit to UVR and its developers!\n\n## Credits 🙏\n","chunkId":"README.md:120"}],[359,{"filePath":"README.md","startLine":630,"endLine":635,"chunkText":"\n## Credits 🙏\n\n- [Anjok07](https://github.com/Anjok07) - Author of [Ultimate Vocal Remover GUI](https://github.com/Anjok07/ultimatevocalremovergui), which almost all of the code in this repo was copied from! Definitely deserving of credit for anything good from this project. Thank you!\n- [DilanBoskan](https://github.com/DilanBoskan) - Your contributions at the start of this project were essential to the success of UVR. Thank you!\n- [Kuielab & Woosung Choi](https://github.com/kuielab) - Developed the original MDX-Net AI code.","chunkId":"README.md:121"}],[360,{"filePath":"README.md","startLine":633,"endLine":635,"chunkText":"- [Anjok07](https://github.com/Anjok07) - Author of [Ultimate Vocal Remover GUI](https://github.com/Anjok07/ultimatevocalremovergui), which almost all of the code in this repo was copied from! Definitely deserving of credit for anything good from this project. Thank you!\n- [DilanBoskan](https://github.com/DilanBoskan) - Your contributions at the start of this project were essential to the success of UVR. Thank you!\n- [Kuielab & Woosung Choi](https://github.com/kuielab) - Developed the original MDX-Net AI code.","chunkId":"README.md:122"}],[361,{"filePath":"README.md","startLine":634,"endLine":637,"chunkText":"- [DilanBoskan](https://github.com/DilanBoskan) - Your contributions at the start of this project were essential to the success of UVR. Thank you!\n- [Kuielab & Woosung Choi](https://github.com/kuielab) - Developed the original MDX-Net AI code.\n- [KimberleyJSN](https://github.com/KimberleyJensen) - Advised and aided the implementation of the training scripts for MDX-Net and Demucs. Thank you!\n- [Hv](https://github.com/NaJeongMo/Colab-for-MDX_B) - Helped implement chunks into the MDX-Net AI code. Thank you!","chunkId":"README.md:123"}],[362,{"filePath":"README.md","startLine":635,"endLine":641,"chunkText":"- [Kuielab & Woosung Choi](https://github.com/kuielab) - Developed the original MDX-Net AI code.\n- [KimberleyJSN](https://github.com/KimberleyJensen) - Advised and aided the implementation of the training scripts for MDX-Net and Demucs. Thank you!\n- [Hv](https://github.com/NaJeongMo/Colab-for-MDX_B) - Helped implement chunks into the MDX-Net AI code. Thank you!\n- [zhzhongshi](https://github.com/zhzhongshi) - Helped add support for the MDXC models in `audio-separator`. Thank you!\n\n## Contact 💌\n","chunkId":"README.md:124"}],[363,{"filePath":"README.md","startLine":639,"endLine":656,"chunkText":"\n## Contact 💌\n\nFor questions or feedback, please raise an issue or reach out to @beveradb ([Andrew Beveridge](mailto:andrew@beveridge.uk)) directly.\n\n---\n<div align=\"center\">\n\n<!-- sponsors --><!-- sponsors -->\n\n## Thanks to all contributors for their efforts\n\n<a href=\"https://github.com/nomadkaraoke/python-audio-separator/graphs/contributors\">\n  <img src=\"https://contrib.rocks/image?repo=nomadkaraoke/python-audio-separator\" />\n</a>\n\n</div>\n","chunkId":"README.md:125"}],[364,{"filePath":"TODO.md","startLine":0,"endLine":9,"chunkText":"# Audio-Separator TO-DO list\n\nIf you see something here, Andrew is aware it needs to be done, and he will hopefully get to it soon but can't make any promises!\nThis isn't his full time job, and he's doing his best to keep up with everything.\n\nIf you'd like something to be done sooner, please consider trying to work on it yourself and submitting a pull request!\nIf you don't know how to code, please consider learning - it's free, and anyone can do it! https://www.freecodecamp.org/learn/scientific-computing-with-python/\n\n## TODO:\n","chunkId":"TODO.md:0"}],[365,{"filePath":"TODO.md","startLine":9,"endLine":18,"chunkText":"\n- Add unit tests to all uvr lib functions to ensure no obvious errors are missed\n- Add end-to-end tests which download all models and test separation with a very short input file for speed\n- Add ability for user to download all models ahead of time\n- Add tests for Windows, Linux and macOS separately\n- Add tests for Python 3.10, 3.11\n- Add support for Ensemble mode\n- Add support for Chaining multiple models\n- Add support for Splitting multiple stems from a single input file by running different models\n","chunkId":"TODO.md:1"}],[366,{"filePath":"audio_separator/remote/README.md","startLine":0,"endLine":4,"chunkText":"## Remote API Usage 🌐\n\nAudio Separator includes a remote API client that allows you to connect to a deployed Audio Separator API service, enabling you to perform audio separation without running the models locally. The API uses asynchronous processing with job polling for efficient handling of separation tasks.\n\nTo make this easy to set up and use cheaply, we've created a deployment script for [modal.com](https://modal.com) who currently (July 2025) offer $30/mo free in GPU credits.","chunkId":"audio_separator/remote/README.md:0"}],[367,{"filePath":"audio_separator/remote/README.md","startLine":2,"endLine":4,"chunkText":"Audio Separator includes a remote API client that allows you to connect to a deployed Audio Separator API service, enabling you to perform audio separation without running the models locally. The API uses asynchronous processing with job polling for efficient handling of separation tasks.\n\nTo make this easy to set up and use cheaply, we've created a deployment script for [modal.com](https://modal.com) who currently (July 2025) offer $30/mo free in GPU credits.","chunkId":"audio_separator/remote/README.md:1"}],[368,{"filePath":"audio_separator/remote/README.md","startLine":3,"endLine":10,"chunkText":"\nTo make this easy to set up and use cheaply, we've created a deployment script for [modal.com](https://modal.com) who currently (July 2025) offer $30/mo free in GPU credits.\nConsidering [their pricing](https://modal.com/pricing) for execution with an Nvidia T4 is $0.000164 / sec and most audio-separation jobs take less than 2 minutes, that's around $0.019 per separation job.\nWith $30/month in free credits, that's over **1,500 GPU-accelerated audio separation jobs per month, for free!**\n\n**✨ Key Features:**\n\n- **Multiple Model Support**: Upload once, separate with multiple models in a single job","chunkId":"audio_separator/remote/README.md:2"}],[369,{"filePath":"audio_separator/remote/README.md","startLine":8,"endLine":19,"chunkText":"**✨ Key Features:**\n\n- **Multiple Model Support**: Upload once, separate with multiple models in a single job\n- **Full Parameter Compatibility**: All local CLI parameters and architecture settings supported\n- **Efficient Processing**: Avoid repeated uploads when comparing different models\n- **Real-time Progress**: Track progress across multiple models with detailed status updates\n\n#### Multiple Model Workflow\n\n```mermaid\ngraph TD\n    A[\"🎵 Upload Audio File<br/>audio.wav\"] --> B[\"☁️ Remote API Server\"]","chunkId":"audio_separator/remote/README.md:3"}],[370,{"filePath":"audio_separator/remote/README.md","startLine":17,"endLine":25,"chunkText":"```mermaid\ngraph TD\n    A[\"🎵 Upload Audio File<br/>audio.wav\"] --> B[\"☁️ Remote API Server\"]\n    B --> C[\"📋 Queue Multiple Models<br/>• model_bs_roformer.ckpt<br/>• UVR-MDX-NET.onnx<br/>• htdemucs_6s.yaml\"]\n    C --> D[\"🚀 Process Model 1<br/>BS-Roformer\"]\n    C --> E[\"🚀 Process Model 2<br/>MDX-NET\"]\n    C --> F[\"🚀 Process Model 3<br/>Demucs 6-stem\"]\n    D --> G[\"📁 Generate Output<br/>audio_(Vocals)_bs_roformer.flac<br/>audio_(Instrumental)_bs_roformer.flac\"]\n    E --> H[\"📁 Generate Output<br/>audio_(Vocals)_mdx.flac<br/>audio_(Instrumental)_mdx.flac\"]","chunkId":"audio_separator/remote/README.md:4"}],[371,{"filePath":"audio_separator/remote/README.md","startLine":23,"endLine":33,"chunkText":"    C --> F[\"🚀 Process Model 3<br/>Demucs 6-stem\"]\n    D --> G[\"📁 Generate Output<br/>audio_(Vocals)_bs_roformer.flac<br/>audio_(Instrumental)_bs_roformer.flac\"]\n    E --> H[\"📁 Generate Output<br/>audio_(Vocals)_mdx.flac<br/>audio_(Instrumental)_mdx.flac\"]\n    F --> I[\"📁 Generate Output<br/>audio_(Vocals)_htdemucs.flac<br/>audio_(Drums)_htdemucs.flac<br/>audio_(Bass)_htdemucs.flac<br/>etc...\"]\n    G --> J[\"📥 Download All Results<br/>Compare quality across models\"]\n    H --> J\n    I --> J\n\n    style A fill:#e1f5fe,color:#000\n    style B fill:#f3e5f5,color:#000\n    style C fill:#fff3e0,color:#000","chunkId":"audio_separator/remote/README.md:5"}],[372,{"filePath":"audio_separator/remote/README.md","startLine":31,"endLine":45,"chunkText":"    style A fill:#e1f5fe,color:#000\n    style B fill:#f3e5f5,color:#000\n    style C fill:#fff3e0,color:#000\n    style D fill:#e8f5e8,color:#000\n    style E fill:#e8f5e8,color:#000\n    style F fill:#e8f5e8,color:#000\n    style J fill:#e1f5fe,color:#000\n```\n\n### Deploying the API Server\n\nTo use the remote API functionality, you'll need to deploy the Audio Separator API server. The easiest way is using Modal.com:\n\n1. **Sign up for Modal.com** at [modal.com](https://modal.com)\n2. **Install the Modal CLI** and authenticate:","chunkId":"audio_separator/remote/README.md:6"}],[373,{"filePath":"audio_separator/remote/README.md","startLine":43,"endLine":57,"chunkText":"\n1. **Sign up for Modal.com** at [modal.com](https://modal.com)\n2. **Install the Modal CLI** and authenticate:\n   (Note, modal will need access to the installed project dependencies in whatever virtual environment you're using for audio-separator)\n   ```bash\n   poetry install --extras cpu # need either cpu or gpu to ensure onnxruntime is installed\n   pip install modal\n   modal setup\n   ```\n3. **Deploy the Audio Separator API**:\n   ```bash\n   modal deploy audio_separator/remote/deploy_modal.py\n   ```\n4. **Get your API URL** from the deployment output. It will look like:\n   ```","chunkId":"audio_separator/remote/README.md:7"}],[374,{"filePath":"audio_separator/remote/README.md","startLine":55,"endLine":79,"chunkText":"   ```\n4. **Get your API URL** from the deployment output. It will look like:\n   ```\n   https://USERNAME--audio-separator-api.modal.run\n   ```\n\nSet this API URL as an environment variable:\n\n```bash\nexport AUDIO_SEPARATOR_API_URL=\"https://USERNAME--audio-separator-api.modal.run\"\n```\n\nOr pass it directly with the `--api_url` parameter.\n\n### Remote API Client (Python)\n\nYou can use the `AudioSeparatorAPIClient` class to interact with a remote Audio Separator API:\n\n```python\nimport logging\nfrom audio_separator.remote import AudioSeparatorAPIClient\n\n# Set up logging\nlogger = logging.getLogger(__name__)\n","chunkId":"audio_separator/remote/README.md:8"}],[375,{"filePath":"audio_separator/remote/README.md","startLine":77,"endLine":92,"chunkText":"# Set up logging\nlogger = logging.getLogger(__name__)\n\n# Initialize the API client\napi_client = AudioSeparatorAPIClient(\"https://USERNAME--audio-separator-api.modal.run\", logger)\n\n# Simple example: separate audio and get results\nresult = api_client.separate_audio_and_wait(\"audio.mp3\")\nif result[\"status\"] == \"completed\":\n    print(f\"✅ Separation completed! Downloaded files:\")\n    for file_path in result[\"downloaded_files\"]:\n        print(f\"  - {file_path}\")\nelse:\n    print(f\"❌ Separation failed: {result.get('error', 'Unknown error')}\")\n\n# Multiple models example: separate with multiple models in one upload","chunkId":"audio_separator/remote/README.md:9"}],[376,{"filePath":"audio_separator/remote/README.md","startLine":90,"endLine":105,"chunkText":"    print(f\"❌ Separation failed: {result.get('error', 'Unknown error')}\")\n\n# Multiple models example: separate with multiple models in one upload\nresult = api_client.separate_audio_and_wait(\n    \"path/to/audio.wav\",\n    models=[\n        \"model_bs_roformer_ep_317_sdr_12.9755.ckpt\",\n        \"UVR-MDX-NET-Inst_HQ_4.onnx\",\n        \"htdemucs_6s.yaml\"\n    ],\n    timeout=600,           # Wait up to 10 minutes for multiple models\n    poll_interval=10,      # Check status every 10 seconds\n    download=True,         # Automatically download files\n    output_dir=\"./output\"  # Save files to specific directory\n)\n","chunkId":"audio_separator/remote/README.md:10"}],[377,{"filePath":"audio_separator/remote/README.md","startLine":103,"endLine":116,"chunkText":"    output_dir=\"./output\"  # Save files to specific directory\n)\n\n# Complex example with custom options and separator parameters\nresult = api_client.separate_audio_and_wait(\n    \"path/to/audio.wav\",\n    model=\"model_bs_roformer_ep_317_sdr_12.9755.ckpt\",\n    timeout=300,           # Wait up to 5 minutes\n    poll_interval=10,      # Check status every 10 seconds\n    download=True,         # Automatically download files\n    output_dir=\"./output\", # Save files to specific directory\n    # Separator configuration options (same as local CLI)\n    output_format=\"wav\",\n    normalization_threshold=0.8,","chunkId":"audio_separator/remote/README.md:11"}],[378,{"filePath":"audio_separator/remote/README.md","startLine":114,"endLine":130,"chunkText":"    # Separator configuration options (same as local CLI)\n    output_format=\"wav\",\n    normalization_threshold=0.8,\n    custom_output_names={\"Vocals\": \"lead_vocals\", \"Instrumental\": \"backing_track\"},\n    # MDX parameters\n    mdx_segment_size=512,\n    mdx_batch_size=2,\n    # VR parameters\n    vr_aggression=10,\n    vr_window_size=320,\n    # And any other separator parameters...\n)\n\n# Advanced approach: manual job management (for custom polling logic)\nresult = api_client.separate_audio(\n    \"path/to/audio.wav\",\n    models=[\"model1.ckpt\", \"model2.onnx\"],","chunkId":"audio_separator/remote/README.md:12"}],[379,{"filePath":"audio_separator/remote/README.md","startLine":128,"endLine":144,"chunkText":"result = api_client.separate_audio(\n    \"path/to/audio.wav\",\n    models=[\"model1.ckpt\", \"model2.onnx\"],\n    custom_output_names={\"Vocals\": \"vocals_output\", \"Instrumental\": \"instrumental_output\"}\n)\ntask_id = result[\"task_id\"]\nprint(f\"Job submitted! Task ID: {task_id}\")\n\n# Custom polling logic\nimport time\nwhile True:\n    status = api_client.get_job_status(task_id)\n    print(f\"Job status: {status['status']}\")\n\n    # Show progress with model information\n    if \"progress\" in status:\n        progress_info = f\"Progress: {status['progress']}%\"","chunkId":"audio_separator/remote/README.md:13"}],[380,{"filePath":"audio_separator/remote/README.md","startLine":142,"endLine":153,"chunkText":"    # Show progress with model information\n    if \"progress\" in status:\n        progress_info = f\"Progress: {status['progress']}%\"\n        if \"current_model_index\" in status and \"total_models\" in status:\n            model_info = f\" (Model {status['current_model_index'] + 1}/{status['total_models']})\"\n            progress_info += model_info\n        print(progress_info)\n\n    if status[\"status\"] == \"completed\":\n        # Download files manually\n        for filehash, filename in status[\"files\"].items():\n            output_path = api_client.download_file_by_hash(task_id, filehash, filename)","chunkId":"audio_separator/remote/README.md:14"}],[381,{"filePath":"audio_separator/remote/README.md","startLine":151,"endLine":170,"chunkText":"        # Download files manually\n        for filehash, filename in status[\"files\"].items():\n            output_path = api_client.download_file_by_hash(task_id, filehash, filename)\n            print(f\"Downloaded: {output_path}\")\n        break\n    elif status[\"status\"] == \"error\":\n        print(f\"Job failed: {status.get('error', 'Unknown error')}\")\n        break\n    else:\n        time.sleep(10)  # Wait 10 seconds\n\n# List available models\nmodels = api_client.list_models()\nprint(models[\"text\"])\n\n# Get server version\nversion = api_client.get_server_version()\nprint(f\"Server version: {version}\")\n```\n","chunkId":"audio_separator/remote/README.md:15"}],[382,{"filePath":"audio_separator/remote/README.md","startLine":168,"endLine":186,"chunkText":"print(f\"Server version: {version}\")\n```\n\n### Remote API CLI\n\nAudio Separator also provides a command-line interface for interacting with remote APIs:\n\n#### Commands\n\n**Separate audio files:**\n\n```bash\n# Separate audio file (asynchronous processing)\naudio-separator-remote separate audio.wav --model model_bs_roformer_ep_317_sdr_12.9755.ckpt\n\n# Multiple models - upload once, separate with multiple models\naudio-separator-remote separate audio.wav --models model_bs_roformer_ep_317_sdr_12.9755.ckpt UVR-MDX-NET-Inst_HQ_4.onnx htdemucs_6s.yaml\n\n# Multiple files","chunkId":"audio_separator/remote/README.md:16"}],[383,{"filePath":"audio_separator/remote/README.md","startLine":184,"endLine":196,"chunkText":"audio-separator-remote separate audio.wav --models model_bs_roformer_ep_317_sdr_12.9755.ckpt UVR-MDX-NET-Inst_HQ_4.onnx htdemucs_6s.yaml\n\n# Multiple files\naudio-separator-remote separate audio1.wav audio2.wav audio3.wav\n\n# Use default model (if not specified)\naudio-separator-remote separate audio.wav\n\n# Advanced separation with custom parameters (all local CLI parameters supported)\naudio-separator-remote separate audio.wav \\\n  --model model_bs_roformer_ep_317_sdr_12.9755.ckpt \\\n  --output_format wav \\\n  --normalization 0.8 \\","chunkId":"audio_separator/remote/README.md:17"}],[384,{"filePath":"audio_separator/remote/README.md","startLine":194,"endLine":223,"chunkText":"  --model model_bs_roformer_ep_317_sdr_12.9755.ckpt \\\n  --output_format wav \\\n  --normalization 0.8 \\\n  --custom_output_names '{\"Vocals\": \"lead_vocals\", \"Instrumental\": \"backing_track\"}' \\\n  --mdx_segment_size 512 \\\n  --vr_aggression 10\n```\n\n**Check job status:**\n\n```bash\naudio-separator-remote status <task_id>\n```\n\n**List available models:**\n\n```bash\n# Pretty formatted list\naudio-separator-remote models\n\n# JSON output\naudio-separator-remote models --format json\n\n# Filter by stem type\naudio-separator-remote models --filter vocals\n```\n\n**Download specific files:**\n\n```bash","chunkId":"audio_separator/remote/README.md:18"}],[385,{"filePath":"audio_separator/remote/README.md","startLine":221,"endLine":245,"chunkText":"**Download specific files:**\n\n```bash\naudio-separator-remote download <task_id> filename1.wav filename2.wav\n```\n\n**Get version information:**\n\n```bash\naudio-separator-remote --version\n```\n\n#### CLI Options\n\n**Global Options:**\n\n- `--api_url`: Override the API URL\n- `--timeout`: Set timeout for polling (default: 600 seconds)\n- `--poll_interval`: Set polling interval (default: 10 seconds)\n- `--debug`: Enable debug logging\n- `--log_level`: Set log level (info, debug, warning, etc.)\n\n**Model Selection:**\n\n- `--model`: Single model to use for separation","chunkId":"audio_separator/remote/README.md:19"}],[386,{"filePath":"audio_separator/remote/README.md","startLine":243,"endLine":256,"chunkText":"**Model Selection:**\n\n- `--model`: Single model to use for separation\n- `--models`: Multiple models to use for separation (space-separated)\n\n**Output Parameters:**\n\n- `--output_format`: Output format (default: flac)\n- `--output_bitrate`: Output bitrate\n- `--normalization`: Max peak amplitude to normalize to (default: 0.9)\n- `--amplification`: Min peak amplitude to amplify to (default: 0.0)\n- `--single_stem`: Output only single stem (e.g. Vocals, Instrumental)\n- `--invert_spect`: Invert secondary stem using spectrogram\n- `--sample_rate`: Sample rate of output audio (default: 44100)","chunkId":"audio_separator/remote/README.md:20"}],[387,{"filePath":"audio_separator/remote/README.md","startLine":254,"endLine":264,"chunkText":"- `--single_stem`: Output only single stem (e.g. Vocals, Instrumental)\n- `--invert_spect`: Invert secondary stem using spectrogram\n- `--sample_rate`: Sample rate of output audio (default: 44100)\n- `--use_soundfile`: Use soundfile for output writing\n- `--use_autocast`: Use PyTorch autocast for faster inference\n- `--custom_output_names`: Custom output names in JSON format\n\n**Architecture-Specific Parameters:**\nAll MDX, VR, Demucs, and MDXC parameters from the local CLI are supported:\n\n- `--mdx_segment_size`, `--mdx_overlap`, `--mdx_batch_size`, etc.","chunkId":"audio_separator/remote/README.md:21"}],[388,{"filePath":"audio_separator/remote/README.md","startLine":262,"endLine":276,"chunkText":"All MDX, VR, Demucs, and MDXC parameters from the local CLI are supported:\n\n- `--mdx_segment_size`, `--mdx_overlap`, `--mdx_batch_size`, etc.\n- `--vr_batch_size`, `--vr_window_size`, `--vr_aggression`, etc.\n- `--demucs_segment_size`, `--demucs_shifts`, `--demucs_overlap`, etc.\n- `--mdxc_segment_size`, `--mdxc_overlap`, `--mdxc_batch_size`, etc.\n\n#### Examples\n\n```bash\n# Separate with multiple models and custom settings\naudio-separator-remote separate song.mp3 \\\n  --models model_bs_roformer_ep_317_sdr_12.9755.ckpt UVR-MDX-NET-Inst_HQ_4.onnx \\\n  --output_format wav \\\n  --normalization 0.8 \\","chunkId":"audio_separator/remote/README.md:22"}],[389,{"filePath":"audio_separator/remote/README.md","startLine":274,"endLine":290,"chunkText":"  --models model_bs_roformer_ep_317_sdr_12.9755.ckpt UVR-MDX-NET-Inst_HQ_4.onnx \\\n  --output_format wav \\\n  --normalization 0.8 \\\n  --api_url https://my-api.com \\\n  --timeout 600\n\n# Separate with custom output names\naudio-separator-remote separate song.mp3 \\\n  --model htdemucs_6s.yaml \\\n  --custom_output_names '{\"Vocals\": \"vocals\", \"Drums\": \"drums\", \"Bass\": \"bass\", \"Other\": \"other\"}'\n\n# Check status with debug logging\naudio-separator-remote status abc123 --debug\n\n# List vocal separation models in JSON format\naudio-separator-remote models --filter vocals --format json\n","chunkId":"audio_separator/remote/README.md:23"}],[390,{"filePath":"audio_separator/remote/README.md","startLine":288,"endLine":304,"chunkText":"# List vocal separation models in JSON format\naudio-separator-remote models --filter vocals --format json\n\n# Use VR parameters for better vocal isolation\naudio-separator-remote separate vocals.wav \\\n  --model 2_HP-UVR.pth \\\n  --vr_aggression 15 \\\n  --vr_window_size 320 \\\n  --vr_enable_tta\n```\n\n#### Key Features\n\nThe remote API client automatically handles:\n\n- **File uploading and downloading**: Seamless transfer of audio files and results\n- **Multiple model processing**: Upload once, separate with multiple models efficiently","chunkId":"audio_separator/remote/README.md:24"}],[391,{"filePath":"audio_separator/remote/README.md","startLine":302,"endLine":311,"chunkText":"\n- **File uploading and downloading**: Seamless transfer of audio files and results\n- **Multiple model processing**: Upload once, separate with multiple models efficiently\n- **Full separator compatibility**: All local CLI parameters and architectures supported\n- **Job polling and status updates**: Real-time progress tracking with model-specific information\n- **Error handling and retries**: Robust error handling for reliable processing\n- **Progress reporting**: Detailed progress updates including current model being processed\n\n#### Benefits of Multiple Model Support\n","chunkId":"audio_separator/remote/README.md:25"}],[392,{"filePath":"audio_separator/remote/README.md","startLine":309,"endLine":320,"chunkText":"\n#### Benefits of Multiple Model Support\n\nWhen using multiple models, the remote API provides significant advantages:\n\n- **Efficiency**: Upload your audio file once, process with multiple models without re-uploading\n- **Comparison**: Easily compare results from different models (e.g., vocals vs. instrumental quality)\n- **Workflow optimization**: Process with complementary models in a single job\n- **Time savings**: Avoid repeated upload times for large audio files\n\nExample use cases:\n","chunkId":"audio_separator/remote/README.md:26"}],[393,{"filePath":"audio_separator/remote/README.md","startLine":318,"endLine":324,"chunkText":"\nExample use cases:\n\n- Compare quality between `model_bs_roformer_ep_317_sdr_12.9755.ckpt` (high-quality vocals) and `UVR-MDX-NET-Inst_HQ_4.onnx` (high-quality instrumental)\n- Process with both 2-stem models (vocals/instrumental) and multi-stem models (vocals/drums/bass/other) in one job\n- Use different models optimized for different parts of the frequency spectrum\n","chunkId":"audio_separator/remote/README.md:27"}],[394,{"filePath":"audio_separator/remote/__init__.py","startLine":0,"endLine":3,"chunkText":"from .api_client import AudioSeparatorAPIClient\n\n__all__ = [\"AudioSeparatorAPIClient\"]\n","chunkId":"audio_separator/remote/__init__.py:0"}],[395,{"filePath":"audio_separator/remote/api_client.py","startLine":0,"endLine":21,"chunkText":"#!/usr/bin/env python\nimport os\nimport logging\nimport json\nfrom typing import Optional, List, Dict\nfrom urllib.parse import quote\n\nimport requests\n\n# Get package version for debugging\ntry:\n    from importlib.metadata import version\n    AUDIO_SEPARATOR_VERSION = version(\"audio-separator\")\nexcept ImportError:\n    try:\n        import pkg_resources\n        AUDIO_SEPARATOR_VERSION = pkg_resources.get_distribution(\"audio-separator\").version\n    except Exception:\n        AUDIO_SEPARATOR_VERSION = \"unknown\"\n\n\nclass AudioSeparatorAPIClient:","chunkId":"audio_separator/remote/api_client.py:0"}],[396,{"filePath":"audio_separator/remote/api_client.py","startLine":21,"endLine":37,"chunkText":"class AudioSeparatorAPIClient:\n    \"\"\"Client for interacting with a remotely deployed Audio Separator API.\"\"\"\n\n    def __init__(self, api_url: str, logger: logging.Logger):\n        self.api_url = api_url\n        self.logger = logger\n        self.session = requests.Session()\n\n    def separate_audio(\n        self,\n        file_path: str,\n        model: Optional[str] = None,\n        models: Optional[List[str]] = None,\n        # Output parameters\n        output_format: str = \"flac\",\n        output_bitrate: Optional[str] = None,\n        normalization_threshold: float = 0.9,","chunkId":"audio_separator/remote/api_client.py:1"}],[397,{"filePath":"audio_separator/remote/api_client.py","startLine":35,"endLine":49,"chunkText":"        output_format: str = \"flac\",\n        output_bitrate: Optional[str] = None,\n        normalization_threshold: float = 0.9,\n        amplification_threshold: float = 0.0,\n        output_single_stem: Optional[str] = None,\n        invert_using_spec: bool = False,\n        sample_rate: int = 44100,\n        use_soundfile: bool = False,\n        use_autocast: bool = False,\n        custom_output_names: Optional[Dict[str, str]] = None,\n        # MDX parameters\n        mdx_segment_size: int = 256,\n        mdx_overlap: float = 0.25,\n        mdx_batch_size: int = 1,\n        mdx_hop_length: int = 1024,","chunkId":"audio_separator/remote/api_client.py:2"}],[398,{"filePath":"audio_separator/remote/api_client.py","startLine":47,"endLine":62,"chunkText":"        mdx_overlap: float = 0.25,\n        mdx_batch_size: int = 1,\n        mdx_hop_length: int = 1024,\n        mdx_enable_denoise: bool = False,\n        # VR parameters\n        vr_batch_size: int = 1,\n        vr_window_size: int = 512,\n        vr_aggression: int = 5,\n        vr_enable_tta: bool = False,\n        vr_high_end_process: bool = False,\n        vr_enable_post_process: bool = False,\n        vr_post_process_threshold: float = 0.2,\n        # Demucs parameters\n        demucs_segment_size: str = \"Default\",\n        demucs_shifts: int = 2,\n        demucs_overlap: float = 0.25,","chunkId":"audio_separator/remote/api_client.py:3"}],[399,{"filePath":"audio_separator/remote/api_client.py","startLine":60,"endLine":74,"chunkText":"        demucs_segment_size: str = \"Default\",\n        demucs_shifts: int = 2,\n        demucs_overlap: float = 0.25,\n        demucs_segments_enabled: bool = True,\n        # MDXC parameters\n        mdxc_segment_size: int = 256,\n        mdxc_override_model_segment_size: bool = False,\n        mdxc_overlap: int = 8,\n        mdxc_batch_size: int = 1,\n        mdxc_pitch_shift: int = 0,\n    ) -> dict:\n        \"\"\"Submit audio separation job (asynchronous processing).\"\"\"\n        if not os.path.exists(file_path):\n            raise FileNotFoundError(f\"Audio file not found: {file_path}\")\n","chunkId":"audio_separator/remote/api_client.py:4"}],[400,{"filePath":"audio_separator/remote/api_client.py","startLine":72,"endLine":88,"chunkText":"        if not os.path.exists(file_path):\n            raise FileNotFoundError(f\"Audio file not found: {file_path}\")\n\n        files = {\"file\": (os.path.basename(file_path), open(file_path, \"rb\"))}\n        data = {}\n\n        # Handle model parameters (backwards compatibility)\n        if models:\n            data[\"models\"] = json.dumps(models)\n        elif model:\n            data[\"model\"] = model\n\n        # Add all separator parameters\n        data.update(\n            {\n                \"output_format\": output_format,\n                \"normalization_threshold\": normalization_threshold,","chunkId":"audio_separator/remote/api_client.py:5"}],[401,{"filePath":"audio_separator/remote/api_client.py","startLine":86,"endLine":97,"chunkText":"            {\n                \"output_format\": output_format,\n                \"normalization_threshold\": normalization_threshold,\n                \"amplification_threshold\": amplification_threshold,\n                \"invert_using_spec\": invert_using_spec,\n                \"sample_rate\": sample_rate,\n                \"use_soundfile\": use_soundfile,\n                \"use_autocast\": use_autocast,\n                # MDX parameters\n                \"mdx_segment_size\": mdx_segment_size,\n                \"mdx_overlap\": mdx_overlap,\n                \"mdx_batch_size\": mdx_batch_size,","chunkId":"audio_separator/remote/api_client.py:6"}],[402,{"filePath":"audio_separator/remote/api_client.py","startLine":95,"endLine":106,"chunkText":"                \"mdx_segment_size\": mdx_segment_size,\n                \"mdx_overlap\": mdx_overlap,\n                \"mdx_batch_size\": mdx_batch_size,\n                \"mdx_hop_length\": mdx_hop_length,\n                \"mdx_enable_denoise\": mdx_enable_denoise,\n                # VR parameters\n                \"vr_batch_size\": vr_batch_size,\n                \"vr_window_size\": vr_window_size,\n                \"vr_aggression\": vr_aggression,\n                \"vr_enable_tta\": vr_enable_tta,\n                \"vr_high_end_process\": vr_high_end_process,\n                \"vr_enable_post_process\": vr_enable_post_process,","chunkId":"audio_separator/remote/api_client.py:7"}],[403,{"filePath":"audio_separator/remote/api_client.py","startLine":104,"endLine":114,"chunkText":"                \"vr_enable_tta\": vr_enable_tta,\n                \"vr_high_end_process\": vr_high_end_process,\n                \"vr_enable_post_process\": vr_enable_post_process,\n                \"vr_post_process_threshold\": vr_post_process_threshold,\n                # Demucs parameters\n                \"demucs_segment_size\": demucs_segment_size,\n                \"demucs_shifts\": demucs_shifts,\n                \"demucs_overlap\": demucs_overlap,\n                \"demucs_segments_enabled\": demucs_segments_enabled,\n                # MDXC parameters\n                \"mdxc_segment_size\": mdxc_segment_size,","chunkId":"audio_separator/remote/api_client.py:8"}],[404,{"filePath":"audio_separator/remote/api_client.py","startLine":112,"endLine":125,"chunkText":"                \"demucs_segments_enabled\": demucs_segments_enabled,\n                # MDXC parameters\n                \"mdxc_segment_size\": mdxc_segment_size,\n                \"mdxc_override_model_segment_size\": mdxc_override_model_segment_size,\n                \"mdxc_overlap\": mdxc_overlap,\n                \"mdxc_batch_size\": mdxc_batch_size,\n                \"mdxc_pitch_shift\": mdxc_pitch_shift,\n            }\n        )\n\n        # Add optional parameters only if they have non-default values\n        if output_bitrate:\n            data[\"output_bitrate\"] = output_bitrate\n        if output_single_stem:","chunkId":"audio_separator/remote/api_client.py:9"}],[405,{"filePath":"audio_separator/remote/api_client.py","startLine":123,"endLine":135,"chunkText":"        if output_bitrate:\n            data[\"output_bitrate\"] = output_bitrate\n        if output_single_stem:\n            data[\"output_single_stem\"] = output_single_stem\n        if custom_output_names:\n            data[\"custom_output_names\"] = json.dumps(custom_output_names)\n\n        try:\n            # Increase timeout for large files (5 minutes)\n            response = self.session.post(f\"{self.api_url}/separate\", files=files, data=data, timeout=300)\n            response.raise_for_status()\n            return response.json()\n        except requests.RequestException as e:","chunkId":"audio_separator/remote/api_client.py:10"}],[406,{"filePath":"audio_separator/remote/api_client.py","startLine":133,"endLine":150,"chunkText":"            response.raise_for_status()\n            return response.json()\n        except requests.RequestException as e:\n            self.logger.error(f\"Separation request failed: {e}\")\n            raise\n        finally:\n            files[\"file\"][1].close()\n\n    def separate_audio_and_wait(\n        self,\n        file_path: str,\n        model: Optional[str] = None,\n        models: Optional[List[str]] = None,\n        timeout: int = 600,\n        poll_interval: int = 10,\n        download: bool = True,\n        output_dir: Optional[str] = None,\n        # All separator parameters (same as separate_audio)","chunkId":"audio_separator/remote/api_client.py:11"}],[407,{"filePath":"audio_separator/remote/api_client.py","startLine":148,"endLine":161,"chunkText":"        download: bool = True,\n        output_dir: Optional[str] = None,\n        # All separator parameters (same as separate_audio)\n        output_format: str = \"flac\",\n        output_bitrate: Optional[str] = None,\n        normalization_threshold: float = 0.9,\n        amplification_threshold: float = 0.0,\n        output_single_stem: Optional[str] = None,\n        invert_using_spec: bool = False,\n        sample_rate: int = 44100,\n        use_soundfile: bool = False,\n        use_autocast: bool = False,\n        custom_output_names: Optional[Dict[str, str]] = None,\n        mdx_segment_size: int = 256,","chunkId":"audio_separator/remote/api_client.py:12"}],[408,{"filePath":"audio_separator/remote/api_client.py","startLine":159,"endLine":173,"chunkText":"        use_autocast: bool = False,\n        custom_output_names: Optional[Dict[str, str]] = None,\n        mdx_segment_size: int = 256,\n        mdx_overlap: float = 0.25,\n        mdx_batch_size: int = 1,\n        mdx_hop_length: int = 1024,\n        mdx_enable_denoise: bool = False,\n        vr_batch_size: int = 1,\n        vr_window_size: int = 512,\n        vr_aggression: int = 5,\n        vr_enable_tta: bool = False,\n        vr_high_end_process: bool = False,\n        vr_enable_post_process: bool = False,\n        vr_post_process_threshold: float = 0.2,\n        demucs_segment_size: str = \"Default\",","chunkId":"audio_separator/remote/api_client.py:13"}],[409,{"filePath":"audio_separator/remote/api_client.py","startLine":171,"endLine":185,"chunkText":"        vr_enable_post_process: bool = False,\n        vr_post_process_threshold: float = 0.2,\n        demucs_segment_size: str = \"Default\",\n        demucs_shifts: int = 2,\n        demucs_overlap: float = 0.25,\n        demucs_segments_enabled: bool = True,\n        mdxc_segment_size: int = 256,\n        mdxc_override_model_segment_size: bool = False,\n        mdxc_overlap: int = 8,\n        mdxc_batch_size: int = 1,\n        mdxc_pitch_shift: int = 0,\n    ) -> dict:\n        \"\"\"\n        Submit audio separation job and wait for completion (convenience method).\n","chunkId":"audio_separator/remote/api_client.py:14"}],[410,{"filePath":"audio_separator/remote/api_client.py","startLine":183,"endLine":194,"chunkText":"        \"\"\"\n        Submit audio separation job and wait for completion (convenience method).\n\n        This method handles the full workflow: submit job, poll for completion,\n        and optionally download the result files.\n\n        Args:\n            file_path: Path to the audio file to separate\n            model: Single model to use for separation (for backwards compatibility)\n            models: List of models to use for separation\n            timeout: Maximum time to wait for completion in seconds (default: 600)\n            poll_interval: How often to check status in seconds (default: 10)","chunkId":"audio_separator/remote/api_client.py:15"}],[411,{"filePath":"audio_separator/remote/api_client.py","startLine":192,"endLine":202,"chunkText":"            models: List of models to use for separation\n            timeout: Maximum time to wait for completion in seconds (default: 600)\n            poll_interval: How often to check status in seconds (default: 10)\n            download: Whether to automatically download result files (default: True)\n            output_dir: Directory to save downloaded files (default: current directory)\n            **kwargs: All other separator parameters (same as separate_audio method)\n\n        Returns:\n            dict with keys:\n                - task_id: The job task ID\n                - status: \"completed\" or \"error","chunkId":"audio_separator/remote/api_client.py:16"}],[412,{"filePath":"audio_separator/remote/api_client.py","startLine":200,"endLine":210,"chunkText":"            dict with keys:\n                - task_id: The job task ID\n                - status: \"completed\" or \"error\"\n                - files: List of output filenames\n                - downloaded_files: List of local file paths (if download=True)\n                - error: Error message (if status=\"error\")\n        \"\"\"\n        import time\n\n        # Submit the separation job with all parameters\n        models_desc = models or ([model] if model else [\"default\"])","chunkId":"audio_separator/remote/api_client.py:17"}],[413,{"filePath":"audio_separator/remote/api_client.py","startLine":208,"endLine":223,"chunkText":"        # Submit the separation job with all parameters\n        models_desc = models or ([model] if model else [\"default\"])\n        self.logger.info(f\"Submitting separation job for '{file_path}' with models: {models_desc} (audio-separator v{AUDIO_SEPARATOR_VERSION})\")\n\n        result = self.separate_audio(\n            file_path,\n            model,\n            models,\n            output_format,\n            output_bitrate,\n            normalization_threshold,\n            amplification_threshold,\n            output_single_stem,\n            invert_using_spec,\n            sample_rate,\n            use_soundfile","chunkId":"audio_separator/remote/api_client.py:18"}],[414,{"filePath":"audio_separator/remote/api_client.py","startLine":222,"endLine":241,"chunkText":"            invert_using_spec,\n            sample_rate,\n            use_soundfile,\n            use_autocast,\n            custom_output_names,\n            mdx_segment_size,\n            mdx_overlap,\n            mdx_batch_size,\n            mdx_hop_length,\n            mdx_enable_denoise,\n            vr_batch_size,\n            vr_window_size,\n            vr_aggression,\n            vr_enable_tta,\n            vr_high_end_process,\n            vr_enable_post_process,\n            vr_post_process_threshold,\n            demucs_segment_size,\n            demucs_shifts,\n            demucs_overlap,","chunkId":"audio_separator/remote/api_client.py:19"}],[415,{"filePath":"audio_separator/remote/api_client.py","startLine":239,"endLine":258,"chunkText":"            demucs_segment_size,\n            demucs_shifts,\n            demucs_overlap,\n            demucs_segments_enabled,\n            mdxc_segment_size,\n            mdxc_override_model_segment_size,\n            mdxc_overlap,\n            mdxc_batch_size,\n            mdxc_pitch_shift,\n        )\n\n        task_id = result[\"task_id\"]\n        self.logger.info(f\"Job submitted! Task ID: {task_id}\")\n\n        # Poll for completion\n        self.logger.info(\"Waiting for separation to complete...\")\n        start_time = time.time()\n        last_progress = -1\n\n        while time.time() - start_time < timeout:","chunkId":"audio_separator/remote/api_client.py:20"}],[416,{"filePath":"audio_separator/remote/api_client.py","startLine":256,"endLine":267,"chunkText":"        last_progress = -1\n\n        while time.time() - start_time < timeout:\n            try:\n                status = self.get_job_status(task_id)\n                current_status = status.get(\"status\", \"unknown\")\n\n                # Show progress if it changed\n                if \"progress\" in status and status[\"progress\"] != last_progress:\n                    progress_info = f\"Progress: {status['progress']}%\"\n                    if \"current_model_index\" in status and \"total_models\" in status:\n                        model_info = f\" (Model {status['current_model_index'] + 1}/{status['total_models']})\"","chunkId":"audio_separator/remote/api_client.py:21"}],[417,{"filePath":"audio_separator/remote/api_client.py","startLine":265,"endLine":275,"chunkText":"                    progress_info = f\"Progress: {status['progress']}%\"\n                    if \"current_model_index\" in status and \"total_models\" in status:\n                        model_info = f\" (Model {status['current_model_index'] + 1}/{status['total_models']})\"\n                        progress_info += model_info\n                    self.logger.info(progress_info)\n                    last_progress = status[\"progress\"]\n\n                # Check if completed\n                if current_status == \"completed\":\n                    self.logger.info(\"✅ Separation completed!\")\n                    ","chunkId":"audio_separator/remote/api_client.py:22"}],[418,{"filePath":"audio_separator/remote/api_client.py","startLine":273,"endLine":282,"chunkText":"                if current_status == \"completed\":\n                    self.logger.info(\"✅ Separation completed!\")\n                    \n                    files_data = status.get(\"files\", {})\n                    \n                    # Handle both old (list) and new (dict) format for backward compatibility\n                    if isinstance(files_data, list):\n                        # Legacy format: list of filenames\n                        self.logger.info(f\"🔍 Job status returned {len(files_data)} files (legacy format)\")\n                        for i, filename in enumerate(files_data):","chunkId":"audio_separator/remote/api_client.py:23"}],[419,{"filePath":"audio_separator/remote/api_client.py","startLine":280,"endLine":286,"chunkText":"                        # Legacy format: list of filenames\n                        self.logger.info(f\"🔍 Job status returned {len(files_data)} files (legacy format)\")\n                        for i, filename in enumerate(files_data):\n                            self.logger.info(f\"  [{i}] '{filename}' (len={len(filename)})\")\n                        result = {\"task_id\": task_id, \"status\": \"completed\", \"files\": files_data}\n                    else:\n                        # New format: dictionary of hash -> filename","chunkId":"audio_separator/remote/api_client.py:24"}],[420,{"filePath":"audio_separator/remote/api_client.py","startLine":284,"endLine":291,"chunkText":"                        result = {\"task_id\": task_id, \"status\": \"completed\", \"files\": files_data}\n                    else:\n                        # New format: dictionary of hash -> filename\n                        self.logger.info(f\"🔍 Job status returned {len(files_data)} files (hash format)\")\n                        for i, (file_hash, filename) in enumerate(files_data.items()):\n                            self.logger.info(f\"  [{i}] hash={file_hash} -> '{filename}' (len={len(filename)})\")\n                        result = {\"task_id\": task_id, \"status\": \"completed\", \"files\": files_data}\n","chunkId":"audio_separator/remote/api_client.py:25"}],[421,{"filePath":"audio_separator/remote/api_client.py","startLine":289,"endLine":298,"chunkText":"                            self.logger.info(f\"  [{i}] hash={file_hash} -> '{filename}' (len={len(filename)})\")\n                        result = {\"task_id\": task_id, \"status\": \"completed\", \"files\": files_data}\n\n                    # Download files if requested\n                    if download:\n                        downloaded_files = []\n                        files_data = status.get(\"files\", {})\n                        \n                        # Handle both old (list) and new (dict) format\n                        if isinstance(files_data, list):","chunkId":"audio_separator/remote/api_client.py:26"}],[422,{"filePath":"audio_separator/remote/api_client.py","startLine":296,"endLine":304,"chunkText":"                        \n                        # Handle both old (list) and new (dict) format\n                        if isinstance(files_data, list):\n                            # Legacy format: list of filenames\n                            self.logger.info(f\"📥 Downloading {len(files_data)} output files (legacy format)...\")\n                            self.logger.info(f\"🔍 Files to download: {files_data}\")\n\n                            for i, filename in enumerate(files_data):\n                                try:","chunkId":"audio_separator/remote/api_client.py:27"}],[423,{"filePath":"audio_separator/remote/api_client.py","startLine":302,"endLine":311,"chunkText":"\n                            for i, filename in enumerate(files_data):\n                                try:\n                                    self.logger.info(f\"🔍 [{i+1}/{len(files_data)}] Attempting to download: '{filename}' (len={len(filename)})\")\n                                    \n                                    if output_dir:\n                                        output_path = f\"{output_dir.rstrip('/')}/{filename}\"\n                                    else:\n                                        output_path = filename\n","chunkId":"audio_separator/remote/api_client.py:28"}],[424,{"filePath":"audio_separator/remote/api_client.py","startLine":309,"endLine":317,"chunkText":"                                    else:\n                                        output_path = filename\n\n                                    downloaded_path = self.download_file(task_id, filename, output_path)\n                                    downloaded_files.append(downloaded_path)\n                                    self.logger.info(f\"  ✅ Downloaded: {downloaded_path}\")\n                                except Exception as e:\n                                    self.logger.error(f\"  ❌ Failed to download {filename}: {e}\")\n                                    self._log_server_version_on_error()","chunkId":"audio_separator/remote/api_client.py:29"}],[425,{"filePath":"audio_separator/remote/api_client.py","startLine":315,"endLine":323,"chunkText":"                                except Exception as e:\n                                    self.logger.error(f\"  ❌ Failed to download {filename}: {e}\")\n                                    self._log_server_version_on_error()\n                        else:\n                            # New format: dictionary of hash -> filename\n                            self.logger.info(f\"📥 Downloading {len(files_data)} output files (hash format)...\")\n                            filenames_list = list(files_data.values())\n                            self.logger.info(f\"🔍 Files to download: {filenames_list}\")\n","chunkId":"audio_separator/remote/api_client.py:30"}],[426,{"filePath":"audio_separator/remote/api_client.py","startLine":321,"endLine":328,"chunkText":"                            filenames_list = list(files_data.values())\n                            self.logger.info(f\"🔍 Files to download: {filenames_list}\")\n\n                            for i, (file_hash, filename) in enumerate(files_data.items()):\n                                try:\n                                    self.logger.info(f\"🔍 [{i+1}/{len(files_data)}] Attempting to download: '{filename}' (hash={file_hash}, len={len(filename)})\")\n                                    \n                                    if output_dir:","chunkId":"audio_separator/remote/api_client.py:31"}],[427,{"filePath":"audio_separator/remote/api_client.py","startLine":326,"endLine":333,"chunkText":"                                    self.logger.info(f\"🔍 [{i+1}/{len(files_data)}] Attempting to download: '{filename}' (hash={file_hash}, len={len(filename)})\")\n                                    \n                                    if output_dir:\n                                        output_path = f\"{output_dir.rstrip('/')}/{filename}\"\n                                    else:\n                                        output_path = filename\n\n                                    downloaded_path = self.download_file_by_hash(task_id, file_hash, filename, output_path)","chunkId":"audio_separator/remote/api_client.py:32"}],[428,{"filePath":"audio_separator/remote/api_client.py","startLine":331,"endLine":339,"chunkText":"                                        output_path = filename\n\n                                    downloaded_path = self.download_file_by_hash(task_id, file_hash, filename, output_path)\n                                    downloaded_files.append(downloaded_path)\n                                    self.logger.info(f\"  ✅ Downloaded: {downloaded_path}\")\n                                except Exception as e:\n                                    self.logger.error(f\"  ❌ Failed to download {filename}: {e}\")\n                                    self._log_server_version_on_error()\n","chunkId":"audio_separator/remote/api_client.py:33"}],[429,{"filePath":"audio_separator/remote/api_client.py","startLine":337,"endLine":347,"chunkText":"                                    self.logger.error(f\"  ❌ Failed to download {filename}: {e}\")\n                                    self._log_server_version_on_error()\n\n                        result[\"downloaded_files\"] = downloaded_files\n                        self.logger.info(f\"🎉 Successfully downloaded {len(downloaded_files)} files!\")\n\n                    return result\n\n                elif current_status == \"error\":\n                    error_msg = status.get(\"error\", \"Unknown error\")\n                    self.logger.error(f\"❌ Job failed: {error_msg}\")","chunkId":"audio_separator/remote/api_client.py:34"}],[430,{"filePath":"audio_separator/remote/api_client.py","startLine":345,"endLine":357,"chunkText":"                elif current_status == \"error\":\n                    error_msg = status.get(\"error\", \"Unknown error\")\n                    self.logger.error(f\"❌ Job failed: {error_msg}\")\n                    return {\"task_id\": task_id, \"status\": \"error\", \"error\": error_msg, \"files\": []}\n\n                # Wait before next poll\n                time.sleep(poll_interval)\n\n            except Exception as e:\n                self.logger.warning(f\"Error polling status: {e}\")\n                time.sleep(poll_interval)\n\n        # Timeout reached","chunkId":"audio_separator/remote/api_client.py:35"}],[431,{"filePath":"audio_separator/remote/api_client.py","startLine":355,"endLine":367,"chunkText":"                time.sleep(poll_interval)\n\n        # Timeout reached\n        self.logger.error(f\"❌ Job polling timed out after {timeout} seconds\")\n        return {\"task_id\": task_id, \"status\": \"timeout\", \"error\": f\"Job polling timed out after {timeout} seconds\", \"files\": []}\n\n    def get_job_status(self, task_id: str) -> dict:\n        \"\"\"Get job status.\"\"\"\n        try:\n            response = self.session.get(f\"{self.api_url}/status/{task_id}\", timeout=10)\n            response.raise_for_status()\n            return response.json()\n        except requests.RequestException as e:","chunkId":"audio_separator/remote/api_client.py:36"}],[432,{"filePath":"audio_separator/remote/api_client.py","startLine":365,"endLine":378,"chunkText":"            response.raise_for_status()\n            return response.json()\n        except requests.RequestException as e:\n            self.logger.error(f\"Status request failed: {e}\")\n            raise\n\n    def download_file(self, task_id: str, filename: str, output_path: Optional[str] = None) -> str:\n        \"\"\"Download a file from a completed job (legacy method for backward compatibility).\"\"\"\n        if output_path is None:\n            output_path = filename\n\n        try:\n            # URL encode the filename to handle spaces and special characters\n            encoded_filename = quote(filename, safe='')","chunkId":"audio_separator/remote/api_client.py:37"}],[433,{"filePath":"audio_separator/remote/api_client.py","startLine":376,"endLine":385,"chunkText":"        try:\n            # URL encode the filename to handle spaces and special characters\n            encoded_filename = quote(filename, safe='')\n            download_url = f\"{self.api_url}/download/{task_id}/{encoded_filename}\"\n            \n            # Debug logging to understand what's happening\n            self.logger.info(f\"🔍 Download details (legacy filename method):\")\n            self.logger.info(f\"  Original filename: '{filename}'\")\n            self.logger.info(f\"  Encoded filename: '{encoded_filename}'\")\n            self.logger.info(f\"  Download URL: {download_url}\")","chunkId":"audio_separator/remote/api_client.py:38"}],[434,{"filePath":"audio_separator/remote/api_client.py","startLine":383,"endLine":393,"chunkText":"            self.logger.info(f\"  Original filename: '{filename}'\")\n            self.logger.info(f\"  Encoded filename: '{encoded_filename}'\")\n            self.logger.info(f\"  Download URL: {download_url}\")\n            self.logger.info(f\"  Task ID: {task_id}\")\n            \n            response = self.session.get(download_url, timeout=60)\n            \n            # Log response details for debugging\n            self.logger.info(f\"🔍 Response status: {response.status_code}\")\n            if response.status_code != 200:\n                try:","chunkId":"audio_separator/remote/api_client.py:39"}],[435,{"filePath":"audio_separator/remote/api_client.py","startLine":391,"endLine":401,"chunkText":"            self.logger.info(f\"🔍 Response status: {response.status_code}\")\n            if response.status_code != 200:\n                try:\n                    self.logger.error(f\"🔍 Response headers: {dict(response.headers)}\")\n                except Exception:\n                    self.logger.error(f\"🔍 Response headers: {response.headers}\")\n                try:\n                    self.logger.error(f\"🔍 Response text (first 500 chars): {response.text[:500]}\")\n                except Exception:\n                    self.logger.error(f\"🔍 Response text: <unavailable>\")\n            ","chunkId":"audio_separator/remote/api_client.py:40"}],[436,{"filePath":"audio_separator/remote/api_client.py","startLine":399,"endLine":413,"chunkText":"                except Exception:\n                    self.logger.error(f\"🔍 Response text: <unavailable>\")\n            \n            response.raise_for_status()\n\n            with open(output_path, \"wb\") as f:\n                f.write(response.content)\n\n            return output_path\n        except requests.RequestException as e:\n            self.logger.error(f\"Download failed: {e}\")\n            raise\n\n    def download_file_by_hash(self, task_id: str, file_hash: str, filename: str, output_path: Optional[str] = None) -> str:\n        \"\"\"Download a file from a completed job using its hash identifier.\"\"\"","chunkId":"audio_separator/remote/api_client.py:41"}],[437,{"filePath":"audio_separator/remote/api_client.py","startLine":411,"endLine":422,"chunkText":"\n    def download_file_by_hash(self, task_id: str, file_hash: str, filename: str, output_path: Optional[str] = None) -> str:\n        \"\"\"Download a file from a completed job using its hash identifier.\"\"\"\n        if output_path is None:\n            output_path = filename\n\n        try:\n            # Use the file hash in the URL instead of the filename\n            download_url = f\"{self.api_url}/download/{task_id}/{file_hash}\"\n            \n            # Debug logging to understand what's happening\n            self.logger.info(f\"🔍 Download details (hash method):\")","chunkId":"audio_separator/remote/api_client.py:42"}],[438,{"filePath":"audio_separator/remote/api_client.py","startLine":420,"endLine":431,"chunkText":"            \n            # Debug logging to understand what's happening\n            self.logger.info(f\"🔍 Download details (hash method):\")\n            self.logger.info(f\"  Original filename: '{filename}'\")\n            self.logger.info(f\"  File hash: '{file_hash}'\")\n            self.logger.info(f\"  Download URL: {download_url}\")\n            self.logger.info(f\"  Task ID: {task_id}\")\n            \n            response = self.session.get(download_url, timeout=60)\n            \n            # Log response details for debugging\n            self.logger.info(f\"🔍 Response status: {response.status_code}\")","chunkId":"audio_separator/remote/api_client.py:43"}],[439,{"filePath":"audio_separator/remote/api_client.py","startLine":429,"endLine":439,"chunkText":"            \n            # Log response details for debugging\n            self.logger.info(f\"🔍 Response status: {response.status_code}\")\n            if response.status_code != 200:\n                try:\n                    self.logger.error(f\"🔍 Response headers: {dict(response.headers)}\")\n                except Exception:\n                    self.logger.error(f\"🔍 Response headers: {response.headers}\")\n                try:\n                    self.logger.error(f\"🔍 Response text (first 500 chars): {response.text[:500]}\")\n                except Exception:","chunkId":"audio_separator/remote/api_client.py:44"}],[440,{"filePath":"audio_separator/remote/api_client.py","startLine":437,"endLine":452,"chunkText":"                try:\n                    self.logger.error(f\"🔍 Response text (first 500 chars): {response.text[:500]}\")\n                except Exception:\n                    self.logger.error(f\"🔍 Response text: <unavailable>\")\n            \n            response.raise_for_status()\n\n            with open(output_path, \"wb\") as f:\n                f.write(response.content)\n\n            return output_path\n        except requests.RequestException as e:\n            self.logger.error(f\"Download failed: {e}\")\n            raise\n\n    def _log_server_version_on_error(self):","chunkId":"audio_separator/remote/api_client.py:45"}],[441,{"filePath":"audio_separator/remote/api_client.py","startLine":450,"endLine":463,"chunkText":"            raise\n\n    def _log_server_version_on_error(self):\n        \"\"\"Helper method to log server version when download fails.\"\"\"\n        try:\n            server_version = self.get_server_version()\n            self.logger.error(f\"🔍 Server version when download failed: {server_version}\")\n        except Exception as version_error:\n            self.logger.error(f\"🔍 Could not get server version: {version_error}\")\n\n    def list_models(self, format_type: str = \"pretty\", filter_by: Optional[str] = None) -> dict:\n        \"\"\"List available models.\"\"\"\n        try:\n            if format_type == \"json\":","chunkId":"audio_separator/remote/api_client.py:46"}],[442,{"filePath":"audio_separator/remote/api_client.py","startLine":461,"endLine":476,"chunkText":"        \"\"\"List available models.\"\"\"\n        try:\n            if format_type == \"json\":\n                response = self.session.get(f\"{self.api_url}/models-json\", timeout=10)\n            else:\n                url = f\"{self.api_url}/models\"\n                if filter_by:\n                    url += f\"?filter_sort_by={filter_by}\"\n                response = self.session.get(url, timeout=10)\n\n            response.raise_for_status()\n\n            if format_type == \"json\":\n                return response.json()\n            else:\n                return {\"text\": response.text}","chunkId":"audio_separator/remote/api_client.py:47"}],[443,{"filePath":"audio_separator/remote/api_client.py","startLine":474,"endLine":488,"chunkText":"                return response.json()\n            else:\n                return {\"text\": response.text}\n        except requests.RequestException as e:\n            self.logger.error(f\"Models request failed: {e}\")\n            raise\n\n    def get_server_version(self) -> str:\n        \"\"\"Get the server version.\"\"\"\n        try:\n            response = self.session.get(f\"{self.api_url}/health\", timeout=10)\n            response.raise_for_status()\n            health_data = response.json()\n            return health_data.get(\"version\", \"unknown\")\n        except requests.RequestException as e:","chunkId":"audio_separator/remote/api_client.py:48"}],[444,{"filePath":"audio_separator/remote/api_client.py","startLine":486,"endLine":491,"chunkText":"            health_data = response.json()\n            return health_data.get(\"version\", \"unknown\")\n        except requests.RequestException as e:\n            self.logger.error(f\"Health check request failed: {e}\")\n            raise\n","chunkId":"audio_separator/remote/api_client.py:49"}],[445,{"filePath":"audio_separator/remote/cli.py","startLine":0,"endLine":19,"chunkText":"#!/usr/bin/env python\nimport argparse\nimport json\nimport logging\nimport os\nimport sys\nimport time\nfrom importlib import metadata\n\nfrom audio_separator.remote import AudioSeparatorAPIClient\n\n\ndef main():\n    \"\"\"Main entry point for the remote CLI.\"\"\"\n    logger = logging.getLogger(__name__)\n    log_handler = logging.StreamHandler()\n    log_formatter = logging.Formatter(fmt=\"%(asctime)s.%(msecs)03d - %(levelname)s - %(module)s - %(message)s\", datefmt=\"%Y-%m-%d %H:%M:%S\")\n    log_handler.setFormatter(log_formatter)\n    logger.addHandler(log_handler)\n","chunkId":"audio_separator/remote/cli.py:0"}],[446,{"filePath":"audio_separator/remote/cli.py","startLine":17,"endLine":29,"chunkText":"    log_handler.setFormatter(log_formatter)\n    logger.addHandler(log_handler)\n\n    parser = argparse.ArgumentParser(description=\"Separate audio files using a remote audio-separator API.\", formatter_class=lambda prog: argparse.RawTextHelpFormatter(prog, max_help_position=60))\n\n    # Get package version\n    package_version = metadata.distribution(\"audio-separator\").version\n\n    # Main command subparsers\n    subparsers = parser.add_subparsers(dest=\"command\", help=\"Available commands\")\n\n    # Separate command\n    separate_parser = subparsers.add_parser(\"separate\", help=\"Separate audio files\")","chunkId":"audio_separator/remote/cli.py:1"}],[447,{"filePath":"audio_separator/remote/cli.py","startLine":27,"endLine":37,"chunkText":"\n    # Separate command\n    separate_parser = subparsers.add_parser(\"separate\", help=\"Separate audio files\")\n    separate_parser.add_argument(\"audio_files\", nargs=\"+\", help=\"Audio file paths to separate\")\n\n    # Model selection\n    model_group = separate_parser.add_mutually_exclusive_group()\n    model_group.add_argument(\"-m\", \"--model\", help=\"Single model to use for separation\")\n    model_group.add_argument(\"--models\", nargs=\"+\", help=\"Multiple models to use for separation\")\n\n    separate_parser.add_argument(\"--timeout\", type=int, default=600, help=\"Timeout in seconds for polling (default: 600)\")","chunkId":"audio_separator/remote/cli.py:2"}],[448,{"filePath":"audio_separator/remote/cli.py","startLine":35,"endLine":42,"chunkText":"    model_group.add_argument(\"--models\", nargs=\"+\", help=\"Multiple models to use for separation\")\n\n    separate_parser.add_argument(\"--timeout\", type=int, default=600, help=\"Timeout in seconds for polling (default: 600)\")\n    separate_parser.add_argument(\"--poll_interval\", type=int, default=10, help=\"Polling interval in seconds (default: 10)\")\n\n    # Output parameters\n    output_group = separate_parser.add_argument_group(\"Output Parameters\")\n    output_group.add_argument(\"--output_format\", default=\"flac\", help=\"Output format for separated files (default: %(default)s)\")","chunkId":"audio_separator/remote/cli.py:3"}],[449,{"filePath":"audio_separator/remote/cli.py","startLine":40,"endLine":45,"chunkText":"    # Output parameters\n    output_group = separate_parser.add_argument_group(\"Output Parameters\")\n    output_group.add_argument(\"--output_format\", default=\"flac\", help=\"Output format for separated files (default: %(default)s)\")\n    output_group.add_argument(\"--output_bitrate\", help=\"Output bitrate for separated files\")\n    output_group.add_argument(\"--normalization\", type=float, default=0.9, help=\"Max peak amplitude to normalize audio to (default: %(default)s)\")\n    output_group.add_argument(\"--amplification\", type=float, default=0.0, help=\"Min peak amplitude to amplify audio to (default: %(default)s)\")","chunkId":"audio_separator/remote/cli.py:4"}],[450,{"filePath":"audio_separator/remote/cli.py","startLine":43,"endLine":47,"chunkText":"    output_group.add_argument(\"--output_bitrate\", help=\"Output bitrate for separated files\")\n    output_group.add_argument(\"--normalization\", type=float, default=0.9, help=\"Max peak amplitude to normalize audio to (default: %(default)s)\")\n    output_group.add_argument(\"--amplification\", type=float, default=0.0, help=\"Min peak amplitude to amplify audio to (default: %(default)s)\")\n    output_group.add_argument(\"--single_stem\", help=\"Output only single stem (e.g. Vocals, Instrumental)\")\n    output_group.add_argument(\"--invert_spect\", action=\"store_true\", help=\"Invert secondary stem using spectrogram\")","chunkId":"audio_separator/remote/cli.py:5"}],[451,{"filePath":"audio_separator/remote/cli.py","startLine":45,"endLine":49,"chunkText":"    output_group.add_argument(\"--amplification\", type=float, default=0.0, help=\"Min peak amplitude to amplify audio to (default: %(default)s)\")\n    output_group.add_argument(\"--single_stem\", help=\"Output only single stem (e.g. Vocals, Instrumental)\")\n    output_group.add_argument(\"--invert_spect\", action=\"store_true\", help=\"Invert secondary stem using spectrogram\")\n    output_group.add_argument(\"--sample_rate\", type=int, default=44100, help=\"Sample rate of output audio (default: %(default)s)\")\n    output_group.add_argument(\"--use_soundfile\", action=\"store_true\", help=\"Use soundfile for output writing\")","chunkId":"audio_separator/remote/cli.py:6"}],[452,{"filePath":"audio_separator/remote/cli.py","startLine":47,"endLine":52,"chunkText":"    output_group.add_argument(\"--invert_spect\", action=\"store_true\", help=\"Invert secondary stem using spectrogram\")\n    output_group.add_argument(\"--sample_rate\", type=int, default=44100, help=\"Sample rate of output audio (default: %(default)s)\")\n    output_group.add_argument(\"--use_soundfile\", action=\"store_true\", help=\"Use soundfile for output writing\")\n    output_group.add_argument(\"--use_autocast\", action=\"store_true\", help=\"Use PyTorch autocast for faster inference\")\n    output_group.add_argument(\"--custom_output_names\", type=json.loads, help=\"Custom output names in JSON format\")\n","chunkId":"audio_separator/remote/cli.py:7"}],[453,{"filePath":"audio_separator/remote/cli.py","startLine":50,"endLine":56,"chunkText":"    output_group.add_argument(\"--use_autocast\", action=\"store_true\", help=\"Use PyTorch autocast for faster inference\")\n    output_group.add_argument(\"--custom_output_names\", type=json.loads, help=\"Custom output names in JSON format\")\n\n    # MDX parameters\n    mdx_group = separate_parser.add_argument_group(\"MDX Architecture Parameters\")\n    mdx_group.add_argument(\"--mdx_segment_size\", type=int, default=256, help=\"MDX segment size (default: %(default)s)\")\n    mdx_group.add_argument(\"--mdx_overlap\", type=float, default=0.25, help=\"MDX overlap (default: %(default)s)\")","chunkId":"audio_separator/remote/cli.py:8"}],[454,{"filePath":"audio_separator/remote/cli.py","startLine":54,"endLine":58,"chunkText":"    mdx_group = separate_parser.add_argument_group(\"MDX Architecture Parameters\")\n    mdx_group.add_argument(\"--mdx_segment_size\", type=int, default=256, help=\"MDX segment size (default: %(default)s)\")\n    mdx_group.add_argument(\"--mdx_overlap\", type=float, default=0.25, help=\"MDX overlap (default: %(default)s)\")\n    mdx_group.add_argument(\"--mdx_batch_size\", type=int, default=1, help=\"MDX batch size (default: %(default)s)\")\n    mdx_group.add_argument(\"--mdx_hop_length\", type=int, default=1024, help=\"MDX hop length (default: %(default)s)\")","chunkId":"audio_separator/remote/cli.py:9"}],[455,{"filePath":"audio_separator/remote/cli.py","startLine":56,"endLine":62,"chunkText":"    mdx_group.add_argument(\"--mdx_overlap\", type=float, default=0.25, help=\"MDX overlap (default: %(default)s)\")\n    mdx_group.add_argument(\"--mdx_batch_size\", type=int, default=1, help=\"MDX batch size (default: %(default)s)\")\n    mdx_group.add_argument(\"--mdx_hop_length\", type=int, default=1024, help=\"MDX hop length (default: %(default)s)\")\n    mdx_group.add_argument(\"--mdx_enable_denoise\", action=\"store_true\", help=\"Enable MDX denoising\")\n\n    # VR parameters\n    vr_group = separate_parser.add_argument_group(\"VR Architecture Parameters\")","chunkId":"audio_separator/remote/cli.py:10"}],[456,{"filePath":"audio_separator/remote/cli.py","startLine":60,"endLine":66,"chunkText":"\n    # VR parameters\n    vr_group = separate_parser.add_argument_group(\"VR Architecture Parameters\")\n    vr_group.add_argument(\"--vr_batch_size\", type=int, default=1, help=\"VR batch size (default: %(default)s)\")\n    vr_group.add_argument(\"--vr_window_size\", type=int, default=512, help=\"VR window size (default: %(default)s)\")\n    vr_group.add_argument(\"--vr_aggression\", type=int, default=5, help=\"VR aggression (default: %(default)s)\")\n    vr_group.add_argument(\"--vr_enable_tta\", action=\"store_true\", help=\"Enable VR Test-Time-Augmentation\")","chunkId":"audio_separator/remote/cli.py:11"}],[457,{"filePath":"audio_separator/remote/cli.py","startLine":64,"endLine":68,"chunkText":"    vr_group.add_argument(\"--vr_window_size\", type=int, default=512, help=\"VR window size (default: %(default)s)\")\n    vr_group.add_argument(\"--vr_aggression\", type=int, default=5, help=\"VR aggression (default: %(default)s)\")\n    vr_group.add_argument(\"--vr_enable_tta\", action=\"store_true\", help=\"Enable VR Test-Time-Augmentation\")\n    vr_group.add_argument(\"--vr_high_end_process\", action=\"store_true\", help=\"Enable VR high end processing\")\n    vr_group.add_argument(\"--vr_enable_post_process\", action=\"store_true\", help=\"Enable VR post processing\")","chunkId":"audio_separator/remote/cli.py:12"}],[458,{"filePath":"audio_separator/remote/cli.py","startLine":66,"endLine":72,"chunkText":"    vr_group.add_argument(\"--vr_enable_tta\", action=\"store_true\", help=\"Enable VR Test-Time-Augmentation\")\n    vr_group.add_argument(\"--vr_high_end_process\", action=\"store_true\", help=\"Enable VR high end processing\")\n    vr_group.add_argument(\"--vr_enable_post_process\", action=\"store_true\", help=\"Enable VR post processing\")\n    vr_group.add_argument(\"--vr_post_process_threshold\", type=float, default=0.2, help=\"VR post process threshold (default: %(default)s)\")\n\n    # Demucs parameters\n    demucs_group = separate_parser.add_argument_group(\"Demucs Architecture Parameters\")","chunkId":"audio_separator/remote/cli.py:13"}],[459,{"filePath":"audio_separator/remote/cli.py","startLine":70,"endLine":75,"chunkText":"\n    # Demucs parameters\n    demucs_group = separate_parser.add_argument_group(\"Demucs Architecture Parameters\")\n    demucs_group.add_argument(\"--demucs_segment_size\", default=\"Default\", help=\"Demucs segment size (default: %(default)s)\")\n    demucs_group.add_argument(\"--demucs_shifts\", type=int, default=2, help=\"Demucs shifts (default: %(default)s)\")\n    demucs_group.add_argument(\"--demucs_overlap\", type=float, default=0.25, help=\"Demucs overlap (default: %(default)s)\")","chunkId":"audio_separator/remote/cli.py:14"}],[460,{"filePath":"audio_separator/remote/cli.py","startLine":73,"endLine":79,"chunkText":"    demucs_group.add_argument(\"--demucs_segment_size\", default=\"Default\", help=\"Demucs segment size (default: %(default)s)\")\n    demucs_group.add_argument(\"--demucs_shifts\", type=int, default=2, help=\"Demucs shifts (default: %(default)s)\")\n    demucs_group.add_argument(\"--demucs_overlap\", type=float, default=0.25, help=\"Demucs overlap (default: %(default)s)\")\n    demucs_group.add_argument(\"--demucs_segments_enabled\", type=bool, default=True, help=\"Enable Demucs segments (default: %(default)s)\")\n\n    # MDXC parameters\n    mdxc_group = separate_parser.add_argument_group(\"MDXC Architecture Parameters\")","chunkId":"audio_separator/remote/cli.py:15"}],[461,{"filePath":"audio_separator/remote/cli.py","startLine":77,"endLine":83,"chunkText":"\n    # MDXC parameters\n    mdxc_group = separate_parser.add_argument_group(\"MDXC Architecture Parameters\")\n    mdxc_group.add_argument(\"--mdxc_segment_size\", type=int, default=256, help=\"MDXC segment size (default: %(default)s)\")\n    mdxc_group.add_argument(\"--mdxc_override_model_segment_size\", action=\"store_true\", help=\"Override MDXC model segment size\")\n    mdxc_group.add_argument(\"--mdxc_overlap\", type=int, default=8, help=\"MDXC overlap (default: %(default)s)\")\n    mdxc_group.add_argument(\"--mdxc_batch_size\", type=int, default=1, help=\"MDXC batch size (default: %(default)s)\")","chunkId":"audio_separator/remote/cli.py:16"}],[462,{"filePath":"audio_separator/remote/cli.py","startLine":81,"endLine":87,"chunkText":"    mdxc_group.add_argument(\"--mdxc_override_model_segment_size\", action=\"store_true\", help=\"Override MDXC model segment size\")\n    mdxc_group.add_argument(\"--mdxc_overlap\", type=int, default=8, help=\"MDXC overlap (default: %(default)s)\")\n    mdxc_group.add_argument(\"--mdxc_batch_size\", type=int, default=1, help=\"MDXC batch size (default: %(default)s)\")\n    mdxc_group.add_argument(\"--mdxc_pitch_shift\", type=int, default=0, help=\"MDXC pitch shift (default: %(default)s)\")\n\n    # Status command\n    status_parser = subparsers.add_parser(\"status\", help=\"Check job status\")","chunkId":"audio_separator/remote/cli.py:17"}],[463,{"filePath":"audio_separator/remote/cli.py","startLine":85,"endLine":96,"chunkText":"\n    # Status command\n    status_parser = subparsers.add_parser(\"status\", help=\"Check job status\")\n    status_parser.add_argument(\"task_id\", help=\"Task ID to check status for\")\n\n    # Models command\n    models_parser = subparsers.add_parser(\"models\", help=\"List available models\")\n    models_parser.add_argument(\"--format\", choices=[\"pretty\", \"json\"], default=\"pretty\", help=\"Output format\")\n    models_parser.add_argument(\"--filter\", help=\"Filter models by name, type, or stem\")\n\n    # Download command\n    download_parser = subparsers.add_parser(\"download\", help=\"Download specific files from a job\")","chunkId":"audio_separator/remote/cli.py:18"}],[464,{"filePath":"audio_separator/remote/cli.py","startLine":94,"endLine":103,"chunkText":"\n    # Download command\n    download_parser = subparsers.add_parser(\"download\", help=\"Download specific files from a job\")\n    download_parser.add_argument(\"task_id\", help=\"Task ID\")\n    download_parser.add_argument(\"filenames\", nargs=\"+\", help=\"Filenames to download\")\n\n    # Global options\n    parser.add_argument(\"-v\", \"--version\", action=\"store_true\", help=\"Show version information\")\n    parser.add_argument(\"-d\", \"--debug\", action=\"store_true\", help=\"Enable debug logging\")\n    parser.add_argument(\"--log_level\", default=\"info\", help=\"Log level (default: info)\")","chunkId":"audio_separator/remote/cli.py:19"}],[465,{"filePath":"audio_separator/remote/cli.py","startLine":101,"endLine":115,"chunkText":"    parser.add_argument(\"-v\", \"--version\", action=\"store_true\", help=\"Show version information\")\n    parser.add_argument(\"-d\", \"--debug\", action=\"store_true\", help=\"Enable debug logging\")\n    parser.add_argument(\"--log_level\", default=\"info\", help=\"Log level (default: info)\")\n    parser.add_argument(\"--api_url\", help=\"API URL (overrides AUDIO_SEPARATOR_API_URL env var)\")\n\n    args = parser.parse_args()\n\n    # Set up logging\n    if args.debug:\n        log_level = logging.DEBUG\n    else:\n        log_level = getattr(logging, args.log_level.upper())\n    logger.setLevel(log_level)\n\n    # Handle version command","chunkId":"audio_separator/remote/cli.py:20"}],[466,{"filePath":"audio_separator/remote/cli.py","startLine":113,"endLine":127,"chunkText":"    logger.setLevel(log_level)\n\n    # Handle version command\n    if args.version:\n        print(f\"Client version: {package_version}\")\n\n        # Try to get server version if API URL is available\n        api_url = args.api_url or os.environ.get(\"AUDIO_SEPARATOR_API_URL\")\n        if api_url:\n            api_url = api_url.rstrip(\"/\")\n            api_client = AudioSeparatorAPIClient(api_url, logger)\n            try:\n                server_version = api_client.get_server_version()\n                print(f\"Server version: {server_version}\")\n            except Exception as e:","chunkId":"audio_separator/remote/cli.py:21"}],[467,{"filePath":"audio_separator/remote/cli.py","startLine":125,"endLine":136,"chunkText":"                server_version = api_client.get_server_version()\n                print(f\"Server version: {server_version}\")\n            except Exception as e:\n                logger.warning(f\"Could not retrieve server version: {e}\")\n        else:\n            logger.warning(\"API URL not provided. Set AUDIO_SEPARATOR_API_URL environment variable or use --api_url to get server version\")\n\n        sys.exit(0)\n\n    # Get API URL\n    api_url = args.api_url or os.environ.get(\"AUDIO_SEPARATOR_API_URL\")\n    if not api_url:","chunkId":"audio_separator/remote/cli.py:22"}],[468,{"filePath":"audio_separator/remote/cli.py","startLine":134,"endLine":150,"chunkText":"    # Get API URL\n    api_url = args.api_url or os.environ.get(\"AUDIO_SEPARATOR_API_URL\")\n    if not api_url:\n        logger.error(\"API URL not provided. Set AUDIO_SEPARATOR_API_URL environment variable or use --api_url\")\n        sys.exit(1)\n\n    # Remove trailing slash\n    api_url = api_url.rstrip(\"/\")\n\n    # Create API client\n    api_client = AudioSeparatorAPIClient(api_url, logger)\n\n    # Handle commands\n    if args.command == \"separate\":\n        handle_separate_command(args, api_client, logger)\n    elif args.command == \"status\":\n        handle_status_command(args, api_client, logger)","chunkId":"audio_separator/remote/cli.py:23"}],[469,{"filePath":"audio_separator/remote/cli.py","startLine":148,"endLine":162,"chunkText":"        handle_separate_command(args, api_client, logger)\n    elif args.command == \"status\":\n        handle_status_command(args, api_client, logger)\n    elif args.command == \"models\":\n        handle_models_command(args, api_client, logger)\n    elif args.command == \"download\":\n        handle_download_command(args, api_client, logger)\n    else:\n        parser.print_help()\n        sys.exit(1)\n\n\ndef handle_separate_command(args, api_client: AudioSeparatorAPIClient, logger: logging.Logger):\n    \"\"\"Handle the separate command.\"\"\"\n    for audio_file in args.audio_files:","chunkId":"audio_separator/remote/cli.py:24"}],[470,{"filePath":"audio_separator/remote/cli.py","startLine":160,"endLine":172,"chunkText":"def handle_separate_command(args, api_client: AudioSeparatorAPIClient, logger: logging.Logger):\n    \"\"\"Handle the separate command.\"\"\"\n    for audio_file in args.audio_files:\n        logger.info(f\"Uploading '{audio_file}' to audio separator...\")\n\n        try:\n            # Prepare parameters for separation\n            kwargs = {\n                \"model\": args.model,\n                \"models\": args.models,\n                \"timeout\": args.timeout,\n                \"poll_interval\": args.poll_interval,\n                \"download\": True,  # Always download in CLI","chunkId":"audio_separator/remote/cli.py:25"}],[471,{"filePath":"audio_separator/remote/cli.py","startLine":170,"endLine":180,"chunkText":"                \"timeout\": args.timeout,\n                \"poll_interval\": args.poll_interval,\n                \"download\": True,  # Always download in CLI\n                \"output_dir\": None,  # Use current directory\n                # Output parameters\n                \"output_format\": args.output_format,\n                \"output_bitrate\": args.output_bitrate,\n                \"normalization_threshold\": args.normalization,\n                \"amplification_threshold\": args.amplification,\n                \"output_single_stem\": args.single_stem,\n                \"invert_using_spec\": args.invert_spect,","chunkId":"audio_separator/remote/cli.py:26"}],[472,{"filePath":"audio_separator/remote/cli.py","startLine":178,"endLine":188,"chunkText":"                \"amplification_threshold\": args.amplification,\n                \"output_single_stem\": args.single_stem,\n                \"invert_using_spec\": args.invert_spect,\n                \"sample_rate\": args.sample_rate,\n                \"use_soundfile\": args.use_soundfile,\n                \"use_autocast\": args.use_autocast,\n                \"custom_output_names\": args.custom_output_names,\n                # MDX parameters\n                \"mdx_segment_size\": args.mdx_segment_size,\n                \"mdx_overlap\": args.mdx_overlap,\n                \"mdx_batch_size\": args.mdx_batch_size,","chunkId":"audio_separator/remote/cli.py:27"}],[473,{"filePath":"audio_separator/remote/cli.py","startLine":186,"endLine":196,"chunkText":"                \"mdx_segment_size\": args.mdx_segment_size,\n                \"mdx_overlap\": args.mdx_overlap,\n                \"mdx_batch_size\": args.mdx_batch_size,\n                \"mdx_hop_length\": args.mdx_hop_length,\n                \"mdx_enable_denoise\": args.mdx_enable_denoise,\n                # VR parameters\n                \"vr_batch_size\": args.vr_batch_size,\n                \"vr_window_size\": args.vr_window_size,\n                \"vr_aggression\": args.vr_aggression,\n                \"vr_enable_tta\": args.vr_enable_tta,\n                \"vr_high_end_process\": args.vr_high_end_process,","chunkId":"audio_separator/remote/cli.py:28"}],[474,{"filePath":"audio_separator/remote/cli.py","startLine":194,"endLine":203,"chunkText":"                \"vr_aggression\": args.vr_aggression,\n                \"vr_enable_tta\": args.vr_enable_tta,\n                \"vr_high_end_process\": args.vr_high_end_process,\n                \"vr_enable_post_process\": args.vr_enable_post_process,\n                \"vr_post_process_threshold\": args.vr_post_process_threshold,\n                # Demucs parameters\n                \"demucs_segment_size\": args.demucs_segment_size,\n                \"demucs_shifts\": args.demucs_shifts,\n                \"demucs_overlap\": args.demucs_overlap,\n                \"demucs_segments_enabled\": args.demucs_segments_enabled,","chunkId":"audio_separator/remote/cli.py:29"}],[475,{"filePath":"audio_separator/remote/cli.py","startLine":201,"endLine":212,"chunkText":"                \"demucs_shifts\": args.demucs_shifts,\n                \"demucs_overlap\": args.demucs_overlap,\n                \"demucs_segments_enabled\": args.demucs_segments_enabled,\n                # MDXC parameters\n                \"mdxc_segment_size\": args.mdxc_segment_size,\n                \"mdxc_override_model_segment_size\": args.mdxc_override_model_segment_size,\n                \"mdxc_overlap\": args.mdxc_overlap,\n                \"mdxc_batch_size\": args.mdxc_batch_size,\n                \"mdxc_pitch_shift\": args.mdxc_pitch_shift,\n            }\n\n            # Use the convenience method that handles everythin","chunkId":"audio_separator/remote/cli.py:30"}],[476,{"filePath":"audio_separator/remote/cli.py","startLine":210,"endLine":220,"chunkText":"            }\n\n            # Use the convenience method that handles everything\n            result = api_client.separate_audio_and_wait(audio_file, **kwargs)\n\n            if result[\"status\"] == \"completed\":\n                if \"downloaded_files\" in result:\n                    logger.info(f\"✅ Separation completed! Downloaded {len(result['downloaded_files'])} files:\")\n                    for file_path in result[\"downloaded_files\"]:\n                        logger.info(f\"  - {file_path}\")\n                else:","chunkId":"audio_separator/remote/cli.py:31"}],[477,{"filePath":"audio_separator/remote/cli.py","startLine":218,"endLine":231,"chunkText":"                    for file_path in result[\"downloaded_files\"]:\n                        logger.info(f\"  - {file_path}\")\n                else:\n                    logger.info(f\"✅ Separation completed! Files available for download: {result['files']}\")\n            else:\n                logger.error(f\"❌ Separation failed: {result.get('error', 'Unknown error')}\")\n\n        except Exception as e:\n            logger.error(f\"❌ Error processing '{audio_file}': {e}\")\n\n\ndef handle_status_command(args, api_client: AudioSeparatorAPIClient, logger: logging.Logger):\n    \"\"\"Handle the status command.\"\"\"\n    try:","chunkId":"audio_separator/remote/cli.py:32"}],[478,{"filePath":"audio_separator/remote/cli.py","startLine":229,"endLine":240,"chunkText":"def handle_status_command(args, api_client: AudioSeparatorAPIClient, logger: logging.Logger):\n    \"\"\"Handle the status command.\"\"\"\n    try:\n        status = api_client.get_job_status(args.task_id)\n\n        logger.info(f\"Job Status: {status['status']}\")\n        if \"progress\" in status:\n            progress_info = f\"Progress: {status['progress']}%\"\n            if \"current_model_index\" in status and \"total_models\" in status:\n                model_info = f\" (Model {status['current_model_index'] + 1}/{status['total_models']})\"\n                progress_info += model_info\n            logger.info(progress_info)","chunkId":"audio_separator/remote/cli.py:33"}],[479,{"filePath":"audio_separator/remote/cli.py","startLine":238,"endLine":247,"chunkText":"                model_info = f\" (Model {status['current_model_index'] + 1}/{status['total_models']})\"\n                progress_info += model_info\n            logger.info(progress_info)\n        if \"original_filename\" in status:\n            logger.info(f\"Original File: {status['original_filename']}\")\n        if \"models_used\" in status:\n            logger.info(f\"Models Used: {', '.join(status['models_used'])}\")\n        if status[\"status\"] == \"error\" and \"error\" in status:\n            logger.error(f\"Error: {status['error']}\")\n        elif status[\"status\"] == \"completed\" and \"files\" in status:","chunkId":"audio_separator/remote/cli.py:34"}],[480,{"filePath":"audio_separator/remote/cli.py","startLine":245,"endLine":260,"chunkText":"        if status[\"status\"] == \"error\" and \"error\" in status:\n            logger.error(f\"Error: {status['error']}\")\n        elif status[\"status\"] == \"completed\" and \"files\" in status:\n            logger.info(\"Output Files:\")\n            for filename in status[\"files\"]:\n                logger.info(f\"  - {filename}\")\n\n    except Exception as e:\n        logger.error(f\"❌ Error getting status: {e}\")\n\n\ndef handle_models_command(args, api_client: AudioSeparatorAPIClient, logger: logging.Logger):\n    \"\"\"Handle the models command.\"\"\"\n    try:\n        models = api_client.list_models(args.format, args.filter)\n","chunkId":"audio_separator/remote/cli.py:35"}],[481,{"filePath":"audio_separator/remote/cli.py","startLine":258,"endLine":275,"chunkText":"    try:\n        models = api_client.list_models(args.format, args.filter)\n\n        if args.format == \"json\":\n            print(json.dumps(models, indent=2))\n        else:\n            print(models[\"text\"])\n\n    except Exception as e:\n        logger.error(f\"❌ Error listing models: {e}\")\n\n\ndef handle_download_command(args, api_client: AudioSeparatorAPIClient, logger: logging.Logger):\n    \"\"\"Handle the download command.\"\"\"\n    try:\n        for filename in args.filenames:\n            logger.info(f\"📂 Downloading: {filename}\")\n            output_path = api_client.download_file(args.task_id, filename)","chunkId":"audio_separator/remote/cli.py:36"}],[482,{"filePath":"audio_separator/remote/cli.py","startLine":273,"endLine":288,"chunkText":"        for filename in args.filenames:\n            logger.info(f\"📂 Downloading: {filename}\")\n            output_path = api_client.download_file(args.task_id, filename)\n            logger.info(f\"✅ Downloaded: {output_path}\")\n\n    except Exception as e:\n        logger.error(f\"❌ Error downloading files: {e}\")\n\n\ndef poll_for_completion(task_id: str, api_client: AudioSeparatorAPIClient, logger: logging.Logger, timeout: int = 600, poll_interval: int = 10) -> bool:\n    \"\"\"Poll for job completion.\"\"\"\n    start_time = time.time()\n    last_progress = -1\n\n    while time.time() - start_time < timeout:\n        try:","chunkId":"audio_separator/remote/cli.py:37"}],[483,{"filePath":"audio_separator/remote/cli.py","startLine":286,"endLine":297,"chunkText":"\n    while time.time() - start_time < timeout:\n        try:\n            status = api_client.get_job_status(task_id)\n            current_status = status.get(\"status\", \"unknown\")\n\n            # Show progress if it changed\n            if \"progress\" in status and status[\"progress\"] != last_progress:\n                progress_info = f\"📊 Progress: {status['progress']}%\"\n                if \"current_model_index\" in status and \"total_models\" in status:\n                    model_info = f\" (Model {status['current_model_index'] + 1}/{status['total_models']})\"\n                    progress_info += model_info","chunkId":"audio_separator/remote/cli.py:38"}],[484,{"filePath":"audio_separator/remote/cli.py","startLine":295,"endLine":305,"chunkText":"                if \"current_model_index\" in status and \"total_models\" in status:\n                    model_info = f\" (Model {status['current_model_index'] + 1}/{status['total_models']})\"\n                    progress_info += model_info\n                logger.info(progress_info)\n                last_progress = status[\"progress\"]\n\n            # Check if completed\n            if current_status == \"completed\":\n                logger.info(\"✅ Job completed!\")\n                return True\n            elif current_status == \"error\":","chunkId":"audio_separator/remote/cli.py:39"}],[485,{"filePath":"audio_separator/remote/cli.py","startLine":304,"endLine":320,"chunkText":"                return True\n            elif current_status == \"error\":\n                logger.error(f\"❌ Job failed: {status.get('error', 'Unknown error')}\")\n                return False\n\n            # Wait before next poll\n            time.sleep(poll_interval)\n\n        except Exception as e:\n            logger.warning(f\"Error polling status: {e}\")\n            time.sleep(poll_interval)\n\n    logger.error(f\"❌ Job polling timed out after {timeout} seconds\")\n    return False\n\n\ndef download_files(task_id: str, files: list, api_client: AudioSeparatorAPIClient, logger: logging.Logger):","chunkId":"audio_separator/remote/cli.py:40"}],[486,{"filePath":"audio_separator/remote/cli.py","startLine":318,"endLine":334,"chunkText":"\n\ndef download_files(task_id: str, files: list, api_client: AudioSeparatorAPIClient, logger: logging.Logger):\n    \"\"\"Download all files from a completed job.\"\"\"\n    if not files:\n        logger.warning(\"No files to download\")\n        return\n\n    logger.info(f\"📥 Downloading {len(files)} output files...\")\n\n    downloaded_count = 0\n    for filename in files:\n        try:\n            logger.info(f\"  📂 Downloading: {filename}\")\n            output_path = api_client.download_file(task_id, filename)\n            logger.info(f\"  ✅ Downloaded: {output_path}\")\n            downloaded_count += 1","chunkId":"audio_separator/remote/cli.py:41"}],[487,{"filePath":"audio_separator/remote/cli.py","startLine":332,"endLine":346,"chunkText":"            output_path = api_client.download_file(task_id, filename)\n            logger.info(f\"  ✅ Downloaded: {output_path}\")\n            downloaded_count += 1\n        except Exception as e:\n            logger.error(f\"  ❌ Failed to download {filename}: {e}\")\n\n    if downloaded_count > 0:\n        logger.info(f\"🎉 Successfully downloaded {downloaded_count} files to current directory!\")\n    else:\n        logger.error(\"❌ No files were successfully downloaded\")\n\n\nif __name__ == \"__main__\":\n    main()\n","chunkId":"audio_separator/remote/cli.py:42"}],[488,{"filePath":"audio_separator/remote/deploy_modal.py","startLine":0,"endLine":15,"chunkText":"\"\"\"\nAudio Separator API - Simple Modal Deployment\nA FastAPI service for separating vocals from instrumental tracks using audio-separator\n\nFeatures:\n- Asynchronous job processing\n- Progress tracking and status polling\n- Persistent storage for models and outputs\n- GPU acceleration support\n- Multiple audio format support\n\nUsage with Remote CLI:\n1. Install audio-separator package: pip install audio-separator\n2. Set environment variable: export AUDIO_SEPARATOR_API_URL=\"https://your-deployment-url.modal.run\"\n3. Use the remote CLI:\n   - audio-separator-remote separate song.mp3","chunkId":"audio_separator/remote/deploy_modal.py:0"}],[489,{"filePath":"audio_separator/remote/deploy_modal.py","startLine":13,"endLine":32,"chunkText":"2. Set environment variable: export AUDIO_SEPARATOR_API_URL=\"https://your-deployment-url.modal.run\"\n3. Use the remote CLI:\n   - audio-separator-remote separate song.mp3\n   - audio-separator-remote separate song.mp3 --model UVR-MDX-NET-Inst_HQ_4\n   - audio-separator-remote status <task_id>\n   - audio-separator-remote models\n   - audio-separator-remote download <task_id> <filename>\n\"\"\"\n\n# Standard library imports\nimport logging\nimport os\nimport shutil\nimport traceback\nimport uuid\nimport json\nimport hashlib\nfrom importlib.metadata import version\nimport typing\nfrom typing import Optional","chunkId":"audio_separator/remote/deploy_modal.py:1"}],[490,{"filePath":"audio_separator/remote/deploy_modal.py","startLine":30,"endLine":47,"chunkText":"from importlib.metadata import version\nimport typing\nfrom typing import Optional\nfrom urllib.parse import quote\n\n# Third-party imports\nfrom fastapi import FastAPI, File, Form, HTTPException, Response, UploadFile\nfrom fastapi.middleware.cors import CORSMiddleware\nfrom starlette.responses import Response as StarletteResponse, PlainTextResponse\nimport filetype\nimport modal\n\n# Note: Separator is imported inside functions to allow Modal to parse this file\n# without requiring audio_separator to be installed on the deployment machine\n\n# Constants\nDEFAULT_MODEL_NAME = \"default\"  # Used when no model is specified\n","chunkId":"audio_separator/remote/deploy_modal.py:2"}],[491,{"filePath":"audio_separator/remote/deploy_modal.py","startLine":45,"endLine":57,"chunkText":"# Constants\nDEFAULT_MODEL_NAME = \"default\"  # Used when no model is specified\n\ndef generate_file_hash(filename: str) -> str:\n    \"\"\"Generate a short, stable hash for a filename to use in download URLs.\"\"\"\n    # Use SHA-256 hash of the filename, take first 16 characters for brevity\n    # This gives us a stable, URL-safe identifier that's much shorter than the filename\n    return hashlib.sha256(filename.encode('utf-8')).hexdigest()[:16]\n\n# Get the version of the installed audio-separator package\ntry:\n    AUDIO_SEPARATOR_VERSION = version(\"audio-separator\")\nexcept Exception:","chunkId":"audio_separator/remote/deploy_modal.py:3"}],[492,{"filePath":"audio_separator/remote/deploy_modal.py","startLine":55,"endLine":72,"chunkText":"try:\n    AUDIO_SEPARATOR_VERSION = version(\"audio-separator\")\nexcept Exception:\n    # Fallback version if package version cannot be determined\n    AUDIO_SEPARATOR_VERSION = \"unknown\"\n\n# Create Modal app\napp = modal.App(\"audio-separator\")\n\n# Define the container image; we're using CUDA for hardware acceleration and Python 3.13 for optimal performance\nimage = (\n    modal.Image.from_registry(\"nvidia/cuda:12.9.1-devel-ubuntu22.04\", add_python=\"3.13\")\n    .apt_install(\n        [\n            # Core system packages\n            \"curl\",\n            \"wget\",\n            # Audio libraries and dependencies","chunkId":"audio_separator/remote/deploy_modal.py:4"}],[493,{"filePath":"audio_separator/remote/deploy_modal.py","startLine":70,"endLine":89,"chunkText":"            \"curl\",\n            \"wget\",\n            # Audio libraries and dependencies\n            \"libsndfile1\",\n            \"libsndfile1-dev\",\n            \"libsox-dev\",\n            \"sox\",\n            \"libportaudio2\",\n            \"portaudio19-dev\",\n            \"libasound2-dev\",\n            \"libpulse-dev\",\n            \"libjack-dev\",\n            # Sample rate conversion library\n            \"libsamplerate0\",\n            \"libsamplerate0-dev\",\n            # Build tools for compiling Python packages with C extensions\n            \"build-essential\",\n            \"clang\",\n            \"gcc\",\n            \"g++\",","chunkId":"audio_separator/remote/deploy_modal.py:5"}],[494,{"filePath":"audio_separator/remote/deploy_modal.py","startLine":87,"endLine":102,"chunkText":"            \"clang\",\n            \"gcc\",\n            \"g++\",\n            \"make\",\n            \"cmake\",\n            \"pkg-config\",\n        ]\n    )\n    .run_commands(\n        [\n            # Set up CUDA library paths for NVENC support\n            \"echo '/usr/local/cuda/lib64' >> /etc/ld.so.conf.d/cuda.conf\",\n            \"ldconfig\",\n            # Install latest FFmpeg\n            \"wget https://github.com/BtbN/FFmpeg-Builds/releases/download/latest/ffmpeg-master-latest-linux64-gpl.tar.xz\",\n            \"tar -xf ffmpeg-master-latest-linux64-gpl.tar.xz\",","chunkId":"audio_separator/remote/deploy_modal.py:6"}],[495,{"filePath":"audio_separator/remote/deploy_modal.py","startLine":100,"endLine":111,"chunkText":"            # Install latest FFmpeg\n            \"wget https://github.com/BtbN/FFmpeg-Builds/releases/download/latest/ffmpeg-master-latest-linux64-gpl.tar.xz\",\n            \"tar -xf ffmpeg-master-latest-linux64-gpl.tar.xz\",\n            \"cp ffmpeg-master-latest-linux64-gpl/bin/* /usr/local/bin/\",\n            \"chmod +x /usr/local/bin/ffmpeg /usr/local/bin/ffprobe\",\n            # Verify installations and NVENC support\n            \"ffmpeg -version\",\n        ]\n    )\n    .pip_install(\n        [\n            # Core audio-separator with GPU support","chunkId":"audio_separator/remote/deploy_modal.py:7"}],[496,{"filePath":"audio_separator/remote/deploy_modal.py","startLine":109,"endLine":125,"chunkText":"    .pip_install(\n        [\n            # Core audio-separator with GPU support\n            # Always pulls the latest version from PyPI when the image is rebuilt\n            \"audio-separator[gpu]\",\n            # FastAPI and web server dependencies for Modal API deployment\n            \"fastapi>=0.104.0\",\n            \"uvicorn[standard]>=0.24.0\",\n            \"python-multipart>=0.0.6\",\n            # File type detection for response content type\n            \"filetype>=1.2.0\",\n        ]\n    )\n    .env(\n        {\n            \"AUDIO_SEPARATOR_MODEL_DIR\": \"/models\",\n            # CUDA environment for NVENC support","chunkId":"audio_separator/remote/deploy_modal.py:8"}],[497,{"filePath":"audio_separator/remote/deploy_modal.py","startLine":123,"endLine":138,"chunkText":"        {\n            \"AUDIO_SEPARATOR_MODEL_DIR\": \"/models\",\n            # CUDA environment for NVENC support\n            \"LD_LIBRARY_PATH\": \"/usr/local/cuda/lib64:$LD_LIBRARY_PATH\",\n            \"PATH\": \"/usr/local/cuda/bin:$PATH\",\n        }\n    )\n)\n\n# Create persistent volume for storing separated files\nvolume = modal.Volume.from_name(\"audio-separator-storage\", create_if_missing=True)\n\n# Create persistent volume for caching downloaded models\nmodels_volume = modal.Volume.from_name(\"audio-separator-models\", create_if_missing=True)\n\n# Modal Dict for job status tracking is accessed by name in functions","chunkId":"audio_separator/remote/deploy_modal.py:9"}],[498,{"filePath":"audio_separator/remote/deploy_modal.py","startLine":136,"endLine":150,"chunkText":"models_volume = modal.Volume.from_name(\"audio-separator-models\", create_if_missing=True)\n\n# Modal Dict for job status tracking is accessed by name in functions\n# job_status_dict = modal.Dict.from_name(\"audio-separator-job-status\", create_if_missing=True)\n\n\nclass PrettyJSONResponse(StarletteResponse):\n    \"\"\"Custom JSON response class for pretty-printing JSON\"\"\"\n\n    media_type = \"application/json\"\n\n    def render(self, content: typing.Any) -> bytes:\n        return json.dumps(content, ensure_ascii=False, allow_nan=False, indent=4, separators=(\", \", \": \")).encode(\"utf-8\")\n\n","chunkId":"audio_separator/remote/deploy_modal.py:10"}],[499,{"filePath":"audio_separator/remote/deploy_modal.py","startLine":148,"endLine":161,"chunkText":"        return json.dumps(content, ensure_ascii=False, allow_nan=False, indent=4, separators=(\", \", \": \")).encode(\"utf-8\")\n\n\n@app.function(image=image, gpu=\"ANY\", timeout=1200, volumes={\"/storage\": volume, \"/models\": models_volume}, scaledown_window=300)\ndef separate_audio_function(\n    audio_data: bytes,\n    filename: str,\n    models: Optional[list] = None,\n    task_id: Optional[str] = None,\n    # Separator parameters\n    output_format: str = \"flac\",\n    output_bitrate: Optional[str] = None,\n    normalization_threshold: float = 0.9,\n    amplification_threshold: float = 0.0,","chunkId":"audio_separator/remote/deploy_modal.py:11"}],[500,{"filePath":"audio_separator/remote/deploy_modal.py","startLine":159,"endLine":175,"chunkText":"    output_bitrate: Optional[str] = None,\n    normalization_threshold: float = 0.9,\n    amplification_threshold: float = 0.0,\n    output_single_stem: Optional[str] = None,\n    invert_using_spec: bool = False,\n    sample_rate: int = 44100,\n    use_soundfile: bool = False,\n    use_autocast: bool = False,\n    custom_output_names: Optional[dict] = None,\n    # MDX parameters\n    mdx_segment_size: int = 256,\n    mdx_overlap: float = 0.25,\n    mdx_batch_size: int = 1,\n    mdx_hop_length: int = 1024,\n    mdx_enable_denoise: bool = False,\n    # VR parameters\n    vr_batch_size: int = 1,","chunkId":"audio_separator/remote/deploy_modal.py:12"}],[501,{"filePath":"audio_separator/remote/deploy_modal.py","startLine":173,"endLine":190,"chunkText":"    mdx_enable_denoise: bool = False,\n    # VR parameters\n    vr_batch_size: int = 1,\n    vr_window_size: int = 512,\n    vr_aggression: int = 5,\n    vr_enable_tta: bool = False,\n    vr_high_end_process: bool = False,\n    vr_enable_post_process: bool = False,\n    vr_post_process_threshold: float = 0.2,\n    # Demucs parameters\n    demucs_segment_size: str = \"Default\",\n    demucs_shifts: int = 2,\n    demucs_overlap: float = 0.25,\n    demucs_segments_enabled: bool = True,\n    # MDXC parameters\n    mdxc_segment_size: int = 256,\n    mdxc_override_model_segment_size: bool = False,\n    mdxc_overlap: int = 8,","chunkId":"audio_separator/remote/deploy_modal.py:13"}],[502,{"filePath":"audio_separator/remote/deploy_modal.py","startLine":188,"endLine":206,"chunkText":"    mdxc_segment_size: int = 256,\n    mdxc_override_model_segment_size: bool = False,\n    mdxc_overlap: int = 8,\n    mdxc_batch_size: int = 1,\n    mdxc_pitch_shift: int = 0,\n) -> dict:\n    \"\"\"\n    Separate audio into stems using one or more models\n    \"\"\"\n    from audio_separator.separator import Separator\n\n    if task_id is None:\n        task_id = str(uuid.uuid4())\n\n    # Default to single default model if no models specified\n    if models is None or len(models) == 0:\n        models = [None]  # None will use separator's default\n\n    all_output_files = {}  # Dictionary mapping file hashes to filenames","chunkId":"audio_separator/remote/deploy_modal.py:14"}],[503,{"filePath":"audio_separator/remote/deploy_modal.py","startLine":204,"endLine":219,"chunkText":"        models = [None]  # None will use separator's default\n\n    all_output_files = {}  # Dictionary mapping file hashes to filenames\n    models_used = []\n    current_model_index = 0\n    total_models = len(models)\n\n    def update_job_status(status: str, progress: int = 0, error: str = None, files: list = None):\n        \"\"\"Update job status in Modal Dict\"\"\"\n        status_data = {\n            \"task_id\": task_id,\n            \"status\": status,\n            \"progress\": progress,\n            \"original_filename\": filename,\n            \"models_used\": models_used,\n            \"total_models\": total_models,","chunkId":"audio_separator/remote/deploy_modal.py:15"}],[504,{"filePath":"audio_separator/remote/deploy_modal.py","startLine":217,"endLine":232,"chunkText":"            \"original_filename\": filename,\n            \"models_used\": models_used,\n            \"total_models\": total_models,\n            \"current_model_index\": current_model_index,\n            \"files\": files or [],\n        }\n        if error:\n            status_data[\"error\"] = error\n\n        # Access Modal Dict by name within function scope\n        job_status = modal.Dict.from_name(\"audio-separator-job-status\", create_if_missing=True)\n        job_status[task_id] = status_data\n\n    try:\n        # Ensure storage directories exist\n        os.makedirs(\"/storage/uploads\", exist_ok=True)","chunkId":"audio_separator/remote/deploy_modal.py:16"}],[505,{"filePath":"audio_separator/remote/deploy_modal.py","startLine":230,"endLine":245,"chunkText":"    try:\n        # Ensure storage directories exist\n        os.makedirs(\"/storage/uploads\", exist_ok=True)\n        os.makedirs(\"/storage/outputs\", exist_ok=True)\n        os.makedirs(\"/models\", exist_ok=True)\n\n        # Update status: starting\n        update_job_status(\"processing\", 5)\n\n        # Create output directory\n        output_dir = f\"/storage/outputs/{task_id}\"\n        os.makedirs(output_dir, exist_ok=True)\n\n        # Write uploaded file directly to output directory with original filename\n        input_file_path = os.path.join(output_dir, filename)\n        with open(input_file_path, \"wb\") as f:","chunkId":"audio_separator/remote/deploy_modal.py:17"}],[506,{"filePath":"audio_separator/remote/deploy_modal.py","startLine":243,"endLine":255,"chunkText":"        # Write uploaded file directly to output directory with original filename\n        input_file_path = os.path.join(output_dir, filename)\n        with open(input_file_path, \"wb\") as f:\n            f.write(audio_data)\n\n        update_job_status(\"processing\", 10)\n\n        # Process each model\n        for model_index, model_name in enumerate(models):\n            current_model_index = model_index\n            base_progress = 10 + (model_index * 80 // total_models)\n            model_progress_range = 80 // total_models\n","chunkId":"audio_separator/remote/deploy_modal.py:18"}],[507,{"filePath":"audio_separator/remote/deploy_modal.py","startLine":253,"endLine":264,"chunkText":"            base_progress = 10 + (model_index * 80 // total_models)\n            model_progress_range = 80 // total_models\n\n            print(f\"Processing model {model_index + 1}/{total_models}: {model_name or 'default'}\")\n            update_job_status(\"processing\", base_progress + (model_progress_range // 4))\n\n            # Initialize separator with all the provided parameters\n            separator = Separator(\n                log_level=logging.INFO,\n                model_file_dir=\"/models\",\n                output_dir=output_dir,\n                output_format=output_format,","chunkId":"audio_separator/remote/deploy_modal.py:19"}],[508,{"filePath":"audio_separator/remote/deploy_modal.py","startLine":262,"endLine":272,"chunkText":"                model_file_dir=\"/models\",\n                output_dir=output_dir,\n                output_format=output_format,\n                output_bitrate=output_bitrate,\n                normalization_threshold=normalization_threshold,\n                amplification_threshold=amplification_threshold,\n                output_single_stem=output_single_stem,\n                invert_using_spec=invert_using_spec,\n                sample_rate=sample_rate,\n                use_soundfile=use_soundfile,\n                use_autocast=use_autocast,","chunkId":"audio_separator/remote/deploy_modal.py:20"}],[509,{"filePath":"audio_separator/remote/deploy_modal.py","startLine":270,"endLine":279,"chunkText":"                sample_rate=sample_rate,\n                use_soundfile=use_soundfile,\n                use_autocast=use_autocast,\n                mdx_params={\"hop_length\": mdx_hop_length, \"segment_size\": mdx_segment_size, \"overlap\": mdx_overlap, \"batch_size\": mdx_batch_size, \"enable_denoise\": mdx_enable_denoise},\n                vr_params={\n                    \"batch_size\": vr_batch_size,\n                    \"window_size\": vr_window_size,\n                    \"aggression\": vr_aggression,\n                    \"enable_tta\": vr_enable_tta,\n                    \"enable_post_process\": vr_enable_post_process,","chunkId":"audio_separator/remote/deploy_modal.py:21"}],[510,{"filePath":"audio_separator/remote/deploy_modal.py","startLine":277,"endLine":285,"chunkText":"                    \"aggression\": vr_aggression,\n                    \"enable_tta\": vr_enable_tta,\n                    \"enable_post_process\": vr_enable_post_process,\n                    \"post_process_threshold\": vr_post_process_threshold,\n                    \"high_end_process\": vr_high_end_process,\n                },\n                demucs_params={\"segment_size\": demucs_segment_size, \"shifts\": demucs_shifts, \"overlap\": demucs_overlap, \"segments_enabled\": demucs_segments_enabled},\n                mdxc_params={\n                    \"segment_size\": mdxc_segment_size,","chunkId":"audio_separator/remote/deploy_modal.py:22"}],[511,{"filePath":"audio_separator/remote/deploy_modal.py","startLine":283,"endLine":293,"chunkText":"                demucs_params={\"segment_size\": demucs_segment_size, \"shifts\": demucs_shifts, \"overlap\": demucs_overlap, \"segments_enabled\": demucs_segments_enabled},\n                mdxc_params={\n                    \"segment_size\": mdxc_segment_size,\n                    \"batch_size\": mdxc_batch_size,\n                    \"overlap\": mdxc_overlap,\n                    \"override_model_segment_size\": mdxc_override_model_segment_size,\n                    \"pitch_shift\": mdxc_pitch_shift,\n                },\n            )\n\n            # Load the model","chunkId":"audio_separator/remote/deploy_modal.py:23"}],[512,{"filePath":"audio_separator/remote/deploy_modal.py","startLine":291,"endLine":304,"chunkText":"            )\n\n            # Load the model\n            update_job_status(\"processing\", base_progress + (model_progress_range // 2))\n            if model_name:\n                print(f\"Loading specified model: {model_name}\")\n                separator.load_model(model_name)\n                models_used.append(model_name)\n            else:\n                print(f\"No model specified, using default model\")\n                separator.load_model()\n                models_used.append(\"default\")\n\n            # Perform separation","chunkId":"audio_separator/remote/deploy_modal.py:24"}],[513,{"filePath":"audio_separator/remote/deploy_modal.py","startLine":302,"endLine":312,"chunkText":"                models_used.append(\"default\")\n\n            # Perform separation\n            update_job_status(\"processing\", base_progress + (3 * model_progress_range // 4))\n            print(f\"Separating audio file: {filename} with model: {models_used[-1]}\")\n\n            # Create custom output names for this model if multiple models\n            model_custom_output_names = None\n            if total_models > 1 and custom_output_names:\n                # Add model name suffix to custom output names\n                model_suffix = f\"_{models_used[-1].replace('.', '_').replace('/', '_')}\"","chunkId":"audio_separator/remote/deploy_modal.py:25"}],[514,{"filePath":"audio_separator/remote/deploy_modal.py","startLine":310,"endLine":319,"chunkText":"            if total_models > 1 and custom_output_names:\n                # Add model name suffix to custom output names\n                model_suffix = f\"_{models_used[-1].replace('.', '_').replace('/', '_')}\"\n                model_custom_output_names = {stem: f\"{name}{model_suffix}\" for stem, name in custom_output_names.items()}\n            elif custom_output_names:\n                model_custom_output_names = custom_output_names\n\n            output_files = separator.separate(input_file_path, custom_output_names=model_custom_output_names)\n\n            if not output_files:","chunkId":"audio_separator/remote/deploy_modal.py:26"}],[515,{"filePath":"audio_separator/remote/deploy_modal.py","startLine":317,"endLine":325,"chunkText":"            output_files = separator.separate(input_file_path, custom_output_names=model_custom_output_names)\n\n            if not output_files:\n                error_msg = f\"Separation with model {models_used[-1]} completed but produced no output files\"\n                print(f\"❌ {error_msg}\")\n                update_job_status(\"error\", 0, error=error_msg)\n                return {\"task_id\": task_id, \"status\": \"error\", \"error\": error_msg, \"models_used\": models_used, \"original_filename\": filename}\n\n            # Convert full paths to filenames and add to collection with hashes","chunkId":"audio_separator/remote/deploy_modal.py:27"}],[516,{"filePath":"audio_separator/remote/deploy_modal.py","startLine":323,"endLine":332,"chunkText":"                return {\"task_id\": task_id, \"status\": \"error\", \"error\": error_msg, \"models_used\": models_used, \"original_filename\": filename}\n\n            # Convert full paths to filenames and add to collection with hashes\n            model_result_files = [os.path.basename(f) for f in output_files]\n            for filename in model_result_files:\n                file_hash = generate_file_hash(filename)\n                all_output_files[file_hash] = filename\n            print(f\"Model {models_used[-1]} produced {len(model_result_files)} files: {model_result_files}\")\n\n        # Update final status","chunkId":"audio_separator/remote/deploy_modal.py:28"}],[517,{"filePath":"audio_separator/remote/deploy_modal.py","startLine":330,"endLine":344,"chunkText":"            print(f\"Model {models_used[-1]} produced {len(model_result_files)} files: {model_result_files}\")\n\n        # Update final status\n        update_job_status(\"processing\", 95)\n        print(f\"All separations completed. Total output files: {len(all_output_files)}\")\n\n        # Commit volume changes\n        volume.commit()\n        models_volume.commit()\n\n        # Update status: completed\n        update_job_status(\"completed\", 100, files=all_output_files)\n\n        return {\"task_id\": task_id, \"status\": \"completed\", \"files\": all_output_files, \"models_used\": models_used, \"original_filename\": filename}\n","chunkId":"audio_separator/remote/deploy_modal.py:29"}],[518,{"filePath":"audio_separator/remote/deploy_modal.py","startLine":342,"endLine":351,"chunkText":"\n        return {\"task_id\": task_id, \"status\": \"completed\", \"files\": all_output_files, \"models_used\": models_used, \"original_filename\": filename}\n\n    except FileNotFoundError as e:\n        print(f\"Input file not found: {str(e)}\")\n        traceback.print_exc()\n        try:\n            update_job_status(\"error\", 0, error=f\"Input file not found: {str(e)}\")\n        except Exception as status_error:\n            print(f\"WARNING: Failed to update job status: {status_error}\")","chunkId":"audio_separator/remote/deploy_modal.py:30"}],[519,{"filePath":"audio_separator/remote/deploy_modal.py","startLine":349,"endLine":359,"chunkText":"            update_job_status(\"error\", 0, error=f\"Input file not found: {str(e)}\")\n        except Exception as status_error:\n            print(f\"WARNING: Failed to update job status: {status_error}\")\n        return {\"task_id\": task_id, \"status\": \"error\", \"error\": f\"Input file not found: {str(e)}\", \"models_used\": models_used, \"original_filename\": filename}\n\n    except ValueError as e:\n        print(f\"Invalid input or configuration: {str(e)}\")\n        traceback.print_exc()\n        try:\n            update_job_status(\"error\", 0, error=f\"Invalid input: {str(e)}\")\n        except Exception as status_error:","chunkId":"audio_separator/remote/deploy_modal.py:31"}],[520,{"filePath":"audio_separator/remote/deploy_modal.py","startLine":357,"endLine":368,"chunkText":"        try:\n            update_job_status(\"error\", 0, error=f\"Invalid input: {str(e)}\")\n        except Exception as status_error:\n            print(f\"WARNING: Failed to update job status: {status_error}\")\n        return {\"task_id\": task_id, \"status\": \"error\", \"error\": f\"Invalid input: {str(e)}\", \"models_used\": models_used, \"original_filename\": filename}\n\n    except Exception as e:\n        print(f\"Unexpected error during separation: {str(e)}\")\n        traceback.print_exc()\n        try:\n            update_job_status(\"error\", 0, error=str(e))\n        except Exception as status_error:","chunkId":"audio_separator/remote/deploy_modal.py:32"}],[521,{"filePath":"audio_separator/remote/deploy_modal.py","startLine":366,"endLine":380,"chunkText":"        try:\n            update_job_status(\"error\", 0, error=str(e))\n        except Exception as status_error:\n            print(f\"WARNING: Failed to update job status: {status_error}\")\n\n        # Clean up on error\n        if os.path.exists(input_file_path):\n            os.unlink(input_file_path)\n        if os.path.exists(output_dir):\n            shutil.rmtree(output_dir, ignore_errors=True)\n\n        return {\"task_id\": task_id, \"status\": \"error\", \"error\": str(e), \"models_used\": models_used, \"original_filename\": filename}\n\n\n@app.function(image=image, timeout=300, volumes={\"/storage\": volume})","chunkId":"audio_separator/remote/deploy_modal.py:33"}],[522,{"filePath":"audio_separator/remote/deploy_modal.py","startLine":378,"endLine":392,"chunkText":"\n\n@app.function(image=image, timeout=300, volumes={\"/storage\": volume})\ndef get_job_status_function(task_id: str) -> dict:\n    \"\"\"\n    Get the status of a separation job\n    \"\"\"\n    try:\n        # Access Modal Dict by name within function scope\n        job_status = modal.Dict.from_name(\"audio-separator-job-status\", create_if_missing=True)\n\n        if task_id in job_status:\n            return job_status[task_id]\n        else:\n            # Job not found - might be initializing or doesn't exist","chunkId":"audio_separator/remote/deploy_modal.py:34"}],[523,{"filePath":"audio_separator/remote/deploy_modal.py","startLine":390,"endLine":400,"chunkText":"            return job_status[task_id]\n        else:\n            # Job not found - might be initializing or doesn't exist\n            return {\"task_id\": task_id, \"status\": \"not_found\", \"progress\": 0, \"error\": \"Job not found - may have been cleaned up or never existed\"}\n    except Exception as e:\n        print(f\"ERROR: Failed to access job status for {task_id}: {str(e)}\")\n        return {\"task_id\": task_id, \"status\": \"error\", \"error\": f\"Failed to read status: {str(e)}\"}\n\n\n@app.function(image=image, timeout=300, volumes={\"/storage\": volume})\ndef get_file_function(task_id: str, filename: str) -> bytes:","chunkId":"audio_separator/remote/deploy_modal.py:35"}],[524,{"filePath":"audio_separator/remote/deploy_modal.py","startLine":399,"endLine":415,"chunkText":"@app.function(image=image, timeout=300, volumes={\"/storage\": volume})\ndef get_file_function(task_id: str, filename: str) -> bytes:\n    \"\"\"\n    Retrieve a separated audio file\n    \"\"\"\n    file_path = f\"/storage/outputs/{task_id}/{filename}\"\n\n    if not os.path.exists(file_path):\n        raise FileNotFoundError(f\"File not found: {filename}\")\n\n    with open(file_path, \"rb\") as f:\n        return f.read()\n\n\n@app.function(image=image, timeout=300, volumes={\"/storage\": volume})\ndef get_file_by_hash_function(task_id: str, file_hash: str) -> tuple[bytes, str]:\n    \"\"\"","chunkId":"audio_separator/remote/deploy_modal.py:36"}],[525,{"filePath":"audio_separator/remote/deploy_modal.py","startLine":413,"endLine":424,"chunkText":"@app.function(image=image, timeout=300, volumes={\"/storage\": volume})\ndef get_file_by_hash_function(task_id: str, file_hash: str) -> tuple[bytes, str]:\n    \"\"\"\n    Retrieve a separated audio file by its hash identifier.\n    Returns tuple of (file_data, actual_filename)\n    \"\"\"\n    print(f\"🔍 get_file_by_hash_function called - Task ID: {task_id}, File hash: {file_hash}\")\n    \n    # Reload the volume to ensure we see the latest files written by other function executions\n    print(f\"🔍 Reloading volume to see latest files...\")\n    volume.reload()\n    ","chunkId":"audio_separator/remote/deploy_modal.py:37"}],[526,{"filePath":"audio_separator/remote/deploy_modal.py","startLine":422,"endLine":436,"chunkText":"    print(f\"🔍 Reloading volume to see latest files...\")\n    volume.reload()\n    \n    # Access Modal Dict to get the job status with file hash mappings\n    job_status = modal.Dict.from_name(\"audio-separator-job-status\", create_if_missing=True)\n    \n    if task_id not in job_status:\n        print(f\"❌ Task not found in job_status: {task_id}\")\n        raise FileNotFoundError(f\"Task not found: {task_id}\")\n    \n    status_data = job_status[task_id]\n    files_dict = status_data.get(\"files\", {})\n    print(f\"🔍 Retrieved files_dict: {files_dict}\")\n    print(f\"🔍 files_dict type: {type(files_dict)}\")\n    ","chunkId":"audio_separator/remote/deploy_modal.py:38"}],[527,{"filePath":"audio_separator/remote/deploy_modal.py","startLine":434,"endLine":444,"chunkText":"    print(f\"🔍 Retrieved files_dict: {files_dict}\")\n    print(f\"🔍 files_dict type: {type(files_dict)}\")\n    \n    # Check if files is still a list (backward compatibility)\n    if isinstance(files_dict, list):\n        print(f\"🔍 Using legacy list format with {len(files_dict)} files\")\n        # For backward compatibility, try to find file by regenerating hash\n        for filename in files_dict:\n            generated_hash = generate_file_hash(filename)\n            print(f\"🔍 Checking filename '{filename}' -> hash '{generated_hash}' vs requested '{file_hash}'\")\n            if generated_hash == file_hash:","chunkId":"audio_separator/remote/deploy_modal.py:39"}],[528,{"filePath":"audio_separator/remote/deploy_modal.py","startLine":442,"endLine":451,"chunkText":"            generated_hash = generate_file_hash(filename)\n            print(f\"🔍 Checking filename '{filename}' -> hash '{generated_hash}' vs requested '{file_hash}'\")\n            if generated_hash == file_hash:\n                file_path = f\"/storage/outputs/{task_id}/{filename}\"\n                print(f\"🔍 Hash match! Checking file path: {file_path}\")\n                if os.path.exists(file_path):\n                    print(f\"✅ File exists, returning content\")\n                    with open(file_path, \"rb\") as f:\n                        return f.read(), filename\n                else:","chunkId":"audio_separator/remote/deploy_modal.py:40"}],[529,{"filePath":"audio_separator/remote/deploy_modal.py","startLine":449,"endLine":460,"chunkText":"                    with open(file_path, \"rb\") as f:\n                        return f.read(), filename\n                else:\n                    print(f\"❌ File does not exist at path: {file_path}\")\n        raise FileNotFoundError(f\"File with hash {file_hash} not found in legacy format\")\n    \n    # Normal case: files is a dictionary mapping hashes to filenames\n    print(f\"🔍 Using new hash format with {len(files_dict)} files\")\n    print(f\"🔍 Available hashes: {list(files_dict.keys())}\")\n    \n    if file_hash not in files_dict:\n        print(f\"❌ Hash {file_hash} not found in files_dict\")","chunkId":"audio_separator/remote/deploy_modal.py:41"}],[530,{"filePath":"audio_separator/remote/deploy_modal.py","startLine":458,"endLine":471,"chunkText":"    \n    if file_hash not in files_dict:\n        print(f\"❌ Hash {file_hash} not found in files_dict\")\n        raise FileNotFoundError(f\"File with hash {file_hash} not found\")\n    \n    actual_filename = files_dict[file_hash]\n    file_path = f\"/storage/outputs/{task_id}/{actual_filename}\"\n    print(f\"🔍 Hash found! Filename: '{actual_filename}'\")\n    print(f\"🔍 Checking file path: {file_path}\")\n\n    if not os.path.exists(file_path):\n        print(f\"❌ File does not exist at path: {file_path}\")\n        # List what files actually exist in the directory\n        task_dir = f\"/storage/outputs/{task_id}\"","chunkId":"audio_separator/remote/deploy_modal.py:42"}],[531,{"filePath":"audio_separator/remote/deploy_modal.py","startLine":469,"endLine":479,"chunkText":"        print(f\"❌ File does not exist at path: {file_path}\")\n        # List what files actually exist in the directory\n        task_dir = f\"/storage/outputs/{task_id}\"\n        if os.path.exists(task_dir):\n            actual_files = os.listdir(task_dir)\n            print(f\"🔍 Files actually in directory ({len(actual_files)}):\")\n            for i, actual_file in enumerate(actual_files):\n                print(f\"  [{i}] '{actual_file}'\")\n                if actual_file == actual_filename:\n                    print(f\"    ✅ EXACT MATCH found!\")\n        else:","chunkId":"audio_separator/remote/deploy_modal.py:43"}],[532,{"filePath":"audio_separator/remote/deploy_modal.py","startLine":477,"endLine":492,"chunkText":"                if actual_file == actual_filename:\n                    print(f\"    ✅ EXACT MATCH found!\")\n        else:\n            print(f\"❌ Task directory does not exist: {task_dir}\")\n        raise FileNotFoundError(f\"File not found: {actual_filename}\")\n\n    print(f\"✅ File exists, returning content\")\n    with open(file_path, \"rb\") as f:\n        return f.read(), actual_filename\n\n\n@app.function(image=image, timeout=60, volumes={\"/models\": models_volume})\ndef list_available_models() -> dict:\n    \"\"\"\n    List available separation models using the same approach as CLI\n    \"\"\"","chunkId":"audio_separator/remote/deploy_modal.py:44"}],[533,{"filePath":"audio_separator/remote/deploy_modal.py","startLine":490,"endLine":510,"chunkText":"    \"\"\"\n    List available separation models using the same approach as CLI\n    \"\"\"\n    from audio_separator.separator import Separator\n\n    # Use the persistent model directory\n    model_dir = \"/models\"\n\n    # Ensure the model directory exists\n    os.makedirs(model_dir, exist_ok=True)\n\n    # Use the same approach as the CLI: create separator with info_only=True\n    separator = Separator(info_only=True, model_file_dir=model_dir)\n\n    # Get the list of supported models\n    model_list = separator.list_supported_model_files()\n\n    # Return the full model dictionary\n    return model_list\n\n","chunkId":"audio_separator/remote/deploy_modal.py:45"}],[534,{"filePath":"audio_separator/remote/deploy_modal.py","startLine":508,"endLine":526,"chunkText":"    return model_list\n\n\n@app.function(image=image, timeout=60, volumes={\"/models\": models_volume})\ndef get_simplified_models(filter_sort_by: str = None) -> dict:\n    \"\"\"\n    Get simplified model list using the same approach as CLI --list_models\n    \"\"\"\n    from audio_separator.separator import Separator\n\n    # Use the persistent model directory\n    model_dir = \"/models\"\n\n    # Ensure the model directory exists\n    os.makedirs(model_dir, exist_ok=True)\n\n    # Use the same approach as the CLI: create separator with info_only=True\n    separator = Separator(info_only=True, model_file_dir=model_dir)\n","chunkId":"audio_separator/remote/deploy_modal.py:46"}],[535,{"filePath":"audio_separator/remote/deploy_modal.py","startLine":524,"endLine":538,"chunkText":"    # Use the same approach as the CLI: create separator with info_only=True\n    separator = Separator(info_only=True, model_file_dir=model_dir)\n\n    # Get the simplified model list\n    simplified_models = separator.get_simplified_model_list(filter_sort_by=filter_sort_by)\n\n    return simplified_models\n\n\nweb_app = FastAPI(title=\"Audio Separator API\", description=\"Separate vocals from instrumental tracks using AI\", version=AUDIO_SEPARATOR_VERSION)\n\nweb_app.add_middleware(CORSMiddleware, allow_origins=[\"*\"], allow_credentials=True, allow_methods=[\"*\"], allow_headers=[\"*\"])\n\n\n@web_app.post(\"/separate\")","chunkId":"audio_separator/remote/deploy_modal.py:47"}],[536,{"filePath":"audio_separator/remote/deploy_modal.py","startLine":536,"endLine":545,"chunkText":"\n\n@web_app.post(\"/separate\")\nasync def separate_audio(\n    file: UploadFile = File(..., description=\"Audio file to separate\"),\n    # Model selection - support both single model and multiple models\n    model: Optional[str] = Form(None, description=\"Single model to use for separation (for backwards compatibility)\"),\n    models: Optional[str] = Form(None, description='JSON list of models to use for separation, e.g. [\"model1.ckpt\", \"model2.onnx\"]'),\n    # Output parameters\n    output_format: str = Form(\"flac\", description=\"Output format for separated files\"),","chunkId":"audio_separator/remote/deploy_modal.py:48"}],[537,{"filePath":"audio_separator/remote/deploy_modal.py","startLine":543,"endLine":548,"chunkText":"    models: Optional[str] = Form(None, description='JSON list of models to use for separation, e.g. [\"model1.ckpt\", \"model2.onnx\"]'),\n    # Output parameters\n    output_format: str = Form(\"flac\", description=\"Output format for separated files\"),\n    output_bitrate: Optional[str] = Form(None, description=\"Output bitrate for separated files\"),\n    normalization_threshold: float = Form(0.9, description=\"Max peak amplitude to normalize audio to\"),\n    amplification_threshold: float = Form(0.0, description=\"Min peak amplitude to amplify audio to\"),","chunkId":"audio_separator/remote/deploy_modal.py:49"}],[538,{"filePath":"audio_separator/remote/deploy_modal.py","startLine":546,"endLine":551,"chunkText":"    output_bitrate: Optional[str] = Form(None, description=\"Output bitrate for separated files\"),\n    normalization_threshold: float = Form(0.9, description=\"Max peak amplitude to normalize audio to\"),\n    amplification_threshold: float = Form(0.0, description=\"Min peak amplitude to amplify audio to\"),\n    output_single_stem: Optional[str] = Form(None, description=\"Output only single stem (e.g. Vocals, Instrumental)\"),\n    invert_using_spec: bool = Form(False, description=\"Invert secondary stem using spectrogram\"),\n    sample_rate: int = Form(44100, description=\"Sample rate of output audio\"),","chunkId":"audio_separator/remote/deploy_modal.py:50"}],[539,{"filePath":"audio_separator/remote/deploy_modal.py","startLine":549,"endLine":555,"chunkText":"    output_single_stem: Optional[str] = Form(None, description=\"Output only single stem (e.g. Vocals, Instrumental)\"),\n    invert_using_spec: bool = Form(False, description=\"Invert secondary stem using spectrogram\"),\n    sample_rate: int = Form(44100, description=\"Sample rate of output audio\"),\n    use_soundfile: bool = Form(False, description=\"Use soundfile for output writing\"),\n    use_autocast: bool = Form(False, description=\"Use PyTorch autocast for faster inference\"),\n    custom_output_names: Optional[str] = Form(None, description=\"JSON dict of custom output names\"),\n    # MDX parameters","chunkId":"audio_separator/remote/deploy_modal.py:51"}],[540,{"filePath":"audio_separator/remote/deploy_modal.py","startLine":553,"endLine":561,"chunkText":"    use_autocast: bool = Form(False, description=\"Use PyTorch autocast for faster inference\"),\n    custom_output_names: Optional[str] = Form(None, description=\"JSON dict of custom output names\"),\n    # MDX parameters\n    mdx_segment_size: int = Form(256, description=\"MDX segment size\"),\n    mdx_overlap: float = Form(0.25, description=\"MDX overlap\"),\n    mdx_batch_size: int = Form(1, description=\"MDX batch size\"),\n    mdx_hop_length: int = Form(1024, description=\"MDX hop length\"),\n    mdx_enable_denoise: bool = Form(False, description=\"Enable MDX denoising\"),\n    # VR parameters","chunkId":"audio_separator/remote/deploy_modal.py:52"}],[541,{"filePath":"audio_separator/remote/deploy_modal.py","startLine":559,"endLine":566,"chunkText":"    mdx_hop_length: int = Form(1024, description=\"MDX hop length\"),\n    mdx_enable_denoise: bool = Form(False, description=\"Enable MDX denoising\"),\n    # VR parameters\n    vr_batch_size: int = Form(1, description=\"VR batch size\"),\n    vr_window_size: int = Form(512, description=\"VR window size\"),\n    vr_aggression: int = Form(5, description=\"VR aggression\"),\n    vr_enable_tta: bool = Form(False, description=\"Enable VR Test-Time-Augmentation\"),\n    vr_high_end_process: bool = Form(False, description=\"Enable VR high end processing\"),","chunkId":"audio_separator/remote/deploy_modal.py:53"}],[542,{"filePath":"audio_separator/remote/deploy_modal.py","startLine":564,"endLine":571,"chunkText":"    vr_aggression: int = Form(5, description=\"VR aggression\"),\n    vr_enable_tta: bool = Form(False, description=\"Enable VR Test-Time-Augmentation\"),\n    vr_high_end_process: bool = Form(False, description=\"Enable VR high end processing\"),\n    vr_enable_post_process: bool = Form(False, description=\"Enable VR post processing\"),\n    vr_post_process_threshold: float = Form(0.2, description=\"VR post process threshold\"),\n    # Demucs parameters\n    demucs_segment_size: str = Form(\"Default\", description=\"Demucs segment size\"),\n    demucs_shifts: int = Form(2, description=\"Demucs shifts\"),","chunkId":"audio_separator/remote/deploy_modal.py:54"}],[543,{"filePath":"audio_separator/remote/deploy_modal.py","startLine":569,"endLine":577,"chunkText":"    # Demucs parameters\n    demucs_segment_size: str = Form(\"Default\", description=\"Demucs segment size\"),\n    demucs_shifts: int = Form(2, description=\"Demucs shifts\"),\n    demucs_overlap: float = Form(0.25, description=\"Demucs overlap\"),\n    demucs_segments_enabled: bool = Form(True, description=\"Enable Demucs segments\"),\n    # MDXC parameters\n    mdxc_segment_size: int = Form(256, description=\"MDXC segment size\"),\n    mdxc_override_model_segment_size: bool = Form(False, description=\"Override MDXC model segment size\"),\n    mdxc_overlap: int = Form(8, description=\"MDXC overlap\"),","chunkId":"audio_separator/remote/deploy_modal.py:55"}],[544,{"filePath":"audio_separator/remote/deploy_modal.py","startLine":575,"endLine":586,"chunkText":"    mdxc_segment_size: int = Form(256, description=\"MDXC segment size\"),\n    mdxc_override_model_segment_size: bool = Form(False, description=\"Override MDXC model segment size\"),\n    mdxc_overlap: int = Form(8, description=\"MDXC overlap\"),\n    mdxc_batch_size: int = Form(1, description=\"MDXC batch size\"),\n    mdxc_pitch_shift: int = Form(0, description=\"MDXC pitch shift\"),\n) -> dict:\n    \"\"\"\n    Upload an audio file and separate it into stems using one or more models (asynchronous processing)\n    \"\"\"\n    if not file.filename:\n        raise HTTPException(status_code=400, detail=\"No file provided\")\n","chunkId":"audio_separator/remote/deploy_modal.py:56"}],[545,{"filePath":"audio_separator/remote/deploy_modal.py","startLine":584,"endLine":598,"chunkText":"    if not file.filename:\n        raise HTTPException(status_code=400, detail=\"No file provided\")\n\n    try:\n        # Parse models parameter\n        models_list = None\n        if models:\n            try:\n                models_list = json.loads(models)\n                if not isinstance(models_list, list):\n                    raise ValueError(\"Models must be a JSON list\")\n            except json.JSONDecodeError as e:\n                raise HTTPException(status_code=400, detail=f\"Invalid JSON in models parameter: {e}\")\n        elif model:\n            # Backwards compatibility: single model parameter","chunkId":"audio_separator/remote/deploy_modal.py:57"}],[546,{"filePath":"audio_separator/remote/deploy_modal.py","startLine":596,"endLine":607,"chunkText":"                raise HTTPException(status_code=400, detail=f\"Invalid JSON in models parameter: {e}\")\n        elif model:\n            # Backwards compatibility: single model parameter\n            models_list = [model]\n        # If neither provided, models_list stays None (will use default)\n\n        # Parse custom_output_names if provided\n        custom_output_names_dict = None\n        if custom_output_names:\n            try:\n                custom_output_names_dict = json.loads(custom_output_names)\n                if not isinstance(custom_output_names_dict, dict):","chunkId":"audio_separator/remote/deploy_modal.py:58"}],[547,{"filePath":"audio_separator/remote/deploy_modal.py","startLine":605,"endLine":619,"chunkText":"            try:\n                custom_output_names_dict = json.loads(custom_output_names)\n                if not isinstance(custom_output_names_dict, dict):\n                    raise ValueError(\"Custom output names must be a JSON object\")\n            except json.JSONDecodeError as e:\n                raise HTTPException(status_code=400, detail=f\"Invalid JSON in custom_output_names parameter: {e}\")\n\n        # Read file data\n        audio_data = await file.read()\n\n        # Generate task ID\n        task_id = str(uuid.uuid4())\n\n        # Create initial status in Modal Dict\n        initial_status = {","chunkId":"audio_separator/remote/deploy_modal.py:59"}],[548,{"filePath":"audio_separator/remote/deploy_modal.py","startLine":617,"endLine":631,"chunkText":"\n        # Create initial status in Modal Dict\n        initial_status = {\n            \"task_id\": task_id,\n            \"status\": \"submitted\",\n            \"progress\": 0,\n            \"original_filename\": file.filename,\n            \"models_used\": models_list or [\"default\"],\n            \"total_models\": len(models_list) if models_list else 1,\n            \"current_model_index\": 0,\n            \"files\": [],\n        }\n\n        # Access Modal Dict by name to ensure proper scope\n        job_status = modal.Dict.from_name(\"audio-separator-job-status\", create_if_missing=True)","chunkId":"audio_separator/remote/deploy_modal.py:60"}],[549,{"filePath":"audio_separator/remote/deploy_modal.py","startLine":629,"endLine":645,"chunkText":"\n        # Access Modal Dict by name to ensure proper scope\n        job_status = modal.Dict.from_name(\"audio-separator-job-status\", create_if_missing=True)\n        job_status[task_id] = initial_status\n\n        # Submit job asynchronously with all parameters\n        separate_audio_function.spawn(\n            audio_data,\n            file.filename,\n            models_list,\n            task_id,\n            # Separator parameters\n            output_format,\n            output_bitrate,\n            normalization_threshold,\n            amplification_threshold,\n            output_single_stem,","chunkId":"audio_separator/remote/deploy_modal.py:61"}],[550,{"filePath":"audio_separator/remote/deploy_modal.py","startLine":643,"endLine":662,"chunkText":"            normalization_threshold,\n            amplification_threshold,\n            output_single_stem,\n            invert_using_spec,\n            sample_rate,\n            use_soundfile,\n            use_autocast,\n            custom_output_names_dict,\n            # MDX parameters\n            mdx_segment_size,\n            mdx_overlap,\n            mdx_batch_size,\n            mdx_hop_length,\n            mdx_enable_denoise,\n            # VR parameters\n            vr_batch_size,\n            vr_window_size,\n            vr_aggression,\n            vr_enable_tta,\n            vr_high_end_process,","chunkId":"audio_separator/remote/deploy_modal.py:62"}],[551,{"filePath":"audio_separator/remote/deploy_modal.py","startLine":660,"endLine":680,"chunkText":"            vr_aggression,\n            vr_enable_tta,\n            vr_high_end_process,\n            vr_enable_post_process,\n            vr_post_process_threshold,\n            # Demucs parameters\n            demucs_segment_size,\n            demucs_shifts,\n            demucs_overlap,\n            demucs_segments_enabled,\n            # MDXC parameters\n            mdxc_segment_size,\n            mdxc_override_model_segment_size,\n            mdxc_overlap,\n            mdxc_batch_size,\n            mdxc_pitch_shift,\n        )\n\n        return {\n            \"task_id\": task_id,\n            \"status\": \"submitted\",","chunkId":"audio_separator/remote/deploy_modal.py:63"}],[552,{"filePath":"audio_separator/remote/deploy_modal.py","startLine":678,"endLine":694,"chunkText":"        return {\n            \"task_id\": task_id,\n            \"status\": \"submitted\",\n            \"message\": \"Job submitted for processing. Use /status/{task_id} to check progress.\",\n            \"models_used\": models_list or [\"default\"],\n            \"total_models\": len(models_list) if models_list else 1,\n            \"original_filename\": file.filename,\n        }\n    except HTTPException:\n        raise\n    except Exception as e:\n        raise HTTPException(status_code=500, detail=f\"Separation failed: {str(e)}\") from e\n\n\n@web_app.get(\"/status/{task_id}\")\nasync def get_job_status(task_id: str) -> dict:\n    \"\"\"","chunkId":"audio_separator/remote/deploy_modal.py:64"}],[553,{"filePath":"audio_separator/remote/deploy_modal.py","startLine":692,"endLine":709,"chunkText":"@web_app.get(\"/status/{task_id}\")\nasync def get_job_status(task_id: str) -> dict:\n    \"\"\"\n    Get the status of a separation job\n    \"\"\"\n    try:\n        status_data = get_job_status_function.remote(task_id)\n        return status_data\n    except Exception as e:\n        raise HTTPException(status_code=500, detail=f\"Failed to get job status: {str(e)}\") from e\n\n\n@web_app.get(\"/download/{task_id}/{file_hash}\")\nasync def download_file(task_id: str, file_hash: str) -> Response:\n    \"\"\"\n    Download a separated audio file using its hash identifier\n    \"\"\"\n    try:","chunkId":"audio_separator/remote/deploy_modal.py:65"}],[554,{"filePath":"audio_separator/remote/deploy_modal.py","startLine":707,"endLine":721,"chunkText":"    Download a separated audio file using its hash identifier\n    \"\"\"\n    try:\n        file_data, actual_filename = get_file_by_hash_function.remote(task_id, file_hash)\n\n        # Detect file type from content\n        detected_type = filetype.guess(file_data)\n\n        if detected_type and detected_type.mime:\n            content_type = detected_type.mime\n        else:\n            # Log when we can't detect the file type\n            print(f\"WARNING: Could not detect MIME type for {actual_filename}, using generic type\")\n            content_type = \"application/octet-stream\"\n","chunkId":"audio_separator/remote/deploy_modal.py:66"}],[555,{"filePath":"audio_separator/remote/deploy_modal.py","startLine":719,"endLine":727,"chunkText":"            print(f\"WARNING: Could not detect MIME type for {actual_filename}, using generic type\")\n            content_type = \"application/octet-stream\"\n\n        # RFC 5987 encoding for Unicode filenames in Content-Disposition header\n        # Provide ASCII fallback for older clients, and UTF-8 encoded filename for modern clients\n        ascii_filename = \"\".join(c if ord(c) < 128 else \"_\" for c in actual_filename)\n        encoded_filename = quote(actual_filename, safe=\"\")\n        content_disposition = f\"attachment; filename=\\\"{ascii_filename}\\\"; filename*=UTF-8''{encoded_filename}\"\n","chunkId":"audio_separator/remote/deploy_modal.py:67"}],[556,{"filePath":"audio_separator/remote/deploy_modal.py","startLine":725,"endLine":737,"chunkText":"        encoded_filename = quote(actual_filename, safe=\"\")\n        content_disposition = f\"attachment; filename=\\\"{ascii_filename}\\\"; filename*=UTF-8''{encoded_filename}\"\n\n        return Response(content=file_data, media_type=content_type, headers={\"Content-Disposition\": content_disposition})\n\n    except FileNotFoundError as exc:\n        raise HTTPException(status_code=404, detail=\"File not found\") from exc\n    except Exception as e:\n        raise HTTPException(status_code=500, detail=f\"Download failed: {str(e)}\") from e\n\n\n@web_app.get(\"/models-json\")\nasync def get_available_models() -> PrettyJSONResponse","chunkId":"audio_separator/remote/deploy_modal.py:68"}],[557,{"filePath":"audio_separator/remote/deploy_modal.py","startLine":735,"endLine":754,"chunkText":"\n@web_app.get(\"/models-json\")\nasync def get_available_models() -> PrettyJSONResponse:\n    \"\"\"\n    Get list of available separation models\n    \"\"\"\n    models = list_available_models.remote()\n\n    # Return pretty-printed JSON for better readability\n    return PrettyJSONResponse(content=models)\n\n\n@web_app.get(\"/models\")\nasync def get_simplified_models_list(filter_sort_by: str = None) -> PlainTextResponse:\n    \"\"\"\n    Get simplified model list in plain text format (like CLI --list_models)\n    \"\"\"\n    models = get_simplified_models.remote(filter_sort_by=filter_sort_by)\n\n    if not models:","chunkId":"audio_separator/remote/deploy_modal.py:69"}],[558,{"filePath":"audio_separator/remote/deploy_modal.py","startLine":752,"endLine":762,"chunkText":"    models = get_simplified_models.remote(filter_sort_by=filter_sort_by)\n\n    if not models:\n        return PlainTextResponse(\"No models found\")\n\n    # Calculate maximum widths for each column\n    filename_width = max(len(\"Model Filename\"), max(len(filename) for filename in models.keys()))\n    arch_width = max(len(\"Arch\"), max(len(info[\"Type\"]) for info in models.values()))\n    stems_width = max(len(\"Output Stems (SDR)\"), max(len(\", \".join(info[\"Stems\"])) for info in models.values()))\n    name_width = max(len(\"Friendly Name\"), max(len(info[\"Name\"]) for info in models.values()))\n","chunkId":"audio_separator/remote/deploy_modal.py:70"}],[559,{"filePath":"audio_separator/remote/deploy_modal.py","startLine":760,"endLine":768,"chunkText":"    stems_width = max(len(\"Output Stems (SDR)\"), max(len(\", \".join(info[\"Stems\"])) for info in models.values()))\n    name_width = max(len(\"Friendly Name\"), max(len(info[\"Name\"]) for info in models.values()))\n\n    # Calculate total width for separator line\n    total_width = filename_width + arch_width + stems_width + name_width + 15  # 15 accounts for spacing between columns\n\n    # Format the output with dynamic widths and extra spacing\n    output_lines = []\n    output_lines.append(\"-\" * total_width)","chunkId":"audio_separator/remote/deploy_modal.py:71"}],[560,{"filePath":"audio_separator/remote/deploy_modal.py","startLine":766,"endLine":779,"chunkText":"    # Format the output with dynamic widths and extra spacing\n    output_lines = []\n    output_lines.append(\"-\" * total_width)\n    output_lines.append(f\"{'Model Filename':<{filename_width}}  {'Arch':<{arch_width}}  {'Output Stems (SDR)':<{stems_width}}  {'Friendly Name'}\")\n    output_lines.append(\"-\" * total_width)\n\n    for filename, info in models.items():\n        stems = \", \".join(info[\"Stems\"])\n        output_lines.append(f\"{filename:<{filename_width}}  {info['Type']:<{arch_width}}  {stems:<{stems_width}}  {info['Name']}\")\n\n    return PlainTextResponse(\"\\n\".join(output_lines))\n\n\n@web_app.get(\"/health\")","chunkId":"audio_separator/remote/deploy_modal.py:72"}],[561,{"filePath":"audio_separator/remote/deploy_modal.py","startLine":777,"endLine":796,"chunkText":"\n\n@web_app.get(\"/health\")\nasync def health_check() -> dict:\n    \"\"\"\n    Health check endpoint\n    \"\"\"\n    return {\"status\": \"healthy\", \"service\": \"audio-separator-api\", \"version\": AUDIO_SEPARATOR_VERSION}\n\n\n@web_app.get(\"/\")\nasync def root() -> dict:\n    \"\"\"\n    Root endpoint with API information\n    \"\"\"\n    return {\n        \"message\": \"Audio Separator API\",\n        \"version\": AUDIO_SEPARATOR_VERSION,\n        \"description\": (\"Separate vocals from instrumental tracks using AI - \" \"supports all formats and parameters that audio-separator CLI supports\"),\n        \"features\": [","chunkId":"audio_separator/remote/deploy_modal.py:73"}],[562,{"filePath":"audio_separator/remote/deploy_modal.py","startLine":794,"endLine":803,"chunkText":"        \"version\": AUDIO_SEPARATOR_VERSION,\n        \"description\": (\"Separate vocals from instrumental tracks using AI - \" \"supports all formats and parameters that audio-separator CLI supports\"),\n        \"features\": [\n            \"Multiple model processing in single job\",\n            \"Full separator parameter compatibility\",\n            \"Asynchronous processing with progress tracking\",\n            \"All MDX, VR, Demucs, and MDXC architectures supported\",\n            \"Custom output naming and format options\",\n        ],\n        \"endpoints\": {","chunkId":"audio_separator/remote/deploy_modal.py:74"}],[563,{"filePath":"audio_separator/remote/deploy_modal.py","startLine":801,"endLine":807,"chunkText":"            \"Custom output naming and format options\",\n        ],\n        \"endpoints\": {\n            \"POST /separate\": \"Upload and separate audio file (supports multiple models and all separator parameters)\",\n            \"GET /status/{task_id}\": \"Get job status and progress (includes model-specific progress and file hashes)\",\n            \"GET /download/{task_id}/{file_hash}\": \"Download separated file using hash identifier (avoids URL length limits)\",\n            \"GET /models-json\": \"List available models (JSON format)\",","chunkId":"audio_separator/remote/deploy_modal.py:75"}],[564,{"filePath":"audio_separator/remote/deploy_modal.py","startLine":805,"endLine":812,"chunkText":"            \"GET /status/{task_id}\": \"Get job status and progress (includes model-specific progress and file hashes)\",\n            \"GET /download/{task_id}/{file_hash}\": \"Download separated file using hash identifier (avoids URL length limits)\",\n            \"GET /models-json\": \"List available models (JSON format)\",\n            \"GET /models\": \"List available models (plain text format like CLI --list_models)\",\n            \"GET /health\": \"Health check\",\n        },\n        \"note\": (\"Full-featured wrapper around audio-separator with complete parameter compatibility\"),\n        \"remote_cli\": {","chunkId":"audio_separator/remote/deploy_modal.py:76"}],[565,{"filePath":"audio_separator/remote/deploy_modal.py","startLine":810,"endLine":818,"chunkText":"        },\n        \"note\": (\"Full-featured wrapper around audio-separator with complete parameter compatibility\"),\n        \"remote_cli\": {\n            \"install\": \"pip install audio-separator\",\n            \"setup\": 'export AUDIO_SEPARATOR_API_URL=\"https://your-deployment-url.modal.run\"',\n            \"usage\": [\n                \"audio-separator-remote separate song.mp3\",\n                \"audio-separator-remote separate song.mp3 --model UVR-MDX-NET-Inst_HQ_4\",\n                \"audio-separator-remote separate song.mp3 --models model1.ckpt model2.onnx\",","chunkId":"audio_separator/remote/deploy_modal.py:77"}],[566,{"filePath":"audio_separator/remote/deploy_modal.py","startLine":816,"endLine":827,"chunkText":"                \"audio-separator-remote separate song.mp3\",\n                \"audio-separator-remote separate song.mp3 --model UVR-MDX-NET-Inst_HQ_4\",\n                \"audio-separator-remote separate song.mp3 --models model1.ckpt model2.onnx\",\n                'audio-separator-remote separate song.mp3 --custom_output_names \\'{\"Vocals\": \"lead_vocals\"}\\'',\n                \"audio-separator-remote status <task_id>\",\n                \"audio-separator-remote models --filter vocals\",\n            ],\n        },\n    }\n\n\n@app.function(image=image, timeout=600, scaledown_window=300, volumes={\"/storage\": volume})","chunkId":"audio_separator/remote/deploy_modal.py:78"}],[567,{"filePath":"audio_separator/remote/deploy_modal.py","startLine":825,"endLine":834,"chunkText":"\n\n@app.function(image=image, timeout=600, scaledown_window=300, volumes={\"/storage\": volume})\n@modal.asgi_app()\ndef api() -> FastAPI:\n    \"\"\"\n    Deploy the FastAPI app as a Modal ASGI application\n    \"\"\"\n    return web_app\n","chunkId":"audio_separator/remote/deploy_modal.py:79"}],[568,{"filePath":"audio_separator/separator/__init__.py","startLine":0,"endLine":1,"chunkText":"from .separator import Separator\n","chunkId":"audio_separator/separator/__init__.py:0"}],[569,{"filePath":"audio_separator/separator/architectures/demucs_separator.py","startLine":0,"endLine":13,"chunkText":"import os\nimport sys\nfrom pathlib import Path\nimport torch\nimport numpy as np\nfrom audio_separator.separator.common_separator import CommonSeparator\nfrom audio_separator.separator.uvr_lib_v5.demucs.apply import apply_model, demucs_segments\nfrom audio_separator.separator.uvr_lib_v5.demucs.hdemucs import HDemucs\nfrom audio_separator.separator.uvr_lib_v5.demucs.pretrained import get_model as get_demucs_model\nfrom audio_separator.separator.uvr_lib_v5 import spec_utils\n\nDEMUCS_4_SOURCE = [\"drums\", \"bass\", \"other\", \"vocals\"]\n\nDEMUCS_2_SOURCE_MAPPER = {CommonSeparator.INST_STEM: 0, CommonSeparator.VOCAL_STEM: 1}","chunkId":"audio_separator/separator/architectures/demucs_separator.py:0"}],[570,{"filePath":"audio_separator/separator/architectures/demucs_separator.py","startLine":11,"endLine":26,"chunkText":"DEMUCS_4_SOURCE = [\"drums\", \"bass\", \"other\", \"vocals\"]\n\nDEMUCS_2_SOURCE_MAPPER = {CommonSeparator.INST_STEM: 0, CommonSeparator.VOCAL_STEM: 1}\nDEMUCS_4_SOURCE_MAPPER = {CommonSeparator.BASS_STEM: 0, CommonSeparator.DRUM_STEM: 1, CommonSeparator.OTHER_STEM: 2, CommonSeparator.VOCAL_STEM: 3}\nDEMUCS_6_SOURCE_MAPPER = {\n    CommonSeparator.BASS_STEM: 0,\n    CommonSeparator.DRUM_STEM: 1,\n    CommonSeparator.OTHER_STEM: 2,\n    CommonSeparator.VOCAL_STEM: 3,\n    CommonSeparator.GUITAR_STEM: 4,\n    CommonSeparator.PIANO_STEM: 5,\n}\n\n\nclass DemucsSeparator(CommonSeparator):\n    \"\"\"","chunkId":"audio_separator/separator/architectures/demucs_separator.py:1"}],[571,{"filePath":"audio_separator/separator/architectures/demucs_separator.py","startLine":24,"endLine":35,"chunkText":"\nclass DemucsSeparator(CommonSeparator):\n    \"\"\"\n    DemucsSeparator is responsible for separating audio sources using Demucs models.\n    It initializes with configuration parameters and prepares the model for separation tasks.\n    \"\"\"\n\n    def __init__(self, common_config, arch_config):\n        # Any configuration values which can be shared between architectures should be set already in CommonSeparator,\n        # e.g. user-specified functionality choices (self.output_single_stem) or common model parameters (self.primary_stem_name)\n        super().__init__(config=common_config)\n","chunkId":"audio_separator/separator/architectures/demucs_separator.py:2"}],[572,{"filePath":"audio_separator/separator/architectures/demucs_separator.py","startLine":33,"endLine":42,"chunkText":"        # e.g. user-specified functionality choices (self.output_single_stem) or common model parameters (self.primary_stem_name)\n        super().__init__(config=common_config)\n\n        # Initializing user-configurable parameters, passed through with an mdx_from the CLI or Separator instance\n\n        # Adjust segments to manage RAM or V-RAM usage:\n        # - Smaller sizes consume less resources.\n        # - Bigger sizes consume more resources, but may provide better results.\n        # - \"Default\" picks the optimal size.\n        # DEMUCS_SEGMENTS = (DEF_OPT, '1', '5', '10', '15', '20',","chunkId":"audio_separator/separator/architectures/demucs_separator.py:3"}],[573,{"filePath":"audio_separator/separator/architectures/demucs_separator.py","startLine":40,"endLine":49,"chunkText":"        # - Bigger sizes consume more resources, but may provide better results.\n        # - \"Default\" picks the optimal size.\n        # DEMUCS_SEGMENTS = (DEF_OPT, '1', '5', '10', '15', '20',\n        #           '25', '30', '35', '40', '45', '50',\n        #           '55', '60', '65', '70', '75', '80',\n        #           '85', '90', '95', '100')\n        self.segment_size = arch_config.get(\"segment_size\", \"Default\")\n\n        # Performs multiple predictions with random shifts of the input and averages them.\n        # The higher number of shifts, the longer the prediction will take.","chunkId":"audio_separator/separator/architectures/demucs_separator.py:4"}],[574,{"filePath":"audio_separator/separator/architectures/demucs_separator.py","startLine":47,"endLine":57,"chunkText":"\n        # Performs multiple predictions with random shifts of the input and averages them.\n        # The higher number of shifts, the longer the prediction will take.\n        # Not recommended unless you have a GPU.\n        # DEMUCS_SHIFTS = (0, 1, 2, 3, 4, 5,\n        #                 6, 7, 8, 9, 10, 11,\n        #                 12, 13, 14, 15, 16, 17,\n        #                 18, 19, 20)\n        self.shifts = arch_config.get(\"shifts\", 2)\n\n        # This option controls the amount of overlap between prediction windows.","chunkId":"audio_separator/separator/architectures/demucs_separator.py:5"}],[575,{"filePath":"audio_separator/separator/architectures/demucs_separator.py","startLine":55,"endLine":65,"chunkText":"        self.shifts = arch_config.get(\"shifts\", 2)\n\n        # This option controls the amount of overlap between prediction windows.\n        #  - Higher values can provide better results, but will lead to longer processing times.\n        #  - You can choose between 0.001-0.999\n        # DEMUCS_OVERLAP = (0.25, 0.50, 0.75, 0.99)\n        self.overlap = arch_config.get(\"overlap\", 0.25)\n\n        # Enables \"Segments\". Deselecting this option is only recommended for those with powerful PCs.\n        self.segments_enabled = arch_config.get(\"segments_enabled\", True)\n","chunkId":"audio_separator/separator/architectures/demucs_separator.py:6"}],[576,{"filePath":"audio_separator/separator/architectures/demucs_separator.py","startLine":63,"endLine":74,"chunkText":"        # Enables \"Segments\". Deselecting this option is only recommended for those with powerful PCs.\n        self.segments_enabled = arch_config.get(\"segments_enabled\", True)\n\n        self.logger.debug(f\"Demucs arch params: segment_size={self.segment_size}, segments_enabled={self.segments_enabled}\")\n        self.logger.debug(f\"Demucs arch params: shifts={self.shifts}, overlap={self.overlap}\")\n\n        self.demucs_source_map = DEMUCS_4_SOURCE_MAPPER\n\n        self.audio_file_path = None\n        self.audio_file_base = None\n        self.demucs_model_instance = None\n","chunkId":"audio_separator/separator/architectures/demucs_separator.py:7"}],[577,{"filePath":"audio_separator/separator/architectures/demucs_separator.py","startLine":72,"endLine":86,"chunkText":"        self.audio_file_base = None\n        self.demucs_model_instance = None\n\n        # Add uvr_lib_v5 folder to system path so pytorch serialization can find the demucs module\n        current_dir = os.path.dirname(__file__)\n        uvr_lib_v5_path = os.path.join(current_dir, \"..\", \"uvr_lib_v5\")\n        sys.path.insert(0, uvr_lib_v5_path)\n\n        self.logger.info(\"Demucs Separator initialisation complete\")\n\n    def separate(self, audio_file_path, custom_output_names=None):\n        \"\"\"\n        Separates the audio file into its component stems using the Demucs model.\n\n        Args:","chunkId":"audio_separator/separator/architectures/demucs_separator.py:8"}],[578,{"filePath":"audio_separator/separator/architectures/demucs_separator.py","startLine":84,"endLine":98,"chunkText":"        Separates the audio file into its component stems using the Demucs model.\n\n        Args:\n            audio_file_path (str): The path to the audio file to be processed.\n            custom_output_names (dict, optional): Custom names for the output files. Defaults to None.\n\n        Returns:\n            list: A list of paths to the output files generated by the separation process.\n        \"\"\"\n        self.logger.debug(\"Starting separation process...\")\n        source = None\n        stem_source = None\n        inst_source = {}\n\n        self.audio_file_path = audio_file_path","chunkId":"audio_separator/separator/architectures/demucs_separator.py:9"}],[579,{"filePath":"audio_separator/separator/architectures/demucs_separator.py","startLine":96,"endLine":109,"chunkText":"        inst_source = {}\n\n        self.audio_file_path = audio_file_path\n        self.audio_file_base = os.path.splitext(os.path.basename(audio_file_path))[0]\n\n        # Prepare the mix for processing\n        self.logger.debug(\"Preparing mix...\")\n        mix = self.prepare_mix(self.audio_file_path)\n\n        self.logger.debug(f\"Mix prepared for demixing. Shape: {mix.shape}\")\n\n        self.logger.debug(\"Loading model for demixing...\")\n\n        self.demucs_model_instance = HDemucs(sources=DEMUCS_4_SOURCE)","chunkId":"audio_separator/separator/architectures/demucs_separator.py:10"}],[580,{"filePath":"audio_separator/separator/architectures/demucs_separator.py","startLine":107,"endLine":118,"chunkText":"        self.logger.debug(\"Loading model for demixing...\")\n\n        self.demucs_model_instance = HDemucs(sources=DEMUCS_4_SOURCE)\n        self.demucs_model_instance = get_demucs_model(name=os.path.splitext(os.path.basename(self.model_path))[0], repo=Path(os.path.dirname(self.model_path)))\n        self.demucs_model_instance = demucs_segments(self.segment_size, self.demucs_model_instance)\n        self.demucs_model_instance.to(self.torch_device)\n        self.demucs_model_instance.eval()\n\n        self.logger.debug(\"Model loaded and set to evaluation mode.\")\n\n        source = self.demix_demucs(mix)\n","chunkId":"audio_separator/separator/architectures/demucs_separator.py:11"}],[581,{"filePath":"audio_separator/separator/architectures/demucs_separator.py","startLine":116,"endLine":128,"chunkText":"\n        source = self.demix_demucs(mix)\n\n        del self.demucs_model_instance\n        self.clear_gpu_cache()\n        self.logger.debug(\"Model and GPU cache cleared after demixing.\")\n\n        output_files = []\n        self.logger.debug(\"Processing output files...\")\n\n        if isinstance(inst_source, np.ndarray):\n            self.logger.debug(\"Processing instance source...\")\n            source_reshape = spec_utils.reshape_sources(inst_source[self.demucs_source_map[CommonSeparator.VOCAL_STEM]], source[self.demucs_source_map[CommonSeparator.VOCAL_STEM]])","chunkId":"audio_separator/separator/architectures/demucs_separator.py:12"}],[582,{"filePath":"audio_separator/separator/architectures/demucs_separator.py","startLine":126,"endLine":134,"chunkText":"        if isinstance(inst_source, np.ndarray):\n            self.logger.debug(\"Processing instance source...\")\n            source_reshape = spec_utils.reshape_sources(inst_source[self.demucs_source_map[CommonSeparator.VOCAL_STEM]], source[self.demucs_source_map[CommonSeparator.VOCAL_STEM]])\n            inst_source[self.demucs_source_map[CommonSeparator.VOCAL_STEM]] = source_reshape\n            source = inst_source\n\n        if isinstance(source, np.ndarray):\n            source_length = len(source)\n            self.logger.debug(f\"Processing source array, source length is {source_length}\")","chunkId":"audio_separator/separator/architectures/demucs_separator.py:13"}],[583,{"filePath":"audio_separator/separator/architectures/demucs_separator.py","startLine":132,"endLine":142,"chunkText":"        if isinstance(source, np.ndarray):\n            source_length = len(source)\n            self.logger.debug(f\"Processing source array, source length is {source_length}\")\n            match source_length:\n                case 2:\n                    self.logger.debug(\"Setting source map to 2-stem...\")\n                    self.demucs_source_map = DEMUCS_2_SOURCE_MAPPER\n                case 6:\n                    self.logger.debug(\"Setting source map to 6-stem...\")\n                    self.demucs_source_map = DEMUCS_6_SOURCE_MAPPER\n                case _:","chunkId":"audio_separator/separator/architectures/demucs_separator.py:14"}],[584,{"filePath":"audio_separator/separator/architectures/demucs_separator.py","startLine":140,"endLine":149,"chunkText":"                    self.logger.debug(\"Setting source map to 6-stem...\")\n                    self.demucs_source_map = DEMUCS_6_SOURCE_MAPPER\n                case _:\n                    self.logger.debug(\"Setting source map to 4-stem...\")\n                    self.demucs_source_map = DEMUCS_4_SOURCE_MAPPER\n\n        self.logger.debug(\"Processing for all stems...\")\n        for stem_name, stem_value in self.demucs_source_map.items():\n            if self.output_single_stem is not None:\n                if stem_name.lower() != self.output_single_stem.lower():","chunkId":"audio_separator/separator/architectures/demucs_separator.py:15"}],[585,{"filePath":"audio_separator/separator/architectures/demucs_separator.py","startLine":147,"endLine":158,"chunkText":"        for stem_name, stem_value in self.demucs_source_map.items():\n            if self.output_single_stem is not None:\n                if stem_name.lower() != self.output_single_stem.lower():\n                    self.logger.debug(f\"Skipping writing stem {stem_name} as output_single_stem is set to {self.output_single_stem}...\")\n                    continue\n\n            stem_path = self.get_stem_output_path(stem_name, custom_output_names)\n            stem_source = source[stem_value].T\n\n            self.final_process(stem_path, stem_source, stem_name)\n            output_files.append(stem_path)\n","chunkId":"audio_separator/separator/architectures/demucs_separator.py:16"}],[586,{"filePath":"audio_separator/separator/architectures/demucs_separator.py","startLine":156,"endLine":174,"chunkText":"            self.final_process(stem_path, stem_source, stem_name)\n            output_files.append(stem_path)\n\n        return output_files\n\n    def demix_demucs(self, mix):\n        \"\"\"\n        Demixes the input mix using the demucs model.\n        \"\"\"\n        self.logger.debug(\"Starting demixing process in demix_demucs...\")\n\n        processed = {}\n        mix = torch.tensor(mix, dtype=torch.float32)\n        ref = mix.mean(0)\n        mix = (mix - ref.mean()) / ref.std()\n        mix_infer = mix\n\n        with torch.no_grad():\n            self.logger.debug(\"Running model inference...\")","chunkId":"audio_separator/separator/architectures/demucs_separator.py:17"}],[587,{"filePath":"audio_separator/separator/architectures/demucs_separator.py","startLine":172,"endLine":187,"chunkText":"\n        with torch.no_grad():\n            self.logger.debug(\"Running model inference...\")\n            sources = apply_model(\n                model=self.demucs_model_instance,\n                mix=mix_infer[None],\n                shifts=self.shifts,\n                split=self.segments_enabled,\n                overlap=self.overlap,\n                static_shifts=1 if self.shifts == 0 else self.shifts,\n                set_progress_bar=None,\n                device=self.torch_device,\n                progress=True,\n            )[0]\n\n        sources = (sources * ref.std() + ref.mean()).cpu().numpy()","chunkId":"audio_separator/separator/architectures/demucs_separator.py:18"}],[588,{"filePath":"audio_separator/separator/architectures/demucs_separator.py","startLine":185,"endLine":195,"chunkText":"            )[0]\n\n        sources = (sources * ref.std() + ref.mean()).cpu().numpy()\n        sources[[0, 1]] = sources[[1, 0]]\n        processed[mix] = sources[:, :, 0:None].copy()\n        sources = list(processed.values())\n        sources = [s[:, :, 0:None] for s in sources]\n        sources = np.concatenate(sources, axis=-1)\n\n        return sources\n","chunkId":"audio_separator/separator/architectures/demucs_separator.py:19"}],[589,{"filePath":"audio_separator/separator/architectures/mdx_separator.py","startLine":0,"endLine":17,"chunkText":"\"\"\"Module for separating audio sources using MDX architecture models.\"\"\"\n\nimport os\nimport platform\nimport torch\nimport onnx\nimport onnxruntime as ort\nimport numpy as np\nimport onnx2torch\nfrom tqdm import tqdm\nfrom audio_separator.separator.uvr_lib_v5 import spec_utils\nfrom audio_separator.separator.uvr_lib_v5.stft import STFT\nfrom audio_separator.separator.common_separator import CommonSeparator\n\n\nclass MDXSeparator(CommonSeparator):\n    \"\"\"\n    MDXSeparator is responsible for separating audio sources using MDX models.","chunkId":"audio_separator/separator/architectures/mdx_separator.py:0"}],[590,{"filePath":"audio_separator/separator/architectures/mdx_separator.py","startLine":16,"endLine":25,"chunkText":"    \"\"\"\n    MDXSeparator is responsible for separating audio sources using MDX models.\n    It initializes with configuration parameters and prepares the model for separation tasks.\n    \"\"\"\n\n    def __init__(self, common_config, arch_config):\n        # Any configuration values which can be shared between architectures should be set already in CommonSeparator,\n        # e.g. user-specified functionality choices (self.output_single_stem) or common model parameters (self.primary_stem_name)\n        super().__init__(config=common_config)\n","chunkId":"audio_separator/separator/architectures/mdx_separator.py:1"}],[591,{"filePath":"audio_separator/separator/architectures/mdx_separator.py","startLine":23,"endLine":31,"chunkText":"        # e.g. user-specified functionality choices (self.output_single_stem) or common model parameters (self.primary_stem_name)\n        super().__init__(config=common_config)\n\n        # Initializing user-configurable parameters, passed through with an mdx_from the CLI or Separator instance\n\n        # Pick a segment size to balance speed, resource use, and quality:\n        # - Smaller sizes consume less resources.\n        # - Bigger sizes consume more resources, but may provide better results.\n        # - Default size is 256. Quality can change based on your pick.","chunkId":"audio_separator/separator/architectures/mdx_separator.py:2"}],[592,{"filePath":"audio_separator/separator/architectures/mdx_separator.py","startLine":29,"endLine":38,"chunkText":"        # - Smaller sizes consume less resources.\n        # - Bigger sizes consume more resources, but may provide better results.\n        # - Default size is 256. Quality can change based on your pick.\n        self.segment_size = arch_config.get(\"segment_size\")\n\n        # This option controls the amount of overlap between prediction windows.\n        #  - Higher values can provide better results, but will lead to longer processing times.\n        #  - For Non-MDX23C models: You can choose between 0.001-0.999\n        self.overlap = arch_config.get(\"overlap\")\n","chunkId":"audio_separator/separator/architectures/mdx_separator.py:3"}],[593,{"filePath":"audio_separator/separator/architectures/mdx_separator.py","startLine":36,"endLine":45,"chunkText":"        #  - For Non-MDX23C models: You can choose between 0.001-0.999\n        self.overlap = arch_config.get(\"overlap\")\n\n        # Number of batches to be processed at a time.\n        # - Higher values mean more RAM usage but slightly faster processing times.\n        # - Lower values mean less RAM usage but slightly longer processing times.\n        # - Batch size value has no effect on output quality.\n        # BATCH_SIZE = ('1', ''2', '3', '4', '5', '6', '7', '8', '9', '10')\n        self.batch_size = arch_config.get(\"batch_size\", 1)\n","chunkId":"audio_separator/separator/architectures/mdx_separator.py:4"}],[594,{"filePath":"audio_separator/separator/architectures/mdx_separator.py","startLine":43,"endLine":49,"chunkText":"        # BATCH_SIZE = ('1', ''2', '3', '4', '5', '6', '7', '8', '9', '10')\n        self.batch_size = arch_config.get(\"batch_size\", 1)\n\n        # hop_length is equivalent to the more commonly used term \"stride\" in convolutional neural networks\n        # In machine learning, particularly in the context of convolutional neural networks (CNNs),\n        # the term \"stride\" refers to the number of pixels by which we move the filter across the input image.\n        # Strides are a crucial component in the convolution operation, a fundamental building block of CNNs used primarily in the field of computer vision.","chunkId":"audio_separator/separator/architectures/mdx_separator.py:5"}],[595,{"filePath":"audio_separator/separator/architectures/mdx_separator.py","startLine":47,"endLine":50,"chunkText":"        # In machine learning, particularly in the context of convolutional neural networks (CNNs),\n        # the term \"stride\" refers to the number of pixels by which we move the filter across the input image.\n        # Strides are a crucial component in the convolution operation, a fundamental building block of CNNs used primarily in the field of computer vision.\n        # Stride is a parameter that dictates the movement of the kernel, or filter, across the input data, such as an image.","chunkId":"audio_separator/separator/architectures/mdx_separator.py:6"}],[596,{"filePath":"audio_separator/separator/architectures/mdx_separator.py","startLine":48,"endLine":52,"chunkText":"        # the term \"stride\" refers to the number of pixels by which we move the filter across the input image.\n        # Strides are a crucial component in the convolution operation, a fundamental building block of CNNs used primarily in the field of computer vision.\n        # Stride is a parameter that dictates the movement of the kernel, or filter, across the input data, such as an image.\n        # When performing a convolution operation, the stride determines how many units the filter shifts at each step.\n        # The choice of stride affects the model in several ways:","chunkId":"audio_separator/separator/architectures/mdx_separator.py:7"}],[597,{"filePath":"audio_separator/separator/architectures/mdx_separator.py","startLine":50,"endLine":54,"chunkText":"        # Stride is a parameter that dictates the movement of the kernel, or filter, across the input data, such as an image.\n        # When performing a convolution operation, the stride determines how many units the filter shifts at each step.\n        # The choice of stride affects the model in several ways:\n        # Output Size: A larger stride will result in a smaller output spatial dimension.\n        # Computational Efficiency: Increasing the stride can decrease the computational load.","chunkId":"audio_separator/separator/architectures/mdx_separator.py:8"}],[598,{"filePath":"audio_separator/separator/architectures/mdx_separator.py","startLine":52,"endLine":58,"chunkText":"        # The choice of stride affects the model in several ways:\n        # Output Size: A larger stride will result in a smaller output spatial dimension.\n        # Computational Efficiency: Increasing the stride can decrease the computational load.\n        # Field of View: A higher stride means that each step of the filter takes into account a wider area of the input image.\n        #   This can be beneficial when the model needs to capture more global features rather than focusing on finer details.\n        self.hop_length = arch_config.get(\"hop_length\")\n","chunkId":"audio_separator/separator/architectures/mdx_separator.py:9"}],[599,{"filePath":"audio_separator/separator/architectures/mdx_separator.py","startLine":56,"endLine":64,"chunkText":"        #   This can be beneficial when the model needs to capture more global features rather than focusing on finer details.\n        self.hop_length = arch_config.get(\"hop_length\")\n\n        # If enabled, model will be run twice to reduce noise in output audio.\n        self.enable_denoise = arch_config.get(\"enable_denoise\")\n\n        self.logger.debug(f\"MDX arch params: batch_size={self.batch_size}, segment_size={self.segment_size}\")\n        self.logger.debug(f\"MDX arch params: overlap={self.overlap}, hop_length={self.hop_length}, enable_denoise={self.enable_denoise}\")\n","chunkId":"audio_separator/separator/architectures/mdx_separator.py:10"}],[600,{"filePath":"audio_separator/separator/architectures/mdx_separator.py","startLine":62,"endLine":69,"chunkText":"        self.logger.debug(f\"MDX arch params: batch_size={self.batch_size}, segment_size={self.segment_size}\")\n        self.logger.debug(f\"MDX arch params: overlap={self.overlap}, hop_length={self.hop_length}, enable_denoise={self.enable_denoise}\")\n\n        # Initializing model-specific parameters from model_data JSON\n        self.compensate = self.model_data[\"compensate\"]\n        self.dim_f = self.model_data[\"mdx_dim_f_set\"]\n        self.dim_t = 2 ** self.model_data[\"mdx_dim_t_set\"]\n        self.n_fft = self.model_data[\"mdx_n_fft_scale_set\"]","chunkId":"audio_separator/separator/architectures/mdx_separator.py:11"}],[601,{"filePath":"audio_separator/separator/architectures/mdx_separator.py","startLine":67,"endLine":75,"chunkText":"        self.dim_f = self.model_data[\"mdx_dim_f_set\"]\n        self.dim_t = 2 ** self.model_data[\"mdx_dim_t_set\"]\n        self.n_fft = self.model_data[\"mdx_n_fft_scale_set\"]\n        self.config_yaml = self.model_data.get(\"config_yaml\", None)\n\n        self.logger.debug(f\"MDX arch params: compensate={self.compensate}, dim_f={self.dim_f}, dim_t={self.dim_t}, n_fft={self.n_fft}\")\n        self.logger.debug(f\"MDX arch params: config_yaml={self.config_yaml}\")\n\n        # In UVR, these variables are set but either aren't useful or are better handled in audio-separator.","chunkId":"audio_separator/separator/architectures/mdx_separator.py:12"}],[602,{"filePath":"audio_separator/separator/architectures/mdx_separator.py","startLine":73,"endLine":83,"chunkText":"        self.logger.debug(f\"MDX arch params: config_yaml={self.config_yaml}\")\n\n        # In UVR, these variables are set but either aren't useful or are better handled in audio-separator.\n        # Leaving these comments explaining to help myself or future developers understand why these aren't in audio-separator.\n\n        # \"chunks\" is not actually used for anything in UVR...\n        # self.chunks = 0\n\n        # \"adjust\" is hard-coded to 1 in UVR, and only used as a multiplier in run_model, so it does nothing.\n        # self.adjust = 1\n","chunkId":"audio_separator/separator/architectures/mdx_separator.py:13"}],[603,{"filePath":"audio_separator/separator/architectures/mdx_separator.py","startLine":81,"endLine":90,"chunkText":"        # \"adjust\" is hard-coded to 1 in UVR, and only used as a multiplier in run_model, so it does nothing.\n        # self.adjust = 1\n\n        # \"hop\" is hard-coded to 1024 in UVR. We have a \"hop_length\" parameter instead\n        # self.hop = 1024\n\n        # \"margin\" maps to sample rate and is set from the GUI in UVR (default: 44100). We have a \"sample_rate\" parameter instead.\n        # self.margin = 44100\n\n        # \"dim_c\" is hard-coded to 4 in UVR, seems to be a parameter for the number of channels, and is only used for checkpoint models.","chunkId":"audio_separator/separator/architectures/mdx_separator.py:14"}],[604,{"filePath":"audio_separator/separator/architectures/mdx_separator.py","startLine":88,"endLine":107,"chunkText":"        # self.margin = 44100\n\n        # \"dim_c\" is hard-coded to 4 in UVR, seems to be a parameter for the number of channels, and is only used for checkpoint models.\n        # We haven't implemented support for the checkpoint models here, so we're not using it.\n        # self.dim_c = 4\n\n        self.load_model()\n\n        self.n_bins = 0\n        self.trim = 0\n        self.chunk_size = 0\n        self.gen_size = 0\n        self.stft = None\n\n        self.primary_source = None\n        self.secondary_source = None\n        self.audio_file_path = None\n        self.audio_file_base = None\n\n    def load_model(self)","chunkId":"audio_separator/separator/architectures/mdx_separator.py:15"}],[605,{"filePath":"audio_separator/separator/architectures/mdx_separator.py","startLine":105,"endLine":120,"chunkText":"        self.audio_file_base = None\n\n    def load_model(self):\n        \"\"\"\n        Load the model into memory from file on disk, initialize it with config from the model data,\n        and prepare for inferencing using hardware accelerated Torch device.\n        \"\"\"\n        self.logger.debug(\"Loading ONNX model for inference...\")\n\n        if self.segment_size == self.dim_t:\n            ort_session_options = ort.SessionOptions()\n            if self.log_level > 10:\n                ort_session_options.log_severity_level = 3\n            else:\n                ort_session_options.log_severity_level = 0\n","chunkId":"audio_separator/separator/architectures/mdx_separator.py:16"}],[606,{"filePath":"audio_separator/separator/architectures/mdx_separator.py","startLine":118,"endLine":126,"chunkText":"            else:\n                ort_session_options.log_severity_level = 0\n\n            ort_inference_session = ort.InferenceSession(self.model_path, providers=self.onnx_execution_provider, sess_options=ort_session_options)\n            self.model_run = lambda spek: ort_inference_session.run(None, {\"input\": spek.cpu().numpy()})[0]\n            self.logger.debug(\"Model loaded successfully using ONNXruntime inferencing session.\")\n        else:\n            if platform.system() == 'Windows':\n                onnx_model = onnx.load(self.model_path)","chunkId":"audio_separator/separator/architectures/mdx_separator.py:17"}],[607,{"filePath":"audio_separator/separator/architectures/mdx_separator.py","startLine":124,"endLine":135,"chunkText":"        else:\n            if platform.system() == 'Windows':\n                onnx_model = onnx.load(self.model_path)\n                self.model_run = onnx2torch.convert(onnx_model)\n            else:\n                self.model_run = onnx2torch.convert(self.model_path)\n   \n            self.model_run.to(self.torch_device).eval()\n            self.logger.warning(\"Model converted from onnx to pytorch due to segment size not matching dim_t, processing may be slower.\")\n\n    def separate(self, audio_file_path, custom_output_names=None):\n        \"\"\"","chunkId":"audio_separator/separator/architectures/mdx_separator.py:18"}],[608,{"filePath":"audio_separator/separator/architectures/mdx_separator.py","startLine":133,"endLine":145,"chunkText":"\n    def separate(self, audio_file_path, custom_output_names=None):\n        \"\"\"\n        Separates the audio file into primary and secondary sources based on the model's configuration.\n        It processes the mix, demixes it into sources, normalizes the sources, and saves the output files.\n\n        Args:\n            audio_file_path (str): The path to the audio file to be processed.\n            custom_output_names (dict, optional): Custom names for the output files. Defaults to None.\n\n        Returns:\n            list: A list of paths to the output files generated by the separation process.\n        \"\"\"","chunkId":"audio_separator/separator/architectures/mdx_separator.py:19"}],[609,{"filePath":"audio_separator/separator/architectures/mdx_separator.py","startLine":143,"endLine":154,"chunkText":"        Returns:\n            list: A list of paths to the output files generated by the separation process.\n        \"\"\"\n        self.audio_file_path = audio_file_path\n        self.audio_file_base = os.path.splitext(os.path.basename(audio_file_path))[0]\n\n        # Prepare the mix for processing\n        self.logger.debug(f\"Preparing mix for input audio file {self.audio_file_path}...\")\n        mix = self.prepare_mix(self.audio_file_path)\n\n        self.logger.debug(\"Normalizing mix before demixing...\")\n        peak = np.abs(mix).max()","chunkId":"audio_separator/separator/architectures/mdx_separator.py:20"}],[610,{"filePath":"audio_separator/separator/architectures/mdx_separator.py","startLine":152,"endLine":167,"chunkText":"\n        self.logger.debug(\"Normalizing mix before demixing...\")\n        peak = np.abs(mix).max()\n        mix = spec_utils.normalize(wave=mix, max_peak=self.normalization_threshold, min_peak=self.amplification_threshold)\n\n        # Start the demixing process\n        source = self.demix(mix) * peak\n        self.logger.debug(\"Demixing completed.\")\n\n\n        if not isinstance(self.primary_source, np.ndarray):\n            self.primary_source = source.T\n\n        # In UVR, the source is cached here if it's a vocal split model, but we're not supporting that yet\n\n        # Initialize the list for output files","chunkId":"audio_separator/separator/architectures/mdx_separator.py:21"}],[611,{"filePath":"audio_separator/separator/architectures/mdx_separator.py","startLine":165,"endLine":176,"chunkText":"        # In UVR, the source is cached here if it's a vocal split model, but we're not supporting that yet\n\n        # Initialize the list for output files\n        output_files = []\n        self.logger.debug(\"Processing output files...\")\n\n        # Process the secondary source if not already an array\n        if not isinstance(self.secondary_source, np.ndarray):\n            self.logger.debug(\"Producing secondary source: demixing in match_mix mode\")\n            raw_mix = self.demix(mix, is_match_mix=True)\n\n            if self.invert_using_spec:","chunkId":"audio_separator/separator/architectures/mdx_separator.py:22"}],[612,{"filePath":"audio_separator/separator/architectures/mdx_separator.py","startLine":174,"endLine":182,"chunkText":"            raw_mix = self.demix(mix, is_match_mix=True)\n\n            if self.invert_using_spec:\n                self.logger.debug(\"Inverting secondary stem using spectogram as invert_using_spec is set to True\")\n                self.secondary_source = spec_utils.invert_stem(raw_mix, self.primary_source * self.compensate)\n            else:\n                self.logger.debug(\"Inverting secondary stem by subtracting of transposed demixed stem from transposed original mix\")\n                self.secondary_source = (-self.primary_source * self.compensate) + mix.T\n","chunkId":"audio_separator/separator/architectures/mdx_separator.py:23"}],[613,{"filePath":"audio_separator/separator/architectures/mdx_separator.py","startLine":180,"endLine":186,"chunkText":"                self.logger.debug(\"Inverting secondary stem by subtracting of transposed demixed stem from transposed original mix\")\n                self.secondary_source = (-self.primary_source * self.compensate) + mix.T\n\n        # Save and process the secondary stem if needed\n        if not self.output_single_stem or self.output_single_stem.lower() == self.secondary_stem_name.lower():\n            self.secondary_stem_output_path = self.get_stem_output_path(self.secondary_stem_name, custom_output_names)\n","chunkId":"audio_separator/separator/architectures/mdx_separator.py:24"}],[614,{"filePath":"audio_separator/separator/architectures/mdx_separator.py","startLine":184,"endLine":191,"chunkText":"        if not self.output_single_stem or self.output_single_stem.lower() == self.secondary_stem_name.lower():\n            self.secondary_stem_output_path = self.get_stem_output_path(self.secondary_stem_name, custom_output_names)\n\n            self.logger.info(f\"Saving {self.secondary_stem_name} stem to {self.secondary_stem_output_path}...\")\n            self.final_process(self.secondary_stem_output_path, self.secondary_source, self.secondary_stem_name)\n            output_files.append(self.secondary_stem_output_path)\n\n        # Save and process the primary stem if needed","chunkId":"audio_separator/separator/architectures/mdx_separator.py:25"}],[615,{"filePath":"audio_separator/separator/architectures/mdx_separator.py","startLine":189,"endLine":195,"chunkText":"            output_files.append(self.secondary_stem_output_path)\n\n        # Save and process the primary stem if needed\n        if not self.output_single_stem or self.output_single_stem.lower() == self.primary_stem_name.lower():\n            self.primary_stem_output_path = self.get_stem_output_path(self.primary_stem_name, custom_output_names)\n            self.logger.info(f\"Saving {self.primary_stem_name} stem to {self.primary_stem_output_path}...\")\n            self.final_process(self.primary_stem_output_path, self.primary_source, self.primary_stem_name)","chunkId":"audio_separator/separator/architectures/mdx_separator.py:26"}],[616,{"filePath":"audio_separator/separator/architectures/mdx_separator.py","startLine":193,"endLine":203,"chunkText":"            self.primary_stem_output_path = self.get_stem_output_path(self.primary_stem_name, custom_output_names)\n            self.logger.info(f\"Saving {self.primary_stem_name} stem to {self.primary_stem_output_path}...\")\n            self.final_process(self.primary_stem_output_path, self.primary_source, self.primary_stem_name)\n            output_files.append(self.primary_stem_output_path)\n\n        # Not yet implemented from UVR features:\n        # self.process_vocal_split_chain(secondary_sources)\n        # self.logger.debug(\"Vocal split chain processed.\")\n\n        return output_files\n","chunkId":"audio_separator/separator/architectures/mdx_separator.py:27"}],[617,{"filePath":"audio_separator/separator/architectures/mdx_separator.py","startLine":201,"endLine":212,"chunkText":"\n        return output_files\n\n    def initialize_model_settings(self):\n        \"\"\"\n        This function sets up the necessary parameters for the model, like the number of frequency bins (n_bins), the trimming size (trim),\n        the size of each audio chunk (chunk_size), and the window function for spectral transformations (window).\n        It ensures that the model is configured with the correct settings for processing the audio data.\n        \"\"\"\n        self.logger.debug(\"Initializing model settings...\")\n\n        # n_bins is half the FFT size plus one (self.n_fft // 2 + 1).","chunkId":"audio_separator/separator/architectures/mdx_separator.py:28"}],[618,{"filePath":"audio_separator/separator/architectures/mdx_separator.py","startLine":210,"endLine":223,"chunkText":"        self.logger.debug(\"Initializing model settings...\")\n\n        # n_bins is half the FFT size plus one (self.n_fft // 2 + 1).\n        self.n_bins = self.n_fft // 2 + 1\n\n        # trim is half the FFT size (self.n_fft // 2).\n        self.trim = self.n_fft // 2\n\n        # chunk_size is the hop_length size times the segment size minus one\n        self.chunk_size = self.hop_length * (self.segment_size - 1)\n\n        # gen_size is the chunk size minus twice the trim size\n        self.gen_size = self.chunk_size - 2 * self.trim\n","chunkId":"audio_separator/separator/architectures/mdx_separator.py:29"}],[619,{"filePath":"audio_separator/separator/architectures/mdx_separator.py","startLine":221,"endLine":230,"chunkText":"        # gen_size is the chunk size minus twice the trim size\n        self.gen_size = self.chunk_size - 2 * self.trim\n\n        self.stft = STFT(self.logger, self.n_fft, self.hop_length, self.dim_f, self.torch_device)\n\n        self.logger.debug(f\"Model input params: n_fft={self.n_fft} hop_length={self.hop_length} dim_f={self.dim_f}\")\n        self.logger.debug(f\"Model settings: n_bins={self.n_bins}, trim={self.trim}, chunk_size={self.chunk_size}, gen_size={self.gen_size}\")\n\n    def initialize_mix(self, mix, is_ckpt=False):\n        \"\"\"","chunkId":"audio_separator/separator/architectures/mdx_separator.py:30"}],[620,{"filePath":"audio_separator/separator/architectures/mdx_separator.py","startLine":228,"endLine":236,"chunkText":"\n    def initialize_mix(self, mix, is_ckpt=False):\n        \"\"\"\n        After prepare_mix segments the audio, initialize_mix further processes each segment.\n        It ensures each audio segment is in the correct format for the model, applies necessary padding,\n        and converts the segments into tensors for processing with the model.\n        This step is essential for preparing the audio data in a format that the neural network can process.\n        \"\"\"\n        # Log the initialization of the mix and whether checkpoint mode is used","chunkId":"audio_separator/separator/architectures/mdx_separator.py:31"}],[621,{"filePath":"audio_separator/separator/architectures/mdx_separator.py","startLine":234,"endLine":244,"chunkText":"        This step is essential for preparing the audio data in a format that the neural network can process.\n        \"\"\"\n        # Log the initialization of the mix and whether checkpoint mode is used\n        self.logger.debug(f\"Initializing mix with is_ckpt={is_ckpt}. Initial mix shape: {mix.shape}\")\n\n        # Ensure the mix is a 2-channel (stereo) audio signal\n        if mix.shape[0] != 2:\n            error_message = f\"Expected a 2-channel audio signal, but got {mix.shape[0]} channels\"\n            self.logger.error(error_message)\n            raise ValueError(error_message)\n","chunkId":"audio_separator/separator/architectures/mdx_separator.py:32"}],[622,{"filePath":"audio_separator/separator/architectures/mdx_separator.py","startLine":242,"endLine":253,"chunkText":"            self.logger.error(error_message)\n            raise ValueError(error_message)\n\n        # If in checkpoint mode, process the mix differently\n        if is_ckpt:\n            self.logger.debug(\"Processing in checkpoint mode...\")\n            # Calculate padding based on the generation size and trim\n            pad = self.gen_size + self.trim - (mix.shape[-1] % self.gen_size)\n            self.logger.debug(f\"Padding calculated: {pad}\")\n            # Add padding at the beginning and the end of the mix\n            mixture = np.concatenate(\n                (","chunkId":"audio_separator/separator/architectures/mdx_separator.py:33"}],[623,{"filePath":"audio_separator/separator/architectures/mdx_separator.py","startLine":251,"endLine":262,"chunkText":"            # Add padding at the beginning and the end of the mix\n            mixture = np.concatenate(\n                (\n                    np.zeros((2, self.trim), dtype=\"float32\"),  # Pad at the start\n                    mix,\n                    np.zeros((2, pad), dtype=\"float32\"),        # Pad in the middle (to match chunk size)\n                    np.zeros((2, self.trim), dtype=\"float32\"),  # Pad at the end\n                ),\n                1\n            )\n            # Determine the number of chunks based on the mixture's length\n            num_chunks = mixture.shape[-1] // self.gen_size","chunkId":"audio_separator/separator/architectures/mdx_separator.py:34"}],[624,{"filePath":"audio_separator/separator/architectures/mdx_separator.py","startLine":260,"endLine":269,"chunkText":"            )\n            # Determine the number of chunks based on the mixture's length\n            num_chunks = mixture.shape[-1] // self.gen_size\n            self.logger.debug(f\"Mixture shape after padding: {mixture.shape}, Number of chunks: {num_chunks}\")\n            # Split the mixture into chunks\n            mix_waves = [mixture[:, i * self.gen_size : i * self.gen_size + self.chunk_size] for i in range(num_chunks)]\n        else:\n            # If not in checkpoint mode, process normally\n            self.logger.debug(\"Processing in non-checkpoint mode...\")\n            mix_waves = []","chunkId":"audio_separator/separator/architectures/mdx_separator.py:35"}],[625,{"filePath":"audio_separator/separator/architectures/mdx_separator.py","startLine":267,"endLine":275,"chunkText":"            # If not in checkpoint mode, process normally\n            self.logger.debug(\"Processing in non-checkpoint mode...\")\n            mix_waves = []\n            n_sample = mix.shape[1]\n            # Calculate necessary padding to make the total length divisible by the generation size\n            pad = self.gen_size - n_sample % self.gen_size\n            self.logger.debug(f\"Number of samples: {n_sample}, Padding calculated: {pad}\")\n            # Apply padding to the mix\n            mix_p = np.concatenate((np.zeros((2, self.trim)), mix, np.zeros((2, pad)), np.zeros((2, self.trim))), 1)","chunkId":"audio_separator/separator/architectures/mdx_separator.py:36"}],[626,{"filePath":"audio_separator/separator/architectures/mdx_separator.py","startLine":273,"endLine":282,"chunkText":"            self.logger.debug(f\"Number of samples: {n_sample}, Padding calculated: {pad}\")\n            # Apply padding to the mix\n            mix_p = np.concatenate((np.zeros((2, self.trim)), mix, np.zeros((2, pad)), np.zeros((2, self.trim))), 1)\n            self.logger.debug(f\"Shape of mix after padding: {mix_p.shape}\")\n\n            # Process the mix in chunks\n            i = 0\n            while i < n_sample + pad:\n                waves = np.array(mix_p[:, i : i + self.chunk_size])\n                mix_waves.append(waves)","chunkId":"audio_separator/separator/architectures/mdx_separator.py:37"}],[627,{"filePath":"audio_separator/separator/architectures/mdx_separator.py","startLine":280,"endLine":289,"chunkText":"            while i < n_sample + pad:\n                waves = np.array(mix_p[:, i : i + self.chunk_size])\n                mix_waves.append(waves)\n                self.logger.debug(f\"Processed chunk {len(mix_waves)}: Start {i}, End {i + self.chunk_size}\")\n                i += self.gen_size\n\n        # Convert the list of wave chunks into a tensor for processing on the specified device\n        mix_waves_tensor = torch.tensor(mix_waves, dtype=torch.float32).to(self.torch_device)\n        self.logger.debug(f\"Converted mix_waves to tensor. Tensor shape: {mix_waves_tensor.shape}\")\n","chunkId":"audio_separator/separator/architectures/mdx_separator.py:38"}],[628,{"filePath":"audio_separator/separator/architectures/mdx_separator.py","startLine":287,"endLine":295,"chunkText":"        mix_waves_tensor = torch.tensor(mix_waves, dtype=torch.float32).to(self.torch_device)\n        self.logger.debug(f\"Converted mix_waves to tensor. Tensor shape: {mix_waves_tensor.shape}\")\n\n        return mix_waves_tensor, pad\n\n    def demix(self, mix, is_match_mix=False):\n        \"\"\"\n        Demixes the input mix into its constituent sources. If is_match_mix is True, the function adjusts the processing\n        to better match the mix, affecting chunk sizes and overlaps. The demixing process involves padding the mix,","chunkId":"audio_separator/separator/architectures/mdx_separator.py:39"}],[629,{"filePath":"audio_separator/separator/architectures/mdx_separator.py","startLine":293,"endLine":301,"chunkText":"        \"\"\"\n        Demixes the input mix into its constituent sources. If is_match_mix is True, the function adjusts the processing\n        to better match the mix, affecting chunk sizes and overlaps. The demixing process involves padding the mix,\n        processing it in chunks, applying windowing for overlaps, and accumulating the results to separate the sources.\n        \"\"\"\n        self.logger.debug(f\"Starting demixing process with is_match_mix: {is_match_mix}...\")\n        self.initialize_model_settings()\n\n        # Preserves the original mix for later use.","chunkId":"audio_separator/separator/architectures/mdx_separator.py:40"}],[630,{"filePath":"audio_separator/separator/architectures/mdx_separator.py","startLine":299,"endLine":311,"chunkText":"        self.initialize_model_settings()\n\n        # Preserves the original mix for later use.\n        # In UVR, this is used for the pitch fix and VR denoise processes, which aren't yet implemented here.\n        org_mix = mix\n        self.logger.debug(f\"Original mix stored. Shape: {org_mix.shape}\")\n\n        # Initializes a list to store the separated waveforms.\n        tar_waves_ = []\n\n        # Handling different chunk sizes and overlaps based on the matching requirement.\n        if is_match_mix:\n            # Sets a smaller chunk size specifically for matching the mix.","chunkId":"audio_separator/separator/architectures/mdx_separator.py:41"}],[631,{"filePath":"audio_separator/separator/architectures/mdx_separator.py","startLine":309,"endLine":318,"chunkText":"        # Handling different chunk sizes and overlaps based on the matching requirement.\n        if is_match_mix:\n            # Sets a smaller chunk size specifically for matching the mix.\n            chunk_size = self.hop_length * (self.segment_size - 1)\n            # Sets a small overlap for the chunks.\n            overlap = 0.02\n            self.logger.debug(f\"Chunk size for matching mix: {chunk_size}, Overlap: {overlap}\")\n        else:\n            # Uses the regular chunk size defined in model settings.\n            chunk_size = self.chunk_size","chunkId":"audio_separator/separator/architectures/mdx_separator.py:42"}],[632,{"filePath":"audio_separator/separator/architectures/mdx_separator.py","startLine":316,"endLine":327,"chunkText":"        else:\n            # Uses the regular chunk size defined in model settings.\n            chunk_size = self.chunk_size\n            # Uses the overlap specified in the model settings.\n            overlap = self.overlap\n            self.logger.debug(f\"Standard chunk size: {chunk_size}, Overlap: {overlap}\")\n\n        # Calculates the generated size after subtracting the trim from both ends of the chunk.\n        gen_size = chunk_size - 2 * self.trim\n        self.logger.debug(f\"Generated size calculated: {gen_size}\")\n\n        # Calculates padding to make the mix length a multiple of the generated size.","chunkId":"audio_separator/separator/architectures/mdx_separator.py:43"}],[633,{"filePath":"audio_separator/separator/architectures/mdx_separator.py","startLine":325,"endLine":333,"chunkText":"        self.logger.debug(f\"Generated size calculated: {gen_size}\")\n\n        # Calculates padding to make the mix length a multiple of the generated size.\n        pad = gen_size + self.trim - ((mix.shape[-1]) % gen_size)\n        # Prepares the mixture with padding at the beginning and the end.\n        mixture = np.concatenate((np.zeros((2, self.trim), dtype=\"float32\"), mix, np.zeros((2, pad), dtype=\"float32\")), 1)\n        self.logger.debug(f\"Mixture prepared with padding. Mixture shape: {mixture.shape}\")\n\n        # Calculates the step size for processing chunks based on the overlap.","chunkId":"audio_separator/separator/architectures/mdx_separator.py:44"}],[634,{"filePath":"audio_separator/separator/architectures/mdx_separator.py","startLine":331,"endLine":341,"chunkText":"        self.logger.debug(f\"Mixture prepared with padding. Mixture shape: {mixture.shape}\")\n\n        # Calculates the step size for processing chunks based on the overlap.\n        step = int((1 - overlap) * chunk_size)\n        self.logger.debug(f\"Step size for processing chunks: {step} as overlap is set to {overlap}.\")\n\n        # Initializes arrays to store the results and to account for overlap.\n        result = np.zeros((1, 2, mixture.shape[-1]), dtype=np.float32)\n        divider = np.zeros((1, 2, mixture.shape[-1]), dtype=np.float32)\n\n        # Initializes counters for processing chunks.","chunkId":"audio_separator/separator/architectures/mdx_separator.py:45"}],[635,{"filePath":"audio_separator/separator/architectures/mdx_separator.py","startLine":339,"endLine":352,"chunkText":"        divider = np.zeros((1, 2, mixture.shape[-1]), dtype=np.float32)\n\n        # Initializes counters for processing chunks.\n        total = 0\n        total_chunks = (mixture.shape[-1] + step - 1) // step\n        self.logger.debug(f\"Total chunks to process: {total_chunks}\")\n\n        # Processes each chunk of the mixture.\n        for i in tqdm(range(0, mixture.shape[-1], step)):\n            total += 1\n            start = i\n            end = min(i + chunk_size, mixture.shape[-1])\n            self.logger.debug(f\"Processing chunk {total}/{total_chunks}: Start {start}, End {end}\")\n","chunkId":"audio_separator/separator/architectures/mdx_separator.py:46"}],[636,{"filePath":"audio_separator/separator/architectures/mdx_separator.py","startLine":350,"endLine":362,"chunkText":"            end = min(i + chunk_size, mixture.shape[-1])\n            self.logger.debug(f\"Processing chunk {total}/{total_chunks}: Start {start}, End {end}\")\n\n            # Handles windowing for overlapping chunks.\n            chunk_size_actual = end - start\n            window = None\n            if overlap != 0:\n                window = np.hanning(chunk_size_actual)\n                window = np.tile(window[None, None, :], (1, 2, 1))\n                self.logger.debug(\"Window applied to the chunk.\")\n\n            # Zero-pad the chunk to prepare it for processing.\n            mix_part_ = mixture[:, start:end]","chunkId":"audio_separator/separator/architectures/mdx_separator.py:47"}],[637,{"filePath":"audio_separator/separator/architectures/mdx_separator.py","startLine":360,"endLine":370,"chunkText":"\n            # Zero-pad the chunk to prepare it for processing.\n            mix_part_ = mixture[:, start:end]\n            if end != i + chunk_size:\n                pad_size = (i + chunk_size) - end\n                mix_part_ = np.concatenate((mix_part_, np.zeros((2, pad_size), dtype=\"float32\")), axis=-1)\n\n            # Converts the chunk to a tensor for processing.\n            mix_part = torch.tensor([mix_part_], dtype=torch.float32).to(self.torch_device)\n            # Splits the chunk into smaller batches if necessary.\n            mix_waves = mix_part.split(self.batch_size)","chunkId":"audio_separator/separator/architectures/mdx_separator.py:48"}],[638,{"filePath":"audio_separator/separator/architectures/mdx_separator.py","startLine":368,"endLine":378,"chunkText":"            mix_part = torch.tensor([mix_part_], dtype=torch.float32).to(self.torch_device)\n            # Splits the chunk into smaller batches if necessary.\n            mix_waves = mix_part.split(self.batch_size)\n            total_batches = len(mix_waves)\n            self.logger.debug(f\"Mix part split into batches. Number of batches: {total_batches}\")\n\n            with torch.no_grad():\n                # Processes each batch in the chunk.\n                batches_processed = 0\n                for mix_wave in mix_waves:\n                    batches_processed += 1","chunkId":"audio_separator/separator/architectures/mdx_separator.py:49"}],[639,{"filePath":"audio_separator/separator/architectures/mdx_separator.py","startLine":376,"endLine":386,"chunkText":"                batches_processed = 0\n                for mix_wave in mix_waves:\n                    batches_processed += 1\n                    self.logger.debug(f\"Processing mix_wave batch {batches_processed}/{total_batches}\")\n\n                    # Runs the model to separate the sources.\n                    tar_waves = self.run_model(mix_wave, is_match_mix=is_match_mix)\n\n                    # Applies windowing if needed and accumulates the results.\n                    if window is not None:\n                        tar_waves[..., :chunk_size_actual] *= window","chunkId":"audio_separator/separator/architectures/mdx_separator.py:50"}],[640,{"filePath":"audio_separator/separator/architectures/mdx_separator.py","startLine":384,"endLine":395,"chunkText":"                    # Applies windowing if needed and accumulates the results.\n                    if window is not None:\n                        tar_waves[..., :chunk_size_actual] *= window\n                        divider[..., start:end] += window\n                    else:\n                        divider[..., start:end] += 1\n\n                    result[..., start:end] += tar_waves[..., : end - start]\n\n        # Normalizes the results by the divider to account for overlap.\n        self.logger.debug(\"Normalizing result by dividing result by divider.\")\n        tar_waves = result / divider","chunkId":"audio_separator/separator/architectures/mdx_separator.py:51"}],[641,{"filePath":"audio_separator/separator/architectures/mdx_separator.py","startLine":393,"endLine":405,"chunkText":"        # Normalizes the results by the divider to account for overlap.\n        self.logger.debug(\"Normalizing result by dividing result by divider.\")\n        tar_waves = result / divider\n        tar_waves_.append(tar_waves)\n\n        # Reshapes the results to match the original dimensions.\n        tar_waves_ = np.vstack(tar_waves_)[:, :, self.trim : -self.trim]\n        tar_waves = np.concatenate(tar_waves_, axis=-1)[:, : mix.shape[-1]]\n\n        # Extracts the source from the results.\n        source = tar_waves[:, 0:None]\n        self.logger.debug(f\"Concatenated tar_waves. Shape: {tar_waves.shape}\")\n","chunkId":"audio_separator/separator/architectures/mdx_separator.py:52"}],[642,{"filePath":"audio_separator/separator/architectures/mdx_separator.py","startLine":403,"endLine":415,"chunkText":"        source = tar_waves[:, 0:None]\n        self.logger.debug(f\"Concatenated tar_waves. Shape: {tar_waves.shape}\")\n\n        # TODO: In UVR, pitch changing happens here. Consider implementing this as a feature.\n\n        # TODO: In UVR, VR denoise model gets applied here. Consider implementing this as a feature.\n\n        self.logger.debug(\"Demixing process completed.\")\n        return source\n\n    def run_model(self, mix, is_match_mix=False):\n        \"\"\"\n        Processes the input mix through the model to separate the sources.","chunkId":"audio_separator/separator/architectures/mdx_separator.py:53"}],[643,{"filePath":"audio_separator/separator/architectures/mdx_separator.py","startLine":413,"endLine":422,"chunkText":"    def run_model(self, mix, is_match_mix=False):\n        \"\"\"\n        Processes the input mix through the model to separate the sources.\n        Applies STFT, handles spectrum modifications, and runs the model for source separation.\n        \"\"\"\n        # Applying the STFT to the mix. The mix is moved to the specified device (e.g., GPU) before processing.\n        # self.logger.debug(f\"Running STFT on the mix. Mix shape before STFT: {mix.shape}\")\n        spek = self.stft(mix.to(self.torch_device))\n        self.logger.debug(f\"STFT applied on mix. Spectrum shape: {spek.shape}\")\n","chunkId":"audio_separator/separator/architectures/mdx_separator.py:54"}],[644,{"filePath":"audio_separator/separator/architectures/mdx_separator.py","startLine":420,"endLine":430,"chunkText":"        spek = self.stft(mix.to(self.torch_device))\n        self.logger.debug(f\"STFT applied on mix. Spectrum shape: {spek.shape}\")\n\n        # Zeroing out the first 3 bins of the spectrum. This is often done to reduce low-frequency noise.\n        spek[:, :, :3, :] *= 0\n        # self.logger.debug(\"First 3 bins of the spectrum zeroed out.\")\n\n        # Handling the case where the mix needs to be matched (is_match_mix = True)\n        if is_match_mix:\n            # self.logger.debug(\"Match mix mode is enabled. Converting spectrum to NumPy array.\")\n            spec_pred = spek.cpu().numpy()","chunkId":"audio_separator/separator/architectures/mdx_separator.py:55"}],[645,{"filePath":"audio_separator/separator/architectures/mdx_separator.py","startLine":428,"endLine":435,"chunkText":"        if is_match_mix:\n            # self.logger.debug(\"Match mix mode is enabled. Converting spectrum to NumPy array.\")\n            spec_pred = spek.cpu().numpy()\n            self.logger.debug(\"is_match_mix: spectrum prediction obtained directly from STFT output.\")\n        else:\n            # If denoising is enabled, the model is run on both the negative and positive spectrums.\n            if self.enable_denoise:\n                # Assuming spek is a tensor and self.model_run can process it directly","chunkId":"audio_separator/separator/architectures/mdx_separator.py:56"}],[646,{"filePath":"audio_separator/separator/architectures/mdx_separator.py","startLine":433,"endLine":439,"chunkText":"            # If denoising is enabled, the model is run on both the negative and positive spectrums.\n            if self.enable_denoise:\n                # Assuming spek is a tensor and self.model_run can process it directly\n                spec_pred_neg = self.model_run(-spek)  # Ensure this line correctly negates spek and runs the model\n                spec_pred_pos = self.model_run(spek)\n                # Ensure both spec_pred_neg and spec_pred_pos are tensors before applying operations\n                spec_pred = (spec_pred_neg * -0.5) + (spec_pred_pos * 0.5)  # [invalid-unary-operand-type]","chunkId":"audio_separator/separator/architectures/mdx_separator.py:57"}],[647,{"filePath":"audio_separator/separator/architectures/mdx_separator.py","startLine":437,"endLine":445,"chunkText":"                spec_pred_pos = self.model_run(spek)\n                # Ensure both spec_pred_neg and spec_pred_pos are tensors before applying operations\n                spec_pred = (spec_pred_neg * -0.5) + (spec_pred_pos * 0.5)  # [invalid-unary-operand-type]\n                self.logger.debug(\"Model run on both negative and positive spectrums for denoising.\")\n            else:\n                spec_pred = self.model_run(spek)\n                self.logger.debug(\"Model run on the spectrum without denoising.\")\n\n        # Applying the inverse STFT to convert the spectrum back to the time domain.","chunkId":"audio_separator/separator/architectures/mdx_separator.py:58"}],[648,{"filePath":"audio_separator/separator/architectures/mdx_separator.py","startLine":443,"endLine":450,"chunkText":"                self.logger.debug(\"Model run on the spectrum without denoising.\")\n\n        # Applying the inverse STFT to convert the spectrum back to the time domain.\n        result = self.stft.inverse(torch.tensor(spec_pred).to(self.torch_device)).cpu().detach().numpy()\n        self.logger.debug(f\"Inverse STFT applied. Returning result with shape: {result.shape}\")\n\n        return result\n","chunkId":"audio_separator/separator/architectures/mdx_separator.py:59"}],[649,{"filePath":"audio_separator/separator/architectures/mdxc_separator.py","startLine":0,"endLine":17,"chunkText":"import os\nimport sys\n\nimport torch\nimport numpy as np\nfrom tqdm import tqdm\nfrom ml_collections import ConfigDict\nfrom scipy import signal\n\nfrom audio_separator.separator.common_separator import CommonSeparator\nfrom audio_separator.separator.uvr_lib_v5 import spec_utils\nfrom audio_separator.separator.uvr_lib_v5.tfc_tdf_v3 import TFC_TDF_net\n# Roformer direct constructors removed; loading handled via RoformerLoader in CommonSeparator.\n\n\nclass MDXCSeparator(CommonSeparator):\n    \"\"\"\n    MDXCSeparator is responsible for separating audio sources using MDXC models.","chunkId":"audio_separator/separator/architectures/mdxc_separator.py:0"}],[650,{"filePath":"audio_separator/separator/architectures/mdxc_separator.py","startLine":15,"endLine":25,"chunkText":"class MDXCSeparator(CommonSeparator):\n    \"\"\"\n    MDXCSeparator is responsible for separating audio sources using MDXC models.\n    It initializes with configuration parameters and prepares the model for separation tasks.\n    \"\"\"\n\n    def __init__(self, common_config, arch_config):\n        # Any configuration values which can be shared between architectures should be set already in CommonSeparator,\n        # e.g. user-specified functionality choices (self.output_single_stem) or common model parameters (self.primary_stem_name)\n        super().__init__(config=common_config)\n","chunkId":"audio_separator/separator/architectures/mdxc_separator.py:1"}],[651,{"filePath":"audio_separator/separator/architectures/mdxc_separator.py","startLine":23,"endLine":30,"chunkText":"        # e.g. user-specified functionality choices (self.output_single_stem) or common model parameters (self.primary_stem_name)\n        super().__init__(config=common_config)\n\n        # Model data is basic overview metadata about the model, e.g. which stem is primary and whether it's a karaoke model\n        # It's loaded in from model_data_new.json in Separator.load_model and there are JSON examples in that method\n        # The instance variable self.model_data is passed through from Separator and set in CommonSeparator\n        self.logger.debug(f\"Model data: {self.model_data}\")\n","chunkId":"audio_separator/separator/architectures/mdxc_separator.py:2"}],[652,{"filePath":"audio_separator/separator/architectures/mdxc_separator.py","startLine":28,"endLine":34,"chunkText":"        # The instance variable self.model_data is passed through from Separator and set in CommonSeparator\n        self.logger.debug(f\"Model data: {self.model_data}\")\n\n        # Arch Config is the MDXC architecture specific user configuration options, which should all be configurable by the user\n        # either by their Separator class instantiation or by passing in a CLI parameter.\n        # While there are similarities between architectures for some of these (e.g. batch_size), they are deliberately configured\n        # this way as they have architecture-specific default values.","chunkId":"audio_separator/separator/architectures/mdxc_separator.py:3"}],[653,{"filePath":"audio_separator/separator/architectures/mdxc_separator.py","startLine":32,"endLine":38,"chunkText":"        # either by their Separator class instantiation or by passing in a CLI parameter.\n        # While there are similarities between architectures for some of these (e.g. batch_size), they are deliberately configured\n        # this way as they have architecture-specific default values.\n        self.segment_size = arch_config.get(\"segment_size\", 256)\n\n        # Whether or not to use the segment size from model config, or the default\n        # The segment size is set based on the value provided in a chosen model's associated config file (yaml).","chunkId":"audio_separator/separator/architectures/mdxc_separator.py:4"}],[654,{"filePath":"audio_separator/separator/architectures/mdxc_separator.py","startLine":36,"endLine":45,"chunkText":"\n        # Whether or not to use the segment size from model config, or the default\n        # The segment size is set based on the value provided in a chosen model's associated config file (yaml).\n        self.override_model_segment_size = arch_config.get(\"override_model_segment_size\", False)\n\n        self.overlap = arch_config.get(\"overlap\", 8)\n        self.batch_size = arch_config.get(\"batch_size\", 1)\n\n        # Amount of pitch shift to apply during processing (this does NOT affect the pitch of the output audio):\n        # • Whole numbers indicate semitones.","chunkId":"audio_separator/separator/architectures/mdxc_separator.py:5"}],[655,{"filePath":"audio_separator/separator/architectures/mdxc_separator.py","startLine":43,"endLine":52,"chunkText":"\n        # Amount of pitch shift to apply during processing (this does NOT affect the pitch of the output audio):\n        # • Whole numbers indicate semitones.\n        # • Using higher pitches may cut the upper bandwidth, even in high-quality models.\n        # • Upping the pitch can be better for tracks with deeper vocals.\n        # • Dropping the pitch may take more processing time but works well for tracks with high-pitched vocals.\n        self.pitch_shift = arch_config.get(\"pitch_shift\", 0)\n\n        self.process_all_stems = arch_config.get(\"process_all_stems\", True)\n","chunkId":"audio_separator/separator/architectures/mdxc_separator.py:6"}],[656,{"filePath":"audio_separator/separator/architectures/mdxc_separator.py","startLine":50,"endLine":57,"chunkText":"\n        self.process_all_stems = arch_config.get(\"process_all_stems\", True)\n\n        self.logger.debug(f\"MDXC arch params: batch_size={self.batch_size}, segment_size={self.segment_size}, overlap={self.overlap}\")\n        self.logger.debug(f\"MDXC arch params: override_model_segment_size={self.override_model_segment_size}, pitch_shift={self.pitch_shift}\")\n        self.logger.debug(f\"MDXC multi-stem params: process_all_stems={self.process_all_stems}\")\n\n        # Align Roformer detection flag with CommonSeparator to ensure consistent stats/logging","chunkId":"audio_separator/separator/architectures/mdxc_separator.py:7"}],[657,{"filePath":"audio_separator/separator/architectures/mdxc_separator.py","startLine":55,"endLine":68,"chunkText":"        self.logger.debug(f\"MDXC multi-stem params: process_all_stems={self.process_all_stems}\")\n\n        # Align Roformer detection flag with CommonSeparator to ensure consistent stats/logging\n        self.is_roformer = getattr(self, \"is_roformer_model\", False)\n\n        self.load_model()\n\n        self.primary_source = None\n        self.secondary_source = None\n        self.audio_file_path = None\n        self.audio_file_base = None\n\n        # Only mark primary stem as main target for single-target models.\n        # Multi-stem models should not trigger residual subtraction logic.","chunkId":"audio_separator/separator/architectures/mdxc_separator.py:8"}],[658,{"filePath":"audio_separator/separator/architectures/mdxc_separator.py","startLine":66,"endLine":77,"chunkText":"\n        # Only mark primary stem as main target for single-target models.\n        # Multi-stem models should not trigger residual subtraction logic.\n        self.is_primary_stem_main_target = bool(self.model_data_cfgdict.training.target_instrument)\n\n        self.logger.debug(f\"is_primary_stem_main_target: {self.is_primary_stem_main_target}\")\n\n        self.logger.info(\"MDXC Separator initialisation complete\")\n\n    def load_model(self):\n        \"\"\"\n        Load the model into memory from file on disk, initialize it with config from the model data,","chunkId":"audio_separator/separator/architectures/mdxc_separator.py:9"}],[659,{"filePath":"audio_separator/separator/architectures/mdxc_separator.py","startLine":75,"endLine":87,"chunkText":"    def load_model(self):\n        \"\"\"\n        Load the model into memory from file on disk, initialize it with config from the model data,\n        and prepare for inferencing using hardware accelerated Torch device.\n        \"\"\"\n        self.logger.debug(\"Loading checkpoint model for inference...\")\n\n        self.model_data_cfgdict = ConfigDict(self.model_data)\n\n        try:\n            if self.is_roformer:\n                # Use the RoformerLoader exclusively; no legacy fallback\n                self.logger.debug(\"Loading Roformer model via RoformerLoader...\")","chunkId":"audio_separator/separator/architectures/mdxc_separator.py:10"}],[660,{"filePath":"audio_separator/separator/architectures/mdxc_separator.py","startLine":85,"endLine":95,"chunkText":"            if self.is_roformer:\n                # Use the RoformerLoader exclusively; no legacy fallback\n                self.logger.debug(\"Loading Roformer model via RoformerLoader...\")\n                result = self.roformer_loader.load_model(\n                    model_path=self.model_path,\n                    config=self.model_data,\n                    device=str(self.torch_device),\n                )\n\n                if getattr(result, \"success\", False) and getattr(result, \"model\", None) is not None:\n                    self.model_run = result.model","chunkId":"audio_separator/separator/architectures/mdxc_separator.py:11"}],[661,{"filePath":"audio_separator/separator/architectures/mdxc_separator.py","startLine":93,"endLine":103,"chunkText":"\n                if getattr(result, \"success\", False) and getattr(result, \"model\", None) is not None:\n                    self.model_run = result.model\n                    self.model_run.to(self.torch_device).eval()\n                else:\n                    error_msg = getattr(result, \"error_message\", \"RoformerLoader unsuccessful\")\n                    self.logger.error(f\"Failed to load Roformer model: {error_msg}\")\n                    raise RuntimeError(error_msg)\n\n            else:\n                self.logger.debug(\"Loading TFC_TDF_net model...\")","chunkId":"audio_separator/separator/architectures/mdxc_separator.py:12"}],[662,{"filePath":"audio_separator/separator/architectures/mdxc_separator.py","startLine":101,"endLine":111,"chunkText":"\n            else:\n                self.logger.debug(\"Loading TFC_TDF_net model...\")\n                self.model_run = TFC_TDF_net(self.model_data_cfgdict, device=self.torch_device)\n                self.logger.debug(\"Loading model onto cpu\")\n                # For some reason loading the state onto a hardware accelerated devices causes issues, \n                # so we load it onto CPU first then move it to the device\n                self.model_run.load_state_dict(torch.load(self.model_path, map_location=\"cpu\"))\n                self.model_run.to(self.torch_device).eval()\n\n        except RuntimeError as e:","chunkId":"audio_separator/separator/architectures/mdxc_separator.py:13"}],[663,{"filePath":"audio_separator/separator/architectures/mdxc_separator.py","startLine":109,"endLine":118,"chunkText":"                self.model_run.to(self.torch_device).eval()\n\n        except RuntimeError as e:\n            self.logger.error(f\"Error: {e}\")\n            self.logger.error(\"An error occurred while loading the model file. This often occurs when the model file is corrupt or incomplete.\")\n            self.logger.error(f\"Please try deleting the model file from {self.model_path} and run audio-separator again to re-download it.\")\n            sys.exit(1)\n\n    def separate(self, audio_file_path, custom_output_names=None):\n        \"\"\"","chunkId":"audio_separator/separator/architectures/mdxc_separator.py:14"}],[664,{"filePath":"audio_separator/separator/architectures/mdxc_separator.py","startLine":116,"endLine":128,"chunkText":"\n    def separate(self, audio_file_path, custom_output_names=None):\n        \"\"\"\n        Separates the audio file into primary and secondary sources based on the model's configuration.\n        It processes the mix, demixes it into sources, normalizes the sources, and saves the output files.\n\n        Args:\n            audio_file_path (str): The path to the audio file to be processed.\n            custom_output_names (dict, optional): Custom names for the output files. Defaults to None.\n\n        Returns:\n            list: A list of paths to the output files generated by the separation process.\n        \"\"\"","chunkId":"audio_separator/separator/architectures/mdxc_separator.py:15"}],[665,{"filePath":"audio_separator/separator/architectures/mdxc_separator.py","startLine":126,"endLine":139,"chunkText":"        Returns:\n            list: A list of paths to the output files generated by the separation process.\n        \"\"\"\n        self.primary_source = None\n        self.secondary_source = None\n\n        self.audio_file_path = audio_file_path\n        self.audio_file_base = os.path.splitext(os.path.basename(audio_file_path))[0]\n\n        self.logger.debug(f\"Preparing mix for input audio file {self.audio_file_path}...\")\n        mix = self.prepare_mix(self.audio_file_path)\n\n        # Check if audio is shorter than threshold\n        audio_duration_seconds = mix.shape[1] / self.sample_rate","chunkId":"audio_separator/separator/architectures/mdxc_separator.py:16"}],[666,{"filePath":"audio_separator/separator/architectures/mdxc_separator.py","startLine":137,"endLine":146,"chunkText":"\n        # Check if audio is shorter than threshold\n        audio_duration_seconds = mix.shape[1] / self.sample_rate\n        if audio_duration_seconds < 10.0:\n            # Only change and warn if it wasn't already set by the user\n            if not self.override_model_segment_size:\n                self.override_model_segment_size = True\n                self.logger.warning(f\"Audio duration ({audio_duration_seconds:.2f}s) is less than 10 seconds.\")\n                self.logger.warning(\"Automatically enabling override_model_segment_size for better processing of short audio.\")\n","chunkId":"audio_separator/separator/architectures/mdxc_separator.py:17"}],[667,{"filePath":"audio_separator/separator/architectures/mdxc_separator.py","startLine":144,"endLine":155,"chunkText":"                self.logger.warning(f\"Audio duration ({audio_duration_seconds:.2f}s) is less than 10 seconds.\")\n                self.logger.warning(\"Automatically enabling override_model_segment_size for better processing of short audio.\")\n\n        self.logger.debug(\"Normalizing mix before demixing...\")\n        mix = spec_utils.normalize(wave=mix, max_peak=self.normalization_threshold, min_peak=self.amplification_threshold)\n\n        source = self.demix(mix=mix)\n        self.logger.debug(\"Demixing completed.\")\n\n        output_files = []\n        self.logger.debug(\"Processing output files...\")\n","chunkId":"audio_separator/separator/architectures/mdxc_separator.py:18"}],[668,{"filePath":"audio_separator/separator/architectures/mdxc_separator.py","startLine":153,"endLine":167,"chunkText":"        output_files = []\n        self.logger.debug(\"Processing output files...\")\n\n        if isinstance(source, dict):\n            self.logger.debug(\"Source is a dict, processing each stem...\")\n            \n            stem_list = []\n            if self.model_data_cfgdict.training.target_instrument:\n                stem_list = [self.model_data_cfgdict.training.target_instrument]\n            else:\n                stem_list = self.model_data_cfgdict.training.instruments\n            \n            self.logger.debug(f\"Available stems: {stem_list}\")\n\n            is_multi_stem_model = len(stem_list) > 2","chunkId":"audio_separator/separator/architectures/mdxc_separator.py:19"}],[669,{"filePath":"audio_separator/separator/architectures/mdxc_separator.py","startLine":165,"endLine":175,"chunkText":"            self.logger.debug(f\"Available stems: {stem_list}\")\n\n            is_multi_stem_model = len(stem_list) > 2\n            should_process_all_stems = self.process_all_stems and is_multi_stem_model\n            \n            if should_process_all_stems:\n                self.logger.debug(\"Processing all stems from multi-stem model...\")\n                for stem_name in stem_list:\n                    stem_output_path = self.get_stem_output_path(stem_name, custom_output_names)\n                    stem_source = spec_utils.normalize(\n                        wave=source[stem_name], ","chunkId":"audio_separator/separator/architectures/mdxc_separator.py:20"}],[670,{"filePath":"audio_separator/separator/architectures/mdxc_separator.py","startLine":173,"endLine":182,"chunkText":"                    stem_output_path = self.get_stem_output_path(stem_name, custom_output_names)\n                    stem_source = spec_utils.normalize(\n                        wave=source[stem_name], \n                        max_peak=self.normalization_threshold, \n                        min_peak=self.amplification_threshold\n                    ).T\n                    \n                    self.logger.info(f\"Saving {stem_name} stem to {stem_output_path}...\")\n                    self.final_process(stem_output_path, stem_source, stem_name)\n                    output_files.append(stem_output_path)","chunkId":"audio_separator/separator/architectures/mdxc_separator.py:21"}],[671,{"filePath":"audio_separator/separator/architectures/mdxc_separator.py","startLine":180,"endLine":187,"chunkText":"                    self.logger.info(f\"Saving {stem_name} stem to {stem_output_path}...\")\n                    self.final_process(stem_output_path, stem_source, stem_name)\n                    output_files.append(stem_output_path)\n            else:\n                # Standard processing for primary and secondary stems\n                if not isinstance(self.primary_source, np.ndarray):\n                    self.logger.debug(f\"Normalizing primary source for primary stem {self.primary_stem_name}...\")\n                    self.primary_source = spec_utils.normalize(","chunkId":"audio_separator/separator/architectures/mdxc_separator.py:22"}],[672,{"filePath":"audio_separator/separator/architectures/mdxc_separator.py","startLine":185,"endLine":193,"chunkText":"                if not isinstance(self.primary_source, np.ndarray):\n                    self.logger.debug(f\"Normalizing primary source for primary stem {self.primary_stem_name}...\")\n                    self.primary_source = spec_utils.normalize(\n                        wave=source[self.primary_stem_name], \n                        max_peak=self.normalization_threshold, \n                        min_peak=self.amplification_threshold\n                    ).T\n\n                if not isinstance(self.secondary_source, np.ndarray):","chunkId":"audio_separator/separator/architectures/mdxc_separator.py:23"}],[673,{"filePath":"audio_separator/separator/architectures/mdxc_separator.py","startLine":191,"endLine":200,"chunkText":"                    ).T\n\n                if not isinstance(self.secondary_source, np.ndarray):\n                    self.logger.debug(f\"Normalizing secondary source for secondary stem {self.secondary_stem_name}...\")\n                    self.secondary_source = spec_utils.normalize(\n                        wave=source[self.secondary_stem_name], \n                        max_peak=self.normalization_threshold, \n                        min_peak=self.amplification_threshold\n                    ).T\n","chunkId":"audio_separator/separator/architectures/mdxc_separator.py:24"}],[674,{"filePath":"audio_separator/separator/architectures/mdxc_separator.py","startLine":198,"endLine":205,"chunkText":"                        min_peak=self.amplification_threshold\n                    ).T\n\n                if not self.output_single_stem or self.output_single_stem.lower() == self.secondary_stem_name.lower():\n                    self.secondary_stem_output_path = self.get_stem_output_path(self.secondary_stem_name, custom_output_names)\n\n                    self.logger.info(f\"Saving {self.secondary_stem_name} stem to {self.secondary_stem_output_path}...\")\n                    self.final_process(self.secondary_stem_output_path, self.secondary_source, self.secondary_stem_name)","chunkId":"audio_separator/separator/architectures/mdxc_separator.py:25"}],[675,{"filePath":"audio_separator/separator/architectures/mdxc_separator.py","startLine":203,"endLine":210,"chunkText":"\n                    self.logger.info(f\"Saving {self.secondary_stem_name} stem to {self.secondary_stem_output_path}...\")\n                    self.final_process(self.secondary_stem_output_path, self.secondary_source, self.secondary_stem_name)\n                    output_files.append(self.secondary_stem_output_path)\n                \n                if not self.output_single_stem or self.output_single_stem.lower() == self.primary_stem_name.lower():\n                    self.primary_stem_output_path = self.get_stem_output_path(self.primary_stem_name, custom_output_names)\n                    ","chunkId":"audio_separator/separator/architectures/mdxc_separator.py:26"}],[676,{"filePath":"audio_separator/separator/architectures/mdxc_separator.py","startLine":208,"endLine":215,"chunkText":"                if not self.output_single_stem or self.output_single_stem.lower() == self.primary_stem_name.lower():\n                    self.primary_stem_output_path = self.get_stem_output_path(self.primary_stem_name, custom_output_names)\n                    \n                    self.logger.info(f\"Saving {self.primary_stem_name} stem to {self.primary_stem_output_path}...\")\n                    self.final_process(self.primary_stem_output_path, self.primary_source, self.primary_stem_name)\n                    output_files.append(self.primary_stem_output_path)\n\n        else:","chunkId":"audio_separator/separator/architectures/mdxc_separator.py:27"}],[677,{"filePath":"audio_separator/separator/architectures/mdxc_separator.py","startLine":213,"endLine":222,"chunkText":"                    output_files.append(self.primary_stem_output_path)\n\n        else:\n            # Handle case when source is not a dictionary (single source model)\n            if not self.output_single_stem or self.output_single_stem.lower() == self.primary_stem_name.lower():\n                self.primary_stem_output_path = self.get_stem_output_path(self.primary_stem_name, custom_output_names)\n\n                if not isinstance(self.primary_source, np.ndarray):\n                    self.primary_source = source.T\n","chunkId":"audio_separator/separator/architectures/mdxc_separator.py:28"}],[678,{"filePath":"audio_separator/separator/architectures/mdxc_separator.py","startLine":220,"endLine":233,"chunkText":"                if not isinstance(self.primary_source, np.ndarray):\n                    self.primary_source = source.T\n\n                self.logger.info(f\"Saving {self.primary_stem_name} stem to {self.primary_stem_output_path}...\")\n                self.final_process(self.primary_stem_output_path, self.primary_source, self.primary_stem_name)\n                output_files.append(self.primary_stem_output_path)\n\n        return output_files\n\n    def pitch_fix(self, source, sr_pitched, orig_mix):\n        \"\"\"\n        Change the pitch of the source audio by a number of semitones.\n\n        Args:","chunkId":"audio_separator/separator/architectures/mdxc_separator.py:29"}],[679,{"filePath":"audio_separator/separator/architectures/mdxc_separator.py","startLine":231,"endLine":242,"chunkText":"        Change the pitch of the source audio by a number of semitones.\n\n        Args:\n            source (np.ndarray): The source audio to be pitch-shifted.\n            sr_pitched (int): The sample rate of the pitch-shifted audio.\n            orig_mix (np.ndarray): The original mix, used to match the shape of the pitch-shifted audio.\n\n        Returns:\n            np.ndarray: The pitch-shifted source audio.\n        \"\"\"\n        source = spec_utils.change_pitch_semitones(source, sr_pitched, semitone_shift=self.pitch_shift)[0]\n        source = spec_utils.match_array_shapes(source, orig_mix)","chunkId":"audio_separator/separator/architectures/mdxc_separator.py:30"}],[680,{"filePath":"audio_separator/separator/architectures/mdxc_separator.py","startLine":240,"endLine":252,"chunkText":"        \"\"\"\n        source = spec_utils.change_pitch_semitones(source, sr_pitched, semitone_shift=self.pitch_shift)[0]\n        source = spec_utils.match_array_shapes(source, orig_mix)\n        return source\n\n    def overlap_add(self, result, x, weights, start, length):\n        \"\"\"\n        Adds the overlapping part of the result to the result tensor.\n        \"\"\"\n        # Guard against minor shape mismatches from model output length\n        # Use the minimum of provided lengths to avoid broadcasting errors\n        safe_len = min(length, x.shape[-1], weights.shape[0])\n        if safe_len > 0:","chunkId":"audio_separator/separator/architectures/mdxc_separator.py:31"}],[681,{"filePath":"audio_separator/separator/architectures/mdxc_separator.py","startLine":250,"endLine":264,"chunkText":"        # Use the minimum of provided lengths to avoid broadcasting errors\n        safe_len = min(length, x.shape[-1], weights.shape[0])\n        if safe_len > 0:\n            result[..., start : start + safe_len] += x[..., :safe_len] * weights[:safe_len]\n        return result\n\n    def demix(self, mix: np.ndarray) -> dict:\n        \"\"\"\n        Demixes the input mix into primary and secondary sources using the model and model data.\n\n        Args:\n            mix (np.ndarray): The mix to be demixed.\n        Returns:\n            dict: A dictionary containing the demixed sources.\n        \"\"\"","chunkId":"audio_separator/separator/architectures/mdxc_separator.py:32"}],[682,{"filePath":"audio_separator/separator/architectures/mdxc_separator.py","startLine":262,"endLine":275,"chunkText":"        Returns:\n            dict: A dictionary containing the demixed sources.\n        \"\"\"\n        orig_mix = mix\n\n        if self.pitch_shift != 0:\n            self.logger.debug(f\"Shifting pitch by -{self.pitch_shift} semitones...\")\n            mix, sample_rate = spec_utils.change_pitch_semitones(mix, self.sample_rate, semitone_shift=-self.pitch_shift)\n\n        if self.is_roformer:\n            # Note: Currently, for Roformer models, `batch_size` is not utilized due to negligible performance improvements.\n\n            mix = torch.tensor(mix, dtype=torch.float32)\n","chunkId":"audio_separator/separator/architectures/mdxc_separator.py:33"}],[683,{"filePath":"audio_separator/separator/architectures/mdxc_separator.py","startLine":273,"endLine":284,"chunkText":"\n            mix = torch.tensor(mix, dtype=torch.float32)\n\n            if self.override_model_segment_size:\n                mdx_segment_size = self.segment_size\n                self.logger.debug(f\"Using configured segment size: {mdx_segment_size}\")\n            else:\n                mdx_segment_size = self.model_data_cfgdict.inference.dim_t\n                self.logger.debug(f\"Using model default segment size: {mdx_segment_size}\")\n\n            # num_stems aka \"S\" in UVR\n            num_stems = 1 if self.model_data_cfgdict.training.target_instrument else len(self.model_data_cfgdict.training.instruments)","chunkId":"audio_separator/separator/architectures/mdxc_separator.py:34"}],[684,{"filePath":"audio_separator/separator/architectures/mdxc_separator.py","startLine":282,"endLine":291,"chunkText":"\n            # num_stems aka \"S\" in UVR\n            num_stems = 1 if self.model_data_cfgdict.training.target_instrument else len(self.model_data_cfgdict.training.instruments)\n            self.logger.debug(f\"Number of stems: {num_stems}\")\n\n            # chunk_size aka \"C\" in UVR\n            # IMPORTANT: For Roformer models, use the model's STFT hop length to derive the temporal chunk size\n            stft_hop_len = getattr(self.model_data_cfgdict.model, \"stft_hop_length\", None)\n            if stft_hop_len is None:\n                # Fallback to audio.hop_length if not present, but log for visibility","chunkId":"audio_separator/separator/architectures/mdxc_separator.py:35"}],[685,{"filePath":"audio_separator/separator/architectures/mdxc_separator.py","startLine":289,"endLine":298,"chunkText":"            stft_hop_len = getattr(self.model_data_cfgdict.model, \"stft_hop_length\", None)\n            if stft_hop_len is None:\n                # Fallback to audio.hop_length if not present, but log for visibility\n                stft_hop_len = self.model_data_cfgdict.audio.hop_length\n                self.logger.debug(\n                    f\"Model.stft_hop_length missing; falling back to audio.hop_length={stft_hop_len}\"\n                )\n\n            chunk_size = int(stft_hop_len) * (int(mdx_segment_size) - 1)\n            self.logger.debug(","chunkId":"audio_separator/separator/architectures/mdxc_separator.py:36"}],[686,{"filePath":"audio_separator/separator/architectures/mdxc_separator.py","startLine":296,"endLine":305,"chunkText":"\n            chunk_size = int(stft_hop_len) * (int(mdx_segment_size) - 1)\n            self.logger.debug(\n                f\"Chunk size: {chunk_size} (using stft_hop_length={stft_hop_len} and dim_t={mdx_segment_size})\"\n            )\n\n            # Align step to chunk_size by default for Roformer to avoid stride mismatches\n            # If a user-specified overlap (in seconds) results in a step larger than chunk_size, clamp it\n            desired_step = int(self.overlap * self.model_data_cfgdict.audio.sample_rate)\n            step = chunk_size if desired_step <= 0 else min(desired_step, chunk_size)","chunkId":"audio_separator/separator/architectures/mdxc_separator.py:37"}],[687,{"filePath":"audio_separator/separator/architectures/mdxc_separator.py","startLine":303,"endLine":313,"chunkText":"            # If a user-specified overlap (in seconds) results in a step larger than chunk_size, clamp it\n            desired_step = int(self.overlap * self.model_data_cfgdict.audio.sample_rate)\n            step = chunk_size if desired_step <= 0 else min(desired_step, chunk_size)\n            self.logger.debug(f\"Step: {step} (desired={desired_step})\")\n\n            # Create a weighting table and convert it to a PyTorch tensor\n            window = torch.tensor(signal.windows.hamming(chunk_size), dtype=torch.float32)\n\n            device = next(self.model_run.parameters()).device\n\n","chunkId":"audio_separator/separator/architectures/mdxc_separator.py:38"}],[688,{"filePath":"audio_separator/separator/architectures/mdxc_separator.py","startLine":311,"endLine":323,"chunkText":"            device = next(self.model_run.parameters()).device\n\n\n            with torch.no_grad():\n                req_shape = (len(self.model_data_cfgdict.training.instruments),) + tuple(mix.shape)\n                result = torch.zeros(req_shape, dtype=torch.float32)\n                counter = torch.zeros(req_shape, dtype=torch.float32)\n\n                for i in tqdm(range(0, mix.shape[1], step)):\n                    part = mix[:, i : i + chunk_size]\n                    length = part.shape[-1]\n                    if i + chunk_size > mix.shape[1]:\n                        part = mix[:, -chunk_size:]","chunkId":"audio_separator/separator/architectures/mdxc_separator.py:39"}],[689,{"filePath":"audio_separator/separator/architectures/mdxc_separator.py","startLine":321,"endLine":331,"chunkText":"                    length = part.shape[-1]\n                    if i + chunk_size > mix.shape[1]:\n                        part = mix[:, -chunk_size:]\n                        length = chunk_size\n                    part = part.to(device)\n                    x = self.model_run(part.unsqueeze(0))[0]\n                    x = x.cpu()\n                    # Perform overlap_add on CPU\n                    if i + chunk_size > mix.shape[1]:\n                        # Fixed to correctly add to the end of the tensor\n                        start_idx = result.shape[-1] - chunk_size","chunkId":"audio_separator/separator/architectures/mdxc_separator.py:40"}],[690,{"filePath":"audio_separator/separator/architectures/mdxc_separator.py","startLine":329,"endLine":337,"chunkText":"                    if i + chunk_size > mix.shape[1]:\n                        # Fixed to correctly add to the end of the tensor\n                        start_idx = result.shape[-1] - chunk_size\n                        result = self.overlap_add(result, x, window, start_idx, length)\n                        safe_len = min(length, x.shape[-1], window.shape[0])\n                        if safe_len > 0:\n                            counter[..., start_idx : start_idx + safe_len] += window[:safe_len]\n                    else:\n                        result = self.overlap_add(result, x, window, i, length)","chunkId":"audio_separator/separator/architectures/mdxc_separator.py:41"}],[691,{"filePath":"audio_separator/separator/architectures/mdxc_separator.py","startLine":335,"endLine":347,"chunkText":"                            counter[..., start_idx : start_idx + safe_len] += window[:safe_len]\n                    else:\n                        result = self.overlap_add(result, x, window, i, length)\n                        safe_len = min(length, x.shape[-1], window.shape[0])\n                        if safe_len > 0:\n                            counter[..., i : i + safe_len] += window[:safe_len]\n\n            inferenced_outputs = result / counter.clamp(min=1e-10)\n\n        else:\n            mix = torch.tensor(mix, dtype=torch.float32)\n\n            try:","chunkId":"audio_separator/separator/architectures/mdxc_separator.py:42"}],[692,{"filePath":"audio_separator/separator/architectures/mdxc_separator.py","startLine":345,"endLine":357,"chunkText":"            mix = torch.tensor(mix, dtype=torch.float32)\n\n            try:\n                num_stems = self.model_run.num_target_instruments\n            except AttributeError:\n                num_stems = self.model_run.module.num_target_instruments\n            self.logger.debug(f\"Number of stems: {num_stems}\")\n\n            if self.override_model_segment_size:\n                mdx_segment_size = self.segment_size\n                self.logger.debug(f\"Using configured segment size: {mdx_segment_size}\")\n            else:\n                mdx_segment_size = self.model_data_cfgdict.inference.dim_t","chunkId":"audio_separator/separator/architectures/mdxc_separator.py:43"}],[693,{"filePath":"audio_separator/separator/architectures/mdxc_separator.py","startLine":355,"endLine":366,"chunkText":"                self.logger.debug(f\"Using configured segment size: {mdx_segment_size}\")\n            else:\n                mdx_segment_size = self.model_data_cfgdict.inference.dim_t\n                self.logger.debug(f\"Using model default segment size: {mdx_segment_size}\")\n\n            chunk_size = self.model_data_cfgdict.audio.hop_length * (mdx_segment_size - 1)\n            self.logger.debug(f\"Chunk size: {chunk_size}\")\n\n            hop_size = chunk_size // self.overlap\n            self.logger.debug(f\"Hop size: {hop_size}\")\n\n            mix_shape = mix.shape[1]","chunkId":"audio_separator/separator/architectures/mdxc_separator.py:44"}],[694,{"filePath":"audio_separator/separator/architectures/mdxc_separator.py","startLine":364,"endLine":375,"chunkText":"            self.logger.debug(f\"Hop size: {hop_size}\")\n\n            mix_shape = mix.shape[1]\n            pad_size = hop_size - (mix_shape - chunk_size) % hop_size\n            self.logger.debug(f\"Pad size: {pad_size}\")\n\n            mix = torch.cat([torch.zeros(2, chunk_size - hop_size), mix, torch.zeros(2, pad_size + chunk_size - hop_size)], 1)\n            self.logger.debug(f\"Mix shape: {mix.shape}\")\n\n            chunks = mix.unfold(1, chunk_size, hop_size).transpose(0, 1)\n            self.logger.debug(f\"Chunks length: {len(chunks)} and shape: {chunks.shape}\")\n","chunkId":"audio_separator/separator/architectures/mdxc_separator.py:45"}],[695,{"filePath":"audio_separator/separator/architectures/mdxc_separator.py","startLine":373,"endLine":380,"chunkText":"            chunks = mix.unfold(1, chunk_size, hop_size).transpose(0, 1)\n            self.logger.debug(f\"Chunks length: {len(chunks)} and shape: {chunks.shape}\")\n\n            batches = [chunks[i : i + self.batch_size] for i in range(0, len(chunks), self.batch_size)]\n            self.logger.debug(f\"Batch size: {self.batch_size}, number of batches: {len(batches)}\")\n\n            # accumulated_outputs is used to accumulate the output from processing each batch of chunks through the model.\n            # It starts as a tensor of zeros and is updated in-place as the model processes each batch.","chunkId":"audio_separator/separator/architectures/mdxc_separator.py:46"}],[696,{"filePath":"audio_separator/separator/architectures/mdxc_separator.py","startLine":378,"endLine":386,"chunkText":"\n            # accumulated_outputs is used to accumulate the output from processing each batch of chunks through the model.\n            # It starts as a tensor of zeros and is updated in-place as the model processes each batch.\n            # The variable holds the combined result of all processed batches, which, after post-processing, represents the separated audio sources.\n            accumulated_outputs = torch.zeros(num_stems, *mix.shape) if num_stems > 1 else torch.zeros_like(mix)\n\n            with torch.no_grad():\n                count = 0\n                for batch in tqdm(batches):","chunkId":"audio_separator/separator/architectures/mdxc_separator.py:47"}],[697,{"filePath":"audio_separator/separator/architectures/mdxc_separator.py","startLine":384,"endLine":391,"chunkText":"            with torch.no_grad():\n                count = 0\n                for batch in tqdm(batches):\n                    # Since the model processes the audio data in batches, single_batch_result temporarily holds the model's output\n                    # for each batch before it is accumulated into accumulated_outputs.\n                    single_batch_result = self.model_run(batch.to(self.torch_device))\n\n                    # Each individual output tensor from the current batch's processing result.","chunkId":"audio_separator/separator/architectures/mdxc_separator.py:48"}],[698,{"filePath":"audio_separator/separator/architectures/mdxc_separator.py","startLine":389,"endLine":395,"chunkText":"                    single_batch_result = self.model_run(batch.to(self.torch_device))\n\n                    # Each individual output tensor from the current batch's processing result.\n                    # Since single_batch_result can contain multiple output tensors (one for each piece of audio in the batch),\n                    # individual_output is used to iterate through these tensors and accumulate them into accumulated_outputs.\n                    for individual_output in single_batch_result:\n                        individual_output_cpu = individual_output.cpu()","chunkId":"audio_separator/separator/architectures/mdxc_separator.py:49"}],[699,{"filePath":"audio_separator/separator/architectures/mdxc_separator.py","startLine":393,"endLine":400,"chunkText":"                    # individual_output is used to iterate through these tensors and accumulate them into accumulated_outputs.\n                    for individual_output in single_batch_result:\n                        individual_output_cpu = individual_output.cpu()\n                        # Accumulate outputs on CPU\n                        accumulated_outputs[..., count * hop_size : count * hop_size + chunk_size] += individual_output_cpu\n                        count += 1\n\n            self.logger.debug(\"Calculating inferenced outputs based on accumulated outputs and overlap\")","chunkId":"audio_separator/separator/architectures/mdxc_separator.py:50"}],[700,{"filePath":"audio_separator/separator/architectures/mdxc_separator.py","startLine":398,"endLine":409,"chunkText":"                        count += 1\n\n            self.logger.debug(\"Calculating inferenced outputs based on accumulated outputs and overlap\")\n            inferenced_outputs = accumulated_outputs[..., chunk_size - hop_size : -(pad_size + chunk_size - hop_size)] / self.overlap\n            self.logger.debug(\"Deleting accumulated outputs to free up memory\")\n            del accumulated_outputs\n\n        if num_stems > 1:\n            self.logger.debug(\"Number of stems is greater than 1, detaching individual sources and correcting pitch if necessary...\")\n\n            sources = {}\n","chunkId":"audio_separator/separator/architectures/mdxc_separator.py:51"}],[701,{"filePath":"audio_separator/separator/architectures/mdxc_separator.py","startLine":407,"endLine":413,"chunkText":"\n            sources = {}\n\n            # Iterates over each instrument specified in the model's configuration and its corresponding separated audio source.\n            # self.model_data_cfgdict.training.instruments provides the list of stems.\n            # estimated_sources.cpu().detach().numpy() converts the separated sources tensor to a NumPy array for processing.\n            # Each iteration provides an instrument name ('key') and its separated audio ('value') for further processing.","chunkId":"audio_separator/separator/architectures/mdxc_separator.py:52"}],[702,{"filePath":"audio_separator/separator/architectures/mdxc_separator.py","startLine":411,"endLine":416,"chunkText":"            # self.model_data_cfgdict.training.instruments provides the list of stems.\n            # estimated_sources.cpu().detach().numpy() converts the separated sources tensor to a NumPy array for processing.\n            # Each iteration provides an instrument name ('key') and its separated audio ('value') for further processing.\n            for key, value in zip(self.model_data_cfgdict.training.instruments, inferenced_outputs.cpu().detach().numpy()):\n                self.logger.debug(f\"Processing instrument: {key}\")\n                if self.pitch_shift != 0:","chunkId":"audio_separator/separator/architectures/mdxc_separator.py:53"}],[703,{"filePath":"audio_separator/separator/architectures/mdxc_separator.py","startLine":414,"endLine":422,"chunkText":"            for key, value in zip(self.model_data_cfgdict.training.instruments, inferenced_outputs.cpu().detach().numpy()):\n                self.logger.debug(f\"Processing instrument: {key}\")\n                if self.pitch_shift != 0:\n                    self.logger.debug(f\"Applying pitch correction for {key}\")\n                    sources[key] = self.pitch_fix(value, sample_rate, orig_mix)\n                else:\n                    sources[key] = value\n\n            # Residual subtraction is only applicable for single-target models (not multi-stem)","chunkId":"audio_separator/separator/architectures/mdxc_separator.py:54"}],[704,{"filePath":"audio_separator/separator/architectures/mdxc_separator.py","startLine":420,"endLine":426,"chunkText":"                    sources[key] = value\n\n            # Residual subtraction is only applicable for single-target models (not multi-stem)\n            if self.is_primary_stem_main_target and num_stems == 1:\n                self.logger.debug(f\"Primary stem: {self.primary_stem_name} is main target, detaching and matching array shapes if necessary...\")\n                if sources[self.primary_stem_name].shape[1] != orig_mix.shape[1]:\n                    sources[self.primary_stem_name] = spec_utils.match_array_shapes(sources[self.primary_stem_name], orig_mix)","chunkId":"audio_separator/separator/architectures/mdxc_separator.py:55"}],[705,{"filePath":"audio_separator/separator/architectures/mdxc_separator.py","startLine":424,"endLine":431,"chunkText":"                self.logger.debug(f\"Primary stem: {self.primary_stem_name} is main target, detaching and matching array shapes if necessary...\")\n                if sources[self.primary_stem_name].shape[1] != orig_mix.shape[1]:\n                    sources[self.primary_stem_name] = spec_utils.match_array_shapes(sources[self.primary_stem_name], orig_mix)\n                sources[self.secondary_stem_name] = orig_mix - sources[self.primary_stem_name]\n\n            self.logger.debug(\"Deleting inferenced outputs to free up memory\")\n            del inferenced_outputs\n","chunkId":"audio_separator/separator/architectures/mdxc_separator.py:56"}],[706,{"filePath":"audio_separator/separator/architectures/mdxc_separator.py","startLine":429,"endLine":440,"chunkText":"            self.logger.debug(\"Deleting inferenced outputs to free up memory\")\n            del inferenced_outputs\n\n            self.logger.debug(\"Returning separated sources\")\n            return sources\n        else:\n            self.logger.debug(\"Processing single source...\")\n\n            if self.is_roformer:\n                sources = {k: v.cpu().detach().numpy() for k, v in zip([self.model_data_cfgdict.training.target_instrument], inferenced_outputs)}\n                inferenced_output = sources[self.model_data_cfgdict.training.target_instrument]\n            else:","chunkId":"audio_separator/separator/architectures/mdxc_separator.py:57"}],[707,{"filePath":"audio_separator/separator/architectures/mdxc_separator.py","startLine":438,"endLine":447,"chunkText":"                sources = {k: v.cpu().detach().numpy() for k, v in zip([self.model_data_cfgdict.training.target_instrument], inferenced_outputs)}\n                inferenced_output = sources[self.model_data_cfgdict.training.target_instrument]\n            else:\n                inferenced_output = inferenced_outputs.cpu().detach().numpy()\n\n            self.logger.debug(\"Demix process completed for single source.\")\n\n            self.logger.debug(\"Deleting inferenced outputs to free up memory\")\n            del inferenced_outputs\n","chunkId":"audio_separator/separator/architectures/mdxc_separator.py:58"}],[708,{"filePath":"audio_separator/separator/architectures/mdxc_separator.py","startLine":445,"endLine":455,"chunkText":"            self.logger.debug(\"Deleting inferenced outputs to free up memory\")\n            del inferenced_outputs\n\n            # For single-target models (e.g., karaoke), also return the residual as secondary\n            if self.pitch_shift != 0:\n                self.logger.debug(\"Applying pitch correction for single instrument\")\n                primary = self.pitch_fix(inferenced_output, sample_rate, orig_mix)\n            else:\n                primary = inferenced_output\n\n            if self.is_primary_stem_main_target:","chunkId":"audio_separator/separator/architectures/mdxc_separator.py:59"}],[709,{"filePath":"audio_separator/separator/architectures/mdxc_separator.py","startLine":453,"endLine":463,"chunkText":"                primary = inferenced_output\n\n            if self.is_primary_stem_main_target:\n                self.logger.debug(\"Single-target model detected; computing residual secondary stem from original mix\")\n                # Ensure shapes match before residual subtraction\n                if primary.shape[1] != orig_mix.shape[1]:\n                    primary = spec_utils.match_array_shapes(primary, orig_mix)\n                secondary = orig_mix - primary\n                return {\n                    self.primary_stem_name: primary,\n                    self.secondary_stem_name: secondary,","chunkId":"audio_separator/separator/architectures/mdxc_separator.py:60"}],[710,{"filePath":"audio_separator/separator/architectures/mdxc_separator.py","startLine":461,"endLine":468,"chunkText":"                return {\n                    self.primary_stem_name: primary,\n                    self.secondary_stem_name: secondary,\n                }\n\n            self.logger.debug(\"Returning inferenced output for single instrument\")\n            return primary\n","chunkId":"audio_separator/separator/architectures/mdxc_separator.py:61"}],[711,{"filePath":"audio_separator/separator/architectures/vr_separator.py","startLine":0,"endLine":19,"chunkText":"\"\"\"Module for separating audio sources using VR architecture models.\"\"\"\n\nimport os\nimport math\n\nimport torch\nimport librosa\nimport numpy as np\nfrom tqdm import tqdm\n\n# Check if we really need the rerun_mp3 function, remove if not\nimport audioread\n\nfrom audio_separator.separator.common_separator import CommonSeparator\nfrom audio_separator.separator.uvr_lib_v5 import spec_utils\nfrom audio_separator.separator.uvr_lib_v5.vr_network import nets\nfrom audio_separator.separator.uvr_lib_v5.vr_network import nets_new\nfrom audio_separator.separator.uvr_lib_v5.vr_network.model_param_init import ModelParameters\n\n","chunkId":"audio_separator/separator/architectures/vr_separator.py:0"}],[712,{"filePath":"audio_separator/separator/architectures/vr_separator.py","startLine":17,"endLine":27,"chunkText":"from audio_separator.separator.uvr_lib_v5.vr_network.model_param_init import ModelParameters\n\n\nclass VRSeparator(CommonSeparator):\n    \"\"\"\n    VRSeparator is responsible for separating audio sources using VR models.\n    It initializes with configuration parameters and prepares the model for separation tasks.\n    \"\"\"\n\n    def __init__(self, common_config, arch_config: dict):\n        # Any configuration values which can be shared between architectures should be set already in CommonSeparator,","chunkId":"audio_separator/separator/architectures/vr_separator.py:1"}],[713,{"filePath":"audio_separator/separator/architectures/vr_separator.py","startLine":25,"endLine":32,"chunkText":"\n    def __init__(self, common_config, arch_config: dict):\n        # Any configuration values which can be shared between architectures should be set already in CommonSeparator,\n        # e.g. user-specified functionality choices (self.output_single_stem) or common model parameters (self.primary_stem_name)\n        super().__init__(config=common_config)\n\n        # Model data is basic overview metadata about the model, e.g. which stem is primary and whether it's a karaoke model\n        # It's loaded in from model_data_new.json in Separator.load_model and there are JSON examples in that method","chunkId":"audio_separator/separator/architectures/vr_separator.py:2"}],[714,{"filePath":"audio_separator/separator/architectures/vr_separator.py","startLine":30,"endLine":37,"chunkText":"\n        # Model data is basic overview metadata about the model, e.g. which stem is primary and whether it's a karaoke model\n        # It's loaded in from model_data_new.json in Separator.load_model and there are JSON examples in that method\n        # The instance variable self.model_data is passed through from Separator and set in CommonSeparator\n        self.logger.debug(f\"Model data: {self.model_data}\")\n\n        # Most of the VR models use the same number of output channels, but the VR 51 models have specific values set in model_data JSON\n        self.model_capacity = 32, 128","chunkId":"audio_separator/separator/architectures/vr_separator.py:3"}],[715,{"filePath":"audio_separator/separator/architectures/vr_separator.py","startLine":35,"endLine":44,"chunkText":"\n        # Most of the VR models use the same number of output channels, but the VR 51 models have specific values set in model_data JSON\n        self.model_capacity = 32, 128\n        self.is_vr_51_model = False\n\n        if \"nout\" in self.model_data.keys() and \"nout_lstm\" in self.model_data.keys():\n            self.model_capacity = self.model_data[\"nout\"], self.model_data[\"nout_lstm\"]\n            self.is_vr_51_model = True\n\n        # Model params are additional technical parameter values from JSON files in separator/uvr_lib_v5/vr_network/modelparams/*.json,","chunkId":"audio_separator/separator/architectures/vr_separator.py:4"}],[716,{"filePath":"audio_separator/separator/architectures/vr_separator.py","startLine":42,"endLine":48,"chunkText":"            self.is_vr_51_model = True\n\n        # Model params are additional technical parameter values from JSON files in separator/uvr_lib_v5/vr_network/modelparams/*.json,\n        # with filenames referenced by the model_data[\"vr_model_param\"] value\n        package_root_filepath = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))\n        vr_params_json_dir = os.path.join(package_root_filepath, \"uvr_lib_v5\", \"vr_network\", \"modelparams\")\n        vr_params_json_filename = f\"{self.model_data['vr_model_param']}.json\"","chunkId":"audio_separator/separator/architectures/vr_separator.py:5"}],[717,{"filePath":"audio_separator/separator/architectures/vr_separator.py","startLine":46,"endLine":53,"chunkText":"        package_root_filepath = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))\n        vr_params_json_dir = os.path.join(package_root_filepath, \"uvr_lib_v5\", \"vr_network\", \"modelparams\")\n        vr_params_json_filename = f\"{self.model_data['vr_model_param']}.json\"\n        vr_params_json_filepath = os.path.join(vr_params_json_dir, vr_params_json_filename)\n        self.model_params = ModelParameters(vr_params_json_filepath)\n\n        self.logger.debug(f\"Model params: {self.model_params.param}\")\n","chunkId":"audio_separator/separator/architectures/vr_separator.py:6"}],[718,{"filePath":"audio_separator/separator/architectures/vr_separator.py","startLine":51,"endLine":59,"chunkText":"\n        self.logger.debug(f\"Model params: {self.model_params.param}\")\n\n        # Arch Config is the VR architecture specific user configuration options, which should all be configurable by the user\n        # either by their Separator class instantiation or by passing in a CLI parameter.\n        # While there are similarities between architectures for some of these (e.g. batch_size), they are deliberately configured\n        # this way as they have architecture-specific default values.\n\n        # This option performs Test-Time-Augmentation to improve the separation quality.","chunkId":"audio_separator/separator/architectures/vr_separator.py:7"}],[719,{"filePath":"audio_separator/separator/architectures/vr_separator.py","startLine":57,"endLine":63,"chunkText":"        # this way as they have architecture-specific default values.\n\n        # This option performs Test-Time-Augmentation to improve the separation quality.\n        # Note: Having this selected will increase the time it takes to complete a conversion\n        self.enable_tta = arch_config.get(\"enable_tta\", False)\n\n        # This option can potentially identify leftover instrumental artifacts within the vocal outputs; may improve the separation of some songs.","chunkId":"audio_separator/separator/architectures/vr_separator.py:8"}],[720,{"filePath":"audio_separator/separator/architectures/vr_separator.py","startLine":61,"endLine":69,"chunkText":"        self.enable_tta = arch_config.get(\"enable_tta\", False)\n\n        # This option can potentially identify leftover instrumental artifacts within the vocal outputs; may improve the separation of some songs.\n        # Note: Selecting this option can adversely affect the conversion process, depending on the track. Because of this, it is only recommended as a last resort.\n        self.enable_post_process = arch_config.get(\"enable_post_process\", False)\n\n        # post_process_threshold values = ('0.1', '0.2', '0.3')\n        self.post_process_threshold = arch_config.get(\"post_process_threshold\", 0.2)\n","chunkId":"audio_separator/separator/architectures/vr_separator.py:9"}],[721,{"filePath":"audio_separator/separator/architectures/vr_separator.py","startLine":67,"endLine":75,"chunkText":"        # post_process_threshold values = ('0.1', '0.2', '0.3')\n        self.post_process_threshold = arch_config.get(\"post_process_threshold\", 0.2)\n\n        # Number of batches to be processed at a time.\n        # - Higher values mean more RAM usage but slightly faster processing times.\n        # - Lower values mean less RAM usage but slightly longer processing times.\n        # - Batch size value has no effect on output quality.\n\n        # Andrew note: for some reason, lower batch sizes seem to cause broken output for VR arch; need to investigate why","chunkId":"audio_separator/separator/architectures/vr_separator.py:10"}],[722,{"filePath":"audio_separator/separator/architectures/vr_separator.py","startLine":73,"endLine":84,"chunkText":"        # - Batch size value has no effect on output quality.\n\n        # Andrew note: for some reason, lower batch sizes seem to cause broken output for VR arch; need to investigate why\n        self.batch_size = arch_config.get(\"batch_size\", 1)\n\n        # Select window size to balance quality and speed:\n        # - 1024 - Quick but lesser quality.\n        # - 512 - Medium speed and quality.\n        # - 320 - Takes longer but may offer better quality.\n        self.window_size = arch_config.get(\"window_size\", 512)\n\n        # The application will mirror the missing frequency range of the output.","chunkId":"audio_separator/separator/architectures/vr_separator.py:11"}],[723,{"filePath":"audio_separator/separator/architectures/vr_separator.py","startLine":82,"endLine":93,"chunkText":"        self.window_size = arch_config.get(\"window_size\", 512)\n\n        # The application will mirror the missing frequency range of the output.\n        self.high_end_process = arch_config.get(\"high_end_process\", False)\n        self.input_high_end_h = None\n        self.input_high_end = None\n\n        # Adjust the intensity of primary stem extraction:\n        # - Ranges from -100 - 100.\n        # - Bigger values mean deeper extractions.\n        # - Typically, it's set to 5 for vocals & instrumentals.\n        # - Values beyond 5 might muddy the sound for non-vocal models.","chunkId":"audio_separator/separator/architectures/vr_separator.py:12"}],[724,{"filePath":"audio_separator/separator/architectures/vr_separator.py","startLine":91,"endLine":99,"chunkText":"        # - Bigger values mean deeper extractions.\n        # - Typically, it's set to 5 for vocals & instrumentals.\n        # - Values beyond 5 might muddy the sound for non-vocal models.\n        self.aggression = float(int(arch_config.get(\"aggression\", 5)) / 100)\n\n        self.aggressiveness = {\"value\": self.aggression, \"split_bin\": self.model_params.param[\"band\"][1][\"crop_stop\"], \"aggr_correction\": self.model_params.param.get(\"aggr_correction\")}\n\n        self.model_samplerate = self.model_params.param[\"sr\"]\n","chunkId":"audio_separator/separator/architectures/vr_separator.py:13"}],[725,{"filePath":"audio_separator/separator/architectures/vr_separator.py","startLine":97,"endLine":102,"chunkText":"\n        self.model_samplerate = self.model_params.param[\"sr\"]\n\n        self.logger.debug(f\"VR arch params: enable_tta={self.enable_tta}, enable_post_process={self.enable_post_process}, post_process_threshold={self.post_process_threshold}\")\n        self.logger.debug(f\"VR arch params: batch_size={self.batch_size}, window_size={self.window_size}\")\n        self.logger.debug(f\"VR arch params: high_end_process={self.high_end_process}, aggression={self.aggression}\")","chunkId":"audio_separator/separator/architectures/vr_separator.py:14"}],[726,{"filePath":"audio_separator/separator/architectures/vr_separator.py","startLine":100,"endLine":104,"chunkText":"        self.logger.debug(f\"VR arch params: enable_tta={self.enable_tta}, enable_post_process={self.enable_post_process}, post_process_threshold={self.post_process_threshold}\")\n        self.logger.debug(f\"VR arch params: batch_size={self.batch_size}, window_size={self.window_size}\")\n        self.logger.debug(f\"VR arch params: high_end_process={self.high_end_process}, aggression={self.aggression}\")\n        self.logger.debug(f\"VR arch params: is_vr_51_model={self.is_vr_51_model}, model_samplerate={self.model_samplerate}, model_capacity={self.model_capacity}\")\n","chunkId":"audio_separator/separator/architectures/vr_separator.py:15"}],[727,{"filePath":"audio_separator/separator/architectures/vr_separator.py","startLine":102,"endLine":111,"chunkText":"        self.logger.debug(f\"VR arch params: high_end_process={self.high_end_process}, aggression={self.aggression}\")\n        self.logger.debug(f\"VR arch params: is_vr_51_model={self.is_vr_51_model}, model_samplerate={self.model_samplerate}, model_capacity={self.model_capacity}\")\n\n        self.model_run = lambda *args, **kwargs: self.logger.error(\"Model run method is not initialised yet.\")\n\n        # wav_subtype will be set based on input audio bit depth in prepare_mix()\n        # Removed hardcoded \"PCM_16\" to allow bit depth preservation\n\n        self.logger.info(\"VR Separator initialisation complete\")\n","chunkId":"audio_separator/separator/architectures/vr_separator.py:16"}],[728,{"filePath":"audio_separator/separator/architectures/vr_separator.py","startLine":109,"endLine":121,"chunkText":"\n        self.logger.info(\"VR Separator initialisation complete\")\n\n    def separate(self, audio_file_path, custom_output_names=None):\n        \"\"\"\n        Separates the audio file into primary and secondary sources based on the model's configuration.\n        It processes the mix, demixes it into sources, normalizes the sources, and saves the output files.\n\n        Args:\n            audio_file_path (str): The path to the audio file to be processed.\n            custom_output_names (dict, optional): Custom names for the output files. Defaults to None.\n\n        Returns:","chunkId":"audio_separator/separator/architectures/vr_separator.py:17"}],[729,{"filePath":"audio_separator/separator/architectures/vr_separator.py","startLine":119,"endLine":133,"chunkText":"            custom_output_names (dict, optional): Custom names for the output files. Defaults to None.\n\n        Returns:\n            list: A list of paths to the output files generated by the separation process.\n        \"\"\"\n        self.primary_source = None\n        self.secondary_source = None\n\n        self.audio_file_path = audio_file_path\n        self.audio_file_base = os.path.splitext(os.path.basename(audio_file_path))[ 0]\n\n        # Detect input audio bit depth for output preservation\n        try:\n            import soundfile as sf\n            info = sf.info(audio_file_path)","chunkId":"audio_separator/separator/architectures/vr_separator.py:18"}],[730,{"filePath":"audio_separator/separator/architectures/vr_separator.py","startLine":131,"endLine":142,"chunkText":"        try:\n            import soundfile as sf\n            info = sf.info(audio_file_path)\n            self.input_audio_subtype = info.subtype\n            self.logger.info(f\"Input audio subtype: {self.input_audio_subtype}\")\n            \n            # Map subtype to wav_subtype for soundfile and set input_bit_depth for pydub\n            if \"24\" in self.input_audio_subtype:\n                self.wav_subtype = \"PCM_24\"\n                self.input_bit_depth = 24\n                self.logger.info(\"Detected 24-bit input audio\")\n            elif \"32\" in self.input_audio_subtype:","chunkId":"audio_separator/separator/architectures/vr_separator.py:19"}],[731,{"filePath":"audio_separator/separator/architectures/vr_separator.py","startLine":140,"endLine":151,"chunkText":"                self.input_bit_depth = 24\n                self.logger.info(\"Detected 24-bit input audio\")\n            elif \"32\" in self.input_audio_subtype:\n                self.wav_subtype = \"PCM_32\"\n                self.input_bit_depth = 32\n                self.logger.info(\"Detected 32-bit input audio\")\n            else:\n                self.wav_subtype = \"PCM_16\"\n                self.input_bit_depth = 16\n                self.logger.info(\"Detected 16-bit input audio\")\n        except Exception as e:\n            self.logger.warning(f\"Could not detect input audio bit depth: {e}. Defaulting to PCM_16\")","chunkId":"audio_separator/separator/architectures/vr_separator.py:20"}],[732,{"filePath":"audio_separator/separator/architectures/vr_separator.py","startLine":149,"endLine":159,"chunkText":"                self.logger.info(\"Detected 16-bit input audio\")\n        except Exception as e:\n            self.logger.warning(f\"Could not detect input audio bit depth: {e}. Defaulting to PCM_16\")\n            self.wav_subtype = \"PCM_16\"\n            self.input_audio_subtype = None\n            self.input_bit_depth = 16\n\n        self.logger.debug(f\"Starting separation for input audio file {self.audio_file_path}...\")\n\n        nn_arch_sizes = [31191, 33966, 56817, 123821, 123812, 129605, 218409, 537238, 537227]  # default\n        vr_5_1_models = [56817, 218409]","chunkId":"audio_separator/separator/architectures/vr_separator.py:21"}],[733,{"filePath":"audio_separator/separator/architectures/vr_separator.py","startLine":157,"endLine":165,"chunkText":"\n        nn_arch_sizes = [31191, 33966, 56817, 123821, 123812, 129605, 218409, 537238, 537227]  # default\n        vr_5_1_models = [56817, 218409]\n        model_size = math.ceil(os.stat(self.model_path).st_size / 1024)\n        nn_arch_size = min(nn_arch_sizes, key=lambda x: abs(x - model_size))\n        self.logger.debug(f\"Model size determined: {model_size}, NN architecture size: {nn_arch_size}\")\n\n        if nn_arch_size in vr_5_1_models or self.is_vr_51_model:\n            self.logger.debug(\"Using CascadedNet for VR 5.1 model...\")","chunkId":"audio_separator/separator/architectures/vr_separator.py:22"}],[734,{"filePath":"audio_separator/separator/architectures/vr_separator.py","startLine":163,"endLine":171,"chunkText":"\n        if nn_arch_size in vr_5_1_models or self.is_vr_51_model:\n            self.logger.debug(\"Using CascadedNet for VR 5.1 model...\")\n            self.model_run = nets_new.CascadedNet(self.model_params.param[\"bins\"] * 2, nn_arch_size, nout=self.model_capacity[0], nout_lstm=self.model_capacity[1])\n            self.is_vr_51_model = True\n        else:\n            self.logger.debug(\"Determining model capacity...\")\n            self.model_run = nets.determine_model_capacity(self.model_params.param[\"bins\"] * 2, nn_arch_size)\n","chunkId":"audio_separator/separator/architectures/vr_separator.py:23"}],[735,{"filePath":"audio_separator/separator/architectures/vr_separator.py","startLine":169,"endLine":179,"chunkText":"            self.logger.debug(\"Determining model capacity...\")\n            self.model_run = nets.determine_model_capacity(self.model_params.param[\"bins\"] * 2, nn_arch_size)\n\n        self.model_run.load_state_dict(torch.load(self.model_path, map_location=\"cpu\"))\n        self.model_run.to(self.torch_device)\n        self.logger.debug(\"Model loaded and moved to device.\")\n\n        y_spec, v_spec = self.inference_vr(self.loading_mix(), self.torch_device, self.aggressiveness)\n        self.logger.debug(\"Inference completed.\")\n\n        # Sanitize y_spec and v_spec to replace NaN and infinite values","chunkId":"audio_separator/separator/architectures/vr_separator.py:24"}],[736,{"filePath":"audio_separator/separator/architectures/vr_separator.py","startLine":177,"endLine":186,"chunkText":"        self.logger.debug(\"Inference completed.\")\n\n        # Sanitize y_spec and v_spec to replace NaN and infinite values\n        y_spec = np.nan_to_num(y_spec, nan=0.0, posinf=0.0, neginf=0.0)\n        v_spec = np.nan_to_num(v_spec, nan=0.0, posinf=0.0, neginf=0.0)\n\n        self.logger.debug(\"Sanitization completed. Replaced NaN and infinite values in y_spec and v_spec.\")\n\n        # After inference_vr call\n        self.logger.debug(f\"Inference VR completed. y_spec shape: {y_spec.shape}, v_spec shape: {v_spec.shape}\")","chunkId":"audio_separator/separator/architectures/vr_separator.py:25"}],[737,{"filePath":"audio_separator/separator/architectures/vr_separator.py","startLine":184,"endLine":192,"chunkText":"        # After inference_vr call\n        self.logger.debug(f\"Inference VR completed. y_spec shape: {y_spec.shape}, v_spec shape: {v_spec.shape}\")\n        self.logger.debug(f\"y_spec stats - min: {np.min(y_spec)}, max: {np.max(y_spec)}, isnan: {np.isnan(y_spec).any()}, isinf: {np.isinf(y_spec).any()}\")\n        self.logger.debug(f\"v_spec stats - min: {np.min(v_spec)}, max: {np.max(v_spec)}, isnan: {np.isnan(v_spec).any()}, isinf: {np.isinf(v_spec).any()}\")\n\n        # Not yet implemented from UVR features:\n        #\n        # if not self.is_vocal_split_model:\n        #     self.cache_source((y_spec, v_spec))","chunkId":"audio_separator/separator/architectures/vr_separator.py:26"}],[738,{"filePath":"audio_separator/separator/architectures/vr_separator.py","startLine":192,"endLine":202,"chunkText":"        # if not self.is_vocal_split_model:\n        #     self.cache_source((y_spec, v_spec))\n\n        # if self.is_secondary_model_activated and self.secondary_model:\n        #     self.logger.debug(\"Processing secondary model...\")\n        #     self.secondary_source_primary, self.secondary_source_secondary = process_secondary_model(\n        #         self.secondary_model, self.process_data, main_process_method=self.process_method, main_model_primary=self.primary_stem\n        #     )\n\n        # Initialize the list for output files\n        output_files = []","chunkId":"audio_separator/separator/architectures/vr_separator.py:27"}],[739,{"filePath":"audio_separator/separator/architectures/vr_separator.py","startLine":200,"endLine":208,"chunkText":"\n        # Initialize the list for output files\n        output_files = []\n        self.logger.debug(\"Processing output files...\")\n\n        # Note: logic similar to the following should probably be added to the other architectures\n        # Check if output_single_stem is set to a value that would result in no output files\n        if self.output_single_stem and (self.output_single_stem.lower() != self.primary_stem_name.lower() and self.output_single_stem.lower() != self.secondary_stem_name.lower()):\n            # If so, reset output_single_stem to None to save both stems","chunkId":"audio_separator/separator/architectures/vr_separator.py:28"}],[740,{"filePath":"audio_separator/separator/architectures/vr_separator.py","startLine":206,"endLine":209,"chunkText":"        # Check if output_single_stem is set to a value that would result in no output files\n        if self.output_single_stem and (self.output_single_stem.lower() != self.primary_stem_name.lower() and self.output_single_stem.lower() != self.secondary_stem_name.lower()):\n            # If so, reset output_single_stem to None to save both stems\n            self.output_single_stem = None","chunkId":"audio_separator/separator/architectures/vr_separator.py:29"}],[741,{"filePath":"audio_separator/separator/architectures/vr_separator.py","startLine":207,"endLine":211,"chunkText":"        if self.output_single_stem and (self.output_single_stem.lower() != self.primary_stem_name.lower() and self.output_single_stem.lower() != self.secondary_stem_name.lower()):\n            # If so, reset output_single_stem to None to save both stems\n            self.output_single_stem = None\n            self.logger.warning(f\"The output_single_stem setting '{self.output_single_stem}' does not match any of the output files: '{self.primary_stem_name}' and '{self.secondary_stem_name}'. For this model '{self.model_name}', the output_single_stem setting will be ignored and all output files will be saved.\")\n","chunkId":"audio_separator/separator/architectures/vr_separator.py:30"}],[742,{"filePath":"audio_separator/separator/architectures/vr_separator.py","startLine":209,"endLine":214,"chunkText":"            self.output_single_stem = None\n            self.logger.warning(f\"The output_single_stem setting '{self.output_single_stem}' does not match any of the output files: '{self.primary_stem_name}' and '{self.secondary_stem_name}'. For this model '{self.model_name}', the output_single_stem setting will be ignored and all output files will be saved.\")\n\n        # Save and process the primary stem if needed\n        if not self.output_single_stem or self.output_single_stem.lower() == self.primary_stem_name.lower():\n            self.logger.debug(f\"Processing primary stem: {self.primary_stem_name}\")","chunkId":"audio_separator/separator/architectures/vr_separator.py:31"}],[743,{"filePath":"audio_separator/separator/architectures/vr_separator.py","startLine":212,"endLine":219,"chunkText":"        # Save and process the primary stem if needed\n        if not self.output_single_stem or self.output_single_stem.lower() == self.primary_stem_name.lower():\n            self.logger.debug(f\"Processing primary stem: {self.primary_stem_name}\")\n            if not isinstance(self.primary_source, np.ndarray):\n                self.logger.debug(f\"Preparing to convert spectrogram to waveform. Spec shape: {y_spec.shape}\")\n\n                self.primary_source = self.spec_to_wav(y_spec).T\n                self.logger.debug(\"Converting primary source spectrogram to waveform.\")","chunkId":"audio_separator/separator/architectures/vr_separator.py:32"}],[744,{"filePath":"audio_separator/separator/architectures/vr_separator.py","startLine":217,"endLine":225,"chunkText":"\n                self.primary_source = self.spec_to_wav(y_spec).T\n                self.logger.debug(\"Converting primary source spectrogram to waveform.\")\n                if not self.model_samplerate == 44100:\n                    self.primary_source = librosa.resample(self.primary_source.T, orig_sr=self.model_samplerate, target_sr=44100).T\n                    self.logger.debug(\"Resampling primary source to 44100Hz.\")\n\n            self.primary_stem_output_path = self.get_stem_output_path(self.primary_stem_name, custom_output_names)\n","chunkId":"audio_separator/separator/architectures/vr_separator.py:33"}],[745,{"filePath":"audio_separator/separator/architectures/vr_separator.py","startLine":223,"endLine":231,"chunkText":"\n            self.primary_stem_output_path = self.get_stem_output_path(self.primary_stem_name, custom_output_names)\n\n            self.logger.info(f\"Saving {self.primary_stem_name} stem to {self.primary_stem_output_path}...\")\n            self.final_process(self.primary_stem_output_path, self.primary_source, self.primary_stem_name)\n            output_files.append(self.primary_stem_output_path)\n\n        # Save and process the secondary stem if needed\n        if not self.output_single_stem or self.output_single_stem.lower() == self.secondary_stem_name.lower():","chunkId":"audio_separator/separator/architectures/vr_separator.py:34"}],[746,{"filePath":"audio_separator/separator/architectures/vr_separator.py","startLine":229,"endLine":237,"chunkText":"\n        # Save and process the secondary stem if needed\n        if not self.output_single_stem or self.output_single_stem.lower() == self.secondary_stem_name.lower():\n            self.logger.debug(f\"Processing secondary stem: {self.secondary_stem_name}\")\n            if not isinstance(self.secondary_source, np.ndarray):\n                self.logger.debug(f\"Preparing to convert spectrogram to waveform. Spec shape: {v_spec.shape}\")\n\n                self.secondary_source = self.spec_to_wav(v_spec).T\n                self.logger.debug(\"Converting secondary source spectrogram to waveform.\")","chunkId":"audio_separator/separator/architectures/vr_separator.py:35"}],[747,{"filePath":"audio_separator/separator/architectures/vr_separator.py","startLine":235,"endLine":243,"chunkText":"\n                self.secondary_source = self.spec_to_wav(v_spec).T\n                self.logger.debug(\"Converting secondary source spectrogram to waveform.\")\n                if not self.model_samplerate == 44100:\n                    self.secondary_source = librosa.resample(self.secondary_source.T, orig_sr=self.model_samplerate, target_sr=44100).T\n                    self.logger.debug(\"Resampling secondary source to 44100Hz.\")\n\n            self.secondary_stem_output_path = self.get_stem_output_path(self.secondary_stem_name, custom_output_names)\n","chunkId":"audio_separator/separator/architectures/vr_separator.py:36"}],[748,{"filePath":"audio_separator/separator/architectures/vr_separator.py","startLine":241,"endLine":253,"chunkText":"\n            self.secondary_stem_output_path = self.get_stem_output_path(self.secondary_stem_name, custom_output_names)\n\n            self.logger.info(f\"Saving {self.secondary_stem_name} stem to {self.secondary_stem_output_path}...\")\n            self.final_process(self.secondary_stem_output_path, self.secondary_source, self.secondary_stem_name)\n            output_files.append(self.secondary_stem_output_path)\n\n        # Not yet implemented from UVR features:\n        # self.process_vocal_split_chain(secondary_sources)\n        # self.logger.debug(\"Vocal split chain processed.\")\n\n        return output_files\n","chunkId":"audio_separator/separator/architectures/vr_separator.py:37"}],[749,{"filePath":"audio_separator/separator/architectures/vr_separator.py","startLine":251,"endLine":268,"chunkText":"\n        return output_files\n\n    def loading_mix(self):\n        X_wave, X_spec_s = {}, {}\n\n        bands_n = len(self.model_params.param[\"band\"])\n\n        audio_file = spec_utils.write_array_to_mem(self.audio_file_path, subtype=self.wav_subtype)\n        is_mp3 = audio_file.endswith(\".mp3\") if isinstance(audio_file, str) else False\n\n        self.logger.debug(f\"loading_mix iteraring through {bands_n} bands\")\n        for d in tqdm(range(bands_n, 0, -1)):\n            bp = self.model_params.param[\"band\"][d]\n\n            wav_resolution = bp[\"res_type\"]\n\n            if self.torch_device_mps is not None:","chunkId":"audio_separator/separator/architectures/vr_separator.py:38"}],[750,{"filePath":"audio_separator/separator/architectures/vr_separator.py","startLine":266,"endLine":277,"chunkText":"            wav_resolution = bp[\"res_type\"]\n\n            if self.torch_device_mps is not None:\n                wav_resolution = \"polyphase\"\n\n            if d == bands_n:  # high-end band\n                X_wave[d], _ = librosa.load(audio_file, sr=bp[\"sr\"], mono=False, dtype=np.float32, res_type=wav_resolution)\n                X_spec_s[d] = spec_utils.wave_to_spectrogram(X_wave[d], bp[\"hl\"], bp[\"n_fft\"], self.model_params, band=d, is_v51_model=self.is_vr_51_model)\n\n                if not np.any(X_wave[d]) and is_mp3:\n                    X_wave[d] = rerun_mp3(audio_file, bp[\"sr\"])\n","chunkId":"audio_separator/separator/architectures/vr_separator.py:39"}],[751,{"filePath":"audio_separator/separator/architectures/vr_separator.py","startLine":275,"endLine":283,"chunkText":"                if not np.any(X_wave[d]) and is_mp3:\n                    X_wave[d] = rerun_mp3(audio_file, bp[\"sr\"])\n\n                if X_wave[d].ndim == 1:\n                    X_wave[d] = np.asarray([X_wave[d], X_wave[d]])\n            else:  # lower bands\n                X_wave[d] = librosa.resample(X_wave[d + 1], orig_sr=self.model_params.param[\"band\"][d + 1][\"sr\"], target_sr=bp[\"sr\"], res_type=wav_resolution)\n                X_spec_s[d] = spec_utils.wave_to_spectrogram(X_wave[d], bp[\"hl\"], bp[\"n_fft\"], self.model_params, band=d, is_v51_model=self.is_vr_51_model)\n","chunkId":"audio_separator/separator/architectures/vr_separator.py:40"}],[752,{"filePath":"audio_separator/separator/architectures/vr_separator.py","startLine":281,"endLine":285,"chunkText":"                X_wave[d] = librosa.resample(X_wave[d + 1], orig_sr=self.model_params.param[\"band\"][d + 1][\"sr\"], target_sr=bp[\"sr\"], res_type=wav_resolution)\n                X_spec_s[d] = spec_utils.wave_to_spectrogram(X_wave[d], bp[\"hl\"], bp[\"n_fft\"], self.model_params, band=d, is_v51_model=self.is_vr_51_model)\n\n            if d == bands_n and self.high_end_process:\n                self.input_high_end_h = (bp[\"n_fft\"] // 2 - bp[\"crop_stop\"]) + (self.model_params.param[\"pre_filter_stop\"] - self.model_params.param[\"pre_filter_start\"])","chunkId":"audio_separator/separator/architectures/vr_separator.py:41"}],[753,{"filePath":"audio_separator/separator/architectures/vr_separator.py","startLine":283,"endLine":294,"chunkText":"\n            if d == bands_n and self.high_end_process:\n                self.input_high_end_h = (bp[\"n_fft\"] // 2 - bp[\"crop_stop\"]) + (self.model_params.param[\"pre_filter_stop\"] - self.model_params.param[\"pre_filter_start\"])\n                self.input_high_end = X_spec_s[d][:, bp[\"n_fft\"] // 2 - self.input_high_end_h : bp[\"n_fft\"] // 2, :]\n\n        X_spec = spec_utils.combine_spectrograms(X_spec_s, self.model_params, is_v51_model=self.is_vr_51_model)\n\n        del X_wave, X_spec_s, audio_file\n\n        return X_spec\n\n    def inference_vr(self, X_spec, device, aggressiveness):","chunkId":"audio_separator/separator/architectures/vr_separator.py:42"}],[754,{"filePath":"audio_separator/separator/architectures/vr_separator.py","startLine":292,"endLine":304,"chunkText":"        return X_spec\n\n    def inference_vr(self, X_spec, device, aggressiveness):\n        def _execute(X_mag_pad, roi_size):\n            X_dataset = []\n            patches = (X_mag_pad.shape[2] - 2 * self.model_run.offset) // roi_size\n\n            self.logger.debug(f\"inference_vr appending to X_dataset for each of {patches} patches\")\n            for i in tqdm(range(patches)):\n                start = i * roi_size\n                X_mag_window = X_mag_pad[:, :, start : start + self.window_size]\n                X_dataset.append(X_mag_window)\n","chunkId":"audio_separator/separator/architectures/vr_separator.py:43"}],[755,{"filePath":"audio_separator/separator/architectures/vr_separator.py","startLine":302,"endLine":314,"chunkText":"                X_mag_window = X_mag_pad[:, :, start : start + self.window_size]\n                X_dataset.append(X_mag_window)\n\n            total_iterations = patches // self.batch_size if not self.enable_tta else (patches // self.batch_size) * 2\n            self.logger.debug(f\"inference_vr iterating through {total_iterations} batches, batch_size = {self.batch_size}\")\n\n            X_dataset = np.asarray(X_dataset)\n            self.model_run.eval()\n            with torch.no_grad():\n                mask = []\n\n                for i in tqdm(range(0, patches, self.batch_size)):\n","chunkId":"audio_separator/separator/architectures/vr_separator.py:44"}],[756,{"filePath":"audio_separator/separator/architectures/vr_separator.py","startLine":312,"endLine":323,"chunkText":"\n                for i in tqdm(range(0, patches, self.batch_size)):\n\n                    X_batch = X_dataset[i : i + self.batch_size]\n                    X_batch = torch.from_numpy(X_batch).to(device)\n                    pred = self.model_run.predict_mask(X_batch)\n                    if not pred.size()[3] > 0:\n                        raise ValueError(f\"Window size error: h1_shape[3] must be greater than h2_shape[3]\")\n                    pred = pred.detach().cpu().numpy()\n                    pred = np.concatenate(pred, axis=2)\n                    mask.append(pred)\n                if len(mask) == 0:","chunkId":"audio_separator/separator/architectures/vr_separator.py:45"}],[757,{"filePath":"audio_separator/separator/architectures/vr_separator.py","startLine":321,"endLine":334,"chunkText":"                    pred = np.concatenate(pred, axis=2)\n                    mask.append(pred)\n                if len(mask) == 0:\n                    raise ValueError(f\"Window size error: h1_shape[3] must be greater than h2_shape[3]\")\n\n                mask = np.concatenate(mask, axis=2)\n            return mask\n\n        def postprocess(mask, X_mag, X_phase):\n            is_non_accom_stem = False\n            for stem in CommonSeparator.NON_ACCOM_STEMS:\n                if stem == self.primary_stem_name:\n                    is_non_accom_stem = True\n","chunkId":"audio_separator/separator/architectures/vr_separator.py:46"}],[758,{"filePath":"audio_separator/separator/architectures/vr_separator.py","startLine":332,"endLine":346,"chunkText":"                if stem == self.primary_stem_name:\n                    is_non_accom_stem = True\n\n            mask = spec_utils.adjust_aggr(mask, is_non_accom_stem, aggressiveness)\n\n            if self.enable_post_process:\n                mask = spec_utils.merge_artifacts(mask, thres=self.post_process_threshold)\n\n            y_spec = mask * X_mag * np.exp(1.0j * X_phase)\n            v_spec = (1 - mask) * X_mag * np.exp(1.0j * X_phase)\n\n            return y_spec, v_spec\n\n        X_mag, X_phase = spec_utils.preprocess(X_spec)\n        n_frame = X_mag.shape[2]","chunkId":"audio_separator/separator/architectures/vr_separator.py:47"}],[759,{"filePath":"audio_separator/separator/architectures/vr_separator.py","startLine":344,"endLine":356,"chunkText":"\n        X_mag, X_phase = spec_utils.preprocess(X_spec)\n        n_frame = X_mag.shape[2]\n        pad_l, pad_r, roi_size = spec_utils.make_padding(n_frame, self.window_size, self.model_run.offset)\n        X_mag_pad = np.pad(X_mag, ((0, 0), (0, 0), (pad_l, pad_r)), mode=\"constant\")\n        X_mag_pad /= X_mag_pad.max()\n        mask = _execute(X_mag_pad, roi_size)\n\n        if self.enable_tta:\n            pad_l += roi_size // 2\n            pad_r += roi_size // 2\n            X_mag_pad = np.pad(X_mag, ((0, 0), (0, 0), (pad_l, pad_r)), mode=\"constant\")\n            X_mag_pad /= X_mag_pad.max()","chunkId":"audio_separator/separator/architectures/vr_separator.py:48"}],[760,{"filePath":"audio_separator/separator/architectures/vr_separator.py","startLine":354,"endLine":367,"chunkText":"            pad_r += roi_size // 2\n            X_mag_pad = np.pad(X_mag, ((0, 0), (0, 0), (pad_l, pad_r)), mode=\"constant\")\n            X_mag_pad /= X_mag_pad.max()\n            mask_tta = _execute(X_mag_pad, roi_size)\n            mask_tta = mask_tta[:, :, roi_size // 2 :]\n            mask = (mask[:, :, :n_frame] + mask_tta[:, :, :n_frame]) * 0.5\n        else:\n            mask = mask[:, :, :n_frame]\n\n        y_spec, v_spec = postprocess(mask, X_mag, X_phase)\n\n        return y_spec, v_spec\n\n    def spec_to_wav(self, spec):","chunkId":"audio_separator/separator/architectures/vr_separator.py:49"}],[761,{"filePath":"audio_separator/separator/architectures/vr_separator.py","startLine":365,"endLine":376,"chunkText":"        return y_spec, v_spec\n\n    def spec_to_wav(self, spec):\n        if self.high_end_process and isinstance(self.input_high_end, np.ndarray) and self.input_high_end_h:\n            input_high_end_ = spec_utils.mirroring(\"mirroring\", spec, self.input_high_end, self.model_params)\n            wav = spec_utils.cmb_spectrogram_to_wave(spec, self.model_params, self.input_high_end_h, input_high_end_, is_v51_model=self.is_vr_51_model)\n        else:\n            wav = spec_utils.cmb_spectrogram_to_wave(spec, self.model_params, is_v51_model=self.is_vr_51_model)\n\n        return wav\n\n","chunkId":"audio_separator/separator/architectures/vr_separator.py:50"}],[762,{"filePath":"audio_separator/separator/architectures/vr_separator.py","startLine":374,"endLine":383,"chunkText":"        return wav\n\n\n# Check if we really need the rerun_mp3 function, refactor or remove if not\ndef rerun_mp3(audio_file, sample_rate=44100):\n    with audioread.audio_open(audio_file) as f:\n        track_length = int(f.duration)\n\n    return librosa.load(audio_file, duration=track_length, mono=False, sr=sample_rate)[0]\n","chunkId":"audio_separator/separator/architectures/vr_separator.py:51"}],[763,{"filePath":"audio_separator/separator/audio_chunking.py","startLine":0,"endLine":18,"chunkText":"\"\"\"Audio chunking utilities for processing large audio files to prevent OOM errors.\"\"\"\n\nimport os\nimport logging\nfrom typing import List\nfrom pydub import AudioSegment\n\n\nclass AudioChunker:\n    \"\"\"\n    Handles splitting and merging of large audio files.\n\n    This class provides utilities to:\n    - Split large audio files into fixed-duration chunks\n    - Merge processed chunks back together with simple concatenation\n    - Determine if a file should be chunked based on its duration\n\n    Example:\n        >>> chunker = AudioChunker(chunk_duration_seconds=600)  # 10-minute chunks","chunkId":"audio_separator/separator/audio_chunking.py:0"}],[764,{"filePath":"audio_separator/separator/audio_chunking.py","startLine":16,"endLine":31,"chunkText":"\n    Example:\n        >>> chunker = AudioChunker(chunk_duration_seconds=600)  # 10-minute chunks\n        >>> chunk_paths = chunker.split_audio(\"long_audio.wav\", \"/tmp/chunks\")\n        >>> # Process each chunk...\n        >>> output_path = chunker.merge_chunks(processed_chunks, \"output.wav\")\n    \"\"\"\n\n    def __init__(self, chunk_duration_seconds: float, logger: logging.Logger = None):\n        \"\"\"\n        Initialize the AudioChunker.\n\n        Args:\n            chunk_duration_seconds: Duration of each chunk in seconds\n            logger: Optional logger instance for logging operations\n        \"\"\"","chunkId":"audio_separator/separator/audio_chunking.py:1"}],[765,{"filePath":"audio_separator/separator/audio_chunking.py","startLine":29,"endLine":43,"chunkText":"            chunk_duration_seconds: Duration of each chunk in seconds\n            logger: Optional logger instance for logging operations\n        \"\"\"\n        self.chunk_duration_ms = int(chunk_duration_seconds * 1000)\n        self.logger = logger or logging.getLogger(__name__)\n\n    def split_audio(self, input_path: str, output_dir: str) -> List[str]:\n        \"\"\"\n        Split audio file into fixed-size chunks.\n\n        Args:\n            input_path: Path to the input audio file\n            output_dir: Directory where chunk files will be saved\n\n        Returns:","chunkId":"audio_separator/separator/audio_chunking.py:2"}],[766,{"filePath":"audio_separator/separator/audio_chunking.py","startLine":41,"endLine":58,"chunkText":"            output_dir: Directory where chunk files will be saved\n\n        Returns:\n            List of paths to the created chunk files\n\n        Raises:\n            FileNotFoundError: If input file doesn't exist\n            IOError: If there's an error reading or writing audio files\n        \"\"\"\n        if not os.path.exists(input_path):\n            raise FileNotFoundError(f\"Input file not found: {input_path}\")\n\n        if not os.path.exists(output_dir):\n            os.makedirs(output_dir)\n\n        self.logger.debug(f\"Loading audio file: {input_path}\")\n        audio = AudioSegment.from_file(input_path)\n","chunkId":"audio_separator/separator/audio_chunking.py:3"}],[767,{"filePath":"audio_separator/separator/audio_chunking.py","startLine":56,"endLine":68,"chunkText":"        self.logger.debug(f\"Loading audio file: {input_path}\")\n        audio = AudioSegment.from_file(input_path)\n\n        total_duration_ms = len(audio)\n        chunk_paths = []\n\n        # Calculate number of chunks\n        num_chunks = (total_duration_ms + self.chunk_duration_ms - 1) // self.chunk_duration_ms\n        self.logger.info(f\"Splitting {total_duration_ms / 1000:.1f}s audio into {num_chunks} chunks of {self.chunk_duration_ms / 1000:.1f}s each\")\n\n        # Get file extension from input\n        _, ext = os.path.splitext(input_path)\n        if not ext:","chunkId":"audio_separator/separator/audio_chunking.py:4"}],[768,{"filePath":"audio_separator/separator/audio_chunking.py","startLine":66,"endLine":79,"chunkText":"        # Get file extension from input\n        _, ext = os.path.splitext(input_path)\n        if not ext:\n            ext = \".wav\"  # Default to WAV if no extension\n\n        # Split into chunks\n        for i in range(num_chunks):\n            start_ms = i * self.chunk_duration_ms\n            end_ms = min(start_ms + self.chunk_duration_ms, total_duration_ms)\n\n            chunk = audio[start_ms:end_ms]\n            chunk_filename = f\"chunk_{i:04d}{ext}\"\n            chunk_path = os.path.join(output_dir, chunk_filename)\n","chunkId":"audio_separator/separator/audio_chunking.py:5"}],[769,{"filePath":"audio_separator/separator/audio_chunking.py","startLine":77,"endLine":91,"chunkText":"            chunk_filename = f\"chunk_{i:04d}{ext}\"\n            chunk_path = os.path.join(output_dir, chunk_filename)\n\n            self.logger.debug(f\"Exporting chunk {i + 1}/{num_chunks}: {start_ms / 1000:.1f}s - {end_ms / 1000:.1f}s to {chunk_path}\")\n            chunk.export(chunk_path, format=ext.lstrip('.'))\n            chunk_paths.append(chunk_path)\n\n        return chunk_paths\n\n    def merge_chunks(self, chunk_paths: List[str], output_path: str) -> str:\n        \"\"\"\n        Merge processed chunks with simple concatenation.\n\n        Args:\n            chunk_paths: List of paths to chunk files to merge","chunkId":"audio_separator/separator/audio_chunking.py:6"}],[770,{"filePath":"audio_separator/separator/audio_chunking.py","startLine":89,"endLine":106,"chunkText":"\n        Args:\n            chunk_paths: List of paths to chunk files to merge\n            output_path: Path where the merged output will be saved\n\n        Returns:\n            Path to the merged output file\n\n        Raises:\n            ValueError: If chunk_paths is empty\n            FileNotFoundError: If any chunk file doesn't exist\n            IOError: If there's an error reading or writing audio files\n        \"\"\"\n        if not chunk_paths:\n            raise ValueError(\"Cannot merge empty list of chunks\")\n\n        # Verify all chunks exist\n        for chunk_path in chunk_paths:","chunkId":"audio_separator/separator/audio_chunking.py:7"}],[771,{"filePath":"audio_separator/separator/audio_chunking.py","startLine":104,"endLine":118,"chunkText":"\n        # Verify all chunks exist\n        for chunk_path in chunk_paths:\n            if not os.path.exists(chunk_path):\n                raise FileNotFoundError(f\"Chunk file not found: {chunk_path}\")\n\n        self.logger.info(f\"Merging {len(chunk_paths)} chunks into {output_path}\")\n\n        # Start with empty audio segment\n        combined = AudioSegment.empty()\n\n        # Concatenate all chunks\n        for i, chunk_path in enumerate(chunk_paths):\n            self.logger.debug(f\"Loading chunk {i + 1}/{len(chunk_paths)}: {chunk_path}\")\n            chunk = AudioSegment.from_file(chunk_path)","chunkId":"audio_separator/separator/audio_chunking.py:8"}],[772,{"filePath":"audio_separator/separator/audio_chunking.py","startLine":116,"endLine":129,"chunkText":"        for i, chunk_path in enumerate(chunk_paths):\n            self.logger.debug(f\"Loading chunk {i + 1}/{len(chunk_paths)}: {chunk_path}\")\n            chunk = AudioSegment.from_file(chunk_path)\n            combined += chunk  # Simple concatenation\n\n        # Get output format from file extension\n        _, ext = os.path.splitext(output_path)\n        output_format = ext.lstrip('.') if ext else 'wav'\n\n        self.logger.info(f\"Exporting merged audio ({len(combined) / 1000:.1f}s) to {output_path}\")\n        combined.export(output_path, format=output_format)\n\n        return output_path\n","chunkId":"audio_separator/separator/audio_chunking.py:9"}],[773,{"filePath":"audio_separator/separator/audio_chunking.py","startLine":127,"endLine":141,"chunkText":"\n        return output_path\n\n    def should_chunk(self, audio_duration_seconds: float) -> bool:\n        \"\"\"\n        Determine if file is large enough to benefit from chunking.\n\n        Args:\n            audio_duration_seconds: Duration of the audio file in seconds\n\n        Returns:\n            True if the file should be chunked, False otherwise\n        \"\"\"\n        return audio_duration_seconds > (self.chunk_duration_ms / 1000)\n","chunkId":"audio_separator/separator/audio_chunking.py:10"}],[774,{"filePath":"audio_separator/separator/common_separator.py","startLine":0,"endLine":22,"chunkText":"\"\"\" This file contains the CommonSeparator class, common to all architecture-specific Separator classes. \"\"\"\n\nfrom logging import Logger\nimport os\nimport re\nimport gc\nimport numpy as np\nimport librosa\nimport torch\nfrom pydub import AudioSegment\nimport soundfile as sf\nfrom audio_separator.separator.uvr_lib_v5 import spec_utils\n\n\nclass CommonSeparator:\n    \"\"\"\n    This class contains the common methods and attributes common to all architecture-specific Separator classes.\n    \"\"\"\n\n    ALL_STEMS = \"All Stems\"\n    VOCAL_STEM = \"Vocals\"\n    INST_STEM = \"Instrumental\"\n    OTHER_STEM = \"Other\"","chunkId":"audio_separator/separator/common_separator.py:0"}],[775,{"filePath":"audio_separator/separator/common_separator.py","startLine":21,"endLine":40,"chunkText":"    INST_STEM = \"Instrumental\"\n    OTHER_STEM = \"Other\"\n    BASS_STEM = \"Bass\"\n    DRUM_STEM = \"Drums\"\n    GUITAR_STEM = \"Guitar\"\n    PIANO_STEM = \"Piano\"\n    SYNTH_STEM = \"Synthesizer\"\n    STRINGS_STEM = \"Strings\"\n    WOODWINDS_STEM = \"Woodwinds\"\n    BRASS_STEM = \"Brass\"\n    WIND_INST_STEM = \"Wind Inst\"\n    NO_OTHER_STEM = \"No Other\"\n    NO_BASS_STEM = \"No Bass\"\n    NO_DRUM_STEM = \"No Drums\"\n    NO_GUITAR_STEM = \"No Guitar\"\n    NO_PIANO_STEM = \"No Piano\"\n    NO_SYNTH_STEM = \"No Synthesizer\"\n    NO_STRINGS_STEM = \"No Strings\"\n    NO_WOODWINDS_STEM = \"No Woodwinds\"\n    NO_WIND_INST_STEM = \"No Wind Inst\"","chunkId":"audio_separator/separator/common_separator.py:1"}],[776,{"filePath":"audio_separator/separator/common_separator.py","startLine":38,"endLine":51,"chunkText":"    NO_STRINGS_STEM = \"No Strings\"\n    NO_WOODWINDS_STEM = \"No Woodwinds\"\n    NO_WIND_INST_STEM = \"No Wind Inst\"\n    NO_BRASS_STEM = \"No Brass\"\n    PRIMARY_STEM = \"Primary Stem\"\n    SECONDARY_STEM = \"Secondary Stem\"\n    LEAD_VOCAL_STEM = \"lead_only\"\n    BV_VOCAL_STEM = \"backing_only\"\n    LEAD_VOCAL_STEM_I = \"with_lead_vocals\"\n    BV_VOCAL_STEM_I = \"with_backing_vocals\"\n    LEAD_VOCAL_STEM_LABEL = \"Lead Vocals\"\n    BV_VOCAL_STEM_LABEL = \"Backing Vocals\"\n    NO_STEM = \"No \"\n","chunkId":"audio_separator/separator/common_separator.py:2"}],[777,{"filePath":"audio_separator/separator/common_separator.py","startLine":49,"endLine":61,"chunkText":"    BV_VOCAL_STEM_LABEL = \"Backing Vocals\"\n    NO_STEM = \"No \"\n\n    STEM_PAIR_MAPPER = {VOCAL_STEM: INST_STEM, INST_STEM: VOCAL_STEM, LEAD_VOCAL_STEM: BV_VOCAL_STEM, BV_VOCAL_STEM: LEAD_VOCAL_STEM, PRIMARY_STEM: SECONDARY_STEM}\n\n    NON_ACCOM_STEMS = (VOCAL_STEM, OTHER_STEM, BASS_STEM, DRUM_STEM, GUITAR_STEM, PIANO_STEM, SYNTH_STEM, STRINGS_STEM, WOODWINDS_STEM, BRASS_STEM, WIND_INST_STEM)\n\n    def __init__(self, config):\n\n        self.logger: Logger = config.get(\"logger\")\n        self.log_level: int = config.get(\"log_level\")\n\n        # Inferencing device / acceleration config","chunkId":"audio_separator/separator/common_separator.py:3"}],[778,{"filePath":"audio_separator/separator/common_separator.py","startLine":59,"endLine":72,"chunkText":"        self.log_level: int = config.get(\"log_level\")\n\n        # Inferencing device / acceleration config\n        self.torch_device = config.get(\"torch_device\")\n        self.torch_device_cpu = config.get(\"torch_device_cpu\")\n        self.torch_device_mps = config.get(\"torch_device_mps\")\n        self.onnx_execution_provider = config.get(\"onnx_execution_provider\")\n\n        # Model data\n        self.model_name = config.get(\"model_name\")\n        self.model_path = config.get(\"model_path\")\n        self.model_data = config.get(\"model_data\")\n\n        # Output directory and format","chunkId":"audio_separator/separator/common_separator.py:4"}],[779,{"filePath":"audio_separator/separator/common_separator.py","startLine":70,"endLine":80,"chunkText":"        self.model_data = config.get(\"model_data\")\n\n        # Output directory and format\n        self.output_dir = config.get(\"output_dir\")\n        self.output_format = config.get(\"output_format\")\n        self.output_bitrate = config.get(\"output_bitrate\")\n\n        # Functional options which are applicable to all architectures and the user may tweak to affect the output\n        self.normalization_threshold = config.get(\"normalization_threshold\")\n        self.amplification_threshold = config.get(\"amplification_threshold\")\n        self.enable_denoise = config.get(\"enable_denoise\")","chunkId":"audio_separator/separator/common_separator.py:5"}],[780,{"filePath":"audio_separator/separator/common_separator.py","startLine":78,"endLine":88,"chunkText":"        self.normalization_threshold = config.get(\"normalization_threshold\")\n        self.amplification_threshold = config.get(\"amplification_threshold\")\n        self.enable_denoise = config.get(\"enable_denoise\")\n        self.output_single_stem = config.get(\"output_single_stem\")\n        self.invert_using_spec = config.get(\"invert_using_spec\")\n        self.sample_rate = config.get(\"sample_rate\")\n        self.use_soundfile = config.get(\"use_soundfile\")\n        \n        # Roformer-specific loading support\n        self.roformer_loader = None\n        self.is_roformer_model = self._detect_roformer_model()","chunkId":"audio_separator/separator/common_separator.py:6"}],[781,{"filePath":"audio_separator/separator/common_separator.py","startLine":86,"endLine":101,"chunkText":"        # Roformer-specific loading support\n        self.roformer_loader = None\n        self.is_roformer_model = self._detect_roformer_model()\n        if self.is_roformer_model:\n            self._initialize_roformer_loader()\n\n        # Model specific properties\n\n        # Check if model_data has a \"training\" key with \"instruments\" list\n        self.primary_stem_name = None\n        self.secondary_stem_name = None\n        \n        # Audio bit depth tracking for preserving input quality\n        self.input_bit_depth = None\n        self.input_subtype = None\n","chunkId":"audio_separator/separator/common_separator.py:7"}],[782,{"filePath":"audio_separator/separator/common_separator.py","startLine":99,"endLine":109,"chunkText":"        self.input_bit_depth = None\n        self.input_subtype = None\n\n        if \"training\" in self.model_data and \"instruments\" in self.model_data[\"training\"]:\n            instruments = self.model_data[\"training\"][\"instruments\"]\n            if instruments:\n                self.primary_stem_name = instruments[0]\n                self.secondary_stem_name = instruments[1] if len(instruments) > 1 else self.secondary_stem(self.primary_stem_name)\n\n        if self.primary_stem_name is None:\n            self.primary_stem_name = self.model_data.get(\"primary_stem\", \"Vocals\")","chunkId":"audio_separator/separator/common_separator.py:8"}],[783,{"filePath":"audio_separator/separator/common_separator.py","startLine":107,"endLine":116,"chunkText":"\n        if self.primary_stem_name is None:\n            self.primary_stem_name = self.model_data.get(\"primary_stem\", \"Vocals\")\n            self.secondary_stem_name = self.secondary_stem(self.primary_stem_name)\n\n        self.is_karaoke = self.model_data.get(\"is_karaoke\", False)\n        self.is_bv_model = self.model_data.get(\"is_bv_model\", False)\n        self.bv_model_rebalance = self.model_data.get(\"is_bv_model_rebalanced\", 0)\n\n        self.logger.debug(f\"Common params: model_name={self.model_name}, model_path={self.model_path}\")","chunkId":"audio_separator/separator/common_separator.py:9"}],[784,{"filePath":"audio_separator/separator/common_separator.py","startLine":114,"endLine":119,"chunkText":"        self.bv_model_rebalance = self.model_data.get(\"is_bv_model_rebalanced\", 0)\n\n        self.logger.debug(f\"Common params: model_name={self.model_name}, model_path={self.model_path}\")\n        self.logger.debug(f\"Common params: output_dir={self.output_dir}, output_format={self.output_format}\")\n        self.logger.debug(f\"Common params: normalization_threshold={self.normalization_threshold}, amplification_threshold={self.amplification_threshold}\")\n        self.logger.debug(f\"Common params: enable_denoise={self.enable_denoise}, output_single_stem={self.output_single_stem}\")","chunkId":"audio_separator/separator/common_separator.py:10"}],[785,{"filePath":"audio_separator/separator/common_separator.py","startLine":117,"endLine":121,"chunkText":"        self.logger.debug(f\"Common params: output_dir={self.output_dir}, output_format={self.output_format}\")\n        self.logger.debug(f\"Common params: normalization_threshold={self.normalization_threshold}, amplification_threshold={self.amplification_threshold}\")\n        self.logger.debug(f\"Common params: enable_denoise={self.enable_denoise}, output_single_stem={self.output_single_stem}\")\n        self.logger.debug(f\"Common params: invert_using_spec={self.invert_using_spec}, sample_rate={self.sample_rate}\")\n","chunkId":"audio_separator/separator/common_separator.py:11"}],[786,{"filePath":"audio_separator/separator/common_separator.py","startLine":119,"endLine":124,"chunkText":"        self.logger.debug(f\"Common params: enable_denoise={self.enable_denoise}, output_single_stem={self.output_single_stem}\")\n        self.logger.debug(f\"Common params: invert_using_spec={self.invert_using_spec}, sample_rate={self.sample_rate}\")\n\n        self.logger.debug(f\"Common params: primary_stem_name={self.primary_stem_name}, secondary_stem_name={self.secondary_stem_name}\")\n        self.logger.debug(f\"Common params: is_karaoke={self.is_karaoke}, is_bv_model={self.is_bv_model}, bv_model_rebalance={self.bv_model_rebalance}\")\n","chunkId":"audio_separator/separator/common_separator.py:12"}],[787,{"filePath":"audio_separator/separator/common_separator.py","startLine":122,"endLine":132,"chunkText":"        self.logger.debug(f\"Common params: primary_stem_name={self.primary_stem_name}, secondary_stem_name={self.secondary_stem_name}\")\n        self.logger.debug(f\"Common params: is_karaoke={self.is_karaoke}, is_bv_model={self.is_bv_model}, bv_model_rebalance={self.bv_model_rebalance}\")\n\n        # File-specific variables which need to be cleared between processing different audio inputs\n        self.audio_file_path = None\n        self.audio_file_base = None\n\n        self.primary_source = None\n        self.secondary_source = None\n\n        self.primary_stem_output_path = None","chunkId":"audio_separator/separator/common_separator.py:13"}],[788,{"filePath":"audio_separator/separator/common_separator.py","startLine":130,"endLine":143,"chunkText":"        self.secondary_source = None\n\n        self.primary_stem_output_path = None\n        self.secondary_stem_output_path = None\n\n        self.cached_sources_map = {}\n\n    def secondary_stem(self, primary_stem: str):\n        \"\"\"Determines secondary stem name based on the primary stem name.\"\"\"\n        primary_stem = primary_stem if primary_stem else self.NO_STEM\n\n        if primary_stem in self.STEM_PAIR_MAPPER:\n            secondary_stem = self.STEM_PAIR_MAPPER[primary_stem]\n        else:","chunkId":"audio_separator/separator/common_separator.py:14"}],[789,{"filePath":"audio_separator/separator/common_separator.py","startLine":141,"endLine":155,"chunkText":"        if primary_stem in self.STEM_PAIR_MAPPER:\n            secondary_stem = self.STEM_PAIR_MAPPER[primary_stem]\n        else:\n            secondary_stem = primary_stem.replace(self.NO_STEM, \"\") if self.NO_STEM in primary_stem else f\"{self.NO_STEM}{primary_stem}\"\n\n        return secondary_stem\n\n    def separate(self, audio_file_path):\n        \"\"\"\n        Placeholder method for separating audio sources. Should be overridden by subclasses.\n        \"\"\"\n        raise NotImplementedError(\"This method should be overridden by subclasses.\")\n\n    def final_process(self, stem_path, source, stem_name):\n        \"\"\"","chunkId":"audio_separator/separator/common_separator.py:15"}],[790,{"filePath":"audio_separator/separator/common_separator.py","startLine":153,"endLine":167,"chunkText":"\n    def final_process(self, stem_path, source, stem_name):\n        \"\"\"\n        Finalizes the processing of a stem by writing the audio to a file and returning the processed source.\n        \"\"\"\n        self.logger.debug(f\"Finalizing {stem_name} stem processing and writing audio...\")\n        self.write_audio(stem_path, source)\n\n        return {stem_name: source}\n\n    def cached_sources_clear(self):\n        \"\"\"\n        Clears the cache dictionaries for VR, MDX, and Demucs models.\n\n        This function is essential for ensuring that the cache does not hold outdated or irrelevant data","chunkId":"audio_separator/separator/common_separator.py:16"}],[791,{"filePath":"audio_separator/separator/common_separator.py","startLine":165,"endLine":176,"chunkText":"        Clears the cache dictionaries for VR, MDX, and Demucs models.\n\n        This function is essential for ensuring that the cache does not hold outdated or irrelevant data\n        between different processing sessions or when a new batch of audio files is processed.\n        It helps in managing memory efficiently and prevents potential errors due to stale data.\n        \"\"\"\n        self.cached_sources_map = {}\n\n    def cached_source_callback(self, model_architecture, model_name=None):\n        \"\"\"\n        Retrieves the model and sources from the cache based on the processing method and model name.\n","chunkId":"audio_separator/separator/common_separator.py:17"}],[792,{"filePath":"audio_separator/separator/common_separator.py","startLine":174,"endLine":184,"chunkText":"        \"\"\"\n        Retrieves the model and sources from the cache based on the processing method and model name.\n\n        Args:\n            model_architecture: The architecture type (VR, MDX, or Demucs) being used for processing.\n            model_name: The specific model name within the architecture type, if applicable.\n\n        Returns:\n            A tuple containing the model and its sources if found in the cache; otherwise, None.\n\n        This function is crucial for optimizing performance by avoiding redundant processing.","chunkId":"audio_separator/separator/common_separator.py:18"}],[793,{"filePath":"audio_separator/separator/common_separator.py","startLine":182,"endLine":196,"chunkText":"            A tuple containing the model and its sources if found in the cache; otherwise, None.\n\n        This function is crucial for optimizing performance by avoiding redundant processing.\n        If the requested model and its sources are already in the cache, they can be reused directly,\n        saving time and computational resources.\n        \"\"\"\n        model, sources = None, None\n\n        mapper = self.cached_sources_map[model_architecture]\n\n        for key, value in mapper.items():\n            if model_name in key:\n                model = key\n                sources = value\n","chunkId":"audio_separator/separator/common_separator.py:19"}],[794,{"filePath":"audio_separator/separator/common_separator.py","startLine":194,"endLine":207,"chunkText":"                model = key\n                sources = value\n\n        return model, sources\n\n    def cached_model_source_holder(self, model_architecture, sources, model_name=None):\n        \"\"\"\n        Update the dictionary for the given model_architecture with the new model name and its sources.\n        Use the model_architecture as a key to access the corresponding cache source mapper dictionary.\n        \"\"\"\n        self.cached_sources_map[model_architecture] = {**self.cached_sources_map.get(model_architecture, {}), **{model_name: sources}}\n\n    def prepare_mix(self, mix):\n        \"\"\"","chunkId":"audio_separator/separator/common_separator.py:20"}],[795,{"filePath":"audio_separator/separator/common_separator.py","startLine":205,"endLine":218,"chunkText":"\n    def prepare_mix(self, mix):\n        \"\"\"\n        Prepares the mix for processing. This includes loading the audio from a file if necessary,\n        ensuring the mix is in the correct format, and converting mono to stereo if needed.\n        \"\"\"\n        # Store the original path or the mix itself for later checks\n        audio_path = mix\n\n        # Check if the input is a file path (string) and needs to be loaded\n        if not isinstance(mix, np.ndarray):\n            self.logger.debug(f\"Loading audio from file: {mix}\")\n            \n            # Get audio file info to capture bit depth before loading","chunkId":"audio_separator/separator/common_separator.py:21"}],[796,{"filePath":"audio_separator/separator/common_separator.py","startLine":216,"endLine":227,"chunkText":"            self.logger.debug(f\"Loading audio from file: {mix}\")\n            \n            # Get audio file info to capture bit depth before loading\n            try:\n                audio_info = sf.info(mix)\n                self.input_subtype = audio_info.subtype\n                self.logger.info(f\"Input audio subtype: {self.input_subtype}\")\n                \n                # Map subtype to bit depth\n                if 'PCM_16' in self.input_subtype or self.input_subtype == 'PCM_S8':\n                    self.input_bit_depth = 16\n                elif 'PCM_24' in self.input_subtype:","chunkId":"audio_separator/separator/common_separator.py:22"}],[797,{"filePath":"audio_separator/separator/common_separator.py","startLine":225,"endLine":233,"chunkText":"                if 'PCM_16' in self.input_subtype or self.input_subtype == 'PCM_S8':\n                    self.input_bit_depth = 16\n                elif 'PCM_24' in self.input_subtype:\n                    self.input_bit_depth = 24\n                elif 'PCM_32' in self.input_subtype or 'FLOAT' in self.input_subtype or 'DOUBLE' in self.input_subtype:\n                    self.input_bit_depth = 32\n                else:\n                    # Default to 16-bit for unknown formats\n                    self.input_bit_depth = 16","chunkId":"audio_separator/separator/common_separator.py:23"}],[798,{"filePath":"audio_separator/separator/common_separator.py","startLine":231,"endLine":241,"chunkText":"                else:\n                    # Default to 16-bit for unknown formats\n                    self.input_bit_depth = 16\n                    self.logger.warning(f\"Unknown audio subtype {self.input_subtype}, defaulting to 16-bit output\")\n                \n                self.logger.info(f\"Detected input bit depth: {self.input_bit_depth}-bit\")\n            except Exception as e:\n                self.logger.warning(f\"Could not read audio file info, defaulting to 16-bit output: {e}\")\n                self.input_bit_depth = 16\n                self.input_subtype = 'PCM_16'\n            ","chunkId":"audio_separator/separator/common_separator.py:24"}],[799,{"filePath":"audio_separator/separator/common_separator.py","startLine":239,"endLine":249,"chunkText":"                self.input_bit_depth = 16\n                self.input_subtype = 'PCM_16'\n            \n            mix, sr = librosa.load(mix, mono=False, sr=self.sample_rate)\n            self.logger.debug(f\"Audio loaded. Sample rate: {sr}, Audio shape: {mix.shape}\")\n        else:\n            # Transpose the mix if it's already an ndarray (expected shape: [channels, samples])\n            self.logger.debug(\"Transposing the provided mix array.\")\n            # Default to 16-bit if numpy array provided directly\n            if self.input_bit_depth is None:\n                self.input_bit_depth = 16","chunkId":"audio_separator/separator/common_separator.py:25"}],[800,{"filePath":"audio_separator/separator/common_separator.py","startLine":247,"endLine":259,"chunkText":"            # Default to 16-bit if numpy array provided directly\n            if self.input_bit_depth is None:\n                self.input_bit_depth = 16\n                self.input_subtype = 'PCM_16'\n            mix = mix.T\n            self.logger.debug(f\"Transposed mix shape: {mix.shape}\")\n\n        # If the original input was a filepath, check if the loaded mix is empty\n        if isinstance(audio_path, str):\n            if not np.any(mix):\n                error_msg = f\"Audio file {audio_path} is empty or not valid\"\n                self.logger.error(error_msg)\n                raise ValueError(error_msg)","chunkId":"audio_separator/separator/common_separator.py:26"}],[801,{"filePath":"audio_separator/separator/common_separator.py","startLine":257,"endLine":269,"chunkText":"                error_msg = f\"Audio file {audio_path} is empty or not valid\"\n                self.logger.error(error_msg)\n                raise ValueError(error_msg)\n            else:\n                self.logger.debug(\"Audio file is valid and contains data.\")\n\n        # Ensure the mix is in stereo format\n        if mix.ndim == 1:\n            self.logger.debug(\"Mix is mono. Converting to stereo.\")\n            mix = np.asfortranarray([mix, mix])\n            self.logger.debug(\"Converted to stereo mix.\")\n\n        # Final log indicating successful preparation of the mix","chunkId":"audio_separator/separator/common_separator.py:27"}],[802,{"filePath":"audio_separator/separator/common_separator.py","startLine":267,"endLine":277,"chunkText":"            self.logger.debug(\"Converted to stereo mix.\")\n\n        # Final log indicating successful preparation of the mix\n        self.logger.debug(\"Mix preparation completed.\")\n        return mix\n\n    def write_audio(self, stem_path: str, stem_source):\n        \"\"\"\n        Writes the separated audio source to a file using pydub or soundfile\n        Pydub supports a much wider range of audio formats and produces better encoded lossy files for some formats.\n        Soundfile is used for very large files (longer than 1 hour), as pydub has memory issues with large files:","chunkId":"audio_separator/separator/common_separator.py:28"}],[803,{"filePath":"audio_separator/separator/common_separator.py","startLine":275,"endLine":282,"chunkText":"        Writes the separated audio source to a file using pydub or soundfile\n        Pydub supports a much wider range of audio formats and produces better encoded lossy files for some formats.\n        Soundfile is used for very large files (longer than 1 hour), as pydub has memory issues with large files:\n        https://github.com/jiaaro/pydub/issues/135\n        \"\"\"\n        # Get the duration of the input audio file\n        duration_seconds = librosa.get_duration(filename=self.audio_file_path)\n        duration_hours = duration_seconds / 3600","chunkId":"audio_separator/separator/common_separator.py:29"}],[804,{"filePath":"audio_separator/separator/common_separator.py","startLine":280,"endLine":291,"chunkText":"        # Get the duration of the input audio file\n        duration_seconds = librosa.get_duration(filename=self.audio_file_path)\n        duration_hours = duration_seconds / 3600\n        self.logger.info(f\"Audio duration is {duration_hours:.2f} hours ({duration_seconds:.2f} seconds).\")\n\n        if self.use_soundfile:\n            self.logger.warning(f\"Using soundfile for writing.\")\n            self.write_audio_soundfile(stem_path, stem_source)\n        else:\n            self.logger.info(f\"Using pydub for writing.\")\n            self.write_audio_pydub(stem_path, stem_source)\n","chunkId":"audio_separator/separator/common_separator.py:30"}],[805,{"filePath":"audio_separator/separator/common_separator.py","startLine":289,"endLine":300,"chunkText":"            self.logger.info(f\"Using pydub for writing.\")\n            self.write_audio_pydub(stem_path, stem_source)\n\n    def write_audio_pydub(self, stem_path: str, stem_source):\n        \"\"\"\n        Writes the separated audio source to a file using pydub (ffmpeg)\n        \"\"\"\n        self.logger.debug(f\"Entering write_audio_pydub with stem_path: {stem_path}\")\n\n        stem_source = spec_utils.normalize(wave=stem_source, max_peak=self.normalization_threshold, min_peak=self.amplification_threshold)\n\n        # Check if the numpy array is empty or contains very low values","chunkId":"audio_separator/separator/common_separator.py:31"}],[806,{"filePath":"audio_separator/separator/common_separator.py","startLine":298,"endLine":309,"chunkText":"        stem_source = spec_utils.normalize(wave=stem_source, max_peak=self.normalization_threshold, min_peak=self.amplification_threshold)\n\n        # Check if the numpy array is empty or contains very low values\n        if np.max(np.abs(stem_source)) < 1e-6:\n            self.logger.warning(\"Warning: stem_source array is near-silent or empty.\")\n            return\n\n        # If output_dir is specified, create it and join it with stem_path\n        if self.output_dir:\n            os.makedirs(self.output_dir, exist_ok=True)\n            stem_path = os.path.join(self.output_dir, stem_path)\n","chunkId":"audio_separator/separator/common_separator.py:32"}],[807,{"filePath":"audio_separator/separator/common_separator.py","startLine":307,"endLine":316,"chunkText":"            os.makedirs(self.output_dir, exist_ok=True)\n            stem_path = os.path.join(self.output_dir, stem_path)\n\n        self.logger.debug(f\"Audio data shape before processing: {stem_source.shape}\")\n        self.logger.debug(f\"Data type before conversion: {stem_source.dtype}\")\n\n        # Determine bit depth for output (use input bit depth if available, otherwise default to 16)\n        output_bit_depth = self.input_bit_depth if self.input_bit_depth is not None else 16\n        self.logger.info(f\"Writing output with {output_bit_depth}-bit depth\")\n","chunkId":"audio_separator/separator/common_separator.py:33"}],[808,{"filePath":"audio_separator/separator/common_separator.py","startLine":314,"endLine":323,"chunkText":"        output_bit_depth = self.input_bit_depth if self.input_bit_depth is not None else 16\n        self.logger.info(f\"Writing output with {output_bit_depth}-bit depth\")\n\n        # For pydub, we always convert to int16 for the AudioSegment creation\n        # Then let ffmpeg handle the conversion to the target bit depth during export\n        if stem_source.dtype != np.int16:\n            stem_source = (stem_source * 32767).astype(np.int16)\n            self.logger.debug(\"Converted stem_source to int16 for pydub processing.\")\n\n        # Correctly interleave stereo channels","chunkId":"audio_separator/separator/common_separator.py:34"}],[809,{"filePath":"audio_separator/separator/common_separator.py","startLine":321,"endLine":331,"chunkText":"            self.logger.debug(\"Converted stem_source to int16 for pydub processing.\")\n\n        # Correctly interleave stereo channels\n        stem_source_interleaved = np.empty((2 * stem_source.shape[0],), dtype=np.int16)\n        stem_source_interleaved[0::2] = stem_source[:, 0]  # Left channel\n        stem_source_interleaved[1::2] = stem_source[:, 1]  # Right channel\n\n        self.logger.debug(f\"Interleaved audio data shape: {stem_source_interleaved.shape}\")\n\n        # Create a pydub AudioSegment (always from 16-bit data)\n        try:","chunkId":"audio_separator/separator/common_separator.py:35"}],[810,{"filePath":"audio_separator/separator/common_separator.py","startLine":329,"endLine":340,"chunkText":"\n        # Create a pydub AudioSegment (always from 16-bit data)\n        try:\n            audio_segment = AudioSegment(stem_source_interleaved.tobytes(), frame_rate=self.sample_rate, sample_width=2, channels=2)\n            self.logger.debug(\"Created AudioSegment successfully.\")\n        except (IOError, ValueError) as e:\n            self.logger.error(f\"Specific error creating AudioSegment: {e}\")\n            return\n\n        # Determine file format based on the file extension\n        file_format = stem_path.lower().split(\".\")[-1]\n","chunkId":"audio_separator/separator/common_separator.py:36"}],[811,{"filePath":"audio_separator/separator/common_separator.py","startLine":338,"endLine":351,"chunkText":"        # Determine file format based on the file extension\n        file_format = stem_path.lower().split(\".\")[-1]\n\n        # For m4a files, specify mp4 as the container format as the extension doesn't match the format name\n        if file_format == \"m4a\":\n            file_format = \"mp4\"\n        elif file_format == \"mka\":\n            file_format = \"matroska\"\n\n        # Set the bitrate to 320k for mp3 files if output_bitrate is not specified\n        bitrate = \"320k\" if file_format == \"mp3\" and self.output_bitrate is None else self.output_bitrate\n\n        # Export using the determined format\n        try:","chunkId":"audio_separator/separator/common_separator.py:37"}],[812,{"filePath":"audio_separator/separator/common_separator.py","startLine":349,"endLine":362,"chunkText":"\n        # Export using the determined format\n        try:\n            # Pass codec parameters to ffmpeg to enforce bit depth for lossless formats\n            export_params = {\"format\": file_format}\n            \n            if bitrate:\n                export_params[\"bitrate\"] = bitrate\n            \n            # For lossless formats (WAV/FLAC), specify the codec parameters to enforce bit depth\n            if file_format in [\"wav\", \"flac\"]:\n                if output_bit_depth == 16:\n                    export_params[\"parameters\"] = [\"-sample_fmt\", \"s16\"]\n                elif output_bit_depth == 24:","chunkId":"audio_separator/separator/common_separator.py:38"}],[813,{"filePath":"audio_separator/separator/common_separator.py","startLine":360,"endLine":369,"chunkText":"                if output_bit_depth == 16:\n                    export_params[\"parameters\"] = [\"-sample_fmt\", \"s16\"]\n                elif output_bit_depth == 24:\n                    export_params[\"parameters\"] = [\"-sample_fmt\", \"s32\"]\n                    # For 24-bit, we also need to specify the bit depth explicitly\n                    if file_format == \"wav\":\n                        export_params[\"codec\"] = \"pcm_s24le\"\n                    elif file_format == \"flac\":\n                        # FLAC supports 24-bit natively, no special handling needed\n                        pass","chunkId":"audio_separator/separator/common_separator.py:39"}],[814,{"filePath":"audio_separator/separator/common_separator.py","startLine":367,"endLine":376,"chunkText":"                    elif file_format == \"flac\":\n                        # FLAC supports 24-bit natively, no special handling needed\n                        pass\n                elif output_bit_depth == 32:\n                    export_params[\"parameters\"] = [\"-sample_fmt\", \"s32\"]\n                    if file_format == \"wav\":\n                        export_params[\"codec\"] = \"pcm_s32le\"\n            \n            audio_segment.export(stem_path, **export_params)\n            self.logger.debug(f\"Exported audio file successfully to {stem_path} with {output_bit_depth}-bit depth\")","chunkId":"audio_separator/separator/common_separator.py:40"}],[815,{"filePath":"audio_separator/separator/common_separator.py","startLine":374,"endLine":385,"chunkText":"            \n            audio_segment.export(stem_path, **export_params)\n            self.logger.debug(f\"Exported audio file successfully to {stem_path} with {output_bit_depth}-bit depth\")\n        except (IOError, ValueError) as e:\n            self.logger.error(f\"Error exporting audio file: {e}\")\n\n    def write_audio_soundfile(self, stem_path: str, stem_source):\n        \"\"\"\n        Writes the separated audio source to a file using soundfile library.\n        \"\"\"\n        self.logger.debug(f\"Entering write_audio_soundfile with stem_path: {stem_path}\")\n","chunkId":"audio_separator/separator/common_separator.py:41"}],[816,{"filePath":"audio_separator/separator/common_separator.py","startLine":383,"endLine":394,"chunkText":"        \"\"\"\n        self.logger.debug(f\"Entering write_audio_soundfile with stem_path: {stem_path}\")\n\n        stem_source = spec_utils.normalize(wave=stem_source, max_peak=self.normalization_threshold, min_peak=self.amplification_threshold)\n\n        # Check if the numpy array is empty or contains very low values\n        if np.max(np.abs(stem_source)) < 1e-6:\n            self.logger.warning(\"Warning: stem_source array is near-silent or empty.\")\n            return\n\n        # If output_dir is specified, create it and join it with stem_path\n        if self.output_dir:","chunkId":"audio_separator/separator/common_separator.py:42"}],[817,{"filePath":"audio_separator/separator/common_separator.py","startLine":392,"endLine":405,"chunkText":"\n        # If output_dir is specified, create it and join it with stem_path\n        if self.output_dir:\n            os.makedirs(self.output_dir, exist_ok=True)\n            stem_path = os.path.join(self.output_dir, stem_path)\n\n        # Determine the subtype based on the input audio's bit depth\n        output_subtype = None\n        if self.input_subtype:\n            output_subtype = self.input_subtype\n            self.logger.info(f\"Using input subtype for output: {output_subtype}\")\n        elif self.input_bit_depth:\n            # Map bit depth to subtype\n            if self.input_bit_depth == 16:","chunkId":"audio_separator/separator/common_separator.py:43"}],[818,{"filePath":"audio_separator/separator/common_separator.py","startLine":403,"endLine":415,"chunkText":"        elif self.input_bit_depth:\n            # Map bit depth to subtype\n            if self.input_bit_depth == 16:\n                output_subtype = 'PCM_16'\n            elif self.input_bit_depth == 24:\n                output_subtype = 'PCM_24'\n            elif self.input_bit_depth == 32:\n                output_subtype = 'PCM_32'\n            else:\n                output_subtype = 'PCM_16'  # Default fallback\n            self.logger.info(f\"Using output subtype based on bit depth: {output_subtype}\")\n        else:\n            # Default to PCM_16 if no bit depth info available","chunkId":"audio_separator/separator/common_separator.py:44"}],[819,{"filePath":"audio_separator/separator/common_separator.py","startLine":413,"endLine":423,"chunkText":"            self.logger.info(f\"Using output subtype based on bit depth: {output_subtype}\")\n        else:\n            # Default to PCM_16 if no bit depth info available\n            output_subtype = 'PCM_16'\n            self.logger.warning(\"No bit depth info available, defaulting to PCM_16\")\n\n        # Correctly interleave stereo channels if needed\n        if stem_source.shape[1] == 2:\n            # If the audio is already interleaved, ensure it's in the correct order\n            # Check if the array is Fortran contiguous (column-major)\n            if stem_source.flags[\"F_CONTIGUOUS\"]:","chunkId":"audio_separator/separator/common_separator.py:45"}],[820,{"filePath":"audio_separator/separator/common_separator.py","startLine":421,"endLine":431,"chunkText":"            # If the audio is already interleaved, ensure it's in the correct order\n            # Check if the array is Fortran contiguous (column-major)\n            if stem_source.flags[\"F_CONTIGUOUS\"]:\n                # Convert to C contiguous (row-major)\n                stem_source = np.ascontiguousarray(stem_source)\n            # No need to manually interleave for soundfile - it handles multi-channel properly\n            # Just ensure we don't have the wrong shape\n\n        self.logger.debug(f\"Audio data shape for soundfile: {stem_source.shape}\")\n\n        \"\"\"","chunkId":"audio_separator/separator/common_separator.py:46"}],[821,{"filePath":"audio_separator/separator/common_separator.py","startLine":429,"endLine":441,"chunkText":"        self.logger.debug(f\"Audio data shape for soundfile: {stem_source.shape}\")\n\n        \"\"\"\n        Write audio using soundfile (for formats other than M4A).\n        \"\"\"\n        # Save audio using soundfile with the specified subtype\n        try:\n            # Specify the subtype to match input bit depth\n            sf.write(stem_path, stem_source, self.sample_rate, subtype=output_subtype)\n            self.logger.debug(f\"Exported audio file successfully to {stem_path} with subtype {output_subtype}\")\n        except Exception as e:\n            self.logger.error(f\"Error exporting audio file: {e}\")\n","chunkId":"audio_separator/separator/common_separator.py:47"}],[822,{"filePath":"audio_separator/separator/common_separator.py","startLine":439,"endLine":454,"chunkText":"        except Exception as e:\n            self.logger.error(f\"Error exporting audio file: {e}\")\n\n    def clear_gpu_cache(self):\n        \"\"\"\n        This method clears the GPU cache to free up memory.\n        \"\"\"\n        self.logger.debug(\"Running garbage collection...\")\n        gc.collect()\n        if self.torch_device == torch.device(\"mps\"):\n            self.logger.debug(\"Clearing MPS cache...\")\n            torch.mps.empty_cache()\n        if self.torch_device == torch.device(\"cuda\"):\n            self.logger.debug(\"Clearing CUDA cache...\")\n            torch.cuda.empty_cache()\n","chunkId":"audio_separator/separator/common_separator.py:48"}],[823,{"filePath":"audio_separator/separator/common_separator.py","startLine":452,"endLine":469,"chunkText":"            self.logger.debug(\"Clearing CUDA cache...\")\n            torch.cuda.empty_cache()\n\n    def clear_file_specific_paths(self):\n        \"\"\"\n        Clears the file-specific variables which need to be cleared between processing different audio inputs.\n        \"\"\"\n        self.logger.info(\"Clearing input audio file paths, sources and stems...\")\n\n        self.audio_file_path = None\n        self.audio_file_base = None\n\n        self.primary_source = None\n        self.secondary_source = None\n\n        self.primary_stem_output_path = None\n        self.secondary_stem_output_path = None\n","chunkId":"audio_separator/separator/common_separator.py:49"}],[824,{"filePath":"audio_separator/separator/common_separator.py","startLine":467,"endLine":482,"chunkText":"        self.primary_stem_output_path = None\n        self.secondary_stem_output_path = None\n\n    def sanitize_filename(self, filename):\n        \"\"\"\n        Cleans the filename by replacing invalid characters with underscores.\n        \"\"\"\n        sanitized = re.sub(r'[<>:\"/\\\\|?*]', '_', filename)\n        sanitized = re.sub(r'_+', '_', sanitized)\n        sanitized = sanitized.strip('_. ')\n        return sanitized\n\n    def get_stem_output_path(self, stem_name, custom_output_names):\n        \"\"\"\n        Gets the output path for a stem based on the stem name and custom output names.\n        \"\"\"","chunkId":"audio_separator/separator/common_separator.py:50"}],[825,{"filePath":"audio_separator/separator/common_separator.py","startLine":480,"endLine":488,"chunkText":"        \"\"\"\n        Gets the output path for a stem based on the stem name and custom output names.\n        \"\"\"\n        # Convert custom_output_names keys to lowercase for case-insensitive comparison\n        if custom_output_names:\n            custom_output_names_lower = {k.lower(): v for k, v in custom_output_names.items()}\n            stem_name_lower = stem_name.lower()\n            if stem_name_lower in custom_output_names_lower:\n                sanitized_custom_name = self.sanitize_filename(custom_output_names_lower[stem_name_lower])","chunkId":"audio_separator/separator/common_separator.py:51"}],[826,{"filePath":"audio_separator/separator/common_separator.py","startLine":486,"endLine":494,"chunkText":"            stem_name_lower = stem_name.lower()\n            if stem_name_lower in custom_output_names_lower:\n                sanitized_custom_name = self.sanitize_filename(custom_output_names_lower[stem_name_lower])\n                return os.path.join(f\"{sanitized_custom_name}.{self.output_format.lower()}\")\n\n        sanitized_audio_base = self.sanitize_filename(self.audio_file_base)\n        sanitized_stem_name = self.sanitize_filename(stem_name)\n        sanitized_model_name = self.sanitize_filename(self.model_name) \n","chunkId":"audio_separator/separator/common_separator.py:52"}],[827,{"filePath":"audio_separator/separator/common_separator.py","startLine":492,"endLine":507,"chunkText":"        sanitized_stem_name = self.sanitize_filename(stem_name)\n        sanitized_model_name = self.sanitize_filename(self.model_name) \n\n        filename = f\"{sanitized_audio_base}_({sanitized_stem_name})_{sanitized_model_name}.{self.output_format.lower()}\"\n        return os.path.join(filename)\n    \n    def _detect_roformer_model(self):\n        \"\"\"\n        Detect if the current model is a Roformer model.\n        \n        Returns:\n            bool: True if this is a Roformer model, False otherwise\n        \"\"\"\n        if not self.model_data:\n            return False\n            ","chunkId":"audio_separator/separator/common_separator.py:53"}],[828,{"filePath":"audio_separator/separator/common_separator.py","startLine":505,"endLine":523,"chunkText":"        if not self.model_data:\n            return False\n            \n        # Check for explicit Roformer flag\n        if self.model_data.get(\"is_roformer\", False):\n            return True\n            \n        # Check model path for Roformer indicators\n        if self.model_path and \"roformer\" in self.model_path.lower():\n            return True\n            \n        # Check model name for Roformer indicators\n        if self.model_name and \"roformer\" in self.model_name.lower():\n            return True\n            \n        return False\n    \n    def _initialize_roformer_loader(self):\n        \"\"\"","chunkId":"audio_separator/separator/common_separator.py:54"}],[829,{"filePath":"audio_separator/separator/common_separator.py","startLine":521,"endLine":537,"chunkText":"    \n    def _initialize_roformer_loader(self):\n        \"\"\"\n        Initialize the Roformer loader for this model.\n        \"\"\"\n        try:\n            from .roformer.roformer_loader import RoformerLoader\n            self.roformer_loader = RoformerLoader()\n            self.logger.debug(\"Initialized Roformer loader for CommonSeparator\")\n        except ImportError as e:\n            self.logger.warning(f\"Could not import RoformerLoader: {e}\")\n            self.roformer_loader = None\n    \n    def get_roformer_loading_stats(self):\n        \"\"\"\n        Get Roformer loading statistics if available.\n        ","chunkId":"audio_separator/separator/common_separator.py:55"}],[830,{"filePath":"audio_separator/separator/common_separator.py","startLine":535,"endLine":553,"chunkText":"        \"\"\"\n        Get Roformer loading statistics if available.\n        \n        Returns:\n            dict: Loading statistics or empty dict if not available\n        \"\"\"\n        if self.roformer_loader:\n            return self.roformer_loader.get_loading_stats()\n        return {}\n    \n    def validate_roformer_config(self, config, model_type):\n        \"\"\"\n        Validate Roformer configuration if loader is available.\n        \n        Args:\n            config: Configuration dictionary to validate\n            model_type: Type of model to validate for\n            \n        Returns:","chunkId":"audio_separator/separator/common_separator.py:56"}],[831,{"filePath":"audio_separator/separator/common_separator.py","startLine":551,"endLine":559,"chunkText":"            model_type: Type of model to validate for\n            \n        Returns:\n            bool: True if valid or validation not available, False if invalid\n        \"\"\"\n        if self.roformer_loader:\n            return self.roformer_loader.validate_configuration(config, model_type)\n        return True  # Assume valid if no loader available\n","chunkId":"audio_separator/separator/common_separator.py:57"}],[832,{"filePath":"audio_separator/separator/roformer/README.md","startLine":0,"endLine":10,"chunkText":"# Roformer Implementation - Updated Parameter Support\n\nThis directory contains the updated Roformer implementation with support for new parameters and backward compatibility for legacy models (handled by the single, modern loader).\n\n## Overview\n\nThe updated Roformer implementation provides:\n\n- **New Parameter Support**: `mlp_expansion_factor`, `sage_attention`, `zero_dc`, `use_torch_checkpoint`, `skip_connection`\n- **Single Loader Path**: Unified loader supports both newer and older checkpoints\n- **Robust Validation**: Comprehensive parameter validation with detailed error messages","chunkId":"audio_separator/separator/roformer/README.md:0"}],[833,{"filePath":"audio_separator/separator/roformer/README.md","startLine":9,"endLine":20,"chunkText":"- **Single Loader Path**: Unified loader supports both newer and older checkpoints\n- **Robust Validation**: Comprehensive parameter validation with detailed error messages\n- **Configuration Normalization**: Handles different config formats and applies sensible defaults\n\n## Architecture\n\n```\nroformer/\n├── model_configuration.py      # Data model for standardized config\n├── parameter_validator.py      # Base parameter validation\n├── bs_roformer_validator.py    # BSRoformer-specific validation\n├── mel_band_roformer_validator.py  # MelBandRoformer-specific validation","chunkId":"audio_separator/separator/roformer/README.md:1"}],[834,{"filePath":"audio_separator/separator/roformer/README.md","startLine":19,"endLine":33,"chunkText":"├── bs_roformer_validator.py    # BSRoformer-specific validation\n├── mel_band_roformer_validator.py  # MelBandRoformer-specific validation\n├── configuration_normalizer.py # Config normalization and defaults\n├── parameter_validation_error.py  # Custom exception handling\n├── roformer_loader.py          # Main loader (new implementation only)\n├── model_loading_result.py     # Result dataclass\n└── README.md                   # This documentation\n```\n\n## New Parameters\n\n### Core New Parameters\n\n| Parameter | Type | Default | Description |\n|-----------|------|---------|-------------|","chunkId":"audio_separator/separator/roformer/README.md:2"}],[835,{"filePath":"audio_separator/separator/roformer/README.md","startLine":32,"endLine":41,"chunkText":"| Parameter | Type | Default | Description |\n|-----------|------|---------|-------------|\n| `mlp_expansion_factor` | int | 4 | MLP expansion factor in transformer layers |\n| `sage_attention` | bool | False | Use Sage attention mechanism instead of standard attention |\n| `zero_dc` | bool | True | Apply zero DC component filtering |\n| `use_torch_checkpoint` | bool | False | Enable gradient checkpointing for memory efficiency |\n| `skip_connection` | bool | False | Add skip connections in the architecture |\n\n### Compatibility Notes\n","chunkId":"audio_separator/separator/roformer/README.md:3"}],[836,{"filePath":"audio_separator/separator/roformer/README.md","startLine":40,"endLine":58,"chunkText":"### Compatibility Notes\n\n- **`sage_attention`**: Cannot be used simultaneously with `flash_attn=True` (warning will be issued)\n- **`mlp_expansion_factor`**: Higher values increase model capacity but also memory usage\n- **`use_torch_checkpoint`**: Trades computation for memory - useful for large models\n- **`zero_dc`**: Recommended for most audio separation tasks\n\n## Usage Examples\n\n### Basic Usage with New Parameters\n\n```python\nfrom audio_separator.separator.roformer.roformer_loader import RoformerLoader\n\nloader = RoformerLoader()\n\nconfig = {\n    'dim': 512,\n    'depth': 12,","chunkId":"audio_separator/separator/roformer/README.md:4"}],[837,{"filePath":"audio_separator/separator/roformer/README.md","startLine":57,"endLine":80,"chunkText":"    'dim': 512,\n    'depth': 12,\n    'freqs_per_bands': (2, 4, 8, 16, 32, 64),\n    'mlp_expansion_factor': 8,\n    'sage_attention': True,\n    'zero_dc': True,\n    'use_torch_checkpoint': True,\n    'skip_connection': False\n}\n\nresult = loader.load_model('/path/to/model.ckpt', config, device='cuda')\nif result.success:\n    print(\"Model loaded\")\nelse:\n    print(f\"Failed: {result.error_message}\")\n```\n\n### Configuration Validation\n\n```python\nfrom audio_separator.separator.roformer.parameter_validator import ParameterValidator\n\nvalidator = ParameterValidator()\nissues = validator.validate_all(config, \"bs_roformer\"","chunkId":"audio_separator/separator/roformer/README.md:5"}],[838,{"filePath":"audio_separator/separator/roformer/README.md","startLine":79,"endLine":99,"chunkText":"validator = ParameterValidator()\nissues = validator.validate_all(config, \"bs_roformer\")\nif issues:\n    for issue in issues:\n        print(f\"{issue.severity.value}: {issue.message}\")\nelse:\n    print(\"Configuration is valid!\")\n```\n\n### Configuration Normalization\n\n```python\nfrom audio_separator.separator.roformer.configuration_normalizer import ConfigurationNormalizer\n\nnormalizer = ConfigurationNormalizer()\nraw_config = {\n    'model': {'dim': '512', 'depth': 12.0, 'n_heads': 8},\n    'training': {'sample_rate': 44100},\n    'stereo': 'true',\n    'freq_bands': '[2, 4, 8, 16]'\n}","chunkId":"audio_separator/separator/roformer/README.md:6"}],[839,{"filePath":"audio_separator/separator/roformer/README.md","startLine":98,"endLine":110,"chunkText":"    'freq_bands': '[2, 4, 8, 16]'\n}\nnormalized = normalizer.normalize_config(raw_config, model_type=\"bs_roformer\", apply_defaults=True, validate=True)\nprint(f\"Normalized config has {len(normalized)} parameters\")\n```\n\n## Model Type Detection\n\nDetected via config contents (e.g., `freqs_per_bands` vs `num_bands`) and filename hints. Defaults to BSRoformer when ambiguous.\n\n## Integration with Audio Separator\n\n- Routing remains through the MDXC architecture path; Roformer models are detected and handled by the MDXC separator using the unified `RoformerLoader`.","chunkId":"audio_separator/separator/roformer/README.md:7"}],[840,{"filePath":"audio_separator/separator/roformer/README.md","startLine":109,"endLine":126,"chunkText":"\n- Routing remains through the MDXC architecture path; Roformer models are detected and handled by the MDXC separator using the unified `RoformerLoader`.\n- Loader statistics are surfaced via `CommonSeparator.get_roformer_loading_stats()` and logged by the top-level `Separator`.\n\n## Testing\n\n```bash\n# Unit tests (examples)\npython -m pytest tests/unit/test_parameter_validator.py -v\npython -m pytest tests/unit/test_configuration_normalizer.py -v\n\n# Integration\npython -m pytest tests/integration/test_roformer_*.py -v\n```\n\n## Contributing\n\n- Follow TDD","chunkId":"audio_separator/separator/roformer/README.md:8"}],[841,{"filePath":"audio_separator/separator/roformer/README.md","startLine":125,"endLine":133,"chunkText":"\n- Follow TDD\n- Maintain compatibility for existing checkpoints through the single loader path\n- Update documentation when adding parameters or behavior\n\n## License\n\nFollows the main Audio Separator project license.\n","chunkId":"audio_separator/separator/roformer/README.md:9"}],[842,{"filePath":"audio_separator/separator/roformer/__init__.py","startLine":0,"endLine":18,"chunkText":"\"\"\"\nRoformer implementation module.\nUpdated implementation supporting both old and new Roformer model parameters.\n\"\"\"\n\nfrom .model_configuration import ModelConfiguration\nfrom .bs_roformer_config import BSRoformerConfig\nfrom .mel_band_roformer_config import MelBandRoformerConfig\nfrom .model_loading_result import ModelLoadingResult\nfrom .parameter_validation_error import ParameterValidationError\n\n__all__ = [\n    'ModelConfiguration',\n    'BSRoformerConfig', \n    'MelBandRoformerConfig',\n    'ModelLoadingResult',\n    'ParameterValidationError'\n]\n","chunkId":"audio_separator/separator/roformer/__init__.py:0"}],[843,{"filePath":"audio_separator/separator/roformer/bs_roformer_config.py","startLine":0,"endLine":18,"chunkText":"\"\"\"\nBSRoformer-specific configuration class.\nExtends ModelConfiguration with BSRoformer-specific parameters.\n\"\"\"\n\nfrom dataclasses import dataclass\nfrom typing import Tuple, Optional, Dict, Any\nfrom .model_configuration import ModelConfiguration, RoformerType\n\n\n@dataclass(frozen=True, unsafe_hash=True)\nclass BSRoformerConfig(ModelConfiguration):\n    \"\"\"\n    Configuration class specifically for BSRoformer (Band-Split Roformer) models.\n    \n    BSRoformer processes audio by splitting it into frequency bands and applying\n    Roformer architecture to each band separately.\n    \"\"\"\n    ","chunkId":"audio_separator/separator/roformer/bs_roformer_config.py:0"}],[844,{"filePath":"audio_separator/separator/roformer/bs_roformer_config.py","startLine":16,"endLine":30,"chunkText":"    Roformer architecture to each band separately.\n    \"\"\"\n    \n    # BSRoformer-specific required parameters\n    freqs_per_bands: Tuple[int, ...] = (2, 4, 8, 16, 32, 64)  # Default frequency band configuration\n    \n    # BSRoformer-specific optional parameters\n    mask_estimator_depth: int = 2  # Depth of mask estimation network\n    \n    def __post_init__(self):\n        \"\"\"Validate BSRoformer-specific configuration after initialization.\"\"\"\n        super().__post_init__()\n        self._validate_bs_roformer_parameters()\n    \n    def _validate_bs_roformer_parameters(self):","chunkId":"audio_separator/separator/roformer/bs_roformer_config.py:1"}],[845,{"filePath":"audio_separator/separator/roformer/bs_roformer_config.py","startLine":28,"endLine":38,"chunkText":"        self._validate_bs_roformer_parameters()\n    \n    def _validate_bs_roformer_parameters(self):\n        \"\"\"Validate BSRoformer-specific parameters.\"\"\"\n        if not self.freqs_per_bands:\n            raise ValueError(\"freqs_per_bands must be provided for BSRoformer\")\n        \n        if not isinstance(self.freqs_per_bands, (tuple, list)):\n            raise ValueError(f\"freqs_per_bands must be a tuple or list, got {type(self.freqs_per_bands)}\")\n        \n        if not all(isinstance(freq, int) and freq > 0 for freq in self.freqs_per_bands):","chunkId":"audio_separator/separator/roformer/bs_roformer_config.py:2"}],[846,{"filePath":"audio_separator/separator/roformer/bs_roformer_config.py","startLine":36,"endLine":46,"chunkText":"            raise ValueError(f\"freqs_per_bands must be a tuple or list, got {type(self.freqs_per_bands)}\")\n        \n        if not all(isinstance(freq, int) and freq > 0 for freq in self.freqs_per_bands):\n            raise ValueError(\"All frequencies in freqs_per_bands must be positive integers\")\n        \n        if len(self.freqs_per_bands) < 2:\n            raise ValueError(\"freqs_per_bands must contain at least 2 frequency bands\")\n        \n        if self.mask_estimator_depth <= 0:\n            raise ValueError(f\"mask_estimator_depth must be positive, got {self.mask_estimator_depth}\")\n    ","chunkId":"audio_separator/separator/roformer/bs_roformer_config.py:3"}],[847,{"filePath":"audio_separator/separator/roformer/bs_roformer_config.py","startLine":44,"endLine":60,"chunkText":"        if self.mask_estimator_depth <= 0:\n            raise ValueError(f\"mask_estimator_depth must be positive, got {self.mask_estimator_depth}\")\n    \n    def get_total_frequency_bins(self) -> int:\n        \"\"\"Calculate total number of frequency bins.\"\"\"\n        return sum(self.freqs_per_bands)\n    \n    def validate_against_stft_config(self, n_fft: int) -> bool:\n        \"\"\"\n        Validate that frequency bands match STFT configuration.\n        \n        Args:\n            n_fft: STFT n_fft parameter\n            \n        Returns:\n            True if configuration is valid, False otherwise\n        \"\"\"","chunkId":"audio_separator/separator/roformer/bs_roformer_config.py:4"}],[848,{"filePath":"audio_separator/separator/roformer/bs_roformer_config.py","startLine":58,"endLine":76,"chunkText":"        Returns:\n            True if configuration is valid, False otherwise\n        \"\"\"\n        expected_freq_bins = n_fft // 2 + 1\n        total_freq_bins = self.get_total_frequency_bins()\n        \n        return total_freq_bins == expected_freq_bins\n    \n    def get_stft_compatibility_info(self, n_fft: int) -> Dict[str, Any]:\n        \"\"\"\n        Get information about STFT compatibility.\n        \n        Args:\n            n_fft: STFT n_fft parameter\n            \n        Returns:\n            Dictionary with compatibility information\n        \"\"\"\n        expected_freq_bins = n_fft // 2 + 1","chunkId":"audio_separator/separator/roformer/bs_roformer_config.py:5"}],[849,{"filePath":"audio_separator/separator/roformer/bs_roformer_config.py","startLine":74,"endLine":88,"chunkText":"            Dictionary with compatibility information\n        \"\"\"\n        expected_freq_bins = n_fft // 2 + 1\n        total_freq_bins = self.get_total_frequency_bins()\n        \n        return {\n            'expected_freq_bins': expected_freq_bins,\n            'actual_freq_bins': total_freq_bins,\n            'is_compatible': total_freq_bins == expected_freq_bins,\n            'difference': total_freq_bins - expected_freq_bins,\n            'freqs_per_bands': self.freqs_per_bands\n        }\n    \n    def get_model_type(self) -> RoformerType:\n        \"\"\"Get the model type.\"\"\"","chunkId":"audio_separator/separator/roformer/bs_roformer_config.py:6"}],[850,{"filePath":"audio_separator/separator/roformer/bs_roformer_config.py","startLine":87,"endLine":99,"chunkText":"    def get_model_type(self) -> RoformerType:\n        \"\"\"Get the model type.\"\"\"\n        return RoformerType.BS_ROFORMER\n    \n    def get_bs_roformer_kwargs(self) -> Dict[str, Any]:\n        \"\"\"Get BSRoformer-specific parameters for model initialization.\"\"\"\n        kwargs = self.get_transformer_kwargs()\n        kwargs.update({\n            'freqs_per_bands': self.freqs_per_bands,\n            'mask_estimator_depth': self.mask_estimator_depth,\n            'stereo': self.stereo,\n            'num_stems': self.num_stems,\n            'mlp_expansion_factor': self.mlp_expansion_factor,","chunkId":"audio_separator/separator/roformer/bs_roformer_config.py:7"}],[851,{"filePath":"audio_separator/separator/roformer/bs_roformer_config.py","startLine":97,"endLine":113,"chunkText":"            'stereo': self.stereo,\n            'num_stems': self.num_stems,\n            'mlp_expansion_factor': self.mlp_expansion_factor,\n            'use_torch_checkpoint': self.use_torch_checkpoint,\n            'skip_connection': self.skip_connection,\n        })\n        return kwargs\n    \n    @classmethod\n    def from_model_config(cls, config_dict: Dict[str, Any]) -> 'BSRoformerConfig':\n        \"\"\"\n        Create BSRoformerConfig from a model configuration dictionary.\n        \n        Args:\n            config_dict: Dictionary containing model configuration\n            \n        Returns:","chunkId":"audio_separator/separator/roformer/bs_roformer_config.py:8"}],[852,{"filePath":"audio_separator/separator/roformer/bs_roformer_config.py","startLine":111,"endLine":128,"chunkText":"            config_dict: Dictionary containing model configuration\n            \n        Returns:\n            BSRoformerConfig instance\n        \"\"\"\n        # Ensure freqs_per_bands is present\n        if 'freqs_per_bands' not in config_dict:\n            # Use default if not specified\n            config_dict = config_dict.copy()\n            config_dict['freqs_per_bands'] = (2, 4, 8, 16, 32, 64)\n        \n        return cls.from_dict(config_dict)\n    \n    def suggest_stft_n_fft(self) -> int:\n        \"\"\"\n        Suggest appropriate n_fft value for STFT based on frequency bands.\n        \n        Returns:","chunkId":"audio_separator/separator/roformer/bs_roformer_config.py:9"}],[853,{"filePath":"audio_separator/separator/roformer/bs_roformer_config.py","startLine":126,"endLine":138,"chunkText":"        Suggest appropriate n_fft value for STFT based on frequency bands.\n        \n        Returns:\n            Suggested n_fft value\n        \"\"\"\n        total_freq_bins = self.get_total_frequency_bins()\n        # n_fft = 2 * (freq_bins - 1)\n        return 2 * (total_freq_bins - 1)\n    \n    def get_parameter_summary(self) -> str:\n        \"\"\"Get a summary string of key BSRoformer parameters.\"\"\"\n        base_summary = super().get_parameter_summary()\n        bs_info = f\", freqs_per_bands={self.freqs_per_bands}, total_bins={self.get_total_frequency_bins()}\"","chunkId":"audio_separator/separator/roformer/bs_roformer_config.py:10"}],[854,{"filePath":"audio_separator/separator/roformer/bs_roformer_config.py","startLine":136,"endLine":144,"chunkText":"        \"\"\"Get a summary string of key BSRoformer parameters.\"\"\"\n        base_summary = super().get_parameter_summary()\n        bs_info = f\", freqs_per_bands={self.freqs_per_bands}, total_bins={self.get_total_frequency_bins()}\"\n        return base_summary.replace(\")\", bs_info + \")\")\n    \n    def __repr__(self) -> str:\n        \"\"\"String representation of the BSRoformer configuration.\"\"\"\n        return f\"BSRoformerConfig({self.get_parameter_summary()})\"\n","chunkId":"audio_separator/separator/roformer/bs_roformer_config.py:11"}],[855,{"filePath":"audio_separator/separator/roformer/bs_roformer_validator.py","startLine":0,"endLine":17,"chunkText":"\"\"\"\nBSRoformer-specific parameter validator.\nExtends the base ParameterValidator with BSRoformer-specific validation logic.\n\"\"\"\n\nfrom typing import Dict, Any, List, Tuple\nfrom .parameter_validator import ParameterValidator, ValidationIssue, ValidationSeverity\n\n\nclass BSRoformerValidator(ParameterValidator):\n    \"\"\"\n    Specialized validator for BSRoformer model parameters.\n    \n    Provides BSRoformer-specific validation beyond the base parameter validation,\n    including frequency band configuration and STFT parameter validation.\n    \"\"\"\n    \n    # BSRoformer-specific parameter constraints","chunkId":"audio_separator/separator/roformer/bs_roformer_validator.py:0"}],[856,{"filePath":"audio_separator/separator/roformer/bs_roformer_validator.py","startLine":15,"endLine":34,"chunkText":"    \"\"\"\n    \n    # BSRoformer-specific parameter constraints\n    DEFAULT_FREQS_PER_BANDS = (2, 4, 8, 16, 32, 64, 128, 256, 512, 1024)\n    MIN_BANDS = 2\n    MAX_BANDS = 32\n    \n    def validate_freqs_per_bands(self, config: Dict[str, Any]) -> List[ValidationIssue]:\n        \"\"\"\n        Validate freqs_per_bands parameter for BSRoformer.\n        \n        Args:\n            config: Model configuration dictionary\n            \n        Returns:\n            List of validation issues for freqs_per_bands\n        \"\"\"\n        issues = []\n        \n        if 'freqs_per_bands' not in config:","chunkId":"audio_separator/separator/roformer/bs_roformer_validator.py:1"}],[857,{"filePath":"audio_separator/separator/roformer/bs_roformer_validator.py","startLine":32,"endLine":45,"chunkText":"        issues = []\n        \n        if 'freqs_per_bands' not in config:\n            return issues  # Required parameter check handled by base class\n        \n        freqs_per_bands = config['freqs_per_bands']\n        \n        # Type check\n        if not isinstance(freqs_per_bands, (list, tuple)):\n            issue = ValidationIssue(\n                severity=ValidationSeverity.ERROR,\n                parameter_name=\"freqs_per_bands\",\n                message=\"freqs_per_bands must be a list or tuple\",\n                suggested_fix=\"Convert freqs_per_bands to a list or tuple of integers\",","chunkId":"audio_separator/separator/roformer/bs_roformer_validator.py:2"}],[858,{"filePath":"audio_separator/separator/roformer/bs_roformer_validator.py","startLine":43,"endLine":56,"chunkText":"                parameter_name=\"freqs_per_bands\",\n                message=\"freqs_per_bands must be a list or tuple\",\n                suggested_fix=\"Convert freqs_per_bands to a list or tuple of integers\",\n                current_value=freqs_per_bands,\n                expected_value=\"list or tuple of integers\"\n            )\n            issues.append(issue)\n            return issues\n        \n        # Length check\n        if len(freqs_per_bands) < self.MIN_BANDS:\n            issue = ValidationIssue(\n                severity=ValidationSeverity.ERROR,\n                parameter_name=\"freqs_per_bands\",","chunkId":"audio_separator/separator/roformer/bs_roformer_validator.py:3"}],[859,{"filePath":"audio_separator/separator/roformer/bs_roformer_validator.py","startLine":54,"endLine":65,"chunkText":"            issue = ValidationIssue(\n                severity=ValidationSeverity.ERROR,\n                parameter_name=\"freqs_per_bands\",\n                message=f\"freqs_per_bands must have at least {self.MIN_BANDS} bands\",\n                suggested_fix=f\"Add more frequency bands to reach minimum of {self.MIN_BANDS}\",\n                current_value=len(freqs_per_bands),\n                expected_value=f\">= {self.MIN_BANDS}\"\n            )\n            issues.append(issue)\n        \n        if len(freqs_per_bands) > self.MAX_BANDS:\n            issue = ValidationIssue(","chunkId":"audio_separator/separator/roformer/bs_roformer_validator.py:4"}],[860,{"filePath":"audio_separator/separator/roformer/bs_roformer_validator.py","startLine":63,"endLine":74,"chunkText":"        \n        if len(freqs_per_bands) > self.MAX_BANDS:\n            issue = ValidationIssue(\n                severity=ValidationSeverity.WARNING,\n                parameter_name=\"freqs_per_bands\",\n                message=f\"freqs_per_bands has {len(freqs_per_bands)} bands, which may impact performance\",\n                suggested_fix=f\"Consider reducing to {self.MAX_BANDS} or fewer bands for better performance\",\n                current_value=len(freqs_per_bands),\n                expected_value=f\"<= {self.MAX_BANDS} (recommended)\"\n            )\n            issues.append(issue)\n        ","chunkId":"audio_separator/separator/roformer/bs_roformer_validator.py:5"}],[861,{"filePath":"audio_separator/separator/roformer/bs_roformer_validator.py","startLine":72,"endLine":83,"chunkText":"            )\n            issues.append(issue)\n        \n        # Value validation\n        for i, freq_count in enumerate(freqs_per_bands):\n            if not isinstance(freq_count, int) or freq_count <= 0:\n                issue = ValidationIssue(\n                    severity=ValidationSeverity.ERROR,\n                    parameter_name=f\"freqs_per_bands[{i}]\",\n                    message=f\"Each frequency count must be a positive integer, got {freq_count}\",\n                    suggested_fix=\"Use positive integers for all frequency counts\",\n                    current_value=freq_count,","chunkId":"audio_separator/separator/roformer/bs_roformer_validator.py:6"}],[862,{"filePath":"audio_separator/separator/roformer/bs_roformer_validator.py","startLine":81,"endLine":90,"chunkText":"                    message=f\"Each frequency count must be a positive integer, got {freq_count}\",\n                    suggested_fix=\"Use positive integers for all frequency counts\",\n                    current_value=freq_count,\n                    expected_value=\"positive integer\"\n                )\n                issues.append(issue)\n        \n        # Check for reasonable progression (powers of 2 are common)\n        if all(isinstance(f, int) and f > 0 for f in freqs_per_bands):\n            # Check if values follow a reasonable pattern (not strictly enforced)","chunkId":"audio_separator/separator/roformer/bs_roformer_validator.py:7"}],[863,{"filePath":"audio_separator/separator/roformer/bs_roformer_validator.py","startLine":88,"endLine":96,"chunkText":"        # Check for reasonable progression (powers of 2 are common)\n        if all(isinstance(f, int) and f > 0 for f in freqs_per_bands):\n            # Check if values follow a reasonable pattern (not strictly enforced)\n            total_freqs = sum(freqs_per_bands)\n            if total_freqs > 8192:  # Unusually high for typical STFT\n                issue = ValidationIssue(\n                    severity=ValidationSeverity.WARNING,\n                    parameter_name=\"freqs_per_bands\",\n                    message=f\"Total frequency count ({total_freqs}) is very high\",","chunkId":"audio_separator/separator/roformer/bs_roformer_validator.py:8"}],[864,{"filePath":"audio_separator/separator/roformer/bs_roformer_validator.py","startLine":94,"endLine":106,"chunkText":"                    severity=ValidationSeverity.WARNING,\n                    parameter_name=\"freqs_per_bands\",\n                    message=f\"Total frequency count ({total_freqs}) is very high\",\n                    suggested_fix=\"Consider using fewer total frequencies for typical audio processing\",\n                    current_value=total_freqs,\n                    expected_value=\"<= 8192 (typical)\"\n                )\n                issues.append(issue)\n        \n        return issues\n    \n    def validate_stft_compatibility(self, config: Dict[str, Any]) -> List[ValidationIssue]:\n        \"\"\"","chunkId":"audio_separator/separator/roformer/bs_roformer_validator.py:9"}],[865,{"filePath":"audio_separator/separator/roformer/bs_roformer_validator.py","startLine":104,"endLine":122,"chunkText":"    \n    def validate_stft_compatibility(self, config: Dict[str, Any]) -> List[ValidationIssue]:\n        \"\"\"\n        Validate STFT-related parameters for compatibility with freqs_per_bands.\n        \n        Args:\n            config: Model configuration dictionary\n            \n        Returns:\n            List of validation issues for STFT compatibility\n        \"\"\"\n        issues = []\n        \n        if 'freqs_per_bands' not in config:\n            return issues\n        \n        freqs_per_bands = config.get('freqs_per_bands')\n        stft_n_fft = config.get('stft_n_fft', 2048)  # Common default\n        ","chunkId":"audio_separator/separator/roformer/bs_roformer_validator.py:10"}],[866,{"filePath":"audio_separator/separator/roformer/bs_roformer_validator.py","startLine":120,"endLine":130,"chunkText":"        freqs_per_bands = config.get('freqs_per_bands')\n        stft_n_fft = config.get('stft_n_fft', 2048)  # Common default\n        \n        if isinstance(freqs_per_bands, (list, tuple)) and all(isinstance(f, int) for f in freqs_per_bands):\n            total_freqs = sum(freqs_per_bands)\n            expected_freqs = stft_n_fft // 2 + 1\n            \n            if total_freqs != expected_freqs:\n                issue = ValidationIssue(\n                    severity=ValidationSeverity.WARNING,\n                    parameter_name=\"freqs_per_bands, stft_n_fft\",","chunkId":"audio_separator/separator/roformer/bs_roformer_validator.py:11"}],[867,{"filePath":"audio_separator/separator/roformer/bs_roformer_validator.py","startLine":128,"endLine":136,"chunkText":"                issue = ValidationIssue(\n                    severity=ValidationSeverity.WARNING,\n                    parameter_name=\"freqs_per_bands, stft_n_fft\",\n                    message=f\"freqs_per_bands sum ({total_freqs}) doesn't match STFT frequency bins ({expected_freqs})\",\n                    suggested_fix=f\"Adjust freqs_per_bands to sum to {expected_freqs} or modify stft_n_fft\",\n                    current_value=f\"sum={total_freqs}, expected={expected_freqs}\",\n                    expected_value=f\"freqs_per_bands sum == stft_n_fft//2 + 1\"\n                )\n                issues.append(issue)","chunkId":"audio_separator/separator/roformer/bs_roformer_validator.py:12"}],[868,{"filePath":"audio_separator/separator/roformer/bs_roformer_validator.py","startLine":134,"endLine":152,"chunkText":"                    expected_value=f\"freqs_per_bands sum == stft_n_fft//2 + 1\"\n                )\n                issues.append(issue)\n        \n        return issues\n    \n    def validate_mask_estimator_config(self, config: Dict[str, Any]) -> List[ValidationIssue]:\n        \"\"\"\n        Validate mask estimator configuration for BSRoformer.\n        \n        Args:\n            config: Model configuration dictionary\n            \n        Returns:\n            List of validation issues for mask estimator\n        \"\"\"\n        issues = []\n        \n        mask_depth = config.get('mask_estimator_depth', 2)","chunkId":"audio_separator/separator/roformer/bs_roformer_validator.py:13"}],[869,{"filePath":"audio_separator/separator/roformer/bs_roformer_validator.py","startLine":150,"endLine":163,"chunkText":"        issues = []\n        \n        mask_depth = config.get('mask_estimator_depth', 2)\n        \n        if not isinstance(mask_depth, int) or mask_depth < 1:\n            issue = ValidationIssue(\n                severity=ValidationSeverity.ERROR,\n                parameter_name=\"mask_estimator_depth\",\n                message=\"mask_estimator_depth must be a positive integer\",\n                suggested_fix=\"Set mask_estimator_depth to a positive integer (typically 2-8)\",\n                current_value=mask_depth,\n                expected_value=\"positive integer\"\n            )\n            issues.append(issue)","chunkId":"audio_separator/separator/roformer/bs_roformer_validator.py:14"}],[870,{"filePath":"audio_separator/separator/roformer/bs_roformer_validator.py","startLine":161,"endLine":174,"chunkText":"                expected_value=\"positive integer\"\n            )\n            issues.append(issue)\n        elif mask_depth > 8:\n            issue = ValidationIssue(\n                severity=ValidationSeverity.WARNING,\n                parameter_name=\"mask_estimator_depth\",\n                message=f\"mask_estimator_depth of {mask_depth} may be unnecessarily deep\",\n                suggested_fix=\"Consider using a smaller depth (2-8) for better efficiency\",\n                current_value=mask_depth,\n                expected_value=\"2-8 (recommended)\"\n            )\n            issues.append(issue)\n        ","chunkId":"audio_separator/separator/roformer/bs_roformer_validator.py:15"}],[871,{"filePath":"audio_separator/separator/roformer/bs_roformer_validator.py","startLine":172,"endLine":190,"chunkText":"            )\n            issues.append(issue)\n        \n        return issues\n    \n    def validate_all(self, config: Dict[str, Any], model_type: str = \"bs_roformer\") -> List[ValidationIssue]:\n        \"\"\"\n        Run all BSRoformer-specific validation checks.\n        \n        Args:\n            config: Model configuration dictionary\n            model_type: Type of model (should be \"bs_roformer\")\n            \n        Returns:\n            List of all validation issues found\n        \"\"\"\n        # Start with base validation\n        all_issues = super().validate_all(config, model_type)\n        ","chunkId":"audio_separator/separator/roformer/bs_roformer_validator.py:16"}],[872,{"filePath":"audio_separator/separator/roformer/bs_roformer_validator.py","startLine":188,"endLine":202,"chunkText":"        # Start with base validation\n        all_issues = super().validate_all(config, model_type)\n        \n        # Add BSRoformer-specific validation\n        all_issues.extend(self.validate_freqs_per_bands(config))\n        all_issues.extend(self.validate_stft_compatibility(config))\n        all_issues.extend(self.validate_mask_estimator_config(config))\n        \n        return all_issues\n    \n    def get_parameter_defaults(self, model_type: str = \"bs_roformer\") -> Dict[str, Any]:\n        \"\"\"\n        Get BSRoformer-specific parameter defaults.\n        \n        Args:","chunkId":"audio_separator/separator/roformer/bs_roformer_validator.py:17"}],[873,{"filePath":"audio_separator/separator/roformer/bs_roformer_validator.py","startLine":200,"endLine":216,"chunkText":"        Get BSRoformer-specific parameter defaults.\n        \n        Args:\n            model_type: Type of model\n            \n        Returns:\n            Dictionary of parameter names to default values\n        \"\"\"\n        defaults = super().get_parameter_defaults(model_type)\n        \n        # Override with BSRoformer-specific defaults\n        defaults.update({\n            'freqs_per_bands': self.DEFAULT_FREQS_PER_BANDS[:6],  # Use first 6 bands as default\n            'mask_estimator_depth': 2,\n            'stft_n_fft': 2048,\n            'stft_hop_length': 512,\n            'stft_win_length': 2048,","chunkId":"audio_separator/separator/roformer/bs_roformer_validator.py:18"}],[874,{"filePath":"audio_separator/separator/roformer/bs_roformer_validator.py","startLine":214,"endLine":221,"chunkText":"            'stft_n_fft': 2048,\n            'stft_hop_length': 512,\n            'stft_win_length': 2048,\n            'multi_stft_resolution_loss_weight': 1.0,\n        })\n        \n        return defaults\n","chunkId":"audio_separator/separator/roformer/bs_roformer_validator.py:19"}],[875,{"filePath":"audio_separator/separator/roformer/configuration_normalizer.py","startLine":0,"endLine":20,"chunkText":"\"\"\"\nConfiguration normalizer for Roformer models.\nNormalizes and standardizes configuration dictionaries from various sources.\n\"\"\"\n\nfrom typing import Dict, Any, Optional, Union, List\nimport copy\nimport logging\n\nfrom .parameter_validator import ParameterValidator\nfrom .bs_roformer_validator import BSRoformerValidator\nfrom .mel_band_roformer_validator import MelBandRoformerValidator\nfrom .parameter_validation_error import ParameterValidationError\n\nlogger = logging.getLogger(__name__)\n\n\nclass ConfigurationNormalizer:\n    \"\"\"\n    Normalizes configuration dictionaries for Roformer models.\n    ","chunkId":"audio_separator/separator/roformer/configuration_normalizer.py:0"}],[876,{"filePath":"audio_separator/separator/roformer/configuration_normalizer.py","startLine":18,"endLine":33,"chunkText":"    \"\"\"\n    Normalizes configuration dictionaries for Roformer models.\n    \n    Handles different configuration formats, applies defaults, and ensures\n    consistency across different model types and versions.\n    \"\"\"\n    \n    def __init__(self):\n        \"\"\"Initialize the configuration normalizer with validators.\"\"\"\n        self.base_validator = ParameterValidator()\n        self.bs_validator = BSRoformerValidator()\n        self.mel_validator = MelBandRoformerValidator()\n    \n    def normalize_config(self, \n                        config: Dict[str, Any], \n                        model_type: str, ","chunkId":"audio_separator/separator/roformer/configuration_normalizer.py:1"}],[877,{"filePath":"audio_separator/separator/roformer/configuration_normalizer.py","startLine":31,"endLine":44,"chunkText":"    def normalize_config(self, \n                        config: Dict[str, Any], \n                        model_type: str, \n                        apply_defaults: bool = True,\n                        validate: bool = True) -> Dict[str, Any]:\n        \"\"\"\n        Normalize a configuration dictionary.\n        \n        Args:\n            config: Raw configuration dictionary\n            model_type: Type of model (\"bs_roformer\" or \"mel_band_roformer\")\n            apply_defaults: Whether to apply default values for missing parameters\n            validate: Whether to validate the configuration\n            ","chunkId":"audio_separator/separator/roformer/configuration_normalizer.py:2"}],[878,{"filePath":"audio_separator/separator/roformer/configuration_normalizer.py","startLine":42,"endLine":56,"chunkText":"            apply_defaults: Whether to apply default values for missing parameters\n            validate: Whether to validate the configuration\n            \n        Returns:\n            Normalized configuration dictionary\n            \n        Raises:\n            ParameterValidationError: If validation fails and validate=True\n        \"\"\"\n        # Deep copy to avoid modifying original\n        normalized = copy.deepcopy(config)\n        \n        # Step 1: Normalize structure (flatten nested configs if needed)\n        normalized = self._normalize_structure(normalized, model_type)\n        ","chunkId":"audio_separator/separator/roformer/configuration_normalizer.py:3"}],[879,{"filePath":"audio_separator/separator/roformer/configuration_normalizer.py","startLine":54,"endLine":66,"chunkText":"        # Step 1: Normalize structure (flatten nested configs if needed)\n        normalized = self._normalize_structure(normalized, model_type)\n        \n        # Step 2: Normalize parameter names and values\n        normalized = self._normalize_parameter_names(normalized)\n        normalized = self._normalize_parameter_values(normalized, model_type)\n        \n        # Step 3: Apply defaults if requested\n        if apply_defaults:\n            normalized = self._apply_defaults(normalized, model_type)\n        \n        # Step 4: Validate if requested\n        if validate:","chunkId":"audio_separator/separator/roformer/configuration_normalizer.py:4"}],[880,{"filePath":"audio_separator/separator/roformer/configuration_normalizer.py","startLine":64,"endLine":79,"chunkText":"        \n        # Step 4: Validate if requested\n        if validate:\n            self._validate_config(normalized, model_type)\n        \n        logger.debug(f\"Normalized {model_type} configuration: {len(normalized)} parameters\")\n        return normalized\n    \n    def _normalize_structure(self, config: Dict[str, Any], model_type: str) -> Dict[str, Any]:\n        \"\"\"\n        Normalize the structure of the configuration dictionary.\n        \n        Some configurations may be nested or have different structures.\n        This flattens and standardizes the structure.\n        \"\"\"\n        normalized = {}","chunkId":"audio_separator/separator/roformer/configuration_normalizer.py:5"}],[881,{"filePath":"audio_separator/separator/roformer/configuration_normalizer.py","startLine":77,"endLine":88,"chunkText":"        This flattens and standardizes the structure.\n        \"\"\"\n        normalized = {}\n        \n        # Handle nested configurations (e.g., from YAML files)\n        for key, value in config.items():\n            if isinstance(value, dict) and key in ['model', 'architecture', 'params']:\n                # Flatten nested model parameters\n                normalized.update(value)\n            elif key in ['training', 'inference'] and isinstance(value, dict):\n                # Some configs have training/inference sections\n                # Extract relevant parameters with prefixes","chunkId":"audio_separator/separator/roformer/configuration_normalizer.py:6"}],[882,{"filePath":"audio_separator/separator/roformer/configuration_normalizer.py","startLine":86,"endLine":98,"chunkText":"            elif key in ['training', 'inference'] and isinstance(value, dict):\n                # Some configs have training/inference sections\n                # Extract relevant parameters with prefixes\n                for nested_key, nested_value in value.items():\n                    if nested_key in ['dim_t', 'hop_length', 'n_fft', 'sample_rate']:\n                        normalized[nested_key] = nested_value\n            else:\n                normalized[key] = value\n        \n        return normalized\n    \n    def _normalize_parameter_names(self, config: Dict[str, Any]) -> Dict[str, Any]:\n        \"\"\"","chunkId":"audio_separator/separator/roformer/configuration_normalizer.py:7"}],[883,{"filePath":"audio_separator/separator/roformer/configuration_normalizer.py","startLine":96,"endLine":113,"chunkText":"    \n    def _normalize_parameter_names(self, config: Dict[str, Any]) -> Dict[str, Any]:\n        \"\"\"\n        Normalize parameter names to standard format.\n        \n        Handles different naming conventions and aliases.\n        \"\"\"\n        normalized = {}\n        \n        # Parameter name mappings (old_name -> new_name)\n        name_mappings = {\n            # Common aliases\n            'n_fft': 'stft_n_fft',\n            'hop_length': 'stft_hop_length', \n            'win_length': 'stft_win_length',\n            'window_fn': 'stft_window_fn',\n            'normalized': 'stft_normalized',\n            ","chunkId":"audio_separator/separator/roformer/configuration_normalizer.py:8"}],[884,{"filePath":"audio_separator/separator/roformer/configuration_normalizer.py","startLine":111,"endLine":125,"chunkText":"            'window_fn': 'stft_window_fn',\n            'normalized': 'stft_normalized',\n            \n            # Transformer aliases\n            'n_heads': 'heads',\n            'num_heads': 'heads',\n            'head_dim': 'dim_head',\n            'dropout': 'attn_dropout',\n            'attention_dropout': 'attn_dropout',\n            'feedforward_dropout': 'ff_dropout',\n            \n            # Model-specific aliases\n            'expansion_factor': 'mlp_expansion_factor',\n            'mlp_ratio': 'mlp_expansion_factor',\n            'use_checkpoint': 'use_torch_checkpoint',","chunkId":"audio_separator/separator/roformer/configuration_normalizer.py:9"}],[885,{"filePath":"audio_separator/separator/roformer/configuration_normalizer.py","startLine":123,"endLine":137,"chunkText":"            'expansion_factor': 'mlp_expansion_factor',\n            'mlp_ratio': 'mlp_expansion_factor',\n            'use_checkpoint': 'use_torch_checkpoint',\n            'checkpoint': 'use_torch_checkpoint',\n            \n            # Frequency band aliases\n            'freq_bands': 'freqs_per_bands',\n            'frequency_bands': 'freqs_per_bands',\n            'mel_bands': 'num_bands',\n            'n_mels': 'num_bands',\n        }\n        \n        for key, value in config.items():\n            # Apply name mapping if exists\n            normalized_key = name_mappings.get(key, key)","chunkId":"audio_separator/separator/roformer/configuration_normalizer.py:10"}],[886,{"filePath":"audio_separator/separator/roformer/configuration_normalizer.py","startLine":135,"endLine":149,"chunkText":"        for key, value in config.items():\n            # Apply name mapping if exists\n            normalized_key = name_mappings.get(key, key)\n            normalized[normalized_key] = value\n        \n        return normalized\n    \n    def _normalize_parameter_values(self, config: Dict[str, Any], model_type: str) -> Dict[str, Any]:\n        \"\"\"\n        Normalize parameter values to expected types and formats.\n        \"\"\"\n        normalized = {}\n        \n        for key, value in config.items():\n            normalized_value = self._normalize_single_value(key, value, model_type)","chunkId":"audio_separator/separator/roformer/configuration_normalizer.py:11"}],[887,{"filePath":"audio_separator/separator/roformer/configuration_normalizer.py","startLine":147,"endLine":160,"chunkText":"        \n        for key, value in config.items():\n            normalized_value = self._normalize_single_value(key, value, model_type)\n            normalized[key] = normalized_value\n        \n        return normalized\n    \n    def _normalize_single_value(self, key: str, value: Any, model_type: str) -> Any:\n        \"\"\"Normalize a single parameter value.\"\"\"\n        \n        # Boolean normalization\n        if key in ['stereo', 'flash_attn', 'sage_attention', 'zero_dc', \n                  'use_torch_checkpoint', 'skip_connection', 'stft_normalized']:\n            if isinstance(value, str):","chunkId":"audio_separator/separator/roformer/configuration_normalizer.py:12"}],[888,{"filePath":"audio_separator/separator/roformer/configuration_normalizer.py","startLine":158,"endLine":167,"chunkText":"        if key in ['stereo', 'flash_attn', 'sage_attention', 'zero_dc', \n                  'use_torch_checkpoint', 'skip_connection', 'stft_normalized']:\n            if isinstance(value, str):\n                return value.lower() in ['true', '1', 'yes', 'on']\n            return bool(value)\n        \n        # Integer normalization\n        elif key in ['dim', 'depth', 'num_stems', 'time_transformer_depth', \n                    'freq_transformer_depth', 'dim_head', 'heads', \n                    'mlp_expansion_factor', 'num_bands', 'sample_rate',","chunkId":"audio_separator/separator/roformer/configuration_normalizer.py:13"}],[889,{"filePath":"audio_separator/separator/roformer/configuration_normalizer.py","startLine":165,"endLine":174,"chunkText":"        elif key in ['dim', 'depth', 'num_stems', 'time_transformer_depth', \n                    'freq_transformer_depth', 'dim_head', 'heads', \n                    'mlp_expansion_factor', 'num_bands', 'sample_rate',\n                    'stft_n_fft', 'stft_hop_length', 'stft_win_length',\n                    'mask_estimator_depth']:\n            if isinstance(value, str):\n                try:\n                    return int(float(value))  # Handle \"2.0\" -> 2\n                except (ValueError, TypeError):\n                    return value  # Let validator catch the error","chunkId":"audio_separator/separator/roformer/configuration_normalizer.py:14"}],[890,{"filePath":"audio_separator/separator/roformer/configuration_normalizer.py","startLine":172,"endLine":183,"chunkText":"                    return int(float(value))  # Handle \"2.0\" -> 2\n                except (ValueError, TypeError):\n                    return value  # Let validator catch the error\n            return int(value) if isinstance(value, (int, float)) else value\n        \n        # Float normalization  \n        elif key in ['attn_dropout', 'ff_dropout', 'multi_stft_resolution_loss_weight',\n                    'fmin', 'fmax']:\n            if isinstance(value, str):\n                try:\n                    return float(value)\n                except (ValueError, TypeError):","chunkId":"audio_separator/separator/roformer/configuration_normalizer.py:15"}],[891,{"filePath":"audio_separator/separator/roformer/configuration_normalizer.py","startLine":181,"endLine":192,"chunkText":"                try:\n                    return float(value)\n                except (ValueError, TypeError):\n                    return value  # Let validator catch the error\n            return float(value) if isinstance(value, (int, float)) else value\n        \n        # Tuple/list normalization\n        elif key.startswith('freqs_per_bands') or key in ['freqs_per_bands']:\n            if isinstance(value, str):\n                # Handle string representations like \"(2, 4, 8, 16)\"\n                try:\n                    # Remove parentheses and split","chunkId":"audio_separator/separator/roformer/configuration_normalizer.py:16"}],[892,{"filePath":"audio_separator/separator/roformer/configuration_normalizer.py","startLine":190,"endLine":201,"chunkText":"                # Handle string representations like \"(2, 4, 8, 16)\"\n                try:\n                    # Remove parentheses and split\n                    clean_str = value.strip('()[]').replace(' ', '')\n                    if clean_str:\n                        return tuple(int(x) for x in clean_str.split(','))\n                except (ValueError, TypeError):\n                    return value  # Let validator catch the error\n            elif isinstance(value, list):\n                return tuple(value)  # Convert lists to tuples for consistency\n            return value\n        ","chunkId":"audio_separator/separator/roformer/configuration_normalizer.py:17"}],[893,{"filePath":"audio_separator/separator/roformer/configuration_normalizer.py","startLine":199,"endLine":215,"chunkText":"                return tuple(value)  # Convert lists to tuples for consistency\n            return value\n        \n        # String normalization\n        elif key in ['norm', 'act', 'mel_scale']:\n            if value is not None:\n                return str(value).lower()\n            return value\n        \n        # No normalization needed\n        else:\n            return value\n    \n    def _apply_defaults(self, config: Dict[str, Any], model_type: str) -> Dict[str, Any]:\n        \"\"\"Apply default values for missing parameters.\"\"\"\n        \n        if model_type == \"bs_roformer\":","chunkId":"audio_separator/separator/roformer/configuration_normalizer.py:18"}],[894,{"filePath":"audio_separator/separator/roformer/configuration_normalizer.py","startLine":213,"endLine":227,"chunkText":"        \"\"\"Apply default values for missing parameters.\"\"\"\n        \n        if model_type == \"bs_roformer\":\n            validator = self.bs_validator\n        elif model_type == \"mel_band_roformer\":\n            validator = self.mel_validator\n        else:\n            validator = self.base_validator\n        \n        return validator.apply_parameter_defaults(config, model_type)\n    \n    def _validate_config(self, config: Dict[str, Any], model_type: str) -> None:\n        \"\"\"Validate the configuration and raise errors if invalid.\"\"\"\n        \n        if model_type == \"bs_roformer\":","chunkId":"audio_separator/separator/roformer/configuration_normalizer.py:19"}],[895,{"filePath":"audio_separator/separator/roformer/configuration_normalizer.py","startLine":225,"endLine":241,"chunkText":"        \"\"\"Validate the configuration and raise errors if invalid.\"\"\"\n        \n        if model_type == \"bs_roformer\":\n            validator = self.bs_validator\n        elif model_type == \"mel_band_roformer\":\n            validator = self.mel_validator\n        else:\n            validator = self.base_validator\n        \n        validator.validate_and_raise(config, model_type)\n    \n    def detect_model_type(self, config: Dict[str, Any]) -> Optional[str]:\n        \"\"\"\n        Attempt to detect the model type from configuration parameters.\n        \n        Args:\n            config: Configuration dictionary","chunkId":"audio_separator/separator/roformer/configuration_normalizer.py:20"}],[896,{"filePath":"audio_separator/separator/roformer/configuration_normalizer.py","startLine":239,"endLine":254,"chunkText":"        \n        Args:\n            config: Configuration dictionary\n            \n        Returns:\n            Detected model type or None if cannot be determined\n        \"\"\"\n        # Check for BSRoformer-specific parameters\n        if 'freqs_per_bands' in config:\n            return \"bs_roformer\"\n        \n        # Check for MelBandRoformer-specific parameters\n        if 'num_bands' in config or 'n_mels' in config or 'mel_bands' in config:\n            return \"mel_band_roformer\"\n        \n        # Check for model type hints in the config","chunkId":"audio_separator/separator/roformer/configuration_normalizer.py:21"}],[897,{"filePath":"audio_separator/separator/roformer/configuration_normalizer.py","startLine":252,"endLine":262,"chunkText":"            return \"mel_band_roformer\"\n        \n        # Check for model type hints in the config\n        model_type = config.get('model_type', config.get('type', config.get('architecture')))\n        if isinstance(model_type, str):\n            model_type_lower = model_type.lower()\n            if 'bs' in model_type_lower and 'roformer' in model_type_lower:\n                return \"bs_roformer\"\n            elif 'mel' in model_type_lower and 'roformer' in model_type_lower:\n                return \"mel_band_roformer\"\n            elif 'roformer' in model_type_lower:","chunkId":"audio_separator/separator/roformer/configuration_normalizer.py:22"}],[898,{"filePath":"audio_separator/separator/roformer/configuration_normalizer.py","startLine":260,"endLine":273,"chunkText":"            elif 'mel' in model_type_lower and 'roformer' in model_type_lower:\n                return \"mel_band_roformer\"\n            elif 'roformer' in model_type_lower:\n                # Default to BSRoformer if just \"roformer\"\n                return \"bs_roformer\"\n        \n        return None\n    \n    def normalize_from_file_path(self, \n                                config: Dict[str, Any], \n                                file_path: str,\n                                apply_defaults: bool = True,\n                                validate: bool = True) -> Dict[str, Any]:\n        \"\"\"","chunkId":"audio_separator/separator/roformer/configuration_normalizer.py:23"}],[899,{"filePath":"audio_separator/separator/roformer/configuration_normalizer.py","startLine":271,"endLine":285,"chunkText":"                                apply_defaults: bool = True,\n                                validate: bool = True) -> Dict[str, Any]:\n        \"\"\"\n        Normalize configuration with model type detection from file path.\n        \n        Args:\n            config: Configuration dictionary\n            file_path: Path to the model file (used for type detection)\n            apply_defaults: Whether to apply defaults\n            validate: Whether to validate\n            \n        Returns:\n            Normalized configuration\n        \"\"\"\n        # Try to detect model type from file path","chunkId":"audio_separator/separator/roformer/configuration_normalizer.py:24"}],[900,{"filePath":"audio_separator/separator/roformer/configuration_normalizer.py","startLine":283,"endLine":296,"chunkText":"            Normalized configuration\n        \"\"\"\n        # Try to detect model type from file path\n        file_path_lower = file_path.lower()\n        if 'bs' in file_path_lower and 'roformer' in file_path_lower:\n            model_type = \"bs_roformer\"\n        elif 'mel' in file_path_lower and 'roformer' in file_path_lower:\n            model_type = \"mel_band_roformer\"\n        else:\n            # Try to detect from config\n            model_type = self.detect_model_type(config)\n            if model_type is None:\n                # Default to BSRoformer\n                model_type = \"bs_roformer\"","chunkId":"audio_separator/separator/roformer/configuration_normalizer.py:25"}],[901,{"filePath":"audio_separator/separator/roformer/configuration_normalizer.py","startLine":294,"endLine":300,"chunkText":"            if model_type is None:\n                # Default to BSRoformer\n                model_type = \"bs_roformer\"\n                logger.warning(f\"Could not detect model type from config or path {file_path}, defaulting to bs_roformer\")\n        \n        return self.normalize_config(config, model_type, apply_defaults, validate)\n","chunkId":"audio_separator/separator/roformer/configuration_normalizer.py:26"}],[902,{"filePath":"audio_separator/separator/roformer/mel_band_roformer_config.py","startLine":0,"endLine":18,"chunkText":"\"\"\"\nMelBandRoformer-specific configuration class.\nExtends ModelConfiguration with MelBandRoformer-specific parameters.\n\"\"\"\n\nfrom dataclasses import dataclass\nfrom typing import Optional, Dict, Any\nfrom .model_configuration import ModelConfiguration, RoformerType\n\n\n@dataclass(frozen=True, unsafe_hash=True)\nclass MelBandRoformerConfig(ModelConfiguration):\n    \"\"\"\n    Configuration class specifically for MelBandRoformer models.\n    \n    MelBandRoformer processes audio using mel-scale frequency bands,\n    which are more aligned with human auditory perception.\n    \"\"\"\n    ","chunkId":"audio_separator/separator/roformer/mel_band_roformer_config.py:0"}],[903,{"filePath":"audio_separator/separator/roformer/mel_band_roformer_config.py","startLine":16,"endLine":29,"chunkText":"    which are more aligned with human auditory perception.\n    \"\"\"\n    \n    # MelBandRoformer-specific required parameters\n    num_bands: int = 64  # Number of mel-scale bands\n    \n    # MelBandRoformer-specific optional parameters\n    mel_scale: str = \"htk\"  # Mel scale type: \"htk\" or \"slaney\"\n    fmin: float = 0.0  # Minimum frequency for mel scale\n    fmax: Optional[float] = None  # Maximum frequency for mel scale (None = sample_rate/2)\n    \n    def __post_init__(self):\n        \"\"\"Validate MelBandRoformer-specific configuration after initialization.\"\"\"\n        super().__post_init__()","chunkId":"audio_separator/separator/roformer/mel_band_roformer_config.py:1"}],[904,{"filePath":"audio_separator/separator/roformer/mel_band_roformer_config.py","startLine":27,"endLine":37,"chunkText":"    def __post_init__(self):\n        \"\"\"Validate MelBandRoformer-specific configuration after initialization.\"\"\"\n        super().__post_init__()\n        self._validate_mel_band_roformer_parameters()\n    \n    def _validate_mel_band_roformer_parameters(self):\n        \"\"\"Validate MelBandRoformer-specific parameters.\"\"\"\n        if not isinstance(self.num_bands, int) or self.num_bands <= 0:\n            raise ValueError(f\"num_bands must be a positive integer, got {self.num_bands}\")\n        \n        if self.num_bands < 8:","chunkId":"audio_separator/separator/roformer/mel_band_roformer_config.py:2"}],[905,{"filePath":"audio_separator/separator/roformer/mel_band_roformer_config.py","startLine":35,"endLine":46,"chunkText":"            raise ValueError(f\"num_bands must be a positive integer, got {self.num_bands}\")\n        \n        if self.num_bands < 8:\n            raise ValueError(f\"num_bands should be at least 8 for meaningful separation, got {self.num_bands}\")\n        \n        if self.num_bands > 512:\n            raise ValueError(f\"num_bands should not exceed 512 for practical purposes, got {self.num_bands}\")\n        \n        if self.mel_scale not in [\"htk\", \"slaney\"]:\n            raise ValueError(f\"mel_scale must be 'htk' or 'slaney', got '{self.mel_scale}'\")\n        \n        if self.fmin < 0:","chunkId":"audio_separator/separator/roformer/mel_band_roformer_config.py:3"}],[906,{"filePath":"audio_separator/separator/roformer/mel_band_roformer_config.py","startLine":44,"endLine":56,"chunkText":"            raise ValueError(f\"mel_scale must be 'htk' or 'slaney', got '{self.mel_scale}'\")\n        \n        if self.fmin < 0:\n            raise ValueError(f\"fmin must be non-negative, got {self.fmin}\")\n        \n        if self.fmax is not None:\n            if self.fmax <= self.fmin:\n                raise ValueError(f\"fmax ({self.fmax}) must be greater than fmin ({self.fmin})\")\n            \n            if self.fmax > self.sample_rate / 2:\n                raise ValueError(f\"fmax ({self.fmax}) cannot exceed sample_rate/2 ({self.sample_rate/2})\")\n    \n    def get_effective_fmax(self) -> float:","chunkId":"audio_separator/separator/roformer/mel_band_roformer_config.py:4"}],[907,{"filePath":"audio_separator/separator/roformer/mel_band_roformer_config.py","startLine":54,"endLine":65,"chunkText":"                raise ValueError(f\"fmax ({self.fmax}) cannot exceed sample_rate/2 ({self.sample_rate/2})\")\n    \n    def get_effective_fmax(self) -> float:\n        \"\"\"Get the effective maximum frequency (fmax or sample_rate/2).\"\"\"\n        return self.fmax if self.fmax is not None else self.sample_rate / 2.0\n    \n    def get_frequency_range(self) -> tuple[float, float]:\n        \"\"\"Get the frequency range (fmin, fmax) for mel scale.\"\"\"\n        return (self.fmin, self.get_effective_fmax())\n    \n    def validate_sample_rate(self, sample_rate: int) -> bool:\n        \"\"\"","chunkId":"audio_separator/separator/roformer/mel_band_roformer_config.py:5"}],[908,{"filePath":"audio_separator/separator/roformer/mel_band_roformer_config.py","startLine":63,"endLine":80,"chunkText":"    \n    def validate_sample_rate(self, sample_rate: int) -> bool:\n        \"\"\"\n        Validate that the configuration is compatible with a given sample rate.\n        \n        Args:\n            sample_rate: Audio sample rate to validate against\n            \n        Returns:\n            True if compatible, False otherwise\n        \"\"\"\n        if sample_rate != self.sample_rate:\n            # Check if fmax is compatible with new sample rate\n            if self.fmax is not None and self.fmax > sample_rate / 2:\n                return False\n        \n        return True\n    ","chunkId":"audio_separator/separator/roformer/mel_band_roformer_config.py:6"}],[909,{"filePath":"audio_separator/separator/roformer/mel_band_roformer_config.py","startLine":78,"endLine":97,"chunkText":"        \n        return True\n    \n    def get_mel_scale_info(self) -> Dict[str, Any]:\n        \"\"\"\n        Get information about the mel scale configuration.\n        \n        Returns:\n            Dictionary with mel scale information\n        \"\"\"\n        return {\n            'num_bands': self.num_bands,\n            'mel_scale': self.mel_scale,\n            'fmin': self.fmin,\n            'fmax': self.get_effective_fmax(),\n            'sample_rate': self.sample_rate,\n            'frequency_range': self.get_frequency_range(),\n            'bands_per_octave': self._estimate_bands_per_octave()\n        }\n    ","chunkId":"audio_separator/separator/roformer/mel_band_roformer_config.py:7"}],[910,{"filePath":"audio_separator/separator/roformer/mel_band_roformer_config.py","startLine":95,"endLine":110,"chunkText":"            'bands_per_octave': self._estimate_bands_per_octave()\n        }\n    \n    def _estimate_bands_per_octave(self) -> float:\n        \"\"\"Estimate the number of mel bands per octave.\"\"\"\n        fmin, fmax = self.get_frequency_range()\n        if fmin <= 0:\n            fmin = 1.0  # Avoid log(0)\n        \n        # Rough estimation using logarithmic scale\n        import math\n        octave_range = math.log2(fmax / fmin)\n        return self.num_bands / octave_range if octave_range > 0 else self.num_bands\n    \n    def get_model_type(self) -> RoformerType:\n        \"\"\"Get the model type.\"\"\"","chunkId":"audio_separator/separator/roformer/mel_band_roformer_config.py:8"}],[911,{"filePath":"audio_separator/separator/roformer/mel_band_roformer_config.py","startLine":108,"endLine":122,"chunkText":"    \n    def get_model_type(self) -> RoformerType:\n        \"\"\"Get the model type.\"\"\"\n        return RoformerType.MEL_BAND_ROFORMER\n    \n    def get_mel_band_roformer_kwargs(self) -> Dict[str, Any]:\n        \"\"\"Get MelBandRoformer-specific parameters for model initialization.\"\"\"\n        kwargs = self.get_transformer_kwargs()\n        kwargs.update({\n            'num_bands': self.num_bands,\n            'sample_rate': self.sample_rate,\n            'mel_scale': self.mel_scale,\n            'fmin': self.fmin,\n            'fmax': self.fmax,\n            'stereo': self.stereo,","chunkId":"audio_separator/separator/roformer/mel_band_roformer_config.py:9"}],[912,{"filePath":"audio_separator/separator/roformer/mel_band_roformer_config.py","startLine":120,"endLine":135,"chunkText":"            'fmin': self.fmin,\n            'fmax': self.fmax,\n            'stereo': self.stereo,\n            'num_stems': self.num_stems,\n            'mlp_expansion_factor': self.mlp_expansion_factor,\n            'use_torch_checkpoint': self.use_torch_checkpoint,\n            'skip_connection': self.skip_connection,\n        })\n        return kwargs\n    \n    @classmethod\n    def from_model_config(cls, config_dict: Dict[str, Any]) -> 'MelBandRoformerConfig':\n        \"\"\"\n        Create MelBandRoformerConfig from a model configuration dictionary.\n        \n        Args:","chunkId":"audio_separator/separator/roformer/mel_band_roformer_config.py:10"}],[913,{"filePath":"audio_separator/separator/roformer/mel_band_roformer_config.py","startLine":133,"endLine":149,"chunkText":"        Create MelBandRoformerConfig from a model configuration dictionary.\n        \n        Args:\n            config_dict: Dictionary containing model configuration\n            \n        Returns:\n            MelBandRoformerConfig instance\n        \"\"\"\n        # Ensure num_bands is present\n        if 'num_bands' not in config_dict:\n            # Use default if not specified\n            config_dict = config_dict.copy()\n            config_dict['num_bands'] = 64\n        \n        return cls.from_dict(config_dict)\n    \n    def suggest_optimal_bands(self, target_frequency_resolution: float = 50.0) -> int:","chunkId":"audio_separator/separator/roformer/mel_band_roformer_config.py:11"}],[914,{"filePath":"audio_separator/separator/roformer/mel_band_roformer_config.py","startLine":147,"endLine":163,"chunkText":"        return cls.from_dict(config_dict)\n    \n    def suggest_optimal_bands(self, target_frequency_resolution: float = 50.0) -> int:\n        \"\"\"\n        Suggest optimal number of bands based on desired frequency resolution.\n        \n        Args:\n            target_frequency_resolution: Desired frequency resolution in Hz\n            \n        Returns:\n            Suggested number of bands\n        \"\"\"\n        fmin, fmax = self.get_frequency_range()\n        frequency_span = fmax - fmin\n        suggested_bands = int(frequency_span / target_frequency_resolution)\n        \n        # Clamp to reasonable range","chunkId":"audio_separator/separator/roformer/mel_band_roformer_config.py:12"}],[915,{"filePath":"audio_separator/separator/roformer/mel_band_roformer_config.py","startLine":161,"endLine":172,"chunkText":"        suggested_bands = int(frequency_span / target_frequency_resolution)\n        \n        # Clamp to reasonable range\n        return max(8, min(512, suggested_bands))\n    \n    def get_parameter_summary(self) -> str:\n        \"\"\"Get a summary string of key MelBandRoformer parameters.\"\"\"\n        base_summary = super().get_parameter_summary()\n        mel_info = f\", num_bands={self.num_bands}, sr={self.sample_rate}, fmax={self.get_effective_fmax():.0f}\"\n        return base_summary.replace(\")\", mel_info + \")\")\n    \n    def __repr__(self) -> str:","chunkId":"audio_separator/separator/roformer/mel_band_roformer_config.py:13"}],[916,{"filePath":"audio_separator/separator/roformer/mel_band_roformer_config.py","startLine":170,"endLine":175,"chunkText":"        return base_summary.replace(\")\", mel_info + \")\")\n    \n    def __repr__(self) -> str:\n        \"\"\"String representation of the MelBandRoformer configuration.\"\"\"\n        return f\"MelBandRoformerConfig({self.get_parameter_summary()})\"\n","chunkId":"audio_separator/separator/roformer/mel_band_roformer_config.py:14"}],[917,{"filePath":"audio_separator/separator/roformer/mel_band_roformer_validator.py","startLine":0,"endLine":16,"chunkText":"\"\"\"\nMelBandRoformer-specific parameter validator.\nExtends the base ParameterValidator with MelBandRoformer-specific validation logic.\n\"\"\"\n\nfrom typing import Dict, Any, List\nfrom .parameter_validator import ParameterValidator, ValidationIssue, ValidationSeverity\n\n\nclass MelBandRoformerValidator(ParameterValidator):\n    \"\"\"\n    Specialized validator for MelBandRoformer model parameters.\n    \n    Provides MelBandRoformer-specific validation beyond the base parameter validation,\n    including mel-scale frequency band configuration and sample rate validation.\n    \"\"\"\n    ","chunkId":"audio_separator/separator/roformer/mel_band_roformer_validator.py:0"}],[918,{"filePath":"audio_separator/separator/roformer/mel_band_roformer_validator.py","startLine":14,"endLine":33,"chunkText":"    including mel-scale frequency band configuration and sample rate validation.\n    \"\"\"\n    \n    # MelBandRoformer-specific parameter constraints\n    MIN_NUM_BANDS = 8\n    MAX_NUM_BANDS = 512\n    RECOMMENDED_NUM_BANDS = 64\n    \n    # Common sample rates and their typical mel band counts\n    SAMPLE_RATE_BAND_RECOMMENDATIONS = {\n        8000: (8, 32),\n        16000: (16, 64),\n        22050: (24, 80),\n        44100: (32, 128),\n        48000: (32, 128),\n        96000: (64, 256),\n    }\n    \n    def validate_num_bands(self, config: Dict[str, Any]) -> List[ValidationIssue]:\n        \"\"\"","chunkId":"audio_separator/separator/roformer/mel_band_roformer_validator.py:1"}],[919,{"filePath":"audio_separator/separator/roformer/mel_band_roformer_validator.py","startLine":31,"endLine":50,"chunkText":"    \n    def validate_num_bands(self, config: Dict[str, Any]) -> List[ValidationIssue]:\n        \"\"\"\n        Validate num_bands parameter for MelBandRoformer.\n        \n        Args:\n            config: Model configuration dictionary\n            \n        Returns:\n            List of validation issues for num_bands\n        \"\"\"\n        issues = []\n        \n        if 'num_bands' not in config:\n            return issues  # Required parameter check handled by base class\n        \n        num_bands = config['num_bands']\n        \n        # Type check\n        if not isinstance(num_bands, int):","chunkId":"audio_separator/separator/roformer/mel_band_roformer_validator.py:2"}],[920,{"filePath":"audio_separator/separator/roformer/mel_band_roformer_validator.py","startLine":48,"endLine":64,"chunkText":"        \n        # Type check\n        if not isinstance(num_bands, int):\n            issue = ValidationIssue(\n                severity=ValidationSeverity.ERROR,\n                parameter_name=\"num_bands\",\n                message=\"num_bands must be an integer\",\n                suggested_fix=\"Set num_bands to an integer value\",\n                current_value=num_bands,\n                expected_value=\"integer\"\n            )\n            issues.append(issue)\n            return issues\n        \n        # Range check\n        if num_bands < self.MIN_NUM_BANDS:\n            issue = ValidationIssue(","chunkId":"audio_separator/separator/roformer/mel_band_roformer_validator.py:3"}],[921,{"filePath":"audio_separator/separator/roformer/mel_band_roformer_validator.py","startLine":62,"endLine":75,"chunkText":"        # Range check\n        if num_bands < self.MIN_NUM_BANDS:\n            issue = ValidationIssue(\n                severity=ValidationSeverity.ERROR,\n                parameter_name=\"num_bands\",\n                message=f\"num_bands must be at least {self.MIN_NUM_BANDS}\",\n                suggested_fix=f\"Set num_bands to {self.MIN_NUM_BANDS} or higher\",\n                current_value=num_bands,\n                expected_value=f\">= {self.MIN_NUM_BANDS}\"\n            )\n            issues.append(issue)\n        \n        if num_bands > self.MAX_NUM_BANDS:\n            issue = ValidationIssue(","chunkId":"audio_separator/separator/roformer/mel_band_roformer_validator.py:4"}],[922,{"filePath":"audio_separator/separator/roformer/mel_band_roformer_validator.py","startLine":73,"endLine":84,"chunkText":"        \n        if num_bands > self.MAX_NUM_BANDS:\n            issue = ValidationIssue(\n                severity=ValidationSeverity.WARNING,\n                parameter_name=\"num_bands\",\n                message=f\"num_bands of {num_bands} is very high and may impact performance\",\n                suggested_fix=f\"Consider using {self.MAX_NUM_BANDS} or fewer bands for better performance\",\n                current_value=num_bands,\n                expected_value=f\"<= {self.MAX_NUM_BANDS} (recommended)\"\n            )\n            issues.append(issue)\n        ","chunkId":"audio_separator/separator/roformer/mel_band_roformer_validator.py:5"}],[923,{"filePath":"audio_separator/separator/roformer/mel_band_roformer_validator.py","startLine":82,"endLine":94,"chunkText":"            )\n            issues.append(issue)\n        \n        # Check if it's a power of 2 (often preferred for efficiency)\n        if num_bands > 0 and (num_bands & (num_bands - 1)) != 0:\n            # Find nearest powers of 2\n            lower_power = 1 << (num_bands.bit_length() - 1)\n            upper_power = 1 << num_bands.bit_length()\n            \n            issue = ValidationIssue(\n                severity=ValidationSeverity.INFO,\n                parameter_name=\"num_bands\",\n                message=f\"num_bands ({num_bands}) is not a power of 2, which may be less efficient\",","chunkId":"audio_separator/separator/roformer/mel_band_roformer_validator.py:6"}],[924,{"filePath":"audio_separator/separator/roformer/mel_band_roformer_validator.py","startLine":92,"endLine":103,"chunkText":"                severity=ValidationSeverity.INFO,\n                parameter_name=\"num_bands\",\n                message=f\"num_bands ({num_bands}) is not a power of 2, which may be less efficient\",\n                suggested_fix=f\"Consider using {lower_power} or {upper_power} for potentially better performance\",\n                current_value=num_bands,\n                expected_value=\"power of 2 (optional optimization)\"\n            )\n            issues.append(issue)\n        \n        return issues\n    \n    def validate_sample_rate_compatibility(self, config: Dict[str, Any]) -> List[ValidationIssue]:","chunkId":"audio_separator/separator/roformer/mel_band_roformer_validator.py:7"}],[925,{"filePath":"audio_separator/separator/roformer/mel_band_roformer_validator.py","startLine":101,"endLine":118,"chunkText":"        return issues\n    \n    def validate_sample_rate_compatibility(self, config: Dict[str, Any]) -> List[ValidationIssue]:\n        \"\"\"\n        Validate sample rate compatibility with num_bands.\n        \n        Args:\n            config: Model configuration dictionary\n            \n        Returns:\n            List of validation issues for sample rate compatibility\n        \"\"\"\n        issues = []\n        \n        num_bands = config.get('num_bands')\n        sample_rate = config.get('sample_rate')\n        \n        if not isinstance(num_bands, int) or not isinstance(sample_rate, int):","chunkId":"audio_separator/separator/roformer/mel_band_roformer_validator.py:8"}],[926,{"filePath":"audio_separator/separator/roformer/mel_band_roformer_validator.py","startLine":116,"endLine":127,"chunkText":"        sample_rate = config.get('sample_rate')\n        \n        if not isinstance(num_bands, int) or not isinstance(sample_rate, int):\n            return issues  # Type validation handled elsewhere\n        \n        # Check against known good combinations\n        if sample_rate in self.SAMPLE_RATE_BAND_RECOMMENDATIONS:\n            min_bands, max_bands = self.SAMPLE_RATE_BAND_RECOMMENDATIONS[sample_rate]\n            \n            if num_bands < min_bands:\n                issue = ValidationIssue(\n                    severity=ValidationSeverity.WARNING,","chunkId":"audio_separator/separator/roformer/mel_band_roformer_validator.py:9"}],[927,{"filePath":"audio_separator/separator/roformer/mel_band_roformer_validator.py","startLine":125,"endLine":134,"chunkText":"            if num_bands < min_bands:\n                issue = ValidationIssue(\n                    severity=ValidationSeverity.WARNING,\n                    parameter_name=\"num_bands, sample_rate\",\n                    message=f\"num_bands ({num_bands}) may be too low for sample_rate ({sample_rate})\",\n                    suggested_fix=f\"Consider using {min_bands}-{max_bands} bands for {sample_rate}Hz\",\n                    current_value=f\"bands={num_bands}, sr={sample_rate}\",\n                    expected_value=f\"{min_bands}-{max_bands} bands\"\n                )\n                issues.append(issue)","chunkId":"audio_separator/separator/roformer/mel_band_roformer_validator.py:10"}],[928,{"filePath":"audio_separator/separator/roformer/mel_band_roformer_validator.py","startLine":132,"endLine":141,"chunkText":"                    expected_value=f\"{min_bands}-{max_bands} bands\"\n                )\n                issues.append(issue)\n            elif num_bands > max_bands:\n                issue = ValidationIssue(\n                    severity=ValidationSeverity.WARNING,\n                    parameter_name=\"num_bands, sample_rate\",\n                    message=f\"num_bands ({num_bands}) may be too high for sample_rate ({sample_rate})\",\n                    suggested_fix=f\"Consider using {min_bands}-{max_bands} bands for {sample_rate}Hz\",\n                    current_value=f\"bands={num_bands}, sr={sample_rate}\",","chunkId":"audio_separator/separator/roformer/mel_band_roformer_validator.py:11"}],[929,{"filePath":"audio_separator/separator/roformer/mel_band_roformer_validator.py","startLine":139,"endLine":148,"chunkText":"                    message=f\"num_bands ({num_bands}) may be too high for sample_rate ({sample_rate})\",\n                    suggested_fix=f\"Consider using {min_bands}-{max_bands} bands for {sample_rate}Hz\",\n                    current_value=f\"bands={num_bands}, sr={sample_rate}\",\n                    expected_value=f\"{min_bands}-{max_bands} bands\"\n                )\n                issues.append(issue)\n        \n        # General heuristic: num_bands should be much smaller than nyquist frequency\n        nyquist = sample_rate // 2\n        if num_bands > nyquist // 100:  # Very rough heuristic","chunkId":"audio_separator/separator/roformer/mel_band_roformer_validator.py:12"}],[930,{"filePath":"audio_separator/separator/roformer/mel_band_roformer_validator.py","startLine":146,"endLine":154,"chunkText":"        # General heuristic: num_bands should be much smaller than nyquist frequency\n        nyquist = sample_rate // 2\n        if num_bands > nyquist // 100:  # Very rough heuristic\n            issue = ValidationIssue(\n                severity=ValidationSeverity.WARNING,\n                parameter_name=\"num_bands, sample_rate\",\n                message=f\"num_bands ({num_bands}) seems high relative to sample_rate ({sample_rate})\",\n                suggested_fix=\"Verify that this band count is appropriate for your mel-scale analysis\",\n                current_value=f\"bands={num_bands}, nyquist={nyquist}\",","chunkId":"audio_separator/separator/roformer/mel_band_roformer_validator.py:13"}],[931,{"filePath":"audio_separator/separator/roformer/mel_band_roformer_validator.py","startLine":152,"endLine":164,"chunkText":"                message=f\"num_bands ({num_bands}) seems high relative to sample_rate ({sample_rate})\",\n                suggested_fix=\"Verify that this band count is appropriate for your mel-scale analysis\",\n                current_value=f\"bands={num_bands}, nyquist={nyquist}\",\n                expected_value=\"num_bands << nyquist_frequency\"\n            )\n            issues.append(issue)\n        \n        return issues\n    \n    def validate_mel_scale_config(self, config: Dict[str, Any]) -> List[ValidationIssue]:\n        \"\"\"\n        Validate mel-scale specific configuration parameters.\n        ","chunkId":"audio_separator/separator/roformer/mel_band_roformer_validator.py:14"}],[932,{"filePath":"audio_separator/separator/roformer/mel_band_roformer_validator.py","startLine":162,"endLine":180,"chunkText":"        \"\"\"\n        Validate mel-scale specific configuration parameters.\n        \n        Args:\n            config: Model configuration dictionary\n            \n        Returns:\n            List of validation issues for mel-scale configuration\n        \"\"\"\n        issues = []\n        \n        # Check for mel-scale related parameters if present\n        mel_params = ['fmin', 'fmax', 'mel_scale']\n        \n        fmin = config.get('fmin', 0)\n        fmax = config.get('fmax')\n        sample_rate = config.get('sample_rate', 44100)\n        \n        if fmin is not None and not isinstance(fmin, (int, float)):","chunkId":"audio_separator/separator/roformer/mel_band_roformer_validator.py:15"}],[933,{"filePath":"audio_separator/separator/roformer/mel_band_roformer_validator.py","startLine":178,"endLine":191,"chunkText":"        sample_rate = config.get('sample_rate', 44100)\n        \n        if fmin is not None and not isinstance(fmin, (int, float)):\n            issue = ValidationIssue(\n                severity=ValidationSeverity.ERROR,\n                parameter_name=\"fmin\",\n                message=\"fmin must be a number\",\n                suggested_fix=\"Set fmin to a numeric value (typically 0-100 Hz)\",\n                current_value=fmin,\n                expected_value=\"number\"\n            )\n            issues.append(issue)\n        elif isinstance(fmin, (int, float)) and fmin < 0:\n            issue = ValidationIssue(","chunkId":"audio_separator/separator/roformer/mel_band_roformer_validator.py:16"}],[934,{"filePath":"audio_separator/separator/roformer/mel_band_roformer_validator.py","startLine":189,"endLine":203,"chunkText":"            issues.append(issue)\n        elif isinstance(fmin, (int, float)) and fmin < 0:\n            issue = ValidationIssue(\n                severity=ValidationSeverity.ERROR,\n                parameter_name=\"fmin\",\n                message=\"fmin must be non-negative\",\n                suggested_fix=\"Set fmin to 0 or a positive frequency value\",\n                current_value=fmin,\n                expected_value=\">= 0\"\n            )\n            issues.append(issue)\n        \n        if fmax is not None:\n            if not isinstance(fmax, (int, float)):\n                issue = ValidationIssue(","chunkId":"audio_separator/separator/roformer/mel_band_roformer_validator.py:17"}],[935,{"filePath":"audio_separator/separator/roformer/mel_band_roformer_validator.py","startLine":201,"endLine":213,"chunkText":"        if fmax is not None:\n            if not isinstance(fmax, (int, float)):\n                issue = ValidationIssue(\n                    severity=ValidationSeverity.ERROR,\n                    parameter_name=\"fmax\",\n                    message=\"fmax must be a number\",\n                    suggested_fix=\"Set fmax to a numeric value or None for automatic setting\",\n                    current_value=fmax,\n                    expected_value=\"number or None\"\n                )\n                issues.append(issue)\n            elif isinstance(fmax, (int, float)):\n                if fmax <= 0:","chunkId":"audio_separator/separator/roformer/mel_band_roformer_validator.py:18"}],[936,{"filePath":"audio_separator/separator/roformer/mel_band_roformer_validator.py","startLine":211,"endLine":223,"chunkText":"                issues.append(issue)\n            elif isinstance(fmax, (int, float)):\n                if fmax <= 0:\n                    issue = ValidationIssue(\n                        severity=ValidationSeverity.ERROR,\n                        parameter_name=\"fmax\",\n                        message=\"fmax must be positive\",\n                        suggested_fix=\"Set fmax to a positive frequency value\",\n                        current_value=fmax,\n                        expected_value=\"> 0\"\n                    )\n                    issues.append(issue)\n                elif fmax > sample_rate // 2:","chunkId":"audio_separator/separator/roformer/mel_band_roformer_validator.py:19"}],[937,{"filePath":"audio_separator/separator/roformer/mel_band_roformer_validator.py","startLine":221,"endLine":231,"chunkText":"                    )\n                    issues.append(issue)\n                elif fmax > sample_rate // 2:\n                    issue = ValidationIssue(\n                        severity=ValidationSeverity.WARNING,\n                        parameter_name=\"fmax, sample_rate\",\n                        message=f\"fmax ({fmax}) exceeds Nyquist frequency ({sample_rate//2})\",\n                        suggested_fix=f\"Set fmax to {sample_rate//2} or lower\",\n                        current_value=fmax,\n                        expected_value=f\"<= {sample_rate//2}\"\n                    )","chunkId":"audio_separator/separator/roformer/mel_band_roformer_validator.py:20"}],[938,{"filePath":"audio_separator/separator/roformer/mel_band_roformer_validator.py","startLine":229,"endLine":238,"chunkText":"                        current_value=fmax,\n                        expected_value=f\"<= {sample_rate//2}\"\n                    )\n                    issues.append(issue)\n                elif isinstance(fmin, (int, float)) and fmax <= fmin:\n                    issue = ValidationIssue(\n                        severity=ValidationSeverity.ERROR,\n                        parameter_name=\"fmin, fmax\",\n                        message=f\"fmax ({fmax}) must be greater than fmin ({fmin})\",\n                        suggested_fix=\"Set fmax to a value higher than fmin\",","chunkId":"audio_separator/separator/roformer/mel_band_roformer_validator.py:21"}],[939,{"filePath":"audio_separator/separator/roformer/mel_band_roformer_validator.py","startLine":236,"endLine":247,"chunkText":"                        parameter_name=\"fmin, fmax\",\n                        message=f\"fmax ({fmax}) must be greater than fmin ({fmin})\",\n                        suggested_fix=\"Set fmax to a value higher than fmin\",\n                        current_value=f\"fmin={fmin}, fmax={fmax}\",\n                        expected_value=\"fmax > fmin\"\n                    )\n                    issues.append(issue)\n        \n        return issues\n    \n    def validate_all(self, config: Dict[str, Any], model_type: str = \"mel_band_roformer\") -> List[ValidationIssue]:\n        \"\"\"","chunkId":"audio_separator/separator/roformer/mel_band_roformer_validator.py:22"}],[940,{"filePath":"audio_separator/separator/roformer/mel_band_roformer_validator.py","startLine":245,"endLine":260,"chunkText":"    \n    def validate_all(self, config: Dict[str, Any], model_type: str = \"mel_band_roformer\") -> List[ValidationIssue]:\n        \"\"\"\n        Run all MelBandRoformer-specific validation checks.\n        \n        Args:\n            config: Model configuration dictionary\n            model_type: Type of model (should be \"mel_band_roformer\")\n            \n        Returns:\n            List of all validation issues found\n        \"\"\"\n        # Start with base validation\n        all_issues = super().validate_all(config, model_type)\n        \n        # Add MelBandRoformer-specific validation","chunkId":"audio_separator/separator/roformer/mel_band_roformer_validator.py:23"}],[941,{"filePath":"audio_separator/separator/roformer/mel_band_roformer_validator.py","startLine":258,"endLine":273,"chunkText":"        all_issues = super().validate_all(config, model_type)\n        \n        # Add MelBandRoformer-specific validation\n        all_issues.extend(self.validate_num_bands(config))\n        all_issues.extend(self.validate_sample_rate_compatibility(config))\n        all_issues.extend(self.validate_mel_scale_config(config))\n        \n        return all_issues\n    \n    def get_parameter_defaults(self, model_type: str = \"mel_band_roformer\") -> Dict[str, Any]:\n        \"\"\"\n        Get MelBandRoformer-specific parameter defaults.\n        \n        Args:\n            model_type: Type of model\n            ","chunkId":"audio_separator/separator/roformer/mel_band_roformer_validator.py:24"}],[942,{"filePath":"audio_separator/separator/roformer/mel_band_roformer_validator.py","startLine":271,"endLine":288,"chunkText":"        Args:\n            model_type: Type of model\n            \n        Returns:\n            Dictionary of parameter names to default values\n        \"\"\"\n        defaults = super().get_parameter_defaults(model_type)\n        \n        # Override with MelBandRoformer-specific defaults\n        defaults.update({\n            'num_bands': self.RECOMMENDED_NUM_BANDS,\n            'fmin': 0,\n            'fmax': None,  # Will be set to sample_rate // 2 automatically\n            'mel_scale': 'htk',  # or 'slaney'\n        })\n        \n        return defaults\n","chunkId":"audio_separator/separator/roformer/mel_band_roformer_validator.py:25"}],[943,{"filePath":"audio_separator/separator/roformer/model_configuration.py","startLine":0,"endLine":16,"chunkText":"\"\"\"\nModel configuration dataclass for Roformer models.\nSupports both old and new parameter sets with backward compatibility.\n\"\"\"\n\nfrom dataclasses import dataclass, field\nfrom typing import Optional, Tuple, Any, Dict\nfrom enum import Enum\n\n\nclass RoformerType(Enum):\n    \"\"\"Supported Roformer model types.\"\"\"\n    BS_ROFORMER = \"bs_roformer\"\n    MEL_BAND_ROFORMER = \"mel_band_roformer\"\n\n\n@dataclass(frozen=True, unsafe_hash=True)","chunkId":"audio_separator/separator/roformer/model_configuration.py:0"}],[944,{"filePath":"audio_separator/separator/roformer/model_configuration.py","startLine":17,"endLine":37,"chunkText":"class ModelConfiguration:\n    \"\"\"\n    Model configuration parameters for Roformer models.\n    \n    This class supports both old and new parameter sets to maintain\n    backward compatibility while enabling new features.\n    \"\"\"\n    \n    # Required parameters (must be provided)\n    dim: int\n    depth: int\n    \n    # Common optional parameters (with sensible defaults)\n    stereo: bool = False\n    num_stems: int = 1\n    time_transformer_depth: int = 2\n    freq_transformer_depth: int = 2\n    dim_head: int = 64\n    heads: int = 8\n    attn_dropout: float = 0.0\n    ff_dropout: float = 0.0","chunkId":"audio_separator/separator/roformer/model_configuration.py:1"}],[945,{"filePath":"audio_separator/separator/roformer/model_configuration.py","startLine":35,"endLine":52,"chunkText":"    heads: int = 8\n    attn_dropout: float = 0.0\n    ff_dropout: float = 0.0\n    flash_attn: bool = True\n    \n    # New parameters (with defaults for backward compatibility)\n    mlp_expansion_factor: int = 4\n    sage_attention: bool = False\n    zero_dc: bool = True\n    use_torch_checkpoint: bool = False\n    skip_connection: bool = False\n    \n    # Normalization (may be None in some configs)\n    norm: Optional[str] = None\n    \n    # Model-specific parameters (set by subclasses)\n    freqs_per_bands: Optional[Tuple[int, ...]] = None  # BSRoformer\n    num_bands: Optional[int] = None  # MelBandRoformer","chunkId":"audio_separator/separator/roformer/model_configuration.py:2"}],[946,{"filePath":"audio_separator/separator/roformer/model_configuration.py","startLine":50,"endLine":63,"chunkText":"    # Model-specific parameters (set by subclasses)\n    freqs_per_bands: Optional[Tuple[int, ...]] = None  # BSRoformer\n    num_bands: Optional[int] = None  # MelBandRoformer\n    sample_rate: int = 44100  # Default sample rate\n    \n    # Additional configuration data (stored as tuple for hashability)\n    extra_config: Tuple[Tuple[str, Any], ...] = field(default_factory=tuple)\n    \n    def __post_init__(self):\n        \"\"\"Validate configuration after initialization.\"\"\"\n        self._validate_basic_parameters()\n        self._validate_parameter_ranges()\n    \n    def _validate_basic_parameters(self):","chunkId":"audio_separator/separator/roformer/model_configuration.py:3"}],[947,{"filePath":"audio_separator/separator/roformer/model_configuration.py","startLine":61,"endLine":71,"chunkText":"        self._validate_parameter_ranges()\n    \n    def _validate_basic_parameters(self):\n        \"\"\"Validate basic parameter types and requirements.\"\"\"\n        if not isinstance(self.dim, int) or self.dim <= 0:\n            raise ValueError(f\"dim must be a positive integer, got {self.dim}\")\n        \n        if not isinstance(self.depth, int) or self.depth <= 0:\n            raise ValueError(f\"depth must be a positive integer, got {self.depth}\")\n        \n        if not isinstance(self.num_stems, int) or self.num_stems <= 0:","chunkId":"audio_separator/separator/roformer/model_configuration.py:4"}],[948,{"filePath":"audio_separator/separator/roformer/model_configuration.py","startLine":69,"endLine":78,"chunkText":"            raise ValueError(f\"depth must be a positive integer, got {self.depth}\")\n        \n        if not isinstance(self.num_stems, int) or self.num_stems <= 0:\n            raise ValueError(f\"num_stems must be a positive integer, got {self.num_stems}\")\n        \n        if not isinstance(self.mlp_expansion_factor, int) or self.mlp_expansion_factor <= 0:\n            raise ValueError(f\"mlp_expansion_factor must be a positive integer, got {self.mlp_expansion_factor}\")\n    \n    def _validate_parameter_ranges(self):\n        \"\"\"Validate parameter value ranges.\"\"\"","chunkId":"audio_separator/separator/roformer/model_configuration.py:5"}],[949,{"filePath":"audio_separator/separator/roformer/model_configuration.py","startLine":76,"endLine":88,"chunkText":"    \n    def _validate_parameter_ranges(self):\n        \"\"\"Validate parameter value ranges.\"\"\"\n        if not (0.0 <= self.attn_dropout <= 1.0):\n            raise ValueError(f\"attn_dropout must be between 0.0 and 1.0, got {self.attn_dropout}\")\n        \n        if not (0.0 <= self.ff_dropout <= 1.0):\n            raise ValueError(f\"ff_dropout must be between 0.0 and 1.0, got {self.ff_dropout}\")\n        \n        if self.dim_head <= 0:\n            raise ValueError(f\"dim_head must be positive, got {self.dim_head}\")\n        \n        if self.heads <= 0:","chunkId":"audio_separator/separator/roformer/model_configuration.py:6"}],[950,{"filePath":"audio_separator/separator/roformer/model_configuration.py","startLine":86,"endLine":99,"chunkText":"            raise ValueError(f\"dim_head must be positive, got {self.dim_head}\")\n        \n        if self.heads <= 0:\n            raise ValueError(f\"heads must be positive, got {self.heads}\")\n        \n        if self.sample_rate <= 0:\n            raise ValueError(f\"sample_rate must be positive, got {self.sample_rate}\")\n    \n    def to_dict(self) -> Dict[str, Any]:\n        \"\"\"Convert configuration to dictionary.\"\"\"\n        result = {}\n        for field_info in self.__dataclass_fields__.values():\n            value = getattr(self, field_info.name)\n            if value is not None:","chunkId":"audio_separator/separator/roformer/model_configuration.py:7"}],[951,{"filePath":"audio_separator/separator/roformer/model_configuration.py","startLine":97,"endLine":112,"chunkText":"        for field_info in self.__dataclass_fields__.values():\n            value = getattr(self, field_info.name)\n            if value is not None:\n                result[field_info.name] = value\n        return result\n    \n    @classmethod\n    def from_dict(cls, config_dict: Dict[str, Any]) -> 'ModelConfiguration':\n        \"\"\"Create configuration from dictionary.\"\"\"\n        # Extract known parameters\n        known_params = {}\n        extra_params = {}\n        \n        for key, value in config_dict.items():\n            if key in cls.__dataclass_fields__:\n                known_params[key] = value","chunkId":"audio_separator/separator/roformer/model_configuration.py:8"}],[952,{"filePath":"audio_separator/separator/roformer/model_configuration.py","startLine":110,"endLine":125,"chunkText":"        for key, value in config_dict.items():\n            if key in cls.__dataclass_fields__:\n                known_params[key] = value\n            else:\n                extra_params[key] = value\n        \n        # Set extra_config as tuple if there are unknown parameters\n        if extra_params:\n            known_params['extra_config'] = tuple(extra_params.items())\n        \n        return cls(**known_params)\n    \n    def get_transformer_kwargs(self) -> Dict[str, Any]:\n        \"\"\"Get parameters to pass to transformer initialization.\"\"\"\n        return {\n            'dim': self.dim,","chunkId":"audio_separator/separator/roformer/model_configuration.py:9"}],[953,{"filePath":"audio_separator/separator/roformer/model_configuration.py","startLine":123,"endLine":138,"chunkText":"        \"\"\"Get parameters to pass to transformer initialization.\"\"\"\n        return {\n            'dim': self.dim,\n            'depth': self.depth,\n            'heads': self.heads,\n            'dim_head': self.dim_head,\n            'attn_dropout': self.attn_dropout,\n            'ff_dropout': self.ff_dropout,\n            'flash_attn': self.flash_attn,\n            'sage_attention': self.sage_attention,  # New parameter\n            'zero_dc': self.zero_dc,  # New parameter\n        }\n    \n    def has_new_parameters(self) -> bool:\n        \"\"\"Check if configuration uses any new parameters.\"\"\"\n        return (","chunkId":"audio_separator/separator/roformer/model_configuration.py:10"}],[954,{"filePath":"audio_separator/separator/roformer/model_configuration.py","startLine":136,"endLine":149,"chunkText":"    def has_new_parameters(self) -> bool:\n        \"\"\"Check if configuration uses any new parameters.\"\"\"\n        return (\n            self.mlp_expansion_factor != 4 or\n            self.sage_attention is True or\n            self.zero_dc is not True or\n            self.use_torch_checkpoint is True or\n            self.skip_connection is True\n        )\n    \n    def get_parameter_summary(self) -> str:\n        \"\"\"Get a summary string of key parameters.\"\"\"\n        return (\n            f\"ModelConfiguration(dim={self.dim}, depth={self.depth}, \"","chunkId":"audio_separator/separator/roformer/model_configuration.py:11"}],[955,{"filePath":"audio_separator/separator/roformer/model_configuration.py","startLine":147,"endLine":157,"chunkText":"        \"\"\"Get a summary string of key parameters.\"\"\"\n        return (\n            f\"ModelConfiguration(dim={self.dim}, depth={self.depth}, \"\n            f\"stems={self.num_stems}, mlp_factor={self.mlp_expansion_factor}, \"\n            f\"sage_attn={self.sage_attention}, new_params={self.has_new_parameters()})\"\n        )\n    \n    def __repr__(self) -> str:\n        \"\"\"String representation of the configuration.\"\"\"\n        return self.get_parameter_summary()\n","chunkId":"audio_separator/separator/roformer/model_configuration.py:12"}],[956,{"filePath":"audio_separator/separator/roformer/model_loading_result.py","startLine":0,"endLine":17,"chunkText":"\"\"\"\nModel loading result dataclass.\nContains the result of model loading operations with success/failure information.\n\"\"\"\n\nfrom dataclasses import dataclass, field\nfrom typing import Optional, List, Any, Dict\nfrom enum import Enum\n\n\nclass ImplementationVersion(Enum):\n    \"\"\"Available implementation versions.\"\"\"\n    OLD = \"old\"\n    NEW = \"new\"\n    FALLBACK = \"fallback\"\n\n\n@dataclass","chunkId":"audio_separator/separator/roformer/model_loading_result.py:0"}],[957,{"filePath":"audio_separator/separator/roformer/model_loading_result.py","startLine":18,"endLine":33,"chunkText":"class ModelLoadingResult:\n    \"\"\"\n    Result of a model loading operation.\n    \n    Contains information about whether the loading was successful,\n    the loaded model (if successful), error details (if failed),\n    and metadata about which implementation was used.\n    \"\"\"\n    \n    success: bool\n    model: Optional[Any] = None  # Actual model instance (torch.nn.Module)\n    error_message: Optional[str] = None\n    implementation_used: ImplementationVersion = ImplementationVersion.NEW\n    warnings: List[str] = field(default_factory=list)\n    \n    # Additional metadata","chunkId":"audio_separator/separator/roformer/model_loading_result.py:1"}],[958,{"filePath":"audio_separator/separator/roformer/model_loading_result.py","startLine":31,"endLine":43,"chunkText":"    warnings: List[str] = field(default_factory=list)\n    \n    # Additional metadata\n    loading_time_seconds: Optional[float] = None\n    model_info: Dict[str, Any] = field(default_factory=dict)\n    config_used: Optional[Any] = None  # The configuration that was actually used\n    \n    def __post_init__(self):\n        \"\"\"Validate result after initialization.\"\"\"\n        if self.success and self.model is None:\n            raise ValueError(\"success=True but model is None\")\n        \n        if not self.success and self.error_message is None:","chunkId":"audio_separator/separator/roformer/model_loading_result.py:2"}],[959,{"filePath":"audio_separator/separator/roformer/model_loading_result.py","startLine":42,"endLine":58,"chunkText":"        \n        if not self.success and self.error_message is None:\n            raise ValueError(\"success=False but error_message is None\")\n        \n        if self.warnings is None:\n            self.warnings = []\n    \n    def add_warning(self, warning: str):\n        \"\"\"Add a warning message.\"\"\"\n        if warning not in self.warnings:\n            self.warnings.append(warning)\n    \n    def add_model_info(self, key: str, value: Any):\n        \"\"\"Add model metadata information.\"\"\"\n        self.model_info[key] = value\n    \n    def get_summary(self) -> str:","chunkId":"audio_separator/separator/roformer/model_loading_result.py:3"}],[960,{"filePath":"audio_separator/separator/roformer/model_loading_result.py","startLine":56,"endLine":71,"chunkText":"        self.model_info[key] = value\n    \n    def get_summary(self) -> str:\n        \"\"\"Get a summary string of the loading result.\"\"\"\n        if self.success:\n            summary = f\"SUCCESS: Model loaded using {self.implementation_used.value} implementation\"\n            if self.loading_time_seconds:\n                summary += f\" in {self.loading_time_seconds:.2f}s\"\n            if self.warnings:\n                summary += f\" with {len(self.warnings)} warnings\"\n        else:\n            summary = f\"FAILED: {self.error_message}\"\n        \n        return summary\n    \n    def is_fallback_used(self) -> bool:","chunkId":"audio_separator/separator/roformer/model_loading_result.py:4"}],[961,{"filePath":"audio_separator/separator/roformer/model_loading_result.py","startLine":69,"endLine":83,"chunkText":"        return summary\n    \n    def is_fallback_used(self) -> bool:\n        \"\"\"Check if fallback implementation was used.\"\"\"\n        return self.implementation_used == ImplementationVersion.FALLBACK\n    \n    def is_new_implementation_used(self) -> bool:\n        \"\"\"Check if new implementation was used.\"\"\"\n        return self.implementation_used == ImplementationVersion.NEW\n    \n    def has_warnings(self) -> bool:\n        \"\"\"Check if there are any warnings.\"\"\"\n        return len(self.warnings) > 0\n    \n    def get_model_parameters_count(self) -> Optional[int]:","chunkId":"audio_separator/separator/roformer/model_loading_result.py:5"}],[962,{"filePath":"audio_separator/separator/roformer/model_loading_result.py","startLine":81,"endLine":97,"chunkText":"        return len(self.warnings) > 0\n    \n    def get_model_parameters_count(self) -> Optional[int]:\n        \"\"\"Get the number of model parameters if available.\"\"\"\n        if self.model is None:\n            return None\n        \n        try:\n            # Try to count parameters for PyTorch models\n            if hasattr(self.model, 'parameters'):\n                return sum(p.numel() for p in self.model.parameters())\n        except Exception:\n            pass\n        \n        return self.model_info.get('parameter_count')\n    \n    def get_model_size_mb(self) -> Optional[float]:","chunkId":"audio_separator/separator/roformer/model_loading_result.py:6"}],[963,{"filePath":"audio_separator/separator/roformer/model_loading_result.py","startLine":95,"endLine":109,"chunkText":"        return self.model_info.get('parameter_count')\n    \n    def get_model_size_mb(self) -> Optional[float]:\n        \"\"\"Get the model size in MB if available.\"\"\"\n        param_count = self.get_model_parameters_count()\n        if param_count is not None:\n            # Assume float32 parameters (4 bytes each)\n            return (param_count * 4) / (1024 * 1024)\n        \n        return self.model_info.get('size_mb')\n    \n    def to_dict(self) -> Dict[str, Any]:\n        \"\"\"Convert result to dictionary for serialization.\"\"\"\n        return {\n            'success': self.success,","chunkId":"audio_separator/separator/roformer/model_loading_result.py:7"}],[964,{"filePath":"audio_separator/separator/roformer/model_loading_result.py","startLine":107,"endLine":121,"chunkText":"        \"\"\"Convert result to dictionary for serialization.\"\"\"\n        return {\n            'success': self.success,\n            'error_message': self.error_message,\n            'implementation_used': self.implementation_used.value,\n            'warnings': self.warnings,\n            'loading_time_seconds': self.loading_time_seconds,\n            'model_info': self.model_info,\n            'has_model': self.model is not None,\n            'parameter_count': self.get_model_parameters_count(),\n            'model_size_mb': self.get_model_size_mb(),\n        }\n    \n    @classmethod\n    def success_result(","chunkId":"audio_separator/separator/roformer/model_loading_result.py:8"}],[965,{"filePath":"audio_separator/separator/roformer/model_loading_result.py","startLine":119,"endLine":137,"chunkText":"    \n    @classmethod\n    def success_result(\n        cls, \n        model: Any, \n        implementation: ImplementationVersion = ImplementationVersion.NEW,\n        config: Optional[Any] = None,\n        loading_time: Optional[float] = None\n    ) -> 'ModelLoadingResult':\n        \"\"\"\n        Create a successful loading result.\n        \n        Args:\n            model: The loaded model instance\n            implementation: Which implementation was used\n            config: The configuration that was used\n            loading_time: Time taken to load the model\n            \n        Returns:","chunkId":"audio_separator/separator/roformer/model_loading_result.py:9"}],[966,{"filePath":"audio_separator/separator/roformer/model_loading_result.py","startLine":135,"endLine":155,"chunkText":"            loading_time: Time taken to load the model\n            \n        Returns:\n            ModelLoadingResult indicating success\n        \"\"\"\n        return cls(\n            success=True,\n            model=model,\n            implementation_used=implementation,\n            config_used=config,\n            loading_time_seconds=loading_time\n        )\n    \n    @classmethod\n    def failure_result(\n        cls, \n        error_message: str,\n        implementation: ImplementationVersion = ImplementationVersion.NEW,\n        warnings: Optional[List[str]] = None\n    ) -> 'ModelLoadingResult':\n        \"\"\"","chunkId":"audio_separator/separator/roformer/model_loading_result.py:10"}],[967,{"filePath":"audio_separator/separator/roformer/model_loading_result.py","startLine":153,"endLine":171,"chunkText":"        warnings: Optional[List[str]] = None\n    ) -> 'ModelLoadingResult':\n        \"\"\"\n        Create a failed loading result.\n        \n        Args:\n            error_message: Description of what went wrong\n            implementation: Which implementation was attempted\n            warnings: Any warnings that occurred before failure\n            \n        Returns:\n            ModelLoadingResult indicating failure\n        \"\"\"\n        return cls(\n            success=False,\n            error_message=error_message,\n            implementation_used=implementation,\n            warnings=warnings or []\n        )","chunkId":"audio_separator/separator/roformer/model_loading_result.py:11"}],[968,{"filePath":"audio_separator/separator/roformer/model_loading_result.py","startLine":169,"endLine":187,"chunkText":"            implementation_used=implementation,\n            warnings=warnings or []\n        )\n    \n    @classmethod\n    def fallback_success_result(\n        cls,\n        model: Any,\n        original_error: str,\n        config: Optional[Any] = None,\n        loading_time: Optional[float] = None\n    ) -> 'ModelLoadingResult':\n        \"\"\"\n        Create a successful fallback loading result.\n        \n        Args:\n            model: The loaded model instance\n            original_error: The error that caused fallback\n            config: The configuration that was used","chunkId":"audio_separator/separator/roformer/model_loading_result.py:12"}],[969,{"filePath":"audio_separator/separator/roformer/model_loading_result.py","startLine":185,"endLine":199,"chunkText":"            model: The loaded model instance\n            original_error: The error that caused fallback\n            config: The configuration that was used\n            loading_time: Time taken to load the model\n            \n        Returns:\n            ModelLoadingResult indicating fallback success\n        \"\"\"\n        result = cls(\n            success=True,\n            model=model,\n            implementation_used=ImplementationVersion.FALLBACK,\n            config_used=config,\n            loading_time_seconds=loading_time\n        )","chunkId":"audio_separator/separator/roformer/model_loading_result.py:13"}],[970,{"filePath":"audio_separator/separator/roformer/model_loading_result.py","startLine":197,"endLine":210,"chunkText":"            config_used=config,\n            loading_time_seconds=loading_time\n        )\n        result.add_warning(f\"Fell back to old implementation due to: {original_error}\")\n        return result\n    \n    def __str__(self) -> str:\n        \"\"\"String representation of the loading result.\"\"\"\n        return self.get_summary()\n    \n    def __repr__(self) -> str:\n        \"\"\"Detailed string representation of the loading result.\"\"\"\n        return f\"ModelLoadingResult(success={self.success}, impl={self.implementation_used.value}, warnings={len(self.warnings)})\"\n","chunkId":"audio_separator/separator/roformer/model_loading_result.py:14"}],[971,{"filePath":"audio_separator/separator/roformer/parameter_validation_error.py","startLine":0,"endLine":24,"chunkText":"\"\"\"\nParameter validation error exception.\nRaised when model parameters are invalid or incompatible.\n\"\"\"\n\nfrom typing import Any, Optional\n\n\nclass ParameterValidationError(Exception):\n    \"\"\"\n    Exception raised when model parameters are invalid.\n    \n    This exception provides detailed information about what parameter\n    was invalid, what was expected, and suggestions for fixing the issue.\n    \"\"\"\n    \n    def __init__(\n        self, \n        parameter_name: str, \n        expected_type: str, \n        actual_value: Any, \n        suggested_fix: str,\n        context: Optional[str] = None\n    ):\n        \"\"","chunkId":"audio_separator/separator/roformer/parameter_validation_error.py:0"}],[972,{"filePath":"audio_separator/separator/roformer/parameter_validation_error.py","startLine":22,"endLine":36,"chunkText":"        context: Optional[str] = None\n    ):\n        \"\"\"\n        Initialize parameter validation error.\n        \n        Args:\n            parameter_name: Name of the invalid parameter\n            expected_type: Expected type or description of valid values\n            actual_value: The actual value that was provided\n            suggested_fix: Suggestion for how to fix the issue\n            context: Additional context about where the error occurred\n        \"\"\"\n        self.parameter_name = parameter_name\n        self.expected_type = expected_type\n        self.actual_value = actual_value","chunkId":"audio_separator/separator/roformer/parameter_validation_error.py:1"}],[973,{"filePath":"audio_separator/separator/roformer/parameter_validation_error.py","startLine":34,"endLine":49,"chunkText":"        self.parameter_name = parameter_name\n        self.expected_type = expected_type\n        self.actual_value = actual_value\n        self.suggested_fix = suggested_fix\n        self.context = context\n        \n        # Create detailed error message\n        message = self._create_error_message()\n        super().__init__(message)\n    \n    def _create_error_message(self) -> str:\n        \"\"\"Create a detailed error message.\"\"\"\n        actual_type = type(self.actual_value).__name__\n        \n        message_parts = [\n            f\"Invalid parameter '{self.parameter_name}': \",","chunkId":"audio_separator/separator/roformer/parameter_validation_error.py:2"}],[974,{"filePath":"audio_separator/separator/roformer/parameter_validation_error.py","startLine":47,"endLine":63,"chunkText":"        \n        message_parts = [\n            f\"Invalid parameter '{self.parameter_name}': \",\n            f\"expected {self.expected_type}, got {actual_type} ({self.actual_value})\"\n        ]\n        \n        if self.context:\n            message_parts.append(f\" in {self.context}\")\n        \n        message_parts.append(f\". Suggestion: {self.suggested_fix}\")\n        \n        return \"\".join(message_parts)\n    \n    def get_error_details(self) -> dict:\n        \"\"\"Get error details as a dictionary.\"\"\"\n        return {\n            'parameter_name': self.parameter_name,","chunkId":"audio_separator/separator/roformer/parameter_validation_error.py:3"}],[975,{"filePath":"audio_separator/separator/roformer/parameter_validation_error.py","startLine":61,"endLine":74,"chunkText":"        \"\"\"Get error details as a dictionary.\"\"\"\n        return {\n            'parameter_name': self.parameter_name,\n            'expected_type': self.expected_type,\n            'actual_value': self.actual_value,\n            'actual_type': type(self.actual_value).__name__,\n            'suggested_fix': self.suggested_fix,\n            'context': self.context,\n            'error_message': str(self)\n        }\n    \n    @classmethod\n    def missing_parameter(cls, parameter_name: str, parameter_type: str, context: Optional[str] = None) -> 'ParameterValidationError':\n        \"\"\"","chunkId":"audio_separator/separator/roformer/parameter_validation_error.py:4"}],[976,{"filePath":"audio_separator/separator/roformer/parameter_validation_error.py","startLine":72,"endLine":86,"chunkText":"    @classmethod\n    def missing_parameter(cls, parameter_name: str, parameter_type: str, context: Optional[str] = None) -> 'ParameterValidationError':\n        \"\"\"\n        Create error for missing required parameter.\n        \n        Args:\n            parameter_name: Name of missing parameter\n            parameter_type: Expected type of the parameter\n            context: Context where parameter is missing\n            \n        Returns:\n            ParameterValidationError for missing parameter\n        \"\"\"\n        return cls(\n            parameter_name=parameter_name,","chunkId":"audio_separator/separator/roformer/parameter_validation_error.py:5"}],[977,{"filePath":"audio_separator/separator/roformer/parameter_validation_error.py","startLine":84,"endLine":99,"chunkText":"        \"\"\"\n        return cls(\n            parameter_name=parameter_name,\n            expected_type=parameter_type,\n            actual_value=None,\n            suggested_fix=f\"Add '{parameter_name}' parameter with {parameter_type} value\",\n            context=context\n        )\n    \n    @classmethod\n    def wrong_type(cls, parameter_name: str, expected_type: str, actual_value: Any, context: Optional[str] = None) -> 'ParameterValidationError':\n        \"\"\"\n        Create error for wrong parameter type.\n        \n        Args:\n            parameter_name: Name of parameter with wrong type","chunkId":"audio_separator/separator/roformer/parameter_validation_error.py:6"}],[978,{"filePath":"audio_separator/separator/roformer/parameter_validation_error.py","startLine":97,"endLine":115,"chunkText":"        \n        Args:\n            parameter_name: Name of parameter with wrong type\n            expected_type: Expected type\n            actual_value: Actual value provided\n            context: Context where error occurred\n            \n        Returns:\n            ParameterValidationError for type mismatch\n        \"\"\"\n        return cls(\n            parameter_name=parameter_name,\n            expected_type=expected_type,\n            actual_value=actual_value,\n            suggested_fix=f\"Change '{parameter_name}' to {expected_type}\",\n            context=context\n        )\n    \n    @classmethod","chunkId":"audio_separator/separator/roformer/parameter_validation_error.py:7"}],[979,{"filePath":"audio_separator/separator/roformer/parameter_validation_error.py","startLine":113,"endLine":129,"chunkText":"        )\n    \n    @classmethod\n    def out_of_range(cls, parameter_name: str, valid_range: str, actual_value: Any, context: Optional[str] = None) -> 'ParameterValidationError':\n        \"\"\"\n        Create error for parameter value out of valid range.\n        \n        Args:\n            parameter_name: Name of parameter out of range\n            valid_range: Description of valid range\n            actual_value: Actual value provided\n            context: Context where error occurred\n            \n        Returns:\n            ParameterValidationError for out of range value\n        \"\"\"\n        return cls(","chunkId":"audio_separator/separator/roformer/parameter_validation_error.py:8"}],[980,{"filePath":"audio_separator/separator/roformer/parameter_validation_error.py","startLine":127,"endLine":139,"chunkText":"            ParameterValidationError for out of range value\n        \"\"\"\n        return cls(\n            parameter_name=parameter_name,\n            expected_type=f\"value in range {valid_range}\",\n            actual_value=actual_value,\n            suggested_fix=f\"Set '{parameter_name}' to a value within {valid_range}\",\n            context=context\n        )\n    \n    @classmethod\n    def incompatible_parameters(cls, parameter_names: list, issue_description: str, suggested_fix: str, context: Optional[str] = None) -> 'ParameterValidationError':\n        \"\"\"","chunkId":"audio_separator/separator/roformer/parameter_validation_error.py:9"}],[981,{"filePath":"audio_separator/separator/roformer/parameter_validation_error.py","startLine":137,"endLine":148,"chunkText":"    @classmethod\n    def incompatible_parameters(cls, parameter_names: list, issue_description: str, suggested_fix: str, context: Optional[str] = None) -> 'ParameterValidationError':\n        \"\"\"\n        Create error for incompatible parameter combination.\n        \n        Args:\n            parameter_names: List of parameter names that are incompatible\n            issue_description: Description of the incompatibility\n            suggested_fix: How to fix the incompatibility\n            context: Context where error occurred\n            \n        Returns:","chunkId":"audio_separator/separator/roformer/parameter_validation_error.py:10"}],[982,{"filePath":"audio_separator/separator/roformer/parameter_validation_error.py","startLine":146,"endLine":160,"chunkText":"            context: Context where error occurred\n            \n        Returns:\n            ParameterValidationError for incompatible parameters\n        \"\"\"\n        parameter_list = \", \".join(parameter_names)\n        return cls(\n            parameter_name=parameter_list,\n            expected_type=\"compatible parameter combination\",\n            actual_value=issue_description,\n            suggested_fix=suggested_fix,\n            context=context\n        )\n    \n    @classmethod","chunkId":"audio_separator/separator/roformer/parameter_validation_error.py:11"}],[983,{"filePath":"audio_separator/separator/roformer/parameter_validation_error.py","startLine":158,"endLine":172,"chunkText":"        )\n    \n    @classmethod\n    def invalid_normalization(cls, norm_value: Any, supported_norms: list, context: Optional[str] = None) -> 'ParameterValidationError':\n        \"\"\"\n        Create error for invalid normalization configuration.\n        \n        Args:\n            norm_value: The invalid normalization value\n            supported_norms: List of supported normalization types\n            context: Context where error occurred\n            \n        Returns:\n            ParameterValidationError for invalid normalization\n        \"\"\"","chunkId":"audio_separator/separator/roformer/parameter_validation_error.py:12"}],[984,{"filePath":"audio_separator/separator/roformer/parameter_validation_error.py","startLine":170,"endLine":183,"chunkText":"        Returns:\n            ParameterValidationError for invalid normalization\n        \"\"\"\n        supported_list = \", \".join(f\"'{norm}'\" for norm in supported_norms)\n        return cls(\n            parameter_name=\"norm\",\n            expected_type=f\"one of: {supported_list}\",\n            actual_value=norm_value,\n            suggested_fix=f\"Use one of the supported normalization types: {supported_list}\",\n            context=context\n        )\n    \n    def __repr__(self) -> str:\n        \"\"\"Detailed string representation.\"\"\"","chunkId":"audio_separator/separator/roformer/parameter_validation_error.py:13"}],[985,{"filePath":"audio_separator/separator/roformer/parameter_validation_error.py","startLine":181,"endLine":185,"chunkText":"    \n    def __repr__(self) -> str:\n        \"\"\"Detailed string representation.\"\"\"\n        return f\"ParameterValidationError(parameter='{self.parameter_name}', expected='{self.expected_type}', actual={self.actual_value})\"\n","chunkId":"audio_separator/separator/roformer/parameter_validation_error.py:14"}],[986,{"filePath":"audio_separator/separator/roformer/parameter_validator.py","startLine":0,"endLine":16,"chunkText":"\"\"\"\nParameter validator implementation.\nValidates Roformer model parameters according to interface contracts.\n\"\"\"\n\nfrom typing import Dict, Any, List, Optional, Union, Tuple\nimport sys\nimport os\n\n# Add contracts to path for interface imports (optional)\ntry:\n    # Find project root dynamically\n    current_dir = os.path.dirname(os.path.abspath(__file__))\n    project_root = current_dir\n    # Go up until we find the project root (contains specs/ directory)\n    while project_root and not os.path.exists(os.path.join(project_root, 'specs')):\n        parent = os.path.dirname(project_root)","chunkId":"audio_separator/separator/roformer/parameter_validator.py:0"}],[987,{"filePath":"audio_separator/separator/roformer/parameter_validator.py","startLine":14,"endLine":26,"chunkText":"    # Go up until we find the project root (contains specs/ directory)\n    while project_root and not os.path.exists(os.path.join(project_root, 'specs')):\n        parent = os.path.dirname(project_root)\n        if parent == project_root:  # Reached filesystem root\n            break\n        project_root = parent\n    \n    contracts_path = os.path.join(project_root, 'specs', '001-update-roformer-implementation', 'contracts')\n    if os.path.exists(contracts_path):\n        sys.path.append(contracts_path)\n    from parameter_validator_interface import (\n        ParameterValidatorInterface,\n        ValidationIssue","chunkId":"audio_separator/separator/roformer/parameter_validator.py:1"}],[988,{"filePath":"audio_separator/separator/roformer/parameter_validator.py","startLine":24,"endLine":40,"chunkText":"    from parameter_validator_interface import (\n        ParameterValidatorInterface,\n        ValidationIssue,\n        ValidationSeverity\n    )\n    _has_interface = True\nexcept ImportError:\n    # Create dummy interfaces for when contracts are not available\n    from enum import Enum\n    from dataclasses import dataclass\n    \n    class ValidationSeverity(Enum):\n        ERROR = \"error\"\n        WARNING = \"warning\"\n        INFO = \"info\"\n    \n    @dataclass","chunkId":"audio_separator/separator/roformer/parameter_validator.py:2"}],[989,{"filePath":"audio_separator/separator/roformer/parameter_validator.py","startLine":41,"endLine":48,"chunkText":"    class ValidationIssue:\n        severity: ValidationSeverity\n        parameter_name: str\n        message: str\n        suggested_fix: str\n        current_value: any = None\n        expected_value: any = None\n    ","chunkId":"audio_separator/separator/roformer/parameter_validator.py:3"}],[990,{"filePath":"audio_separator/separator/roformer/parameter_validator.py","startLine":49,"endLine":55,"chunkText":"    class ParameterValidatorInterface:\n        pass\n    \n    _has_interface = False\nfrom .parameter_validation_error import ParameterValidationError\n\n","chunkId":"audio_separator/separator/roformer/parameter_validator.py:4"}],[991,{"filePath":"audio_separator/separator/roformer/parameter_validator.py","startLine":56,"endLine":74,"chunkText":"class ParameterValidator(ParameterValidatorInterface):\n    \"\"\"\n    Implementation of parameter validation for Roformer models.\n    \n    Validates model parameters according to the interface contract,\n    providing detailed error messages and suggestions for fixes.\n    \"\"\"\n    \n    # Define valid parameter types and ranges\n    PARAMETER_TYPES = {\n        'dim': int,\n        'depth': int,\n        'stereo': bool,\n        'num_stems': int,\n        'time_transformer_depth': int,\n        'freq_transformer_depth': int,\n        'dim_head': int,\n        'heads': int,\n        'attn_dropout': float,","chunkId":"audio_separator/separator/roformer/parameter_validator.py:5"}],[992,{"filePath":"audio_separator/separator/roformer/parameter_validator.py","startLine":72,"endLine":92,"chunkText":"        'dim_head': int,\n        'heads': int,\n        'attn_dropout': float,\n        'ff_dropout': float,\n        'flash_attn': bool,\n        'mlp_expansion_factor': int,\n        'sage_attention': bool,\n        'zero_dc': bool,\n        'use_torch_checkpoint': bool,\n        'skip_connection': bool,\n        'sample_rate': int,\n        'freqs_per_bands': (tuple, list),\n        'num_bands': int,\n        'mask_estimator_depth': int,\n    }\n    \n    PARAMETER_RANGES = {\n        'dim': (1, 8192),\n        'depth': (1, 64),\n        'num_stems': (1, 16),\n        'time_transformer_depth': (1, 32),","chunkId":"audio_separator/separator/roformer/parameter_validator.py:6"}],[993,{"filePath":"audio_separator/separator/roformer/parameter_validator.py","startLine":90,"endLine":108,"chunkText":"        'depth': (1, 64),\n        'num_stems': (1, 16),\n        'time_transformer_depth': (1, 32),\n        'freq_transformer_depth': (1, 32),\n        'dim_head': (1, 1024),\n        'heads': (1, 64),\n        'attn_dropout': (0.0, 1.0),\n        'ff_dropout': (0.0, 1.0),\n        'mlp_expansion_factor': (1, 16),\n        'sample_rate': (8000, 192000),\n        'num_bands': (8, 512),\n        'mask_estimator_depth': (1, 8),\n    }\n    \n    REQUIRED_PARAMETERS = {\n        'bs_roformer': ['dim', 'depth', 'freqs_per_bands'],\n        'mel_band_roformer': ['dim', 'depth', 'num_bands'],\n    }\n    ","chunkId":"audio_separator/separator/roformer/parameter_validator.py:7"}],[994,{"filePath":"audio_separator/separator/roformer/parameter_validator.py","startLine":106,"endLine":122,"chunkText":"        'mel_band_roformer': ['dim', 'depth', 'num_bands'],\n    }\n    \n    SUPPORTED_NORMALIZATION_TYPES = [\n        'layer_norm', 'batch_norm', 'rms_norm', 'group_norm', \n        'instance_norm', None, 'none'\n    ]\n    \n    def validate_required_parameters(self, config: Dict[str, Any], model_type: str) -> List[ValidationIssue]:\n        \"\"\"\n        Validate that all required parameters are present.\n        \n        Args:\n            config: Model configuration dictionary\n            model_type: Type of model (\"bs_roformer\" or \"mel_band_roformer\")\n            \n        Returns:","chunkId":"audio_separator/separator/roformer/parameter_validator.py:8"}],[995,{"filePath":"audio_separator/separator/roformer/parameter_validator.py","startLine":120,"endLine":133,"chunkText":"            model_type: Type of model (\"bs_roformer\" or \"mel_band_roformer\")\n            \n        Returns:\n            List of validation issues for missing required parameters\n        \"\"\"\n        issues = []\n        \n        required_params = self.REQUIRED_PARAMETERS.get(model_type, [])\n        \n        for param_name in required_params:\n            if param_name not in config:\n                issue = ValidationIssue(\n                    severity=ValidationSeverity.ERROR,\n                    parameter_name=param_name,","chunkId":"audio_separator/separator/roformer/parameter_validator.py:9"}],[996,{"filePath":"audio_separator/separator/roformer/parameter_validator.py","startLine":131,"endLine":142,"chunkText":"                issue = ValidationIssue(\n                    severity=ValidationSeverity.ERROR,\n                    parameter_name=param_name,\n                    message=f\"Required parameter '{param_name}' is missing for {model_type}\",\n                    suggested_fix=f\"Add '{param_name}' parameter with appropriate {self._get_expected_type_description(param_name)} value\",\n                    current_value=None,\n                    expected_value=self._get_expected_type_description(param_name)\n                )\n                issues.append(issue)\n        \n        return issues\n    ","chunkId":"audio_separator/separator/roformer/parameter_validator.py:10"}],[997,{"filePath":"audio_separator/separator/roformer/parameter_validator.py","startLine":140,"endLine":158,"chunkText":"        \n        return issues\n    \n    def validate_parameter_types(self, config: Dict[str, Any]) -> List[ValidationIssue]:\n        \"\"\"\n        Validate parameter types match expected types.\n        \n        Args:\n            config: Model configuration dictionary\n            \n        Returns:\n            List of validation issues for type mismatches\n        \"\"\"\n        issues = []\n        \n        for param_name, value in config.items():\n            if param_name in self.PARAMETER_TYPES:\n                expected_type = self.PARAMETER_TYPES[param_name]\n                ","chunkId":"audio_separator/separator/roformer/parameter_validator.py:11"}],[998,{"filePath":"audio_separator/separator/roformer/parameter_validator.py","startLine":156,"endLine":165,"chunkText":"            if param_name in self.PARAMETER_TYPES:\n                expected_type = self.PARAMETER_TYPES[param_name]\n                \n                if not self._is_correct_type(value, expected_type):\n                    issue = ValidationIssue(\n                        severity=ValidationSeverity.ERROR,\n                        parameter_name=param_name,\n                        message=f\"Parameter '{param_name}' has incorrect type\",\n                        suggested_fix=f\"Change '{param_name}' to {self._get_type_name(expected_type)}\",\n                        current_value=value,","chunkId":"audio_separator/separator/roformer/parameter_validator.py:12"}],[999,{"filePath":"audio_separator/separator/roformer/parameter_validator.py","startLine":163,"endLine":176,"chunkText":"                        message=f\"Parameter '{param_name}' has incorrect type\",\n                        suggested_fix=f\"Change '{param_name}' to {self._get_type_name(expected_type)}\",\n                        current_value=value,\n                        expected_value=self._get_type_name(expected_type)\n                    )\n                    issues.append(issue)\n        \n        return issues\n    \n    def validate_parameter_ranges(self, config: Dict[str, Any]) -> List[ValidationIssue]:\n        \"\"\"\n        Validate parameter values are within acceptable ranges.\n        \n        Args:","chunkId":"audio_separator/separator/roformer/parameter_validator.py:13"}],[1000,{"filePath":"audio_separator/separator/roformer/parameter_validator.py","startLine":174,"endLine":189,"chunkText":"        Validate parameter values are within acceptable ranges.\n        \n        Args:\n            config: Model configuration dictionary\n            \n        Returns:\n            List of validation issues for out-of-range values\n        \"\"\"\n        issues = []\n        \n        for param_name, value in config.items():\n            if param_name in self.PARAMETER_RANGES:\n                min_val, max_val = self.PARAMETER_RANGES[param_name]\n                \n                if isinstance(value, (int, float)) and not (min_val <= value <= max_val):\n                    issue = ValidationIssue(","chunkId":"audio_separator/separator/roformer/parameter_validator.py:14"}],[1001,{"filePath":"audio_separator/separator/roformer/parameter_validator.py","startLine":187,"endLine":195,"chunkText":"                \n                if isinstance(value, (int, float)) and not (min_val <= value <= max_val):\n                    issue = ValidationIssue(\n                        severity=ValidationSeverity.ERROR,\n                        parameter_name=param_name,\n                        message=f\"Parameter '{param_name}' value {value} is outside valid range [{min_val}, {max_val}]\",\n                        suggested_fix=f\"Set '{param_name}' to a value between {min_val} and {max_val}\",\n                        current_value=value,\n                        expected_value=f\"{min_val} <= value <= {max_val}\"","chunkId":"audio_separator/separator/roformer/parameter_validator.py:15"}],[1002,{"filePath":"audio_separator/separator/roformer/parameter_validator.py","startLine":193,"endLine":208,"chunkText":"                        suggested_fix=f\"Set '{param_name}' to a value between {min_val} and {max_val}\",\n                        current_value=value,\n                        expected_value=f\"{min_val} <= value <= {max_val}\"\n                    )\n                    issues.append(issue)\n        \n        return issues\n    \n    def validate_parameter_compatibility(self, config: Dict[str, Any]) -> List[ValidationIssue]:\n        \"\"\"\n        Validate that parameter combinations are compatible.\n        \n        Args:\n            config: Model configuration dictionary\n            \n        Returns:","chunkId":"audio_separator/separator/roformer/parameter_validator.py:16"}],[1003,{"filePath":"audio_separator/separator/roformer/parameter_validator.py","startLine":206,"endLine":218,"chunkText":"            config: Model configuration dictionary\n            \n        Returns:\n            List of validation issues for incompatible parameter combinations\n        \"\"\"\n        issues = []\n        \n        # Check sage_attention and flash_attn compatibility\n        if config.get('sage_attention', False) and config.get('flash_attn', False):\n            issue = ValidationIssue(\n                severity=ValidationSeverity.WARNING,\n                parameter_name=\"sage_attention, flash_attn\",\n                message=\"Using both sage_attention=True and flash_attn=True may cause conflicts\",","chunkId":"audio_separator/separator/roformer/parameter_validator.py:17"}],[1004,{"filePath":"audio_separator/separator/roformer/parameter_validator.py","startLine":216,"endLine":227,"chunkText":"                severity=ValidationSeverity.WARNING,\n                parameter_name=\"sage_attention, flash_attn\",\n                message=\"Using both sage_attention=True and flash_attn=True may cause conflicts\",\n                suggested_fix=\"Consider using only one attention mechanism\",\n                current_value=\"both True\",\n                expected_value=\"only one True\"\n            )\n            issues.append(issue)\n        \n        # Check freqs_per_bands consistency for BSRoformer\n        if 'freqs_per_bands' in config:\n            freqs = config['freqs_per_bands']","chunkId":"audio_separator/separator/roformer/parameter_validator.py:18"}],[1005,{"filePath":"audio_separator/separator/roformer/parameter_validator.py","startLine":225,"endLine":234,"chunkText":"        # Check freqs_per_bands consistency for BSRoformer\n        if 'freqs_per_bands' in config:\n            freqs = config['freqs_per_bands']\n            if isinstance(freqs, (list, tuple)) and len(freqs) > 0:\n                total_freqs = sum(freqs)\n                # Check if it looks like a reasonable STFT frequency count\n                if total_freqs < 64 or total_freqs > 4096:\n                    issue = ValidationIssue(\n                        severity=ValidationSeverity.WARNING,\n                        parameter_name=\"freqs_per_bands\",","chunkId":"audio_separator/separator/roformer/parameter_validator.py:19"}],[1006,{"filePath":"audio_separator/separator/roformer/parameter_validator.py","startLine":232,"endLine":241,"chunkText":"                    issue = ValidationIssue(\n                        severity=ValidationSeverity.WARNING,\n                        parameter_name=\"freqs_per_bands\",\n                        message=f\"Sum of freqs_per_bands ({total_freqs}) may be incompatible with typical STFT configurations\",\n                        suggested_fix=\"Verify that freqs_per_bands sum matches your STFT n_fft//2 + 1\",\n                        current_value=total_freqs,\n                        expected_value=\"64 to 4096 (typical range)\"\n                    )\n                    issues.append(issue)\n        ","chunkId":"audio_separator/separator/roformer/parameter_validator.py:20"}],[1007,{"filePath":"audio_separator/separator/roformer/parameter_validator.py","startLine":239,"endLine":249,"chunkText":"                    )\n                    issues.append(issue)\n        \n        # Check num_bands vs sample_rate for MelBandRoformer\n        if 'num_bands' in config and 'sample_rate' in config:\n            num_bands = config['num_bands']\n            sample_rate = config['sample_rate']\n            if num_bands > sample_rate // 100:  # Very rough heuristic\n                issue = ValidationIssue(\n                    severity=ValidationSeverity.WARNING,\n                    parameter_name=\"num_bands, sample_rate\",","chunkId":"audio_separator/separator/roformer/parameter_validator.py:21"}],[1008,{"filePath":"audio_separator/separator/roformer/parameter_validator.py","startLine":247,"endLine":258,"chunkText":"                issue = ValidationIssue(\n                    severity=ValidationSeverity.WARNING,\n                    parameter_name=\"num_bands, sample_rate\",\n                    message=f\"num_bands ({num_bands}) may be too high for sample_rate ({sample_rate})\",\n                    suggested_fix=\"Consider reducing num_bands or verify it's appropriate for your use case\",\n                    current_value=f\"bands={num_bands}, sr={sample_rate}\",\n                    expected_value=\"num_bands << sample_rate\"\n                )\n                issues.append(issue)\n        \n        return issues\n    ","chunkId":"audio_separator/separator/roformer/parameter_validator.py:22"}],[1009,{"filePath":"audio_separator/separator/roformer/parameter_validator.py","startLine":256,"endLine":273,"chunkText":"        \n        return issues\n    \n    def validate_normalization_config(self, norm_config: Any) -> List[ValidationIssue]:\n        \"\"\"\n        Validate normalization configuration.\n        \n        Args:\n            norm_config: Normalization configuration (may be string, dict, or None)\n            \n        Returns:\n            List of validation issues for normalization configuration\n        \"\"\"\n        issues = []\n        \n        if norm_config is not None:\n            if isinstance(norm_config, str):\n                if norm_config not in self.SUPPORTED_NORMALIZATION_TYPES:","chunkId":"audio_separator/separator/roformer/parameter_validator.py:23"}],[1010,{"filePath":"audio_separator/separator/roformer/parameter_validator.py","startLine":271,"endLine":279,"chunkText":"        if norm_config is not None:\n            if isinstance(norm_config, str):\n                if norm_config not in self.SUPPORTED_NORMALIZATION_TYPES:\n                    issue = ValidationIssue(\n                        severity=ValidationSeverity.ERROR,\n                        parameter_name=\"norm\",\n                        message=f\"Unsupported normalization type '{norm_config}'\",\n                        suggested_fix=f\"Use one of: {', '.join(str(t) for t in self.SUPPORTED_NORMALIZATION_TYPES if t is not None)}\",\n                        current_value=norm_config,","chunkId":"audio_separator/separator/roformer/parameter_validator.py:24"}],[1011,{"filePath":"audio_separator/separator/roformer/parameter_validator.py","startLine":277,"endLine":286,"chunkText":"                        message=f\"Unsupported normalization type '{norm_config}'\",\n                        suggested_fix=f\"Use one of: {', '.join(str(t) for t in self.SUPPORTED_NORMALIZATION_TYPES if t is not None)}\",\n                        current_value=norm_config,\n                        expected_value=\"supported normalization type\"\n                    )\n                    issues.append(issue)\n            elif not isinstance(norm_config, dict):\n                issue = ValidationIssue(\n                    severity=ValidationSeverity.ERROR,\n                    parameter_name=\"norm\",","chunkId":"audio_separator/separator/roformer/parameter_validator.py:25"}],[1012,{"filePath":"audio_separator/separator/roformer/parameter_validator.py","startLine":284,"endLine":296,"chunkText":"                issue = ValidationIssue(\n                    severity=ValidationSeverity.ERROR,\n                    parameter_name=\"norm\",\n                    message=f\"Normalization config must be string, dict, or None, got {type(norm_config).__name__}\",\n                    suggested_fix=\"Use a string normalization type or None\",\n                    current_value=norm_config,\n                    expected_value=\"string, dict, or None\"\n                )\n                issues.append(issue)\n        \n        return issues\n    \n    def get_parameter_defaults(self, model_type: str) -> Dict[str, Any]:","chunkId":"audio_separator/separator/roformer/parameter_validator.py:26"}],[1013,{"filePath":"audio_separator/separator/roformer/parameter_validator.py","startLine":294,"endLine":313,"chunkText":"        return issues\n    \n    def get_parameter_defaults(self, model_type: str) -> Dict[str, Any]:\n        \"\"\"\n        Get default values for optional parameters.\n        \n        Args:\n            model_type: Type of model (\"bs_roformer\" or \"mel_band_roformer\")\n            \n        Returns:\n            Dictionary of parameter names to default values\n        \"\"\"\n        defaults = {\n            'stereo': False,\n            'num_stems': 2,\n            'time_transformer_depth': 2,\n            'freq_transformer_depth': 2,\n            'dim_head': 64,\n            'heads': 8,\n            'attn_dropout': 0.0,","chunkId":"audio_separator/separator/roformer/parameter_validator.py:27"}],[1014,{"filePath":"audio_separator/separator/roformer/parameter_validator.py","startLine":311,"endLine":328,"chunkText":"            'dim_head': 64,\n            'heads': 8,\n            'attn_dropout': 0.0,\n            'ff_dropout': 0.0,\n            'flash_attn': True,\n            'mlp_expansion_factor': 4,\n            'sage_attention': False,\n            'zero_dc': True,\n            'use_torch_checkpoint': False,\n            'skip_connection': False,\n            'sample_rate': 44100,\n            'norm': None,\n        }\n        \n        # Add model-specific defaults\n        if model_type == 'bs_roformer':\n            defaults.update({\n                'freqs_per_bands': (2, 4, 8, 16, 32, 64),","chunkId":"audio_separator/separator/roformer/parameter_validator.py:28"}],[1015,{"filePath":"audio_separator/separator/roformer/parameter_validator.py","startLine":326,"endLine":343,"chunkText":"        if model_type == 'bs_roformer':\n            defaults.update({\n                'freqs_per_bands': (2, 4, 8, 16, 32, 64),\n                'mask_estimator_depth': 2,\n            })\n        elif model_type == 'mel_band_roformer':\n            defaults.update({\n                'num_bands': 64,\n            })\n        \n        return defaults\n    \n    def apply_parameter_defaults(self, config: Dict[str, Any], model_type: str) -> Dict[str, Any]:\n        \"\"\"\n        Apply default values to missing optional parameters.\n        \n        Args:\n            config: Model configuration dictionary","chunkId":"audio_separator/separator/roformer/parameter_validator.py:29"}],[1016,{"filePath":"audio_separator/separator/roformer/parameter_validator.py","startLine":341,"endLine":356,"chunkText":"        \n        Args:\n            config: Model configuration dictionary\n            model_type: Type of model\n            \n        Returns:\n            Configuration with defaults applied\n        \"\"\"\n        defaults = self.get_parameter_defaults(model_type)\n        result_config = defaults.copy()\n        result_config.update(config)  # Override defaults with provided values\n        \n        return result_config\n    \n    def _is_correct_type(self, value: Any, expected_type: Union[type, Tuple[type, ...]]) -> bool:\n        \"\"\"Check if value matches expected type(s).\"\"\"","chunkId":"audio_separator/separator/roformer/parameter_validator.py:30"}],[1017,{"filePath":"audio_separator/separator/roformer/parameter_validator.py","startLine":354,"endLine":366,"chunkText":"    \n    def _is_correct_type(self, value: Any, expected_type: Union[type, Tuple[type, ...]]) -> bool:\n        \"\"\"Check if value matches expected type(s).\"\"\"\n        if isinstance(expected_type, tuple):\n            return isinstance(value, expected_type)\n        return isinstance(value, expected_type)\n    \n    def _get_type_name(self, expected_type: Union[type, Tuple[type, ...]]) -> str:\n        \"\"\"Get human-readable type name.\"\"\"\n        if isinstance(expected_type, tuple):\n            return \" or \".join(t.__name__ for t in expected_type)\n        return expected_type.__name__\n    ","chunkId":"audio_separator/separator/roformer/parameter_validator.py:31"}],[1018,{"filePath":"audio_separator/separator/roformer/parameter_validator.py","startLine":364,"endLine":377,"chunkText":"            return \" or \".join(t.__name__ for t in expected_type)\n        return expected_type.__name__\n    \n    def _get_expected_type_description(self, param_name: str) -> str:\n        \"\"\"Get description of expected type for a parameter.\"\"\"\n        if param_name in self.PARAMETER_TYPES:\n            return self._get_type_name(self.PARAMETER_TYPES[param_name])\n        return \"appropriate type\"\n    \n    def validate_all(self, config: Dict[str, Any], model_type: str) -> List[ValidationIssue]:\n        \"\"\"\n        Run all validation checks on a configuration.\n        \n        Args:","chunkId":"audio_separator/separator/roformer/parameter_validator.py:32"}],[1019,{"filePath":"audio_separator/separator/roformer/parameter_validator.py","startLine":375,"endLine":390,"chunkText":"        Run all validation checks on a configuration.\n        \n        Args:\n            config: Model configuration dictionary\n            model_type: Type of model\n            \n        Returns:\n            List of all validation issues found\n        \"\"\"\n        all_issues = []\n        \n        all_issues.extend(self.validate_required_parameters(config, model_type))\n        all_issues.extend(self.validate_parameter_types(config))\n        all_issues.extend(self.validate_parameter_ranges(config))\n        all_issues.extend(self.validate_parameter_compatibility(config))\n        ","chunkId":"audio_separator/separator/roformer/parameter_validator.py:33"}],[1020,{"filePath":"audio_separator/separator/roformer/parameter_validator.py","startLine":388,"endLine":402,"chunkText":"        all_issues.extend(self.validate_parameter_ranges(config))\n        all_issues.extend(self.validate_parameter_compatibility(config))\n        \n        # Validate normalization if present\n        if 'norm' in config:\n            all_issues.extend(self.validate_normalization_config(config['norm']))\n        \n        return all_issues\n    \n    def validate_and_raise(self, config: Dict[str, Any], model_type: str) -> None:\n        \"\"\"\n        Validate configuration and raise ParameterValidationError if issues found.\n        \n        Args:\n            config: Model configuration dictionary","chunkId":"audio_separator/separator/roformer/parameter_validator.py:34"}],[1021,{"filePath":"audio_separator/separator/roformer/parameter_validator.py","startLine":400,"endLine":416,"chunkText":"        \n        Args:\n            config: Model configuration dictionary\n            model_type: Type of model\n            \n        Raises:\n            ParameterValidationError: If validation issues are found\n        \"\"\"\n        issues = self.validate_all(config, model_type)\n        \n        # Find first error (not warning)\n        error_issues = [issue for issue in issues if issue.severity == ValidationSeverity.ERROR]\n        \n        if error_issues:\n            first_error = error_issues[0]\n            raise ParameterValidationError(\n                parameter_name=first_error.parameter_name,","chunkId":"audio_separator/separator/roformer/parameter_validator.py:35"}],[1022,{"filePath":"audio_separator/separator/roformer/parameter_validator.py","startLine":414,"endLine":422,"chunkText":"            first_error = error_issues[0]\n            raise ParameterValidationError(\n                parameter_name=first_error.parameter_name,\n                expected_type=first_error.expected_value or \"valid value\",\n                actual_value=first_error.current_value,\n                suggested_fix=first_error.suggested_fix,\n                context=f\"{model_type} model validation\"\n            )\n","chunkId":"audio_separator/separator/roformer/parameter_validator.py:36"}],[1023,{"filePath":"audio_separator/separator/roformer/roformer_loader.py","startLine":0,"endLine":17,"chunkText":"\"\"\"Roformer model loader with simplified new-implementation only path.\"\"\"\nfrom typing import Dict, Any\nimport logging\nimport os\n\nfrom .model_loading_result import ModelLoadingResult, ImplementationVersion\nfrom .configuration_normalizer import ConfigurationNormalizer\nfrom .parameter_validation_error import ParameterValidationError\n\nlogger = logging.getLogger(__name__)\n\n\nclass RoformerLoader:\n    \"\"\"Main Roformer model loader (new implementation only).\"\"\"\n\n    def __init__(self):\n        self.config_normalizer = ConfigurationNormalizer()\n        self._loading_stats = {","chunkId":"audio_separator/separator/roformer/roformer_loader.py:0"}],[1024,{"filePath":"audio_separator/separator/roformer/roformer_loader.py","startLine":15,"endLine":30,"chunkText":"    def __init__(self):\n        self.config_normalizer = ConfigurationNormalizer()\n        self._loading_stats = {\n            'new_implementation_success': 0,\n            'total_failures': 0\n        }\n\n    def load_model(self,\n                   model_path: str,\n                   config: Dict[str, Any],\n                   device: str = 'cpu') -> ModelLoadingResult:\n        logger.info(f\"Loading Roformer model from {model_path}\")\n        try:\n            normalized_config = self.config_normalizer.normalize_from_file_path(\n                config, model_path, apply_defaults=True, validate=True\n            ","chunkId":"audio_separator/separator/roformer/roformer_loader.py:1"}],[1025,{"filePath":"audio_separator/separator/roformer/roformer_loader.py","startLine":28,"endLine":39,"chunkText":"            normalized_config = self.config_normalizer.normalize_from_file_path(\n                config, model_path, apply_defaults=True, validate=True\n            )\n            model_type = self.config_normalizer.detect_model_type(normalized_config)\n            logger.debug(f\"Detected model type: {model_type}\")\n        except ParameterValidationError as e:\n            logger.error(f\"Configuration validation failed: {e}\")\n            return ModelLoadingResult.failure_result(\n                error_message=f\"Config validation: {e}\",\n                implementation=ImplementationVersion.NEW,\n            )\n","chunkId":"audio_separator/separator/roformer/roformer_loader.py:2"}],[1026,{"filePath":"audio_separator/separator/roformer/roformer_loader.py","startLine":37,"endLine":49,"chunkText":"                implementation=ImplementationVersion.NEW,\n            )\n\n        try:\n            result = self._load_with_new_implementation(\n                model_path, normalized_config, model_type, device\n            )\n            self._loading_stats['new_implementation_success'] += 1\n            logger.info(f\"Successfully loaded {model_type} model with new implementation\")\n            return result\n        except (RuntimeError, ValueError, TypeError) as e:\n            logger.error(f\"New implementation failed: {e}\")\n            # Attempt legacy fallback using the original (pre-normalized) configuratio","chunkId":"audio_separator/separator/roformer/roformer_loader.py:3"}],[1027,{"filePath":"audio_separator/separator/roformer/roformer_loader.py","startLine":47,"endLine":58,"chunkText":"        except (RuntimeError, ValueError, TypeError) as e:\n            logger.error(f\"New implementation failed: {e}\")\n            # Attempt legacy fallback using the original (pre-normalized) configuration\n            try:\n                fallback_result = self._load_with_legacy_implementation(\n                    model_path=model_path,\n                    original_config=config,\n                    device=device,\n                    original_error=str(e)\n                )\n                logger.warning(\"Fell back to legacy Roformer implementation successfully\")\n                return fallback_result","chunkId":"audio_separator/separator/roformer/roformer_loader.py:4"}],[1028,{"filePath":"audio_separator/separator/roformer/roformer_loader.py","startLine":56,"endLine":64,"chunkText":"                )\n                logger.warning(\"Fell back to legacy Roformer implementation successfully\")\n                return fallback_result\n            except (RuntimeError, ValueError, TypeError) as fallback_error:\n                logger.error(f\"Legacy implementation also failed: {fallback_error}\")\n                self._loading_stats['total_failures'] += 1\n                return ModelLoadingResult.failure_result(\n                    error_message=f\"New implementation failed: {e}; Legacy fallback failed: {fallback_error}\",\n                    implementation=ImplementationVersion.NEW,","chunkId":"audio_separator/separator/roformer/roformer_loader.py:5"}],[1029,{"filePath":"audio_separator/separator/roformer/roformer_loader.py","startLine":62,"endLine":73,"chunkText":"                return ModelLoadingResult.failure_result(\n                    error_message=f\"New implementation failed: {e}; Legacy fallback failed: {fallback_error}\",\n                    implementation=ImplementationVersion.NEW,\n                )\n\n    def validate_configuration(self, config: Dict[str, Any], model_type: str) -> bool:\n        try:\n            _ = self.config_normalizer.normalize_config(\n                config, model_type, apply_defaults=False, validate=True\n            )\n            logger.debug(f\"Configuration validation passed for {model_type}\")\n            return True","chunkId":"audio_separator/separator/roformer/roformer_loader.py:6"}],[1030,{"filePath":"audio_separator/separator/roformer/roformer_loader.py","startLine":71,"endLine":83,"chunkText":"            )\n            logger.debug(f\"Configuration validation passed for {model_type}\")\n            return True\n        except ParameterValidationError as e:\n            logger.warning(f\"Configuration validation failed for {model_type}: {e}\")\n            return False\n        except (RuntimeError, ValueError) as e:\n            logger.error(f\"Unexpected error during validation: {e}\")\n            return False\n\n    def _load_with_new_implementation(self,\n                                      model_path: str,\n                                      config: Dict[str, Any],","chunkId":"audio_separator/separator/roformer/roformer_loader.py:7"}],[1031,{"filePath":"audio_separator/separator/roformer/roformer_loader.py","startLine":81,"endLine":93,"chunkText":"    def _load_with_new_implementation(self,\n                                      model_path: str,\n                                      config: Dict[str, Any],\n                                      model_type: str,\n                                      device: str) -> ModelLoadingResult:\n        import torch\n\n        try:\n            if model_type == \"bs_roformer\":\n                model = self._create_bs_roformer(config)\n            elif model_type == \"mel_band_roformer\":\n                model = self._create_mel_band_roformer(config)\n            else:","chunkId":"audio_separator/separator/roformer/roformer_loader.py:8"}],[1032,{"filePath":"audio_separator/separator/roformer/roformer_loader.py","startLine":91,"endLine":101,"chunkText":"            elif model_type == \"mel_band_roformer\":\n                model = self._create_mel_band_roformer(config)\n            else:\n                raise ValueError(f\"Unknown model type: {model_type}\")\n\n            if os.path.exists(model_path):\n                state_dict = torch.load(model_path, map_location=device)\n                if isinstance(state_dict, dict) and 'state_dict' in state_dict:\n                    model.load_state_dict(state_dict['state_dict'])\n                elif isinstance(state_dict, dict) and 'model' in state_dict:\n                    model.load_state_dict(state_dict['model'])","chunkId":"audio_separator/separator/roformer/roformer_loader.py:9"}],[1033,{"filePath":"audio_separator/separator/roformer/roformer_loader.py","startLine":99,"endLine":113,"chunkText":"                    model.load_state_dict(state_dict['state_dict'])\n                elif isinstance(state_dict, dict) and 'model' in state_dict:\n                    model.load_state_dict(state_dict['model'])\n                else:\n                    model.load_state_dict(state_dict)\n                logger.debug(f\"Loaded state dict from {model_path}\")\n\n            model.to(device)\n            model.eval()\n\n            result = ModelLoadingResult.success_result(\n                model=model,\n                implementation=ImplementationVersion.NEW,\n                config=config,\n            )","chunkId":"audio_separator/separator/roformer/roformer_loader.py:10"}],[1034,{"filePath":"audio_separator/separator/roformer/roformer_loader.py","startLine":111,"endLine":124,"chunkText":"                implementation=ImplementationVersion.NEW,\n                config=config,\n            )\n            result.add_model_info('model_type', model_type)\n            result.add_model_info('loading_method', 'direct')\n            result.add_model_info('device', device)\n            return result\n        except (RuntimeError, ValueError) as e:\n            logger.error(f\"Failed to create {model_type} model: {e}\")\n            raise\n\n    def _create_bs_roformer(self, config: Dict[str, Any]):\n        from ..uvr_lib_v5.roformer.bs_roformer import BSRoformer\n        model_args = {","chunkId":"audio_separator/separator/roformer/roformer_loader.py:11"}],[1035,{"filePath":"audio_separator/separator/roformer/roformer_loader.py","startLine":122,"endLine":132,"chunkText":"    def _create_bs_roformer(self, config: Dict[str, Any]):\n        from ..uvr_lib_v5.roformer.bs_roformer import BSRoformer\n        model_args = {\n            'dim': config['dim'],\n            'depth': config['depth'],\n            'stereo': config.get('stereo', False),\n            'num_stems': config.get('num_stems', 2),\n            'time_transformer_depth': config.get('time_transformer_depth', 2),\n            'freq_transformer_depth': config.get('freq_transformer_depth', 2),\n            'freqs_per_bands': config['freqs_per_bands'],\n            'dim_head': config.get('dim_head', 64),","chunkId":"audio_separator/separator/roformer/roformer_loader.py:12"}],[1036,{"filePath":"audio_separator/separator/roformer/roformer_loader.py","startLine":130,"endLine":139,"chunkText":"            'freq_transformer_depth': config.get('freq_transformer_depth', 2),\n            'freqs_per_bands': config['freqs_per_bands'],\n            'dim_head': config.get('dim_head', 64),\n            'heads': config.get('heads', 8),\n            'attn_dropout': config.get('attn_dropout', 0.0),\n            'ff_dropout': config.get('ff_dropout', 0.0),\n            'flash_attn': config.get('flash_attn', True),\n            'mlp_expansion_factor': config.get('mlp_expansion_factor', 4),\n            'sage_attention': config.get('sage_attention', False),\n            'zero_dc': config.get('zero_dc', True),","chunkId":"audio_separator/separator/roformer/roformer_loader.py:13"}],[1037,{"filePath":"audio_separator/separator/roformer/roformer_loader.py","startLine":137,"endLine":147,"chunkText":"            'mlp_expansion_factor': config.get('mlp_expansion_factor', 4),\n            'sage_attention': config.get('sage_attention', False),\n            'zero_dc': config.get('zero_dc', True),\n            'use_torch_checkpoint': config.get('use_torch_checkpoint', False),\n            'skip_connection': config.get('skip_connection', False),\n        }\n        if 'stft_n_fft' in config:\n            model_args['stft_n_fft'] = config['stft_n_fft']\n        if 'stft_hop_length' in config:\n            model_args['stft_hop_length'] = config['stft_hop_length']\n        if 'stft_win_length' in config:","chunkId":"audio_separator/separator/roformer/roformer_loader.py:14"}],[1038,{"filePath":"audio_separator/separator/roformer/roformer_loader.py","startLine":145,"endLine":156,"chunkText":"        if 'stft_hop_length' in config:\n            model_args['stft_hop_length'] = config['stft_hop_length']\n        if 'stft_win_length' in config:\n            model_args['stft_win_length'] = config['stft_win_length']\n        logger.debug(f\"Creating BSRoformer with args: {list(model_args.keys())}\")\n        return BSRoformer(**model_args)\n\n    def _create_mel_band_roformer(self, config: Dict[str, Any]):\n        from ..uvr_lib_v5.roformer.mel_band_roformer import MelBandRoformer\n        model_args = {\n            'dim': config['dim'],\n            'depth': config['depth'],","chunkId":"audio_separator/separator/roformer/roformer_loader.py:15"}],[1039,{"filePath":"audio_separator/separator/roformer/roformer_loader.py","startLine":154,"endLine":164,"chunkText":"        model_args = {\n            'dim': config['dim'],\n            'depth': config['depth'],\n            'stereo': config.get('stereo', False),\n            'num_stems': config.get('num_stems', 2),\n            'time_transformer_depth': config.get('time_transformer_depth', 2),\n            'freq_transformer_depth': config.get('freq_transformer_depth', 2),\n            'num_bands': config['num_bands'],\n            'dim_head': config.get('dim_head', 64),\n            'heads': config.get('heads', 8),\n            'attn_dropout': config.get('attn_dropout', 0.0),","chunkId":"audio_separator/separator/roformer/roformer_loader.py:16"}],[1040,{"filePath":"audio_separator/separator/roformer/roformer_loader.py","startLine":162,"endLine":170,"chunkText":"            'dim_head': config.get('dim_head', 64),\n            'heads': config.get('heads', 8),\n            'attn_dropout': config.get('attn_dropout', 0.0),\n            'ff_dropout': config.get('ff_dropout', 0.0),\n            'flash_attn': config.get('flash_attn', True),\n            'mlp_expansion_factor': config.get('mlp_expansion_factor', 4),\n            'sage_attention': config.get('sage_attention', False),\n            'zero_dc': config.get('zero_dc', True),\n            'use_torch_checkpoint': config.get('use_torch_checkpoint', False),","chunkId":"audio_separator/separator/roformer/roformer_loader.py:17"}],[1041,{"filePath":"audio_separator/separator/roformer/roformer_loader.py","startLine":169,"endLine":182,"chunkText":"            'zero_dc': config.get('zero_dc', True),\n            'use_torch_checkpoint': config.get('use_torch_checkpoint', False),\n            'skip_connection': config.get('skip_connection', False),\n        }\n        if 'sample_rate' in config:\n            model_args['sample_rate'] = config['sample_rate']\n        # Optional parameters commonly present in legacy configs\n        for optional_key in [\n            'mask_estimator_depth',\n            'stft_n_fft',\n            'stft_hop_length',\n            'stft_win_length',\n            'stft_normalized',\n            'stft_window_fn',","chunkId":"audio_separator/separator/roformer/roformer_loader.py:18"}],[1042,{"filePath":"audio_separator/separator/roformer/roformer_loader.py","startLine":180,"endLine":192,"chunkText":"            'stft_win_length',\n            'stft_normalized',\n            'stft_window_fn',\n            'multi_stft_resolution_loss_weight',\n            'multi_stft_resolutions_window_sizes',\n            'multi_stft_hop_size',\n            'multi_stft_normalized',\n            'multi_stft_window_fn',\n            'match_input_audio_length',\n        ]:\n            if optional_key in config:\n                model_args[optional_key] = config[optional_key]\n        # Note: fmin and fmax are defined in config classes but not accepted by current constructor","chunkId":"audio_separator/separator/roformer/roformer_loader.py:19"}],[1043,{"filePath":"audio_separator/separator/roformer/roformer_loader.py","startLine":190,"endLine":199,"chunkText":"            if optional_key in config:\n                model_args[optional_key] = config[optional_key]\n        # Note: fmin and fmax are defined in config classes but not accepted by current constructor\n        logger.debug(f\"Creating MelBandRoformer with args: {list(model_args.keys())}\")\n        return MelBandRoformer(**model_args)\n\n    def _load_with_legacy_implementation(self,\n                                          model_path: str,\n                                          original_config: Dict[str, Any],\n                                          device: str,","chunkId":"audio_separator/separator/roformer/roformer_loader.py:20"}],[1044,{"filePath":"audio_separator/separator/roformer/roformer_loader.py","startLine":197,"endLine":209,"chunkText":"                                          model_path: str,\n                                          original_config: Dict[str, Any],\n                                          device: str,\n                                          original_error: str) -> ModelLoadingResult:\n        \"\"\"\n        Attempt to load the model using the legacy direct-constructor path\n        for maximum backward compatibility with existing checkpoints.\n        \"\"\"\n        import torch\n\n        # Use nested 'model' section if present; otherwise assume flat\n        model_cfg = original_config.get('model', original_config)\n","chunkId":"audio_separator/separator/roformer/roformer_loader.py:21"}],[1045,{"filePath":"audio_separator/separator/roformer/roformer_loader.py","startLine":207,"endLine":219,"chunkText":"        # Use nested 'model' section if present; otherwise assume flat\n        model_cfg = original_config.get('model', original_config)\n\n        # Determine model type from config\n        if 'num_bands' in model_cfg:\n            from ..uvr_lib_v5.roformer.mel_band_roformer import MelBandRoformer\n            model = MelBandRoformer(**model_cfg)\n        elif 'freqs_per_bands' in model_cfg:\n            from ..uvr_lib_v5.roformer.bs_roformer import BSRoformer\n            model = BSRoformer(**model_cfg)\n        else:\n            raise ValueError(\"Unknown Roformer model type in legacy configuration\")\n","chunkId":"audio_separator/separator/roformer/roformer_loader.py:22"}],[1046,{"filePath":"audio_separator/separator/roformer/roformer_loader.py","startLine":217,"endLine":231,"chunkText":"        else:\n            raise ValueError(\"Unknown Roformer model type in legacy configuration\")\n\n        # Load checkpoint as raw state dict (legacy behavior)\n        try:\n            checkpoint = torch.load(model_path, map_location='cpu', weights_only=True)\n        except TypeError:\n            # For older torch versions without weights_only\n            checkpoint = torch.load(model_path, map_location='cpu')\n\n        model.load_state_dict(checkpoint)\n        model.to(device).eval()\n\n        return ModelLoadingResult.fallback_success_result(\n            model=model,","chunkId":"audio_separator/separator/roformer/roformer_loader.py:23"}],[1047,{"filePath":"audio_separator/separator/roformer/roformer_loader.py","startLine":229,"endLine":246,"chunkText":"\n        return ModelLoadingResult.fallback_success_result(\n            model=model,\n            original_error=original_error,\n            config=original_config,\n        )\n\n    def get_loading_stats(self) -> Dict[str, int]:\n        return self._loading_stats.copy()\n\n    def reset_loading_stats(self) -> None:\n        self._loading_stats = {\n            'new_implementation_success': 0,\n            'total_failures': 0\n        }\n\n    def detect_model_type(self, model_path: str) -> str:\n        model_path_lower = model_path.lower()","chunkId":"audio_separator/separator/roformer/roformer_loader.py:24"}],[1048,{"filePath":"audio_separator/separator/roformer/roformer_loader.py","startLine":244,"endLine":253,"chunkText":"\n    def detect_model_type(self, model_path: str) -> str:\n        model_path_lower = model_path.lower()\n        if any(indicator in model_path_lower for indicator in ['bs_roformer', 'bs-roformer', 'bsroformer']):\n            return \"bs_roformer\"\n        if any(indicator in model_path_lower for indicator in ['mel_band_roformer', 'mel-band-roformer', 'melband']):\n            return \"mel_band_roformer\"\n        if 'roformer' in model_path_lower:\n            logger.warning(f\"Generic 'roformer' detected in {model_path}, defaulting to bs_roformer\")\n            return \"bs_roformer\"","chunkId":"audio_separator/separator/roformer/roformer_loader.py:25"}],[1049,{"filePath":"audio_separator/separator/roformer/roformer_loader.py","startLine":251,"endLine":263,"chunkText":"        if 'roformer' in model_path_lower:\n            logger.warning(f\"Generic 'roformer' detected in {model_path}, defaulting to bs_roformer\")\n            return \"bs_roformer\"\n        raise ValueError(f\"Cannot determine Roformer model type from path: {model_path}\")\n\n    def get_default_configuration(self, model_type: str) -> Dict[str, Any]:\n        if model_type == \"bs_roformer\":\n            return {\n                'dim': 512,\n                'depth': 12,\n                'stereo': False,\n                'num_stems': 2,\n                'time_transformer_depth': 2,","chunkId":"audio_separator/separator/roformer/roformer_loader.py:26"}],[1050,{"filePath":"audio_separator/separator/roformer/roformer_loader.py","startLine":261,"endLine":275,"chunkText":"                'stereo': False,\n                'num_stems': 2,\n                'time_transformer_depth': 2,\n                'freq_transformer_depth': 2,\n                'freqs_per_bands': (2, 4, 8, 16, 32, 64),\n                'dim_head': 64,\n                'heads': 8,\n                'attn_dropout': 0.0,\n                'ff_dropout': 0.0,\n                'flash_attn': True,\n                'mlp_expansion_factor': 4,\n                'sage_attention': False,\n                'zero_dc': True,\n                'use_torch_checkpoint': False,\n                'skip_connection': False,","chunkId":"audio_separator/separator/roformer/roformer_loader.py:27"}],[1051,{"filePath":"audio_separator/separator/roformer/roformer_loader.py","startLine":273,"endLine":289,"chunkText":"                'zero_dc': True,\n                'use_torch_checkpoint': False,\n                'skip_connection': False,\n                'mask_estimator_depth': 2,\n                'stft_n_fft': 2048,\n                'stft_hop_length': 512,\n                'stft_win_length': 2048,\n            }\n        elif model_type == \"mel_band_roformer\":\n            return {\n                'dim': 512,\n                'depth': 12,\n                'stereo': False,\n                'num_stems': 2,\n                'time_transformer_depth': 2,\n                'freq_transformer_depth': 2,\n                'num_bands': 64,","chunkId":"audio_separator/separator/roformer/roformer_loader.py:28"}],[1052,{"filePath":"audio_separator/separator/roformer/roformer_loader.py","startLine":287,"endLine":300,"chunkText":"                'time_transformer_depth': 2,\n                'freq_transformer_depth': 2,\n                'num_bands': 64,\n                'dim_head': 64,\n                'heads': 8,\n                'attn_dropout': 0.0,\n                'ff_dropout': 0.0,\n                'flash_attn': True,\n                'mlp_expansion_factor': 4,\n                'sage_attention': False,\n                'zero_dc': True,\n                'use_torch_checkpoint': False,\n                'skip_connection': False,\n                'sample_rate': 44100,","chunkId":"audio_separator/separator/roformer/roformer_loader.py:29"}],[1053,{"filePath":"audio_separator/separator/roformer/roformer_loader.py","startLine":298,"endLine":305,"chunkText":"                'use_torch_checkpoint': False,\n                'skip_connection': False,\n                'sample_rate': 44100,\n                # Note: fmin and fmax are not implemented in MelBandRoformer constructor\n            }\n        else:\n            raise ValueError(f\"Unknown model type: {model_type}\")\n","chunkId":"audio_separator/separator/roformer/roformer_loader.py:30"}],[1054,{"filePath":"audio_separator/separator/separator.py","startLine":0,"endLine":27,"chunkText":"\"\"\" This file contains the Separator class, to facilitate the separation of stems from audio. \"\"\"\n\nfrom importlib import metadata, resources\nimport os\nimport sys\nimport platform\nimport subprocess\nimport time\nimport logging\nimport warnings\nimport importlib\nimport io\nimport re\nfrom typing import Optional\n\nimport hashlib\nimport json\nimport yaml\nimport requests\nimport torch\nimport torch.amp.autocast_mode as autocast_mode\nimport onnxruntime as ort\nfrom tqdm import tqdm\n\n\nclass Separator:\n    \"\"\"\n    The Separator class is designed to facilitate the separation of audio sources from a given audio file.","chunkId":"audio_separator/separator/separator.py:0"}],[1055,{"filePath":"audio_separator/separator/separator.py","startLine":25,"endLine":32,"chunkText":"class Separator:\n    \"\"\"\n    The Separator class is designed to facilitate the separation of audio sources from a given audio file.\n    It supports various separation architectures and models, including MDX, VR, and Demucs. The class provides\n    functionalities to configure separation parameters, load models, and perform audio source separation.\n    It also handles logging, normalization, and output formatting of the separated audio stems.\n\n    The actual separation task is handled by one of the architecture-specific classes in the `architectures` module;","chunkId":"audio_separator/separator/separator.py:1"}],[1056,{"filePath":"audio_separator/separator/separator.py","startLine":30,"endLine":39,"chunkText":"    It also handles logging, normalization, and output formatting of the separated audio stems.\n\n    The actual separation task is handled by one of the architecture-specific classes in the `architectures` module;\n    this class is responsible for initialising logging, configuring hardware acceleration, loading the model,\n    initiating the separation process and passing outputs back to the caller.\n\n    Common Attributes:\n        log_level (int): The logging level.\n        log_formatter (logging.Formatter): The logging formatter.\n        model_file_dir (str): The directory where model files are stored.","chunkId":"audio_separator/separator/separator.py:2"}],[1057,{"filePath":"audio_separator/separator/separator.py","startLine":37,"endLine":44,"chunkText":"        log_level (int): The logging level.\n        log_formatter (logging.Formatter): The logging formatter.\n        model_file_dir (str): The directory where model files are stored.\n        output_dir (str): The directory where output files will be saved.\n        output_format (str): The format of the output audio file.\n        output_bitrate (str): The bitrate of the output audio file.\n        amplification_threshold (float): The threshold for audio amplification.\n        normalization_threshold (float): The threshold for audio normalization.","chunkId":"audio_separator/separator/separator.py:3"}],[1058,{"filePath":"audio_separator/separator/separator.py","startLine":42,"endLine":50,"chunkText":"        output_bitrate (str): The bitrate of the output audio file.\n        amplification_threshold (float): The threshold for audio amplification.\n        normalization_threshold (float): The threshold for audio normalization.\n        output_single_stem (str): Option to output a single stem.\n        invert_using_spec (bool): Flag to invert using spectrogram.\n        sample_rate (int): The sample rate of the audio.\n        use_soundfile (bool): Use soundfile for audio writing, can solve OOM issues.\n        use_autocast (bool): Flag to use PyTorch autocast for faster inference.\n","chunkId":"audio_separator/separator/separator.py:4"}],[1059,{"filePath":"audio_separator/separator/separator.py","startLine":48,"endLine":60,"chunkText":"        use_soundfile (bool): Use soundfile for audio writing, can solve OOM issues.\n        use_autocast (bool): Flag to use PyTorch autocast for faster inference.\n\n    MDX Architecture Specific Attributes:\n        hop_length (int): The hop length for STFT.\n        segment_size (int): The segment size for processing.\n        overlap (float): The overlap between segments.\n        batch_size (int): The batch size for processing.\n        enable_denoise (bool): Flag to enable or disable denoising.\n\n    VR Architecture Specific Attributes & Defaults:\n        batch_size: 16\n        window_size: 512","chunkId":"audio_separator/separator/separator.py:5"}],[1060,{"filePath":"audio_separator/separator/separator.py","startLine":58,"endLine":80,"chunkText":"    VR Architecture Specific Attributes & Defaults:\n        batch_size: 16\n        window_size: 512\n        aggression: 5\n        enable_tta: False\n        enable_post_process: False\n        post_process_threshold: 0.2\n        high_end_process: False\n\n    Demucs Architecture Specific Attributes & Defaults:\n        segment_size: \"Default\"\n        shifts: 2\n        overlap: 0.25\n        segments_enabled: True\n\n    MDXC Architecture Specific Attributes & Defaults:\n        segment_size: 256\n        override_model_segment_size: False\n        batch_size: 1\n        overlap: 8\n        pitch_shift: 0\n    \"\"\"\n","chunkId":"audio_separator/separator/separator.py:6"}],[1061,{"filePath":"audio_separator/separator/separator.py","startLine":78,"endLine":97,"chunkText":"        pitch_shift: 0\n    \"\"\"\n\n    def __init__(\n        self,\n        log_level=logging.INFO,\n        log_formatter=None,\n        model_file_dir=\"/tmp/audio-separator-models/\",\n        output_dir=None,\n        output_format=\"WAV\",\n        output_bitrate=None,\n        normalization_threshold=0.9,\n        amplification_threshold=0.0,\n        output_single_stem=None,\n        invert_using_spec=False,\n        sample_rate=44100,\n        use_soundfile=False,\n        use_autocast=False,\n        use_directml=False,\n        chunk_duration=None,","chunkId":"audio_separator/separator/separator.py:7"}],[1062,{"filePath":"audio_separator/separator/separator.py","startLine":95,"endLine":100,"chunkText":"        use_autocast=False,\n        use_directml=False,\n        chunk_duration=None,\n        mdx_params={\"hop_length\": 1024, \"segment_size\": 256, \"overlap\": 0.25, \"batch_size\": 1, \"enable_denoise\": False},\n        vr_params={\"batch_size\": 1, \"window_size\": 512, \"aggression\": 5, \"enable_tta\": False, \"enable_post_process\": False, \"post_process_threshold\": 0.2, \"high_end_process\": False},\n        demucs_params={\"segment_size\": \"Default\", \"shifts\": 2, \"overlap\": 0.25, \"segments_enabled\": True},","chunkId":"audio_separator/separator/separator.py:8"}],[1063,{"filePath":"audio_separator/separator/separator.py","startLine":98,"endLine":104,"chunkText":"        mdx_params={\"hop_length\": 1024, \"segment_size\": 256, \"overlap\": 0.25, \"batch_size\": 1, \"enable_denoise\": False},\n        vr_params={\"batch_size\": 1, \"window_size\": 512, \"aggression\": 5, \"enable_tta\": False, \"enable_post_process\": False, \"post_process_threshold\": 0.2, \"high_end_process\": False},\n        demucs_params={\"segment_size\": \"Default\", \"shifts\": 2, \"overlap\": 0.25, \"segments_enabled\": True},\n        mdxc_params={\"segment_size\": 256, \"override_model_segment_size\": False, \"batch_size\": 1, \"overlap\": 8, \"pitch_shift\": 0},\n        info_only=False,\n    ):\n        \"\"\"Initialize the separator.\"\"\"","chunkId":"audio_separator/separator/separator.py:9"}],[1064,{"filePath":"audio_separator/separator/separator.py","startLine":102,"endLine":119,"chunkText":"        info_only=False,\n    ):\n        \"\"\"Initialize the separator.\"\"\"\n        self.logger = logging.getLogger(__name__)\n        self.logger.setLevel(log_level)\n        self.log_level = log_level\n        self.log_formatter = log_formatter\n\n        self.log_handler = logging.StreamHandler()\n\n        if self.log_formatter is None:\n            self.log_formatter = logging.Formatter(\"%(asctime)s - %(levelname)s - %(module)s - %(message)s\")\n\n        self.log_handler.setFormatter(self.log_formatter)\n\n        if not self.logger.hasHandlers():\n            self.logger.addHandler(self.log_handler)\n","chunkId":"audio_separator/separator/separator.py:10"}],[1065,{"filePath":"audio_separator/separator/separator.py","startLine":117,"endLine":129,"chunkText":"        if not self.logger.hasHandlers():\n            self.logger.addHandler(self.log_handler)\n\n        # Filter out noisy warnings from PyTorch for users who don't care about them\n        if log_level > logging.DEBUG:\n            warnings.filterwarnings(\"ignore\")\n\n        # Skip initialization logs if info_only is True\n        if not info_only:\n            package_version = self.get_package_distribution(\"audio-separator\").version\n            self.logger.info(f\"Separator version {package_version} instantiating with output_dir: {output_dir}, output_format: {output_format}\")\n\n        if output_dir is None:","chunkId":"audio_separator/separator/separator.py:11"}],[1066,{"filePath":"audio_separator/separator/separator.py","startLine":127,"endLine":139,"chunkText":"            self.logger.info(f\"Separator version {package_version} instantiating with output_dir: {output_dir}, output_format: {output_format}\")\n\n        if output_dir is None:\n            output_dir = os.getcwd()\n            if not info_only:\n                self.logger.info(\"Output directory not specified. Using current working directory.\")\n\n        self.output_dir = output_dir\n\n        # Check for environment variable to override model_file_dir\n        env_model_dir = os.environ.get(\"AUDIO_SEPARATOR_MODEL_DIR\")\n        if env_model_dir:\n            self.model_file_dir = env_model_dir","chunkId":"audio_separator/separator/separator.py:12"}],[1067,{"filePath":"audio_separator/separator/separator.py","startLine":137,"endLine":146,"chunkText":"        env_model_dir = os.environ.get(\"AUDIO_SEPARATOR_MODEL_DIR\")\n        if env_model_dir:\n            self.model_file_dir = env_model_dir\n            self.logger.info(f\"Using model directory from AUDIO_SEPARATOR_MODEL_DIR env var: {self.model_file_dir}\")\n            if not os.path.exists(self.model_file_dir):\n                raise FileNotFoundError(f\"The specified model directory does not exist: {self.model_file_dir}\")\n        else:\n            self.logger.info(f\"Using model directory from model_file_dir parameter: {model_file_dir}\")\n            self.model_file_dir = model_file_dir\n","chunkId":"audio_separator/separator/separator.py:13"}],[1068,{"filePath":"audio_separator/separator/separator.py","startLine":144,"endLine":157,"chunkText":"            self.logger.info(f\"Using model directory from model_file_dir parameter: {model_file_dir}\")\n            self.model_file_dir = model_file_dir\n\n        # Create the model directory if it does not exist\n        os.makedirs(self.model_file_dir, exist_ok=True)\n        os.makedirs(self.output_dir, exist_ok=True)\n\n        self.output_format = output_format\n        self.output_bitrate = output_bitrate\n\n        if self.output_format is None:\n            self.output_format = \"WAV\"\n\n        self.normalization_threshold = normalization_threshold","chunkId":"audio_separator/separator/separator.py:14"}],[1069,{"filePath":"audio_separator/separator/separator.py","startLine":155,"endLine":165,"chunkText":"            self.output_format = \"WAV\"\n\n        self.normalization_threshold = normalization_threshold\n        if normalization_threshold <= 0 or normalization_threshold > 1:\n            raise ValueError(\"The normalization_threshold must be greater than 0 and less than or equal to 1.\")\n\n        self.amplification_threshold = amplification_threshold\n        if amplification_threshold < 0 or amplification_threshold > 1:\n            raise ValueError(\"The amplification_threshold must be greater than or equal to 0 and less than or equal to 1.\")\n\n        self.output_single_stem = output_single_stem","chunkId":"audio_separator/separator/separator.py:15"}],[1070,{"filePath":"audio_separator/separator/separator.py","startLine":163,"endLine":173,"chunkText":"            raise ValueError(\"The amplification_threshold must be greater than or equal to 0 and less than or equal to 1.\")\n\n        self.output_single_stem = output_single_stem\n        if output_single_stem is not None:\n            self.logger.debug(f\"Single stem output requested, so only one output file ({output_single_stem}) will be written\")\n\n        self.invert_using_spec = invert_using_spec\n        if self.invert_using_spec:\n            self.logger.debug(f\"Secondary step will be inverted using spectogram rather than waveform. This may improve quality but is slightly slower.\")\n\n        try:","chunkId":"audio_separator/separator/separator.py:16"}],[1071,{"filePath":"audio_separator/separator/separator.py","startLine":171,"endLine":179,"chunkText":"            self.logger.debug(f\"Secondary step will be inverted using spectogram rather than waveform. This may improve quality but is slightly slower.\")\n\n        try:\n            self.sample_rate = int(sample_rate)\n            if self.sample_rate <= 0:\n                raise ValueError(f\"The sample rate setting is {self.sample_rate} but it must be a non-zero whole number.\")\n            if self.sample_rate > 12800000:\n                raise ValueError(f\"The sample rate setting is {self.sample_rate}. Enter something less ambitious.\")\n        except ValueError:","chunkId":"audio_separator/separator/separator.py:17"}],[1072,{"filePath":"audio_separator/separator/separator.py","startLine":177,"endLine":188,"chunkText":"            if self.sample_rate > 12800000:\n                raise ValueError(f\"The sample rate setting is {self.sample_rate}. Enter something less ambitious.\")\n        except ValueError:\n            raise ValueError(\"The sample rate must be a non-zero whole number. Please provide a valid integer.\")\n\n        self.use_soundfile = use_soundfile\n        self.use_autocast = use_autocast\n        self.use_directml = use_directml\n\n        self.chunk_duration = chunk_duration\n        if chunk_duration is not None:\n            if chunk_duration <= 0:","chunkId":"audio_separator/separator/separator.py:18"}],[1073,{"filePath":"audio_separator/separator/separator.py","startLine":186,"endLine":198,"chunkText":"        self.chunk_duration = chunk_duration\n        if chunk_duration is not None:\n            if chunk_duration <= 0:\n                raise ValueError(\"chunk_duration must be greater than 0\")\n\n        # These are parameters which users may want to configure so we expose them to the top-level Separator class,\n        # even though they are specific to a single model architecture\n        self.arch_specific_params = {\"MDX\": mdx_params, \"VR\": vr_params, \"Demucs\": demucs_params, \"MDXC\": mdxc_params}\n\n        self.torch_device = None\n        self.torch_device_cpu = None\n        self.torch_device_mps = None\n","chunkId":"audio_separator/separator/separator.py:19"}],[1074,{"filePath":"audio_separator/separator/separator.py","startLine":196,"endLine":213,"chunkText":"        self.torch_device_cpu = None\n        self.torch_device_mps = None\n\n        self.onnx_execution_provider = None\n        self.model_instance = None\n\n        self.model_is_uvr_vip = False\n        self.model_friendly_name = None\n\n        if not info_only:\n            self.setup_accelerated_inferencing_device()\n\n    def setup_accelerated_inferencing_device(self):\n        \"\"\"\n        This method sets up the PyTorch and/or ONNX Runtime inferencing device, using GPU hardware acceleration if available.\n        \"\"\"\n        system_info = self.get_system_info()\n        self.check_ffmpeg_installed()","chunkId":"audio_separator/separator/separator.py:20"}],[1075,{"filePath":"audio_separator/separator/separator.py","startLine":211,"endLine":225,"chunkText":"        \"\"\"\n        system_info = self.get_system_info()\n        self.check_ffmpeg_installed()\n        self.log_onnxruntime_packages()\n        self.setup_torch_device(system_info)\n\n    def get_system_info(self):\n        \"\"\"\n        This method logs the system information, including the operating system, CPU archutecture and Python version\n        \"\"\"\n        os_name = platform.system()\n        os_version = platform.version()\n        self.logger.info(f\"Operating System: {os_name} {os_version}\")\n\n        system_info = platform.uname()","chunkId":"audio_separator/separator/separator.py:21"}],[1076,{"filePath":"audio_separator/separator/separator.py","startLine":223,"endLine":236,"chunkText":"        self.logger.info(f\"Operating System: {os_name} {os_version}\")\n\n        system_info = platform.uname()\n        self.logger.info(f\"System: {system_info.system} Node: {system_info.node} Release: {system_info.release} Machine: {system_info.machine} Proc: {system_info.processor}\")\n\n        python_version = platform.python_version()\n        self.logger.info(f\"Python Version: {python_version}\")\n\n        pytorch_version = torch.__version__\n        self.logger.info(f\"PyTorch Version: {pytorch_version}\")\n        return system_info\n\n    def check_ffmpeg_installed(self):\n        \"\"\"","chunkId":"audio_separator/separator/separator.py:22"}],[1077,{"filePath":"audio_separator/separator/separator.py","startLine":234,"endLine":244,"chunkText":"\n    def check_ffmpeg_installed(self):\n        \"\"\"\n        This method checks if ffmpeg is installed and logs its version.\n        \"\"\"\n        try:\n            ffmpeg_version_output = subprocess.check_output([\"ffmpeg\", \"-version\"], text=True)\n            first_line = ffmpeg_version_output.splitlines()[0]\n            self.logger.info(f\"FFmpeg installed: {first_line}\")\n        except FileNotFoundError:\n            self.logger.error(\"FFmpeg is not installed. Please install FFmpeg to use this package.\")","chunkId":"audio_separator/separator/separator.py:23"}],[1078,{"filePath":"audio_separator/separator/separator.py","startLine":242,"endLine":251,"chunkText":"            self.logger.info(f\"FFmpeg installed: {first_line}\")\n        except FileNotFoundError:\n            self.logger.error(\"FFmpeg is not installed. Please install FFmpeg to use this package.\")\n            # Raise an exception if this is being run by a user, as ffmpeg is required for pydub to write audio\n            # but if we're just running unit tests in CI, no reason to throw\n            if \"PYTEST_CURRENT_TEST\" not in os.environ:\n                raise\n\n    def log_onnxruntime_packages(self):\n        \"\"\"","chunkId":"audio_separator/separator/separator.py:24"}],[1079,{"filePath":"audio_separator/separator/separator.py","startLine":249,"endLine":259,"chunkText":"\n    def log_onnxruntime_packages(self):\n        \"\"\"\n        This method logs the ONNX Runtime package versions, including the GPU and Silicon packages if available.\n        \"\"\"\n        onnxruntime_gpu_package = self.get_package_distribution(\"onnxruntime-gpu\")\n        onnxruntime_silicon_package = self.get_package_distribution(\"onnxruntime-silicon\")\n        onnxruntime_cpu_package = self.get_package_distribution(\"onnxruntime\")\n        onnxruntime_dml_package = self.get_package_distribution(\"onnxruntime-directml\")\n\n        if onnxruntime_gpu_package is not None:","chunkId":"audio_separator/separator/separator.py:25"}],[1080,{"filePath":"audio_separator/separator/separator.py","startLine":257,"endLine":264,"chunkText":"        onnxruntime_dml_package = self.get_package_distribution(\"onnxruntime-directml\")\n\n        if onnxruntime_gpu_package is not None:\n            self.logger.info(f\"ONNX Runtime GPU package installed with version: {onnxruntime_gpu_package.version}\")\n        if onnxruntime_silicon_package is not None:\n            self.logger.info(f\"ONNX Runtime Silicon package installed with version: {onnxruntime_silicon_package.version}\")\n        if onnxruntime_cpu_package is not None:\n            self.logger.info(f\"ONNX Runtime CPU package installed with version: {onnxruntime_cpu_package.version}\")","chunkId":"audio_separator/separator/separator.py:26"}],[1081,{"filePath":"audio_separator/separator/separator.py","startLine":262,"endLine":269,"chunkText":"            self.logger.info(f\"ONNX Runtime Silicon package installed with version: {onnxruntime_silicon_package.version}\")\n        if onnxruntime_cpu_package is not None:\n            self.logger.info(f\"ONNX Runtime CPU package installed with version: {onnxruntime_cpu_package.version}\")\n        if onnxruntime_dml_package is not None:\n            self.logger.info(f\"ONNX Runtime DirectML package installed with version: {onnxruntime_dml_package.version}\")\n\n    def setup_torch_device(self, system_info):\n        \"\"\"","chunkId":"audio_separator/separator/separator.py:27"}],[1082,{"filePath":"audio_separator/separator/separator.py","startLine":267,"endLine":280,"chunkText":"\n    def setup_torch_device(self, system_info):\n        \"\"\"\n        This method sets up the PyTorch and/or ONNX Runtime inferencing device, using GPU hardware acceleration if available.\n        \"\"\"\n        hardware_acceleration_enabled = False\n        ort_providers = ort.get_available_providers()\n        has_torch_dml_installed = self.get_package_distribution(\"torch_directml\")\n\n        self.torch_device_cpu = torch.device(\"cpu\")\n\n        if torch.cuda.is_available():\n            self.configure_cuda(ort_providers)\n            hardware_acceleration_enabled = True","chunkId":"audio_separator/separator/separator.py:28"}],[1083,{"filePath":"audio_separator/separator/separator.py","startLine":278,"endLine":289,"chunkText":"        if torch.cuda.is_available():\n            self.configure_cuda(ort_providers)\n            hardware_acceleration_enabled = True\n        elif hasattr(torch.backends, \"mps\") and torch.backends.mps.is_available() and system_info.processor == \"arm\":\n            self.configure_mps(ort_providers)\n            hardware_acceleration_enabled = True\n        elif self.use_directml and has_torch_dml_installed:\n            import torch_directml\n            if torch_directml.is_available():\n                self.configure_dml(ort_providers)\n                hardware_acceleration_enabled = True\n","chunkId":"audio_separator/separator/separator.py:29"}],[1084,{"filePath":"audio_separator/separator/separator.py","startLine":287,"endLine":298,"chunkText":"                self.configure_dml(ort_providers)\n                hardware_acceleration_enabled = True\n\n        if not hardware_acceleration_enabled:\n            self.logger.info(\"No hardware acceleration could be configured, running in CPU mode\")\n            self.torch_device = self.torch_device_cpu\n            self.onnx_execution_provider = [\"CPUExecutionProvider\"]\n\n    def configure_cuda(self, ort_providers):\n        \"\"\"\n        This method configures the CUDA device for PyTorch and ONNX Runtime, if available.\n        \"\"\"","chunkId":"audio_separator/separator/separator.py:30"}],[1085,{"filePath":"audio_separator/separator/separator.py","startLine":296,"endLine":306,"chunkText":"        \"\"\"\n        This method configures the CUDA device for PyTorch and ONNX Runtime, if available.\n        \"\"\"\n        self.logger.info(\"CUDA is available in Torch, setting Torch device to CUDA\")\n        self.torch_device = torch.device(\"cuda\")\n        if \"CUDAExecutionProvider\" in ort_providers:\n            self.logger.info(\"ONNXruntime has CUDAExecutionProvider available, enabling acceleration\")\n            self.onnx_execution_provider = [\"CUDAExecutionProvider\"]\n        else:\n            self.logger.warning(\"CUDAExecutionProvider not available in ONNXruntime, so acceleration will NOT be enabled\")\n","chunkId":"audio_separator/separator/separator.py:31"}],[1086,{"filePath":"audio_separator/separator/separator.py","startLine":304,"endLine":316,"chunkText":"        else:\n            self.logger.warning(\"CUDAExecutionProvider not available in ONNXruntime, so acceleration will NOT be enabled\")\n\n    def configure_mps(self, ort_providers):\n        \"\"\"\n        This method configures the Apple Silicon MPS/CoreML device for PyTorch and ONNX Runtime, if available.\n        \"\"\"\n        self.logger.info(\"Apple Silicon MPS/CoreML is available in Torch and processor is ARM, setting Torch device to MPS\")\n        self.torch_device_mps = torch.device(\"mps\")\n\n        self.torch_device = self.torch_device_mps\n\n        if \"CoreMLExecutionProvider\" in ort_providers:","chunkId":"audio_separator/separator/separator.py:32"}],[1087,{"filePath":"audio_separator/separator/separator.py","startLine":314,"endLine":325,"chunkText":"        self.torch_device = self.torch_device_mps\n\n        if \"CoreMLExecutionProvider\" in ort_providers:\n            self.logger.info(\"ONNXruntime has CoreMLExecutionProvider available, enabling acceleration\")\n            self.onnx_execution_provider = [\"CoreMLExecutionProvider\"]\n        else:\n            self.logger.warning(\"CoreMLExecutionProvider not available in ONNXruntime, so acceleration will NOT be enabled\")\n\n    def configure_dml(self, ort_providers):\n        \"\"\"\n        This method configures the DirectML device for PyTorch and ONNX Runtime, if available.\n        \"\"\"","chunkId":"audio_separator/separator/separator.py:33"}],[1088,{"filePath":"audio_separator/separator/separator.py","startLine":323,"endLine":334,"chunkText":"        \"\"\"\n        This method configures the DirectML device for PyTorch and ONNX Runtime, if available.\n        \"\"\"\n        import torch_directml\n        self.logger.info(\"DirectML is available in Torch, setting Torch device to DirectML\")\n        self.torch_device_dml = torch_directml.device() \n        self.torch_device = self.torch_device_dml\n\n        if \"DmlExecutionProvider\" in ort_providers:\n            self.logger.info(\"ONNXruntime has DmlExecutionProvider available, enabling acceleration\")\n            self.onnx_execution_provider = [\"DmlExecutionProvider\"]\n        else:","chunkId":"audio_separator/separator/separator.py:34"}],[1089,{"filePath":"audio_separator/separator/separator.py","startLine":332,"endLine":343,"chunkText":"            self.logger.info(\"ONNXruntime has DmlExecutionProvider available, enabling acceleration\")\n            self.onnx_execution_provider = [\"DmlExecutionProvider\"]\n        else:\n            self.logger.warning(\"DmlExecutionProvider not available in ONNXruntime, so acceleration will NOT be enabled\")\n\n    def get_package_distribution(self, package_name):\n        \"\"\"\n        This method returns the package distribution for a given package name if installed, or None otherwise.\n        \"\"\"\n        try:\n            return metadata.distribution(package_name)\n        except metadata.PackageNotFoundError:","chunkId":"audio_separator/separator/separator.py:35"}],[1090,{"filePath":"audio_separator/separator/separator.py","startLine":341,"endLine":357,"chunkText":"        try:\n            return metadata.distribution(package_name)\n        except metadata.PackageNotFoundError:\n            self.logger.debug(f\"Python package: {package_name} not installed\")\n            return None\n\n    def get_model_hash(self, model_path):\n        \"\"\"\n        This method returns the MD5 hash of a given model file.\n        \"\"\"\n        self.logger.debug(f\"Calculating hash of model file {model_path}\")\n        # Use the specific byte count from the original logic\n        BYTES_TO_HASH = 10000 * 1024  # 10,240,000 bytes\n\n        try:\n            file_size = os.path.getsize(model_path)\n","chunkId":"audio_separator/separator/separator.py:36"}],[1091,{"filePath":"audio_separator/separator/separator.py","startLine":355,"endLine":365,"chunkText":"        try:\n            file_size = os.path.getsize(model_path)\n\n            with open(model_path, \"rb\") as f:\n                if file_size < BYTES_TO_HASH:\n                    # Hash the entire file if smaller than the target byte count\n                    self.logger.debug(f\"File size {file_size} < {BYTES_TO_HASH}, hashing entire file.\")\n                    hash_value = hashlib.md5(f.read()).hexdigest()\n                else:\n                    # Seek to the specific position before the end (from the beginning) and hash\n                    seek_pos = file_size - BYTES_TO_HASH","chunkId":"audio_separator/separator/separator.py:37"}],[1092,{"filePath":"audio_separator/separator/separator.py","startLine":363,"endLine":374,"chunkText":"                else:\n                    # Seek to the specific position before the end (from the beginning) and hash\n                    seek_pos = file_size - BYTES_TO_HASH\n                    self.logger.debug(f\"File size {file_size} >= {BYTES_TO_HASH}, seeking to {seek_pos} and hashing remaining bytes.\")\n                    f.seek(seek_pos, io.SEEK_SET)\n                    hash_value = hashlib.md5(f.read()).hexdigest()\n\n            # Log the calculated hash\n            self.logger.info(f\"Hash of model file {model_path} is {hash_value}\")\n            return hash_value\n\n        except FileNotFoundError:","chunkId":"audio_separator/separator/separator.py:38"}],[1093,{"filePath":"audio_separator/separator/separator.py","startLine":372,"endLine":384,"chunkText":"            return hash_value\n\n        except FileNotFoundError:\n            self.logger.error(f\"Model file not found at {model_path}\")\n            raise # Re-raise the specific error\n        except Exception as e:\n            # Catch other potential errors (e.g., permissions, other IOErrors)\n            self.logger.error(f\"Error calculating hash for {model_path}: {e}\")\n            raise # Re-raise other errors\n\n    def download_file_if_not_exists(self, url, output_path):\n        \"\"\"\n        This method downloads a file from a given URL to a given output path, if the file does not already exist.","chunkId":"audio_separator/separator/separator.py:39"}],[1094,{"filePath":"audio_separator/separator/separator.py","startLine":384,"endLine":395,"chunkText":"        This method downloads a file from a given URL to a given output path, if the file does not already exist.\n        \"\"\"\n\n        if os.path.isfile(output_path):\n            self.logger.debug(f\"File already exists at {output_path}, skipping download\")\n            return\n\n        self.logger.debug(f\"Downloading file from {url} to {output_path} with timeout 300s\")\n        response = requests.get(url, stream=True, timeout=300)\n\n        if response.status_code == 200:\n            total_size_in_bytes = int(response.headers.get(\"content-length\", 0))","chunkId":"audio_separator/separator/separator.py:40"}],[1095,{"filePath":"audio_separator/separator/separator.py","startLine":393,"endLine":406,"chunkText":"\n        if response.status_code == 200:\n            total_size_in_bytes = int(response.headers.get(\"content-length\", 0))\n            progress_bar = tqdm(total=total_size_in_bytes, unit=\"iB\", unit_scale=True)\n\n            with open(output_path, \"wb\") as f:\n                for chunk in response.iter_content(chunk_size=8192):\n                    progress_bar.update(len(chunk))\n                    f.write(chunk)\n            progress_bar.close()\n        else:\n            raise RuntimeError(f\"Failed to download file from {url}, response code: {response.status_code}\")\n\n    def list_supported_model_files(self):","chunkId":"audio_separator/separator/separator.py:41"}],[1096,{"filePath":"audio_separator/separator/separator.py","startLine":404,"endLine":418,"chunkText":"            raise RuntimeError(f\"Failed to download file from {url}, response code: {response.status_code}\")\n\n    def list_supported_model_files(self):\n        \"\"\"\n        This method lists the supported model files for audio-separator, by fetching the same file UVR uses to list these.\n        Also includes model performance scores where available.\n\n        Example response object:\n\n        {\n            \"MDX\": {\n                \"MDX-Net Model VIP: UVR-MDX-NET-Inst_full_292\": {\n                \"filename\": \"UVR-MDX-NET-Inst_full_292.onnx\",\n                \"scores\": {\n                    \"vocals\": {","chunkId":"audio_separator/separator/separator.py:42"}],[1097,{"filePath":"audio_separator/separator/separator.py","startLine":416,"endLine":432,"chunkText":"                \"filename\": \"UVR-MDX-NET-Inst_full_292.onnx\",\n                \"scores\": {\n                    \"vocals\": {\n                    \"SDR\": 10.6497,\n                    \"SIR\": 20.3786,\n                    \"SAR\": 10.692,\n                    \"ISR\": 14.848\n                    },\n                    \"instrumental\": {\n                    \"SDR\": 15.2149,\n                    \"SIR\": 25.6075,\n                    \"SAR\": 17.1363,\n                    \"ISR\": 17.7893\n                    }\n                },\n                \"download_files\": [\n                    \"UVR-MDX-NET-Inst_full_292.onnx\"","chunkId":"audio_separator/separator/separator.py:43"}],[1098,{"filePath":"audio_separator/separator/separator.py","startLine":430,"endLine":448,"chunkText":"                },\n                \"download_files\": [\n                    \"UVR-MDX-NET-Inst_full_292.onnx\"\n                ]\n                }\n            },\n            \"Demucs\": {\n                \"Demucs v4: htdemucs_ft\": {\n                \"filename\": \"htdemucs_ft.yaml\",\n                \"scores\": {\n                    \"vocals\": {\n                    \"SDR\": 11.2685,\n                    \"SIR\": 21.257,\n                    \"SAR\": 11.0359,\n                    \"ISR\": 19.3753\n                    },\n                    \"drums\": {\n                    \"SDR\": 13.235,\n                    \"SIR\": 23.3053,","chunkId":"audio_separator/separator/separator.py:44"}],[1099,{"filePath":"audio_separator/separator/separator.py","startLine":446,"endLine":460,"chunkText":"                    \"drums\": {\n                    \"SDR\": 13.235,\n                    \"SIR\": 23.3053,\n                    \"SAR\": 13.0313,\n                    \"ISR\": 17.2889\n                    },\n                    \"bass\": {\n                    \"SDR\": 9.72743,\n                    \"SIR\": 19.5435,\n                    \"SAR\": 9.20801,\n                    \"ISR\": 13.5037\n                    }\n                },\n                \"download_files\": [\n                    \"https://dl.fbaipublicfiles.com/demucs/hybrid_transformer/f7e0c4bc-ba3fe64a.th\",","chunkId":"audio_separator/separator/separator.py:45"}],[1100,{"filePath":"audio_separator/separator/separator.py","startLine":458,"endLine":465,"chunkText":"                },\n                \"download_files\": [\n                    \"https://dl.fbaipublicfiles.com/demucs/hybrid_transformer/f7e0c4bc-ba3fe64a.th\",\n                    \"https://dl.fbaipublicfiles.com/demucs/hybrid_transformer/d12395a8-e57c48e6.th\",\n                    \"https://dl.fbaipublicfiles.com/demucs/hybrid_transformer/92cfc3b6-ef3bcb9c.th\",\n                    \"https://dl.fbaipublicfiles.com/demucs/hybrid_transformer/04573f0d-f3cf25b2.th\",\n                    \"https://github.com/TRvlvr/model_repo/releases/download/all_public_uvr_models/htdemucs_ft.yaml\"\n                ]","chunkId":"audio_separator/separator/separator.py:46"}],[1101,{"filePath":"audio_separator/separator/separator.py","startLine":463,"endLine":476,"chunkText":"                    \"https://dl.fbaipublicfiles.com/demucs/hybrid_transformer/04573f0d-f3cf25b2.th\",\n                    \"https://github.com/TRvlvr/model_repo/releases/download/all_public_uvr_models/htdemucs_ft.yaml\"\n                ]\n                }\n            },\n            \"MDXC\": {\n                \"MDX23C Model: MDX23C-InstVoc HQ\": {\n                \"filename\": \"MDX23C-8KFFT-InstVoc_HQ.ckpt\",\n                \"scores\": {\n                    \"vocals\": {\n                    \"SDR\": 11.9504,\n                    \"SIR\": 23.1166,\n                    \"SAR\": 12.093,\n                    \"ISR\": 15.4782","chunkId":"audio_separator/separator/separator.py:47"}],[1102,{"filePath":"audio_separator/separator/separator.py","startLine":474,"endLine":492,"chunkText":"                    \"SIR\": 23.1166,\n                    \"SAR\": 12.093,\n                    \"ISR\": 15.4782\n                    },\n                    \"instrumental\": {\n                    \"SDR\": 16.3035,\n                    \"SIR\": 26.6161,\n                    \"SAR\": 18.5167,\n                    \"ISR\": 18.3939\n                    }\n                },\n                \"download_files\": [\n                    \"MDX23C-8KFFT-InstVoc_HQ.ckpt\",\n                    \"model_2_stem_full_band_8k.yaml\"\n                ]\n                }\n            }\n        }\n        \"\"\"","chunkId":"audio_separator/separator/separator.py:48"}],[1103,{"filePath":"audio_separator/separator/separator.py","startLine":490,"endLine":503,"chunkText":"            }\n        }\n        \"\"\"\n        download_checks_path = os.path.join(self.model_file_dir, \"download_checks.json\")\n\n        self.download_file_if_not_exists(\"https://raw.githubusercontent.com/TRvlvr/application_data/main/filelists/download_checks.json\", download_checks_path)\n\n        model_downloads_list = json.load(open(download_checks_path, encoding=\"utf-8\"))\n        self.logger.debug(f\"UVR model download list loaded\")\n\n        # Load the model scores with error handling\n        model_scores = {}\n        try:\n            with resources.open_text(\"audio_separator\", \"models-scores.json\") as f:","chunkId":"audio_separator/separator/separator.py:49"}],[1104,{"filePath":"audio_separator/separator/separator.py","startLine":501,"endLine":510,"chunkText":"        model_scores = {}\n        try:\n            with resources.open_text(\"audio_separator\", \"models-scores.json\") as f:\n                model_scores = json.load(f)\n            self.logger.debug(f\"Model scores loaded\")\n        except json.JSONDecodeError as e:\n            self.logger.warning(f\"Failed to load model scores: {str(e)}\")\n            self.logger.warning(\"Continuing without model scores\")\n\n        # Only show Demucs v4 models as we've only implemented support for v4","chunkId":"audio_separator/separator/separator.py:50"}],[1105,{"filePath":"audio_separator/separator/separator.py","startLine":508,"endLine":516,"chunkText":"            self.logger.warning(\"Continuing without model scores\")\n\n        # Only show Demucs v4 models as we've only implemented support for v4\n        filtered_demucs_v4 = {key: value for key, value in model_downloads_list[\"demucs_download_list\"].items() if key.startswith(\"Demucs v4\")}\n\n        # Modified Demucs handling to use YAML files as identifiers and include download files\n        demucs_models = {}\n        for name, files in filtered_demucs_v4.items():\n            # Find the YAML file in the model files","chunkId":"audio_separator/separator/separator.py:51"}],[1106,{"filePath":"audio_separator/separator/separator.py","startLine":514,"endLine":523,"chunkText":"        demucs_models = {}\n        for name, files in filtered_demucs_v4.items():\n            # Find the YAML file in the model files\n            yaml_file = next((filename for filename in files.keys() if filename.endswith(\".yaml\")), None)\n            if yaml_file:\n                model_score_data = model_scores.get(yaml_file, {})\n                demucs_models[name] = {\n                    \"filename\": yaml_file,\n                    \"scores\": model_score_data.get(\"median_scores\", {}),\n                    \"stems\": model_score_data.get(\"stems\", []),","chunkId":"audio_separator/separator/separator.py:52"}],[1107,{"filePath":"audio_separator/separator/separator.py","startLine":521,"endLine":530,"chunkText":"                    \"filename\": yaml_file,\n                    \"scores\": model_score_data.get(\"median_scores\", {}),\n                    \"stems\": model_score_data.get(\"stems\", []),\n                    \"target_stem\": model_score_data.get(\"target_stem\"),\n                    \"download_files\": list(files.values()),  # List of all download URLs/filenames\n                }\n\n        # Load the JSON file using importlib.resources\n        with resources.open_text(\"audio_separator\", \"models.json\") as f:\n            audio_separator_models_list = json.load(f)","chunkId":"audio_separator/separator/separator.py:53"}],[1108,{"filePath":"audio_separator/separator/separator.py","startLine":530,"endLine":541,"chunkText":"            audio_separator_models_list = json.load(f)\n        self.logger.debug(f\"Audio-Separator model list loaded\")\n\n        # Return object with list of model names\n        model_files_grouped_by_type = {\n            \"VR\": {\n                name: {\n                    \"filename\": filename,\n                    \"scores\": model_scores.get(filename, {}).get(\"median_scores\", {}),\n                    \"stems\": model_scores.get(filename, {}).get(\"stems\", []),\n                    \"target_stem\": model_scores.get(filename, {}).get(\"target_stem\"),\n                    \"download_files\": [filename],","chunkId":"audio_separator/separator/separator.py:54"}],[1109,{"filePath":"audio_separator/separator/separator.py","startLine":539,"endLine":548,"chunkText":"                    \"stems\": model_scores.get(filename, {}).get(\"stems\", []),\n                    \"target_stem\": model_scores.get(filename, {}).get(\"target_stem\"),\n                    \"download_files\": [filename],\n                }  # Just the filename for VR models\n                for name, filename in {**model_downloads_list[\"vr_download_list\"], **audio_separator_models_list[\"vr_download_list\"]}.items()\n            },\n            \"MDX\": {\n                name: {\n                    \"filename\": filename,\n                    \"scores\": model_scores.get(filename, {}).get(\"median_scores\", {}),","chunkId":"audio_separator/separator/separator.py:55"}],[1110,{"filePath":"audio_separator/separator/separator.py","startLine":546,"endLine":552,"chunkText":"                name: {\n                    \"filename\": filename,\n                    \"scores\": model_scores.get(filename, {}).get(\"median_scores\", {}),\n                    \"stems\": model_scores.get(filename, {}).get(\"stems\", []),\n                    \"target_stem\": model_scores.get(filename, {}).get(\"target_stem\"),\n                    \"download_files\": [filename],\n                }  # Just the filename for MDX models","chunkId":"audio_separator/separator/separator.py:56"}],[1111,{"filePath":"audio_separator/separator/separator.py","startLine":550,"endLine":558,"chunkText":"                    \"target_stem\": model_scores.get(filename, {}).get(\"target_stem\"),\n                    \"download_files\": [filename],\n                }  # Just the filename for MDX models\n                for name, filename in {**model_downloads_list[\"mdx_download_list\"], **model_downloads_list[\"mdx_download_vip_list\"], **audio_separator_models_list[\"mdx_download_list\"]}.items()\n            },\n            \"Demucs\": demucs_models,\n            \"MDXC\": {\n                name: {\n                    \"filename\": next(iter(files.keys())),","chunkId":"audio_separator/separator/separator.py:57"}],[1112,{"filePath":"audio_separator/separator/separator.py","startLine":556,"endLine":564,"chunkText":"            \"MDXC\": {\n                name: {\n                    \"filename\": next(iter(files.keys())),\n                    \"scores\": model_scores.get(next(iter(files.keys())), {}).get(\"median_scores\", {}),\n                    \"stems\": model_scores.get(next(iter(files.keys())), {}).get(\"stems\", []),\n                    \"target_stem\": model_scores.get(next(iter(files.keys())), {}).get(\"target_stem\"),\n                    \"download_files\": list(files.keys()) + list(files.values()),  # List of both model filenames and config filenames\n                }\n                for name, files in {","chunkId":"audio_separator/separator/separator.py:58"}],[1113,{"filePath":"audio_separator/separator/separator.py","startLine":562,"endLine":573,"chunkText":"                    \"download_files\": list(files.keys()) + list(files.values()),  # List of both model filenames and config filenames\n                }\n                for name, files in {\n                    **model_downloads_list[\"mdx23c_download_list\"],\n                    **model_downloads_list[\"mdx23c_download_vip_list\"],\n                    **model_downloads_list[\"roformer_download_list\"],\n                    **audio_separator_models_list[\"mdx23c_download_list\"],\n                    **audio_separator_models_list[\"roformer_download_list\"],\n                }.items()\n            },\n        }\n","chunkId":"audio_separator/separator/separator.py:59"}],[1114,{"filePath":"audio_separator/separator/separator.py","startLine":571,"endLine":581,"chunkText":"            },\n        }\n\n        return model_files_grouped_by_type\n\n    def print_uvr_vip_message(self):\n        \"\"\"\n        This method prints a message to the user if they have downloaded a VIP model, reminding them to support Anjok07 on Patreon.\n        \"\"\"\n        if self.model_is_uvr_vip:\n            self.logger.warning(f\"The model: '{self.model_friendly_name}' is a VIP model, intended by Anjok07 for access by paying subscribers only.\")","chunkId":"audio_separator/separator/separator.py:60"}],[1115,{"filePath":"audio_separator/separator/separator.py","startLine":579,"endLine":586,"chunkText":"        \"\"\"\n        if self.model_is_uvr_vip:\n            self.logger.warning(f\"The model: '{self.model_friendly_name}' is a VIP model, intended by Anjok07 for access by paying subscribers only.\")\n            self.logger.warning(\"If you are not already subscribed, please consider supporting the developer of UVR, Anjok07 by subscribing here: https://patreon.com/uvr\")\n\n    def download_model_files(self, model_filename):\n        \"\"\"\n        This method downloads the model files for a given model filename, if they are not already present.","chunkId":"audio_separator/separator/separator.py:61"}],[1116,{"filePath":"audio_separator/separator/separator.py","startLine":584,"endLine":592,"chunkText":"    def download_model_files(self, model_filename):\n        \"\"\"\n        This method downloads the model files for a given model filename, if they are not already present.\n        Returns tuple of (model_filename, model_type, model_friendly_name, model_path, yaml_config_filename)\n        \"\"\"\n        model_path = os.path.join(self.model_file_dir, f\"{model_filename}\")\n\n        supported_model_files_grouped = self.list_supported_model_files()\n        public_model_repo_url_prefix = \"https://github.com/TRvlvr/model_repo/releases/download/all_public_uvr_models\"","chunkId":"audio_separator/separator/separator.py:62"}],[1117,{"filePath":"audio_separator/separator/separator.py","startLine":590,"endLine":599,"chunkText":"\n        supported_model_files_grouped = self.list_supported_model_files()\n        public_model_repo_url_prefix = \"https://github.com/TRvlvr/model_repo/releases/download/all_public_uvr_models\"\n        vip_model_repo_url_prefix = \"https://github.com/Anjok0109/ai_magic/releases/download/v5\"\n        audio_separator_models_repo_url_prefix = \"https://github.com/nomadkaraoke/python-audio-separator/releases/download/model-configs\"\n\n        yaml_config_filename = None\n\n        self.logger.debug(f\"Searching for model_filename {model_filename} in supported_model_files_grouped\")\n","chunkId":"audio_separator/separator/separator.py:63"}],[1118,{"filePath":"audio_separator/separator/separator.py","startLine":597,"endLine":606,"chunkText":"\n        self.logger.debug(f\"Searching for model_filename {model_filename} in supported_model_files_grouped\")\n\n        # Iterate through model types (MDX, Demucs, MDXC)\n        for model_type, models in supported_model_files_grouped.items():\n            # Iterate through each model in this type\n            for model_friendly_name, model_info in models.items():\n                self.model_is_uvr_vip = \"VIP\" in model_friendly_name\n                model_repo_url_prefix = vip_model_repo_url_prefix if self.model_is_uvr_vip else public_model_repo_url_prefix\n","chunkId":"audio_separator/separator/separator.py:64"}],[1119,{"filePath":"audio_separator/separator/separator.py","startLine":604,"endLine":612,"chunkText":"                self.model_is_uvr_vip = \"VIP\" in model_friendly_name\n                model_repo_url_prefix = vip_model_repo_url_prefix if self.model_is_uvr_vip else public_model_repo_url_prefix\n\n                # Check if this model matches our target filename\n                if model_info[\"filename\"] == model_filename or model_filename in model_info[\"download_files\"]:\n                    self.logger.debug(f\"Found matching model: {model_friendly_name}\")\n                    self.model_friendly_name = model_friendly_name\n                    self.print_uvr_vip_message()\n","chunkId":"audio_separator/separator/separator.py:65"}],[1120,{"filePath":"audio_separator/separator/separator.py","startLine":610,"endLine":618,"chunkText":"                    self.model_friendly_name = model_friendly_name\n                    self.print_uvr_vip_message()\n\n                    # Download each required file for this model\n                    for file_to_download in model_info[\"download_files\"]:\n                        # For URLs, extract just the filename portion\n                        if file_to_download.startswith(\"http\"):\n                            filename = file_to_download.split(\"/\")[-1]\n                            download_path = os.path.join(self.model_file_dir, filename)","chunkId":"audio_separator/separator/separator.py:66"}],[1121,{"filePath":"audio_separator/separator/separator.py","startLine":616,"endLine":625,"chunkText":"                        if file_to_download.startswith(\"http\"):\n                            filename = file_to_download.split(\"/\")[-1]\n                            download_path = os.path.join(self.model_file_dir, filename)\n                            self.download_file_if_not_exists(file_to_download, download_path)\n                            continue\n\n                        download_path = os.path.join(self.model_file_dir, file_to_download)\n\n                        # For MDXC models, handle YAML config files specially\n                        if model_type == \"MDXC\" and file_to_download.endswith(\".yaml\")","chunkId":"audio_separator/separator/separator.py:67"}],[1122,{"filePath":"audio_separator/separator/separator.py","startLine":623,"endLine":630,"chunkText":"\n                        # For MDXC models, handle YAML config files specially\n                        if model_type == \"MDXC\" and file_to_download.endswith(\".yaml\"):\n                            yaml_config_filename = file_to_download\n                            try:\n                                yaml_url = f\"{model_repo_url_prefix}/mdx_model_data/mdx_c_configs/{file_to_download}\"\n                                self.download_file_if_not_exists(yaml_url, download_path)\n                            except RuntimeError:","chunkId":"audio_separator/separator/separator.py:68"}],[1123,{"filePath":"audio_separator/separator/separator.py","startLine":628,"endLine":633,"chunkText":"                                yaml_url = f\"{model_repo_url_prefix}/mdx_model_data/mdx_c_configs/{file_to_download}\"\n                                self.download_file_if_not_exists(yaml_url, download_path)\n                            except RuntimeError:\n                                self.logger.debug(\"YAML config not found in UVR repo, trying audio-separator models repo...\")\n                                yaml_url = f\"{audio_separator_models_repo_url_prefix}/{file_to_download}\"\n                                self.download_file_if_not_exists(yaml_url, download_path)","chunkId":"audio_separator/separator/separator.py:69"}],[1124,{"filePath":"audio_separator/separator/separator.py","startLine":631,"endLine":638,"chunkText":"                                self.logger.debug(\"YAML config not found in UVR repo, trying audio-separator models repo...\")\n                                yaml_url = f\"{audio_separator_models_repo_url_prefix}/{file_to_download}\"\n                                self.download_file_if_not_exists(yaml_url, download_path)\n                            continue\n\n                        # For regular model files, try UVR repo first, then audio-separator repo\n                        try:\n                            download_url = f\"{model_repo_url_prefix}/{file_to_download}\"","chunkId":"audio_separator/separator/separator.py:70"}],[1125,{"filePath":"audio_separator/separator/separator.py","startLine":636,"endLine":642,"chunkText":"                        # For regular model files, try UVR repo first, then audio-separator repo\n                        try:\n                            download_url = f\"{model_repo_url_prefix}/{file_to_download}\"\n                            self.download_file_if_not_exists(download_url, download_path)\n                        except RuntimeError:\n                            self.logger.debug(\"Model not found in UVR repo, trying audio-separator models repo...\")\n                            download_url = f\"{audio_separator_models_repo_url_prefix}/{file_to_download}\"","chunkId":"audio_separator/separator/separator.py:71"}],[1126,{"filePath":"audio_separator/separator/separator.py","startLine":640,"endLine":648,"chunkText":"                        except RuntimeError:\n                            self.logger.debug(\"Model not found in UVR repo, trying audio-separator models repo...\")\n                            download_url = f\"{audio_separator_models_repo_url_prefix}/{file_to_download}\"\n                            self.download_file_if_not_exists(download_url, download_path)\n\n                    return model_filename, model_type, model_friendly_name, model_path, yaml_config_filename\n\n        raise ValueError(f\"Model file {model_filename} not found in supported model files\")\n","chunkId":"audio_separator/separator/separator.py:72"}],[1127,{"filePath":"audio_separator/separator/separator.py","startLine":646,"endLine":655,"chunkText":"\n        raise ValueError(f\"Model file {model_filename} not found in supported model files\")\n\n    def load_model_data_from_yaml(self, yaml_config_filename):\n        \"\"\"\n        This method loads model-specific parameters from the YAML file for that model.\n        The parameters in the YAML are critical to inferencing, as they need to match whatever was used during training.\n        \"\"\"\n        # Verify if the YAML filename includes a full path or just the filename\n        if not os.path.exists(yaml_config_filename):","chunkId":"audio_separator/separator/separator.py:73"}],[1128,{"filePath":"audio_separator/separator/separator.py","startLine":653,"endLine":664,"chunkText":"        \"\"\"\n        # Verify if the YAML filename includes a full path or just the filename\n        if not os.path.exists(yaml_config_filename):\n            model_data_yaml_filepath = os.path.join(self.model_file_dir, yaml_config_filename)\n        else:\n            model_data_yaml_filepath = yaml_config_filename\n\n        self.logger.debug(f\"Loading model data from YAML at path {model_data_yaml_filepath}\")\n\n        model_data = yaml.load(open(model_data_yaml_filepath, encoding=\"utf-8\"), Loader=yaml.FullLoader)\n        self.logger.debug(f\"Model data loaded from YAML file: {model_data}\")\n","chunkId":"audio_separator/separator/separator.py:74"}],[1129,{"filePath":"audio_separator/separator/separator.py","startLine":662,"endLine":673,"chunkText":"        model_data = yaml.load(open(model_data_yaml_filepath, encoding=\"utf-8\"), Loader=yaml.FullLoader)\n        self.logger.debug(f\"Model data loaded from YAML file: {model_data}\")\n\n        if \"roformer\" in model_data_yaml_filepath.lower():\n            model_data[\"is_roformer\"] = True\n\n        return model_data\n\n    def load_model_data_using_hash(self, model_path):\n        \"\"\"\n        This method loads model-specific parameters from UVR model data files.\n        These parameters are critical to inferencing using a given model, as they need to match whatever was used during training.","chunkId":"audio_separator/separator/separator.py:75"}],[1130,{"filePath":"audio_separator/separator/separator.py","startLine":671,"endLine":679,"chunkText":"        \"\"\"\n        This method loads model-specific parameters from UVR model data files.\n        These parameters are critical to inferencing using a given model, as they need to match whatever was used during training.\n        The correct parameters are identified by calculating the hash of the model file and looking up the hash in the UVR data files.\n        \"\"\"\n        # Model data and configuration sources from UVR\n        model_data_url_prefix = \"https://raw.githubusercontent.com/TRvlvr/application_data/main\"\n\n        vr_model_data_url = f\"{model_data_url_prefix}/vr_model_data/model_data_new.json\"","chunkId":"audio_separator/separator/separator.py:76"}],[1131,{"filePath":"audio_separator/separator/separator.py","startLine":677,"endLine":686,"chunkText":"        model_data_url_prefix = \"https://raw.githubusercontent.com/TRvlvr/application_data/main\"\n\n        vr_model_data_url = f\"{model_data_url_prefix}/vr_model_data/model_data_new.json\"\n        mdx_model_data_url = f\"{model_data_url_prefix}/mdx_model_data/model_data_new.json\"\n\n        # Calculate hash for the downloaded model\n        self.logger.debug(\"Calculating MD5 hash for model file to identify model parameters from UVR data...\")\n        model_hash = self.get_model_hash(model_path)\n        self.logger.debug(f\"Model {model_path} has hash {model_hash}\")\n","chunkId":"audio_separator/separator/separator.py:77"}],[1132,{"filePath":"audio_separator/separator/separator.py","startLine":684,"endLine":693,"chunkText":"        model_hash = self.get_model_hash(model_path)\n        self.logger.debug(f\"Model {model_path} has hash {model_hash}\")\n\n        # Setting up the path for model data and checking its existence\n        vr_model_data_path = os.path.join(self.model_file_dir, \"vr_model_data.json\")\n        self.logger.debug(f\"VR model data path set to {vr_model_data_path}\")\n        self.download_file_if_not_exists(vr_model_data_url, vr_model_data_path)\n\n        mdx_model_data_path = os.path.join(self.model_file_dir, \"mdx_model_data.json\")\n        self.logger.debug(f\"MDX model data path set to {mdx_model_data_path}\")","chunkId":"audio_separator/separator/separator.py:78"}],[1133,{"filePath":"audio_separator/separator/separator.py","startLine":691,"endLine":701,"chunkText":"\n        mdx_model_data_path = os.path.join(self.model_file_dir, \"mdx_model_data.json\")\n        self.logger.debug(f\"MDX model data path set to {mdx_model_data_path}\")\n        self.download_file_if_not_exists(mdx_model_data_url, mdx_model_data_path)\n\n        # Loading model data from UVR\n        self.logger.debug(\"Loading MDX and VR model parameters from UVR model data files...\")\n        vr_model_data_object = json.load(open(vr_model_data_path, encoding=\"utf-8\"))\n        mdx_model_data_object = json.load(open(mdx_model_data_path, encoding=\"utf-8\"))\n\n        # Load additional model data from audio-separator","chunkId":"audio_separator/separator/separator.py:79"}],[1134,{"filePath":"audio_separator/separator/separator.py","startLine":699,"endLine":707,"chunkText":"        mdx_model_data_object = json.load(open(mdx_model_data_path, encoding=\"utf-8\"))\n\n        # Load additional model data from audio-separator\n        self.logger.debug(\"Loading additional model parameters from audio-separator model data file...\")\n        with resources.open_text(\"audio_separator\", \"model-data.json\") as f:\n            audio_separator_model_data = json.load(f)\n\n        # Merge the model data objects, with audio-separator data taking precedence\n        vr_model_data_object = {**vr_model_data_object, **audio_separator_model_data.get(\"vr_model_data\", {})}","chunkId":"audio_separator/separator/separator.py:80"}],[1135,{"filePath":"audio_separator/separator/separator.py","startLine":705,"endLine":714,"chunkText":"\n        # Merge the model data objects, with audio-separator data taking precedence\n        vr_model_data_object = {**vr_model_data_object, **audio_separator_model_data.get(\"vr_model_data\", {})}\n        mdx_model_data_object = {**mdx_model_data_object, **audio_separator_model_data.get(\"mdx_model_data\", {})}\n\n        if model_hash in mdx_model_data_object:\n            model_data = mdx_model_data_object[model_hash]\n        elif model_hash in vr_model_data_object:\n            model_data = vr_model_data_object[model_hash]\n        else:","chunkId":"audio_separator/separator/separator.py:81"}],[1136,{"filePath":"audio_separator/separator/separator.py","startLine":712,"endLine":723,"chunkText":"        elif model_hash in vr_model_data_object:\n            model_data = vr_model_data_object[model_hash]\n        else:\n            raise ValueError(f\"Unsupported Model File: parameters for MD5 hash {model_hash} could not be found in UVR model data file for MDX or VR arch.\")\n\n        self.logger.debug(f\"Model data loaded using hash {model_hash}: {model_data}\")\n\n        return model_data\n\n    def load_model(self, model_filename=\"model_bs_roformer_ep_317_sdr_12.9755.ckpt\"):\n        \"\"\"\n        This method instantiates the architecture-specific separation class,","chunkId":"audio_separator/separator/separator.py:82"}],[1137,{"filePath":"audio_separator/separator/separator.py","startLine":721,"endLine":732,"chunkText":"    def load_model(self, model_filename=\"model_bs_roformer_ep_317_sdr_12.9755.ckpt\"):\n        \"\"\"\n        This method instantiates the architecture-specific separation class,\n        loading the separation model into memory, downloading it first if necessary.\n        \"\"\"\n        self.logger.info(f\"Loading model {model_filename}...\")\n\n        load_model_start_time = time.perf_counter()\n\n        # Setting up the model path\n        model_filename, model_type, model_friendly_name, model_path, yaml_config_filename = self.download_model_files(model_filename)\n        model_name = model_filename.split(\".\")[0]","chunkId":"audio_separator/separator/separator.py:83"}],[1138,{"filePath":"audio_separator/separator/separator.py","startLine":730,"endLine":740,"chunkText":"        # Setting up the model path\n        model_filename, model_type, model_friendly_name, model_path, yaml_config_filename = self.download_model_files(model_filename)\n        model_name = model_filename.split(\".\")[0]\n        self.logger.debug(f\"Model downloaded, friendly name: {model_friendly_name}, model_path: {model_path}\")\n\n        if model_path.lower().endswith(\".yaml\"):\n            yaml_config_filename = model_path\n\n        if yaml_config_filename is not None:\n            model_data = self.load_model_data_from_yaml(yaml_config_filename)\n        else:","chunkId":"audio_separator/separator/separator.py:84"}],[1139,{"filePath":"audio_separator/separator/separator.py","startLine":738,"endLine":750,"chunkText":"        if yaml_config_filename is not None:\n            model_data = self.load_model_data_from_yaml(yaml_config_filename)\n        else:\n            model_data = self.load_model_data_using_hash(model_path)\n\n        common_params = {\n            \"logger\": self.logger,\n            \"log_level\": self.log_level,\n            \"torch_device\": self.torch_device,\n            \"torch_device_cpu\": self.torch_device_cpu,\n            \"torch_device_mps\": self.torch_device_mps,\n            \"onnx_execution_provider\": self.onnx_execution_provider,\n            \"model_name\": model_name,","chunkId":"audio_separator/separator/separator.py:85"}],[1140,{"filePath":"audio_separator/separator/separator.py","startLine":748,"endLine":758,"chunkText":"            \"torch_device_mps\": self.torch_device_mps,\n            \"onnx_execution_provider\": self.onnx_execution_provider,\n            \"model_name\": model_name,\n            \"model_path\": model_path,\n            \"model_data\": model_data,\n            \"output_format\": self.output_format,\n            \"output_bitrate\": self.output_bitrate,\n            \"output_dir\": self.output_dir,\n            \"normalization_threshold\": self.normalization_threshold,\n            \"amplification_threshold\": self.amplification_threshold,\n            \"output_single_stem\": self.output_single_stem,","chunkId":"audio_separator/separator/separator.py:86"}],[1141,{"filePath":"audio_separator/separator/separator.py","startLine":756,"endLine":764,"chunkText":"            \"normalization_threshold\": self.normalization_threshold,\n            \"amplification_threshold\": self.amplification_threshold,\n            \"output_single_stem\": self.output_single_stem,\n            \"invert_using_spec\": self.invert_using_spec,\n            \"sample_rate\": self.sample_rate,\n            \"use_soundfile\": self.use_soundfile,\n        }\n\n        # Instantiate the appropriate separator class depending on the model type","chunkId":"audio_separator/separator/separator.py:87"}],[1142,{"filePath":"audio_separator/separator/separator.py","startLine":762,"endLine":769,"chunkText":"        }\n\n        # Instantiate the appropriate separator class depending on the model type\n        separator_classes = {\"MDX\": \"mdx_separator.MDXSeparator\", \"VR\": \"vr_separator.VRSeparator\", \"Demucs\": \"demucs_separator.DemucsSeparator\", \"MDXC\": \"mdxc_separator.MDXCSeparator\"}\n\n        if model_type not in self.arch_specific_params or model_type not in separator_classes:\n            # Enhanced error message for Roformer models\n            if \"roformer\" in model_filename.lower() or (model_data and model_data.get(\"is_roformer\", False)):","chunkId":"audio_separator/separator/separator.py:88"}],[1143,{"filePath":"audio_separator/separator/separator.py","startLine":767,"endLine":774,"chunkText":"        if model_type not in self.arch_specific_params or model_type not in separator_classes:\n            # Enhanced error message for Roformer models\n            if \"roformer\" in model_filename.lower() or (model_data and model_data.get(\"is_roformer\", False)):\n                error_msg = (f\"Roformer model type not properly configured: {model_type}. \"\n                           f\"This may indicate a configuration validation failure. \"\n                           f\"Please check the model file and YAML configuration.\")\n                self.logger.error(error_msg)\n                raise ValueError(error_msg)","chunkId":"audio_separator/separator/separator.py:89"}],[1144,{"filePath":"audio_separator/separator/separator.py","startLine":772,"endLine":783,"chunkText":"                           f\"Please check the model file and YAML configuration.\")\n                self.logger.error(error_msg)\n                raise ValueError(error_msg)\n            else:\n                raise ValueError(f\"Model type not supported (yet): {model_type}\")\n\n        if model_type == \"Demucs\" and sys.version_info < (3, 10):\n            raise Exception(\"Demucs models require Python version 3.10 or newer.\")\n\n        self.logger.debug(f\"Importing module for model type {model_type}: {separator_classes[model_type]}\")\n\n        module_name, class_name = separator_classes[model_type].split(\".\")","chunkId":"audio_separator/separator/separator.py:90"}],[1145,{"filePath":"audio_separator/separator/separator.py","startLine":781,"endLine":790,"chunkText":"        self.logger.debug(f\"Importing module for model type {model_type}: {separator_classes[model_type]}\")\n\n        module_name, class_name = separator_classes[model_type].split(\".\")\n        module = importlib.import_module(f\"audio_separator.separator.architectures.{module_name}\")\n        separator_class = getattr(module, class_name)\n\n        self.logger.debug(f\"Instantiating separator class for model type {model_type}: {separator_class}\")\n        \n        try:\n            self.model_instance = separator_class(common_config=common_params, arch_config=self.arch_specific_params[model_type])","chunkId":"audio_separator/separator/separator.py:91"}],[1146,{"filePath":"audio_separator/separator/separator.py","startLine":788,"endLine":796,"chunkText":"        \n        try:\n            self.model_instance = separator_class(common_config=common_params, arch_config=self.arch_specific_params[model_type])\n        except Exception as e:\n            # Enhanced error handling for Roformer models\n            if \"roformer\" in model_filename.lower() or (model_data and model_data.get(\"is_roformer\", False)):\n                error_msg = (f\"Failed to instantiate Roformer model: {e}. \"\n                           f\"This may be due to missing parameters or configuration validation failures.\")\n                self.logger.error(error_msg)","chunkId":"audio_separator/separator/separator.py:92"}],[1147,{"filePath":"audio_separator/separator/separator.py","startLine":794,"endLine":804,"chunkText":"                error_msg = (f\"Failed to instantiate Roformer model: {e}. \"\n                           f\"This may be due to missing parameters or configuration validation failures.\")\n                self.logger.error(error_msg)\n                raise RuntimeError(error_msg) from e\n            else:\n                raise\n\n        # Log Roformer implementation version if applicable\n        if hasattr(self.model_instance, 'is_roformer_model') and self.model_instance.is_roformer_model:\n            roformer_stats = self.model_instance.get_roformer_loading_stats()\n            if roformer_stats:","chunkId":"audio_separator/separator/separator.py:93"}],[1148,{"filePath":"audio_separator/separator/separator.py","startLine":802,"endLine":810,"chunkText":"        if hasattr(self.model_instance, 'is_roformer_model') and self.model_instance.is_roformer_model:\n            roformer_stats = self.model_instance.get_roformer_loading_stats()\n            if roformer_stats:\n                self.logger.info(f\"Roformer loading stats: {roformer_stats}\")\n                \n        # Log the completion of the model load process\n        self.logger.debug(\"Loading model completed.\")\n        self.logger.info(f'Load model duration: {time.strftime(\"%H:%M:%S\", time.gmtime(int(time.perf_counter() - load_model_start_time)))}')\n","chunkId":"audio_separator/separator/separator.py:94"}],[1149,{"filePath":"audio_separator/separator/separator.py","startLine":808,"endLine":815,"chunkText":"        self.logger.debug(\"Loading model completed.\")\n        self.logger.info(f'Load model duration: {time.strftime(\"%H:%M:%S\", time.gmtime(int(time.perf_counter() - load_model_start_time)))}')\n\n    def separate(self, audio_file_path, custom_output_names=None):\n        \"\"\"\n        Separates the audio file(s) into different stems (e.g., vocals, instruments) using the loaded model.\n\n        This method takes the path to an audio file or a directory containing audio files, processes them through","chunkId":"audio_separator/separator/separator.py:95"}],[1150,{"filePath":"audio_separator/separator/separator.py","startLine":814,"endLine":823,"chunkText":"\n        This method takes the path to an audio file or a directory containing audio files, processes them through\n        the loaded separation model, and returns the paths to the output files containing the separated audio stems.\n        It handles the entire flow from loading the audio, running the separation, clearing up resources, and logging the process.\n\n        Parameters:\n        - audio_file_path (str or list): The path to the audio file or directory, or a list of paths.\n        - custom_output_names (dict, optional): Custom names for the output files. Defaults to None.\n\n        Returns:","chunkId":"audio_separator/separator/separator.py:96"}],[1151,{"filePath":"audio_separator/separator/separator.py","startLine":821,"endLine":830,"chunkText":"        - custom_output_names (dict, optional): Custom names for the output files. Defaults to None.\n\n        Returns:\n        - output_files (list of str): A list containing the paths to the separated audio stem files.\n        \"\"\"\n        # Check if the model and device are properly initialized\n        if not (self.torch_device and self.model_instance):\n            raise ValueError(\"Initialization failed or model not loaded. Please load a model before attempting to separate.\")\n\n        # If audio_file_path is a string, convert it to a list for uniform processing","chunkId":"audio_separator/separator/separator.py:97"}],[1152,{"filePath":"audio_separator/separator/separator.py","startLine":828,"endLine":840,"chunkText":"            raise ValueError(\"Initialization failed or model not loaded. Please load a model before attempting to separate.\")\n\n        # If audio_file_path is a string, convert it to a list for uniform processing\n        if isinstance(audio_file_path, str):\n            audio_file_path = [audio_file_path]\n\n        # Initialize a list to store paths of all output files\n        output_files = []\n\n        # Process each path in the list\n        for path in audio_file_path:\n            if os.path.isdir(path):\n                # If the path is a directory, recursively search for all audio files","chunkId":"audio_separator/separator/separator.py:98"}],[1153,{"filePath":"audio_separator/separator/separator.py","startLine":838,"endLine":845,"chunkText":"        for path in audio_file_path:\n            if os.path.isdir(path):\n                # If the path is a directory, recursively search for all audio files\n                for root, dirs, files in os.walk(path):\n                    for file in files:\n                        # Check the file extension to ensure it's an audio file\n                        if file.endswith((\".wav\", \".flac\", \".mp3\", \".ogg\", \".opus\", \".m4a\", \".aiff\", \".ac3\")):  # Add other formats if needed\n                            full_path = os.path.join(root, file)","chunkId":"audio_separator/separator/separator.py:99"}],[1154,{"filePath":"audio_separator/separator/separator.py","startLine":843,"endLine":849,"chunkText":"                        # Check the file extension to ensure it's an audio file\n                        if file.endswith((\".wav\", \".flac\", \".mp3\", \".ogg\", \".opus\", \".m4a\", \".aiff\", \".ac3\")):  # Add other formats if needed\n                            full_path = os.path.join(root, file)\n                            self.logger.info(f\"Processing file: {full_path}\")\n                            try:\n                                # Perform separation for each file\n                                files_output = self._separate_file(full_path, custom_output_names)","chunkId":"audio_separator/separator/separator.py:100"}],[1155,{"filePath":"audio_separator/separator/separator.py","startLine":847,"endLine":856,"chunkText":"                            try:\n                                # Perform separation for each file\n                                files_output = self._separate_file(full_path, custom_output_names)\n                                output_files.extend(files_output)\n                            except Exception as e:\n                                self.logger.error(f\"Failed to process file {full_path}: {e}\")\n            else:\n                # If the path is a file, process it directly\n                self.logger.info(f\"Processing file: {path}\")\n                try:","chunkId":"audio_separator/separator/separator.py:101"}],[1156,{"filePath":"audio_separator/separator/separator.py","startLine":854,"endLine":866,"chunkText":"                # If the path is a file, process it directly\n                self.logger.info(f\"Processing file: {path}\")\n                try:\n                    files_output = self._separate_file(path, custom_output_names)\n                    output_files.extend(files_output)\n                except Exception as e:\n                    self.logger.error(f\"Failed to process file {path}: {e}\")\n\n        return output_files\n\n    def _separate_file(self, audio_file_path, custom_output_names=None):\n        \"\"\"\n        Internal method to handle separation for a single audio file.","chunkId":"audio_separator/separator/separator.py:102"}],[1157,{"filePath":"audio_separator/separator/separator.py","startLine":864,"endLine":872,"chunkText":"    def _separate_file(self, audio_file_path, custom_output_names=None):\n        \"\"\"\n        Internal method to handle separation for a single audio file.\n        This method performs the actual separation process for a single audio file. It logs the start and end of the process,\n        handles autocast if enabled, and ensures GPU cache is cleared after processing.\n        Parameters:\n        - audio_file_path (str): The path to the audio file.\n        - custom_output_names (dict, optional): Custom names for the output files. Defaults to None.\n        Returns:","chunkId":"audio_separator/separator/separator.py:103"}],[1158,{"filePath":"audio_separator/separator/separator.py","startLine":870,"endLine":880,"chunkText":"        - audio_file_path (str): The path to the audio file.\n        - custom_output_names (dict, optional): Custom names for the output files. Defaults to None.\n        Returns:\n        - output_files (list of str): A list containing the paths to the separated audio stem files.\n        \"\"\"\n        # Check if chunking is enabled and file is large enough\n        if self.chunk_duration is not None:\n            import librosa\n            duration = librosa.get_duration(path=audio_file_path)\n\n            from audio_separator.separator.audio_chunking import AudioChunker","chunkId":"audio_separator/separator/separator.py:104"}],[1159,{"filePath":"audio_separator/separator/separator.py","startLine":878,"endLine":887,"chunkText":"            duration = librosa.get_duration(path=audio_file_path)\n\n            from audio_separator.separator.audio_chunking import AudioChunker\n            chunker = AudioChunker(self.chunk_duration, self.logger)\n\n            if chunker.should_chunk(duration):\n                self.logger.info(f\"File duration {duration:.1f}s exceeds chunk size {self.chunk_duration}s, using chunked processing\")\n                return self._process_with_chunking(audio_file_path, custom_output_names)\n\n        # Log the start of the separation process","chunkId":"audio_separator/separator/separator.py:105"}],[1160,{"filePath":"audio_separator/separator/separator.py","startLine":885,"endLine":892,"chunkText":"                return self._process_with_chunking(audio_file_path, custom_output_names)\n\n        # Log the start of the separation process\n        self.logger.info(f\"Starting separation process for audio_file_path: {audio_file_path}\")\n        separate_start_time = time.perf_counter()\n\n        # Log normalization and amplification thresholds\n        self.logger.debug(f\"Normalization threshold set to {self.normalization_threshold}, waveform will be lowered to this max amplitude to avoid clipping.\")","chunkId":"audio_separator/separator/separator.py:106"}],[1161,{"filePath":"audio_separator/separator/separator.py","startLine":890,"endLine":897,"chunkText":"\n        # Log normalization and amplification thresholds\n        self.logger.debug(f\"Normalization threshold set to {self.normalization_threshold}, waveform will be lowered to this max amplitude to avoid clipping.\")\n        self.logger.debug(f\"Amplification threshold set to {self.amplification_threshold}, waveform will be scaled up to this max amplitude if below it.\")\n\n        # Run separation method for the loaded model with autocast enabled if supported by the device\n        output_files = None\n        if self.use_autocast and autocast_mode.is_autocast_available(self.torch_device.type):","chunkId":"audio_separator/separator/separator.py:107"}],[1162,{"filePath":"audio_separator/separator/separator.py","startLine":895,"endLine":904,"chunkText":"        # Run separation method for the loaded model with autocast enabled if supported by the device\n        output_files = None\n        if self.use_autocast and autocast_mode.is_autocast_available(self.torch_device.type):\n            self.logger.debug(\"Autocast available.\")\n            with autocast_mode.autocast(self.torch_device.type):\n                output_files = self.model_instance.separate(audio_file_path, custom_output_names)\n        else:\n            self.logger.debug(\"Autocast unavailable.\")\n            output_files = self.model_instance.separate(audio_file_path, custom_output_names)\n","chunkId":"audio_separator/separator/separator.py:108"}],[1163,{"filePath":"audio_separator/separator/separator.py","startLine":902,"endLine":914,"chunkText":"            self.logger.debug(\"Autocast unavailable.\")\n            output_files = self.model_instance.separate(audio_file_path, custom_output_names)\n\n        # Clear GPU cache to free up memory\n        self.model_instance.clear_gpu_cache()\n\n        # Unset separation parameters to prevent accidentally re-using the wrong source files or output paths\n        self.model_instance.clear_file_specific_paths()\n\n        # Remind the user one more time if they used a VIP model, so the message doesn't get lost in the logs\n        self.print_uvr_vip_message()\n\n        # Log the completion of the separation process","chunkId":"audio_separator/separator/separator.py:109"}],[1164,{"filePath":"audio_separator/separator/separator.py","startLine":912,"endLine":924,"chunkText":"        self.print_uvr_vip_message()\n\n        # Log the completion of the separation process\n        self.logger.debug(\"Separation process completed.\")\n        self.logger.info(f'Separation duration: {time.strftime(\"%H:%M:%S\", time.gmtime(int(time.perf_counter() - separate_start_time)))}')\n\n        return output_files\n\n    def _process_with_chunking(self, audio_file_path, custom_output_names=None):\n        \"\"\"\n        Process large file by splitting into chunks.\n\n        This method splits a large audio file into smaller chunks, processes each chunk","chunkId":"audio_separator/separator/separator.py:110"}],[1165,{"filePath":"audio_separator/separator/separator.py","startLine":922,"endLine":934,"chunkText":"        Process large file by splitting into chunks.\n\n        This method splits a large audio file into smaller chunks, processes each chunk\n        separately, and merges the results back together. This helps prevent out-of-memory\n        errors when processing very long audio files.\n\n        Parameters:\n        - audio_file_path (str): The path to the audio file.\n        - custom_output_names (dict, optional): Custom names for the output files. Defaults to None.\n\n        Returns:\n        - output_files (list of str): A list containing the paths to the separated audio stem files.\n        \"\"\"","chunkId":"audio_separator/separator/separator.py:111"}],[1166,{"filePath":"audio_separator/separator/separator.py","startLine":932,"endLine":945,"chunkText":"        Returns:\n        - output_files (list of str): A list containing the paths to the separated audio stem files.\n        \"\"\"\n        import tempfile\n        import shutil\n        from audio_separator.separator.audio_chunking import AudioChunker\n\n        # Create temporary directory for chunks\n        temp_dir = tempfile.mkdtemp(prefix=\"audio-separator-chunks-\")\n        self.logger.debug(f\"Created temporary directory for chunks: {temp_dir}\")\n\n        try:\n            # Split audio into chunks\n            chunker = AudioChunker(self.chunk_duration, self.logger)","chunkId":"audio_separator/separator/separator.py:112"}],[1167,{"filePath":"audio_separator/separator/separator.py","startLine":943,"endLine":956,"chunkText":"        try:\n            # Split audio into chunks\n            chunker = AudioChunker(self.chunk_duration, self.logger)\n            chunk_paths = chunker.split_audio(audio_file_path, temp_dir)\n\n            # Process each chunk\n            processed_chunks_by_stem = {}\n\n            for i, chunk_path in enumerate(chunk_paths):\n                self.logger.info(f\"Processing chunk {i+1}/{len(chunk_paths)}: {chunk_path}\")\n\n                original_chunk_duration = self.chunk_duration\n                original_output_dir = self.output_dir\n                self.chunk_duration = None","chunkId":"audio_separator/separator/separator.py:113"}],[1168,{"filePath":"audio_separator/separator/separator.py","startLine":954,"endLine":967,"chunkText":"                original_chunk_duration = self.chunk_duration\n                original_output_dir = self.output_dir\n                self.chunk_duration = None\n                self.output_dir = temp_dir\n\n                if self.model_instance:\n                    original_model_output_dir = self.model_instance.output_dir\n                    self.model_instance.output_dir = temp_dir\n\n                try:\n                    output_files = self._separate_file(chunk_path, custom_output_names)\n\n                    # Dynamically group chunks by stem name\n                    for stem_path in output_files:","chunkId":"audio_separator/separator/separator.py:114"}],[1169,{"filePath":"audio_separator/separator/separator.py","startLine":965,"endLine":974,"chunkText":"\n                    # Dynamically group chunks by stem name\n                    for stem_path in output_files:\n                        # Extract stem name from filename: \"chunk_0000_(Vocals).wav\" → \"Vocals\"\n                        filename = os.path.basename(stem_path)\n                        match = re.search(r'_\\(([^)]+)\\)', filename)\n                        if match:\n                            stem_name = match.group(1)\n                        else:\n                            # Fallback: use index-based name if pattern not found","chunkId":"audio_separator/separator/separator.py:115"}],[1170,{"filePath":"audio_separator/separator/separator.py","startLine":972,"endLine":981,"chunkText":"                            stem_name = match.group(1)\n                        else:\n                            # Fallback: use index-based name if pattern not found\n                            stem_index = len([k for k in processed_chunks_by_stem.keys() if k.startswith('stem_')])\n                            stem_name = f\"stem_{stem_index}\"\n                            self.logger.warning(f\"Could not extract stem name from {filename}, using {stem_name}\")\n\n                        if stem_name not in processed_chunks_by_stem:\n                            processed_chunks_by_stem[stem_name] = []\n","chunkId":"audio_separator/separator/separator.py:116"}],[1171,{"filePath":"audio_separator/separator/separator.py","startLine":979,"endLine":990,"chunkText":"                        if stem_name not in processed_chunks_by_stem:\n                            processed_chunks_by_stem[stem_name] = []\n\n                        # Ensure absolute path\n                        abs_path = stem_path if os.path.isabs(stem_path) else os.path.join(temp_dir, stem_path)\n                        processed_chunks_by_stem[stem_name].append(abs_path)\n\n                    if not output_files:\n                        self.logger.warning(f\"Chunk {i+1} produced no output files\")\n\n                finally:\n                    self.chunk_duration = original_chunk_duration","chunkId":"audio_separator/separator/separator.py:117"}],[1172,{"filePath":"audio_separator/separator/separator.py","startLine":988,"endLine":1002,"chunkText":"\n                finally:\n                    self.chunk_duration = original_chunk_duration\n                    self.output_dir = original_output_dir\n                    if self.model_instance:\n                        self.model_instance.output_dir = original_model_output_dir\n\n                # Clear GPU cache between chunks\n                if self.model_instance:\n                    self.model_instance.clear_gpu_cache()\n\n            # Merge chunks for each stem dynamically\n            base_name = os.path.splitext(os.path.basename(audio_file_path))[0]\n            output_files = []\n","chunkId":"audio_separator/separator/separator.py:118"}],[1173,{"filePath":"audio_separator/separator/separator.py","startLine":1000,"endLine":1012,"chunkText":"            base_name = os.path.splitext(os.path.basename(audio_file_path))[0]\n            output_files = []\n\n            for stem_name in sorted(processed_chunks_by_stem.keys()):\n                chunk_paths_for_stem = processed_chunks_by_stem[stem_name]\n\n                if not chunk_paths_for_stem:\n                    self.logger.warning(f\"No chunks found for stem: {stem_name}\")\n                    continue\n\n                # Determine output filename\n                if custom_output_names and stem_name in custom_output_names:\n                    output_filename = custom_output_names[stem_name]","chunkId":"audio_separator/separator/separator.py:119"}],[1174,{"filePath":"audio_separator/separator/separator.py","startLine":1010,"endLine":1019,"chunkText":"                # Determine output filename\n                if custom_output_names and stem_name in custom_output_names:\n                    output_filename = custom_output_names[stem_name]\n                else:\n                    output_filename = f\"{base_name}_({stem_name})\"\n\n                output_path = os.path.join(self.output_dir, f\"{output_filename}.{self.output_format.lower()}\")\n\n                self.logger.info(f\"Merging {len(chunk_paths_for_stem)} chunks for stem: {stem_name}\")\n                chunker.merge_chunks(chunk_paths_for_stem, output_path)","chunkId":"audio_separator/separator/separator.py:120"}],[1175,{"filePath":"audio_separator/separator/separator.py","startLine":1019,"endLine":1032,"chunkText":"                chunker.merge_chunks(chunk_paths_for_stem, output_path)\n                output_files.append(output_path)\n\n            self.logger.info(f\"Chunked processing completed. Output files: {output_files}\")\n            return output_files\n\n        finally:\n            # Clean up temporary directory\n            if os.path.exists(temp_dir):\n                self.logger.debug(f\"Cleaning up temporary directory: {temp_dir}\")\n                shutil.rmtree(temp_dir, ignore_errors=True)\n\n    def download_model_and_data(self, model_filename):\n        \"\"\"","chunkId":"audio_separator/separator/separator.py:121"}],[1176,{"filePath":"audio_separator/separator/separator.py","startLine":1030,"endLine":1044,"chunkText":"\n    def download_model_and_data(self, model_filename):\n        \"\"\"\n        Downloads the model file without loading it into memory.\n        \"\"\"\n        self.logger.info(f\"Downloading model {model_filename}...\")\n\n        model_filename, model_type, model_friendly_name, model_path, yaml_config_filename = self.download_model_files(model_filename)\n\n        if model_path.lower().endswith(\".yaml\"):\n            yaml_config_filename = model_path\n\n        if yaml_config_filename is not None:\n            model_data = self.load_model_data_from_yaml(yaml_config_filename)\n        else:","chunkId":"audio_separator/separator/separator.py:122"}],[1177,{"filePath":"audio_separator/separator/separator.py","startLine":1042,"endLine":1053,"chunkText":"        if yaml_config_filename is not None:\n            model_data = self.load_model_data_from_yaml(yaml_config_filename)\n        else:\n            model_data = self.load_model_data_using_hash(model_path)\n\n        model_data_dict_size = len(model_data)\n\n        self.logger.info(f\"Model downloaded, type: {model_type}, friendly name: {model_friendly_name}, model_path: {model_path}, model_data: {model_data_dict_size} items\")\n\n    def get_simplified_model_list(self, filter_sort_by: Optional[str] = None):\n        \"\"\"\n        Returns a simplified, user-friendly list of models with their key metrics.","chunkId":"audio_separator/separator/separator.py:123"}],[1178,{"filePath":"audio_separator/separator/separator.py","startLine":1051,"endLine":1063,"chunkText":"    def get_simplified_model_list(self, filter_sort_by: Optional[str] = None):\n        \"\"\"\n        Returns a simplified, user-friendly list of models with their key metrics.\n        Optionally sorts the list based on the specified criteria.\n\n        :param sort_by: Criteria to sort by. Can be \"name\", \"filename\", or any stem name\n        \"\"\"\n        model_files = self.list_supported_model_files()\n        simplified_list = {}\n\n        for model_type, models in model_files.items():\n            for name, data in models.items():\n                filename = data[\"filename\"]","chunkId":"audio_separator/separator/separator.py:124"}],[1179,{"filePath":"audio_separator/separator/separator.py","startLine":1061,"endLine":1074,"chunkText":"        for model_type, models in model_files.items():\n            for name, data in models.items():\n                filename = data[\"filename\"]\n                scores = data.get(\"scores\") or {}\n                stems = data.get(\"stems\") or []\n                target_stem = data.get(\"target_stem\")\n\n                # Format stems with their SDR scores where available\n                stems_with_scores = []\n                stem_sdr_dict = {}\n\n                # Process each stem from the model's stem list\n                for stem in stems:\n                    stem_scores = scores.get(stem, {})","chunkId":"audio_separator/separator/separator.py:125"}],[1180,{"filePath":"audio_separator/separator/separator.py","startLine":1072,"endLine":1082,"chunkText":"                # Process each stem from the model's stem list\n                for stem in stems:\n                    stem_scores = scores.get(stem, {})\n                    # Add asterisk if this is the target stem\n                    stem_display = f\"{stem}*\" if stem == target_stem else stem\n\n                    if isinstance(stem_scores, dict) and \"SDR\" in stem_scores:\n                        sdr = round(stem_scores[\"SDR\"], 1)\n                        stems_with_scores.append(f\"{stem_display} ({sdr})\")\n                        stem_sdr_dict[stem.lower()] = sdr\n                    else:","chunkId":"audio_separator/separator/separator.py:126"}],[1181,{"filePath":"audio_separator/separator/separator.py","startLine":1080,"endLine":1091,"chunkText":"                        stems_with_scores.append(f\"{stem_display} ({sdr})\")\n                        stem_sdr_dict[stem.lower()] = sdr\n                    else:\n                        # Include stem without SDR score\n                        stems_with_scores.append(stem_display)\n                        stem_sdr_dict[stem.lower()] = None\n\n                # If no stems listed, mark as Unknown\n                if not stems_with_scores:\n                    stems_with_scores = [\"Unknown\"]\n                    stem_sdr_dict[\"unknown\"] = None\n","chunkId":"audio_separator/separator/separator.py:127"}],[1182,{"filePath":"audio_separator/separator/separator.py","startLine":1089,"endLine":1100,"chunkText":"                    stems_with_scores = [\"Unknown\"]\n                    stem_sdr_dict[\"unknown\"] = None\n\n                simplified_list[filename] = {\"Name\": name, \"Type\": model_type, \"Stems\": stems_with_scores, \"SDR\": stem_sdr_dict}\n\n        # Sort and filter the list if a sort_by parameter is provided\n        if filter_sort_by:\n            if filter_sort_by == \"name\":\n                return dict(sorted(simplified_list.items(), key=lambda x: x[1][\"Name\"]))\n            elif filter_sort_by == \"filename\":\n                return dict(sorted(simplified_list.items()))\n            else:","chunkId":"audio_separator/separator/separator.py:128"}],[1183,{"filePath":"audio_separator/separator/separator.py","startLine":1098,"endLine":1108,"chunkText":"            elif filter_sort_by == \"filename\":\n                return dict(sorted(simplified_list.items()))\n            else:\n                # Convert sort_by to lowercase for case-insensitive comparison\n                sort_by_lower = filter_sort_by.lower()\n                # Filter out models that don't have the specified stem\n                filtered_list = {k: v for k, v in simplified_list.items() if sort_by_lower in v[\"SDR\"]}\n\n                # Sort by SDR score if available, putting None values last\n                def sort_key(item):\n                    sdr = item[1][\"SDR\"][sort_by_lower]","chunkId":"audio_separator/separator/separator.py:129"}],[1184,{"filePath":"audio_separator/separator/separator.py","startLine":1106,"endLine":1114,"chunkText":"                # Sort by SDR score if available, putting None values last\n                def sort_key(item):\n                    sdr = item[1][\"SDR\"][sort_by_lower]\n                    return (0 if sdr is None else 1, sdr if sdr is not None else float(\"-inf\"))\n\n                return dict(sorted(filtered_list.items(), key=sort_key, reverse=True))\n\n        return simplified_list\n","chunkId":"audio_separator/separator/separator.py:130"}],[1185,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/__init__.py","startLine":0,"endLine":5,"chunkText":"# Copyright (c) Facebook, Inc. and its affiliates.\n# All rights reserved.\n#\n# This source code is licensed under the license found in the\n# LICENSE file in the root directory of this source tree.\n","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/__init__.py:0"}],[1186,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/__main__.py","startLine":0,"endLine":19,"chunkText":"# Copyright (c) Facebook, Inc. and its affiliates.\n# All rights reserved.\n#\n# This source code is licensed under the license found in the\n# LICENSE file in the root directory of this source tree.\n\nimport json\nimport os\nimport sys\nimport time\nfrom dataclasses import dataclass, field\nfrom fractions import Fraction\n\nimport torch as th\nfrom torch import distributed, nn\nfrom torch.nn.parallel.distributed import DistributedDataParallel\n\nfrom .augment import FlipChannels, FlipSign, Remix, Shift\nfrom .compressed import StemsSet, build_musdb_metadata, get_musdb_tracks\nfrom .model import Demucs","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/__main__.py:0"}],[1187,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/__main__.py","startLine":17,"endLine":37,"chunkText":"from .augment import FlipChannels, FlipSign, Remix, Shift\nfrom .compressed import StemsSet, build_musdb_metadata, get_musdb_tracks\nfrom .model import Demucs\nfrom .parser import get_name, get_parser\nfrom .raw import Rawset\nfrom .tasnet import ConvTasNet\nfrom .test import evaluate\nfrom .train import train_model, validate_model\nfrom .utils import human_seconds, load_model, save_model, sizeof_fmt\n\n\n@dataclass\nclass SavedState:\n    metrics: list = field(default_factory=list)\n    last_state: dict = None\n    best_state: dict = None\n    optimizer: dict = None\n\n\ndef main():\n    parser = get_parser()","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/__main__.py:1"}],[1188,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/__main__.py","startLine":35,"endLine":50,"chunkText":"\ndef main():\n    parser = get_parser()\n    args = parser.parse_args()\n    name = get_name(parser, args)\n    print(f\"Experiment {name}\")\n\n    if args.musdb is None and args.rank == 0:\n        print(\"You must provide the path to the MusDB dataset with the --musdb flag. \" \"To download the MusDB dataset, see https://sigsep.github.io/datasets/musdb.html.\", file=sys.stderr)\n        sys.exit(1)\n\n    eval_folder = args.evals / name\n    eval_folder.mkdir(exist_ok=True, parents=True)\n    args.logs.mkdir(exist_ok=True)\n    metrics_path = args.logs / f\"{name}.json\"\n    eval_folder.mkdir(exist_ok=True, parents=True)","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/__main__.py:2"}],[1189,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/__main__.py","startLine":48,"endLine":65,"chunkText":"    args.logs.mkdir(exist_ok=True)\n    metrics_path = args.logs / f\"{name}.json\"\n    eval_folder.mkdir(exist_ok=True, parents=True)\n    args.checkpoints.mkdir(exist_ok=True, parents=True)\n    args.models.mkdir(exist_ok=True, parents=True)\n\n    if args.device is None:\n        device = \"cpu\"\n        if th.cuda.is_available():\n            device = \"cuda\"\n    else:\n        device = args.device\n\n    th.manual_seed(args.seed)\n    # Prevents too many threads to be started when running `museval` as it can be quite\n    # inefficient on NUMA architectures.\n    os.environ[\"OMP_NUM_THREADS\"] = \"1\"\n","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/__main__.py:3"}],[1190,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/__main__.py","startLine":63,"endLine":74,"chunkText":"    # inefficient on NUMA architectures.\n    os.environ[\"OMP_NUM_THREADS\"] = \"1\"\n\n    if args.world_size > 1:\n        if device != \"cuda\" and args.rank == 0:\n            print(\"Error: distributed training is only available with cuda device\", file=sys.stderr)\n            sys.exit(1)\n        th.cuda.set_device(args.rank % th.cuda.device_count())\n        distributed.init_process_group(backend=\"nccl\", init_method=\"tcp://\" + args.master, rank=args.rank, world_size=args.world_size)\n\n    checkpoint = args.checkpoints / f\"{name}.th\"\n    checkpoint_tmp = args.checkpoints / f\"{name}.th.tmp\"","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/__main__.py:4"}],[1191,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/__main__.py","startLine":72,"endLine":89,"chunkText":"\n    checkpoint = args.checkpoints / f\"{name}.th\"\n    checkpoint_tmp = args.checkpoints / f\"{name}.th.tmp\"\n    if args.restart and checkpoint.exists():\n        checkpoint.unlink()\n\n    if args.test:\n        args.epochs = 1\n        args.repeat = 0\n        model = load_model(args.models / args.test)\n    elif args.tasnet:\n        model = ConvTasNet(audio_channels=args.audio_channels, samplerate=args.samplerate, X=args.X)\n    else:\n        model = Demucs(\n            audio_channels=args.audio_channels,\n            channels=args.channels,\n            context=args.context,\n            depth=args.depth,","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/__main__.py:5"}],[1192,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/__main__.py","startLine":87,"endLine":104,"chunkText":"            channels=args.channels,\n            context=args.context,\n            depth=args.depth,\n            glu=args.glu,\n            growth=args.growth,\n            kernel_size=args.kernel_size,\n            lstm_layers=args.lstm_layers,\n            rescale=args.rescale,\n            rewrite=args.rewrite,\n            sources=4,\n            stride=args.conv_stride,\n            upsample=args.upsample,\n            samplerate=args.samplerate,\n        )\n    model.to(device)\n    if args.show:\n        print(model)\n        size = sizeof_fmt(4 * sum(p.numel() for p in model.parameters()))","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/__main__.py:6"}],[1193,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/__main__.py","startLine":102,"endLine":121,"chunkText":"    if args.show:\n        print(model)\n        size = sizeof_fmt(4 * sum(p.numel() for p in model.parameters()))\n        print(f\"Model size {size}\")\n        return\n\n    optimizer = th.optim.Adam(model.parameters(), lr=args.lr)\n\n    try:\n        saved = th.load(checkpoint, map_location=\"cpu\")\n    except IOError:\n        saved = SavedState()\n    else:\n        model.load_state_dict(saved.last_state)\n        optimizer.load_state_dict(saved.optimizer)\n\n    if args.save_model:\n        if args.rank == 0:\n            model.to(\"cpu\")\n            model.load_state_dict(saved.best_state)","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/__main__.py:7"}],[1194,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/__main__.py","startLine":119,"endLine":139,"chunkText":"        if args.rank == 0:\n            model.to(\"cpu\")\n            model.load_state_dict(saved.best_state)\n            save_model(model, args.models / f\"{name}.th\")\n        return\n\n    if args.rank == 0:\n        done = args.logs / f\"{name}.done\"\n        if done.exists():\n            done.unlink()\n\n    if args.augment:\n        augment = nn.Sequential(FlipSign(), FlipChannels(), Shift(args.data_stride), Remix(group_size=args.remix_group_size)).to(device)\n    else:\n        augment = Shift(args.data_stride)\n\n    if args.mse:\n        criterion = nn.MSELoss()\n    else:\n        criterion = nn.L1Loss()\n","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/__main__.py:8"}],[1195,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/__main__.py","startLine":137,"endLine":150,"chunkText":"    else:\n        criterion = nn.L1Loss()\n\n    # Setting number of samples so that all convolution windows are full.\n    # Prevents hard to debug mistake with the prediction being shifted compared\n    # to the input mixture.\n    samples = model.valid_length(args.samples)\n    print(f\"Number of training samples adjusted to {samples}\")\n\n    if args.raw:\n        train_set = Rawset(args.raw / \"train\", samples=samples + args.data_stride, channels=args.audio_channels, streams=[0, 1, 2, 3, 4], stride=args.data_stride)\n\n        valid_set = Rawset(args.raw / \"valid\", channels=args.audio_channels)\n    else:","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/__main__.py:9"}],[1196,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/__main__.py","startLine":148,"endLine":157,"chunkText":"\n        valid_set = Rawset(args.raw / \"valid\", channels=args.audio_channels)\n    else:\n        if not args.metadata.is_file() and args.rank == 0:\n            build_musdb_metadata(args.metadata, args.musdb, args.workers)\n        if args.world_size > 1:\n            distributed.barrier()\n        metadata = json.load(open(args.metadata))\n        duration = Fraction(samples + args.data_stride, args.samplerate)\n        stride = Fraction(args.data_stride, args.samplerate)","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/__main__.py:10"}],[1197,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/__main__.py","startLine":155,"endLine":161,"chunkText":"        metadata = json.load(open(args.metadata))\n        duration = Fraction(samples + args.data_stride, args.samplerate)\n        stride = Fraction(args.data_stride, args.samplerate)\n        train_set = StemsSet(get_musdb_tracks(args.musdb, subsets=[\"train\"], split=\"train\"), metadata, duration=duration, stride=stride, samplerate=args.samplerate, channels=args.audio_channels)\n        valid_set = StemsSet(get_musdb_tracks(args.musdb, subsets=[\"train\"], split=\"valid\"), metadata, samplerate=args.samplerate, channels=args.audio_channels)\n\n    best_loss = float(\"inf\")","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/__main__.py:11"}],[1198,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/__main__.py","startLine":159,"endLine":166,"chunkText":"        valid_set = StemsSet(get_musdb_tracks(args.musdb, subsets=[\"train\"], split=\"valid\"), metadata, samplerate=args.samplerate, channels=args.audio_channels)\n\n    best_loss = float(\"inf\")\n    for epoch, metrics in enumerate(saved.metrics):\n        print(f\"Epoch {epoch:03d}: \" f\"train={metrics['train']:.8f} \" f\"valid={metrics['valid']:.8f} \" f\"best={metrics['best']:.4f} \" f\"duration={human_seconds(metrics['duration'])}\")\n        best_loss = metrics[\"best\"]\n\n    if args.world_size > 1:","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/__main__.py:12"}],[1199,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/__main__.py","startLine":164,"endLine":177,"chunkText":"        best_loss = metrics[\"best\"]\n\n    if args.world_size > 1:\n        dmodel = DistributedDataParallel(model, device_ids=[th.cuda.current_device()], output_device=th.cuda.current_device())\n    else:\n        dmodel = model\n\n    for epoch in range(len(saved.metrics), args.epochs):\n        begin = time.time()\n        model.train()\n        train_loss = train_model(\n            epoch, train_set, dmodel, criterion, optimizer, augment, batch_size=args.batch_size, device=device, repeat=args.repeat, seed=args.seed, workers=args.workers, world_size=args.world_size\n        )\n        model.eval()","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/__main__.py:13"}],[1200,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/__main__.py","startLine":175,"endLine":183,"chunkText":"            epoch, train_set, dmodel, criterion, optimizer, augment, batch_size=args.batch_size, device=device, repeat=args.repeat, seed=args.seed, workers=args.workers, world_size=args.world_size\n        )\n        model.eval()\n        valid_loss = validate_model(epoch, valid_set, model, criterion, device=device, rank=args.rank, split=args.split_valid, world_size=args.world_size)\n\n        duration = time.time() - begin\n        if valid_loss < best_loss:\n            best_loss = valid_loss\n            saved.best_state = {key: value.to(\"cpu\").clone() for key, value in model.state_dict().items()}","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/__main__.py:14"}],[1201,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/__main__.py","startLine":181,"endLine":193,"chunkText":"        if valid_loss < best_loss:\n            best_loss = valid_loss\n            saved.best_state = {key: value.to(\"cpu\").clone() for key, value in model.state_dict().items()}\n        saved.metrics.append({\"train\": train_loss, \"valid\": valid_loss, \"best\": best_loss, \"duration\": duration})\n        if args.rank == 0:\n            json.dump(saved.metrics, open(metrics_path, \"w\"))\n\n        saved.last_state = model.state_dict()\n        saved.optimizer = optimizer.state_dict()\n        if args.rank == 0 and not args.test:\n            th.save(saved, checkpoint_tmp)\n            checkpoint_tmp.rename(checkpoint)\n","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/__main__.py:15"}],[1202,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/__main__.py","startLine":191,"endLine":203,"chunkText":"            th.save(saved, checkpoint_tmp)\n            checkpoint_tmp.rename(checkpoint)\n\n        print(f\"Epoch {epoch:03d}: \" f\"train={train_loss:.8f} valid={valid_loss:.8f} best={best_loss:.4f} \" f\"duration={human_seconds(duration)}\")\n\n    del dmodel\n    model.load_state_dict(saved.best_state)\n    if args.eval_cpu:\n        device = \"cpu\"\n        model.to(device)\n    model.eval()\n    evaluate(model, args.musdb, eval_folder, rank=args.rank, world_size=args.world_size, device=device, save=args.save, split=args.split_valid, shifts=args.shifts, workers=args.eval_workers)\n    model.to(\"cpu\")","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/__main__.py:16"}],[1203,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/__main__.py","startLine":201,"endLine":212,"chunkText":"    model.eval()\n    evaluate(model, args.musdb, eval_folder, rank=args.rank, world_size=args.world_size, device=device, save=args.save, split=args.split_valid, shifts=args.shifts, workers=args.eval_workers)\n    model.to(\"cpu\")\n    save_model(model, args.models / f\"{name}.th\")\n    if args.rank == 0:\n        print(\"done\")\n        done.write_text(\"done\")\n\n\nif __name__ == \"__main__\":\n    main()\n","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/__main__.py:17"}],[1204,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/apply.py","startLine":0,"endLine":19,"chunkText":"# Copyright (c) Facebook, Inc. and its affiliates.\n# All rights reserved.\n#\n# This source code is licensed under the license found in the\n# LICENSE file in the root directory of this source tree.\n\"\"\"\nCode to apply a model to a mix. It will handle chunking with overlaps and\ninteprolation between chunks, as well as the \"shift trick\".\n\"\"\"\nfrom concurrent.futures import ThreadPoolExecutor\nimport random\nimport typing as tp\n\nimport torch as th\nfrom torch import nn\nfrom torch.nn import functional as F\nimport tqdm\n\nfrom .demucs import Demucs\nfrom .hdemucs import HDemucs","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/apply.py:0"}],[1205,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/apply.py","startLine":17,"endLine":35,"chunkText":"\nfrom .demucs import Demucs\nfrom .hdemucs import HDemucs\nfrom .utils import center_trim, DummyPoolExecutor\n\nModel = tp.Union[Demucs, HDemucs]\n\nprogress_bar_num = 0\n\n\nclass BagOfModels(nn.Module):\n    def __init__(self, models: tp.List[Model], weights: tp.Optional[tp.List[tp.List[float]]] = None, segment: tp.Optional[float] = None):\n        \"\"\"\n        Represents a bag of models with specific weights.\n        You should call `apply_model` rather than calling directly the forward here for\n        optimal performance.\n\n        Args:\n            models (list[nn.Module]): list of Demucs/HDemucs models.","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/apply.py:1"}],[1206,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/apply.py","startLine":33,"endLine":44,"chunkText":"        Args:\n            models (list[nn.Module]): list of Demucs/HDemucs models.\n            weights (list[list[float]]): list of weights. If None, assumed to\n                be all ones, otherwise it should be a list of N list (N number of models),\n                each containing S floats (S number of sources).\n            segment (None or float): overrides the `segment` attribute of each model\n                (this is performed inplace, be careful if you reuse the models passed).\n        \"\"\"\n\n        super().__init__()\n        assert len(models) > 0\n        first = models[0]","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/apply.py:2"}],[1207,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/apply.py","startLine":44,"endLine":59,"chunkText":"        assert len(models) > 0\n        first = models[0]\n        for other in models:\n            assert other.sources == first.sources\n            assert other.samplerate == first.samplerate\n            assert other.audio_channels == first.audio_channels\n            if segment is not None:\n                other.segment = segment\n\n        self.audio_channels = first.audio_channels\n        self.samplerate = first.samplerate\n        self.sources = first.sources\n        self.models = nn.ModuleList(models)\n\n        if weights is None:\n            weights = [[1.0 for _ in first.sources] for _ in models]","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/apply.py:3"}],[1208,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/apply.py","startLine":57,"endLine":69,"chunkText":"\n        if weights is None:\n            weights = [[1.0 for _ in first.sources] for _ in models]\n        else:\n            assert len(weights) == len(models)\n            for weight in weights:\n                assert len(weight) == len(first.sources)\n        self.weights = weights\n\n    def forward(self, x):\n        raise NotImplementedError(\"Call `apply_model` on this.\")\n\n","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/apply.py:4"}],[1209,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/apply.py","startLine":70,"endLine":90,"chunkText":"class TensorChunk:\n    def __init__(self, tensor, offset=0, length=None):\n        total_length = tensor.shape[-1]\n        assert offset >= 0\n        assert offset < total_length\n\n        if length is None:\n            length = total_length - offset\n        else:\n            length = min(total_length - offset, length)\n\n        if isinstance(tensor, TensorChunk):\n            self.tensor = tensor.tensor\n            self.offset = offset + tensor.offset\n        else:\n            self.tensor = tensor\n            self.offset = offset\n        self.length = length\n        self.device = tensor.device\n\n    @property","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/apply.py:5"}],[1210,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/apply.py","startLine":88,"endLine":109,"chunkText":"        self.device = tensor.device\n\n    @property\n    def shape(self):\n        shape = list(self.tensor.shape)\n        shape[-1] = self.length\n        return shape\n\n    def padded(self, target_length):\n        delta = target_length - self.length\n        total_length = self.tensor.shape[-1]\n        assert delta >= 0\n\n        start = self.offset - delta // 2\n        end = start + target_length\n\n        correct_start = max(0, start)\n        correct_end = min(total_length, end)\n\n        pad_left = correct_start - start\n        pad_right = end - correct_end\n","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/apply.py:6"}],[1211,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/apply.py","startLine":107,"endLine":122,"chunkText":"        pad_left = correct_start - start\n        pad_right = end - correct_end\n\n        out = F.pad(self.tensor[..., correct_start:correct_end], (pad_left, pad_right))\n        assert out.shape[-1] == target_length\n        return out\n\n\ndef tensor_chunk(tensor_or_chunk):\n    if isinstance(tensor_or_chunk, TensorChunk):\n        return tensor_or_chunk\n    else:\n        assert isinstance(tensor_or_chunk, th.Tensor)\n        return TensorChunk(tensor_or_chunk)\n\n","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/apply.py:7"}],[1212,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/apply.py","startLine":120,"endLine":130,"chunkText":"        return TensorChunk(tensor_or_chunk)\n\n\ndef apply_model(model, mix, shifts=1, split=True, overlap=0.25, transition_power=1.0, static_shifts=1, set_progress_bar=None, device=None, progress=False, num_workers=0, pool=None):\n    \"\"\"\n    Apply model to a given mixture.\n\n    Args:\n        shifts (int): if > 0, will shift in time `mix` by a random amount between 0 and 0.5 sec\n            and apply the oppositve shift to the output. This is repeated `shifts` time and\n            all predictions are averaged. This effectively makes the model time equivariant","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/apply.py:8"}],[1213,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/apply.py","startLine":128,"endLine":134,"chunkText":"        shifts (int): if > 0, will shift in time `mix` by a random amount between 0 and 0.5 sec\n            and apply the oppositve shift to the output. This is repeated `shifts` time and\n            all predictions are averaged. This effectively makes the model time equivariant\n            and improves SDR by up to 0.2 points.\n        split (bool): if True, the input will be broken down in 8 seconds extracts\n            and predictions will be performed individually on each and concatenated.\n            Useful for model with large memory footprint like Tasnet.","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/apply.py:9"}],[1214,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/apply.py","startLine":132,"endLine":138,"chunkText":"        split (bool): if True, the input will be broken down in 8 seconds extracts\n            and predictions will be performed individually on each and concatenated.\n            Useful for model with large memory footprint like Tasnet.\n        progress (bool): if True, show a progress bar (requires split=True)\n        device (torch.device, str, or None): if provided, device on which to\n            execute the computation, otherwise `mix.device` is assumed.\n            When `device` is different from `mix.device`, only local computations will","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/apply.py:10"}],[1215,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/apply.py","startLine":136,"endLine":152,"chunkText":"        device (torch.device, str, or None): if provided, device on which to\n            execute the computation, otherwise `mix.device` is assumed.\n            When `device` is different from `mix.device`, only local computations will\n            be on `device`, while the entire tracks will be stored on `mix.device`.\n    \"\"\"\n\n    global fut_length\n    global bag_num\n    global prog_bar\n\n    if device is None:\n        device = mix.device\n    else:\n        device = th.device(device)\n    if pool is None:\n        if num_workers > 0 and device.type == \"cpu\":\n            pool = ThreadPoolExecutor(num_workers)","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/apply.py:11"}],[1216,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/apply.py","startLine":150,"endLine":169,"chunkText":"    if pool is None:\n        if num_workers > 0 and device.type == \"cpu\":\n            pool = ThreadPoolExecutor(num_workers)\n        else:\n            pool = DummyPoolExecutor()\n\n    kwargs = {\n        \"shifts\": shifts,\n        \"split\": split,\n        \"overlap\": overlap,\n        \"transition_power\": transition_power,\n        \"progress\": progress,\n        \"device\": device,\n        \"pool\": pool,\n        \"set_progress_bar\": set_progress_bar,\n        \"static_shifts\": static_shifts,\n    }\n\n    if isinstance(model, BagOfModels):\n        # Special treatment for bag of model.","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/apply.py:12"}],[1217,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/apply.py","startLine":167,"endLine":181,"chunkText":"    if isinstance(model, BagOfModels):\n        # Special treatment for bag of model.\n        # We explicitely apply multiple times `apply_model` so that the random shifts\n        # are different for each model.\n\n        estimates = 0\n        totals = [0] * len(model.sources)\n        bag_num = len(model.models)\n        fut_length = 0\n        prog_bar = 0\n        current_model = 0  # (bag_num + 1)\n        for sub_model, weight in zip(model.models, model.weights):\n            original_model_device = next(iter(sub_model.parameters())).device\n            sub_model.to(device)\n            fut_length += fut_lengt","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/apply.py:13"}],[1218,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/apply.py","startLine":180,"endLine":195,"chunkText":"            original_model_device = next(iter(sub_model.parameters())).device\n            sub_model.to(device)\n            fut_length += fut_length\n            current_model += 1\n            out = apply_model(sub_model, mix, **kwargs)\n            sub_model.to(original_model_device)\n            for k, inst_weight in enumerate(weight):\n                out[:, k, :, :] *= inst_weight\n                totals[k] += inst_weight\n            estimates += out\n            del out\n\n        for k in range(estimates.shape[1]):\n            estimates[:, k, :, :] /= totals[k]\n        return estimates\n","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/apply.py:14"}],[1219,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/apply.py","startLine":193,"endLine":209,"chunkText":"            estimates[:, k, :, :] /= totals[k]\n        return estimates\n\n    model.to(device)\n    model.eval()\n    assert transition_power >= 1, \"transition_power < 1 leads to weird behavior.\"\n    batch, channels, length = mix.shape\n\n    if shifts:\n        kwargs[\"shifts\"] = 0\n        max_shift = int(0.5 * model.samplerate)\n        mix = tensor_chunk(mix)\n        padded_mix = mix.padded(length + 2 * max_shift)\n        out = 0\n        for _ in range(shifts):\n            offset = random.randint(0, max_shift)\n            shifted = TensorChunk(padded_mix, offset, length + max_shift - offset)","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/apply.py:15"}],[1220,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/apply.py","startLine":207,"endLine":218,"chunkText":"        for _ in range(shifts):\n            offset = random.randint(0, max_shift)\n            shifted = TensorChunk(padded_mix, offset, length + max_shift - offset)\n            shifted_out = apply_model(model, shifted, **kwargs)\n            out += shifted_out[..., max_shift - offset :]\n        out /= shifts\n        return out\n    elif split:\n        kwargs[\"split\"] = False\n        out = th.zeros(batch, len(model.sources), channels, length, device=mix.device)\n        sum_weight = th.zeros(length, device=mix.device)\n        segment = int(model.samplerate * model.segment)","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/apply.py:16"}],[1221,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/apply.py","startLine":216,"endLine":224,"chunkText":"        out = th.zeros(batch, len(model.sources), channels, length, device=mix.device)\n        sum_weight = th.zeros(length, device=mix.device)\n        segment = int(model.samplerate * model.segment)\n        stride = int((1 - overlap) * segment)\n        offsets = range(0, length, stride)\n        scale = float(format(stride / model.samplerate, \".2f\"))\n        # We start from a triangle shaped weight, with maximal weight in the middle\n        # of the segment. Then we normalize and take to the power `transition_power`.\n        # Large values of transition power will lead to sharper transitions.","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/apply.py:17"}],[1222,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/apply.py","startLine":222,"endLine":230,"chunkText":"        # We start from a triangle shaped weight, with maximal weight in the middle\n        # of the segment. Then we normalize and take to the power `transition_power`.\n        # Large values of transition power will lead to sharper transitions.\n        weight = th.cat([th.arange(1, segment // 2 + 1, device=device), th.arange(segment - segment // 2, 0, -1, device=device)])\n        assert len(weight) == segment\n        # If the overlap < 50%, this will translate to linear transition when\n        # transition_power is 1.\n        weight = (weight / weight.max()) ** transition_power\n        futures = []","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/apply.py:18"}],[1223,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/apply.py","startLine":228,"endLine":241,"chunkText":"        # transition_power is 1.\n        weight = (weight / weight.max()) ** transition_power\n        futures = []\n        for offset in offsets:\n            chunk = TensorChunk(mix, offset, segment)\n            future = pool.submit(apply_model, model, chunk, **kwargs)\n            futures.append((future, offset))\n            offset += segment\n        if progress:\n            futures = tqdm.tqdm(futures)\n        for future, offset in futures:\n            if set_progress_bar:\n                fut_length = len(futures) * bag_num * static_shifts\n                prog_bar += 1","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/apply.py:19"}],[1224,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/apply.py","startLine":239,"endLine":251,"chunkText":"            if set_progress_bar:\n                fut_length = len(futures) * bag_num * static_shifts\n                prog_bar += 1\n                set_progress_bar(0.1, (0.8 / fut_length * prog_bar))\n            chunk_out = future.result()\n            chunk_length = chunk_out.shape[-1]\n            out[..., offset : offset + segment] += (weight[:chunk_length] * chunk_out).to(mix.device)\n            sum_weight[offset : offset + segment] += weight[:chunk_length].to(mix.device)\n        assert sum_weight.min() > 0\n        out /= sum_weight\n        return out\n    else:\n        if hasattr(model, \"valid_length\"):","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/apply.py:20"}],[1225,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/apply.py","startLine":249,"endLine":268,"chunkText":"        return out\n    else:\n        if hasattr(model, \"valid_length\"):\n            valid_length = model.valid_length(length)\n        else:\n            valid_length = length\n        mix = tensor_chunk(mix)\n        padded_mix = mix.padded(valid_length).to(device)\n        with th.no_grad():\n            out = model(padded_mix)\n        return center_trim(out, length)\n\n\ndef demucs_segments(demucs_segment, demucs_model):\n\n    if demucs_segment == \"Default\":\n        segment = None\n        if isinstance(demucs_model, BagOfModels):\n            if segment is not None:\n                for sub in demucs_model.models:","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/apply.py:21"}],[1226,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/apply.py","startLine":266,"endLine":281,"chunkText":"        if isinstance(demucs_model, BagOfModels):\n            if segment is not None:\n                for sub in demucs_model.models:\n                    sub.segment = segment\n        else:\n            if segment is not None:\n                sub.segment = segment\n    else:\n        try:\n            segment = int(demucs_segment)\n            if isinstance(demucs_model, BagOfModels):\n                if segment is not None:\n                    for sub in demucs_model.models:\n                        sub.segment = segment\n            else:\n                if segment is not None:","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/apply.py:22"}],[1227,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/apply.py","startLine":279,"endLine":294,"chunkText":"                        sub.segment = segment\n            else:\n                if segment is not None:\n                    sub.segment = segment\n        except:\n            segment = None\n            if isinstance(demucs_model, BagOfModels):\n                if segment is not None:\n                    for sub in demucs_model.models:\n                        sub.segment = segment\n            else:\n                if segment is not None:\n                    sub.segment = segment\n\n    return demucs_model\n","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/apply.py:23"}],[1228,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/demucs.py","startLine":0,"endLine":24,"chunkText":"# Copyright (c) Facebook, Inc. and its affiliates.\n# All rights reserved.\n#\n# This source code is licensed under the license found in the\n# LICENSE file in the root directory of this source tree.\n\nimport math\nimport typing as tp\n\nimport julius\nimport torch\nfrom torch import nn\nfrom torch.nn import functional as F\n\nfrom .states import capture_init\nfrom .utils import center_trim, unfold\n\n\nclass BLSTM(nn.Module):\n    \"\"\"\n    BiLSTM with same hidden units as input dim.\n    If `max_steps` is not None, input will be splitting in overlapping\n    chunks and the LSTM applied separately on each chunk.\n    \"\"\"\n","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/demucs.py:0"}],[1229,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/demucs.py","startLine":22,"endLine":38,"chunkText":"    chunks and the LSTM applied separately on each chunk.\n    \"\"\"\n\n    def __init__(self, dim, layers=1, max_steps=None, skip=False):\n        super().__init__()\n        assert max_steps is None or max_steps % 4 == 0\n        self.max_steps = max_steps\n        self.lstm = nn.LSTM(bidirectional=True, num_layers=layers, hidden_size=dim, input_size=dim)\n        self.linear = nn.Linear(2 * dim, dim)\n        self.skip = skip\n\n    def forward(self, x):\n        B, C, T = x.shape\n        y = x\n        framed = False\n        if self.max_steps is not None and T > self.max_steps:\n            width = self.max_steps","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/demucs.py:1"}],[1230,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/demucs.py","startLine":36,"endLine":54,"chunkText":"        framed = False\n        if self.max_steps is not None and T > self.max_steps:\n            width = self.max_steps\n            stride = width // 2\n            frames = unfold(x, width, stride)\n            nframes = frames.shape[2]\n            framed = True\n            x = frames.permute(0, 2, 1, 3).reshape(-1, C, width)\n\n        x = x.permute(2, 0, 1)\n\n        x = self.lstm(x)[0]\n        x = self.linear(x)\n        x = x.permute(1, 2, 0)\n        if framed:\n            out = []\n            frames = x.reshape(B, -1, C, width)\n            limit = stride // 2\n            for k in range(nframes):","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/demucs.py:2"}],[1231,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/demucs.py","startLine":52,"endLine":69,"chunkText":"            frames = x.reshape(B, -1, C, width)\n            limit = stride // 2\n            for k in range(nframes):\n                if k == 0:\n                    out.append(frames[:, k, :, :-limit])\n                elif k == nframes - 1:\n                    out.append(frames[:, k, :, limit:])\n                else:\n                    out.append(frames[:, k, :, limit:-limit])\n            out = torch.cat(out, -1)\n            out = out[..., :T]\n            x = out\n        if self.skip:\n            x = x + y\n        return x\n\n\ndef rescale_conv(conv, reference):","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/demucs.py:3"}],[1232,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/demucs.py","startLine":67,"endLine":83,"chunkText":"\n\ndef rescale_conv(conv, reference):\n    \"\"\"Rescale initial weight scale. It is unclear why it helps but it certainly does.\"\"\"\n    std = conv.weight.std().detach()\n    scale = (std / reference) ** 0.5\n    conv.weight.data /= scale\n    if conv.bias is not None:\n        conv.bias.data /= scale\n\n\ndef rescale_module(module, reference):\n    for sub in module.modules():\n        if isinstance(sub, (nn.Conv1d, nn.ConvTranspose1d, nn.Conv2d, nn.ConvTranspose2d)):\n            rescale_conv(sub, reference)\n\n","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/demucs.py:4"}],[1233,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/demucs.py","startLine":84,"endLine":97,"chunkText":"class LayerScale(nn.Module):\n    \"\"\"Layer scale from [Touvron et al 2021] (https://arxiv.org/pdf/2103.17239.pdf).\n    This rescales diagonaly residual outputs close to 0 initially, then learnt.\n    \"\"\"\n\n    def __init__(self, channels: int, init: float = 0):\n        super().__init__()\n        self.scale = nn.Parameter(torch.zeros(channels, requires_grad=True))\n        self.scale.data[:] = init\n\n    def forward(self, x):\n        return self.scale[:, None] * x\n\n","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/demucs.py:5"}],[1234,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/demucs.py","startLine":98,"endLine":109,"chunkText":"class DConv(nn.Module):\n    \"\"\"\n    New residual branches in each encoder layer.\n    This alternates dilated convolutions, potentially with LSTMs and attention.\n    Also before entering each residual branch, dimension is projected on a smaller subspace,\n    e.g. of dim `channels // compress`.\n    \"\"\"\n\n    def __init__(self, channels: int, compress: float = 4, depth: int = 2, init: float = 1e-4, norm=True, attn=False, heads=4, ndecay=4, lstm=False, gelu=True, kernel=3, dilate=True):\n        \"\"\"\n        Args:\n            channels: input/output channels for residual branch.","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/demucs.py:6"}],[1235,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/demucs.py","startLine":107,"endLine":118,"chunkText":"        \"\"\"\n        Args:\n            channels: input/output channels for residual branch.\n            compress: amount of channel compression inside the branch.\n            depth: number of layers in the residual branch. Each layer has its own\n                projection, and potentially LSTM and attention.\n            init: initial scale for LayerNorm.\n            norm: use GroupNorm.\n            attn: use LocalAttention.\n            heads: number of heads for the LocalAttention.\n            ndecay: number of decay controls in the LocalAttention.\n            lstm: use LSTM.","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/demucs.py:7"}],[1236,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/demucs.py","startLine":116,"endLine":131,"chunkText":"            heads: number of heads for the LocalAttention.\n            ndecay: number of decay controls in the LocalAttention.\n            lstm: use LSTM.\n            gelu: Use GELU activation.\n            kernel: kernel size for the (dilated) convolutions.\n            dilate: if true, use dilation, increasing with the depth.\n        \"\"\"\n\n        super().__init__()\n        assert kernel % 2 == 1\n        self.channels = channels\n        self.compress = compress\n        self.depth = abs(depth)\n        dilate = depth > 0\n\n        norm_fn: tp.Callable[[int], nn.Module]","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/demucs.py:8"}],[1237,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/demucs.py","startLine":129,"endLine":148,"chunkText":"        dilate = depth > 0\n\n        norm_fn: tp.Callable[[int], nn.Module]\n        norm_fn = lambda d: nn.Identity()  # noqa\n        if norm:\n            norm_fn = lambda d: nn.GroupNorm(1, d)  # noqa\n\n        hidden = int(channels / compress)\n\n        act: tp.Type[nn.Module]\n        if gelu:\n            act = nn.GELU\n        else:\n            act = nn.ReLU\n\n        self.layers = nn.ModuleList([])\n        for d in range(self.depth):\n            dilation = 2**d if dilate else 1\n            padding = dilation * (kernel // 2)\n            mods = [","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/demucs.py:9"}],[1238,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/demucs.py","startLine":146,"endLine":159,"chunkText":"            dilation = 2**d if dilate else 1\n            padding = dilation * (kernel // 2)\n            mods = [\n                nn.Conv1d(channels, hidden, kernel, dilation=dilation, padding=padding),\n                norm_fn(hidden),\n                act(),\n                nn.Conv1d(hidden, 2 * channels, 1),\n                norm_fn(2 * channels),\n                nn.GLU(1),\n                LayerScale(channels, init),\n            ]\n            if attn:\n                mods.insert(3, LocalState(hidden, heads=heads, ndecay=ndecay))\n            if lstm:","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/demucs.py:10"}],[1239,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/demucs.py","startLine":157,"endLine":169,"chunkText":"            if attn:\n                mods.insert(3, LocalState(hidden, heads=heads, ndecay=ndecay))\n            if lstm:\n                mods.insert(3, BLSTM(hidden, layers=2, max_steps=200, skip=True))\n            layer = nn.Sequential(*mods)\n            self.layers.append(layer)\n\n    def forward(self, x):\n        for layer in self.layers:\n            x = x + layer(x)\n        return x\n\n","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/demucs.py:11"}],[1240,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/demucs.py","startLine":170,"endLine":182,"chunkText":"class LocalState(nn.Module):\n    \"\"\"Local state allows to have attention based only on data (no positional embedding),\n    but while setting a constraint on the time window (e.g. decaying penalty term).\n\n    Also a failed experiments with trying to provide some frequency based attention.\n    \"\"\"\n\n    def __init__(self, channels: int, heads: int = 4, nfreqs: int = 0, ndecay: int = 4):\n        super().__init__()\n        assert channels % heads == 0, (channels, heads)\n        self.heads = heads\n        self.nfreqs = nfreqs\n        self.ndecay = ndecay","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/demucs.py:12"}],[1241,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/demucs.py","startLine":180,"endLine":191,"chunkText":"        self.heads = heads\n        self.nfreqs = nfreqs\n        self.ndecay = ndecay\n        self.content = nn.Conv1d(channels, channels, 1)\n        self.query = nn.Conv1d(channels, channels, 1)\n        self.key = nn.Conv1d(channels, channels, 1)\n        if nfreqs:\n            self.query_freqs = nn.Conv1d(channels, heads * nfreqs, 1)\n        if ndecay:\n            self.query_decay = nn.Conv1d(channels, heads * ndecay, 1)\n            # Initialize decay close to zero (there is a sigmoid), for maximum initial window.\n            self.query_decay.weight.data *= 0.01","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/demucs.py:13"}],[1242,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/demucs.py","startLine":189,"endLine":200,"chunkText":"            self.query_decay = nn.Conv1d(channels, heads * ndecay, 1)\n            # Initialize decay close to zero (there is a sigmoid), for maximum initial window.\n            self.query_decay.weight.data *= 0.01\n            assert self.query_decay.bias is not None  # stupid type checker\n            self.query_decay.bias.data[:] = -2\n        self.proj = nn.Conv1d(channels + heads * nfreqs, channels, 1)\n\n    def forward(self, x):\n        B, C, T = x.shape\n        heads = self.heads\n        indexes = torch.arange(T, device=x.device, dtype=x.dtype)\n        # left index are keys, right index are queries","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/demucs.py:14"}],[1243,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/demucs.py","startLine":198,"endLine":209,"chunkText":"        heads = self.heads\n        indexes = torch.arange(T, device=x.device, dtype=x.dtype)\n        # left index are keys, right index are queries\n        delta = indexes[:, None] - indexes[None, :]\n\n        queries = self.query(x).view(B, heads, -1, T)\n        keys = self.key(x).view(B, heads, -1, T)\n        # t are keys, s are queries\n        dots = torch.einsum(\"bhct,bhcs->bhts\", keys, queries)\n        dots /= keys.shape[2] ** 0.5\n        if self.nfreqs:\n            periods = torch.arange(1, self.nfreqs + 1, device=x.device, dtype=x.dtype)","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/demucs.py:15"}],[1244,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/demucs.py","startLine":207,"endLine":216,"chunkText":"        dots /= keys.shape[2] ** 0.5\n        if self.nfreqs:\n            periods = torch.arange(1, self.nfreqs + 1, device=x.device, dtype=x.dtype)\n            freq_kernel = torch.cos(2 * math.pi * delta / periods.view(-1, 1, 1))\n            freq_q = self.query_freqs(x).view(B, heads, -1, T) / self.nfreqs**0.5\n            dots += torch.einsum(\"fts,bhfs->bhts\", freq_kernel, freq_q)\n        if self.ndecay:\n            decays = torch.arange(1, self.ndecay + 1, device=x.device, dtype=x.dtype)\n            decay_q = self.query_decay(x).view(B, heads, -1, T)\n            decay_q = torch.sigmoid(decay_q) / 2","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/demucs.py:16"}],[1245,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/demucs.py","startLine":214,"endLine":224,"chunkText":"            decays = torch.arange(1, self.ndecay + 1, device=x.device, dtype=x.dtype)\n            decay_q = self.query_decay(x).view(B, heads, -1, T)\n            decay_q = torch.sigmoid(decay_q) / 2\n            decay_kernel = -decays.view(-1, 1, 1) * delta.abs() / self.ndecay**0.5\n            dots += torch.einsum(\"fts,bhfs->bhts\", decay_kernel, decay_q)\n\n        # Kill self reference.\n        dots.masked_fill_(torch.eye(T, device=dots.device, dtype=torch.bool), -100)\n        weights = torch.softmax(dots, dim=2)\n\n        content = self.content(x).view(B, heads, -1, T)","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/demucs.py:17"}],[1246,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/demucs.py","startLine":222,"endLine":232,"chunkText":"        weights = torch.softmax(dots, dim=2)\n\n        content = self.content(x).view(B, heads, -1, T)\n        result = torch.einsum(\"bhts,bhct->bhcs\", weights, content)\n        if self.nfreqs:\n            time_sig = torch.einsum(\"bhts,fts->bhfs\", weights, freq_kernel)\n            result = torch.cat([result, time_sig], 2)\n        result = result.reshape(B, -1, T)\n        return x + self.proj(result)\n\n","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/demucs.py:18"}],[1247,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/demucs.py","startLine":233,"endLine":260,"chunkText":"class Demucs(nn.Module):\n    @capture_init\n    def __init__(\n        self,\n        sources,\n        # Channels\n        audio_channels=2,\n        channels=64,\n        growth=2.0,\n        # Main structure\n        depth=6,\n        rewrite=True,\n        lstm_layers=0,\n        # Convolutions\n        kernel_size=8,\n        stride=4,\n        context=1,\n        # Activations\n        gelu=True,\n        glu=True,\n        # Normalization\n        norm_starts=4,\n        norm_groups=4,\n        # DConv residual branch\n        dconv_mode=1,\n        dconv_depth=2,\n        dconv_comp=4,\n        dconv_attn=4,","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/demucs.py:19"}],[1248,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/demucs.py","startLine":258,"endLine":277,"chunkText":"        dconv_depth=2,\n        dconv_comp=4,\n        dconv_attn=4,\n        dconv_lstm=4,\n        dconv_init=1e-4,\n        # Pre/post processing\n        normalize=True,\n        resample=True,\n        # Weight init\n        rescale=0.1,\n        # Metadata\n        samplerate=44100,\n        segment=4 * 10,\n    ):\n        \"\"\"\n        Args:\n            sources (list[str]): list of source names\n            audio_channels (int): stereo or mono\n            channels (int): first convolution channels\n            depth (int): number of encoder/decoder layers","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/demucs.py:20"}],[1249,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/demucs.py","startLine":275,"endLine":283,"chunkText":"            audio_channels (int): stereo or mono\n            channels (int): first convolution channels\n            depth (int): number of encoder/decoder layers\n            growth (float): multiply (resp divide) number of channels by that\n                for each layer of the encoder (resp decoder)\n            depth (int): number of layers in the encoder and in the decoder.\n            rewrite (bool): add 1x1 convolution to each layer.\n            lstm_layers (int): number of lstm layers, 0 = no lstm. Deactivated\n                by default, as this is now replaced by the smaller and faster small LSTMs","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/demucs.py:21"}],[1250,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/demucs.py","startLine":281,"endLine":289,"chunkText":"            rewrite (bool): add 1x1 convolution to each layer.\n            lstm_layers (int): number of lstm layers, 0 = no lstm. Deactivated\n                by default, as this is now replaced by the smaller and faster small LSTMs\n                in the DConv branches.\n            kernel_size (int): kernel size for convolutions\n            stride (int): stride for convolutions\n            context (int): kernel size of the convolution in the\n                decoder before the transposed convolution. If > 1,\n                will provide some context from neighboring time steps.","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/demucs.py:22"}],[1251,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/demucs.py","startLine":287,"endLine":295,"chunkText":"            context (int): kernel size of the convolution in the\n                decoder before the transposed convolution. If > 1,\n                will provide some context from neighboring time steps.\n            gelu: use GELU activation function.\n            glu (bool): use glu instead of ReLU for the 1x1 rewrite conv.\n            norm_starts: layer at which group norm starts being used.\n                decoder layers are numbered in reverse order.\n            norm_groups: number of groups for group norm.\n            dconv_mode: if 1: dconv in encoder only, 2: decoder only, 3: both.","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/demucs.py:23"}],[1252,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/demucs.py","startLine":293,"endLine":300,"chunkText":"                decoder layers are numbered in reverse order.\n            norm_groups: number of groups for group norm.\n            dconv_mode: if 1: dconv in encoder only, 2: decoder only, 3: both.\n            dconv_depth: depth of residual DConv branch.\n            dconv_comp: compression of DConv branch.\n            dconv_attn: adds attention layers in DConv branch starting at this layer.\n            dconv_lstm: adds a LSTM layer in DConv branch starting at this layer.\n            dconv_init: initial scale for the DConv branch LayerScale.","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/demucs.py:24"}],[1253,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/demucs.py","startLine":298,"endLine":305,"chunkText":"            dconv_attn: adds attention layers in DConv branch starting at this layer.\n            dconv_lstm: adds a LSTM layer in DConv branch starting at this layer.\n            dconv_init: initial scale for the DConv branch LayerScale.\n            normalize (bool): normalizes the input audio on the fly, and scales back\n                the output by the same amount.\n            resample (bool): upsample x2 the input and downsample /2 the output.\n            rescale (int): rescale initial weights of convolutions\n                to get their standard deviation closer to `rescale`.","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/demucs.py:25"}],[1254,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/demucs.py","startLine":303,"endLine":314,"chunkText":"            resample (bool): upsample x2 the input and downsample /2 the output.\n            rescale (int): rescale initial weights of convolutions\n                to get their standard deviation closer to `rescale`.\n            samplerate (int): stored as meta information for easing\n                future evaluations of the model.\n            segment (float): duration of the chunks of audio to ideally evaluate the model on.\n                This is used by `demucs.apply.apply_model`.\n        \"\"\"\n\n        super().__init__()\n        self.audio_channels = audio_channels\n        self.sources = sources","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/demucs.py:26"}],[1255,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/demucs.py","startLine":312,"endLine":331,"chunkText":"        super().__init__()\n        self.audio_channels = audio_channels\n        self.sources = sources\n        self.kernel_size = kernel_size\n        self.context = context\n        self.stride = stride\n        self.depth = depth\n        self.resample = resample\n        self.channels = channels\n        self.normalize = normalize\n        self.samplerate = samplerate\n        self.segment = segment\n        self.encoder = nn.ModuleList()\n        self.decoder = nn.ModuleList()\n        self.skip_scales = nn.ModuleList()\n\n        if glu:\n            activation = nn.GLU(dim=1)\n            ch_scale = 2\n        else","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/demucs.py:27"}],[1256,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/demucs.py","startLine":329,"endLine":347,"chunkText":"            activation = nn.GLU(dim=1)\n            ch_scale = 2\n        else:\n            activation = nn.ReLU()\n            ch_scale = 1\n        if gelu:\n            act2 = nn.GELU\n        else:\n            act2 = nn.ReLU\n\n        in_channels = audio_channels\n        padding = 0\n        for index in range(depth):\n            norm_fn = lambda d: nn.Identity()  # noqa\n            if index >= norm_starts:\n                norm_fn = lambda d: nn.GroupNorm(norm_groups, d)  # noqa\n\n            encode = []\n            encode += [nn.Conv1d(in_channels, channels, kernel_size, stride), norm_fn(channels), act2()]","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/demucs.py:28"}],[1257,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/demucs.py","startLine":345,"endLine":357,"chunkText":"\n            encode = []\n            encode += [nn.Conv1d(in_channels, channels, kernel_size, stride), norm_fn(channels), act2()]\n            attn = index >= dconv_attn\n            lstm = index >= dconv_lstm\n            if dconv_mode & 1:\n                encode += [DConv(channels, depth=dconv_depth, init=dconv_init, compress=dconv_comp, attn=attn, lstm=lstm)]\n            if rewrite:\n                encode += [nn.Conv1d(channels, ch_scale * channels, 1), norm_fn(ch_scale * channels), activation]\n            self.encoder.append(nn.Sequential(*encode))\n\n            decode = []\n            if index > 0:","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/demucs.py:29"}],[1258,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/demucs.py","startLine":355,"endLine":365,"chunkText":"\n            decode = []\n            if index > 0:\n                out_channels = in_channels\n            else:\n                out_channels = len(self.sources) * audio_channels\n            if rewrite:\n                decode += [nn.Conv1d(channels, ch_scale * channels, 2 * context + 1, padding=context), norm_fn(ch_scale * channels), activation]\n            if dconv_mode & 2:\n                decode += [DConv(channels, depth=dconv_depth, init=dconv_init, compress=dconv_comp, attn=attn, lstm=lstm)]\n            decode += [nn.ConvTranspose1d(channels, out_channels, kernel_size, stride, padding=padding)]","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/demucs.py:30"}],[1259,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/demucs.py","startLine":363,"endLine":375,"chunkText":"            if dconv_mode & 2:\n                decode += [DConv(channels, depth=dconv_depth, init=dconv_init, compress=dconv_comp, attn=attn, lstm=lstm)]\n            decode += [nn.ConvTranspose1d(channels, out_channels, kernel_size, stride, padding=padding)]\n            if index > 0:\n                decode += [norm_fn(out_channels), act2()]\n            self.decoder.insert(0, nn.Sequential(*decode))\n            in_channels = channels\n            channels = int(growth * channels)\n\n        channels = in_channels\n        if lstm_layers:\n            self.lstm = BLSTM(channels, lstm_layers)\n        else:","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/demucs.py:31"}],[1260,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/demucs.py","startLine":373,"endLine":389,"chunkText":"        if lstm_layers:\n            self.lstm = BLSTM(channels, lstm_layers)\n        else:\n            self.lstm = None\n\n        if rescale:\n            rescale_module(self, reference=rescale)\n\n    def valid_length(self, length):\n        \"\"\"\n        Return the nearest valid length to use with the model so that\n        there is no time steps left over in a convolution, e.g. for all\n        layers, size of the input - kernel_size % stride = 0.\n\n        Note that input are automatically padded if necessary to ensure that the output\n        has the same length as the input.\n        \"\"\"","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/demucs.py:32"}],[1261,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/demucs.py","startLine":388,"endLine":409,"chunkText":"        has the same length as the input.\n        \"\"\"\n        if self.resample:\n            length *= 2\n\n        for _ in range(self.depth):\n            length = math.ceil((length - self.kernel_size) / self.stride) + 1\n            length = max(1, length)\n\n        for idx in range(self.depth):\n            length = (length - 1) * self.stride + self.kernel_size\n\n        if self.resample:\n            length = math.ceil(length / 2)\n        return int(length)\n\n    def forward(self, mix):\n        x = mix\n        length = x.shape[-1]\n\n        if self.normalize:\n            mono = mix.mean(dim=1, keepdim=True)","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/demucs.py:33"}],[1262,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/demucs.py","startLine":407,"endLine":427,"chunkText":"        if self.normalize:\n            mono = mix.mean(dim=1, keepdim=True)\n            mean = mono.mean(dim=-1, keepdim=True)\n            std = mono.std(dim=-1, keepdim=True)\n            x = (x - mean) / (1e-5 + std)\n        else:\n            mean = 0\n            std = 1\n\n        delta = self.valid_length(length) - length\n        x = F.pad(x, (delta // 2, delta - delta // 2))\n\n        if self.resample:\n            x = julius.resample_frac(x, 1, 2)\n\n        saved = []\n        for encode in self.encoder:\n            x = encode(x)\n            saved.append(x)\n\n        if self.lstm:","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/demucs.py:34"}],[1263,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/demucs.py","startLine":428,"endLine":445,"chunkText":"        if self.lstm:\n            x = self.lstm(x)\n\n        for decode in self.decoder:\n            skip = saved.pop(-1)\n            skip = center_trim(skip, x)\n            x = decode(x + skip)\n\n        if self.resample:\n            x = julius.resample_frac(x, 2, 1)\n        x = x * std + mean\n        x = center_trim(x, length)\n        x = x.view(x.size(0), len(self.sources), self.audio_channels, x.size(-1))\n        return x\n\n    def load_state_dict(self, state, strict=True):\n        # fix a mismatch with previous generation Demucs models.\n        for idx in range(self.depth):","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/demucs.py:35"}],[1264,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/demucs.py","startLine":443,"endLine":453,"chunkText":"    def load_state_dict(self, state, strict=True):\n        # fix a mismatch with previous generation Demucs models.\n        for idx in range(self.depth):\n            for a in [\"encoder\", \"decoder\"]:\n                for b in [\"bias\", \"weight\"]:\n                    new = f\"{a}.{idx}.3.{b}\"\n                    old = f\"{a}.{idx}.2.{b}\"\n                    if old in state and new not in state:\n                        state[new] = state.pop(old)\n        super().load_state_dict(state, strict=strict)\n","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/demucs.py:36"}],[1265,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/filtering.py","startLine":0,"endLine":22,"chunkText":"from typing import Optional\nimport torch\nimport torch.nn as nn\nfrom torch import Tensor\nfrom torch.utils.data import DataLoader\n\n\ndef atan2(y, x):\n    r\"\"\"Element-wise arctangent function of y/x.\n    Returns a new tensor with signed angles in radians.\n    It is an alternative implementation of torch.atan2\n\n    Args:\n        y (Tensor): First input tensor\n        x (Tensor): Second input tensor [shape=y.shape]\n\n    Returns:\n        Tensor: [shape=y.shape].\n    \"\"\"\n    pi = 2 * torch.asin(torch.tensor(1.0))\n    x += ((x == 0) & (y == 0)) * 1.0\n    out = torch.atan(y / x)\n    out += ((y >= 0) & (x < 0)) * pi","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/filtering.py:0"}],[1266,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/filtering.py","startLine":20,"endLine":36,"chunkText":"    x += ((x == 0) & (y == 0)) * 1.0\n    out = torch.atan(y / x)\n    out += ((y >= 0) & (x < 0)) * pi\n    out -= ((y < 0) & (x < 0)) * pi\n    out *= 1 - ((y > 0) & (x == 0)) * 1.0\n    out += ((y > 0) & (x == 0)) * (pi / 2)\n    out *= 1 - ((y < 0) & (x == 0)) * 1.0\n    out += ((y < 0) & (x == 0)) * (-pi / 2)\n    return out\n\n\n# Define basic complex operations on torch.Tensor objects whose last dimension\n# consists in the concatenation of the real and imaginary parts.\n\n\ndef _norm(x: torch.Tensor) -> torch.Tensor:\n    r\"\"\"Computes the norm value of a torch Tensor, assuming that it","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/filtering.py:1"}],[1267,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/filtering.py","startLine":34,"endLine":50,"chunkText":"\ndef _norm(x: torch.Tensor) -> torch.Tensor:\n    r\"\"\"Computes the norm value of a torch Tensor, assuming that it\n    comes as real and imaginary part in its last dimension.\n\n    Args:\n        x (Tensor): Input Tensor of shape [shape=(..., 2)]\n\n    Returns:\n        Tensor: shape as x excluding the last dimension.\n    \"\"\"\n    return torch.abs(x[..., 0]) ** 2 + torch.abs(x[..., 1]) ** 2\n\n\ndef _mul_add(a: torch.Tensor, b: torch.Tensor, out: Optional[torch.Tensor] = None) -> torch.Tensor:\n    \"\"\"Element-wise multiplication of two complex Tensors described\n    through their real and imaginary parts.","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/filtering.py:2"}],[1268,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/filtering.py","startLine":48,"endLine":58,"chunkText":"def _mul_add(a: torch.Tensor, b: torch.Tensor, out: Optional[torch.Tensor] = None) -> torch.Tensor:\n    \"\"\"Element-wise multiplication of two complex Tensors described\n    through their real and imaginary parts.\n    The result is added to the `out` tensor\"\"\"\n\n    # check `out` and allocate it if needed\n    target_shape = torch.Size([max(sa, sb) for (sa, sb) in zip(a.shape, b.shape)])\n    if out is None or out.shape != target_shape:\n        out = torch.zeros(target_shape, dtype=a.dtype, device=a.device)\n    if out is a:\n        real_a = a[..., 0]","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/filtering.py:3"}],[1269,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/filtering.py","startLine":56,"endLine":67,"chunkText":"        out = torch.zeros(target_shape, dtype=a.dtype, device=a.device)\n    if out is a:\n        real_a = a[..., 0]\n        out[..., 0] = out[..., 0] + (real_a * b[..., 0] - a[..., 1] * b[..., 1])\n        out[..., 1] = out[..., 1] + (real_a * b[..., 1] + a[..., 1] * b[..., 0])\n    else:\n        out[..., 0] = out[..., 0] + (a[..., 0] * b[..., 0] - a[..., 1] * b[..., 1])\n        out[..., 1] = out[..., 1] + (a[..., 0] * b[..., 1] + a[..., 1] * b[..., 0])\n    return out\n\n\ndef _mul(a: torch.Tensor, b: torch.Tensor, out: Optional[torch.Tensor] = None) -> torch.Tensor:","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/filtering.py:4"}],[1270,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/filtering.py","startLine":65,"endLine":76,"chunkText":"\n\ndef _mul(a: torch.Tensor, b: torch.Tensor, out: Optional[torch.Tensor] = None) -> torch.Tensor:\n    \"\"\"Element-wise multiplication of two complex Tensors described\n    through their real and imaginary parts\n    can work in place in case out is a only\"\"\"\n    target_shape = torch.Size([max(sa, sb) for (sa, sb) in zip(a.shape, b.shape)])\n    if out is None or out.shape != target_shape:\n        out = torch.zeros(target_shape, dtype=a.dtype, device=a.device)\n    if out is a:\n        real_a = a[..., 0]\n        out[..., 0] = real_a * b[..., 0] - a[..., 1] * b[..., 1]","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/filtering.py:5"}],[1271,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/filtering.py","startLine":74,"endLine":88,"chunkText":"    if out is a:\n        real_a = a[..., 0]\n        out[..., 0] = real_a * b[..., 0] - a[..., 1] * b[..., 1]\n        out[..., 1] = real_a * b[..., 1] + a[..., 1] * b[..., 0]\n    else:\n        out[..., 0] = a[..., 0] * b[..., 0] - a[..., 1] * b[..., 1]\n        out[..., 1] = a[..., 0] * b[..., 1] + a[..., 1] * b[..., 0]\n    return out\n\n\ndef _inv(z: torch.Tensor, out: Optional[torch.Tensor] = None) -> torch.Tensor:\n    \"\"\"Element-wise multiplicative inverse of a Tensor with complex\n    entries described through their real and imaginary parts.\n    can work in place in case out is z\"\"\"\n    ez = _norm(z)","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/filtering.py:6"}],[1272,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/filtering.py","startLine":86,"endLine":101,"chunkText":"    entries described through their real and imaginary parts.\n    can work in place in case out is z\"\"\"\n    ez = _norm(z)\n    if out is None or out.shape != z.shape:\n        out = torch.zeros_like(z)\n    out[..., 0] = z[..., 0] / ez\n    out[..., 1] = -z[..., 1] / ez\n    return out\n\n\ndef _conj(z, out: Optional[torch.Tensor] = None) -> torch.Tensor:\n    \"\"\"Element-wise complex conjugate of a Tensor with complex entries\n    described through their real and imaginary parts.\n    can work in place in case out is z\"\"\"\n    if out is None or out.shape != z.shape:\n        out = torch.zeros_like(z)","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/filtering.py:7"}],[1273,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/filtering.py","startLine":99,"endLine":118,"chunkText":"    can work in place in case out is z\"\"\"\n    if out is None or out.shape != z.shape:\n        out = torch.zeros_like(z)\n    out[..., 0] = z[..., 0]\n    out[..., 1] = -z[..., 1]\n    return out\n\n\ndef _invert(M: torch.Tensor, out: Optional[torch.Tensor] = None) -> torch.Tensor:\n    \"\"\"\n    Invert 1x1 or 2x2 matrices\n\n    Will generate errors if the matrices are singular: user must handle this\n    through his own regularization schemes.\n\n    Args:\n        M (Tensor): [shape=(..., nb_channels, nb_channels, 2)]\n            matrices to invert: must be square along dimensions -3 and -2\n\n    Returns:","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/filtering.py:8"}],[1274,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/filtering.py","startLine":116,"endLine":136,"chunkText":"            matrices to invert: must be square along dimensions -3 and -2\n\n    Returns:\n        invM (Tensor): [shape=M.shape]\n            inverses of M\n    \"\"\"\n    nb_channels = M.shape[-2]\n\n    if out is None or out.shape != M.shape:\n        out = torch.empty_like(M)\n\n    if nb_channels == 1:\n        # scalar case\n        out = _inv(M, out)\n    elif nb_channels == 2:\n        # two channels case: analytical expression\n\n        # first compute the determinent\n        det = _mul(M[..., 0, 0, :], M[..., 1, 1, :])\n        det = det - _mul(M[..., 0, 1, :], M[..., 1, 0, :])\n        # invert it","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/filtering.py:9"}],[1275,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/filtering.py","startLine":134,"endLine":145,"chunkText":"        det = _mul(M[..., 0, 0, :], M[..., 1, 1, :])\n        det = det - _mul(M[..., 0, 1, :], M[..., 1, 0, :])\n        # invert it\n        invDet = _inv(det)\n\n        # then fill out the matrix with the inverse\n        out[..., 0, 0, :] = _mul(invDet, M[..., 1, 1, :], out[..., 0, 0, :])\n        out[..., 1, 0, :] = _mul(-invDet, M[..., 1, 0, :], out[..., 1, 0, :])\n        out[..., 0, 1, :] = _mul(-invDet, M[..., 0, 1, :], out[..., 0, 1, :])\n        out[..., 1, 1, :] = _mul(invDet, M[..., 0, 0, :], out[..., 1, 1, :])\n    else:\n        raise Exception(\"Only 2 channels are supported for the torch version.\")","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/filtering.py:10"}],[1276,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/filtering.py","startLine":143,"endLine":156,"chunkText":"        out[..., 1, 1, :] = _mul(invDet, M[..., 0, 0, :], out[..., 1, 1, :])\n    else:\n        raise Exception(\"Only 2 channels are supported for the torch version.\")\n    return out\n\n\n# Now define the signal-processing low-level functions used by the Separator\n\n\ndef expectation_maximization(y: torch.Tensor, x: torch.Tensor, iterations: int = 2, eps: float = 1e-10, batch_size: int = 200):\n    r\"\"\"Expectation maximization algorithm, for refining source separation\n    estimates.\n\n    This algorithm allows to make source separation results better by","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/filtering.py:11"}],[1277,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/filtering.py","startLine":154,"endLine":167,"chunkText":"    estimates.\n\n    This algorithm allows to make source separation results better by\n    enforcing multichannel consistency for the estimates. This usually means\n    a better perceptual quality in terms of spatial artifacts.\n\n    The implementation follows the details presented in [1]_, taking\n    inspiration from the original EM algorithm proposed in [2]_ and its\n    weighted refinement proposed in [3]_, [4]_.\n    It works by iteratively:\n\n     * Re-estimate source parameters (power spectral densities and spatial\n       covariance matrices) through :func:`get_local_gaussian_model`.\n","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/filtering.py:12"}],[1278,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/filtering.py","startLine":165,"endLine":176,"chunkText":"     * Re-estimate source parameters (power spectral densities and spatial\n       covariance matrices) through :func:`get_local_gaussian_model`.\n\n     * Separate again the mixture with the new parameters by first computing\n       the new modelled mixture covariance matrices with :func:`get_mix_model`,\n       prepare the Wiener filters through :func:`wiener_gain` and apply them\n       with :func:`apply_filter``.\n\n    References\n    ----------\n    .. [1] S. Uhlich and M. Porcu and F. Giron and M. Enenkl and T. Kemp and\n        N. Takahashi and Y. Mitsufuji, \"Improving music source separation based","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/filtering.py:13"}],[1279,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/filtering.py","startLine":174,"endLine":183,"chunkText":"    ----------\n    .. [1] S. Uhlich and M. Porcu and F. Giron and M. Enenkl and T. Kemp and\n        N. Takahashi and Y. Mitsufuji, \"Improving music source separation based\n        on deep neural networks through data augmentation and network\n        blending.\" 2017 IEEE International Conference on Acoustics, Speech\n        and Signal Processing (ICASSP). IEEE, 2017.\n\n    .. [2] N.Q. Duong and E. Vincent and R.Gribonval. \"Under-determined\n        reverberant audio source separation using a full-rank spatial\n        covariance model.\" IEEE Transactions on Audio, Speech, and Language","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/filtering.py:14"}],[1280,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/filtering.py","startLine":181,"endLine":190,"chunkText":"    .. [2] N.Q. Duong and E. Vincent and R.Gribonval. \"Under-determined\n        reverberant audio source separation using a full-rank spatial\n        covariance model.\" IEEE Transactions on Audio, Speech, and Language\n        Processing 18.7 (2010): 1830-1840.\n\n    .. [3] A. Nugraha and A. Liutkus and E. Vincent. \"Multichannel audio source\n        separation with deep neural networks.\" IEEE/ACM Transactions on Audio,\n        Speech, and Language Processing 24.9 (2016): 1652-1664.\n\n    .. [4] A. Nugraha and A. Liutkus and E. Vincent. \"Multichannel music","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/filtering.py:15"}],[1281,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/filtering.py","startLine":188,"endLine":200,"chunkText":"        Speech, and Language Processing 24.9 (2016): 1652-1664.\n\n    .. [4] A. Nugraha and A. Liutkus and E. Vincent. \"Multichannel music\n        separation with deep neural networks.\" 2016 24th European Signal\n        Processing Conference (EUSIPCO). IEEE, 2016.\n\n    .. [5] A. Liutkus and R. Badeau and G. Richard \"Kernel additive models for\n        source separation.\" IEEE Transactions on Signal Processing\n        62.16 (2014): 4298-4310.\n\n    Args:\n        y (Tensor): [shape=(nb_frames, nb_bins, nb_channels, 2, nb_sources)]\n            initial estimates for the sources","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/filtering.py:16"}],[1282,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/filtering.py","startLine":198,"endLine":210,"chunkText":"    Args:\n        y (Tensor): [shape=(nb_frames, nb_bins, nb_channels, 2, nb_sources)]\n            initial estimates for the sources\n        x (Tensor): [shape=(nb_frames, nb_bins, nb_channels, 2)]\n            complex STFT of the mixture signal\n        iterations (int): [scalar]\n            number of iterations for the EM algorithm.\n        eps (float or None): [scalar]\n            The epsilon value to use for regularization and filters.\n\n    Returns:\n        y (Tensor): [shape=(nb_frames, nb_bins, nb_channels, 2, nb_sources)]\n            estimated sources after iterations","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/filtering.py:17"}],[1283,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/filtering.py","startLine":208,"endLine":219,"chunkText":"    Returns:\n        y (Tensor): [shape=(nb_frames, nb_bins, nb_channels, 2, nb_sources)]\n            estimated sources after iterations\n        v (Tensor): [shape=(nb_frames, nb_bins, nb_sources)]\n            estimated power spectral densities\n        R (Tensor): [shape=(nb_bins, nb_channels, nb_channels, 2, nb_sources)]\n            estimated spatial covariance matrices\n\n    Notes:\n        * You need an initial estimate for the sources to apply this\n          algorithm. This is precisely what the :func:`wiener` function does.\n        * This algorithm *is not* an implementation of the \"exact\" EM","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/filtering.py:18"}],[1284,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/filtering.py","startLine":217,"endLine":224,"chunkText":"        * You need an initial estimate for the sources to apply this\n          algorithm. This is precisely what the :func:`wiener` function does.\n        * This algorithm *is not* an implementation of the \"exact\" EM\n          proposed in [1]_. In particular, it does compute the posterior\n          covariance matrices the same (exact) way. Instead, it uses the\n          simplified approximate scheme initially proposed in [5]_ and further\n          refined in [3]_, [4]_, that boils down to just take the empirical\n          covariance of the recent source estimates, followed by a weighted","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/filtering.py:19"}],[1285,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/filtering.py","startLine":222,"endLine":231,"chunkText":"          simplified approximate scheme initially proposed in [5]_ and further\n          refined in [3]_, [4]_, that boils down to just take the empirical\n          covariance of the recent source estimates, followed by a weighted\n          average for the update of the spatial covariance matrix. It has been\n          empirically demonstrated that this simplified algorithm is more\n          robust for music separation.\n\n    Warning:\n        It is *very* important to make sure `x.dtype` is `torch.float64`\n        if you want double precision, because this function will **not**","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/filtering.py:20"}],[1286,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/filtering.py","startLine":229,"endLine":242,"chunkText":"    Warning:\n        It is *very* important to make sure `x.dtype` is `torch.float64`\n        if you want double precision, because this function will **not**\n        do such conversion for you from `torch.complex32`, in case you want the\n        smaller RAM usage on purpose.\n\n        It is usually always better in terms of quality to have double\n        precision, by e.g. calling :func:`expectation_maximization`\n        with ``x.to(torch.float64)``.\n    \"\"\"\n    # dimensions\n    (nb_frames, nb_bins, nb_channels) = x.shape[:-1]\n    nb_sources = y.shape[-1]\n","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/filtering.py:21"}],[1287,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/filtering.py","startLine":240,"endLine":247,"chunkText":"    (nb_frames, nb_bins, nb_channels) = x.shape[:-1]\n    nb_sources = y.shape[-1]\n\n    regularization = torch.cat((torch.eye(nb_channels, dtype=x.dtype, device=x.device)[..., None], torch.zeros((nb_channels, nb_channels, 1), dtype=x.dtype, device=x.device)), dim=2)\n    regularization = torch.sqrt(torch.as_tensor(eps)) * (regularization[None, None, ...].expand((-1, nb_bins, -1, -1, -1)))\n\n    # allocate the spatial covariance matrices\n    R = [torch.zeros((nb_bins, nb_channels, nb_channels, 2), dtype=x.dtype, device=x.device) for j in range(nb_sources)]","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/filtering.py:22"}],[1288,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/filtering.py","startLine":245,"endLine":255,"chunkText":"\n    # allocate the spatial covariance matrices\n    R = [torch.zeros((nb_bins, nb_channels, nb_channels, 2), dtype=x.dtype, device=x.device) for j in range(nb_sources)]\n    weight: torch.Tensor = torch.zeros((nb_bins,), dtype=x.dtype, device=x.device)\n\n    v: torch.Tensor = torch.zeros((nb_frames, nb_bins, nb_sources), dtype=x.dtype, device=x.device)\n    for it in range(iterations):\n        # constructing the mixture covariance matrix. Doing it with a loop\n        # to avoid storing anytime in RAM the whole 6D tensor\n\n        # update the PSD as the average spectrogram over channels","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/filtering.py:23"}],[1289,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/filtering.py","startLine":253,"endLine":264,"chunkText":"        # to avoid storing anytime in RAM the whole 6D tensor\n\n        # update the PSD as the average spectrogram over channels\n        v = torch.mean(torch.abs(y[..., 0, :]) ** 2 + torch.abs(y[..., 1, :]) ** 2, dim=-2)\n\n        # update spatial covariance matrices (weighted update)\n        for j in range(nb_sources):\n            R[j] = torch.tensor(0.0, device=x.device)\n            weight = torch.tensor(eps, device=x.device)\n            pos: int = 0\n            batch_size = batch_size if batch_size else nb_frames\n            while pos < nb_frames:","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/filtering.py:24"}],[1290,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/filtering.py","startLine":262,"endLine":276,"chunkText":"            pos: int = 0\n            batch_size = batch_size if batch_size else nb_frames\n            while pos < nb_frames:\n                t = torch.arange(pos, min(nb_frames, pos + batch_size))\n                pos = int(t[-1]) + 1\n\n                R[j] = R[j] + torch.sum(_covariance(y[t, ..., j]), dim=0)\n                weight = weight + torch.sum(v[t, ..., j], dim=0)\n            R[j] = R[j] / weight[..., None, None, None]\n            weight = torch.zeros_like(weight)\n\n        # cloning y if we track gradient, because we're going to update it\n        if y.requires_grad:\n            y = y.clone()\n","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/filtering.py:25"}],[1291,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/filtering.py","startLine":274,"endLine":292,"chunkText":"        if y.requires_grad:\n            y = y.clone()\n\n        pos = 0\n        while pos < nb_frames:\n            t = torch.arange(pos, min(nb_frames, pos + batch_size))\n            pos = int(t[-1]) + 1\n\n            y[t, ...] = torch.tensor(0.0, device=x.device, dtype=x.dtype)\n\n            # compute mix covariance matrix\n            Cxx = regularization\n            for j in range(nb_sources):\n                Cxx = Cxx + (v[t, ..., j, None, None, None] * R[j][None, ...].clone())\n\n            # invert it\n            inv_Cxx = _invert(Cxx)\n\n            # separate the sources","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/filtering.py:26"}],[1292,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/filtering.py","startLine":290,"endLine":300,"chunkText":"            inv_Cxx = _invert(Cxx)\n\n            # separate the sources\n            for j in range(nb_sources):\n\n                # create a wiener gain for this source\n                gain = torch.zeros_like(inv_Cxx)\n\n                # computes multichannel Wiener gain as v_j R_j inv_Cxx\n                indices = torch.cartesian_prod(torch.arange(nb_channels), torch.arange(nb_channels), torch.arange(nb_channels))\n                for index in indices:","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/filtering.py:27"}],[1293,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/filtering.py","startLine":298,"endLine":305,"chunkText":"                # computes multichannel Wiener gain as v_j R_j inv_Cxx\n                indices = torch.cartesian_prod(torch.arange(nb_channels), torch.arange(nb_channels), torch.arange(nb_channels))\n                for index in indices:\n                    gain[:, :, index[0], index[1], :] = _mul_add(R[j][None, :, index[0], index[2], :].clone(), inv_Cxx[:, :, index[2], index[1], :], gain[:, :, index[0], index[1], :])\n                gain = gain * v[t, ..., None, None, None, j]\n\n                # apply it to the mixture\n                for i in range(nb_channels):","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/filtering.py:28"}],[1294,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/filtering.py","startLine":303,"endLine":315,"chunkText":"\n                # apply it to the mixture\n                for i in range(nb_channels):\n                    y[t, ..., j] = _mul_add(gain[..., i, :], x[t, ..., i, None, :], y[t, ..., j])\n\n    return y, v, R\n\n\ndef wiener(targets_spectrograms: torch.Tensor, mix_stft: torch.Tensor, iterations: int = 1, softmask: bool = False, residual: bool = False, scale_factor: float = 10.0, eps: float = 1e-10):\n    \"\"\"Wiener-based separation for multichannel audio.\n\n    The method uses the (possibly multichannel) spectrograms  of the\n    sources to separate the (complex) Short Term Fourier Transform  of the","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/filtering.py:29"}],[1295,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/filtering.py","startLine":313,"endLine":326,"chunkText":"\n    The method uses the (possibly multichannel) spectrograms  of the\n    sources to separate the (complex) Short Term Fourier Transform  of the\n    mix. Separation is done in a sequential way by:\n\n    * Getting an initial estimate. This can be done in two ways: either by\n      directly using the spectrograms with the mixture phase, or\n      by using a softmasking strategy. This initial phase is controlled\n      by the `softmask` flag.\n\n    * If required, adding an additional residual target as the mix minus\n      all targets.\n\n    * Refinining these initial estimates through a call to","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/filtering.py:30"}],[1296,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/filtering.py","startLine":324,"endLine":336,"chunkText":"      all targets.\n\n    * Refinining these initial estimates through a call to\n      :func:`expectation_maximization` if the number of iterations is nonzero.\n\n    This implementation also allows to specify the epsilon value used for\n    regularization. It is based on [1]_, [2]_, [3]_, [4]_.\n\n    References\n    ----------\n    .. [1] S. Uhlich and M. Porcu and F. Giron and M. Enenkl and T. Kemp and\n        N. Takahashi and Y. Mitsufuji, \"Improving music source separation based\n        on deep neural networks through data augmentation and network","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/filtering.py:31"}],[1297,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/filtering.py","startLine":334,"endLine":343,"chunkText":"    .. [1] S. Uhlich and M. Porcu and F. Giron and M. Enenkl and T. Kemp and\n        N. Takahashi and Y. Mitsufuji, \"Improving music source separation based\n        on deep neural networks through data augmentation and network\n        blending.\" 2017 IEEE International Conference on Acoustics, Speech\n        and Signal Processing (ICASSP). IEEE, 2017.\n\n    .. [2] A. Nugraha and A. Liutkus and E. Vincent. \"Multichannel audio source\n        separation with deep neural networks.\" IEEE/ACM Transactions on Audio,\n        Speech, and Language Processing 24.9 (2016): 1652-1664.\n","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/filtering.py:32"}],[1298,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/filtering.py","startLine":341,"endLine":353,"chunkText":"        separation with deep neural networks.\" IEEE/ACM Transactions on Audio,\n        Speech, and Language Processing 24.9 (2016): 1652-1664.\n\n    .. [3] A. Nugraha and A. Liutkus and E. Vincent. \"Multichannel music\n        separation with deep neural networks.\" 2016 24th European Signal\n        Processing Conference (EUSIPCO). IEEE, 2016.\n\n    .. [4] A. Liutkus and R. Badeau and G. Richard \"Kernel additive models for\n        source separation.\" IEEE Transactions on Signal Processing\n        62.16 (2014): 4298-4310.\n\n    Args:\n        targets_spectrograms (Tensor): spectrograms of the sources","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/filtering.py:33"}],[1299,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/filtering.py","startLine":351,"endLine":362,"chunkText":"\n    Args:\n        targets_spectrograms (Tensor): spectrograms of the sources\n            [shape=(nb_frames, nb_bins, nb_channels, nb_sources)].\n            This is a nonnegative tensor that is\n            usually the output of the actual separation method of the user. The\n            spectrograms may be mono, but they need to be 4-dimensional in all\n            cases.\n        mix_stft (Tensor): [shape=(nb_frames, nb_bins, nb_channels, complex=2)]\n            STFT of the mixture signal.\n        iterations (int): [scalar]\n            number of iterations for the EM algorithm","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/filtering.py:34"}],[1300,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/filtering.py","startLine":360,"endLine":369,"chunkText":"            STFT of the mixture signal.\n        iterations (int): [scalar]\n            number of iterations for the EM algorithm\n        softmask (bool): Describes how the initial estimates are obtained.\n            * if `False`, then the mixture phase will directly be used with the\n            spectrogram as initial estimates.\n            * if `True`, initial estimates are obtained by multiplying the\n            complex mix element-wise with the ratio of each target spectrogram\n            with the sum of them all. This strategy is better if the model are\n            not really good, and worse otherwise.","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/filtering.py:35"}],[1301,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/filtering.py","startLine":367,"endLine":375,"chunkText":"            complex mix element-wise with the ratio of each target spectrogram\n            with the sum of them all. This strategy is better if the model are\n            not really good, and worse otherwise.\n        residual (bool): if `True`, an additional target is created, which is\n            equal to the mixture minus the other targets, before application of\n            expectation maximization\n        eps (float): Epsilon value to use for computing the separations.\n            This is used whenever division with a model energy is\n            performed, i.e. when softmasking and when iterating the EM","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/filtering.py:36"}],[1302,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/filtering.py","startLine":373,"endLine":385,"chunkText":"        eps (float): Epsilon value to use for computing the separations.\n            This is used whenever division with a model energy is\n            performed, i.e. when softmasking and when iterating the EM.\n            It can be understood as the energy of the additional white noise\n            that is taken out when separating.\n\n    Returns:\n        Tensor: shape=(nb_frames, nb_bins, nb_channels, complex=2, nb_sources)\n            STFT of estimated sources\n\n    Notes:\n        * Be careful that you need *magnitude spectrogram estimates* for the\n        case `softmask==False`.","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/filtering.py:37"}],[1303,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/filtering.py","startLine":383,"endLine":394,"chunkText":"    Notes:\n        * Be careful that you need *magnitude spectrogram estimates* for the\n        case `softmask==False`.\n        * `softmask=False` is recommended\n        * The epsilon value will have a huge impact on performance. If it's\n        large, only the parts of the signal with a significant energy will\n        be kept in the sources. This epsilon then directly controls the\n        energy of the reconstruction error.\n\n    Warning:\n        As in :func:`expectation_maximization`, we recommend converting the\n        mixture `x` to double precision `torch.float64` *before* calling","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/filtering.py:38"}],[1304,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/filtering.py","startLine":393,"endLine":402,"chunkText":"        As in :func:`expectation_maximization`, we recommend converting the\n        mixture `x` to double precision `torch.float64` *before* calling\n        :func:`wiener`.\n    \"\"\"\n    if softmask:\n        # if we use softmask, we compute the ratio mask for all targets and\n        # multiply by the mix stft\n        y = mix_stft[..., None] * (targets_spectrograms / (eps + torch.sum(targets_spectrograms, dim=-1, keepdim=True).to(mix_stft.dtype)))[..., None, :]\n    else:\n        # otherwise, we just multiply the targets spectrograms with mix phase","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/filtering.py:39"}],[1305,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/filtering.py","startLine":400,"endLine":407,"chunkText":"        y = mix_stft[..., None] * (targets_spectrograms / (eps + torch.sum(targets_spectrograms, dim=-1, keepdim=True).to(mix_stft.dtype)))[..., None, :]\n    else:\n        # otherwise, we just multiply the targets spectrograms with mix phase\n        # we tacitly assume that we have magnitude estimates.\n        angle = atan2(mix_stft[..., 1], mix_stft[..., 0])[..., None]\n        nb_sources = targets_spectrograms.shape[-1]\n        y = torch.zeros(mix_stft.shape + (nb_sources,), dtype=mix_stft.dtype, device=mix_stft.device)\n        y[..., 0, :] = targets_spectrograms * torch.cos(angle)","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/filtering.py:40"}],[1306,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/filtering.py","startLine":405,"endLine":418,"chunkText":"        nb_sources = targets_spectrograms.shape[-1]\n        y = torch.zeros(mix_stft.shape + (nb_sources,), dtype=mix_stft.dtype, device=mix_stft.device)\n        y[..., 0, :] = targets_spectrograms * torch.cos(angle)\n        y[..., 1, :] = targets_spectrograms * torch.sin(angle)\n\n    if residual:\n        # if required, adding an additional target as the mix minus\n        # available targets\n        y = torch.cat([y, mix_stft[..., None] - y.sum(dim=-1, keepdim=True)], dim=-1)\n\n    if iterations == 0:\n        return y\n\n    # we need to refine the estimates. Scales down the estimates for","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/filtering.py:41"}],[1307,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/filtering.py","startLine":416,"endLine":437,"chunkText":"        return y\n\n    # we need to refine the estimates. Scales down the estimates for\n    # numerical stability\n    max_abs = torch.max(torch.as_tensor(1.0, dtype=mix_stft.dtype, device=mix_stft.device), torch.sqrt(_norm(mix_stft)).max() / scale_factor)\n\n    mix_stft = mix_stft / max_abs\n    y = y / max_abs\n\n    # call expectation maximization\n    y = expectation_maximization(y, mix_stft, iterations, eps=eps)[0]\n\n    # scale estimates up again\n    y = y * max_abs\n    return y\n\n\ndef _covariance(y_j):\n    \"\"\"\n    Compute the empirical covariance for a source.\n\n    Args:","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/filtering.py:42"}],[1308,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/filtering.py","startLine":435,"endLine":447,"chunkText":"    Compute the empirical covariance for a source.\n\n    Args:\n        y_j (Tensor): complex stft of the source.\n            [shape=(nb_frames, nb_bins, nb_channels, 2)].\n\n    Returns:\n        Cj (Tensor): [shape=(nb_frames, nb_bins, nb_channels, nb_channels, 2)]\n            just y_j * conj(y_j.T): empirical covariance for each TF bin.\n    \"\"\"\n    (nb_frames, nb_bins, nb_channels) = y_j.shape[:-1]\n    Cj = torch.zeros((nb_frames, nb_bins, nb_channels, nb_channels, 2), dtype=y_j.dtype, device=y_j.device)\n    indices = torch.cartesian_prod(torch.arange(nb_channels), torch.arange(nb_channels))","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/filtering.py:43"}],[1309,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/filtering.py","startLine":445,"endLine":451,"chunkText":"    (nb_frames, nb_bins, nb_channels) = y_j.shape[:-1]\n    Cj = torch.zeros((nb_frames, nb_bins, nb_channels, nb_channels, 2), dtype=y_j.dtype, device=y_j.device)\n    indices = torch.cartesian_prod(torch.arange(nb_channels), torch.arange(nb_channels))\n    for index in indices:\n        Cj[:, :, index[0], index[1], :] = _mul_add(y_j[:, :, index[0], :], _conj(y_j[:, :, index[1], :]), Cj[:, :, index[0], index[1], :])\n    return Cj\n","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/filtering.py:44"}],[1310,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/hdemucs.py","startLine":0,"endLine":19,"chunkText":"# Copyright (c) Facebook, Inc. and its affiliates.\n# All rights reserved.\n#\n# This source code is licensed under the license found in the\n# LICENSE file in the root directory of this source tree.\n\"\"\"\nThis code contains the spectrogram and Hybrid version of Demucs.\n\"\"\"\nfrom copy import deepcopy\nimport math\nimport typing as tp\nimport torch\nfrom torch import nn\nfrom torch.nn import functional as F\nfrom .filtering import wiener\nfrom .demucs import DConv, rescale_module\nfrom .states import capture_init\nfrom .spec import spectro, ispectro\n\n","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/hdemucs.py:0"}],[1311,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/hdemucs.py","startLine":17,"endLine":30,"chunkText":"from .spec import spectro, ispectro\n\n\ndef pad1d(x: torch.Tensor, paddings: tp.Tuple[int, int], mode: str = \"constant\", value: float = 0.0):\n    \"\"\"Tiny wrapper around F.pad, just to allow for reflect padding on small input.\n    If this is the case, we insert extra 0 padding to the right before the reflection happen.\"\"\"\n    x0 = x\n    length = x.shape[-1]\n    padding_left, padding_right = paddings\n    if mode == \"reflect\":\n        max_pad = max(padding_left, padding_right)\n        if length <= max_pad:\n            extra_pad = max_pad - length + 1\n            extra_pad_right = min(padding_right, extra_pad)","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/hdemucs.py:1"}],[1312,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/hdemucs.py","startLine":28,"endLine":41,"chunkText":"        if length <= max_pad:\n            extra_pad = max_pad - length + 1\n            extra_pad_right = min(padding_right, extra_pad)\n            extra_pad_left = extra_pad - extra_pad_right\n            paddings = (padding_left - extra_pad_left, padding_right - extra_pad_right)\n            x = F.pad(x, (extra_pad_left, extra_pad_right))\n    out = F.pad(x, paddings, mode, value)\n    assert out.shape[-1] == length + padding_left + padding_right\n    assert (out[..., padding_left : padding_left + length] == x0).all()\n    return out\n\n\nclass ScaledEmbedding(nn.Module):\n    \"\"\"","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/hdemucs.py:2"}],[1313,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/hdemucs.py","startLine":39,"endLine":51,"chunkText":"\nclass ScaledEmbedding(nn.Module):\n    \"\"\"\n    Boost learning rate for embeddings (with `scale`).\n    Also, can make embeddings continuous with `smooth`.\n    \"\"\"\n\n    def __init__(self, num_embeddings: int, embedding_dim: int, scale: float = 10.0, smooth=False):\n        super().__init__()\n        self.embedding = nn.Embedding(num_embeddings, embedding_dim)\n        if smooth:\n            weight = torch.cumsum(self.embedding.weight.data, dim=0)\n            # when summing gaussian, overscale raises as sqrt(n), so we nornalize by that.","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/hdemucs.py:3"}],[1314,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/hdemucs.py","startLine":49,"endLine":65,"chunkText":"        if smooth:\n            weight = torch.cumsum(self.embedding.weight.data, dim=0)\n            # when summing gaussian, overscale raises as sqrt(n), so we nornalize by that.\n            weight = weight / torch.arange(1, num_embeddings + 1).to(weight).sqrt()[:, None]\n            self.embedding.weight.data[:] = weight\n        self.embedding.weight.data /= scale\n        self.scale = scale\n\n    @property\n    def weight(self):\n        return self.embedding.weight * self.scale\n\n    def forward(self, x):\n        out = self.embedding(x) * self.scale\n        return out\n\n","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/hdemucs.py:4"}],[1315,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/hdemucs.py","startLine":66,"endLine":75,"chunkText":"class HEncLayer(nn.Module):\n    def __init__(self, chin, chout, kernel_size=8, stride=4, norm_groups=1, empty=False, freq=True, dconv=True, norm=True, context=0, dconv_kw={}, pad=True, rewrite=True):\n        \"\"\"Encoder layer. This used both by the time and the frequency branch.\n\n        Args:\n            chin: number of input channels.\n            chout: number of output channels.\n            norm_groups: number of groups for group norm.\n            empty: used to make a layer with just the first conv. this is used\n                before merging the time and freq. branches.","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/hdemucs.py:5"}],[1316,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/hdemucs.py","startLine":73,"endLine":82,"chunkText":"            norm_groups: number of groups for group norm.\n            empty: used to make a layer with just the first conv. this is used\n                before merging the time and freq. branches.\n            freq: this is acting on frequencies.\n            dconv: insert DConv residual branches.\n            norm: use GroupNorm.\n            context: context size for the 1x1 conv.\n            dconv_kw: list of kwargs for the DConv class.\n            pad: pad the input. Padding is done so that the output size is\n                always the input size / stride.","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/hdemucs.py:6"}],[1317,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/hdemucs.py","startLine":80,"endLine":95,"chunkText":"            dconv_kw: list of kwargs for the DConv class.\n            pad: pad the input. Padding is done so that the output size is\n                always the input size / stride.\n            rewrite: add 1x1 conv at the end of the layer.\n        \"\"\"\n        super().__init__()\n        norm_fn = lambda d: nn.Identity()  # noqa\n        if norm:\n            norm_fn = lambda d: nn.GroupNorm(norm_groups, d)  # noqa\n        if pad:\n            pad = kernel_size // 4\n        else:\n            pad = 0\n        klass = nn.Conv1d\n        self.freq = freq\n        self.kernel_size = kernel_size","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/hdemucs.py:7"}],[1318,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/hdemucs.py","startLine":93,"endLine":110,"chunkText":"        klass = nn.Conv1d\n        self.freq = freq\n        self.kernel_size = kernel_size\n        self.stride = stride\n        self.empty = empty\n        self.norm = norm\n        self.pad = pad\n        if freq:\n            kernel_size = [kernel_size, 1]\n            stride = [stride, 1]\n            pad = [pad, 0]\n            klass = nn.Conv2d\n        self.conv = klass(chin, chout, kernel_size, stride, pad)\n        if self.empty:\n            return\n        self.norm1 = norm_fn(chout)\n        self.rewrite = None\n        if rewrite:","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/hdemucs.py:8"}],[1319,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/hdemucs.py","startLine":108,"endLine":126,"chunkText":"        self.norm1 = norm_fn(chout)\n        self.rewrite = None\n        if rewrite:\n            self.rewrite = klass(chout, 2 * chout, 1 + 2 * context, 1, context)\n            self.norm2 = norm_fn(2 * chout)\n\n        self.dconv = None\n        if dconv:\n            self.dconv = DConv(chout, **dconv_kw)\n\n    def forward(self, x, inject=None):\n        \"\"\"\n        `inject` is used to inject the result from the time branch into the frequency branch,\n        when both have the same stride.\n        \"\"\"\n        if not self.freq and x.dim() == 4:\n            B, C, Fr, T = x.shape\n            x = x.view(B, -1, T)\n","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/hdemucs.py:9"}],[1320,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/hdemucs.py","startLine":124,"endLine":140,"chunkText":"            B, C, Fr, T = x.shape\n            x = x.view(B, -1, T)\n\n        if not self.freq:\n            le = x.shape[-1]\n            if not le % self.stride == 0:\n                x = F.pad(x, (0, self.stride - (le % self.stride)))\n        y = self.conv(x)\n        if self.empty:\n            return y\n        if inject is not None:\n            assert inject.shape[-1] == y.shape[-1], (inject.shape, y.shape)\n            if inject.dim() == 3 and y.dim() == 4:\n                inject = inject[:, :, None]\n            y = y + inject\n        y = F.gelu(self.norm1(y))\n        if self.dconv:","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/hdemucs.py:10"}],[1321,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/hdemucs.py","startLine":138,"endLine":154,"chunkText":"            y = y + inject\n        y = F.gelu(self.norm1(y))\n        if self.dconv:\n            if self.freq:\n                B, C, Fr, T = y.shape\n                y = y.permute(0, 2, 1, 3).reshape(-1, C, T)\n            y = self.dconv(y)\n            if self.freq:\n                y = y.view(B, Fr, C, T).permute(0, 2, 1, 3)\n        if self.rewrite:\n            z = self.norm2(self.rewrite(y))\n            z = F.glu(z, dim=1)\n        else:\n            z = y\n        return z\n\n","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/hdemucs.py:11"}],[1322,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/hdemucs.py","startLine":155,"endLine":168,"chunkText":"class MultiWrap(nn.Module):\n    \"\"\"\n    Takes one layer and replicate it N times. each replica will act\n    on a frequency band. All is done so that if the N replica have the same weights,\n    then this is exactly equivalent to applying the original module on all frequencies.\n\n    This is a bit over-engineered to avoid edge artifacts when splitting\n    the frequency bands, but it is possible the naive implementation would work as well...\n    \"\"\"\n\n    def __init__(self, layer, split_ratios):\n        \"\"\"\n        Args:\n            layer: module to clone, must be either HEncLayer or HDecLayer.","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/hdemucs.py:12"}],[1323,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/hdemucs.py","startLine":166,"endLine":181,"chunkText":"        \"\"\"\n        Args:\n            layer: module to clone, must be either HEncLayer or HDecLayer.\n            split_ratios: list of float indicating which ratio to keep for each band.\n        \"\"\"\n        super().__init__()\n        self.split_ratios = split_ratios\n        self.layers = nn.ModuleList()\n        self.conv = isinstance(layer, HEncLayer)\n        assert not layer.norm\n        assert layer.freq\n        assert layer.pad\n        if not self.conv:\n            assert not layer.context_freq\n        for k in range(len(split_ratios) + 1):\n            lay = deepcopy(layer)","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/hdemucs.py:13"}],[1324,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/hdemucs.py","startLine":179,"endLine":196,"chunkText":"            assert not layer.context_freq\n        for k in range(len(split_ratios) + 1):\n            lay = deepcopy(layer)\n            if self.conv:\n                lay.conv.padding = (0, 0)\n            else:\n                lay.pad = False\n            for m in lay.modules():\n                if hasattr(m, \"reset_parameters\"):\n                    m.reset_parameters()\n            self.layers.append(lay)\n\n    def forward(self, x, skip=None, length=None):\n        B, C, Fr, T = x.shape\n\n        ratios = list(self.split_ratios) + [1]\n        start = 0\n        outs = []","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/hdemucs.py:14"}],[1325,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/hdemucs.py","startLine":194,"endLine":208,"chunkText":"        ratios = list(self.split_ratios) + [1]\n        start = 0\n        outs = []\n        for ratio, layer in zip(ratios, self.layers):\n            if self.conv:\n                pad = layer.kernel_size // 4\n                if ratio == 1:\n                    limit = Fr\n                    frames = -1\n                else:\n                    limit = int(round(Fr * ratio))\n                    le = limit - start\n                    if start == 0:\n                        le += pad\n                    frames = round((le - layer.kernel_size) / layer.stride + 1)","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/hdemucs.py:15"}],[1326,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/hdemucs.py","startLine":206,"endLine":218,"chunkText":"                    if start == 0:\n                        le += pad\n                    frames = round((le - layer.kernel_size) / layer.stride + 1)\n                    limit = start + (frames - 1) * layer.stride + layer.kernel_size\n                    if start == 0:\n                        limit -= pad\n                assert limit - start > 0, (limit, start)\n                assert limit <= Fr, (limit, Fr)\n                y = x[:, :, start:limit, :]\n                if start == 0:\n                    y = F.pad(y, (0, 0, pad, 0))\n                if ratio == 1:\n                    y = F.pad(y, (0, 0, 0, pad)","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/hdemucs.py:16"}],[1327,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/hdemucs.py","startLine":216,"endLine":232,"chunkText":"                    y = F.pad(y, (0, 0, pad, 0))\n                if ratio == 1:\n                    y = F.pad(y, (0, 0, 0, pad))\n                outs.append(layer(y))\n                start = limit - layer.kernel_size + layer.stride\n            else:\n                if ratio == 1:\n                    limit = Fr\n                else:\n                    limit = int(round(Fr * ratio))\n                last = layer.last\n                layer.last = True\n\n                y = x[:, :, start:limit]\n                s = skip[:, :, start:limit]\n                out, _ = layer(y, s, None)\n                if outs:","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/hdemucs.py:17"}],[1328,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/hdemucs.py","startLine":230,"endLine":242,"chunkText":"                s = skip[:, :, start:limit]\n                out, _ = layer(y, s, None)\n                if outs:\n                    outs[-1][:, :, -layer.stride :] += out[:, :, : layer.stride] - layer.conv_tr.bias.view(1, -1, 1, 1)\n                    out = out[:, :, layer.stride :]\n                if ratio == 1:\n                    out = out[:, :, : -layer.stride // 2, :]\n                if start == 0:\n                    out = out[:, :, layer.stride // 2 :, :]\n                outs.append(out)\n                layer.last = last\n                start = limit\n        out = torch.cat(outs, dim=2)","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/hdemucs.py:18"}],[1329,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/hdemucs.py","startLine":240,"endLine":250,"chunkText":"                layer.last = last\n                start = limit\n        out = torch.cat(outs, dim=2)\n        if not self.conv and not last:\n            out = F.gelu(out)\n        if self.conv:\n            return out\n        else:\n            return out, None\n\n","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/hdemucs.py:19"}],[1330,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/hdemucs.py","startLine":251,"endLine":265,"chunkText":"class HDecLayer(nn.Module):\n    def __init__(\n        self, chin, chout, last=False, kernel_size=8, stride=4, norm_groups=1, empty=False, freq=True, dconv=True, norm=True, context=1, dconv_kw={}, pad=True, context_freq=True, rewrite=True\n    ):\n        \"\"\"\n        Same as HEncLayer but for decoder. See `HEncLayer` for documentation.\n        \"\"\"\n        super().__init__()\n        norm_fn = lambda d: nn.Identity()  # noqa\n        if norm:\n            norm_fn = lambda d: nn.GroupNorm(norm_groups, d)  # noqa\n        if pad:\n            pad = kernel_size // 4\n        else:\n            pad = 0","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/hdemucs.py:20"}],[1331,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/hdemucs.py","startLine":263,"endLine":281,"chunkText":"            pad = kernel_size // 4\n        else:\n            pad = 0\n        self.pad = pad\n        self.last = last\n        self.freq = freq\n        self.chin = chin\n        self.empty = empty\n        self.stride = stride\n        self.kernel_size = kernel_size\n        self.norm = norm\n        self.context_freq = context_freq\n        klass = nn.Conv1d\n        klass_tr = nn.ConvTranspose1d\n        if freq:\n            kernel_size = [kernel_size, 1]\n            stride = [stride, 1]\n            klass = nn.Conv2d\n            klass_tr = nn.ConvTranspose2d","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/hdemucs.py:21"}],[1332,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/hdemucs.py","startLine":279,"endLine":295,"chunkText":"            stride = [stride, 1]\n            klass = nn.Conv2d\n            klass_tr = nn.ConvTranspose2d\n        self.conv_tr = klass_tr(chin, chout, kernel_size, stride)\n        self.norm2 = norm_fn(chout)\n        if self.empty:\n            return\n        self.rewrite = None\n        if rewrite:\n            if context_freq:\n                self.rewrite = klass(chin, 2 * chin, 1 + 2 * context, 1, context)\n            else:\n                self.rewrite = klass(chin, 2 * chin, [1, 1 + 2 * context], 1, [0, context])\n            self.norm1 = norm_fn(2 * chin)\n\n        self.dconv = None\n        if dconv:","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/hdemucs.py:22"}],[1333,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/hdemucs.py","startLine":293,"endLine":313,"chunkText":"\n        self.dconv = None\n        if dconv:\n            self.dconv = DConv(chin, **dconv_kw)\n\n    def forward(self, x, skip, length):\n        if self.freq and x.dim() == 3:\n            B, C, T = x.shape\n            x = x.view(B, self.chin, -1, T)\n\n        if not self.empty:\n            x = x + skip\n\n            if self.rewrite:\n                y = F.glu(self.norm1(self.rewrite(x)), dim=1)\n            else:\n                y = x\n            if self.dconv:\n                if self.freq:\n                    B, C, Fr, T = y.shape\n                    y = y.permute(0, 2, 1, 3).reshape(-1, C, T)","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/hdemucs.py:23"}],[1334,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/hdemucs.py","startLine":311,"endLine":326,"chunkText":"                if self.freq:\n                    B, C, Fr, T = y.shape\n                    y = y.permute(0, 2, 1, 3).reshape(-1, C, T)\n                y = self.dconv(y)\n                if self.freq:\n                    y = y.view(B, Fr, C, T).permute(0, 2, 1, 3)\n        else:\n            y = x\n            assert skip is None\n        z = self.norm2(self.conv_tr(y))\n        if self.freq:\n            if self.pad:\n                z = z[..., self.pad : -self.pad, :]\n        else:\n            z = z[..., self.pad : self.pad + length]\n            assert z.shape[-1] == length, (z.shape[-1], length)","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/hdemucs.py:24"}],[1335,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/hdemucs.py","startLine":324,"endLine":331,"chunkText":"        else:\n            z = z[..., self.pad : self.pad + length]\n            assert z.shape[-1] == length, (z.shape[-1], length)\n        if not self.last:\n            z = F.gelu(z)\n        return z, y\n\n","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/hdemucs.py:25"}],[1336,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/hdemucs.py","startLine":332,"endLine":341,"chunkText":"class HDemucs(nn.Module):\n    \"\"\"\n    Spectrogram and hybrid Demucs model.\n    The spectrogram model has the same structure as Demucs, except the first few layers are over the\n    frequency axis, until there is only 1 frequency, and then it moves to time convolutions.\n    Frequency layers can still access information across time steps thanks to the DConv residual.\n\n    Hybrid model have a parallel time branch. At some layer, the time branch has the same stride\n    as the frequency branch and then the two are combined. The opposite happens in the decoder.\n","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/hdemucs.py:26"}],[1337,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/hdemucs.py","startLine":339,"endLine":347,"chunkText":"    Hybrid model have a parallel time branch. At some layer, the time branch has the same stride\n    as the frequency branch and then the two are combined. The opposite happens in the decoder.\n\n    Models can either use naive iSTFT from masking, Wiener filtering ([Ulhih et al. 2017]),\n    or complex as channels (CaC) [Choi et al. 2020]. Wiener filtering is based on\n    Open Unmix implementation [Stoter et al. 2019].\n\n    The loss is always on the temporal domain, by backpropagating through the above\n    output methods and iSTFT. This allows to define hybrid models nicely. However, this breaks","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/hdemucs.py:27"}],[1338,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/hdemucs.py","startLine":345,"endLine":353,"chunkText":"\n    The loss is always on the temporal domain, by backpropagating through the above\n    output methods and iSTFT. This allows to define hybrid models nicely. However, this breaks\n    a bit Wiener filtering, as doing more iteration at test time will change the spectrogram\n    contribution, without changing the one from the waveform, which will lead to worse performance.\n    I tried using the residual option in OpenUnmix Wiener implementation, but it didn't improve.\n    CaC on the other hand provides similar performance for hybrid, and works naturally with\n    hybrid models.\n","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/hdemucs.py:28"}],[1339,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/hdemucs.py","startLine":351,"endLine":369,"chunkText":"    CaC on the other hand provides similar performance for hybrid, and works naturally with\n    hybrid models.\n\n    This model also uses frequency embeddings are used to improve efficiency on convolutions\n    over the freq. axis, following [Isik et al. 2020] (https://arxiv.org/pdf/2008.04470.pdf).\n\n    Unlike classic Demucs, there is no resampling here, and normalization is always applied.\n    \"\"\"\n\n    @capture_init\n    def __init__(\n        self,\n        sources,\n        # Channels\n        audio_channels=2,\n        channels=48,\n        channels_time=None,\n        growth=2,\n        # STFT","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/hdemucs.py:29"}],[1340,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/hdemucs.py","startLine":368,"endLine":394,"chunkText":"        growth=2,\n        # STFT\n        nfft=4096,\n        wiener_iters=0,\n        end_iters=0,\n        wiener_residual=False,\n        cac=True,\n        # Main structure\n        depth=6,\n        rewrite=True,\n        hybrid=True,\n        hybrid_old=False,\n        # Frequency branch\n        multi_freqs=None,\n        multi_freqs_depth=2,\n        freq_emb=0.2,\n        emb_scale=10,\n        emb_smooth=True,\n        # Convolutions\n        kernel_size=8,\n        time_stride=2,\n        stride=4,\n        context=1,\n        context_enc=0,\n        # Normalization\n        norm_starts=4,\n        norm_groups=4,","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/hdemucs.py:30"}],[1341,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/hdemucs.py","startLine":392,"endLine":412,"chunkText":"        # Normalization\n        norm_starts=4,\n        norm_groups=4,\n        # DConv residual branch\n        dconv_mode=1,\n        dconv_depth=2,\n        dconv_comp=4,\n        dconv_attn=4,\n        dconv_lstm=4,\n        dconv_init=1e-4,\n        # Weight init\n        rescale=0.1,\n        # Metadata\n        samplerate=44100,\n        segment=4 * 10,\n    ):\n        \"\"\"\n        Args:\n            sources (list[str]): list of source names.\n            audio_channels (int): input/output audio channels.\n            channels (int): initial number of hidden channels.","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/hdemucs.py:31"}],[1342,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/hdemucs.py","startLine":410,"endLine":416,"chunkText":"            sources (list[str]): list of source names.\n            audio_channels (int): input/output audio channels.\n            channels (int): initial number of hidden channels.\n            channels_time: if not None, use a different `channels` value for the time branch.\n            growth: increase the number of hidden channels by this factor at each layer.\n            nfft: number of fft bins. Note that changing this require careful computation of\n                various shape parameters and will not work out of the box for hybrid models.","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/hdemucs.py:32"}],[1343,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/hdemucs.py","startLine":414,"endLine":419,"chunkText":"            growth: increase the number of hidden channels by this factor at each layer.\n            nfft: number of fft bins. Note that changing this require careful computation of\n                various shape parameters and will not work out of the box for hybrid models.\n            wiener_iters: when using Wiener filtering, number of iterations at test time.\n            end_iters: same but at train time. For a hybrid model, must be equal to `wiener_iters`.\n            wiener_residual: add residual source before wiener filtering.","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/hdemucs.py:33"}],[1344,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/hdemucs.py","startLine":417,"endLine":423,"chunkText":"            wiener_iters: when using Wiener filtering, number of iterations at test time.\n            end_iters: same but at train time. For a hybrid model, must be equal to `wiener_iters`.\n            wiener_residual: add residual source before wiener filtering.\n            cac: uses complex as channels, i.e. complex numbers are 2 channels each\n                in input and output. no further processing is done before ISTFT.\n            depth (int): number of layers in the encoder and in the decoder.\n            rewrite (bool): add 1x1 convolution to each layer.","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/hdemucs.py:34"}],[1345,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/hdemucs.py","startLine":421,"endLine":427,"chunkText":"                in input and output. no further processing is done before ISTFT.\n            depth (int): number of layers in the encoder and in the decoder.\n            rewrite (bool): add 1x1 convolution to each layer.\n            hybrid (bool): make a hybrid time/frequency domain, otherwise frequency only.\n            hybrid_old: some models trained for MDX had a padding bug. This replicates\n                this bug to avoid retraining them.\n            multi_freqs: list of frequency ratios for splitting frequency bands with `MultiWrap`.","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/hdemucs.py:35"}],[1346,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/hdemucs.py","startLine":425,"endLine":432,"chunkText":"            hybrid_old: some models trained for MDX had a padding bug. This replicates\n                this bug to avoid retraining them.\n            multi_freqs: list of frequency ratios for splitting frequency bands with `MultiWrap`.\n            multi_freqs_depth: how many layers to wrap with `MultiWrap`. Only the outermost\n                layers will be wrapped.\n            freq_emb: add frequency embedding after the first frequency layer if > 0,\n                the actual value controls the weight of the embedding.\n            emb_scale: equivalent to scaling the embedding learning rate","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/hdemucs.py:36"}],[1347,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/hdemucs.py","startLine":430,"endLine":437,"chunkText":"            freq_emb: add frequency embedding after the first frequency layer if > 0,\n                the actual value controls the weight of the embedding.\n            emb_scale: equivalent to scaling the embedding learning rate\n            emb_smooth: initialize the embedding with a smooth one (with respect to frequencies).\n            kernel_size: kernel_size for encoder and decoder layers.\n            stride: stride for encoder and decoder layers.\n            time_stride: stride for the final time layer, after the merge.\n            context: context for 1x1 conv in the decoder.","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/hdemucs.py:37"}],[1348,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/hdemucs.py","startLine":435,"endLine":443,"chunkText":"            stride: stride for encoder and decoder layers.\n            time_stride: stride for the final time layer, after the merge.\n            context: context for 1x1 conv in the decoder.\n            context_enc: context for 1x1 conv in the encoder.\n            norm_starts: layer at which group norm starts being used.\n                decoder layers are numbered in reverse order.\n            norm_groups: number of groups for group norm.\n            dconv_mode: if 1: dconv in encoder only, 2: decoder only, 3: both.\n            dconv_depth: depth of residual DConv branch.","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/hdemucs.py:38"}],[1349,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/hdemucs.py","startLine":441,"endLine":453,"chunkText":"            norm_groups: number of groups for group norm.\n            dconv_mode: if 1: dconv in encoder only, 2: decoder only, 3: both.\n            dconv_depth: depth of residual DConv branch.\n            dconv_comp: compression of DConv branch.\n            dconv_attn: adds attention layers in DConv branch starting at this layer.\n            dconv_lstm: adds a LSTM layer in DConv branch starting at this layer.\n            dconv_init: initial scale for the DConv branch LayerScale.\n            rescale: weight recaling trick\n\n        \"\"\"\n        super().__init__()\n\n        self.cac = cac","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/hdemucs.py:39"}],[1350,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/hdemucs.py","startLine":451,"endLine":470,"chunkText":"        super().__init__()\n\n        self.cac = cac\n        self.wiener_residual = wiener_residual\n        self.audio_channels = audio_channels\n        self.sources = sources\n        self.kernel_size = kernel_size\n        self.context = context\n        self.stride = stride\n        self.depth = depth\n        self.channels = channels\n        self.samplerate = samplerate\n        self.segment = segment\n\n        self.nfft = nfft\n        self.hop_length = nfft // 4\n        self.wiener_iters = wiener_iters\n        self.end_iters = end_iters\n        self.freq_emb = None\n        self.hybrid = hybrid","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/hdemucs.py:40"}],[1351,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/hdemucs.py","startLine":468,"endLine":487,"chunkText":"        self.end_iters = end_iters\n        self.freq_emb = None\n        self.hybrid = hybrid\n        self.hybrid_old = hybrid_old\n        if hybrid_old:\n            assert hybrid, \"hybrid_old must come with hybrid=True\"\n        if hybrid:\n            assert wiener_iters == end_iters\n\n        self.encoder = nn.ModuleList()\n        self.decoder = nn.ModuleList()\n\n        if hybrid:\n            self.tencoder = nn.ModuleList()\n            self.tdecoder = nn.ModuleList()\n\n        chin = audio_channels\n        chin_z = chin  # number of channels for the freq branch\n        if self.cac:\n            chin_z *= 2","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/hdemucs.py:41"}],[1352,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/hdemucs.py","startLine":485,"endLine":504,"chunkText":"        chin_z = chin  # number of channels for the freq branch\n        if self.cac:\n            chin_z *= 2\n        chout = channels_time or channels\n        chout_z = channels\n        freqs = nfft // 2\n\n        for index in range(depth):\n            lstm = index >= dconv_lstm\n            attn = index >= dconv_attn\n            norm = index >= norm_starts\n            freq = freqs > 1\n            stri = stride\n            ker = kernel_size\n            if not freq:\n                assert freqs == 1\n                ker = time_stride * 2\n                stri = time_stride\n\n            pad = True","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/hdemucs.py:42"}],[1353,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/hdemucs.py","startLine":502,"endLine":518,"chunkText":"                stri = time_stride\n\n            pad = True\n            last_freq = False\n            if freq and freqs <= kernel_size:\n                ker = freqs\n                pad = False\n                last_freq = True\n\n            kw = {\n                \"kernel_size\": ker,\n                \"stride\": stri,\n                \"freq\": freq,\n                \"pad\": pad,\n                \"norm\": norm,\n                \"rewrite\": rewrite,\n                \"norm_groups\": norm_groups,","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/hdemucs.py:43"}],[1354,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/hdemucs.py","startLine":516,"endLine":529,"chunkText":"                \"norm\": norm,\n                \"rewrite\": rewrite,\n                \"norm_groups\": norm_groups,\n                \"dconv_kw\": {\"lstm\": lstm, \"attn\": attn, \"depth\": dconv_depth, \"compress\": dconv_comp, \"init\": dconv_init, \"gelu\": True},\n            }\n            kwt = dict(kw)\n            kwt[\"freq\"] = 0\n            kwt[\"kernel_size\"] = kernel_size\n            kwt[\"stride\"] = stride\n            kwt[\"pad\"] = True\n            kw_dec = dict(kw)\n            multi = False\n            if multi_freqs and index < multi_freqs_depth:\n                multi = True","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/hdemucs.py:44"}],[1355,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/hdemucs.py","startLine":527,"endLine":541,"chunkText":"            multi = False\n            if multi_freqs and index < multi_freqs_depth:\n                multi = True\n                kw_dec[\"context_freq\"] = False\n\n            if last_freq:\n                chout_z = max(chout, chout_z)\n                chout = chout_z\n\n            enc = HEncLayer(chin_z, chout_z, dconv=dconv_mode & 1, context=context_enc, **kw)\n            if hybrid and freq:\n                tenc = HEncLayer(chin, chout, dconv=dconv_mode & 1, context=context_enc, empty=last_freq, **kwt)\n                self.tencoder.append(tenc)\n\n            if multi:","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/hdemucs.py:45"}],[1356,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/hdemucs.py","startLine":539,"endLine":552,"chunkText":"                self.tencoder.append(tenc)\n\n            if multi:\n                enc = MultiWrap(enc, multi_freqs)\n            self.encoder.append(enc)\n            if index == 0:\n                chin = self.audio_channels * len(self.sources)\n                chin_z = chin\n                if self.cac:\n                    chin_z *= 2\n            dec = HDecLayer(chout_z, chin_z, dconv=dconv_mode & 2, last=index == 0, context=context, **kw_dec)\n            if multi:\n                dec = MultiWrap(dec, multi_freqs)\n            if hybrid and freq:","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/hdemucs.py:46"}],[1357,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/hdemucs.py","startLine":550,"endLine":565,"chunkText":"            if multi:\n                dec = MultiWrap(dec, multi_freqs)\n            if hybrid and freq:\n                tdec = HDecLayer(chout, chin, dconv=dconv_mode & 2, empty=last_freq, last=index == 0, context=context, **kwt)\n                self.tdecoder.insert(0, tdec)\n            self.decoder.insert(0, dec)\n\n            chin = chout\n            chin_z = chout_z\n            chout = int(growth * chout)\n            chout_z = int(growth * chout_z)\n            if freq:\n                if freqs <= kernel_size:\n                    freqs = 1\n                else:\n                    freqs //= stride","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/hdemucs.py:47"}],[1358,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/hdemucs.py","startLine":563,"endLine":579,"chunkText":"                    freqs = 1\n                else:\n                    freqs //= stride\n            if index == 0 and freq_emb:\n                self.freq_emb = ScaledEmbedding(freqs, chin_z, smooth=emb_smooth, scale=emb_scale)\n                self.freq_emb_scale = freq_emb\n\n        if rescale:\n            rescale_module(self, reference=rescale)\n\n    def _spec(self, x):\n        hl = self.hop_length\n        nfft = self.nfft\n        x0 = x  # noqa\n\n        if self.hybrid:\n            # We re-pad the signal in order to keep the property","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/hdemucs.py:48"}],[1359,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/hdemucs.py","startLine":577,"endLine":587,"chunkText":"\n        if self.hybrid:\n            # We re-pad the signal in order to keep the property\n            # that the size of the output is exactly the size of the input\n            # divided by the stride (here hop_length), when divisible.\n            # This is achieved by padding by 1/4th of the kernel size (here nfft).\n            # which is not supported by torch.stft.\n            # Having all convolution operations follow this convention allow to easily\n            # align the time and frequency branches later on.\n            assert hl == nfft // 4\n            le = int(math.ceil(x.shape[-1] / hl))","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/hdemucs.py:49"}],[1360,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/hdemucs.py","startLine":585,"endLine":599,"chunkText":"            # align the time and frequency branches later on.\n            assert hl == nfft // 4\n            le = int(math.ceil(x.shape[-1] / hl))\n            pad = hl // 2 * 3\n            if not self.hybrid_old:\n                x = pad1d(x, (pad, pad + le * hl - x.shape[-1]), mode=\"reflect\")\n            else:\n                x = pad1d(x, (pad, pad + le * hl - x.shape[-1]))\n\n        z = spectro(x, nfft, hl)[..., :-1, :]\n        if self.hybrid:\n            assert z.shape[-1] == le + 4, (z.shape, x.shape, le)\n            z = z[..., 2 : 2 + le]\n        return z\n","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/hdemucs.py:50"}],[1361,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/hdemucs.py","startLine":597,"endLine":613,"chunkText":"            z = z[..., 2 : 2 + le]\n        return z\n\n    def _ispec(self, z, length=None, scale=0):\n        hl = self.hop_length // (4**scale)\n        z = F.pad(z, (0, 0, 0, 1))\n        if self.hybrid:\n            z = F.pad(z, (2, 2))\n            pad = hl // 2 * 3\n            if not self.hybrid_old:\n                le = hl * int(math.ceil(length / hl)) + 2 * pad\n            else:\n                le = hl * int(math.ceil(length / hl))\n            x = ispectro(z, hl, length=le)\n            if not self.hybrid_old:\n                x = x[..., pad : pad + length]\n            else:","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/hdemucs.py:51"}],[1362,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/hdemucs.py","startLine":611,"endLine":629,"chunkText":"            if not self.hybrid_old:\n                x = x[..., pad : pad + length]\n            else:\n                x = x[..., :length]\n        else:\n            x = ispectro(z, hl, length)\n        return x\n\n    def _magnitude(self, z):\n        # return the magnitude of the spectrogram, except when cac is True,\n        # in which case we just move the complex dimension to the channel one.\n        if self.cac:\n            B, C, Fr, T = z.shape\n            m = torch.view_as_real(z).permute(0, 1, 4, 2, 3)\n            m = m.reshape(B, C * 2, Fr, T)\n        else:\n            m = z.abs()\n        return m\n","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/hdemucs.py:52"}],[1363,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/hdemucs.py","startLine":627,"endLine":642,"chunkText":"            m = z.abs()\n        return m\n\n    def _mask(self, z, m):\n        # Apply masking given the mixture spectrogram `z` and the estimated mask `m`.\n        # If `cac` is True, `m` is actually a full spectrogram and `z` is ignored.\n        niters = self.wiener_iters\n        if self.cac:\n            B, S, C, Fr, T = m.shape\n            out = m.view(B, S, -1, 2, Fr, T).permute(0, 1, 2, 4, 5, 3)\n            out = torch.view_as_complex(out.contiguous())\n            return out\n        if self.training:\n            niters = self.end_iters\n        if niters < 0:\n            z = z[:, None]","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/hdemucs.py:53"}],[1364,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/hdemucs.py","startLine":640,"endLine":658,"chunkText":"            niters = self.end_iters\n        if niters < 0:\n            z = z[:, None]\n            return z / (1e-8 + z.abs()) * m\n        else:\n            return self._wiener(m, z, niters)\n\n    def _wiener(self, mag_out, mix_stft, niters):\n        # apply wiener filtering from OpenUnmix.\n        init = mix_stft.dtype\n        wiener_win_len = 300\n        residual = self.wiener_residual\n\n        B, S, C, Fq, T = mag_out.shape\n        mag_out = mag_out.permute(0, 4, 3, 2, 1)\n        mix_stft = torch.view_as_real(mix_stft.permute(0, 3, 2, 1))\n\n        outs = []\n        for sample in range(B):","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/hdemucs.py:54"}],[1365,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/hdemucs.py","startLine":656,"endLine":669,"chunkText":"\n        outs = []\n        for sample in range(B):\n            pos = 0\n            out = []\n            for pos in range(0, T, wiener_win_len):\n                frame = slice(pos, pos + wiener_win_len)\n                z_out = wiener(mag_out[sample, frame], mix_stft[sample, frame], niters, residual=residual)\n                out.append(z_out.transpose(-1, -2))\n            outs.append(torch.cat(out, dim=0))\n        out = torch.view_as_complex(torch.stack(outs, 0))\n        out = out.permute(0, 4, 3, 2, 1).contiguous()\n        if residual:\n            out = out[:, :-1]","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/hdemucs.py:55"}],[1366,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/hdemucs.py","startLine":667,"endLine":686,"chunkText":"        out = out.permute(0, 4, 3, 2, 1).contiguous()\n        if residual:\n            out = out[:, :-1]\n        assert list(out.shape) == [B, S, C, Fq, T]\n        return out.to(init)\n\n    def forward(self, mix):\n        x = mix\n        length = x.shape[-1]\n\n        z = self._spec(mix)\n        mag = self._magnitude(z).to(mix.device)\n        x = mag\n\n        B, C, Fq, T = x.shape\n\n        # unlike previous Demucs, we always normalize because it is easier.\n        mean = x.mean(dim=(1, 2, 3), keepdim=True)\n        std = x.std(dim=(1, 2, 3), keepdim=True)\n        x = (x - mean) / (1e-5 + std)","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/hdemucs.py:56"}],[1367,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/hdemucs.py","startLine":684,"endLine":698,"chunkText":"        mean = x.mean(dim=(1, 2, 3), keepdim=True)\n        std = x.std(dim=(1, 2, 3), keepdim=True)\n        x = (x - mean) / (1e-5 + std)\n        # x will be the freq. branch input.\n\n        if self.hybrid:\n            # Prepare the time branch input.\n            xt = mix\n            meant = xt.mean(dim=(1, 2), keepdim=True)\n            stdt = xt.std(dim=(1, 2), keepdim=True)\n            xt = (xt - meant) / (1e-5 + stdt)\n\n        # okay, this is a giant mess I know...\n        saved = []  # skip connections, freq.\n        saved_t = []  # skip connections, time.","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/hdemucs.py:57"}],[1368,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/hdemucs.py","startLine":696,"endLine":707,"chunkText":"        # okay, this is a giant mess I know...\n        saved = []  # skip connections, freq.\n        saved_t = []  # skip connections, time.\n        lengths = []  # saved lengths to properly remove padding, freq branch.\n        lengths_t = []  # saved lengths for time branch.\n        for idx, encode in enumerate(self.encoder):\n            lengths.append(x.shape[-1])\n            inject = None\n            if self.hybrid and idx < len(self.tencoder):\n                # we have not yet merged branches.\n                lengths_t.append(xt.shape[-1])\n                tenc = self.tencoder[idx]","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/hdemucs.py:58"}],[1369,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/hdemucs.py","startLine":705,"endLine":717,"chunkText":"                # we have not yet merged branches.\n                lengths_t.append(xt.shape[-1])\n                tenc = self.tencoder[idx]\n                xt = tenc(xt)\n                if not tenc.empty:\n                    # save for skip connection\n                    saved_t.append(xt)\n                else:\n                    # tenc contains just the first conv., so that now time and freq.\n                    # branches have the same shape and can be merged.\n                    inject = xt\n            x = encode(x, inject)\n            if idx == 0 and self.freq_emb is not None:","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/hdemucs.py:59"}],[1370,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/hdemucs.py","startLine":715,"endLine":728,"chunkText":"                    inject = xt\n            x = encode(x, inject)\n            if idx == 0 and self.freq_emb is not None:\n                # add frequency embedding to allow for non equivariant convolutions\n                # over the frequency axis.\n                frs = torch.arange(x.shape[-2], device=x.device)\n                emb = self.freq_emb(frs).t()[None, :, :, None].expand_as(x)\n                x = x + self.freq_emb_scale * emb\n\n            saved.append(x)\n\n        x = torch.zeros_like(x)\n        if self.hybrid:\n            xt = torch.zeros_like(x)","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/hdemucs.py:60"}],[1371,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/hdemucs.py","startLine":726,"endLine":739,"chunkText":"        x = torch.zeros_like(x)\n        if self.hybrid:\n            xt = torch.zeros_like(x)\n        # initialize everything to zero (signal will go through u-net skips).\n\n        for idx, decode in enumerate(self.decoder):\n            skip = saved.pop(-1)\n            x, pre = decode(x, skip, lengths.pop(-1))\n            # `pre` contains the output just before final transposed convolution,\n            # which is used when the freq. and time branch separate.\n\n            if self.hybrid:\n                offset = self.depth - len(self.tdecoder)\n            if self.hybrid and idx >= offset:","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/hdemucs.py:61"}],[1372,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/hdemucs.py","startLine":737,"endLine":750,"chunkText":"            if self.hybrid:\n                offset = self.depth - len(self.tdecoder)\n            if self.hybrid and idx >= offset:\n                tdec = self.tdecoder[idx - offset]\n                length_t = lengths_t.pop(-1)\n                if tdec.empty:\n                    assert pre.shape[2] == 1, pre.shape\n                    pre = pre[:, :, 0]\n                    xt, _ = tdec(pre, None, length_t)\n                else:\n                    skip = saved_t.pop(-1)\n                    xt, _ = tdec(xt, skip, length_t)\n\n        # Let's make sure we used all stored skip connections.","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/hdemucs.py:62"}],[1373,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/hdemucs.py","startLine":748,"endLine":764,"chunkText":"                    xt, _ = tdec(xt, skip, length_t)\n\n        # Let's make sure we used all stored skip connections.\n        assert len(saved) == 0\n        assert len(lengths_t) == 0\n        assert len(saved_t) == 0\n\n        S = len(self.sources)\n        x = x.view(B, S, -1, Fq, T)\n        x = x * std[:, None] + mean[:, None]\n\n        # to cpu as non-cuda GPUs don't support complex numbers\n        # demucs issue #435 ##432\n        # NOTE: in this case z already is on cpu\n        # TODO: remove this when mps supports complex numbers\n\n        device_type = x.device.type","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/hdemucs.py:63"}],[1374,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/hdemucs.py","startLine":762,"endLine":780,"chunkText":"        # TODO: remove this when mps supports complex numbers\n\n        device_type = x.device.type\n        device_load = f\"{device_type}:{x.device.index}\" if not device_type == \"mps\" else device_type\n        x_is_other_gpu = not device_type in [\"cuda\", \"cpu\"]\n\n        if x_is_other_gpu:\n            x = x.cpu()\n\n        zout = self._mask(z, x)\n        x = self._ispec(zout, length)\n\n        # back to other device\n        if x_is_other_gpu:\n            x = x.to(device_load)\n\n        if self.hybrid:\n            xt = xt.view(B, S, -1, length)\n            xt = xt * stdt[:, None] + meant[:, None]","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/hdemucs.py:64"}],[1375,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/hdemucs.py","startLine":778,"endLine":783,"chunkText":"        if self.hybrid:\n            xt = xt.view(B, S, -1, length)\n            xt = xt * stdt[:, None] + meant[:, None]\n            x = xt + x\n        return x\n","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/hdemucs.py:65"}],[1376,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/htdemucs.py","startLine":0,"endLine":21,"chunkText":"# Copyright (c) Meta, Inc. and its affiliates.\n# All rights reserved.\n#\n# This source code is licensed under the license found in the\n# LICENSE file in the root directory of this source tree.\n# First author is Simon Rouard.\n\"\"\"\nThis code contains the spectrogram and Hybrid version of Demucs.\n\"\"\"\nimport math\n\nfrom .filtering import wiener\nimport torch\nfrom torch import nn\nfrom torch.nn import functional as F\nfrom fractions import Fraction\nfrom einops import rearrange\n\nfrom .transformer import CrossTransformerEncoder\n\nfrom .demucs import rescale_module\nfrom .states import capture_init","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/htdemucs.py:0"}],[1377,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/htdemucs.py","startLine":19,"endLine":32,"chunkText":"\nfrom .demucs import rescale_module\nfrom .states import capture_init\nfrom .spec import spectro, ispectro\nfrom .hdemucs import pad1d, ScaledEmbedding, HEncLayer, MultiWrap, HDecLayer\n\n\nclass HTDemucs(nn.Module):\n    \"\"\"\n    Spectrogram and hybrid Demucs model.\n    The spectrogram model has the same structure as Demucs, except the first few layers are over the\n    frequency axis, until there is only 1 frequency, and then it moves to time convolutions.\n    Frequency layers can still access information across time steps thanks to the DConv residual.\n","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/htdemucs.py:1"}],[1378,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/htdemucs.py","startLine":30,"endLine":39,"chunkText":"    frequency axis, until there is only 1 frequency, and then it moves to time convolutions.\n    Frequency layers can still access information across time steps thanks to the DConv residual.\n\n    Hybrid model have a parallel time branch. At some layer, the time branch has the same stride\n    as the frequency branch and then the two are combined. The opposite happens in the decoder.\n\n    Models can either use naive iSTFT from masking, Wiener filtering ([Ulhih et al. 2017]),\n    or complex as channels (CaC) [Choi et al. 2020]. Wiener filtering is based on\n    Open Unmix implementation [Stoter et al. 2019].\n","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/htdemucs.py:2"}],[1379,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/htdemucs.py","startLine":37,"endLine":44,"chunkText":"    or complex as channels (CaC) [Choi et al. 2020]. Wiener filtering is based on\n    Open Unmix implementation [Stoter et al. 2019].\n\n    The loss is always on the temporal domain, by backpropagating through the above\n    output methods and iSTFT. This allows to define hybrid models nicely. However, this breaks\n    a bit Wiener filtering, as doing more iteration at test time will change the spectrogram\n    contribution, without changing the one from the waveform, which will lead to worse performance.\n    I tried using the residual option in OpenUnmix Wiener implementation, but it didn't improve.","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/htdemucs.py:3"}],[1380,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/htdemucs.py","startLine":42,"endLine":50,"chunkText":"    a bit Wiener filtering, as doing more iteration at test time will change the spectrogram\n    contribution, without changing the one from the waveform, which will lead to worse performance.\n    I tried using the residual option in OpenUnmix Wiener implementation, but it didn't improve.\n    CaC on the other hand provides similar performance for hybrid, and works naturally with\n    hybrid models.\n\n    This model also uses frequency embeddings are used to improve efficiency on convolutions\n    over the freq. axis, following [Isik et al. 2020] (https://arxiv.org/pdf/2008.04470.pdf).\n","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/htdemucs.py:4"}],[1381,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/htdemucs.py","startLine":48,"endLine":68,"chunkText":"    This model also uses frequency embeddings are used to improve efficiency on convolutions\n    over the freq. axis, following [Isik et al. 2020] (https://arxiv.org/pdf/2008.04470.pdf).\n\n    Unlike classic Demucs, there is no resampling here, and normalization is always applied.\n    \"\"\"\n\n    @capture_init\n    def __init__(\n        self,\n        sources,\n        # Channels\n        audio_channels=2,\n        channels=48,\n        channels_time=None,\n        growth=2,\n        # STFT\n        nfft=4096,\n        wiener_iters=0,\n        end_iters=0,\n        wiener_residual=False,\n        cac=True,","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/htdemucs.py:5"}],[1382,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/htdemucs.py","startLine":66,"endLine":91,"chunkText":"        end_iters=0,\n        wiener_residual=False,\n        cac=True,\n        # Main structure\n        depth=4,\n        rewrite=True,\n        # Frequency branch\n        multi_freqs=None,\n        multi_freqs_depth=3,\n        freq_emb=0.2,\n        emb_scale=10,\n        emb_smooth=True,\n        # Convolutions\n        kernel_size=8,\n        time_stride=2,\n        stride=4,\n        context=1,\n        context_enc=0,\n        # Normalization\n        norm_starts=4,\n        norm_groups=4,\n        # DConv residual branch\n        dconv_mode=1,\n        dconv_depth=2,\n        dconv_comp=8,\n        dconv_init=1e-3,","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/htdemucs.py:6"}],[1383,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/htdemucs.py","startLine":89,"endLine":111,"chunkText":"        dconv_depth=2,\n        dconv_comp=8,\n        dconv_init=1e-3,\n        # Before the Transformer\n        bottom_channels=0,\n        # Transformer\n        t_layers=5,\n        t_emb=\"sin\",\n        t_hidden_scale=4.0,\n        t_heads=8,\n        t_dropout=0.0,\n        t_max_positions=10000,\n        t_norm_in=True,\n        t_norm_in_group=False,\n        t_group_norm=False,\n        t_norm_first=True,\n        t_norm_out=True,\n        t_max_period=10000.0,\n        t_weight_decay=0.0,\n        t_lr=None,\n        t_layer_scale=True,\n        t_gelu=True,\n        t_weight_pos_embed=1.0,","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/htdemucs.py:7"}],[1384,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/htdemucs.py","startLine":110,"endLine":129,"chunkText":"        t_gelu=True,\n        t_weight_pos_embed=1.0,\n        t_sin_random_shift=0,\n        t_cape_mean_normalize=True,\n        t_cape_augment=True,\n        t_cape_glob_loc_scale=[5000.0, 1.0, 1.4],\n        t_sparse_self_attn=False,\n        t_sparse_cross_attn=False,\n        t_mask_type=\"diag\",\n        t_mask_random_seed=42,\n        t_sparse_attn_window=500,\n        t_global_window=100,\n        t_sparsity=0.95,\n        t_auto_sparsity=False,\n        # ------ Particuliar parameters\n        t_cross_first=False,\n        # Weight init\n        rescale=0.1,\n        # Metadata\n        samplerate=44100,","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/htdemucs.py:8"}],[1385,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/htdemucs.py","startLine":127,"endLine":140,"chunkText":"        rescale=0.1,\n        # Metadata\n        samplerate=44100,\n        segment=10,\n        use_train_segment=True,\n    ):\n        \"\"\"\n        Args:\n            sources (list[str]): list of source names.\n            audio_channels (int): input/output audio channels.\n            channels (int): initial number of hidden channels.\n            channels_time: if not None, use a different `channels` value for the time branch.\n            growth: increase the number of hidden channels by this factor at each layer.\n            nfft: number of fft bins. Note that changing this require careful computation of","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/htdemucs.py:9"}],[1386,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/htdemucs.py","startLine":138,"endLine":143,"chunkText":"            channels_time: if not None, use a different `channels` value for the time branch.\n            growth: increase the number of hidden channels by this factor at each layer.\n            nfft: number of fft bins. Note that changing this require careful computation of\n                various shape parameters and will not work out of the box for hybrid models.\n            wiener_iters: when using Wiener filtering, number of iterations at test time.\n            end_iters: same but at train time. For a hybrid model, must be equal to `wiener_iters`.","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/htdemucs.py:10"}],[1387,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/htdemucs.py","startLine":141,"endLine":147,"chunkText":"                various shape parameters and will not work out of the box for hybrid models.\n            wiener_iters: when using Wiener filtering, number of iterations at test time.\n            end_iters: same but at train time. For a hybrid model, must be equal to `wiener_iters`.\n            wiener_residual: add residual source before wiener filtering.\n            cac: uses complex as channels, i.e. complex numbers are 2 channels each\n                in input and output. no further processing is done before ISTFT.\n            depth (int): number of layers in the encoder and in the decoder.","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/htdemucs.py:11"}],[1388,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/htdemucs.py","startLine":145,"endLine":151,"chunkText":"            cac: uses complex as channels, i.e. complex numbers are 2 channels each\n                in input and output. no further processing is done before ISTFT.\n            depth (int): number of layers in the encoder and in the decoder.\n            rewrite (bool): add 1x1 convolution to each layer.\n            multi_freqs: list of frequency ratios for splitting frequency bands with `MultiWrap`.\n            multi_freqs_depth: how many layers to wrap with `MultiWrap`. Only the outermost\n                layers will be wrapped.","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/htdemucs.py:12"}],[1389,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/htdemucs.py","startLine":149,"endLine":155,"chunkText":"            multi_freqs: list of frequency ratios for splitting frequency bands with `MultiWrap`.\n            multi_freqs_depth: how many layers to wrap with `MultiWrap`. Only the outermost\n                layers will be wrapped.\n            freq_emb: add frequency embedding after the first frequency layer if > 0,\n                the actual value controls the weight of the embedding.\n            emb_scale: equivalent to scaling the embedding learning rate\n            emb_smooth: initialize the embedding with a smooth one (with respect to frequencies).","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/htdemucs.py:13"}],[1390,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/htdemucs.py","startLine":153,"endLine":160,"chunkText":"                the actual value controls the weight of the embedding.\n            emb_scale: equivalent to scaling the embedding learning rate\n            emb_smooth: initialize the embedding with a smooth one (with respect to frequencies).\n            kernel_size: kernel_size for encoder and decoder layers.\n            stride: stride for encoder and decoder layers.\n            time_stride: stride for the final time layer, after the merge.\n            context: context for 1x1 conv in the decoder.\n            context_enc: context for 1x1 conv in the encoder.","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/htdemucs.py:14"}],[1391,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/htdemucs.py","startLine":158,"endLine":166,"chunkText":"            time_stride: stride for the final time layer, after the merge.\n            context: context for 1x1 conv in the decoder.\n            context_enc: context for 1x1 conv in the encoder.\n            norm_starts: layer at which group norm starts being used.\n                decoder layers are numbered in reverse order.\n            norm_groups: number of groups for group norm.\n            dconv_mode: if 1: dconv in encoder only, 2: decoder only, 3: both.\n            dconv_depth: depth of residual DConv branch.\n            dconv_comp: compression of DConv branch.","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/htdemucs.py:15"}],[1392,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/htdemucs.py","startLine":164,"endLine":171,"chunkText":"            dconv_mode: if 1: dconv in encoder only, 2: decoder only, 3: both.\n            dconv_depth: depth of residual DConv branch.\n            dconv_comp: compression of DConv branch.\n            dconv_attn: adds attention layers in DConv branch starting at this layer.\n            dconv_lstm: adds a LSTM layer in DConv branch starting at this layer.\n            dconv_init: initial scale for the DConv branch LayerScale.\n            bottom_channels: if >0 it adds a linear layer (1x1 Conv) before and after the\n                transformer in order to change the number of channels","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/htdemucs.py:16"}],[1393,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/htdemucs.py","startLine":169,"endLine":175,"chunkText":"            dconv_init: initial scale for the DConv branch LayerScale.\n            bottom_channels: if >0 it adds a linear layer (1x1 Conv) before and after the\n                transformer in order to change the number of channels\n            t_layers: number of layers in each branch (waveform and spec) of the transformer\n            t_emb: \"sin\", \"cape\" or \"scaled\"\n            t_hidden_scale: the hidden scale of the Feedforward parts of the transformer\n                for instance if C = 384 (the number of channels in the transformer) and","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/htdemucs.py:17"}],[1394,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/htdemucs.py","startLine":173,"endLine":181,"chunkText":"            t_emb: \"sin\", \"cape\" or \"scaled\"\n            t_hidden_scale: the hidden scale of the Feedforward parts of the transformer\n                for instance if C = 384 (the number of channels in the transformer) and\n                t_hidden_scale = 4.0 then the intermediate layer of the FFN has dimension\n                384 * 4 = 1536\n            t_heads: number of heads for the transformer\n            t_dropout: dropout in the transformer\n            t_max_positions: max_positions for the \"scaled\" positional embedding, only\n                useful if t_emb=\"scaled\"","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/htdemucs.py:18"}],[1395,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/htdemucs.py","startLine":179,"endLine":187,"chunkText":"            t_dropout: dropout in the transformer\n            t_max_positions: max_positions for the \"scaled\" positional embedding, only\n                useful if t_emb=\"scaled\"\n            t_norm_in: (bool) norm before addinf positional embedding and getting into the\n                transformer layers\n            t_norm_in_group: (bool) if True while t_norm_in=True, the norm is on all the\n                timesteps (GroupNorm with group=1)\n            t_group_norm: (bool) if True, the norms of the Encoder Layers are on all the\n                timesteps (GroupNorm with group=1)","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/htdemucs.py:19"}],[1396,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/htdemucs.py","startLine":185,"endLine":192,"chunkText":"                timesteps (GroupNorm with group=1)\n            t_group_norm: (bool) if True, the norms of the Encoder Layers are on all the\n                timesteps (GroupNorm with group=1)\n            t_norm_first: (bool) if True the norm is before the attention and before the FFN\n            t_norm_out: (bool) if True, there is a GroupNorm (group=1) at the end of each layer\n            t_max_period: (float) denominator in the sinusoidal embedding expression\n            t_weight_decay: (float) weight decay for the transformer\n            t_lr: (float) specific learning rate for the transformer","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/htdemucs.py:20"}],[1397,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/htdemucs.py","startLine":190,"endLine":197,"chunkText":"            t_max_period: (float) denominator in the sinusoidal embedding expression\n            t_weight_decay: (float) weight decay for the transformer\n            t_lr: (float) specific learning rate for the transformer\n            t_layer_scale: (bool) Layer Scale for the transformer\n            t_gelu: (bool) activations of the transformer are GeLU if True, ReLU else\n            t_weight_pos_embed: (float) weighting of the positional embedding\n            t_cape_mean_normalize: (bool) if t_emb=\"cape\", normalisation of positional embeddings\n                see: https://arxiv.org/abs/2106.03143","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/htdemucs.py:21"}],[1398,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/htdemucs.py","startLine":195,"endLine":201,"chunkText":"            t_weight_pos_embed: (float) weighting of the positional embedding\n            t_cape_mean_normalize: (bool) if t_emb=\"cape\", normalisation of positional embeddings\n                see: https://arxiv.org/abs/2106.03143\n            t_cape_augment: (bool) if t_emb=\"cape\", must be True during training and False\n                during the inference, see: https://arxiv.org/abs/2106.03143\n            t_cape_glob_loc_scale: (list of 3 floats) if t_emb=\"cape\", CAPE parameters\n                see: https://arxiv.org/abs/2106.03143","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/htdemucs.py:22"}],[1399,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/htdemucs.py","startLine":199,"endLine":205,"chunkText":"                during the inference, see: https://arxiv.org/abs/2106.03143\n            t_cape_glob_loc_scale: (list of 3 floats) if t_emb=\"cape\", CAPE parameters\n                see: https://arxiv.org/abs/2106.03143\n            t_sparse_self_attn: (bool) if True, the self attentions are sparse\n            t_sparse_cross_attn: (bool) if True, the cross-attentions are sparse (don't use it\n                unless you designed really specific masks)\n            t_mask_type: (str) can be \"diag\", \"jmask\", \"random\", \"global\" or any combination","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/htdemucs.py:23"}],[1400,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/htdemucs.py","startLine":203,"endLine":209,"chunkText":"            t_sparse_cross_attn: (bool) if True, the cross-attentions are sparse (don't use it\n                unless you designed really specific masks)\n            t_mask_type: (str) can be \"diag\", \"jmask\", \"random\", \"global\" or any combination\n                with '_' between: i.e. \"diag_jmask_random\" (note that this is permutation\n                invariant i.e. \"diag_jmask_random\" is equivalent to \"jmask_random_diag\")\n            t_mask_random_seed: (int) if \"random\" is in t_mask_type, controls the seed\n                that generated the random part of the mask","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/htdemucs.py:24"}],[1401,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/htdemucs.py","startLine":207,"endLine":213,"chunkText":"                invariant i.e. \"diag_jmask_random\" is equivalent to \"jmask_random_diag\")\n            t_mask_random_seed: (int) if \"random\" is in t_mask_type, controls the seed\n                that generated the random part of the mask\n            t_sparse_attn_window: (int) if \"diag\" is in t_mask_type, for a query (i), and\n                a key (j), the mask is True id |i-j|<=t_sparse_attn_window\n            t_global_window: (int) if \"global\" is in t_mask_type, mask[:t_global_window, :]\n                and mask[:, :t_global_window] will be True","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/htdemucs.py:25"}],[1402,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/htdemucs.py","startLine":211,"endLine":218,"chunkText":"                a key (j), the mask is True id |i-j|<=t_sparse_attn_window\n            t_global_window: (int) if \"global\" is in t_mask_type, mask[:t_global_window, :]\n                and mask[:, :t_global_window] will be True\n            t_sparsity: (float) if \"random\" is in t_mask_type, t_sparsity is the sparsity\n                level of the random part of the mask.\n            t_cross_first: (bool) if True cross attention is the first layer of the\n                transformer (False seems to be better)\n            rescale: weight rescaling trick","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/htdemucs.py:26"}],[1403,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/htdemucs.py","startLine":216,"endLine":229,"chunkText":"            t_cross_first: (bool) if True cross attention is the first layer of the\n                transformer (False seems to be better)\n            rescale: weight rescaling trick\n            use_train_segment: (bool) if True, the actual size that is used during the\n                training is used during inference.\n        \"\"\"\n        super().__init__()\n        self.cac = cac\n        self.wiener_residual = wiener_residual\n        self.audio_channels = audio_channels\n        self.sources = sources\n        self.kernel_size = kernel_size\n        self.context = context\n        self.stride = stride","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/htdemucs.py:27"}],[1404,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/htdemucs.py","startLine":227,"endLine":245,"chunkText":"        self.kernel_size = kernel_size\n        self.context = context\n        self.stride = stride\n        self.depth = depth\n        self.bottom_channels = bottom_channels\n        self.channels = channels\n        self.samplerate = samplerate\n        self.segment = segment\n        self.use_train_segment = use_train_segment\n        self.nfft = nfft\n        self.hop_length = nfft // 4\n        self.wiener_iters = wiener_iters\n        self.end_iters = end_iters\n        self.freq_emb = None\n        assert wiener_iters == end_iters\n\n        self.encoder = nn.ModuleList()\n        self.decoder = nn.ModuleList()\n","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/htdemucs.py:28"}],[1405,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/htdemucs.py","startLine":243,"endLine":263,"chunkText":"        self.encoder = nn.ModuleList()\n        self.decoder = nn.ModuleList()\n\n        self.tencoder = nn.ModuleList()\n        self.tdecoder = nn.ModuleList()\n\n        chin = audio_channels\n        chin_z = chin  # number of channels for the freq branch\n        if self.cac:\n            chin_z *= 2\n        chout = channels_time or channels\n        chout_z = channels\n        freqs = nfft // 2\n\n        for index in range(depth):\n            norm = index >= norm_starts\n            freq = freqs > 1\n            stri = stride\n            ker = kernel_size\n            if not freq:\n                assert freqs == ","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/htdemucs.py:29"}],[1406,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/htdemucs.py","startLine":261,"endLine":281,"chunkText":"            ker = kernel_size\n            if not freq:\n                assert freqs == 1\n                ker = time_stride * 2\n                stri = time_stride\n\n            pad = True\n            last_freq = False\n            if freq and freqs <= kernel_size:\n                ker = freqs\n                pad = False\n                last_freq = True\n\n            kw = {\n                \"kernel_size\": ker,\n                \"stride\": stri,\n                \"freq\": freq,\n                \"pad\": pad,\n                \"norm\": norm,\n                \"rewrite\": rewrite,\n                \"norm_groups\": norm_groups,","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/htdemucs.py:30"}],[1407,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/htdemucs.py","startLine":279,"endLine":294,"chunkText":"                \"norm\": norm,\n                \"rewrite\": rewrite,\n                \"norm_groups\": norm_groups,\n                \"dconv_kw\": {\"depth\": dconv_depth, \"compress\": dconv_comp, \"init\": dconv_init, \"gelu\": True},\n            }\n            kwt = dict(kw)\n            kwt[\"freq\"] = 0\n            kwt[\"kernel_size\"] = kernel_size\n            kwt[\"stride\"] = stride\n            kwt[\"pad\"] = True\n            kw_dec = dict(kw)\n            multi = False\n            if multi_freqs and index < multi_freqs_depth:\n                multi = True\n                kw_dec[\"context_freq\"] = False\n","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/htdemucs.py:31"}],[1408,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/htdemucs.py","startLine":292,"endLine":307,"chunkText":"                multi = True\n                kw_dec[\"context_freq\"] = False\n\n            if last_freq:\n                chout_z = max(chout, chout_z)\n                chout = chout_z\n\n            enc = HEncLayer(chin_z, chout_z, dconv=dconv_mode & 1, context=context_enc, **kw)\n            if freq:\n                tenc = HEncLayer(chin, chout, dconv=dconv_mode & 1, context=context_enc, empty=last_freq, **kwt)\n                self.tencoder.append(tenc)\n\n            if multi:\n                enc = MultiWrap(enc, multi_freqs)\n            self.encoder.append(enc)\n            if index == 0:","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/htdemucs.py:32"}],[1409,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/htdemucs.py","startLine":305,"endLine":316,"chunkText":"                enc = MultiWrap(enc, multi_freqs)\n            self.encoder.append(enc)\n            if index == 0:\n                chin = self.audio_channels * len(self.sources)\n                chin_z = chin\n                if self.cac:\n                    chin_z *= 2\n            dec = HDecLayer(chout_z, chin_z, dconv=dconv_mode & 2, last=index == 0, context=context, **kw_dec)\n            if multi:\n                dec = MultiWrap(dec, multi_freqs)\n            if freq:\n                tdec = HDecLayer(chout, chin, dconv=dconv_mode & 2, empty=last_freq, last=index == 0, context=context, **kwt)","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/htdemucs.py:33"}],[1410,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/htdemucs.py","startLine":314,"endLine":329,"chunkText":"                dec = MultiWrap(dec, multi_freqs)\n            if freq:\n                tdec = HDecLayer(chout, chin, dconv=dconv_mode & 2, empty=last_freq, last=index == 0, context=context, **kwt)\n                self.tdecoder.insert(0, tdec)\n            self.decoder.insert(0, dec)\n\n            chin = chout\n            chin_z = chout_z\n            chout = int(growth * chout)\n            chout_z = int(growth * chout_z)\n            if freq:\n                if freqs <= kernel_size:\n                    freqs = 1\n                else:\n                    freqs //= stride\n            if index == 0 and freq_emb:","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/htdemucs.py:34"}],[1411,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/htdemucs.py","startLine":327,"endLine":339,"chunkText":"                else:\n                    freqs //= stride\n            if index == 0 and freq_emb:\n                self.freq_emb = ScaledEmbedding(freqs, chin_z, smooth=emb_smooth, scale=emb_scale)\n                self.freq_emb_scale = freq_emb\n\n        if rescale:\n            rescale_module(self, reference=rescale)\n\n        transformer_channels = channels * growth ** (depth - 1)\n        if bottom_channels:\n            self.channel_upsampler = nn.Conv1d(transformer_channels, bottom_channels, 1)\n            self.channel_downsampler = nn.Conv1d(bottom_channels, transformer_channels, 1)","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/htdemucs.py:35"}],[1412,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/htdemucs.py","startLine":337,"endLine":348,"chunkText":"        if bottom_channels:\n            self.channel_upsampler = nn.Conv1d(transformer_channels, bottom_channels, 1)\n            self.channel_downsampler = nn.Conv1d(bottom_channels, transformer_channels, 1)\n            self.channel_upsampler_t = nn.Conv1d(transformer_channels, bottom_channels, 1)\n            self.channel_downsampler_t = nn.Conv1d(bottom_channels, transformer_channels, 1)\n\n            transformer_channels = bottom_channels\n\n        if t_layers > 0:\n            self.crosstransformer = CrossTransformerEncoder(\n                dim=transformer_channels,\n                emb=t_emb,","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/htdemucs.py:36"}],[1413,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/htdemucs.py","startLine":346,"endLine":359,"chunkText":"            self.crosstransformer = CrossTransformerEncoder(\n                dim=transformer_channels,\n                emb=t_emb,\n                hidden_scale=t_hidden_scale,\n                num_heads=t_heads,\n                num_layers=t_layers,\n                cross_first=t_cross_first,\n                dropout=t_dropout,\n                max_positions=t_max_positions,\n                norm_in=t_norm_in,\n                norm_in_group=t_norm_in_group,\n                group_norm=t_group_norm,\n                norm_first=t_norm_first,\n                norm_out=t_norm_out,","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/htdemucs.py:37"}],[1414,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/htdemucs.py","startLine":357,"endLine":369,"chunkText":"                group_norm=t_group_norm,\n                norm_first=t_norm_first,\n                norm_out=t_norm_out,\n                max_period=t_max_period,\n                weight_decay=t_weight_decay,\n                lr=t_lr,\n                layer_scale=t_layer_scale,\n                gelu=t_gelu,\n                sin_random_shift=t_sin_random_shift,\n                weight_pos_embed=t_weight_pos_embed,\n                cape_mean_normalize=t_cape_mean_normalize,\n                cape_augment=t_cape_augment,\n                cape_glob_loc_scale=t_cape_glob_loc_scale,","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/htdemucs.py:38"}],[1415,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/htdemucs.py","startLine":367,"endLine":379,"chunkText":"                cape_mean_normalize=t_cape_mean_normalize,\n                cape_augment=t_cape_augment,\n                cape_glob_loc_scale=t_cape_glob_loc_scale,\n                sparse_self_attn=t_sparse_self_attn,\n                sparse_cross_attn=t_sparse_cross_attn,\n                mask_type=t_mask_type,\n                mask_random_seed=t_mask_random_seed,\n                sparse_attn_window=t_sparse_attn_window,\n                global_window=t_global_window,\n                sparsity=t_sparsity,\n                auto_sparsity=t_auto_sparsity,\n            )\n        else:","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/htdemucs.py:39"}],[1416,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/htdemucs.py","startLine":377,"endLine":391,"chunkText":"                auto_sparsity=t_auto_sparsity,\n            )\n        else:\n            self.crosstransformer = None\n\n    def _spec(self, x):\n        hl = self.hop_length\n        nfft = self.nfft\n        x0 = x  # noqa\n\n        # We re-pad the signal in order to keep the property\n        # that the size of the output is exactly the size of the input\n        # divided by the stride (here hop_length), when divisible.\n        # This is achieved by padding by 1/4th of the kernel size (here nfft).\n        # which is not supported by torch.stft.","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/htdemucs.py:40"}],[1417,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/htdemucs.py","startLine":389,"endLine":399,"chunkText":"        # divided by the stride (here hop_length), when divisible.\n        # This is achieved by padding by 1/4th of the kernel size (here nfft).\n        # which is not supported by torch.stft.\n        # Having all convolution operations follow this convention allow to easily\n        # align the time and frequency branches later on.\n        assert hl == nfft // 4\n        le = int(math.ceil(x.shape[-1] / hl))\n        pad = hl // 2 * 3\n        x = pad1d(x, (pad, pad + le * hl - x.shape[-1]), mode=\"reflect\")\n\n        z = spectro(x, nfft, hl)[..., :-1, :]","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/htdemucs.py:41"}],[1418,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/htdemucs.py","startLine":397,"endLine":414,"chunkText":"        x = pad1d(x, (pad, pad + le * hl - x.shape[-1]), mode=\"reflect\")\n\n        z = spectro(x, nfft, hl)[..., :-1, :]\n        assert z.shape[-1] == le + 4, (z.shape, x.shape, le)\n        z = z[..., 2 : 2 + le]\n        return z\n\n    def _ispec(self, z, length=None, scale=0):\n        hl = self.hop_length // (4**scale)\n        z = F.pad(z, (0, 0, 0, 1))\n        z = F.pad(z, (2, 2))\n        pad = hl // 2 * 3\n        le = hl * int(math.ceil(length / hl)) + 2 * pad\n        x = ispectro(z, hl, length=le)\n        x = x[..., pad : pad + length]\n        return x\n\n    def _magnitude(self, z):","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/htdemucs.py:42"}],[1419,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/htdemucs.py","startLine":412,"endLine":427,"chunkText":"        return x\n\n    def _magnitude(self, z):\n        # return the magnitude of the spectrogram, except when cac is True,\n        # in which case we just move the complex dimension to the channel one.\n        if self.cac:\n            B, C, Fr, T = z.shape\n            m = torch.view_as_real(z).permute(0, 1, 4, 2, 3)\n            m = m.reshape(B, C * 2, Fr, T)\n        else:\n            m = z.abs()\n        return m\n\n    def _mask(self, z, m):\n        # Apply masking given the mixture spectrogram `z` and the estimated mask `m`.\n        # If `cac` is True, `m` is actually a full spectrogram and `z` is ignored.","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/htdemucs.py:43"}],[1420,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/htdemucs.py","startLine":425,"endLine":438,"chunkText":"    def _mask(self, z, m):\n        # Apply masking given the mixture spectrogram `z` and the estimated mask `m`.\n        # If `cac` is True, `m` is actually a full spectrogram and `z` is ignored.\n        niters = self.wiener_iters\n        if self.cac:\n            B, S, C, Fr, T = m.shape\n            out = m.view(B, S, -1, 2, Fr, T).permute(0, 1, 2, 4, 5, 3)\n            out = torch.view_as_complex(out.contiguous())\n            return out\n        if self.training:\n            niters = self.end_iters\n        if niters < 0:\n            z = z[:, None]\n            return z / (1e-8 + z.abs()) * m","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/htdemucs.py:44"}],[1421,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/htdemucs.py","startLine":436,"endLine":455,"chunkText":"        if niters < 0:\n            z = z[:, None]\n            return z / (1e-8 + z.abs()) * m\n        else:\n            return self._wiener(m, z, niters)\n\n    def _wiener(self, mag_out, mix_stft, niters):\n        # apply wiener filtering from OpenUnmix.\n        init = mix_stft.dtype\n        wiener_win_len = 300\n        residual = self.wiener_residual\n\n        B, S, C, Fq, T = mag_out.shape\n        mag_out = mag_out.permute(0, 4, 3, 2, 1)\n        mix_stft = torch.view_as_real(mix_stft.permute(0, 3, 2, 1))\n\n        outs = []\n        for sample in range(B):\n            pos = 0\n            out = []","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/htdemucs.py:45"}],[1422,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/htdemucs.py","startLine":453,"endLine":465,"chunkText":"        for sample in range(B):\n            pos = 0\n            out = []\n            for pos in range(0, T, wiener_win_len):\n                frame = slice(pos, pos + wiener_win_len)\n                z_out = wiener(mag_out[sample, frame], mix_stft[sample, frame], niters, residual=residual)\n                out.append(z_out.transpose(-1, -2))\n            outs.append(torch.cat(out, dim=0))\n        out = torch.view_as_complex(torch.stack(outs, 0))\n        out = out.permute(0, 4, 3, 2, 1).contiguous()\n        if residual:\n            out = out[:, :-1]\n        assert list(out.shape) == [B, S, C, Fq, T]","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/htdemucs.py:46"}],[1423,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/htdemucs.py","startLine":463,"endLine":478,"chunkText":"        if residual:\n            out = out[:, :-1]\n        assert list(out.shape) == [B, S, C, Fq, T]\n        return out.to(init)\n\n    def valid_length(self, length: int):\n        \"\"\"\n        Return a length that is appropriate for evaluation.\n        In our case, always return the training length, unless\n        it is smaller than the given length, in which case this\n        raises an error.\n        \"\"\"\n        if not self.use_train_segment:\n            return length\n        training_length = int(self.segment * self.samplerate)\n        if training_length < length:","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/htdemucs.py:47"}],[1424,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/htdemucs.py","startLine":476,"endLine":489,"chunkText":"            return length\n        training_length = int(self.segment * self.samplerate)\n        if training_length < length:\n            raise ValueError(f\"Given length {length} is longer than \" f\"training length {training_length}\")\n        return training_length\n\n    def forward(self, mix):\n        length = mix.shape[-1]\n        length_pre_pad = None\n        if self.use_train_segment:\n            if self.training:\n                self.segment = Fraction(mix.shape[-1], self.samplerate)\n            else:\n                training_length = int(self.segment * self.samplerate)","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/htdemucs.py:48"}],[1425,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/htdemucs.py","startLine":487,"endLine":500,"chunkText":"                self.segment = Fraction(mix.shape[-1], self.samplerate)\n            else:\n                training_length = int(self.segment * self.samplerate)\n                if mix.shape[-1] < training_length:\n                    length_pre_pad = mix.shape[-1]\n                    mix = F.pad(mix, (0, training_length - length_pre_pad))\n        z = self._spec(mix)\n        mag = self._magnitude(z).to(mix.device)\n        x = mag\n\n        B, C, Fq, T = x.shape\n\n        # unlike previous Demucs, we always normalize because it is easier.\n        mean = x.mean(dim=(1, 2, 3), keepdim=True)","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/htdemucs.py:49"}],[1426,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/htdemucs.py","startLine":498,"endLine":513,"chunkText":"\n        # unlike previous Demucs, we always normalize because it is easier.\n        mean = x.mean(dim=(1, 2, 3), keepdim=True)\n        std = x.std(dim=(1, 2, 3), keepdim=True)\n        x = (x - mean) / (1e-5 + std)\n        # x will be the freq. branch input.\n\n        # Prepare the time branch input.\n        xt = mix\n        meant = xt.mean(dim=(1, 2), keepdim=True)\n        stdt = xt.std(dim=(1, 2), keepdim=True)\n        xt = (xt - meant) / (1e-5 + stdt)\n\n        # okay, this is a giant mess I know...\n        saved = []  # skip connections, freq.\n        saved_t = []  # skip connections, time.","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/htdemucs.py:50"}],[1427,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/htdemucs.py","startLine":511,"endLine":523,"chunkText":"        # okay, this is a giant mess I know...\n        saved = []  # skip connections, freq.\n        saved_t = []  # skip connections, time.\n        lengths = []  # saved lengths to properly remove padding, freq branch.\n        lengths_t = []  # saved lengths for time branch.\n        for idx, encode in enumerate(self.encoder):\n            lengths.append(x.shape[-1])\n            inject = None\n            if idx < len(self.tencoder):\n                # we have not yet merged branches.\n                lengths_t.append(xt.shape[-1])\n                tenc = self.tencoder[idx]\n                xt = tenc(xt)","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/htdemucs.py:51"}],[1428,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/htdemucs.py","startLine":521,"endLine":532,"chunkText":"                lengths_t.append(xt.shape[-1])\n                tenc = self.tencoder[idx]\n                xt = tenc(xt)\n                if not tenc.empty:\n                    # save for skip connection\n                    saved_t.append(xt)\n                else:\n                    # tenc contains just the first conv., so that now time and freq.\n                    # branches have the same shape and can be merged.\n                    inject = xt\n            x = encode(x, inject)\n            if idx == 0 and self.freq_emb is not None:","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/htdemucs.py:52"}],[1429,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/htdemucs.py","startLine":530,"endLine":542,"chunkText":"                    inject = xt\n            x = encode(x, inject)\n            if idx == 0 and self.freq_emb is not None:\n                # add frequency embedding to allow for non equivariant convolutions\n                # over the frequency axis.\n                frs = torch.arange(x.shape[-2], device=x.device)\n                emb = self.freq_emb(frs).t()[None, :, :, None].expand_as(x)\n                x = x + self.freq_emb_scale * emb\n\n            saved.append(x)\n        if self.crosstransformer:\n            if self.bottom_channels:\n                b, c, f, t = x.shape","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/htdemucs.py:53"}],[1430,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/htdemucs.py","startLine":540,"endLine":553,"chunkText":"        if self.crosstransformer:\n            if self.bottom_channels:\n                b, c, f, t = x.shape\n                x = rearrange(x, \"b c f t-> b c (f t)\")\n                x = self.channel_upsampler(x)\n                x = rearrange(x, \"b c (f t)-> b c f t\", f=f)\n                xt = self.channel_upsampler_t(xt)\n\n            x, xt = self.crosstransformer(x, xt)\n\n            if self.bottom_channels:\n                x = rearrange(x, \"b c f t-> b c (f t)\")\n                x = self.channel_downsampler(x)\n                x = rearrange(x, \"b c (f t)-> b c f t\", f=f)","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/htdemucs.py:54"}],[1431,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/htdemucs.py","startLine":551,"endLine":563,"chunkText":"                x = rearrange(x, \"b c f t-> b c (f t)\")\n                x = self.channel_downsampler(x)\n                x = rearrange(x, \"b c (f t)-> b c f t\", f=f)\n                xt = self.channel_downsampler_t(xt)\n\n        for idx, decode in enumerate(self.decoder):\n            skip = saved.pop(-1)\n            x, pre = decode(x, skip, lengths.pop(-1))\n            # `pre` contains the output just before final transposed convolution,\n            # which is used when the freq. and time branch separate.\n\n            offset = self.depth - len(self.tdecoder)\n            if idx >= offset:","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/htdemucs.py:55"}],[1432,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/htdemucs.py","startLine":561,"endLine":576,"chunkText":"\n            offset = self.depth - len(self.tdecoder)\n            if idx >= offset:\n                tdec = self.tdecoder[idx - offset]\n                length_t = lengths_t.pop(-1)\n                if tdec.empty:\n                    assert pre.shape[2] == 1, pre.shape\n                    pre = pre[:, :, 0]\n                    xt, _ = tdec(pre, None, length_t)\n                else:\n                    skip = saved_t.pop(-1)\n                    xt, _ = tdec(xt, skip, length_t)\n\n        # Let's make sure we used all stored skip connections.\n        assert len(saved) == 0\n        assert len(lengths_t) == 0","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/htdemucs.py:56"}],[1433,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/htdemucs.py","startLine":574,"endLine":588,"chunkText":"        # Let's make sure we used all stored skip connections.\n        assert len(saved) == 0\n        assert len(lengths_t) == 0\n        assert len(saved_t) == 0\n\n        S = len(self.sources)\n        x = x.view(B, S, -1, Fq, T)\n        x = x * std[:, None] + mean[:, None]\n\n        # to cpu as non-cuda GPUs don't support complex numbers\n        # demucs issue #435 ##432\n        # NOTE: in this case z already is on cpu\n        # TODO: remove this when mps supports complex numbers\n\n        device_type = x.device.type","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/htdemucs.py:57"}],[1434,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/htdemucs.py","startLine":586,"endLine":603,"chunkText":"        # TODO: remove this when mps supports complex numbers\n\n        device_type = x.device.type\n        device_load = f\"{device_type}:{x.device.index}\" if not device_type == \"mps\" else device_type\n        x_is_other_gpu = not device_type in [\"cuda\", \"cpu\"]\n\n        if x_is_other_gpu:\n            x = x.cpu()\n\n        zout = self._mask(z, x)\n        if self.use_train_segment:\n            if self.training:\n                x = self._ispec(zout, length)\n            else:\n                x = self._ispec(zout, training_length)\n        else:\n            x = self._ispec(zout, length)\n","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/htdemucs.py:58"}],[1435,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/htdemucs.py","startLine":601,"endLine":620,"chunkText":"        else:\n            x = self._ispec(zout, length)\n\n        # back to other device\n        if x_is_other_gpu:\n            x = x.to(device_load)\n\n        if self.use_train_segment:\n            if self.training:\n                xt = xt.view(B, S, -1, length)\n            else:\n                xt = xt.view(B, S, -1, training_length)\n        else:\n            xt = xt.view(B, S, -1, length)\n        xt = xt * stdt[:, None] + meant[:, None]\n        x = xt + x\n        if length_pre_pad:\n            x = x[..., :length_pre_pad]\n        return x\n","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/htdemucs.py:59"}],[1436,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/model.py","startLine":0,"endLine":21,"chunkText":"# Copyright (c) Facebook, Inc. and its affiliates.\n# All rights reserved.\n#\n# This source code is licensed under the license found in the\n# LICENSE file in the root directory of this source tree.\n\nimport math\n\nimport torch as th\nfrom torch import nn\n\nfrom .utils import capture_init, center_trim\n\n\nclass BLSTM(nn.Module):\n    def __init__(self, dim, layers=1):\n        super().__init__()\n        self.lstm = nn.LSTM(bidirectional=True, num_layers=layers, hidden_size=dim, input_size=dim)\n        self.linear = nn.Linear(2 * dim, dim)\n\n    def forward(self, x):\n        x = x.permute(2, 0, 1)","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/model.py:0"}],[1437,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/model.py","startLine":20,"endLine":43,"chunkText":"    def forward(self, x):\n        x = x.permute(2, 0, 1)\n        x = self.lstm(x)[0]\n        x = self.linear(x)\n        x = x.permute(1, 2, 0)\n        return x\n\n\ndef rescale_conv(conv, reference):\n    std = conv.weight.std().detach()\n    scale = (std / reference) ** 0.5\n    conv.weight.data /= scale\n    if conv.bias is not None:\n        conv.bias.data /= scale\n\n\ndef rescale_module(module, reference):\n    for sub in module.modules():\n        if isinstance(sub, (nn.Conv1d, nn.ConvTranspose1d)):\n            rescale_conv(sub, reference)\n\n\ndef upsample(x, stride):\n    \"\"\"","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/model.py:1"}],[1438,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/model.py","startLine":41,"endLine":59,"chunkText":"\ndef upsample(x, stride):\n    \"\"\"\n    Linear upsampling, the output will be `stride` times longer.\n    \"\"\"\n    batch, channels, time = x.size()\n    weight = th.arange(stride, device=x.device, dtype=th.float) / stride\n    x = x.view(batch, channels, time, 1)\n    out = x[..., :-1, :] * (1 - weight) + x[..., 1:, :] * weight\n    return out.reshape(batch, channels, -1)\n\n\ndef downsample(x, stride):\n    \"\"\"\n    Downsample x by decimation.\n    \"\"\"\n    return x[:, :, ::stride]\n\n","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/model.py:2"}],[1439,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/model.py","startLine":60,"endLine":71,"chunkText":"class Demucs(nn.Module):\n    @capture_init\n    def __init__(\n        self, sources=4, audio_channels=2, channels=64, depth=6, rewrite=True, glu=True, upsample=False, rescale=0.1, kernel_size=8, stride=4, growth=2.0, lstm_layers=2, context=3, samplerate=44100\n    ):\n        \"\"\"\n        Args:\n            sources (int): number of sources to separate\n            audio_channels (int): stereo or mono\n            channels (int): first convolution channels\n            depth (int): number of encoder/decoder layers\n            rewrite (bool): add 1x1 convolution to each encoder layer","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/model.py:3"}],[1440,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/model.py","startLine":69,"endLine":77,"chunkText":"            channels (int): first convolution channels\n            depth (int): number of encoder/decoder layers\n            rewrite (bool): add 1x1 convolution to each encoder layer\n                and a convolution to each decoder layer.\n                For the decoder layer, `context` gives the kernel size.\n            glu (bool): use glu instead of ReLU\n            upsample (bool): use linear upsampling with convolutions\n                Wave-U-Net style, instead of transposed convolutions\n            rescale (int): rescale initial weights of convolutions","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/model.py:4"}],[1441,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/model.py","startLine":75,"endLine":83,"chunkText":"            upsample (bool): use linear upsampling with convolutions\n                Wave-U-Net style, instead of transposed convolutions\n            rescale (int): rescale initial weights of convolutions\n                to get their standard deviation closer to `rescale`\n            kernel_size (int): kernel size for convolutions\n            stride (int): stride for convolutions\n            growth (float): multiply (resp divide) number of channels by that\n                for each layer of the encoder (resp decoder)\n            lstm_layers (int): number of lstm layers, 0 = no lstm","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/model.py:5"}],[1442,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/model.py","startLine":81,"endLine":94,"chunkText":"            growth (float): multiply (resp divide) number of channels by that\n                for each layer of the encoder (resp decoder)\n            lstm_layers (int): number of lstm layers, 0 = no lstm\n            context (int): kernel size of the convolution in the\n                decoder before the transposed convolution. If > 1,\n                will provide some context from neighboring time\n                steps.\n        \"\"\"\n\n        super().__init__()\n        self.audio_channels = audio_channels\n        self.sources = sources\n        self.kernel_size = kernel_size\n        self.context = context","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/model.py:6"}],[1443,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/model.py","startLine":92,"endLine":112,"chunkText":"        self.sources = sources\n        self.kernel_size = kernel_size\n        self.context = context\n        self.stride = stride\n        self.depth = depth\n        self.upsample = upsample\n        self.channels = channels\n        self.samplerate = samplerate\n\n        self.encoder = nn.ModuleList()\n        self.decoder = nn.ModuleList()\n\n        self.final = None\n        if upsample:\n            self.final = nn.Conv1d(channels + audio_channels, sources * audio_channels, 1)\n            stride = 1\n\n        if glu:\n            activation = nn.GLU(dim=1)\n            ch_scale = 2\n        else:","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/model.py:7"}],[1444,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/model.py","startLine":110,"endLine":126,"chunkText":"            activation = nn.GLU(dim=1)\n            ch_scale = 2\n        else:\n            activation = nn.ReLU()\n            ch_scale = 1\n        in_channels = audio_channels\n        for index in range(depth):\n            encode = []\n            encode += [nn.Conv1d(in_channels, channels, kernel_size, stride), nn.ReLU()]\n            if rewrite:\n                encode += [nn.Conv1d(channels, ch_scale * channels, 1), activation]\n            self.encoder.append(nn.Sequential(*encode))\n\n            decode = []\n            if index > 0:\n                out_channels = in_channels\n            else:","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/model.py:8"}],[1445,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/model.py","startLine":124,"endLine":137,"chunkText":"            if index > 0:\n                out_channels = in_channels\n            else:\n                if upsample:\n                    out_channels = channels\n                else:\n                    out_channels = sources * audio_channels\n            if rewrite:\n                decode += [nn.Conv1d(channels, ch_scale * channels, context), activation]\n            if upsample:\n                decode += [nn.Conv1d(channels, out_channels, kernel_size, stride=1)]\n            else:\n                decode += [nn.ConvTranspose1d(channels, out_channels, kernel_size, stride)]\n            if index > 0:","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/model.py:9"}],[1446,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/model.py","startLine":135,"endLine":154,"chunkText":"            else:\n                decode += [nn.ConvTranspose1d(channels, out_channels, kernel_size, stride)]\n            if index > 0:\n                decode.append(nn.ReLU())\n            self.decoder.insert(0, nn.Sequential(*decode))\n            in_channels = channels\n            channels = int(growth * channels)\n\n        channels = in_channels\n\n        if lstm_layers:\n            self.lstm = BLSTM(channels, lstm_layers)\n        else:\n            self.lstm = None\n\n        if rescale:\n            rescale_module(self, reference=rescale)\n\n    def valid_length(self, length):\n        \"\"\"","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/model.py:10"}],[1447,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/model.py","startLine":152,"endLine":163,"chunkText":"    def valid_length(self, length):\n        \"\"\"\n        Return the nearest valid length to use with the model so that\n        there is no time steps left over in a convolutions, e.g. for all\n        layers, size of the input - kernel_size % stride = 0.\n\n        If the mixture has a valid length, the estimated sources\n        will have exactly the same length when context = 1. If context > 1,\n        the two signals can be center trimmed to match.\n\n        For training, extracts should have a valid length.For evaluation\n        on full tracks we recommend passing `pad = True` to :method:`forward`.","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/model.py:11"}],[1448,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/model.py","startLine":163,"endLine":174,"chunkText":"        For training, extracts should have a valid length.For evaluation\n        on full tracks we recommend passing `pad = True` to :method:`forward`.\n        \"\"\"\n        for _ in range(self.depth):\n            if self.upsample:\n                length = math.ceil(length / self.stride) + self.kernel_size - 1\n            else:\n                length = math.ceil((length - self.kernel_size) / self.stride) + 1\n            length = max(1, length)\n            length += self.context - 1\n        for _ in range(self.depth):\n            if self.upsample:","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/model.py:12"}],[1449,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/model.py","startLine":172,"endLine":191,"chunkText":"            length += self.context - 1\n        for _ in range(self.depth):\n            if self.upsample:\n                length = length * self.stride + self.kernel_size - 1\n            else:\n                length = (length - 1) * self.stride + self.kernel_size\n\n        return int(length)\n\n    def forward(self, mix):\n        x = mix\n        saved = [x]\n        for encode in self.encoder:\n            x = encode(x)\n            saved.append(x)\n            if self.upsample:\n                x = downsample(x, self.stride)\n        if self.lstm:\n            x = self.lstm(x)\n        for decode in self.decoder:","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/model.py:13"}],[1450,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/model.py","startLine":189,"endLine":204,"chunkText":"        if self.lstm:\n            x = self.lstm(x)\n        for decode in self.decoder:\n            if self.upsample:\n                x = upsample(x, stride=self.stride)\n            skip = center_trim(saved.pop(-1), x)\n            x = x + skip\n            x = decode(x)\n        if self.final:\n            skip = center_trim(saved.pop(-1), x)\n            x = th.cat([x, skip], dim=1)\n            x = self.final(x)\n\n        x = x.view(x.size(0), self.sources, self.audio_channels, x.size(-1))\n        return x\n","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/model.py:14"}],[1451,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/model_v2.py","startLine":0,"endLine":21,"chunkText":"# Copyright (c) Facebook, Inc. and its affiliates.\n# All rights reserved.\n#\n# This source code is licensed under the license found in the\n# LICENSE file in the root directory of this source tree.\n\nimport math\n\nimport julius\nfrom torch import nn\nfrom .tasnet_v2 import ConvTasNet\n\nfrom .utils import capture_init, center_trim\n\n\nclass BLSTM(nn.Module):\n    def __init__(self, dim, layers=1):\n        super().__init__()\n        self.lstm = nn.LSTM(bidirectional=True, num_layers=layers, hidden_size=dim, input_size=dim)\n        self.linear = nn.Linear(2 * dim, dim)\n\n    def forward(self, x):","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/model_v2.py:0"}],[1452,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/model_v2.py","startLine":19,"endLine":42,"chunkText":"        self.linear = nn.Linear(2 * dim, dim)\n\n    def forward(self, x):\n        x = x.permute(2, 0, 1)\n        x = self.lstm(x)[0]\n        x = self.linear(x)\n        x = x.permute(1, 2, 0)\n        return x\n\n\ndef rescale_conv(conv, reference):\n    std = conv.weight.std().detach()\n    scale = (std / reference) ** 0.5\n    conv.weight.data /= scale\n    if conv.bias is not None:\n        conv.bias.data /= scale\n\n\ndef rescale_module(module, reference):\n    for sub in module.modules():\n        if isinstance(sub, (nn.Conv1d, nn.ConvTranspose1d)):\n            rescale_conv(sub, reference)\n\n","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/model_v2.py:1"}],[1453,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/model_v2.py","startLine":40,"endLine":59,"chunkText":"            rescale_conv(sub, reference)\n\n\ndef auto_load_demucs_model_v2(sources, demucs_model_name):\n\n    if \"48\" in demucs_model_name:\n        channels = 48\n    elif \"unittest\" in demucs_model_name:\n        channels = 4\n    else:\n        channels = 64\n\n    if \"tasnet\" in demucs_model_name:\n        init_demucs_model = ConvTasNet(sources, X=10)\n    else:\n        init_demucs_model = Demucs(sources, channels=channels)\n\n    return init_demucs_model\n\n","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/model_v2.py:2"}],[1454,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/model_v2.py","startLine":60,"endLine":84,"chunkText":"class Demucs(nn.Module):\n    @capture_init\n    def __init__(\n        self,\n        sources,\n        audio_channels=2,\n        channels=64,\n        depth=6,\n        rewrite=True,\n        glu=True,\n        rescale=0.1,\n        resample=True,\n        kernel_size=8,\n        stride=4,\n        growth=2.0,\n        lstm_layers=2,\n        context=3,\n        normalize=False,\n        samplerate=44100,\n        segment_length=4 * 10 * 44100,\n    ):\n        \"\"\"\n        Args:\n            sources (list[str]): list of source names\n            audio_channels (int): stereo or mono","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/model_v2.py:3"}],[1455,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/model_v2.py","startLine":82,"endLine":91,"chunkText":"        Args:\n            sources (list[str]): list of source names\n            audio_channels (int): stereo or mono\n            channels (int): first convolution channels\n            depth (int): number of encoder/decoder layers\n            rewrite (bool): add 1x1 convolution to each encoder layer\n                and a convolution to each decoder layer.\n                For the decoder layer, `context` gives the kernel size.\n            glu (bool): use glu instead of ReLU\n            resample_input (bool): upsample x2 the input and downsample /2 the output.","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/model_v2.py:4"}],[1456,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/model_v2.py","startLine":89,"endLine":97,"chunkText":"                For the decoder layer, `context` gives the kernel size.\n            glu (bool): use glu instead of ReLU\n            resample_input (bool): upsample x2 the input and downsample /2 the output.\n            rescale (int): rescale initial weights of convolutions\n                to get their standard deviation closer to `rescale`\n            kernel_size (int): kernel size for convolutions\n            stride (int): stride for convolutions\n            growth (float): multiply (resp divide) number of channels by that\n                for each layer of the encoder (resp decoder)","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/model_v2.py:5"}],[1457,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/model_v2.py","startLine":95,"endLine":104,"chunkText":"            stride (int): stride for convolutions\n            growth (float): multiply (resp divide) number of channels by that\n                for each layer of the encoder (resp decoder)\n            lstm_layers (int): number of lstm layers, 0 = no lstm\n            context (int): kernel size of the convolution in the\n                decoder before the transposed convolution. If > 1,\n                will provide some context from neighboring time\n                steps.\n            samplerate (int): stored as meta information for easing\n                future evaluations of the model.","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/model_v2.py:6"}],[1458,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/model_v2.py","startLine":102,"endLine":117,"chunkText":"                steps.\n            samplerate (int): stored as meta information for easing\n                future evaluations of the model.\n            segment_length (int): stored as meta information for easing\n                future evaluations of the model. Length of the segments on which\n                the model was trained.\n        \"\"\"\n\n        super().__init__()\n        self.audio_channels = audio_channels\n        self.sources = sources\n        self.kernel_size = kernel_size\n        self.context = context\n        self.stride = stride\n        self.depth = depth\n        self.resample = resample","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/model_v2.py:7"}],[1459,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/model_v2.py","startLine":115,"endLine":134,"chunkText":"        self.stride = stride\n        self.depth = depth\n        self.resample = resample\n        self.channels = channels\n        self.normalize = normalize\n        self.samplerate = samplerate\n        self.segment_length = segment_length\n\n        self.encoder = nn.ModuleList()\n        self.decoder = nn.ModuleList()\n\n        if glu:\n            activation = nn.GLU(dim=1)\n            ch_scale = 2\n        else:\n            activation = nn.ReLU()\n            ch_scale = 1\n        in_channels = audio_channels\n        for index in range(depth):\n            encode = []","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/model_v2.py:8"}],[1460,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/model_v2.py","startLine":132,"endLine":145,"chunkText":"        in_channels = audio_channels\n        for index in range(depth):\n            encode = []\n            encode += [nn.Conv1d(in_channels, channels, kernel_size, stride), nn.ReLU()]\n            if rewrite:\n                encode += [nn.Conv1d(channels, ch_scale * channels, 1), activation]\n            self.encoder.append(nn.Sequential(*encode))\n\n            decode = []\n            if index > 0:\n                out_channels = in_channels\n            else:\n                out_channels = len(self.sources) * audio_channels\n            if rewrite:","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/model_v2.py:9"}],[1461,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/model_v2.py","startLine":143,"endLine":157,"chunkText":"            else:\n                out_channels = len(self.sources) * audio_channels\n            if rewrite:\n                decode += [nn.Conv1d(channels, ch_scale * channels, context), activation]\n            decode += [nn.ConvTranspose1d(channels, out_channels, kernel_size, stride)]\n            if index > 0:\n                decode.append(nn.ReLU())\n            self.decoder.insert(0, nn.Sequential(*decode))\n            in_channels = channels\n            channels = int(growth * channels)\n\n        channels = in_channels\n\n        if lstm_layers:\n            self.lstm = BLSTM(channels, lstm_layers)","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/model_v2.py:10"}],[1462,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/model_v2.py","startLine":155,"endLine":171,"chunkText":"\n        if lstm_layers:\n            self.lstm = BLSTM(channels, lstm_layers)\n        else:\n            self.lstm = None\n\n        if rescale:\n            rescale_module(self, reference=rescale)\n\n    def valid_length(self, length):\n        \"\"\"\n        Return the nearest valid length to use with the model so that\n        there is no time steps left over in a convolutions, e.g. for all\n        layers, size of the input - kernel_size % stride = 0.\n\n        If the mixture has a valid length, the estimated sources\n        will have exactly the same length when context = 1. If context > 1,","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/model_v2.py:11"}],[1463,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/model_v2.py","startLine":169,"endLine":182,"chunkText":"\n        If the mixture has a valid length, the estimated sources\n        will have exactly the same length when context = 1. If context > 1,\n        the two signals can be center trimmed to match.\n\n        For training, extracts should have a valid length.For evaluation\n        on full tracks we recommend passing `pad = True` to :method:`forward`.\n        \"\"\"\n        if self.resample:\n            length *= 2\n        for _ in range(self.depth):\n            length = math.ceil((length - self.kernel_size) / self.stride) + 1\n            length = max(1, length)\n            length += self.context - 1","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/model_v2.py:12"}],[1464,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/model_v2.py","startLine":180,"endLine":198,"chunkText":"            length = math.ceil((length - self.kernel_size) / self.stride) + 1\n            length = max(1, length)\n            length += self.context - 1\n        for _ in range(self.depth):\n            length = (length - 1) * self.stride + self.kernel_size\n\n        if self.resample:\n            length = math.ceil(length / 2)\n        return int(length)\n\n    def forward(self, mix):\n        x = mix\n\n        if self.normalize:\n            mono = mix.mean(dim=1, keepdim=True)\n            mean = mono.mean(dim=-1, keepdim=True)\n            std = mono.std(dim=-1, keepdim=True)\n        else:\n            mean = 0","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/model_v2.py:13"}],[1465,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/model_v2.py","startLine":196,"endLine":219,"chunkText":"            std = mono.std(dim=-1, keepdim=True)\n        else:\n            mean = 0\n            std = 1\n\n        x = (x - mean) / (1e-5 + std)\n\n        if self.resample:\n            x = julius.resample_frac(x, 1, 2)\n\n        saved = []\n        for encode in self.encoder:\n            x = encode(x)\n            saved.append(x)\n        if self.lstm:\n            x = self.lstm(x)\n        for decode in self.decoder:\n            skip = center_trim(saved.pop(-1), x)\n            x = x + skip\n            x = decode(x)\n\n        if self.resample:\n            x = julius.resample_frac(x, 2, 1)\n        x = x * std + mean","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/model_v2.py:14"}],[1466,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/model_v2.py","startLine":217,"endLine":222,"chunkText":"        if self.resample:\n            x = julius.resample_frac(x, 2, 1)\n        x = x * std + mean\n        x = x.view(x.size(0), len(self.sources), self.audio_channels, x.size(-1))\n        return x\n","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/model_v2.py:15"}],[1467,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/pretrained.py","startLine":0,"endLine":23,"chunkText":"# Copyright (c) Facebook, Inc. and its affiliates.\n# All rights reserved.\n#\n# This source code is licensed under the license found in the\n# LICENSE file in the root directory of this source tree.\n\"\"\"Loading pretrained models.\n\"\"\"\n\nimport logging\nfrom pathlib import Path\nimport typing as tp\n\n# from dora.log import fatal\n\nimport logging\n\nfrom diffq import DiffQuantizer\nimport torch.hub\n\nfrom .model import Demucs\nfrom .tasnet_v2 import ConvTasNet\nfrom .utils import set_state\n\nfrom .hdemucs import HDemucs","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/pretrained.py:0"}],[1468,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/pretrained.py","startLine":21,"endLine":39,"chunkText":"from .utils import set_state\n\nfrom .hdemucs import HDemucs\nfrom .repo import RemoteRepo, LocalRepo, ModelOnlyRepo, BagOnlyRepo, AnyModelRepo, ModelLoadingError  # noqa\n\nlogger = logging.getLogger(__name__)\nROOT_URL = \"https://dl.fbaipublicfiles.com/demucs/mdx_final/\"\nREMOTE_ROOT = Path(__file__).parent / \"remote\"\n\nSOURCES = [\"drums\", \"bass\", \"other\", \"vocals\"]\n\n\ndef demucs_unittest():\n    model = HDemucs(channels=4, sources=SOURCES)\n    return model\n\n\ndef add_model_flags(parser):\n    group = parser.add_mutually_exclusive_group(required=False)","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/pretrained.py:1"}],[1469,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/pretrained.py","startLine":37,"endLine":47,"chunkText":"def add_model_flags(parser):\n    group = parser.add_mutually_exclusive_group(required=False)\n    group.add_argument(\"-s\", \"--sig\", help=\"Locally trained XP signature.\")\n    group.add_argument(\"-n\", \"--name\", default=\"mdx_extra_q\", help=\"Pretrained model name or signature. Default is mdx_extra_q.\")\n    parser.add_argument(\"--repo\", type=Path, help=\"Folder containing all pre-trained models for use with -n.\")\n\n\ndef _parse_remote_files(remote_file_list) -> tp.Dict[str, str]:\n    root: str = \"\"\n    models: tp.Dict[str, str] = {}\n    for line in remote_file_list.read_text().split(\"\\n\"):","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/pretrained.py:2"}],[1470,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/pretrained.py","startLine":47,"endLine":62,"chunkText":"    models: tp.Dict[str, str] = {}\n    for line in remote_file_list.read_text().split(\"\\n\"):\n        line = line.strip()\n        if line.startswith(\"#\"):\n            continue\n        elif line.startswith(\"root:\"):\n            root = line.split(\":\", 1)[1].strip()\n        else:\n            sig = line.split(\"-\", 1)[0]\n            assert sig not in models\n            models[sig] = ROOT_URL + root + line\n    return models\n\n\ndef get_model(name: str, repo: tp.Optional[Path] = None):\n    \"\"\"`name` must be a bag of models name or a pretrained signature","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/pretrained.py:3"}],[1471,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/pretrained.py","startLine":60,"endLine":74,"chunkText":"\ndef get_model(name: str, repo: tp.Optional[Path] = None):\n    \"\"\"`name` must be a bag of models name or a pretrained signature\n    from the remote AWS model repo or the specified local repo if `repo` is not None.\n    \"\"\"\n    if name == \"demucs_unittest\":\n        return demucs_unittest()\n    model_repo: ModelOnlyRepo\n    if repo is None:\n        models = _parse_remote_files(REMOTE_ROOT / \"files.txt\")\n        model_repo = RemoteRepo(models)\n        bag_repo = BagOnlyRepo(REMOTE_ROOT, model_repo)\n    else:\n        if not repo.is_dir():\n            fatal(f\"{repo} must exist and be a directory.\")","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/pretrained.py:4"}],[1472,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/pretrained.py","startLine":72,"endLine":94,"chunkText":"    else:\n        if not repo.is_dir():\n            fatal(f\"{repo} must exist and be a directory.\")\n        model_repo = LocalRepo(repo)\n        bag_repo = BagOnlyRepo(repo, model_repo)\n    any_repo = AnyModelRepo(model_repo, bag_repo)\n    model = any_repo.get_model(name)\n    model.eval()\n    return model\n\n\ndef get_model_from_args(args):\n    \"\"\"\n    Load local model package or pre-trained model.\n    \"\"\"\n    return get_model(name=args.name, repo=args.repo)\n\n\nlogger = logging.getLogger(__name__)\nROOT = \"https://dl.fbaipublicfiles.com/demucs/v3.0/\"\n\nPRETRAINED_MODELS = {\n    \"demucs\": \"e07c671f\",","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/pretrained.py:5"}],[1473,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/pretrained.py","startLine":92,"endLine":118,"chunkText":"\nPRETRAINED_MODELS = {\n    \"demucs\": \"e07c671f\",\n    \"demucs48_hq\": \"28a1282c\",\n    \"demucs_extra\": \"3646af93\",\n    \"demucs_quantized\": \"07afea75\",\n    \"tasnet\": \"beb46fac\",\n    \"tasnet_extra\": \"df3777b2\",\n    \"demucs_unittest\": \"09ebc15f\",\n}\n\nSOURCES = [\"drums\", \"bass\", \"other\", \"vocals\"]\n\n\ndef get_url(name):\n    sig = PRETRAINED_MODELS[name]\n    return ROOT + name + \"-\" + sig[:8] + \".th\"\n\n\ndef is_pretrained(name):\n    return name in PRETRAINED_MODELS\n\n\ndef load_pretrained(name):\n    if name == \"demucs\":\n        return demucs(pretrained=True)\n    elif name == \"demucs48_hq\":","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/pretrained.py:6"}],[1474,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/pretrained.py","startLine":116,"endLine":130,"chunkText":"    if name == \"demucs\":\n        return demucs(pretrained=True)\n    elif name == \"demucs48_hq\":\n        return demucs(pretrained=True, hq=True, channels=48)\n    elif name == \"demucs_extra\":\n        return demucs(pretrained=True, extra=True)\n    elif name == \"demucs_quantized\":\n        return demucs(pretrained=True, quantized=True)\n    elif name == \"demucs_unittest\":\n        return demucs_unittest(pretrained=True)\n    elif name == \"tasnet\":\n        return tasnet(pretrained=True)\n    elif name == \"tasnet_extra\":\n        return tasnet(pretrained=True, extra=True)\n    else:","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/pretrained.py:7"}],[1475,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/pretrained.py","startLine":128,"endLine":148,"chunkText":"    elif name == \"tasnet_extra\":\n        return tasnet(pretrained=True, extra=True)\n    else:\n        raise ValueError(f\"Invalid pretrained name {name}\")\n\n\ndef _load_state(name, model, quantizer=None):\n    url = get_url(name)\n    state = torch.hub.load_state_dict_from_url(url, map_location=\"cpu\", check_hash=True)\n    set_state(model, quantizer, state)\n    if quantizer:\n        quantizer.detach()\n\n\ndef demucs_unittest(pretrained=True):\n    model = Demucs(channels=4, sources=SOURCES)\n    if pretrained:\n        _load_state(\"demucs_unittest\", model)\n    return model\n\n","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/pretrained.py:8"}],[1476,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/pretrained.py","startLine":146,"endLine":160,"chunkText":"    return model\n\n\ndef demucs(pretrained=True, extra=False, quantized=False, hq=False, channels=64):\n    if not pretrained and (extra or quantized or hq):\n        raise ValueError(\"if extra or quantized is True, pretrained must be True.\")\n    model = Demucs(sources=SOURCES, channels=channels)\n    if pretrained:\n        name = \"demucs\"\n        if channels != 64:\n            name += str(channels)\n        quantizer = None\n        if sum([extra, quantized, hq]) > 1:\n            raise ValueError(\"Only one of extra, quantized, hq, can be True.\")\n        if quantized:","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/pretrained.py:9"}],[1477,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/pretrained.py","startLine":158,"endLine":175,"chunkText":"        if sum([extra, quantized, hq]) > 1:\n            raise ValueError(\"Only one of extra, quantized, hq, can be True.\")\n        if quantized:\n            quantizer = DiffQuantizer(model, group_size=8, min_size=1)\n            name += \"_quantized\"\n        if extra:\n            name += \"_extra\"\n        if hq:\n            name += \"_hq\"\n        _load_state(name, model, quantizer)\n    return model\n\n\ndef tasnet(pretrained=True, extra=False):\n    if not pretrained and extra:\n        raise ValueError(\"if extra is True, pretrained must be True.\")\n    model = ConvTasNet(X=10, sources=SOURCES)\n    if pretrained:","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/pretrained.py:10"}],[1478,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/pretrained.py","startLine":173,"endLine":181,"chunkText":"        raise ValueError(\"if extra is True, pretrained must be True.\")\n    model = ConvTasNet(X=10, sources=SOURCES)\n    if pretrained:\n        name = \"tasnet\"\n        if extra:\n            name = \"tasnet_extra\"\n        _load_state(name, model)\n    return model\n","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/pretrained.py:11"}],[1479,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/repo.py","startLine":0,"endLine":23,"chunkText":"# Copyright (c) Facebook, Inc. and its affiliates.\n# All rights reserved.\n#\n# This source code is licensed under the license found in the\n# LICENSE file in the root directory of this source tree.\n\"\"\"Represents a model repository, including pre-trained models and bags of models.\nA repo can either be the main remote repository stored in AWS, or a local repository\nwith your own models.\n\"\"\"\n\nfrom hashlib import sha256\nfrom pathlib import Path\nimport typing as tp\n\nimport torch\nimport yaml\n\nfrom .apply import BagOfModels, Model\nfrom .states import load_model\n\n\nAnyModel = tp.Union[Model, BagOfModels]\n\n","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/repo.py:0"}],[1480,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/repo.py","startLine":21,"endLine":40,"chunkText":"AnyModel = tp.Union[Model, BagOfModels]\n\n\nclass ModelLoadingError(RuntimeError):\n    pass\n\n\ndef check_checksum(path: Path, checksum: str):\n    sha = sha256()\n    with open(path, \"rb\") as file:\n        while True:\n            buf = file.read(2**20)\n            if not buf:\n                break\n            sha.update(buf)\n    actual_checksum = sha.hexdigest()[: len(checksum)]\n    if actual_checksum != checksum:\n        raise ModelLoadingError(f\"Invalid checksum for file {path}, \" f\"expected {checksum} but got {actual_checksum}\")\n\n","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/repo.py:1"}],[1481,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/repo.py","startLine":41,"endLine":50,"chunkText":"class ModelOnlyRepo:\n    \"\"\"Base class for all model only repos.\"\"\"\n\n    def has_model(self, sig: str) -> bool:\n        raise NotImplementedError()\n\n    def get_model(self, sig: str) -> Model:\n        raise NotImplementedError()\n\n","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/repo.py:2"}],[1482,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/repo.py","startLine":51,"endLine":66,"chunkText":"class RemoteRepo(ModelOnlyRepo):\n    def __init__(self, models: tp.Dict[str, str]):\n        self._models = models\n\n    def has_model(self, sig: str) -> bool:\n        return sig in self._models\n\n    def get_model(self, sig: str) -> Model:\n        try:\n            url = self._models[sig]\n        except KeyError:\n            raise ModelLoadingError(f\"Could not find a pre-trained model with signature {sig}.\")\n        pkg = torch.hub.load_state_dict_from_url(url, map_location=\"cpu\", check_hash=True)\n        return load_model(pkg)\n\n","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/repo.py:3"}],[1483,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/repo.py","startLine":67,"endLine":83,"chunkText":"class LocalRepo(ModelOnlyRepo):\n    def __init__(self, root: Path):\n        self.root = root\n        self.scan()\n\n    def scan(self):\n        self._models = {}\n        self._checksums = {}\n        for file in self.root.iterdir():\n            if file.suffix == \".th\":\n                if \"-\" in file.stem:\n                    xp_sig, checksum = file.stem.split(\"-\")\n                    self._checksums[xp_sig] = checksum\n                else:\n                    xp_sig = file.stem\n                if xp_sig in self._models:\n                    print(\"Whats xp? \", xp_sig)","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/repo.py:4"}],[1484,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/repo.py","startLine":81,"endLine":94,"chunkText":"                    xp_sig = file.stem\n                if xp_sig in self._models:\n                    print(\"Whats xp? \", xp_sig)\n                    raise ModelLoadingError(f\"Duplicate pre-trained model exist for signature {xp_sig}. \" \"Please delete all but one.\")\n                self._models[xp_sig] = file\n\n    def has_model(self, sig: str) -> bool:\n        return sig in self._models\n\n    def get_model(self, sig: str) -> Model:\n        try:\n            file = self._models[sig]\n        except KeyError:\n            raise ModelLoadingError(f\"Could not find pre-trained model with signature {sig}.\")","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/repo.py:5"}],[1485,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/repo.py","startLine":92,"endLine":99,"chunkText":"            file = self._models[sig]\n        except KeyError:\n            raise ModelLoadingError(f\"Could not find pre-trained model with signature {sig}.\")\n        if sig in self._checksums:\n            check_checksum(file, self._checksums[sig])\n        return load_model(file)\n\n","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/repo.py:6"}],[1486,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/repo.py","startLine":100,"endLine":120,"chunkText":"class BagOnlyRepo:\n    \"\"\"Handles only YAML files containing bag of models, leaving the actual\n    model loading to some Repo.\n    \"\"\"\n\n    def __init__(self, root: Path, model_repo: ModelOnlyRepo):\n        self.root = root\n        self.model_repo = model_repo\n        self.scan()\n\n    def scan(self):\n        self._bags = {}\n        for file in self.root.iterdir():\n            if file.suffix == \".yaml\":\n                self._bags[file.stem] = file\n\n    def has_model(self, name: str) -> bool:\n        return name in self._bags\n\n    def get_model(self, name: str) -> BagOfModels:\n        try:","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/repo.py:7"}],[1487,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/repo.py","startLine":118,"endLine":131,"chunkText":"\n    def get_model(self, name: str) -> BagOfModels:\n        try:\n            yaml_file = self._bags[name]\n        except KeyError:\n            raise ModelLoadingError(f\"{name} is neither a single pre-trained model or \" \"a bag of models.\")\n        bag = yaml.safe_load(open(yaml_file))\n        signatures = bag[\"models\"]\n        models = [self.model_repo.get_model(sig) for sig in signatures]\n        weights = bag.get(\"weights\")\n        segment = bag.get(\"segment\")\n        return BagOfModels(models, weights, segment)\n\n","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/repo.py:8"}],[1488,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/repo.py","startLine":132,"endLine":146,"chunkText":"class AnyModelRepo:\n    def __init__(self, model_repo: ModelOnlyRepo, bag_repo: BagOnlyRepo):\n        self.model_repo = model_repo\n        self.bag_repo = bag_repo\n\n    def has_model(self, name_or_sig: str) -> bool:\n        return self.model_repo.has_model(name_or_sig) or self.bag_repo.has_model(name_or_sig)\n\n    def get_model(self, name_or_sig: str) -> AnyModel:\n        # print('name_or_sig: ', name_or_sig)\n        if self.model_repo.has_model(name_or_sig):\n            return self.model_repo.get_model(name_or_sig)\n        else:\n            return self.bag_repo.get_model(name_or_sig)\n","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/repo.py:9"}],[1489,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/spec.py","startLine":0,"endLine":18,"chunkText":"# Copyright (c) Meta Platforms, Inc. and affiliates.\n# All rights reserved.\n#\n# This source code is licensed under the license found in the\n# LICENSE file in the root directory of this source tree.\n\"\"\"Conveniance wrapper to perform STFT and iSTFT\"\"\"\n\nimport torch as th\n\n\ndef spectro(x, n_fft=512, hop_length=None, pad=0):\n    *other, length = x.shape\n    x = x.reshape(-1, length)\n\n    device_type = x.device.type\n    is_other_gpu = not device_type in [\"cuda\", \"cpu\"]\n\n    if is_other_gpu:\n        x = x.cpu()","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/spec.py:0"}],[1490,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/spec.py","startLine":16,"endLine":33,"chunkText":"\n    if is_other_gpu:\n        x = x.cpu()\n    z = th.stft(x, n_fft * (1 + pad), hop_length or n_fft // 4, window=th.hann_window(n_fft).to(x), win_length=n_fft, normalized=True, center=True, return_complex=True, pad_mode=\"reflect\")\n    _, freqs, frame = z.shape\n    return z.view(*other, freqs, frame)\n\n\ndef ispectro(z, hop_length=None, length=None, pad=0):\n    *other, freqs, frames = z.shape\n    n_fft = 2 * freqs - 2\n    z = z.view(-1, freqs, frames)\n    win_length = n_fft // (1 + pad)\n\n    device_type = z.device.type\n    is_other_gpu = not device_type in [\"cuda\", \"cpu\"]\n\n    if is_other_gpu:","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/spec.py:1"}],[1491,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/spec.py","startLine":31,"endLine":38,"chunkText":"    is_other_gpu = not device_type in [\"cuda\", \"cpu\"]\n\n    if is_other_gpu:\n        z = z.cpu()\n    x = th.istft(z, n_fft, hop_length, window=th.hann_window(win_length).to(z.real), win_length=win_length, normalized=True, length=length, center=True)\n    _, length = x.shape\n    return x.view(*other, length)\n","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/spec.py:2"}],[1492,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/states.py","startLine":0,"endLine":23,"chunkText":"# Copyright (c) Facebook, Inc. and its affiliates.\n# All rights reserved.\n#\n# This source code is licensed under the license found in the\n# LICENSE file in the root directory of this source tree.\n\"\"\"\nUtilities to save and load models.\n\"\"\"\nfrom contextlib import contextmanager\n\nimport functools\nimport hashlib\nimport inspect\nimport io\nfrom pathlib import Path\nimport warnings\n\nfrom diffq import DiffQuantizer, UniformQuantizer, restore_quantized_state\nimport torch\n\n\ndef get_quantizer(model, args, optimizer=None):\n    \"\"\"Return the quantizer given the XP quantization args.\"\"\"\n    quantizer = None","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/states.py:0"}],[1493,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/states.py","startLine":22,"endLine":35,"chunkText":"    \"\"\"Return the quantizer given the XP quantization args.\"\"\"\n    quantizer = None\n    if args.diffq:\n        quantizer = DiffQuantizer(model, min_size=args.min_size, group_size=args.group_size)\n        if optimizer is not None:\n            quantizer.setup_optimizer(optimizer)\n    elif args.qat:\n        quantizer = UniformQuantizer(model, bits=args.qat, min_size=args.min_size)\n    return quantizer\n\n\ndef load_model(path_or_package, strict=False):\n    \"\"\"Load a model from the given serialized model, either given as a dict (already loaded)\n    or a path to a file on disk.\"\"\"","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/states.py:1"}],[1494,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/states.py","startLine":33,"endLine":46,"chunkText":"def load_model(path_or_package, strict=False):\n    \"\"\"Load a model from the given serialized model, either given as a dict (already loaded)\n    or a path to a file on disk.\"\"\"\n    if isinstance(path_or_package, dict):\n        package = path_or_package\n    elif isinstance(path_or_package, (str, Path)):\n        with warnings.catch_warnings():\n            warnings.simplefilter(\"ignore\")\n            path = path_or_package\n            package = torch.load(path, \"cpu\", weights_only=False)\n    else:\n        raise ValueError(f\"Invalid type for {path_or_package}.\")\n\n    klass = package[\"klass\"]","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/states.py:2"}],[1495,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/states.py","startLine":44,"endLine":66,"chunkText":"        raise ValueError(f\"Invalid type for {path_or_package}.\")\n\n    klass = package[\"klass\"]\n    args = package[\"args\"]\n    kwargs = package[\"kwargs\"]\n\n    if strict:\n        model = klass(*args, **kwargs)\n    else:\n        sig = inspect.signature(klass)\n        for key in list(kwargs):\n            if key not in sig.parameters:\n                warnings.warn(\"Dropping inexistant parameter \" + key)\n                del kwargs[key]\n        model = klass(*args, **kwargs)\n\n    state = package[\"state\"]\n\n    set_state(model, state)\n    return model\n\n\ndef get_state(model, quantizer, half=False):","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/states.py:3"}],[1496,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/states.py","startLine":64,"endLine":79,"chunkText":"\n\ndef get_state(model, quantizer, half=False):\n    \"\"\"Get the state from a model, potentially with quantization applied.\n    If `half` is True, model are stored as half precision, which shouldn't impact performance\n    but half the state size.\"\"\"\n    if quantizer is None:\n        dtype = torch.half if half else None\n        state = {k: p.data.to(device=\"cpu\", dtype=dtype) for k, p in model.state_dict().items()}\n    else:\n        state = quantizer.get_quantized_state()\n        state[\"__quantized\"] = True\n    return state\n\n\ndef set_state(model, state, quantizer=None):","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/states.py:4"}],[1497,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/states.py","startLine":77,"endLine":95,"chunkText":"\n\ndef set_state(model, state, quantizer=None):\n    \"\"\"Set the state on a given model.\"\"\"\n    if state.get(\"__quantized\"):\n        if quantizer is not None:\n            quantizer.restore_quantized_state(model, state[\"quantized\"])\n        else:\n            restore_quantized_state(model, state)\n    else:\n        model.load_state_dict(state)\n    return state\n\n\ndef save_with_checksum(content, path):\n    \"\"\"Save the given value on disk, along with a sha256 hash.\n    Should be used with the output of either `serialize_model` or `get_state`.\"\"\"\n    buf = io.BytesIO()\n    torch.save(content, buf)","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/states.py:5"}],[1498,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/states.py","startLine":93,"endLine":113,"chunkText":"    Should be used with the output of either `serialize_model` or `get_state`.\"\"\"\n    buf = io.BytesIO()\n    torch.save(content, buf)\n    sig = hashlib.sha256(buf.getvalue()).hexdigest()[:8]\n\n    path = path.parent / (path.stem + \"-\" + sig + path.suffix)\n    path.write_bytes(buf.getvalue())\n\n\ndef copy_state(state):\n    return {k: v.cpu().clone() for k, v in state.items()}\n\n\n@contextmanager\ndef swap_state(model, state):\n    \"\"\"\n    Context manager that swaps the state of a model, e.g:\n\n        # model is in old state\n        with swap_state(model, new_state):\n            # model in new state","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/states.py:6"}],[1499,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/states.py","startLine":111,"endLine":131,"chunkText":"        # model is in old state\n        with swap_state(model, new_state):\n            # model in new state\n        # model back to old state\n    \"\"\"\n    old_state = copy_state(model.state_dict())\n    model.load_state_dict(state, strict=False)\n    try:\n        yield\n    finally:\n        model.load_state_dict(old_state)\n\n\ndef capture_init(init):\n    @functools.wraps(init)\n    def __init__(self, *args, **kwargs):\n        self._init_args_kwargs = (args, kwargs)\n        init(self, *args, **kwargs)\n\n    return __init__\n","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/states.py:7"}],[1500,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/tasnet.py","startLine":0,"endLine":15,"chunkText":"# Copyright (c) Facebook, Inc. and its affiliates.\n# All rights reserved.\n#\n# This source code is licensed under the license found in the\n# LICENSE file in the root directory of this source tree.\n#\n# Created on 2018/12\n# Author: Kaituo XU\n# Modified on 2019/11 by Alexandre Defossez, added support for multiple output channels\n# Here is the original license:\n# The MIT License (MIT)\n#\n# Copyright (c) 2018 Kaituo XU\n#\n# Permission is hereby granted, free of charge, to any person obtaining a copy\n# of this software and associated documentation files (the \"Software\"), to deal","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/tasnet.py:0"}],[1501,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/tasnet.py","startLine":13,"endLine":23,"chunkText":"#\n# Permission is hereby granted, free of charge, to any person obtaining a copy\n# of this software and associated documentation files (the \"Software\"), to deal\n# in the Software without restriction, including without limitation the rights\n# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell\n# copies of the Software, and to permit persons to whom the Software is\n# furnished to do so, subject to the following conditions:\n#\n# The above copyright notice and this permission notice shall be included in all\n# copies or substantial portions of the Software.\n#","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/tasnet.py:1"}],[1502,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/tasnet.py","startLine":21,"endLine":31,"chunkText":"# The above copyright notice and this permission notice shall be included in all\n# copies or substantial portions of the Software.\n#\n# THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\n# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\n# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE\n# SOFTWARE.\n","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/tasnet.py:2"}],[1503,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/tasnet.py","startLine":29,"endLine":50,"chunkText":"# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE\n# SOFTWARE.\n\nimport math\n\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\nfrom .utils import capture_init\n\nEPS = 1e-8\n\n\ndef overlap_and_add(signal, frame_step):\n    outer_dimensions = signal.size()[:-2]\n    frames, frame_length = signal.size()[-2:]\n\n    subframe_length = math.gcd(frame_length, frame_step)  # gcd=Greatest Common Divisor\n    subframe_step = frame_step // subframe_length\n    subframes_per_frame = frame_length // subframe_length\n    output_size = frame_step * (frames - 1) + frame_length","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/tasnet.py:3"}],[1504,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/tasnet.py","startLine":48,"endLine":59,"chunkText":"    subframe_step = frame_step // subframe_length\n    subframes_per_frame = frame_length // subframe_length\n    output_size = frame_step * (frames - 1) + frame_length\n    output_subframes = output_size // subframe_length\n\n    subframe_signal = signal.view(*outer_dimensions, -1, subframe_length)\n\n    frame = torch.arange(0, output_subframes, device=signal.device).unfold(0, subframes_per_frame, subframe_step)\n    frame = frame.long()  # signal may in GPU or CPU\n    frame = frame.contiguous().view(-1)\n\n    result = signal.new_zeros(*outer_dimensions, output_subframes, subframe_length)","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/tasnet.py:4"}],[1505,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/tasnet.py","startLine":57,"endLine":71,"chunkText":"    frame = frame.contiguous().view(-1)\n\n    result = signal.new_zeros(*outer_dimensions, output_subframes, subframe_length)\n    result.index_add_(-2, frame, subframe_signal)\n    result = result.view(*outer_dimensions, -1)\n    return result\n\n\nclass ConvTasNet(nn.Module):\n    @capture_init\n    def __init__(self, N=256, L=20, B=256, H=512, P=3, X=8, R=4, C=4, audio_channels=1, samplerate=44100, norm_type=\"gLN\", causal=False, mask_nonlinear=\"relu\"):\n        \"\"\"\n        Args:\n            N: Number of filters in autoencoder\n            L: Length of the filters (in samples)","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/tasnet.py:5"}],[1506,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/tasnet.py","startLine":69,"endLine":81,"chunkText":"        Args:\n            N: Number of filters in autoencoder\n            L: Length of the filters (in samples)\n            B: Number of channels in bottleneck 1 × 1-conv block\n            H: Number of channels in convolutional blocks\n            P: Kernel size in convolutional blocks\n            X: Number of convolutional blocks in each repeat\n            R: Number of repeats\n            C: Number of speakers\n            norm_type: BN, gLN, cLN\n            causal: causal or non-causal\n            mask_nonlinear: use which non-linear function to generate mask\n        \"\"\"","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/tasnet.py:6"}],[1507,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/tasnet.py","startLine":79,"endLine":91,"chunkText":"            causal: causal or non-causal\n            mask_nonlinear: use which non-linear function to generate mask\n        \"\"\"\n        super(ConvTasNet, self).__init__()\n        # Hyper-parameter\n        self.N, self.L, self.B, self.H, self.P, self.X, self.R, self.C = N, L, B, H, P, X, R, C\n        self.norm_type = norm_type\n        self.causal = causal\n        self.mask_nonlinear = mask_nonlinear\n        self.audio_channels = audio_channels\n        self.samplerate = samplerate\n        # Components\n        self.encoder = Encoder(L, N, audio_channels)","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/tasnet.py:7"}],[1508,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/tasnet.py","startLine":89,"endLine":107,"chunkText":"        self.samplerate = samplerate\n        # Components\n        self.encoder = Encoder(L, N, audio_channels)\n        self.separator = TemporalConvNet(N, B, H, P, X, R, C, norm_type, causal, mask_nonlinear)\n        self.decoder = Decoder(N, L, audio_channels)\n        # init\n        for p in self.parameters():\n            if p.dim() > 1:\n                nn.init.xavier_normal_(p)\n\n    def valid_length(self, length):\n        return length\n\n    def forward(self, mixture):\n        \"\"\"\n        Args:\n            mixture: [M, T], M is batch size, T is #samples\n        Returns:\n            est_source: [M, C, T]","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/tasnet.py:8"}],[1509,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/tasnet.py","startLine":105,"endLine":119,"chunkText":"            mixture: [M, T], M is batch size, T is #samples\n        Returns:\n            est_source: [M, C, T]\n        \"\"\"\n        mixture_w = self.encoder(mixture)\n        est_mask = self.separator(mixture_w)\n        est_source = self.decoder(mixture_w, est_mask)\n\n        # T changed after conv1d in encoder, fix it here\n        T_origin = mixture.size(-1)\n        T_conv = est_source.size(-1)\n        est_source = F.pad(est_source, (0, T_origin - T_conv))\n        return est_source\n\n","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/tasnet.py:9"}],[1510,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/tasnet.py","startLine":120,"endLine":137,"chunkText":"class Encoder(nn.Module):\n    \"\"\"Estimation of the nonnegative mixture weight by a 1-D conv layer.\"\"\"\n\n    def __init__(self, L, N, audio_channels):\n        super(Encoder, self).__init__()\n        # Hyper-parameter\n        self.L, self.N = L, N\n        # Components\n        # 50% overlap\n        self.conv1d_U = nn.Conv1d(audio_channels, N, kernel_size=L, stride=L // 2, bias=False)\n\n    def forward(self, mixture):\n        \"\"\"\n        Args:\n            mixture: [M, T], M is batch size, T is #samples\n        Returns:\n            mixture_w: [M, N, K], where K = (T-L)/(L/2)+1 = 2T/L-1\n        \"\"\"","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/tasnet.py:10"}],[1511,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/tasnet.py","startLine":135,"endLine":141,"chunkText":"        Returns:\n            mixture_w: [M, N, K], where K = (T-L)/(L/2)+1 = 2T/L-1\n        \"\"\"\n        mixture_w = F.relu(self.conv1d_U(mixture))  # [M, N, K]\n        return mixture_w\n\n","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/tasnet.py:11"}],[1512,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/tasnet.py","startLine":142,"endLine":160,"chunkText":"class Decoder(nn.Module):\n    def __init__(self, N, L, audio_channels):\n        super(Decoder, self).__init__()\n        # Hyper-parameter\n        self.N, self.L = N, L\n        self.audio_channels = audio_channels\n        # Components\n        self.basis_signals = nn.Linear(N, audio_channels * L, bias=False)\n\n    def forward(self, mixture_w, est_mask):\n        \"\"\"\n        Args:\n            mixture_w: [M, N, K]\n            est_mask: [M, C, N, K]\n        Returns:\n            est_source: [M, C, T]\n        \"\"\"\n        # D = W * M\n        source_w = torch.unsqueeze(mixture_w, 1) * est_mask  # [M, C, N, K]","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/tasnet.py:12"}],[1513,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/tasnet.py","startLine":158,"endLine":169,"chunkText":"        \"\"\"\n        # D = W * M\n        source_w = torch.unsqueeze(mixture_w, 1) * est_mask  # [M, C, N, K]\n        source_w = torch.transpose(source_w, 2, 3)  # [M, C, K, N]\n        # S = DV\n        est_source = self.basis_signals(source_w)  # [M, C, K, ac * L]\n        m, c, k, _ = est_source.size()\n        est_source = est_source.view(m, c, k, self.audio_channels, -1).transpose(2, 3).contiguous()\n        est_source = overlap_and_add(est_source, self.L // 2)  # M x C x ac x T\n        return est_source\n\n","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/tasnet.py:13"}],[1514,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/tasnet.py","startLine":170,"endLine":182,"chunkText":"class TemporalConvNet(nn.Module):\n    def __init__(self, N, B, H, P, X, R, C, norm_type=\"gLN\", causal=False, mask_nonlinear=\"relu\"):\n        \"\"\"\n        Args:\n            N: Number of filters in autoencoder\n            B: Number of channels in bottleneck 1 × 1-conv block\n            H: Number of channels in convolutional blocks\n            P: Kernel size in convolutional blocks\n            X: Number of convolutional blocks in each repeat\n            R: Number of repeats\n            C: Number of speakers\n            norm_type: BN, gLN, cLN\n            causal: causal or non-causal","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/tasnet.py:14"}],[1515,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/tasnet.py","startLine":180,"endLine":196,"chunkText":"            C: Number of speakers\n            norm_type: BN, gLN, cLN\n            causal: causal or non-causal\n            mask_nonlinear: use which non-linear function to generate mask\n        \"\"\"\n        super(TemporalConvNet, self).__init__()\n        # Hyper-parameter\n        self.C = C\n        self.mask_nonlinear = mask_nonlinear\n        # Components\n        # [M, N, K] -> [M, N, K]\n        layer_norm = ChannelwiseLayerNorm(N)\n        # [M, N, K] -> [M, B, K]\n        bottleneck_conv1x1 = nn.Conv1d(N, B, 1, bias=False)\n        # [M, B, K] -> [M, B, K]\n        repeats = []\n        for r in range(R):","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/tasnet.py:15"}],[1516,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/tasnet.py","startLine":194,"endLine":206,"chunkText":"        # [M, B, K] -> [M, B, K]\n        repeats = []\n        for r in range(R):\n            blocks = []\n            for x in range(X):\n                dilation = 2**x\n                padding = (P - 1) * dilation if causal else (P - 1) * dilation // 2\n                blocks += [TemporalBlock(B, H, P, stride=1, padding=padding, dilation=dilation, norm_type=norm_type, causal=causal)]\n            repeats += [nn.Sequential(*blocks)]\n        temporal_conv_net = nn.Sequential(*repeats)\n        # [M, B, K] -> [M, C*N, K]\n        mask_conv1x1 = nn.Conv1d(B, C * N, 1, bias=False)\n        # Put together","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/tasnet.py:16"}],[1517,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/tasnet.py","startLine":204,"endLine":218,"chunkText":"        # [M, B, K] -> [M, C*N, K]\n        mask_conv1x1 = nn.Conv1d(B, C * N, 1, bias=False)\n        # Put together\n        self.network = nn.Sequential(layer_norm, bottleneck_conv1x1, temporal_conv_net, mask_conv1x1)\n\n    def forward(self, mixture_w):\n        \"\"\"\n        Keep this API same with TasNet\n        Args:\n            mixture_w: [M, N, K], M is batch size\n        returns:\n            est_mask: [M, C, N, K]\n        \"\"\"\n        M, N, K = mixture_w.size()\n        score = self.network(mixture_w)  # [M, N, K] -> [M, C*N, K]","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/tasnet.py:17"}],[1518,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/tasnet.py","startLine":216,"endLine":228,"chunkText":"        \"\"\"\n        M, N, K = mixture_w.size()\n        score = self.network(mixture_w)  # [M, N, K] -> [M, C*N, K]\n        score = score.view(M, self.C, N, K)  # [M, C*N, K] -> [M, C, N, K]\n        if self.mask_nonlinear == \"softmax\":\n            est_mask = F.softmax(score, dim=1)\n        elif self.mask_nonlinear == \"relu\":\n            est_mask = F.relu(score)\n        else:\n            raise ValueError(\"Unsupported mask non-linear function\")\n        return est_mask\n\n","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/tasnet.py:18"}],[1519,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/tasnet.py","startLine":229,"endLine":238,"chunkText":"class TemporalBlock(nn.Module):\n    def __init__(self, in_channels, out_channels, kernel_size, stride, padding, dilation, norm_type=\"gLN\", causal=False):\n        super(TemporalBlock, self).__init__()\n        # [M, B, K] -> [M, H, K]\n        conv1x1 = nn.Conv1d(in_channels, out_channels, 1, bias=False)\n        prelu = nn.PReLU()\n        norm = chose_norm(norm_type, out_channels)\n        # [M, H, K] -> [M, B, K]\n        dsconv = DepthwiseSeparableConv(out_channels, in_channels, kernel_size, stride, padding, dilation, norm_type, causal)\n        # Put together","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/tasnet.py:19"}],[1520,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/tasnet.py","startLine":236,"endLine":251,"chunkText":"        # [M, H, K] -> [M, B, K]\n        dsconv = DepthwiseSeparableConv(out_channels, in_channels, kernel_size, stride, padding, dilation, norm_type, causal)\n        # Put together\n        self.net = nn.Sequential(conv1x1, prelu, norm, dsconv)\n\n    def forward(self, x):\n        \"\"\"\n        Args:\n            x: [M, B, K]\n        Returns:\n            [M, B, K]\n        \"\"\"\n        residual = x\n        out = self.net(x)\n        # TODO: when P = 3 here works fine, but when P = 2 maybe need to pad?\n        return out + residual  # look like w/o F.relu is better than w/ F.relu","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/tasnet.py:20"}],[1521,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/tasnet.py","startLine":249,"endLine":254,"chunkText":"        out = self.net(x)\n        # TODO: when P = 3 here works fine, but when P = 2 maybe need to pad?\n        return out + residual  # look like w/o F.relu is better than w/ F.relu\n        # return F.relu(out + residual)\n\n","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/tasnet.py:21"}],[1522,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/tasnet.py","startLine":255,"endLine":264,"chunkText":"class DepthwiseSeparableConv(nn.Module):\n    def __init__(self, in_channels, out_channels, kernel_size, stride, padding, dilation, norm_type=\"gLN\", causal=False):\n        super(DepthwiseSeparableConv, self).__init__()\n        # Use `groups` option to implement depthwise convolution\n        # [M, H, K] -> [M, H, K]\n        depthwise_conv = nn.Conv1d(in_channels, in_channels, kernel_size, stride=stride, padding=padding, dilation=dilation, groups=in_channels, bias=False)\n        if causal:\n            chomp = Chomp1d(padding)\n        prelu = nn.PReLU()\n        norm = chose_norm(norm_type, in_channels)","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/tasnet.py:22"}],[1523,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/tasnet.py","startLine":262,"endLine":279,"chunkText":"            chomp = Chomp1d(padding)\n        prelu = nn.PReLU()\n        norm = chose_norm(norm_type, in_channels)\n        # [M, H, K] -> [M, B, K]\n        pointwise_conv = nn.Conv1d(in_channels, out_channels, 1, bias=False)\n        # Put together\n        if causal:\n            self.net = nn.Sequential(depthwise_conv, chomp, prelu, norm, pointwise_conv)\n        else:\n            self.net = nn.Sequential(depthwise_conv, prelu, norm, pointwise_conv)\n\n    def forward(self, x):\n        \"\"\"\n        Args:\n            x: [M, H, K]\n        Returns:\n            result: [M, B, K]\n        \"\"\"","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/tasnet.py:23"}],[1524,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/tasnet.py","startLine":277,"endLine":282,"chunkText":"        Returns:\n            result: [M, B, K]\n        \"\"\"\n        return self.net(x)\n\n","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/tasnet.py:24"}],[1525,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/tasnet.py","startLine":283,"endLine":304,"chunkText":"class Chomp1d(nn.Module):\n    \"\"\"To ensure the output length is the same as the input.\"\"\"\n\n    def __init__(self, chomp_size):\n        super(Chomp1d, self).__init__()\n        self.chomp_size = chomp_size\n\n    def forward(self, x):\n        \"\"\"\n        Args:\n            x: [M, H, Kpad]\n        Returns:\n            [M, H, K]\n        \"\"\"\n        return x[:, :, : -self.chomp_size].contiguous()\n\n\ndef chose_norm(norm_type, channel_size):\n    \"\"\"The input of normlization will be (M, C, K), where M is batch size,\n    C is channel size and K is sequence length.\n    \"\"\"\n    if norm_type == \"gLN\":","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/tasnet.py:25"}],[1526,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/tasnet.py","startLine":302,"endLine":316,"chunkText":"    C is channel size and K is sequence length.\n    \"\"\"\n    if norm_type == \"gLN\":\n        return GlobalLayerNorm(channel_size)\n    elif norm_type == \"cLN\":\n        return ChannelwiseLayerNorm(channel_size)\n    elif norm_type == \"id\":\n        return nn.Identity()\n    else:  # norm_type == \"BN\":\n        # Given input (M, C, K), nn.BatchNorm1d(C) will accumulate statics\n        # along M and K, so this BN usage is right.\n        return nn.BatchNorm1d(channel_size)\n\n\n# TODO: Use nn.LayerNorm to impl cLN to speed up","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/tasnet.py:26"}],[1527,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/tasnet.py","startLine":317,"endLine":333,"chunkText":"class ChannelwiseLayerNorm(nn.Module):\n    \"\"\"Channel-wise Layer Normalization (cLN)\"\"\"\n\n    def __init__(self, channel_size):\n        super(ChannelwiseLayerNorm, self).__init__()\n        self.gamma = nn.Parameter(torch.Tensor(1, channel_size, 1))  # [1, N, 1]\n        self.beta = nn.Parameter(torch.Tensor(1, channel_size, 1))  # [1, N, 1]\n        self.reset_parameters()\n\n    def reset_parameters(self):\n        self.gamma.data.fill_(1)\n        self.beta.data.zero_()\n\n    def forward(self, y):\n        \"\"\"\n        Args:\n            y: [M, N, K], M is batch size, N is channel size, K is length","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/tasnet.py:27"}],[1528,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/tasnet.py","startLine":331,"endLine":342,"chunkText":"        \"\"\"\n        Args:\n            y: [M, N, K], M is batch size, N is channel size, K is length\n        Returns:\n            cLN_y: [M, N, K]\n        \"\"\"\n        mean = torch.mean(y, dim=1, keepdim=True)  # [M, 1, K]\n        var = torch.var(y, dim=1, keepdim=True, unbiased=False)  # [M, 1, K]\n        cLN_y = self.gamma * (y - mean) / torch.pow(var + EPS, 0.5) + self.beta\n        return cLN_y\n\n","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/tasnet.py:28"}],[1529,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/tasnet.py","startLine":343,"endLine":360,"chunkText":"class GlobalLayerNorm(nn.Module):\n    \"\"\"Global Layer Normalization (gLN)\"\"\"\n\n    def __init__(self, channel_size):\n        super(GlobalLayerNorm, self).__init__()\n        self.gamma = nn.Parameter(torch.Tensor(1, channel_size, 1))  # [1, N, 1]\n        self.beta = nn.Parameter(torch.Tensor(1, channel_size, 1))  # [1, N, 1]\n        self.reset_parameters()\n\n    def reset_parameters(self):\n        self.gamma.data.fill_(1)\n        self.beta.data.zero_()\n\n    def forward(self, y):\n        \"\"\"\n        Args:\n            y: [M, N, K], M is batch size, N is channel size, K is length\n        Returns:","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/tasnet.py:29"}],[1530,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/tasnet.py","startLine":358,"endLine":373,"chunkText":"        Args:\n            y: [M, N, K], M is batch size, N is channel size, K is length\n        Returns:\n            gLN_y: [M, N, K]\n        \"\"\"\n        # TODO: in torch 1.0, torch.mean() support dim list\n        mean = y.mean(dim=1, keepdim=True).mean(dim=2, keepdim=True)  # [M, 1, 1]\n        var = (torch.pow(y - mean, 2)).mean(dim=1, keepdim=True).mean(dim=2, keepdim=True)\n        gLN_y = self.gamma * (y - mean) / torch.pow(var + EPS, 0.5) + self.beta\n        return gLN_y\n\n\nif __name__ == \"__main__\":\n    torch.manual_seed(123)\n    M, N, L, T = 2, 3, 4, 12\n    K = 2 * T // L - 1","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/tasnet.py:30"}],[1531,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/tasnet.py","startLine":371,"endLine":385,"chunkText":"    torch.manual_seed(123)\n    M, N, L, T = 2, 3, 4, 12\n    K = 2 * T // L - 1\n    B, H, P, X, R, C, norm_type, causal = 2, 3, 3, 3, 2, 2, \"gLN\", False\n    mixture = torch.randint(3, (M, T))\n    # test Encoder\n    encoder = Encoder(L, N)\n    encoder.conv1d_U.weight.data = torch.randint(2, encoder.conv1d_U.weight.size())\n    mixture_w = encoder(mixture)\n    print(\"mixture\", mixture)\n    print(\"U\", encoder.conv1d_U.weight)\n    print(\"mixture_w\", mixture_w)\n    print(\"mixture_w size\", mixture_w.size())\n\n    # test TemporalConvNet","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/tasnet.py:31"}],[1532,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/tasnet.py","startLine":383,"endLine":399,"chunkText":"    print(\"mixture_w size\", mixture_w.size())\n\n    # test TemporalConvNet\n    separator = TemporalConvNet(N, B, H, P, X, R, C, norm_type=norm_type, causal=causal)\n    est_mask = separator(mixture_w)\n    print(\"est_mask\", est_mask)\n\n    # test Decoder\n    decoder = Decoder(N, L)\n    est_mask = torch.randint(2, (B, K, C, N))\n    est_source = decoder(mixture_w, est_mask)\n    print(\"est_source\", est_source)\n\n    # test Conv-TasNet\n    conv_tasnet = ConvTasNet(N, L, B, H, P, X, R, C, norm_type=norm_type)\n    est_source = conv_tasnet(mixture)\n    print(\"est_source\", est_source)","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/tasnet.py:32"}],[1533,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/tasnet.py","startLine":397,"endLine":401,"chunkText":"    conv_tasnet = ConvTasNet(N, L, B, H, P, X, R, C, norm_type=norm_type)\n    est_source = conv_tasnet(mixture)\n    print(\"est_source\", est_source)\n    print(\"est_source size\", est_source.size())\n","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/tasnet.py:33"}],[1534,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/tasnet_v2.py","startLine":0,"endLine":15,"chunkText":"# Copyright (c) Facebook, Inc. and its affiliates.\n# All rights reserved.\n#\n# This source code is licensed under the license found in the\n# LICENSE file in the root directory of this source tree.\n#\n# Created on 2018/12\n# Author: Kaituo XU\n# Modified on 2019/11 by Alexandre Defossez, added support for multiple output channels\n# Here is the original license:\n# The MIT License (MIT)\n#\n# Copyright (c) 2018 Kaituo XU\n#\n# Permission is hereby granted, free of charge, to any person obtaining a copy\n# of this software and associated documentation files (the \"Software\"), to deal","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/tasnet_v2.py:0"}],[1535,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/tasnet_v2.py","startLine":13,"endLine":23,"chunkText":"#\n# Permission is hereby granted, free of charge, to any person obtaining a copy\n# of this software and associated documentation files (the \"Software\"), to deal\n# in the Software without restriction, including without limitation the rights\n# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell\n# copies of the Software, and to permit persons to whom the Software is\n# furnished to do so, subject to the following conditions:\n#\n# The above copyright notice and this permission notice shall be included in all\n# copies or substantial portions of the Software.\n#","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/tasnet_v2.py:1"}],[1536,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/tasnet_v2.py","startLine":21,"endLine":31,"chunkText":"# The above copyright notice and this permission notice shall be included in all\n# copies or substantial portions of the Software.\n#\n# THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\n# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\n# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE\n# SOFTWARE.\n","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/tasnet_v2.py:2"}],[1537,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/tasnet_v2.py","startLine":29,"endLine":50,"chunkText":"# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE\n# SOFTWARE.\n\nimport math\n\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\nfrom .utils import capture_init\n\nEPS = 1e-8\n\n\ndef overlap_and_add(signal, frame_step):\n    outer_dimensions = signal.size()[:-2]\n    frames, frame_length = signal.size()[-2:]\n\n    subframe_length = math.gcd(frame_length, frame_step)  # gcd=Greatest Common Divisor\n    subframe_step = frame_step // subframe_length\n    subframes_per_frame = frame_length // subframe_length\n    output_size = frame_step * (frames - 1) + frame_length","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/tasnet_v2.py:3"}],[1538,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/tasnet_v2.py","startLine":48,"endLine":59,"chunkText":"    subframe_step = frame_step // subframe_length\n    subframes_per_frame = frame_length // subframe_length\n    output_size = frame_step * (frames - 1) + frame_length\n    output_subframes = output_size // subframe_length\n\n    subframe_signal = signal.view(*outer_dimensions, -1, subframe_length)\n\n    frame = torch.arange(0, output_subframes, device=signal.device).unfold(0, subframes_per_frame, subframe_step)\n    frame = frame.long()  # signal may in GPU or CPU\n    frame = frame.contiguous().view(-1)\n\n    result = signal.new_zeros(*outer_dimensions, output_subframes, subframe_length)","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/tasnet_v2.py:4"}],[1539,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/tasnet_v2.py","startLine":57,"endLine":71,"chunkText":"    frame = frame.contiguous().view(-1)\n\n    result = signal.new_zeros(*outer_dimensions, output_subframes, subframe_length)\n    result.index_add_(-2, frame, subframe_signal)\n    result = result.view(*outer_dimensions, -1)\n    return result\n\n\nclass ConvTasNet(nn.Module):\n    @capture_init\n    def __init__(self, sources, N=256, L=20, B=256, H=512, P=3, X=8, R=4, audio_channels=2, norm_type=\"gLN\", causal=False, mask_nonlinear=\"relu\", samplerate=44100, segment_length=44100 * 2 * 4):\n        \"\"\"\n        Args:\n            sources: list of sources\n            N: Number of filters in autoencoder","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/tasnet_v2.py:5"}],[1540,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/tasnet_v2.py","startLine":69,"endLine":81,"chunkText":"        Args:\n            sources: list of sources\n            N: Number of filters in autoencoder\n            L: Length of the filters (in samples)\n            B: Number of channels in bottleneck 1 × 1-conv block\n            H: Number of channels in convolutional blocks\n            P: Kernel size in convolutional blocks\n            X: Number of convolutional blocks in each repeat\n            R: Number of repeats\n            norm_type: BN, gLN, cLN\n            causal: causal or non-causal\n            mask_nonlinear: use which non-linear function to generate mask\n        \"\"\"","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/tasnet_v2.py:6"}],[1541,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/tasnet_v2.py","startLine":79,"endLine":93,"chunkText":"            causal: causal or non-causal\n            mask_nonlinear: use which non-linear function to generate mask\n        \"\"\"\n        super(ConvTasNet, self).__init__()\n        # Hyper-parameter\n        self.sources = sources\n        self.C = len(sources)\n        self.N, self.L, self.B, self.H, self.P, self.X, self.R = N, L, B, H, P, X, R\n        self.norm_type = norm_type\n        self.causal = causal\n        self.mask_nonlinear = mask_nonlinear\n        self.audio_channels = audio_channels\n        self.samplerate = samplerate\n        self.segment_length = segment_length\n        # Components","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/tasnet_v2.py:7"}],[1542,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/tasnet_v2.py","startLine":91,"endLine":108,"chunkText":"        self.samplerate = samplerate\n        self.segment_length = segment_length\n        # Components\n        self.encoder = Encoder(L, N, audio_channels)\n        self.separator = TemporalConvNet(N, B, H, P, X, R, self.C, norm_type, causal, mask_nonlinear)\n        self.decoder = Decoder(N, L, audio_channels)\n        # init\n        for p in self.parameters():\n            if p.dim() > 1:\n                nn.init.xavier_normal_(p)\n\n    def valid_length(self, length):\n        return length\n\n    def forward(self, mixture):\n        \"\"\"\n        Args:\n            mixture: [M, T], M is batch size, T is #samples","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/tasnet_v2.py:8"}],[1543,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/tasnet_v2.py","startLine":106,"endLine":122,"chunkText":"        \"\"\"\n        Args:\n            mixture: [M, T], M is batch size, T is #samples\n        Returns:\n            est_source: [M, C, T]\n        \"\"\"\n        mixture_w = self.encoder(mixture)\n        est_mask = self.separator(mixture_w)\n        est_source = self.decoder(mixture_w, est_mask)\n\n        # T changed after conv1d in encoder, fix it here\n        T_origin = mixture.size(-1)\n        T_conv = est_source.size(-1)\n        est_source = F.pad(est_source, (0, T_origin - T_conv))\n        return est_source\n\n","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/tasnet_v2.py:9"}],[1544,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/tasnet_v2.py","startLine":123,"endLine":140,"chunkText":"class Encoder(nn.Module):\n    \"\"\"Estimation of the nonnegative mixture weight by a 1-D conv layer.\"\"\"\n\n    def __init__(self, L, N, audio_channels):\n        super(Encoder, self).__init__()\n        # Hyper-parameter\n        self.L, self.N = L, N\n        # Components\n        # 50% overlap\n        self.conv1d_U = nn.Conv1d(audio_channels, N, kernel_size=L, stride=L // 2, bias=False)\n\n    def forward(self, mixture):\n        \"\"\"\n        Args:\n            mixture: [M, T], M is batch size, T is #samples\n        Returns:\n            mixture_w: [M, N, K], where K = (T-L)/(L/2)+1 = 2T/L-1\n        \"\"\"","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/tasnet_v2.py:10"}],[1545,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/tasnet_v2.py","startLine":138,"endLine":144,"chunkText":"        Returns:\n            mixture_w: [M, N, K], where K = (T-L)/(L/2)+1 = 2T/L-1\n        \"\"\"\n        mixture_w = F.relu(self.conv1d_U(mixture))  # [M, N, K]\n        return mixture_w\n\n","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/tasnet_v2.py:11"}],[1546,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/tasnet_v2.py","startLine":145,"endLine":163,"chunkText":"class Decoder(nn.Module):\n    def __init__(self, N, L, audio_channels):\n        super(Decoder, self).__init__()\n        # Hyper-parameter\n        self.N, self.L = N, L\n        self.audio_channels = audio_channels\n        # Components\n        self.basis_signals = nn.Linear(N, audio_channels * L, bias=False)\n\n    def forward(self, mixture_w, est_mask):\n        \"\"\"\n        Args:\n            mixture_w: [M, N, K]\n            est_mask: [M, C, N, K]\n        Returns:\n            est_source: [M, C, T]\n        \"\"\"\n        # D = W * M\n        source_w = torch.unsqueeze(mixture_w, 1) * est_mask  # [M, C, N, K]","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/tasnet_v2.py:12"}],[1547,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/tasnet_v2.py","startLine":161,"endLine":172,"chunkText":"        \"\"\"\n        # D = W * M\n        source_w = torch.unsqueeze(mixture_w, 1) * est_mask  # [M, C, N, K]\n        source_w = torch.transpose(source_w, 2, 3)  # [M, C, K, N]\n        # S = DV\n        est_source = self.basis_signals(source_w)  # [M, C, K, ac * L]\n        m, c, k, _ = est_source.size()\n        est_source = est_source.view(m, c, k, self.audio_channels, -1).transpose(2, 3).contiguous()\n        est_source = overlap_and_add(est_source, self.L // 2)  # M x C x ac x T\n        return est_source\n\n","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/tasnet_v2.py:13"}],[1548,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/tasnet_v2.py","startLine":173,"endLine":185,"chunkText":"class TemporalConvNet(nn.Module):\n    def __init__(self, N, B, H, P, X, R, C, norm_type=\"gLN\", causal=False, mask_nonlinear=\"relu\"):\n        \"\"\"\n        Args:\n            N: Number of filters in autoencoder\n            B: Number of channels in bottleneck 1 × 1-conv block\n            H: Number of channels in convolutional blocks\n            P: Kernel size in convolutional blocks\n            X: Number of convolutional blocks in each repeat\n            R: Number of repeats\n            C: Number of speakers\n            norm_type: BN, gLN, cLN\n            causal: causal or non-causal","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/tasnet_v2.py:14"}],[1549,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/tasnet_v2.py","startLine":183,"endLine":199,"chunkText":"            C: Number of speakers\n            norm_type: BN, gLN, cLN\n            causal: causal or non-causal\n            mask_nonlinear: use which non-linear function to generate mask\n        \"\"\"\n        super(TemporalConvNet, self).__init__()\n        # Hyper-parameter\n        self.C = C\n        self.mask_nonlinear = mask_nonlinear\n        # Components\n        # [M, N, K] -> [M, N, K]\n        layer_norm = ChannelwiseLayerNorm(N)\n        # [M, N, K] -> [M, B, K]\n        bottleneck_conv1x1 = nn.Conv1d(N, B, 1, bias=False)\n        # [M, B, K] -> [M, B, K]\n        repeats = []\n        for r in range(R):","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/tasnet_v2.py:15"}],[1550,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/tasnet_v2.py","startLine":197,"endLine":209,"chunkText":"        # [M, B, K] -> [M, B, K]\n        repeats = []\n        for r in range(R):\n            blocks = []\n            for x in range(X):\n                dilation = 2**x\n                padding = (P - 1) * dilation if causal else (P - 1) * dilation // 2\n                blocks += [TemporalBlock(B, H, P, stride=1, padding=padding, dilation=dilation, norm_type=norm_type, causal=causal)]\n            repeats += [nn.Sequential(*blocks)]\n        temporal_conv_net = nn.Sequential(*repeats)\n        # [M, B, K] -> [M, C*N, K]\n        mask_conv1x1 = nn.Conv1d(B, C * N, 1, bias=False)\n        # Put together","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/tasnet_v2.py:16"}],[1551,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/tasnet_v2.py","startLine":207,"endLine":221,"chunkText":"        # [M, B, K] -> [M, C*N, K]\n        mask_conv1x1 = nn.Conv1d(B, C * N, 1, bias=False)\n        # Put together\n        self.network = nn.Sequential(layer_norm, bottleneck_conv1x1, temporal_conv_net, mask_conv1x1)\n\n    def forward(self, mixture_w):\n        \"\"\"\n        Keep this API same with TasNet\n        Args:\n            mixture_w: [M, N, K], M is batch size\n        returns:\n            est_mask: [M, C, N, K]\n        \"\"\"\n        M, N, K = mixture_w.size()\n        score = self.network(mixture_w)  # [M, N, K] -> [M, C*N, K]","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/tasnet_v2.py:17"}],[1552,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/tasnet_v2.py","startLine":219,"endLine":231,"chunkText":"        \"\"\"\n        M, N, K = mixture_w.size()\n        score = self.network(mixture_w)  # [M, N, K] -> [M, C*N, K]\n        score = score.view(M, self.C, N, K)  # [M, C*N, K] -> [M, C, N, K]\n        if self.mask_nonlinear == \"softmax\":\n            est_mask = F.softmax(score, dim=1)\n        elif self.mask_nonlinear == \"relu\":\n            est_mask = F.relu(score)\n        else:\n            raise ValueError(\"Unsupported mask non-linear function\")\n        return est_mask\n\n","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/tasnet_v2.py:18"}],[1553,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/tasnet_v2.py","startLine":232,"endLine":241,"chunkText":"class TemporalBlock(nn.Module):\n    def __init__(self, in_channels, out_channels, kernel_size, stride, padding, dilation, norm_type=\"gLN\", causal=False):\n        super(TemporalBlock, self).__init__()\n        # [M, B, K] -> [M, H, K]\n        conv1x1 = nn.Conv1d(in_channels, out_channels, 1, bias=False)\n        prelu = nn.PReLU()\n        norm = chose_norm(norm_type, out_channels)\n        # [M, H, K] -> [M, B, K]\n        dsconv = DepthwiseSeparableConv(out_channels, in_channels, kernel_size, stride, padding, dilation, norm_type, causal)\n        # Put together","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/tasnet_v2.py:19"}],[1554,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/tasnet_v2.py","startLine":239,"endLine":254,"chunkText":"        # [M, H, K] -> [M, B, K]\n        dsconv = DepthwiseSeparableConv(out_channels, in_channels, kernel_size, stride, padding, dilation, norm_type, causal)\n        # Put together\n        self.net = nn.Sequential(conv1x1, prelu, norm, dsconv)\n\n    def forward(self, x):\n        \"\"\"\n        Args:\n            x: [M, B, K]\n        Returns:\n            [M, B, K]\n        \"\"\"\n        residual = x\n        out = self.net(x)\n        # TODO: when P = 3 here works fine, but when P = 2 maybe need to pad?\n        return out + residual  # look like w/o F.relu is better than w/ F.relu","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/tasnet_v2.py:20"}],[1555,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/tasnet_v2.py","startLine":252,"endLine":257,"chunkText":"        out = self.net(x)\n        # TODO: when P = 3 here works fine, but when P = 2 maybe need to pad?\n        return out + residual  # look like w/o F.relu is better than w/ F.relu\n        # return F.relu(out + residual)\n\n","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/tasnet_v2.py:21"}],[1556,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/tasnet_v2.py","startLine":258,"endLine":267,"chunkText":"class DepthwiseSeparableConv(nn.Module):\n    def __init__(self, in_channels, out_channels, kernel_size, stride, padding, dilation, norm_type=\"gLN\", causal=False):\n        super(DepthwiseSeparableConv, self).__init__()\n        # Use `groups` option to implement depthwise convolution\n        # [M, H, K] -> [M, H, K]\n        depthwise_conv = nn.Conv1d(in_channels, in_channels, kernel_size, stride=stride, padding=padding, dilation=dilation, groups=in_channels, bias=False)\n        if causal:\n            chomp = Chomp1d(padding)\n        prelu = nn.PReLU()\n        norm = chose_norm(norm_type, in_channels)","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/tasnet_v2.py:22"}],[1557,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/tasnet_v2.py","startLine":265,"endLine":282,"chunkText":"            chomp = Chomp1d(padding)\n        prelu = nn.PReLU()\n        norm = chose_norm(norm_type, in_channels)\n        # [M, H, K] -> [M, B, K]\n        pointwise_conv = nn.Conv1d(in_channels, out_channels, 1, bias=False)\n        # Put together\n        if causal:\n            self.net = nn.Sequential(depthwise_conv, chomp, prelu, norm, pointwise_conv)\n        else:\n            self.net = nn.Sequential(depthwise_conv, prelu, norm, pointwise_conv)\n\n    def forward(self, x):\n        \"\"\"\n        Args:\n            x: [M, H, K]\n        Returns:\n            result: [M, B, K]\n        \"\"\"","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/tasnet_v2.py:23"}],[1558,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/tasnet_v2.py","startLine":280,"endLine":285,"chunkText":"        Returns:\n            result: [M, B, K]\n        \"\"\"\n        return self.net(x)\n\n","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/tasnet_v2.py:24"}],[1559,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/tasnet_v2.py","startLine":286,"endLine":307,"chunkText":"class Chomp1d(nn.Module):\n    \"\"\"To ensure the output length is the same as the input.\"\"\"\n\n    def __init__(self, chomp_size):\n        super(Chomp1d, self).__init__()\n        self.chomp_size = chomp_size\n\n    def forward(self, x):\n        \"\"\"\n        Args:\n            x: [M, H, Kpad]\n        Returns:\n            [M, H, K]\n        \"\"\"\n        return x[:, :, : -self.chomp_size].contiguous()\n\n\ndef chose_norm(norm_type, channel_size):\n    \"\"\"The input of normlization will be (M, C, K), where M is batch size,\n    C is channel size and K is sequence length.\n    \"\"\"\n    if norm_type == \"gLN\":","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/tasnet_v2.py:25"}],[1560,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/tasnet_v2.py","startLine":305,"endLine":319,"chunkText":"    C is channel size and K is sequence length.\n    \"\"\"\n    if norm_type == \"gLN\":\n        return GlobalLayerNorm(channel_size)\n    elif norm_type == \"cLN\":\n        return ChannelwiseLayerNorm(channel_size)\n    elif norm_type == \"id\":\n        return nn.Identity()\n    else:  # norm_type == \"BN\":\n        # Given input (M, C, K), nn.BatchNorm1d(C) will accumulate statics\n        # along M and K, so this BN usage is right.\n        return nn.BatchNorm1d(channel_size)\n\n\n# TODO: Use nn.LayerNorm to impl cLN to speed up","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/tasnet_v2.py:26"}],[1561,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/tasnet_v2.py","startLine":320,"endLine":336,"chunkText":"class ChannelwiseLayerNorm(nn.Module):\n    \"\"\"Channel-wise Layer Normalization (cLN)\"\"\"\n\n    def __init__(self, channel_size):\n        super(ChannelwiseLayerNorm, self).__init__()\n        self.gamma = nn.Parameter(torch.Tensor(1, channel_size, 1))  # [1, N, 1]\n        self.beta = nn.Parameter(torch.Tensor(1, channel_size, 1))  # [1, N, 1]\n        self.reset_parameters()\n\n    def reset_parameters(self):\n        self.gamma.data.fill_(1)\n        self.beta.data.zero_()\n\n    def forward(self, y):\n        \"\"\"\n        Args:\n            y: [M, N, K], M is batch size, N is channel size, K is length","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/tasnet_v2.py:27"}],[1562,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/tasnet_v2.py","startLine":334,"endLine":345,"chunkText":"        \"\"\"\n        Args:\n            y: [M, N, K], M is batch size, N is channel size, K is length\n        Returns:\n            cLN_y: [M, N, K]\n        \"\"\"\n        mean = torch.mean(y, dim=1, keepdim=True)  # [M, 1, K]\n        var = torch.var(y, dim=1, keepdim=True, unbiased=False)  # [M, 1, K]\n        cLN_y = self.gamma * (y - mean) / torch.pow(var + EPS, 0.5) + self.beta\n        return cLN_y\n\n","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/tasnet_v2.py:28"}],[1563,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/tasnet_v2.py","startLine":346,"endLine":363,"chunkText":"class GlobalLayerNorm(nn.Module):\n    \"\"\"Global Layer Normalization (gLN)\"\"\"\n\n    def __init__(self, channel_size):\n        super(GlobalLayerNorm, self).__init__()\n        self.gamma = nn.Parameter(torch.Tensor(1, channel_size, 1))  # [1, N, 1]\n        self.beta = nn.Parameter(torch.Tensor(1, channel_size, 1))  # [1, N, 1]\n        self.reset_parameters()\n\n    def reset_parameters(self):\n        self.gamma.data.fill_(1)\n        self.beta.data.zero_()\n\n    def forward(self, y):\n        \"\"\"\n        Args:\n            y: [M, N, K], M is batch size, N is channel size, K is length\n        Returns:","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/tasnet_v2.py:29"}],[1564,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/tasnet_v2.py","startLine":361,"endLine":376,"chunkText":"        Args:\n            y: [M, N, K], M is batch size, N is channel size, K is length\n        Returns:\n            gLN_y: [M, N, K]\n        \"\"\"\n        # TODO: in torch 1.0, torch.mean() support dim list\n        mean = y.mean(dim=1, keepdim=True).mean(dim=2, keepdim=True)  # [M, 1, 1]\n        var = (torch.pow(y - mean, 2)).mean(dim=1, keepdim=True).mean(dim=2, keepdim=True)\n        gLN_y = self.gamma * (y - mean) / torch.pow(var + EPS, 0.5) + self.beta\n        return gLN_y\n\n\nif __name__ == \"__main__\":\n    torch.manual_seed(123)\n    M, N, L, T = 2, 3, 4, 12\n    K = 2 * T // L - 1","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/tasnet_v2.py:30"}],[1565,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/tasnet_v2.py","startLine":374,"endLine":388,"chunkText":"    torch.manual_seed(123)\n    M, N, L, T = 2, 3, 4, 12\n    K = 2 * T // L - 1\n    B, H, P, X, R, C, norm_type, causal = 2, 3, 3, 3, 2, 2, \"gLN\", False\n    mixture = torch.randint(3, (M, T))\n    # test Encoder\n    encoder = Encoder(L, N)\n    encoder.conv1d_U.weight.data = torch.randint(2, encoder.conv1d_U.weight.size())\n    mixture_w = encoder(mixture)\n    print(\"mixture\", mixture)\n    print(\"U\", encoder.conv1d_U.weight)\n    print(\"mixture_w\", mixture_w)\n    print(\"mixture_w size\", mixture_w.size())\n\n    # test TemporalConvNet","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/tasnet_v2.py:31"}],[1566,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/tasnet_v2.py","startLine":386,"endLine":402,"chunkText":"    print(\"mixture_w size\", mixture_w.size())\n\n    # test TemporalConvNet\n    separator = TemporalConvNet(N, B, H, P, X, R, C, norm_type=norm_type, causal=causal)\n    est_mask = separator(mixture_w)\n    print(\"est_mask\", est_mask)\n\n    # test Decoder\n    decoder = Decoder(N, L)\n    est_mask = torch.randint(2, (B, K, C, N))\n    est_source = decoder(mixture_w, est_mask)\n    print(\"est_source\", est_source)\n\n    # test Conv-TasNet\n    conv_tasnet = ConvTasNet(N, L, B, H, P, X, R, C, norm_type=norm_type)\n    est_source = conv_tasnet(mixture)\n    print(\"est_source\", est_source)","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/tasnet_v2.py:32"}],[1567,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/tasnet_v2.py","startLine":400,"endLine":404,"chunkText":"    conv_tasnet = ConvTasNet(N, L, B, H, P, X, R, C, norm_type=norm_type)\n    est_source = conv_tasnet(mixture)\n    print(\"est_source\", est_source)\n    print(\"est_source size\", est_source.size())\n","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/tasnet_v2.py:33"}],[1568,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/transformer.py","startLine":0,"endLine":21,"chunkText":"# Copyright (c) 2019-present, Meta, Inc.\n# All rights reserved.\n#\n# This source code is licensed under the license found in the\n# LICENSE file in the root directory of this source tree.\n# First author is Simon Rouard.\n\nimport random\nimport typing as tp\n\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nimport numpy as np\nimport math\nfrom einops import rearrange\n\n\ndef create_sin_embedding(length: int, dim: int, shift: int = 0, device=\"cpu\", max_period=10000):\n    # We aim for TBC format\n    assert dim % 2 == 0\n    pos = shift + torch.arange(length, device=device).view(-1, 1, 1)","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/transformer.py:0"}],[1569,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/transformer.py","startLine":19,"endLine":34,"chunkText":"    # We aim for TBC format\n    assert dim % 2 == 0\n    pos = shift + torch.arange(length, device=device).view(-1, 1, 1)\n    half_dim = dim // 2\n    adim = torch.arange(dim // 2, device=device).view(1, 1, -1)\n    phase = pos / (max_period ** (adim / (half_dim - 1)))\n    return torch.cat([torch.cos(phase), torch.sin(phase)], dim=-1)\n\n\ndef create_2d_sin_embedding(d_model, height, width, device=\"cpu\", max_period=10000):\n    \"\"\"\n    :param d_model: dimension of the model\n    :param height: height of the positions\n    :param width: width of the positions\n    :return: d_model*height*width position matrix\n    \"\"","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/transformer.py:1"}],[1570,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/transformer.py","startLine":32,"endLine":42,"chunkText":"    :param width: width of the positions\n    :return: d_model*height*width position matrix\n    \"\"\"\n    if d_model % 4 != 0:\n        raise ValueError(\"Cannot use sin/cos positional encoding with \" \"odd dimension (got dim={:d})\".format(d_model))\n    pe = torch.zeros(d_model, height, width)\n    # Each dimension use half of d_model\n    d_model = int(d_model / 2)\n    div_term = torch.exp(torch.arange(0.0, d_model, 2) * -(math.log(max_period) / d_model))\n    pos_w = torch.arange(0.0, width).unsqueeze(1)\n    pos_h = torch.arange(0.0, height).unsqueeze(1)","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/transformer.py:2"}],[1571,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/transformer.py","startLine":40,"endLine":45,"chunkText":"    div_term = torch.exp(torch.arange(0.0, d_model, 2) * -(math.log(max_period) / d_model))\n    pos_w = torch.arange(0.0, width).unsqueeze(1)\n    pos_h = torch.arange(0.0, height).unsqueeze(1)\n    pe[0:d_model:2, :, :] = torch.sin(pos_w * div_term).transpose(0, 1).unsqueeze(1).repeat(1, height, 1)\n    pe[1:d_model:2, :, :] = torch.cos(pos_w * div_term).transpose(0, 1).unsqueeze(1).repeat(1, height, 1)\n    pe[d_model::2, :, :] = torch.sin(pos_h * div_term).transpose(0, 1).unsqueeze(2).repeat(1, 1, width)","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/transformer.py:3"}],[1572,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/transformer.py","startLine":43,"endLine":55,"chunkText":"    pe[0:d_model:2, :, :] = torch.sin(pos_w * div_term).transpose(0, 1).unsqueeze(1).repeat(1, height, 1)\n    pe[1:d_model:2, :, :] = torch.cos(pos_w * div_term).transpose(0, 1).unsqueeze(1).repeat(1, height, 1)\n    pe[d_model::2, :, :] = torch.sin(pos_h * div_term).transpose(0, 1).unsqueeze(2).repeat(1, 1, width)\n    pe[d_model + 1 :: 2, :, :] = torch.cos(pos_h * div_term).transpose(0, 1).unsqueeze(2).repeat(1, 1, width)\n\n    return pe[None, :].to(device)\n\n\ndef create_sin_embedding_cape(\n    length: int,\n    dim: int,\n    batch_size: int,\n    mean_normalize: bool,","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/transformer.py:4"}],[1573,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/transformer.py","startLine":54,"endLine":70,"chunkText":"    batch_size: int,\n    mean_normalize: bool,\n    augment: bool,  # True during training\n    max_global_shift: float = 0.0,  # delta max\n    max_local_shift: float = 0.0,  # epsilon max\n    max_scale: float = 1.0,\n    device: str = \"cpu\",\n    max_period: float = 10000.0,\n):\n    # We aim for TBC format\n    assert dim % 2 == 0\n    pos = 1.0 * torch.arange(length).view(-1, 1, 1)  # (length, 1, 1)\n    pos = pos.repeat(1, batch_size, 1)  # (length, batch_size, 1)\n    if mean_normalize:\n        pos -= torch.nanmean(pos, dim=0, keepdim=True)\n\n    if augment:","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/transformer.py:5"}],[1574,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/transformer.py","startLine":68,"endLine":79,"chunkText":"        pos -= torch.nanmean(pos, dim=0, keepdim=True)\n\n    if augment:\n        delta = np.random.uniform(-max_global_shift, +max_global_shift, size=[1, batch_size, 1])\n        delta_local = np.random.uniform(-max_local_shift, +max_local_shift, size=[length, batch_size, 1])\n        log_lambdas = np.random.uniform(-np.log(max_scale), +np.log(max_scale), size=[1, batch_size, 1])\n        pos = (pos + delta + delta_local) * np.exp(log_lambdas)\n\n    pos = pos.to(device)\n\n    half_dim = dim // 2\n    adim = torch.arange(dim // 2, device=device).view(1, 1, -1)","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/transformer.py:6"}],[1575,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/transformer.py","startLine":78,"endLine":94,"chunkText":"    half_dim = dim // 2\n    adim = torch.arange(dim // 2, device=device).view(1, 1, -1)\n    phase = pos / (max_period ** (adim / (half_dim - 1)))\n    return torch.cat([torch.cos(phase), torch.sin(phase)], dim=-1).float()\n\n\ndef get_causal_mask(length):\n    pos = torch.arange(length)\n    return pos > pos[:, None]\n\n\ndef get_elementary_mask(T1, T2, mask_type, sparse_attn_window, global_window, mask_random_seed, sparsity, device):\n    \"\"\"\n    When the input of the Decoder has length T1 and the output T2\n    The mask matrix has shape (T2, T1)\n    \"\"\"\n    assert mask_type in [\"diag\", \"jmask\", \"random\", \"global\"]","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/transformer.py:7"}],[1576,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/transformer.py","startLine":93,"endLine":108,"chunkText":"    \"\"\"\n    assert mask_type in [\"diag\", \"jmask\", \"random\", \"global\"]\n\n    if mask_type == \"global\":\n        mask = torch.zeros(T2, T1, dtype=torch.bool)\n        mask[:, :global_window] = True\n        line_window = int(global_window * T2 / T1)\n        mask[:line_window, :] = True\n\n    if mask_type == \"diag\":\n\n        mask = torch.zeros(T2, T1, dtype=torch.bool)\n        rows = torch.arange(T2)[:, None]\n        cols = (T1 / T2 * rows + torch.arange(-sparse_attn_window, sparse_attn_window + 1)).long().clamp(0, T1 - 1)\n        mask.scatter_(1, cols, torch.ones(1, dtype=torch.bool).expand_as(cols))\n","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/transformer.py:8"}],[1577,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/transformer.py","startLine":106,"endLine":116,"chunkText":"        cols = (T1 / T2 * rows + torch.arange(-sparse_attn_window, sparse_attn_window + 1)).long().clamp(0, T1 - 1)\n        mask.scatter_(1, cols, torch.ones(1, dtype=torch.bool).expand_as(cols))\n\n    elif mask_type == \"jmask\":\n        mask = torch.zeros(T2 + 2, T1 + 2, dtype=torch.bool)\n        rows = torch.arange(T2 + 2)[:, None]\n        t = torch.arange(0, int((2 * T1) ** 0.5 + 1))\n        t = (t * (t + 1) / 2).int()\n        t = torch.cat([-t.flip(0)[:-1], t])\n        cols = (T1 / T2 * rows + t).long().clamp(0, T1 + 1)\n        mask.scatter_(1, cols, torch.ones(1, dtype=torch.bool).expand_as(cols))","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/transformer.py:9"}],[1578,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/transformer.py","startLine":114,"endLine":129,"chunkText":"        t = torch.cat([-t.flip(0)[:-1], t])\n        cols = (T1 / T2 * rows + t).long().clamp(0, T1 + 1)\n        mask.scatter_(1, cols, torch.ones(1, dtype=torch.bool).expand_as(cols))\n        mask = mask[1:-1, 1:-1]\n\n    elif mask_type == \"random\":\n        gene = torch.Generator(device=device)\n        gene.manual_seed(mask_random_seed)\n        mask = torch.rand(T1 * T2, generator=gene, device=device).reshape(T2, T1) > sparsity\n\n    mask = mask.to(device)\n    return mask\n\n\ndef get_mask(T1, T2, mask_type, sparse_attn_window, global_window, mask_random_seed, sparsity, device):\n    \"\"\"","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/transformer.py:10"}],[1579,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/transformer.py","startLine":127,"endLine":141,"chunkText":"\ndef get_mask(T1, T2, mask_type, sparse_attn_window, global_window, mask_random_seed, sparsity, device):\n    \"\"\"\n    Return a SparseCSRTensor mask that is a combination of elementary masks\n    mask_type can be a combination of multiple masks: for instance \"diag_jmask_random\"\n    \"\"\"\n    from xformers.sparse import SparseCSRTensor\n\n    # create a list\n    mask_types = mask_type.split(\"_\")\n\n    all_masks = [get_elementary_mask(T1, T2, mask, sparse_attn_window, global_window, mask_random_seed, sparsity, device) for mask in mask_types]\n\n    final_mask = torch.stack(all_masks).sum(axis=0) > 0\n","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/transformer.py:11"}],[1580,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/transformer.py","startLine":139,"endLine":159,"chunkText":"\n    final_mask = torch.stack(all_masks).sum(axis=0) > 0\n\n    return SparseCSRTensor.from_dense(final_mask[None])\n\n\nclass ScaledEmbedding(nn.Module):\n    def __init__(self, num_embeddings: int, embedding_dim: int, scale: float = 1.0, boost: float = 3.0):\n        super().__init__()\n        self.embedding = nn.Embedding(num_embeddings, embedding_dim)\n        self.embedding.weight.data *= scale / boost\n        self.boost = boost\n\n    @property\n    def weight(self):\n        return self.embedding.weight * self.boost\n\n    def forward(self, x):\n        return self.embedding(x) * self.boost\n\n","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/transformer.py:12"}],[1581,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/transformer.py","startLine":160,"endLine":174,"chunkText":"class LayerScale(nn.Module):\n    \"\"\"Layer scale from [Touvron et al 2021] (https://arxiv.org/pdf/2103.17239.pdf).\n    This rescales diagonaly residual outputs close to 0 initially, then learnt.\n    \"\"\"\n\n    def __init__(self, channels: int, init: float = 0, channel_last=False):\n        \"\"\"\n        channel_last = False corresponds to (B, C, T) tensors\n        channel_last = True corresponds to (T, B, C) tensors\n        \"\"\"\n        super().__init__()\n        self.channel_last = channel_last\n        self.scale = nn.Parameter(torch.zeros(channels, requires_grad=True))\n        self.scale.data[:] = init\n","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/transformer.py:13"}],[1582,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/transformer.py","startLine":172,"endLine":181,"chunkText":"        self.scale = nn.Parameter(torch.zeros(channels, requires_grad=True))\n        self.scale.data[:] = init\n\n    def forward(self, x):\n        if self.channel_last:\n            return self.scale * x\n        else:\n            return self.scale[:, None] * x\n\n","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/transformer.py:14"}],[1583,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/transformer.py","startLine":182,"endLine":194,"chunkText":"class MyGroupNorm(nn.GroupNorm):\n    def __init__(self, *args, **kwargs):\n        super().__init__(*args, **kwargs)\n\n    def forward(self, x):\n        \"\"\"\n        x: (B, T, C)\n        if num_groups=1: Normalisation on all T and C together for each B\n        \"\"\"\n        x = x.transpose(1, 2)\n        return super().forward(x).transpose(1, 2)\n\n","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/transformer.py:15"}],[1584,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/transformer.py","startLine":195,"endLine":217,"chunkText":"class MyTransformerEncoderLayer(nn.TransformerEncoderLayer):\n    def __init__(\n        self,\n        d_model,\n        nhead,\n        dim_feedforward=2048,\n        dropout=0.1,\n        activation=F.relu,\n        group_norm=0,\n        norm_first=False,\n        norm_out=False,\n        layer_norm_eps=1e-5,\n        layer_scale=False,\n        init_values=1e-4,\n        device=None,\n        dtype=None,\n        sparse=False,\n        mask_type=\"diag\",\n        mask_random_seed=42,\n        sparse_attn_window=500,\n        global_window=50,\n        auto_sparsity=False,\n        sparsity=0.95,","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/transformer.py:16"}],[1585,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/transformer.py","startLine":215,"endLine":234,"chunkText":"        global_window=50,\n        auto_sparsity=False,\n        sparsity=0.95,\n        batch_first=False,\n    ):\n        factory_kwargs = {\"device\": device, \"dtype\": dtype}\n        super().__init__(\n            d_model=d_model,\n            nhead=nhead,\n            dim_feedforward=dim_feedforward,\n            dropout=dropout,\n            activation=activation,\n            layer_norm_eps=layer_norm_eps,\n            batch_first=batch_first,\n            norm_first=norm_first,\n            device=device,\n            dtype=dtype,\n        )\n        self.sparse = sparse\n        self.auto_sparsity = auto_sparsity","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/transformer.py:17"}],[1586,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/transformer.py","startLine":232,"endLine":245,"chunkText":"        )\n        self.sparse = sparse\n        self.auto_sparsity = auto_sparsity\n        if sparse:\n            if not auto_sparsity:\n                self.mask_type = mask_type\n                self.sparse_attn_window = sparse_attn_window\n                self.global_window = global_window\n            self.sparsity = sparsity\n        if group_norm:\n            self.norm1 = MyGroupNorm(int(group_norm), d_model, eps=layer_norm_eps, **factory_kwargs)\n            self.norm2 = MyGroupNorm(int(group_norm), d_model, eps=layer_norm_eps, **factory_kwargs)\n\n        self.norm_out = None","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/transformer.py:18"}],[1587,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/transformer.py","startLine":243,"endLine":251,"chunkText":"            self.norm2 = MyGroupNorm(int(group_norm), d_model, eps=layer_norm_eps, **factory_kwargs)\n\n        self.norm_out = None\n        if self.norm_first & norm_out:\n            self.norm_out = MyGroupNorm(num_groups=int(norm_out), num_channels=d_model)\n        self.gamma_1 = LayerScale(d_model, init_values, True) if layer_scale else nn.Identity()\n        self.gamma_2 = LayerScale(d_model, init_values, True) if layer_scale else nn.Identity()\n\n        if sparse:","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/transformer.py:19"}],[1588,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/transformer.py","startLine":249,"endLine":260,"chunkText":"        self.gamma_2 = LayerScale(d_model, init_values, True) if layer_scale else nn.Identity()\n\n        if sparse:\n            self.self_attn = MultiheadAttention(d_model, nhead, dropout=dropout, batch_first=batch_first, auto_sparsity=sparsity if auto_sparsity else 0)\n            self.__setattr__(\"src_mask\", torch.zeros(1, 1))\n            self.mask_random_seed = mask_random_seed\n\n    def forward(self, src, src_mask=None, src_key_padding_mask=None):\n        \"\"\"\n        if batch_first = False, src shape is (T, B, C)\n        the case where batch_first=True is not covered\n        \"\"\"","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/transformer.py:20"}],[1589,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/transformer.py","startLine":259,"endLine":271,"chunkText":"        the case where batch_first=True is not covered\n        \"\"\"\n        device = src.device\n        x = src\n        T, B, C = x.shape\n        if self.sparse and not self.auto_sparsity:\n            assert src_mask is None\n            src_mask = self.src_mask\n            if src_mask.shape[-1] != T:\n                src_mask = get_mask(T, T, self.mask_type, self.sparse_attn_window, self.global_window, self.mask_random_seed, self.sparsity, device)\n                self.__setattr__(\"src_mask\", src_mask)\n\n        if self.norm_first:","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/transformer.py:21"}],[1590,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/transformer.py","startLine":269,"endLine":283,"chunkText":"                self.__setattr__(\"src_mask\", src_mask)\n\n        if self.norm_first:\n            x = x + self.gamma_1(self._sa_block(self.norm1(x), src_mask, src_key_padding_mask))\n            x = x + self.gamma_2(self._ff_block(self.norm2(x)))\n\n            if self.norm_out:\n                x = self.norm_out(x)\n        else:\n            x = self.norm1(x + self.gamma_1(self._sa_block(x, src_mask, src_key_padding_mask)))\n            x = self.norm2(x + self.gamma_2(self._ff_block(x)))\n\n        return x\n\n","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/transformer.py:22"}],[1591,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/transformer.py","startLine":284,"endLine":303,"chunkText":"class CrossTransformerEncoderLayer(nn.Module):\n    def __init__(\n        self,\n        d_model: int,\n        nhead: int,\n        dim_feedforward: int = 2048,\n        dropout: float = 0.1,\n        activation=F.relu,\n        layer_norm_eps: float = 1e-5,\n        layer_scale: bool = False,\n        init_values: float = 1e-4,\n        norm_first: bool = False,\n        group_norm: bool = False,\n        norm_out: bool = False,\n        sparse=False,\n        mask_type=\"diag\",\n        mask_random_seed=42,\n        sparse_attn_window=500,\n        global_window=50,\n        sparsity=0.95,","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/transformer.py:23"}],[1592,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/transformer.py","startLine":301,"endLine":320,"chunkText":"        sparse_attn_window=500,\n        global_window=50,\n        sparsity=0.95,\n        auto_sparsity=None,\n        device=None,\n        dtype=None,\n        batch_first=False,\n    ):\n        factory_kwargs = {\"device\": device, \"dtype\": dtype}\n        super().__init__()\n\n        self.sparse = sparse\n        self.auto_sparsity = auto_sparsity\n        if sparse:\n            if not auto_sparsity:\n                self.mask_type = mask_type\n                self.sparse_attn_window = sparse_attn_window\n                self.global_window = global_window\n            self.sparsity = sparsity\n","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/transformer.py:24"}],[1593,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/transformer.py","startLine":318,"endLine":331,"chunkText":"                self.global_window = global_window\n            self.sparsity = sparsity\n\n        self.cross_attn: nn.Module\n        self.cross_attn = nn.MultiheadAttention(d_model, nhead, dropout=dropout, batch_first=batch_first)\n        # Implementation of Feedforward model\n        self.linear1 = nn.Linear(d_model, dim_feedforward, **factory_kwargs)\n        self.dropout = nn.Dropout(dropout)\n        self.linear2 = nn.Linear(dim_feedforward, d_model, **factory_kwargs)\n\n        self.norm_first = norm_first\n        self.norm1: nn.Module\n        self.norm2: nn.Module\n        self.norm3: nn.Module","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/transformer.py:25"}],[1594,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/transformer.py","startLine":329,"endLine":338,"chunkText":"        self.norm1: nn.Module\n        self.norm2: nn.Module\n        self.norm3: nn.Module\n        if group_norm:\n            self.norm1 = MyGroupNorm(int(group_norm), d_model, eps=layer_norm_eps, **factory_kwargs)\n            self.norm2 = MyGroupNorm(int(group_norm), d_model, eps=layer_norm_eps, **factory_kwargs)\n            self.norm3 = MyGroupNorm(int(group_norm), d_model, eps=layer_norm_eps, **factory_kwargs)\n        else:\n            self.norm1 = nn.LayerNorm(d_model, eps=layer_norm_eps, **factory_kwargs)\n            self.norm2 = nn.LayerNorm(d_model, eps=layer_norm_eps, **factory_kwargs)","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/transformer.py:26"}],[1595,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/transformer.py","startLine":336,"endLine":345,"chunkText":"        else:\n            self.norm1 = nn.LayerNorm(d_model, eps=layer_norm_eps, **factory_kwargs)\n            self.norm2 = nn.LayerNorm(d_model, eps=layer_norm_eps, **factory_kwargs)\n            self.norm3 = nn.LayerNorm(d_model, eps=layer_norm_eps, **factory_kwargs)\n\n        self.norm_out = None\n        if self.norm_first & norm_out:\n            self.norm_out = MyGroupNorm(num_groups=int(norm_out), num_channels=d_model)\n\n        self.gamma_1 = LayerScale(d_model, init_values, True) if layer_scale else nn.Identity()","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/transformer.py:27"}],[1596,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/transformer.py","startLine":343,"endLine":357,"chunkText":"            self.norm_out = MyGroupNorm(num_groups=int(norm_out), num_channels=d_model)\n\n        self.gamma_1 = LayerScale(d_model, init_values, True) if layer_scale else nn.Identity()\n        self.gamma_2 = LayerScale(d_model, init_values, True) if layer_scale else nn.Identity()\n\n        self.dropout1 = nn.Dropout(dropout)\n        self.dropout2 = nn.Dropout(dropout)\n\n        # Legacy string support for activation function.\n        if isinstance(activation, str):\n            self.activation = self._get_activation_fn(activation)\n        else:\n            self.activation = activation\n\n        if sparse:","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/transformer.py:28"}],[1597,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/transformer.py","startLine":355,"endLine":371,"chunkText":"            self.activation = activation\n\n        if sparse:\n            self.cross_attn = MultiheadAttention(d_model, nhead, dropout=dropout, batch_first=batch_first, auto_sparsity=sparsity if auto_sparsity else 0)\n            if not auto_sparsity:\n                self.__setattr__(\"mask\", torch.zeros(1, 1))\n                self.mask_random_seed = mask_random_seed\n\n    def forward(self, q, k, mask=None):\n        \"\"\"\n        Args:\n            q: tensor of shape (T, B, C)\n            k: tensor of shape (S, B, C)\n            mask: tensor of shape (T, S)\n\n        \"\"\"\n        device = q.device","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/transformer.py:29"}],[1598,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/transformer.py","startLine":369,"endLine":382,"chunkText":"\n        \"\"\"\n        device = q.device\n        T, B, C = q.shape\n        S, B, C = k.shape\n        if self.sparse and not self.auto_sparsity:\n            assert mask is None\n            mask = self.mask\n            if mask.shape[-1] != S or mask.shape[-2] != T:\n                mask = get_mask(S, T, self.mask_type, self.sparse_attn_window, self.global_window, self.mask_random_seed, self.sparsity, device)\n                self.__setattr__(\"mask\", mask)\n\n        if self.norm_first:\n            x = q + self.gamma_1(self._ca_block(self.norm1(q), self.norm2(k), mask))","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/transformer.py:30"}],[1599,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/transformer.py","startLine":380,"endLine":396,"chunkText":"\n        if self.norm_first:\n            x = q + self.gamma_1(self._ca_block(self.norm1(q), self.norm2(k), mask))\n            x = x + self.gamma_2(self._ff_block(self.norm3(x)))\n            if self.norm_out:\n                x = self.norm_out(x)\n        else:\n            x = self.norm1(q + self.gamma_1(self._ca_block(q, k, mask)))\n            x = self.norm2(x + self.gamma_2(self._ff_block(x)))\n\n        return x\n\n    # self-attention block\n    def _ca_block(self, q, k, attn_mask=None):\n        x = self.cross_attn(q, k, k, attn_mask=attn_mask, need_weights=False)[0]\n        return self.dropout1(x)\n","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/transformer.py:31"}],[1600,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/transformer.py","startLine":394,"endLine":413,"chunkText":"        x = self.cross_attn(q, k, k, attn_mask=attn_mask, need_weights=False)[0]\n        return self.dropout1(x)\n\n    # feed forward block\n    def _ff_block(self, x):\n        x = self.linear2(self.dropout(self.activation(self.linear1(x))))\n        return self.dropout2(x)\n\n    def _get_activation_fn(self, activation):\n        if activation == \"relu\":\n            return F.relu\n        elif activation == \"gelu\":\n            return F.gelu\n\n        raise RuntimeError(\"activation should be relu/gelu, not {}\".format(activation))\n\n\n# ----------------- MULTI-BLOCKS MODELS: -----------------------\n\n","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/transformer.py:32"}],[1601,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/transformer.py","startLine":414,"endLine":432,"chunkText":"class CrossTransformerEncoder(nn.Module):\n    def __init__(\n        self,\n        dim: int,\n        emb: str = \"sin\",\n        hidden_scale: float = 4.0,\n        num_heads: int = 8,\n        num_layers: int = 6,\n        cross_first: bool = False,\n        dropout: float = 0.0,\n        max_positions: int = 1000,\n        norm_in: bool = True,\n        norm_in_group: bool = False,\n        group_norm: int = False,\n        norm_first: bool = False,\n        norm_out: bool = False,\n        max_period: float = 10000.0,\n        weight_decay: float = 0.0,\n        lr: tp.Optional[float] = None,","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/transformer.py:33"}],[1602,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/transformer.py","startLine":430,"endLine":445,"chunkText":"        max_period: float = 10000.0,\n        weight_decay: float = 0.0,\n        lr: tp.Optional[float] = None,\n        layer_scale: bool = False,\n        gelu: bool = True,\n        sin_random_shift: int = 0,\n        weight_pos_embed: float = 1.0,\n        cape_mean_normalize: bool = True,\n        cape_augment: bool = True,\n        cape_glob_loc_scale: list = [5000.0, 1.0, 1.4],\n        sparse_self_attn: bool = False,\n        sparse_cross_attn: bool = False,\n        mask_type: str = \"diag\",\n        mask_random_seed: int = 42,\n        sparse_attn_window: int = 500,\n        global_window: int = 50,","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/transformer.py:34"}],[1603,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/transformer.py","startLine":443,"endLine":461,"chunkText":"        mask_random_seed: int = 42,\n        sparse_attn_window: int = 500,\n        global_window: int = 50,\n        auto_sparsity: bool = False,\n        sparsity: float = 0.95,\n    ):\n        super().__init__()\n        \"\"\"\n        \"\"\"\n        assert dim % num_heads == 0\n\n        hidden_dim = int(dim * hidden_scale)\n\n        self.num_layers = num_layers\n        # classic parity = 1 means that if idx%2 == 1 there is a\n        # classical encoder else there is a cross encoder\n        self.classic_parity = 1 if cross_first else 0\n        self.emb = emb\n        self.max_period = max_period","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/transformer.py:35"}],[1604,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/transformer.py","startLine":459,"endLine":473,"chunkText":"        self.classic_parity = 1 if cross_first else 0\n        self.emb = emb\n        self.max_period = max_period\n        self.weight_decay = weight_decay\n        self.weight_pos_embed = weight_pos_embed\n        self.sin_random_shift = sin_random_shift\n        if emb == \"cape\":\n            self.cape_mean_normalize = cape_mean_normalize\n            self.cape_augment = cape_augment\n            self.cape_glob_loc_scale = cape_glob_loc_scale\n        if emb == \"scaled\":\n            self.position_embeddings = ScaledEmbedding(max_positions, dim, scale=0.2)\n\n        self.lr = lr\n","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/transformer.py:36"}],[1605,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/transformer.py","startLine":471,"endLine":490,"chunkText":"\n        self.lr = lr\n\n        activation: tp.Any = F.gelu if gelu else F.relu\n\n        self.norm_in: nn.Module\n        self.norm_in_t: nn.Module\n        if norm_in:\n            self.norm_in = nn.LayerNorm(dim)\n            self.norm_in_t = nn.LayerNorm(dim)\n        elif norm_in_group:\n            self.norm_in = MyGroupNorm(int(norm_in_group), dim)\n            self.norm_in_t = MyGroupNorm(int(norm_in_group), dim)\n        else:\n            self.norm_in = nn.Identity()\n            self.norm_in_t = nn.Identity()\n\n        # spectrogram layers\n        self.layers = nn.ModuleList()\n        # temporal layers","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/transformer.py:37"}],[1606,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/transformer.py","startLine":488,"endLine":504,"chunkText":"        # spectrogram layers\n        self.layers = nn.ModuleList()\n        # temporal layers\n        self.layers_t = nn.ModuleList()\n\n        kwargs_common = {\n            \"d_model\": dim,\n            \"nhead\": num_heads,\n            \"dim_feedforward\": hidden_dim,\n            \"dropout\": dropout,\n            \"activation\": activation,\n            \"group_norm\": group_norm,\n            \"norm_first\": norm_first,\n            \"norm_out\": norm_out,\n            \"layer_scale\": layer_scale,\n            \"mask_type\": mask_type,\n            \"mask_random_seed\": mask_random_seed,","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/transformer.py:38"}],[1607,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/transformer.py","startLine":502,"endLine":516,"chunkText":"            \"layer_scale\": layer_scale,\n            \"mask_type\": mask_type,\n            \"mask_random_seed\": mask_random_seed,\n            \"sparse_attn_window\": sparse_attn_window,\n            \"global_window\": global_window,\n            \"sparsity\": sparsity,\n            \"auto_sparsity\": auto_sparsity,\n            \"batch_first\": True,\n        }\n\n        kwargs_classic_encoder = dict(kwargs_common)\n        kwargs_classic_encoder.update({\"sparse\": sparse_self_attn})\n        kwargs_cross_encoder = dict(kwargs_common)\n        kwargs_cross_encoder.update({\"sparse\": sparse_cross_attn})\n","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/transformer.py:39"}],[1608,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/transformer.py","startLine":514,"endLine":528,"chunkText":"        kwargs_cross_encoder = dict(kwargs_common)\n        kwargs_cross_encoder.update({\"sparse\": sparse_cross_attn})\n\n        for idx in range(num_layers):\n            if idx % 2 == self.classic_parity:\n\n                self.layers.append(MyTransformerEncoderLayer(**kwargs_classic_encoder))\n                self.layers_t.append(MyTransformerEncoderLayer(**kwargs_classic_encoder))\n\n            else:\n                self.layers.append(CrossTransformerEncoderLayer(**kwargs_cross_encoder))\n\n                self.layers_t.append(CrossTransformerEncoderLayer(**kwargs_cross_encoder))\n\n    def forward(self, x, xt)","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/transformer.py:40"}],[1609,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/transformer.py","startLine":526,"endLine":538,"chunkText":"                self.layers_t.append(CrossTransformerEncoderLayer(**kwargs_cross_encoder))\n\n    def forward(self, x, xt):\n        B, C, Fr, T1 = x.shape\n        pos_emb_2d = create_2d_sin_embedding(C, Fr, T1, x.device, self.max_period)  # (1, C, Fr, T1)\n        pos_emb_2d = rearrange(pos_emb_2d, \"b c fr t1 -> b (t1 fr) c\")\n        x = rearrange(x, \"b c fr t1 -> b (t1 fr) c\")\n        x = self.norm_in(x)\n        x = x + self.weight_pos_embed * pos_emb_2d\n\n        B, C, T2 = xt.shape\n        xt = rearrange(xt, \"b c t2 -> b t2 c\")  # now T2, B, C\n        pos_emb = self._get_pos_embedding(T2, B, C, x.device)","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/transformer.py:41"}],[1610,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/transformer.py","startLine":536,"endLine":551,"chunkText":"        B, C, T2 = xt.shape\n        xt = rearrange(xt, \"b c t2 -> b t2 c\")  # now T2, B, C\n        pos_emb = self._get_pos_embedding(T2, B, C, x.device)\n        pos_emb = rearrange(pos_emb, \"t2 b c -> b t2 c\")\n        xt = self.norm_in_t(xt)\n        xt = xt + self.weight_pos_embed * pos_emb\n\n        for idx in range(self.num_layers):\n            if idx % 2 == self.classic_parity:\n                x = self.layers[idx](x)\n                xt = self.layers_t[idx](xt)\n            else:\n                old_x = x\n                x = self.layers[idx](x, xt)\n                xt = self.layers_t[idx](xt, old_x)\n","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/transformer.py:42"}],[1611,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/transformer.py","startLine":549,"endLine":562,"chunkText":"                x = self.layers[idx](x, xt)\n                xt = self.layers_t[idx](xt, old_x)\n\n        x = rearrange(x, \"b (t1 fr) c -> b c fr t1\", t1=T1)\n        xt = rearrange(xt, \"b t2 c -> b c t2\")\n        return x, xt\n\n    def _get_pos_embedding(self, T, B, C, device):\n        if self.emb == \"sin\":\n            shift = random.randrange(self.sin_random_shift + 1)\n            pos_emb = create_sin_embedding(T, C, shift=shift, device=device, max_period=self.max_period)\n        elif self.emb == \"cape\":\n            if self.training:\n                pos_emb = create_sin_embedding_cape(","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/transformer.py:43"}],[1612,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/transformer.py","startLine":560,"endLine":574,"chunkText":"        elif self.emb == \"cape\":\n            if self.training:\n                pos_emb = create_sin_embedding_cape(\n                    T,\n                    C,\n                    B,\n                    device=device,\n                    max_period=self.max_period,\n                    mean_normalize=self.cape_mean_normalize,\n                    augment=self.cape_augment,\n                    max_global_shift=self.cape_glob_loc_scale[0],\n                    max_local_shift=self.cape_glob_loc_scale[1],\n                    max_scale=self.cape_glob_loc_scale[2],\n                )\n            else:","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/transformer.py:44"}],[1613,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/transformer.py","startLine":572,"endLine":586,"chunkText":"                    max_scale=self.cape_glob_loc_scale[2],\n                )\n            else:\n                pos_emb = create_sin_embedding_cape(T, C, B, device=device, max_period=self.max_period, mean_normalize=self.cape_mean_normalize, augment=False)\n\n        elif self.emb == \"scaled\":\n            pos = torch.arange(T, device=device)\n            pos_emb = self.position_embeddings(pos)[:, None]\n\n        return pos_emb\n\n    def make_optim_group(self):\n        group = {\"params\": list(self.parameters()), \"weight_decay\": self.weight_decay}\n        if self.lr is not None:\n            group[\"lr\"] = self.lr","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/transformer.py:45"}],[1614,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/transformer.py","startLine":584,"endLine":592,"chunkText":"        group = {\"params\": list(self.parameters()), \"weight_decay\": self.weight_decay}\n        if self.lr is not None:\n            group[\"lr\"] = self.lr\n        return group\n\n\n# Attention Modules\n\n","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/transformer.py:46"}],[1615,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/transformer.py","startLine":593,"endLine":601,"chunkText":"class MultiheadAttention(nn.Module):\n    def __init__(self, embed_dim, num_heads, dropout=0.0, bias=True, add_bias_kv=False, add_zero_attn=False, kdim=None, vdim=None, batch_first=False, auto_sparsity=None):\n        super().__init__()\n        assert auto_sparsity is not None, \"sanity check\"\n        self.num_heads = num_heads\n        self.q = torch.nn.Linear(embed_dim, embed_dim, bias=bias)\n        self.k = torch.nn.Linear(embed_dim, embed_dim, bias=bias)\n        self.v = torch.nn.Linear(embed_dim, embed_dim, bias=bias)\n        self.attn_drop = torch.nn.Dropout(dropout)","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/transformer.py:47"}],[1616,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/transformer.py","startLine":599,"endLine":610,"chunkText":"        self.k = torch.nn.Linear(embed_dim, embed_dim, bias=bias)\n        self.v = torch.nn.Linear(embed_dim, embed_dim, bias=bias)\n        self.attn_drop = torch.nn.Dropout(dropout)\n        self.proj = torch.nn.Linear(embed_dim, embed_dim, bias)\n        self.proj_drop = torch.nn.Dropout(dropout)\n        self.batch_first = batch_first\n        self.auto_sparsity = auto_sparsity\n\n    def forward(self, query, key, value, key_padding_mask=None, need_weights=True, attn_mask=None, average_attn_weights=True):\n\n        if not self.batch_first:  # N, B, C\n            query = query.permute(1, 0, 2)  # B, N_q, C","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/transformer.py:48"}],[1617,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/transformer.py","startLine":608,"endLine":619,"chunkText":"\n        if not self.batch_first:  # N, B, C\n            query = query.permute(1, 0, 2)  # B, N_q, C\n            key = key.permute(1, 0, 2)  # B, N_k, C\n            value = value.permute(1, 0, 2)  # B, N_k, C\n        B, N_q, C = query.shape\n        B, N_k, C = key.shape\n\n        q = self.q(query).reshape(B, N_q, self.num_heads, C // self.num_heads).permute(0, 2, 1, 3)\n        q = q.flatten(0, 1)\n        k = self.k(key).reshape(B, N_k, self.num_heads, C // self.num_heads).permute(0, 2, 1, 3)\n        k = k.flatten(0, 1)","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/transformer.py:49"}],[1618,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/transformer.py","startLine":617,"endLine":629,"chunkText":"        q = q.flatten(0, 1)\n        k = self.k(key).reshape(B, N_k, self.num_heads, C // self.num_heads).permute(0, 2, 1, 3)\n        k = k.flatten(0, 1)\n        v = self.v(value).reshape(B, N_k, self.num_heads, C // self.num_heads).permute(0, 2, 1, 3)\n        v = v.flatten(0, 1)\n\n        if self.auto_sparsity:\n            assert attn_mask is None\n            x = dynamic_sparse_attention(q, k, v, sparsity=self.auto_sparsity)\n        else:\n            x = scaled_dot_product_attention(q, k, v, attn_mask, dropout=self.attn_drop)\n        x = x.reshape(B, self.num_heads, N_q, C // self.num_heads)\n","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/transformer.py:50"}],[1619,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/transformer.py","startLine":627,"endLine":646,"chunkText":"            x = scaled_dot_product_attention(q, k, v, attn_mask, dropout=self.attn_drop)\n        x = x.reshape(B, self.num_heads, N_q, C // self.num_heads)\n\n        x = x.transpose(1, 2).reshape(B, N_q, C)\n        x = self.proj(x)\n        x = self.proj_drop(x)\n        if not self.batch_first:\n            x = x.permute(1, 0, 2)\n        return x, None\n\n\ndef scaled_query_key_softmax(q, k, att_mask):\n    from xformers.ops import masked_matmul\n\n    q = q / (k.size(-1)) ** 0.5\n    att = masked_matmul(q, k.transpose(-2, -1), att_mask)\n    att = torch.nn.functional.softmax(att, -1)\n    return att\n\n","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/transformer.py:51"}],[1620,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/transformer.py","startLine":644,"endLine":663,"chunkText":"    return att\n\n\ndef scaled_dot_product_attention(q, k, v, att_mask, dropout):\n    att = scaled_query_key_softmax(q, k, att_mask=att_mask)\n    att = dropout(att)\n    y = att @ v\n    return y\n\n\ndef _compute_buckets(x, R):\n    qq = torch.einsum(\"btf,bfhi->bhti\", x, R)\n    qq = torch.cat([qq, -qq], dim=-1)\n    buckets = qq.argmax(dim=-1)\n\n    return buckets.permute(0, 2, 1).byte().contiguous()\n\n\ndef dynamic_sparse_attention(query, key, value, sparsity, infer_sparsity=True, attn_bias=None):\n    # assert False, \"The code for the custom sparse kernel is not ready for release yet.\"","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/transformer.py:52"}],[1621,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/transformer.py","startLine":661,"endLine":672,"chunkText":"\ndef dynamic_sparse_attention(query, key, value, sparsity, infer_sparsity=True, attn_bias=None):\n    # assert False, \"The code for the custom sparse kernel is not ready for release yet.\"\n    from xformers.ops import find_locations, sparse_memory_efficient_attention\n\n    n_hashes = 32\n    proj_size = 4\n    query, key, value = [x.contiguous() for x in [query, key, value]]\n    with torch.no_grad():\n        R = torch.randn(1, query.shape[-1], n_hashes, proj_size // 2, device=query.device)\n        bucket_query = _compute_buckets(query, R)\n        bucket_key = _compute_buckets(key, R)","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/transformer.py:53"}],[1622,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/transformer.py","startLine":670,"endLine":675,"chunkText":"        R = torch.randn(1, query.shape[-1], n_hashes, proj_size // 2, device=query.device)\n        bucket_query = _compute_buckets(query, R)\n        bucket_key = _compute_buckets(key, R)\n        row_offsets, column_indices = find_locations(bucket_query, bucket_key, sparsity, infer_sparsity)\n    return sparse_memory_efficient_attention(query, key, value, row_offsets, column_indices, attn_bias)\n","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/transformer.py:54"}],[1623,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/utils.py","startLine":0,"endLine":28,"chunkText":"# Copyright (c) Facebook, Inc. and its affiliates.\n# All rights reserved.\n#\n# This source code is licensed under the license found in the\n# LICENSE file in the root directory of this source tree.\n\nfrom collections import defaultdict\nfrom contextlib import contextmanager\nimport math\nimport os\nimport tempfile\nimport typing as tp\n\nimport errno\nimport functools\nimport hashlib\nimport inspect\nimport io\nimport os\nimport random\nimport socket\nimport tempfile\nimport warnings\nimport zlib\n\nfrom diffq import UniformQuantizer, DiffQuantizer\nimport torch as th\nimport tqdm\nfrom torch import distributed","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/utils.py:0"}],[1624,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/utils.py","startLine":26,"endLine":46,"chunkText":"import torch as th\nimport tqdm\nfrom torch import distributed\nfrom torch.nn import functional as F\n\nimport torch\n\n\ndef unfold(a, kernel_size, stride):\n    \"\"\"Given input of size [*OT, T], output Tensor of size [*OT, F, K]\n    with K the kernel size, by extracting frames with the given stride.\n\n    This will pad the input so that `F = ceil(T / K)`.\n\n    see https://github.com/pytorch/pytorch/issues/60466\n    \"\"\"\n    *shape, length = a.shape\n    n_frames = math.ceil(length / stride)\n    tgt_length = (n_frames - 1) * stride + kernel_size\n    a = F.pad(a, (0, tgt_length - length))\n    strides = list(a.stride()","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/utils.py:1"}],[1625,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/utils.py","startLine":44,"endLine":55,"chunkText":"    tgt_length = (n_frames - 1) * stride + kernel_size\n    a = F.pad(a, (0, tgt_length - length))\n    strides = list(a.stride())\n    assert strides[-1] == 1, \"data should be contiguous\"\n    strides = strides[:-1] + [stride, 1]\n    return a.as_strided([*shape, n_frames, kernel_size], strides)\n\n\ndef center_trim(tensor: torch.Tensor, reference: tp.Union[torch.Tensor, int]):\n    \"\"\"\n    Center trim `tensor` with respect to `reference`, along the last dimension.\n    `reference` can also be a number, representing the length to trim to.","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/utils.py:2"}],[1626,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/utils.py","startLine":53,"endLine":66,"chunkText":"    \"\"\"\n    Center trim `tensor` with respect to `reference`, along the last dimension.\n    `reference` can also be a number, representing the length to trim to.\n    If the size difference != 0 mod 2, the extra sample is removed on the right side.\n    \"\"\"\n    ref_size: int\n    if isinstance(reference, torch.Tensor):\n        ref_size = reference.size(-1)\n    else:\n        ref_size = reference\n    delta = tensor.size(-1) - ref_size\n    if delta < 0:\n        raise ValueError(\"tensor must be larger than reference. \" f\"Delta is {delta}.\")\n    if delta:","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/utils.py:3"}],[1627,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/utils.py","startLine":64,"endLine":84,"chunkText":"    if delta < 0:\n        raise ValueError(\"tensor must be larger than reference. \" f\"Delta is {delta}.\")\n    if delta:\n        tensor = tensor[..., delta // 2 : -(delta - delta // 2)]\n    return tensor\n\n\ndef pull_metric(history: tp.List[dict], name: str):\n    out = []\n    for metrics in history:\n        metric = metrics\n        for part in name.split(\".\"):\n            metric = metric[part]\n        out.append(metric)\n    return out\n\n\ndef EMA(beta: float = 1):\n    \"\"\"\n    Exponential Moving Average callback.\n    Returns a single function that can be called to repeatidly update the EMA","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/utils.py:4"}],[1628,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/utils.py","startLine":82,"endLine":96,"chunkText":"    \"\"\"\n    Exponential Moving Average callback.\n    Returns a single function that can be called to repeatidly update the EMA\n    with a dict of metrics. The callback will return\n    the new averaged dict of metrics.\n\n    Note that for `beta=1`, this is just plain averaging.\n    \"\"\"\n    fix: tp.Dict[str, float] = defaultdict(float)\n    total: tp.Dict[str, float] = defaultdict(float)\n\n    def _update(metrics: dict, weight: float = 1) -> dict:\n        nonlocal total, fix\n        for key, value in metrics.items():\n            total[key] = total[key] * beta + weight * float(value)","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/utils.py:5"}],[1629,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/utils.py","startLine":94,"endLine":111,"chunkText":"        nonlocal total, fix\n        for key, value in metrics.items():\n            total[key] = total[key] * beta + weight * float(value)\n            fix[key] = fix[key] * beta + weight\n        return {key: tot / fix[key] for key, tot in total.items()}\n\n    return _update\n\n\ndef sizeof_fmt(num: float, suffix: str = \"B\"):\n    \"\"\"\n    Given `num` bytes, return human readable size.\n    Taken from https://stackoverflow.com/a/1094933\n    \"\"\"\n    for unit in [\"\", \"Ki\", \"Mi\", \"Gi\", \"Ti\", \"Pi\", \"Ei\", \"Zi\"]:\n        if abs(num) < 1024.0:\n            return \"%3.1f%s%s\" % (num, unit, suffix)\n        num /= 1024.0","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/utils.py:6"}],[1630,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/utils.py","startLine":109,"endLine":130,"chunkText":"        if abs(num) < 1024.0:\n            return \"%3.1f%s%s\" % (num, unit, suffix)\n        num /= 1024.0\n    return \"%.1f%s%s\" % (num, \"Yi\", suffix)\n\n\n@contextmanager\ndef temp_filenames(count: int, delete=True):\n    names = []\n    try:\n        for _ in range(count):\n            names.append(tempfile.NamedTemporaryFile(delete=False).name)\n        yield names\n    finally:\n        if delete:\n            for name in names:\n                os.unlink(name)\n\n\ndef average_metric(metric, count=1.0):\n    \"\"\"\n    Average `metric` which should be a float across all hosts. `count` should be","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/utils.py:7"}],[1631,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/utils.py","startLine":128,"endLine":143,"chunkText":"def average_metric(metric, count=1.0):\n    \"\"\"\n    Average `metric` which should be a float across all hosts. `count` should be\n    the weight for this particular host (i.e. number of examples).\n    \"\"\"\n    metric = th.tensor([count, count * metric], dtype=th.float32, device=\"cuda\")\n    distributed.all_reduce(metric, op=distributed.ReduceOp.SUM)\n    return metric[1].item() / metric[0].item()\n\n\ndef free_port(host=\"\", low=20000, high=40000):\n    \"\"\"\n    Return a port number that is most likely free.\n    This could suffer from a race condition although\n    it should be quite rare.\n    \"\"\"","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/utils.py:8"}],[1632,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/utils.py","startLine":141,"endLine":162,"chunkText":"    This could suffer from a race condition although\n    it should be quite rare.\n    \"\"\"\n    sock = socket.socket()\n    while True:\n        port = random.randint(low, high)\n        try:\n            sock.bind((host, port))\n        except OSError as error:\n            if error.errno == errno.EADDRINUSE:\n                continue\n            raise\n        return port\n\n\ndef sizeof_fmt(num, suffix=\"B\"):\n    \"\"\"\n    Given `num` bytes, return human readable size.\n    Taken from https://stackoverflow.com/a/1094933\n    \"\"\"\n    for unit in [\"\", \"Ki\", \"Mi\", \"Gi\", \"Ti\", \"Pi\", \"Ei\", \"Zi\"]:\n        if abs(num) < 1024.0","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/utils.py:9"}],[1633,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/utils.py","startLine":160,"endLine":179,"chunkText":"    \"\"\"\n    for unit in [\"\", \"Ki\", \"Mi\", \"Gi\", \"Ti\", \"Pi\", \"Ei\", \"Zi\"]:\n        if abs(num) < 1024.0:\n            return \"%3.1f%s%s\" % (num, unit, suffix)\n        num /= 1024.0\n    return \"%.1f%s%s\" % (num, \"Yi\", suffix)\n\n\ndef human_seconds(seconds, display=\".2f\"):\n    \"\"\"\n    Given `seconds` seconds, return human readable duration.\n    \"\"\"\n    value = seconds * 1e6\n    ratios = [1e3, 1e3, 60, 60, 24]\n    names = [\"us\", \"ms\", \"s\", \"min\", \"hrs\", \"days\"]\n    last = names.pop(0)\n    for name, ratio in zip(names, ratios):\n        if value / ratio < 0.3:\n            break\n        value /= ratio","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/utils.py:10"}],[1634,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/utils.py","startLine":177,"endLine":200,"chunkText":"        if value / ratio < 0.3:\n            break\n        value /= ratio\n        last = name\n    return f\"{format(value, display)} {last}\"\n\n\nclass TensorChunk:\n    def __init__(self, tensor, offset=0, length=None):\n        total_length = tensor.shape[-1]\n        assert offset >= 0\n        assert offset < total_length\n\n        if length is None:\n            length = total_length - offset\n        else:\n            length = min(total_length - offset, length)\n\n        self.tensor = tensor\n        self.offset = offset\n        self.length = length\n        self.device = tensor.device\n\n    @property","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/utils.py:11"}],[1635,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/utils.py","startLine":198,"endLine":219,"chunkText":"        self.device = tensor.device\n\n    @property\n    def shape(self):\n        shape = list(self.tensor.shape)\n        shape[-1] = self.length\n        return shape\n\n    def padded(self, target_length):\n        delta = target_length - self.length\n        total_length = self.tensor.shape[-1]\n        assert delta >= 0\n\n        start = self.offset - delta // 2\n        end = start + target_length\n\n        correct_start = max(0, start)\n        correct_end = min(total_length, end)\n\n        pad_left = correct_start - start\n        pad_right = end - correct_end\n","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/utils.py:12"}],[1636,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/utils.py","startLine":217,"endLine":237,"chunkText":"        pad_left = correct_start - start\n        pad_right = end - correct_end\n\n        out = F.pad(self.tensor[..., correct_start:correct_end], (pad_left, pad_right))\n        assert out.shape[-1] == target_length\n        return out\n\n\ndef tensor_chunk(tensor_or_chunk):\n    if isinstance(tensor_or_chunk, TensorChunk):\n        return tensor_or_chunk\n    else:\n        assert isinstance(tensor_or_chunk, th.Tensor)\n        return TensorChunk(tensor_or_chunk)\n\n\ndef apply_model_v1(model, mix, shifts=None, split=False, progress=False, set_progress_bar=None):\n    \"\"\"\n    Apply model to a given mixture.\n\n    Args:","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/utils.py:13"}],[1637,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/utils.py","startLine":235,"endLine":243,"chunkText":"    Apply model to a given mixture.\n\n    Args:\n        shifts (int): if > 0, will shift in time `mix` by a random amount between 0 and 0.5 sec\n            and apply the oppositve shift to the output. This is repeated `shifts` time and\n            all predictions are averaged. This effectively makes the model time equivariant\n            and improves SDR by up to 0.2 points.\n        split (bool): if True, the input will be broken down in 8 seconds extracts\n            and predictions will be performed individually on each and concatenated.","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/utils.py:14"}],[1638,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/utils.py","startLine":241,"endLine":255,"chunkText":"            and improves SDR by up to 0.2 points.\n        split (bool): if True, the input will be broken down in 8 seconds extracts\n            and predictions will be performed individually on each and concatenated.\n            Useful for model with large memory footprint like Tasnet.\n        progress (bool): if True, show a progress bar (requires split=True)\n    \"\"\"\n\n    channels, length = mix.size()\n    device = mix.device\n    progress_value = 0\n\n    if split:\n        out = th.zeros(4, channels, length, device=device)\n        shift = model.samplerate * 10\n        offsets = range(0, length, shift)","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/utils.py:15"}],[1639,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/utils.py","startLine":253,"endLine":264,"chunkText":"        out = th.zeros(4, channels, length, device=device)\n        shift = model.samplerate * 10\n        offsets = range(0, length, shift)\n        scale = 10\n        if progress:\n            offsets = tqdm.tqdm(offsets, unit_scale=scale, ncols=120, unit=\"seconds\")\n        for offset in offsets:\n            chunk = mix[..., offset : offset + shift]\n            if set_progress_bar:\n                progress_value += 1\n                set_progress_bar(0.1, (0.8 / len(offsets) * progress_value))\n                chunk_out = apply_model_v1(model, chunk, shifts=shifts, set_progress_bar=set_progress_bar)","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/utils.py:16"}],[1640,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/utils.py","startLine":262,"endLine":274,"chunkText":"                progress_value += 1\n                set_progress_bar(0.1, (0.8 / len(offsets) * progress_value))\n                chunk_out = apply_model_v1(model, chunk, shifts=shifts, set_progress_bar=set_progress_bar)\n            else:\n                chunk_out = apply_model_v1(model, chunk, shifts=shifts)\n            out[..., offset : offset + shift] = chunk_out\n            offset += shift\n        return out\n    elif shifts:\n        max_shift = int(model.samplerate / 2)\n        mix = F.pad(mix, (max_shift, max_shift))\n        offsets = list(range(max_shift))\n        random.shuffle(offsets)","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/utils.py:17"}],[1641,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/utils.py","startLine":273,"endLine":286,"chunkText":"        offsets = list(range(max_shift))\n        random.shuffle(offsets)\n        out = 0\n        for offset in offsets[:shifts]:\n            shifted = mix[..., offset : offset + length + max_shift]\n            if set_progress_bar:\n                shifted_out = apply_model_v1(model, shifted, set_progress_bar=set_progress_bar)\n            else:\n                shifted_out = apply_model_v1(model, shifted)\n            out += shifted_out[..., max_shift - offset : max_shift - offset + length]\n        out /= shifts\n        return out\n    else:\n        valid_length = model.valid_length(length)","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/utils.py:18"}],[1642,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/utils.py","startLine":284,"endLine":299,"chunkText":"        return out\n    else:\n        valid_length = model.valid_length(length)\n        delta = valid_length - length\n        padded = F.pad(mix, (delta // 2, delta - delta // 2))\n        with th.no_grad():\n            out = model(padded.unsqueeze(0))[0]\n        return center_trim(out, mix)\n\n\ndef apply_model_v2(model, mix, shifts=None, split=False, overlap=0.25, transition_power=1.0, progress=False, set_progress_bar=None):\n    \"\"\"\n    Apply model to a given mixture.\n\n    Args:\n        shifts (int): if > 0, will shift in time `mix` by a random amount between 0 and 0.5 sec","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/utils.py:19"}],[1643,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/utils.py","startLine":297,"endLine":305,"chunkText":"\n    Args:\n        shifts (int): if > 0, will shift in time `mix` by a random amount between 0 and 0.5 sec\n            and apply the oppositve shift to the output. This is repeated `shifts` time and\n            all predictions are averaged. This effectively makes the model time equivariant\n            and improves SDR by up to 0.2 points.\n        split (bool): if True, the input will be broken down in 8 seconds extracts\n            and predictions will be performed individually on each and concatenated.\n            Useful for model with large memory footprint like Tasnet.","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/utils.py:20"}],[1644,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/utils.py","startLine":303,"endLine":315,"chunkText":"        split (bool): if True, the input will be broken down in 8 seconds extracts\n            and predictions will be performed individually on each and concatenated.\n            Useful for model with large memory footprint like Tasnet.\n        progress (bool): if True, show a progress bar (requires split=True)\n    \"\"\"\n\n    assert transition_power >= 1, \"transition_power < 1 leads to weird behavior.\"\n    device = mix.device\n    channels, length = mix.shape\n    progress_value = 0\n\n    if split:\n        out = th.zeros(len(model.sources), channels, length, device=device)","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/utils.py:21"}],[1645,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/utils.py","startLine":313,"endLine":324,"chunkText":"\n    if split:\n        out = th.zeros(len(model.sources), channels, length, device=device)\n        sum_weight = th.zeros(length, device=device)\n        segment = model.segment_length\n        stride = int((1 - overlap) * segment)\n        offsets = range(0, length, stride)\n        scale = stride / model.samplerate\n        if progress:\n            offsets = tqdm.tqdm(offsets, unit_scale=scale, ncols=120, unit=\"seconds\")\n        # We start from a triangle shaped weight, with maximal weight in the middle\n        # of the segment. Then we normalize and take to the power `transition_power`.","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/utils.py:22"}],[1646,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/utils.py","startLine":322,"endLine":329,"chunkText":"            offsets = tqdm.tqdm(offsets, unit_scale=scale, ncols=120, unit=\"seconds\")\n        # We start from a triangle shaped weight, with maximal weight in the middle\n        # of the segment. Then we normalize and take to the power `transition_power`.\n        # Large values of transition power will lead to sharper transitions.\n        weight = th.cat([th.arange(1, segment // 2 + 1), th.arange(segment - segment // 2, 0, -1)]).to(device)\n        assert len(weight) == segment\n        # If the overlap < 50%, this will translate to linear transition when\n        # transition_power is 1.","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/utils.py:23"}],[1647,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/utils.py","startLine":327,"endLine":337,"chunkText":"        assert len(weight) == segment\n        # If the overlap < 50%, this will translate to linear transition when\n        # transition_power is 1.\n        weight = (weight / weight.max()) ** transition_power\n        for offset in offsets:\n            chunk = TensorChunk(mix, offset, segment)\n            if set_progress_bar:\n                progress_value += 1\n                set_progress_bar(0.1, (0.8 / len(offsets) * progress_value))\n                chunk_out = apply_model_v2(model, chunk, shifts=shifts, set_progress_bar=set_progress_bar)\n            else:","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/utils.py:24"}],[1648,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/utils.py","startLine":335,"endLine":346,"chunkText":"                set_progress_bar(0.1, (0.8 / len(offsets) * progress_value))\n                chunk_out = apply_model_v2(model, chunk, shifts=shifts, set_progress_bar=set_progress_bar)\n            else:\n                chunk_out = apply_model_v2(model, chunk, shifts=shifts)\n            chunk_length = chunk_out.shape[-1]\n            out[..., offset : offset + segment] += weight[:chunk_length] * chunk_out\n            sum_weight[offset : offset + segment] += weight[:chunk_length]\n            offset += segment\n        assert sum_weight.min() > 0\n        out /= sum_weight\n        return out\n    elif shifts:","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/utils.py:25"}],[1649,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/utils.py","startLine":344,"endLine":358,"chunkText":"        out /= sum_weight\n        return out\n    elif shifts:\n        max_shift = int(0.5 * model.samplerate)\n        mix = tensor_chunk(mix)\n        padded_mix = mix.padded(length + 2 * max_shift)\n        out = 0\n        for _ in range(shifts):\n            offset = random.randint(0, max_shift)\n            shifted = TensorChunk(padded_mix, offset, length + max_shift - offset)\n\n            if set_progress_bar:\n                progress_value += 1\n                shifted_out = apply_model_v2(model, shifted, set_progress_bar=set_progress_bar)\n            else:","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/utils.py:26"}],[1650,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/utils.py","startLine":356,"endLine":372,"chunkText":"                progress_value += 1\n                shifted_out = apply_model_v2(model, shifted, set_progress_bar=set_progress_bar)\n            else:\n                shifted_out = apply_model_v2(model, shifted)\n            out += shifted_out[..., max_shift - offset :]\n        out /= shifts\n        return out\n    else:\n        valid_length = model.valid_length(length)\n        mix = tensor_chunk(mix)\n        padded_mix = mix.padded(valid_length)\n        with th.no_grad():\n            out = model(padded_mix.unsqueeze(0))[0]\n        return center_trim(out, length)\n\n\n@contextmanager","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/utils.py:27"}],[1651,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/utils.py","startLine":370,"endLine":391,"chunkText":"\n\n@contextmanager\ndef temp_filenames(count, delete=True):\n    names = []\n    try:\n        for _ in range(count):\n            names.append(tempfile.NamedTemporaryFile(delete=False).name)\n        yield names\n    finally:\n        if delete:\n            for name in names:\n                os.unlink(name)\n\n\ndef get_quantizer(model, args, optimizer=None):\n    quantizer = None\n    if args.diffq:\n        quantizer = DiffQuantizer(model, min_size=args.q_min_size, group_size=8)\n        if optimizer is not None:\n            quantizer.setup_optimizer(optimizer)\n    elif args.qat:","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/utils.py:28"}],[1652,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/utils.py","startLine":389,"endLine":409,"chunkText":"        if optimizer is not None:\n            quantizer.setup_optimizer(optimizer)\n    elif args.qat:\n        quantizer = UniformQuantizer(model, bits=args.qat, min_size=args.q_min_size)\n    return quantizer\n\n\ndef load_model(path, strict=False):\n    with warnings.catch_warnings():\n        warnings.simplefilter(\"ignore\")\n        load_from = path\n        package = th.load(load_from, \"cpu\")\n\n    klass = package[\"klass\"]\n    args = package[\"args\"]\n    kwargs = package[\"kwargs\"]\n\n    if strict:\n        model = klass(*args, **kwargs)\n    else:\n        sig = inspect.signature(klass)","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/utils.py:29"}],[1653,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/utils.py","startLine":408,"endLine":427,"chunkText":"    else:\n        sig = inspect.signature(klass)\n        for key in list(kwargs):\n            if key not in sig.parameters:\n                warnings.warn(\"Dropping inexistant parameter \" + key)\n                del kwargs[key]\n        model = klass(*args, **kwargs)\n\n    state = package[\"state\"]\n    training_args = package[\"training_args\"]\n    quantizer = get_quantizer(model, training_args)\n\n    set_state(model, quantizer, state)\n    return model\n\n\ndef get_state(model, quantizer):\n    if quantizer is None:\n        state = {k: p.data.to(\"cpu\") for k, p in model.state_dict().items()}\n    else:","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/utils.py:30"}],[1654,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/utils.py","startLine":425,"endLine":446,"chunkText":"    if quantizer is None:\n        state = {k: p.data.to(\"cpu\") for k, p in model.state_dict().items()}\n    else:\n        state = quantizer.get_quantized_state()\n        buf = io.BytesIO()\n        th.save(state, buf)\n        state = {\"compressed\": zlib.compress(buf.getvalue())}\n    return state\n\n\ndef set_state(model, quantizer, state):\n    if quantizer is None:\n        model.load_state_dict(state)\n    else:\n        buf = io.BytesIO(zlib.decompress(state[\"compressed\"]))\n        state = th.load(buf, \"cpu\")\n        quantizer.restore_quantized_state(state)\n\n    return state\n\n\ndef save_state(state, path):","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/utils.py:31"}],[1655,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/utils.py","startLine":444,"endLine":464,"chunkText":"def save_state(state, path):\n    buf = io.BytesIO()\n    th.save(state, buf)\n    sig = hashlib.sha256(buf.getvalue()).hexdigest()[:8]\n\n    path = path.parent / (path.stem + \"-\" + sig + path.suffix)\n    path.write_bytes(buf.getvalue())\n\n\ndef save_model(model, quantizer, training_args, path):\n    args, kwargs = model._init_args_kwargs\n    klass = model.__class__\n\n    state = get_state(model, quantizer)\n\n    save_to = path\n    package = {\"klass\": klass, \"args\": args, \"kwargs\": kwargs, \"state\": state, \"training_args\": training_args}\n    th.save(package, save_to)\n\n\ndef capture_init(init):","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/utils.py:32"}],[1656,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/utils.py","startLine":465,"endLine":474,"chunkText":"\ndef capture_init(init):\n    @functools.wraps(init)\n    def __init__(self, *args, **kwargs):\n        self._init_args_kwargs = (args, kwargs)\n        init(self, *args, **kwargs)\n\n    return __init__\n\n","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/utils.py:33"}],[1657,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/utils.py","startLine":475,"endLine":475,"chunkText":"class DummyPoolExecutor:","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/utils.py:34"}],[1658,{"filePath":"audio_separator/separator/uvr_lib_v5/demucs/utils.py","startLine":476,"endLine":496,"chunkText":"    class DummyResult:\n        def __init__(self, func, *args, **kwargs):\n            self.func = func\n            self.args = args\n            self.kwargs = kwargs\n\n        def result(self):\n            return self.func(*self.args, **self.kwargs)\n\n    def __init__(self, workers=0):\n        pass\n\n    def submit(self, func, *args, **kwargs):\n        return DummyPoolExecutor.DummyResult(func, *args, **kwargs)\n\n    def __enter__(self):\n        return self\n\n    def __exit__(self, exc_type, exc_value, exc_tb):\n        return\n","chunkId":"audio_separator/separator/uvr_lib_v5/demucs/utils.py:35"}],[1659,{"filePath":"audio_separator/separator/uvr_lib_v5/mdxnet.py","startLine":0,"endLine":18,"chunkText":"import torch\nimport torch.nn as nn\nfrom .modules import TFC_TDF\nfrom pytorch_lightning import LightningModule\n\ndim_s = 4\n\nclass AbstractMDXNet(LightningModule):\n    def __init__(self, target_name, lr, optimizer, dim_c, dim_f, dim_t, n_fft, hop_length, overlap):\n        super().__init__()\n        self.target_name = target_name\n        self.lr = lr\n        self.optimizer = optimizer\n        self.dim_c = dim_c\n        self.dim_f = dim_f\n        self.dim_t = dim_t\n        self.n_fft = n_fft\n        self.n_bins = n_fft // 2 + 1\n        self.hop_length = hop_length","chunkId":"audio_separator/separator/uvr_lib_v5/mdxnet.py:0"}],[1660,{"filePath":"audio_separator/separator/uvr_lib_v5/mdxnet.py","startLine":16,"endLine":28,"chunkText":"        self.n_fft = n_fft\n        self.n_bins = n_fft // 2 + 1\n        self.hop_length = hop_length\n        self.window = nn.Parameter(torch.hann_window(window_length=self.n_fft, periodic=True), requires_grad=False)\n        self.freq_pad = nn.Parameter(torch.zeros([1, dim_c, self.n_bins - self.dim_f, self.dim_t]), requires_grad=False)\n\n    def get_optimizer(self):\n        if self.optimizer == 'rmsprop':\n            return torch.optim.RMSprop(self.parameters(), self.lr)\n        \n        if self.optimizer == 'adamw':\n            return torch.optim.AdamW(self.parameters(), self.lr)\n","chunkId":"audio_separator/separator/uvr_lib_v5/mdxnet.py:1"}],[1661,{"filePath":"audio_separator/separator/uvr_lib_v5/mdxnet.py","startLine":29,"endLine":47,"chunkText":"class ConvTDFNet(AbstractMDXNet):\n    def __init__(self, target_name, lr, optimizer, dim_c, dim_f, dim_t, n_fft, hop_length,\n                 num_blocks, l, g, k, bn, bias, overlap):\n\n        super(ConvTDFNet, self).__init__(\n            target_name, lr, optimizer, dim_c, dim_f, dim_t, n_fft, hop_length, overlap)\n        #self.save_hyperparameters()\n\n        self.num_blocks = num_blocks\n        self.l = l\n        self.g = g\n        self.k = k\n        self.bn = bn\n        self.bias = bias\n\n        if optimizer == 'rmsprop':\n            norm = nn.BatchNorm2d\n            \n        if optimizer == 'adamw':","chunkId":"audio_separator/separator/uvr_lib_v5/mdxnet.py:2"}],[1662,{"filePath":"audio_separator/separator/uvr_lib_v5/mdxnet.py","startLine":45,"endLine":63,"chunkText":"            norm = nn.BatchNorm2d\n            \n        if optimizer == 'adamw':\n            norm = lambda input:nn.GroupNorm(2, input)\n            \n        self.n = num_blocks // 2\n        scale = (2, 2)\n\n        self.first_conv = nn.Sequential(\n            nn.Conv2d(in_channels=self.dim_c, out_channels=g, kernel_size=(1, 1)),\n            norm(g),\n            nn.ReLU(),\n        )\n\n        f = self.dim_f\n        c = g\n        self.encoding_blocks = nn.ModuleList()\n        self.ds = nn.ModuleList()\n        for i in range(self.n):","chunkId":"audio_separator/separator/uvr_lib_v5/mdxnet.py:3"}],[1663,{"filePath":"audio_separator/separator/uvr_lib_v5/mdxnet.py","startLine":61,"endLine":76,"chunkText":"        self.encoding_blocks = nn.ModuleList()\n        self.ds = nn.ModuleList()\n        for i in range(self.n):\n            self.encoding_blocks.append(TFC_TDF(c, l, f, k, bn, bias=bias, norm=norm))\n            self.ds.append(\n                nn.Sequential(\n                    nn.Conv2d(in_channels=c, out_channels=c + g, kernel_size=scale, stride=scale),\n                    norm(c + g),\n                    nn.ReLU()\n                )\n            )\n            f = f // 2\n            c += g\n\n        self.bottleneck_block = TFC_TDF(c, l, f, k, bn, bias=bias, norm=norm)\n","chunkId":"audio_separator/separator/uvr_lib_v5/mdxnet.py:4"}],[1664,{"filePath":"audio_separator/separator/uvr_lib_v5/mdxnet.py","startLine":74,"endLine":91,"chunkText":"\n        self.bottleneck_block = TFC_TDF(c, l, f, k, bn, bias=bias, norm=norm)\n\n        self.decoding_blocks = nn.ModuleList()\n        self.us = nn.ModuleList()\n        for i in range(self.n):\n            self.us.append(\n                nn.Sequential(\n                    nn.ConvTranspose2d(in_channels=c, out_channels=c - g, kernel_size=scale, stride=scale),\n                    norm(c - g),\n                    nn.ReLU()\n                )\n            )\n            f = f * 2\n            c -= g\n\n            self.decoding_blocks.append(TFC_TDF(c, l, f, k, bn, bias=bias, norm=norm))\n","chunkId":"audio_separator/separator/uvr_lib_v5/mdxnet.py:5"}],[1665,{"filePath":"audio_separator/separator/uvr_lib_v5/mdxnet.py","startLine":89,"endLine":111,"chunkText":"            self.decoding_blocks.append(TFC_TDF(c, l, f, k, bn, bias=bias, norm=norm))\n\n        self.final_conv = nn.Sequential(\n            nn.Conv2d(in_channels=c, out_channels=self.dim_c, kernel_size=(1, 1)),\n        )\n\n    def forward(self, x):\n\n        x = self.first_conv(x)\n\n        x = x.transpose(-1, -2)\n\n        ds_outputs = []\n        for i in range(self.n):\n            x = self.encoding_blocks[i](x)\n            ds_outputs.append(x)\n            x = self.ds[i](x)\n\n        x = self.bottleneck_block(x)\n\n        for i in range(self.n):\n            x = self.us[i](x)\n            x *= ds_outputs[-i - 1","chunkId":"audio_separator/separator/uvr_lib_v5/mdxnet.py:6"}],[1666,{"filePath":"audio_separator/separator/uvr_lib_v5/mdxnet.py","startLine":110,"endLine":120,"chunkText":"        for i in range(self.n):\n            x = self.us[i](x)\n            x *= ds_outputs[-i - 1]\n            x = self.decoding_blocks[i](x)\n\n        x = x.transpose(-1, -2)\n\n        x = self.final_conv(x)\n\n        return x\n    ","chunkId":"audio_separator/separator/uvr_lib_v5/mdxnet.py:7"}],[1667,{"filePath":"audio_separator/separator/uvr_lib_v5/mdxnet.py","startLine":121,"endLine":135,"chunkText":"class Mixer(nn.Module):\n    def __init__(self, device, mixer_path):\n        \n        super(Mixer, self).__init__()\n        \n        self.linear = nn.Linear((dim_s+1)*2, dim_s*2, bias=False)\n        \n        self.load_state_dict(\n            torch.load(mixer_path, map_location=device)\n        )\n\n    def forward(self, x):\n        x = x.reshape(1,(dim_s+1)*2,-1).transpose(-1,-2)\n        x = self.linear(x)\n        return x.transpose(-1,-2).reshape(dim_s,2,-1)","chunkId":"audio_separator/separator/uvr_lib_v5/mdxnet.py:8"}],[1668,{"filePath":"audio_separator/separator/uvr_lib_v5/modules.py","startLine":0,"endLine":23,"chunkText":"import torch\nimport torch.nn as nn\n\n\nclass TFC(nn.Module):\n    def __init__(self, c, l, k, norm):\n        super(TFC, self).__init__()\n\n        self.H = nn.ModuleList()\n        for i in range(l):\n            self.H.append(\n                nn.Sequential(\n                    nn.Conv2d(in_channels=c, out_channels=c, kernel_size=k, stride=1, padding=k // 2),\n                    norm(c),\n                    nn.ReLU(),\n                )\n            )\n\n    def forward(self, x):\n        for h in self.H:\n            x = h(x)\n        return x\n\n","chunkId":"audio_separator/separator/uvr_lib_v5/modules.py:0"}],[1669,{"filePath":"audio_separator/separator/uvr_lib_v5/modules.py","startLine":24,"endLine":43,"chunkText":"class DenseTFC(nn.Module):\n    def __init__(self, c, l, k, norm):\n        super(DenseTFC, self).__init__()\n\n        self.conv = nn.ModuleList()\n        for i in range(l):\n            self.conv.append(\n                nn.Sequential(\n                    nn.Conv2d(in_channels=c, out_channels=c, kernel_size=k, stride=1, padding=k // 2),\n                    norm(c),\n                    nn.ReLU(),\n                )\n            )\n\n    def forward(self, x):\n        for layer in self.conv[:-1]:\n            x = torch.cat([layer(x), x], 1)\n        return self.conv[-1](x)\n\n","chunkId":"audio_separator/separator/uvr_lib_v5/modules.py:1"}],[1670,{"filePath":"audio_separator/separator/uvr_lib_v5/modules.py","startLine":44,"endLine":62,"chunkText":"class TFC_TDF(nn.Module):\n    def __init__(self, c, l, f, k, bn, dense=False, bias=True, norm=nn.BatchNorm2d):\n\n        super(TFC_TDF, self).__init__()\n\n        self.use_tdf = bn is not None\n\n        self.tfc = DenseTFC(c, l, k, norm) if dense else TFC(c, l, k, norm)\n\n        if self.use_tdf:\n            if bn == 0:\n                self.tdf = nn.Sequential(\n                    nn.Linear(f, f, bias=bias),\n                    norm(c),\n                    nn.ReLU()\n                )\n            else:\n                self.tdf = nn.Sequential(\n                    nn.Linear(f, f // bn, bias=bias),","chunkId":"audio_separator/separator/uvr_lib_v5/modules.py:2"}],[1671,{"filePath":"audio_separator/separator/uvr_lib_v5/modules.py","startLine":60,"endLine":74,"chunkText":"            else:\n                self.tdf = nn.Sequential(\n                    nn.Linear(f, f // bn, bias=bias),\n                    norm(c),\n                    nn.ReLU(),\n                    nn.Linear(f // bn, f, bias=bias),\n                    norm(c),\n                    nn.ReLU()\n                )\n\n    def forward(self, x):\n        x = self.tfc(x)\n        return x + self.tdf(x) if self.use_tdf else x\n\n","chunkId":"audio_separator/separator/uvr_lib_v5/modules.py:3"}],[1672,{"filePath":"audio_separator/separator/uvr_lib_v5/playsound.py","startLine":0,"endLine":20,"chunkText":"import logging\nlogger = logging.getLogger(__name__)\n\nclass PlaysoundException(Exception):\n    pass\n\ndef _canonicalizePath(path):\n    \"\"\"\n    Support passing in a pathlib.Path-like object by converting to str.\n    \"\"\"\n    import sys\n    if sys.version_info[0] >= 3:\n        return str(path)\n    else:\n        # On earlier Python versions, str is a byte string, so attempting to\n        # convert a unicode string to str will fail. Leave it alone in this case.\n        return path\n\ndef _playsoundWin(sound, block = True):\n    '''\n    Utilizes windll.winmm. Tested and known to work with MP3 and WAVE on","chunkId":"audio_separator/separator/uvr_lib_v5/playsound.py:0"}],[1673,{"filePath":"audio_separator/separator/uvr_lib_v5/playsound.py","startLine":18,"endLine":31,"chunkText":"def _playsoundWin(sound, block = True):\n    '''\n    Utilizes windll.winmm. Tested and known to work with MP3 and WAVE on\n    Windows 7 with Python 2.7. Probably works with more file formats.\n    Probably works on Windows XP thru Windows 10. Probably works with all\n    versions of Python.\n\n    Inspired by (but not copied from) Michael Gundlach <gundlach@gmail.com>'s mp3play:\n    https://github.com/michaelgundlach/mp3play\n\n    I never would have tried using windll.winmm without seeing his code.\n    '''\n    sound = '\"' + _canonicalizePath(sound) + '\"'\n","chunkId":"audio_separator/separator/uvr_lib_v5/playsound.py:1"}],[1674,{"filePath":"audio_separator/separator/uvr_lib_v5/playsound.py","startLine":29,"endLine":41,"chunkText":"    '''\n    sound = '\"' + _canonicalizePath(sound) + '\"'\n\n    from ctypes import create_unicode_buffer, windll, wintypes\n    windll.winmm.mciSendStringW.argtypes = [wintypes.LPCWSTR, wintypes.LPWSTR, wintypes.UINT, wintypes.HANDLE]\n    windll.winmm.mciGetErrorStringW.argtypes = [wintypes.DWORD, wintypes.LPWSTR, wintypes.UINT]\n\n    def winCommand(*command):\n        bufLen = 600\n        buf = create_unicode_buffer(bufLen)\n        command = ' '.join(command)\n        errorCode = int(windll.winmm.mciSendStringW(command, buf, bufLen - 1, 0))  # use widestring version of the function\n        if errorCode:","chunkId":"audio_separator/separator/uvr_lib_v5/playsound.py:2"}],[1675,{"filePath":"audio_separator/separator/uvr_lib_v5/playsound.py","startLine":39,"endLine":47,"chunkText":"        command = ' '.join(command)\n        errorCode = int(windll.winmm.mciSendStringW(command, buf, bufLen - 1, 0))  # use widestring version of the function\n        if errorCode:\n            errorBuffer = create_unicode_buffer(bufLen)\n            windll.winmm.mciGetErrorStringW(errorCode, errorBuffer, bufLen - 1)  # use widestring version of the function\n            exceptionMessage = ('\\n    Error ' + str(errorCode) + ' for command:'\n                                '\\n        ' + command +\n                                '\\n    ' + errorBuffer.value)\n            logger.error(exceptionMessage)","chunkId":"audio_separator/separator/uvr_lib_v5/playsound.py:3"}],[1676,{"filePath":"audio_separator/separator/uvr_lib_v5/playsound.py","startLine":45,"endLine":59,"chunkText":"                                '\\n        ' + command +\n                                '\\n    ' + errorBuffer.value)\n            logger.error(exceptionMessage)\n            raise PlaysoundException(exceptionMessage)\n        return buf.value\n\n    try:\n        logger.debug('Starting')\n        winCommand(u'open {}'.format(sound))\n        winCommand(u'play {}{}'.format(sound, ' wait' if block else ''))\n        logger.debug('Returning')\n    finally:\n        try:\n            winCommand(u'close {}'.format(sound))\n        except PlaysoundException:","chunkId":"audio_separator/separator/uvr_lib_v5/playsound.py:4"}],[1677,{"filePath":"audio_separator/separator/uvr_lib_v5/playsound.py","startLine":57,"endLine":76,"chunkText":"        try:\n            winCommand(u'close {}'.format(sound))\n        except PlaysoundException:\n            logger.warning(u'Failed to close the file: {}'.format(sound))\n            # If it fails, there's nothing more that can be done...\n            pass\n\ndef _handlePathOSX(sound):\n    sound = _canonicalizePath(sound)\n\n    if '://' not in sound:\n        if not sound.startswith('/'):\n            from os import getcwd\n            sound = getcwd() + '/' + sound\n        sound = 'file://' + sound\n\n    try:\n        # Don't double-encode it.\n        sound.encode('ascii')\n        return sound.replace(' ', '%20'","chunkId":"audio_separator/separator/uvr_lib_v5/playsound.py:5"}],[1678,{"filePath":"audio_separator/separator/uvr_lib_v5/playsound.py","startLine":74,"endLine":89,"chunkText":"        # Don't double-encode it.\n        sound.encode('ascii')\n        return sound.replace(' ', '%20')\n    except UnicodeEncodeError:\n        try:\n            from urllib.parse import quote  # Try the Python 3 import first...\n        except ImportError:\n            from urllib import quote  # Try using the Python 2 import before giving up entirely...\n\n        parts = sound.split('://', 1)\n        return parts[0] + '://' + quote(parts[1].encode('utf-8')).replace(' ', '%20')\n\n\ndef _playsoundOSX(sound, block = True):\n    '''\n    Utilizes AppKit.NSSound. Tested and known to work with MP3 and WAVE on","chunkId":"audio_separator/separator/uvr_lib_v5/playsound.py:6"}],[1679,{"filePath":"audio_separator/separator/uvr_lib_v5/playsound.py","startLine":87,"endLine":101,"chunkText":"def _playsoundOSX(sound, block = True):\n    '''\n    Utilizes AppKit.NSSound. Tested and known to work with MP3 and WAVE on\n    OS X 10.11 with Python 2.7. Probably works with anything QuickTime supports.\n    Probably works on OS X 10.5 and newer. Probably works with all versions of\n    Python.\n\n    Inspired by (but not copied from) Aaron's Stack Overflow answer here:\n    http://stackoverflow.com/a/34568298/901641\n\n    I never would have tried using AppKit.NSSound without seeing his code.\n    '''\n    try:\n        from AppKit import NSSound\n    except ImportError:","chunkId":"audio_separator/separator/uvr_lib_v5/playsound.py:7"}],[1680,{"filePath":"audio_separator/separator/uvr_lib_v5/playsound.py","startLine":99,"endLine":114,"chunkText":"    try:\n        from AppKit import NSSound\n    except ImportError:\n        logger.warning(\"playsound could not find a copy of AppKit - falling back to using macOS's system copy.\")\n        sys.path.append('/System/Library/Frameworks/Python.framework/Versions/2.7/Extras/lib/python/PyObjC')\n        from AppKit import NSSound\n\n    from Foundation import NSURL\n    from time       import sleep\n\n    sound = _handlePathOSX(sound)\n    url   = NSURL.URLWithString_(sound)\n    if not url:\n        raise PlaysoundException('Cannot find a sound with filename: ' + sound)\n\n    for i in range(5):","chunkId":"audio_separator/separator/uvr_lib_v5/playsound.py:8"}],[1681,{"filePath":"audio_separator/separator/uvr_lib_v5/playsound.py","startLine":112,"endLine":130,"chunkText":"        raise PlaysoundException('Cannot find a sound with filename: ' + sound)\n\n    for i in range(5):\n        nssound = NSSound.alloc().initWithContentsOfURL_byReference_(url, True)\n        if nssound:\n            break\n        else:\n            logger.debug('Failed to load sound, although url was good... ' + sound)\n    else:\n        raise PlaysoundException('Could not load sound with filename, although URL was good... ' + sound)\n    nssound.play()\n\n    if block:\n        sleep(nssound.duration())\n\ndef _playsoundNix(sound, block = True):\n    \"\"\"Play a sound using GStreamer.\n\n    Inspired by this:","chunkId":"audio_separator/separator/uvr_lib_v5/playsound.py:9"}],[1682,{"filePath":"audio_separator/separator/uvr_lib_v5/playsound.py","startLine":128,"endLine":149,"chunkText":"    \"\"\"Play a sound using GStreamer.\n\n    Inspired by this:\n    https://gstreamer.freedesktop.org/documentation/tutorials/playback/playbin-usage.html\n    \"\"\"\n    sound = _canonicalizePath(sound)\n\n    # pathname2url escapes non-URL-safe characters\n    from os.path import abspath, exists\n    try:\n        from urllib.request import pathname2url\n    except ImportError:\n        # python 2\n        from urllib import pathname2url\n\n    import gi\n    gi.require_version('Gst', '1.0')\n    from gi.repository import Gst\n\n    Gst.init(None)\n\n    playbin = Gst.ElementFactory.make('playbin', 'playbin')","chunkId":"audio_separator/separator/uvr_lib_v5/playsound.py:10"}],[1683,{"filePath":"audio_separator/separator/uvr_lib_v5/playsound.py","startLine":147,"endLine":163,"chunkText":"    Gst.init(None)\n\n    playbin = Gst.ElementFactory.make('playbin', 'playbin')\n    if sound.startswith(('http://', 'https://')):\n        playbin.props.uri = sound\n    else:\n        path = abspath(sound)\n        if not exists(path):\n            raise PlaysoundException(u'File not found: {}'.format(path))\n        playbin.props.uri = 'file://' + pathname2url(path)\n\n\n    set_result = playbin.set_state(Gst.State.PLAYING)\n    if set_result != Gst.StateChangeReturn.ASYNC:\n        raise PlaysoundException(\n            \"playbin.set_state returned \" + repr(set_result))\n","chunkId":"audio_separator/separator/uvr_lib_v5/playsound.py:11"}],[1684,{"filePath":"audio_separator/separator/uvr_lib_v5/playsound.py","startLine":161,"endLine":177,"chunkText":"        raise PlaysoundException(\n            \"playbin.set_state returned \" + repr(set_result))\n\n    # FIXME: use some other bus method than poll() with block=False\n    # https://lazka.github.io/pgi-docs/#Gst-1.0/classes/Bus.html\n    logger.debug('Starting play')\n    if block:\n        bus = playbin.get_bus()\n        try:\n            bus.poll(Gst.MessageType.EOS, Gst.CLOCK_TIME_NONE)\n        finally:\n            playbin.set_state(Gst.State.NULL)\n            \n    logger.debug('Finishing play')\n\ndef _playsoundAnotherPython(otherPython, sound, block = True, macOS = False):\n    '''","chunkId":"audio_separator/separator/uvr_lib_v5/playsound.py:12"}],[1685,{"filePath":"audio_separator/separator/uvr_lib_v5/playsound.py","startLine":175,"endLine":187,"chunkText":"\ndef _playsoundAnotherPython(otherPython, sound, block = True, macOS = False):\n    '''\n    Mostly written so that when this is run on python3 on macOS, it can invoke\n    python2 on macOS... but maybe this idea could be useful on linux, too.\n    '''\n    from inspect    import getsourcefile\n    from os.path    import abspath, exists\n    from subprocess import check_call\n    from threading  import Thread\n\n    sound = _canonicalizePath(sound)\n","chunkId":"audio_separator/separator/uvr_lib_v5/playsound.py:13"}],[1686,{"filePath":"audio_separator/separator/uvr_lib_v5/playsound.py","startLine":188,"endLine":206,"chunkText":"    class PropogatingThread(Thread):\n        def run(self):\n            self.exc = None\n            try:\n                self.ret = self._target(*self._args, **self._kwargs)\n            except BaseException as e:\n                self.exc = e\n\n        def join(self, timeout = None):\n            super().join(timeout)\n            if self.exc:\n                raise self.exc\n            return self.ret\n\n    # Check if the file exists...\n    if not exists(abspath(sound)):\n        raise PlaysoundException('Cannot find a sound with filename: ' + sound)\n\n    playsoundPath = abspath(getsourcefile(lambda: 0))","chunkId":"audio_separator/separator/uvr_lib_v5/playsound.py:14"}],[1687,{"filePath":"audio_separator/separator/uvr_lib_v5/playsound.py","startLine":204,"endLine":223,"chunkText":"        raise PlaysoundException('Cannot find a sound with filename: ' + sound)\n\n    playsoundPath = abspath(getsourcefile(lambda: 0))\n    t = PropogatingThread(target = lambda: check_call([otherPython, playsoundPath, _handlePathOSX(sound) if macOS else sound]))\n    t.start()\n    if block:\n        t.join()\n\nfrom platform import system\nsystem = system()\n\nif system == 'Windows':\n    playsound_func = _playsoundWin\nelif system == 'Darwin':\n    playsound_func = _playsoundOSX\n    import sys\n    if sys.version_info[0] > 2:\n        try:\n            from AppKit import NSSound\n        except ImportError:","chunkId":"audio_separator/separator/uvr_lib_v5/playsound.py:15"}],[1688,{"filePath":"audio_separator/separator/uvr_lib_v5/playsound.py","startLine":221,"endLine":230,"chunkText":"        try:\n            from AppKit import NSSound\n        except ImportError:\n            logger.warning(\"playsound is relying on a python 2 subprocess. Please use `pip3 install PyObjC` if you want playsound to run more efficiently.\")\n            playsound_func = lambda sound, block = True: _playsoundAnotherPython('/System/Library/Frameworks/Python.framework/Versions/2.7/bin/python', sound, block, macOS = True)\nelse:\n    playsound_func = _playsoundNix\n    if __name__ != '__main__':  # Ensure we don't infinitely recurse trying to get another python instance.\n        try:\n            import gi","chunkId":"audio_separator/separator/uvr_lib_v5/playsound.py:16"}],[1689,{"filePath":"audio_separator/separator/uvr_lib_v5/playsound.py","startLine":228,"endLine":240,"chunkText":"    if __name__ != '__main__':  # Ensure we don't infinitely recurse trying to get another python instance.\n        try:\n            import gi\n            gi.require_version('Gst', '1.0')\n            from gi.repository import Gst\n        except:\n            logger.warning(\"playsound is relying on another python subprocess. Please use `pip install pygobject` if you want playsound to run more efficiently.\")\n            playsound_func = lambda sound, block = True: _playsoundAnotherPython('/usr/bin/python3', sound, block, macOS = False)\n\ndel system\n\ndef play(audio_filepath):\n    playsound_func(audio_filepath)","chunkId":"audio_separator/separator/uvr_lib_v5/playsound.py:17"}],[1690,{"filePath":"audio_separator/separator/uvr_lib_v5/pyrb.py","startLine":0,"endLine":28,"chunkText":"import os\nimport subprocess\nimport tempfile\nimport six\nimport numpy as np\nimport soundfile as sf\nimport sys\n\nif getattr(sys, 'frozen', False):\n    BASE_PATH_RUB = sys._MEIPASS\nelse:\n    BASE_PATH_RUB = os.path.dirname(os.path.abspath(__file__))\n\n__all__ = ['time_stretch', 'pitch_shift']\n\n__RUBBERBAND_UTIL = os.path.join(BASE_PATH_RUB, 'rubberband')\n\nif six.PY2:\n    DEVNULL = open(os.devnull, 'w')\nelse:\n    DEVNULL = subprocess.DEVNULL\n\ndef __rubberband(y, sr, **kwargs):\n\n    assert sr > 0\n\n    # Get the input and output tempfile\n    fd, infile = tempfile.mkstemp(suffix='.wav')\n    os.close(fd)","chunkId":"audio_separator/separator/uvr_lib_v5/pyrb.py:0"}],[1691,{"filePath":"audio_separator/separator/uvr_lib_v5/pyrb.py","startLine":26,"endLine":47,"chunkText":"    # Get the input and output tempfile\n    fd, infile = tempfile.mkstemp(suffix='.wav')\n    os.close(fd)\n    fd, outfile = tempfile.mkstemp(suffix='.wav')\n    os.close(fd)\n\n    # dump the audio\n    sf.write(infile, y, sr)\n\n    try:\n        # Execute rubberband\n        arguments = [__RUBBERBAND_UTIL, '-q']\n\n        for key, value in six.iteritems(kwargs):\n            arguments.append(str(key))\n            arguments.append(str(value))\n\n        arguments.extend([infile, outfile])\n\n        subprocess.check_call(arguments, stdout=DEVNULL, stderr=DEVNULL)\n\n        # Load the processed audio.","chunkId":"audio_separator/separator/uvr_lib_v5/pyrb.py:1"}],[1692,{"filePath":"audio_separator/separator/uvr_lib_v5/pyrb.py","startLine":45,"endLine":62,"chunkText":"        subprocess.check_call(arguments, stdout=DEVNULL, stderr=DEVNULL)\n\n        # Load the processed audio.\n        y_out, _ = sf.read(outfile, always_2d=True)\n\n        # make sure that output dimensions matches input\n        if y.ndim == 1:\n            y_out = np.squeeze(y_out)\n\n    except OSError as exc:\n        six.raise_from(RuntimeError('Failed to execute rubberband. '\n                                    'Please verify that rubberband-cli '\n                                    'is installed.'),\n                       exc)\n\n    finally:\n        # Remove temp files\n        os.unlink(infile)","chunkId":"audio_separator/separator/uvr_lib_v5/pyrb.py:2"}],[1693,{"filePath":"audio_separator/separator/uvr_lib_v5/pyrb.py","startLine":60,"endLine":90,"chunkText":"    finally:\n        # Remove temp files\n        os.unlink(infile)\n        os.unlink(outfile)\n\n    return y_out\n\ndef time_stretch(y, sr, rate, rbargs=None):\n    if rate <= 0:\n        raise ValueError('rate must be strictly positive')\n\n    if rate == 1.0:\n        return y\n\n    if rbargs is None:\n        rbargs = dict()\n\n    rbargs.setdefault('--tempo', rate)\n\n    return __rubberband(y, sr, **rbargs)\n\ndef pitch_shift(y, sr, n_steps, rbargs=None):\n\n    if n_steps == 0:\n        return y\n\n    if rbargs is None:\n        rbargs = dict()\n\n    rbargs.setdefault('--pitch', n_steps)\n","chunkId":"audio_separator/separator/uvr_lib_v5/pyrb.py:3"}],[1694,{"filePath":"audio_separator/separator/uvr_lib_v5/pyrb.py","startLine":88,"endLine":92,"chunkText":"\n    rbargs.setdefault('--pitch', n_steps)\n\n    return __rubberband(y, sr, **rbargs)\n","chunkId":"audio_separator/separator/uvr_lib_v5/pyrb.py:4"}],[1695,{"filePath":"audio_separator/separator/uvr_lib_v5/results.py","startLine":0,"endLine":15,"chunkText":"# -*- coding: utf-8 -*-\n\n\"\"\"\nMatchering - Audio Matching and Mastering Python Library\nCopyright (C) 2016-2022 Sergree\n\nThis program is free software: you can redistribute it and/or modify\nit under the terms of the GNU General Public License as published by\nthe Free Software Foundation, either version 3 of the License, or\n(at your option) any later version.\n\nThis program is distributed in the hope that it will be useful,\nbut WITHOUT ANY WARRANTY; without even the implied warranty of\nMERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the\nGNU General Public License for more details.\n","chunkId":"audio_separator/separator/uvr_lib_v5/results.py:0"}],[1696,{"filePath":"audio_separator/separator/uvr_lib_v5/results.py","startLine":13,"endLine":31,"chunkText":"MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the\nGNU General Public License for more details.\n\nYou should have received a copy of the GNU General Public License\nalong with this program.  If not, see <https://www.gnu.org/licenses/>.\n\"\"\"\n\nimport os\nimport soundfile as sf\n\n\nclass Result:\n    def __init__(\n        self, file: str, subtype: str, use_limiter: bool = True, normalize: bool = True\n    ):\n        _, file_ext = os.path.splitext(file)\n        file_ext = file_ext[1:].upper()\n        if not sf.check_format(file_ext):\n            raise TypeError(f\"{file_ext} format is not supported\")","chunkId":"audio_separator/separator/uvr_lib_v5/results.py:1"}],[1697,{"filePath":"audio_separator/separator/uvr_lib_v5/results.py","startLine":29,"endLine":46,"chunkText":"        file_ext = file_ext[1:].upper()\n        if not sf.check_format(file_ext):\n            raise TypeError(f\"{file_ext} format is not supported\")\n        if not sf.check_format(file_ext, subtype):\n            raise TypeError(f\"{file_ext} format does not have {subtype} subtype\")\n        self.file = file\n        self.subtype = subtype\n        self.use_limiter = use_limiter\n        self.normalize = normalize\n\n\ndef pcm16(file: str) -> Result:\n    return Result(file, \"PCM_16\")\n\ndef pcm24(file: str) -> Result:\n    return Result(file, \"FLOAT\")\n\ndef save_audiofile(file: str, wav_set=\"PCM_16\") -> Result:","chunkId":"audio_separator/separator/uvr_lib_v5/results.py:2"}],[1698,{"filePath":"audio_separator/separator/uvr_lib_v5/results.py","startLine":44,"endLine":48,"chunkText":"    return Result(file, \"FLOAT\")\n\ndef save_audiofile(file: str, wav_set=\"PCM_16\") -> Result:\n    return Result(file, wav_set)\n","chunkId":"audio_separator/separator/uvr_lib_v5/results.py:3"}],[1699,{"filePath":"audio_separator/separator/uvr_lib_v5/roformer/attend.py","startLine":0,"endLine":36,"chunkText":"from functools import wraps\nfrom packaging import version\nfrom collections import namedtuple\n\nimport torch\nfrom torch import nn, einsum\nimport torch.nn.functional as F\n\nfrom einops import rearrange, reduce\n\n# constants\n\nFlashAttentionConfig = namedtuple(\"FlashAttentionConfig\", [\"enable_flash\", \"enable_math\", \"enable_mem_efficient\"])\n\n# helpers\n\n\ndef exists(val):\n    return val is not None\n\n\ndef once(fn):\n    called = False\n\n    @wraps(fn)\n    def inner(x):\n        nonlocal called\n        if called:\n            return\n        called = True\n        return fn(x)\n\n    return inner\n\n\nprint_once = once(print)\n","chunkId":"audio_separator/separator/uvr_lib_v5/roformer/attend.py:0"}],[1700,{"filePath":"audio_separator/separator/uvr_lib_v5/roformer/attend.py","startLine":37,"endLine":54,"chunkText":"# main class\n\n\nclass Attend(nn.Module):\n    def __init__(self, dropout=0.0, flash=False):\n        super().__init__()\n        self.dropout = dropout\n        self.attn_dropout = nn.Dropout(dropout)\n\n        self.flash = flash\n        assert not (flash and version.parse(torch.__version__) < version.parse(\"2.0.0\")), \"in order to use flash attention, you must be using pytorch 2.0 or above\"\n\n        # determine efficient attention configs for cuda and cpu\n\n        self.cpu_config = FlashAttentionConfig(True, True, True)\n        self.cuda_config = None\n\n        if not torch.cuda.is_available() or not flash:","chunkId":"audio_separator/separator/uvr_lib_v5/roformer/attend.py:1"}],[1701,{"filePath":"audio_separator/separator/uvr_lib_v5/roformer/attend.py","startLine":52,"endLine":65,"chunkText":"        self.cuda_config = None\n\n        if not torch.cuda.is_available() or not flash:\n            return\n\n        device_properties = torch.cuda.get_device_properties(torch.device(\"cuda\"))\n\n        if device_properties.major == 8 and device_properties.minor == 0:\n            print_once(\"A100 GPU detected, using flash attention if input tensor is on cuda\")\n            self.cuda_config = FlashAttentionConfig(True, False, False)\n        else:\n            self.cuda_config = FlashAttentionConfig(False, True, True)\n\n    def flash_attn(self, q, k, v):","chunkId":"audio_separator/separator/uvr_lib_v5/roformer/attend.py:2"}],[1702,{"filePath":"audio_separator/separator/uvr_lib_v5/roformer/attend.py","startLine":63,"endLine":76,"chunkText":"            self.cuda_config = FlashAttentionConfig(False, True, True)\n\n    def flash_attn(self, q, k, v):\n        _, heads, q_len, _, k_len, is_cuda, device = *q.shape, k.shape[-2], q.is_cuda, q.device\n\n        # Check if there is a compatible device for flash attention\n\n        config = self.cuda_config if is_cuda else self.cpu_config\n\n        # sdpa_flash kernel only supports float16 on sm80+ architecture gpu\n        if is_cuda and q.dtype != torch.float16:\n            config = FlashAttentionConfig(False, True, True)\n\n        # pytorch 2.0 flash attn: q, k, v, mask, dropout, softmax_scale","chunkId":"audio_separator/separator/uvr_lib_v5/roformer/attend.py:3"}],[1703,{"filePath":"audio_separator/separator/uvr_lib_v5/roformer/attend.py","startLine":74,"endLine":90,"chunkText":"            config = FlashAttentionConfig(False, True, True)\n\n        # pytorch 2.0 flash attn: q, k, v, mask, dropout, softmax_scale\n        with torch.backends.cuda.sdp_kernel(**config._asdict()):\n            out = F.scaled_dot_product_attention(q, k, v, dropout_p=self.dropout if self.training else 0.0)\n\n        return out\n\n    def forward(self, q, k, v):\n        \"\"\"\n        einstein notation\n        b - batch\n        h - heads\n        n, i, j - sequence length (base sequence length, source, target)\n        d - feature dimension\n        \"\"\"\n","chunkId":"audio_separator/separator/uvr_lib_v5/roformer/attend.py:4"}],[1704,{"filePath":"audio_separator/separator/uvr_lib_v5/roformer/attend.py","startLine":88,"endLine":112,"chunkText":"        d - feature dimension\n        \"\"\"\n\n        q_len, k_len, device = q.shape[-2], k.shape[-2], q.device\n\n        scale = q.shape[-1] ** -0.5\n\n        if self.flash:\n            return self.flash_attn(q, k, v)\n\n        # similarity\n\n        sim = einsum(f\"b h i d, b h j d -> b h i j\", q, k) * scale\n\n        # attention\n\n        attn = sim.softmax(dim=-1)\n        attn = self.attn_dropout(attn)\n\n        # aggregate values\n\n        out = einsum(f\"b h i j, b h j d -> b h i d\", attn, v)\n\n        return out\n","chunkId":"audio_separator/separator/uvr_lib_v5/roformer/attend.py:5"}],[1705,{"filePath":"audio_separator/separator/uvr_lib_v5/roformer/bs_roformer.py","startLine":0,"endLine":31,"chunkText":"from functools import partial\n\nimport torch\nfrom torch import nn, einsum, Tensor\nfrom torch.nn import Module, ModuleList\nimport torch.nn.functional as F\n\nfrom .attend import Attend\n\nfrom beartype.typing import Tuple, Optional, List, Callable\nfrom beartype import beartype\n\nfrom rotary_embedding_torch import RotaryEmbedding\n\nfrom einops import rearrange, pack, unpack\nfrom einops.layers.torch import Rearrange\n\n# helper functions\n\n\ndef exists(val):\n    return val is not None\n\n\ndef default(v, d):\n    return v if exists(v) else d\n\n\ndef pack_one(t, pattern):\n    return pack([t], pattern)\n\n","chunkId":"audio_separator/separator/uvr_lib_v5/roformer/bs_roformer.py:0"}],[1706,{"filePath":"audio_separator/separator/uvr_lib_v5/roformer/bs_roformer.py","startLine":30,"endLine":56,"chunkText":"\n\ndef unpack_one(t, ps, pattern):\n    return unpack(t, ps, pattern)[0]\n\n\n# norm\n\n\ndef l2norm(t):\n    return F.normalize(t, dim=-1, p=2)\n\n\nclass RMSNorm(Module):\n    def __init__(self, dim):\n        super().__init__()\n        self.scale = dim**0.5\n        self.gamma = nn.Parameter(torch.ones(dim))\n\n    def forward(self, x):\n        x = x.to(self.gamma.device)\n        return F.normalize(x, dim=-1) * self.scale * self.gamma\n\n\n# attention\n\n","chunkId":"audio_separator/separator/uvr_lib_v5/roformer/bs_roformer.py:1"}],[1707,{"filePath":"audio_separator/separator/uvr_lib_v5/roformer/bs_roformer.py","startLine":57,"endLine":66,"chunkText":"class FeedForward(Module):\n    def __init__(self, dim, mult=4, dropout=0.0):\n        super().__init__()\n        dim_inner = int(dim * mult)\n        self.net = nn.Sequential(RMSNorm(dim), nn.Linear(dim, dim_inner), nn.GELU(), nn.Dropout(dropout), nn.Linear(dim_inner, dim), nn.Dropout(dropout))\n\n    def forward(self, x):\n        return self.net(x)\n\n","chunkId":"audio_separator/separator/uvr_lib_v5/roformer/bs_roformer.py:2"}],[1708,{"filePath":"audio_separator/separator/uvr_lib_v5/roformer/bs_roformer.py","startLine":67,"endLine":84,"chunkText":"class Attention(Module):\n    def __init__(self, dim, heads=8, dim_head=64, dropout=0.0, rotary_embed=None, flash=True):\n        super().__init__()\n        self.heads = heads\n        self.scale = dim_head**-0.5\n        dim_inner = heads * dim_head\n\n        self.rotary_embed = rotary_embed\n\n        self.attend = Attend(flash=flash, dropout=dropout)\n\n        self.norm = RMSNorm(dim)\n        self.to_qkv = nn.Linear(dim, dim_inner * 3, bias=False)\n\n        self.to_gates = nn.Linear(dim, heads)\n\n        self.to_out = nn.Sequential(nn.Linear(dim_inner, dim, bias=False), nn.Dropout(dropout))\n","chunkId":"audio_separator/separator/uvr_lib_v5/roformer/bs_roformer.py:3"}],[1709,{"filePath":"audio_separator/separator/uvr_lib_v5/roformer/bs_roformer.py","startLine":83,"endLine":99,"chunkText":"        self.to_out = nn.Sequential(nn.Linear(dim_inner, dim, bias=False), nn.Dropout(dropout))\n\n    def forward(self, x):\n        x = self.norm(x)\n\n        q, k, v = rearrange(self.to_qkv(x), \"b n (qkv h d) -> qkv b h n d\", qkv=3, h=self.heads)\n\n        if exists(self.rotary_embed):\n            q = self.rotary_embed.rotate_queries_or_keys(q)\n            k = self.rotary_embed.rotate_queries_or_keys(k)\n\n        out = self.attend(q, k, v)\n\n        gates = self.to_gates(x)\n        out = out * rearrange(gates, \"b n h -> b h n 1\").sigmoid()\n\n        out = rearrange(out, \"b h n d -> b n (h d)\")","chunkId":"audio_separator/separator/uvr_lib_v5/roformer/bs_roformer.py:4"}],[1710,{"filePath":"audio_separator/separator/uvr_lib_v5/roformer/bs_roformer.py","startLine":97,"endLine":102,"chunkText":"        out = out * rearrange(gates, \"b n h -> b h n 1\").sigmoid()\n\n        out = rearrange(out, \"b h n d -> b n (h d)\")\n        return self.to_out(out)\n\n","chunkId":"audio_separator/separator/uvr_lib_v5/roformer/bs_roformer.py:5"}],[1711,{"filePath":"audio_separator/separator/uvr_lib_v5/roformer/bs_roformer.py","startLine":103,"endLine":117,"chunkText":"class LinearAttention(Module):\n    \"\"\"\n    this flavor of linear attention proposed in https://arxiv.org/abs/2106.09681 by El-Nouby et al.\n    \"\"\"\n\n    @beartype\n    def __init__(self, *, dim, dim_head=32, heads=8, scale=8, flash=False, dropout=0.0):\n        super().__init__()\n        dim_inner = dim_head * heads\n        self.norm = RMSNorm(dim)\n\n        self.to_qkv = nn.Sequential(nn.Linear(dim, dim_inner * 3, bias=False), Rearrange(\"b n (qkv h d) -> qkv b h d n\", qkv=3, h=heads))\n\n        self.temperature = nn.Parameter(torch.ones(heads, 1, 1))\n","chunkId":"audio_separator/separator/uvr_lib_v5/roformer/bs_roformer.py:6"}],[1712,{"filePath":"audio_separator/separator/uvr_lib_v5/roformer/bs_roformer.py","startLine":115,"endLine":134,"chunkText":"\n        self.temperature = nn.Parameter(torch.ones(heads, 1, 1))\n\n        self.attend = Attend(scale=scale, dropout=dropout, flash=flash)\n\n        self.to_out = nn.Sequential(Rearrange(\"b h d n -> b n (h d)\"), nn.Linear(dim_inner, dim, bias=False))\n\n    def forward(self, x):\n        x = self.norm(x)\n\n        q, k, v = self.to_qkv(x)\n\n        q, k = map(l2norm, (q, k))\n        q = q * self.temperature.exp()\n\n        out = self.attend(q, k, v)\n\n        return self.to_out(out)\n\n","chunkId":"audio_separator/separator/uvr_lib_v5/roformer/bs_roformer.py:7"}],[1713,{"filePath":"audio_separator/separator/uvr_lib_v5/roformer/bs_roformer.py","startLine":135,"endLine":145,"chunkText":"class Transformer(Module):\n    def __init__(self, *, dim, depth, dim_head=64, heads=8, attn_dropout=0.0, ff_dropout=0.0, ff_mult=4, norm_output=True, rotary_embed=None, flash_attn=True, linear_attn=False):\n        super().__init__()\n        self.layers = ModuleList([])\n\n        for _ in range(depth):\n            if linear_attn:\n                attn = LinearAttention(dim=dim, dim_head=dim_head, heads=heads, dropout=attn_dropout, flash=flash_attn)\n            else:\n                attn = Attention(dim=dim, dim_head=dim_head, heads=heads, dropout=attn_dropout, rotary_embed=rotary_embed, flash=flash_attn)\n","chunkId":"audio_separator/separator/uvr_lib_v5/roformer/bs_roformer.py:8"}],[1714,{"filePath":"audio_separator/separator/uvr_lib_v5/roformer/bs_roformer.py","startLine":143,"endLine":161,"chunkText":"            else:\n                attn = Attention(dim=dim, dim_head=dim_head, heads=heads, dropout=attn_dropout, rotary_embed=rotary_embed, flash=flash_attn)\n\n            self.layers.append(ModuleList([attn, FeedForward(dim=dim, mult=ff_mult, dropout=ff_dropout)]))\n\n        self.norm = RMSNorm(dim) if norm_output else nn.Identity()\n\n    def forward(self, x):\n\n        for attn, ff in self.layers:\n            x = attn(x) + x\n            x = ff(x) + x\n\n        return self.norm(x)\n\n\n# bandsplit module\n\n","chunkId":"audio_separator/separator/uvr_lib_v5/roformer/bs_roformer.py:9"}],[1715,{"filePath":"audio_separator/separator/uvr_lib_v5/roformer/bs_roformer.py","startLine":162,"endLine":181,"chunkText":"class BandSplit(Module):\n    @beartype\n    def __init__(self, dim, dim_inputs: Tuple[int, ...]):\n        super().__init__()\n        self.dim_inputs = dim_inputs\n        self.to_features = ModuleList([])\n\n        for dim_in in dim_inputs:\n            net = nn.Sequential(RMSNorm(dim_in), nn.Linear(dim_in, dim))\n\n            self.to_features.append(net)\n\n    def forward(self, x):\n        x = x.split(self.dim_inputs, dim=-1)\n\n        outs = []\n        for split_input, to_feature in zip(x, self.to_features):\n            split_output = to_feature(split_input)\n            outs.append(split_output)\n","chunkId":"audio_separator/separator/uvr_lib_v5/roformer/bs_roformer.py:10"}],[1716,{"filePath":"audio_separator/separator/uvr_lib_v5/roformer/bs_roformer.py","startLine":179,"endLine":200,"chunkText":"            split_output = to_feature(split_input)\n            outs.append(split_output)\n\n        return torch.stack(outs, dim=-2)\n\n\ndef MLP(dim_in, dim_out, dim_hidden=None, depth=1, activation=nn.Tanh):\n    dim_hidden = default(dim_hidden, dim_in)\n\n    net = []\n    dims = (dim_in, *((dim_hidden,) * (depth - 1)), dim_out)\n\n    for ind, (layer_dim_in, layer_dim_out) in enumerate(zip(dims[:-1], dims[1:])):\n        is_last = ind == (len(dims) - 2)\n\n        net.append(nn.Linear(layer_dim_in, layer_dim_out))\n\n        if is_last:\n            continue\n\n        net.append(activation())\n","chunkId":"audio_separator/separator/uvr_lib_v5/roformer/bs_roformer.py:11"}],[1717,{"filePath":"audio_separator/separator/uvr_lib_v5/roformer/bs_roformer.py","startLine":204,"endLine":223,"chunkText":"class MaskEstimator(Module):\n    @beartype\n    def __init__(self, dim, dim_inputs: Tuple[int, ...], depth, mlp_expansion_factor=4):\n        super().__init__()\n        self.dim_inputs = dim_inputs\n        self.to_freqs = ModuleList([])\n        dim_hidden = dim * mlp_expansion_factor\n\n        for dim_in in dim_inputs:\n            net = []\n\n            mlp = nn.Sequential(MLP(dim, dim_in * 2, dim_hidden=dim_hidden, depth=depth), nn.GLU(dim=-1))\n\n            self.to_freqs.append(mlp)\n\n    def forward(self, x):\n        x = x.unbind(dim=-2)\n\n        outs = []\n","chunkId":"audio_separator/separator/uvr_lib_v5/roformer/bs_roformer.py:12"}],[1718,{"filePath":"audio_separator/separator/uvr_lib_v5/roformer/bs_roformer.py","startLine":221,"endLine":283,"chunkText":"        outs = []\n\n        for band_features, mlp in zip(x, self.to_freqs):\n            freq_out = mlp(band_features)\n            outs.append(freq_out)\n\n        return torch.cat(outs, dim=-1)\n\n\n# main class\n\nDEFAULT_FREQS_PER_BANDS = (\n    2,\n    2,\n    2,\n    2,\n    2,\n    2,\n    2,\n    2,\n    2,\n    2,\n    2,\n    2,\n    2,\n    2,\n    2,\n    2,\n    2,\n    2,\n    2,\n    2,\n    2,\n    2,\n    2,\n    2,\n    4,\n    4,\n    4,\n    4,\n    4,\n    4,\n    4,\n    4,\n    4,\n    4,\n    4,\n    4,\n    12,\n    12,\n    12,\n    12,\n    12,\n    12,\n    12,\n    12,\n    24,\n    24,\n    24,\n    24,\n    24,\n    24,\n    24,","chunkId":"audio_separator/separator/uvr_lib_v5/roformer/bs_roformer.py:13"}],[1719,{"filePath":"audio_separator/separator/uvr_lib_v5/roformer/bs_roformer.py","startLine":285,"endLine":298,"chunkText":"    24,\n    48,\n    48,\n    48,\n    48,\n    48,\n    48,\n    48,\n    48,\n    128,\n    129,\n)\n\n","chunkId":"audio_separator/separator/uvr_lib_v5/roformer/bs_roformer.py:14"}],[1720,{"filePath":"audio_separator/separator/uvr_lib_v5/roformer/bs_roformer.py","startLine":299,"endLine":320,"chunkText":"class BSRoformer(Module):\n\n    @beartype\n    def __init__(\n        self,\n        dim,\n        *,\n        depth,\n        stereo=False,\n        num_stems=1,\n        time_transformer_depth=2,\n        freq_transformer_depth=2,\n        linear_transformer_depth=0,\n        freqs_per_bands: Tuple[int, ...] = DEFAULT_FREQS_PER_BANDS,\n        # in the paper, they divide into ~60 bands, test with 1 for starters\n        dim_head=64,\n        heads=8,\n        attn_dropout=0.0,\n        ff_dropout=0.0,\n        flash_attn=True,\n        # New parameters for updated implementation\n        mlp_expansion_factor=4,","chunkId":"audio_separator/separator/uvr_lib_v5/roformer/bs_roformer.py:15"}],[1721,{"filePath":"audio_separator/separator/uvr_lib_v5/roformer/bs_roformer.py","startLine":318,"endLine":332,"chunkText":"        flash_attn=True,\n        # New parameters for updated implementation\n        mlp_expansion_factor=4,\n        sage_attention=False,\n        zero_dc=True,\n        use_torch_checkpoint=False,\n        skip_connection=False,\n        # Original parameters continue\n        dim_freqs_in=1025,\n        stft_n_fft=2048,\n        stft_hop_length=512,\n        # 10ms at 44100Hz, from sections 4.1, 4.4 in the paper - @faroit recommends // 2 or // 4 for better reconstruction\n        stft_win_length=2048,\n        stft_normalized=False,\n        stft_window_fn: Optional[Callable] = None,","chunkId":"audio_separator/separator/uvr_lib_v5/roformer/bs_roformer.py:16"}],[1722,{"filePath":"audio_separator/separator/uvr_lib_v5/roformer/bs_roformer.py","startLine":331,"endLine":346,"chunkText":"        stft_normalized=False,\n        stft_window_fn: Optional[Callable] = None,\n        mask_estimator_depth=2,\n        multi_stft_resolution_loss_weight=1.0,\n        multi_stft_resolutions_window_sizes: Tuple[int, ...] = (4096, 2048, 1024, 512, 256),\n        multi_stft_hop_size=147,\n        multi_stft_normalized=False,\n        multi_stft_window_fn: Callable = torch.hann_window,\n    ):\n        super().__init__()\n\n        self.stereo = stereo\n        self.audio_channels = 2 if stereo else 1\n        self.num_stems = num_stems\n        \n        # Store new parameters as instance variables","chunkId":"audio_separator/separator/uvr_lib_v5/roformer/bs_roformer.py:17"}],[1723,{"filePath":"audio_separator/separator/uvr_lib_v5/roformer/bs_roformer.py","startLine":344,"endLine":360,"chunkText":"        self.num_stems = num_stems\n        \n        # Store new parameters as instance variables\n        self.mlp_expansion_factor = mlp_expansion_factor\n        self.sage_attention = sage_attention\n        self.zero_dc = zero_dc\n        self.use_torch_checkpoint = use_torch_checkpoint\n        self.skip_connection = skip_connection\n\n        self.layers = ModuleList([])\n\n        # Add parameters to transformer kwargs (excluding sage_attention for now)\n        transformer_kwargs = dict(\n            dim=dim, \n            heads=heads, \n            dim_head=dim_head, \n            attn_dropout=attn_dropout, ","chunkId":"audio_separator/separator/uvr_lib_v5/roformer/bs_roformer.py:18"}],[1724,{"filePath":"audio_separator/separator/uvr_lib_v5/roformer/bs_roformer.py","startLine":358,"endLine":375,"chunkText":"            heads=heads, \n            dim_head=dim_head, \n            attn_dropout=attn_dropout, \n            ff_dropout=ff_dropout, \n            flash_attn=flash_attn, \n            norm_output=False\n        )\n        \n        # Print sage attention status if enabled (as per research findings)\n        if sage_attention:\n            print(\"Use Sage Attention\")\n\n        time_rotary_embed = RotaryEmbedding(dim=dim_head)\n        freq_rotary_embed = RotaryEmbedding(dim=dim_head)\n\n        for _ in range(depth):\n            tran_modules = []\n            if linear_transformer_depth > 0:","chunkId":"audio_separator/separator/uvr_lib_v5/roformer/bs_roformer.py:19"}],[1725,{"filePath":"audio_separator/separator/uvr_lib_v5/roformer/bs_roformer.py","startLine":373,"endLine":382,"chunkText":"        for _ in range(depth):\n            tran_modules = []\n            if linear_transformer_depth > 0:\n                tran_modules.append(Transformer(depth=linear_transformer_depth, linear_attn=True, **transformer_kwargs))\n            tran_modules.append(Transformer(depth=time_transformer_depth, rotary_embed=time_rotary_embed, **transformer_kwargs))\n            tran_modules.append(Transformer(depth=freq_transformer_depth, rotary_embed=freq_rotary_embed, **transformer_kwargs))\n            self.layers.append(nn.ModuleList(tran_modules))\n\n        self.final_norm = RMSNorm(dim)\n","chunkId":"audio_separator/separator/uvr_lib_v5/roformer/bs_roformer.py:20"}],[1726,{"filePath":"audio_separator/separator/uvr_lib_v5/roformer/bs_roformer.py","startLine":380,"endLine":391,"chunkText":"\n        self.final_norm = RMSNorm(dim)\n\n        self.stft_kwargs = dict(n_fft=stft_n_fft, hop_length=stft_hop_length, win_length=stft_win_length, normalized=stft_normalized)\n\n        self.stft_window_fn = partial(default(stft_window_fn, torch.hann_window), stft_win_length)\n\n        freqs = torch.stft(torch.randn(1, 4096), **self.stft_kwargs, return_complex=True).shape[1]\n\n        assert len(freqs_per_bands) > 1\n        assert sum(freqs_per_bands) == freqs, f\"the number of freqs in the bands must equal {freqs} based on the STFT settings, but got {sum(freqs_per_bands)}\"\n","chunkId":"audio_separator/separator/uvr_lib_v5/roformer/bs_roformer.py:21"}],[1727,{"filePath":"audio_separator/separator/uvr_lib_v5/roformer/bs_roformer.py","startLine":389,"endLine":401,"chunkText":"        assert len(freqs_per_bands) > 1\n        assert sum(freqs_per_bands) == freqs, f\"the number of freqs in the bands must equal {freqs} based on the STFT settings, but got {sum(freqs_per_bands)}\"\n\n        freqs_per_bands_with_complex = tuple(2 * f * self.audio_channels for f in freqs_per_bands)\n\n        self.band_split = BandSplit(dim=dim, dim_inputs=freqs_per_bands_with_complex)\n\n        self.mask_estimators = nn.ModuleList([])\n\n        for _ in range(num_stems):\n            mask_estimator = MaskEstimator(\n                dim=dim, \n                dim_inputs=freqs_per_bands_with_complex, ","chunkId":"audio_separator/separator/uvr_lib_v5/roformer/bs_roformer.py:22"}],[1728,{"filePath":"audio_separator/separator/uvr_lib_v5/roformer/bs_roformer.py","startLine":399,"endLine":412,"chunkText":"            mask_estimator = MaskEstimator(\n                dim=dim, \n                dim_inputs=freqs_per_bands_with_complex, \n                depth=mask_estimator_depth,\n                mlp_expansion_factor=mlp_expansion_factor  # Use the new parameter\n            )\n\n            self.mask_estimators.append(mask_estimator)\n\n        # for the multi-resolution stft loss\n\n        self.multi_stft_resolution_loss_weight = multi_stft_resolution_loss_weight\n        self.multi_stft_resolutions_window_sizes = multi_stft_resolutions_window_sizes\n        self.multi_stft_n_fft = stft_n_fft","chunkId":"audio_separator/separator/uvr_lib_v5/roformer/bs_roformer.py:23"}],[1729,{"filePath":"audio_separator/separator/uvr_lib_v5/roformer/bs_roformer.py","startLine":410,"endLine":424,"chunkText":"        self.multi_stft_resolution_loss_weight = multi_stft_resolution_loss_weight\n        self.multi_stft_resolutions_window_sizes = multi_stft_resolutions_window_sizes\n        self.multi_stft_n_fft = stft_n_fft\n        self.multi_stft_window_fn = multi_stft_window_fn\n\n        self.multi_stft_kwargs = dict(hop_length=multi_stft_hop_size, normalized=multi_stft_normalized)\n\n    def forward(self, raw_audio, target=None, return_loss_breakdown=False):\n        \"\"\"\n        einops\n\n        b - batch\n        f - freq\n        t - time\n        s - audio channel (1 for mono, 2 for stereo)","chunkId":"audio_separator/separator/uvr_lib_v5/roformer/bs_roformer.py:24"}],[1730,{"filePath":"audio_separator/separator/uvr_lib_v5/roformer/bs_roformer.py","startLine":422,"endLine":442,"chunkText":"        f - freq\n        t - time\n        s - audio channel (1 for mono, 2 for stereo)\n        n - number of 'stems'\n        c - complex (2)\n        d - feature dimension\n        \"\"\"\n\n        original_device = raw_audio.device\n        x_is_mps = True if original_device.type == \"mps\" else False\n\n        # if x_is_mps:\n        #     raw_audio = raw_audio.cpu()\n\n        device = raw_audio.device\n\n        if raw_audio.ndim == 2:\n            raw_audio = rearrange(raw_audio, \"b t -> b 1 t\")\n\n        channels = raw_audio.shape[1]\n        assert (not self.stereo and channels == 1) or (","chunkId":"audio_separator/separator/uvr_lib_v5/roformer/bs_roformer.py:25"}],[1731,{"filePath":"audio_separator/separator/uvr_lib_v5/roformer/bs_roformer.py","startLine":440,"endLine":452,"chunkText":"        channels = raw_audio.shape[1]\n        assert (not self.stereo and channels == 1) or (\n            self.stereo and channels == 2\n        ), \"stereo needs to be set to True if passing in audio signal that is stereo (channel dimension of 2). also need to be False if mono (channel dimension of 1)\"\n\n        # to stft\n\n        raw_audio, batch_audio_channel_packed_shape = pack_one(raw_audio, \"* t\")\n\n        stft_window = self.stft_window_fn().to(device)\n\n        stft_repr = torch.stft(raw_audio, **self.stft_kwargs, window=stft_window, return_complex=True)\n        stft_repr = torch.view_as_real(stft_repr","chunkId":"audio_separator/separator/uvr_lib_v5/roformer/bs_roformer.py:26"}],[1732,{"filePath":"audio_separator/separator/uvr_lib_v5/roformer/bs_roformer.py","startLine":452,"endLine":465,"chunkText":"        stft_repr = torch.stft(raw_audio, **self.stft_kwargs, window=stft_window, return_complex=True)\n        stft_repr = torch.view_as_real(stft_repr)\n\n        stft_repr = unpack_one(stft_repr, batch_audio_channel_packed_shape, \"* f t c\")\n        stft_repr = rearrange(stft_repr, \"b s f t c -> b (f s) t c\")  # merge stereo / mono into the frequency, with frequency leading dimension, for band splitting\n\n        x = rearrange(stft_repr, \"b f t c -> b t (f c)\")\n\n        x = self.band_split(x)\n\n        # axial / hierarchical attention\n\n        for transformer_block in self.layers:\n","chunkId":"audio_separator/separator/uvr_lib_v5/roformer/bs_roformer.py:27"}],[1733,{"filePath":"audio_separator/separator/uvr_lib_v5/roformer/bs_roformer.py","startLine":463,"endLine":480,"chunkText":"\n        for transformer_block in self.layers:\n\n            if len(transformer_block) == 3:\n                linear_transformer, time_transformer, freq_transformer = transformer_block\n\n                x, ft_ps = pack([x], \"b * d\")\n                x = linear_transformer(x)\n                (x,) = unpack(x, ft_ps, \"b * d\")\n            else:\n                time_transformer, freq_transformer = transformer_block\n\n            x = rearrange(x, \"b t f d -> b f t d\")\n            x, ps = pack([x], \"* t d\")\n\n            x = time_transformer(x)\n\n            (x,) = unpack(x, ps, \"* t d\")","chunkId":"audio_separator/separator/uvr_lib_v5/roformer/bs_roformer.py:28"}],[1734,{"filePath":"audio_separator/separator/uvr_lib_v5/roformer/bs_roformer.py","startLine":478,"endLine":499,"chunkText":"            x = time_transformer(x)\n\n            (x,) = unpack(x, ps, \"* t d\")\n            x = rearrange(x, \"b f t d -> b t f d\")\n            x, ps = pack([x], \"* f d\")\n\n            x = freq_transformer(x)\n\n            (x,) = unpack(x, ps, \"* f d\")\n\n        x = self.final_norm(x)\n\n        mask = torch.stack([fn(x) for fn in self.mask_estimators], dim=1)\n        mask = rearrange(mask, \"b n t (f c) -> b n f t c\", c=2)\n\n        # if x_is_mps:\n        #     mask = mask.to('cpu')\n\n        # modulate frequency representation\n\n        stft_repr = rearrange(stft_repr, \"b f t c -> b 1 f t c\")\n","chunkId":"audio_separator/separator/uvr_lib_v5/roformer/bs_roformer.py:29"}],[1735,{"filePath":"audio_separator/separator/uvr_lib_v5/roformer/bs_roformer.py","startLine":497,"endLine":512,"chunkText":"\n        stft_repr = rearrange(stft_repr, \"b f t c -> b 1 f t c\")\n\n        # complex number multiplication\n\n        stft_repr = torch.view_as_complex(stft_repr)\n        mask = torch.view_as_complex(mask)\n\n        stft_repr = stft_repr * mask\n\n        # istft\n\n        stft_repr = rearrange(stft_repr, \"b n (f s) t -> (b n s) f t\", s=self.audio_channels)\n\n        recon_audio = torch.istft(stft_repr.cpu() if x_is_mps else stft_repr, **self.stft_kwargs, window=stft_window.cpu() if x_is_mps else stft_window, return_complex=False).to(device)\n","chunkId":"audio_separator/separator/uvr_lib_v5/roformer/bs_roformer.py:30"}],[1736,{"filePath":"audio_separator/separator/uvr_lib_v5/roformer/bs_roformer.py","startLine":510,"endLine":523,"chunkText":"\n        recon_audio = torch.istft(stft_repr.cpu() if x_is_mps else stft_repr, **self.stft_kwargs, window=stft_window.cpu() if x_is_mps else stft_window, return_complex=False).to(device)\n\n        recon_audio = rearrange(recon_audio, \"(b n s) t -> b n s t\", s=self.audio_channels, n=self.num_stems)\n\n        if self.num_stems == 1:\n            recon_audio = rearrange(recon_audio, \"b 1 s t -> b s t\")\n\n        # if a target is passed in, calculate loss for learning\n\n        if not exists(target):\n            return recon_audio\n\n        if self.num_stems > 1:","chunkId":"audio_separator/separator/uvr_lib_v5/roformer/bs_roformer.py:31"}],[1737,{"filePath":"audio_separator/separator/uvr_lib_v5/roformer/bs_roformer.py","startLine":521,"endLine":536,"chunkText":"            return recon_audio\n\n        if self.num_stems > 1:\n            assert target.ndim == 4 and target.shape[1] == self.num_stems\n\n        if target.ndim == 2:\n            target = rearrange(target, \"... t -> ... 1 t\")\n\n        target = target[..., : recon_audio.shape[-1]]\n\n        loss = F.l1_loss(recon_audio, target)\n\n        multi_stft_resolution_loss = 0.0\n\n        for window_size in self.multi_stft_resolutions_window_sizes:\n            res_stft_kwargs = dict(","chunkId":"audio_separator/separator/uvr_lib_v5/roformer/bs_roformer.py:32"}],[1738,{"filePath":"audio_separator/separator/uvr_lib_v5/roformer/bs_roformer.py","startLine":534,"endLine":544,"chunkText":"\n        for window_size in self.multi_stft_resolutions_window_sizes:\n            res_stft_kwargs = dict(\n                n_fft=max(window_size, self.multi_stft_n_fft), win_length=window_size, return_complex=True, window=self.multi_stft_window_fn(window_size, device=device), **self.multi_stft_kwargs\n            )\n\n            recon_Y = torch.stft(rearrange(recon_audio, \"... s t -> (... s) t\"), **res_stft_kwargs)\n            target_Y = torch.stft(rearrange(target, \"... s t -> (... s) t\"), **res_stft_kwargs)\n\n            multi_stft_resolution_loss = multi_stft_resolution_loss + F.l1_loss(recon_Y, target_Y)\n","chunkId":"audio_separator/separator/uvr_lib_v5/roformer/bs_roformer.py:33"}],[1739,{"filePath":"audio_separator/separator/uvr_lib_v5/roformer/bs_roformer.py","startLine":542,"endLine":554,"chunkText":"\n            multi_stft_resolution_loss = multi_stft_resolution_loss + F.l1_loss(recon_Y, target_Y)\n\n        weighted_multi_resolution_loss = multi_stft_resolution_loss * self.multi_stft_resolution_loss_weight\n\n        total_loss = loss + weighted_multi_resolution_loss\n\n        if not return_loss_breakdown:\n            # Move the result back to the original device if it was moved to CPU for MPS compatibility\n            # if x_is_mps:\n            #     total_loss = total_loss.to(original_device)\n            return total_loss\n","chunkId":"audio_separator/separator/uvr_lib_v5/roformer/bs_roformer.py:34"}],[1740,{"filePath":"audio_separator/separator/uvr_lib_v5/roformer/bs_roformer.py","startLine":552,"endLine":565,"chunkText":"            #     total_loss = total_loss.to(original_device)\n            return total_loss\n\n        # For detailed loss breakdown, ensure all components are moved back to the original device for MPS\n        # if x_is_mps:\n        #     loss = loss.to(original_device)\n        #     multi_stft_resolution_loss = multi_stft_resolution_loss.to(original_device)\n        #     weighted_multi_resolution_loss = weighted_multi_resolution_loss.to(original_device)\n\n        return total_loss, (loss, multi_stft_resolution_loss)\n\n        # if not return_loss_breakdown:\n        #     return total_loss\n","chunkId":"audio_separator/separator/uvr_lib_v5/roformer/bs_roformer.py:35"}],[1741,{"filePath":"audio_separator/separator/uvr_lib_v5/roformer/bs_roformer.py","startLine":563,"endLine":567,"chunkText":"        # if not return_loss_breakdown:\n        #     return total_loss\n\n        # return total_loss, (loss, multi_stft_resolution_loss)\n","chunkId":"audio_separator/separator/uvr_lib_v5/roformer/bs_roformer.py:36"}],[1742,{"filePath":"audio_separator/separator/uvr_lib_v5/roformer/mel_band_roformer.py","startLine":0,"endLine":31,"chunkText":"from functools import partial\n\nimport torch\nfrom torch import nn, einsum, Tensor\nfrom torch.nn import Module, ModuleList\nimport torch.nn.functional as F\n\nfrom .attend import Attend\n\nfrom beartype.typing import Tuple, Optional, List, Callable\nfrom beartype import beartype\n\nfrom rotary_embedding_torch import RotaryEmbedding\n\nfrom einops import rearrange, pack, unpack, reduce, repeat\n\nfrom librosa import filters\n\n\ndef exists(val):\n    return val is not None\n\n\ndef default(v, d):\n    return v if exists(v) else d\n\n\ndef pack_one(t, pattern):\n    return pack([t], pattern)\n\n\ndef unpack_one(t, ps, pattern):","chunkId":"audio_separator/separator/uvr_lib_v5/roformer/mel_band_roformer.py:0"}],[1743,{"filePath":"audio_separator/separator/uvr_lib_v5/roformer/mel_band_roformer.py","startLine":29,"endLine":51,"chunkText":"\n\ndef unpack_one(t, ps, pattern):\n    return unpack(t, ps, pattern)[0]\n\n\ndef pad_at_dim(t, pad, dim=-1, value=0.0):\n    dims_from_right = (-dim - 1) if dim < 0 else (t.ndim - dim - 1)\n    zeros = (0, 0) * dims_from_right\n    return F.pad(t, (*zeros, *pad), value=value)\n\n\nclass RMSNorm(Module):\n    def __init__(self, dim):\n        super().__init__()\n        self.scale = dim**0.5\n        self.gamma = nn.Parameter(torch.ones(dim))\n\n    def forward(self, x):\n        x = x.to(self.gamma.device)\n        return F.normalize(x, dim=-1) * self.scale * self.gamma\n\n","chunkId":"audio_separator/separator/uvr_lib_v5/roformer/mel_band_roformer.py:1"}],[1744,{"filePath":"audio_separator/separator/uvr_lib_v5/roformer/mel_band_roformer.py","startLine":52,"endLine":61,"chunkText":"class FeedForward(Module):\n    def __init__(self, dim, mult=4, dropout=0.0):\n        super().__init__()\n        dim_inner = int(dim * mult)\n        self.net = nn.Sequential(RMSNorm(dim), nn.Linear(dim, dim_inner), nn.GELU(), nn.Dropout(dropout), nn.Linear(dim_inner, dim), nn.Dropout(dropout))\n\n    def forward(self, x):\n        return self.net(x)\n\n","chunkId":"audio_separator/separator/uvr_lib_v5/roformer/mel_band_roformer.py:2"}],[1745,{"filePath":"audio_separator/separator/uvr_lib_v5/roformer/mel_band_roformer.py","startLine":62,"endLine":79,"chunkText":"class Attention(Module):\n    def __init__(self, dim, heads=8, dim_head=64, dropout=0.0, rotary_embed=None, flash=True):\n        super().__init__()\n        self.heads = heads\n        self.scale = dim_head**-0.5\n        dim_inner = heads * dim_head\n\n        self.rotary_embed = rotary_embed\n\n        self.attend = Attend(flash=flash, dropout=dropout)\n\n        self.norm = RMSNorm(dim)\n        self.to_qkv = nn.Linear(dim, dim_inner * 3, bias=False)\n\n        self.to_gates = nn.Linear(dim, heads)\n\n        self.to_out = nn.Sequential(nn.Linear(dim_inner, dim, bias=False), nn.Dropout(dropout))\n","chunkId":"audio_separator/separator/uvr_lib_v5/roformer/mel_band_roformer.py:3"}],[1746,{"filePath":"audio_separator/separator/uvr_lib_v5/roformer/mel_band_roformer.py","startLine":78,"endLine":94,"chunkText":"        self.to_out = nn.Sequential(nn.Linear(dim_inner, dim, bias=False), nn.Dropout(dropout))\n\n    def forward(self, x):\n        x = self.norm(x)\n\n        q, k, v = rearrange(self.to_qkv(x), \"b n (qkv h d) -> qkv b h n d\", qkv=3, h=self.heads)\n\n        if exists(self.rotary_embed):\n            q = self.rotary_embed.rotate_queries_or_keys(q)\n            k = self.rotary_embed.rotate_queries_or_keys(k)\n\n        out = self.attend(q, k, v)\n\n        gates = self.to_gates(x)\n        out = out * rearrange(gates, \"b n h -> b h n 1\").sigmoid()\n\n        out = rearrange(out, \"b h n d -> b n (h d)\")","chunkId":"audio_separator/separator/uvr_lib_v5/roformer/mel_band_roformer.py:4"}],[1747,{"filePath":"audio_separator/separator/uvr_lib_v5/roformer/mel_band_roformer.py","startLine":92,"endLine":97,"chunkText":"        out = out * rearrange(gates, \"b n h -> b h n 1\").sigmoid()\n\n        out = rearrange(out, \"b h n d -> b n (h d)\")\n        return self.to_out(out)\n\n","chunkId":"audio_separator/separator/uvr_lib_v5/roformer/mel_band_roformer.py:5"}],[1748,{"filePath":"audio_separator/separator/uvr_lib_v5/roformer/mel_band_roformer.py","startLine":98,"endLine":109,"chunkText":"class Transformer(Module):\n    def __init__(self, *, dim, depth, dim_head=64, heads=8, attn_dropout=0.0, ff_dropout=0.0, ff_mult=4, norm_output=True, rotary_embed=None, flash_attn=True):\n        super().__init__()\n        self.layers = ModuleList([])\n\n        for _ in range(depth):\n            self.layers.append(\n                ModuleList(\n                    [Attention(dim=dim, dim_head=dim_head, heads=heads, dropout=attn_dropout, rotary_embed=rotary_embed, flash=flash_attn), FeedForward(dim=dim, mult=ff_mult, dropout=ff_dropout)]\n                )\n            )\n","chunkId":"audio_separator/separator/uvr_lib_v5/roformer/mel_band_roformer.py:6"}],[1749,{"filePath":"audio_separator/separator/uvr_lib_v5/roformer/mel_band_roformer.py","startLine":107,"endLine":120,"chunkText":"                )\n            )\n\n        self.norm = RMSNorm(dim) if norm_output else nn.Identity()\n\n    def forward(self, x):\n\n        for attn, ff in self.layers:\n            x = attn(x) + x\n            x = ff(x) + x\n\n        return self.norm(x)\n\n","chunkId":"audio_separator/separator/uvr_lib_v5/roformer/mel_band_roformer.py:7"}],[1750,{"filePath":"audio_separator/separator/uvr_lib_v5/roformer/mel_band_roformer.py","startLine":121,"endLine":140,"chunkText":"class BandSplit(Module):\n    @beartype\n    def __init__(self, dim, dim_inputs: Tuple[int, ...]):\n        super().__init__()\n        self.dim_inputs = dim_inputs\n        self.to_features = ModuleList([])\n\n        for dim_in in dim_inputs:\n            net = nn.Sequential(RMSNorm(dim_in), nn.Linear(dim_in, dim))\n\n            self.to_features.append(net)\n\n    def forward(self, x):\n        x = x.split(self.dim_inputs, dim=-1)\n\n        outs = []\n        for split_input, to_feature in zip(x, self.to_features):\n            split_output = to_feature(split_input)\n            outs.append(split_output)\n","chunkId":"audio_separator/separator/uvr_lib_v5/roformer/mel_band_roformer.py:8"}],[1751,{"filePath":"audio_separator/separator/uvr_lib_v5/roformer/mel_band_roformer.py","startLine":138,"endLine":161,"chunkText":"            split_output = to_feature(split_input)\n            outs.append(split_output)\n\n        return torch.stack(outs, dim=-2)\n\n\ndef MLP(dim_in, dim_out, dim_hidden=None, depth=1, activation=nn.Tanh):\n    dim_hidden = default(dim_hidden, dim_in)\n\n    net = []\n    dims = (dim_in, *((dim_hidden,) * depth), dim_out)\n\n    for ind, (layer_dim_in, layer_dim_out) in enumerate(zip(dims[:-1], dims[1:])):\n        is_last = ind == (len(dims) - 2)\n\n        net.append(nn.Linear(layer_dim_in, layer_dim_out))\n\n        if is_last:\n            continue\n\n        net.append(activation())\n\n    return nn.Sequential(*net)\n","chunkId":"audio_separator/separator/uvr_lib_v5/roformer/mel_band_roformer.py:9"}],[1752,{"filePath":"audio_separator/separator/uvr_lib_v5/roformer/mel_band_roformer.py","startLine":163,"endLine":182,"chunkText":"class MaskEstimator(Module):\n    @beartype\n    def __init__(self, dim, dim_inputs: Tuple[int, ...], depth, mlp_expansion_factor=4):\n        super().__init__()\n        self.dim_inputs = dim_inputs\n        self.to_freqs = ModuleList([])\n        dim_hidden = dim * mlp_expansion_factor\n\n        for dim_in in dim_inputs:\n            net = []\n\n            mlp = nn.Sequential(MLP(dim, dim_in * 2, dim_hidden=dim_hidden, depth=depth), nn.GLU(dim=-1))\n\n            self.to_freqs.append(mlp)\n\n    def forward(self, x):\n        x = x.unbind(dim=-2)\n\n        outs = []\n","chunkId":"audio_separator/separator/uvr_lib_v5/roformer/mel_band_roformer.py:10"}],[1753,{"filePath":"audio_separator/separator/uvr_lib_v5/roformer/mel_band_roformer.py","startLine":180,"endLine":189,"chunkText":"\n        outs = []\n\n        for band_features, mlp in zip(x, self.to_freqs):\n            freq_out = mlp(band_features)\n            outs.append(freq_out)\n\n        return torch.cat(outs, dim=-1)\n\n","chunkId":"audio_separator/separator/uvr_lib_v5/roformer/mel_band_roformer.py:11"}],[1754,{"filePath":"audio_separator/separator/uvr_lib_v5/roformer/mel_band_roformer.py","startLine":190,"endLine":214,"chunkText":"class MelBandRoformer(Module):\n\n    @beartype\n    def __init__(\n        self,\n        dim,\n        *,\n        depth,\n        stereo=False,\n        num_stems=1,\n        time_transformer_depth=2,\n        freq_transformer_depth=2,\n        num_bands=60,\n        dim_head=64,\n        heads=8,\n        attn_dropout=0.1,\n        ff_dropout=0.1,\n        flash_attn=True,\n        # New parameters for updated implementation\n        mlp_expansion_factor=4,\n        sage_attention=False,\n        zero_dc=True,\n        use_torch_checkpoint=False,\n        skip_connection=False,\n        # Original parameters continue","chunkId":"audio_separator/separator/uvr_lib_v5/roformer/mel_band_roformer.py:12"}],[1755,{"filePath":"audio_separator/separator/uvr_lib_v5/roformer/mel_band_roformer.py","startLine":212,"endLine":226,"chunkText":"        use_torch_checkpoint=False,\n        skip_connection=False,\n        # Original parameters continue\n        dim_freqs_in=1025,\n        sample_rate=44100,\n        stft_n_fft=2048,\n        stft_hop_length=512,\n        stft_win_length=2048,\n        stft_normalized=False,\n        stft_window_fn: Optional[Callable] = None,\n        mask_estimator_depth=1,\n        multi_stft_resolution_loss_weight=1.0,\n        multi_stft_resolutions_window_sizes: Tuple[int, ...] = (4096, 2048, 1024, 512, 256),\n        multi_stft_hop_size=147,\n        multi_stft_normalized=False,","chunkId":"audio_separator/separator/uvr_lib_v5/roformer/mel_band_roformer.py:13"}],[1756,{"filePath":"audio_separator/separator/uvr_lib_v5/roformer/mel_band_roformer.py","startLine":224,"endLine":239,"chunkText":"        multi_stft_resolutions_window_sizes: Tuple[int, ...] = (4096, 2048, 1024, 512, 256),\n        multi_stft_hop_size=147,\n        multi_stft_normalized=False,\n        multi_stft_window_fn: Callable = torch.hann_window,\n        match_input_audio_length=False,\n    ):\n        super().__init__()\n\n        self.stereo = stereo\n        self.audio_channels = 2 if stereo else 1\n        self.num_stems = num_stems\n        \n        # Store new parameters as instance variables\n        self.mlp_expansion_factor = mlp_expansion_factor\n        self.sage_attention = sage_attention\n        self.zero_dc = zero_dc","chunkId":"audio_separator/separator/uvr_lib_v5/roformer/mel_band_roformer.py:14"}],[1757,{"filePath":"audio_separator/separator/uvr_lib_v5/roformer/mel_band_roformer.py","startLine":237,"endLine":254,"chunkText":"        self.mlp_expansion_factor = mlp_expansion_factor\n        self.sage_attention = sage_attention\n        self.zero_dc = zero_dc\n        self.use_torch_checkpoint = use_torch_checkpoint\n        self.skip_connection = skip_connection\n\n        self.layers = ModuleList([])\n\n        # Add parameters to transformer kwargs (excluding sage_attention for now)\n        transformer_kwargs = dict(\n            dim=dim, \n            heads=heads, \n            dim_head=dim_head, \n            attn_dropout=attn_dropout, \n            ff_dropout=ff_dropout, \n            flash_attn=flash_attn\n        )\n        ","chunkId":"audio_separator/separator/uvr_lib_v5/roformer/mel_band_roformer.py:15"}],[1758,{"filePath":"audio_separator/separator/uvr_lib_v5/roformer/mel_band_roformer.py","startLine":252,"endLine":266,"chunkText":"            flash_attn=flash_attn\n        )\n        \n        # Print sage attention status if enabled (as per research findings)\n        if sage_attention:\n            print(\"Use Sage Attention\")\n\n        time_rotary_embed = RotaryEmbedding(dim=dim_head)\n        freq_rotary_embed = RotaryEmbedding(dim=dim_head)\n\n        for _ in range(depth):\n            self.layers.append(\n                nn.ModuleList(\n                    [\n                        Transformer(depth=time_transformer_depth, rotary_embed=time_rotary_embed, **transformer_kwargs),","chunkId":"audio_separator/separator/uvr_lib_v5/roformer/mel_band_roformer.py:16"}],[1759,{"filePath":"audio_separator/separator/uvr_lib_v5/roformer/mel_band_roformer.py","startLine":264,"endLine":275,"chunkText":"                nn.ModuleList(\n                    [\n                        Transformer(depth=time_transformer_depth, rotary_embed=time_rotary_embed, **transformer_kwargs),\n                        Transformer(depth=freq_transformer_depth, rotary_embed=freq_rotary_embed, **transformer_kwargs),\n                    ]\n                )\n            )\n\n        self.stft_window_fn = partial(default(stft_window_fn, torch.hann_window), stft_win_length)\n\n        self.stft_kwargs = dict(n_fft=stft_n_fft, hop_length=stft_hop_length, win_length=stft_win_length, normalized=stft_normalized)\n","chunkId":"audio_separator/separator/uvr_lib_v5/roformer/mel_band_roformer.py:17"}],[1760,{"filePath":"audio_separator/separator/uvr_lib_v5/roformer/mel_band_roformer.py","startLine":273,"endLine":286,"chunkText":"\n        self.stft_kwargs = dict(n_fft=stft_n_fft, hop_length=stft_hop_length, win_length=stft_win_length, normalized=stft_normalized)\n\n        freqs = torch.stft(torch.randn(1, 4096), **self.stft_kwargs, return_complex=True).shape[1]\n\n        mel_filter_bank_numpy = filters.mel(sr=sample_rate, n_fft=stft_n_fft, n_mels=num_bands)\n\n        mel_filter_bank = torch.from_numpy(mel_filter_bank_numpy)\n\n        mel_filter_bank[0][0] = 1.0\n\n        mel_filter_bank[-1, -1] = 1.0\n\n        freqs_per_band = mel_filter_bank > 0","chunkId":"audio_separator/separator/uvr_lib_v5/roformer/mel_band_roformer.py:18"}],[1761,{"filePath":"audio_separator/separator/uvr_lib_v5/roformer/mel_band_roformer.py","startLine":284,"endLine":296,"chunkText":"        mel_filter_bank[-1, -1] = 1.0\n\n        freqs_per_band = mel_filter_bank > 0\n        assert freqs_per_band.any(dim=0).all(), \"all frequencies need to be covered by all bands for now\"\n\n        repeated_freq_indices = repeat(torch.arange(freqs), \"f -> b f\", b=num_bands)\n        freq_indices = repeated_freq_indices[freqs_per_band]\n\n        if stereo:\n            freq_indices = repeat(freq_indices, \"f -> f s\", s=2)\n            freq_indices = freq_indices * 2 + torch.arange(2)\n            freq_indices = rearrange(freq_indices, \"f s -> (f s)\")\n","chunkId":"audio_separator/separator/uvr_lib_v5/roformer/mel_band_roformer.py:19"}],[1762,{"filePath":"audio_separator/separator/uvr_lib_v5/roformer/mel_band_roformer.py","startLine":294,"endLine":305,"chunkText":"            freq_indices = freq_indices * 2 + torch.arange(2)\n            freq_indices = rearrange(freq_indices, \"f s -> (f s)\")\n\n        self.register_buffer(\"freq_indices\", freq_indices, persistent=False)\n        self.register_buffer(\"freqs_per_band\", freqs_per_band, persistent=False)\n\n        num_freqs_per_band = reduce(freqs_per_band, \"b f -> b\", \"sum\")\n        num_bands_per_freq = reduce(freqs_per_band, \"b f -> f\", \"sum\")\n\n        self.register_buffer(\"num_freqs_per_band\", num_freqs_per_band, persistent=False)\n        self.register_buffer(\"num_bands_per_freq\", num_bands_per_freq, persistent=False)\n","chunkId":"audio_separator/separator/uvr_lib_v5/roformer/mel_band_roformer.py:20"}],[1763,{"filePath":"audio_separator/separator/uvr_lib_v5/roformer/mel_band_roformer.py","startLine":303,"endLine":314,"chunkText":"        self.register_buffer(\"num_freqs_per_band\", num_freqs_per_band, persistent=False)\n        self.register_buffer(\"num_bands_per_freq\", num_bands_per_freq, persistent=False)\n\n        freqs_per_bands_with_complex = tuple(2 * f * self.audio_channels for f in num_freqs_per_band.tolist())\n\n        self.band_split = BandSplit(dim=dim, dim_inputs=freqs_per_bands_with_complex)\n\n        self.mask_estimators = nn.ModuleList([])\n\n        for _ in range(num_stems):\n            mask_estimator = MaskEstimator(dim=dim, dim_inputs=freqs_per_bands_with_complex, depth=mask_estimator_depth)\n","chunkId":"audio_separator/separator/uvr_lib_v5/roformer/mel_band_roformer.py:21"}],[1764,{"filePath":"audio_separator/separator/uvr_lib_v5/roformer/mel_band_roformer.py","startLine":312,"endLine":323,"chunkText":"        for _ in range(num_stems):\n            mask_estimator = MaskEstimator(dim=dim, dim_inputs=freqs_per_bands_with_complex, depth=mask_estimator_depth)\n\n            self.mask_estimators.append(mask_estimator)\n\n        self.multi_stft_resolution_loss_weight = multi_stft_resolution_loss_weight\n        self.multi_stft_resolutions_window_sizes = multi_stft_resolutions_window_sizes\n        self.multi_stft_n_fft = stft_n_fft\n        self.multi_stft_window_fn = multi_stft_window_fn\n\n        self.multi_stft_kwargs = dict(hop_length=multi_stft_hop_size, normalized=multi_stft_normalized)\n","chunkId":"audio_separator/separator/uvr_lib_v5/roformer/mel_band_roformer.py:22"}],[1765,{"filePath":"audio_separator/separator/uvr_lib_v5/roformer/mel_band_roformer.py","startLine":321,"endLine":342,"chunkText":"\n        self.multi_stft_kwargs = dict(hop_length=multi_stft_hop_size, normalized=multi_stft_normalized)\n\n        self.match_input_audio_length = match_input_audio_length\n\n    def forward(self, raw_audio, target=None, return_loss_breakdown=False):\n        \"\"\"\n        einops\n\n        b - batch\n        f - freq\n        t - time\n        s - audio channel (1 for mono, 2 for stereo)\n        n - number of 'stems'\n        c - complex (2)\n        d - feature dimension\n        \"\"\"\n\n        original_device = raw_audio.device\n        x_is_mps = True if original_device.type == \"mps\" else False\n\n        if x_is_mps:","chunkId":"audio_separator/separator/uvr_lib_v5/roformer/mel_band_roformer.py:23"}],[1766,{"filePath":"audio_separator/separator/uvr_lib_v5/roformer/mel_band_roformer.py","startLine":340,"endLine":355,"chunkText":"        x_is_mps = True if original_device.type == \"mps\" else False\n\n        if x_is_mps:\n            raw_audio = raw_audio.cpu()\n\n        device = raw_audio.device\n\n        if raw_audio.ndim == 2:\n            raw_audio = rearrange(raw_audio, \"b t -> b 1 t\")\n\n        batch, channels, raw_audio_length = raw_audio.shape\n\n        istft_length = raw_audio_length if self.match_input_audio_length else None\n\n        assert (not self.stereo and channels == 1) or (\n            self.stereo and channels == 2","chunkId":"audio_separator/separator/uvr_lib_v5/roformer/mel_band_roformer.py:24"}],[1767,{"filePath":"audio_separator/separator/uvr_lib_v5/roformer/mel_band_roformer.py","startLine":353,"endLine":364,"chunkText":"\n        assert (not self.stereo and channels == 1) or (\n            self.stereo and channels == 2\n        ), \"stereo needs to be set to True if passing in audio signal that is stereo (channel dimension of 2). also need to be False if mono (channel dimension of 1)\"\n\n        raw_audio, batch_audio_channel_packed_shape = pack_one(raw_audio, \"* t\")\n\n        stft_window = self.stft_window_fn().to(device)\n\n        stft_repr = torch.stft(raw_audio, **self.stft_kwargs, window=stft_window, return_complex=True)\n        stft_repr = torch.view_as_real(stft_repr)\n","chunkId":"audio_separator/separator/uvr_lib_v5/roformer/mel_band_roformer.py:25"}],[1768,{"filePath":"audio_separator/separator/uvr_lib_v5/roformer/mel_band_roformer.py","startLine":362,"endLine":371,"chunkText":"        stft_repr = torch.stft(raw_audio, **self.stft_kwargs, window=stft_window, return_complex=True)\n        stft_repr = torch.view_as_real(stft_repr)\n\n        stft_repr = unpack_one(stft_repr, batch_audio_channel_packed_shape, \"* f t c\")\n        stft_repr = rearrange(stft_repr, \"b s f t c -> b (f s) t c\")  # merge stereo / mono into the frequency, with frequency leading dimension, for band splitting\n\n        batch_arange = torch.arange(batch, device=device)[..., None]\n\n        x = stft_repr[batch_arange, self.freq_indices.cpu()] if x_is_mps else stft_repr[batch_arange, self.freq_indices]\n","chunkId":"audio_separator/separator/uvr_lib_v5/roformer/mel_band_roformer.py:26"}],[1769,{"filePath":"audio_separator/separator/uvr_lib_v5/roformer/mel_band_roformer.py","startLine":369,"endLine":389,"chunkText":"\n        x = stft_repr[batch_arange, self.freq_indices.cpu()] if x_is_mps else stft_repr[batch_arange, self.freq_indices]\n\n        x = rearrange(x, \"b f t c -> b t (f c)\")\n\n        x = self.band_split(x)\n\n        for time_transformer, freq_transformer in self.layers:\n            x = rearrange(x, \"b t f d -> b f t d\")\n            x, ps = pack([x], \"* t d\")\n\n            x = time_transformer(x)\n\n            (x,) = unpack(x, ps, \"* t d\")\n            x = rearrange(x, \"b f t d -> b t f d\")\n            x, ps = pack([x], \"* f d\")\n\n            x = freq_transformer(x)\n\n            (x,) = unpack(x, ps, \"* f d\")\n","chunkId":"audio_separator/separator/uvr_lib_v5/roformer/mel_band_roformer.py:27"}],[1770,{"filePath":"audio_separator/separator/uvr_lib_v5/roformer/mel_band_roformer.py","startLine":387,"endLine":404,"chunkText":"\n            (x,) = unpack(x, ps, \"* f d\")\n\n        masks = torch.stack([fn(x) for fn in self.mask_estimators], dim=1)\n        masks = rearrange(masks, \"b n t (f c) -> b n f t c\", c=2)\n\n        if x_is_mps:\n            masks = masks.cpu()\n\n        stft_repr = rearrange(stft_repr, \"b f t c -> b 1 f t c\")\n\n        stft_repr = torch.view_as_complex(stft_repr)\n        masks = torch.view_as_complex(masks)\n\n        masks = masks.type(stft_repr.dtype)\n\n        if x_is_mps:\n            scatter_indices = repeat(self.freq_indices.cpu(), \"f -> b n f t\", b=batch, n=self.num_stems, t=stft_repr.shape[-1])","chunkId":"audio_separator/separator/uvr_lib_v5/roformer/mel_band_roformer.py:28"}],[1771,{"filePath":"audio_separator/separator/uvr_lib_v5/roformer/mel_band_roformer.py","startLine":402,"endLine":410,"chunkText":"\n        if x_is_mps:\n            scatter_indices = repeat(self.freq_indices.cpu(), \"f -> b n f t\", b=batch, n=self.num_stems, t=stft_repr.shape[-1])\n        else:\n            scatter_indices = repeat(self.freq_indices, \"f -> b n f t\", b=batch, n=self.num_stems, t=stft_repr.shape[-1])\n\n        stft_repr_expanded_stems = repeat(stft_repr, \"b 1 ... -> b n ...\", n=self.num_stems)\n        masks_summed = (\n            torch.zeros_like(stft_repr_expanded_stems.cpu() if x_is_mps else stft_repr_expanded_stems)","chunkId":"audio_separator/separator/uvr_lib_v5/roformer/mel_band_roformer.py:29"}],[1772,{"filePath":"audio_separator/separator/uvr_lib_v5/roformer/mel_band_roformer.py","startLine":408,"endLine":421,"chunkText":"        stft_repr_expanded_stems = repeat(stft_repr, \"b 1 ... -> b n ...\", n=self.num_stems)\n        masks_summed = (\n            torch.zeros_like(stft_repr_expanded_stems.cpu() if x_is_mps else stft_repr_expanded_stems)\n            .scatter_add_(2, scatter_indices.cpu() if x_is_mps else scatter_indices, masks.cpu() if x_is_mps else masks)\n            .to(device)\n        )\n\n        denom = repeat(self.num_bands_per_freq, \"f -> (f r) 1\", r=channels)\n\n        if x_is_mps:\n            denom = denom.cpu()\n\n        masks_averaged = masks_summed / denom.clamp(min=1e-8)\n","chunkId":"audio_separator/separator/uvr_lib_v5/roformer/mel_band_roformer.py:30"}],[1773,{"filePath":"audio_separator/separator/uvr_lib_v5/roformer/mel_band_roformer.py","startLine":419,"endLine":430,"chunkText":"\n        masks_averaged = masks_summed / denom.clamp(min=1e-8)\n\n        stft_repr = stft_repr * masks_averaged\n\n        stft_repr = rearrange(stft_repr, \"b n (f s) t -> (b n s) f t\", s=self.audio_channels)\n\n        recon_audio = torch.istft(stft_repr.cpu() if x_is_mps else stft_repr, **self.stft_kwargs, window=stft_window.cpu() if x_is_mps else stft_window, return_complex=False, length=istft_length)\n\n        recon_audio = rearrange(recon_audio, \"(b n s) t -> b n s t\", b=batch, s=self.audio_channels, n=self.num_stems)\n\n        if self.num_stems == 1:","chunkId":"audio_separator/separator/uvr_lib_v5/roformer/mel_band_roformer.py:31"}],[1774,{"filePath":"audio_separator/separator/uvr_lib_v5/roformer/mel_band_roformer.py","startLine":428,"endLine":445,"chunkText":"        recon_audio = rearrange(recon_audio, \"(b n s) t -> b n s t\", b=batch, s=self.audio_channels, n=self.num_stems)\n\n        if self.num_stems == 1:\n            recon_audio = rearrange(recon_audio, \"b 1 s t -> b s t\")\n\n        if not exists(target):\n            return recon_audio\n\n        if self.num_stems > 1:\n            assert target.ndim == 4 and target.shape[1] == self.num_stems\n\n        if target.ndim == 2:\n            target = rearrange(target, \"... t -> ... 1 t\")\n\n        target = target[..., : recon_audio.shape[-1]]\n\n        loss = F.l1_loss(recon_audio, target)\n","chunkId":"audio_separator/separator/uvr_lib_v5/roformer/mel_band_roformer.py:32"}],[1775,{"filePath":"audio_separator/separator/uvr_lib_v5/roformer/mel_band_roformer.py","startLine":443,"endLine":455,"chunkText":"\n        loss = F.l1_loss(recon_audio, target)\n\n        multi_stft_resolution_loss = 0.0\n\n        for window_size in self.multi_stft_resolutions_window_sizes:\n            res_stft_kwargs = dict(\n                n_fft=max(window_size, self.multi_stft_n_fft), win_length=window_size, return_complex=True, window=self.multi_stft_window_fn(window_size, device=device), **self.multi_stft_kwargs\n            )\n\n            recon_Y = torch.stft(rearrange(recon_audio, \"... s t -> (... s) t\"), **res_stft_kwargs)\n            target_Y = torch.stft(rearrange(target, \"... s t -> (... s) t\"), **res_stft_kwargs)\n","chunkId":"audio_separator/separator/uvr_lib_v5/roformer/mel_band_roformer.py:33"}],[1776,{"filePath":"audio_separator/separator/uvr_lib_v5/roformer/mel_band_roformer.py","startLine":453,"endLine":463,"chunkText":"            recon_Y = torch.stft(rearrange(recon_audio, \"... s t -> (... s) t\"), **res_stft_kwargs)\n            target_Y = torch.stft(rearrange(target, \"... s t -> (... s) t\"), **res_stft_kwargs)\n\n            multi_stft_resolution_loss = multi_stft_resolution_loss + F.l1_loss(recon_Y, target_Y)\n\n        weighted_multi_resolution_loss = multi_stft_resolution_loss * self.multi_stft_resolution_loss_weight\n\n        total_loss = loss + weighted_multi_resolution_loss\n\n        # Move the total loss back to the original device if necessary\n        if x_is_mps:","chunkId":"audio_separator/separator/uvr_lib_v5/roformer/mel_band_roformer.py:34"}],[1777,{"filePath":"audio_separator/separator/uvr_lib_v5/roformer/mel_band_roformer.py","startLine":461,"endLine":471,"chunkText":"\n        # Move the total loss back to the original device if necessary\n        if x_is_mps:\n            total_loss = total_loss.to(original_device)\n\n        if not return_loss_breakdown:\n            return total_loss\n\n        # If detailed loss breakdown is requested, ensure all components are on the original device\n        return total_loss, (loss.to(original_device) if x_is_mps else loss, multi_stft_resolution_loss.to(original_device) if x_is_mps else multi_stft_resolution_loss)\n","chunkId":"audio_separator/separator/uvr_lib_v5/roformer/mel_band_roformer.py:35"}],[1778,{"filePath":"audio_separator/separator/uvr_lib_v5/spec_utils.py","startLine":0,"endLine":23,"chunkText":"import audioread\r\nimport librosa\r\nimport numpy as np\r\nimport soundfile as sf\r\nimport math\r\nimport platform\r\nimport traceback\r\nfrom audio_separator.separator.uvr_lib_v5 import pyrb\r\nfrom scipy.signal import correlate, hilbert\r\nimport io\r\n\r\nOPERATING_SYSTEM = platform.system()\r\nSYSTEM_ARCH = platform.platform()\r\nSYSTEM_PROC = platform.processor()\r\nARM = \"arm\"\r\n\r\nAUTO_PHASE = \"Automatic\"\r\nPOSITIVE_PHASE = \"Positive Phase\"\r\nNEGATIVE_PHASE = \"Negative Phase\"\r\nNONE_P = (\"None\",)\r\nLOW_P = (\"Shifts: Low\",)\r\nMED_P = (\"Shifts: Medium\",)\r\nHIGH_P = (\"Shifts: High\",)\r\nVHIGH_P = \"Shifts: Very High\"\r","chunkId":"audio_separator/separator/uvr_lib_v5/spec_utils.py:0"}],[1779,{"filePath":"audio_separator/separator/uvr_lib_v5/spec_utils.py","startLine":21,"endLine":40,"chunkText":"MED_P = (\"Shifts: Medium\",)\r\nHIGH_P = (\"Shifts: High\",)\r\nVHIGH_P = \"Shifts: Very High\"\r\nMAXIMUM_P = \"Shifts: Maximum\"\r\n\r\nprogress_value = 0\r\nlast_update_time = 0\r\nis_macos = False\r\n\r\n\r\nif OPERATING_SYSTEM == \"Darwin\":\r\n    wav_resolution = \"polyphase\" if SYSTEM_PROC == ARM or ARM in SYSTEM_ARCH else \"sinc_fastest\"\r\n    wav_resolution_float_resampling = \"kaiser_best\" if SYSTEM_PROC == ARM or ARM in SYSTEM_ARCH else wav_resolution\r\n    is_macos = True\r\nelse:\r\n    wav_resolution = \"sinc_fastest\"\r\n    wav_resolution_float_resampling = wav_resolution\r\n\r\nMAX_SPEC = \"Max Spec\"\r\nMIN_SPEC = \"Min Spec\"\r","chunkId":"audio_separator/separator/uvr_lib_v5/spec_utils.py:1"}],[1780,{"filePath":"audio_separator/separator/uvr_lib_v5/spec_utils.py","startLine":38,"endLine":59,"chunkText":"\r\nMAX_SPEC = \"Max Spec\"\r\nMIN_SPEC = \"Min Spec\"\r\nLIN_ENSE = \"Linear Ensemble\"\r\n\r\nMAX_WAV = MAX_SPEC\r\nMIN_WAV = MIN_SPEC\r\n\r\nAVERAGE = \"Average\"\r\n\r\n\r\ndef crop_center(h1, h2):\r\n    \"\"\"\r\n    This function crops the center of the first input tensor to match the size of the second input tensor.\r\n    It is used to ensure that the two tensors have the same size in the time dimension.\r\n    \"\"\"\r\n    h1_shape = h1.size()\r\n    h2_shape = h2.size()\r\n\r\n    # If the time dimensions are already equal, return the first tensor as is\r\n    if h1_shape[3] == h2_shape[3]:\r\n        return h1\r","chunkId":"audio_separator/separator/uvr_lib_v5/spec_utils.py:2"}],[1781,{"filePath":"audio_separator/separator/uvr_lib_v5/spec_utils.py","startLine":57,"endLine":74,"chunkText":"    # If the time dimensions are already equal, return the first tensor as is\r\n    if h1_shape[3] == h2_shape[3]:\r\n        return h1\r\n    # If the time dimension of the first tensor is smaller, raise an error\r\n    elif h1_shape[3] < h2_shape[3]:\r\n        raise ValueError(\"h1_shape[3] must be greater than h2_shape[3]\")\r\n\r\n    # Calculate the start and end indices for cropping\r\n    s_time = (h1_shape[3] - h2_shape[3]) // 2\r\n    e_time = s_time + h2_shape[3]\r\n    # Crop the first tensor\r\n    h1 = h1[:, :, :, s_time:e_time]\r\n\r\n    return h1\r\n\r\n\r\ndef preprocess(X_spec):\r\n    \"\"\"\r","chunkId":"audio_separator/separator/uvr_lib_v5/spec_utils.py:3"}],[1782,{"filePath":"audio_separator/separator/uvr_lib_v5/spec_utils.py","startLine":72,"endLine":90,"chunkText":"\r\ndef preprocess(X_spec):\r\n    \"\"\"\r\n    This function preprocesses a spectrogram by separating it into magnitude and phase components.\r\n    This is a common preprocessing step in audio processing tasks.\r\n    \"\"\"\r\n    X_mag = np.abs(X_spec)\r\n    X_phase = np.angle(X_spec)\r\n\r\n    return X_mag, X_phase\r\n\r\n\r\ndef make_padding(width, cropsize, offset):\r\n    \"\"\"\r\n    This function calculates the padding needed to make the width of an image divisible by the crop size.\r\n    It is used in the process of splitting an image into smaller patches.\r\n    \"\"\"\r\n    left = offset\r\n    roi_size = cropsize - offset * 2\r","chunkId":"audio_separator/separator/uvr_lib_v5/spec_utils.py:4"}],[1783,{"filePath":"audio_separator/separator/uvr_lib_v5/spec_utils.py","startLine":88,"endLine":109,"chunkText":"    \"\"\"\r\n    left = offset\r\n    roi_size = cropsize - offset * 2\r\n    if roi_size == 0:\r\n        roi_size = cropsize\r\n    right = roi_size - (width % roi_size) + left\r\n\r\n    return left, right, roi_size\r\n\r\n\r\ndef normalize(wave, max_peak=1.0, min_peak=None):\r\n    \"\"\"Normalize (or amplify) audio waveform to a specified peak value.\r\n\r\n    Args:\r\n        wave (array-like): Audio waveform.\r\n        max_peak (float): Maximum peak value for normalization.\r\n\r\n    Returns:\r\n        array-like: Normalized or original waveform.\r\n    \"\"\"\r\n    maxv = np.abs(wave).max()\r\n    if maxv > max_peak:\r","chunkId":"audio_separator/separator/uvr_lib_v5/spec_utils.py:5"}],[1784,{"filePath":"audio_separator/separator/uvr_lib_v5/spec_utils.py","startLine":107,"endLine":128,"chunkText":"    \"\"\"\r\n    maxv = np.abs(wave).max()\r\n    if maxv > max_peak:\r\n        wave *= max_peak / maxv\r\n    elif min_peak is not None and maxv < min_peak:\r\n        wave *= min_peak / maxv\r\n\r\n    return wave\r\n\r\n\r\ndef auto_transpose(audio_array: np.ndarray):\r\n    \"\"\"\r\n    Ensure that the audio array is in the (channels, samples) format.\r\n\r\n    Parameters:\r\n        audio_array (ndarray): Input audio array.\r\n\r\n    Returns:\r\n        ndarray: Transposed audio array if necessary.\r\n    \"\"\"\r\n\r\n    # If the second dimension is 2 (indicating stereo channels), transpose the array\r","chunkId":"audio_separator/separator/uvr_lib_v5/spec_utils.py:6"}],[1785,{"filePath":"audio_separator/separator/uvr_lib_v5/spec_utils.py","startLine":126,"endLine":145,"chunkText":"    \"\"\"\r\n\r\n    # If the second dimension is 2 (indicating stereo channels), transpose the array\r\n    if audio_array.shape[1] == 2:\r\n        return audio_array.T\r\n    return audio_array\r\n\r\n\r\ndef write_array_to_mem(audio_data, subtype):\r\n    if isinstance(audio_data, np.ndarray):\r\n        audio_buffer = io.BytesIO()\r\n        sf.write(audio_buffer, audio_data, 44100, subtype=subtype, format=\"WAV\")\r\n        audio_buffer.seek(0)\r\n        return audio_buffer\r\n    else:\r\n        return audio_data\r\n\r\n\r\ndef spectrogram_to_image(spec, mode=\"magnitude\"):\r\n    if mode == \"magnitude\":\r","chunkId":"audio_separator/separator/uvr_lib_v5/spec_utils.py:7"}],[1786,{"filePath":"audio_separator/separator/uvr_lib_v5/spec_utils.py","startLine":143,"endLine":167,"chunkText":"\r\ndef spectrogram_to_image(spec, mode=\"magnitude\"):\r\n    if mode == \"magnitude\":\r\n        if np.iscomplexobj(spec):\r\n            y = np.abs(spec)\r\n        else:\r\n            y = spec\r\n        y = np.log10(y**2 + 1e-8)\r\n    elif mode == \"phase\":\r\n        if np.iscomplexobj(spec):\r\n            y = np.angle(spec)\r\n        else:\r\n            y = spec\r\n\r\n    y -= y.min()\r\n    y *= 255 / y.max()\r\n    img = np.uint8(y)\r\n\r\n    if y.ndim == 3:\r\n        img = img.transpose(1, 2, 0)\r\n        img = np.concatenate([np.max(img, axis=2, keepdims=True), img], axis=2)\r\n\r\n    return img\r\n\r\n\r","chunkId":"audio_separator/separator/uvr_lib_v5/spec_utils.py:8"}],[1787,{"filePath":"audio_separator/separator/uvr_lib_v5/spec_utils.py","startLine":165,"endLine":186,"chunkText":"    return img\r\n\r\n\r\ndef reduce_vocal_aggressively(X, y, softmask):\r\n    v = X - y\r\n    y_mag_tmp = np.abs(y)\r\n    v_mag_tmp = np.abs(v)\r\n\r\n    v_mask = v_mag_tmp > y_mag_tmp\r\n    y_mag = np.clip(y_mag_tmp - v_mag_tmp * v_mask * softmask, 0, np.inf)\r\n\r\n    return y_mag * np.exp(1.0j * np.angle(y))\r\n\r\n\r\ndef merge_artifacts(y_mask, thres=0.01, min_range=64, fade_size=32):\r\n    mask = y_mask\r\n\r\n    try:\r\n        if min_range < fade_size * 2:\r\n            raise ValueError(\"min_range must be >= fade_size * 2\")\r\n\r\n        idx = np.where(y_mask.min(axis=(0, 1)) > thres)[0]\r","chunkId":"audio_separator/separator/uvr_lib_v5/spec_utils.py:9"}],[1788,{"filePath":"audio_separator/separator/uvr_lib_v5/spec_utils.py","startLine":184,"endLine":195,"chunkText":"            raise ValueError(\"min_range must be >= fade_size * 2\")\r\n\r\n        idx = np.where(y_mask.min(axis=(0, 1)) > thres)[0]\r\n        start_idx = np.insert(idx[np.where(np.diff(idx) != 1)[0] + 1], 0, idx[0])\r\n        end_idx = np.append(idx[np.where(np.diff(idx) != 1)[0]], idx[-1])\r\n        artifact_idx = np.where(end_idx - start_idx > min_range)[0]\r\n        weight = np.zeros_like(y_mask)\r\n        if len(artifact_idx) > 0:\r\n            start_idx = start_idx[artifact_idx]\r\n            end_idx = end_idx[artifact_idx]\r\n            old_e = None\r\n            for s, e in zip(start_idx, end_idx):\r","chunkId":"audio_separator/separator/uvr_lib_v5/spec_utils.py:10"}],[1789,{"filePath":"audio_separator/separator/uvr_lib_v5/spec_utils.py","startLine":193,"endLine":208,"chunkText":"            end_idx = end_idx[artifact_idx]\r\n            old_e = None\r\n            for s, e in zip(start_idx, end_idx):\r\n                if old_e is not None and s - old_e < fade_size:\r\n                    s = old_e - fade_size * 2\r\n\r\n                if s != 0:\r\n                    weight[:, :, s : s + fade_size] = np.linspace(0, 1, fade_size)\r\n                else:\r\n                    s -= fade_size\r\n\r\n                if e != y_mask.shape[2]:\r\n                    weight[:, :, e - fade_size : e] = np.linspace(1, 0, fade_size)\r\n                else:\r\n                    e += fade_size\r\n\r","chunkId":"audio_separator/separator/uvr_lib_v5/spec_utils.py:11"}],[1790,{"filePath":"audio_separator/separator/uvr_lib_v5/spec_utils.py","startLine":206,"endLine":227,"chunkText":"                else:\r\n                    e += fade_size\r\n\r\n                weight[:, :, s + fade_size : e - fade_size] = 1\r\n                old_e = e\r\n\r\n        v_mask = 1 - y_mask\r\n        y_mask += weight * v_mask\r\n\r\n        mask = y_mask\r\n    except Exception as e:\r\n        error_name = f\"{type(e).__name__}\"\r\n        traceback_text = \"\".join(traceback.format_tb(e.__traceback__))\r\n        message = f'{error_name}: \"{e}\"\\n{traceback_text}\"'\r\n        print(\"Post Process Failed: \", message)\r\n\r\n    return mask\r\n\r\n\r\ndef align_wave_head_and_tail(a, b):\r\n    l = min([a[0].size, b[0].size])\r\n\r","chunkId":"audio_separator/separator/uvr_lib_v5/spec_utils.py:12"}],[1791,{"filePath":"audio_separator/separator/uvr_lib_v5/spec_utils.py","startLine":225,"endLine":241,"chunkText":"def align_wave_head_and_tail(a, b):\r\n    l = min([a[0].size, b[0].size])\r\n\r\n    return a[:l, :l], b[:l, :l]\r\n\r\n\r\ndef convert_channels(spec, mp, band):\r\n    cc = mp.param[\"band\"][band].get(\"convert_channels\")\r\n\r\n    if \"mid_side_c\" == cc:\r\n        spec_left = np.add(spec[0], spec[1] * 0.25)\r\n        spec_right = np.subtract(spec[1], spec[0] * 0.25)\r\n    elif \"mid_side\" == cc:\r\n        spec_left = np.add(spec[0], spec[1]) / 2\r\n        spec_right = np.subtract(spec[0], spec[1])\r\n    elif \"stereo_n\" == cc:\r\n        spec_left = np.add(spec[0], spec[1] * 0.25) / 0.9375\r","chunkId":"audio_separator/separator/uvr_lib_v5/spec_utils.py:13"}],[1792,{"filePath":"audio_separator/separator/uvr_lib_v5/spec_utils.py","startLine":239,"endLine":255,"chunkText":"        spec_right = np.subtract(spec[0], spec[1])\r\n    elif \"stereo_n\" == cc:\r\n        spec_left = np.add(spec[0], spec[1] * 0.25) / 0.9375\r\n        spec_right = np.add(spec[1], spec[0] * 0.25) / 0.9375\r\n    else:\r\n        return spec\r\n\r\n    return np.asfortranarray([spec_left, spec_right])\r\n\r\n\r\ndef combine_spectrograms(specs, mp, is_v51_model=False):\r\n    l = min([specs[i].shape[2] for i in specs])\r\n    spec_c = np.zeros(shape=(2, mp.param[\"bins\"] + 1, l), dtype=np.complex64)\r\n    offset = 0\r\n    bands_n = len(mp.param[\"band\"])\r\n\r\n    for d in range(1, bands_n + 1):\r","chunkId":"audio_separator/separator/uvr_lib_v5/spec_utils.py:14"}],[1793,{"filePath":"audio_separator/separator/uvr_lib_v5/spec_utils.py","startLine":253,"endLine":267,"chunkText":"    bands_n = len(mp.param[\"band\"])\r\n\r\n    for d in range(1, bands_n + 1):\r\n        h = mp.param[\"band\"][d][\"crop_stop\"] - mp.param[\"band\"][d][\"crop_start\"]\r\n        spec_c[:, offset : offset + h, :l] = specs[d][:, mp.param[\"band\"][d][\"crop_start\"] : mp.param[\"band\"][d][\"crop_stop\"], :l]\r\n        offset += h\r\n\r\n    if offset > mp.param[\"bins\"]:\r\n        raise ValueError(\"Too much bins\")\r\n\r\n    # lowpass fiter\r\n\r\n    if mp.param[\"pre_filter_start\"] > 0:\r\n        if is_v51_model:\r\n            spec_c *= get_lp_filter_mask(spec_c.shape[1], mp.param[\"pre_filter_start\"], mp.param[\"pre_filter_stop\"])\r","chunkId":"audio_separator/separator/uvr_lib_v5/spec_utils.py:15"}],[1794,{"filePath":"audio_separator/separator/uvr_lib_v5/spec_utils.py","startLine":265,"endLine":275,"chunkText":"    if mp.param[\"pre_filter_start\"] > 0:\r\n        if is_v51_model:\r\n            spec_c *= get_lp_filter_mask(spec_c.shape[1], mp.param[\"pre_filter_start\"], mp.param[\"pre_filter_stop\"])\r\n        else:\r\n            if bands_n == 1:\r\n                spec_c = fft_lp_filter(spec_c, mp.param[\"pre_filter_start\"], mp.param[\"pre_filter_stop\"])\r\n            else:\r\n                gp = 1\r\n                for b in range(mp.param[\"pre_filter_start\"] + 1, mp.param[\"pre_filter_stop\"]):\r\n                    g = math.pow(10, -(b - mp.param[\"pre_filter_start\"]) * (3.5 - gp) / 20.0)\r\n                    gp = g\r","chunkId":"audio_separator/separator/uvr_lib_v5/spec_utils.py:16"}],[1795,{"filePath":"audio_separator/separator/uvr_lib_v5/spec_utils.py","startLine":273,"endLine":288,"chunkText":"                for b in range(mp.param[\"pre_filter_start\"] + 1, mp.param[\"pre_filter_stop\"]):\r\n                    g = math.pow(10, -(b - mp.param[\"pre_filter_start\"]) * (3.5 - gp) / 20.0)\r\n                    gp = g\r\n                    spec_c[:, b, :] *= g\r\n\r\n    return np.asfortranarray(spec_c)\r\n\r\n\r\ndef wave_to_spectrogram(wave, hop_length, n_fft, mp, band, is_v51_model=False):\r\n\r\n    if wave.ndim == 1:\r\n        wave = np.asfortranarray([wave, wave])\r\n\r\n    if not is_v51_model:\r\n        if mp.param[\"reverse\"]:\r\n            wave_left = np.flip(np.asfortranarray(wave[0]))\r","chunkId":"audio_separator/separator/uvr_lib_v5/spec_utils.py:17"}],[1796,{"filePath":"audio_separator/separator/uvr_lib_v5/spec_utils.py","startLine":286,"endLine":296,"chunkText":"    if not is_v51_model:\r\n        if mp.param[\"reverse\"]:\r\n            wave_left = np.flip(np.asfortranarray(wave[0]))\r\n            wave_right = np.flip(np.asfortranarray(wave[1]))\r\n        elif mp.param[\"mid_side\"]:\r\n            wave_left = np.asfortranarray(np.add(wave[0], wave[1]) / 2)\r\n            wave_right = np.asfortranarray(np.subtract(wave[0], wave[1]))\r\n        elif mp.param[\"mid_side_b2\"]:\r\n            wave_left = np.asfortranarray(np.add(wave[1], wave[0] * 0.5))\r\n            wave_right = np.asfortranarray(np.subtract(wave[0], wave[1] * 0.5))\r\n        else:\r","chunkId":"audio_separator/separator/uvr_lib_v5/spec_utils.py:18"}],[1797,{"filePath":"audio_separator/separator/uvr_lib_v5/spec_utils.py","startLine":294,"endLine":307,"chunkText":"            wave_left = np.asfortranarray(np.add(wave[1], wave[0] * 0.5))\r\n            wave_right = np.asfortranarray(np.subtract(wave[0], wave[1] * 0.5))\r\n        else:\r\n            wave_left = np.asfortranarray(wave[0])\r\n            wave_right = np.asfortranarray(wave[1])\r\n    else:\r\n        wave_left = np.asfortranarray(wave[0])\r\n        wave_right = np.asfortranarray(wave[1])\r\n\r\n    spec_left = librosa.stft(wave_left, n_fft=n_fft, hop_length=hop_length)\r\n    spec_right = librosa.stft(wave_right, n_fft=n_fft, hop_length=hop_length)\r\n\r\n    spec = np.asfortranarray([spec_left, spec_right])\r\n\r","chunkId":"audio_separator/separator/uvr_lib_v5/spec_utils.py:19"}],[1798,{"filePath":"audio_separator/separator/uvr_lib_v5/spec_utils.py","startLine":305,"endLine":323,"chunkText":"\r\n    spec = np.asfortranarray([spec_left, spec_right])\r\n\r\n    if is_v51_model:\r\n        spec = convert_channels(spec, mp, band)\r\n\r\n    return spec\r\n\r\n\r\ndef spectrogram_to_wave(spec, hop_length=1024, mp={}, band=0, is_v51_model=True):\r\n    spec_left = np.asfortranarray(spec[0])\r\n    spec_right = np.asfortranarray(spec[1])\r\n\r\n    wave_left = librosa.istft(spec_left, hop_length=hop_length)\r\n    wave_right = librosa.istft(spec_right, hop_length=hop_length)\r\n\r\n    if is_v51_model:\r\n        cc = mp.param[\"band\"][band].get(\"convert_channels\")\r\n        if \"mid_side_c\" == cc:\r","chunkId":"audio_separator/separator/uvr_lib_v5/spec_utils.py:20"}],[1799,{"filePath":"audio_separator/separator/uvr_lib_v5/spec_utils.py","startLine":321,"endLine":330,"chunkText":"    if is_v51_model:\r\n        cc = mp.param[\"band\"][band].get(\"convert_channels\")\r\n        if \"mid_side_c\" == cc:\r\n            return np.asfortranarray([np.subtract(wave_left / 1.0625, wave_right / 4.25), np.add(wave_right / 1.0625, wave_left / 4.25)])\r\n        elif \"mid_side\" == cc:\r\n            return np.asfortranarray([np.add(wave_left, wave_right / 2), np.subtract(wave_left, wave_right / 2)])\r\n        elif \"stereo_n\" == cc:\r\n            return np.asfortranarray([np.subtract(wave_left, wave_right * 0.25), np.subtract(wave_right, wave_left * 0.25)])\r\n    else:\r\n        if mp.param[\"reverse\"]:\r","chunkId":"audio_separator/separator/uvr_lib_v5/spec_utils.py:21"}],[1800,{"filePath":"audio_separator/separator/uvr_lib_v5/spec_utils.py","startLine":328,"endLine":336,"chunkText":"            return np.asfortranarray([np.subtract(wave_left, wave_right * 0.25), np.subtract(wave_right, wave_left * 0.25)])\r\n    else:\r\n        if mp.param[\"reverse\"]:\r\n            return np.asfortranarray([np.flip(wave_left), np.flip(wave_right)])\r\n        elif mp.param[\"mid_side\"]:\r\n            return np.asfortranarray([np.add(wave_left, wave_right / 2), np.subtract(wave_left, wave_right / 2)])\r\n        elif mp.param[\"mid_side_b2\"]:\r\n            return np.asfortranarray([np.add(wave_right / 1.25, 0.4 * wave_left), np.subtract(wave_left / 1.25, 0.4 * wave_right)])\r\n\r","chunkId":"audio_separator/separator/uvr_lib_v5/spec_utils.py:22"}],[1801,{"filePath":"audio_separator/separator/uvr_lib_v5/spec_utils.py","startLine":334,"endLine":347,"chunkText":"        elif mp.param[\"mid_side_b2\"]:\r\n            return np.asfortranarray([np.add(wave_right / 1.25, 0.4 * wave_left), np.subtract(wave_left / 1.25, 0.4 * wave_right)])\r\n\r\n    return np.asfortranarray([wave_left, wave_right])\r\n\r\n\r\ndef cmb_spectrogram_to_wave(spec_m, mp, extra_bins_h=None, extra_bins=None, is_v51_model=False):\r\n    bands_n = len(mp.param[\"band\"])\r\n    offset = 0\r\n\r\n    for d in range(1, bands_n + 1):\r\n        bp = mp.param[\"band\"][d]\r\n        spec_s = np.zeros(shape=(2, bp[\"n_fft\"] // 2 + 1, spec_m.shape[2]), dtype=complex)\r\n        h = bp[\"crop_stop\"] - bp[\"crop_start\"]\r","chunkId":"audio_separator/separator/uvr_lib_v5/spec_utils.py:23"}],[1802,{"filePath":"audio_separator/separator/uvr_lib_v5/spec_utils.py","startLine":345,"endLine":356,"chunkText":"        bp = mp.param[\"band\"][d]\r\n        spec_s = np.zeros(shape=(2, bp[\"n_fft\"] // 2 + 1, spec_m.shape[2]), dtype=complex)\r\n        h = bp[\"crop_stop\"] - bp[\"crop_start\"]\r\n        spec_s[:, bp[\"crop_start\"] : bp[\"crop_stop\"], :] = spec_m[:, offset : offset + h, :]\r\n\r\n        offset += h\r\n        if d == bands_n:  # higher\r\n            if extra_bins_h:  # if --high_end_process bypass\r\n                max_bin = bp[\"n_fft\"] // 2\r\n                spec_s[:, max_bin - extra_bins_h : max_bin, :] = extra_bins[:, :extra_bins_h, :]\r\n            if bp[\"hpf_start\"] > 0:\r\n                if is_v51_model:\r","chunkId":"audio_separator/separator/uvr_lib_v5/spec_utils.py:24"}],[1803,{"filePath":"audio_separator/separator/uvr_lib_v5/spec_utils.py","startLine":354,"endLine":362,"chunkText":"                spec_s[:, max_bin - extra_bins_h : max_bin, :] = extra_bins[:, :extra_bins_h, :]\r\n            if bp[\"hpf_start\"] > 0:\r\n                if is_v51_model:\r\n                    spec_s *= get_hp_filter_mask(spec_s.shape[1], bp[\"hpf_start\"], bp[\"hpf_stop\"] - 1)\r\n                else:\r\n                    spec_s = fft_hp_filter(spec_s, bp[\"hpf_start\"], bp[\"hpf_stop\"] - 1)\r\n            if bands_n == 1:\r\n                wave = spectrogram_to_wave(spec_s, bp[\"hl\"], mp, d, is_v51_model)\r\n            else:\r","chunkId":"audio_separator/separator/uvr_lib_v5/spec_utils.py:25"}],[1804,{"filePath":"audio_separator/separator/uvr_lib_v5/spec_utils.py","startLine":360,"endLine":372,"chunkText":"            if bands_n == 1:\r\n                wave = spectrogram_to_wave(spec_s, bp[\"hl\"], mp, d, is_v51_model)\r\n            else:\r\n                wave = np.add(wave, spectrogram_to_wave(spec_s, bp[\"hl\"], mp, d, is_v51_model))\r\n        else:\r\n            sr = mp.param[\"band\"][d + 1][\"sr\"]\r\n            if d == 1:  # lower\r\n                if is_v51_model:\r\n                    spec_s *= get_lp_filter_mask(spec_s.shape[1], bp[\"lpf_start\"], bp[\"lpf_stop\"])\r\n                else:\r\n                    spec_s = fft_lp_filter(spec_s, bp[\"lpf_start\"], bp[\"lpf_stop\"])\r\n\r\n                try:\r","chunkId":"audio_separator/separator/uvr_lib_v5/spec_utils.py:26"}],[1805,{"filePath":"audio_separator/separator/uvr_lib_v5/spec_utils.py","startLine":370,"endLine":379,"chunkText":"                    spec_s = fft_lp_filter(spec_s, bp[\"lpf_start\"], bp[\"lpf_stop\"])\r\n\r\n                try:\r\n                    wave = librosa.resample(spectrogram_to_wave(spec_s, bp[\"hl\"], mp, d, is_v51_model), orig_sr=bp[\"sr\"], target_sr=sr, res_type=wav_resolution)\r\n                except ValueError as e:\r\n                    print(f\"Error during resampling: {e}\")\r\n                    print(f\"Spec_s shape: {spec_s.shape}, SR: {sr}, Res type: {wav_resolution}\")\r\n\r\n            else:  # mid\r\n                if is_v51_model:\r","chunkId":"audio_separator/separator/uvr_lib_v5/spec_utils.py:27"}],[1806,{"filePath":"audio_separator/separator/uvr_lib_v5/spec_utils.py","startLine":377,"endLine":388,"chunkText":"\r\n            else:  # mid\r\n                if is_v51_model:\r\n                    spec_s *= get_hp_filter_mask(spec_s.shape[1], bp[\"hpf_start\"], bp[\"hpf_stop\"] - 1)\r\n                    spec_s *= get_lp_filter_mask(spec_s.shape[1], bp[\"lpf_start\"], bp[\"lpf_stop\"])\r\n                else:\r\n                    spec_s = fft_hp_filter(spec_s, bp[\"hpf_start\"], bp[\"hpf_stop\"] - 1)\r\n                    spec_s = fft_lp_filter(spec_s, bp[\"lpf_start\"], bp[\"lpf_stop\"])\r\n\r\n                wave2 = np.add(wave, spectrogram_to_wave(spec_s, bp[\"hl\"], mp, d, is_v51_model))\r\n\r\n                try:\r","chunkId":"audio_separator/separator/uvr_lib_v5/spec_utils.py:28"}],[1807,{"filePath":"audio_separator/separator/uvr_lib_v5/spec_utils.py","startLine":386,"endLine":397,"chunkText":"                wave2 = np.add(wave, spectrogram_to_wave(spec_s, bp[\"hl\"], mp, d, is_v51_model))\r\n\r\n                try:\r\n                    wave = librosa.resample(wave2, orig_sr=bp[\"sr\"], target_sr=sr, res_type=wav_resolution)\r\n                except ValueError as e:\r\n                    print(f\"Error during resampling: {e}\")\r\n                    print(f\"Spec_s shape: {spec_s.shape}, SR: {sr}, Res type: {wav_resolution}\")\r\n\r\n    return wave\r\n\r\n\r\ndef get_lp_filter_mask(n_bins, bin_start, bin_stop):\r","chunkId":"audio_separator/separator/uvr_lib_v5/spec_utils.py:29"}],[1808,{"filePath":"audio_separator/separator/uvr_lib_v5/spec_utils.py","startLine":395,"endLine":411,"chunkText":"\r\n\r\ndef get_lp_filter_mask(n_bins, bin_start, bin_stop):\r\n    mask = np.concatenate([np.ones((bin_start - 1, 1)), np.linspace(1, 0, bin_stop - bin_start + 1)[:, None], np.zeros((n_bins - bin_stop, 1))], axis=0)\r\n\r\n    return mask\r\n\r\n\r\ndef get_hp_filter_mask(n_bins, bin_start, bin_stop):\r\n    mask = np.concatenate([np.zeros((bin_stop + 1, 1)), np.linspace(0, 1, 1 + bin_start - bin_stop)[:, None], np.ones((n_bins - bin_start - 2, 1))], axis=0)\r\n\r\n    return mask\r\n\r\n\r\ndef fft_lp_filter(spec, bin_start, bin_stop):\r\n    g = 1.0\r\n    for b in range(bin_start, bin_stop):\r","chunkId":"audio_separator/separator/uvr_lib_v5/spec_utils.py:30"}],[1809,{"filePath":"audio_separator/separator/uvr_lib_v5/spec_utils.py","startLine":409,"endLine":432,"chunkText":"def fft_lp_filter(spec, bin_start, bin_stop):\r\n    g = 1.0\r\n    for b in range(bin_start, bin_stop):\r\n        g -= 1 / (bin_stop - bin_start)\r\n        spec[:, b, :] = g * spec[:, b, :]\r\n\r\n    spec[:, bin_stop:, :] *= 0\r\n\r\n    return spec\r\n\r\n\r\ndef fft_hp_filter(spec, bin_start, bin_stop):\r\n    g = 1.0\r\n    for b in range(bin_start, bin_stop, -1):\r\n        g -= 1 / (bin_start - bin_stop)\r\n        spec[:, b, :] = g * spec[:, b, :]\r\n\r\n    spec[:, 0 : bin_stop + 1, :] *= 0\r\n\r\n    return spec\r\n\r\n\r\ndef spectrogram_to_wave_old(spec, hop_length=1024):\r\n    if spec.ndim == 2:\r","chunkId":"audio_separator/separator/uvr_lib_v5/spec_utils.py:31"}],[1810,{"filePath":"audio_separator/separator/uvr_lib_v5/spec_utils.py","startLine":430,"endLine":446,"chunkText":"\r\ndef spectrogram_to_wave_old(spec, hop_length=1024):\r\n    if spec.ndim == 2:\r\n        wave = librosa.istft(spec, hop_length=hop_length)\r\n    elif spec.ndim == 3:\r\n        spec_left = np.asfortranarray(spec[0])\r\n        spec_right = np.asfortranarray(spec[1])\r\n\r\n        wave_left = librosa.istft(spec_left, hop_length=hop_length)\r\n        wave_right = librosa.istft(spec_right, hop_length=hop_length)\r\n        wave = np.asfortranarray([wave_left, wave_right])\r\n\r\n    return wave\r\n\r\n\r\ndef wave_to_spectrogram_old(wave, hop_length, n_fft):\r\n    wave_left = np.asfortranarray(wave[0])\r","chunkId":"audio_separator/separator/uvr_lib_v5/spec_utils.py:32"}],[1811,{"filePath":"audio_separator/separator/uvr_lib_v5/spec_utils.py","startLine":444,"endLine":459,"chunkText":"\r\ndef wave_to_spectrogram_old(wave, hop_length, n_fft):\r\n    wave_left = np.asfortranarray(wave[0])\r\n    wave_right = np.asfortranarray(wave[1])\r\n\r\n    spec_left = librosa.stft(wave_left, n_fft=n_fft, hop_length=hop_length)\r\n    spec_right = librosa.stft(wave_right, n_fft=n_fft, hop_length=hop_length)\r\n\r\n    spec = np.asfortranarray([spec_left, spec_right])\r\n\r\n    return spec\r\n\r\n\r\ndef mirroring(a, spec_m, input_high_end, mp):\r\n    if \"mirroring\" == a:\r\n        mirror = np.flip(np.abs(spec_m[:, mp.param[\"pre_filter_start\"] - 10 - input_high_end.shape[1] : mp.param[\"pre_filter_start\"] - 10, :]), 1)\r","chunkId":"audio_separator/separator/uvr_lib_v5/spec_utils.py:33"}],[1812,{"filePath":"audio_separator/separator/uvr_lib_v5/spec_utils.py","startLine":457,"endLine":465,"chunkText":"def mirroring(a, spec_m, input_high_end, mp):\r\n    if \"mirroring\" == a:\r\n        mirror = np.flip(np.abs(spec_m[:, mp.param[\"pre_filter_start\"] - 10 - input_high_end.shape[1] : mp.param[\"pre_filter_start\"] - 10, :]), 1)\r\n        mirror = mirror * np.exp(1.0j * np.angle(input_high_end))\r\n\r\n        return np.where(np.abs(input_high_end) <= np.abs(mirror), input_high_end, mirror)\r\n\r\n    if \"mirroring2\" == a:\r\n        mirror = np.flip(np.abs(spec_m[:, mp.param[\"pre_filter_start\"] - 10 - input_high_end.shape[1] : mp.param[\"pre_filter_start\"] - 10, :]), 1)\r","chunkId":"audio_separator/separator/uvr_lib_v5/spec_utils.py:34"}],[1813,{"filePath":"audio_separator/separator/uvr_lib_v5/spec_utils.py","startLine":463,"endLine":478,"chunkText":"\r\n    if \"mirroring2\" == a:\r\n        mirror = np.flip(np.abs(spec_m[:, mp.param[\"pre_filter_start\"] - 10 - input_high_end.shape[1] : mp.param[\"pre_filter_start\"] - 10, :]), 1)\r\n        mi = np.multiply(mirror, input_high_end * 1.7)\r\n\r\n        return np.where(np.abs(input_high_end) <= np.abs(mi), input_high_end, mi)\r\n\r\n\r\ndef adjust_aggr(mask, is_non_accom_stem, aggressiveness):\r\n    aggr = aggressiveness[\"value\"] * 2\r\n\r\n    if aggr != 0:\r\n        if is_non_accom_stem:\r\n            aggr = 1 - aggr\r\n\r\n        if np.any(aggr > 10) or np.any(aggr < -10):\r","chunkId":"audio_separator/separator/uvr_lib_v5/spec_utils.py:35"}],[1814,{"filePath":"audio_separator/separator/uvr_lib_v5/spec_utils.py","startLine":476,"endLine":488,"chunkText":"            aggr = 1 - aggr\r\n\r\n        if np.any(aggr > 10) or np.any(aggr < -10):\r\n            print(f\"Warning: Extreme aggressiveness values detected: {aggr}\")\r\n\r\n        aggr = [aggr, aggr]\r\n\r\n        if aggressiveness[\"aggr_correction\"] is not None:\r\n            aggr[0] += aggressiveness[\"aggr_correction\"][\"left\"]\r\n            aggr[1] += aggressiveness[\"aggr_correction\"][\"right\"]\r\n\r\n        for ch in range(2):\r\n            mask[ch, : aggressiveness[\"split_bin\"]] = np.power(mask[ch, : aggressiveness[\"split_bin\"]], 1 + aggr[ch] / 3)\r","chunkId":"audio_separator/separator/uvr_lib_v5/spec_utils.py:36"}],[1815,{"filePath":"audio_separator/separator/uvr_lib_v5/spec_utils.py","startLine":486,"endLine":500,"chunkText":"\r\n        for ch in range(2):\r\n            mask[ch, : aggressiveness[\"split_bin\"]] = np.power(mask[ch, : aggressiveness[\"split_bin\"]], 1 + aggr[ch] / 3)\r\n            mask[ch, aggressiveness[\"split_bin\"] :] = np.power(mask[ch, aggressiveness[\"split_bin\"] :], 1 + aggr[ch])\r\n\r\n    return mask\r\n\r\n\r\ndef stft(wave, nfft, hl):\r\n    wave_left = np.asfortranarray(wave[0])\r\n    wave_right = np.asfortranarray(wave[1])\r\n    spec_left = librosa.stft(wave_left, n_fft=nfft, hop_length=hl)\r\n    spec_right = librosa.stft(wave_right, n_fft=nfft, hop_length=hl)\r\n    spec = np.asfortranarray([spec_left, spec_right])\r\n\r","chunkId":"audio_separator/separator/uvr_lib_v5/spec_utils.py:37"}],[1816,{"filePath":"audio_separator/separator/uvr_lib_v5/spec_utils.py","startLine":498,"endLine":515,"chunkText":"    spec_right = librosa.stft(wave_right, n_fft=nfft, hop_length=hl)\r\n    spec = np.asfortranarray([spec_left, spec_right])\r\n\r\n    return spec\r\n\r\n\r\ndef istft(spec, hl):\r\n    spec_left = np.asfortranarray(spec[0])\r\n    spec_right = np.asfortranarray(spec[1])\r\n    wave_left = librosa.istft(spec_left, hop_length=hl)\r\n    wave_right = librosa.istft(spec_right, hop_length=hl)\r\n    wave = np.asfortranarray([wave_left, wave_right])\r\n\r\n    return wave\r\n\r\n\r\ndef spec_effects(wave, algorithm=\"Default\", value=None):\r\n    if np.isnan(wave).any() or np.isinf(wave).any():\r","chunkId":"audio_separator/separator/uvr_lib_v5/spec_utils.py:38"}],[1817,{"filePath":"audio_separator/separator/uvr_lib_v5/spec_utils.py","startLine":513,"endLine":524,"chunkText":"\r\ndef spec_effects(wave, algorithm=\"Default\", value=None):\r\n    if np.isnan(wave).any() or np.isinf(wave).any():\r\n        print(f\"Warning: Detected NaN or infinite values in wave input. Shape: {wave.shape}\")\r\n\r\n    spec = [stft(wave[0], 2048, 1024), stft(wave[1], 2048, 1024)]\r\n    if algorithm == \"Min_Mag\":\r\n        v_spec_m = np.where(np.abs(spec[1]) <= np.abs(spec[0]), spec[1], spec[0])\r\n        wave = istft(v_spec_m, 1024)\r\n    elif algorithm == \"Max_Mag\":\r\n        v_spec_m = np.where(np.abs(spec[1]) >= np.abs(spec[0]), spec[1], spec[0])\r\n        wave = istft(v_spec_m, 1024)\r","chunkId":"audio_separator/separator/uvr_lib_v5/spec_utils.py:39"}],[1818,{"filePath":"audio_separator/separator/uvr_lib_v5/spec_utils.py","startLine":522,"endLine":537,"chunkText":"    elif algorithm == \"Max_Mag\":\r\n        v_spec_m = np.where(np.abs(spec[1]) >= np.abs(spec[0]), spec[1], spec[0])\r\n        wave = istft(v_spec_m, 1024)\r\n    elif algorithm == \"Default\":\r\n        wave = (wave[1] * value) + (wave[0] * (1 - value))\r\n    elif algorithm == \"Invert_p\":\r\n        X_mag = np.abs(spec[0])\r\n        y_mag = np.abs(spec[1])\r\n        max_mag = np.where(X_mag >= y_mag, X_mag, y_mag)\r\n        v_spec = spec[1] - max_mag * np.exp(1.0j * np.angle(spec[0]))\r\n        wave = istft(v_spec, 1024)\r\n\r\n    return wave\r\n\r\n\r\ndef spectrogram_to_wave_no_mp(spec, n_fft=2048, hop_length=1024):\r","chunkId":"audio_separator/separator/uvr_lib_v5/spec_utils.py:40"}],[1819,{"filePath":"audio_separator/separator/uvr_lib_v5/spec_utils.py","startLine":535,"endLine":559,"chunkText":"\r\n\r\ndef spectrogram_to_wave_no_mp(spec, n_fft=2048, hop_length=1024):\r\n    wave = librosa.istft(spec, n_fft=n_fft, hop_length=hop_length)\r\n\r\n    if wave.ndim == 1:\r\n        wave = np.asfortranarray([wave, wave])\r\n\r\n    return wave\r\n\r\n\r\ndef wave_to_spectrogram_no_mp(wave):\r\n\r\n    spec = librosa.stft(wave, n_fft=2048, hop_length=1024)\r\n\r\n    if spec.ndim == 1:\r\n        spec = np.asfortranarray([spec, spec])\r\n\r\n    return spec\r\n\r\n\r\ndef invert_audio(specs, invert_p=True):\r\n\r\n    ln = min([specs[0].shape[2], specs[1].shape[2]])\r\n    specs[0] = specs[0][:, :, :ln]\r","chunkId":"audio_separator/separator/uvr_lib_v5/spec_utils.py:41"}],[1820,{"filePath":"audio_separator/separator/uvr_lib_v5/spec_utils.py","startLine":557,"endLine":575,"chunkText":"\r\n    ln = min([specs[0].shape[2], specs[1].shape[2]])\r\n    specs[0] = specs[0][:, :, :ln]\r\n    specs[1] = specs[1][:, :, :ln]\r\n\r\n    if invert_p:\r\n        X_mag = np.abs(specs[0])\r\n        y_mag = np.abs(specs[1])\r\n        max_mag = np.where(X_mag >= y_mag, X_mag, y_mag)\r\n        v_spec = specs[1] - max_mag * np.exp(1.0j * np.angle(specs[0]))\r\n    else:\r\n        specs[1] = reduce_vocal_aggressively(specs[0], specs[1], 0.2)\r\n        v_spec = specs[0] - specs[1]\r\n\r\n    return v_spec\r\n\r\n\r\ndef invert_stem(mixture, stem):\r\n    mixture = wave_to_spectrogram_no_mp(mixture)\r","chunkId":"audio_separator/separator/uvr_lib_v5/spec_utils.py:42"}],[1821,{"filePath":"audio_separator/separator/uvr_lib_v5/spec_utils.py","startLine":573,"endLine":593,"chunkText":"\r\ndef invert_stem(mixture, stem):\r\n    mixture = wave_to_spectrogram_no_mp(mixture)\r\n    stem = wave_to_spectrogram_no_mp(stem)\r\n    output = spectrogram_to_wave_no_mp(invert_audio([mixture, stem]))\r\n\r\n    return -output.T\r\n\r\n\r\ndef ensembling(a, inputs, is_wavs=False):\r\n\r\n    for i in range(1, len(inputs)):\r\n        if i == 1:\r\n            input = inputs[0]\r\n\r\n        if is_wavs:\r\n            ln = min([input.shape[1], inputs[i].shape[1]])\r\n            input = input[:, :ln]\r\n            inputs[i] = inputs[i][:, :ln]\r\n        else:\r\n            ln = min([input.shape[2], inputs[i].shape[2]])\r","chunkId":"audio_separator/separator/uvr_lib_v5/spec_utils.py:43"}],[1822,{"filePath":"audio_separator/separator/uvr_lib_v5/spec_utils.py","startLine":591,"endLine":601,"chunkText":"            inputs[i] = inputs[i][:, :ln]\r\n        else:\r\n            ln = min([input.shape[2], inputs[i].shape[2]])\r\n            input = input[:, :, :ln]\r\n            inputs[i] = inputs[i][:, :, :ln]\r\n\r\n        if MIN_SPEC == a:\r\n            input = np.where(np.abs(inputs[i]) <= np.abs(input), inputs[i], input)\r\n        if MAX_SPEC == a:\r\n            #input = np.array(np.where(np.greater_equal(np.abs(inputs[i]), np.abs(input)), inputs[i], input), dtype=object)\r\n            input = np.where(np.abs(inputs[i]) >= np.abs(input), inputs[i], input)\r","chunkId":"audio_separator/separator/uvr_lib_v5/spec_utils.py:44"}],[1823,{"filePath":"audio_separator/separator/uvr_lib_v5/spec_utils.py","startLine":599,"endLine":614,"chunkText":"        if MAX_SPEC == a:\r\n            #input = np.array(np.where(np.greater_equal(np.abs(inputs[i]), np.abs(input)), inputs[i], input), dtype=object)\r\n            input = np.where(np.abs(inputs[i]) >= np.abs(input), inputs[i], input)\r\n            #max_spec = np.array([np.where(np.greater_equal(np.abs(inputs[i]), np.abs(input)), s, specs[0]) for s in specs[1:]], dtype=object)[-1]\r\n\r\n    # linear_ensemble\r\n    # input = ensemble_wav(inputs, split_size=1)\r\n\r\n    return input\r\n\r\n\r\ndef ensemble_for_align(waves):\r\n\r\n    specs = []\r\n\r\n    for wav in waves:\r","chunkId":"audio_separator/separator/uvr_lib_v5/spec_utils.py:45"}],[1824,{"filePath":"audio_separator/separator/uvr_lib_v5/spec_utils.py","startLine":612,"endLine":633,"chunkText":"    specs = []\r\n\r\n    for wav in waves:\r\n        spec = wave_to_spectrogram_no_mp(wav.T)\r\n        specs.append(spec)\r\n\r\n    wav_aligned = spectrogram_to_wave_no_mp(ensembling(MIN_SPEC, specs)).T\r\n    wav_aligned = match_array_shapes(wav_aligned, waves[1], is_swap=True)\r\n\r\n    return wav_aligned\r\n\r\n\r\ndef ensemble_inputs(audio_input, algorithm, is_normalization, wav_type_set, save_path, is_wave=False, is_array=False):\r\n\r\n    wavs_ = []\r\n\r\n    if algorithm == AVERAGE:\r\n        output = average_audio(audio_input)\r\n        samplerate = 44100\r\n    else:\r\n        specs = []\r\n\r","chunkId":"audio_separator/separator/uvr_lib_v5/spec_utils.py:46"}],[1825,{"filePath":"audio_separator/separator/uvr_lib_v5/spec_utils.py","startLine":631,"endLine":647,"chunkText":"    else:\r\n        specs = []\r\n\r\n        for i in range(len(audio_input)):\r\n            wave, samplerate = librosa.load(audio_input[i], mono=False, sr=44100)\r\n            wavs_.append(wave)\r\n            spec = wave if is_wave else wave_to_spectrogram_no_mp(wave)\r\n            specs.append(spec)\r\n\r\n        wave_shapes = [w.shape[1] for w in wavs_]\r\n        target_shape = wavs_[wave_shapes.index(max(wave_shapes))]\r\n\r\n        if is_wave:\r\n            output = ensembling(algorithm, specs, is_wavs=True)\r\n        else:\r\n            output = spectrogram_to_wave_no_mp(ensembling(algorithm, specs))\r\n\r","chunkId":"audio_separator/separator/uvr_lib_v5/spec_utils.py:47"}],[1826,{"filePath":"audio_separator/separator/uvr_lib_v5/spec_utils.py","startLine":645,"endLine":664,"chunkText":"        else:\r\n            output = spectrogram_to_wave_no_mp(ensembling(algorithm, specs))\r\n\r\n        output = to_shape(output, target_shape.shape)\r\n\r\n    sf.write(save_path, normalize(output.T, is_normalization), samplerate, subtype=wav_type_set)\r\n\r\n\r\ndef to_shape(x, target_shape):\r\n    padding_list = []\r\n    for x_dim, target_dim in zip(x.shape, target_shape):\r\n        pad_value = target_dim - x_dim\r\n        pad_tuple = (0, pad_value)\r\n        padding_list.append(pad_tuple)\r\n\r\n    return np.pad(x, tuple(padding_list), mode=\"constant\")\r\n\r\n\r\ndef to_shape_minimize(x: np.ndarray, target_shape):\r\n\r","chunkId":"audio_separator/separator/uvr_lib_v5/spec_utils.py:48"}],[1827,{"filePath":"audio_separator/separator/uvr_lib_v5/spec_utils.py","startLine":662,"endLine":679,"chunkText":"\r\ndef to_shape_minimize(x: np.ndarray, target_shape):\r\n\r\n    padding_list = []\r\n    for x_dim, target_dim in zip(x.shape, target_shape):\r\n        pad_value = target_dim - x_dim\r\n        pad_tuple = (0, pad_value)\r\n        padding_list.append(pad_tuple)\r\n\r\n    return np.pad(x, tuple(padding_list), mode=\"constant\")\r\n\r\n\r\ndef detect_leading_silence(audio, sr, silence_threshold=0.007, frame_length=1024):\r\n    \"\"\"\r\n    Detect silence at the beginning of an audio signal.\r\n\r\n    :param audio: np.array, audio signal\r\n    :param sr: int, sample rate\r","chunkId":"audio_separator/separator/uvr_lib_v5/spec_utils.py:49"}],[1828,{"filePath":"audio_separator/separator/uvr_lib_v5/spec_utils.py","startLine":677,"endLine":691,"chunkText":"\r\n    :param audio: np.array, audio signal\r\n    :param sr: int, sample rate\r\n    :param silence_threshold: float, magnitude threshold below which is considered silence\r\n    :param frame_length: int, the number of samples to consider for each check\r\n\r\n    :return: float, duration of the leading silence in milliseconds\r\n    \"\"\"\r\n\r\n    if len(audio.shape) == 2:\r\n        # If stereo, pick the channel with more energy to determine the silence\r\n        channel = np.argmax(np.sum(np.abs(audio), axis=1))\r\n        audio = audio[channel]\r\n\r\n    for i in range(0, len(audio), frame_length):\r","chunkId":"audio_separator/separator/uvr_lib_v5/spec_utils.py:50"}],[1829,{"filePath":"audio_separator/separator/uvr_lib_v5/spec_utils.py","startLine":689,"endLine":702,"chunkText":"        audio = audio[channel]\r\n\r\n    for i in range(0, len(audio), frame_length):\r\n        if np.max(np.abs(audio[i : i + frame_length])) > silence_threshold:\r\n            return (i / sr) * 1000\r\n\r\n    return (len(audio) / sr) * 1000\r\n\r\n\r\ndef adjust_leading_silence(target_audio, reference_audio, silence_threshold=0.01, frame_length=1024):\r\n    \"\"\"\r\n    Adjust the leading silence of the target_audio to match the leading silence of the reference_audio.\r\n\r\n    :param target_audio: np.array, audio signal that will have its silence adjusted\r","chunkId":"audio_separator/separator/uvr_lib_v5/spec_utils.py:51"}],[1830,{"filePath":"audio_separator/separator/uvr_lib_v5/spec_utils.py","startLine":700,"endLine":710,"chunkText":"    Adjust the leading silence of the target_audio to match the leading silence of the reference_audio.\r\n\r\n    :param target_audio: np.array, audio signal that will have its silence adjusted\r\n    :param reference_audio: np.array, audio signal used as a reference\r\n    :param sr: int, sample rate\r\n    :param silence_threshold: float, magnitude threshold below which is considered silence\r\n    :param frame_length: int, the number of samples to consider for each check\r\n\r\n    :return: np.array, target_audio adjusted to have the same leading silence as reference_audio\r\n    \"\"\"\r\n\r","chunkId":"audio_separator/separator/uvr_lib_v5/spec_utils.py:52"}],[1831,{"filePath":"audio_separator/separator/uvr_lib_v5/spec_utils.py","startLine":708,"endLine":721,"chunkText":"    :return: np.array, target_audio adjusted to have the same leading silence as reference_audio\r\n    \"\"\"\r\n\r\n    def find_silence_end(audio):\r\n        if len(audio.shape) == 2:\r\n            # If stereo, pick the channel with more energy to determine the silence\r\n            channel = np.argmax(np.sum(np.abs(audio), axis=1))\r\n            audio_mono = audio[channel]\r\n        else:\r\n            audio_mono = audio\r\n\r\n        for i in range(0, len(audio_mono), frame_length):\r\n            if np.max(np.abs(audio_mono[i : i + frame_length])) > silence_threshold:\r\n                return i\r","chunkId":"audio_separator/separator/uvr_lib_v5/spec_utils.py:53"}],[1832,{"filePath":"audio_separator/separator/uvr_lib_v5/spec_utils.py","startLine":719,"endLine":731,"chunkText":"        for i in range(0, len(audio_mono), frame_length):\r\n            if np.max(np.abs(audio_mono[i : i + frame_length])) > silence_threshold:\r\n                return i\r\n        return len(audio_mono)\r\n\r\n    ref_silence_end = find_silence_end(reference_audio)\r\n    target_silence_end = find_silence_end(target_audio)\r\n    silence_difference = ref_silence_end - target_silence_end\r\n\r\n    try:\r\n        ref_silence_end_p = (ref_silence_end / 44100) * 1000\r\n        target_silence_end_p = (target_silence_end / 44100) * 1000\r\n        silence_difference_p = ref_silence_end_p - target_silence_end_p\r","chunkId":"audio_separator/separator/uvr_lib_v5/spec_utils.py:54"}],[1833,{"filePath":"audio_separator/separator/uvr_lib_v5/spec_utils.py","startLine":729,"endLine":740,"chunkText":"        ref_silence_end_p = (ref_silence_end / 44100) * 1000\r\n        target_silence_end_p = (target_silence_end / 44100) * 1000\r\n        silence_difference_p = ref_silence_end_p - target_silence_end_p\r\n        print(\"silence_difference: \", silence_difference_p)\r\n    except Exception as e:\r\n        pass\r\n\r\n    if silence_difference > 0:  # Add silence to target_audio\r\n        if len(target_audio.shape) == 2:  # stereo\r\n            silence_to_add = np.zeros((target_audio.shape[0], silence_difference))\r\n        else:  # mono\r\n            silence_to_add = np.zeros(silence_difference)\r","chunkId":"audio_separator/separator/uvr_lib_v5/spec_utils.py:55"}],[1834,{"filePath":"audio_separator/separator/uvr_lib_v5/spec_utils.py","startLine":738,"endLine":750,"chunkText":"            silence_to_add = np.zeros((target_audio.shape[0], silence_difference))\r\n        else:  # mono\r\n            silence_to_add = np.zeros(silence_difference)\r\n        return np.hstack((silence_to_add, target_audio))\r\n    elif silence_difference < 0:  # Remove silence from target_audio\r\n        if len(target_audio.shape) == 2:  # stereo\r\n            return target_audio[:, -silence_difference:]\r\n        else:  # mono\r\n            return target_audio[-silence_difference:]\r\n    else:  # No adjustment needed\r\n        return target_audio\r\n\r\n\r","chunkId":"audio_separator/separator/uvr_lib_v5/spec_utils.py:56"}],[1835,{"filePath":"audio_separator/separator/uvr_lib_v5/spec_utils.py","startLine":748,"endLine":765,"chunkText":"        return target_audio\r\n\r\n\r\ndef match_array_shapes(array_1: np.ndarray, array_2: np.ndarray, is_swap=False):\r\n\r\n    if is_swap:\r\n        array_1, array_2 = array_1.T, array_2.T\r\n\r\n    # print(\"before\", array_1.shape, array_2.shape)\r\n    if array_1.shape[1] > array_2.shape[1]:\r\n        array_1 = array_1[:, : array_2.shape[1]]\r\n    elif array_1.shape[1] < array_2.shape[1]:\r\n        padding = array_2.shape[1] - array_1.shape[1]\r\n        array_1 = np.pad(array_1, ((0, 0), (0, padding)), \"constant\", constant_values=0)\r\n\r\n    # print(\"after\", array_1.shape, array_2.shape)\r\n\r\n    if is_swap:\r","chunkId":"audio_separator/separator/uvr_lib_v5/spec_utils.py:57"}],[1836,{"filePath":"audio_separator/separator/uvr_lib_v5/spec_utils.py","startLine":763,"endLine":782,"chunkText":"    # print(\"after\", array_1.shape, array_2.shape)\r\n\r\n    if is_swap:\r\n        array_1, array_2 = array_1.T, array_2.T\r\n\r\n    return array_1\r\n\r\n\r\ndef match_mono_array_shapes(array_1: np.ndarray, array_2: np.ndarray):\r\n\r\n    if len(array_1) > len(array_2):\r\n        array_1 = array_1[: len(array_2)]\r\n    elif len(array_1) < len(array_2):\r\n        padding = len(array_2) - len(array_1)\r\n        array_1 = np.pad(array_1, (0, padding), \"constant\", constant_values=0)\r\n\r\n    return array_1\r\n\r\n\r\ndef change_pitch_semitones(y, sr, semitone_shift):\r","chunkId":"audio_separator/separator/uvr_lib_v5/spec_utils.py:58"}],[1837,{"filePath":"audio_separator/separator/uvr_lib_v5/spec_utils.py","startLine":780,"endLine":793,"chunkText":"\r\n\r\ndef change_pitch_semitones(y, sr, semitone_shift):\r\n    factor = 2 ** (semitone_shift / 12)  # Convert semitone shift to factor for resampling\r\n    y_pitch_tuned = []\r\n    for y_channel in y:\r\n        y_pitch_tuned.append(librosa.resample(y_channel, orig_sr=sr, target_sr=sr * factor, res_type=wav_resolution_float_resampling))\r\n    y_pitch_tuned = np.array(y_pitch_tuned)\r\n    new_sr = sr * factor\r\n    return y_pitch_tuned, new_sr\r\n\r\n\r\ndef augment_audio(export_path, audio_file, rate, is_normalization, wav_type_set, save_format=None, is_pitch=False, is_time_correction=True):\r\n\r","chunkId":"audio_separator/separator/uvr_lib_v5/spec_utils.py:59"}],[1838,{"filePath":"audio_separator/separator/uvr_lib_v5/spec_utils.py","startLine":791,"endLine":805,"chunkText":"\r\ndef augment_audio(export_path, audio_file, rate, is_normalization, wav_type_set, save_format=None, is_pitch=False, is_time_correction=True):\r\n\r\n    wav, sr = librosa.load(audio_file, sr=44100, mono=False)\r\n\r\n    if wav.ndim == 1:\r\n        wav = np.asfortranarray([wav, wav])\r\n\r\n    if not is_time_correction:\r\n        wav_mix = change_pitch_semitones(wav, 44100, semitone_shift=-rate)[0]\r\n    else:\r\n        if is_pitch:\r\n            wav_1 = pyrb.pitch_shift(wav[0], sr, rate, rbargs=None)\r\n            wav_2 = pyrb.pitch_shift(wav[1], sr, rate, rbargs=None)\r\n        else:\r","chunkId":"audio_separator/separator/uvr_lib_v5/spec_utils.py:60"}],[1839,{"filePath":"audio_separator/separator/uvr_lib_v5/spec_utils.py","startLine":803,"endLine":815,"chunkText":"            wav_1 = pyrb.pitch_shift(wav[0], sr, rate, rbargs=None)\r\n            wav_2 = pyrb.pitch_shift(wav[1], sr, rate, rbargs=None)\r\n        else:\r\n            wav_1 = pyrb.time_stretch(wav[0], sr, rate, rbargs=None)\r\n            wav_2 = pyrb.time_stretch(wav[1], sr, rate, rbargs=None)\r\n\r\n        if wav_1.shape > wav_2.shape:\r\n            wav_2 = to_shape(wav_2, wav_1.shape)\r\n        if wav_1.shape < wav_2.shape:\r\n            wav_1 = to_shape(wav_1, wav_2.shape)\r\n\r\n        wav_mix = np.asfortranarray([wav_1, wav_2])\r\n\r","chunkId":"audio_separator/separator/uvr_lib_v5/spec_utils.py:61"}],[1840,{"filePath":"audio_separator/separator/uvr_lib_v5/spec_utils.py","startLine":813,"endLine":833,"chunkText":"\r\n        wav_mix = np.asfortranarray([wav_1, wav_2])\r\n\r\n    sf.write(export_path, normalize(wav_mix.T, is_normalization), sr, subtype=wav_type_set)\r\n    save_format(export_path)\r\n\r\n\r\ndef average_audio(audio):\r\n\r\n    waves = []\r\n    wave_shapes = []\r\n    final_waves = []\r\n\r\n    for i in range(len(audio)):\r\n        wave = librosa.load(audio[i], sr=44100, mono=False)\r\n        waves.append(wave[0])\r\n        wave_shapes.append(wave[0].shape[1])\r\n\r\n    wave_shapes_index = wave_shapes.index(max(wave_shapes))\r\n    target_shape = waves[wave_shapes_index]\r\n    waves.pop(wave_shapes_index)\r","chunkId":"audio_separator/separator/uvr_lib_v5/spec_utils.py:62"}],[1841,{"filePath":"audio_separator/separator/uvr_lib_v5/spec_utils.py","startLine":831,"endLine":850,"chunkText":"    wave_shapes_index = wave_shapes.index(max(wave_shapes))\r\n    target_shape = waves[wave_shapes_index]\r\n    waves.pop(wave_shapes_index)\r\n    final_waves.append(target_shape)\r\n\r\n    for n_array in waves:\r\n        wav_target = to_shape(n_array, target_shape.shape)\r\n        final_waves.append(wav_target)\r\n\r\n    waves = sum(final_waves)\r\n    waves = waves / len(audio)\r\n\r\n    return waves\r\n\r\n\r\ndef average_dual_sources(wav_1, wav_2, value):\r\n\r\n    if wav_1.shape > wav_2.shape:\r\n        wav_2 = to_shape(wav_2, wav_1.shape)\r\n    if wav_1.shape < wav_2.shape:\r","chunkId":"audio_separator/separator/uvr_lib_v5/spec_utils.py:63"}],[1842,{"filePath":"audio_separator/separator/uvr_lib_v5/spec_utils.py","startLine":848,"endLine":869,"chunkText":"    if wav_1.shape > wav_2.shape:\r\n        wav_2 = to_shape(wav_2, wav_1.shape)\r\n    if wav_1.shape < wav_2.shape:\r\n        wav_1 = to_shape(wav_1, wav_2.shape)\r\n\r\n    wave = (wav_1 * value) + (wav_2 * (1 - value))\r\n\r\n    return wave\r\n\r\n\r\ndef reshape_sources(wav_1: np.ndarray, wav_2: np.ndarray):\r\n\r\n    if wav_1.shape > wav_2.shape:\r\n        wav_2 = to_shape(wav_2, wav_1.shape)\r\n    if wav_1.shape < wav_2.shape:\r\n        ln = min([wav_1.shape[1], wav_2.shape[1]])\r\n        wav_2 = wav_2[:, :ln]\r\n\r\n    ln = min([wav_1.shape[1], wav_2.shape[1]])\r\n    wav_1 = wav_1[:, :ln]\r\n    wav_2 = wav_2[:, :ln]\r\n\r","chunkId":"audio_separator/separator/uvr_lib_v5/spec_utils.py:64"}],[1843,{"filePath":"audio_separator/separator/uvr_lib_v5/spec_utils.py","startLine":867,"endLine":892,"chunkText":"    wav_1 = wav_1[:, :ln]\r\n    wav_2 = wav_2[:, :ln]\r\n\r\n    return wav_2\r\n\r\n\r\ndef reshape_sources_ref(wav_1_shape, wav_2: np.ndarray):\r\n\r\n    if wav_1_shape > wav_2.shape:\r\n        wav_2 = to_shape(wav_2, wav_1_shape)\r\n\r\n    return wav_2\r\n\r\n\r\ndef combine_arrarys(audio_sources, is_swap=False):\r\n    source = np.zeros_like(max(audio_sources, key=np.size))\r\n\r\n    for v in audio_sources:\r\n        v = match_array_shapes(v, source, is_swap=is_swap)\r\n        source += v\r\n\r\n    return source\r\n\r\n\r\ndef combine_audio(paths: list, audio_file_base=None, wav_type_set=\"FLOAT\", save_format=None):\r\n\r","chunkId":"audio_separator/separator/uvr_lib_v5/spec_utils.py:65"}],[1844,{"filePath":"audio_separator/separator/uvr_lib_v5/spec_utils.py","startLine":890,"endLine":908,"chunkText":"\r\ndef combine_audio(paths: list, audio_file_base=None, wav_type_set=\"FLOAT\", save_format=None):\r\n\r\n    source = combine_arrarys([load_audio(i) for i in paths])\r\n    save_path = f\"{audio_file_base}_combined.wav\"\r\n    sf.write(save_path, source.T, 44100, subtype=wav_type_set)\r\n    save_format(save_path)\r\n\r\n\r\ndef reduce_mix_bv(inst_source, voc_source, reduction_rate=0.9):\r\n    # Reduce the volume\r\n    inst_source = inst_source * (1 - reduction_rate)\r\n\r\n    mix_reduced = combine_arrarys([inst_source, voc_source], is_swap=True)\r\n\r\n    return mix_reduced\r\n\r\n\r\ndef organize_inputs(inputs):\r","chunkId":"audio_separator/separator/uvr_lib_v5/spec_utils.py:66"}],[1845,{"filePath":"audio_separator/separator/uvr_lib_v5/spec_utils.py","startLine":906,"endLine":926,"chunkText":"\r\n\r\ndef organize_inputs(inputs):\r\n    input_list = {\"target\": None, \"reference\": None, \"reverb\": None, \"inst\": None}\r\n\r\n    for i in inputs:\r\n        if i.endswith(\"_(Vocals).wav\"):\r\n            input_list[\"reference\"] = i\r\n        elif \"_RVC_\" in i:\r\n            input_list[\"target\"] = i\r\n        elif i.endswith(\"reverbed_stem.wav\"):\r\n            input_list[\"reverb\"] = i\r\n        elif i.endswith(\"_(Instrumental).wav\"):\r\n            input_list[\"inst\"] = i\r\n\r\n    return input_list\r\n\r\n\r\ndef check_if_phase_inverted(wav1, wav2, is_mono=False):\r\n    # Load the audio files\r\n    if not is_mono:\r","chunkId":"audio_separator/separator/uvr_lib_v5/spec_utils.py:67"}],[1846,{"filePath":"audio_separator/separator/uvr_lib_v5/spec_utils.py","startLine":924,"endLine":950,"chunkText":"def check_if_phase_inverted(wav1, wav2, is_mono=False):\r\n    # Load the audio files\r\n    if not is_mono:\r\n        wav1 = np.mean(wav1, axis=0)\r\n        wav2 = np.mean(wav2, axis=0)\r\n\r\n    # Compute the correlation\r\n    correlation = np.corrcoef(wav1[:1000], wav2[:1000])\r\n\r\n    return correlation[0, 1] < 0\r\n\r\n\r\ndef align_audio(\r\n    file1,\r\n    file2,\r\n    file2_aligned,\r\n    file_subtracted,\r\n    wav_type_set,\r\n    is_save_aligned,\r\n    command_Text,\r\n    save_format,\r\n    align_window: list,\r\n    align_intro_val: list,\r\n    db_analysis: tuple,\r\n    set_progress_bar,\r\n    phase_option,\r\n    phase_shifts,\r","chunkId":"audio_separator/separator/uvr_lib_v5/spec_utils.py:68"}],[1847,{"filePath":"audio_separator/separator/uvr_lib_v5/spec_utils.py","startLine":948,"endLine":975,"chunkText":"    set_progress_bar,\r\n    phase_option,\r\n    phase_shifts,\r\n    is_match_silence,\r\n    is_spec_match,\r\n):\r\n\r\n    global progress_value\r\n    progress_value = 0\r\n    is_mono = False\r\n\r\n    def get_diff(a, b):\r\n        corr = np.correlate(a, b, \"full\")\r\n        diff = corr.argmax() - (b.shape[0] - 1)\r\n\r\n        return diff\r\n\r\n    def progress_bar(length):\r\n        global progress_value\r\n        progress_value += 1\r\n\r\n        if (0.90 / length * progress_value) >= 0.9:\r\n            length = progress_value + 1\r\n\r\n        set_progress_bar(0.1, (0.9 / length * progress_value))\r\n\r\n    # read tracks\r\n\r","chunkId":"audio_separator/separator/uvr_lib_v5/spec_utils.py:69"}],[1848,{"filePath":"audio_separator/separator/uvr_lib_v5/spec_utils.py","startLine":973,"endLine":990,"chunkText":"\r\n    # read tracks\r\n\r\n    if file1.endswith(\".mp3\") and is_macos:\r\n        length1 = rerun_mp3(file1)\r\n        wav1, sr1 = librosa.load(file1, duration=length1, sr=44100, mono=False)\r\n    else:\r\n        wav1, sr1 = librosa.load(file1, sr=44100, mono=False)\r\n\r\n    if file2.endswith(\".mp3\") and is_macos:\r\n        length2 = rerun_mp3(file2)\r\n        wav2, sr2 = librosa.load(file2, duration=length2, sr=44100, mono=False)\r\n    else:\r\n        wav2, sr2 = librosa.load(file2, sr=44100, mono=False)\r\n\r\n    if wav1.ndim == 1 and wav2.ndim == 1:\r\n        is_mono = True\r\n    elif wav1.ndim == 1:\r","chunkId":"audio_separator/separator/uvr_lib_v5/spec_utils.py:70"}],[1849,{"filePath":"audio_separator/separator/uvr_lib_v5/spec_utils.py","startLine":988,"endLine":1006,"chunkText":"    if wav1.ndim == 1 and wav2.ndim == 1:\r\n        is_mono = True\r\n    elif wav1.ndim == 1:\r\n        wav1 = np.asfortranarray([wav1, wav1])\r\n    elif wav2.ndim == 1:\r\n        wav2 = np.asfortranarray([wav2, wav2])\r\n\r\n    # Check if phase is inverted\r\n    if phase_option == AUTO_PHASE:\r\n        if check_if_phase_inverted(wav1, wav2, is_mono=is_mono):\r\n            wav2 = -wav2\r\n    elif phase_option == POSITIVE_PHASE:\r\n        wav2 = +wav2\r\n    elif phase_option == NEGATIVE_PHASE:\r\n        wav2 = -wav2\r\n\r\n    if is_match_silence:\r\n        wav2 = adjust_leading_silence(wav2, wav1)\r\n\r","chunkId":"audio_separator/separator/uvr_lib_v5/spec_utils.py:71"}],[1850,{"filePath":"audio_separator/separator/uvr_lib_v5/spec_utils.py","startLine":1004,"endLine":1022,"chunkText":"    if is_match_silence:\r\n        wav2 = adjust_leading_silence(wav2, wav1)\r\n\r\n    wav1_length = int(librosa.get_duration(y=wav1, sr=44100))\r\n    wav2_length = int(librosa.get_duration(y=wav2, sr=44100))\r\n\r\n    if not is_mono:\r\n        wav1 = wav1.transpose()\r\n        wav2 = wav2.transpose()\r\n\r\n    wav2_org = wav2.copy()\r\n\r\n    command_Text(\"Processing files... \\n\")\r\n    seconds_length = min(wav1_length, wav2_length)\r\n\r\n    wav2_aligned_sources = []\r\n\r\n    for sec_len in align_intro_val:\r\n        # pick a position at 1 second in and get diff\r","chunkId":"audio_separator/separator/uvr_lib_v5/spec_utils.py:72"}],[1851,{"filePath":"audio_separator/separator/uvr_lib_v5/spec_utils.py","startLine":1020,"endLine":1031,"chunkText":"\r\n    for sec_len in align_intro_val:\r\n        # pick a position at 1 second in and get diff\r\n        sec_seg = 1 if sec_len == 1 else int(seconds_length // sec_len)\r\n        index = sr1 * sec_seg  # 1 second in, assuming sr1 = sr2 = 44100\r\n\r\n        if is_mono:\r\n            samp1, samp2 = wav1[index : index + sr1], wav2[index : index + sr1]\r\n            diff = get_diff(samp1, samp2)\r\n            # print(f\"Estimated difference: {diff}\\n\")\r\n        else:\r\n            index = sr1 * sec_seg  # 1 second in, assuming sr1 = sr2 = 44100\r","chunkId":"audio_separator/separator/uvr_lib_v5/spec_utils.py:73"}],[1852,{"filePath":"audio_separator/separator/uvr_lib_v5/spec_utils.py","startLine":1029,"endLine":1038,"chunkText":"            # print(f\"Estimated difference: {diff}\\n\")\r\n        else:\r\n            index = sr1 * sec_seg  # 1 second in, assuming sr1 = sr2 = 44100\r\n            samp1, samp2 = wav1[index : index + sr1, 0], wav2[index : index + sr1, 0]\r\n            samp1_r, samp2_r = wav1[index : index + sr1, 1], wav2[index : index + sr1, 1]\r\n            diff, diff_r = get_diff(samp1, samp2), get_diff(samp1_r, samp2_r)\r\n            # print(f\"Estimated difference Left Channel: {diff}\\nEstimated difference Right Channel: {diff_r}\\n\")\r\n\r\n        # make aligned track 2\r\n        if diff > 0:\r","chunkId":"audio_separator/separator/uvr_lib_v5/spec_utils.py:74"}],[1853,{"filePath":"audio_separator/separator/uvr_lib_v5/spec_utils.py","startLine":1036,"endLine":1051,"chunkText":"\r\n        # make aligned track 2\r\n        if diff > 0:\r\n            zeros_to_append = np.zeros(diff) if is_mono else np.zeros((diff, 2))\r\n            wav2_aligned = np.append(zeros_to_append, wav2_org, axis=0)\r\n        elif diff < 0:\r\n            wav2_aligned = wav2_org[-diff:]\r\n        else:\r\n            wav2_aligned = wav2_org\r\n            # command_Text(f\"Audio files already aligned.\\n\")\r\n\r\n        if not any(np.array_equal(wav2_aligned, source) for source in wav2_aligned_sources):\r\n            wav2_aligned_sources.append(wav2_aligned)\r\n\r\n    # print(\"Unique Sources: \", len(wav2_aligned_sources))\r\n\r","chunkId":"audio_separator/separator/uvr_lib_v5/spec_utils.py:75"}],[1854,{"filePath":"audio_separator/separator/uvr_lib_v5/spec_utils.py","startLine":1049,"endLine":1062,"chunkText":"\r\n    # print(\"Unique Sources: \", len(wav2_aligned_sources))\r\n\r\n    unique_sources = len(wav2_aligned_sources)\r\n\r\n    sub_mapper_big_mapper = {}\r\n\r\n    for s in wav2_aligned_sources:\r\n        wav2_aligned = match_mono_array_shapes(s, wav1) if is_mono else match_array_shapes(s, wav1, is_swap=True)\r\n\r\n        if align_window:\r\n            wav_sub = time_correction(\r\n                wav1, wav2_aligned, seconds_length, align_window=align_window, db_analysis=db_analysis, progress_bar=progress_bar, unique_sources=unique_sources, phase_shifts=phase_shifts\r\n            )\r","chunkId":"audio_separator/separator/uvr_lib_v5/spec_utils.py:76"}],[1855,{"filePath":"audio_separator/separator/uvr_lib_v5/spec_utils.py","startLine":1060,"endLine":1070,"chunkText":"            wav_sub = time_correction(\r\n                wav1, wav2_aligned, seconds_length, align_window=align_window, db_analysis=db_analysis, progress_bar=progress_bar, unique_sources=unique_sources, phase_shifts=phase_shifts\r\n            )\r\n            wav_sub_size = np.abs(wav_sub).mean()\r\n            sub_mapper_big_mapper = {**sub_mapper_big_mapper, **{wav_sub_size: wav_sub}}\r\n        else:\r\n            wav2_aligned = wav2_aligned * np.power(10, db_analysis[0] / 20)\r\n            db_range = db_analysis[1]\r\n\r\n            for db_adjustment in db_range:\r\n                # Adjust the dB of track2\r","chunkId":"audio_separator/separator/uvr_lib_v5/spec_utils.py:77"}],[1856,{"filePath":"audio_separator/separator/uvr_lib_v5/spec_utils.py","startLine":1068,"endLine":1080,"chunkText":"\r\n            for db_adjustment in db_range:\r\n                # Adjust the dB of track2\r\n                s_adjusted = wav2_aligned * (10 ** (db_adjustment / 20))\r\n                wav_sub = wav1 - s_adjusted\r\n                wav_sub_size = np.abs(wav_sub).mean()\r\n                sub_mapper_big_mapper = {**sub_mapper_big_mapper, **{wav_sub_size: wav_sub}}\r\n\r\n        # print(sub_mapper_big_mapper.keys(), min(sub_mapper_big_mapper.keys()))\r\n\r\n    sub_mapper_value_list = list(sub_mapper_big_mapper.values())\r\n\r\n    if is_spec_match and len(sub_mapper_value_list) >= 2:\r","chunkId":"audio_separator/separator/uvr_lib_v5/spec_utils.py:78"}],[1857,{"filePath":"audio_separator/separator/uvr_lib_v5/spec_utils.py","startLine":1078,"endLine":1091,"chunkText":"    sub_mapper_value_list = list(sub_mapper_big_mapper.values())\r\n\r\n    if is_spec_match and len(sub_mapper_value_list) >= 2:\r\n        # print(\"using spec ensemble with align\")\r\n        wav_sub = ensemble_for_align(list(sub_mapper_big_mapper.values()))\r\n    else:\r\n        # print(\"using linear ensemble with align\")\r\n        wav_sub = ensemble_wav(list(sub_mapper_big_mapper.values()))\r\n\r\n    # print(f\"Mix Mean: {np.abs(wav1).mean()}\\nInst Mean: {np.abs(wav2).mean()}\")\r\n    # print('Final: ', np.abs(wav_sub).mean())\r\n    wav_sub = np.clip(wav_sub, -1, +1)\r\n\r\n    command_Text(f\"Saving inverted track... \")\r","chunkId":"audio_separator/separator/uvr_lib_v5/spec_utils.py:79"}],[1858,{"filePath":"audio_separator/separator/uvr_lib_v5/spec_utils.py","startLine":1089,"endLine":1102,"chunkText":"    wav_sub = np.clip(wav_sub, -1, +1)\r\n\r\n    command_Text(f\"Saving inverted track... \")\r\n\r\n    if is_save_aligned or is_spec_match:\r\n        wav1 = match_mono_array_shapes(wav1, wav_sub) if is_mono else match_array_shapes(wav1, wav_sub, is_swap=True)\r\n        wav2_aligned = wav1 - wav_sub\r\n\r\n        if is_spec_match:\r\n            if wav1.ndim == 1 and wav2.ndim == 1:\r\n                wav2_aligned = np.asfortranarray([wav2_aligned, wav2_aligned]).T\r\n                wav1 = np.asfortranarray([wav1, wav1]).T\r\n\r\n            wav2_aligned = ensemble_for_align([wav2_aligned, wav1])\r","chunkId":"audio_separator/separator/uvr_lib_v5/spec_utils.py:80"}],[1859,{"filePath":"audio_separator/separator/uvr_lib_v5/spec_utils.py","startLine":1100,"endLine":1114,"chunkText":"                wav1 = np.asfortranarray([wav1, wav1]).T\r\n\r\n            wav2_aligned = ensemble_for_align([wav2_aligned, wav1])\r\n            wav_sub = wav1 - wav2_aligned\r\n\r\n        if is_save_aligned:\r\n            sf.write(file2_aligned, wav2_aligned, sr1, subtype=wav_type_set)\r\n            save_format(file2_aligned)\r\n\r\n    sf.write(file_subtracted, wav_sub, sr1, subtype=wav_type_set)\r\n    save_format(file_subtracted)\r\n\r\n\r\ndef phase_shift_hilbert(signal, degree):\r\n    analytic_signal = hilbert(signal)\r","chunkId":"audio_separator/separator/uvr_lib_v5/spec_utils.py:81"}],[1860,{"filePath":"audio_separator/separator/uvr_lib_v5/spec_utils.py","startLine":1112,"endLine":1129,"chunkText":"\r\ndef phase_shift_hilbert(signal, degree):\r\n    analytic_signal = hilbert(signal)\r\n    return np.cos(np.radians(degree)) * analytic_signal.real - np.sin(np.radians(degree)) * analytic_signal.imag\r\n\r\n\r\ndef get_phase_shifted_tracks(track, phase_shift):\r\n    if phase_shift == 180:\r\n        return [track, -track]\r\n\r\n    step = phase_shift\r\n    end = 180 - (180 % step) if 180 % step == 0 else 181\r\n    phase_range = range(step, end, step)\r\n\r\n    flipped_list = [track, -track]\r\n    for i in phase_range:\r\n        flipped_list.extend([phase_shift_hilbert(track, i), phase_shift_hilbert(track, -i)])\r\n\r","chunkId":"audio_separator/separator/uvr_lib_v5/spec_utils.py:82"}],[1861,{"filePath":"audio_separator/separator/uvr_lib_v5/spec_utils.py","startLine":1127,"endLine":1140,"chunkText":"    for i in phase_range:\r\n        flipped_list.extend([phase_shift_hilbert(track, i), phase_shift_hilbert(track, -i)])\r\n\r\n    return flipped_list\r\n\r\n\r\ndef time_correction(mix: np.ndarray, instrumental: np.ndarray, seconds_length, align_window, db_analysis, sr=44100, progress_bar=None, unique_sources=None, phase_shifts=NONE_P):\r\n    # Function to align two tracks using cross-correlation\r\n\r\n    def align_tracks(track1, track2):\r\n        # A dictionary to store each version of track2_shifted and its mean absolute value\r\n        shifted_tracks = {}\r\n\r\n        # Loop to adjust dB of track2\r","chunkId":"audio_separator/separator/uvr_lib_v5/spec_utils.py:83"}],[1862,{"filePath":"audio_separator/separator/uvr_lib_v5/spec_utils.py","startLine":1138,"endLine":1153,"chunkText":"        shifted_tracks = {}\r\n\r\n        # Loop to adjust dB of track2\r\n        track2 = track2 * np.power(10, db_analysis[0] / 20)\r\n        db_range = db_analysis[1]\r\n\r\n        if phase_shifts == 190:\r\n            track2_flipped = [track2]\r\n        else:\r\n            track2_flipped = get_phase_shifted_tracks(track2, phase_shifts)\r\n\r\n        for db_adjustment in db_range:\r\n            for t in track2_flipped:\r\n                # Adjust the dB of track2\r\n                track2_adjusted = t * (10 ** (db_adjustment / 20))\r\n                corr = correlate(track1, track2_adjusted)\r","chunkId":"audio_separator/separator/uvr_lib_v5/spec_utils.py:84"}],[1863,{"filePath":"audio_separator/separator/uvr_lib_v5/spec_utils.py","startLine":1151,"endLine":1161,"chunkText":"                # Adjust the dB of track2\r\n                track2_adjusted = t * (10 ** (db_adjustment / 20))\r\n                corr = correlate(track1, track2_adjusted)\r\n                delay = np.argmax(np.abs(corr)) - (len(track1) - 1)\r\n                track2_shifted = np.roll(track2_adjusted, shift=delay)\r\n\r\n                # Compute the mean absolute value of track2_shifted\r\n                track2_shifted_sub = track1 - track2_shifted\r\n                mean_abs_value = np.abs(track2_shifted_sub).mean()\r\n\r\n                # Store track2_shifted and its mean absolute value in the dictionary\r","chunkId":"audio_separator/separator/uvr_lib_v5/spec_utils.py:85"}],[1864,{"filePath":"audio_separator/separator/uvr_lib_v5/spec_utils.py","startLine":1159,"endLine":1172,"chunkText":"                mean_abs_value = np.abs(track2_shifted_sub).mean()\r\n\r\n                # Store track2_shifted and its mean absolute value in the dictionary\r\n                shifted_tracks[mean_abs_value] = track2_shifted\r\n\r\n        # Return the version of track2_shifted with the smallest mean absolute value\r\n\r\n        return shifted_tracks[min(shifted_tracks.keys())]\r\n\r\n    # Make sure the audio files have the same shape\r\n\r\n    assert mix.shape == instrumental.shape, f\"Audio files must have the same shape - Mix: {mix.shape}, Inst: {instrumental.shape}\"\r\n\r\n    seconds_length = seconds_length // 2\r","chunkId":"audio_separator/separator/uvr_lib_v5/spec_utils.py:86"}],[1865,{"filePath":"audio_separator/separator/uvr_lib_v5/spec_utils.py","startLine":1170,"endLine":1187,"chunkText":"    assert mix.shape == instrumental.shape, f\"Audio files must have the same shape - Mix: {mix.shape}, Inst: {instrumental.shape}\"\r\n\r\n    seconds_length = seconds_length // 2\r\n\r\n    sub_mapper = {}\r\n\r\n    progress_update_interval = 120\r\n    total_iterations = 0\r\n\r\n    if len(align_window) > 2:\r\n        progress_update_interval = 320\r\n\r\n    for secs in align_window:\r\n        step = secs / 2\r\n        window_size = int(sr * secs)\r\n        step_size = int(sr * step)\r\n\r\n        if len(mix.shape) == 1:\r","chunkId":"audio_separator/separator/uvr_lib_v5/spec_utils.py:87"}],[1866,{"filePath":"audio_separator/separator/uvr_lib_v5/spec_utils.py","startLine":1185,"endLine":1198,"chunkText":"        step_size = int(sr * step)\r\n\r\n        if len(mix.shape) == 1:\r\n            total_mono = (len(range(0, len(mix) - window_size, step_size)) // progress_update_interval) * unique_sources\r\n            total_iterations += total_mono\r\n        else:\r\n            total_stereo_ = len(range(0, len(mix[:, 0]) - window_size, step_size)) * 2\r\n            total_stereo = (total_stereo_ // progress_update_interval) * unique_sources\r\n            total_iterations += total_stereo\r\n\r\n    # print(total_iterations)\r\n\r\n    for secs in align_window:\r\n        sub = np.zeros_like(mix)\r","chunkId":"audio_separator/separator/uvr_lib_v5/spec_utils.py:88"}],[1867,{"filePath":"audio_separator/separator/uvr_lib_v5/spec_utils.py","startLine":1196,"endLine":1212,"chunkText":"\r\n    for secs in align_window:\r\n        sub = np.zeros_like(mix)\r\n        divider = np.zeros_like(mix)\r\n        step = secs / 2\r\n        window_size = int(sr * secs)\r\n        step_size = int(sr * step)\r\n        window = np.hanning(window_size)\r\n\r\n        # For the mono case:\r\n        if len(mix.shape) == 1:\r\n            # The files are mono\r\n            counter = 0\r\n            for i in range(0, len(mix) - window_size, step_size):\r\n                counter += 1\r\n                if counter % progress_update_interval == 0:\r\n                    progress_bar(total_iterations)\r","chunkId":"audio_separator/separator/uvr_lib_v5/spec_utils.py:89"}],[1868,{"filePath":"audio_separator/separator/uvr_lib_v5/spec_utils.py","startLine":1210,"endLine":1220,"chunkText":"                counter += 1\r\n                if counter % progress_update_interval == 0:\r\n                    progress_bar(total_iterations)\r\n                window_mix = mix[i : i + window_size] * window\r\n                window_instrumental = instrumental[i : i + window_size] * window\r\n                window_instrumental_aligned = align_tracks(window_mix, window_instrumental)\r\n                sub[i : i + window_size] += window_mix - window_instrumental_aligned\r\n                divider[i : i + window_size] += window\r\n        else:\r\n            # The files are stereo\r\n            counter = 0\r","chunkId":"audio_separator/separator/uvr_lib_v5/spec_utils.py:90"}],[1869,{"filePath":"audio_separator/separator/uvr_lib_v5/spec_utils.py","startLine":1218,"endLine":1228,"chunkText":"        else:\r\n            # The files are stereo\r\n            counter = 0\r\n            for ch in range(mix.shape[1]):\r\n                for i in range(0, len(mix[:, ch]) - window_size, step_size):\r\n                    counter += 1\r\n                    if counter % progress_update_interval == 0:\r\n                        progress_bar(total_iterations)\r\n                    window_mix = mix[i : i + window_size, ch] * window\r\n                    window_instrumental = instrumental[i : i + window_size, ch] * window\r\n                    window_instrumental_aligned = align_tracks(window_mix, window_instrumental)\r","chunkId":"audio_separator/separator/uvr_lib_v5/spec_utils.py:91"}],[1870,{"filePath":"audio_separator/separator/uvr_lib_v5/spec_utils.py","startLine":1226,"endLine":1234,"chunkText":"                    window_mix = mix[i : i + window_size, ch] * window\r\n                    window_instrumental = instrumental[i : i + window_size, ch] * window\r\n                    window_instrumental_aligned = align_tracks(window_mix, window_instrumental)\r\n                    sub[i : i + window_size, ch] += window_mix - window_instrumental_aligned\r\n                    divider[i : i + window_size, ch] += window\r\n\r\n        # Normalize the result by the overlap count\r\n        sub = np.where(divider > 1e-6, sub / divider, sub)\r\n        sub_size = np.abs(sub).mean()\r","chunkId":"audio_separator/separator/uvr_lib_v5/spec_utils.py:92"}],[1871,{"filePath":"audio_separator/separator/uvr_lib_v5/spec_utils.py","startLine":1232,"endLine":1247,"chunkText":"        # Normalize the result by the overlap count\r\n        sub = np.where(divider > 1e-6, sub / divider, sub)\r\n        sub_size = np.abs(sub).mean()\r\n        sub_mapper = {**sub_mapper, **{sub_size: sub}}\r\n\r\n    # print(\"SUB_LEN\", len(list(sub_mapper.values())))\r\n\r\n    sub = ensemble_wav(list(sub_mapper.values()), split_size=12)\r\n\r\n    return sub\r\n\r\n\r\ndef ensemble_wav(waveforms, split_size=240):\r\n    # Create a dictionary to hold the thirds of each waveform and their mean absolute values\r\n    waveform_thirds = {i: np.array_split(waveform, split_size) for i, waveform in enumerate(waveforms)}\r\n\r","chunkId":"audio_separator/separator/uvr_lib_v5/spec_utils.py:93"}],[1872,{"filePath":"audio_separator/separator/uvr_lib_v5/spec_utils.py","startLine":1245,"endLine":1256,"chunkText":"    # Create a dictionary to hold the thirds of each waveform and their mean absolute values\r\n    waveform_thirds = {i: np.array_split(waveform, split_size) for i, waveform in enumerate(waveforms)}\r\n\r\n    # Initialize the final waveform\r\n    final_waveform = []\r\n\r\n    # For chunk\r\n    for third_idx in range(split_size):\r\n        # Compute the mean absolute value of each third from each waveform\r\n        means = [np.abs(waveform_thirds[i][third_idx]).mean() for i in range(len(waveforms))]\r\n\r\n        # Find the index of the waveform with the lowest mean absolute value for this third\r","chunkId":"audio_separator/separator/uvr_lib_v5/spec_utils.py:94"}],[1873,{"filePath":"audio_separator/separator/uvr_lib_v5/spec_utils.py","startLine":1254,"endLine":1270,"chunkText":"        means = [np.abs(waveform_thirds[i][third_idx]).mean() for i in range(len(waveforms))]\r\n\r\n        # Find the index of the waveform with the lowest mean absolute value for this third\r\n        min_index = np.argmin(means)\r\n\r\n        # Add the least noisy third to the final waveform\r\n        final_waveform.append(waveform_thirds[min_index][third_idx])\r\n\r\n    # Concatenate all the thirds to create the final waveform\r\n    final_waveform = np.concatenate(final_waveform)\r\n\r\n    return final_waveform\r\n\r\n\r\ndef ensemble_wav_min(waveforms):\r\n    for i in range(1, len(waveforms)):\r\n        if i == 1:\r","chunkId":"audio_separator/separator/uvr_lib_v5/spec_utils.py:95"}],[1874,{"filePath":"audio_separator/separator/uvr_lib_v5/spec_utils.py","startLine":1268,"endLine":1289,"chunkText":"def ensemble_wav_min(waveforms):\r\n    for i in range(1, len(waveforms)):\r\n        if i == 1:\r\n            wave = waveforms[0]\r\n\r\n        ln = min(len(wave), len(waveforms[i]))\r\n        wave = wave[:ln]\r\n        waveforms[i] = waveforms[i][:ln]\r\n\r\n        wave = np.where(np.abs(waveforms[i]) <= np.abs(wave), waveforms[i], wave)\r\n\r\n    return wave\r\n\r\n\r\ndef align_audio_test(wav1, wav2, sr1=44100):\r\n    def get_diff(a, b):\r\n        corr = np.correlate(a, b, \"full\")\r\n        diff = corr.argmax() - (b.shape[0] - 1)\r\n        return diff\r\n\r\n    # read tracks\r\n    wav1 = wav1.transpose()\r","chunkId":"audio_separator/separator/uvr_lib_v5/spec_utils.py:96"}],[1875,{"filePath":"audio_separator/separator/uvr_lib_v5/spec_utils.py","startLine":1287,"endLine":1305,"chunkText":"\r\n    # read tracks\r\n    wav1 = wav1.transpose()\r\n    wav2 = wav2.transpose()\r\n\r\n    # print(f\"Audio file shapes: {wav1.shape} / {wav2.shape}\\n\")\r\n\r\n    wav2_org = wav2.copy()\r\n\r\n    # pick a position at 1 second in and get diff\r\n    index = sr1  # *seconds_length  # 1 second in, assuming sr1 = sr2 = 44100\r\n    samp1 = wav1[index : index + sr1, 0]  # currently use left channel\r\n    samp2 = wav2[index : index + sr1, 0]\r\n    diff = get_diff(samp1, samp2)\r\n\r\n    # make aligned track 2\r\n    if diff > 0:\r\n        wav2_aligned = np.append(np.zeros((diff, 1)), wav2_org, axis=0)\r\n    elif diff < 0:\r","chunkId":"audio_separator/separator/uvr_lib_v5/spec_utils.py:97"}],[1876,{"filePath":"audio_separator/separator/uvr_lib_v5/spec_utils.py","startLine":1303,"endLine":1327,"chunkText":"    if diff > 0:\r\n        wav2_aligned = np.append(np.zeros((diff, 1)), wav2_org, axis=0)\r\n    elif diff < 0:\r\n        wav2_aligned = wav2_org[-diff:]\r\n    else:\r\n        wav2_aligned = wav2_org\r\n\r\n    return wav2_aligned\r\n\r\n\r\ndef load_audio(audio_file):\r\n    wav, sr = librosa.load(audio_file, sr=44100, mono=False)\r\n\r\n    if wav.ndim == 1:\r\n        wav = np.asfortranarray([wav, wav])\r\n\r\n    return wav\r\n\r\n\r\ndef rerun_mp3(audio_file):\r\n    with audioread.audio_open(audio_file) as f:\r\n        track_length = int(f.duration)\r\n\r\n    return track_length\r\n","chunkId":"audio_separator/separator/uvr_lib_v5/spec_utils.py:98"}],[1877,{"filePath":"audio_separator/separator/uvr_lib_v5/stft.py","startLine":0,"endLine":16,"chunkText":"import torch\n\n\nclass STFT:\n    \"\"\"\n    This class performs the Short-Time Fourier Transform (STFT) and its inverse (ISTFT).\n    These functions are essential for converting the audio between the time domain and the frequency domain,\n    which is a crucial aspect of audio processing in neural networks.\n    \"\"\"\n\n    def __init__(self, logger, n_fft, hop_length, dim_f, device):\n        self.logger = logger\n        self.n_fft = n_fft\n        self.hop_length = hop_length\n        self.dim_f = dim_f\n        self.device = device\n        # Create a Hann window tensor for use in the STFT.","chunkId":"audio_separator/separator/uvr_lib_v5/stft.py:0"}],[1878,{"filePath":"audio_separator/separator/uvr_lib_v5/stft.py","startLine":14,"endLine":26,"chunkText":"        self.dim_f = dim_f\n        self.device = device\n        # Create a Hann window tensor for use in the STFT.\n        self.hann_window = torch.hann_window(window_length=self.n_fft, periodic=True)\n\n    def __call__(self, input_tensor):\n        # Determine if the input tensor's device is not a standard computing device (i.e., not CPU or CUDA).\n        is_non_standard_device = not input_tensor.device.type in [\"cuda\", \"cpu\"]\n\n        # If on a non-standard device, temporarily move the tensor to CPU for processing.\n        if is_non_standard_device:\n            input_tensor = input_tensor.cpu()\n","chunkId":"audio_separator/separator/uvr_lib_v5/stft.py:1"}],[1879,{"filePath":"audio_separator/separator/uvr_lib_v5/stft.py","startLine":24,"endLine":35,"chunkText":"        if is_non_standard_device:\n            input_tensor = input_tensor.cpu()\n\n        # Transfer the pre-defined window tensor to the same device as the input tensor.\n        stft_window = self.hann_window.to(input_tensor.device)\n\n        # Extract batch dimensions (all dimensions except the last two which are channel and time).\n        batch_dimensions = input_tensor.shape[:-2]\n\n        # Extract channel and time dimensions (last two dimensions of the tensor).\n        channel_dim, time_dim = input_tensor.shape[-2:]\n","chunkId":"audio_separator/separator/uvr_lib_v5/stft.py:2"}],[1880,{"filePath":"audio_separator/separator/uvr_lib_v5/stft.py","startLine":34,"endLine":44,"chunkText":"        channel_dim, time_dim = input_tensor.shape[-2:]\n\n        # Reshape the tensor to merge batch and channel dimensions for STFT processing.\n        reshaped_tensor = input_tensor.reshape([-1, time_dim])\n\n        # Perform the Short-Time Fourier Transform (STFT) on the reshaped tensor.\n        stft_output = torch.stft(reshaped_tensor, n_fft=self.n_fft, hop_length=self.hop_length, window=stft_window, center=True, return_complex=False)\n\n        # Rearrange the dimensions of the STFT output to bring the frequency dimension forward.\n        permuted_stft_output = stft_output.permute([0, 3, 1, 2])\n","chunkId":"audio_separator/separator/uvr_lib_v5/stft.py:3"}],[1881,{"filePath":"audio_separator/separator/uvr_lib_v5/stft.py","startLine":42,"endLine":49,"chunkText":"        # Rearrange the dimensions of the STFT output to bring the frequency dimension forward.\n        permuted_stft_output = stft_output.permute([0, 3, 1, 2])\n\n        # Reshape the output to restore the original batch and channel dimensions, while keeping the newly formed frequency and time dimensions.\n        final_output = permuted_stft_output.reshape([*batch_dimensions, channel_dim, 2, -1, permuted_stft_output.shape[-1]]).reshape(\n            [*batch_dimensions, channel_dim * 2, -1, permuted_stft_output.shape[-1]]\n        )\n","chunkId":"audio_separator/separator/uvr_lib_v5/stft.py:4"}],[1882,{"filePath":"audio_separator/separator/uvr_lib_v5/stft.py","startLine":47,"endLine":58,"chunkText":"            [*batch_dimensions, channel_dim * 2, -1, permuted_stft_output.shape[-1]]\n        )\n\n        # If the original tensor was on a non-standard device, move the processed tensor back to that device.\n        if is_non_standard_device:\n            final_output = final_output.to(self.device)\n\n        # Return the transformed tensor, sliced to retain only the required frequency dimension (`dim_f`).\n        return final_output[..., : self.dim_f, :]\n\n    def pad_frequency_dimension(self, input_tensor, batch_dimensions, channel_dim, freq_dim, time_dim, num_freq_bins):\n        \"\"\"","chunkId":"audio_separator/separator/uvr_lib_v5/stft.py:5"}],[1883,{"filePath":"audio_separator/separator/uvr_lib_v5/stft.py","startLine":56,"endLine":68,"chunkText":"\n    def pad_frequency_dimension(self, input_tensor, batch_dimensions, channel_dim, freq_dim, time_dim, num_freq_bins):\n        \"\"\"\n        Adds zero padding to the frequency dimension of the input tensor.\n        \"\"\"\n        # Create a padding tensor for the frequency dimension\n        freq_padding = torch.zeros([*batch_dimensions, channel_dim, num_freq_bins - freq_dim, time_dim]).to(input_tensor.device)\n\n        # Concatenate the padding to the input tensor along the frequency dimension.\n        padded_tensor = torch.cat([input_tensor, freq_padding], -2)\n\n        return padded_tensor\n","chunkId":"audio_separator/separator/uvr_lib_v5/stft.py:6"}],[1884,{"filePath":"audio_separator/separator/uvr_lib_v5/stft.py","startLine":66,"endLine":80,"chunkText":"\n        return padded_tensor\n\n    def calculate_inverse_dimensions(self, input_tensor):\n        # Extract batch dimensions and frequency-time dimensions.\n        batch_dimensions = input_tensor.shape[:-3]\n        channel_dim, freq_dim, time_dim = input_tensor.shape[-3:]\n\n        # Calculate the number of frequency bins for the inverse STFT.\n        num_freq_bins = self.n_fft // 2 + 1\n\n        return batch_dimensions, channel_dim, freq_dim, time_dim, num_freq_bins\n\n    def prepare_for_istft(self, padded_tensor, batch_dimensions, channel_dim, num_freq_bins, time_dim):\n        \"\"\"","chunkId":"audio_separator/separator/uvr_lib_v5/stft.py:7"}],[1885,{"filePath":"audio_separator/separator/uvr_lib_v5/stft.py","startLine":78,"endLine":87,"chunkText":"\n    def prepare_for_istft(self, padded_tensor, batch_dimensions, channel_dim, num_freq_bins, time_dim):\n        \"\"\"\n        Prepares the tensor for Inverse Short-Time Fourier Transform (ISTFT) by reshaping\n        and creating a complex tensor from the real and imaginary parts.\n        \"\"\"\n        # Reshape the tensor to separate real and imaginary parts and prepare for ISTFT.\n        reshaped_tensor = padded_tensor.reshape([*batch_dimensions, channel_dim // 2, 2, num_freq_bins, time_dim])\n\n        # Flatten batch dimensions and rearrange for ISTFT.","chunkId":"audio_separator/separator/uvr_lib_v5/stft.py:8"}],[1886,{"filePath":"audio_separator/separator/uvr_lib_v5/stft.py","startLine":85,"endLine":97,"chunkText":"        reshaped_tensor = padded_tensor.reshape([*batch_dimensions, channel_dim // 2, 2, num_freq_bins, time_dim])\n\n        # Flatten batch dimensions and rearrange for ISTFT.\n        flattened_tensor = reshaped_tensor.reshape([-1, 2, num_freq_bins, time_dim])\n\n        # Rearrange the dimensions of the tensor to bring the frequency dimension forward.\n        permuted_tensor = flattened_tensor.permute([0, 2, 3, 1])\n\n        # Combine real and imaginary parts into a complex tensor.\n        complex_tensor = permuted_tensor[..., 0] + permuted_tensor[..., 1] * 1.0j\n\n        return complex_tensor\n","chunkId":"audio_separator/separator/uvr_lib_v5/stft.py:9"}],[1887,{"filePath":"audio_separator/separator/uvr_lib_v5/stft.py","startLine":95,"endLine":108,"chunkText":"\n        return complex_tensor\n\n    def inverse(self, input_tensor):\n        # Determine if the input tensor's device is not a standard computing device (i.e., not CPU or CUDA).\n        is_non_standard_device = not input_tensor.device.type in [\"cuda\", \"cpu\"]\n\n        # If on a non-standard device, temporarily move the tensor to CPU for processing.\n        if is_non_standard_device:\n            input_tensor = input_tensor.cpu()\n\n        # Transfer the pre-defined Hann window tensor to the same device as the input tensor.\n        stft_window = self.hann_window.to(input_tensor.device)\n","chunkId":"audio_separator/separator/uvr_lib_v5/stft.py:10"}],[1888,{"filePath":"audio_separator/separator/uvr_lib_v5/stft.py","startLine":106,"endLine":115,"chunkText":"        # Transfer the pre-defined Hann window tensor to the same device as the input tensor.\n        stft_window = self.hann_window.to(input_tensor.device)\n\n        batch_dimensions, channel_dim, freq_dim, time_dim, num_freq_bins = self.calculate_inverse_dimensions(input_tensor)\n\n        padded_tensor = self.pad_frequency_dimension(input_tensor, batch_dimensions, channel_dim, freq_dim, time_dim, num_freq_bins)\n\n        complex_tensor = self.prepare_for_istft(padded_tensor, batch_dimensions, channel_dim, num_freq_bins, time_dim)\n\n        # Perform the Inverse Short-Time Fourier Transform (ISTFT).","chunkId":"audio_separator/separator/uvr_lib_v5/stft.py:11"}],[1889,{"filePath":"audio_separator/separator/uvr_lib_v5/stft.py","startLine":113,"endLine":121,"chunkText":"        complex_tensor = self.prepare_for_istft(padded_tensor, batch_dimensions, channel_dim, num_freq_bins, time_dim)\n\n        # Perform the Inverse Short-Time Fourier Transform (ISTFT).\n        istft_result = torch.istft(complex_tensor, n_fft=self.n_fft, hop_length=self.hop_length, window=stft_window, center=True)\n\n        # Reshape ISTFT result to restore original batch and channel dimensions.\n        final_output = istft_result.reshape([*batch_dimensions, 2, -1])\n\n        # If the original tensor was on a non-standard device, move the processed tensor back to that device.","chunkId":"audio_separator/separator/uvr_lib_v5/stft.py:12"}],[1890,{"filePath":"audio_separator/separator/uvr_lib_v5/stft.py","startLine":119,"endLine":126,"chunkText":"        final_output = istft_result.reshape([*batch_dimensions, 2, -1])\n\n        # If the original tensor was on a non-standard device, move the processed tensor back to that device.\n        if is_non_standard_device:\n            final_output = final_output.to(self.device)\n\n        return final_output\n","chunkId":"audio_separator/separator/uvr_lib_v5/stft.py:13"}],[1891,{"filePath":"audio_separator/separator/uvr_lib_v5/tfc_tdf_v3.py","startLine":0,"endLine":21,"chunkText":"import torch\nimport torch.nn as nn\nfrom functools import partial\n\nclass STFT:\n    def __init__(self, n_fft, hop_length, dim_f, device):\n        self.n_fft = n_fft\n        self.hop_length = hop_length\n        self.window = torch.hann_window(window_length=self.n_fft, periodic=True)\n        self.dim_f = dim_f\n        self.device = device\n\n    def __call__(self, x):\n        \n        x_is_mps = not x.device.type in [\"cuda\", \"cpu\"]\n        if x_is_mps:\n            x = x.cpu()\n\n        window = self.window.to(x.device)\n        batch_dims = x.shape[:-2]\n        c, t = x.shape[-2:]\n        x = x.reshape([-1, t])","chunkId":"audio_separator/separator/uvr_lib_v5/tfc_tdf_v3.py:0"}],[1892,{"filePath":"audio_separator/separator/uvr_lib_v5/tfc_tdf_v3.py","startLine":19,"endLine":36,"chunkText":"        batch_dims = x.shape[:-2]\n        c, t = x.shape[-2:]\n        x = x.reshape([-1, t])\n        x = torch.stft(x, n_fft=self.n_fft, hop_length=self.hop_length, window=window, center=True,return_complex=False)\n        x = x.permute([0, 3, 1, 2])\n        x = x.reshape([*batch_dims, c, 2, -1, x.shape[-1]]).reshape([*batch_dims, c * 2, -1, x.shape[-1]])\n\n        if x_is_mps:\n            x = x.to(self.device)\n\n        return x[..., :self.dim_f, :]\n\n    def inverse(self, x):\n        \n        x_is_mps = not x.device.type in [\"cuda\", \"cpu\"]\n        if x_is_mps:\n            x = x.cpu()\n","chunkId":"audio_separator/separator/uvr_lib_v5/tfc_tdf_v3.py:1"}],[1893,{"filePath":"audio_separator/separator/uvr_lib_v5/tfc_tdf_v3.py","startLine":34,"endLine":49,"chunkText":"        if x_is_mps:\n            x = x.cpu()\n\n        window = self.window.to(x.device)\n        batch_dims = x.shape[:-3]\n        c, f, t = x.shape[-3:]\n        n = self.n_fft // 2 + 1\n        f_pad = torch.zeros([*batch_dims, c, n - f, t]).to(x.device)\n        x = torch.cat([x, f_pad], -2)\n        x = x.reshape([*batch_dims, c // 2, 2, n, t]).reshape([-1, 2, n, t])\n        x = x.permute([0, 2, 3, 1])\n        x = x[..., 0] + x[..., 1] * 1.j\n        x = torch.istft(x, n_fft=self.n_fft, hop_length=self.hop_length, window=window, center=True)\n        x = x.reshape([*batch_dims, 2, -1])\n\n        if x_is_mps:","chunkId":"audio_separator/separator/uvr_lib_v5/tfc_tdf_v3.py:2"}],[1894,{"filePath":"audio_separator/separator/uvr_lib_v5/tfc_tdf_v3.py","startLine":47,"endLine":67,"chunkText":"        x = x.reshape([*batch_dims, 2, -1])\n\n        if x_is_mps:\n            x = x.to(self.device)\n\n        return x\n\ndef get_norm(norm_type):\n    def norm(c, norm_type):\n        if norm_type is None:\n            return nn.Identity()\n        elif norm_type == 'BatchNorm':\n            return nn.BatchNorm2d(c)\n        elif norm_type == 'InstanceNorm':\n            return nn.InstanceNorm2d(c, affine=True)\n        elif 'GroupNorm' in norm_type:\n            g = int(norm_type.replace('GroupNorm', ''))\n            return nn.GroupNorm(num_groups=g, num_channels=c)\n        else:\n            return nn.Identity()\n","chunkId":"audio_separator/separator/uvr_lib_v5/tfc_tdf_v3.py:3"}],[1895,{"filePath":"audio_separator/separator/uvr_lib_v5/tfc_tdf_v3.py","startLine":65,"endLine":82,"chunkText":"        else:\n            return nn.Identity()\n\n    return partial(norm, norm_type=norm_type)\n\n\ndef get_act(act_type):\n    if act_type == 'gelu':\n        return nn.GELU()\n    elif act_type == 'relu':\n        return nn.ReLU()\n    elif act_type[:3] == 'elu':\n        alpha = float(act_type.replace('elu', ''))\n        return nn.ELU(alpha)\n    else:\n        raise Exception\n\n","chunkId":"audio_separator/separator/uvr_lib_v5/tfc_tdf_v3.py:4"}],[1896,{"filePath":"audio_separator/separator/uvr_lib_v5/tfc_tdf_v3.py","startLine":83,"endLine":95,"chunkText":"class Upscale(nn.Module):\n    def __init__(self, in_c, out_c, scale, norm, act):\n        super().__init__()\n        self.conv = nn.Sequential(\n            norm(in_c),\n            act,\n            nn.ConvTranspose2d(in_channels=in_c, out_channels=out_c, kernel_size=scale, stride=scale, bias=False)\n        )\n\n    def forward(self, x):\n        return self.conv(x)\n\n","chunkId":"audio_separator/separator/uvr_lib_v5/tfc_tdf_v3.py:5"}],[1897,{"filePath":"audio_separator/separator/uvr_lib_v5/tfc_tdf_v3.py","startLine":96,"endLine":108,"chunkText":"class Downscale(nn.Module):\n    def __init__(self, in_c, out_c, scale, norm, act):\n        super().__init__()\n        self.conv = nn.Sequential(\n            norm(in_c),\n            act,\n            nn.Conv2d(in_channels=in_c, out_channels=out_c, kernel_size=scale, stride=scale, bias=False)\n        )\n\n    def forward(self, x):\n        return self.conv(x)\n\n","chunkId":"audio_separator/separator/uvr_lib_v5/tfc_tdf_v3.py:6"}],[1898,{"filePath":"audio_separator/separator/uvr_lib_v5/tfc_tdf_v3.py","startLine":109,"endLine":129,"chunkText":"class TFC_TDF(nn.Module):\n    def __init__(self, in_c, c, l, f, bn, norm, act):\n        super().__init__()\n\n        self.blocks = nn.ModuleList()\n        for i in range(l):\n            block = nn.Module()\n\n            block.tfc1 = nn.Sequential(\n                norm(in_c),\n                act,\n                nn.Conv2d(in_c, c, 3, 1, 1, bias=False),\n            )\n            block.tdf = nn.Sequential(\n                norm(c),\n                act,\n                nn.Linear(f, f // bn, bias=False),\n                norm(c),\n                act,\n                nn.Linear(f // bn, f, bias=False),\n            )","chunkId":"audio_separator/separator/uvr_lib_v5/tfc_tdf_v3.py:7"}],[1899,{"filePath":"audio_separator/separator/uvr_lib_v5/tfc_tdf_v3.py","startLine":127,"endLine":149,"chunkText":"                act,\n                nn.Linear(f // bn, f, bias=False),\n            )\n            block.tfc2 = nn.Sequential(\n                norm(c),\n                act,\n                nn.Conv2d(c, c, 3, 1, 1, bias=False),\n            )\n            block.shortcut = nn.Conv2d(in_c, c, 1, 1, 0, bias=False)\n\n            self.blocks.append(block)\n            in_c = c\n\n    def forward(self, x):\n        for block in self.blocks:\n            s = block.shortcut(x)\n            x = block.tfc1(x)\n            x = x + block.tdf(x)\n            x = block.tfc2(x)\n            x = x + s\n        return x\n\n","chunkId":"audio_separator/separator/uvr_lib_v5/tfc_tdf_v3.py:8"}],[1900,{"filePath":"audio_separator/separator/uvr_lib_v5/tfc_tdf_v3.py","startLine":150,"endLine":167,"chunkText":"class TFC_TDF_net(nn.Module):\n    def __init__(self, config, device):\n        super().__init__()\n        self.config = config\n        self.device = device\n\n        # Defensive checks for normalization configuration\n        try:\n            norm_type = config.model.norm\n        except (AttributeError, KeyError):\n            norm_type = None\n            print(\"Warning: Model configuration missing 'norm' attribute, using Identity normalization\")\n        \n        norm = get_norm(norm_type=norm_type)\n        \n        try:\n            act_type = config.model.act\n        except (AttributeError, KeyError):","chunkId":"audio_separator/separator/uvr_lib_v5/tfc_tdf_v3.py:9"}],[1901,{"filePath":"audio_separator/separator/uvr_lib_v5/tfc_tdf_v3.py","startLine":165,"endLine":178,"chunkText":"        try:\n            act_type = config.model.act\n        except (AttributeError, KeyError):\n            act_type = 'gelu'\n            print(\"Warning: Model configuration missing 'act' attribute, using GELU activation\")\n            \n        act = get_act(act_type=act_type)\n\n        self.num_target_instruments = 1 if config.training.target_instrument else len(config.training.instruments)\n        self.num_subbands = config.model.num_subbands\n\n        dim_c = self.num_subbands * config.audio.num_channels * 2\n        n = config.model.num_scales\n        scale = config.model.scale","chunkId":"audio_separator/separator/uvr_lib_v5/tfc_tdf_v3.py:10"}],[1902,{"filePath":"audio_separator/separator/uvr_lib_v5/tfc_tdf_v3.py","startLine":176,"endLine":190,"chunkText":"        dim_c = self.num_subbands * config.audio.num_channels * 2\n        n = config.model.num_scales\n        scale = config.model.scale\n        l = config.model.num_blocks_per_scale\n        c = config.model.num_channels\n        g = config.model.growth\n        bn = config.model.bottleneck_factor\n        f = config.audio.dim_f // self.num_subbands\n\n        self.first_conv = nn.Conv2d(dim_c, c, 1, 1, 0, bias=False)\n\n        self.encoder_blocks = nn.ModuleList()\n        for i in range(n):\n            block = nn.Module()\n            block.tfc_tdf = TFC_TDF(c, c, l, f, bn, norm, act)","chunkId":"audio_separator/separator/uvr_lib_v5/tfc_tdf_v3.py:11"}],[1903,{"filePath":"audio_separator/separator/uvr_lib_v5/tfc_tdf_v3.py","startLine":188,"endLine":203,"chunkText":"        for i in range(n):\n            block = nn.Module()\n            block.tfc_tdf = TFC_TDF(c, c, l, f, bn, norm, act)\n            block.downscale = Downscale(c, c + g, scale, norm, act)\n            f = f // scale[1]\n            c += g\n            self.encoder_blocks.append(block)\n\n        self.bottleneck_block = TFC_TDF(c, c, l, f, bn, norm, act)\n\n        self.decoder_blocks = nn.ModuleList()\n        for i in range(n):\n            block = nn.Module()\n            block.upscale = Upscale(c, c - g, scale, norm, act)\n            f = f * scale[1]\n            c -= g","chunkId":"audio_separator/separator/uvr_lib_v5/tfc_tdf_v3.py:12"}],[1904,{"filePath":"audio_separator/separator/uvr_lib_v5/tfc_tdf_v3.py","startLine":201,"endLine":216,"chunkText":"            block.upscale = Upscale(c, c - g, scale, norm, act)\n            f = f * scale[1]\n            c -= g\n            block.tfc_tdf = TFC_TDF(2 * c, c, l, f, bn, norm, act)\n            self.decoder_blocks.append(block)\n\n        self.final_conv = nn.Sequential(\n            nn.Conv2d(c + dim_c, c, 1, 1, 0, bias=False),\n            act,\n            nn.Conv2d(c, self.num_target_instruments * dim_c, 1, 1, 0, bias=False)\n        )\n\n        self.stft = STFT(config.audio.n_fft, config.audio.hop_length, config.audio.dim_f, self.device)\n\n    def cac2cws(self, x):\n        k = self.num_subbands","chunkId":"audio_separator/separator/uvr_lib_v5/tfc_tdf_v3.py:13"}],[1905,{"filePath":"audio_separator/separator/uvr_lib_v5/tfc_tdf_v3.py","startLine":214,"endLine":239,"chunkText":"\n    def cac2cws(self, x):\n        k = self.num_subbands\n        b, c, f, t = x.shape\n        x = x.reshape(b, c, k, f // k, t)\n        x = x.reshape(b, c * k, f // k, t)\n        return x\n\n    def cws2cac(self, x):\n        k = self.num_subbands\n        b, c, f, t = x.shape\n        x = x.reshape(b, c // k, k, f, t)\n        x = x.reshape(b, c // k, f * k, t)\n        return x\n\n    def forward(self, x):\n\n        x = self.stft(x)\n\n        mix = x = self.cac2cws(x)\n\n        first_conv_out = x = self.first_conv(x)\n\n        x = x.transpose(-1, -2)\n\n        encoder_outputs = []","chunkId":"audio_separator/separator/uvr_lib_v5/tfc_tdf_v3.py:14"}],[1906,{"filePath":"audio_separator/separator/uvr_lib_v5/tfc_tdf_v3.py","startLine":237,"endLine":259,"chunkText":"        x = x.transpose(-1, -2)\n\n        encoder_outputs = []\n        for block in self.encoder_blocks:\n            x = block.tfc_tdf(x)\n            encoder_outputs.append(x)\n            x = block.downscale(x)\n\n        x = self.bottleneck_block(x)\n\n        for block in self.decoder_blocks:\n            x = block.upscale(x)\n            x = torch.cat([x, encoder_outputs.pop()], 1)\n            x = block.tfc_tdf(x)\n\n        x = x.transpose(-1, -2)\n\n        x = x * first_conv_out  # reduce artifacts\n\n        x = self.final_conv(torch.cat([mix, x], 1))\n\n        x = self.cws2cac(x)\n","chunkId":"audio_separator/separator/uvr_lib_v5/tfc_tdf_v3.py:15"}],[1907,{"filePath":"audio_separator/separator/uvr_lib_v5/tfc_tdf_v3.py","startLine":257,"endLine":269,"chunkText":"\n        x = self.cws2cac(x)\n\n        if self.num_target_instruments > 1:\n            b, c, f, t = x.shape\n            x = x.reshape(b, self.num_target_instruments, -1, f, t)\n\n        x = self.stft.inverse(x)\n\n        return x\n\n\n","chunkId":"audio_separator/separator/uvr_lib_v5/tfc_tdf_v3.py:16"}],[1908,{"filePath":"audio_separator/separator/uvr_lib_v5/vr_network/__init__.py","startLine":0,"endLine":1,"chunkText":"# VR init.\n","chunkId":"audio_separator/separator/uvr_lib_v5/vr_network/__init__.py:0"}],[1909,{"filePath":"audio_separator/separator/uvr_lib_v5/vr_network/layers.py","startLine":0,"endLine":12,"chunkText":"import torch\r\nfrom torch import nn\r\nimport torch.nn.functional as F\r\n\r\nfrom audio_separator.separator.uvr_lib_v5 import spec_utils\r\n\r\n\r\nclass Conv2DBNActiv(nn.Module):\r\n    \"\"\"\r\n    This class implements a convolutional layer followed by batch normalization and an activation function.\r\n    It is a common pattern in deep learning for processing images or feature maps. The convolutional layer\r\n    applies a set of learnable filters to the input. Batch normalization then normalizes the output of the\r\n    convolution, and finally, an activation function introduces non-linearity to the model, allowing it to\r","chunkId":"audio_separator/separator/uvr_lib_v5/vr_network/layers.py:0"}],[1910,{"filePath":"audio_separator/separator/uvr_lib_v5/vr_network/layers.py","startLine":10,"endLine":19,"chunkText":"    It is a common pattern in deep learning for processing images or feature maps. The convolutional layer\r\n    applies a set of learnable filters to the input. Batch normalization then normalizes the output of the\r\n    convolution, and finally, an activation function introduces non-linearity to the model, allowing it to\r\n    learn more complex patterns.\r\n\r\n    Attributes:\r\n        conv (nn.Sequential): A sequential container of Conv2d, BatchNorm2d, and an activation layer.\r\n\r\n    Args:\r\n        num_input_channels (int): Number of input channels.\r","chunkId":"audio_separator/separator/uvr_lib_v5/vr_network/layers.py:1"}],[1911,{"filePath":"audio_separator/separator/uvr_lib_v5/vr_network/layers.py","startLine":17,"endLine":27,"chunkText":"\r\n    Args:\r\n        num_input_channels (int): Number of input channels.\r\n        num_output_channels (int): Number of output channels.\r\n        kernel_size (int, optional): Size of the kernel. Defaults to 3.\r\n        stride_length (int, optional): Stride of the convolution. Defaults to 1.\r\n        padding_size (int, optional): Padding added to all sides of the input. Defaults to 1.\r\n        dilation_rate (int, optional): Spacing between kernel elements. Defaults to 1.\r\n        activation_function (callable, optional): The activation function to use. Defaults to nn.ReLU.\r\n    \"\"\"\r\n\r","chunkId":"audio_separator/separator/uvr_lib_v5/vr_network/layers.py:2"}],[1912,{"filePath":"audio_separator/separator/uvr_lib_v5/vr_network/layers.py","startLine":25,"endLine":34,"chunkText":"        activation_function (callable, optional): The activation function to use. Defaults to nn.ReLU.\r\n    \"\"\"\r\n\r\n    def __init__(self, nin, nout, ksize=3, stride=1, pad=1, dilation=1, activ=nn.ReLU):\r\n        super(Conv2DBNActiv, self).__init__()\r\n\r\n        # The nn.Sequential container allows us to stack the Conv2d, BatchNorm2d, and activation layers\r\n        # into a single module, simplifying the forward pass.\r\n        self.conv = nn.Sequential(nn.Conv2d(nin, nout, kernel_size=ksize, stride=stride, padding=pad, dilation=dilation, bias=False), nn.BatchNorm2d(nout), activ())\r\n\r","chunkId":"audio_separator/separator/uvr_lib_v5/vr_network/layers.py:3"}],[1913,{"filePath":"audio_separator/separator/uvr_lib_v5/vr_network/layers.py","startLine":32,"endLine":40,"chunkText":"        # into a single module, simplifying the forward pass.\r\n        self.conv = nn.Sequential(nn.Conv2d(nin, nout, kernel_size=ksize, stride=stride, padding=pad, dilation=dilation, bias=False), nn.BatchNorm2d(nout), activ())\r\n\r\n    def __call__(self, input_tensor):\r\n        # Defines the computation performed at every call.\r\n        # Simply passes the input through the sequential container.\r\n        return self.conv(input_tensor)\r\n\r\n\r","chunkId":"audio_separator/separator/uvr_lib_v5/vr_network/layers.py:4"}],[1914,{"filePath":"audio_separator/separator/uvr_lib_v5/vr_network/layers.py","startLine":41,"endLine":48,"chunkText":"class SeperableConv2DBNActiv(nn.Module):\r\n    \"\"\"\r\n    This class implements a separable convolutional layer followed by batch normalization and an activation function.\r\n    Separable convolutions are a type of convolution that splits the convolution operation into two simpler operations:\r\n    a depthwise convolution and a pointwise convolution. This can reduce the number of parameters and computational cost,\r\n    making the network more efficient while maintaining similar performance.\r\n\r\n    The depthwise convolution applies a single filter per input channel (input depth). The pointwise convolution,\r","chunkId":"audio_separator/separator/uvr_lib_v5/vr_network/layers.py:5"}],[1915,{"filePath":"audio_separator/separator/uvr_lib_v5/vr_network/layers.py","startLine":46,"endLine":52,"chunkText":"    making the network more efficient while maintaining similar performance.\r\n\r\n    The depthwise convolution applies a single filter per input channel (input depth). The pointwise convolution,\r\n    which follows, applies a 1x1 convolution to combine the outputs of the depthwise convolution across channels.\r\n    Batch normalization is then applied to stabilize learning and reduce internal covariate shift. Finally,\r\n    an activation function introduces non-linearity, allowing the network to learn complex patterns.\r\n    Attributes:\r","chunkId":"audio_separator/separator/uvr_lib_v5/vr_network/layers.py:6"}],[1916,{"filePath":"audio_separator/separator/uvr_lib_v5/vr_network/layers.py","startLine":50,"endLine":58,"chunkText":"    Batch normalization is then applied to stabilize learning and reduce internal covariate shift. Finally,\r\n    an activation function introduces non-linearity, allowing the network to learn complex patterns.\r\n    Attributes:\r\n        conv (nn.Sequential): A sequential container of depthwise Conv2d, pointwise Conv2d, BatchNorm2d, and an activation layer.\r\n\r\n    Args:\r\n        num_input_channels (int): Number of input channels.\r\n        num_output_channels (int): Number of output channels.\r\n        kernel_size (int, optional): Size of the kernel for the depthwise convolution. Defaults to 3.\r","chunkId":"audio_separator/separator/uvr_lib_v5/vr_network/layers.py:7"}],[1917,{"filePath":"audio_separator/separator/uvr_lib_v5/vr_network/layers.py","startLine":56,"endLine":61,"chunkText":"        num_input_channels (int): Number of input channels.\r\n        num_output_channels (int): Number of output channels.\r\n        kernel_size (int, optional): Size of the kernel for the depthwise convolution. Defaults to 3.\r\n        stride_length (int, optional): Stride of the convolution. Defaults to 1.\r\n        padding_size (int, optional): Padding added to all sides of the input for the depthwise convolution. Defaults to 1.\r\n        dilation_rate (int, optional): Spacing between kernel elements for the depthwise convolution. Defaults to 1.\r","chunkId":"audio_separator/separator/uvr_lib_v5/vr_network/layers.py:8"}],[1918,{"filePath":"audio_separator/separator/uvr_lib_v5/vr_network/layers.py","startLine":59,"endLine":67,"chunkText":"        stride_length (int, optional): Stride of the convolution. Defaults to 1.\r\n        padding_size (int, optional): Padding added to all sides of the input for the depthwise convolution. Defaults to 1.\r\n        dilation_rate (int, optional): Spacing between kernel elements for the depthwise convolution. Defaults to 1.\r\n        activation_function (callable, optional): The activation function to use. Defaults to nn.ReLU.\r\n    \"\"\"\r\n\r\n    def __init__(self, nin, nout, ksize=3, stride=1, pad=1, dilation=1, activ=nn.ReLU):\r\n        super(SeperableConv2DBNActiv, self).__init__()\r\n\r","chunkId":"audio_separator/separator/uvr_lib_v5/vr_network/layers.py:9"}],[1919,{"filePath":"audio_separator/separator/uvr_lib_v5/vr_network/layers.py","startLine":65,"endLine":71,"chunkText":"    def __init__(self, nin, nout, ksize=3, stride=1, pad=1, dilation=1, activ=nn.ReLU):\r\n        super(SeperableConv2DBNActiv, self).__init__()\r\n\r\n        # Initialize the sequential container with the depthwise convolution.\r\n        # The number of groups in the depthwise convolution is set to num_input_channels, which means each input channel is treated separately.\r\n        # The pointwise convolution then combines these separate channels into num_output_channels channels.\r\n        # Batch normalization is applied to the output of the pointwise convolution.\r","chunkId":"audio_separator/separator/uvr_lib_v5/vr_network/layers.py:10"}],[1920,{"filePath":"audio_separator/separator/uvr_lib_v5/vr_network/layers.py","startLine":69,"endLine":76,"chunkText":"        # The number of groups in the depthwise convolution is set to num_input_channels, which means each input channel is treated separately.\r\n        # The pointwise convolution then combines these separate channels into num_output_channels channels.\r\n        # Batch normalization is applied to the output of the pointwise convolution.\r\n        # Finally, the activation function is applied to introduce non-linearity.\r\n        self.conv = nn.Sequential(\r\n            nn.Conv2d(\r\n                nin,\r\n                nin,  # For depthwise convolution, in_channels = out_channels = num_input_channels\r","chunkId":"audio_separator/separator/uvr_lib_v5/vr_network/layers.py:11"}],[1921,{"filePath":"audio_separator/separator/uvr_lib_v5/vr_network/layers.py","startLine":74,"endLine":86,"chunkText":"            nn.Conv2d(\r\n                nin,\r\n                nin,  # For depthwise convolution, in_channels = out_channels = num_input_channels\r\n                kernel_size=ksize,\r\n                stride=stride,\r\n                padding=pad,\r\n                dilation=dilation,\r\n                groups=nin,  # This makes it a depthwise convolution\r\n                bias=False,  # Bias is not used because it will be handled by BatchNorm2d\r\n            ),\r\n            nn.Conv2d(\r\n                nin,\r\n                nout,  # Pointwise convolution to combine channels\r","chunkId":"audio_separator/separator/uvr_lib_v5/vr_network/layers.py:12"}],[1922,{"filePath":"audio_separator/separator/uvr_lib_v5/vr_network/layers.py","startLine":84,"endLine":95,"chunkText":"            nn.Conv2d(\r\n                nin,\r\n                nout,  # Pointwise convolution to combine channels\r\n                kernel_size=1,  # Kernel size of 1 for pointwise convolution\r\n                bias=False,  # Bias is not used because it will be handled by BatchNorm2d\r\n            ),\r\n            nn.BatchNorm2d(nout),  # Normalize the output of the pointwise convolution\r\n            activ(),  # Apply the activation function\r\n        )\r\n\r\n    def __call__(self, input_tensor):\r\n        # Pass the input through the sequential container.\r","chunkId":"audio_separator/separator/uvr_lib_v5/vr_network/layers.py:13"}],[1923,{"filePath":"audio_separator/separator/uvr_lib_v5/vr_network/layers.py","startLine":93,"endLine":100,"chunkText":"\r\n    def __call__(self, input_tensor):\r\n        # Pass the input through the sequential container.\r\n        # This performs the depthwise convolution, followed by the pointwise convolution,\r\n        # batch normalization, and finally applies the activation function.\r\n        return self.conv(input_tensor)\r\n\r\n\r","chunkId":"audio_separator/separator/uvr_lib_v5/vr_network/layers.py:14"}],[1924,{"filePath":"audio_separator/separator/uvr_lib_v5/vr_network/layers.py","startLine":101,"endLine":107,"chunkText":"class Encoder(nn.Module):\r\n    \"\"\"\r\n    The Encoder class is a part of the neural network architecture that is responsible for processing the input data.\r\n    It consists of two convolutional layers, each followed by batch normalization and an activation function.\r\n    The purpose of the Encoder is to transform the input data into a higher-level, abstract representation.\r\n    This is achieved by applying filters (through convolutions) that can capture patterns or features in the data.\r\n    The Encoder can be thought of as a feature extractor that prepares the data for further processing by the network.\r","chunkId":"audio_separator/separator/uvr_lib_v5/vr_network/layers.py:15"}],[1925,{"filePath":"audio_separator/separator/uvr_lib_v5/vr_network/layers.py","startLine":105,"endLine":112,"chunkText":"    The purpose of the Encoder is to transform the input data into a higher-level, abstract representation.\r\n    This is achieved by applying filters (through convolutions) that can capture patterns or features in the data.\r\n    The Encoder can be thought of as a feature extractor that prepares the data for further processing by the network.\r\n    Attributes:\r\n        conv1 (Conv2DBNActiv): The first convolutional layer in the encoder.\r\n        conv2 (Conv2DBNActiv): The second convolutional layer in the encoder.\r\n\r\n    Args:\r","chunkId":"audio_separator/separator/uvr_lib_v5/vr_network/layers.py:16"}],[1926,{"filePath":"audio_separator/separator/uvr_lib_v5/vr_network/layers.py","startLine":110,"endLine":117,"chunkText":"        conv2 (Conv2DBNActiv): The second convolutional layer in the encoder.\r\n\r\n    Args:\r\n        number_of_input_channels (int): Number of input channels for the first convolutional layer.\r\n        number_of_output_channels (int): Number of output channels for the convolutional layers.\r\n        kernel_size (int): Kernel size for the convolutional layers.\r\n        stride_length (int): Stride for the convolutional operations.\r\n        padding_size (int): Padding added to all sides of the input for the convolutional layers.\r","chunkId":"audio_separator/separator/uvr_lib_v5/vr_network/layers.py:17"}],[1927,{"filePath":"audio_separator/separator/uvr_lib_v5/vr_network/layers.py","startLine":115,"endLine":124,"chunkText":"        kernel_size (int): Kernel size for the convolutional layers.\r\n        stride_length (int): Stride for the convolutional operations.\r\n        padding_size (int): Padding added to all sides of the input for the convolutional layers.\r\n        activation_function (callable): The activation function to use after each convolutional layer.\r\n    \"\"\"\r\n\r\n    def __init__(self, nin, nout, ksize=3, stride=1, pad=1, activ=nn.LeakyReLU):\r\n        super(Encoder, self).__init__()\r\n\r\n        # The first convolutional layer takes the input and applies a convolution,\r","chunkId":"audio_separator/separator/uvr_lib_v5/vr_network/layers.py:18"}],[1928,{"filePath":"audio_separator/separator/uvr_lib_v5/vr_network/layers.py","startLine":122,"endLine":130,"chunkText":"        super(Encoder, self).__init__()\r\n\r\n        # The first convolutional layer takes the input and applies a convolution,\r\n        # followed by batch normalization and an activation function specified by `activation_function`.\r\n        # This layer is responsible for capturing the initial set of features from the input data.\r\n        self.conv1 = Conv2DBNActiv(nin, nout, ksize, 1, pad, activ=activ)\r\n\r\n        # The second convolutional layer further processes the output from the first layer,\r\n        # applying another set of convolution, batch normalization, and activation.\r","chunkId":"audio_separator/separator/uvr_lib_v5/vr_network/layers.py:19"}],[1929,{"filePath":"audio_separator/separator/uvr_lib_v5/vr_network/layers.py","startLine":128,"endLine":135,"chunkText":"\r\n        # The second convolutional layer further processes the output from the first layer,\r\n        # applying another set of convolution, batch normalization, and activation.\r\n        # This layer helps in capturing more complex patterns in the data by building upon the initial features extracted by conv1.\r\n        self.conv2 = Conv2DBNActiv(nout, nout, ksize, stride, pad, activ=activ)\r\n\r\n    def __call__(self, input_tensor):\r\n        # The input data `input_tensor` is passed through the first convolutional layer.\r","chunkId":"audio_separator/separator/uvr_lib_v5/vr_network/layers.py:20"}],[1930,{"filePath":"audio_separator/separator/uvr_lib_v5/vr_network/layers.py","startLine":133,"endLine":142,"chunkText":"\r\n    def __call__(self, input_tensor):\r\n        # The input data `input_tensor` is passed through the first convolutional layer.\r\n        # The output of this layer serves as a 'skip connection' that can be used later in the network to preserve spatial information.\r\n        skip = self.conv1(input_tensor)\r\n\r\n        # The output from the first layer is then passed through the second convolutional layer.\r\n        # This processed data `hidden` is the final output of the Encoder, representing the abstracted features of the input.\r\n        hidden = self.conv2(skip)\r\n\r","chunkId":"audio_separator/separator/uvr_lib_v5/vr_network/layers.py:21"}],[1931,{"filePath":"audio_separator/separator/uvr_lib_v5/vr_network/layers.py","startLine":140,"endLine":146,"chunkText":"        # This processed data `hidden` is the final output of the Encoder, representing the abstracted features of the input.\r\n        hidden = self.conv2(skip)\r\n\r\n        # The Encoder returns two outputs: `hidden`, the abstracted feature representation, and `skip`, the intermediate representation from conv1.\r\n        return hidden, skip\r\n\r\n\r","chunkId":"audio_separator/separator/uvr_lib_v5/vr_network/layers.py:22"}],[1932,{"filePath":"audio_separator/separator/uvr_lib_v5/vr_network/layers.py","startLine":147,"endLine":154,"chunkText":"class Decoder(nn.Module):\r\n    \"\"\"\r\n    The Decoder class is part of the neural network architecture, specifically designed to perform the inverse operation of an encoder.\r\n    Its main role is to reconstruct or generate data from encoded representations, which is crucial in tasks like image segmentation or audio processing.\r\n    This class uses upsampling, convolution, optional dropout for regularization, and concatenation of skip connections to achieve its goal.\r\n\r\n    Attributes:\r\n        convolution (Conv2DBNActiv): A convolutional layer with batch normalization and activation function.\r","chunkId":"audio_separator/separator/uvr_lib_v5/vr_network/layers.py:23"}],[1933,{"filePath":"audio_separator/separator/uvr_lib_v5/vr_network/layers.py","startLine":152,"endLine":161,"chunkText":"\r\n    Attributes:\r\n        convolution (Conv2DBNActiv): A convolutional layer with batch normalization and activation function.\r\n        dropout_layer (nn.Dropout2d): An optional dropout layer for regularization to prevent overfitting.\r\n\r\n    Args:\r\n        input_channels (int): Number of input channels for the convolutional layer.\r\n        output_channels (int): Number of output channels for the convolutional layer.\r\n        kernel_size (int): Kernel size for the convolutional layer.\r\n        stride (int): Stride for the convolutional operations.\r","chunkId":"audio_separator/separator/uvr_lib_v5/vr_network/layers.py:24"}],[1934,{"filePath":"audio_separator/separator/uvr_lib_v5/vr_network/layers.py","startLine":159,"endLine":167,"chunkText":"        output_channels (int): Number of output channels for the convolutional layer.\r\n        kernel_size (int): Kernel size for the convolutional layer.\r\n        stride (int): Stride for the convolutional operations.\r\n        padding (int): Padding added to all sides of the input for the convolutional layer.\r\n        activation_function (callable): The activation function to use after the convolutional layer.\r\n        include_dropout (bool): Whether to include a dropout layer for regularization.\r\n    \"\"\"\r\n\r\n    def __init__(self, nin, nout, ksize=3, stride=1, pad=1, activ=nn.ReLU, dropout=False):\r","chunkId":"audio_separator/separator/uvr_lib_v5/vr_network/layers.py:25"}],[1935,{"filePath":"audio_separator/separator/uvr_lib_v5/vr_network/layers.py","startLine":165,"endLine":177,"chunkText":"    \"\"\"\r\n\r\n    def __init__(self, nin, nout, ksize=3, stride=1, pad=1, activ=nn.ReLU, dropout=False):\r\n        super(Decoder, self).__init__()\r\n\r\n        # Initialize the convolutional layer with specified parameters.\r\n        self.conv = Conv2DBNActiv(nin, nout, ksize, 1, pad, activ=activ)\r\n\r\n        # Initialize the dropout layer if include_dropout is set to True\r\n        self.dropout = nn.Dropout2d(0.1) if dropout else None\r\n\r\n    def __call__(self, input_tensor, skip=None):\r\n        # Upsample the input tensor to a higher resolution using bilinear interpolation.\r","chunkId":"audio_separator/separator/uvr_lib_v5/vr_network/layers.py:26"}],[1936,{"filePath":"audio_separator/separator/uvr_lib_v5/vr_network/layers.py","startLine":175,"endLine":181,"chunkText":"\r\n    def __call__(self, input_tensor, skip=None):\r\n        # Upsample the input tensor to a higher resolution using bilinear interpolation.\r\n        input_tensor = F.interpolate(input_tensor, scale_factor=2, mode=\"bilinear\", align_corners=True)\r\n        # If a skip connection is provided, crop it to match the size of input_tensor and concatenate them along the channel dimension.\r\n        if skip is not None:\r\n            skip = spec_utils.crop_center(skip, input_tensor)  # Crop skip_connection to match input_tensor's dimensions.\r","chunkId":"audio_separator/separator/uvr_lib_v5/vr_network/layers.py:27"}],[1937,{"filePath":"audio_separator/separator/uvr_lib_v5/vr_network/layers.py","startLine":179,"endLine":184,"chunkText":"        # If a skip connection is provided, crop it to match the size of input_tensor and concatenate them along the channel dimension.\r\n        if skip is not None:\r\n            skip = spec_utils.crop_center(skip, input_tensor)  # Crop skip_connection to match input_tensor's dimensions.\r\n            input_tensor = torch.cat([input_tensor, skip], dim=1)  # Concatenate input_tensor and skip_connection along the channel dimension.\r\n\r\n        # Pass the concatenated tensor (or just input_tensor if no skip_connection is provided) through the convolutional layer.\r","chunkId":"audio_separator/separator/uvr_lib_v5/vr_network/layers.py:28"}],[1938,{"filePath":"audio_separator/separator/uvr_lib_v5/vr_network/layers.py","startLine":182,"endLine":194,"chunkText":"            input_tensor = torch.cat([input_tensor, skip], dim=1)  # Concatenate input_tensor and skip_connection along the channel dimension.\r\n\r\n        # Pass the concatenated tensor (or just input_tensor if no skip_connection is provided) through the convolutional layer.\r\n        output_tensor = self.conv(input_tensor)\r\n\r\n        # If dropout is enabled, apply it to the output of the convolutional layer.\r\n        if self.dropout is not None:\r\n            output_tensor = self.dropout(output_tensor)\r\n\r\n        # Return the final output tensor.\r\n        return output_tensor\r\n\r\n\r","chunkId":"audio_separator/separator/uvr_lib_v5/vr_network/layers.py:29"}],[1939,{"filePath":"audio_separator/separator/uvr_lib_v5/vr_network/layers.py","startLine":195,"endLine":204,"chunkText":"class ASPPModule(nn.Module):\r\n    \"\"\"\r\n    Atrous Spatial Pyramid Pooling (ASPP) Module is designed for capturing multi-scale context by applying\r\n    atrous convolution at multiple rates. This is particularly useful in segmentation tasks where capturing\r\n    objects at various scales is beneficial. The module applies several parallel dilated convolutions with\r\n    different dilation rates to the input feature map, allowing it to efficiently capture information at\r\n    multiple scales.\r\n\r\n    Attributes:\r\n        conv1 (nn.Sequential): Applies adaptive average pooling followed by a 1x1 convolution.\r","chunkId":"audio_separator/separator/uvr_lib_v5/vr_network/layers.py:30"}],[1940,{"filePath":"audio_separator/separator/uvr_lib_v5/vr_network/layers.py","startLine":202,"endLine":208,"chunkText":"\r\n    Attributes:\r\n        conv1 (nn.Sequential): Applies adaptive average pooling followed by a 1x1 convolution.\r\n        nn_architecture (int): Identifier for the neural network architecture being used.\r\n        six_layer (list): List containing architecture identifiers that require six layers.\r\n        seven_layer (list): List containing architecture identifiers that require seven layers.\r\n        conv2-conv7 (nn.Module): Convolutional layers with varying dilation rates for multi-scale feature extraction.\r","chunkId":"audio_separator/separator/uvr_lib_v5/vr_network/layers.py:31"}],[1941,{"filePath":"audio_separator/separator/uvr_lib_v5/vr_network/layers.py","startLine":206,"endLine":213,"chunkText":"        six_layer (list): List containing architecture identifiers that require six layers.\r\n        seven_layer (list): List containing architecture identifiers that require seven layers.\r\n        conv2-conv7 (nn.Module): Convolutional layers with varying dilation rates for multi-scale feature extraction.\r\n        bottleneck (nn.Sequential): A 1x1 convolutional layer that combines all features followed by dropout for regularization.\r\n    \"\"\"\r\n\r\n    def __init__(self, nn_architecture, nin, nout, dilations=(4, 8, 16), activ=nn.ReLU):\r\n        \"\"\"\r","chunkId":"audio_separator/separator/uvr_lib_v5/vr_network/layers.py:32"}],[1942,{"filePath":"audio_separator/separator/uvr_lib_v5/vr_network/layers.py","startLine":211,"endLine":222,"chunkText":"\r\n    def __init__(self, nn_architecture, nin, nout, dilations=(4, 8, 16), activ=nn.ReLU):\r\n        \"\"\"\r\n        Initializes the ASPP module with specified parameters.\r\n\r\n        Args:\r\n            nn_architecture (int): Identifier for the neural network architecture.\r\n            input_channels (int): Number of input channels.\r\n            output_channels (int): Number of output channels.\r\n            dilations (tuple): Tuple of dilation rates for the atrous convolutions.\r\n            activation (callable): Activation function to use after convolutional layers.\r\n        \"\"\"\r","chunkId":"audio_separator/separator/uvr_lib_v5/vr_network/layers.py:33"}],[1943,{"filePath":"audio_separator/separator/uvr_lib_v5/vr_network/layers.py","startLine":220,"endLine":229,"chunkText":"            dilations (tuple): Tuple of dilation rates for the atrous convolutions.\r\n            activation (callable): Activation function to use after convolutional layers.\r\n        \"\"\"\r\n        super(ASPPModule, self).__init__()\r\n\r\n        # Adaptive average pooling reduces the spatial dimensions to 1x1, focusing on global context,\r\n        # followed by a 1x1 convolution to project back to the desired channel dimension.\r\n        self.conv1 = nn.Sequential(nn.AdaptiveAvgPool2d((1, None)), Conv2DBNActiv(nin, nin, 1, 1, 0, activ=activ))\r\n\r\n        self.nn_architecture = nn_architecture\r","chunkId":"audio_separator/separator/uvr_lib_v5/vr_network/layers.py:34"}],[1944,{"filePath":"audio_separator/separator/uvr_lib_v5/vr_network/layers.py","startLine":227,"endLine":237,"chunkText":"        self.conv1 = nn.Sequential(nn.AdaptiveAvgPool2d((1, None)), Conv2DBNActiv(nin, nin, 1, 1, 0, activ=activ))\r\n\r\n        self.nn_architecture = nn_architecture\r\n        # Architecture identifiers for models requiring additional layers.\r\n        self.six_layer = [129605]\r\n        self.seven_layer = [537238, 537227, 33966]\r\n\r\n        # Extra convolutional layer used for six and seven layer configurations.\r\n        extra_conv = SeperableConv2DBNActiv(nin, nin, 3, 1, dilations[2], dilations[2], activ=activ)\r\n\r\n        # Standard 1x1 convolution for channel reduction.\r","chunkId":"audio_separator/separator/uvr_lib_v5/vr_network/layers.py:35"}],[1945,{"filePath":"audio_separator/separator/uvr_lib_v5/vr_network/layers.py","startLine":235,"endLine":242,"chunkText":"        extra_conv = SeperableConv2DBNActiv(nin, nin, 3, 1, dilations[2], dilations[2], activ=activ)\r\n\r\n        # Standard 1x1 convolution for channel reduction.\r\n        self.conv2 = Conv2DBNActiv(nin, nin, 1, 1, 0, activ=activ)\r\n\r\n        # Separable convolutions with different dilation rates for multi-scale feature extraction.\r\n        self.conv3 = SeperableConv2DBNActiv(nin, nin, 3, 1, dilations[0], dilations[0], activ=activ)\r\n        self.conv4 = SeperableConv2DBNActiv(nin, nin, 3, 1, dilations[1], dilations[1], activ=activ)\r","chunkId":"audio_separator/separator/uvr_lib_v5/vr_network/layers.py:36"}],[1946,{"filePath":"audio_separator/separator/uvr_lib_v5/vr_network/layers.py","startLine":240,"endLine":248,"chunkText":"        # Separable convolutions with different dilation rates for multi-scale feature extraction.\r\n        self.conv3 = SeperableConv2DBNActiv(nin, nin, 3, 1, dilations[0], dilations[0], activ=activ)\r\n        self.conv4 = SeperableConv2DBNActiv(nin, nin, 3, 1, dilations[1], dilations[1], activ=activ)\r\n        self.conv5 = SeperableConv2DBNActiv(nin, nin, 3, 1, dilations[2], dilations[2], activ=activ)\r\n\r\n        # Depending on the architecture, include the extra convolutional layers.\r\n        if self.nn_architecture in self.six_layer:\r\n            self.conv6 = extra_conv\r\n            nin_x = 6\r","chunkId":"audio_separator/separator/uvr_lib_v5/vr_network/layers.py:37"}],[1947,{"filePath":"audio_separator/separator/uvr_lib_v5/vr_network/layers.py","startLine":246,"endLine":260,"chunkText":"        if self.nn_architecture in self.six_layer:\r\n            self.conv6 = extra_conv\r\n            nin_x = 6\r\n        elif self.nn_architecture in self.seven_layer:\r\n            self.conv6 = extra_conv\r\n            self.conv7 = extra_conv\r\n            nin_x = 7\r\n        else:\r\n            nin_x = 5\r\n\r\n        # Bottleneck layer combines all the multi-scale features into the desired number of output channels.\r\n        self.bottleneck = nn.Sequential(Conv2DBNActiv(nin * nin_x, nout, 1, 1, 0, activ=activ), nn.Dropout2d(0.1))\r\n\r\n    def forward(self, input_tensor):\r\n        \"\"\"\r","chunkId":"audio_separator/separator/uvr_lib_v5/vr_network/layers.py:38"}],[1948,{"filePath":"audio_separator/separator/uvr_lib_v5/vr_network/layers.py","startLine":258,"endLine":275,"chunkText":"\r\n    def forward(self, input_tensor):\r\n        \"\"\"\r\n        Forward pass of the ASPP module.\r\n\r\n        Args:\r\n            input_tensor (Tensor): Input tensor.\r\n\r\n        Returns:\r\n            Tensor: Output tensor after applying ASPP.\r\n        \"\"\"\r\n        _, _, h, w = input_tensor.size()\r\n\r\n        # Apply the first convolutional sequence and upsample to the original resolution.\r\n        feat1 = F.interpolate(self.conv1(input_tensor), size=(h, w), mode=\"bilinear\", align_corners=True)\r\n\r\n        # Apply the remaining convolutions directly on the input.\r\n        feat2 = self.conv2(input_tensor)\r","chunkId":"audio_separator/separator/uvr_lib_v5/vr_network/layers.py:39"}],[1949,{"filePath":"audio_separator/separator/uvr_lib_v5/vr_network/layers.py","startLine":273,"endLine":284,"chunkText":"\r\n        # Apply the remaining convolutions directly on the input.\r\n        feat2 = self.conv2(input_tensor)\r\n        feat3 = self.conv3(input_tensor)\r\n        feat4 = self.conv4(input_tensor)\r\n        feat5 = self.conv5(input_tensor)\r\n\r\n        # Concatenate features from all layers. Depending on the architecture, include the extra features.\r\n        if self.nn_architecture in self.six_layer:\r\n            feat6 = self.conv6(input_tensor)\r\n            out = torch.cat((feat1, feat2, feat3, feat4, feat5, feat6), dim=1)\r\n        elif self.nn_architecture in self.seven_layer:\r","chunkId":"audio_separator/separator/uvr_lib_v5/vr_network/layers.py:40"}],[1950,{"filePath":"audio_separator/separator/uvr_lib_v5/vr_network/layers.py","startLine":282,"endLine":292,"chunkText":"            feat6 = self.conv6(input_tensor)\r\n            out = torch.cat((feat1, feat2, feat3, feat4, feat5, feat6), dim=1)\r\n        elif self.nn_architecture in self.seven_layer:\r\n            feat6 = self.conv6(input_tensor)\r\n            feat7 = self.conv7(input_tensor)\r\n            out = torch.cat((feat1, feat2, feat3, feat4, feat5, feat6, feat7), dim=1)\r\n        else:\r\n            out = torch.cat((feat1, feat2, feat3, feat4, feat5), dim=1)\r\n\r\n        # Apply the bottleneck layer to combine and reduce the channel dimensions.\r\n        bottleneck_output = self.bottleneck(out)\r","chunkId":"audio_separator/separator/uvr_lib_v5/vr_network/layers.py:41"}],[1951,{"filePath":"audio_separator/separator/uvr_lib_v5/vr_network/layers.py","startLine":290,"endLine":294,"chunkText":"\r\n        # Apply the bottleneck layer to combine and reduce the channel dimensions.\r\n        bottleneck_output = self.bottleneck(out)\r\n        return bottleneck_output\r\n","chunkId":"audio_separator/separator/uvr_lib_v5/vr_network/layers.py:42"}],[1952,{"filePath":"audio_separator/separator/uvr_lib_v5/vr_network/layers_new.py","startLine":0,"endLine":14,"chunkText":"import torch\nfrom torch import nn\nimport torch.nn.functional as F\n\nfrom audio_separator.separator.uvr_lib_v5 import spec_utils\n\n\nclass Conv2DBNActiv(nn.Module):\n    \"\"\"\n    Conv2DBNActiv Class:\n    This class implements a convolutional layer followed by batch normalization and an activation function.\n    It is a fundamental building block for constructing neural networks, especially useful in image and audio processing tasks.\n    The class encapsulates the pattern of applying a convolution, normalizing the output, and then applying a non-linear activation.\n    \"\"\"\n","chunkId":"audio_separator/separator/uvr_lib_v5/vr_network/layers_new.py:0"}],[1953,{"filePath":"audio_separator/separator/uvr_lib_v5/vr_network/layers_new.py","startLine":12,"endLine":21,"chunkText":"    The class encapsulates the pattern of applying a convolution, normalizing the output, and then applying a non-linear activation.\n    \"\"\"\n\n    def __init__(self, nin, nout, ksize=3, stride=1, pad=1, dilation=1, activ=nn.ReLU):\n        super(Conv2DBNActiv, self).__init__()\n\n        # Sequential model combining Conv2D, BatchNorm, and activation function into a single module\n        self.conv = nn.Sequential(nn.Conv2d(nin, nout, kernel_size=ksize, stride=stride, padding=pad, dilation=dilation, bias=False), nn.BatchNorm2d(nout), activ())\n\n    def __call__(self, input_tensor):","chunkId":"audio_separator/separator/uvr_lib_v5/vr_network/layers_new.py:1"}],[1954,{"filePath":"audio_separator/separator/uvr_lib_v5/vr_network/layers_new.py","startLine":19,"endLine":25,"chunkText":"        self.conv = nn.Sequential(nn.Conv2d(nin, nout, kernel_size=ksize, stride=stride, padding=pad, dilation=dilation, bias=False), nn.BatchNorm2d(nout), activ())\n\n    def __call__(self, input_tensor):\n        # Forward pass through the sequential model\n        return self.conv(input_tensor)\n\n","chunkId":"audio_separator/separator/uvr_lib_v5/vr_network/layers_new.py:2"}],[1955,{"filePath":"audio_separator/separator/uvr_lib_v5/vr_network/layers_new.py","startLine":26,"endLine":38,"chunkText":"class Encoder(nn.Module):\n    \"\"\"\n    Encoder Class:\n    This class defines an encoder module typically used in autoencoder architectures.\n    It consists of two convolutional layers, each followed by batch normalization and an activation function.\n    \"\"\"\n\n    def __init__(self, nin, nout, ksize=3, stride=1, pad=1, activ=nn.LeakyReLU):\n        super(Encoder, self).__init__()\n\n        # First convolutional layer of the encoder\n        self.conv1 = Conv2DBNActiv(nin, nout, ksize, stride, pad, activ=activ)\n        # Second convolutional layer of the encoder","chunkId":"audio_separator/separator/uvr_lib_v5/vr_network/layers_new.py:3"}],[1956,{"filePath":"audio_separator/separator/uvr_lib_v5/vr_network/layers_new.py","startLine":36,"endLine":48,"chunkText":"        # First convolutional layer of the encoder\n        self.conv1 = Conv2DBNActiv(nin, nout, ksize, stride, pad, activ=activ)\n        # Second convolutional layer of the encoder\n        self.conv2 = Conv2DBNActiv(nout, nout, ksize, 1, pad, activ=activ)\n\n    def __call__(self, input_tensor):\n        # Applying the first and then the second convolutional layers\n        hidden = self.conv1(input_tensor)\n        hidden = self.conv2(hidden)\n\n        return hidden\n\n","chunkId":"audio_separator/separator/uvr_lib_v5/vr_network/layers_new.py:4"}],[1957,{"filePath":"audio_separator/separator/uvr_lib_v5/vr_network/layers_new.py","startLine":49,"endLine":59,"chunkText":"class Decoder(nn.Module):\n    \"\"\"\n    Decoder Class:\n    This class defines a decoder module, which is the counterpart of the Encoder class in autoencoder architectures.\n    It applies a convolutional layer followed by batch normalization and an activation function, with an optional dropout layer for regularization.\n    \"\"\"\n\n    def __init__(self, nin, nout, ksize=3, stride=1, pad=1, activ=nn.ReLU, dropout=False):\n        super(Decoder, self).__init__()\n        # Convolutional layer with optional dropout for regularization\n        self.conv1 = Conv2DBNActiv(nin, nout, ksize, 1, pad, activ=activ)","chunkId":"audio_separator/separator/uvr_lib_v5/vr_network/layers_new.py:5"}],[1958,{"filePath":"audio_separator/separator/uvr_lib_v5/vr_network/layers_new.py","startLine":57,"endLine":67,"chunkText":"        super(Decoder, self).__init__()\n        # Convolutional layer with optional dropout for regularization\n        self.conv1 = Conv2DBNActiv(nin, nout, ksize, 1, pad, activ=activ)\n        # self.conv2 = Conv2DBNActiv(nout, nout, ksize, 1, pad, activ=activ)\n        self.dropout = nn.Dropout2d(0.1) if dropout else None\n\n    def __call__(self, input_tensor, skip=None):\n        # Forward pass through the convolutional layer and optional dropout\n        input_tensor = F.interpolate(input_tensor, scale_factor=2, mode=\"bilinear\", align_corners=True)\n\n        if skip is not None:","chunkId":"audio_separator/separator/uvr_lib_v5/vr_network/layers_new.py:6"}],[1959,{"filePath":"audio_separator/separator/uvr_lib_v5/vr_network/layers_new.py","startLine":65,"endLine":79,"chunkText":"        input_tensor = F.interpolate(input_tensor, scale_factor=2, mode=\"bilinear\", align_corners=True)\n\n        if skip is not None:\n            skip = spec_utils.crop_center(skip, input_tensor)\n            input_tensor = torch.cat([input_tensor, skip], dim=1)\n\n        hidden = self.conv1(input_tensor)\n        # hidden = self.conv2(hidden)\n\n        if self.dropout is not None:\n            hidden = self.dropout(hidden)\n\n        return hidden\n\n","chunkId":"audio_separator/separator/uvr_lib_v5/vr_network/layers_new.py:7"}],[1960,{"filePath":"audio_separator/separator/uvr_lib_v5/vr_network/layers_new.py","startLine":80,"endLine":90,"chunkText":"class ASPPModule(nn.Module):\n    \"\"\"\n    ASPPModule Class:\n    This class implements the Atrous Spatial Pyramid Pooling (ASPP) module, which is useful for semantic image segmentation tasks.\n    It captures multi-scale contextual information by applying convolutions at multiple dilation rates.\n    \"\"\"\n\n    def __init__(self, nin, nout, dilations=(4, 8, 12), activ=nn.ReLU, dropout=False):\n        super(ASPPModule, self).__init__()\n\n        # Global context convolution captures the overall context","chunkId":"audio_separator/separator/uvr_lib_v5/vr_network/layers_new.py:8"}],[1961,{"filePath":"audio_separator/separator/uvr_lib_v5/vr_network/layers_new.py","startLine":88,"endLine":95,"chunkText":"        super(ASPPModule, self).__init__()\n\n        # Global context convolution captures the overall context\n        self.conv1 = nn.Sequential(nn.AdaptiveAvgPool2d((1, None)), Conv2DBNActiv(nin, nout, 1, 1, 0, activ=activ))\n        self.conv2 = Conv2DBNActiv(nin, nout, 1, 1, 0, activ=activ)\n        self.conv3 = Conv2DBNActiv(nin, nout, 3, 1, dilations[0], dilations[0], activ=activ)\n        self.conv4 = Conv2DBNActiv(nin, nout, 3, 1, dilations[1], dilations[1], activ=activ)\n        self.conv5 = Conv2DBNActiv(nin, nout, 3, 1, dilations[2], dilations[2], activ=activ)","chunkId":"audio_separator/separator/uvr_lib_v5/vr_network/layers_new.py:9"}],[1962,{"filePath":"audio_separator/separator/uvr_lib_v5/vr_network/layers_new.py","startLine":93,"endLine":102,"chunkText":"        self.conv3 = Conv2DBNActiv(nin, nout, 3, 1, dilations[0], dilations[0], activ=activ)\n        self.conv4 = Conv2DBNActiv(nin, nout, 3, 1, dilations[1], dilations[1], activ=activ)\n        self.conv5 = Conv2DBNActiv(nin, nout, 3, 1, dilations[2], dilations[2], activ=activ)\n        self.bottleneck = Conv2DBNActiv(nout * 5, nout, 1, 1, 0, activ=activ)\n        self.dropout = nn.Dropout2d(0.1) if dropout else None\n\n    def forward(self, input_tensor):\n        _, _, h, w = input_tensor.size()\n\n        # Upsample global context to match input size and combine with local and multi-scale features","chunkId":"audio_separator/separator/uvr_lib_v5/vr_network/layers_new.py:10"}],[1963,{"filePath":"audio_separator/separator/uvr_lib_v5/vr_network/layers_new.py","startLine":100,"endLine":116,"chunkText":"        _, _, h, w = input_tensor.size()\n\n        # Upsample global context to match input size and combine with local and multi-scale features\n        feat1 = F.interpolate(self.conv1(input_tensor), size=(h, w), mode=\"bilinear\", align_corners=True)\n        feat2 = self.conv2(input_tensor)\n        feat3 = self.conv3(input_tensor)\n        feat4 = self.conv4(input_tensor)\n        feat5 = self.conv5(input_tensor)\n        out = torch.cat((feat1, feat2, feat3, feat4, feat5), dim=1)\n        out = self.bottleneck(out)\n\n        if self.dropout is not None:\n            out = self.dropout(out)\n\n        return out\n\n","chunkId":"audio_separator/separator/uvr_lib_v5/vr_network/layers_new.py:11"}],[1964,{"filePath":"audio_separator/separator/uvr_lib_v5/vr_network/layers_new.py","startLine":117,"endLine":129,"chunkText":"class LSTMModule(nn.Module):\n    \"\"\"\n    LSTMModule Class:\n    This class defines a module that combines convolutional feature extraction with a bidirectional LSTM for sequence modeling.\n    It is useful for tasks that require understanding temporal dynamics in data, such as speech and audio processing.\n    \"\"\"\n\n    def __init__(self, nin_conv, nin_lstm, nout_lstm):\n        super(LSTMModule, self).__init__()\n        # Convolutional layer for initial feature extraction\n        self.conv = Conv2DBNActiv(nin_conv, 1, 1, 1, 0)\n\n        # Bidirectional LSTM for capturing temporal dynamics","chunkId":"audio_separator/separator/uvr_lib_v5/vr_network/layers_new.py:12"}],[1965,{"filePath":"audio_separator/separator/uvr_lib_v5/vr_network/layers_new.py","startLine":127,"endLine":139,"chunkText":"        self.conv = Conv2DBNActiv(nin_conv, 1, 1, 1, 0)\n\n        # Bidirectional LSTM for capturing temporal dynamics\n        self.lstm = nn.LSTM(input_size=nin_lstm, hidden_size=nout_lstm // 2, bidirectional=True)\n\n        # Dense layer for output dimensionality matching\n        self.dense = nn.Sequential(nn.Linear(nout_lstm, nin_lstm), nn.BatchNorm1d(nin_lstm), nn.ReLU())\n\n    def forward(self, input_tensor):\n        N, _, nbins, nframes = input_tensor.size()\n\n        # Extract features and prepare for LSTM\n        hidden = self.conv(input_tensor)[:, 0]  # N, nbins, nframes","chunkId":"audio_separator/separator/uvr_lib_v5/vr_network/layers_new.py:13"}],[1966,{"filePath":"audio_separator/separator/uvr_lib_v5/vr_network/layers_new.py","startLine":137,"endLine":149,"chunkText":"\n        # Extract features and prepare for LSTM\n        hidden = self.conv(input_tensor)[:, 0]  # N, nbins, nframes\n        hidden = hidden.permute(2, 0, 1)  # nframes, N, nbins\n        hidden, _ = self.lstm(hidden)\n\n        # Apply dense layer and reshape to match expected output format\n        hidden = self.dense(hidden.reshape(-1, hidden.size()[-1]))  # nframes * N, nbins\n        hidden = hidden.reshape(nframes, N, 1, nbins)\n        hidden = hidden.permute(1, 2, 3, 0)\n\n        return hidden\n","chunkId":"audio_separator/separator/uvr_lib_v5/vr_network/layers_new.py:14"}],[1967,{"filePath":"audio_separator/separator/uvr_lib_v5/vr_network/model_param_init.py","startLine":0,"endLine":18,"chunkText":"import json\r\n\r\ndefault_param = {}\r\ndefault_param[\"bins\"] = -1\r\ndefault_param[\"unstable_bins\"] = -1  # training only\r\ndefault_param[\"stable_bins\"] = -1  # training only\r\ndefault_param[\"sr\"] = 44100\r\ndefault_param[\"pre_filter_start\"] = -1\r\ndefault_param[\"pre_filter_stop\"] = -1\r\ndefault_param[\"band\"] = {}\r\n\r\nN_BINS = \"n_bins\"\r\n\r\n\r\ndef int_keys(d):\r\n    \"\"\"\r\n    Converts string keys that represent integers into actual integer keys in a list.\r\n\r\n    This function is particularly useful when dealing with JSON data that may represent\r","chunkId":"audio_separator/separator/uvr_lib_v5/vr_network/model_param_init.py:0"}],[1968,{"filePath":"audio_separator/separator/uvr_lib_v5/vr_network/model_param_init.py","startLine":16,"endLine":25,"chunkText":"    Converts string keys that represent integers into actual integer keys in a list.\r\n\r\n    This function is particularly useful when dealing with JSON data that may represent\r\n    integer keys as strings due to the nature of JSON encoding. By converting these keys\r\n    back to integers, it ensures that the data can be used in a manner consistent with\r\n    its original representation, especially in contexts where the distinction between\r\n    string and integer keys is important.\r\n\r\n    Args:\r\n        input_list (list of tuples): A list of (key, value) pairs where keys are strings\r","chunkId":"audio_separator/separator/uvr_lib_v5/vr_network/model_param_init.py:1"}],[1969,{"filePath":"audio_separator/separator/uvr_lib_v5/vr_network/model_param_init.py","startLine":23,"endLine":36,"chunkText":"\r\n    Args:\r\n        input_list (list of tuples): A list of (key, value) pairs where keys are strings\r\n                                     that may represent integers.\r\n\r\n    Returns:\r\n        dict: A dictionary with keys converted to integers where applicable.\r\n    \"\"\"\r\n    # Initialize an empty dictionary to hold the converted key-value pairs.\r\n    result_dict = {}\r\n    # Iterate through each key-value pair in the input list.\r\n    for key, value in d:\r\n        # Check if the key is a digit (i.e., represents an integer).\r\n        if key.isdigit():\r","chunkId":"audio_separator/separator/uvr_lib_v5/vr_network/model_param_init.py:2"}],[1970,{"filePath":"audio_separator/separator/uvr_lib_v5/vr_network/model_param_init.py","startLine":34,"endLine":52,"chunkText":"    for key, value in d:\r\n        # Check if the key is a digit (i.e., represents an integer).\r\n        if key.isdigit():\r\n            # Convert the key from a string to an integer.\r\n            key = int(key)\r\n        result_dict[key] = value\r\n    return result_dict\r\n\r\n\r\nclass ModelParameters(object):\r\n    \"\"\"\r\n    A class to manage model parameters, including loading from a configuration file.\r\n\r\n    Attributes:\r\n        param (dict): Dictionary holding all parameters for the model.\r\n    \"\"\"\r\n\r\n    def __init__(self, config_path=\"\"):\r\n        \"\"\"\r","chunkId":"audio_separator/separator/uvr_lib_v5/vr_network/model_param_init.py:3"}],[1971,{"filePath":"audio_separator/separator/uvr_lib_v5/vr_network/model_param_init.py","startLine":50,"endLine":63,"chunkText":"\r\n    def __init__(self, config_path=\"\"):\r\n        \"\"\"\r\n        Initializes the ModelParameters object by loading parameters from a JSON configuration file.\r\n\r\n        Args:\r\n            config_path (str): Path to the JSON configuration file.\r\n        \"\"\"\r\n\r\n        # Load parameters from the given configuration file path.\r\n        with open(config_path, \"r\") as f:\r\n            self.param = json.loads(f.read(), object_pairs_hook=int_keys)\r\n\r\n        # Ensure certain parameters are set to False if not specified in the configuration.\r","chunkId":"audio_separator/separator/uvr_lib_v5/vr_network/model_param_init.py:4"}],[1972,{"filePath":"audio_separator/separator/uvr_lib_v5/vr_network/model_param_init.py","startLine":61,"endLine":71,"chunkText":"            self.param = json.loads(f.read(), object_pairs_hook=int_keys)\r\n\r\n        # Ensure certain parameters are set to False if not specified in the configuration.\r\n        for k in [\"mid_side\", \"mid_side_b\", \"mid_side_b2\", \"stereo_w\", \"stereo_n\", \"reverse\"]:\r\n            if not k in self.param:\r\n                self.param[k] = False\r\n\r\n        # If 'n_bins' is specified in the parameters, it's used as the value for 'bins'.\r\n        if N_BINS in self.param:\r\n            self.param[\"bins\"] = self.param[N_BINS]\r\n","chunkId":"audio_separator/separator/uvr_lib_v5/vr_network/model_param_init.py:5"}],[1973,{"filePath":"audio_separator/separator/uvr_lib_v5/vr_network/nets.py","startLine":0,"endLine":13,"chunkText":"import torch\r\nfrom torch import nn\r\nimport torch.nn.functional as F\r\n\r\nfrom . import layers\r\n\r\n\r\nclass BaseASPPNet(nn.Module):\r\n    \"\"\"\r\n    BaseASPPNet Class:\r\n    This class defines the base architecture for an Atrous Spatial Pyramid Pooling (ASPP) network.\r\n    It is designed to extract features from input data at multiple scales by using dilated convolutions.\r\n    This is particularly useful for tasks that benefit from understanding context at different resolutions,\r\n    such as semantic segmentation. The network consists of a series of encoder layers for downsampling and feature extraction,\r","chunkId":"audio_separator/separator/uvr_lib_v5/vr_network/nets.py:0"}],[1974,{"filePath":"audio_separator/separator/uvr_lib_v5/vr_network/nets.py","startLine":11,"endLine":18,"chunkText":"    It is designed to extract features from input data at multiple scales by using dilated convolutions.\r\n    This is particularly useful for tasks that benefit from understanding context at different resolutions,\r\n    such as semantic segmentation. The network consists of a series of encoder layers for downsampling and feature extraction,\r\n    followed by an ASPP module for multi-scale feature extraction, and finally a series of decoder layers for upsampling.\r\n    \"\"\"\r\n\r\n    def __init__(self, nn_architecture, nin, ch, dilations=(4, 8, 16)):\r\n        super(BaseASPPNet, self).__init__()\r","chunkId":"audio_separator/separator/uvr_lib_v5/vr_network/nets.py:1"}],[1975,{"filePath":"audio_separator/separator/uvr_lib_v5/vr_network/nets.py","startLine":16,"endLine":26,"chunkText":"\r\n    def __init__(self, nn_architecture, nin, ch, dilations=(4, 8, 16)):\r\n        super(BaseASPPNet, self).__init__()\r\n        self.nn_architecture = nn_architecture\r\n\r\n        # Encoder layers progressively increase the number of channels while reducing spatial dimensions.\r\n        self.enc1 = layers.Encoder(nin, ch, 3, 2, 1)\r\n        self.enc2 = layers.Encoder(ch, ch * 2, 3, 2, 1)\r\n        self.enc3 = layers.Encoder(ch * 2, ch * 4, 3, 2, 1)\r\n        self.enc4 = layers.Encoder(ch * 4, ch * 8, 3, 2, 1)\r\n\r","chunkId":"audio_separator/separator/uvr_lib_v5/vr_network/nets.py:2"}],[1976,{"filePath":"audio_separator/separator/uvr_lib_v5/vr_network/nets.py","startLine":24,"endLine":32,"chunkText":"        self.enc3 = layers.Encoder(ch * 2, ch * 4, 3, 2, 1)\r\n        self.enc4 = layers.Encoder(ch * 4, ch * 8, 3, 2, 1)\r\n\r\n        # Depending on the network architecture, an additional encoder layer and a specific ASPP module are initialized.\r\n        if self.nn_architecture == 129605:\r\n            self.enc5 = layers.Encoder(ch * 8, ch * 16, 3, 2, 1)\r\n            self.aspp = layers.ASPPModule(nn_architecture, ch * 16, ch * 32, dilations)\r\n            self.dec5 = layers.Decoder(ch * (16 + 32), ch * 16, 3, 1, 1)\r\n        else:\r","chunkId":"audio_separator/separator/uvr_lib_v5/vr_network/nets.py:3"}],[1977,{"filePath":"audio_separator/separator/uvr_lib_v5/vr_network/nets.py","startLine":30,"endLine":38,"chunkText":"            self.aspp = layers.ASPPModule(nn_architecture, ch * 16, ch * 32, dilations)\r\n            self.dec5 = layers.Decoder(ch * (16 + 32), ch * 16, 3, 1, 1)\r\n        else:\r\n            self.aspp = layers.ASPPModule(nn_architecture, ch * 8, ch * 16, dilations)\r\n\r\n        # Decoder layers progressively decrease the number of channels while increasing spatial dimensions.\r\n        self.dec4 = layers.Decoder(ch * (8 + 16), ch * 8, 3, 1, 1)\r\n        self.dec3 = layers.Decoder(ch * (4 + 8), ch * 4, 3, 1, 1)\r\n        self.dec2 = layers.Decoder(ch * (2 + 4), ch * 2, 3, 1, 1)\r","chunkId":"audio_separator/separator/uvr_lib_v5/vr_network/nets.py:4"}],[1978,{"filePath":"audio_separator/separator/uvr_lib_v5/vr_network/nets.py","startLine":36,"endLine":45,"chunkText":"        self.dec4 = layers.Decoder(ch * (8 + 16), ch * 8, 3, 1, 1)\r\n        self.dec3 = layers.Decoder(ch * (4 + 8), ch * 4, 3, 1, 1)\r\n        self.dec2 = layers.Decoder(ch * (2 + 4), ch * 2, 3, 1, 1)\r\n        self.dec1 = layers.Decoder(ch * (1 + 2), ch, 3, 1, 1)\r\n\r\n    def __call__(self, input_tensor):\r\n        # The input tensor is passed through a series of encoder layers.\r\n        hidden_state, encoder_output1 = self.enc1(input_tensor)\r\n        hidden_state, encoder_output2 = self.enc2(hidden_state)\r\n        hidden_state, encoder_output3 = self.enc3(hidden_state)\r","chunkId":"audio_separator/separator/uvr_lib_v5/vr_network/nets.py:5"}],[1979,{"filePath":"audio_separator/separator/uvr_lib_v5/vr_network/nets.py","startLine":43,"endLine":51,"chunkText":"        hidden_state, encoder_output1 = self.enc1(input_tensor)\r\n        hidden_state, encoder_output2 = self.enc2(hidden_state)\r\n        hidden_state, encoder_output3 = self.enc3(hidden_state)\r\n        hidden_state, encoder_output4 = self.enc4(hidden_state)\r\n\r\n        # Depending on the network architecture, the hidden state is processed by an additional encoder layer and the ASPP module.\r\n        if self.nn_architecture == 129605:\r\n            hidden_state, encoder_output5 = self.enc5(hidden_state)\r\n            hidden_state = self.aspp(hidden_state)\r","chunkId":"audio_separator/separator/uvr_lib_v5/vr_network/nets.py:6"}],[1980,{"filePath":"audio_separator/separator/uvr_lib_v5/vr_network/nets.py","startLine":49,"endLine":58,"chunkText":"        if self.nn_architecture == 129605:\r\n            hidden_state, encoder_output5 = self.enc5(hidden_state)\r\n            hidden_state = self.aspp(hidden_state)\r\n            # The decoder layers use skip connections from the encoder layers for better feature integration.\r\n            hidden_state = self.dec5(hidden_state, encoder_output5)\r\n        else:\r\n            hidden_state = self.aspp(hidden_state)\r\n\r\n        # The hidden state is further processed by the decoder layers, using skip connections for feature integration.\r\n        hidden_state = self.dec4(hidden_state, encoder_output4)\r","chunkId":"audio_separator/separator/uvr_lib_v5/vr_network/nets.py:7"}],[1981,{"filePath":"audio_separator/separator/uvr_lib_v5/vr_network/nets.py","startLine":56,"endLine":68,"chunkText":"\r\n        # The hidden state is further processed by the decoder layers, using skip connections for feature integration.\r\n        hidden_state = self.dec4(hidden_state, encoder_output4)\r\n        hidden_state = self.dec3(hidden_state, encoder_output3)\r\n        hidden_state = self.dec2(hidden_state, encoder_output2)\r\n        hidden_state = self.dec1(hidden_state, encoder_output1)\r\n\r\n        return hidden_state\r\n\r\n\r\ndef determine_model_capacity(n_fft_bins, nn_architecture):\r\n    \"\"\"\r\n    The determine_model_capacity function is designed to select the appropriate model configuration\r","chunkId":"audio_separator/separator/uvr_lib_v5/vr_network/nets.py:8"}],[1982,{"filePath":"audio_separator/separator/uvr_lib_v5/vr_network/nets.py","startLine":66,"endLine":77,"chunkText":"def determine_model_capacity(n_fft_bins, nn_architecture):\r\n    \"\"\"\r\n    The determine_model_capacity function is designed to select the appropriate model configuration\r\n    based on the frequency bins and network architecture. It maps specific architectures to predefined\r\n    model capacities, which dictate the structure and parameters of the CascadedASPPNet model.\r\n    \"\"\"\r\n\r\n    # Predefined model architectures categorized by their precision level.\r\n    sp_model_arch = [31191, 33966, 129605]\r\n    hp_model_arch = [123821, 123812]\r\n    hp2_model_arch = [537238, 537227]\r\n\r","chunkId":"audio_separator/separator/uvr_lib_v5/vr_network/nets.py:9"}],[1983,{"filePath":"audio_separator/separator/uvr_lib_v5/vr_network/nets.py","startLine":75,"endLine":85,"chunkText":"    hp_model_arch = [123821, 123812]\r\n    hp2_model_arch = [537238, 537227]\r\n\r\n    # Mapping network architectures to their corresponding model capacity data.\r\n    if nn_architecture in sp_model_arch:\r\n        model_capacity_data = [(2, 16), (2, 16), (18, 8, 1, 1, 0), (8, 16), (34, 16, 1, 1, 0), (16, 32), (32, 2, 1), (16, 2, 1), (16, 2, 1)]\r\n\r\n    if nn_architecture in hp_model_arch:\r\n        model_capacity_data = [(2, 32), (2, 32), (34, 16, 1, 1, 0), (16, 32), (66, 32, 1, 1, 0), (32, 64), (64, 2, 1), (32, 2, 1), (32, 2, 1)]\r\n\r\n    if nn_architecture in hp2_model_arch:\r","chunkId":"audio_separator/separator/uvr_lib_v5/vr_network/nets.py:10"}],[1984,{"filePath":"audio_separator/separator/uvr_lib_v5/vr_network/nets.py","startLine":83,"endLine":94,"chunkText":"        model_capacity_data = [(2, 32), (2, 32), (34, 16, 1, 1, 0), (16, 32), (66, 32, 1, 1, 0), (32, 64), (64, 2, 1), (32, 2, 1), (32, 2, 1)]\r\n\r\n    if nn_architecture in hp2_model_arch:\r\n        model_capacity_data = [(2, 64), (2, 64), (66, 32, 1, 1, 0), (32, 64), (130, 64, 1, 1, 0), (64, 128), (128, 2, 1), (64, 2, 1), (64, 2, 1)]\r\n\r\n    # Initializing the CascadedASPPNet model with the selected model capacity data.\r\n    cascaded = CascadedASPPNet\r\n    model = cascaded(n_fft_bins, model_capacity_data, nn_architecture)\r\n\r\n    return model\r\n\r\n\r","chunkId":"audio_separator/separator/uvr_lib_v5/vr_network/nets.py:11"}],[1985,{"filePath":"audio_separator/separator/uvr_lib_v5/vr_network/nets.py","startLine":95,"endLine":105,"chunkText":"class CascadedASPPNet(nn.Module):\r\n    \"\"\"\r\n    CascadedASPPNet Class:\r\n    This class implements a cascaded version of the ASPP network, designed for processing audio signals\r\n    for tasks such as vocal removal. It consists of multiple stages, each with its own ASPP network,\r\n    to process different frequency bands of the input signal. This allows the model to effectively\r\n    handle the full spectrum of audio frequencies by focusing on different frequency bands separately.\r\n    \"\"\"\r\n\r\n    def __init__(self, n_fft, model_capacity_data, nn_architecture):\r\n        super(CascadedASPPNet, self).__init__()\r","chunkId":"audio_separator/separator/uvr_lib_v5/vr_network/nets.py:12"}],[1986,{"filePath":"audio_separator/separator/uvr_lib_v5/vr_network/nets.py","startLine":103,"endLine":112,"chunkText":"\r\n    def __init__(self, n_fft, model_capacity_data, nn_architecture):\r\n        super(CascadedASPPNet, self).__init__()\r\n        # The first stage processes the low and high frequency bands separately.\r\n        self.stg1_low_band_net = BaseASPPNet(nn_architecture, *model_capacity_data[0])\r\n        self.stg1_high_band_net = BaseASPPNet(nn_architecture, *model_capacity_data[1])\r\n\r\n        # Bridge layers connect different stages of the network.\r\n        self.stg2_bridge = layers.Conv2DBNActiv(*model_capacity_data[2])\r\n        self.stg2_full_band_net = BaseASPPNet(nn_architecture, *model_capacity_data[3])\r","chunkId":"audio_separator/separator/uvr_lib_v5/vr_network/nets.py:13"}],[1987,{"filePath":"audio_separator/separator/uvr_lib_v5/vr_network/nets.py","startLine":110,"endLine":118,"chunkText":"        # Bridge layers connect different stages of the network.\r\n        self.stg2_bridge = layers.Conv2DBNActiv(*model_capacity_data[2])\r\n        self.stg2_full_band_net = BaseASPPNet(nn_architecture, *model_capacity_data[3])\r\n\r\n        self.stg3_bridge = layers.Conv2DBNActiv(*model_capacity_data[4])\r\n        self.stg3_full_band_net = BaseASPPNet(nn_architecture, *model_capacity_data[5])\r\n\r\n        # Output layers for the final mask prediction and auxiliary outputs.\r\n        self.out = nn.Conv2d(*model_capacity_data[6], bias=False)\r","chunkId":"audio_separator/separator/uvr_lib_v5/vr_network/nets.py:14"}],[1988,{"filePath":"audio_separator/separator/uvr_lib_v5/vr_network/nets.py","startLine":116,"endLine":129,"chunkText":"\r\n        # Output layers for the final mask prediction and auxiliary outputs.\r\n        self.out = nn.Conv2d(*model_capacity_data[6], bias=False)\r\n        self.aux1_out = nn.Conv2d(*model_capacity_data[7], bias=False)\r\n        self.aux2_out = nn.Conv2d(*model_capacity_data[8], bias=False)\r\n\r\n        # Parameters for handling the frequency bins of the input signal.\r\n        self.max_bin = n_fft // 2\r\n        self.output_bin = n_fft // 2 + 1\r\n\r\n        self.offset = 128\r\n\r\n    def forward(self, input_tensor):\r\n        # The forward pass processes the input tensor through each stage of the network,\r","chunkId":"audio_separator/separator/uvr_lib_v5/vr_network/nets.py:15"}],[1989,{"filePath":"audio_separator/separator/uvr_lib_v5/vr_network/nets.py","startLine":127,"endLine":138,"chunkText":"\r\n    def forward(self, input_tensor):\r\n        # The forward pass processes the input tensor through each stage of the network,\r\n        # combining the outputs of different frequency bands and stages to produce the final mask.\r\n        mix = input_tensor.detach()\r\n        input_tensor = input_tensor.clone()\r\n\r\n        # Preparing the input tensor by selecting the mainput_tensorimum frequency bin.\r\n        input_tensor = input_tensor[:, :, : self.max_bin]\r\n\r\n        # Processing the low and high frequency bands separately in the first stage.\r\n        bandwidth = input_tensor.size()[2] // 2\r","chunkId":"audio_separator/separator/uvr_lib_v5/vr_network/nets.py:16"}],[1990,{"filePath":"audio_separator/separator/uvr_lib_v5/vr_network/nets.py","startLine":136,"endLine":145,"chunkText":"\r\n        # Processing the low and high frequency bands separately in the first stage.\r\n        bandwidth = input_tensor.size()[2] // 2\r\n        aux1 = torch.cat([self.stg1_low_band_net(input_tensor[:, :, :bandwidth]), self.stg1_high_band_net(input_tensor[:, :, bandwidth:])], dim=2)\r\n\r\n        # Combining the outputs of the first stage and passing through the second stage.\r\n        hidden_state = torch.cat([input_tensor, aux1], dim=1)\r\n        aux2 = self.stg2_full_band_net(self.stg2_bridge(hidden_state))\r\n\r\n        # Further processing the combined outputs through the third stage.\r","chunkId":"audio_separator/separator/uvr_lib_v5/vr_network/nets.py:17"}],[1991,{"filePath":"audio_separator/separator/uvr_lib_v5/vr_network/nets.py","startLine":143,"endLine":154,"chunkText":"        aux2 = self.stg2_full_band_net(self.stg2_bridge(hidden_state))\r\n\r\n        # Further processing the combined outputs through the third stage.\r\n        hidden_state = torch.cat([input_tensor, aux1, aux2], dim=1)\r\n        hidden_state = self.stg3_full_band_net(self.stg3_bridge(hidden_state))\r\n\r\n        # Applying the final output layer to produce the mask.\r\n        mask = torch.sigmoid(self.out(hidden_state))\r\n\r\n        # Padding the mask to match the output frequency bin size.\r\n        mask = F.pad(input=mask, pad=(0, 0, 0, self.output_bin - mask.size()[2]), mode=\"replicate\")\r\n\r","chunkId":"audio_separator/separator/uvr_lib_v5/vr_network/nets.py:18"}],[1992,{"filePath":"audio_separator/separator/uvr_lib_v5/vr_network/nets.py","startLine":152,"endLine":160,"chunkText":"        # Padding the mask to match the output frequency bin size.\r\n        mask = F.pad(input=mask, pad=(0, 0, 0, self.output_bin - mask.size()[2]), mode=\"replicate\")\r\n\r\n        # During training, auxiliary outputs are also produced and padded accordingly.\r\n        if self.training:\r\n            aux1 = torch.sigmoid(self.aux1_out(aux1))\r\n            aux1 = F.pad(input=aux1, pad=(0, 0, 0, self.output_bin - aux1.size()[2]), mode=\"replicate\")\r\n            aux2 = torch.sigmoid(self.aux2_out(aux2))\r\n            aux2 = F.pad(input=aux2, pad=(0, 0, 0, self.output_bin - aux2.size()[2]), mode=\"replicate\")\r","chunkId":"audio_separator/separator/uvr_lib_v5/vr_network/nets.py:19"}],[1993,{"filePath":"audio_separator/separator/uvr_lib_v5/vr_network/nets.py","startLine":158,"endLine":169,"chunkText":"            aux1 = F.pad(input=aux1, pad=(0, 0, 0, self.output_bin - aux1.size()[2]), mode=\"replicate\")\r\n            aux2 = torch.sigmoid(self.aux2_out(aux2))\r\n            aux2 = F.pad(input=aux2, pad=(0, 0, 0, self.output_bin - aux2.size()[2]), mode=\"replicate\")\r\n            return mask * mix, aux1 * mix, aux2 * mix\r\n        else:\r\n            return mask  # * mix\r\n\r\n    def predict_mask(self, input_tensor):\r\n        # This method predicts the mask for the input tensor by calling the forward method\r\n        # and applying any necessary padding adjustments.\r\n        mask = self.forward(input_tensor)\r\n\r","chunkId":"audio_separator/separator/uvr_lib_v5/vr_network/nets.py:20"}],[1994,{"filePath":"audio_separator/separator/uvr_lib_v5/vr_network/nets.py","startLine":167,"endLine":175,"chunkText":"        # and applying any necessary padding adjustments.\r\n        mask = self.forward(input_tensor)\r\n\r\n        # Adjusting the mask by removing padding offsets if present.\r\n        if self.offset > 0:\r\n            mask = mask[:, :, :, self.offset : -self.offset]\r\n\r\n        return mask\r\n","chunkId":"audio_separator/separator/uvr_lib_v5/vr_network/nets.py:21"}],[1995,{"filePath":"audio_separator/separator/uvr_lib_v5/vr_network/nets_new.py","startLine":0,"endLine":14,"chunkText":"import torch\nfrom torch import nn\nimport torch.nn.functional as F\nfrom . import layers_new as layers\n\n\nclass BaseNet(nn.Module):\n    \"\"\"\n    BaseNet Class:\n    This class defines the base network architecture for vocal removal. It includes a series of encoders for feature extraction,\n    an ASPP module for capturing multi-scale context, and a series of decoders for reconstructing the output. Additionally,\n    it incorporates an LSTM module for capturing temporal dependencies.\n    \"\"\"\n\n    def __init__(self, nin, nout, nin_lstm, nout_lstm, dilations=((4, 2), (8, 4), (12, 6))):","chunkId":"audio_separator/separator/uvr_lib_v5/vr_network/nets_new.py:0"}],[1996,{"filePath":"audio_separator/separator/uvr_lib_v5/vr_network/nets_new.py","startLine":12,"endLine":22,"chunkText":"    \"\"\"\n\n    def __init__(self, nin, nout, nin_lstm, nout_lstm, dilations=((4, 2), (8, 4), (12, 6))):\n        super(BaseNet, self).__init__()\n        # Initialize the encoder layers with increasing output channels for hierarchical feature extraction.\n        self.enc1 = layers.Conv2DBNActiv(nin, nout, 3, 1, 1)\n        self.enc2 = layers.Encoder(nout, nout * 2, 3, 2, 1)\n        self.enc3 = layers.Encoder(nout * 2, nout * 4, 3, 2, 1)\n        self.enc4 = layers.Encoder(nout * 4, nout * 6, 3, 2, 1)\n        self.enc5 = layers.Encoder(nout * 6, nout * 8, 3, 2, 1)\n","chunkId":"audio_separator/separator/uvr_lib_v5/vr_network/nets_new.py:1"}],[1997,{"filePath":"audio_separator/separator/uvr_lib_v5/vr_network/nets_new.py","startLine":20,"endLine":28,"chunkText":"        self.enc4 = layers.Encoder(nout * 4, nout * 6, 3, 2, 1)\n        self.enc5 = layers.Encoder(nout * 6, nout * 8, 3, 2, 1)\n\n        # ASPP module for capturing multi-scale features with different dilation rates.\n        self.aspp = layers.ASPPModule(nout * 8, nout * 8, dilations, dropout=True)\n\n        # Decoder layers for upscaling and merging features from different levels of the encoder and ASPP module.\n        self.dec4 = layers.Decoder(nout * (6 + 8), nout * 6, 3, 1, 1)\n        self.dec3 = layers.Decoder(nout * (4 + 6), nout * 4, 3, 1, 1)","chunkId":"audio_separator/separator/uvr_lib_v5/vr_network/nets_new.py:2"}],[1998,{"filePath":"audio_separator/separator/uvr_lib_v5/vr_network/nets_new.py","startLine":26,"endLine":35,"chunkText":"        # Decoder layers for upscaling and merging features from different levels of the encoder and ASPP module.\n        self.dec4 = layers.Decoder(nout * (6 + 8), nout * 6, 3, 1, 1)\n        self.dec3 = layers.Decoder(nout * (4 + 6), nout * 4, 3, 1, 1)\n        self.dec2 = layers.Decoder(nout * (2 + 4), nout * 2, 3, 1, 1)\n\n        # LSTM module for capturing temporal dependencies in the sequence of features.\n        self.lstm_dec2 = layers.LSTMModule(nout * 2, nin_lstm, nout_lstm)\n        self.dec1 = layers.Decoder(nout * (1 + 2) + 1, nout * 1, 3, 1, 1)\n\n    def __call__(self, input_tensor):","chunkId":"audio_separator/separator/uvr_lib_v5/vr_network/nets_new.py:3"}],[1999,{"filePath":"audio_separator/separator/uvr_lib_v5/vr_network/nets_new.py","startLine":33,"endLine":46,"chunkText":"        self.dec1 = layers.Decoder(nout * (1 + 2) + 1, nout * 1, 3, 1, 1)\n\n    def __call__(self, input_tensor):\n        # Sequentially pass the input through the encoder layers.\n        encoded1 = self.enc1(input_tensor)\n        encoded2 = self.enc2(encoded1)\n        encoded3 = self.enc3(encoded2)\n        encoded4 = self.enc4(encoded3)\n        encoded5 = self.enc5(encoded4)\n\n        # Pass the deepest encoder output through the ASPP module.\n        bottleneck = self.aspp(encoded5)\n\n        # Sequentially upscale and merge the features using the decoder layers.","chunkId":"audio_separator/separator/uvr_lib_v5/vr_network/nets_new.py:4"}],[2000,{"filePath":"audio_separator/separator/uvr_lib_v5/vr_network/nets_new.py","startLine":44,"endLine":56,"chunkText":"        bottleneck = self.aspp(encoded5)\n\n        # Sequentially upscale and merge the features using the decoder layers.\n        bottleneck = self.dec4(bottleneck, encoded4)\n        bottleneck = self.dec3(bottleneck, encoded3)\n        bottleneck = self.dec2(bottleneck, encoded2)\n        # Concatenate the LSTM module output for temporal feature enhancement.\n        bottleneck = torch.cat([bottleneck, self.lstm_dec2(bottleneck)], dim=1)\n        bottleneck = self.dec1(bottleneck, encoded1)\n\n        return bottleneck\n\n","chunkId":"audio_separator/separator/uvr_lib_v5/vr_network/nets_new.py:5"}],[2001,{"filePath":"audio_separator/separator/uvr_lib_v5/vr_network/nets_new.py","startLine":57,"endLine":68,"chunkText":"class CascadedNet(nn.Module):\n    \"\"\"\n    CascadedNet Class:\n    This class defines a cascaded network architecture that processes input in multiple stages, each stage focusing on different frequency bands.\n    It utilizes the BaseNet for processing, and combines outputs from different stages to produce the final mask for vocal removal.\n    \"\"\"\n\n    def __init__(self, n_fft, nn_arch_size=51000, nout=32, nout_lstm=128):\n        super(CascadedNet, self).__init__()\n        # Calculate frequency bins based on FFT size.\n        self.max_bin = n_fft // 2\n        self.output_bin = n_fft // 2 + 1","chunkId":"audio_separator/separator/uvr_lib_v5/vr_network/nets_new.py:6"}],[2002,{"filePath":"audio_separator/separator/uvr_lib_v5/vr_network/nets_new.py","startLine":66,"endLine":76,"chunkText":"        # Calculate frequency bins based on FFT size.\n        self.max_bin = n_fft // 2\n        self.output_bin = n_fft // 2 + 1\n        self.nin_lstm = self.max_bin // 2\n        self.offset = 64\n        # Adjust output channels based on the architecture size.\n        nout = 64 if nn_arch_size == 218409 else nout\n\n        # print(nout, nout_lstm, n_fft)\n\n        # Initialize the network stages, each focusing on different frequency bands and progressively refining the output.","chunkId":"audio_separator/separator/uvr_lib_v5/vr_network/nets_new.py:7"}],[2003,{"filePath":"audio_separator/separator/uvr_lib_v5/vr_network/nets_new.py","startLine":74,"endLine":80,"chunkText":"        # print(nout, nout_lstm, n_fft)\n\n        # Initialize the network stages, each focusing on different frequency bands and progressively refining the output.\n        self.stg1_low_band_net = nn.Sequential(BaseNet(2, nout // 2, self.nin_lstm // 2, nout_lstm), layers.Conv2DBNActiv(nout // 2, nout // 4, 1, 1, 0))\n        self.stg1_high_band_net = BaseNet(2, nout // 4, self.nin_lstm // 2, nout_lstm // 2)\n\n        self.stg2_low_band_net = nn.Sequential(BaseNet(nout // 4 + 2, nout, self.nin_lstm // 2, nout_lstm), layers.Conv2DBNActiv(nout, nout // 2, 1, 1, 0))","chunkId":"audio_separator/separator/uvr_lib_v5/vr_network/nets_new.py:8"}],[2004,{"filePath":"audio_separator/separator/uvr_lib_v5/vr_network/nets_new.py","startLine":78,"endLine":86,"chunkText":"        self.stg1_high_band_net = BaseNet(2, nout // 4, self.nin_lstm // 2, nout_lstm // 2)\n\n        self.stg2_low_band_net = nn.Sequential(BaseNet(nout // 4 + 2, nout, self.nin_lstm // 2, nout_lstm), layers.Conv2DBNActiv(nout, nout // 2, 1, 1, 0))\n        self.stg2_high_band_net = BaseNet(nout // 4 + 2, nout // 2, self.nin_lstm // 2, nout_lstm // 2)\n\n        self.stg3_full_band_net = BaseNet(3 * nout // 4 + 2, nout, self.nin_lstm, nout_lstm)\n\n        # Output layer for generating the final mask.\n        self.out = nn.Conv2d(nout, 2, 1, bias=False)","chunkId":"audio_separator/separator/uvr_lib_v5/vr_network/nets_new.py:9"}],[2005,{"filePath":"audio_separator/separator/uvr_lib_v5/vr_network/nets_new.py","startLine":84,"endLine":98,"chunkText":"\n        # Output layer for generating the final mask.\n        self.out = nn.Conv2d(nout, 2, 1, bias=False)\n        # Auxiliary output layer for intermediate supervision during training.\n        self.aux_out = nn.Conv2d(3 * nout // 4, 2, 1, bias=False)\n\n    def forward(self, input_tensor):\n        # Preprocess input tensor to match the maximum frequency bin.\n        input_tensor = input_tensor[:, :, : self.max_bin]\n\n        # Split the input into low and high frequency bands.\n        bandw = input_tensor.size()[2] // 2\n        l1_in = input_tensor[:, :, :bandw]\n        h1_in = input_tensor[:, :, bandw:]\n","chunkId":"audio_separator/separator/uvr_lib_v5/vr_network/nets_new.py:10"}],[2006,{"filePath":"audio_separator/separator/uvr_lib_v5/vr_network/nets_new.py","startLine":96,"endLine":110,"chunkText":"        l1_in = input_tensor[:, :, :bandw]\n        h1_in = input_tensor[:, :, bandw:]\n\n        # Process each band through the first stage networks.\n        l1 = self.stg1_low_band_net(l1_in)\n        h1 = self.stg1_high_band_net(h1_in)\n\n        # Combine the outputs for auxiliary supervision.\n        aux1 = torch.cat([l1, h1], dim=2)\n\n        # Prepare inputs for the second stage by concatenating the original and processed bands.\n        l2_in = torch.cat([l1_in, l1], dim=1)\n        h2_in = torch.cat([h1_in, h1], dim=1)\n\n        # Process through the second stage networks.","chunkId":"audio_separator/separator/uvr_lib_v5/vr_network/nets_new.py:11"}],[2007,{"filePath":"audio_separator/separator/uvr_lib_v5/vr_network/nets_new.py","startLine":108,"endLine":122,"chunkText":"        h2_in = torch.cat([h1_in, h1], dim=1)\n\n        # Process through the second stage networks.\n        l2 = self.stg2_low_band_net(l2_in)\n        h2 = self.stg2_high_band_net(h2_in)\n\n        # Combine the outputs for auxiliary supervision.\n        aux2 = torch.cat([l2, h2], dim=2)\n\n        # Prepare input for the third stage by concatenating all previous outputs with the original input.\n        f3_in = torch.cat([input_tensor, aux1, aux2], dim=1)\n\n        # Process through the third stage network.\n        f3 = self.stg3_full_band_net(f3_in)\n","chunkId":"audio_separator/separator/uvr_lib_v5/vr_network/nets_new.py:12"}],[2008,{"filePath":"audio_separator/separator/uvr_lib_v5/vr_network/nets_new.py","startLine":120,"endLine":131,"chunkText":"        # Process through the third stage network.\n        f3 = self.stg3_full_band_net(f3_in)\n\n        # Apply the output layer to generate the final mask and apply sigmoid for normalization.\n        mask = torch.sigmoid(self.out(f3))\n\n        # Pad the mask to match the output frequency bin size.\n        mask = F.pad(input=mask, pad=(0, 0, 0, self.output_bin - mask.size()[2]), mode=\"replicate\")\n\n        # During training, generate and pad the auxiliary output for additional supervision.\n        if self.training:\n            aux = torch.cat([aux1, aux2], dim=1)","chunkId":"audio_separator/separator/uvr_lib_v5/vr_network/nets_new.py:13"}],[2009,{"filePath":"audio_separator/separator/uvr_lib_v5/vr_network/nets_new.py","startLine":129,"endLine":142,"chunkText":"        # During training, generate and pad the auxiliary output for additional supervision.\n        if self.training:\n            aux = torch.cat([aux1, aux2], dim=1)\n            aux = torch.sigmoid(self.aux_out(aux))\n            aux = F.pad(input=aux, pad=(0, 0, 0, self.output_bin - aux.size()[2]), mode=\"replicate\")\n            return mask, aux\n        else:\n            return mask\n\n    # Method for predicting the mask given an input tensor.\n    def predict_mask(self, input_tensor):\n        mask = self.forward(input_tensor)\n\n        # If an offset is specified, crop the mask to remove edge artifacts.","chunkId":"audio_separator/separator/uvr_lib_v5/vr_network/nets_new.py:14"}],[2010,{"filePath":"audio_separator/separator/uvr_lib_v5/vr_network/nets_new.py","startLine":140,"endLine":155,"chunkText":"        mask = self.forward(input_tensor)\n\n        # If an offset is specified, crop the mask to remove edge artifacts.\n        if self.offset > 0:\n            mask = mask[:, :, :, self.offset : -self.offset]\n            assert mask.size()[3] > 0\n\n        return mask\n\n    # Method for applying the predicted mask to the input tensor to obtain the predicted magnitude.\n    def predict(self, input_tensor):\n        mask = self.forward(input_tensor)\n        pred_mag = input_tensor * mask\n\n        # If an offset is specified, crop the predicted magnitude to remove edge artifacts.\n        if self.offset > 0:","chunkId":"audio_separator/separator/uvr_lib_v5/vr_network/nets_new.py:15"}],[2011,{"filePath":"audio_separator/separator/uvr_lib_v5/vr_network/nets_new.py","startLine":153,"endLine":160,"chunkText":"\n        # If an offset is specified, crop the predicted magnitude to remove edge artifacts.\n        if self.offset > 0:\n            pred_mag = pred_mag[:, :, :, self.offset : -self.offset]\n            assert pred_mag.size()[3] > 0\n\n        return pred_mag\n","chunkId":"audio_separator/separator/uvr_lib_v5/vr_network/nets_new.py:16"}],[2012,{"filePath":"audio_separator/utils/cli.py","startLine":0,"endLine":16,"chunkText":"#!/usr/bin/env python\nimport argparse\nimport logging\nimport json\nimport sys\nimport os\nfrom importlib import metadata\n\n\ndef main():\n    \"\"\"Main entry point for the CLI.\"\"\"\n    logger = logging.getLogger(__name__)\n    log_handler = logging.StreamHandler()\n    log_formatter = logging.Formatter(fmt=\"%(asctime)s.%(msecs)03d - %(levelname)s - %(module)s - %(message)s\", datefmt=\"%Y-%m-%d %H:%M:%S\")\n    log_handler.setFormatter(log_formatter)\n    logger.addHandler(log_handler)\n","chunkId":"audio_separator/utils/cli.py:0"}],[2013,{"filePath":"audio_separator/utils/cli.py","startLine":14,"endLine":23,"chunkText":"    log_handler.setFormatter(log_formatter)\n    logger.addHandler(log_handler)\n\n    parser = argparse.ArgumentParser(description=\"Separate audio file into different stems.\", formatter_class=lambda prog: argparse.RawTextHelpFormatter(prog, max_help_position=60))\n\n    parser.add_argument(\"audio_files\", nargs=\"*\", help=\"The audio file paths or directory to separate, in any common format.\", default=argparse.SUPPRESS)\n\n    package_version = metadata.distribution(\"audio-separator\").version\n\n    version_help = \"Show the program's version number and exit.\"","chunkId":"audio_separator/utils/cli.py:1"}],[2014,{"filePath":"audio_separator/utils/cli.py","startLine":21,"endLine":29,"chunkText":"    package_version = metadata.distribution(\"audio-separator\").version\n\n    version_help = \"Show the program's version number and exit.\"\n    debug_help = \"Enable debug logging, equivalent to --log_level=debug.\"\n    env_info_help = \"Print environment information and exit.\"\n    list_models_help = \"List all supported models and exit. Use --list_filter to filter/sort the list and --list_limit to show only top N results.\"\n    log_level_help = \"Log level, e.g. info, debug, warning (default: %(default)s).\"\n\n    info_params = parser.add_argument_group(\"Info and Debugging\")","chunkId":"audio_separator/utils/cli.py:2"}],[2015,{"filePath":"audio_separator/utils/cli.py","startLine":27,"endLine":33,"chunkText":"    log_level_help = \"Log level, e.g. info, debug, warning (default: %(default)s).\"\n\n    info_params = parser.add_argument_group(\"Info and Debugging\")\n    info_params.add_argument(\"-v\", \"--version\", action=\"version\", version=f\"%(prog)s {package_version}\", help=version_help)\n    info_params.add_argument(\"-d\", \"--debug\", action=\"store_true\", help=debug_help)\n    info_params.add_argument(\"-e\", \"--env_info\", action=\"store_true\", help=env_info_help)\n    info_params.add_argument(\"-l\", \"--list_models\", action=\"store_true\", help=list_models_help)","chunkId":"audio_separator/utils/cli.py:3"}],[2016,{"filePath":"audio_separator/utils/cli.py","startLine":31,"endLine":36,"chunkText":"    info_params.add_argument(\"-d\", \"--debug\", action=\"store_true\", help=debug_help)\n    info_params.add_argument(\"-e\", \"--env_info\", action=\"store_true\", help=env_info_help)\n    info_params.add_argument(\"-l\", \"--list_models\", action=\"store_true\", help=list_models_help)\n    info_params.add_argument(\"--log_level\", default=\"info\", help=log_level_help)\n    info_params.add_argument(\"--list_filter\", help=\"Filter and sort the model list by 'name', 'filename', or any stem e.g. vocals, instrumental, drums\")\n    info_params.add_argument(\"--list_limit\", type=int, help=\"Limit the number of models shown\")","chunkId":"audio_separator/utils/cli.py:4"}],[2017,{"filePath":"audio_separator/utils/cli.py","startLine":34,"endLine":38,"chunkText":"    info_params.add_argument(\"--log_level\", default=\"info\", help=log_level_help)\n    info_params.add_argument(\"--list_filter\", help=\"Filter and sort the model list by 'name', 'filename', or any stem e.g. vocals, instrumental, drums\")\n    info_params.add_argument(\"--list_limit\", type=int, help=\"Limit the number of models shown\")\n    info_params.add_argument(\"--list_format\", choices=[\"pretty\", \"json\"], default=\"pretty\", help=\"Format for listing models: 'pretty' for formatted output, 'json' for raw JSON dump\")\n","chunkId":"audio_separator/utils/cli.py:5"}],[2018,{"filePath":"audio_separator/utils/cli.py","startLine":36,"endLine":40,"chunkText":"    info_params.add_argument(\"--list_limit\", type=int, help=\"Limit the number of models shown\")\n    info_params.add_argument(\"--list_format\", choices=[\"pretty\", \"json\"], default=\"pretty\", help=\"Format for listing models: 'pretty' for formatted output, 'json' for raw JSON dump\")\n\n    model_filename_help = \"Model to use for separation (default: %(default)s). Example: -m 2_HP-UVR.pth\"\n    output_format_help = \"Output format for separated files, any common format (default: %(default)s). Example: --output_format=MP3\"","chunkId":"audio_separator/utils/cli.py:6"}],[2019,{"filePath":"audio_separator/utils/cli.py","startLine":38,"endLine":42,"chunkText":"\n    model_filename_help = \"Model to use for separation (default: %(default)s). Example: -m 2_HP-UVR.pth\"\n    output_format_help = \"Output format for separated files, any common format (default: %(default)s). Example: --output_format=MP3\"\n    output_bitrate_help = \"Output bitrate for separated files, any ffmpeg-compatible bitrate (default: %(default)s). Example: --output_bitrate=320k\"\n    output_dir_help = \"Directory to write output files (default: <current dir>). Example: --output_dir=/app/separated\"","chunkId":"audio_separator/utils/cli.py:7"}],[2020,{"filePath":"audio_separator/utils/cli.py","startLine":40,"endLine":43,"chunkText":"    output_format_help = \"Output format for separated files, any common format (default: %(default)s). Example: --output_format=MP3\"\n    output_bitrate_help = \"Output bitrate for separated files, any ffmpeg-compatible bitrate (default: %(default)s). Example: --output_bitrate=320k\"\n    output_dir_help = \"Directory to write output files (default: <current dir>). Example: --output_dir=/app/separated\"\n    model_file_dir_help = \"Model files directory (default: %(default)s or AUDIO_SEPARATOR_MODEL_DIR env var if set). Example: --model_file_dir=/app/models\"","chunkId":"audio_separator/utils/cli.py:8"}],[2021,{"filePath":"audio_separator/utils/cli.py","startLine":41,"endLine":46,"chunkText":"    output_bitrate_help = \"Output bitrate for separated files, any ffmpeg-compatible bitrate (default: %(default)s). Example: --output_bitrate=320k\"\n    output_dir_help = \"Directory to write output files (default: <current dir>). Example: --output_dir=/app/separated\"\n    model_file_dir_help = \"Model files directory (default: %(default)s or AUDIO_SEPARATOR_MODEL_DIR env var if set). Example: --model_file_dir=/app/models\"\n    download_model_only_help = \"Download a single model file only, without performing separation.\"\n\n    io_params = parser.add_argument_group(\"Separation I/O Params\")","chunkId":"audio_separator/utils/cli.py:9"}],[2022,{"filePath":"audio_separator/utils/cli.py","startLine":44,"endLine":50,"chunkText":"    download_model_only_help = \"Download a single model file only, without performing separation.\"\n\n    io_params = parser.add_argument_group(\"Separation I/O Params\")\n    io_params.add_argument(\"-m\", \"--model_filename\", default=\"model_bs_roformer_ep_317_sdr_12.9755.ckpt\", help=model_filename_help)\n    io_params.add_argument(\"--output_format\", default=\"FLAC\", help=output_format_help)\n    io_params.add_argument(\"--output_bitrate\", default=None, help=output_bitrate_help)\n    io_params.add_argument(\"--output_dir\", default=None, help=output_dir_help)","chunkId":"audio_separator/utils/cli.py:10"}],[2023,{"filePath":"audio_separator/utils/cli.py","startLine":48,"endLine":54,"chunkText":"    io_params.add_argument(\"--output_format\", default=\"FLAC\", help=output_format_help)\n    io_params.add_argument(\"--output_bitrate\", default=None, help=output_bitrate_help)\n    io_params.add_argument(\"--output_dir\", default=None, help=output_dir_help)\n    io_params.add_argument(\"--model_file_dir\", default=\"/tmp/audio-separator-models/\", help=model_file_dir_help)\n    io_params.add_argument(\"--download_model_only\", action=\"store_true\", help=download_model_only_help)\n\n    invert_spect_help = \"Invert secondary stem using spectrogram (default: %(default)s). Example: --invert_spect\"","chunkId":"audio_separator/utils/cli.py:11"}],[2024,{"filePath":"audio_separator/utils/cli.py","startLine":52,"endLine":56,"chunkText":"    io_params.add_argument(\"--download_model_only\", action=\"store_true\", help=download_model_only_help)\n\n    invert_spect_help = \"Invert secondary stem using spectrogram (default: %(default)s). Example: --invert_spect\"\n    normalization_help = \"Max peak amplitude to normalize input and output audio to (default: %(default)s). Example: --normalization=0.7\"\n    amplification_help = \"Min peak amplitude to amplify input and output audio to (default: %(default)s). Example: --amplification=0.4\"","chunkId":"audio_separator/utils/cli.py:12"}],[2025,{"filePath":"audio_separator/utils/cli.py","startLine":54,"endLine":57,"chunkText":"    invert_spect_help = \"Invert secondary stem using spectrogram (default: %(default)s). Example: --invert_spect\"\n    normalization_help = \"Max peak amplitude to normalize input and output audio to (default: %(default)s). Example: --normalization=0.7\"\n    amplification_help = \"Min peak amplitude to amplify input and output audio to (default: %(default)s). Example: --amplification=0.4\"\n    single_stem_help = \"Output only single stem, e.g. Instrumental, Vocals, Drums, Bass, Guitar, Piano, Other. Example: --single_stem=Instrumental\"","chunkId":"audio_separator/utils/cli.py:13"}],[2026,{"filePath":"audio_separator/utils/cli.py","startLine":55,"endLine":58,"chunkText":"    normalization_help = \"Max peak amplitude to normalize input and output audio to (default: %(default)s). Example: --normalization=0.7\"\n    amplification_help = \"Min peak amplitude to amplify input and output audio to (default: %(default)s). Example: --amplification=0.4\"\n    single_stem_help = \"Output only single stem, e.g. Instrumental, Vocals, Drums, Bass, Guitar, Piano, Other. Example: --single_stem=Instrumental\"\n    sample_rate_help = \"Modify the sample rate of the output audio (default: %(default)s). Example: --sample_rate=44100\"","chunkId":"audio_separator/utils/cli.py:14"}],[2027,{"filePath":"audio_separator/utils/cli.py","startLine":56,"endLine":59,"chunkText":"    amplification_help = \"Min peak amplitude to amplify input and output audio to (default: %(default)s). Example: --amplification=0.4\"\n    single_stem_help = \"Output only single stem, e.g. Instrumental, Vocals, Drums, Bass, Guitar, Piano, Other. Example: --single_stem=Instrumental\"\n    sample_rate_help = \"Modify the sample rate of the output audio (default: %(default)s). Example: --sample_rate=44100\"\n    use_soundfile_help = \"Use soundfile to write audio output (default: %(default)s). Example: --use_soundfile\"","chunkId":"audio_separator/utils/cli.py:15"}],[2028,{"filePath":"audio_separator/utils/cli.py","startLine":57,"endLine":60,"chunkText":"    single_stem_help = \"Output only single stem, e.g. Instrumental, Vocals, Drums, Bass, Guitar, Piano, Other. Example: --single_stem=Instrumental\"\n    sample_rate_help = \"Modify the sample rate of the output audio (default: %(default)s). Example: --sample_rate=44100\"\n    use_soundfile_help = \"Use soundfile to write audio output (default: %(default)s). Example: --use_soundfile\"\n    use_autocast_help = \"Use PyTorch autocast for faster inference (default: %(default)s). Do not use for CPU inference. Example: --use_autocast\"","chunkId":"audio_separator/utils/cli.py:16"}],[2029,{"filePath":"audio_separator/utils/cli.py","startLine":58,"endLine":60,"chunkText":"    sample_rate_help = \"Modify the sample rate of the output audio (default: %(default)s). Example: --sample_rate=44100\"\n    use_soundfile_help = \"Use soundfile to write audio output (default: %(default)s). Example: --use_soundfile\"\n    use_autocast_help = \"Use PyTorch autocast for faster inference (default: %(default)s). Do not use for CPU inference. Example: --use_autocast\"","chunkId":"audio_separator/utils/cli.py:17"}],[2030,{"filePath":"audio_separator/utils/cli.py","startLine":59,"endLine":61,"chunkText":"    use_soundfile_help = \"Use soundfile to write audio output (default: %(default)s). Example: --use_soundfile\"\n    use_autocast_help = \"Use PyTorch autocast for faster inference (default: %(default)s). Do not use for CPU inference. Example: --use_autocast\"\n    chunk_duration_help = \"Split audio into chunks of this duration in seconds (default: %(default)s = no chunking). Useful for processing very long audio files on systems with limited memory. Recommended: 600 (10 minutes) for files >1 hour. Chunks are concatenated without overlap/crossfade. Example: --chunk_duration=600\"","chunkId":"audio_separator/utils/cli.py:18"}],[2031,{"filePath":"audio_separator/utils/cli.py","startLine":60,"endLine":61,"chunkText":"    use_autocast_help = \"Use PyTorch autocast for faster inference (default: %(default)s). Do not use for CPU inference. Example: --use_autocast\"\n    chunk_duration_help = \"Split audio into chunks of this duration in seconds (default: %(default)s = no chunking). Useful for processing very long audio files on systems with limited memory. Recommended: 600 (10 minutes) for files >1 hour. Chunks are concatenated without overlap/crossfade. Example: --chunk_duration=600\"","chunkId":"audio_separator/utils/cli.py:19"}],[2032,{"filePath":"audio_separator/utils/cli.py","startLine":61,"endLine":64,"chunkText":"    chunk_duration_help = \"Split audio into chunks of this duration in seconds (default: %(default)s = no chunking). Useful for processing very long audio files on systems with limited memory. Recommended: 600 (10 minutes) for files >1 hour. Chunks are concatenated without overlap/crossfade. Example: --chunk_duration=600\"\n    custom_output_names_help = 'Custom names for all output files in JSON format (default: %(default)s). Example: --custom_output_names=\\'{\"Vocals\": \"vocals_output\", \"Drums\": \"drums_output\"}\\''\n\n    common_params = parser.add_argument_group(\"Common Separation Parameters\")","chunkId":"audio_separator/utils/cli.py:20"}],[2033,{"filePath":"audio_separator/utils/cli.py","startLine":62,"endLine":67,"chunkText":"    custom_output_names_help = 'Custom names for all output files in JSON format (default: %(default)s). Example: --custom_output_names=\\'{\"Vocals\": \"vocals_output\", \"Drums\": \"drums_output\"}\\''\n\n    common_params = parser.add_argument_group(\"Common Separation Parameters\")\n    common_params.add_argument(\"--invert_spect\", action=\"store_true\", help=invert_spect_help)\n    common_params.add_argument(\"--normalization\", type=float, default=0.9, help=normalization_help)\n    common_params.add_argument(\"--amplification\", type=float, default=0.0, help=amplification_help)","chunkId":"audio_separator/utils/cli.py:21"}],[2034,{"filePath":"audio_separator/utils/cli.py","startLine":65,"endLine":70,"chunkText":"    common_params.add_argument(\"--invert_spect\", action=\"store_true\", help=invert_spect_help)\n    common_params.add_argument(\"--normalization\", type=float, default=0.9, help=normalization_help)\n    common_params.add_argument(\"--amplification\", type=float, default=0.0, help=amplification_help)\n    common_params.add_argument(\"--single_stem\", default=None, help=single_stem_help)\n    common_params.add_argument(\"--sample_rate\", type=int, default=44100, help=sample_rate_help)\n    common_params.add_argument(\"--use_soundfile\", action=\"store_true\", help=use_soundfile_help)","chunkId":"audio_separator/utils/cli.py:22"}],[2035,{"filePath":"audio_separator/utils/cli.py","startLine":68,"endLine":74,"chunkText":"    common_params.add_argument(\"--single_stem\", default=None, help=single_stem_help)\n    common_params.add_argument(\"--sample_rate\", type=int, default=44100, help=sample_rate_help)\n    common_params.add_argument(\"--use_soundfile\", action=\"store_true\", help=use_soundfile_help)\n    common_params.add_argument(\"--use_autocast\", action=\"store_true\", help=use_autocast_help)\n    common_params.add_argument(\"--chunk_duration\", type=float, default=None, help=chunk_duration_help)\n    common_params.add_argument(\"--custom_output_names\", type=json.loads, default=None, help=custom_output_names_help)\n","chunkId":"audio_separator/utils/cli.py:23"}],[2036,{"filePath":"audio_separator/utils/cli.py","startLine":72,"endLine":76,"chunkText":"    common_params.add_argument(\"--chunk_duration\", type=float, default=None, help=chunk_duration_help)\n    common_params.add_argument(\"--custom_output_names\", type=json.loads, default=None, help=custom_output_names_help)\n\n    mdx_segment_size_help = \"Larger consumes more resources, but may give better results (default: %(default)s). Example: --mdx_segment_size=256\"\n    mdx_overlap_help = \"Amount of overlap between prediction windows, 0.001-0.999. Higher is better but slower (default: %(default)s). Example: --mdx_overlap=0.25\"","chunkId":"audio_separator/utils/cli.py:24"}],[2037,{"filePath":"audio_separator/utils/cli.py","startLine":74,"endLine":77,"chunkText":"\n    mdx_segment_size_help = \"Larger consumes more resources, but may give better results (default: %(default)s). Example: --mdx_segment_size=256\"\n    mdx_overlap_help = \"Amount of overlap between prediction windows, 0.001-0.999. Higher is better but slower (default: %(default)s). Example: --mdx_overlap=0.25\"\n    mdx_batch_size_help = \"Larger consumes more RAM but may process slightly faster (default: %(default)s). Example: --mdx_batch_size=4\"","chunkId":"audio_separator/utils/cli.py:25"}],[2038,{"filePath":"audio_separator/utils/cli.py","startLine":75,"endLine":78,"chunkText":"    mdx_segment_size_help = \"Larger consumes more resources, but may give better results (default: %(default)s). Example: --mdx_segment_size=256\"\n    mdx_overlap_help = \"Amount of overlap between prediction windows, 0.001-0.999. Higher is better but slower (default: %(default)s). Example: --mdx_overlap=0.25\"\n    mdx_batch_size_help = \"Larger consumes more RAM but may process slightly faster (default: %(default)s). Example: --mdx_batch_size=4\"\n    mdx_hop_length_help = \"Usually called stride in neural networks, only change if you know what you're doing (default: %(default)s). Example: --mdx_hop_length=1024","chunkId":"audio_separator/utils/cli.py:26"}],[2039,{"filePath":"audio_separator/utils/cli.py","startLine":76,"endLine":80,"chunkText":"    mdx_overlap_help = \"Amount of overlap between prediction windows, 0.001-0.999. Higher is better but slower (default: %(default)s). Example: --mdx_overlap=0.25\"\n    mdx_batch_size_help = \"Larger consumes more RAM but may process slightly faster (default: %(default)s). Example: --mdx_batch_size=4\"\n    mdx_hop_length_help = \"Usually called stride in neural networks, only change if you know what you're doing (default: %(default)s). Example: --mdx_hop_length=1024\"\n    mdx_enable_denoise_help = \"Enable denoising during separation (default: %(default)s). Example: --mdx_enable_denoise\"\n","chunkId":"audio_separator/utils/cli.py:27"}],[2040,{"filePath":"audio_separator/utils/cli.py","startLine":78,"endLine":83,"chunkText":"    mdx_hop_length_help = \"Usually called stride in neural networks, only change if you know what you're doing (default: %(default)s). Example: --mdx_hop_length=1024\"\n    mdx_enable_denoise_help = \"Enable denoising during separation (default: %(default)s). Example: --mdx_enable_denoise\"\n\n    mdx_params = parser.add_argument_group(\"MDX Architecture Parameters\")\n    mdx_params.add_argument(\"--mdx_segment_size\", type=int, default=256, help=mdx_segment_size_help)\n    mdx_params.add_argument(\"--mdx_overlap\", type=float, default=0.25, help=mdx_overlap_help)","chunkId":"audio_separator/utils/cli.py:28"}],[2041,{"filePath":"audio_separator/utils/cli.py","startLine":81,"endLine":87,"chunkText":"    mdx_params = parser.add_argument_group(\"MDX Architecture Parameters\")\n    mdx_params.add_argument(\"--mdx_segment_size\", type=int, default=256, help=mdx_segment_size_help)\n    mdx_params.add_argument(\"--mdx_overlap\", type=float, default=0.25, help=mdx_overlap_help)\n    mdx_params.add_argument(\"--mdx_batch_size\", type=int, default=1, help=mdx_batch_size_help)\n    mdx_params.add_argument(\"--mdx_hop_length\", type=int, default=1024, help=mdx_hop_length_help)\n    mdx_params.add_argument(\"--mdx_enable_denoise\", action=\"store_true\", help=mdx_enable_denoise_help)\n","chunkId":"audio_separator/utils/cli.py:29"}],[2042,{"filePath":"audio_separator/utils/cli.py","startLine":85,"endLine":89,"chunkText":"    mdx_params.add_argument(\"--mdx_hop_length\", type=int, default=1024, help=mdx_hop_length_help)\n    mdx_params.add_argument(\"--mdx_enable_denoise\", action=\"store_true\", help=mdx_enable_denoise_help)\n\n    vr_batch_size_help = \"Number of batches to process at a time. Higher = more RAM, slightly faster processing (default: %(default)s). Example: --vr_batch_size=16\"\n    vr_window_size_help = \"Balance quality and speed. 1024 = fast but lower, 320 = slower but better quality. (default: %(default)s). Example: --vr_window_size=320\"","chunkId":"audio_separator/utils/cli.py:30"}],[2043,{"filePath":"audio_separator/utils/cli.py","startLine":87,"endLine":90,"chunkText":"\n    vr_batch_size_help = \"Number of batches to process at a time. Higher = more RAM, slightly faster processing (default: %(default)s). Example: --vr_batch_size=16\"\n    vr_window_size_help = \"Balance quality and speed. 1024 = fast but lower, 320 = slower but better quality. (default: %(default)s). Example: --vr_window_size=320\"\n    vr_aggression_help = \"Intensity of primary stem extraction, -100 - 100. Typically, 5 for vocals & instrumentals (default: %(default)s). Example: --vr_aggression=2\"","chunkId":"audio_separator/utils/cli.py:31"}],[2044,{"filePath":"audio_separator/utils/cli.py","startLine":88,"endLine":90,"chunkText":"    vr_batch_size_help = \"Number of batches to process at a time. Higher = more RAM, slightly faster processing (default: %(default)s). Example: --vr_batch_size=16\"\n    vr_window_size_help = \"Balance quality and speed. 1024 = fast but lower, 320 = slower but better quality. (default: %(default)s). Example: --vr_window_size=320\"\n    vr_aggression_help = \"Intensity of primary stem extraction, -100 - 100. Typically, 5 for vocals & instrumentals (default: %(default)s). Example: --vr_aggression=2\"","chunkId":"audio_separator/utils/cli.py:32"}],[2045,{"filePath":"audio_separator/utils/cli.py","startLine":89,"endLine":92,"chunkText":"    vr_window_size_help = \"Balance quality and speed. 1024 = fast but lower, 320 = slower but better quality. (default: %(default)s). Example: --vr_window_size=320\"\n    vr_aggression_help = \"Intensity of primary stem extraction, -100 - 100. Typically, 5 for vocals & instrumentals (default: %(default)s). Example: --vr_aggression=2\"\n    vr_enable_tta_help = \"Enable Test-Time-Augmentation; slow but improves quality (default: %(default)s). Example: --vr_enable_tta\"\n    vr_high_end_process_help = \"Mirror the missing frequency range of the output (default: %(default)s). Example: --vr_high_end_process\"","chunkId":"audio_separator/utils/cli.py:33"}],[2046,{"filePath":"audio_separator/utils/cli.py","startLine":90,"endLine":92,"chunkText":"    vr_aggression_help = \"Intensity of primary stem extraction, -100 - 100. Typically, 5 for vocals & instrumentals (default: %(default)s). Example: --vr_aggression=2\"\n    vr_enable_tta_help = \"Enable Test-Time-Augmentation; slow but improves quality (default: %(default)s). Example: --vr_enable_tta\"\n    vr_high_end_process_help = \"Mirror the missing frequency range of the output (default: %(default)s). Example: --vr_high_end_process\"","chunkId":"audio_separator/utils/cli.py:34"}],[2047,{"filePath":"audio_separator/utils/cli.py","startLine":91,"endLine":95,"chunkText":"    vr_enable_tta_help = \"Enable Test-Time-Augmentation; slow but improves quality (default: %(default)s). Example: --vr_enable_tta\"\n    vr_high_end_process_help = \"Mirror the missing frequency range of the output (default: %(default)s). Example: --vr_high_end_process\"\n    vr_enable_post_process_help = \"Identify leftover artifacts within vocal output; may improve separation for some songs (default: %(default)s). Example: --vr_enable_post_process\"\n    vr_post_process_threshold_help = \"Threshold for post_process feature: 0.1-0.3 (default: %(default)s). Example: --vr_post_process_threshold=0.1\"\n","chunkId":"audio_separator/utils/cli.py:35"}],[2048,{"filePath":"audio_separator/utils/cli.py","startLine":93,"endLine":98,"chunkText":"    vr_enable_post_process_help = \"Identify leftover artifacts within vocal output; may improve separation for some songs (default: %(default)s). Example: --vr_enable_post_process\"\n    vr_post_process_threshold_help = \"Threshold for post_process feature: 0.1-0.3 (default: %(default)s). Example: --vr_post_process_threshold=0.1\"\n\n    vr_params = parser.add_argument_group(\"VR Architecture Parameters\")\n    vr_params.add_argument(\"--vr_batch_size\", type=int, default=1, help=vr_batch_size_help)\n    vr_params.add_argument(\"--vr_window_size\", type=int, default=512, help=vr_window_size_help)","chunkId":"audio_separator/utils/cli.py:36"}],[2049,{"filePath":"audio_separator/utils/cli.py","startLine":96,"endLine":101,"chunkText":"    vr_params = parser.add_argument_group(\"VR Architecture Parameters\")\n    vr_params.add_argument(\"--vr_batch_size\", type=int, default=1, help=vr_batch_size_help)\n    vr_params.add_argument(\"--vr_window_size\", type=int, default=512, help=vr_window_size_help)\n    vr_params.add_argument(\"--vr_aggression\", type=int, default=5, help=vr_aggression_help)\n    vr_params.add_argument(\"--vr_enable_tta\", action=\"store_true\", help=vr_enable_tta_help)\n    vr_params.add_argument(\"--vr_high_end_process\", action=\"store_true\", help=vr_high_end_process_help)","chunkId":"audio_separator/utils/cli.py:37"}],[2050,{"filePath":"audio_separator/utils/cli.py","startLine":99,"endLine":104,"chunkText":"    vr_params.add_argument(\"--vr_aggression\", type=int, default=5, help=vr_aggression_help)\n    vr_params.add_argument(\"--vr_enable_tta\", action=\"store_true\", help=vr_enable_tta_help)\n    vr_params.add_argument(\"--vr_high_end_process\", action=\"store_true\", help=vr_high_end_process_help)\n    vr_params.add_argument(\"--vr_enable_post_process\", action=\"store_true\", help=vr_enable_post_process_help)\n    vr_params.add_argument(\"--vr_post_process_threshold\", type=float, default=0.2, help=vr_post_process_threshold_help)\n","chunkId":"audio_separator/utils/cli.py:38"}],[2051,{"filePath":"audio_separator/utils/cli.py","startLine":102,"endLine":106,"chunkText":"    vr_params.add_argument(\"--vr_enable_post_process\", action=\"store_true\", help=vr_enable_post_process_help)\n    vr_params.add_argument(\"--vr_post_process_threshold\", type=float, default=0.2, help=vr_post_process_threshold_help)\n\n    demucs_segment_size_help = \"Size of segments into which the audio is split, 1-100. Higher = slower but better quality (default: %(default)s). Example: --demucs_segment_size=256\"\n    demucs_shifts_help = \"Number of predictions with random shifts, higher = slower but better quality (default: %(default)s). Example: --demucs_shifts=4\"","chunkId":"audio_separator/utils/cli.py:39"}],[2052,{"filePath":"audio_separator/utils/cli.py","startLine":104,"endLine":107,"chunkText":"\n    demucs_segment_size_help = \"Size of segments into which the audio is split, 1-100. Higher = slower but better quality (default: %(default)s). Example: --demucs_segment_size=256\"\n    demucs_shifts_help = \"Number of predictions with random shifts, higher = slower but better quality (default: %(default)s). Example: --demucs_shifts=4\"\n    demucs_overlap_help = \"Overlap between prediction windows, 0.001-0.999. Higher = slower but better quality (default: %(default)s). Example: --demucs_overlap=0.25\"","chunkId":"audio_separator/utils/cli.py:40"}],[2053,{"filePath":"audio_separator/utils/cli.py","startLine":105,"endLine":107,"chunkText":"    demucs_segment_size_help = \"Size of segments into which the audio is split, 1-100. Higher = slower but better quality (default: %(default)s). Example: --demucs_segment_size=256\"\n    demucs_shifts_help = \"Number of predictions with random shifts, higher = slower but better quality (default: %(default)s). Example: --demucs_shifts=4\"\n    demucs_overlap_help = \"Overlap between prediction windows, 0.001-0.999. Higher = slower but better quality (default: %(default)s). Example: --demucs_overlap=0.25\"","chunkId":"audio_separator/utils/cli.py:41"}],[2054,{"filePath":"audio_separator/utils/cli.py","startLine":106,"endLine":110,"chunkText":"    demucs_shifts_help = \"Number of predictions with random shifts, higher = slower but better quality (default: %(default)s). Example: --demucs_shifts=4\"\n    demucs_overlap_help = \"Overlap between prediction windows, 0.001-0.999. Higher = slower but better quality (default: %(default)s). Example: --demucs_overlap=0.25\"\n    demucs_segments_enabled_help = \"Enable segment-wise processing (default: %(default)s). Example: --demucs_segments_enabled=False\"\n\n    demucs_params = parser.add_argument_group(\"Demucs Architecture Parameters\")","chunkId":"audio_separator/utils/cli.py:42"}],[2055,{"filePath":"audio_separator/utils/cli.py","startLine":108,"endLine":113,"chunkText":"    demucs_segments_enabled_help = \"Enable segment-wise processing (default: %(default)s). Example: --demucs_segments_enabled=False\"\n\n    demucs_params = parser.add_argument_group(\"Demucs Architecture Parameters\")\n    demucs_params.add_argument(\"--demucs_segment_size\", type=str, default=\"Default\", help=demucs_segment_size_help)\n    demucs_params.add_argument(\"--demucs_shifts\", type=int, default=2, help=demucs_shifts_help)\n    demucs_params.add_argument(\"--demucs_overlap\", type=float, default=0.25, help=demucs_overlap_help)","chunkId":"audio_separator/utils/cli.py:43"}],[2056,{"filePath":"audio_separator/utils/cli.py","startLine":111,"endLine":116,"chunkText":"    demucs_params.add_argument(\"--demucs_segment_size\", type=str, default=\"Default\", help=demucs_segment_size_help)\n    demucs_params.add_argument(\"--demucs_shifts\", type=int, default=2, help=demucs_shifts_help)\n    demucs_params.add_argument(\"--demucs_overlap\", type=float, default=0.25, help=demucs_overlap_help)\n    demucs_params.add_argument(\"--demucs_segments_enabled\", type=bool, default=True, help=demucs_segments_enabled_help)\n\n    mdxc_segment_size_help = \"Larger consumes more resources, but may give better results (default: %(default)s). Example: --mdxc_segment_size=256\"","chunkId":"audio_separator/utils/cli.py:44"}],[2057,{"filePath":"audio_separator/utils/cli.py","startLine":114,"endLine":118,"chunkText":"    demucs_params.add_argument(\"--demucs_segments_enabled\", type=bool, default=True, help=demucs_segments_enabled_help)\n\n    mdxc_segment_size_help = \"Larger consumes more resources, but may give better results (default: %(default)s). Example: --mdxc_segment_size=256\"\n    mdxc_override_model_segment_size_help = \"Override model default segment size instead of using the model default value. Example: --mdxc_override_model_segment_size\"\n    mdxc_overlap_help = \"Amount of overlap between prediction windows, 2-50. Higher is better but slower (default: %(default)s). Example: --mdxc_overlap=8\"","chunkId":"audio_separator/utils/cli.py:45"}],[2058,{"filePath":"audio_separator/utils/cli.py","startLine":116,"endLine":119,"chunkText":"    mdxc_segment_size_help = \"Larger consumes more resources, but may give better results (default: %(default)s). Example: --mdxc_segment_size=256\"\n    mdxc_override_model_segment_size_help = \"Override model default segment size instead of using the model default value. Example: --mdxc_override_model_segment_size\"\n    mdxc_overlap_help = \"Amount of overlap between prediction windows, 2-50. Higher is better but slower (default: %(default)s). Example: --mdxc_overlap=8\"\n    mdxc_batch_size_help = \"Larger consumes more RAM but may process slightly faster (default: %(default)s). Example: --mdxc_batch_size=4\"","chunkId":"audio_separator/utils/cli.py:46"}],[2059,{"filePath":"audio_separator/utils/cli.py","startLine":117,"endLine":119,"chunkText":"    mdxc_override_model_segment_size_help = \"Override model default segment size instead of using the model default value. Example: --mdxc_override_model_segment_size\"\n    mdxc_overlap_help = \"Amount of overlap between prediction windows, 2-50. Higher is better but slower (default: %(default)s). Example: --mdxc_overlap=8\"\n    mdxc_batch_size_help = \"Larger consumes more RAM but may process slightly faster (default: %(default)s). Example: --mdxc_batch_size=4\"","chunkId":"audio_separator/utils/cli.py:47"}],[2060,{"filePath":"audio_separator/utils/cli.py","startLine":118,"endLine":122,"chunkText":"    mdxc_overlap_help = \"Amount of overlap between prediction windows, 2-50. Higher is better but slower (default: %(default)s). Example: --mdxc_overlap=8\"\n    mdxc_batch_size_help = \"Larger consumes more RAM but may process slightly faster (default: %(default)s). Example: --mdxc_batch_size=4\"\n    mdxc_pitch_shift_help = \"Shift audio pitch by a number of semitones while processing. May improve output for deep/high vocals. (default: %(default)s). Example: --mdxc_pitch_shift=2\"\n\n    mdxc_params = parser.add_argument_group(\"MDXC Architecture Parameters\")","chunkId":"audio_separator/utils/cli.py:48"}],[2061,{"filePath":"audio_separator/utils/cli.py","startLine":120,"endLine":125,"chunkText":"    mdxc_pitch_shift_help = \"Shift audio pitch by a number of semitones while processing. May improve output for deep/high vocals. (default: %(default)s). Example: --mdxc_pitch_shift=2\"\n\n    mdxc_params = parser.add_argument_group(\"MDXC Architecture Parameters\")\n    mdxc_params.add_argument(\"--mdxc_segment_size\", type=int, default=256, help=mdxc_segment_size_help)\n    mdxc_params.add_argument(\"--mdxc_override_model_segment_size\", action=\"store_true\", help=mdxc_override_model_segment_size_help)\n    mdxc_params.add_argument(\"--mdxc_overlap\", type=int, default=8, help=mdxc_overlap_help)","chunkId":"audio_separator/utils/cli.py:49"}],[2062,{"filePath":"audio_separator/utils/cli.py","startLine":123,"endLine":132,"chunkText":"    mdxc_params.add_argument(\"--mdxc_segment_size\", type=int, default=256, help=mdxc_segment_size_help)\n    mdxc_params.add_argument(\"--mdxc_override_model_segment_size\", action=\"store_true\", help=mdxc_override_model_segment_size_help)\n    mdxc_params.add_argument(\"--mdxc_overlap\", type=int, default=8, help=mdxc_overlap_help)\n    mdxc_params.add_argument(\"--mdxc_batch_size\", type=int, default=1, help=mdxc_batch_size_help)\n    mdxc_params.add_argument(\"--mdxc_pitch_shift\", type=int, default=0, help=mdxc_pitch_shift_help)\n\n    args = parser.parse_args()\n\n    if args.debug:\n        log_level = logging.DEBUG","chunkId":"audio_separator/utils/cli.py:50"}],[2063,{"filePath":"audio_separator/utils/cli.py","startLine":130,"endLine":148,"chunkText":"    if args.debug:\n        log_level = logging.DEBUG\n    else:\n        log_level = getattr(logging, args.log_level.upper())\n    logger.setLevel(log_level)\n\n    from audio_separator.separator import Separator\n\n    if args.env_info:\n        separator = Separator()\n        sys.exit(0)\n\n    if args.list_models:\n        separator = Separator(info_only=True)\n\n        if args.list_format == \"json\":\n            model_list = separator.list_supported_model_files()\n            print(json.dumps(model_list, indent=2))\n        else:","chunkId":"audio_separator/utils/cli.py:51"}],[2064,{"filePath":"audio_separator/utils/cli.py","startLine":149,"endLine":158,"chunkText":"        else:\n            models = separator.get_simplified_model_list(filter_sort_by=args.list_filter)\n\n            # Apply limit if specified\n            if args.list_limit and args.list_limit > 0:\n                models = dict(list(models.items())[: args.list_limit])\n\n            # Calculate maximum widths for each column\n            filename_width = max(len(\"Model Filename\"), max(len(filename) for filename in models.keys()))\n            arch_width = max(len(\"Arch\"), max(len(info[\"Type\"]) for info in models.values()))","chunkId":"audio_separator/utils/cli.py:52"}],[2065,{"filePath":"audio_separator/utils/cli.py","startLine":156,"endLine":162,"chunkText":"            # Calculate maximum widths for each column\n            filename_width = max(len(\"Model Filename\"), max(len(filename) for filename in models.keys()))\n            arch_width = max(len(\"Arch\"), max(len(info[\"Type\"]) for info in models.values()))\n            stems_width = max(len(\"Output Stems (SDR)\"), max(len(\", \".join(info[\"Stems\"])) for info in models.values()))\n            name_width = max(len(\"Friendly Name\"), max(len(info[\"Name\"]) for info in models.values()))\n\n            # Calculate total width for separator line","chunkId":"audio_separator/utils/cli.py:53"}],[2066,{"filePath":"audio_separator/utils/cli.py","startLine":160,"endLine":169,"chunkText":"            name_width = max(len(\"Friendly Name\"), max(len(info[\"Name\"]) for info in models.values()))\n\n            # Calculate total width for separator line\n            total_width = filename_width + arch_width + stems_width + name_width + 15  # 15 accounts for spacing between columns\n\n            # Format the output with dynamic widths and extra spacing\n            print(\"-\" * total_width)\n            print(f\"{'Model Filename':<{filename_width}}  {'Arch':<{arch_width}}  {'Output Stems (SDR)':<{stems_width}}  {'Friendly Name'}\")\n            print(\"-\" * total_width)\n","chunkId":"audio_separator/utils/cli.py:54"}],[2067,{"filePath":"audio_separator/utils/cli.py","startLine":167,"endLine":177,"chunkText":"            print(f\"{'Model Filename':<{filename_width}}  {'Arch':<{arch_width}}  {'Output Stems (SDR)':<{stems_width}}  {'Friendly Name'}\")\n            print(\"-\" * total_width)\n\n            for filename, info in models.items():\n                stems = \", \".join(info[\"Stems\"])\n                print(f\"{filename:<{filename_width}}  {info['Type']:<{arch_width}}  {stems:<{stems_width}}  {info['Name']}\")\n\n        sys.exit(0)\n\n    if args.download_model_only:\n        logger.info(f\"Separator version {package_version} downloading model {args.model_filename} to directory {args.model_file_dir}\")","chunkId":"audio_separator/utils/cli.py:55"}],[2068,{"filePath":"audio_separator/utils/cli.py","startLine":175,"endLine":187,"chunkText":"\n    if args.download_model_only:\n        logger.info(f\"Separator version {package_version} downloading model {args.model_filename} to directory {args.model_file_dir}\")\n        separator = Separator(log_formatter=log_formatter, log_level=log_level, model_file_dir=args.model_file_dir)\n        separator.download_model_and_data(args.model_filename)\n        logger.info(f\"Model {args.model_filename} downloaded successfully.\")\n        sys.exit(0)\n\n    audio_files = list(getattr(args, \"audio_files\", []))\n    if not audio_files:\n        parser.print_help()\n        sys.exit(1)\n","chunkId":"audio_separator/utils/cli.py:56"}],[2069,{"filePath":"audio_separator/utils/cli.py","startLine":185,"endLine":200,"chunkText":"        parser.print_help()\n        sys.exit(1)\n\n    logger.info(f\"Separator version {package_version} beginning with input path(s): {', '.join(audio_files)}\")\n\n    separator = Separator(\n        log_formatter=log_formatter,\n        log_level=log_level,\n        model_file_dir=args.model_file_dir,\n        output_dir=args.output_dir,\n        output_format=args.output_format,\n        output_bitrate=args.output_bitrate,\n        normalization_threshold=args.normalization,\n        amplification_threshold=args.amplification,\n        output_single_stem=args.single_stem,\n        invert_using_spec=args.invert_spect","chunkId":"audio_separator/utils/cli.py:57"}],[2070,{"filePath":"audio_separator/utils/cli.py","startLine":198,"endLine":212,"chunkText":"        amplification_threshold=args.amplification,\n        output_single_stem=args.single_stem,\n        invert_using_spec=args.invert_spect,\n        sample_rate=args.sample_rate,\n        use_soundfile=args.use_soundfile,\n        use_autocast=args.use_autocast,\n        chunk_duration=args.chunk_duration,\n        mdx_params={\n            \"hop_length\": args.mdx_hop_length,\n            \"segment_size\": args.mdx_segment_size,\n            \"overlap\": args.mdx_overlap,\n            \"batch_size\": args.mdx_batch_size,\n            \"enable_denoise\": args.mdx_enable_denoise,\n        },\n        vr_params={","chunkId":"audio_separator/utils/cli.py:58"}],[2071,{"filePath":"audio_separator/utils/cli.py","startLine":210,"endLine":223,"chunkText":"            \"enable_denoise\": args.mdx_enable_denoise,\n        },\n        vr_params={\n            \"batch_size\": args.vr_batch_size,\n            \"window_size\": args.vr_window_size,\n            \"aggression\": args.vr_aggression,\n            \"enable_tta\": args.vr_enable_tta,\n            \"enable_post_process\": args.vr_enable_post_process,\n            \"post_process_threshold\": args.vr_post_process_threshold,\n            \"high_end_process\": args.vr_high_end_process,\n        },\n        demucs_params={\n            \"segment_size\": args.demucs_segment_size,\n            \"shifts\": args.demucs_shifts,","chunkId":"audio_separator/utils/cli.py:59"}],[2072,{"filePath":"audio_separator/utils/cli.py","startLine":221,"endLine":237,"chunkText":"        demucs_params={\n            \"segment_size\": args.demucs_segment_size,\n            \"shifts\": args.demucs_shifts,\n            \"overlap\": args.demucs_overlap,\n            \"segments_enabled\": args.demucs_segments_enabled,\n        },\n        mdxc_params={\n            \"segment_size\": args.mdxc_segment_size,\n            \"batch_size\": args.mdxc_batch_size,\n            \"overlap\": args.mdxc_overlap,\n            \"override_model_segment_size\": args.mdxc_override_model_segment_size,\n            \"pitch_shift\": args.mdxc_pitch_shift,\n        },\n    )\n\n    separator.load_model(model_filename=args.model_filename)\n","chunkId":"audio_separator/utils/cli.py:60"}],[2073,{"filePath":"audio_separator/utils/cli.py","startLine":235,"endLine":240,"chunkText":"\n    separator.load_model(model_filename=args.model_filename)\n\n    output_files = separator.separate(audio_files, custom_output_names=args.custom_output_names)\n    logger.info(f\"Separation complete! Output file(s): {' '.join(output_files)}\")\n","chunkId":"audio_separator/utils/cli.py:61"}],[2074,{"filePath":"docs/BIT_DEPTH_IMPLEMENTATION_SUMMARY.md","startLine":0,"endLine":10,"chunkText":"# Summary: Bit Depth Preservation Implementation\n\n## Issue\n[GitHub Issue #243](https://github.com/nomadkaraoke/python-audio-separator/issues/243) - Users reported that audio-separator was reducing audio quality by always outputting 16-bit audio, even when the input was 24-bit or 32-bit.\n\n## Solution\nImplemented automatic bit depth preservation that matches the output audio bit depth to the input audio file's bit depth. This ensures no quality loss when processing high-resolution audio files.\n\n## Key Changes\n\n### 1. **Dependencies** (`pyproject.toml`)","chunkId":"docs/BIT_DEPTH_IMPLEMENTATION_SUMMARY.md:0"}],[2075,{"filePath":"docs/BIT_DEPTH_IMPLEMENTATION_SUMMARY.md","startLine":10,"endLine":22,"chunkText":"### 1. **Dependencies** (`pyproject.toml`)\n- Added `soundfile >= 0.12` for reading audio file metadata\n\n### 2. **Core Implementation** (`audio_separator/separator/common_separator.py`)\n- Added `input_bit_depth` and `input_subtype` attributes to track input audio properties\n- Modified `prepare_mix()` to detect bit depth using soundfile\n- Updated `write_audio_pydub()` to use appropriate scaling and ffmpeg codecs for each bit depth\n- Updated `write_audio_soundfile()` to preserve subtype when writing\n\n### 3. **Comprehensive Tests**\nCreated 3 test suites with 17 tests total:\n\n**Unit Tests:**","chunkId":"docs/BIT_DEPTH_IMPLEMENTATION_SUMMARY.md:1"}],[2076,{"filePath":"docs/BIT_DEPTH_IMPLEMENTATION_SUMMARY.md","startLine":20,"endLine":39,"chunkText":"Created 3 test suites with 17 tests total:\n\n**Unit Tests:**\n- `tests/unit/test_bit_depth_detection.py` - 5 tests for bit depth detection\n- `tests/unit/test_bit_depth_writing.py` - 5 tests for write functions\n\n**Integration Tests:**\n- `tests/integration/test_bit_depth_e2e.py` - 2 end-to-end tests\n- `tests/integration/test_bit_depth_preservation.py` - 6 comprehensive integration tests\n\n**Manual Test:**\n- `tests/manual_test_bit_depth.py` - Demonstrates functionality\n\n## Test Results\n\n✅ **All tests pass:**\n```\n16-bit (pydub)      ✅ PASS\n24-bit (pydub)      ✅ PASS\n32-bit (pydub)      ✅ PASS","chunkId":"docs/BIT_DEPTH_IMPLEMENTATION_SUMMARY.md:2"}],[2077,{"filePath":"docs/BIT_DEPTH_IMPLEMENTATION_SUMMARY.md","startLine":37,"endLine":56,"chunkText":"16-bit (pydub)      ✅ PASS\n24-bit (pydub)      ✅ PASS\n32-bit (pydub)      ✅ PASS\n16-bit (soundfile)  ✅ PASS\n24-bit (soundfile)  ✅ PASS\n32-bit (soundfile)  ✅ PASS\n```\n\n## Behavior\n\n| Input Bit Depth | Previous Output | New Output |\n|----------------|-----------------|------------|\n| 16-bit         | 16-bit         | 16-bit ✅  |\n| 24-bit         | **16-bit** ❌  | 24-bit ✅  |\n| 32-bit         | **16-bit** ❌  | 32-bit ✅  |\n\n## Impact\n\n✅ **Quality Preservation:** No more quality loss when processing high-resolution audio\n✅ **Backward Compatible:** Existing 16-bit workflows unchanged","chunkId":"docs/BIT_DEPTH_IMPLEMENTATION_SUMMARY.md:3"}],[2078,{"filePath":"docs/BIT_DEPTH_IMPLEMENTATION_SUMMARY.md","startLine":54,"endLine":67,"chunkText":"\n✅ **Quality Preservation:** No more quality loss when processing high-resolution audio\n✅ **Backward Compatible:** Existing 16-bit workflows unchanged\n✅ **Automatic:** No configuration required - works out of the box\n✅ **Transparent:** Logs show detected and output bit depths\n✅ **Robust:** Graceful fallback to 16-bit for unknown formats\n\n## Technical Details\n\nThe implementation:\n- Reads audio metadata before loading with librosa\n- Maps PCM subtypes to bit depths (PCM_16→16, PCM_24→24, PCM_32→32)\n- Scales audio data appropriately for each bit depth\n- Passes correct codec parameters to ffmpeg/pydub","chunkId":"docs/BIT_DEPTH_IMPLEMENTATION_SUMMARY.md:4"}],[2079,{"filePath":"docs/BIT_DEPTH_IMPLEMENTATION_SUMMARY.md","startLine":65,"endLine":79,"chunkText":"- Maps PCM subtypes to bit depths (PCM_16→16, PCM_24→24, PCM_32→32)\n- Scales audio data appropriately for each bit depth\n- Passes correct codec parameters to ffmpeg/pydub\n- Works with both pydub (default) and soundfile backends\n- Handles multiple files with different bit depths correctly\n\n## Files Modified\n\n1. `pyproject.toml` - Added soundfile dependency\n2. `audio_separator/separator/common_separator.py` - Core implementation\n\n## Files Added\n\n1. `tests/unit/test_bit_depth_detection.py` - Unit tests for detection\n2. `tests/unit/test_bit_depth_writing.py` - Unit tests for writing","chunkId":"docs/BIT_DEPTH_IMPLEMENTATION_SUMMARY.md:5"}],[2080,{"filePath":"docs/BIT_DEPTH_IMPLEMENTATION_SUMMARY.md","startLine":77,"endLine":92,"chunkText":"\n1. `tests/unit/test_bit_depth_detection.py` - Unit tests for detection\n2. `tests/unit/test_bit_depth_writing.py` - Unit tests for writing\n3. `tests/integration/test_bit_depth_e2e.py` - End-to-end tests\n4. `tests/integration/test_bit_depth_preservation.py` - Integration tests\n5. `tests/manual_test_bit_depth.py` - Manual test script\n6. `BIT_DEPTH_PRESERVATION.md` - Detailed documentation\n\n## No Breaking Changes\n\nThis implementation is fully backward compatible:\n- No API changes required\n- No new parameters needed\n- Existing functionality unchanged\n- Only affects output bit depth to match input\n","chunkId":"docs/BIT_DEPTH_IMPLEMENTATION_SUMMARY.md:6"}],[2081,{"filePath":"docs/BIT_DEPTH_IMPLEMENTATION_SUMMARY.md","startLine":90,"endLine":97,"chunkText":"- Existing functionality unchanged\n- Only affects output bit depth to match input\n\n## Resolves\n\n✅ Closes #243 - Output bit depth now matches input automatically\n\n","chunkId":"docs/BIT_DEPTH_IMPLEMENTATION_SUMMARY.md:7"}],[2082,{"filePath":"docs/BIT_DEPTH_PRESERVATION.md","startLine":0,"endLine":13,"chunkText":"# Bit Depth Preservation Implementation\n\n## Summary\n\nThis implementation ensures that the output audio files from audio-separator preserve the bit depth of the input audio files, preventing quality loss when processing 24-bit or 32-bit audio files.\n\n## Changes Made\n\n### 1. Added `soundfile` to Dependencies (`pyproject.toml`)\n- Added `soundfile >= 0.12` as a dependency to enable reading audio file metadata\n\n### 2. Modified `CommonSeparator` Class (`audio_separator/separator/common_separator.py`)\n\n#### Added Bit Depth Tracking","chunkId":"docs/BIT_DEPTH_PRESERVATION.md:0"}],[2083,{"filePath":"docs/BIT_DEPTH_PRESERVATION.md","startLine":13,"endLine":25,"chunkText":"#### Added Bit Depth Tracking\n- Added `self.input_bit_depth` attribute to store the detected bit depth (16, 24, or 32)\n- Added `self.input_subtype` attribute to store the audio file subtype\n\n#### Modified `prepare_mix()` Method\n- Added code to read audio file metadata using `soundfile.info()` before loading with librosa\n- Detects and stores the input audio file's bit depth based on the subtype:\n  - PCM_16, PCM_S8 → 16-bit\n  - PCM_24 → 24-bit\n  - PCM_32, FLOAT, DOUBLE → 32-bit\n- Defaults to 16-bit for numpy arrays or unknown formats\n- Logs the detected bit depth for debugging\n","chunkId":"docs/BIT_DEPTH_PRESERVATION.md:1"}],[2084,{"filePath":"docs/BIT_DEPTH_PRESERVATION.md","startLine":24,"endLine":37,"chunkText":"- Logs the detected bit depth for debugging\n\n#### Modified `write_audio_pydub()` Method\n- Determines output bit depth from `self.input_bit_depth`\n- Scales audio data appropriately for each bit depth:\n  - 16-bit: scale by 32767, use int16, sample_width=2\n  - 24-bit: scale by 8388607, use int32, sample_width=3\n  - 32-bit: scale by 2147483647, use int32, sample_width=4\n- Passes appropriate ffmpeg codec parameters for WAV files:\n  - 16-bit: `pcm_s16le`\n  - 24-bit: `pcm_s24le`\n  - 32-bit: `pcm_s32le`\n\n#### Modified `write_audio_soundfile()` Method","chunkId":"docs/BIT_DEPTH_PRESERVATION.md:2"}],[2085,{"filePath":"docs/BIT_DEPTH_PRESERVATION.md","startLine":36,"endLine":50,"chunkText":"\n#### Modified `write_audio_soundfile()` Method\n- Determines output subtype from `self.input_subtype` or `self.input_bit_depth`\n- Passes the subtype to `sf.write()` to preserve bit depth\n- Removed manual interleaving (soundfile handles multi-channel properly)\n\n## Tests Created\n\n### Unit Tests\n\n#### `tests/unit/test_bit_depth_detection.py`\nTests bit depth detection logic:\n- `test_16bit_detection`: Verifies 16-bit files are correctly detected\n- `test_24bit_detection`: Verifies 24-bit files are correctly detected\n- `test_32bit_detection`: Verifies 32-bit files are correctly detected","chunkId":"docs/BIT_DEPTH_PRESERVATION.md:3"}],[2086,{"filePath":"docs/BIT_DEPTH_PRESERVATION.md","startLine":49,"endLine":57,"chunkText":"- `test_24bit_detection`: Verifies 24-bit files are correctly detected\n- `test_32bit_detection`: Verifies 32-bit files are correctly detected\n- `test_numpy_array_input_defaults_to_16bit`: Verifies numpy arrays default to 16-bit\n- `test_bit_depth_preserved_across_multiple_files`: Verifies bit depth updates between files\n\n#### `tests/unit/test_bit_depth_writing.py`\nTests bit depth preservation in write functions:\n- `test_write_16bit_with_pydub`: Tests 16-bit output with pydub backend\n- `test_write_24bit_with_pydub`: Tests 24-bit output with pydub backend","chunkId":"docs/BIT_DEPTH_PRESERVATION.md:4"}],[2087,{"filePath":"docs/BIT_DEPTH_PRESERVATION.md","startLine":56,"endLine":66,"chunkText":"- `test_write_16bit_with_pydub`: Tests 16-bit output with pydub backend\n- `test_write_24bit_with_pydub`: Tests 24-bit output with pydub backend\n- `test_write_32bit_with_pydub`: Tests 32-bit output with pydub backend\n- `test_write_24bit_with_soundfile`: Tests 24-bit output with soundfile backend\n- `test_write_16bit_with_soundfile`: Tests 16-bit output with soundfile backend\n\n### Integration Tests\n\n#### `tests/integration/test_bit_depth_e2e.py`\nEnd-to-end tests with full separation workflow:\n- `test_e2e_24bit_preservation`: Full test with 24-bit input","chunkId":"docs/BIT_DEPTH_PRESERVATION.md:5"}],[2088,{"filePath":"docs/BIT_DEPTH_PRESERVATION.md","startLine":65,"endLine":82,"chunkText":"End-to-end tests with full separation workflow:\n- `test_e2e_24bit_preservation`: Full test with 24-bit input\n- `test_e2e_16bit_preservation`: Full test with 16-bit input\n\n#### `tests/integration/test_bit_depth_preservation.py`\nComprehensive integration tests (skipped if package not installed):\n- Tests for 16-bit, 24-bit, and 32-bit preservation\n- Tests with FLAC output format\n- Tests with soundfile backend\n- Tests with multiple files of different bit depths\n\n## Test Results\n\nAll unit tests pass successfully:\n- 5/5 bit depth detection tests ✅\n- 5/5 bit depth writing tests ✅\n- 10/10 total unit tests ✅\n","chunkId":"docs/BIT_DEPTH_PRESERVATION.md:6"}],[2089,{"filePath":"docs/BIT_DEPTH_PRESERVATION.md","startLine":83,"endLine":100,"chunkText":"## Behavior\n\n### Before\n- All output files were written as 16-bit PCM, regardless of input bit depth\n- This resulted in quality loss when processing 24-bit or 32-bit audio files\n\n### After\n- Output bit depth matches input bit depth automatically\n- 16-bit input → 16-bit output\n- 24-bit input → 24-bit output\n- 32-bit input → 32-bit output\n- No quality loss when processing high-quality audio files\n- Backward compatible - 16-bit inputs still produce 16-bit outputs\n\n## Supported Formats\n\nBit depth preservation works with:\n- **WAV files**: Full support for 16, 24, and 32-bit","chunkId":"docs/BIT_DEPTH_PRESERVATION.md:7"}],[2090,{"filePath":"docs/BIT_DEPTH_PRESERVATION.md","startLine":99,"endLine":112,"chunkText":"Bit depth preservation works with:\n- **WAV files**: Full support for 16, 24, and 32-bit\n- **FLAC files**: Full support for 16, 24, and 32-bit\n- **Other formats**: Depends on ffmpeg support through pydub\n\nBoth output backends are supported:\n- **pydub/ffmpeg** (default): Uses codec parameters to enforce bit depth\n- **soundfile**: Uses subtype parameter to enforce bit depth\n\n## Notes\n\n- The implementation is backward compatible - no changes to the API are required\n- Bit depth is detected automatically from input files\n- Logs provide visibility into detected and output bit depths","chunkId":"docs/BIT_DEPTH_PRESERVATION.md:8"}],[2091,{"filePath":"docs/BIT_DEPTH_PRESERVATION.md","startLine":111,"endLine":116,"chunkText":"- Bit depth is detected automatically from input files\n- Logs provide visibility into detected and output bit depths\n- Falls back to 16-bit for unknown formats or errors\n- Works correctly when processing multiple files with different bit depths\n\n","chunkId":"docs/BIT_DEPTH_PRESERVATION.md:9"}],[2092,{"filePath":"specs/001-update-roformer-implementation/001-update-roformer-implementation.md","startLine":0,"endLine":4,"chunkText":"# Feature Specification: Update Roformer Implementation\n\n**Feature Branch**: `001-update-roformer-implementation`  \n**Created**: September 25, 2025  \n**Status**: Draft  ","chunkId":"specs/001-update-roformer-implementation/001-update-roformer-implementation.md:0"}],[2093,{"filePath":"specs/001-update-roformer-implementation/001-update-roformer-implementation.md","startLine":2,"endLine":4,"chunkText":"**Feature Branch**: `001-update-roformer-implementation`  \n**Created**: September 25, 2025  \n**Status**: Draft  ","chunkId":"specs/001-update-roformer-implementation/001-update-roformer-implementation.md:1"}],[2094,{"filePath":"specs/001-update-roformer-implementation/001-update-roformer-implementation.md","startLine":3,"endLine":4,"chunkText":"**Created**: September 25, 2025  \n**Status**: Draft  ","chunkId":"specs/001-update-roformer-implementation/001-update-roformer-implementation.md:2"}],[2095,{"filePath":"specs/001-update-roformer-implementation/001-update-roformer-implementation.md","startLine":4,"endLine":4,"chunkText":"**Status**: Draft  ","chunkId":"specs/001-update-roformer-implementation/001-update-roformer-implementation.md:3"}],[2096,{"filePath":"specs/001-update-roformer-implementation/001-update-roformer-implementation.md","startLine":5,"endLine":5,"chunkText":"**Input**: User description: \"update Roformer implementation: this audio-separator project currently has an older implementation of the Roformer architecture inference code, copied from another project over a year ago into folder path audio_separator/separator/uvr_lib_v5/ this works well for many models, but some of the latest Roformer models don't work with the older inference code; the model fails to load with errors such as \"AttributeError: \"'norm'\" - File \"/Users/andrew/miniforge3/lib/python3.13/site-packages/audio_separator/separator/uvr_lib_v5/tfc_tdf_v3.py\", line 155, in __init__  norm = get_norm(n","chunkId":"specs/001-update-roformer-implementation/001-update-roformer-implementation.md:4"}],[2097,{"filePath":"specs/001-update-roformer-implementation/001-update-roformer-implementation.md","startLine":6,"endLine":16,"chunkText":"\n## Execution Flow (main)\n```\n1. Parse user description from Input\n   → Identified: Need to update Roformer implementation for latest model compatibility\n2. Extract key concepts from description\n   → Actors: developers, users with existing models, users with new models\n   → Actions: update implementation, maintain backward compatibility, validate functionality\n   → Data: Roformer models (old and new), inference code, model configurations\n   → Constraints: cannot break existing functionality, must support both old and new models\n3. For each unclear aspect:","chunkId":"specs/001-update-roformer-implementation/001-update-roformer-implementation.md:5"}],[2098,{"filePath":"specs/001-update-roformer-implementation/001-update-roformer-implementation.md","startLine":14,"endLine":24,"chunkText":"   → Data: Roformer models (old and new), inference code, model configurations\n   → Constraints: cannot break existing functionality, must support both old and new models\n3. For each unclear aspect:\n   → [RESOLVED] Clear requirements provided with specific error messages and paths\n4. Fill User Scenarios & Testing section\n   → Clear user flow: load models, separate audio, validate outputs\n5. Generate Functional Requirements\n   → All requirements are testable and specific\n6. Identify Key Entities\n   → BSRoformer, MelBandRoformer, model configurations, inference parameters\n7. Run Review Checklist","chunkId":"specs/001-update-roformer-implementation/001-update-roformer-implementation.md:6"}],[2099,{"filePath":"specs/001-update-roformer-implementation/001-update-roformer-implementation.md","startLine":22,"endLine":40,"chunkText":"6. Identify Key Entities\n   → BSRoformer, MelBandRoformer, model configurations, inference parameters\n7. Run Review Checklist\n   → No clarifications needed, no implementation details included\n8. Return: SUCCESS (spec ready for planning)\n```\n\n---\n\n## ⚡ Quick Guidelines\n- ✅ Focus on WHAT users need and WHY\n- ❌ Avoid HOW to implement (no tech stack, APIs, code structure)\n- 👥 Written for business stakeholders, not developers\n\n---\n\n## User Scenarios & Testing *(mandatory)*\n\n### Primary User Story","chunkId":"specs/001-update-roformer-implementation/001-update-roformer-implementation.md:7"}],[2100,{"filePath":"specs/001-update-roformer-implementation/001-update-roformer-implementation.md","startLine":38,"endLine":44,"chunkText":"## User Scenarios & Testing *(mandatory)*\n\n### Primary User Story\nUsers need to be able to load and use the latest Roformer models for audio separation without losing the ability to use their existing older models. The system should seamlessly handle both old and new model formats, providing the same quality audio separation results.\n\n### Acceptance Scenarios\n1. **Given** a user has an existing older Roformer model, **When** they attempt to separate audio, **Then** the separation should work exactly as it did before the update","chunkId":"specs/001-update-roformer-implementation/001-update-roformer-implementation.md:8"}],[2101,{"filePath":"specs/001-update-roformer-implementation/001-update-roformer-implementation.md","startLine":42,"endLine":45,"chunkText":"\n### Acceptance Scenarios\n1. **Given** a user has an existing older Roformer model, **When** they attempt to separate audio, **Then** the separation should work exactly as it did before the update\n2. **Given** a user has a newer Roformer model with updated parameters (mlp_expansion_factor, sage_attention, zero_dc), **When** they attempt to load and use the model, **Then** the model should load successfully and produce high-quality audio separation","chunkId":"specs/001-update-roformer-implementation/001-update-roformer-implementation.md:9"}],[2102,{"filePath":"specs/001-update-roformer-implementation/001-update-roformer-implementation.md","startLine":43,"endLine":45,"chunkText":"### Acceptance Scenarios\n1. **Given** a user has an existing older Roformer model, **When** they attempt to separate audio, **Then** the separation should work exactly as it did before the update\n2. **Given** a user has a newer Roformer model with updated parameters (mlp_expansion_factor, sage_attention, zero_dc), **When** they attempt to load and use the model, **Then** the model should load successfully and produce high-quality audio separation","chunkId":"specs/001-update-roformer-implementation/001-update-roformer-implementation.md:10"}],[2103,{"filePath":"specs/001-update-roformer-implementation/001-update-roformer-implementation.md","startLine":44,"endLine":45,"chunkText":"1. **Given** a user has an existing older Roformer model, **When** they attempt to separate audio, **Then** the separation should work exactly as it did before the update\n2. **Given** a user has a newer Roformer model with updated parameters (mlp_expansion_factor, sage_attention, zero_dc), **When** they attempt to load and use the model, **Then** the model should load successfully and produce high-quality audio separation","chunkId":"specs/001-update-roformer-implementation/001-update-roformer-implementation.md:11"}],[2104,{"filePath":"specs/001-update-roformer-implementation/001-update-roformer-implementation.md","startLine":45,"endLine":46,"chunkText":"2. **Given** a user has a newer Roformer model with updated parameters (mlp_expansion_factor, sage_attention, zero_dc), **When** they attempt to load and use the model, **Then** the model should load successfully and produce high-quality audio separation\n3. **Given** a user has models that use different normalization configurations, **When** they load these models, **Then** the system should handle the normalization appropriately without AttributeError exceptions","chunkId":"specs/001-update-roformer-implementation/001-update-roformer-implementation.md:12"}],[2105,{"filePath":"specs/001-update-roformer-implementation/001-update-roformer-implementation.md","startLine":46,"endLine":51,"chunkText":"3. **Given** a user has models that use different normalization configurations, **When** they load these models, **Then** the system should handle the normalization appropriately without AttributeError exceptions\n4. **Given** a user switches between old and new model types, **When** they perform multiple separations in sequence, **Then** all separations should complete successfully without conflicts\n\n### Edge Cases\n- What happens when a model configuration contains parameters that exist in new implementation but not old?\n- How does system handle models with missing or invalid configuration parameters?","chunkId":"specs/001-update-roformer-implementation/001-update-roformer-implementation.md:13"}],[2106,{"filePath":"specs/001-update-roformer-implementation/001-update-roformer-implementation.md","startLine":49,"endLine":57,"chunkText":"### Edge Cases\n- What happens when a model configuration contains parameters that exist in new implementation but not old?\n- How does system handle models with missing or invalid configuration parameters?\n- What happens when a user tries to load a corrupted or incompatible model file?\n- How does the system behave when switching between different Roformer variants (BSRoformer vs MelBandRoformer)?\n\n## Requirements *(mandatory)*\n\n### Functional Requirements","chunkId":"specs/001-update-roformer-implementation/001-update-roformer-implementation.md:14"}],[2107,{"filePath":"specs/001-update-roformer-implementation/001-update-roformer-implementation.md","startLine":55,"endLine":59,"chunkText":"## Requirements *(mandatory)*\n\n### Functional Requirements\n- **FR-001**: System MUST load and execute older Roformer models without any regression in functionality or performance, preferentially using new inference code with fallback to old code if loading fails\n- **FR-002**: System MUST load and execute newer Roformer models that include updated parameters (mlp_expansion_factor, sage_attention, zero_dc, use_torch_checkpoint, skip_connection) using the new inference code","chunkId":"specs/001-update-roformer-implementation/001-update-roformer-implementation.md:15"}],[2108,{"filePath":"specs/001-update-roformer-implementation/001-update-roformer-implementation.md","startLine":57,"endLine":59,"chunkText":"### Functional Requirements\n- **FR-001**: System MUST load and execute older Roformer models without any regression in functionality or performance, preferentially using new inference code with fallback to old code if loading fails\n- **FR-002**: System MUST load and execute newer Roformer models that include updated parameters (mlp_expansion_factor, sage_attention, zero_dc, use_torch_checkpoint, skip_connection) using the new inference code","chunkId":"specs/001-update-roformer-implementation/001-update-roformer-implementation.md:16"}],[2109,{"filePath":"specs/001-update-roformer-implementation/001-update-roformer-implementation.md","startLine":58,"endLine":60,"chunkText":"- **FR-001**: System MUST load and execute older Roformer models without any regression in functionality or performance, preferentially using new inference code with fallback to old code if loading fails\n- **FR-002**: System MUST load and execute newer Roformer models that include updated parameters (mlp_expansion_factor, sage_attention, zero_dc, use_torch_checkpoint, skip_connection) using the new inference code\n- **FR-003**: System MUST handle both BSRoformer and MelBandRoformer model variants, attempting new implementation first and falling back to old implementation only if necessary","chunkId":"specs/001-update-roformer-implementation/001-update-roformer-implementation.md:17"}],[2110,{"filePath":"specs/001-update-roformer-implementation/001-update-roformer-implementation.md","startLine":59,"endLine":61,"chunkText":"- **FR-002**: System MUST load and execute newer Roformer models that include updated parameters (mlp_expansion_factor, sage_attention, zero_dc, use_torch_checkpoint, skip_connection) using the new inference code\n- **FR-003**: System MUST handle both BSRoformer and MelBandRoformer model variants, attempting new implementation first and falling back to old implementation only if necessary\n- **FR-004**: System MUST gracefully handle model configurations with missing required parameters by failing with detailed error messages that specify which parameters are missing and their expected types/values","chunkId":"specs/001-update-roformer-implementation/001-update-roformer-implementation.md:18"}],[2111,{"filePath":"specs/001-update-roformer-implementation/001-update-roformer-implementation.md","startLine":60,"endLine":62,"chunkText":"- **FR-003**: System MUST handle both BSRoformer and MelBandRoformer model variants, attempting new implementation first and falling back to old implementation only if necessary\n- **FR-004**: System MUST gracefully handle model configurations with missing required parameters by failing with detailed error messages that specify which parameters are missing and their expected types/values\n- **FR-005**: System MUST resolve normalization configuration issues that cause AttributeError exceptions in tfc_tdf_v3.py","chunkId":"specs/001-update-roformer-implementation/001-update-roformer-implementation.md:19"}],[2112,{"filePath":"specs/001-update-roformer-implementation/001-update-roformer-implementation.md","startLine":61,"endLine":63,"chunkText":"- **FR-004**: System MUST gracefully handle model configurations with missing required parameters by failing with detailed error messages that specify which parameters are missing and their expected types/values\n- **FR-005**: System MUST resolve normalization configuration issues that cause AttributeError exceptions in tfc_tdf_v3.py\n- **FR-006**: System MUST maintain identical audio separation quality for existing models after the update, validated using spectral analysis comparison with waveform and spectrogram similarity thresholds (≥0.90 for waveform, ≥0.80 for spectrogram)","chunkId":"specs/001-update-roformer-implementation/001-update-roformer-implementation.md:20"}],[2113,{"filePath":"specs/001-update-roformer-implementation/001-update-roformer-implementation.md","startLine":62,"endLine":64,"chunkText":"- **FR-005**: System MUST resolve normalization configuration issues that cause AttributeError exceptions in tfc_tdf_v3.py\n- **FR-006**: System MUST maintain identical audio separation quality for existing models after the update, validated using spectral analysis comparison with waveform and spectrogram similarity thresholds (≥0.90 for waveform, ≥0.80 for spectrogram)\n- **FR-007**: System MUST provide clear error messages when model loading fails due to incompatible configurations, including specific parameter mismatches and suggested corrections","chunkId":"specs/001-update-roformer-implementation/001-update-roformer-implementation.md:21"}],[2114,{"filePath":"specs/001-update-roformer-implementation/001-update-roformer-implementation.md","startLine":63,"endLine":65,"chunkText":"- **FR-006**: System MUST maintain identical audio separation quality for existing models after the update, validated using spectral analysis comparison with waveform and spectrogram similarity thresholds (≥0.90 for waveform, ≥0.80 for spectrogram)\n- **FR-007**: System MUST provide clear error messages when model loading fails due to incompatible configurations, including specific parameter mismatches and suggested corrections\n- **FR-008**: System MUST support backward compatibility for all existing model files and configurations","chunkId":"specs/001-update-roformer-implementation/001-update-roformer-implementation.md:22"}],[2115,{"filePath":"specs/001-update-roformer-implementation/001-update-roformer-implementation.md","startLine":64,"endLine":67,"chunkText":"- **FR-007**: System MUST provide clear error messages when model loading fails due to incompatible configurations, including specific parameter mismatches and suggested corrections\n- **FR-008**: System MUST support backward compatibility for all existing model files and configurations\n- **FR-009**: System MUST validate model configurations before attempting to instantiate models\n- **FR-010**: System MUST allow seamless switching between different Roformer model types within the same session","chunkId":"specs/001-update-roformer-implementation/001-update-roformer-implementation.md:23"}],[2116,{"filePath":"specs/001-update-roformer-implementation/001-update-roformer-implementation.md","startLine":65,"endLine":70,"chunkText":"- **FR-008**: System MUST support backward compatibility for all existing model files and configurations\n- **FR-009**: System MUST validate model configurations before attempting to instantiate models\n- **FR-010**: System MUST allow seamless switching between different Roformer model types within the same session\n- **FR-011**: System MUST undergo manual testing validation after completing major implementation milestones (e.g., new inference code integration, backward compatibility implementation, error handling updates)\n\n### Key Entities *(include if feature involves data)*","chunkId":"specs/001-update-roformer-implementation/001-update-roformer-implementation.md:24"}],[2117,{"filePath":"specs/001-update-roformer-implementation/001-update-roformer-implementation.md","startLine":68,"endLine":72,"chunkText":"- **FR-011**: System MUST undergo manual testing validation after completing major implementation milestones (e.g., new inference code integration, backward compatibility implementation, error handling updates)\n\n### Key Entities *(include if feature involves data)*\n- **Roformer Model**: Audio separation model with specific architecture parameters, exists in old and new variants with different parameter sets\n- **Model Configuration**: Dictionary containing model parameters including dimension, depth, attention settings, and normalization preferences","chunkId":"specs/001-update-roformer-implementation/001-update-roformer-implementation.md:25"}],[2118,{"filePath":"specs/001-update-roformer-implementation/001-update-roformer-implementation.md","startLine":70,"endLine":74,"chunkText":"### Key Entities *(include if feature involves data)*\n- **Roformer Model**: Audio separation model with specific architecture parameters, exists in old and new variants with different parameter sets\n- **Model Configuration**: Dictionary containing model parameters including dimension, depth, attention settings, and normalization preferences\n- **BSRoformer**: Band-split Roformer variant that processes audio in frequency bands, requires freqs_per_bands parameter\n- **MelBandRoformer**: Mel-scale band Roformer variant that uses mel-scale frequency bands, requires num_bands parameter","chunkId":"specs/001-update-roformer-implementation/001-update-roformer-implementation.md:26"}],[2119,{"filePath":"specs/001-update-roformer-implementation/001-update-roformer-implementation.md","startLine":72,"endLine":79,"chunkText":"- **Model Configuration**: Dictionary containing model parameters including dimension, depth, attention settings, and normalization preferences\n- **BSRoformer**: Band-split Roformer variant that processes audio in frequency bands, requires freqs_per_bands parameter\n- **MelBandRoformer**: Mel-scale band Roformer variant that uses mel-scale frequency bands, requires num_bands parameter\n- **Model Parameters**: Configuration values including mlp_expansion_factor, sage_attention, zero_dc, and other architecture-specific settings\n\n## Clarifications\n\n### Session 2025-09-25","chunkId":"specs/001-update-roformer-implementation/001-update-roformer-implementation.md:27"}],[2120,{"filePath":"specs/001-update-roformer-implementation/001-update-roformer-implementation.md","startLine":77,"endLine":81,"chunkText":"## Clarifications\n\n### Session 2025-09-25\n- Q: The spec mentions \"identical audio separation quality\" must be maintained, but doesn't specify how quality should be measured or what tolerance is acceptable for validation testing. → A: Use spectral analysis comparison with defined similarity thresholds\n- Q: How should the system determine appropriate defaults for missing model parameters? → A: Fail gracefully with detailed error messages rather than assuming defaults","chunkId":"specs/001-update-roformer-implementation/001-update-roformer-implementation.md:28"}],[2121,{"filePath":"specs/001-update-roformer-implementation/001-update-roformer-implementation.md","startLine":79,"endLine":81,"chunkText":"### Session 2025-09-25\n- Q: The spec mentions \"identical audio separation quality\" must be maintained, but doesn't specify how quality should be measured or what tolerance is acceptable for validation testing. → A: Use spectral analysis comparison with defined similarity thresholds\n- Q: How should the system determine appropriate defaults for missing model parameters? → A: Fail gracefully with detailed error messages rather than assuming defaults","chunkId":"specs/001-update-roformer-implementation/001-update-roformer-implementation.md:29"}],[2122,{"filePath":"specs/001-update-roformer-implementation/001-update-roformer-implementation.md","startLine":80,"endLine":82,"chunkText":"- Q: The spec mentions \"identical audio separation quality\" must be maintained, but doesn't specify how quality should be measured or what tolerance is acceptable for validation testing. → A: Use spectral analysis comparison with defined similarity thresholds\n- Q: How should the system determine appropriate defaults for missing model parameters? → A: Fail gracefully with detailed error messages rather than assuming defaults\n- Q: How should the system detect whether a model uses the old or new Roformer format? → A: All Roformer models should use new inference code (test first), fallback to old if fails","chunkId":"specs/001-update-roformer-implementation/001-update-roformer-implementation.md:30"}],[2123,{"filePath":"specs/001-update-roformer-implementation/001-update-roformer-implementation.md","startLine":81,"endLine":89,"chunkText":"- Q: How should the system determine appropriate defaults for missing model parameters? → A: Fail gracefully with detailed error messages rather than assuming defaults\n- Q: How should the system detect whether a model uses the old or new Roformer format? → A: All Roformer models should use new inference code (test first), fallback to old if fails\n- Q: When should manual testing validation be performed during the implementation process? → A: Test only after completing major implementation milestones\n\n---\n\n## Review & Acceptance Checklist\n*GATE: Automated checks run during main() execution*\n","chunkId":"specs/001-update-roformer-implementation/001-update-roformer-implementation.md:31"}],[2124,{"filePath":"specs/001-update-roformer-implementation/001-update-roformer-implementation.md","startLine":87,"endLine":107,"chunkText":"## Review & Acceptance Checklist\n*GATE: Automated checks run during main() execution*\n\n### Content Quality\n- [x] No implementation details (languages, frameworks, APIs)\n- [x] Focused on user value and business needs\n- [x] Written for non-technical stakeholders\n- [x] All mandatory sections completed\n\n### Requirement Completeness\n- [x] No [NEEDS CLARIFICATION] markers remain\n- [x] Requirements are testable and unambiguous  \n- [x] Success criteria are measurable\n- [x] Scope is clearly bounded\n- [x] Dependencies and assumptions identified\n\n---\n\n## Execution Status\n*Updated by main() during processing*\n","chunkId":"specs/001-update-roformer-implementation/001-update-roformer-implementation.md:32"}],[2125,{"filePath":"specs/001-update-roformer-implementation/001-update-roformer-implementation.md","startLine":105,"endLine":117,"chunkText":"## Execution Status\n*Updated by main() during processing*\n\n- [x] User description parsed\n- [x] Key concepts extracted\n- [x] Ambiguities marked\n- [x] User scenarios defined\n- [x] Requirements generated\n- [x] Entities identified\n- [x] Review checklist passed\n\n---\n","chunkId":"specs/001-update-roformer-implementation/001-update-roformer-implementation.md:33"}],[2126,{"filePath":"specs/001-update-roformer-implementation/contracts/fallback_loader_interface.py","startLine":0,"endLine":21,"chunkText":"\"\"\"\nInterface contract for fallback loader implementations.\nDefines the expected behavior for fallback loading mechanisms.\n\"\"\"\n\nfrom abc import ABC, abstractmethod\nfrom typing import Dict, Any\nfrom dataclasses import dataclass\n\n@dataclass\nclass ModelLoadingResult:\n    \"\"\"Result of a model loading attempt.\"\"\"\n    model: Any\n    model_type: str\n    config_used: Dict[str, Any]\n    implementation_version: str\n    loading_method: str\n    device: str\n    success: bool\n    error_message: str = None\n\n","chunkId":"specs/001-update-roformer-implementation/contracts/fallback_loader_interface.py:0"}],[2127,{"filePath":"specs/001-update-roformer-implementation/contracts/fallback_loader_interface.py","startLine":22,"endLine":38,"chunkText":"class FallbackLoaderInterface(ABC):\n    \"\"\"\n    Interface for fallback model loading implementations.\n    \n    Defines the contract that fallback loaders must implement to provide\n    compatibility with legacy models when new implementations fail.\n    \"\"\"\n    \n    @abstractmethod\n    def try_new_implementation(self, \n                             model_path: str, \n                             config: Dict[str, Any], \n                             device: str = 'cpu') -> ModelLoadingResult:\n        \"\"\"\n        Try loading with new implementation.\n        \n        Args:","chunkId":"specs/001-update-roformer-implementation/contracts/fallback_loader_interface.py:1"}],[2128,{"filePath":"specs/001-update-roformer-implementation/contracts/fallback_loader_interface.py","startLine":36,"endLine":53,"chunkText":"        Try loading with new implementation.\n        \n        Args:\n            model_path: Path to the model file\n            config: Model configuration dictionary\n            device: Device to load model on\n            \n        Returns:\n            ModelLoadingResult with attempt results\n        \"\"\"\n        pass\n    \n    @abstractmethod\n    def try_legacy_implementation(self, \n                                model_path: str, \n                                config: Dict[str, Any], \n                                device: str = 'cpu') -> ModelLoadingResult:\n        \"\"\"","chunkId":"specs/001-update-roformer-implementation/contracts/fallback_loader_interface.py:2"}],[2129,{"filePath":"specs/001-update-roformer-implementation/contracts/fallback_loader_interface.py","startLine":51,"endLine":65,"chunkText":"                                config: Dict[str, Any], \n                                device: str = 'cpu') -> ModelLoadingResult:\n        \"\"\"\n        Try loading with legacy implementation.\n        \n        Args:\n            model_path: Path to the model file\n            config: Model configuration dictionary\n            device: Device to load model on\n            \n        Returns:\n            ModelLoadingResult with fallback attempt results\n        \"\"\"\n        pass\n","chunkId":"specs/001-update-roformer-implementation/contracts/fallback_loader_interface.py:3"}],[2130,{"filePath":"specs/001-update-roformer-implementation/contracts/parameter_validator_interface.py","startLine":0,"endLine":18,"chunkText":"\"\"\"\nAPI Contract: Parameter Validator Interface\nThis defines the interface for validating Roformer model parameters.\n\"\"\"\n\nfrom abc import ABC, abstractmethod\nfrom typing import Dict, Any, List, Optional, Tuple\nfrom dataclasses import dataclass\nfrom enum import Enum\n\n\nclass ValidationSeverity(Enum):\n    \"\"\"Severity levels for validation issues.\"\"\"\n    ERROR = \"error\"      # Blocks model loading\n    WARNING = \"warning\"  # Allows loading but may affect performance\n    INFO = \"info\"        # Informational only\n\n\n@dataclass","chunkId":"specs/001-update-roformer-implementation/contracts/parameter_validator_interface.py:0"}],[2131,{"filePath":"specs/001-update-roformer-implementation/contracts/parameter_validator_interface.py","startLine":19,"endLine":28,"chunkText":"class ValidationIssue:\n    \"\"\"Represents a validation issue found in model configuration.\"\"\"\n    severity: ValidationSeverity\n    parameter_name: str\n    message: str\n    suggested_fix: str\n    current_value: Any = None\n    expected_value: Any = None\n\n","chunkId":"specs/001-update-roformer-implementation/contracts/parameter_validator_interface.py:1"}],[2132,{"filePath":"specs/001-update-roformer-implementation/contracts/parameter_validator_interface.py","startLine":29,"endLine":45,"chunkText":"class ParameterValidatorInterface(ABC):\n    \"\"\"Abstract interface for validating model parameters.\"\"\"\n    \n    @abstractmethod\n    def validate_required_parameters(self, config: Dict[str, Any], model_type: str) -> List[ValidationIssue]:\n        \"\"\"\n        Validate that all required parameters are present.\n        \n        Args:\n            config: Model configuration dictionary\n            model_type: Type of model (\"bs_roformer\" or \"mel_band_roformer\")\n            \n        Returns:\n            List of validation issues for missing required parameters\n        \"\"\"\n        pass\n    ","chunkId":"specs/001-update-roformer-implementation/contracts/parameter_validator_interface.py:2"}],[2133,{"filePath":"specs/001-update-roformer-implementation/contracts/parameter_validator_interface.py","startLine":43,"endLine":64,"chunkText":"        \"\"\"\n        pass\n    \n    @abstractmethod\n    def validate_parameter_types(self, config: Dict[str, Any]) -> List[ValidationIssue]:\n        \"\"\"\n        Validate parameter types match expected types.\n        \n        Args:\n            config: Model configuration dictionary\n            \n        Returns:\n            List of validation issues for type mismatches\n        \"\"\"\n        pass\n    \n    @abstractmethod\n    def validate_parameter_ranges(self, config: Dict[str, Any]) -> List[ValidationIssue]:\n        \"\"\"\n        Validate parameter values are within acceptable ranges.\n        \n        Args:","chunkId":"specs/001-update-roformer-implementation/contracts/parameter_validator_interface.py:3"}],[2134,{"filePath":"specs/001-update-roformer-implementation/contracts/parameter_validator_interface.py","startLine":62,"endLine":80,"chunkText":"        Validate parameter values are within acceptable ranges.\n        \n        Args:\n            config: Model configuration dictionary\n            \n        Returns:\n            List of validation issues for out-of-range values\n        \"\"\"\n        pass\n    \n    @abstractmethod\n    def validate_parameter_compatibility(self, config: Dict[str, Any]) -> List[ValidationIssue]:\n        \"\"\"\n        Validate that parameter combinations are compatible.\n        \n        Args:\n            config: Model configuration dictionary\n            \n        Returns:","chunkId":"specs/001-update-roformer-implementation/contracts/parameter_validator_interface.py:4"}],[2135,{"filePath":"specs/001-update-roformer-implementation/contracts/parameter_validator_interface.py","startLine":78,"endLine":98,"chunkText":"            config: Model configuration dictionary\n            \n        Returns:\n            List of validation issues for incompatible parameter combinations\n        \"\"\"\n        pass\n    \n    @abstractmethod\n    def validate_normalization_config(self, norm_config: Any) -> List[ValidationIssue]:\n        \"\"\"\n        Validate normalization configuration.\n        \n        Args:\n            norm_config: Normalization configuration (may be string, dict, or None)\n            \n        Returns:\n            List of validation issues for normalization configuration\n        \"\"\"\n        pass\n    \n    @abstractmethod","chunkId":"specs/001-update-roformer-implementation/contracts/parameter_validator_interface.py:5"}],[2136,{"filePath":"specs/001-update-roformer-implementation/contracts/parameter_validator_interface.py","startLine":96,"endLine":116,"chunkText":"        pass\n    \n    @abstractmethod\n    def get_parameter_defaults(self, model_type: str) -> Dict[str, Any]:\n        \"\"\"\n        Get default values for optional parameters.\n        \n        Args:\n            model_type: Type of model (\"bs_roformer\" or \"mel_band_roformer\")\n            \n        Returns:\n            Dictionary of parameter names to default values\n        \"\"\"\n        pass\n    \n    @abstractmethod\n    def apply_parameter_defaults(self, config: Dict[str, Any], model_type: str) -> Dict[str, Any]:\n        \"\"\"\n        Apply default values to missing optional parameters.\n        \n        Args:","chunkId":"specs/001-update-roformer-implementation/contracts/parameter_validator_interface.py:6"}],[2137,{"filePath":"specs/001-update-roformer-implementation/contracts/parameter_validator_interface.py","startLine":114,"endLine":125,"chunkText":"        Apply default values to missing optional parameters.\n        \n        Args:\n            config: Model configuration dictionary\n            model_type: Type of model\n            \n        Returns:\n            Configuration with defaults applied\n        \"\"\"\n        pass\n\n","chunkId":"specs/001-update-roformer-implementation/contracts/parameter_validator_interface.py:7"}],[2138,{"filePath":"specs/001-update-roformer-implementation/contracts/parameter_validator_interface.py","startLine":126,"endLine":143,"chunkText":"class BSRoformerValidatorInterface(ABC):\n    \"\"\"Specialized validator for BSRoformer models.\"\"\"\n    \n    @abstractmethod\n    def validate_freqs_per_bands(self, freqs_per_bands: Tuple[int, ...], stft_config: Dict[str, Any]) -> List[ValidationIssue]:\n        \"\"\"\n        Validate frequency bands configuration.\n        \n        Args:\n            freqs_per_bands: Tuple of frequencies per band\n            stft_config: STFT configuration parameters\n            \n        Returns:\n            List of validation issues for frequency bands\n        \"\"\"\n        pass\n    \n    @abstractmethod","chunkId":"specs/001-update-roformer-implementation/contracts/parameter_validator_interface.py:8"}],[2139,{"filePath":"specs/001-update-roformer-implementation/contracts/parameter_validator_interface.py","startLine":141,"endLine":156,"chunkText":"        pass\n    \n    @abstractmethod\n    def calculate_expected_freqs(self, stft_n_fft: int) -> int:\n        \"\"\"\n        Calculate expected number of frequency bins from STFT configuration.\n        \n        Args:\n            stft_n_fft: STFT n_fft parameter\n            \n        Returns:\n            Expected number of frequency bins\n        \"\"\"\n        pass\n\n","chunkId":"specs/001-update-roformer-implementation/contracts/parameter_validator_interface.py:9"}],[2140,{"filePath":"specs/001-update-roformer-implementation/contracts/parameter_validator_interface.py","startLine":157,"endLine":174,"chunkText":"class MelBandRoformerValidatorInterface(ABC):\n    \"\"\"Specialized validator for MelBandRoformer models.\"\"\"\n    \n    @abstractmethod\n    def validate_num_bands(self, num_bands: int, sample_rate: int) -> List[ValidationIssue]:\n        \"\"\"\n        Validate number of mel bands.\n        \n        Args:\n            num_bands: Number of mel-scale bands\n            sample_rate: Audio sample rate\n            \n        Returns:\n            List of validation issues for mel bands configuration\n        \"\"\"\n        pass\n    \n    @abstractmethod","chunkId":"specs/001-update-roformer-implementation/contracts/parameter_validator_interface.py:10"}],[2141,{"filePath":"specs/001-update-roformer-implementation/contracts/parameter_validator_interface.py","startLine":172,"endLine":187,"chunkText":"        pass\n    \n    @abstractmethod\n    def validate_sample_rate(self, sample_rate: int) -> List[ValidationIssue]:\n        \"\"\"\n        Validate audio sample rate.\n        \n        Args:\n            sample_rate: Audio sample rate in Hz\n            \n        Returns:\n            List of validation issues for sample rate\n        \"\"\"\n        pass\n\n","chunkId":"specs/001-update-roformer-implementation/contracts/parameter_validator_interface.py:11"}],[2142,{"filePath":"specs/001-update-roformer-implementation/contracts/parameter_validator_interface.py","startLine":188,"endLine":204,"chunkText":"class ConfigurationNormalizerInterface(ABC):\n    \"\"\"Interface for normalizing configuration between old and new formats.\"\"\"\n    \n    @abstractmethod\n    def normalize_config_format(self, raw_config: Any) -> Dict[str, Any]:\n        \"\"\"\n        Normalize configuration from various input formats to standard dictionary.\n        \n        Args:\n            raw_config: Configuration in any format (dict, object, etc.)\n            \n        Returns:\n            Normalized configuration dictionary\n        \"\"\"\n        pass\n    \n    @abstractmethod","chunkId":"specs/001-update-roformer-implementation/contracts/parameter_validator_interface.py:12"}],[2143,{"filePath":"specs/001-update-roformer-implementation/contracts/parameter_validator_interface.py","startLine":202,"endLine":222,"chunkText":"        pass\n    \n    @abstractmethod\n    def map_legacy_parameters(self, config: Dict[str, Any]) -> Dict[str, Any]:\n        \"\"\"\n        Map legacy parameter names to current parameter names.\n        \n        Args:\n            config: Configuration with potentially legacy parameter names\n            \n        Returns:\n            Configuration with current parameter names\n        \"\"\"\n        pass\n    \n    @abstractmethod\n    def extract_nested_config(self, config: Any, path: str) -> Any:\n        \"\"\"\n        Extract nested configuration value using dot notation path.\n        \n        Args:","chunkId":"specs/001-update-roformer-implementation/contracts/parameter_validator_interface.py:13"}],[2144,{"filePath":"specs/001-update-roformer-implementation/contracts/parameter_validator_interface.py","startLine":220,"endLine":230,"chunkText":"        Extract nested configuration value using dot notation path.\n        \n        Args:\n            config: Configuration object or dictionary\n            path: Dot notation path (e.g., \"model.norm\")\n            \n        Returns:\n            Extracted configuration value or None if not found\n        \"\"\"\n        pass\n","chunkId":"specs/001-update-roformer-implementation/contracts/parameter_validator_interface.py:14"}],[2145,{"filePath":"specs/001-update-roformer-implementation/contracts/roformer_loader_interface.py","startLine":0,"endLine":16,"chunkText":"\"\"\"\nAPI Contract: Roformer Model Loader Interface\nThis defines the interface for loading Roformer models with backward compatibility.\n\"\"\"\n\nfrom abc import ABC, abstractmethod\nfrom typing import Optional, List, Dict, Any, Tuple\nfrom dataclasses import dataclass\nfrom enum import Enum\n\n\nclass RoformerType(Enum):\n    \"\"\"Supported Roformer model types.\"\"\"\n    BS_ROFORMER = \"bs_roformer\"\n    MEL_BAND_ROFORMER = \"mel_band_roformer\"\n\n","chunkId":"specs/001-update-roformer-implementation/contracts/roformer_loader_interface.py:0"}],[2146,{"filePath":"specs/001-update-roformer-implementation/contracts/roformer_loader_interface.py","startLine":17,"endLine":24,"chunkText":"class ImplementationVersion(Enum):\n    \"\"\"Available implementation versions.\"\"\"\n    OLD = \"old\"\n    NEW = \"new\"\n    FALLBACK = \"fallback\"\n\n\n@dataclass","chunkId":"specs/001-update-roformer-implementation/contracts/roformer_loader_interface.py:1"}],[2147,{"filePath":"specs/001-update-roformer-implementation/contracts/roformer_loader_interface.py","startLine":25,"endLine":38,"chunkText":"class ModelLoadingResult:\n    \"\"\"Result of model loading operation.\"\"\"\n    success: bool\n    model: Optional[Any] = None  # Actual model instance\n    error_message: Optional[str] = None\n    implementation_used: ImplementationVersion = ImplementationVersion.NEW\n    warnings: List[str] = None\n    \n    def __post_init__(self):\n        if self.warnings is None:\n            self.warnings = []\n\n\n@dataclass","chunkId":"specs/001-update-roformer-implementation/contracts/roformer_loader_interface.py:2"}],[2148,{"filePath":"specs/001-update-roformer-implementation/contracts/roformer_loader_interface.py","startLine":39,"endLine":60,"chunkText":"class ModelConfiguration:\n    \"\"\"Model configuration parameters.\"\"\"\n    # Required parameters\n    dim: int\n    depth: int\n    \n    # Common optional parameters\n    stereo: bool = False\n    num_stems: int = 1\n    time_transformer_depth: int = 2\n    freq_transformer_depth: int = 2\n    dim_head: int = 64\n    heads: int = 8\n    attn_dropout: float = 0.0\n    ff_dropout: float = 0.0\n    flash_attn: bool = True\n    \n    # New parameters (with defaults for backward compatibility)\n    mlp_expansion_factor: int = 4\n    sage_attention: bool = False\n    zero_dc: bool = True\n    use_torch_checkpoint: bool = False","chunkId":"specs/001-update-roformer-implementation/contracts/roformer_loader_interface.py:3"}],[2149,{"filePath":"specs/001-update-roformer-implementation/contracts/roformer_loader_interface.py","startLine":58,"endLine":70,"chunkText":"    sage_attention: bool = False\n    zero_dc: bool = True\n    use_torch_checkpoint: bool = False\n    skip_connection: bool = False\n    \n    # Normalization (may be None in some configs)\n    norm: Optional[str] = None\n    \n    # Model-specific parameters\n    freqs_per_bands: Optional[Tuple[int, ...]] = None  # BSRoformer\n    num_bands: Optional[int] = None  # MelBandRoformer\n\n","chunkId":"specs/001-update-roformer-implementation/contracts/roformer_loader_interface.py:4"}],[2150,{"filePath":"specs/001-update-roformer-implementation/contracts/roformer_loader_interface.py","startLine":71,"endLine":84,"chunkText":"class ParameterValidationError(Exception):\n    \"\"\"Raised when model parameters are invalid.\"\"\"\n    \n    def __init__(self, parameter_name: str, expected_type: str, actual_value: Any, suggested_fix: str):\n        self.parameter_name = parameter_name\n        self.expected_type = expected_type\n        self.actual_value = actual_value\n        self.suggested_fix = suggested_fix\n        \n        message = (\n            f\"Invalid parameter '{parameter_name}': \"\n            f\"expected {expected_type}, got {type(actual_value).__name__} ({actual_value}). \"\n            f\"Suggestion: {suggested_fix}\"\n        )","chunkId":"specs/001-update-roformer-implementation/contracts/roformer_loader_interface.py:5"}],[2151,{"filePath":"specs/001-update-roformer-implementation/contracts/roformer_loader_interface.py","startLine":82,"endLine":87,"chunkText":"            f\"expected {expected_type}, got {type(actual_value).__name__} ({actual_value}). \"\n            f\"Suggestion: {suggested_fix}\"\n        )\n        super().__init__(message)\n\n","chunkId":"specs/001-update-roformer-implementation/contracts/roformer_loader_interface.py:6"}],[2152,{"filePath":"specs/001-update-roformer-implementation/contracts/roformer_loader_interface.py","startLine":88,"endLine":103,"chunkText":"class RoformerLoaderInterface(ABC):\n    \"\"\"Abstract interface for loading Roformer models.\"\"\"\n    \n    @abstractmethod\n    def load_model(self, model_path: str, config: Optional[Dict[str, Any]] = None) -> ModelLoadingResult:\n        \"\"\"\n        Load a Roformer model from the given path.\n        \n        Args:\n            model_path: Path to the model file (.ckpt, .pth)\n            config: Optional configuration override\n            \n        Returns:\n            ModelLoadingResult with success status and model or error details\n            \n        Raises:","chunkId":"specs/001-update-roformer-implementation/contracts/roformer_loader_interface.py:7"}],[2153,{"filePath":"specs/001-update-roformer-implementation/contracts/roformer_loader_interface.py","startLine":101,"endLine":118,"chunkText":"            ModelLoadingResult with success status and model or error details\n            \n        Raises:\n            ParameterValidationError: If model configuration is invalid\n            FileNotFoundError: If model file doesn't exist\n        \"\"\"\n        pass\n    \n    @abstractmethod\n    def validate_configuration(self, config: ModelConfiguration, model_type: RoformerType) -> List[str]:\n        \"\"\"\n        Validate model configuration parameters.\n        \n        Args:\n            config: Model configuration to validate\n            model_type: Type of Roformer model\n            \n        Returns:","chunkId":"specs/001-update-roformer-implementation/contracts/roformer_loader_interface.py:8"}],[2154,{"filePath":"specs/001-update-roformer-implementation/contracts/roformer_loader_interface.py","startLine":116,"endLine":139,"chunkText":"            model_type: Type of Roformer model\n            \n        Returns:\n            List of validation error messages (empty if valid)\n        \"\"\"\n        pass\n    \n    @abstractmethod\n    def detect_model_type(self, model_path: str) -> RoformerType:\n        \"\"\"\n        Detect the type of Roformer model from the file.\n        \n        Args:\n            model_path: Path to the model file\n            \n        Returns:\n            Detected RoformerType\n            \n        Raises:\n            ValueError: If model type cannot be determined\n        \"\"\"\n        pass\n    \n    @abstractmethod","chunkId":"specs/001-update-roformer-implementation/contracts/roformer_loader_interface.py:9"}],[2155,{"filePath":"specs/001-update-roformer-implementation/contracts/roformer_loader_interface.py","startLine":137,"endLine":152,"chunkText":"        pass\n    \n    @abstractmethod\n    def get_default_configuration(self, model_type: RoformerType) -> ModelConfiguration:\n        \"\"\"\n        Get default configuration for a model type.\n        \n        Args:\n            model_type: Type of Roformer model\n            \n        Returns:\n            Default ModelConfiguration for the type\n        \"\"\"\n        pass\n\n","chunkId":"specs/001-update-roformer-implementation/contracts/roformer_loader_interface.py:10"}],[2156,{"filePath":"specs/001-update-roformer-implementation/contracts/roformer_loader_interface.py","startLine":153,"endLine":174,"chunkText":"class RoformerModelInterface(ABC):\n    \"\"\"Abstract interface for Roformer model instances.\"\"\"\n    \n    @abstractmethod\n    def separate_audio(self, audio_data: Any, **kwargs) -> Any:\n        \"\"\"\n        Separate audio into stems using the model.\n        \n        Args:\n            audio_data: Input audio data\n            **kwargs: Additional separation parameters\n            \n        Returns:\n            Separated audio stems\n        \"\"\"\n        pass\n    \n    @abstractmethod\n    def get_model_info(self) -> Dict[str, Any]:\n        \"\"\"\n        Get information about the loaded model.\n        ","chunkId":"specs/001-update-roformer-implementation/contracts/roformer_loader_interface.py:11"}],[2157,{"filePath":"specs/001-update-roformer-implementation/contracts/roformer_loader_interface.py","startLine":172,"endLine":185,"chunkText":"        \"\"\"\n        Get information about the loaded model.\n        \n        Returns:\n            Dictionary with model metadata\n        \"\"\"\n        pass\n    \n    @abstractmethod\n    def cleanup(self) -> None:\n        \"\"\"Clean up model resources.\"\"\"\n        pass\n\n","chunkId":"specs/001-update-roformer-implementation/contracts/roformer_loader_interface.py:12"}],[2158,{"filePath":"specs/001-update-roformer-implementation/contracts/roformer_loader_interface.py","startLine":186,"endLine":203,"chunkText":"class FallbackLoaderInterface(ABC):\n    \"\"\"Interface for fallback loading mechanism.\"\"\"\n    \n    @abstractmethod\n    def try_new_implementation(self, model_path: str, config: ModelConfiguration) -> ModelLoadingResult:\n        \"\"\"\n        Attempt to load model with new implementation.\n        \n        Args:\n            model_path: Path to model file\n            config: Model configuration\n            \n        Returns:\n            ModelLoadingResult indicating success or failure\n        \"\"\"\n        pass\n    \n    @abstractmethod","chunkId":"specs/001-update-roformer-implementation/contracts/roformer_loader_interface.py:13"}],[2159,{"filePath":"specs/001-update-roformer-implementation/contracts/roformer_loader_interface.py","startLine":201,"endLine":220,"chunkText":"        pass\n    \n    @abstractmethod\n    def try_old_implementation(self, model_path: str, config: ModelConfiguration) -> ModelLoadingResult:\n        \"\"\"\n        Attempt to load model with old implementation (fallback).\n        \n        Args:\n            model_path: Path to model file\n            config: Model configuration\n            \n        Returns:\n            ModelLoadingResult indicating success or failure\n        \"\"\"\n        pass\n    \n    @abstractmethod\n    def should_fallback(self, error: Exception) -> bool:\n        \"\"\"\n        Determine if the error warrants falling back to old implementation.","chunkId":"specs/001-update-roformer-implementation/contracts/roformer_loader_interface.py:14"}],[2160,{"filePath":"specs/001-update-roformer-implementation/contracts/roformer_loader_interface.py","startLine":218,"endLine":229,"chunkText":"    def should_fallback(self, error: Exception) -> bool:\n        \"\"\"\n        Determine if the error warrants falling back to old implementation.\n        \n        Args:\n            error: Exception from new implementation attempt\n            \n        Returns:\n            True if should attempt fallback, False otherwise\n        \"\"\"\n        pass\n","chunkId":"specs/001-update-roformer-implementation/contracts/roformer_loader_interface.py:15"}],[2161,{"filePath":"specs/001-update-roformer-implementation/data-model.md","startLine":0,"endLine":15,"chunkText":"# Data Model: Roformer Implementation Update\n\n## Core Entities\n\n### RoformerModel\nRepresents a Roformer audio separation model with configuration parameters.\n\n**Fields:**\n- `model_path: str` - Path to the model file (.ckpt, .pth)\n- `config: ModelConfiguration` - Model configuration parameters\n- `model_type: RoformerType` - Type of Roformer (BSRoformer, MelBandRoformer)\n- `implementation_version: str` - Which implementation version (\"old\", \"new\")\n- `loaded_successfully: bool` - Whether model loaded without errors\n\n**Validation Rules:**\n- `model_path` must exist and be readable","chunkId":"specs/001-update-roformer-implementation/data-model.md:0"}],[2162,{"filePath":"specs/001-update-roformer-implementation/data-model.md","startLine":13,"endLine":31,"chunkText":"\n**Validation Rules:**\n- `model_path` must exist and be readable\n- `config` must contain required parameters for the model type\n- `model_type` must be one of supported variants\n\n**State Transitions:**\n- `Unloaded → Loading → Loaded` (success path)\n- `Unloaded → Loading → Failed` (error path)\n- `Loaded → Unloaded` (cleanup)\n\n### ModelConfiguration\nDictionary-like object containing model architecture parameters.\n\n**Fields:**\n- `dim: int` - Model dimension\n- `depth: int` - Number of transformer layers\n- `stereo: bool` - Whether model handles stereo audio\n- `num_stems: int` - Number of output stems","chunkId":"specs/001-update-roformer-implementation/data-model.md:1"}],[2163,{"filePath":"specs/001-update-roformer-implementation/data-model.md","startLine":29,"endLine":39,"chunkText":"- `depth: int` - Number of transformer layers\n- `stereo: bool` - Whether model handles stereo audio\n- `num_stems: int` - Number of output stems\n- `time_transformer_depth: int` - Depth of time transformer\n- `freq_transformer_depth: int` - Depth of frequency transformer\n- `freqs_per_bands: Tuple[int, ...]` - Frequency bands configuration\n- `dim_head: int` - Attention head dimension\n- `heads: int` - Number of attention heads\n- `attn_dropout: float` - Attention dropout rate\n- `ff_dropout: float` - Feed-forward dropout rate\n- `flash_attn: bool` - Whether to use flash attention","chunkId":"specs/001-update-roformer-implementation/data-model.md:2"}],[2164,{"filePath":"specs/001-update-roformer-implementation/data-model.md","startLine":37,"endLine":50,"chunkText":"- `attn_dropout: float` - Attention dropout rate\n- `ff_dropout: float` - Feed-forward dropout rate\n- `flash_attn: bool` - Whether to use flash attention\n- `norm: str` - Normalization type (new field handling)\n\n**New Parameters (for updated models):**\n- `mlp_expansion_factor: int = 4` - MLP expansion ratio\n- `sage_attention: bool = False` - Enable Sage attention\n- `zero_dc: bool = True` - Zero DC component handling\n- `use_torch_checkpoint: bool = False` - Enable gradient checkpointing\n- `skip_connection: bool = False` - Enable skip connections\n\n**Validation Rules:**\n- All numeric fields must be positive","chunkId":"specs/001-update-roformer-implementation/data-model.md:3"}],[2165,{"filePath":"specs/001-update-roformer-implementation/data-model.md","startLine":48,"endLine":64,"chunkText":"\n**Validation Rules:**\n- All numeric fields must be positive\n- Dropout rates must be between 0.0 and 1.0\n- `freqs_per_bands` must sum to expected frequency count\n- `norm` must be valid normalization type or None\n\n### BSRoformerConfig\nSpecialized configuration for Band-Split Roformer models.\n\n**Fields:**\n- Inherits all from `ModelConfiguration`\n- `freqs_per_bands: Tuple[int, ...]` - Required, defines frequency band splits\n- `mask_estimator_depth: int = 2` - Depth of mask estimation network\n\n**Validation Rules:**\n- `freqs_per_bands` must be provided and non-empty","chunkId":"specs/001-update-roformer-implementation/data-model.md:4"}],[2166,{"filePath":"specs/001-update-roformer-implementation/data-model.md","startLine":62,"endLine":82,"chunkText":"\n**Validation Rules:**\n- `freqs_per_bands` must be provided and non-empty\n- Sum of `freqs_per_bands` must match STFT frequency bins\n\n### MelBandRoformerConfig  \nSpecialized configuration for Mel-Band Roformer models.\n\n**Fields:**\n- Inherits all from `ModelConfiguration`\n- `num_bands: int` - Number of mel-scale bands\n- `sample_rate: int = 44100` - Audio sample rate for mel calculation\n\n**Validation Rules:**\n- `num_bands` must be positive integer\n- `sample_rate` must be valid audio sample rate\n\n### ModelLoadingResult\nResult object returned from model loading attempts.\n\n**Fields:**","chunkId":"specs/001-update-roformer-implementation/data-model.md:5"}],[2167,{"filePath":"specs/001-update-roformer-implementation/data-model.md","startLine":80,"endLine":94,"chunkText":"Result object returned from model loading attempts.\n\n**Fields:**\n- `success: bool` - Whether loading succeeded\n- `model: Optional[RoformerModel]` - Loaded model if successful\n- `error_message: Optional[str]` - Error description if failed\n- `implementation_used: str` - Which implementation was used (\"old\", \"new\", \"fallback\")\n- `warnings: List[str]` - Non-fatal warnings during loading\n\n**State Transitions:**\n- `Attempting → Success` (with model)\n- `Attempting → Failure` (with error message)\n- `Success → Cleanup` (model disposal)\n\n### ParameterValidationError","chunkId":"specs/001-update-roformer-implementation/data-model.md:6"}],[2168,{"filePath":"specs/001-update-roformer-implementation/data-model.md","startLine":94,"endLine":115,"chunkText":"### ParameterValidationError\nException raised when model parameters are invalid.\n\n**Fields:**\n- `parameter_name: str` - Name of invalid parameter\n- `expected_type: str` - Expected parameter type\n- `actual_value: Any` - Actual value provided\n- `suggested_fix: str` - Suggestion for fixing the issue\n\n## Entity Relationships\n\n```\nRoformerModel\n├── has_one ModelConfiguration\n│   ├── extends_to BSRoformerConfig\n│   └── extends_to MelBandRoformerConfig\n├── produces ModelLoadingResult\n└── may_raise ParameterValidationError\n\nModelConfiguration\n├── validates_against ValidationRules\n└── contains ParameterSet","chunkId":"specs/001-update-roformer-implementation/data-model.md:7"}],[2169,{"filePath":"specs/001-update-roformer-implementation/data-model.md","startLine":113,"endLine":133,"chunkText":"ModelConfiguration\n├── validates_against ValidationRules\n└── contains ParameterSet\n    ├── required_parameters\n    ├── optional_parameters\n    └── new_parameters\n```\n\n## Data Flow\n\n### Model Loading Flow\n1. `ModelLoader.load(model_path)` → `ModelLoadingResult`\n2. Parse configuration from model file\n3. Validate configuration parameters\n4. Attempt loading with new implementation\n5. On failure, attempt loading with old implementation\n6. Return result with success/failure status\n\n### Parameter Validation Flow\n1. Extract parameters from model configuration\n2. Check required parameters exist","chunkId":"specs/001-update-roformer-implementation/data-model.md:8"}],[2170,{"filePath":"specs/001-update-roformer-implementation/data-model.md","startLine":131,"endLine":147,"chunkText":"### Parameter Validation Flow\n1. Extract parameters from model configuration\n2. Check required parameters exist\n3. Validate parameter types and ranges\n4. Check parameter compatibility\n5. Raise `ParameterValidationError` with specific details if invalid\n\n### Configuration Normalization Flow\n1. Load raw configuration from model\n2. Map old parameter names to new parameter names\n3. Set default values for missing optional parameters\n4. Validate final configuration\n5. Return normalized `ModelConfiguration`\n\n## Storage Considerations\n\n- Model files are read-only external artifacts","chunkId":"specs/001-update-roformer-implementation/data-model.md:9"}],[2171,{"filePath":"specs/001-update-roformer-implementation/data-model.md","startLine":145,"endLine":160,"chunkText":"## Storage Considerations\n\n- Model files are read-only external artifacts\n- Configuration is derived from model metadata\n- No persistent state storage required\n- Memory usage scales with model size\n- Cleanup required after model disposal\n\n## Performance Considerations\n\n- Model loading is I/O intensive operation\n- Configuration validation should be fast\n- Parameter defaults should be computed once\n- Error messages should be pre-formatted for common cases\n- Fallback mechanism adds latency but ensures compatibility\n","chunkId":"specs/001-update-roformer-implementation/data-model.md:10"}],[2172,{"filePath":"specs/001-update-roformer-implementation/plan.md","startLine":0,"endLine":10,"chunkText":"# Implementation Plan: Update Roformer Implementation\n\n**Branch**: `001-update-roformer-implementation` | **Date**: September 25, 2025 | **Spec**: [/specs/001-update-roformer-implementation.md](../001-update-roformer-implementation.md)\n**Input**: Feature specification from `/specs/001-update-roformer-implementation.md`\n\n## Execution Flow (/plan command scope)\n```\n1. Load feature spec from Input path\n   → If not found: ERROR \"No feature spec at {path}\"\n2. Fill Technical Context (scan for NEEDS CLARIFICATION)\n   → Detect Project Type from context (web=frontend+backend, mobile=app+api)","chunkId":"specs/001-update-roformer-implementation/plan.md:0"}],[2173,{"filePath":"specs/001-update-roformer-implementation/plan.md","startLine":8,"endLine":17,"chunkText":"   → If not found: ERROR \"No feature spec at {path}\"\n2. Fill Technical Context (scan for NEEDS CLARIFICATION)\n   → Detect Project Type from context (web=frontend+backend, mobile=app+api)\n   → Set Structure Decision based on project type\n3. Fill the Constitution Check section based on the content of the constitution document.\n4. Evaluate Constitution Check section below\n   → If violations exist: Document in Complexity Tracking\n   → If no justification possible: ERROR \"Simplify approach first\"\n   → Update Progress Tracking: Initial Constitution Check\n5. Execute Phase 0 → research.md","chunkId":"specs/001-update-roformer-implementation/plan.md:1"}],[2174,{"filePath":"specs/001-update-roformer-implementation/plan.md","startLine":15,"endLine":21,"chunkText":"   → If no justification possible: ERROR \"Simplify approach first\"\n   → Update Progress Tracking: Initial Constitution Check\n5. Execute Phase 0 → research.md\n   → If NEEDS CLARIFICATION remain: ERROR \"Resolve unknowns\"\n6. Execute Phase 1 → contracts, data-model.md, quickstart.md, agent-specific template file (e.g., `CLAUDE.md` for Claude Code, `.github/copilot-instructions.md` for GitHub Copilot, `GEMINI.md` for Gemini CLI, `QWEN.md` for Qwen Code or `AGENTS.md` for opencode).\n7. Re-evaluate Constitution Check section\n   → If new violations: Refactor design, return to Phase 1","chunkId":"specs/001-update-roformer-implementation/plan.md:2"}],[2175,{"filePath":"specs/001-update-roformer-implementation/plan.md","startLine":19,"endLine":26,"chunkText":"6. Execute Phase 1 → contracts, data-model.md, quickstart.md, agent-specific template file (e.g., `CLAUDE.md` for Claude Code, `.github/copilot-instructions.md` for GitHub Copilot, `GEMINI.md` for Gemini CLI, `QWEN.md` for Qwen Code or `AGENTS.md` for opencode).\n7. Re-evaluate Constitution Check section\n   → If new violations: Refactor design, return to Phase 1\n   → Update Progress Tracking: Post-Design Constitution Check\n8. Plan Phase 2 → Describe task generation approach (DO NOT create tasks.md)\n9. STOP - Ready for /tasks command\n```\n","chunkId":"specs/001-update-roformer-implementation/plan.md:3"}],[2176,{"filePath":"specs/001-update-roformer-implementation/plan.md","startLine":24,"endLine":31,"chunkText":"9. STOP - Ready for /tasks command\n```\n\n**IMPORTANT**: The /plan command STOPS at step 7. Phases 2-4 are executed by other commands:\n- Phase 2: /tasks command creates tasks.md\n- Phase 3-4: Implementation execution (manual or via tools)\n\n## Summary","chunkId":"specs/001-update-roformer-implementation/plan.md:4"}],[2177,{"filePath":"specs/001-update-roformer-implementation/plan.md","startLine":29,"endLine":35,"chunkText":"- Phase 3-4: Implementation execution (manual or via tools)\n\n## Summary\nUpdate the Roformer implementation to support latest model architectures while maintaining backward compatibility. The primary requirement is enabling newer Roformer models with updated parameters (mlp_expansion_factor, sage_attention, zero_dc) while preserving functionality for existing older models. Technical approach involves integrating new inference code with fallback mechanisms and comprehensive validation using existing spectral analysis testing framework.\n\n## Technical Context\n**Language/Version**: Python 3.11+  ","chunkId":"specs/001-update-roformer-implementation/plan.md:5"}],[2178,{"filePath":"specs/001-update-roformer-implementation/plan.md","startLine":33,"endLine":41,"chunkText":"\n## Technical Context\n**Language/Version**: Python 3.11+  \n**Primary Dependencies**: PyTorch, librosa, soundfile, numpy, onnxruntime  \n**Storage**: Model files (.ckpt, .pth, .onnx), audio files (FLAC, WAV, MP3)  \n**Testing**: pytest with custom audio validation (SSIM comparison)  \n**Target Platform**: Cross-platform (Windows, macOS, Linux) with GPU acceleration support  \n**Project Type**: single - Python library with CLI wrapper  \n**Performance Goals**: Maintain existing separation quality (≥0.90 waveform, ≥0.80 spectrogram similarity)  ","chunkId":"specs/001-update-roformer-implementation/plan.md:6"}],[2179,{"filePath":"specs/001-update-roformer-implementation/plan.md","startLine":39,"endLine":48,"chunkText":"**Target Platform**: Cross-platform (Windows, macOS, Linux) with GPU acceleration support  \n**Project Type**: single - Python library with CLI wrapper  \n**Performance Goals**: Maintain existing separation quality (≥0.90 waveform, ≥0.80 spectrogram similarity)  \n**Constraints**: Zero regression in existing model functionality, backward compatibility mandatory  \n**Scale/Scope**: Support for BSRoformer and MelBandRoformer variants, integration with existing 4 model architectures\n\n## Constitution Check\n*GATE: Must pass before Phase 0 research. Re-check after Phase 1 design.*\n\n### I. Library-First Architecture","chunkId":"specs/001-update-roformer-implementation/plan.md:7"}],[2180,{"filePath":"specs/001-update-roformer-implementation/plan.md","startLine":46,"endLine":57,"chunkText":"*GATE: Must pass before Phase 0 research. Re-check after Phase 1 design.*\n\n### I. Library-First Architecture\n- [x] Core functionality implemented in `Separator` class or similar library pattern\n- [x] CLI/Remote API are thin wrappers, not containing business logic\n- [x] Clear separation between model architectures (MDX, VR, Demucs, MDXC)\n\n### II. Multi-Interface Consistency  \n- [x] Feature accessible via Python API, CLI, and Remote API (if applicable)\n- [x] Parameter names identical across all interfaces\n- [x] Same model architectures supported across interfaces\n","chunkId":"specs/001-update-roformer-implementation/plan.md:8"}],[2181,{"filePath":"specs/001-update-roformer-implementation/plan.md","startLine":55,"endLine":68,"chunkText":"- [x] Parameter names identical across all interfaces\n- [x] Same model architectures supported across interfaces\n\n### III. Test-First Development (NON-NEGOTIABLE)\n- [x] Tests written before implementation\n- [x] Unit tests for all core functionality\n- [x] Integration tests with audio validation (SSIM comparison)\n- [x] CLI tests for all exposed functionality\n\n### IV. Performance & Resource Efficiency\n- [x] Hardware acceleration support considered (CUDA, CoreML, DirectML)\n- [x] Memory optimization for large files (streaming/batch processing)\n- [x] Tunable parameters for different hardware capabilities\n","chunkId":"specs/001-update-roformer-implementation/plan.md:9"}],[2182,{"filePath":"specs/001-update-roformer-implementation/plan.md","startLine":66,"endLine":81,"chunkText":"- [x] Memory optimization for large files (streaming/batch processing)\n- [x] Tunable parameters for different hardware capabilities\n\n### V. Model Architecture Separation\n- [x] Each architecture in separate modules\n- [x] Inherits from `CommonSeparator` pattern\n- [x] Architecture-specific parameters isolated\n- [x] Loading one architecture doesn't load others\n\n## Project Structure\n\n### Documentation (this feature)\n```\nspecs/001-update-roformer-implementation/\n├── plan.md              # This file (/plan command output)\n├── research.md          # Phase 0 output (/plan command)","chunkId":"specs/001-update-roformer-implementation/plan.md:10"}],[2183,{"filePath":"specs/001-update-roformer-implementation/plan.md","startLine":79,"endLine":93,"chunkText":"specs/001-update-roformer-implementation/\n├── plan.md              # This file (/plan command output)\n├── research.md          # Phase 0 output (/plan command)\n├── data-model.md        # Phase 1 output (/plan command)\n├── quickstart.md        # Phase 1 output (/plan command)\n├── contracts/           # Phase 1 output (/plan command)\n└── tasks.md             # Phase 2 output (/tasks command - NOT created by /plan)\n```\n\n### Source Code (repository root)\n```\n# Option 1: Single project (DEFAULT)\naudio_separator/\n├── separator/\n│   ├── uvr_lib_v5/          # Existing old Roformer implementation","chunkId":"specs/001-update-roformer-implementation/plan.md:11"}],[2184,{"filePath":"specs/001-update-roformer-implementation/plan.md","startLine":91,"endLine":108,"chunkText":"audio_separator/\n├── separator/\n│   ├── uvr_lib_v5/          # Existing old Roformer implementation\n│   ├── msst-models-new/     # New reference implementation\n│   ├── common_separator.py  # Base class for all architectures\n│   └── roformer_separator.py # Updated Roformer implementation\n└── utils/\n\ntests/\n├── contract/\n├── integration/\n└── unit/\n```\n\n**Structure Decision**: Option 1 - Single project structure, as this is a library enhancement rather than web/mobile application\n\n## Phase 0: Outline & Research\n1. **Extract unknowns from Technical Context** above:","chunkId":"specs/001-update-roformer-implementation/plan.md:12"}],[2185,{"filePath":"specs/001-update-roformer-implementation/plan.md","startLine":106,"endLine":117,"chunkText":"\n## Phase 0: Outline & Research\n1. **Extract unknowns from Technical Context** above:\n   - Analyze differences between old and new Roformer implementations\n   - Research parameter compatibility between BSRoformer and MelBandRoformer variants\n   - Investigate normalization configuration handling patterns\n   - Study fallback mechanism implementation strategies\n\n2. **Generate and dispatch research agents**:\n   ```\n   Task: \"Research differences between old uvr_lib_v5 and new msst-models Roformer implementations\"\n   Task: \"Find best practices for backward compatibility in PyTorch model loading\"","chunkId":"specs/001-update-roformer-implementation/plan.md:13"}],[2186,{"filePath":"specs/001-update-roformer-implementation/plan.md","startLine":115,"endLine":128,"chunkText":"   ```\n   Task: \"Research differences between old uvr_lib_v5 and new msst-models Roformer implementations\"\n   Task: \"Find best practices for backward compatibility in PyTorch model loading\"\n   Task: \"Research parameter validation patterns for ML model configurations\"\n   Task: \"Study fallback mechanism implementations in audio processing libraries\"\n   ```\n\n3. **Consolidate findings** in `research.md` using format:\n   - Decision: [what was chosen]\n   - Rationale: [why chosen]\n   - Alternatives considered: [what else evaluated]\n\n**Output**: research.md with all technical unknowns resolved\n","chunkId":"specs/001-update-roformer-implementation/plan.md:14"}],[2187,{"filePath":"specs/001-update-roformer-implementation/plan.md","startLine":126,"endLine":140,"chunkText":"\n**Output**: research.md with all technical unknowns resolved\n\n## Phase 1: Design & Contracts\n*Prerequisites: research.md complete*\n\n1. **Extract entities from feature spec** → `data-model.md`:\n   - RoformerModel: Configuration parameters, validation rules\n   - ModelConfiguration: Parameter dictionaries, normalization settings\n   - BSRoformerConfig: Band-split specific parameters\n   - MelBandRoformerConfig: Mel-scale band specific parameters\n\n2. **Generate API contracts** from functional requirements:\n   - Model loading interface with fallback capability\n   - Parameter validation interface","chunkId":"specs/001-update-roformer-implementation/plan.md:15"}],[2188,{"filePath":"specs/001-update-roformer-implementation/plan.md","startLine":138,"endLine":151,"chunkText":"2. **Generate API contracts** from functional requirements:\n   - Model loading interface with fallback capability\n   - Parameter validation interface\n   - Error reporting interface for configuration mismatches\n   - Output OpenAPI/GraphQL schema to `/contracts/`\n\n3. **Generate contract tests** from contracts:\n   - Test model loading with old format\n   - Test model loading with new format\n   - Test fallback mechanism activation\n   - Test parameter validation and error reporting\n\n4. **Extract test scenarios** from user stories:\n   - Load existing older Roformer model → separation works identically","chunkId":"specs/001-update-roformer-implementation/plan.md:16"}],[2189,{"filePath":"specs/001-update-roformer-implementation/plan.md","startLine":149,"endLine":162,"chunkText":"\n4. **Extract test scenarios** from user stories:\n   - Load existing older Roformer model → separation works identically\n   - Load newer Roformer model → separation works with new parameters\n   - Switch between model types → no conflicts or failures\n\n5. **Update agent file incrementally** (O(1) operation):\n   - Run `.specify/scripts/bash/update-agent-context.sh cursor`\n   - Add Roformer implementation context\n   - Preserve existing audio separation knowledge\n   - Update with new model parameter handling\n\n**Output**: data-model.md, /contracts/*, failing tests, quickstart.md, agent-specific file\n","chunkId":"specs/001-update-roformer-implementation/plan.md:17"}],[2190,{"filePath":"specs/001-update-roformer-implementation/plan.md","startLine":160,"endLine":175,"chunkText":"\n**Output**: data-model.md, /contracts/*, failing tests, quickstart.md, agent-specific file\n\n## Phase 2: Task Planning Approach\n*This section describes what the /tasks command will do - DO NOT execute during /plan*\n\n**Task Generation Strategy**:\n- Load `.specify/templates/tasks-template.md` as base\n- Generate tasks from Phase 1 design docs (contracts, data model, quickstart)\n- Each contract → contract test task [P]\n- Each entity → model creation task [P] \n- Each user story → integration test task\n- Implementation tasks to make tests pass\n\n**Ordering Strategy**:\n- TDD order: Tests before implementation ","chunkId":"specs/001-update-roformer-implementation/plan.md:18"}],[2191,{"filePath":"specs/001-update-roformer-implementation/plan.md","startLine":173,"endLine":187,"chunkText":"\n**Ordering Strategy**:\n- TDD order: Tests before implementation \n- Dependency order: Models before services before UI\n- Mark [P] for parallel execution (independent files)\n\n**Estimated Output**: 20-25 numbered, ordered tasks in tasks.md\n\n**IMPORTANT**: This phase is executed by the /tasks command, NOT by /plan\n\n## Phase 3+: Future Implementation\n*These phases are beyond the scope of the /plan command*\n\n**Phase 3**: Task execution (/tasks command creates tasks.md)  \n**Phase 4**: Implementation (execute tasks.md following constitutional principles)  ","chunkId":"specs/001-update-roformer-implementation/plan.md:19"}],[2192,{"filePath":"specs/001-update-roformer-implementation/plan.md","startLine":185,"endLine":199,"chunkText":"\n**Phase 3**: Task execution (/tasks command creates tasks.md)  \n**Phase 4**: Implementation (execute tasks.md following constitutional principles)  \n**Phase 5**: Validation (run tests, execute quickstart.md, performance validation)\n\n## Complexity Tracking\n*No constitutional violations identified - all checks passed*\n\n## Progress Tracking\n*This checklist is updated during execution flow*\n\n**Phase Status**:\n- [x] Phase 0: Research complete (/plan command)\n- [x] Phase 1: Design complete (/plan command)\n- [x] Phase 2: Task planning complete (/plan command - describe approach only)","chunkId":"specs/001-update-roformer-implementation/plan.md:20"}],[2193,{"filePath":"specs/001-update-roformer-implementation/plan.md","startLine":197,"endLine":212,"chunkText":"- [x] Phase 0: Research complete (/plan command)\n- [x] Phase 1: Design complete (/plan command)\n- [x] Phase 2: Task planning complete (/plan command - describe approach only)\n- [x] Phase 3: Tasks generated (/tasks command)\n- [ ] Phase 4: Implementation complete\n- [ ] Phase 5: Validation passed\n\n**Gate Status**:\n- [x] Initial Constitution Check: PASS\n- [x] Post-Design Constitution Check: PASS\n- [x] All NEEDS CLARIFICATION resolved\n- [x] Complexity deviations documented\n\n---\n*Based on Constitution v1.0.0 - See `.specify/memory/constitution.md`*\n","chunkId":"specs/001-update-roformer-implementation/plan.md:21"}],[2194,{"filePath":"specs/001-update-roformer-implementation/post-implementation-issues.md","startLine":0,"endLine":5,"chunkText":"### Post-Implementation Issues: Roformer Routing and Execution Paths\n\n- **Observed behavior**: New Roformer models load and separate successfully, but execution is routed through `MDXCSeparator` rather than a dedicated Roformer separator. Logs show the MDXC path with Roformer-specific branching inside `MDXCSeparator`.\n\n- **Root cause**:\n  - `Separator.list_supported_model_files()` groups Roformer entries under the `MDXC` model type, so `Separator.load_model()` instantiates `architectures.mdxc_separator.MDXCSeparator` for Roformer files.","chunkId":"specs/001-update-roformer-implementation/post-implementation-issues.md:0"}],[2195,{"filePath":"specs/001-update-roformer-implementation/post-implementation-issues.md","startLine":4,"endLine":6,"chunkText":"- **Root cause**:\n  - `Separator.list_supported_model_files()` groups Roformer entries under the `MDXC` model type, so `Separator.load_model()` instantiates `architectures.mdxc_separator.MDXCSeparator` for Roformer files.\n  - `separator_classes` in `separator.py` has no mapping for a `Roformer` type; the dedicated `architectures/roformer_separator.py` is never routed to and is effectively unused.","chunkId":"specs/001-update-roformer-implementation/post-implementation-issues.md:1"}],[2196,{"filePath":"specs/001-update-roformer-implementation/post-implementation-issues.md","startLine":5,"endLine":8,"chunkText":"  - `Separator.list_supported_model_files()` groups Roformer entries under the `MDXC` model type, so `Separator.load_model()` instantiates `architectures.mdxc_separator.MDXCSeparator` for Roformer files.\n  - `separator_classes` in `separator.py` has no mapping for a `Roformer` type; the dedicated `architectures/roformer_separator.py` is never routed to and is effectively unused.\n  - `MDXCSeparator` contains an internal Roformer branch (constructs `BSRoformer`/`MelBandRoformer`, loads checkpoints, and implements Roformer chunking/overlap-add). This duplicates responsibility with the new `roformer_loader`.\n","chunkId":"specs/001-update-roformer-implementation/post-implementation-issues.md:2"}],[2197,{"filePath":"specs/001-update-roformer-implementation/post-implementation-issues.md","startLine":7,"endLine":13,"chunkText":"  - `MDXCSeparator` contains an internal Roformer branch (constructs `BSRoformer`/`MelBandRoformer`, loads checkpoints, and implements Roformer chunking/overlap-add). This duplicates responsibility with the new `roformer_loader`.\n\n- **Impact**:\n  - The new Roformer loader (normalization/validation/fallback) is not used in actual runs; loader stats remain zero in logs.\n  - Two overlapping Roformer code paths (inside `MDXCSeparator` and the new loader + `RoformerSeparator`) create confusion and increase maintenance cost.\n  - `RoformerSeparator` is dead code under current routing.\n","chunkId":"specs/001-update-roformer-implementation/post-implementation-issues.md:3"}],[2198,{"filePath":"specs/001-update-roformer-implementation/post-implementation-issues.md","startLine":12,"endLine":19,"chunkText":"  - `RoformerSeparator` is dead code under current routing.\n\n- **Decision**: Proceed with Option A (minimal-change refactor)\n  - Keep routing via `MDXCSeparator` to avoid broad changes.\n  - Refactor the Roformer branch in `MDXCSeparator` to use `RoformerLoader` for model loading/validation/fallback, while preserving the existing Roformer chunking/overlap-add execution.\n  - Align the Roformer detection flag naming (`is_roformer_model`) with `CommonSeparator`, and surface loader stats via existing `CommonSeparator.get_roformer_loading_stats()` and `Separator` logging.\n\n- **Planned edits (Option A)**:","chunkId":"specs/001-update-roformer-implementation/post-implementation-issues.md:4"}],[2199,{"filePath":"specs/001-update-roformer-implementation/post-implementation-issues.md","startLine":18,"endLine":24,"chunkText":"\n- **Planned edits (Option A)**:\n  - `architectures/mdxc_separator.py`:\n    - Use `self.is_roformer_model` (from `CommonSeparator`) instead of a separate `self.is_roformer` detection.\n    - In `load_model()`, when Roformer is detected, call `self.roformer_loader.load_model(model_path=self.model_path, config=self.model_data['model'], device=str(self.torch_device))`. On success, set `self.model_run = result.model`; on failure, fall back to existing direct instantiation path.\n    - Keep current Roformer chunking/overlap-add logic in `demix()` unchanged.\n  - `roformer/roformer_loader.py`:","chunkId":"specs/001-update-roformer-implementation/post-implementation-issues.md:5"}],[2200,{"filePath":"specs/001-update-roformer-implementation/post-implementation-issues.md","startLine":23,"endLine":29,"chunkText":"    - Keep current Roformer chunking/overlap-add logic in `demix()` unchanged.\n  - `roformer/roformer_loader.py`:\n    - Ensure it returns a `ModelLoadingResult` that matches the local dataclass (use `success_result`, `failure_result`, `fallback_success_result` helpers), and populate metadata via `model_info` rather than custom fields.\n\n- **Expected outcomes**:\n  - No change in separation outputs or performance characteristics.\n  - Loader stats in logs reflect actual usage (non-zero `new_implementation_success` and/or `fallback_success`).","chunkId":"specs/001-update-roformer-implementation/post-implementation-issues.md:6"}],[2201,{"filePath":"specs/001-update-roformer-implementation/post-implementation-issues.md","startLine":28,"endLine":33,"chunkText":"  - No change in separation outputs or performance characteristics.\n  - Loader stats in logs reflect actual usage (non-zero `new_implementation_success` and/or `fallback_success`).\n  - Reduced duplication and clearer ownership, while deferring larger routing changes (introducing `RoformerSeparator`) to a follow-up if desired.\n\n\n","chunkId":"specs/001-update-roformer-implementation/post-implementation-issues.md:7"}],[2202,{"filePath":"specs/001-update-roformer-implementation/quickstart.md","startLine":0,"endLine":24,"chunkText":"# Quickstart Guide: Updated Roformer Implementation\n\nThis guide demonstrates how to use the updated Roformer implementation with both old and new models.\n\n## Prerequisites\n\n- Python 3.11+\n- PyTorch installed\n- audio-separator package installed\n- Access to Roformer model files (.ckpt or .pth)\n\n## Basic Usage\n\n### Loading a Roformer Model\n\n```python\nfrom audio_separator import Separator\n\n# Initialize separator with Roformer model\nseparator = Separator(\n    model_file_dir='path/to/models',\n    output_dir='path/to/output'\n)\n\n# Load a Roformer model (automatically detects old vs new format)","chunkId":"specs/001-update-roformer-implementation/quickstart.md:0"}],[2203,{"filePath":"specs/001-update-roformer-implementation/quickstart.md","startLine":22,"endLine":49,"chunkText":")\n\n# Load a Roformer model (automatically detects old vs new format)\nseparator.load_model('model_bs_roformer_ep_317_sdr_12.9755.ckpt')\n```\n\n### Separating Audio\n\n```python\n# Separate audio file\noutput_files = separator.separate('input_audio.flac')\n\nprint(f\"Separation complete. Output files: {output_files}\")\n```\n\n## Model Types\n\n### BSRoformer Models\n\n```python\n# BSRoformer models work with frequency band splitting\nseparator.load_model('bs_roformer_model.ckpt')\noutputs = separator.separate('audio.flac')\n# Outputs: ['audio_(Vocals).flac', 'audio_(Instrumental).flav']\n```\n\n### MelBandRoformer Models  \n","chunkId":"specs/001-update-roformer-implementation/quickstart.md:1"}],[2204,{"filePath":"specs/001-update-roformer-implementation/quickstart.md","startLine":48,"endLine":76,"chunkText":"### MelBandRoformer Models  \n\n```python\n# MelBandRoformer models work with mel-scale bands\nseparator.load_model('mel_band_roformer_model.ckpt')\noutputs = separator.separate('audio.flac')\n# Outputs depend on model configuration\n```\n\n## Advanced Configuration\n\n### Manual Configuration Override\n\n```python\n# Override model configuration if needed\nconfig_override = {\n    'mlp_expansion_factor': 4,\n    'sage_attention': True,\n    'zero_dc': True,\n    'use_torch_checkpoint': False\n}\n\nseparator.load_model('model.ckpt', config=config_override)\n```\n\n### Error Handling\n\n```python\ntry:","chunkId":"specs/001-update-roformer-implementation/quickstart.md:2"}],[2205,{"filePath":"specs/001-update-roformer-implementation/quickstart.md","startLine":74,"endLine":97,"chunkText":"\n```python\ntry:\n    separator.load_model('problematic_model.ckpt')\n    outputs = separator.separate('audio.flac')\nexcept ParameterValidationError as e:\n    print(f\"Configuration error: {e}\")\n    print(f\"Suggestion: {e.suggested_fix}\")\nexcept Exception as e:\n    print(f\"Loading failed: {e}\")\n```\n\n## Testing Scenarios\n\n### Scenario 1: Existing Older Model\n\nTest that existing models continue to work without changes.\n\n```python\n# This should work exactly as before\nseparator = Separator()\nseparator.load_model('old_roformer_model.ckpt')\noutputs = separator.separate('test_audio.flac')\n","chunkId":"specs/001-update-roformer-implementation/quickstart.md:3"}],[2206,{"filePath":"specs/001-update-roformer-implementation/quickstart.md","startLine":95,"endLine":113,"chunkText":"separator.load_model('old_roformer_model.ckpt')\noutputs = separator.separate('test_audio.flac')\n\n# Verify outputs match previous results\nassert len(outputs) == 2  # Expecting vocal and instrumental\nassert outputs[0].endswith('_(Vocals).flav')\nassert outputs[1].endswith('_(Instrumental).flav')\n```\n\n### Scenario 2: Newer Model with New Parameters\n\nTest that newer models with additional parameters work correctly.\n\n```python\n# This should work with the updated implementation\nseparator = Separator()\nseparator.load_model('new_roformer_with_sage_attention.ckpt')\noutputs = separator.separate('test_audio.flac')\n","chunkId":"specs/001-update-roformer-implementation/quickstart.md:4"}],[2207,{"filePath":"specs/001-update-roformer-implementation/quickstart.md","startLine":111,"endLine":132,"chunkText":"separator.load_model('new_roformer_with_sage_attention.ckpt')\noutputs = separator.separate('test_audio.flac')\n\n# Verify outputs are generated successfully\nassert len(outputs) >= 1\nfor output in outputs:\n    assert os.path.exists(output)\n    assert os.path.getsize(output) > 0\n```\n\n### Scenario 3: Model Type Switching\n\nTest switching between different Roformer variants in the same session.\n\n```python\nseparator = Separator()\n\n# Load BSRoformer model\nseparator.load_model('bs_roformer.ckpt')\nbs_outputs = separator.separate('test1.flac')\n\n# Switch to MelBandRoformer model  ","chunkId":"specs/001-update-roformer-implementation/quickstart.md:5"}],[2208,{"filePath":"specs/001-update-roformer-implementation/quickstart.md","startLine":130,"endLine":150,"chunkText":"bs_outputs = separator.separate('test1.flac')\n\n# Switch to MelBandRoformer model  \nseparator.load_model('mel_band_roformer.ckpt')\nmel_outputs = separator.separate('test2.flac')\n\n# Both should work without conflicts\nassert len(bs_outputs) > 0\nassert len(mel_outputs) > 0\n```\n\n### Scenario 4: Configuration Validation\n\nTest that invalid configurations are caught with helpful error messages.\n\n```python\n# Test missing required parameter\ntry:\n    separator.load_model('model_with_missing_config.ckpt')\n    assert False, \"Should have raised validation error\"\nexcept ParameterValidationError as e:","chunkId":"specs/001-update-roformer-implementation/quickstart.md:6"}],[2209,{"filePath":"specs/001-update-roformer-implementation/quickstart.md","startLine":148,"endLine":164,"chunkText":"    separator.load_model('model_with_missing_config.ckpt')\n    assert False, \"Should have raised validation error\"\nexcept ParameterValidationError as e:\n    assert \"missing\" in e.suggested_fix.lower()\n    assert e.parameter_name is not None\n```\n\n### Scenario 5: Fallback Mechanism\n\nTest that fallback from new to old implementation works.\n\n```python\n# This would internally try new implementation first, then fallback to old\nseparator = Separator(log_level='DEBUG')  # Enable debug logging\nseparator.load_model('edge_case_model.ckpt')\n\n# Check logs to verify fallback occurred if needed","chunkId":"specs/001-update-roformer-implementation/quickstart.md:7"}],[2210,{"filePath":"specs/001-update-roformer-implementation/quickstart.md","startLine":162,"endLine":189,"chunkText":"separator.load_model('edge_case_model.ckpt')\n\n# Check logs to verify fallback occurred if needed\n# (Implementation should log which version was used)\n```\n\n## Performance Validation\n\n### Audio Quality Validation\n\n```python\nimport subprocess\nimport os\n\n# Run integration test to validate audio quality\nresult = subprocess.run([\n    'python', '-m', 'pytest', \n    'tests/integration/test_roformer_quality.py',\n    '-v'\n], capture_output=True, text=True)\n\nassert result.returncode == 0, f\"Quality tests failed: {result.stderr}\"\n```\n\n### Regression Testing\n\n```python\n# Ensure existing models produce identical result","chunkId":"specs/001-update-roformer-implementation/quickstart.md:8"}],[2211,{"filePath":"specs/001-update-roformer-implementation/quickstart.md","startLine":187,"endLine":205,"chunkText":"\n```python\n# Ensure existing models produce identical results\nreference_outputs = load_reference_outputs('test_audio.flac')\ncurrent_outputs = separator.separate('test_audio.flav')\n\nfor ref, curr in zip(reference_outputs, current_outputs):\n    similarity = calculate_audio_similarity(ref, curr)\n    assert similarity >= 0.90, f\"Audio similarity {similarity} below threshold\"\n```\n\n## Troubleshooting\n\n### Common Issues\n\n1. **AttributeError: 'norm'**\n   - Cause: Model configuration has different normalization structure\n   - Solution: The updated implementation handles this automatically\n","chunkId":"specs/001-update-roformer-implementation/quickstart.md:9"}],[2212,{"filePath":"specs/001-update-roformer-implementation/quickstart.md","startLine":203,"endLine":219,"chunkText":"   - Cause: Model configuration has different normalization structure\n   - Solution: The updated implementation handles this automatically\n\n2. **TypeError: unexpected keyword argument 'mlp_expansion_factor'**\n   - Cause: Trying to use new model with old implementation\n   - Solution: The fallback mechanism handles this automatically\n\n3. **Model loading fails completely**\n   - Check model file exists and is readable\n   - Verify model is a supported Roformer variant\n   - Check logs for specific error details\n\n### Debug Information\n\n```python\n# Enable detailed logging\nimport logging","chunkId":"specs/001-update-roformer-implementation/quickstart.md:10"}],[2213,{"filePath":"specs/001-update-roformer-implementation/quickstart.md","startLine":217,"endLine":235,"chunkText":"```python\n# Enable detailed logging\nimport logging\nlogging.basicConfig(level=logging.DEBUG)\n\nseparator = Separator()\nseparator.load_model('model.ckpt')\n# Check logs for implementation version used and any warnings\n```\n\n## Manual Testing Checklist\n\nAfter implementation changes, manually verify:\n\n- [ ] Existing BSRoformer models load and separate correctly\n- [ ] Existing MelBandRoformer models load and separate correctly  \n- [ ] New models with additional parameters work\n- [ ] Audio quality matches reference outputs (SSIM ≥ 0.90/0.80)\n- [ ] Error messages are clear and helpful","chunkId":"specs/001-update-roformer-implementation/quickstart.md:11"}],[2214,{"filePath":"specs/001-update-roformer-implementation/quickstart.md","startLine":233,"endLine":249,"chunkText":"- [ ] New models with additional parameters work\n- [ ] Audio quality matches reference outputs (SSIM ≥ 0.90/0.80)\n- [ ] Error messages are clear and helpful\n- [ ] Performance is not significantly degraded\n- [ ] Memory usage is reasonable\n- [ ] Multiple models can be loaded in sequence\n\n## Next Steps\n\nAfter validating the quickstart scenarios:\n\n1. Run full integration test suite\n2. Perform manual testing with real model files\n3. Validate performance benchmarks\n4. Update documentation if needed\n5. Prepare for production deployment\n","chunkId":"specs/001-update-roformer-implementation/quickstart.md:12"}],[2215,{"filePath":"specs/001-update-roformer-implementation/research.md","startLine":0,"endLine":13,"chunkText":"# Research Findings: Roformer Implementation Update\n\n## Key Differences Between Old and New Implementations\n\n### 1. BSRoformer Constructor Parameters\n\n**Decision**: The new implementation includes 5 additional parameters that are missing from the old implementation:\n- `zero_dc = True` (line 385)\n- `mlp_expansion_factor = 4` (line 392) \n- `use_torch_checkpoint = False` (line 393)\n- `skip_connection = False` (line 394)\n- `sage_attention = False` (line 395)\n\n**Rationale**: These parameters enable advanced features in newer models:","chunkId":"specs/001-update-roformer-implementation/research.md:0"}],[2216,{"filePath":"specs/001-update-roformer-implementation/research.md","startLine":11,"endLine":21,"chunkText":"- `sage_attention = False` (line 395)\n\n**Rationale**: These parameters enable advanced features in newer models:\n- `mlp_expansion_factor`: Controls MLP layer expansion ratio for better model capacity\n- `sage_attention`: Enables Sage attention mechanism for improved performance\n- `zero_dc`: Controls DC component handling in STFT processing\n- `use_torch_checkpoint`: Enables gradient checkpointing for memory efficiency\n- `skip_connection`: Enables residual connections between layers\n\n**Alternatives considered**: \n- Hardcoding defaults in old implementation (rejected - not maintainable)","chunkId":"specs/001-update-roformer-implementation/research.md:1"}],[2217,{"filePath":"specs/001-update-roformer-implementation/research.md","startLine":19,"endLine":35,"chunkText":"\n**Alternatives considered**: \n- Hardcoding defaults in old implementation (rejected - not maintainable)\n- Creating separate classes (rejected - increases complexity)\n- Parameter validation with fallback (chosen approach)\n\n### 2. Transformer Configuration Changes\n\n**Decision**: New implementation includes `sage_attention` parameter in transformer_kwargs and conditional logic for Sage attention activation.\n\n**Rationale**: The new implementation adds:\n```python\nif sage_attention:\n    print(\"Use Sage Attention\")\n\ntransformer_kwargs = dict(\n    # ... existing parameters ...","chunkId":"specs/001-update-roformer-implementation/research.md:2"}],[2218,{"filePath":"specs/001-update-roformer-implementation/research.md","startLine":33,"endLine":49,"chunkText":"\ntransformer_kwargs = dict(\n    # ... existing parameters ...\n    sage_attention=sage_attention,  # New parameter\n)\n```\n\n**Alternatives considered**: Ignoring sage_attention (rejected - breaks newer models), implementing stub (chosen for backward compatibility)\n\n### 3. Instance Variable Additions\n\n**Decision**: New implementation tracks additional state variables:\n- `self.use_torch_checkpoint` \n- `self.skip_connection`\n\n**Rationale**: These are used throughout the forward pass and need to be accessible as instance variables.\n","chunkId":"specs/001-update-roformer-implementation/research.md:3"}],[2219,{"filePath":"specs/001-update-roformer-implementation/research.md","startLine":47,"endLine":56,"chunkText":"**Rationale**: These are used throughout the forward pass and need to be accessible as instance variables.\n\n**Alternatives considered**: Local variables only (rejected - needed in forward method), property methods (rejected - unnecessary complexity)\n\n### 4. Normalization Configuration Issues\n\n**Decision**: The error `AttributeError: \"'norm'\"` in `tfc_tdf_v3.py` line 155 occurs when `config.model.norm` is accessed but the configuration object structure is different between old and new models.\n\n**Rationale**: Analysis of `tfc_tdf_v3.py` shows:\n```python","chunkId":"specs/001-update-roformer-implementation/research.md:4"}],[2220,{"filePath":"specs/001-update-roformer-implementation/research.md","startLine":56,"endLine":72,"chunkText":"**Rationale**: Analysis of `tfc_tdf_v3.py` shows:\n```python\nnorm = get_norm(norm_type=config.model.norm)  # Line 155\n```\n\nThe `get_norm` function expects a string value but newer model configs may have different structure or missing `norm` attribute.\n\n**Alternatives considered**: \n- Modify get_norm to handle missing attributes (chosen)\n- Update config parsing (rejected - too invasive)\n- Separate normalization handling (rejected - duplicates code)\n\n### 5. Model Loading Strategy\n\n**Decision**: Implement try-new-first-fallback-to-old pattern for model loading.\n\n**Rationale**: This approach:","chunkId":"specs/001-update-roformer-implementation/research.md:5"}],[2221,{"filePath":"specs/001-update-roformer-implementation/research.md","startLine":70,"endLine":84,"chunkText":"**Decision**: Implement try-new-first-fallback-to-old pattern for model loading.\n\n**Rationale**: This approach:\n1. Attempts to load with new implementation first\n2. Falls back to old implementation if new fails\n3. Provides clear error messages for debugging\n4. Maintains zero regression for existing models\n\n**Alternatives considered**: \n- Version detection from model files (rejected - unreliable)\n- User-specified format (rejected - poor UX)\n- Parallel implementations (rejected - maintenance burden)\n\n### 6. Parameter Validation Patterns\n","chunkId":"specs/001-update-roformer-implementation/research.md:6"}],[2222,{"filePath":"specs/001-update-roformer-implementation/research.md","startLine":82,"endLine":96,"chunkText":"\n### 6. Parameter Validation Patterns\n\n**Decision**: Implement explicit parameter validation with detailed error messages rather than silent defaults.\n\n**Rationale**: Based on clarification session, failing fast with clear error messages is preferred over assuming defaults. This helps users understand what's wrong with their model configurations.\n\n**Alternatives considered**: \n- Silent defaults (rejected per clarification)\n- Configuration auto-correction (rejected - unpredictable)\n- Hardcoded fallbacks (rejected - not maintainable)\n\n## Implementation Strategy\n\n### Phase 1: Extend Old Implementation","chunkId":"specs/001-update-roformer-implementation/research.md:7"}],[2223,{"filePath":"specs/001-update-roformer-implementation/research.md","startLine":94,"endLine":107,"chunkText":"## Implementation Strategy\n\n### Phase 1: Extend Old Implementation\n1. Add new parameters to BSRoformer.__init__ with default values\n2. Add new parameters to MelBandRoformer.__init__ with default values  \n3. Update transformer_kwargs to include new parameters\n4. Add instance variable assignments for checkpoint and skip_connection\n\n### Phase 2: Normalization Compatibility\n1. Modify get_norm function to handle missing norm attributes\n2. Add defensive checks in TFC_TDF_net.__init__ \n3. Provide clear error messages for configuration issues\n\n### Phase 3: Fallback Mechanism","chunkId":"specs/001-update-roformer-implementation/research.md:8"}],[2224,{"filePath":"specs/001-update-roformer-implementation/research.md","startLine":105,"endLine":119,"chunkText":"3. Provide clear error messages for configuration issues\n\n### Phase 3: Fallback Mechanism\n1. Implement model loading wrapper that tries new implementation first\n2. Add fallback to old implementation on specific exceptions\n3. Log which implementation was used for debugging\n\n### Phase 4: Testing Integration\n1. Extend existing integration tests to cover new parameters\n2. Add specific tests for fallback mechanism\n3. Validate that existing models continue to work identically\n\n## Risk Mitigation\n\n1. **Backward Compatibility**: All new parameters have sensible defaults that maintain existing behavior","chunkId":"specs/001-update-roformer-implementation/research.md:9"}],[2225,{"filePath":"specs/001-update-roformer-implementation/research.md","startLine":117,"endLine":124,"chunkText":"## Risk Mitigation\n\n1. **Backward Compatibility**: All new parameters have sensible defaults that maintain existing behavior\n2. **Error Handling**: Clear error messages help users identify configuration issues\n3. **Testing**: Comprehensive test coverage ensures no regressions\n4. **Fallback Safety**: Old implementation remains available if new implementation fails\n5. **Manual Validation**: Manual testing after major milestones ensures real-world compatibility\n","chunkId":"specs/001-update-roformer-implementation/research.md:10"}],[2226,{"filePath":"specs/001-update-roformer-implementation/tasks.md","startLine":0,"endLine":13,"chunkText":"# Tasks: Update Roformer Implementation\n\n**Input**: Design documents from `/specs/001-update-roformer-implementation/`\n**Prerequisites**: plan.md (required), research.md, data-model.md, contracts/\n\n## Execution Flow (main)\n```\n1. Load plan.md from feature directory\n   → Extract: Python 3.11+, PyTorch, librosa, soundfile, numpy, onnxruntime\n   → Structure: single project (audio_separator/ library with CLI wrapper)\n2. Load design documents:\n   → data-model.md: 6 entities → model tasks\n   → contracts/: 2 interface files → contract test tasks\n   → quickstart.md: 5 test scenarios → integration test tasks","chunkId":"specs/001-update-roformer-implementation/tasks.md:0"}],[2227,{"filePath":"specs/001-update-roformer-implementation/tasks.md","startLine":11,"endLine":21,"chunkText":"   → data-model.md: 6 entities → model tasks\n   → contracts/: 2 interface files → contract test tasks\n   → quickstart.md: 5 test scenarios → integration test tasks\n3. Generate tasks by category:\n   → Setup: dependencies, linting, project structure\n   → Tests: contract tests, integration tests (TDD)\n   → Core: models, parameter validation, fallback mechanism\n   → Integration: existing separator integration, CLI updates\n   → Polish: unit tests, performance validation, docs\n4. Apply TDD ordering: Tests before implementation\n5. Mark [P] for parallel execution (different files)","chunkId":"specs/001-update-roformer-implementation/tasks.md:1"}],[2228,{"filePath":"specs/001-update-roformer-implementation/tasks.md","startLine":19,"endLine":32,"chunkText":"   → Polish: unit tests, performance validation, docs\n4. Apply TDD ordering: Tests before implementation\n5. Mark [P] for parallel execution (different files)\n6. Validate: All contracts tested, all entities implemented\n```\n\n## Format: `[ID] [P?] Description`\n- **[P]**: Can run in parallel (different files, no dependencies)\n- Include exact file paths in descriptions\n\n## Phase 3.1: Setup\n\n- [x] T001 Update project dependencies in pyproject.toml to ensure PyTorch compatibility for new Roformer parameters\n- [x] T002 [P] Configure linting rules in pyproject.toml for new Roformer implementation files","chunkId":"specs/001-update-roformer-implementation/tasks.md:2"}],[2229,{"filePath":"specs/001-update-roformer-implementation/tasks.md","startLine":30,"endLine":40,"chunkText":"\n- [x] T001 Update project dependencies in pyproject.toml to ensure PyTorch compatibility for new Roformer parameters\n- [x] T002 [P] Configure linting rules in pyproject.toml for new Roformer implementation files\n- [x] T003 [P] Create backup of existing uvr_lib_v5/roformer/ directory for rollback safety\n\n## Phase 3.2: Tests First (TDD) ⚠️ MUST COMPLETE BEFORE 3.3\n\n**CRITICAL: These tests MUST be written and MUST FAIL before ANY implementation**\n\n### Contract Tests\n- [x] T004 [P] Contract test RoformerLoaderInterface.load_model in tests/contract/test_roformer_loader_interface.py","chunkId":"specs/001-update-roformer-implementation/tasks.md:3"}],[2230,{"filePath":"specs/001-update-roformer-implementation/tasks.md","startLine":38,"endLine":43,"chunkText":"\n### Contract Tests\n- [x] T004 [P] Contract test RoformerLoaderInterface.load_model in tests/contract/test_roformer_loader_interface.py\n- [x] T005 [P] Contract test RoformerLoaderInterface.validate_configuration in tests/contract/test_roformer_loader_interface.py\n- [x] T006 [P] Contract test ParameterValidatorInterface.validate_required_parameters in tests/contract/test_parameter_validator_interface.py\n- [x] T007 [P] Contract test ParameterValidatorInterface.validate_normalization_config in tests/contract/test_parameter_validator_interface.py","chunkId":"specs/001-update-roformer-implementation/tasks.md:4"}],[2231,{"filePath":"specs/001-update-roformer-implementation/tasks.md","startLine":41,"endLine":46,"chunkText":"- [x] T005 [P] Contract test RoformerLoaderInterface.validate_configuration in tests/contract/test_roformer_loader_interface.py\n- [x] T006 [P] Contract test ParameterValidatorInterface.validate_required_parameters in tests/contract/test_parameter_validator_interface.py\n- [x] T007 [P] Contract test ParameterValidatorInterface.validate_normalization_config in tests/contract/test_parameter_validator_interface.py\n- [x] T008 [P] Contract test FallbackLoaderInterface.try_new_implementation in tests/contract/test_fallback_loader_interface.py\n\n### Integration Tests (from quickstart scenarios)","chunkId":"specs/001-update-roformer-implementation/tasks.md:5"}],[2232,{"filePath":"specs/001-update-roformer-implementation/tasks.md","startLine":44,"endLine":49,"chunkText":"- [x] T008 [P] Contract test FallbackLoaderInterface.try_new_implementation in tests/contract/test_fallback_loader_interface.py\n\n### Integration Tests (from quickstart scenarios)\n- [x] T009 [P] Integration test existing older model compatibility in tests/integration/test_roformer_backward_compatibility.py\n- [x] T010 [P] Integration test newer model with new parameters in tests/integration/test_roformer_new_parameters.py\n- [x] T011 [P] Integration test model type switching (BSRoformer ↔ MelBandRoformer) in tests/integration/test_roformer_model_switching.py","chunkId":"specs/001-update-roformer-implementation/tasks.md:6"}],[2233,{"filePath":"specs/001-update-roformer-implementation/tasks.md","startLine":47,"endLine":50,"chunkText":"- [x] T009 [P] Integration test existing older model compatibility in tests/integration/test_roformer_backward_compatibility.py\n- [x] T010 [P] Integration test newer model with new parameters in tests/integration/test_roformer_new_parameters.py\n- [x] T011 [P] Integration test model type switching (BSRoformer ↔ MelBandRoformer) in tests/integration/test_roformer_model_switching.py\n- [x] T012 [P] Integration test configuration validation error handling in tests/integration/test_roformer_config_validation.py","chunkId":"specs/001-update-roformer-implementation/tasks.md:7"}],[2234,{"filePath":"specs/001-update-roformer-implementation/tasks.md","startLine":48,"endLine":53,"chunkText":"- [x] T010 [P] Integration test newer model with new parameters in tests/integration/test_roformer_new_parameters.py\n- [x] T011 [P] Integration test model type switching (BSRoformer ↔ MelBandRoformer) in tests/integration/test_roformer_model_switching.py\n- [x] T012 [P] Integration test configuration validation error handling in tests/integration/test_roformer_config_validation.py\n- [x] T013 [P] Integration test fallback mechanism activation in tests/integration/test_roformer_fallback_mechanism.py\n\n### Audio Quality Validation Tests","chunkId":"specs/001-update-roformer-implementation/tasks.md:8"}],[2235,{"filePath":"specs/001-update-roformer-implementation/tasks.md","startLine":51,"endLine":58,"chunkText":"- [x] T013 [P] Integration test fallback mechanism activation in tests/integration/test_roformer_fallback_mechanism.py\n\n### Audio Quality Validation Tests\n- [x] T014 [P] Audio quality regression test for existing BSRoformer models in tests/integration/test_roformer_audio_quality.py\n- [x] T015 [P] Audio quality validation test for MelBandRoformer models in tests/integration/test_roformer_audio_quality.py\n\n### Roformer Chunking and Overlap Unit Tests\n- [x] T051 [P] Assert chunk_size uses model.stft_hop_length: tests/unit/test_mdxc_roformer_chunking.py::test_chunk_size_uses_model_stft_hop_length","chunkId":"specs/001-update-roformer-implementation/tasks.md:9"}],[2236,{"filePath":"specs/001-update-roformer-implementation/tasks.md","startLine":56,"endLine":60,"chunkText":"\n### Roformer Chunking and Overlap Unit Tests\n- [x] T051 [P] Assert chunk_size uses model.stft_hop_length: tests/unit/test_mdxc_roformer_chunking.py::test_chunk_size_uses_model_stft_hop_length\n- [x] T052 [P] Fallback to audio.hop_length if model.stft_hop_length missing: tests/unit/test_mdxc_roformer_chunking.py::test_chunk_size_falls_back_to_audio_hop_length\n- [x] T053 [P] Step clamped to chunk_size (desired_step > chunk_size or ≤ 0): tests/unit/test_mdxc_roformer_chunking.py::test_step_clamped_to_chunk_size","chunkId":"specs/001-update-roformer-implementation/tasks.md:10"}],[2237,{"filePath":"specs/001-update-roformer-implementation/tasks.md","startLine":58,"endLine":60,"chunkText":"- [x] T051 [P] Assert chunk_size uses model.stft_hop_length: tests/unit/test_mdxc_roformer_chunking.py::test_chunk_size_uses_model_stft_hop_length\n- [x] T052 [P] Fallback to audio.hop_length if model.stft_hop_length missing: tests/unit/test_mdxc_roformer_chunking.py::test_chunk_size_falls_back_to_audio_hop_length\n- [x] T053 [P] Step clamped to chunk_size (desired_step > chunk_size or ≤ 0): tests/unit/test_mdxc_roformer_chunking.py::test_step_clamped_to_chunk_size","chunkId":"specs/001-update-roformer-implementation/tasks.md:11"}],[2238,{"filePath":"specs/001-update-roformer-implementation/tasks.md","startLine":59,"endLine":62,"chunkText":"- [x] T052 [P] Fallback to audio.hop_length if model.stft_hop_length missing: tests/unit/test_mdxc_roformer_chunking.py::test_chunk_size_falls_back_to_audio_hop_length\n- [x] T053 [P] Step clamped to chunk_size (desired_step > chunk_size or ≤ 0): tests/unit/test_mdxc_roformer_chunking.py::test_step_clamped_to_chunk_size\n- [x] T054 [P] overlap_add handles shorter model output safely (safe_len): tests/unit/test_mdxc_roformer_chunking.py::test_overlap_add_short_output_safe\n- [x] T055 [P] Counter increments match overlap_add safe span: tests/unit/test_mdxc_roformer_chunking.py::test_counter_updates_safe_len","chunkId":"specs/001-update-roformer-implementation/tasks.md:12"}],[2239,{"filePath":"specs/001-update-roformer-implementation/tasks.md","startLine":60,"endLine":63,"chunkText":"- [x] T053 [P] Step clamped to chunk_size (desired_step > chunk_size or ≤ 0): tests/unit/test_mdxc_roformer_chunking.py::test_step_clamped_to_chunk_size\n- [x] T054 [P] overlap_add handles shorter model output safely (safe_len): tests/unit/test_mdxc_roformer_chunking.py::test_overlap_add_short_output_safe\n- [x] T055 [P] Counter increments match overlap_add safe span: tests/unit/test_mdxc_roformer_chunking.py::test_counter_updates_safe_len\n- [x] T056 [P] No NaN/inf on normalization (counter clamp): tests/unit/test_mdxc_roformer_chunking.py::test_counter_clamp_no_nan","chunkId":"specs/001-update-roformer-implementation/tasks.md:13"}],[2240,{"filePath":"specs/001-update-roformer-implementation/tasks.md","startLine":61,"endLine":64,"chunkText":"- [x] T054 [P] overlap_add handles shorter model output safely (safe_len): tests/unit/test_mdxc_roformer_chunking.py::test_overlap_add_short_output_safe\n- [x] T055 [P] Counter increments match overlap_add safe span: tests/unit/test_mdxc_roformer_chunking.py::test_counter_updates_safe_len\n- [x] T056 [P] No NaN/inf on normalization (counter clamp): tests/unit/test_mdxc_roformer_chunking.py::test_counter_clamp_no_nan\n- [x] T057 [P] Short-audio last-block path works and preserves length: tests/unit/test_mdxc_roformer_chunking.py::test_short_audio_last_block","chunkId":"specs/001-update-roformer-implementation/tasks.md:14"}],[2241,{"filePath":"specs/001-update-roformer-implementation/tasks.md","startLine":62,"endLine":67,"chunkText":"- [x] T055 [P] Counter increments match overlap_add safe span: tests/unit/test_mdxc_roformer_chunking.py::test_counter_updates_safe_len\n- [x] T056 [P] No NaN/inf on normalization (counter clamp): tests/unit/test_mdxc_roformer_chunking.py::test_counter_clamp_no_nan\n- [x] T057 [P] Short-audio last-block path works and preserves length: tests/unit/test_mdxc_roformer_chunking.py::test_short_audio_last_block\n- [x] T058 [P] Parametrized invariants across dim_t and hop configs: tests/unit/test_mdxc_roformer_chunking.py::test_parametrized_shape_invariants\n\n### Roformer Detection Tests","chunkId":"specs/001-update-roformer-implementation/tasks.md:15"}],[2242,{"filePath":"specs/001-update-roformer-implementation/tasks.md","startLine":65,"endLine":71,"chunkText":"- [x] T058 [P] Parametrized invariants across dim_t and hop configs: tests/unit/test_mdxc_roformer_chunking.py::test_parametrized_shape_invariants\n\n### Roformer Detection Tests\n- [x] T059 [P] YAML path containing \"roformer\" sets is_roformer and routes Roformer path: tests/unit/test_separator_detection.py::test_is_roformer_set_from_yaml_path\n\n### Roformer Integration (E2E)\n- [x] T060 [P] New BSRoformer SW-Fixed end-to-end separation succeeds: tests/integration/test_roformer_e2e.py::test_bs_roformer_sw_fixed_e2e","chunkId":"specs/001-update-roformer-implementation/tasks.md:16"}],[2243,{"filePath":"specs/001-update-roformer-implementation/tasks.md","startLine":69,"endLine":77,"chunkText":"\n### Roformer Integration (E2E)\n- [x] T060 [P] New BSRoformer SW-Fixed end-to-end separation succeeds: tests/integration/test_roformer_e2e.py::test_bs_roformer_sw_fixed_e2e\n- [x] T061 [P] Legacy Roformer end-to-end separation still succeeds: tests/integration/test_roformer_e2e.py::test_legacy_roformer_e2e\n\n### Regression: Size mismatch family\n- [x] T062 [P] Reproduce shorter outputs (N∈{1,16,32,236}) and assert no broadcast errors, output length preserved: tests/regression/test_roformer_size_mismatch.py::test_overlap_add_safe_lengths\n\n### Logging and Performance Sanity","chunkId":"specs/001-update-roformer-implementation/tasks.md:17"}],[2244,{"filePath":"specs/001-update-roformer-implementation/tasks.md","startLine":75,"endLine":82,"chunkText":"- [x] T062 [P] Reproduce shorter outputs (N∈{1,16,32,236}) and assert no broadcast errors, output length preserved: tests/regression/test_roformer_size_mismatch.py::test_overlap_add_safe_lengths\n\n### Logging and Performance Sanity\n- [x] T063 [P] Logs include hop/step sources (stft_hop_length, dim_t, desired vs actual step): tests/unit/test_mdxc_roformer_chunking.py::test_logging_for_hop_and_step\n- [x] T064 [P] Iteration count reasonable (ceil calculation within ±1): tests/unit/test_mdxc_roformer_chunking.py::test_iteration_count_reasonable\n\n## Phase 3.3: Core Implementation (ONLY after tests are failing)\n","chunkId":"specs/001-update-roformer-implementation/tasks.md:18"}],[2245,{"filePath":"specs/001-update-roformer-implementation/tasks.md","startLine":80,"endLine":87,"chunkText":"\n## Phase 3.3: Core Implementation (ONLY after tests are failing)\n\n### Data Models and Configuration\n- [x] T016 [P] Implement ModelConfiguration dataclass in audio_separator/separator/roformer/model_configuration.py\n- [x] T017 [P] Implement BSRoformerConfig class in audio_separator/separator/roformer/bs_roformer_config.py\n- [x] T018 [P] Implement MelBandRoformerConfig class in audio_separator/separator/roformer/mel_band_roformer_config.py\n- [x] T019 [P] Implement ModelLoadingResult dataclass in audio_separator/separator/roformer/model_loading_result.py","chunkId":"specs/001-update-roformer-implementation/tasks.md:19"}],[2246,{"filePath":"specs/001-update-roformer-implementation/tasks.md","startLine":85,"endLine":90,"chunkText":"- [x] T017 [P] Implement BSRoformerConfig class in audio_separator/separator/roformer/bs_roformer_config.py\n- [x] T018 [P] Implement MelBandRoformerConfig class in audio_separator/separator/roformer/mel_band_roformer_config.py\n- [x] T019 [P] Implement ModelLoadingResult dataclass in audio_separator/separator/roformer/model_loading_result.py\n- [x] T020 [P] Implement ParameterValidationError exception in audio_separator/separator/roformer/parameter_validation_error.py\n\n### Parameter Validation System","chunkId":"specs/001-update-roformer-implementation/tasks.md:20"}],[2247,{"filePath":"specs/001-update-roformer-implementation/tasks.md","startLine":88,"endLine":93,"chunkText":"- [x] T020 [P] Implement ParameterValidationError exception in audio_separator/separator/roformer/parameter_validation_error.py\n\n### Parameter Validation System\n- [x] T021 [P] Implement ParameterValidator class in audio_separator/separator/roformer/parameter_validator.py\n- [x] T022 [P] Implement BSRoformerValidator class in audio_separator/separator/roformer/bs_roformer_validator.py\n- [x] T023 [P] Implement MelBandRoformerValidator class in audio_separator/separator/roformer/mel_band_roformer_validator.py","chunkId":"specs/001-update-roformer-implementation/tasks.md:21"}],[2248,{"filePath":"specs/001-update-roformer-implementation/tasks.md","startLine":91,"endLine":96,"chunkText":"- [x] T021 [P] Implement ParameterValidator class in audio_separator/separator/roformer/parameter_validator.py\n- [x] T022 [P] Implement BSRoformerValidator class in audio_separator/separator/roformer/bs_roformer_validator.py\n- [x] T023 [P] Implement MelBandRoformerValidator class in audio_separator/separator/roformer/mel_band_roformer_validator.py\n- [x] T024 [P] Implement ConfigurationNormalizer class in audio_separator/separator/roformer/configuration_normalizer.py\n\n### Updated Roformer Models","chunkId":"specs/001-update-roformer-implementation/tasks.md:22"}],[2249,{"filePath":"specs/001-update-roformer-implementation/tasks.md","startLine":94,"endLine":99,"chunkText":"- [x] T024 [P] Implement ConfigurationNormalizer class in audio_separator/separator/roformer/configuration_normalizer.py\n\n### Updated Roformer Models\n- [x] T025 Update BSRoformer.__init__ method in audio_separator/separator/uvr_lib_v5/roformer/bs_roformer.py to add new parameters (mlp_expansion_factor, sage_attention, zero_dc, use_torch_checkpoint, skip_connection)\n- [x] T026 Update MelBandRoformer.__init__ method in audio_separator/separator/uvr_lib_v5/roformer/mel_band_roformer.py to add new parameters\n- [x] T027 Update transformer_kwargs in BSRoformer to include sage_attention parameter","chunkId":"specs/001-update-roformer-implementation/tasks.md:23"}],[2250,{"filePath":"specs/001-update-roformer-implementation/tasks.md","startLine":97,"endLine":102,"chunkText":"- [x] T025 Update BSRoformer.__init__ method in audio_separator/separator/uvr_lib_v5/roformer/bs_roformer.py to add new parameters (mlp_expansion_factor, sage_attention, zero_dc, use_torch_checkpoint, skip_connection)\n- [x] T026 Update MelBandRoformer.__init__ method in audio_separator/separator/uvr_lib_v5/roformer/mel_band_roformer.py to add new parameters\n- [x] T027 Update transformer_kwargs in BSRoformer to include sage_attention parameter\n- [x] T028 Update transformer_kwargs in MelBandRoformer to include sage_attention parameter\n\n### Normalization Fixes","chunkId":"specs/001-update-roformer-implementation/tasks.md:24"}],[2251,{"filePath":"specs/001-update-roformer-implementation/tasks.md","startLine":100,"endLine":108,"chunkText":"- [x] T028 Update transformer_kwargs in MelBandRoformer to include sage_attention parameter\n\n### Normalization Fixes\n- [x] T029 Update get_norm function in audio_separator/separator/uvr_lib_v5/tfc_tdf_v3.py to handle missing norm attributes gracefully\n- [x] T030 Add defensive checks in TFC_TDF_net.__init__ for normalization configuration\n\n### Fallback Loading System\n- [x] T031 [P] Implement RoformerLoader class with fallback mechanism in audio_separator/separator/roformer/roformer_loader.py\n- [x] T032 [P] Implement FallbackLoader class in audio_separator/separator/roformer/fallback_loader.py","chunkId":"specs/001-update-roformer-implementation/tasks.md:25"}],[2252,{"filePath":"specs/001-update-roformer-implementation/tasks.md","startLine":106,"endLine":113,"chunkText":"### Fallback Loading System\n- [x] T031 [P] Implement RoformerLoader class with fallback mechanism in audio_separator/separator/roformer/roformer_loader.py\n- [x] T032 [P] Implement FallbackLoader class in audio_separator/separator/roformer/fallback_loader.py\n- [x] T033 Update RoformerSeparator class in audio_separator/separator/architectures/roformer_separator.py to use new loading system\n\n## Phase 3.4: Integration\n\n- [x] T034 Integrate new RoformerLoader into CommonSeparator base class in audio_separator/separator/common_separator.py","chunkId":"specs/001-update-roformer-implementation/tasks.md:26"}],[2253,{"filePath":"specs/001-update-roformer-implementation/tasks.md","startLine":111,"endLine":120,"chunkText":"## Phase 3.4: Integration\n\n- [x] T034 Integrate new RoformerLoader into CommonSeparator base class in audio_separator/separator/common_separator.py\n- [x] T035 Update CLI model loading logic to use new fallback mechanism in audio_separator/separator/separator.py\n- [x] T036 Add logging for implementation version used (old/new/fallback) in audio_separator/separator/roformer/roformer_loader.py\n- [x] T037 Update error handling and user messages for configuration validation failures\n- [x] T038 Integrate with existing integration test framework (test_cli_integration.py compatibility)\n\n## Phase 3.5: Polish\n","chunkId":"specs/001-update-roformer-implementation/tasks.md:27"}],[2254,{"filePath":"specs/001-update-roformer-implementation/tasks.md","startLine":118,"endLine":128,"chunkText":"\n## Phase 3.5: Polish\n\n### Unit Tests\n- [x] T039 [P] Unit tests for ModelConfiguration validation in tests/unit/test_model_configuration.py\n- [x] T040 [P] Unit tests for ParameterValidator methods in tests/unit/test_parameter_validator.py\n- [x] T041 [P] Unit tests for ConfigurationNormalizer methods in tests/unit/test_configuration_normalizer.py\n- [x] T042 [P] Unit tests for fallback mechanism logic in tests/unit/test_fallback_loader.py\n\n### Performance and Quality Validation\n- [x] T043 Run existing integration tests to ensure no regression in audio quality (SSIM ≥ 0.90/0.80)","chunkId":"specs/001-update-roformer-implementation/tasks.md:28"}],[2255,{"filePath":"specs/001-update-roformer-implementation/tasks.md","startLine":126,"endLine":135,"chunkText":"\n### Performance and Quality Validation\n- [x] T043 Run existing integration tests to ensure no regression in audio quality (SSIM ≥ 0.90/0.80)\n- [x] T044 Performance benchmark for model loading time (should not significantly increase)\n- [x] T045 Memory usage validation for new parameter handling\n- [x] T046 [P] Update documentation in audio_separator/separator/roformer/README.md for new parameters\n\n### Manual Testing\n- [x] T047 Manual test with existing BSRoformer models from integration test suite\n- [x] T048 Manual test with existing MelBandRoformer models from integration test suite  ","chunkId":"specs/001-update-roformer-implementation/tasks.md:29"}],[2256,{"filePath":"specs/001-update-roformer-implementation/tasks.md","startLine":133,"endLine":144,"chunkText":"### Manual Testing\n- [x] T047 Manual test with existing BSRoformer models from integration test suite\n- [x] T048 Manual test with existing MelBandRoformer models from integration test suite  \n- [x] T049 Manual test with newer models containing new parameters (if available)\n- [x] T050 Manual validation of error messages for common configuration issues\n\n## Dependencies\n\n### Critical Dependencies (TDD)\n- Tests (T004-T015) MUST complete and FAIL before implementation (T016-T033)\n- T001-T003 (setup) before everything else\n- T016-T020 (data models) before T021-T024 (validators)","chunkId":"specs/001-update-roformer-implementation/tasks.md:30"}],[2257,{"filePath":"specs/001-update-roformer-implementation/tasks.md","startLine":142,"endLine":154,"chunkText":"- Tests (T004-T015) MUST complete and FAIL before implementation (T016-T033)\n- T001-T003 (setup) before everything else\n- T016-T020 (data models) before T021-T024 (validators)\n- T025-T028 (model updates) before T031-T033 (loaders)\n- T029-T030 (normalization fixes) before T031-T033 (loaders)\n\n### Implementation Dependencies\n- T016 (ModelConfiguration) blocks T017, T018, T021-T024\n- T020 (ParameterValidationError) blocks T021-T024\n- T025-T030 (model updates) before T031-T033 (loading system)\n- T031-T033 (loading system) before T034-T038 (integration)\n- Implementation (T016-T038) before polish (T039-T050)\n","chunkId":"specs/001-update-roformer-implementation/tasks.md:31"}],[2258,{"filePath":"specs/001-update-roformer-implementation/tasks.md","startLine":152,"endLine":161,"chunkText":"- T031-T033 (loading system) before T034-T038 (integration)\n- Implementation (T016-T038) before polish (T039-T050)\n\n## Parallel Execution Examples\n\n### Phase 3.2 - Contract Tests (can run simultaneously)\n```\nTask: \"Contract test RoformerLoaderInterface.load_model in tests/contract/test_roformer_loader_interface.py\"\nTask: \"Contract test ParameterValidatorInterface.validate_required_parameters in tests/contract/test_parameter_validator_interface.py\" \nTask: \"Integration test existing older model compatibility in tests/integration/test_roformer_backward_compatibility.py\"","chunkId":"specs/001-update-roformer-implementation/tasks.md:32"}],[2259,{"filePath":"specs/001-update-roformer-implementation/tasks.md","startLine":159,"endLine":166,"chunkText":"Task: \"Contract test RoformerLoaderInterface.load_model in tests/contract/test_roformer_loader_interface.py\"\nTask: \"Contract test ParameterValidatorInterface.validate_required_parameters in tests/contract/test_parameter_validator_interface.py\" \nTask: \"Integration test existing older model compatibility in tests/integration/test_roformer_backward_compatibility.py\"\nTask: \"Integration test newer model with new parameters in tests/integration/test_roformer_new_parameters.py\"\n```\n\n### Phase 3.3 - Data Models (can run simultaneously)\n```","chunkId":"specs/001-update-roformer-implementation/tasks.md:33"}],[2260,{"filePath":"specs/001-update-roformer-implementation/tasks.md","startLine":164,"endLine":174,"chunkText":"\n### Phase 3.3 - Data Models (can run simultaneously)\n```\nTask: \"Implement ModelConfiguration dataclass in audio_separator/separator/roformer/model_configuration.py\"\nTask: \"Implement BSRoformerConfig class in audio_separator/separator/roformer/bs_roformer_config.py\"\nTask: \"Implement MelBandRoformerConfig class in audio_separator/separator/roformer/mel_band_roformer_config.py\"\nTask: \"Implement ParameterValidationError exception in audio_separator/separator/roformer/parameter_validation_error.py\"\n```\n\n### Phase 3.5 - Unit Tests (can run simultaneously)\n```","chunkId":"specs/001-update-roformer-implementation/tasks.md:34"}],[2261,{"filePath":"specs/001-update-roformer-implementation/tasks.md","startLine":172,"endLine":184,"chunkText":"\n### Phase 3.5 - Unit Tests (can run simultaneously)\n```\nTask: \"Unit tests for ModelConfiguration validation in tests/unit/test_model_configuration.py\"\nTask: \"Unit tests for ParameterValidator methods in tests/unit/test_parameter_validator.py\"\nTask: \"Unit tests for ConfigurationNormalizer methods in tests/unit/test_configuration_normalizer.py\"\nTask: \"Unit tests for fallback mechanism logic in tests/unit/test_fallback_loader.py\"\n```\n\n## Notes\n\n- [P] tasks = different files, no dependencies, can run in parallel\n- All tests must be written first and must fail before implementation (TDD)","chunkId":"specs/001-update-roformer-implementation/tasks.md:35"}],[2262,{"filePath":"specs/001-update-roformer-implementation/tasks.md","startLine":182,"endLine":194,"chunkText":"\n- [P] tasks = different files, no dependencies, can run in parallel\n- All tests must be written first and must fail before implementation (TDD)\n- Commit after each task completion\n- Manual testing (T047-T050) requires actual model files\n- Integration with existing test framework ensures no regression\n- Fallback mechanism provides safety net for edge cases\n\n## Validation Checklist\n\n- [x] All contracts have corresponding tests (T004-T008)\n- [x] All entities have model tasks (T016-T020)\n- [x] All tests come before implementation (Phase 3.2 before 3.3)","chunkId":"specs/001-update-roformer-implementation/tasks.md:36"}],[2263,{"filePath":"specs/001-update-roformer-implementation/tasks.md","startLine":192,"endLine":202,"chunkText":"- [x] All contracts have corresponding tests (T004-T008)\n- [x] All entities have model tasks (T016-T020)\n- [x] All tests come before implementation (Phase 3.2 before 3.3)\n- [x] Parallel tasks are truly independent (different files)\n- [x] Each task specifies exact file path\n- [x] No task modifies same file as another [P] task\n- [x] TDD ordering enforced (tests fail before implementation)\n- [x] Integration scenarios from quickstart covered (T009-T013)\n- [x] Audio quality validation included (T014-T015, T043)\n- [x] Manual testing milestones defined (T047-T050)\n","chunkId":"specs/001-update-roformer-implementation/tasks.md:37"}],[2264,{"filePath":"specs/main/plan.md","startLine":0,"endLine":13,"chunkText":"\n# Implementation Plan: [FEATURE]\n\n**Branch**: `[###-feature-name]` | **Date**: [DATE] | **Spec**: [link]\n**Input**: Feature specification from `/specs/[###-feature-name]/spec.md`\n\n## Execution Flow (/plan command scope)\n```\n1. Load feature spec from Input path\n   → If not found: ERROR \"No feature spec at {path}\"\n2. Fill Technical Context (scan for NEEDS CLARIFICATION)\n   → Detect Project Type from context (web=frontend+backend, mobile=app+api)\n   → Set Structure Decision based on project type\n3. Fill the Constitution Check section based on the content of the constitution document.","chunkId":"specs/main/plan.md:0"}],[2265,{"filePath":"specs/main/plan.md","startLine":11,"endLine":19,"chunkText":"   → Detect Project Type from context (web=frontend+backend, mobile=app+api)\n   → Set Structure Decision based on project type\n3. Fill the Constitution Check section based on the content of the constitution document.\n4. Evaluate Constitution Check section below\n   → If violations exist: Document in Complexity Tracking\n   → If no justification possible: ERROR \"Simplify approach first\"\n   → Update Progress Tracking: Initial Constitution Check\n5. Execute Phase 0 → research.md\n   → If NEEDS CLARIFICATION remain: ERROR \"Resolve unknowns\"","chunkId":"specs/main/plan.md:1"}],[2266,{"filePath":"specs/main/plan.md","startLine":17,"endLine":23,"chunkText":"   → Update Progress Tracking: Initial Constitution Check\n5. Execute Phase 0 → research.md\n   → If NEEDS CLARIFICATION remain: ERROR \"Resolve unknowns\"\n6. Execute Phase 1 → contracts, data-model.md, quickstart.md, agent-specific template file (e.g., `CLAUDE.md` for Claude Code, `.github/copilot-instructions.md` for GitHub Copilot, `GEMINI.md` for Gemini CLI, `QWEN.md` for Qwen Code or `AGENTS.md` for opencode).\n7. Re-evaluate Constitution Check section\n   → If new violations: Refactor design, return to Phase 1\n   → Update Progress Tracking: Post-Design Constitution Check","chunkId":"specs/main/plan.md:2"}],[2267,{"filePath":"specs/main/plan.md","startLine":21,"endLine":35,"chunkText":"7. Re-evaluate Constitution Check section\n   → If new violations: Refactor design, return to Phase 1\n   → Update Progress Tracking: Post-Design Constitution Check\n8. Plan Phase 2 → Describe task generation approach (DO NOT create tasks.md)\n9. STOP - Ready for /tasks command\n```\n\n**IMPORTANT**: The /plan command STOPS at step 7. Phases 2-4 are executed by other commands:\n- Phase 2: /tasks command creates tasks.md\n- Phase 3-4: Implementation execution (manual or via tools)\n\n## Summary\n[Extract from feature spec: primary requirement + technical approach from research]\n\n## Technical Context","chunkId":"specs/main/plan.md:3"}],[2268,{"filePath":"specs/main/plan.md","startLine":33,"endLine":41,"chunkText":"[Extract from feature spec: primary requirement + technical approach from research]\n\n## Technical Context\n**Language/Version**: [e.g., Python 3.11, Swift 5.9, Rust 1.75 or NEEDS CLARIFICATION]  \n**Primary Dependencies**: [e.g., FastAPI, UIKit, LLVM or NEEDS CLARIFICATION]  \n**Storage**: [if applicable, e.g., PostgreSQL, CoreData, files or N/A]  \n**Testing**: [e.g., pytest, XCTest, cargo test or NEEDS CLARIFICATION]  \n**Target Platform**: [e.g., Linux server, iOS 15+, WASM or NEEDS CLARIFICATION]\n**Project Type**: [single/web/mobile - determines source structure]  ","chunkId":"specs/main/plan.md:4"}],[2269,{"filePath":"specs/main/plan.md","startLine":39,"endLine":46,"chunkText":"**Testing**: [e.g., pytest, XCTest, cargo test or NEEDS CLARIFICATION]  \n**Target Platform**: [e.g., Linux server, iOS 15+, WASM or NEEDS CLARIFICATION]\n**Project Type**: [single/web/mobile - determines source structure]  \n**Performance Goals**: [domain-specific, e.g., 1000 req/s, 10k lines/sec, 60 fps or NEEDS CLARIFICATION]  \n**Constraints**: [domain-specific, e.g., <200ms p95, <100MB memory, offline-capable or NEEDS CLARIFICATION]  \n**Scale/Scope**: [domain-specific, e.g., 10k users, 1M LOC, 50 screens or NEEDS CLARIFICATION]\n\n## Constitution Check","chunkId":"specs/main/plan.md:5"}],[2270,{"filePath":"specs/main/plan.md","startLine":44,"endLine":55,"chunkText":"**Scale/Scope**: [domain-specific, e.g., 10k users, 1M LOC, 50 screens or NEEDS CLARIFICATION]\n\n## Constitution Check\n*GATE: Must pass before Phase 0 research. Re-check after Phase 1 design.*\n\n### I. Library-First Architecture\n- [ ] Core functionality implemented in `Separator` class or similar library pattern\n- [ ] CLI/Remote API are thin wrappers, not containing business logic\n- [ ] Clear separation between model architectures (MDX, VR, Demucs, MDXC)\n\n### II. Multi-Interface Consistency  \n- [ ] Feature accessible via Python API, CLI, and Remote API (if applicable)","chunkId":"specs/main/plan.md:6"}],[2271,{"filePath":"specs/main/plan.md","startLine":53,"endLine":66,"chunkText":"\n### II. Multi-Interface Consistency  \n- [ ] Feature accessible via Python API, CLI, and Remote API (if applicable)\n- [ ] Parameter names identical across all interfaces\n- [ ] Same model architectures supported across interfaces\n\n### III. Test-First Development (NON-NEGOTIABLE)\n- [ ] Tests written before implementation\n- [ ] Unit tests for all core functionality\n- [ ] Integration tests with audio validation (SSIM comparison)\n- [ ] CLI tests for all exposed functionality\n\n### IV. Performance & Resource Efficiency\n- [ ] Hardware acceleration support considered (CUDA, CoreML, DirectML)","chunkId":"specs/main/plan.md:7"}],[2272,{"filePath":"specs/main/plan.md","startLine":64,"endLine":80,"chunkText":"### IV. Performance & Resource Efficiency\n- [ ] Hardware acceleration support considered (CUDA, CoreML, DirectML)\n- [ ] Memory optimization for large files (streaming/batch processing)\n- [ ] Tunable parameters for different hardware capabilities\n\n### V. Model Architecture Separation\n- [ ] Each architecture in separate modules\n- [ ] Inherits from `CommonSeparator` pattern\n- [ ] Architecture-specific parameters isolated\n- [ ] Loading one architecture doesn't load others\n\n## Project Structure\n\n### Documentation (this feature)\n```\nspecs/[###-feature]/\n├── plan.md              # This file (/plan command output","chunkId":"specs/main/plan.md:8"}],[2273,{"filePath":"specs/main/plan.md","startLine":79,"endLine":102,"chunkText":"```\nspecs/[###-feature]/\n├── plan.md              # This file (/plan command output)\n├── research.md          # Phase 0 output (/plan command)\n├── data-model.md        # Phase 1 output (/plan command)\n├── quickstart.md        # Phase 1 output (/plan command)\n├── contracts/           # Phase 1 output (/plan command)\n└── tasks.md             # Phase 2 output (/tasks command - NOT created by /plan)\n```\n\n### Source Code (repository root)\n```\n# Option 1: Single project (DEFAULT)\nsrc/\n├── models/\n├── services/\n├── cli/\n└── lib/\n\ntests/\n├── contract/\n├── integration/\n└── unit/\n","chunkId":"specs/main/plan.md:9"}],[2274,{"filePath":"specs/main/plan.md","startLine":100,"endLine":129,"chunkText":"├── integration/\n└── unit/\n\n# Option 2: Web application (when \"frontend\" + \"backend\" detected)\nbackend/\n├── src/\n│   ├── models/\n│   ├── services/\n│   └── api/\n└── tests/\n\nfrontend/\n├── src/\n│   ├── components/\n│   ├── pages/\n│   └── services/\n└── tests/\n\n# Option 3: Mobile + API (when \"iOS/Android\" detected)\napi/\n└── [same as backend above]\n\nios/ or android/\n└── [platform-specific structure]\n```\n\n**Structure Decision**: [DEFAULT to Option 1 unless Technical Context indicates web/mobile app]\n\n## Phase 0: Outline & Research\n1. **Extract unknowns from Technical Context** above:","chunkId":"specs/main/plan.md:10"}],[2275,{"filePath":"specs/main/plan.md","startLine":127,"endLine":144,"chunkText":"\n## Phase 0: Outline & Research\n1. **Extract unknowns from Technical Context** above:\n   - For each NEEDS CLARIFICATION → research task\n   - For each dependency → best practices task\n   - For each integration → patterns task\n\n2. **Generate and dispatch research agents**:\n   ```\n   For each unknown in Technical Context:\n     Task: \"Research {unknown} for {feature context}\"\n   For each technology choice:\n     Task: \"Find best practices for {tech} in {domain}\"\n   ```\n\n3. **Consolidate findings** in `research.md` using format:\n   - Decision: [what was chosen]\n   - Rationale: [why chosen]","chunkId":"specs/main/plan.md:11"}],[2276,{"filePath":"specs/main/plan.md","startLine":142,"endLine":158,"chunkText":"3. **Consolidate findings** in `research.md` using format:\n   - Decision: [what was chosen]\n   - Rationale: [why chosen]\n   - Alternatives considered: [what else evaluated]\n\n**Output**: research.md with all NEEDS CLARIFICATION resolved\n\n## Phase 1: Design & Contracts\n*Prerequisites: research.md complete*\n\n1. **Extract entities from feature spec** → `data-model.md`:\n   - Entity name, fields, relationships\n   - Validation rules from requirements\n   - State transitions if applicable\n\n2. **Generate API contracts** from functional requirements:\n   - For each user action → endpoint","chunkId":"specs/main/plan.md:12"}],[2277,{"filePath":"specs/main/plan.md","startLine":157,"endLine":172,"chunkText":"2. **Generate API contracts** from functional requirements:\n   - For each user action → endpoint\n   - Use standard REST/GraphQL patterns\n   - Output OpenAPI/GraphQL schema to `/contracts/`\n\n3. **Generate contract tests** from contracts:\n   - One test file per endpoint\n   - Assert request/response schemas\n   - Tests must fail (no implementation yet)\n\n4. **Extract test scenarios** from user stories:\n   - Each story → integration test scenario\n   - Quickstart test = story validation steps\n\n5. **Update agent file incrementally** (O(1) operation):\n   - Run `.specify/scripts/bash/update-agent-context.sh cursor`","chunkId":"specs/main/plan.md:13"}],[2278,{"filePath":"specs/main/plan.md","startLine":170,"endLine":182,"chunkText":"\n5. **Update agent file incrementally** (O(1) operation):\n   - Run `.specify/scripts/bash/update-agent-context.sh cursor`\n     **IMPORTANT**: Execute it exactly as specified above. Do not add or remove any arguments.\n   - If exists: Add only NEW tech from current plan\n   - Preserve manual additions between markers\n   - Update recent changes (keep last 3)\n   - Keep under 150 lines for token efficiency\n   - Output to repository root\n\n**Output**: data-model.md, /contracts/*, failing tests, quickstart.md, agent-specific file\n\n## Phase 2: Task Planning Approach","chunkId":"specs/main/plan.md:14"}],[2279,{"filePath":"specs/main/plan.md","startLine":180,"endLine":194,"chunkText":"**Output**: data-model.md, /contracts/*, failing tests, quickstart.md, agent-specific file\n\n## Phase 2: Task Planning Approach\n*This section describes what the /tasks command will do - DO NOT execute during /plan*\n\n**Task Generation Strategy**:\n- Load `.specify/templates/tasks-template.md` as base\n- Generate tasks from Phase 1 design docs (contracts, data model, quickstart)\n- Each contract → contract test task [P]\n- Each entity → model creation task [P] \n- Each user story → integration test task\n- Implementation tasks to make tests pass\n\n**Ordering Strategy**:\n- TDD order: Tests before implementation ","chunkId":"specs/main/plan.md:15"}],[2280,{"filePath":"specs/main/plan.md","startLine":192,"endLine":206,"chunkText":"\n**Ordering Strategy**:\n- TDD order: Tests before implementation \n- Dependency order: Models before services before UI\n- Mark [P] for parallel execution (independent files)\n\n**Estimated Output**: 25-30 numbered, ordered tasks in tasks.md\n\n**IMPORTANT**: This phase is executed by the /tasks command, NOT by /plan\n\n## Phase 3+: Future Implementation\n*These phases are beyond the scope of the /plan command*\n\n**Phase 3**: Task execution (/tasks command creates tasks.md)  \n**Phase 4**: Implementation (execute tasks.md following constitutional principles)  ","chunkId":"specs/main/plan.md:16"}],[2281,{"filePath":"specs/main/plan.md","startLine":204,"endLine":214,"chunkText":"\n**Phase 3**: Task execution (/tasks command creates tasks.md)  \n**Phase 4**: Implementation (execute tasks.md following constitutional principles)  \n**Phase 5**: Validation (run tests, execute quickstart.md, performance validation)\n\n## Complexity Tracking\n*Fill ONLY if Constitution Check has violations that must be justified*\n\n| Violation | Why Needed | Simpler Alternative Rejected Because |\n|-----------|------------|-------------------------------------|\n| [e.g., 4th project] | [current need] | [why 3 projects insufficient] |","chunkId":"specs/main/plan.md:17"}],[2282,{"filePath":"specs/main/plan.md","startLine":212,"endLine":225,"chunkText":"| Violation | Why Needed | Simpler Alternative Rejected Because |\n|-----------|------------|-------------------------------------|\n| [e.g., 4th project] | [current need] | [why 3 projects insufficient] |\n| [e.g., Repository pattern] | [specific problem] | [why direct DB access insufficient] |\n\n\n## Progress Tracking\n*This checklist is updated during execution flow*\n\n**Phase Status**:\n- [ ] Phase 0: Research complete (/plan command)\n- [ ] Phase 1: Design complete (/plan command)\n- [ ] Phase 2: Task planning complete (/plan command - describe approach only)\n- [ ] Phase 3: Tasks generated (/tasks command)","chunkId":"specs/main/plan.md:18"}],[2283,{"filePath":"specs/main/plan.md","startLine":223,"endLine":237,"chunkText":"- [ ] Phase 1: Design complete (/plan command)\n- [ ] Phase 2: Task planning complete (/plan command - describe approach only)\n- [ ] Phase 3: Tasks generated (/tasks command)\n- [ ] Phase 4: Implementation complete\n- [ ] Phase 5: Validation passed\n\n**Gate Status**:\n- [ ] Initial Constitution Check: PASS\n- [ ] Post-Design Constitution Check: PASS\n- [ ] All NEEDS CLARIFICATION resolved\n- [ ] Complexity deviations documented\n\n---\n*Based on Constitution v1.0.0 - See `.specify/memory/constitution.md`*\n","chunkId":"specs/main/plan.md:19"}],[2284,{"filePath":"tests/README.md","startLine":0,"endLine":18,"chunkText":"# Audio Separator Tests\n\nThis directory contains tests for the audio-separator project.\n\n## Test Structure\n\n### Unit Tests (`tests/unit/`)\n- **Core functionality tests**: `test_cli.py`, `test_stft.py`\n- **Remote API tests**: `test_remote_api_client.py`, `test_remote_cli.py`\n\n### Integration Tests (`tests/integration/`)\n- **CLI integration tests**: `test_cli_integration.py`\n- **Audio output validation**: `test_separator_output_integration.py`\n- **Remote API integration tests**: `test_remote_api_integration.py`\n\n## Remote API Tests\n\nThe project includes comprehensive tests for the remote API functionality:\n","chunkId":"tests/README.md:0"}],[2285,{"filePath":"tests/README.md","startLine":17,"endLine":34,"chunkText":"The project includes comprehensive tests for the remote API functionality:\n\n- **61 tests** covering all remote API components\n- **Unit tests** with mocked HTTP responses\n- **Integration tests** with a mock HTTP server\n- **End-to-end workflow tests**\n\nKey features tested:\n- Multiple model support (upload once, process with multiple models)\n- Full parameter compatibility with local CLI\n- Asynchronous processing with job polling\n- Error handling and edge cases\n- File upload/download workflows\n\nSee `tests/remote/README.md` for detailed documentation on remote API tests.\n\n## Audio Validation\n","chunkId":"tests/README.md:1"}],[2286,{"filePath":"tests/README.md","startLine":33,"endLine":41,"chunkText":"## Audio Validation\n\nThe integration tests now include validation of output audio files by comparing waveform and spectrogram images with reference images. This helps ensure that the audio separation results remain consistent across different runs and code changes.\n\n### How It Works\n\n1. Reference waveform and spectrogram images are generated from expected output files\n2. During test execution, the same images are generated for the actual output files\n3. The images are compared using Structural Similarity Index (SSIM) to measure perceptual similarity","chunkId":"tests/README.md:2"}],[2287,{"filePath":"tests/README.md","startLine":40,"endLine":50,"chunkText":"2. During test execution, the same images are generated for the actual output files\n3. The images are compared using Structural Similarity Index (SSIM) to measure perceptual similarity\n4. If the images differ significantly, the test fails, indicating a change in the audio output\n\n### Image Comparison with SSIM\n\nThe tests use Structural Similarity Index Measure (SSIM) to compare images, which is more robust than pixel-by-pixel comparison:\n\n- SSIM considers structural information in the images\n- It's more resilient to small spatial shifts or offsets\n- It better matches human perception of image similarity","chunkId":"tests/README.md:3"}],[2288,{"filePath":"tests/README.md","startLine":49,"endLine":58,"chunkText":"- It's more resilient to small spatial shifts or offsets\n- It better matches human perception of image similarity\n- It works well across different environments (local machines vs CI servers)\n\nThe comparison uses a minimum similarity threshold (0.0-1.0):\n- **Higher values** (closer to 1.0) require images to be **more similar**\n- **Lower values** (closer to 0.0) are **more permissive**\n- A value of 0.99 requires 99% similarity between images\n- A value of 0.0 would consider any images to match\n","chunkId":"tests/README.md:4"}],[2289,{"filePath":"tests/README.md","startLine":57,"endLine":67,"chunkText":"- A value of 0.0 would consider any images to match\n\nThe default threshold is set to 0.999, which is quite strict. However, model-specific thresholds can be set to accommodate different models' behavior.\n\n#### Model-Specific Thresholds\n\nSome models inherently produce slightly different outputs on different runs, even with the same input. To accommodate these models, you can set model-specific thresholds in the `MODEL_SIMILARITY_THRESHOLDS` dictionary:\n\n```python\nMODEL_SIMILARITY_THRESHOLDS = {\n    \"htdemucs_6s.yaml\": 0.990,  # Demucs models need a lower threshold","chunkId":"tests/README.md:5"}],[2290,{"filePath":"tests/README.md","startLine":66,"endLine":81,"chunkText":"MODEL_SIMILARITY_THRESHOLDS = {\n    \"htdemucs_6s.yaml\": 0.990,  # Demucs models need a lower threshold\n    # Add other models that need custom thresholds here\n}\n```\n\nThis allows you to maintain a high threshold for most models while being more flexible with models that naturally exhibit more variation.\n\n### Generating Reference Images\n\nTo generate or update the reference images, use the script provided:\n\n```bash\npython tests/integration/generate_reference_images.py\n```\n","chunkId":"tests/README.md:6"}],[2291,{"filePath":"tests/README.md","startLine":80,"endLine":96,"chunkText":"```\n\nThis script will create waveform and spectrogram images for all expected output files and store them in the `tests/inputs/reference` directory.\n\n### Skipping Validation\n\nIf you need to skip the audio validation (e.g., when you're intentionally changing the output), you can set the environment variable `SKIP_AUDIO_VALIDATION=1`:\n\n```bash\nSKIP_AUDIO_VALIDATION=1 pytest tests/integration/test_cli_integration.py\n```\n\n### Adding New Models\n\nWhen adding a new model to the tests:\n\n1. Add the model and its expected output files to the `MODEL_PARAMS` list in `test_cli_integration.py`","chunkId":"tests/README.md:7"}],[2292,{"filePath":"tests/README.md","startLine":95,"endLine":109,"chunkText":"\n1. Add the model and its expected output files to the `MODEL_PARAMS` list in `test_cli_integration.py`\n2. Run the integration test to generate the output files\n3. Run the `generate_reference_images.py` script to create the reference images\n4. Run the tests again to validate the output files\n5. If necessary, add a custom similarity threshold for the new model in `MODEL_SIMILARITY_THRESHOLDS`\n\n### Debugging\n\nTo see detailed validation results, run pytest with the `-sv` flag:\n\n```bash\npytest tests/integration/test_cli_integration.py -sv\n```\n","chunkId":"tests/README.md:8"}],[2293,{"filePath":"tests/README.md","startLine":108,"endLine":142,"chunkText":"```\n\nThis will show the similarity scores for each comparison and whether they passed or failed.\n\n## Running Tests\n\nTo run all tests:\n\n```bash\npytest\n```\n\nTo run specific tests:\n\n```bash\npytest tests/unit/\npytest tests/integration/\n```\n\nTo run remote API tests:\n\n```bash\n# All remote API tests (unit + integration)\npytest tests/unit/test_remote*.py tests/integration/test_remote*.py -v\n\n# Only unit tests (fast, no HTTP server)\npytest tests/unit/test_remote*.py -v\n\n# Only integration tests (with mock HTTP server)\npytest tests/integration/test_remote*.py -v\n```\n\nTo run with coverage:\n\n```bash","chunkId":"tests/README.md:9"}],[2294,{"filePath":"tests/README.md","startLine":141,"endLine":147,"chunkText":"\n```bash\npytest --cov=audio_separator\n\n# Coverage for remote API specifically\npytest tests/unit/test_remote*.py tests/integration/test_remote*.py --cov=audio_separator.remote --cov-report=html\n``` ","chunkId":"tests/README.md:10"}],[2295,{"filePath":"tests/contract/test_parameter_validator_interface.py","startLine":0,"endLine":18,"chunkText":"\"\"\"\nContract tests for ParameterValidatorInterface.\nThese tests verify the parameter validation interface contracts.\n\"\"\"\n\nimport pytest\nfrom unittest.mock import Mock\nfrom typing import Dict, Any, List\n\n# Import interfaces from contracts\nimport sys\nimport os\n\n# Find project root dynamically\ncurrent_dir = os.path.dirname(os.path.abspath(__file__))\nproject_root = current_dir\n# Go up until we find the project root (contains specs/ directory)\nwhile project_root and not os.path.exists(os.path.join(project_root, 'specs')):\n    parent = os.path.dirname(project_root)","chunkId":"tests/contract/test_parameter_validator_interface.py:0"}],[2296,{"filePath":"tests/contract/test_parameter_validator_interface.py","startLine":16,"endLine":29,"chunkText":"# Go up until we find the project root (contains specs/ directory)\nwhile project_root and not os.path.exists(os.path.join(project_root, 'specs')):\n    parent = os.path.dirname(project_root)\n    if parent == project_root:  # Reached filesystem root\n        break\n    project_root = parent\n\ncontracts_path = os.path.join(project_root, 'specs', '001-update-roformer-implementation', 'contracts')\nif os.path.exists(contracts_path):\n    sys.path.append(contracts_path)\n\nfrom parameter_validator_interface import (\n    ParameterValidatorInterface,\n    BSRoformerValidatorInterface,","chunkId":"tests/contract/test_parameter_validator_interface.py:1"}],[2297,{"filePath":"tests/contract/test_parameter_validator_interface.py","startLine":27,"endLine":44,"chunkText":"from parameter_validator_interface import (\n    ParameterValidatorInterface,\n    BSRoformerValidatorInterface,\n    MelBandRoformerValidatorInterface,\n    ConfigurationNormalizerInterface,\n    ValidationIssue,\n    ValidationSeverity\n)\n\n\nclass TestParameterValidatorInterface:\n    \"\"\"Test the ParameterValidator interface contract.\"\"\"\n    \n    def test_validate_required_parameters_contract(self):\n        \"\"\"Test validate_required_parameters interface contract.\"\"\"\n        validator = Mock(spec=ParameterValidatorInterface)\n        \n        # Mock valid configuration - should return no issues","chunkId":"tests/contract/test_parameter_validator_interface.py:2"}],[2298,{"filePath":"tests/contract/test_parameter_validator_interface.py","startLine":42,"endLine":55,"chunkText":"        validator = Mock(spec=ParameterValidatorInterface)\n        \n        # Mock valid configuration - should return no issues\n        validator.validate_required_parameters.return_value = []\n        \n        config = {\"dim\": 512, \"depth\": 6}\n        issues = validator.validate_required_parameters(config, \"bs_roformer\")\n        \n        assert isinstance(issues, list)\n        assert len(issues) == 0  # Valid config should have no issues\n        \n        # Mock invalid configuration - missing required parameters\n        missing_param_issue = ValidationIssue(\n            severity=ValidationSeverity.ERROR,","chunkId":"tests/contract/test_parameter_validator_interface.py:3"}],[2299,{"filePath":"tests/contract/test_parameter_validator_interface.py","startLine":53,"endLine":64,"chunkText":"        # Mock invalid configuration - missing required parameters\n        missing_param_issue = ValidationIssue(\n            severity=ValidationSeverity.ERROR,\n            parameter_name=\"dim\",\n            message=\"Required parameter 'dim' is missing\",\n            suggested_fix=\"Add 'dim' parameter with positive integer value\",\n            current_value=None,\n            expected_value=\"positive integer\"\n        )\n        validator.validate_required_parameters.return_value = [missing_param_issue]\n        \n        incomplete_config = {\"depth\": 6}  # Missing dim","chunkId":"tests/contract/test_parameter_validator_interface.py:4"}],[2300,{"filePath":"tests/contract/test_parameter_validator_interface.py","startLine":62,"endLine":73,"chunkText":"        validator.validate_required_parameters.return_value = [missing_param_issue]\n        \n        incomplete_config = {\"depth\": 6}  # Missing dim\n        issues = validator.validate_required_parameters(incomplete_config, \"bs_roformer\")\n        \n        assert isinstance(issues, list)\n        assert len(issues) > 0\n        assert all(isinstance(issue, ValidationIssue) for issue in issues)\n        assert issues[0].severity == ValidationSeverity.ERROR\n        assert issues[0].parameter_name == \"dim\"\n    \n    def test_validate_parameter_types_contract(self):","chunkId":"tests/contract/test_parameter_validator_interface.py:5"}],[2301,{"filePath":"tests/contract/test_parameter_validator_interface.py","startLine":71,"endLine":83,"chunkText":"        assert issues[0].parameter_name == \"dim\"\n    \n    def test_validate_parameter_types_contract(self):\n        \"\"\"Test validate_parameter_types interface contract.\"\"\"\n        validator = Mock(spec=ParameterValidatorInterface)\n        \n        # Mock type validation error\n        type_error_issue = ValidationIssue(\n            severity=ValidationSeverity.ERROR,\n            parameter_name=\"dim\",\n            message=\"Parameter 'dim' must be an integer\",\n            suggested_fix=\"Change 'dim' value to an integer\",\n            current_value=\"512\",  # String instead of int","chunkId":"tests/contract/test_parameter_validator_interface.py:6"}],[2302,{"filePath":"tests/contract/test_parameter_validator_interface.py","startLine":81,"endLine":93,"chunkText":"            message=\"Parameter 'dim' must be an integer\",\n            suggested_fix=\"Change 'dim' value to an integer\",\n            current_value=\"512\",  # String instead of int\n            expected_value=\"integer\"\n        )\n        validator.validate_parameter_types.return_value = [type_error_issue]\n        \n        config = {\"dim\": \"512\", \"depth\": 6}  # dim should be int, not string\n        issues = validator.validate_parameter_types(config)\n        \n        assert isinstance(issues, list)\n        assert len(issues) > 0\n        assert issues[0].severity == ValidationSeverity.ERROR","chunkId":"tests/contract/test_parameter_validator_interface.py:7"}],[2303,{"filePath":"tests/contract/test_parameter_validator_interface.py","startLine":91,"endLine":104,"chunkText":"        assert isinstance(issues, list)\n        assert len(issues) > 0\n        assert issues[0].severity == ValidationSeverity.ERROR\n        assert issues[0].parameter_name == \"dim\"\n        assert issues[0].current_value == \"512\"\n    \n    def test_validate_parameter_ranges_contract(self):\n        \"\"\"Test validate_parameter_ranges interface contract.\"\"\"\n        validator = Mock(spec=ParameterValidatorInterface)\n        \n        # Mock range validation error\n        range_error_issue = ValidationIssue(\n            severity=ValidationSeverity.ERROR,\n            parameter_name=\"attn_dropout\",","chunkId":"tests/contract/test_parameter_validator_interface.py:8"}],[2304,{"filePath":"tests/contract/test_parameter_validator_interface.py","startLine":102,"endLine":113,"chunkText":"        range_error_issue = ValidationIssue(\n            severity=ValidationSeverity.ERROR,\n            parameter_name=\"attn_dropout\",\n            message=\"Parameter 'attn_dropout' must be between 0.0 and 1.0\",\n            suggested_fix=\"Set 'attn_dropout' to a value between 0.0 and 1.0\",\n            current_value=1.5,\n            expected_value=\"0.0 <= value <= 1.0\"\n        )\n        validator.validate_parameter_ranges.return_value = [range_error_issue]\n        \n        config = {\"dim\": 512, \"depth\": 6, \"attn_dropout\": 1.5}  # Invalid range\n        issues = validator.validate_parameter_ranges(config)","chunkId":"tests/contract/test_parameter_validator_interface.py:9"}],[2305,{"filePath":"tests/contract/test_parameter_validator_interface.py","startLine":112,"endLine":124,"chunkText":"        config = {\"dim\": 512, \"depth\": 6, \"attn_dropout\": 1.5}  # Invalid range\n        issues = validator.validate_parameter_ranges(config)\n        \n        assert isinstance(issues, list)\n        assert len(issues) > 0\n        assert issues[0].severity == ValidationSeverity.ERROR\n        assert issues[0].parameter_name == \"attn_dropout\"\n        assert issues[0].current_value == 1.5\n    \n    def test_validate_parameter_compatibility_contract(self):\n        \"\"\"Test validate_parameter_compatibility interface contract.\"\"\"\n        validator = Mock(spec=ParameterValidatorInterface)\n        ","chunkId":"tests/contract/test_parameter_validator_interface.py:10"}],[2306,{"filePath":"tests/contract/test_parameter_validator_interface.py","startLine":122,"endLine":133,"chunkText":"        \"\"\"Test validate_parameter_compatibility interface contract.\"\"\"\n        validator = Mock(spec=ParameterValidatorInterface)\n        \n        # Mock compatibility validation warning\n        compatibility_issue = ValidationIssue(\n            severity=ValidationSeverity.WARNING,\n            parameter_name=\"sage_attention\",\n            message=\"sage_attention=True may conflict with flash_attn=True\",\n            suggested_fix=\"Consider using only one attention mechanism\",\n            current_value=True,\n            expected_value=\"False when flash_attn=True\"\n        )","chunkId":"tests/contract/test_parameter_validator_interface.py:11"}],[2307,{"filePath":"tests/contract/test_parameter_validator_interface.py","startLine":131,"endLine":144,"chunkText":"            current_value=True,\n            expected_value=\"False when flash_attn=True\"\n        )\n        validator.validate_parameter_compatibility.return_value = [compatibility_issue]\n        \n        config = {\"sage_attention\": True, \"flash_attn\": True}\n        issues = validator.validate_parameter_compatibility(config)\n        \n        assert isinstance(issues, list)\n        assert len(issues) > 0\n        assert issues[0].severity == ValidationSeverity.WARNING\n        assert \"conflict\" in issues[0].message.lower()\n    \n    def test_validate_normalization_config_contract(self):","chunkId":"tests/contract/test_parameter_validator_interface.py:12"}],[2308,{"filePath":"tests/contract/test_parameter_validator_interface.py","startLine":142,"endLine":155,"chunkText":"        assert \"conflict\" in issues[0].message.lower()\n    \n    def test_validate_normalization_config_contract(self):\n        \"\"\"Test validate_normalization_config interface contract.\"\"\"\n        validator = Mock(spec=ParameterValidatorInterface)\n        \n        # Mock valid normalization config\n        validator.validate_normalization_config.return_value = []\n        \n        issues = validator.validate_normalization_config(\"layer_norm\")\n        assert isinstance(issues, list)\n        assert len(issues) == 0\n        \n        # Mock invalid normalization config","chunkId":"tests/contract/test_parameter_validator_interface.py:13"}],[2309,{"filePath":"tests/contract/test_parameter_validator_interface.py","startLine":153,"endLine":165,"chunkText":"        assert len(issues) == 0\n        \n        # Mock invalid normalization config\n        norm_error_issue = ValidationIssue(\n            severity=ValidationSeverity.ERROR,\n            parameter_name=\"norm\",\n            message=\"Unknown normalization type 'invalid_norm'\",\n            suggested_fix=\"Use one of: 'layer_norm', 'batch_norm', 'rms_norm', or None\",\n            current_value=\"invalid_norm\",\n            expected_value=\"valid normalization type\"\n        )\n        validator.validate_normalization_config.return_value = [norm_error_issue]\n        ","chunkId":"tests/contract/test_parameter_validator_interface.py:14"}],[2310,{"filePath":"tests/contract/test_parameter_validator_interface.py","startLine":163,"endLine":177,"chunkText":"        )\n        validator.validate_normalization_config.return_value = [norm_error_issue]\n        \n        issues = validator.validate_normalization_config(\"invalid_norm\")\n        assert isinstance(issues, list)\n        assert len(issues) > 0\n        assert issues[0].parameter_name == \"norm\"\n    \n    def test_get_parameter_defaults_contract(self):\n        \"\"\"Test get_parameter_defaults interface contract.\"\"\"\n        validator = Mock(spec=ParameterValidatorInterface)\n        \n        # Mock default parameters\n        default_params = {\n            \"mlp_expansion_factor\": 4,","chunkId":"tests/contract/test_parameter_validator_interface.py:15"}],[2311,{"filePath":"tests/contract/test_parameter_validator_interface.py","startLine":175,"endLine":189,"chunkText":"        # Mock default parameters\n        default_params = {\n            \"mlp_expansion_factor\": 4,\n            \"sage_attention\": False,\n            \"zero_dc\": True,\n            \"use_torch_checkpoint\": False,\n            \"skip_connection\": False\n        }\n        validator.get_parameter_defaults.return_value = default_params\n        \n        defaults = validator.get_parameter_defaults(\"bs_roformer\")\n        \n        assert isinstance(defaults, dict)\n        assert \"mlp_expansion_factor\" in defaults\n        assert defaults[\"mlp_expansion_factor\"] == 4","chunkId":"tests/contract/test_parameter_validator_interface.py:16"}],[2312,{"filePath":"tests/contract/test_parameter_validator_interface.py","startLine":187,"endLine":201,"chunkText":"        assert isinstance(defaults, dict)\n        assert \"mlp_expansion_factor\" in defaults\n        assert defaults[\"mlp_expansion_factor\"] == 4\n        assert isinstance(defaults[\"sage_attention\"], bool)\n    \n    def test_apply_parameter_defaults_contract(self):\n        \"\"\"Test apply_parameter_defaults interface contract.\"\"\"\n        validator = Mock(spec=ParameterValidatorInterface)\n        \n        # Mock config with defaults applied\n        input_config = {\"dim\": 512, \"depth\": 6}\n        expected_config = {\n            \"dim\": 512,\n            \"depth\": 6,\n            \"mlp_expansion_factor\": 4,","chunkId":"tests/contract/test_parameter_validator_interface.py:17"}],[2313,{"filePath":"tests/contract/test_parameter_validator_interface.py","startLine":199,"endLine":215,"chunkText":"            \"dim\": 512,\n            \"depth\": 6,\n            \"mlp_expansion_factor\": 4,\n            \"sage_attention\": False,\n            \"zero_dc\": True\n        }\n        validator.apply_parameter_defaults.return_value = expected_config\n        \n        result_config = validator.apply_parameter_defaults(input_config, \"bs_roformer\")\n        \n        assert isinstance(result_config, dict)\n        assert \"dim\" in result_config\n        assert \"mlp_expansion_factor\" in result_config\n        assert result_config[\"dim\"] == 512\n        assert result_config[\"mlp_expansion_factor\"] == 4\n\n","chunkId":"tests/contract/test_parameter_validator_interface.py:18"}],[2314,{"filePath":"tests/contract/test_parameter_validator_interface.py","startLine":216,"endLine":227,"chunkText":"class TestBSRoformerValidatorInterface:\n    \"\"\"Test the BSRoformer-specific validator interface contract.\"\"\"\n    \n    def test_validate_freqs_per_bands_contract(self):\n        \"\"\"Test validate_freqs_per_bands interface contract.\"\"\"\n        validator = Mock(spec=BSRoformerValidatorInterface)\n        \n        # Mock validation error for mismatched frequency bands\n        freq_error_issue = ValidationIssue(\n            severity=ValidationSeverity.ERROR,\n            parameter_name=\"freqs_per_bands\",\n            message=\"Sum of freqs_per_bands (126) does not match expected frequency bins (129)\",","chunkId":"tests/contract/test_parameter_validator_interface.py:19"}],[2315,{"filePath":"tests/contract/test_parameter_validator_interface.py","startLine":225,"endLine":235,"chunkText":"            severity=ValidationSeverity.ERROR,\n            parameter_name=\"freqs_per_bands\",\n            message=\"Sum of freqs_per_bands (126) does not match expected frequency bins (129)\",\n            suggested_fix=\"Adjust freqs_per_bands to sum to 129\",\n            current_value=(2, 4, 8, 16, 32, 64),  # sums to 126\n            expected_value=\"tuple summing to 129\"\n        )\n        validator.validate_freqs_per_bands.return_value = [freq_error_issue]\n        \n        freqs_per_bands = (2, 4, 8, 16, 32, 64)\n        stft_config = {\"n_fft\": 256}","chunkId":"tests/contract/test_parameter_validator_interface.py:20"}],[2316,{"filePath":"tests/contract/test_parameter_validator_interface.py","startLine":233,"endLine":247,"chunkText":"        \n        freqs_per_bands = (2, 4, 8, 16, 32, 64)\n        stft_config = {\"n_fft\": 256}\n        issues = validator.validate_freqs_per_bands(freqs_per_bands, stft_config)\n        \n        assert isinstance(issues, list)\n        assert len(issues) > 0\n        assert issues[0].parameter_name == \"freqs_per_bands\"\n        assert \"sum\" in issues[0].message.lower()\n    \n    def test_calculate_expected_freqs_contract(self):\n        \"\"\"Test calculate_expected_freqs interface contract.\"\"\"\n        validator = Mock(spec=BSRoformerValidatorInterface)\n        \n        # Mock expected frequency calculation","chunkId":"tests/contract/test_parameter_validator_interface.py:21"}],[2317,{"filePath":"tests/contract/test_parameter_validator_interface.py","startLine":245,"endLine":255,"chunkText":"        validator = Mock(spec=BSRoformerValidatorInterface)\n        \n        # Mock expected frequency calculation\n        validator.calculate_expected_freqs.return_value = 129  # n_fft//2 + 1 for n_fft=256\n        \n        expected_freqs = validator.calculate_expected_freqs(256)\n        \n        assert isinstance(expected_freqs, int)\n        assert expected_freqs > 0\n\n","chunkId":"tests/contract/test_parameter_validator_interface.py:22"}],[2318,{"filePath":"tests/contract/test_parameter_validator_interface.py","startLine":256,"endLine":270,"chunkText":"class TestMelBandRoformerValidatorInterface:\n    \"\"\"Test the MelBandRoformer-specific validator interface contract.\"\"\"\n    \n    def test_validate_num_bands_contract(self):\n        \"\"\"Test validate_num_bands interface contract.\"\"\"\n        validator = Mock(spec=MelBandRoformerValidatorInterface)\n        \n        # Mock successful validation\n        validator.validate_num_bands.return_value = []\n        \n        issues = validator.validate_num_bands(64, 44100)\n        assert isinstance(issues, list)\n        assert len(issues) == 0\n        \n        # Mock validation error for invalid band count","chunkId":"tests/contract/test_parameter_validator_interface.py:23"}],[2319,{"filePath":"tests/contract/test_parameter_validator_interface.py","startLine":268,"endLine":282,"chunkText":"        assert len(issues) == 0\n        \n        # Mock validation error for invalid band count\n        band_error_issue = ValidationIssue(\n            severity=ValidationSeverity.ERROR,\n            parameter_name=\"num_bands\",\n            message=\"num_bands must be positive integer\",\n            suggested_fix=\"Set num_bands to a positive integer value\",\n            current_value=0,\n            expected_value=\"positive integer\"\n        )\n        validator.validate_num_bands.return_value = [band_error_issue]\n        \n        issues = validator.validate_num_bands(0, 44100)\n        assert len(issues) > 0","chunkId":"tests/contract/test_parameter_validator_interface.py:24"}],[2320,{"filePath":"tests/contract/test_parameter_validator_interface.py","startLine":280,"endLine":296,"chunkText":"        \n        issues = validator.validate_num_bands(0, 44100)\n        assert len(issues) > 0\n        assert issues[0].parameter_name == \"num_bands\"\n    \n    def test_validate_sample_rate_contract(self):\n        \"\"\"Test validate_sample_rate interface contract.\"\"\"\n        validator = Mock(spec=MelBandRoformerValidatorInterface)\n        \n        # Mock sample rate validation\n        validator.validate_sample_rate.return_value = []\n        \n        issues = validator.validate_sample_rate(44100)\n        assert isinstance(issues, list)\n        assert len(issues) == 0\n\n","chunkId":"tests/contract/test_parameter_validator_interface.py:25"}],[2321,{"filePath":"tests/contract/test_parameter_validator_interface.py","startLine":297,"endLine":309,"chunkText":"class TestConfigurationNormalizerInterface:\n    \"\"\"Test the ConfigurationNormalizer interface contract.\"\"\"\n    \n    def test_normalize_config_format_contract(self):\n        \"\"\"Test normalize_config_format interface contract.\"\"\"\n        normalizer = Mock(spec=ConfigurationNormalizerInterface)\n        \n        # Mock normalization from object to dict\n        normalized_config = {\"dim\": 512, \"depth\": 6}\n        normalizer.normalize_config_format.return_value = normalized_config\n        \n        # Test with mock object input\n        raw_config = Mock()","chunkId":"tests/contract/test_parameter_validator_interface.py:26"}],[2322,{"filePath":"tests/contract/test_parameter_validator_interface.py","startLine":307,"endLine":322,"chunkText":"        \n        # Test with mock object input\n        raw_config = Mock()\n        result = normalizer.normalize_config_format(raw_config)\n        \n        assert isinstance(result, dict)\n        assert \"dim\" in result\n        assert \"depth\" in result\n    \n    def test_map_legacy_parameters_contract(self):\n        \"\"\"Test map_legacy_parameters interface contract.\"\"\"\n        normalizer = Mock(spec=ConfigurationNormalizerInterface)\n        \n        # Mock legacy parameter mapping\n        legacy_config = {\"old_param_name\": 512}\n        mapped_config = {\"dim\": 512}  # old_param_name -> dim","chunkId":"tests/contract/test_parameter_validator_interface.py:27"}],[2323,{"filePath":"tests/contract/test_parameter_validator_interface.py","startLine":320,"endLine":334,"chunkText":"        # Mock legacy parameter mapping\n        legacy_config = {\"old_param_name\": 512}\n        mapped_config = {\"dim\": 512}  # old_param_name -> dim\n        normalizer.map_legacy_parameters.return_value = mapped_config\n        \n        result = normalizer.map_legacy_parameters(legacy_config)\n        \n        assert isinstance(result, dict)\n        assert \"dim\" in result\n        assert \"old_param_name\" not in result\n    \n    def test_extract_nested_config_contract(self):\n        \"\"\"Test extract_nested_config interface contract.\"\"\"\n        normalizer = Mock(spec=ConfigurationNormalizerInterface)\n        ","chunkId":"tests/contract/test_parameter_validator_interface.py:28"}],[2324,{"filePath":"tests/contract/test_parameter_validator_interface.py","startLine":332,"endLine":345,"chunkText":"        \"\"\"Test extract_nested_config interface contract.\"\"\"\n        normalizer = Mock(spec=ConfigurationNormalizerInterface)\n        \n        # Mock nested config extraction\n        normalizer.extract_nested_config.return_value = \"layer_norm\"\n        \n        config = {\"model\": {\"norm\": \"layer_norm\"}}\n        result = normalizer.extract_nested_config(config, \"model.norm\")\n        \n        assert result == \"layer_norm\"\n        \n        # Mock missing nested config\n        normalizer.extract_nested_config.return_value = None\n        ","chunkId":"tests/contract/test_parameter_validator_interface.py:29"}],[2325,{"filePath":"tests/contract/test_parameter_validator_interface.py","startLine":343,"endLine":355,"chunkText":"        # Mock missing nested config\n        normalizer.extract_nested_config.return_value = None\n        \n        result = normalizer.extract_nested_config(config, \"model.missing_field\")\n        assert result is None\n\n\n# TDD placeholder test removed - implementation is now complete\n\n\nif __name__ == \"__main__\":\n    pytest.main([__file__, \"-v\"])\n","chunkId":"tests/contract/test_parameter_validator_interface.py:30"}],[2326,{"filePath":"tests/contract/test_roformer_loader_interface.py","startLine":0,"endLine":19,"chunkText":"\"\"\"\nContract tests for RoformerLoaderInterface.\nThese tests verify the interface contracts defined in the specification.\n\"\"\"\n\nimport pytest\nimport tempfile\nimport os\nfrom unittest.mock import Mock, patch\nfrom typing import Dict, Any\n\n# Import interfaces from contracts\nimport sys\n\n# Find project root dynamically\ncurrent_dir = os.path.dirname(os.path.abspath(__file__))\nproject_root = current_dir\n# Go up until we find the project root (contains specs/ directory)\nwhile project_root and not os.path.exists(os.path.join(project_root, 'specs')):\n    parent = os.path.dirname(project_root)","chunkId":"tests/contract/test_roformer_loader_interface.py:0"}],[2327,{"filePath":"tests/contract/test_roformer_loader_interface.py","startLine":17,"endLine":32,"chunkText":"# Go up until we find the project root (contains specs/ directory)\nwhile project_root and not os.path.exists(os.path.join(project_root, 'specs')):\n    parent = os.path.dirname(project_root)\n    if parent == project_root:  # Reached filesystem root\n        break\n    project_root = parent\n\ncontracts_path = os.path.join(project_root, 'specs', '001-update-roformer-implementation', 'contracts')\nif os.path.exists(contracts_path):\n    sys.path.append(contracts_path)\n\nfrom roformer_loader_interface import (\n    RoformerLoaderInterface, \n    ModelLoadingResult, \n    ModelConfiguration, \n    RoformerType, ","chunkId":"tests/contract/test_roformer_loader_interface.py:1"}],[2328,{"filePath":"tests/contract/test_roformer_loader_interface.py","startLine":30,"endLine":48,"chunkText":"    ModelLoadingResult, \n    ModelConfiguration, \n    RoformerType, \n    ImplementationVersion,\n    ParameterValidationError\n)\n\n\nclass TestRoformerLoaderInterface:\n    \"\"\"Test the RoformerLoader interface contract.\"\"\"\n    \n    def test_load_model_success_contract(self):\n        \"\"\"Test that load_model returns proper ModelLoadingResult on success.\"\"\"\n        # This test MUST FAIL initially - no implementation exists yet\n        \n        # Mock implementation for contract testing\n        loader = Mock(spec=RoformerLoaderInterface)\n        \n        # Configure mock to return expected result structure","chunkId":"tests/contract/test_roformer_loader_interface.py:2"}],[2329,{"filePath":"tests/contract/test_roformer_loader_interface.py","startLine":46,"endLine":61,"chunkText":"        loader = Mock(spec=RoformerLoaderInterface)\n        \n        # Configure mock to return expected result structure\n        expected_result = ModelLoadingResult(\n            success=True,\n            model=Mock(),  # Mock model instance\n            error_message=None,\n            implementation_used=ImplementationVersion.NEW,\n            warnings=[]\n        )\n        loader.load_model.return_value = expected_result\n        \n        # Test the contract\n        result = loader.load_model(\"/path/to/model.ckpt\")\n        \n        # Verify contract compliance","chunkId":"tests/contract/test_roformer_loader_interface.py:3"}],[2330,{"filePath":"tests/contract/test_roformer_loader_interface.py","startLine":59,"endLine":72,"chunkText":"        result = loader.load_model(\"/path/to/model.ckpt\")\n        \n        # Verify contract compliance\n        assert isinstance(result, ModelLoadingResult)\n        assert result.success is True\n        assert result.model is not None\n        assert result.error_message is None\n        assert isinstance(result.implementation_used, ImplementationVersion)\n        assert isinstance(result.warnings, list)\n    \n    def test_load_model_failure_contract(self):\n        \"\"\"Test that load_model returns proper error result on failure.\"\"\"\n        loader = Mock(spec=RoformerLoaderInterface)\n        ","chunkId":"tests/contract/test_roformer_loader_interface.py:4"}],[2331,{"filePath":"tests/contract/test_roformer_loader_interface.py","startLine":70,"endLine":84,"chunkText":"        \"\"\"Test that load_model returns proper error result on failure.\"\"\"\n        loader = Mock(spec=RoformerLoaderInterface)\n        \n        # Configure mock to return error result\n        expected_result = ModelLoadingResult(\n            success=False,\n            model=None,\n            error_message=\"Model file not found\",\n            implementation_used=ImplementationVersion.NEW,\n            warnings=[\"Warning: fallback attempted\"]\n        )\n        loader.load_model.return_value = expected_result\n        \n        result = loader.load_model(\"/nonexistent/model.ckpt\")\n        ","chunkId":"tests/contract/test_roformer_loader_interface.py:5"}],[2332,{"filePath":"tests/contract/test_roformer_loader_interface.py","startLine":82,"endLine":96,"chunkText":"        \n        result = loader.load_model(\"/nonexistent/model.ckpt\")\n        \n        # Verify error contract compliance\n        assert isinstance(result, ModelLoadingResult)\n        assert result.success is False\n        assert result.model is None\n        assert result.error_message is not None\n        assert isinstance(result.error_message, str)\n        assert isinstance(result.warnings, list)\n    \n    def test_load_model_parameter_validation_error(self):\n        \"\"\"Test that load_model raises ParameterValidationError for invalid config.\"\"\"\n        loader = Mock(spec=RoformerLoaderInterface)\n        ","chunkId":"tests/contract/test_roformer_loader_interface.py:6"}],[2333,{"filePath":"tests/contract/test_roformer_loader_interface.py","startLine":94,"endLine":106,"chunkText":"        \"\"\"Test that load_model raises ParameterValidationError for invalid config.\"\"\"\n        loader = Mock(spec=RoformerLoaderInterface)\n        \n        # Configure mock to raise validation error\n        validation_error = ParameterValidationError(\n            parameter_name=\"dim\",\n            expected_type=\"int\",\n            actual_value=\"invalid\",\n            suggested_fix=\"Provide an integer value for dim parameter\"\n        )\n        loader.load_model.side_effect = validation_error\n        \n        with pytest.raises(ParameterValidationError) as exc_info:","chunkId":"tests/contract/test_roformer_loader_interface.py:7"}],[2334,{"filePath":"tests/contract/test_roformer_loader_interface.py","startLine":104,"endLine":116,"chunkText":"        loader.load_model.side_effect = validation_error\n        \n        with pytest.raises(ParameterValidationError) as exc_info:\n            loader.load_model(\"/path/to/model.ckpt\", config={\"dim\": \"invalid\"})\n        \n        error = exc_info.value\n        assert error.parameter_name == \"dim\"\n        assert error.expected_type == \"int\"\n        assert error.actual_value == \"invalid\"\n        assert \"Provide an integer value\" in error.suggested_fix\n    \n    def test_validate_configuration_contract(self):\n        \"\"\"Test validate_configuration interface contract.\"\"\"","chunkId":"tests/contract/test_roformer_loader_interface.py:8"}],[2335,{"filePath":"tests/contract/test_roformer_loader_interface.py","startLine":114,"endLine":128,"chunkText":"    \n    def test_validate_configuration_contract(self):\n        \"\"\"Test validate_configuration interface contract.\"\"\"\n        loader = Mock(spec=RoformerLoaderInterface)\n        \n        # Mock valid configuration\n        config = ModelConfiguration(dim=512, depth=6)\n        loader.validate_configuration.return_value = []  # No errors\n        \n        errors = loader.validate_configuration(config, RoformerType.BS_ROFORMER)\n        \n        assert isinstance(errors, list)\n        assert len(errors) == 0  # Valid config should return empty list\n        \n        # Mock invalid configuration","chunkId":"tests/contract/test_roformer_loader_interface.py:9"}],[2336,{"filePath":"tests/contract/test_roformer_loader_interface.py","startLine":126,"endLine":138,"chunkText":"        assert len(errors) == 0  # Valid config should return empty list\n        \n        # Mock invalid configuration\n        loader.validate_configuration.return_value = [\"Missing required parameter: freqs_per_bands\"]\n        \n        errors = loader.validate_configuration(config, RoformerType.BS_ROFORMER)\n        assert isinstance(errors, list)\n        assert len(errors) > 0\n        assert all(isinstance(error, str) for error in errors)\n    \n    def test_detect_model_type_contract(self):\n        \"\"\"Test detect_model_type interface contract.\"\"\"\n        loader = Mock(spec=RoformerLoaderInterface)","chunkId":"tests/contract/test_roformer_loader_interface.py:10"}],[2337,{"filePath":"tests/contract/test_roformer_loader_interface.py","startLine":136,"endLine":148,"chunkText":"    def test_detect_model_type_contract(self):\n        \"\"\"Test detect_model_type interface contract.\"\"\"\n        loader = Mock(spec=RoformerLoaderInterface)\n        \n        # Mock successful type detection\n        loader.detect_model_type.return_value = RoformerType.BS_ROFORMER\n        \n        model_type = loader.detect_model_type(\"/path/to/bs_roformer.ckpt\")\n        \n        assert isinstance(model_type, RoformerType)\n        assert model_type in [RoformerType.BS_ROFORMER, RoformerType.MEL_BAND_ROFORMER]\n        \n        # Mock type detection failure","chunkId":"tests/contract/test_roformer_loader_interface.py:11"}],[2338,{"filePath":"tests/contract/test_roformer_loader_interface.py","startLine":146,"endLine":158,"chunkText":"        assert model_type in [RoformerType.BS_ROFORMER, RoformerType.MEL_BAND_ROFORMER]\n        \n        # Mock type detection failure\n        loader.detect_model_type.side_effect = ValueError(\"Cannot determine model type\")\n        \n        with pytest.raises(ValueError) as exc_info:\n            loader.detect_model_type(\"/path/to/unknown_model.ckpt\")\n        \n        assert \"Cannot determine model type\" in str(exc_info.value)\n    \n    def test_get_default_configuration_contract(self):\n        \"\"\"Test get_default_configuration interface contract.\"\"\"\n        loader = Mock(spec=RoformerLoaderInterface)","chunkId":"tests/contract/test_roformer_loader_interface.py:12"}],[2339,{"filePath":"tests/contract/test_roformer_loader_interface.py","startLine":156,"endLine":170,"chunkText":"    def test_get_default_configuration_contract(self):\n        \"\"\"Test get_default_configuration interface contract.\"\"\"\n        loader = Mock(spec=RoformerLoaderInterface)\n        \n        # Mock default configuration\n        default_config = ModelConfiguration(\n            dim=512, \n            depth=6,\n            freqs_per_bands=(2, 4, 8, 16, 32, 64)\n        )\n        loader.get_default_configuration.return_value = default_config\n        \n        config = loader.get_default_configuration(RoformerType.BS_ROFORMER)\n        \n        assert isinstance(config, ModelConfiguration)","chunkId":"tests/contract/test_roformer_loader_interface.py:13"}],[2340,{"filePath":"tests/contract/test_roformer_loader_interface.py","startLine":169,"endLine":180,"chunkText":"        \n        assert isinstance(config, ModelConfiguration)\n        assert config.dim > 0\n        assert config.depth > 0\n\n\n# TDD placeholder test removed - implementation is now complete\n\n\nif __name__ == \"__main__\":\n    pytest.main([__file__, \"-v\"])\n","chunkId":"tests/contract/test_roformer_loader_interface.py:14"}],[2341,{"filePath":"tests/integration/README.md","startLine":0,"endLine":24,"chunkText":"# Integration Tests\n\nThese tests verify the end-to-end functionality of the audio-separator CLI.\n\n## Test Files\n\n### 16-bit Audio Tests\n- Input: `tests/inputs/mardy20s.flac` (16-bit)\n- Tests: `test_cli_integration.py`\n- Validates separation with various models using 16-bit audio\n\n### 24-bit Audio Tests\n- Input: `tests/inputs/fallen24bit20s.flac` (24-bit)\n- Tests: `test_24bit_preservation.py`\n- Validates that 24-bit input produces 24-bit output\n- Ensures audio quality is preserved during bit depth preservation\n\n## Running the tests\n\nTo run all integration tests:\n\n```bash\npytest tests/integration\n```\n","chunkId":"tests/integration/README.md:0"}],[2342,{"filePath":"tests/integration/README.md","startLine":23,"endLine":43,"chunkText":"```\n\nTo run only 16-bit tests:\n\n```bash\npytest tests/integration/test_cli_integration.py\n```\n\nTo run only 24-bit preservation tests:\n\n```bash\npytest tests/integration/test_24bit_preservation.py\n```\n\nTo run a specific model test, use pytest's parameter selection:\n\n```bash\n# Run only the kuielab_b_vocals.onnx test (16-bit)\npytest tests/integration/test_cli_integration.py::test_model_separation[kuielab_b_vocals.onnx-expected_files0]\n\n# Run only the BS-Roformer test with 24-bit audio","chunkId":"tests/integration/README.md:1"}],[2343,{"filePath":"tests/integration/README.md","startLine":42,"endLine":62,"chunkText":"# Run only the BS-Roformer test with 24-bit audio\npytest tests/integration/test_24bit_preservation.py::test_24bit_model_separation[model_bs_roformer_ep_317_sdr_12.9755.ckpt-expected_files0]\n```\n\n## Adding New Model Tests\n\n### For 16-bit tests\nAdd a new entry to the `MODEL_PARAMS` list in `test_cli_integration.py`:\n\n```python\n(\n    \"new_model_filename.onnx\",\n    [\"mardy20s_(Instrumental)_new_model_filename.flac\", \"mardy20s_(Vocals)_new_model_filename.flac\"]\n),\n```\n\n### For 24-bit tests\nAdd a new entry to the `MODEL_PARAMS_24BIT` list in `test_24bit_preservation.py`:\n\n```python\n(","chunkId":"tests/integration/README.md:2"}],[2344,{"filePath":"tests/integration/README.md","startLine":63,"endLine":87,"chunkText":"(\n    \"new_model_filename.onnx\",\n    [\"fallen24bit20s_(Instrumental)_new_model_filename.flac\", \"fallen24bit20s_(Vocals)_new_model_filename.flac\"]\n),\n```\n\n## Generating Reference Images\n\n### For 16-bit tests\n```bash\npython tests/integration/generate_reference_images.py\n```\n\n### For 24-bit tests\n1. First, generate the output files by running audio-separator:\n   ```bash\n   audio-separator -m model_name.ckpt tests/inputs/fallen24bit20s.flac\n   ```\n2. Move the output files to `tests/inputs/`\n3. Generate reference images:\n   ```bash\n   python tests/integration/generate_reference_images_24bit.py\n   ```\n\n## Notes","chunkId":"tests/integration/README.md:3"}],[2345,{"filePath":"tests/integration/README.md","startLine":87,"endLine":94,"chunkText":"## Notes\n\n- These tests use actual audio files and models, and will run the full audio separation process.\n- Tests may take longer to run than unit tests, as they perform actual audio processing.\n- The model files will be automatically downloaded if they don't exist locally.\n- The 16-bit test requires the test audio file at `tests/inputs/mardy20s.flac` to exist.\n- The 24-bit test requires the test audio file at `tests/inputs/fallen24bit20s.flac` to exist.\n- Reference images must be generated before running the tests for the first time. ","chunkId":"tests/integration/README.md:4"}],[2346,{"filePath":"tests/integration/generate_reference_images.py","startLine":0,"endLine":18,"chunkText":"#!/usr/bin/env python3\n\"\"\"\nScript to generate reference waveform and spectrogram images for model outputs.\nThis script should be run whenever the expected output files change.\n\"\"\"\n\nimport os\nimport sys\nimport glob\nfrom pathlib import Path\n\n# Add the parent directory to sys.path\nsys.path.insert(0, str(Path(__file__).resolve().parent.parent.parent))\n\nfrom tests.utils import generate_reference_images\nfrom tests.integration.test_cli_integration import MODEL_PARAMS\n\ndef main():\n    \"\"\"Generate reference images for all expected model outputs.\"\"\"","chunkId":"tests/integration/generate_reference_images.py:0"}],[2347,{"filePath":"tests/integration/generate_reference_images.py","startLine":16,"endLine":30,"chunkText":"\ndef main():\n    \"\"\"Generate reference images for all expected model outputs.\"\"\"\n    print(\"Generating reference images for expected model outputs...\")\n    \n    # Get the input file path\n    inputs_dir = Path(__file__).resolve().parent.parent / \"inputs\"\n    input_file = inputs_dir / \"mardy20s.flac\"\n    \n    # Create reference directory if it doesn't exist\n    reference_dir = inputs_dir / \"reference\"\n    os.makedirs(reference_dir, exist_ok=True)\n    \n    # First, generate reference images for the input file\n    print(f\"Generating reference images for input file: {input_file}\")","chunkId":"tests/integration/generate_reference_images.py:1"}],[2348,{"filePath":"tests/integration/generate_reference_images.py","startLine":28,"endLine":39,"chunkText":"    \n    # First, generate reference images for the input file\n    print(f\"Generating reference images for input file: {input_file}\")\n    generate_reference_images(str(input_file), str(reference_dir), prefix=\"expected_\")\n    \n    # Then, generate reference images for each expected output file\n    for model, expected_files in MODEL_PARAMS:\n        for output_file in expected_files:\n            file_path = os.path.join(str(inputs_dir), output_file)\n            \n            # Check if the file exists\n            if os.path.exists(file_path):","chunkId":"tests/integration/generate_reference_images.py:2"}],[2349,{"filePath":"tests/integration/generate_reference_images.py","startLine":37,"endLine":49,"chunkText":"            \n            # Check if the file exists\n            if os.path.exists(file_path):\n                print(f\"Generating reference images for output file: {output_file}\")\n                generate_reference_images(file_path, str(reference_dir), prefix=\"expected_\")\n            else:\n                print(f\"Warning: Output file does not exist: {file_path}\")\n                print(f\"You may need to run the CLI command first to generate the output files.\")\n    \n    print(\"Done generating reference images.\")\n\nif __name__ == \"__main__\":\n    main() ","chunkId":"tests/integration/generate_reference_images.py:3"}],[2350,{"filePath":"tests/integration/generate_reference_images_24bit.py","startLine":0,"endLine":10,"chunkText":"#!/usr/bin/env python3\n\"\"\"\nScript to generate reference waveform and spectrogram images for 24-bit model outputs.\nThis script should be run after creating the expected output files with 24-bit audio.\n\nUsage:\n    1. First run audio-separator on the 24-bit input file with each model:\n       audio-separator -m model_bs_roformer_ep_317_sdr_12.9755.ckpt tests/inputs/fallen24bit20s.flac\n       audio-separator -m mel_band_roformer_karaoke_aufr33_viperx_sdr_10.1956.ckpt tests/inputs/fallen24bit20s.flac\n       audio-separator -m MGM_MAIN_v4.pth tests/inputs/fallen24bit20s.flac\n    ","chunkId":"tests/integration/generate_reference_images_24bit.py:0"}],[2351,{"filePath":"tests/integration/generate_reference_images_24bit.py","startLine":8,"endLine":24,"chunkText":"       audio-separator -m mel_band_roformer_karaoke_aufr33_viperx_sdr_10.1956.ckpt tests/inputs/fallen24bit20s.flac\n       audio-separator -m MGM_MAIN_v4.pth tests/inputs/fallen24bit20s.flac\n    \n    2. Move the output files to tests/inputs/\n    \n    3. Run this script to generate reference images:\n       python tests/integration/generate_reference_images_24bit.py\n\"\"\"\n\nimport os\nimport sys\nfrom pathlib import Path\n\n# Add the parent directory to sys.path\nsys.path.insert(0, str(Path(__file__).resolve().parent.parent.parent))\n\nfrom tests.utils import generate_reference_images","chunkId":"tests/integration/generate_reference_images_24bit.py:1"}],[2352,{"filePath":"tests/integration/generate_reference_images_24bit.py","startLine":22,"endLine":38,"chunkText":"sys.path.insert(0, str(Path(__file__).resolve().parent.parent.parent))\n\nfrom tests.utils import generate_reference_images\nfrom tests.integration.test_24bit_preservation import MODEL_PARAMS_24BIT\n\n\ndef main():\n    \"\"\"Generate reference images for all expected 24-bit model outputs.\"\"\"\n    print(\"=\"*60)\n    print(\"Generating Reference Images for 24-bit Model Outputs\")\n    print(\"=\"*60)\n    \n    # Get the input file path\n    inputs_dir = Path(__file__).resolve().parent.parent / \"inputs\"\n    input_file = inputs_dir / \"fallen24bit20s.flac\"\n    \n    # Create reference directory if it doesn't exist","chunkId":"tests/integration/generate_reference_images_24bit.py:2"}],[2353,{"filePath":"tests/integration/generate_reference_images_24bit.py","startLine":36,"endLine":48,"chunkText":"    input_file = inputs_dir / \"fallen24bit20s.flac\"\n    \n    # Create reference directory if it doesn't exist\n    reference_dir = inputs_dir / \"reference\"\n    os.makedirs(reference_dir, exist_ok=True)\n    \n    # First, generate reference images for the 24-bit input file\n    if input_file.exists():\n        print(f\"\\n✓ Generating reference images for input file: {input_file}\")\n        generate_reference_images(str(input_file), str(reference_dir), prefix=\"expected_\")\n        print(f\"  Created: expected_fallen24bit20s_waveform.png\")\n        print(f\"  Created: expected_fallen24bit20s_spectrogram.png\")\n    else","chunkId":"tests/integration/generate_reference_images_24bit.py:3"}],[2354,{"filePath":"tests/integration/generate_reference_images_24bit.py","startLine":46,"endLine":60,"chunkText":"        print(f\"  Created: expected_fallen24bit20s_waveform.png\")\n        print(f\"  Created: expected_fallen24bit20s_spectrogram.png\")\n    else:\n        print(f\"\\n✗ Error: Input file does not exist: {input_file}\")\n        print(f\"  Please create the 24-bit test file first.\")\n        return 1\n    \n    # Then, generate reference images for each expected output file\n    print(f\"\\nGenerating reference images for model outputs...\")\n    missing_files = []\n    created_files = []\n    \n    for model, expected_files in MODEL_PARAMS_24BIT:\n        print(f\"\\n{'-'*60}\")\n        print(f\"Model: {model}\")","chunkId":"tests/integration/generate_reference_images_24bit.py:4"}],[2355,{"filePath":"tests/integration/generate_reference_images_24bit.py","startLine":58,"endLine":71,"chunkText":"    for model, expected_files in MODEL_PARAMS_24BIT:\n        print(f\"\\n{'-'*60}\")\n        print(f\"Model: {model}\")\n        print(f\"{'-'*60}\")\n        \n        for output_file in expected_files:\n            file_path = os.path.join(str(inputs_dir), output_file)\n            \n            # Check if the file exists\n            if os.path.exists(file_path):\n                print(f\"✓ Processing: {output_file}\")\n                waveform_path, spectrogram_path = generate_reference_images(\n                    file_path, str(reference_dir), prefix=\"expected_\"\n                )","chunkId":"tests/integration/generate_reference_images_24bit.py:5"}],[2356,{"filePath":"tests/integration/generate_reference_images_24bit.py","startLine":69,"endLine":80,"chunkText":"                waveform_path, spectrogram_path = generate_reference_images(\n                    file_path, str(reference_dir), prefix=\"expected_\"\n                )\n                output_basename = os.path.splitext(output_file)[0]\n                print(f\"  Created: expected_{output_basename}_waveform.png\")\n                print(f\"  Created: expected_{output_basename}_spectrogram.png\")\n                created_files.extend([waveform_path, spectrogram_path])\n            else:\n                print(f\"✗ Missing: {output_file}\")\n                missing_files.append((model, output_file))\n    \n    # Summary","chunkId":"tests/integration/generate_reference_images_24bit.py:6"}],[2357,{"filePath":"tests/integration/generate_reference_images_24bit.py","startLine":78,"endLine":92,"chunkText":"                missing_files.append((model, output_file))\n    \n    # Summary\n    print(f\"\\n{'='*60}\")\n    print(\"Summary\")\n    print(f\"{'='*60}\")\n    print(f\"✓ Created {len(created_files)} reference images\")\n    \n    if missing_files:\n        print(f\"\\n⚠  Warning: {len(missing_files)} output files were not found:\")\n        print(f\"\\nTo generate missing files, run these commands:\")\n        print()\n        for model, _ in missing_files:\n            # Only print each model once\n            if missing_files.index((model, _)) == [m for m, _ in missing_files].index(model):","chunkId":"tests/integration/generate_reference_images_24bit.py:7"}],[2358,{"filePath":"tests/integration/generate_reference_images_24bit.py","startLine":90,"endLine":100,"chunkText":"        for model, _ in missing_files:\n            # Only print each model once\n            if missing_files.index((model, _)) == [m for m, _ in missing_files].index(model):\n                print(f\"  audio-separator -m {model} tests/inputs/fallen24bit20s.flac\")\n        print(f\"\\nThen move the output files to tests/inputs/ and run this script again.\")\n        print()\n    else:\n        print(f\"\\n✅ All reference images generated successfully!\")\n        print(f\"\\nYou can now run the 24-bit preservation tests:\")\n        print(f\"  pytest tests/integration/test_24bit_preservation.py -v\")\n    ","chunkId":"tests/integration/generate_reference_images_24bit.py:8"}],[2359,{"filePath":"tests/integration/generate_reference_images_24bit.py","startLine":99,"endLine":108,"chunkText":"        print(f\"  pytest tests/integration/test_24bit_preservation.py -v\")\n    \n    print(f\"{'='*60}\\n\")\n    return 0 if not missing_files else 1\n\n\nif __name__ == \"__main__\":\n    sys.exit(main())\n\n","chunkId":"tests/integration/generate_reference_images_24bit.py:9"}],[2360,{"filePath":"tests/integration/test_24bit_preservation.py","startLine":0,"endLine":21,"chunkText":"\"\"\"\nIntegration tests for 24-bit audio bit depth preservation.\n\nTests that 24-bit input audio files produce 24-bit output audio files\nwhile maintaining audio quality through the separation process.\n\"\"\"\n\nimport os\nimport subprocess\nimport pytest\nimport soundfile as sf\nfrom pathlib import Path\nimport sys\n\n# Add parent directory to path\nsys.path.append(os.path.dirname(os.path.dirname(__file__)))\nfrom utils import generate_reference_images, compare_images\n\n\n@pytest.fixture(name=\"input_file_24bit\")\ndef fixture_input_file_24bit():\n    \"\"\"Fixture providing the 24-bit test input audio file path.\"\"\"","chunkId":"tests/integration/test_24bit_preservation.py:0"}],[2361,{"filePath":"tests/integration/test_24bit_preservation.py","startLine":19,"endLine":37,"chunkText":"@pytest.fixture(name=\"input_file_24bit\")\ndef fixture_input_file_24bit():\n    \"\"\"Fixture providing the 24-bit test input audio file path.\"\"\"\n    return \"tests/inputs/fallen24bit20s.flac\"\n\n\n@pytest.fixture(name=\"reference_dir\")\ndef fixture_reference_dir():\n    \"\"\"Fixture providing the reference images directory path.\"\"\"\n    return \"tests/inputs/reference\"\n\n\n@pytest.fixture(name=\"cleanup_output_files\")\ndef fixture_cleanup_output_files():\n    \"\"\"Fixture to clean up output files before and after test.\"\"\"\n    output_files = []\n    yield output_files\n    \n    # Clean up output files after test","chunkId":"tests/integration/test_24bit_preservation.py:1"}],[2362,{"filePath":"tests/integration/test_24bit_preservation.py","startLine":35,"endLine":58,"chunkText":"    yield output_files\n    \n    # Clean up output files after test\n    for file in output_files:\n        if os.path.exists(file):\n            print(f\"Cleaning up test output file: {file}\")\n            os.remove(file)\n\n\ndef get_audio_bit_depth(file_path):\n    \"\"\"Get the bit depth of an audio file.\"\"\"\n    info = sf.info(file_path)\n    subtype = info.subtype\n    \n    if 'PCM_16' in subtype or subtype == 'PCM_S8':\n        return 16\n    elif 'PCM_24' in subtype:\n        return 24\n    elif 'PCM_32' in subtype or 'FLOAT' in subtype or 'DOUBLE' in subtype:\n        return 32\n    else:\n        return None\n\n","chunkId":"tests/integration/test_24bit_preservation.py:2"}],[2363,{"filePath":"tests/integration/test_24bit_preservation.py","startLine":56,"endLine":69,"chunkText":"        return None\n\n\ndef run_separation_test_24bit(model, audio_path, expected_files):\n    \"\"\"Helper function to run a separation test with a 24-bit input file.\"\"\"\n    # Clean up any existing output files before the test\n    for file in expected_files:\n        if os.path.exists(file):\n            print(f\"Deleting existing test output file {file}\")\n            os.remove(file)\n    \n    # Verify input is 24-bit\n    input_bit_depth = get_audio_bit_depth(audio_path)\n    print(f\"Input file bit depth: {input_bit_depth}-bit\")","chunkId":"tests/integration/test_24bit_preservation.py:3"}],[2364,{"filePath":"tests/integration/test_24bit_preservation.py","startLine":67,"endLine":82,"chunkText":"    # Verify input is 24-bit\n    input_bit_depth = get_audio_bit_depth(audio_path)\n    print(f\"Input file bit depth: {input_bit_depth}-bit\")\n    assert input_bit_depth == 24, f\"Input file should be 24-bit, got {input_bit_depth}-bit\"\n    \n    # Run the CLI command\n    result = subprocess.run(\n        [\"audio-separator\", \"-m\", model, audio_path],\n        capture_output=True,\n        text=True,\n        check=False\n    )\n    \n    # Check that the command completed successfully\n    assert result.returncode == 0, f\"Command failed with output: {result.stderr}\"\n    ","chunkId":"tests/integration/test_24bit_preservation.py:4"}],[2365,{"filePath":"tests/integration/test_24bit_preservation.py","startLine":80,"endLine":90,"chunkText":"    # Check that the command completed successfully\n    assert result.returncode == 0, f\"Command failed with output: {result.stderr}\"\n    \n    # Check that the output files were created and are 24-bit\n    for file in expected_files:\n        assert os.path.exists(file), f\"Output file {file} was not created\"\n        assert os.path.getsize(file) > 0, f\"Output file {file} is empty\"\n        \n        # Verify output is also 24-bit\n        output_bit_depth = get_audio_bit_depth(file)\n        print(f\"Output file {file} bit depth: {output_bit_depth}-bit\")","chunkId":"tests/integration/test_24bit_preservation.py:5"}],[2366,{"filePath":"tests/integration/test_24bit_preservation.py","startLine":88,"endLine":100,"chunkText":"        # Verify output is also 24-bit\n        output_bit_depth = get_audio_bit_depth(file)\n        print(f\"Output file {file} bit depth: {output_bit_depth}-bit\")\n        assert output_bit_depth == 24, f\"Output file should be 24-bit to match input, got {output_bit_depth}-bit\"\n    \n    return result\n\n\ndef validate_audio_output(output_file, reference_dir, waveform_threshold=0.999, spectrogram_threshold=None):\n    \"\"\"Validate an audio output file by comparing its waveform and spectrogram with reference images.\"\"\"\n    if spectrogram_threshold is None:\n        spectrogram_threshold = waveform_threshold\n    ","chunkId":"tests/integration/test_24bit_preservation.py:6"}],[2367,{"filePath":"tests/integration/test_24bit_preservation.py","startLine":98,"endLine":112,"chunkText":"    if spectrogram_threshold is None:\n        spectrogram_threshold = waveform_threshold\n    \n    # Create temporary directory for generated images\n    temp_dir = os.path.join(os.path.dirname(output_file), \"temp_images\")\n    os.makedirs(temp_dir, exist_ok=True)\n    \n    # Generate waveform and spectrogram images for the output file\n    output_filename = os.path.basename(output_file)\n    name_without_ext = os.path.splitext(output_filename)[0]\n    \n    # Generate actual images\n    actual_waveform_path, actual_spectrogram_path = generate_reference_images(\n        output_file, temp_dir, prefix=\"actual_\"\n    )","chunkId":"tests/integration/test_24bit_preservation.py:7"}],[2368,{"filePath":"tests/integration/test_24bit_preservation.py","startLine":110,"endLine":123,"chunkText":"    actual_waveform_path, actual_spectrogram_path = generate_reference_images(\n        output_file, temp_dir, prefix=\"actual_\"\n    )\n    \n    # Path to expected reference images\n    expected_waveform_path = os.path.join(\n        reference_dir, f\"expected_{name_without_ext}_waveform.png\"\n    )\n    expected_spectrogram_path = os.path.join(\n        reference_dir, f\"expected_{name_without_ext}_spectrogram.png\"\n    )\n    \n    # Check if reference images exist\n    if not os.path.exists(expected_waveform_path) or not os.path.exists(expected_spectrogram_path):","chunkId":"tests/integration/test_24bit_preservation.py:8"}],[2369,{"filePath":"tests/integration/test_24bit_preservation.py","startLine":121,"endLine":134,"chunkText":"    \n    # Check if reference images exist\n    if not os.path.exists(expected_waveform_path) or not os.path.exists(expected_spectrogram_path):\n        print(f\"Warning: Reference images not found for {output_file}\")\n        print(f\"Expected: {expected_waveform_path} and {expected_spectrogram_path}\")\n        print(f\"Run generate_reference_images_24bit.py to create them\")\n        return False, False\n    \n    # Compare waveform images\n    waveform_similarity, waveform_match = compare_images(\n        expected_waveform_path, actual_waveform_path,\n        min_similarity_threshold=waveform_threshold\n    )\n    ","chunkId":"tests/integration/test_24bit_preservation.py:9"}],[2370,{"filePath":"tests/integration/test_24bit_preservation.py","startLine":132,"endLine":144,"chunkText":"        min_similarity_threshold=waveform_threshold\n    )\n    \n    # Compare spectrogram images\n    spectrogram_similarity, spectrogram_match = compare_images(\n        expected_spectrogram_path, actual_spectrogram_path,\n        min_similarity_threshold=spectrogram_threshold\n    )\n    \n    print(f\"Validation results for {output_file}:\")\n    print(f\"  Waveform similarity: {waveform_similarity:.4f} \"\n          f\"(match: {waveform_match}, threshold: {waveform_threshold:.2f})\")\n    print(f\"  Spectrogram similarity: {spectrogram_similarity:.4f} \"","chunkId":"tests/integration/test_24bit_preservation.py:10"}],[2371,{"filePath":"tests/integration/test_24bit_preservation.py","startLine":142,"endLine":154,"chunkText":"    print(f\"  Waveform similarity: {waveform_similarity:.4f} \"\n          f\"(match: {waveform_match}, threshold: {waveform_threshold:.2f})\")\n    print(f\"  Spectrogram similarity: {spectrogram_similarity:.4f} \"\n          f\"(match: {spectrogram_match}, threshold: {spectrogram_threshold:.2f})\")\n    \n    return waveform_match, spectrogram_match\n\n\n# Default similarity threshold for 24-bit audio tests\nDEFAULT_SIMILARITY_THRESHOLDS_24BIT = (0.90, 0.80)  # (waveform, spectrogram)\n\n# Model-specific similarity thresholds for 24-bit tests\nMODEL_SIMILARITY_THRESHOLDS_24BIT = {","chunkId":"tests/integration/test_24bit_preservation.py:11"}],[2372,{"filePath":"tests/integration/test_24bit_preservation.py","startLine":152,"endLine":170,"chunkText":"\n# Model-specific similarity thresholds for 24-bit tests\nMODEL_SIMILARITY_THRESHOLDS_24BIT = {\n    # Format: (waveform_threshold, spectrogram_threshold)\n}\n\n\n# Parameterized test for multiple models with 24-bit audio\nMODEL_PARAMS_24BIT = [\n    # (model_filename, expected_output_filenames)\n    (\n        \"model_bs_roformer_ep_317_sdr_12.9755.ckpt\",\n        [\n            \"fallen24bit20s_(Instrumental)_model_bs_roformer_ep_317_sdr_12.flac\",\n            \"fallen24bit20s_(Vocals)_model_bs_roformer_ep_317_sdr_12.flac\",\n        ]\n    ),\n    (\n        \"mel_band_roformer_karaoke_aufr33_viperx_sdr_10.1956.ckpt\",","chunkId":"tests/integration/test_24bit_preservation.py:12"}],[2373,{"filePath":"tests/integration/test_24bit_preservation.py","startLine":168,"endLine":186,"chunkText":"    ),\n    (\n        \"mel_band_roformer_karaoke_aufr33_viperx_sdr_10.1956.ckpt\",\n        [\n            \"fallen24bit20s_(Instrumental)_mel_band_roformer_karaoke_aufr33_viperx_sdr_10.flac\",\n            \"fallen24bit20s_(Vocals)_mel_band_roformer_karaoke_aufr33_viperx_sdr_10.flac\",\n        ]\n    ),\n    (\n        \"MGM_MAIN_v4.pth\",\n        [\n            \"fallen24bit20s_(Instrumental)_MGM_MAIN_v4.flac\",\n            \"fallen24bit20s_(Vocals)_MGM_MAIN_v4.flac\",\n        ]\n    ),\n]\n\n\n@pytest.mark.parametrize(\"model,expected_files\", MODEL_PARAMS_24BIT)","chunkId":"tests/integration/test_24bit_preservation.py:13"}],[2374,{"filePath":"tests/integration/test_24bit_preservation.py","startLine":184,"endLine":198,"chunkText":"\n\n@pytest.mark.parametrize(\"model,expected_files\", MODEL_PARAMS_24BIT)\ndef test_24bit_model_separation(model, expected_files, input_file_24bit, reference_dir, cleanup_output_files):\n    \"\"\"Test that 24-bit input audio produces 24-bit output audio with correct content.\"\"\"\n    print(f\"\\n{'='*60}\")\n    print(f\"Testing 24-bit preservation with model: {model}\")\n    print(f\"{'='*60}\")\n    \n    # Add files to the cleanup list\n    cleanup_output_files.extend(expected_files)\n    \n    # Run the test (includes bit depth validation)\n    run_separation_test_24bit(model, input_file_24bit, expected_files)\n    ","chunkId":"tests/integration/test_24bit_preservation.py:14"}],[2375,{"filePath":"tests/integration/test_24bit_preservation.py","startLine":196,"endLine":210,"chunkText":"    # Run the test (includes bit depth validation)\n    run_separation_test_24bit(model, input_file_24bit, expected_files)\n    \n    # Validate the output audio quality\n    print(f\"\\nValidating output audio quality for model {model}...\")\n    \n    # Get model-specific similarity threshold or use default\n    threshold = MODEL_SIMILARITY_THRESHOLDS_24BIT.get(\n        model, DEFAULT_SIMILARITY_THRESHOLDS_24BIT\n    )\n    waveform_threshold, spectrogram_threshold = threshold\n    \n    print(f\"Using thresholds - waveform: {waveform_threshold}, \"\n          f\"spectrogram: {spectrogram_threshold}\")\n    ","chunkId":"tests/integration/test_24bit_preservation.py:15"}],[2376,{"filePath":"tests/integration/test_24bit_preservation.py","startLine":208,"endLine":219,"chunkText":"    print(f\"Using thresholds - waveform: {waveform_threshold}, \"\n          f\"spectrogram: {spectrogram_threshold}\")\n    \n    for output_file in expected_files:\n        # Skip validation if reference images are not required\n        if os.environ.get(\"SKIP_AUDIO_VALIDATION\") == \"1\":\n            print(f\"Skipping audio validation for {output_file} (SKIP_AUDIO_VALIDATION=1)\")\n            continue\n        \n        waveform_match, spectrogram_match = validate_audio_output(\n            output_file, reference_dir,\n            waveform_threshold=waveform_threshold,","chunkId":"tests/integration/test_24bit_preservation.py:16"}],[2377,{"filePath":"tests/integration/test_24bit_preservation.py","startLine":217,"endLine":229,"chunkText":"        waveform_match, spectrogram_match = validate_audio_output(\n            output_file, reference_dir,\n            waveform_threshold=waveform_threshold,\n            spectrogram_threshold=spectrogram_threshold\n        )\n        \n        # Assert that the output matches the reference\n        assert waveform_match, f\"Waveform for {output_file} does not match the reference\"\n        assert spectrogram_match, f\"Spectrogram for {output_file} does not match the reference\"\n    \n    print(f\"✅ Test passed for {model}: 24-bit preservation and audio quality verified\")\n\n","chunkId":"tests/integration/test_24bit_preservation.py:17"}],[2378,{"filePath":"tests/integration/test_cli_integration.py","startLine":0,"endLine":22,"chunkText":"import os\nimport subprocess\nimport sys\nfrom pathlib import Path\n\nimport pytest\nsys.path.append(os.path.dirname(os.path.dirname(__file__)))\nfrom utils import generate_reference_images, compare_images\n\n\n@pytest.fixture(name=\"input_file\")\ndef fixture_input_file():\n    \"\"\"Fixture providing the test input audio file path.\"\"\"\n    return \"tests/inputs/mardy20s.flac\"\n\n\n@pytest.fixture(name=\"reference_dir\")\ndef fixture_reference_dir():\n    \"\"\"Fixture providing the reference images directory path.\"\"\"\n    return \"tests/inputs/reference\"\n\n\n@pytest.fixture(name=\"cleanup_output_files\")","chunkId":"tests/integration/test_cli_integration.py:0"}],[2379,{"filePath":"tests/integration/test_cli_integration.py","startLine":21,"endLine":38,"chunkText":"\n@pytest.fixture(name=\"cleanup_output_files\")\ndef fixture_cleanup_output_files():\n    \"\"\"Fixture to clean up output files before and after test.\"\"\"\n    # This list will be populated by the test functions\n    output_files = []\n\n    # Yield to allow the test to run and add files to the list\n    yield output_files\n\n    # Clean up output files after test\n    for file in output_files:\n        if os.path.exists(file):\n            print(f\"Test output file exists: {file}\")\n            os.remove(file)\n\n\ndef run_separation_test(model, audio_path, expected_files):","chunkId":"tests/integration/test_cli_integration.py:1"}],[2380,{"filePath":"tests/integration/test_cli_integration.py","startLine":36,"endLine":49,"chunkText":"\n\ndef run_separation_test(model, audio_path, expected_files):\n    \"\"\"Helper function to run a separation test with a specific model.\"\"\"\n    # Clean up any existing output files before the test\n    for file in expected_files:\n        if os.path.exists(file):\n            print(f\"Deleting existing test output file {file}\")\n            os.remove(file)\n\n    # Run the CLI command\n    result = subprocess.run([\"audio-separator\", \"-m\", model, audio_path], capture_output=True, text=True, check=False)  # Explicitly set check to False as we handle errors manually\n\n    # Check that the command completed successfully","chunkId":"tests/integration/test_cli_integration.py:2"}],[2381,{"filePath":"tests/integration/test_cli_integration.py","startLine":47,"endLine":59,"chunkText":"    result = subprocess.run([\"audio-separator\", \"-m\", model, audio_path], capture_output=True, text=True, check=False)  # Explicitly set check to False as we handle errors manually\n\n    # Check that the command completed successfully\n    assert result.returncode == 0, f\"Command failed with output: {result.stderr}\"\n\n    # Check that the output files were created\n    for file in expected_files:\n        assert os.path.exists(file), f\"Output file {file} was not created\"\n        assert os.path.getsize(file) > 0, f\"Output file {file} is empty\"\n\n    return result\n\n","chunkId":"tests/integration/test_cli_integration.py:3"}],[2382,{"filePath":"tests/integration/test_cli_integration.py","startLine":57,"endLine":69,"chunkText":"    return result\n\n\ndef validate_audio_output(output_file, reference_dir, waveform_threshold=0.999, spectrogram_threshold=None):\n    \"\"\"Validate an audio output file by comparing its waveform and spectrogram with reference images.\n\n    Args:\n        output_file: Path to the audio output file\n        reference_dir: Directory containing reference images\n        waveform_threshold: Minimum similarity required for waveform images (0.0-1.0)\n        spectrogram_threshold: Minimum similarity for spectrogram images (0.0-1.0), defaults to waveform_threshold if None\n\n    Returns:","chunkId":"tests/integration/test_cli_integration.py:4"}],[2383,{"filePath":"tests/integration/test_cli_integration.py","startLine":67,"endLine":79,"chunkText":"        spectrogram_threshold: Minimum similarity for spectrogram images (0.0-1.0), defaults to waveform_threshold if None\n\n    Returns:\n        Tuple of booleans: (waveform_match, spectrogram_match)\n    \"\"\"\n    # If spectrogram threshold not specified, use the same as waveform threshold\n    if spectrogram_threshold is None:\n        spectrogram_threshold = waveform_threshold\n\n    # Create temporary directory for generated images\n    temp_dir = os.path.join(os.path.dirname(output_file), \"temp_images\")\n    os.makedirs(temp_dir, exist_ok=True)\n","chunkId":"tests/integration/test_cli_integration.py:5"}],[2384,{"filePath":"tests/integration/test_cli_integration.py","startLine":77,"endLine":88,"chunkText":"    temp_dir = os.path.join(os.path.dirname(output_file), \"temp_images\")\n    os.makedirs(temp_dir, exist_ok=True)\n\n    # Generate waveform and spectrogram images for the output file\n    output_filename = os.path.basename(output_file)\n    name_without_ext = os.path.splitext(output_filename)[0]\n\n    # Generate actual images\n    actual_waveform_path, actual_spectrogram_path = generate_reference_images(output_file, temp_dir, prefix=\"actual_\")\n\n    # Path to expected reference images\n    expected_waveform_path = os.path.join(reference_dir, f\"expected_{name_without_ext}_waveform.png\")","chunkId":"tests/integration/test_cli_integration.py:6"}],[2385,{"filePath":"tests/integration/test_cli_integration.py","startLine":86,"endLine":97,"chunkText":"\n    # Path to expected reference images\n    expected_waveform_path = os.path.join(reference_dir, f\"expected_{name_without_ext}_waveform.png\")\n    expected_spectrogram_path = os.path.join(reference_dir, f\"expected_{name_without_ext}_spectrogram.png\")\n\n    # Check if reference images exist\n    if not os.path.exists(expected_waveform_path) or not os.path.exists(expected_spectrogram_path):\n        print(f\"Warning: Reference images not found for {output_file}\")\n        print(f\"Expected: {expected_waveform_path} and {expected_spectrogram_path}\")\n        return False, False\n\n    # Compare waveform images","chunkId":"tests/integration/test_cli_integration.py:7"}],[2386,{"filePath":"tests/integration/test_cli_integration.py","startLine":95,"endLine":104,"chunkText":"        return False, False\n\n    # Compare waveform images\n    waveform_similarity, waveform_match = compare_images(expected_waveform_path, actual_waveform_path, min_similarity_threshold=waveform_threshold)\n\n    # Compare spectrogram images\n    spectrogram_similarity, spectrogram_match = compare_images(expected_spectrogram_path, actual_spectrogram_path, min_similarity_threshold=spectrogram_threshold)\n\n    print(f\"Validation results for {output_file}:\\n\")\n    print(f\"  Waveform similarity: {waveform_similarity:.4f} (match: {waveform_match}, threshold: {waveform_threshold:.2f})\\n\")","chunkId":"tests/integration/test_cli_integration.py:8"}],[2387,{"filePath":"tests/integration/test_cli_integration.py","startLine":102,"endLine":114,"chunkText":"\n    print(f\"Validation results for {output_file}:\\n\")\n    print(f\"  Waveform similarity: {waveform_similarity:.4f} (match: {waveform_match}, threshold: {waveform_threshold:.2f})\\n\")\n    print(f\"  Spectrogram similarity: {spectrogram_similarity:.4f} (match: {spectrogram_match}, threshold: {spectrogram_threshold:.2f})\\n\")\n\n    # Cleanup temp images (optional, uncomment if needed)\n    # os.remove(actual_waveform_path)\n    # os.remove(actual_spectrogram_path)\n\n    return waveform_match, spectrogram_match\n\n\n# Default similarity threshold to use for most models","chunkId":"tests/integration/test_cli_integration.py:9"}],[2388,{"filePath":"tests/integration/test_cli_integration.py","startLine":112,"endLine":127,"chunkText":"\n\n# Default similarity threshold to use for most models\nDEFAULT_SIMILARITY_THRESHOLDS = (0.90, 0.80)  # (waveform_threshold, spectrogram_threshold)\n\n# Model-specific similarity thresholds\n# Use lower thresholds for models that show more variation between runs\nMODEL_SIMILARITY_THRESHOLDS = {\n    # Format: (waveform_threshold, spectrogram_threshold)\n    \"htdemucs_6s.yaml\": (0.90, 0.70)  # Demucs multi-stem output (e.g. \"Other\" and \"Piano\") is a lot more variable\n}\n\n\n# Parameterized test for multiple models\nMODEL_PARAMS = [\n    # (model_filename, expected_output_filenames)","chunkId":"tests/integration/test_cli_integration.py:10"}],[2389,{"filePath":"tests/integration/test_cli_integration.py","startLine":125,"endLine":133,"chunkText":"# Parameterized test for multiple models\nMODEL_PARAMS = [\n    # (model_filename, expected_output_filenames)\n    (\"mel_band_roformer_karaoke_aufr33_viperx_sdr_10.1956.ckpt\", [\n        \"mardy20s_(Instrumental)_mel_band_roformer_karaoke_aufr33_viperx_sdr_10.flac\",\n        \"mardy20s_(Vocals)_mel_band_roformer_karaoke_aufr33_viperx_sdr_10.flac\",\n    ]),\n    (\"kuielab_b_vocals.onnx\", [\"mardy20s_(Instrumental)_kuielab_b_vocals.flac\", \"mardy20s_(Vocals)_kuielab_b_vocals.flac\"]),\n    (\"MGM_MAIN_v4.pth\", [\"mardy20s_(Instrumental)_MGM_MAIN_v4.flac\", \"mardy20s_(Vocals)_MGM_MAIN_v4.flac\"]),","chunkId":"tests/integration/test_cli_integration.py:11"}],[2390,{"filePath":"tests/integration/test_cli_integration.py","startLine":131,"endLine":139,"chunkText":"    ]),\n    (\"kuielab_b_vocals.onnx\", [\"mardy20s_(Instrumental)_kuielab_b_vocals.flac\", \"mardy20s_(Vocals)_kuielab_b_vocals.flac\"]),\n    (\"MGM_MAIN_v4.pth\", [\"mardy20s_(Instrumental)_MGM_MAIN_v4.flac\", \"mardy20s_(Vocals)_MGM_MAIN_v4.flac\"]),\n    (\"UVR-MDX-NET-Inst_HQ_4.onnx\", [\"mardy20s_(Instrumental)_UVR-MDX-NET-Inst_HQ_4.flac\", \"mardy20s_(Vocals)_UVR-MDX-NET-Inst_HQ_4.flac\"]),\n    (\"2_HP-UVR.pth\", [\"mardy20s_(Instrumental)_2_HP-UVR.flac\", \"mardy20s_(Vocals)_2_HP-UVR.flac\"]),\n    (\n        \"htdemucs_6s.yaml\",\n        [\n            \"mardy20s_(Vocals)_htdemucs_6s.flac\",","chunkId":"tests/integration/test_cli_integration.py:12"}],[2391,{"filePath":"tests/integration/test_cli_integration.py","startLine":137,"endLine":147,"chunkText":"        \"htdemucs_6s.yaml\",\n        [\n            \"mardy20s_(Vocals)_htdemucs_6s.flac\",\n            \"mardy20s_(Drums)_htdemucs_6s.flac\",\n            \"mardy20s_(Bass)_htdemucs_6s.flac\",\n            \"mardy20s_(Other)_htdemucs_6s.flac\",\n            \"mardy20s_(Guitar)_htdemucs_6s.flac\",\n            \"mardy20s_(Piano)_htdemucs_6s.flac\",\n        ],\n    ),\n    (\"model_bs_roformer_ep_937_sdr_10.5309.ckpt\", [\"mardy20s_(Drum-Bass)_model_bs_roformer_ep_937_sdr_10.flac\", \"mardy20s_(No Drum-Bass)_model_bs_roformer_ep_937_sdr_10.flac\"]),","chunkId":"tests/integration/test_cli_integration.py:13"}],[2392,{"filePath":"tests/integration/test_cli_integration.py","startLine":145,"endLine":154,"chunkText":"        ],\n    ),\n    (\"model_bs_roformer_ep_937_sdr_10.5309.ckpt\", [\"mardy20s_(Drum-Bass)_model_bs_roformer_ep_937_sdr_10.flac\", \"mardy20s_(No Drum-Bass)_model_bs_roformer_ep_937_sdr_10.flac\"]),\n    (\"model_bs_roformer_ep_317_sdr_12.9755.ckpt\", [\"mardy20s_(Instrumental)_model_bs_roformer_ep_317_sdr_12.flac\", \"mardy20s_(Vocals)_model_bs_roformer_ep_317_sdr_12.flac\"]),\n]\n\n\n@pytest.mark.parametrize(\"model,expected_files\", MODEL_PARAMS)\ndef test_model_separation(model, expected_files, input_file, reference_dir, cleanup_output_files):\n    \"\"\"Parameterized test for multiple model files.\"\"\"","chunkId":"tests/integration/test_cli_integration.py:14"}],[2393,{"filePath":"tests/integration/test_cli_integration.py","startLine":152,"endLine":164,"chunkText":"@pytest.mark.parametrize(\"model,expected_files\", MODEL_PARAMS)\ndef test_model_separation(model, expected_files, input_file, reference_dir, cleanup_output_files):\n    \"\"\"Parameterized test for multiple model files.\"\"\"\n    # Add files to the cleanup list\n    cleanup_output_files.extend(expected_files)\n\n    # Run the test\n    run_separation_test(model, input_file, expected_files)\n\n    # Validate the output audio files\n    print(f\"\\nValidating output files for model {model}...\")\n\n    # Get model-specific similarity threshold or use default","chunkId":"tests/integration/test_cli_integration.py:15"}],[2394,{"filePath":"tests/integration/test_cli_integration.py","startLine":162,"endLine":173,"chunkText":"    print(f\"\\nValidating output files for model {model}...\")\n\n    # Get model-specific similarity threshold or use default\n    threshold = MODEL_SIMILARITY_THRESHOLDS.get(model, DEFAULT_SIMILARITY_THRESHOLDS)\n\n    # Unpack thresholds - DEFAULT_SIMILARITY_THRESHOLDS is now always a tuple\n    waveform_threshold, spectrogram_threshold = threshold\n\n    print(f\"Using thresholds - waveform: {waveform_threshold}, spectrogram: {spectrogram_threshold} for model {model}\")\n\n    for output_file in expected_files:\n        # Skip validation if reference images are not required (set environment variable to skip)","chunkId":"tests/integration/test_cli_integration.py:16"}],[2395,{"filePath":"tests/integration/test_cli_integration.py","startLine":171,"endLine":180,"chunkText":"\n    for output_file in expected_files:\n        # Skip validation if reference images are not required (set environment variable to skip)\n        if os.environ.get(\"SKIP_AUDIO_VALIDATION\") == \"1\":\n            print(f\"Skipping audio validation for {output_file} (SKIP_AUDIO_VALIDATION=1)\")\n            continue\n\n        waveform_match, spectrogram_match = validate_audio_output(output_file, reference_dir, waveform_threshold=waveform_threshold, spectrogram_threshold=spectrogram_threshold)\n\n        # Assert that the output matches the reference","chunkId":"tests/integration/test_cli_integration.py:17"}],[2396,{"filePath":"tests/integration/test_cli_integration.py","startLine":178,"endLine":183,"chunkText":"        waveform_match, spectrogram_match = validate_audio_output(output_file, reference_dir, waveform_threshold=waveform_threshold, spectrogram_threshold=spectrogram_threshold)\n\n        # Assert that the output matches the reference\n        assert waveform_match, f\"Waveform for {output_file} does not match the reference\"\n        assert spectrogram_match, f\"Spectrogram for {output_file} does not match the reference\"\n","chunkId":"tests/integration/test_cli_integration.py:18"}],[2397,{"filePath":"tests/integration/test_remote_api_integration.py","startLine":0,"endLine":22,"chunkText":"import json\nimport pytest\nimport logging\nimport tempfile\nimport os\nimport time\nfrom pathlib import Path\nfrom unittest.mock import Mock, patch\nfrom http.server import HTTPServer, BaseHTTPRequestHandler\nimport threading\nimport urllib.parse\nimport io\n\nfrom audio_separator.remote import AudioSeparatorAPIClient\n\n\n# Mock API Server for Integration Tests\nclass MockAPIHandler(BaseHTTPRequestHandler):\n    \"\"\"Mock HTTP handler for simulating the Audio Separator API.\"\"\"\n\n    # Class variables to store state across requests\n    jobs = {}\n    models_data = {","chunkId":"tests/integration/test_remote_api_integration.py:0"}],[2398,{"filePath":"tests/integration/test_remote_api_integration.py","startLine":20,"endLine":32,"chunkText":"    # Class variables to store state across requests\n    jobs = {}\n    models_data = {\n        \"model1.ckpt\": {\"Type\": \"MDXC\", \"Stems\": [\"vocals (12.9)\", \"instrumental (17.0)\"], \"Name\": \"Test Model 1\"},\n        \"model2.onnx\": {\"Type\": \"MDX\", \"Stems\": [\"vocals (10.5)\", \"instrumental (15.2)\"], \"Name\": \"Test Model 2\"},\n        \"htdemucs_6s.yaml\": {\"Type\": \"Demucs\", \"Stems\": [\"vocals (9.7)\", \"drums (8.5)\", \"bass (10.0)\", \"guitar\", \"piano\", \"other\"], \"Name\": \"Test Demucs 6s\"},\n    }\n\n    def log_message(self, format, *args):\n        \"\"\"Suppress HTTP server logs.\"\"\"\n        pass\n\n    def do_GET(self):","chunkId":"tests/integration/test_remote_api_integration.py:1"}],[2399,{"filePath":"tests/integration/test_remote_api_integration.py","startLine":30,"endLine":46,"chunkText":"        pass\n\n    def do_GET(self):\n        \"\"\"Handle GET requests.\"\"\"\n        path = self.path\n\n        if path == \"/health\":\n            self.send_json_response({\"status\": \"healthy\", \"version\": \"1.0.0\"})\n\n        elif path.startswith(\"/status/\"):\n            task_id = path.split(\"/\")[-1]\n            if task_id in self.jobs:\n                self.send_json_response(self.jobs[task_id])\n            else:\n                self.send_json_response({\"task_id\": task_id, \"status\": \"not_found\", \"error\": \"Job not found\"}, status=404)\n\n        elif path.startswith(\"/download/\"):","chunkId":"tests/integration/test_remote_api_integration.py:2"}],[2400,{"filePath":"tests/integration/test_remote_api_integration.py","startLine":44,"endLine":55,"chunkText":"                self.send_json_response({\"task_id\": task_id, \"status\": \"not_found\", \"error\": \"Job not found\"}, status=404)\n\n        elif path.startswith(\"/download/\"):\n            # Parse task_id and filename from path like /download/task123/output.wav\n            parts = path.split(\"/\")\n            if len(parts) >= 4:\n                task_id = parts[2]\n                filename = urllib.parse.unquote(parts[3])\n\n                # Check if job exists and is completed\n                if task_id in self.jobs and self.jobs[task_id][\"status\"] == \"completed\":\n                    # Simulate audio file content","chunkId":"tests/integration/test_remote_api_integration.py:3"}],[2401,{"filePath":"tests/integration/test_remote_api_integration.py","startLine":53,"endLine":62,"chunkText":"                # Check if job exists and is completed\n                if task_id in self.jobs and self.jobs[task_id][\"status\"] == \"completed\":\n                    # Simulate audio file content\n                    audio_content = b\"fake audio file content for \" + filename.encode()\n                    self.send_response(200)\n                    self.send_header(\"Content-Type\", \"audio/wav\")\n                    self.send_header(\"Content-Disposition\", f\"attachment; filename={filename}\")\n                    self.end_headers()\n                    self.wfile.write(audio_content)\n                else:","chunkId":"tests/integration/test_remote_api_integration.py:4"}],[2402,{"filePath":"tests/integration/test_remote_api_integration.py","startLine":60,"endLine":71,"chunkText":"                    self.end_headers()\n                    self.wfile.write(audio_content)\n                else:\n                    self.send_json_response({\"error\": \"File not found\"}, status=404)\n            else:\n                self.send_json_response({\"error\": \"Invalid download path\"}, status=400)\n\n        elif path == \"/models\" or path.startswith(\"/models?\"):\n            # Parse query parameters\n            if \"?\" in path:\n                query_string = path.split(\"?\", 1)[1]\n                params = urllib.parse.parse_qs(query_string)","chunkId":"tests/integration/test_remote_api_integration.py:5"}],[2403,{"filePath":"tests/integration/test_remote_api_integration.py","startLine":70,"endLine":81,"chunkText":"                query_string = path.split(\"?\", 1)[1]\n                params = urllib.parse.parse_qs(query_string)\n                filter_by = params.get(\"filter_sort_by\", [None])[0]\n            else:\n                filter_by = None\n\n            # Filter models if requested\n            filtered_models = self.models_data\n            if filter_by:\n                filtered_models = {k: v for k, v in self.models_data.items() if filter_by.lower() in \" \".join(v[\"Stems\"]).lower() or filter_by.lower() in v[\"Name\"].lower()}\n\n            # Format as plain text (like the real API)","chunkId":"tests/integration/test_remote_api_integration.py:6"}],[2404,{"filePath":"tests/integration/test_remote_api_integration.py","startLine":79,"endLine":91,"chunkText":"                filtered_models = {k: v for k, v in self.models_data.items() if filter_by.lower() in \" \".join(v[\"Stems\"]).lower() or filter_by.lower() in v[\"Name\"].lower()}\n\n            # Format as plain text (like the real API)\n            text_output = self._format_models_as_text(filtered_models)\n            self.send_response(200)\n            self.send_header(\"Content-Type\", \"text/plain\")\n            self.end_headers()\n            self.wfile.write(text_output.encode())\n\n        elif path == \"/models-json\":\n            self.send_json_response(self.models_data)\n\n        else:","chunkId":"tests/integration/test_remote_api_integration.py:7"}],[2405,{"filePath":"tests/integration/test_remote_api_integration.py","startLine":89,"endLine":106,"chunkText":"            self.send_json_response(self.models_data)\n\n        else:\n            self.send_json_response({\"error\": \"Not found\"}, status=404)\n\n    def do_POST(self):\n        \"\"\"Handle POST requests.\"\"\"\n        if self.path == \"/separate\":\n            content_length = int(self.headers[\"Content-Length\"])\n            post_data = self.rfile.read(content_length)\n\n            # Simple task ID generation\n            task_id = f\"task-{len(self.jobs) + 1:03d}\"\n\n            # Initialize job status\n            self.jobs[task_id] = {\n                \"task_id\": task_id,\n                \"status\": \"submitted\",","chunkId":"tests/integration/test_remote_api_integration.py:8"}],[2406,{"filePath":"tests/integration/test_remote_api_integration.py","startLine":104,"endLine":118,"chunkText":"            self.jobs[task_id] = {\n                \"task_id\": task_id,\n                \"status\": \"submitted\",\n                \"progress\": 0,\n                \"original_filename\": \"test.wav\",\n                \"models_used\": [\"default\"],\n                \"total_models\": 1,\n                \"current_model_index\": 0,\n                \"files\": [],\n            }\n\n            # Simulate processing in background (for testing polling)\n            threading.Thread(target=self._simulate_processing, args=(task_id,)).start()\n\n            self.send_json_response(","chunkId":"tests/integration/test_remote_api_integration.py:9"}],[2407,{"filePath":"tests/integration/test_remote_api_integration.py","startLine":116,"endLine":128,"chunkText":"            threading.Thread(target=self._simulate_processing, args=(task_id,)).start()\n\n            self.send_json_response(\n                {\"task_id\": task_id, \"status\": \"submitted\", \"message\": \"Job submitted for processing\", \"models_used\": [\"default\"], \"total_models\": 1, \"original_filename\": \"test.wav\"}\n            )\n        else:\n            self.send_json_response({\"error\": \"Not found\"}, status=404)\n\n    def _simulate_processing(self, task_id):\n        \"\"\"Simulate job processing in background.\"\"\"\n        time.sleep(0.1)  # Brief delay\n\n        # Update to processing","chunkId":"tests/integration/test_remote_api_integration.py:10"}],[2408,{"filePath":"tests/integration/test_remote_api_integration.py","startLine":126,"endLine":146,"chunkText":"        time.sleep(0.1)  # Brief delay\n\n        # Update to processing\n        self.jobs[task_id].update({\"status\": \"processing\", \"progress\": 25})\n\n        time.sleep(0.1)\n\n        # Update progress\n        self.jobs[task_id].update({\"progress\": 50})\n\n        time.sleep(0.1)\n\n        # Update progress\n        self.jobs[task_id].update({\"progress\": 75})\n\n        time.sleep(0.1)\n\n        # Complete\n        self.jobs[task_id].update({\"status\": \"completed\", \"progress\": 100, \"files\": [\"test_(Vocals)_default.flac\", \"test_(Instrumental)_default.flac\"]})\n\n    def _format_models_as_text(self, models):","chunkId":"tests/integration/test_remote_api_integration.py:11"}],[2409,{"filePath":"tests/integration/test_remote_api_integration.py","startLine":144,"endLine":153,"chunkText":"        self.jobs[task_id].update({\"status\": \"completed\", \"progress\": 100, \"files\": [\"test_(Vocals)_default.flac\", \"test_(Instrumental)_default.flac\"]})\n\n    def _format_models_as_text(self, models):\n        \"\"\"Format models dictionary as plain text table.\"\"\"\n        if not models:\n            return \"No models found\"\n\n        # Calculate column widths\n        filename_width = max(len(\"Model Filename\"), max(len(k) for k in models.keys()))\n        arch_width = max(len(\"Arch\"), max(len(v[\"Type\"]) for v in models.values()))","chunkId":"tests/integration/test_remote_api_integration.py:12"}],[2410,{"filePath":"tests/integration/test_remote_api_integration.py","startLine":151,"endLine":160,"chunkText":"        # Calculate column widths\n        filename_width = max(len(\"Model Filename\"), max(len(k) for k in models.keys()))\n        arch_width = max(len(\"Arch\"), max(len(v[\"Type\"]) for v in models.values()))\n        stems_width = max(len(\"Output Stems (SDR)\"), max(len(\", \".join(v[\"Stems\"])) for v in models.values()))\n        name_width = max(len(\"Friendly Name\"), max(len(v[\"Name\"]) for v in models.values()))\n\n        total_width = filename_width + arch_width + stems_width + name_width + 15\n\n        lines = []\n        lines.append(\"-\" * total_width)","chunkId":"tests/integration/test_remote_api_integration.py:13"}],[2411,{"filePath":"tests/integration/test_remote_api_integration.py","startLine":158,"endLine":171,"chunkText":"\n        lines = []\n        lines.append(\"-\" * total_width)\n        lines.append(f\"{'Model Filename':<{filename_width}}  {'Arch':<{arch_width}}  {'Output Stems (SDR)':<{stems_width}}  {'Friendly Name'}\")\n        lines.append(\"-\" * total_width)\n\n        for filename, info in models.items():\n            stems = \", \".join(info[\"Stems\"])\n            lines.append(f\"{filename:<{filename_width}}  {info['Type']:<{arch_width}}  {stems:<{stems_width}}  {info['Name']}\")\n\n        return \"\\n\".join(lines)\n\n    def send_json_response(self, data, status=200):\n        \"\"\"Send JSON response.\"\"\"","chunkId":"tests/integration/test_remote_api_integration.py:14"}],[2412,{"filePath":"tests/integration/test_remote_api_integration.py","startLine":169,"endLine":186,"chunkText":"\n    def send_json_response(self, data, status=200):\n        \"\"\"Send JSON response.\"\"\"\n        self.send_response(status)\n        self.send_header(\"Content-Type\", \"application/json\")\n        self.end_headers()\n        response = json.dumps(data).encode()\n        self.wfile.write(response)\n\n\n@pytest.fixture(scope=\"function\")\ndef mock_http_server():\n    \"\"\"Start a mock HTTP server for testing.\"\"\"\n    server = HTTPServer((\"localhost\", 0), MockAPIHandler)\n    server_thread = threading.Thread(target=server.serve_forever)\n    server_thread.daemon = True\n    server_thread.start()\n","chunkId":"tests/integration/test_remote_api_integration.py:15"}],[2413,{"filePath":"tests/integration/test_remote_api_integration.py","startLine":184,"endLine":206,"chunkText":"    server_thread.daemon = True\n    server_thread.start()\n\n    # Get the actual port the server is using\n    port = server.server_address[1]\n    base_url = f\"http://localhost:{port}\"\n\n    yield base_url\n\n    server.shutdown()\n    server.server_close()\n\n\n@pytest.fixture\ndef api_client(mock_http_server):\n    \"\"\"Create an API client connected to the mock server.\"\"\"\n    logger = logging.getLogger(\"test\")\n    return AudioSeparatorAPIClient(mock_http_server, logger)\n\n\n@pytest.fixture\ndef test_audio_file():\n    \"\"\"Create a temporary test audio file.\"\"\"","chunkId":"tests/integration/test_remote_api_integration.py:16"}],[2414,{"filePath":"tests/integration/test_remote_api_integration.py","startLine":205,"endLine":212,"chunkText":"def test_audio_file():\n    \"\"\"Create a temporary test audio file.\"\"\"\n    with tempfile.NamedTemporaryFile(suffix=\".wav\", delete=False) as f:\n        f.write(b\"fake audio content for testing\")\n        yield f.name\n    os.unlink(f.name)\n\n","chunkId":"tests/integration/test_remote_api_integration.py:17"}],[2415,{"filePath":"tests/integration/test_remote_api_integration.py","startLine":213,"endLine":228,"chunkText":"class TestRemoteAPIIntegration:\n    \"\"\"Integration tests for the Remote API functionality.\"\"\"\n\n    def test_server_health_check(self, api_client):\n        \"\"\"Test server health check endpoint.\"\"\"\n        version = api_client.get_server_version()\n        assert version == \"1.0.0\"\n\n    def test_list_models_pretty_format(self, api_client):\n        \"\"\"Test listing models in pretty format.\"\"\"\n        result = api_client.list_models(format_type=\"pretty\")\n\n        assert \"text\" in result\n        text = result[\"text\"]\n        assert \"Model Filename\" in text\n        assert \"model1.ckpt\" in text","chunkId":"tests/integration/test_remote_api_integration.py:18"}],[2416,{"filePath":"tests/integration/test_remote_api_integration.py","startLine":226,"endLine":240,"chunkText":"        text = result[\"text\"]\n        assert \"Model Filename\" in text\n        assert \"model1.ckpt\" in text\n        assert \"model2.onnx\" in text\n        assert \"htdemucs_6s.yaml\" in text\n        assert \"Test Model 1\" in text\n\n    def test_list_models_json_format(self, api_client):\n        \"\"\"Test listing models in JSON format.\"\"\"\n        result = api_client.list_models(format_type=\"json\")\n\n        assert \"model1.ckpt\" in result\n        assert \"model2.onnx\" in result\n        assert \"htdemucs_6s.yaml\" in result\n        assert result[\"model1.ckpt\"][\"Type\"] == \"MDXC\"","chunkId":"tests/integration/test_remote_api_integration.py:19"}],[2417,{"filePath":"tests/integration/test_remote_api_integration.py","startLine":238,"endLine":251,"chunkText":"        assert \"model2.onnx\" in result\n        assert \"htdemucs_6s.yaml\" in result\n        assert result[\"model1.ckpt\"][\"Type\"] == \"MDXC\"\n        assert result[\"model2.onnx\"][\"Type\"] == \"MDX\"\n        assert result[\"htdemucs_6s.yaml\"][\"Type\"] == \"Demucs\"\n\n    def test_list_models_with_filter(self, api_client):\n        \"\"\"Test listing models with filter.\"\"\"\n        result = api_client.list_models(filter_by=\"vocals\")\n\n        assert \"text\" in result\n        text = result[\"text\"]\n        # Should include models that have \"vocals\" in their stems\n        assert \"model1.ckpt\" in text","chunkId":"tests/integration/test_remote_api_integration.py:20"}],[2418,{"filePath":"tests/integration/test_remote_api_integration.py","startLine":249,"endLine":262,"chunkText":"        text = result[\"text\"]\n        # Should include models that have \"vocals\" in their stems\n        assert \"model1.ckpt\" in text\n        assert \"model2.onnx\" in text\n        assert \"htdemucs_6s.yaml\" in text\n\n    def test_separate_audio_submission(self, api_client, test_audio_file):\n        \"\"\"Test audio separation job submission.\"\"\"\n        result = api_client.separate_audio(test_audio_file)\n\n        assert \"task_id\" in result\n        assert result[\"status\"] == \"submitted\"\n        assert result[\"models_used\"] == [\"default\"]\n        assert result[\"total_models\"] == 1","chunkId":"tests/integration/test_remote_api_integration.py:21"}],[2419,{"filePath":"tests/integration/test_remote_api_integration.py","startLine":260,"endLine":274,"chunkText":"        assert result[\"status\"] == \"submitted\"\n        assert result[\"models_used\"] == [\"default\"]\n        assert result[\"total_models\"] == 1\n        assert result[\"original_filename\"] == \"test.wav\"\n\n        task_id = result[\"task_id\"]\n        assert task_id.startswith(\"task-\")\n\n    def test_separate_audio_with_custom_parameters(self, api_client, test_audio_file):\n        \"\"\"Test audio separation with custom parameters.\"\"\"\n        result = api_client.separate_audio(\n            test_audio_file,\n            model=\"model1.ckpt\",\n            output_format=\"wav\",\n            normalization_threshold=0.8,","chunkId":"tests/integration/test_remote_api_integration.py:22"}],[2420,{"filePath":"tests/integration/test_remote_api_integration.py","startLine":272,"endLine":288,"chunkText":"            model=\"model1.ckpt\",\n            output_format=\"wav\",\n            normalization_threshold=0.8,\n            mdx_segment_size=512,\n            vr_aggression=10,\n            custom_output_names={\"Vocals\": \"lead_vocals\", \"Instrumental\": \"backing_track\"},\n        )\n\n        assert result[\"status\"] == \"submitted\"\n        assert \"task_id\" in result\n\n    def test_job_status_polling(self, api_client, test_audio_file):\n        \"\"\"Test job status polling through completion.\"\"\"\n        # Submit job\n        result = api_client.separate_audio(test_audio_file)\n        task_id = result[\"task_id\"]\n","chunkId":"tests/integration/test_remote_api_integration.py:23"}],[2421,{"filePath":"tests/integration/test_remote_api_integration.py","startLine":286,"endLine":302,"chunkText":"        result = api_client.separate_audio(test_audio_file)\n        task_id = result[\"task_id\"]\n\n        # Poll until completion\n        max_attempts = 20  # Prevent infinite loop\n        attempts = 0\n\n        while attempts < max_attempts:\n            status = api_client.get_job_status(task_id)\n\n            assert status[\"task_id\"] == task_id\n            assert \"status\" in status\n\n            if status[\"status\"] == \"completed\":\n                assert status[\"progress\"] == 100\n                assert \"files\" in status\n                assert len(status[\"files\"]) == 2  # Vocals and Instrumental","chunkId":"tests/integration/test_remote_api_integration.py:24"}],[2422,{"filePath":"tests/integration/test_remote_api_integration.py","startLine":300,"endLine":314,"chunkText":"                assert status[\"progress\"] == 100\n                assert \"files\" in status\n                assert len(status[\"files\"]) == 2  # Vocals and Instrumental\n                break\n            elif status[\"status\"] in [\"submitted\", \"processing\"]:\n                # Continue polling\n                time.sleep(0.05)  # Small delay between polls\n            else:\n                pytest.fail(f\"Unexpected job status: {status['status']}\")\n\n            attempts += 1\n\n        if attempts >= max_attempts:\n            pytest.fail(\"Job did not complete within expected time\")\n","chunkId":"tests/integration/test_remote_api_integration.py:25"}],[2423,{"filePath":"tests/integration/test_remote_api_integration.py","startLine":312,"endLine":327,"chunkText":"        if attempts >= max_attempts:\n            pytest.fail(\"Job did not complete within expected time\")\n\n    def test_file_download(self, api_client, test_audio_file):\n        \"\"\"Test downloading files from completed job.\"\"\"\n        # Submit and wait for completion\n        result = api_client.separate_audio(test_audio_file)\n        task_id = result[\"task_id\"]\n\n        # Wait for completion (simplified polling)\n        for _ in range(20):\n            status = api_client.get_job_status(task_id)\n            if status[\"status\"] == \"completed\":\n                break\n            time.sleep(0.05)\n","chunkId":"tests/integration/test_remote_api_integration.py:26"}],[2424,{"filePath":"tests/integration/test_remote_api_integration.py","startLine":325,"endLine":342,"chunkText":"                break\n            time.sleep(0.05)\n\n        assert status[\"status\"] == \"completed\"\n        assert len(status[\"files\"]) == 2\n\n        # Download first file\n        filename = status[\"files\"][0]\n        with tempfile.NamedTemporaryFile(delete=False) as temp_output:\n            output_path = temp_output.name\n\n        try:\n            downloaded_path = api_client.download_file(task_id, filename, output_path)\n            assert downloaded_path == output_path\n            assert os.path.exists(output_path)\n\n            # Verify file content\n            with open(output_path, \"rb\") as f:","chunkId":"tests/integration/test_remote_api_integration.py:27"}],[2425,{"filePath":"tests/integration/test_remote_api_integration.py","startLine":340,"endLine":352,"chunkText":"\n            # Verify file content\n            with open(output_path, \"rb\") as f:\n                content = f.read()\n                assert content.startswith(b\"fake audio file content\")\n                assert filename.encode() in content\n        finally:\n            if os.path.exists(output_path):\n                os.unlink(output_path)\n\n    def test_separate_audio_and_wait_success(self, api_client, test_audio_file):\n        \"\"\"Test the convenience method for separating audio and waiting for completion.\"\"\"\n        with tempfile.TemporaryDirectory() as temp_dir:","chunkId":"tests/integration/test_remote_api_integration.py:28"}],[2426,{"filePath":"tests/integration/test_remote_api_integration.py","startLine":350,"endLine":359,"chunkText":"    def test_separate_audio_and_wait_success(self, api_client, test_audio_file):\n        \"\"\"Test the convenience method for separating audio and waiting for completion.\"\"\"\n        with tempfile.TemporaryDirectory() as temp_dir:\n            result = api_client.separate_audio_and_wait(test_audio_file, timeout=5, poll_interval=0.05, download=True, output_dir=temp_dir)\n\n            assert result[\"status\"] == \"completed\"\n            assert \"task_id\" in result\n            assert \"files\" in result\n            assert \"downloaded_files\" in result\n            assert len(result[\"files\"]) == 2","chunkId":"tests/integration/test_remote_api_integration.py:29"}],[2427,{"filePath":"tests/integration/test_remote_api_integration.py","startLine":357,"endLine":368,"chunkText":"            assert \"files\" in result\n            assert \"downloaded_files\" in result\n            assert len(result[\"files\"]) == 2\n            assert len(result[\"downloaded_files\"]) == 2\n\n            # Verify files were actually downloaded\n            for file_path in result[\"downloaded_files\"]:\n                assert os.path.exists(file_path)\n                assert file_path.startswith(temp_dir)\n\n    def test_separate_audio_and_wait_no_download(self, api_client, test_audio_file):\n        \"\"\"Test convenience method without downloading files.\"\"\"","chunkId":"tests/integration/test_remote_api_integration.py:30"}],[2428,{"filePath":"tests/integration/test_remote_api_integration.py","startLine":366,"endLine":379,"chunkText":"\n    def test_separate_audio_and_wait_no_download(self, api_client, test_audio_file):\n        \"\"\"Test convenience method without downloading files.\"\"\"\n        result = api_client.separate_audio_and_wait(test_audio_file, timeout=5, poll_interval=0.05, download=False)\n\n        assert result[\"status\"] == \"completed\"\n        assert \"files\" in result\n        assert \"downloaded_files\" not in result\n\n    def test_job_status_not_found(self, api_client):\n        \"\"\"Test getting status for non-existent job.\"\"\"\n        import requests\n\n        with pytest.raises(requests.exceptions.HTTPError):","chunkId":"tests/integration/test_remote_api_integration.py:31"}],[2429,{"filePath":"tests/integration/test_remote_api_integration.py","startLine":377,"endLine":392,"chunkText":"        import requests\n\n        with pytest.raises(requests.exceptions.HTTPError):\n            api_client.get_job_status(\"nonexistent-task-id\")\n\n    def test_download_file_not_found(self, api_client):\n        \"\"\"Test downloading file for non-existent job.\"\"\"\n        with pytest.raises(Exception):  # Should raise an exception for 404\n            api_client.download_file(\"nonexistent-task-id\", \"file.wav\")\n\n    def test_multiple_concurrent_jobs(self, api_client, test_audio_file):\n        \"\"\"Test handling multiple concurrent jobs.\"\"\"\n        # Submit multiple jobs\n        num_jobs = 3\n        jobs = []\n","chunkId":"tests/integration/test_remote_api_integration.py:32"}],[2430,{"filePath":"tests/integration/test_remote_api_integration.py","startLine":390,"endLine":407,"chunkText":"        num_jobs = 3\n        jobs = []\n\n        for i in range(num_jobs):\n            result = api_client.separate_audio(test_audio_file)\n            jobs.append(result[\"task_id\"])\n\n        # Wait for all jobs to complete\n        completed_jobs = []\n        max_attempts = 30\n\n        for attempt in range(max_attempts):\n            for task_id in jobs:\n                if task_id not in completed_jobs:\n                    status = api_client.get_job_status(task_id)\n                    if status[\"status\"] == \"completed\":\n                        completed_jobs.append(task_id)\n","chunkId":"tests/integration/test_remote_api_integration.py:33"}],[2431,{"filePath":"tests/integration/test_remote_api_integration.py","startLine":405,"endLine":419,"chunkText":"                    if status[\"status\"] == \"completed\":\n                        completed_jobs.append(task_id)\n\n            if len(completed_jobs) == num_jobs:\n                break\n\n            time.sleep(0.05)\n\n        assert len(completed_jobs) == num_jobs, \"Not all jobs completed in expected time\"\n\n    def test_separate_audio_with_multiple_models(self, api_client, test_audio_file):\n        \"\"\"Test separation with multiple models (parameter passing).\"\"\"\n        models = [\"model1.ckpt\", \"model2.onnx\"]\n        result = api_client.separate_audio(test_audio_file, models=models)\n","chunkId":"tests/integration/test_remote_api_integration.py:34"}],[2432,{"filePath":"tests/integration/test_remote_api_integration.py","startLine":417,"endLine":426,"chunkText":"        models = [\"model1.ckpt\", \"model2.onnx\"]\n        result = api_client.separate_audio(test_audio_file, models=models)\n\n        assert result[\"status\"] == \"submitted\"\n        assert \"task_id\" in result\n        # Note: The mock server doesn't fully simulate multiple model processing,\n        # but we can test that the parameters are accepted\n\n\n# CLI Integration Tests (using the mock server)","chunkId":"tests/integration/test_remote_api_integration.py:35"}],[2433,{"filePath":"tests/integration/test_remote_api_integration.py","startLine":427,"endLine":439,"chunkText":"class TestRemoteCLIIntegration:\n    \"\"\"Integration tests for the remote CLI.\"\"\"\n\n    @patch(\"audio_separator.remote.cli.AudioSeparatorAPIClient\")\n    def test_cli_separate_command_integration(self, mock_client_class, test_audio_file):\n        \"\"\"Test CLI separate command integration.\"\"\"\n        from audio_separator.remote.cli import handle_separate_command\n\n        # Set up mock client\n        mock_client = Mock()\n        mock_client.separate_audio_and_wait.return_value = {\"status\": \"completed\", \"downloaded_files\": [\"output1.wav\", \"output2.wav\"]}\n        mock_client_class.return_value = mock_client\n","chunkId":"tests/integration/test_remote_api_integration.py:36"}],[2434,{"filePath":"tests/integration/test_remote_api_integration.py","startLine":437,"endLine":451,"chunkText":"        mock_client.separate_audio_and_wait.return_value = {\"status\": \"completed\", \"downloaded_files\": [\"output1.wav\", \"output2.wav\"]}\n        mock_client_class.return_value = mock_client\n\n        # Mock arguments (simplified)\n        args = Mock()\n        args.audio_files = [test_audio_file]\n        args.model = \"test_model.ckpt\"\n        args.models = None\n        args.timeout = 600\n        args.poll_interval = 10\n\n        # Set all required attributes with appropriate defaults\n        default_attrs = {\n            \"output_format\": \"flac\",\n            \"output_bitrate\": None,","chunkId":"tests/integration/test_remote_api_integration.py:37"}],[2435,{"filePath":"tests/integration/test_remote_api_integration.py","startLine":449,"endLine":465,"chunkText":"        default_attrs = {\n            \"output_format\": \"flac\",\n            \"output_bitrate\": None,\n            \"normalization\": 0.9,\n            \"amplification\": 0.0,\n            \"single_stem\": None,\n            \"invert_spect\": False,\n            \"sample_rate\": 44100,\n            \"use_soundfile\": False,\n            \"use_autocast\": False,\n            \"custom_output_names\": None,\n            \"mdx_segment_size\": 256,\n            \"mdx_overlap\": 0.25,\n            \"mdx_batch_size\": 1,\n            \"mdx_hop_length\": 1024,\n            \"mdx_enable_denoise\": False,\n            \"vr_batch_size\": 1,","chunkId":"tests/integration/test_remote_api_integration.py:38"}],[2436,{"filePath":"tests/integration/test_remote_api_integration.py","startLine":463,"endLine":477,"chunkText":"            \"mdx_hop_length\": 1024,\n            \"mdx_enable_denoise\": False,\n            \"vr_batch_size\": 1,\n            \"vr_window_size\": 512,\n            \"vr_aggression\": 5,\n            \"vr_enable_tta\": False,\n            \"vr_high_end_process\": False,\n            \"vr_enable_post_process\": False,\n            \"vr_post_process_threshold\": 0.2,\n            \"demucs_segment_size\": \"Default\",\n            \"demucs_shifts\": 2,\n            \"demucs_overlap\": 0.25,\n            \"demucs_segments_enabled\": True,\n            \"mdxc_segment_size\": 256,\n            \"mdxc_override_model_segment_size\": False,","chunkId":"tests/integration/test_remote_api_integration.py:39"}],[2437,{"filePath":"tests/integration/test_remote_api_integration.py","startLine":475,"endLine":494,"chunkText":"            \"demucs_segments_enabled\": True,\n            \"mdxc_segment_size\": 256,\n            \"mdxc_override_model_segment_size\": False,\n            \"mdxc_overlap\": 8,\n            \"mdxc_batch_size\": 1,\n            \"mdxc_pitch_shift\": 0,\n        }\n\n        for attr, value in default_attrs.items():\n            setattr(args, attr, value)\n\n        logger = Mock()\n\n        # Execute the command\n        handle_separate_command(args, mock_client, logger)\n\n        # Verify the API client method was called\n        mock_client.separate_audio_and_wait.assert_called_once()\n\n        # Verify success was logged","chunkId":"tests/integration/test_remote_api_integration.py:40"}],[2438,{"filePath":"tests/integration/test_remote_api_integration.py","startLine":492,"endLine":498,"chunkText":"        mock_client.separate_audio_and_wait.assert_called_once()\n\n        # Verify success was logged\n        logger.info.assert_called()\n\n\n# End-to-End Test with Real Audio File","chunkId":"tests/integration/test_remote_api_integration.py:41"}],[2439,{"filePath":"tests/integration/test_remote_api_integration.py","startLine":499,"endLine":510,"chunkText":"class TestRemoteAPIEndToEnd:\n    \"\"\"End-to-end tests using realistic audio data.\"\"\"\n\n    def test_end_to_end_workflow(self, api_client):\n        \"\"\"Test complete workflow from submission to download.\"\"\"\n        # Create a more realistic \"audio\" file\n        with tempfile.NamedTemporaryFile(suffix=\".wav\", delete=False) as f:\n            # Write some data that could represent a small WAV file\n            f.write(b\"RIFF\")  # WAV header\n            f.write(b\"\\x24\\x00\\x00\\x00\")  # File size\n            f.write(b\"WAVE\")  # Format\n            f.write(b\"fake audio data for testing\" * 10)  # Some fake audio data","chunkId":"tests/integration/test_remote_api_integration.py:42"}],[2440,{"filePath":"tests/integration/test_remote_api_integration.py","startLine":508,"endLine":523,"chunkText":"            f.write(b\"\\x24\\x00\\x00\\x00\")  # File size\n            f.write(b\"WAVE\")  # Format\n            f.write(b\"fake audio data for testing\" * 10)  # Some fake audio data\n            test_file = f.name\n\n        try:\n            # Step 1: Check server health\n            version = api_client.get_server_version()\n            assert version is not None\n\n            # Step 2: List available models\n            models = api_client.list_models()\n            assert \"text\" in models\n\n            # Step 3: Submit separation job\n            result = api_client.separate_audio(test_file)","chunkId":"tests/integration/test_remote_api_integration.py:43"}],[2441,{"filePath":"tests/integration/test_remote_api_integration.py","startLine":521,"endLine":535,"chunkText":"\n            # Step 3: Submit separation job\n            result = api_client.separate_audio(test_file)\n            task_id = result[\"task_id\"]\n            assert result[\"status\"] == \"submitted\"\n\n            # Step 4: Poll for completion\n            completed = False\n            for _ in range(20):\n                status = api_client.get_job_status(task_id)\n                if status[\"status\"] == \"completed\":\n                    completed = True\n                    break\n                elif status[\"status\"] == \"error\":\n                    pytest.fail(f\"Job failed: {status.get('error', 'Unknown error')}\")","chunkId":"tests/integration/test_remote_api_integration.py:44"}],[2442,{"filePath":"tests/integration/test_remote_api_integration.py","startLine":533,"endLine":546,"chunkText":"                    break\n                elif status[\"status\"] == \"error\":\n                    pytest.fail(f\"Job failed: {status.get('error', 'Unknown error')}\")\n                time.sleep(0.05)\n\n            assert completed, \"Job did not complete in expected time\"\n\n            # Step 5: Download results\n            files = status[\"files\"]\n            assert len(files) > 0\n\n            with tempfile.TemporaryDirectory() as temp_dir:\n                for filename in files:\n                    output_path = os.path.join(temp_dir, filename)","chunkId":"tests/integration/test_remote_api_integration.py:45"}],[2443,{"filePath":"tests/integration/test_remote_api_integration.py","startLine":544,"endLine":558,"chunkText":"            with tempfile.TemporaryDirectory() as temp_dir:\n                for filename in files:\n                    output_path = os.path.join(temp_dir, filename)\n                    downloaded_path = api_client.download_file(task_id, filename, output_path)\n                    assert os.path.exists(downloaded_path)\n                    assert os.path.getsize(downloaded_path) > 0\n\n        finally:\n            os.unlink(test_file)\n\n    def test_error_handling_workflow(self, api_client):\n        \"\"\"Test error handling in various scenarios.\"\"\"\n        import requests\n\n        # Test with non-existent file","chunkId":"tests/integration/test_remote_api_integration.py:46"}],[2444,{"filePath":"tests/integration/test_remote_api_integration.py","startLine":556,"endLine":569,"chunkText":"        import requests\n\n        # Test with non-existent file\n        with pytest.raises(FileNotFoundError):\n            api_client.separate_audio(\"/non/existent/file.wav\")\n\n        # Test status for non-existent job\n        with pytest.raises(requests.exceptions.HTTPError):\n            api_client.get_job_status(\"invalid-task-id\")\n\n        # Test download for non-existent job/file\n        with pytest.raises(requests.exceptions.HTTPError):\n            api_client.download_file(\"invalid-task-id\", \"file.wav\")\n","chunkId":"tests/integration/test_remote_api_integration.py:47"}],[2445,{"filePath":"tests/integration/test_roformer_audio_quality.py","startLine":0,"endLine":23,"chunkText":"\"\"\"\nAudio quality validation tests for Roformer models.\nThese tests ensure audio quality hasn't regressed after the update.\n\"\"\"\n\nimport pytest\nimport os\nimport tempfile\nimport numpy as np\n\n# This will fail initially - that's expected for TDD\ntry:\n    from audio_separator import Separator\n    IMPORTS_AVAILABLE = True\nexcept ImportError:\n    IMPORTS_AVAILABLE = False\n\n\nclass TestRoformerAudioQuality:\n    \"\"\"Test audio quality validation for Roformer models.\"\"\"\n    \n    @pytest.fixture\n    def reference_audio_file(self):\n        \"\"\"Create reference audio file for testing.\"\"\"","chunkId":"tests/integration/test_roformer_audio_quality.py:0"}],[2446,{"filePath":"tests/integration/test_roformer_audio_quality.py","startLine":21,"endLine":32,"chunkText":"    @pytest.fixture\n    def reference_audio_file(self):\n        \"\"\"Create reference audio file for testing.\"\"\"\n        with tempfile.NamedTemporaryFile(suffix='.wav', delete=False) as tmp:\n            # Create mock audio data (in real implementation, this would be actual audio)\n            sample_rate = 44100\n            duration = 1.0  # 1 second\n            t = np.linspace(0, duration, int(sample_rate * duration))\n            audio_data = np.sin(2 * np.pi * 440 * t)  # 440 Hz sine wave\n            \n            # Write as simple binary data for mock\n            tmp.write(audio_data.tobytes())","chunkId":"tests/integration/test_roformer_audio_quality.py:1"}],[2447,{"filePath":"tests/integration/test_roformer_audio_quality.py","startLine":30,"endLine":44,"chunkText":"            \n            # Write as simple binary data for mock\n            tmp.write(audio_data.tobytes())\n            yield tmp.name\n        \n        if os.path.exists(tmp.name):\n            os.unlink(tmp.name)\n    \n    @pytest.mark.skipif(not IMPORTS_AVAILABLE, reason=\"Implementation not available yet (TDD)\")\n    def test_bs_roformer_audio_quality_regression(self, reference_audio_file):\n        \"\"\"Test that BSRoformer models maintain audio quality after update.\"\"\"\n        \n        with tempfile.TemporaryDirectory() as output_dir:\n            separator = Separator(output_dir=output_dir)\n            ","chunkId":"tests/integration/test_roformer_audio_quality.py:2"}],[2448,{"filePath":"tests/integration/test_roformer_audio_quality.py","startLine":42,"endLine":54,"chunkText":"        with tempfile.TemporaryDirectory() as output_dir:\n            separator = Separator(output_dir=output_dir)\n            \n            # This would load an actual BSRoformer model in real implementation\n            # For now, it's a placeholder that will fail (TDD requirement)\n            separator.load_model(\"bs_roformer_test_model.ckpt\")\n            \n            outputs = separator.separate(reference_audio_file)\n            \n            # Verify outputs exist\n            assert len(outputs) >= 2  # Expecting vocal and instrumental\n            \n            for output_file in outputs:","chunkId":"tests/integration/test_roformer_audio_quality.py:3"}],[2449,{"filePath":"tests/integration/test_roformer_audio_quality.py","startLine":52,"endLine":62,"chunkText":"            assert len(outputs) >= 2  # Expecting vocal and instrumental\n            \n            for output_file in outputs:\n                assert os.path.exists(output_file)\n                assert os.path.getsize(output_file) > 0\n                \n                # In real implementation, this would:\n                # 1. Load reference output from before update\n                # 2. Calculate SSIM similarity between waveforms\n                # 3. Assert similarity >= 0.90 (per FR-006)\n                # 4. Calculate spectrogram SSIM","chunkId":"tests/integration/test_roformer_audio_quality.py:4"}],[2450,{"filePath":"tests/integration/test_roformer_audio_quality.py","startLine":60,"endLine":69,"chunkText":"                # 2. Calculate SSIM similarity between waveforms\n                # 3. Assert similarity >= 0.90 (per FR-006)\n                # 4. Calculate spectrogram SSIM\n                # 5. Assert spectrogram similarity >= 0.80 (per FR-006)\n    \n    @pytest.mark.skipif(not IMPORTS_AVAILABLE, reason=\"Implementation not available yet (TDD)\")\n    def test_mel_band_roformer_audio_quality_validation(self, reference_audio_file):\n        \"\"\"Test that MelBandRoformer models produce high-quality separation.\"\"\"\n        \n        with tempfile.TemporaryDirectory() as output_dir:","chunkId":"tests/integration/test_roformer_audio_quality.py:5"}],[2451,{"filePath":"tests/integration/test_roformer_audio_quality.py","startLine":67,"endLine":81,"chunkText":"        \"\"\"Test that MelBandRoformer models produce high-quality separation.\"\"\"\n        \n        with tempfile.TemporaryDirectory() as output_dir:\n            separator = Separator(output_dir=output_dir)\n            \n            # This would load an actual MelBandRoformer model\n            separator.load_model(\"mel_band_roformer_test_model.ckpt\")\n            \n            outputs = separator.separate(reference_audio_file)\n            \n            # Quality validation\n            assert len(outputs) > 0\n            \n            for output_file in outputs:\n                assert os.path.exists(output_file)","chunkId":"tests/integration/test_roformer_audio_quality.py:6"}],[2452,{"filePath":"tests/integration/test_roformer_audio_quality.py","startLine":79,"endLine":91,"chunkText":"            \n            for output_file in outputs:\n                assert os.path.exists(output_file)\n                \n                # In real implementation:\n                # 1. Analyze audio for artifacts\n                # 2. Check frequency response\n                # 3. Validate stem separation quality\n                # 4. Ensure no clipping or distortion\n    \n    # TDD placeholder test removed - implementation is now complete\n    \n    @pytest.mark.skipif(not IMPORTS_AVAILABLE, reason=\"Implementation not available yet (TDD)\")","chunkId":"tests/integration/test_roformer_audio_quality.py:7"}],[2453,{"filePath":"tests/integration/test_roformer_audio_quality.py","startLine":89,"endLine":99,"chunkText":"    # TDD placeholder test removed - implementation is now complete\n    \n    @pytest.mark.skipif(not IMPORTS_AVAILABLE, reason=\"Implementation not available yet (TDD)\")\n    def test_audio_similarity_calculation_framework(self, reference_audio_file):\n        \"\"\"Test the audio similarity calculation framework.\"\"\"\n        \n        # This test would verify the SSIM calculation framework works correctly\n        # It's a placeholder for the actual similarity calculation implementation\n        \n        # Mock similarity calculation\n        def calculate_waveform_similarity(audio1, audio2):","chunkId":"tests/integration/test_roformer_audio_quality.py:8"}],[2454,{"filePath":"tests/integration/test_roformer_audio_quality.py","startLine":97,"endLine":109,"chunkText":"        \n        # Mock similarity calculation\n        def calculate_waveform_similarity(audio1, audio2):\n            # Placeholder - real implementation would use SSIM\n            return 0.95  # Mock high similarity\n        \n        def calculate_spectrogram_similarity(audio1, audio2):\n            # Placeholder - real implementation would convert to spectrogram and use SSIM\n            return 0.85  # Mock good similarity\n        \n        # Test the calculation functions work\n        mock_audio1 = np.random.random(1000)\n        mock_audio2 = mock_audio1 + np.random.random(1000) * 0.1  # Similar with noise","chunkId":"tests/integration/test_roformer_audio_quality.py:9"}],[2455,{"filePath":"tests/integration/test_roformer_audio_quality.py","startLine":107,"endLine":119,"chunkText":"        # Test the calculation functions work\n        mock_audio1 = np.random.random(1000)\n        mock_audio2 = mock_audio1 + np.random.random(1000) * 0.1  # Similar with noise\n        \n        waveform_sim = calculate_waveform_similarity(mock_audio1, mock_audio2)\n        spectrogram_sim = calculate_spectrogram_similarity(mock_audio1, mock_audio2)\n        \n        assert waveform_sim >= 0.90, f\"Waveform similarity {waveform_sim} below threshold\"\n        assert spectrogram_sim >= 0.80, f\"Spectrogram similarity {spectrogram_sim} below threshold\"\n\n\nif __name__ == \"__main__\":\n    pytest.main([__file__, \"-v\"]","chunkId":"tests/integration/test_roformer_audio_quality.py:10"}],[2456,{"filePath":"tests/integration/test_roformer_backward_compatibility.py","startLine":0,"endLine":21,"chunkText":"\"\"\"\nIntegration test for existing older model compatibility.\nThis test ensures that existing models continue to work without regression.\n\"\"\"\n\nimport pytest\nimport os\nimport tempfile\nimport numpy as np\nfrom unittest.mock import patch, Mock\nimport torch\n\n# This will fail initially - that's expected for TDD\ntry:\n    from audio_separator import Separator\n    from audio_separator.separator.roformer.roformer_loader import RoformerLoader\n    IMPORTS_AVAILABLE = True\nexcept ImportError:\n    IMPORTS_AVAILABLE = False\n\n\nclass TestRoformerBackwardCompatibility:","chunkId":"tests/integration/test_roformer_backward_compatibility.py:0"}],[2457,{"filePath":"tests/integration/test_roformer_backward_compatibility.py","startLine":19,"endLine":32,"chunkText":"\n\nclass TestRoformerBackwardCompatibility:\n    \"\"\"Test backward compatibility with existing older Roformer models.\"\"\"\n    \n    @pytest.fixture\n    def mock_old_roformer_model(self):\n        \"\"\"Create a mock old Roformer model file for testing.\"\"\"\n        with tempfile.NamedTemporaryFile(suffix='.ckpt', delete=False) as tmp:\n            # Create a minimal mock model state dict that represents an old Roformer\n            mock_state = {\n                'state_dict': {\n                    'model.dim': torch.tensor(512),\n                    'model.depth': torch.tensor(6),","chunkId":"tests/integration/test_roformer_backward_compatibility.py:1"}],[2458,{"filePath":"tests/integration/test_roformer_backward_compatibility.py","startLine":30,"endLine":42,"chunkText":"                'state_dict': {\n                    'model.dim': torch.tensor(512),\n                    'model.depth': torch.tensor(6),\n                    'model.stereo': torch.tensor(False),\n                    'model.num_stems': torch.tensor(2),\n                    # Old model doesn't have new parameters like mlp_expansion_factor\n                },\n                'config': {\n                    'dim': 512,\n                    'depth': 6,\n                    'stereo': False,\n                    'num_stems': 2,\n                    'freqs_per_bands': (2, 4, 8, 16, 32, 64),","chunkId":"tests/integration/test_roformer_backward_compatibility.py:2"}],[2459,{"filePath":"tests/integration/test_roformer_backward_compatibility.py","startLine":40,"endLine":56,"chunkText":"                    'stereo': False,\n                    'num_stems': 2,\n                    'freqs_per_bands': (2, 4, 8, 16, 32, 64),\n                    # Missing new parameters: mlp_expansion_factor, sage_attention, etc.\n                }\n            }\n            torch.save(mock_state, tmp.name)\n            yield tmp.name\n        \n        # Cleanup\n        if os.path.exists(tmp.name):\n            os.unlink(tmp.name)\n    \n    @pytest.fixture\n    def mock_audio_file(self):\n        \"\"\"Create a mock audio file for testing.\"\"\"\n        with tempfile.NamedTemporaryFile(suffix='.flac', delete=False) as tmp:","chunkId":"tests/integration/test_roformer_backward_compatibility.py:3"}],[2460,{"filePath":"tests/integration/test_roformer_backward_compatibility.py","startLine":54,"endLine":65,"chunkText":"    def mock_audio_file(self):\n        \"\"\"Create a mock audio file for testing.\"\"\"\n        with tempfile.NamedTemporaryFile(suffix='.flac', delete=False) as tmp:\n            # Create minimal mock audio data (this would normally be actual audio)\n            tmp.write(b'mock_audio_data')\n            yield tmp.name\n        \n        # Cleanup\n        if os.path.exists(tmp.name):\n            os.unlink(tmp.name)\n    \n    @pytest.mark.skipif(not IMPORTS_AVAILABLE, reason=\"Implementation not available yet (TDD)\")","chunkId":"tests/integration/test_roformer_backward_compatibility.py:4"}],[2461,{"filePath":"tests/integration/test_roformer_backward_compatibility.py","startLine":63,"endLine":72,"chunkText":"            os.unlink(tmp.name)\n    \n    @pytest.mark.skipif(not IMPORTS_AVAILABLE, reason=\"Implementation not available yet (TDD)\")\n    def test_load_existing_older_model_without_regression(self, mock_old_roformer_model, mock_audio_file):\n        \"\"\"Test that existing older models load and work identically to before update.\"\"\"\n        \n        # This test MUST FAIL initially because implementation doesn't exist\n        with tempfile.TemporaryDirectory() as output_dir:\n            separator = Separator(\n                model_file_dir=os.path.dirname(mock_old_roformer_model),","chunkId":"tests/integration/test_roformer_backward_compatibility.py:5"}],[2462,{"filePath":"tests/integration/test_roformer_backward_compatibility.py","startLine":70,"endLine":82,"chunkText":"        with tempfile.TemporaryDirectory() as output_dir:\n            separator = Separator(\n                model_file_dir=os.path.dirname(mock_old_roformer_model),\n                output_dir=output_dir\n            )\n            \n            # Load the old model - should work with fallback mechanism\n            separator.load_model(os.path.basename(mock_old_roformer_model))\n            \n            # Separate audio - should produce same results as before\n            output_files = separator.separate(mock_audio_file)\n            \n            # Verify outputs exist and are valid","chunkId":"tests/integration/test_roformer_backward_compatibility.py:6"}],[2463,{"filePath":"tests/integration/test_roformer_backward_compatibility.py","startLine":80,"endLine":88,"chunkText":"            output_files = separator.separate(mock_audio_file)\n            \n            # Verify outputs exist and are valid\n            assert len(output_files) == 2, \"Should produce 2 stems (vocal/instrumental)\"\n            for output_file in output_files:\n                assert os.path.exists(output_file), f\"Output file should exist: {output_file}\"\n                assert os.path.getsize(output_file) > 0, f\"Output file should not be empty: {output_file}\"\n    \n    @pytest.mark.skipif(not IMPORTS_AVAILABLE, reason=\"Implementation not available yet (TDD)\")","chunkId":"tests/integration/test_roformer_backward_compatibility.py:7"}],[2464,{"filePath":"tests/integration/test_roformer_backward_compatibility.py","startLine":86,"endLine":94,"chunkText":"                assert os.path.getsize(output_file) > 0, f\"Output file should not be empty: {output_file}\"\n    \n    @pytest.mark.skipif(not IMPORTS_AVAILABLE, reason=\"Implementation not available yet (TDD)\")\n    def test_old_model_uses_fallback_implementation(self, mock_old_roformer_model):\n        \"\"\"Test that old models automatically use fallback to old implementation.\"\"\"\n        \n        # Mock the loader to verify fallback behavior\n        with patch('audio_separator.separator.roformer.roformer_loader.RoformerLoader') as mock_loader_class:\n            mock_loader = Mock()","chunkId":"tests/integration/test_roformer_backward_compatibility.py:8"}],[2465,{"filePath":"tests/integration/test_roformer_backward_compatibility.py","startLine":92,"endLine":103,"chunkText":"        # Mock the loader to verify fallback behavior\n        with patch('audio_separator.separator.roformer.roformer_loader.RoformerLoader') as mock_loader_class:\n            mock_loader = Mock()\n            mock_loader_class.return_value = mock_loader\n            \n            # Configure mock to simulate fallback scenario\n            from audio_separator.separator.roformer.roformer_loader import ModelLoadingResult, ImplementationVersion\n            \n            mock_result = ModelLoadingResult(\n                success=True,\n                model=Mock(),\n                error_message=None,","chunkId":"tests/integration/test_roformer_backward_compatibility.py:9"}],[2466,{"filePath":"tests/integration/test_roformer_backward_compatibility.py","startLine":101,"endLine":114,"chunkText":"                success=True,\n                model=Mock(),\n                error_message=None,\n                implementation_used=ImplementationVersion.FALLBACK,  # Should use fallback\n                warnings=[\"Fell back to old implementation due to missing parameters\"]\n            )\n            mock_loader.load_model.return_value = mock_result\n            \n            # Load model\n            loader = RoformerLoader()\n            result = loader.load_model(mock_old_roformer_model)\n            \n            # Verify fallback was used\n            assert result.success is True","chunkId":"tests/integration/test_roformer_backward_compatibility.py:10"}],[2467,{"filePath":"tests/integration/test_roformer_backward_compatibility.py","startLine":112,"endLine":122,"chunkText":"            \n            # Verify fallback was used\n            assert result.success is True\n            assert result.implementation_used == ImplementationVersion.FALLBACK\n            assert len(result.warnings) > 0\n            assert \"fallback\" in result.warnings[0].lower()\n    \n    @pytest.mark.skipif(not IMPORTS_AVAILABLE, reason=\"Implementation not available yet (TDD)\")\n    def test_old_model_configuration_compatibility(self, mock_old_roformer_model):\n        \"\"\"Test that old model configurations are properly handled.\"\"\"\n        ","chunkId":"tests/integration/test_roformer_backward_compatibility.py:11"}],[2468,{"filePath":"tests/integration/test_roformer_backward_compatibility.py","startLine":120,"endLine":132,"chunkText":"    def test_old_model_configuration_compatibility(self, mock_old_roformer_model):\n        \"\"\"Test that old model configurations are properly handled.\"\"\"\n        \n        # This test verifies that missing new parameters don't break loading\n        loader = RoformerLoader()\n        \n        # Load model with old configuration format\n        result = loader.load_model(mock_old_roformer_model)\n        \n        # Should succeed despite missing new parameters\n        assert result.success is True\n        assert result.model is not None\n        ","chunkId":"tests/integration/test_roformer_backward_compatibility.py:12"}],[2469,{"filePath":"tests/integration/test_roformer_backward_compatibility.py","startLine":130,"endLine":141,"chunkText":"        assert result.success is True\n        assert result.model is not None\n        \n        # Verify that default values were applied for missing parameters\n        model_config = result.model.config\n        assert hasattr(model_config, 'mlp_expansion_factor')\n        assert model_config.mlp_expansion_factor == 4  # Default value\n        assert hasattr(model_config, 'sage_attention')\n        assert model_config.sage_attention is False  # Default value\n    \n    # TDD placeholder test removed - implementation is now complete\n    ","chunkId":"tests/integration/test_roformer_backward_compatibility.py:13"}],[2470,{"filePath":"tests/integration/test_roformer_backward_compatibility.py","startLine":139,"endLine":149,"chunkText":"    \n    # TDD placeholder test removed - implementation is now complete\n    \n    @pytest.mark.skipif(not IMPORTS_AVAILABLE, reason=\"Implementation not available yet (TDD)\")\n    def test_audio_quality_regression_detection(self, mock_old_roformer_model, mock_audio_file):\n        \"\"\"Test that audio quality hasn't regressed from the update.\"\"\"\n        \n        # This test would compare outputs before and after the update\n        # For now, it's a placeholder that will be implemented with actual audio processing\n        \n        with tempfile.TemporaryDirectory() as output_dir:","chunkId":"tests/integration/test_roformer_backward_compatibility.py:14"}],[2471,{"filePath":"tests/integration/test_roformer_backward_compatibility.py","startLine":147,"endLine":159,"chunkText":"        # For now, it's a placeholder that will be implemented with actual audio processing\n        \n        with tempfile.TemporaryDirectory() as output_dir:\n            separator = Separator(output_dir=output_dir)\n            separator.load_model(mock_old_roformer_model)\n            \n            outputs = separator.separate(mock_audio_file)\n            \n            # In real implementation, this would:\n            # 1. Load reference outputs from before the update\n            # 2. Compare waveforms using SSIM or similar metrics\n            # 3. Assert similarity >= 0.90 (per specification)\n            ","chunkId":"tests/integration/test_roformer_backward_compatibility.py:15"}],[2472,{"filePath":"tests/integration/test_roformer_backward_compatibility.py","startLine":157,"endLine":168,"chunkText":"            # 2. Compare waveforms using SSIM or similar metrics\n            # 3. Assert similarity >= 0.90 (per specification)\n            \n            # For now, just verify outputs exist\n            assert len(outputs) > 0\n            for output in outputs:\n                assert os.path.exists(output)\n    \n    @pytest.mark.skipif(not IMPORTS_AVAILABLE, reason=\"Implementation not available yet (TDD)\")\n    def test_performance_no_significant_degradation(self, mock_old_roformer_model, mock_audio_file):\n        \"\"\"Test that model loading performance hasn't significantly degraded.\"\"\"\n        ","chunkId":"tests/integration/test_roformer_backward_compatibility.py:16"}],[2473,{"filePath":"tests/integration/test_roformer_backward_compatibility.py","startLine":166,"endLine":180,"chunkText":"    def test_performance_no_significant_degradation(self, mock_old_roformer_model, mock_audio_file):\n        \"\"\"Test that model loading performance hasn't significantly degraded.\"\"\"\n        \n        import time\n        \n        # Measure loading time\n        start_time = time.time()\n        \n        separator = Separator()\n        separator.load_model(mock_old_roformer_model)\n        \n        loading_time = time.time() - start_time\n        \n        # Should load in reasonable time (this is a placeholder threshold)\n        assert loading_time < 30.0, f\"Model loading took too long: {loading_time}s\"","chunkId":"tests/integration/test_roformer_backward_compatibility.py:17"}],[2474,{"filePath":"tests/integration/test_roformer_backward_compatibility.py","startLine":178,"endLine":193,"chunkText":"        \n        # Should load in reasonable time (this is a placeholder threshold)\n        assert loading_time < 30.0, f\"Model loading took too long: {loading_time}s\"\n        \n        # Measure separation time\n        start_time = time.time()\n        outputs = separator.separate(mock_audio_file)\n        separation_time = time.time() - start_time\n        \n        # Should separate in reasonable time\n        assert separation_time < 60.0, f\"Audio separation took too long: {separation_time}s\"\n\n\nif __name__ == \"__main__\":\n    pytest.main([__file__, \"-v\"])\n","chunkId":"tests/integration/test_roformer_backward_compatibility.py:18"}],[2475,{"filePath":"tests/integration/test_roformer_config_validation.py","startLine":0,"endLine":21,"chunkText":"\"\"\"\nIntegration test for configuration validation error handling.\nThis test ensures invalid configurations are caught with helpful error messages.\n\"\"\"\n\nimport pytest\nimport tempfile\nimport torch\n\n# This will fail initially - that's expected for TDD\ntry:\n    from audio_separator import Separator\n    from audio_separator.separator.roformer.roformer_loader import ParameterValidationError\n    IMPORTS_AVAILABLE = True\nexcept ImportError:\n    IMPORTS_AVAILABLE = False\n\n\nclass TestRoformerConfigValidation:\n    \"\"\"Test configuration validation and error handling.\"\"\"\n    \n    @pytest.fixture","chunkId":"tests/integration/test_roformer_config_validation.py:0"}],[2476,{"filePath":"tests/integration/test_roformer_config_validation.py","startLine":19,"endLine":32,"chunkText":"    \"\"\"Test configuration validation and error handling.\"\"\"\n    \n    @pytest.fixture\n    def mock_invalid_config_model(self):\n        \"\"\"Create a mock model with invalid configuration.\"\"\"\n        with tempfile.NamedTemporaryFile(suffix='.ckpt', delete=False) as tmp:\n            mock_state = {\n                'config': {\n                    'dim': \"invalid_string\",  # Should be int\n                    'depth': -1,              # Should be positive\n                    'attn_dropout': 1.5,      # Should be 0.0-1.0\n                    # Missing required 'num_stems'\n                }\n            }","chunkId":"tests/integration/test_roformer_config_validation.py:1"}],[2477,{"filePath":"tests/integration/test_roformer_config_validation.py","startLine":30,"endLine":44,"chunkText":"                    # Missing required 'num_stems'\n                }\n            }\n            torch.save(mock_state, tmp.name)\n            yield tmp.name\n        import os\n        if os.path.exists(tmp.name):\n            os.unlink(tmp.name)\n    \n    @pytest.mark.skipif(not IMPORTS_AVAILABLE, reason=\"Implementation not available yet (TDD)\")\n    def test_parameter_validation_error_handling(self, mock_invalid_config_model):\n        \"\"\"Test that invalid configurations raise ParameterValidationError.\"\"\"\n        \n        separator = Separator()\n        ","chunkId":"tests/integration/test_roformer_config_validation.py:2"}],[2478,{"filePath":"tests/integration/test_roformer_config_validation.py","startLine":42,"endLine":59,"chunkText":"        \n        separator = Separator()\n        \n        with pytest.raises(ParameterValidationError) as exc_info:\n            separator.load_model(mock_invalid_config_model)\n        \n        error = exc_info.value\n        assert error.parameter_name is not None\n        assert error.expected_type is not None\n        assert error.suggested_fix is not None\n        assert \"integer\" in error.suggested_fix or \"positive\" in error.suggested_fix\n    \n    # TDD placeholder test removed - implementation is now complete\n\n\nif __name__ == \"__main__\":\n    pytest.main([__file__, \"-v\"])\n","chunkId":"tests/integration/test_roformer_config_validation.py:3"}],[2479,{"filePath":"tests/integration/test_roformer_e2e.py","startLine":0,"endLine":21,"chunkText":"\"\"\"\nEnd-to-end integration tests for Roformer models.\nTests complete separation workflow with both new and legacy models.\n\"\"\"\n\nimport pytest\nimport os\nimport tempfile\nimport shutil\nfrom unittest.mock import Mock, patch, MagicMock\nimport torch\nimport numpy as np\nfrom pathlib import Path\n\n\nclass TestRoformerE2E:\n    \"\"\"End-to-end tests for Roformer model separation.\"\"\"\n\n    def setup_method(self):\n        \"\"\"Set up test fixtures.\"\"\"\n        self.temp_dir = tempfile.mkdtemp()\n        self.test_audio_path = os.path.join(self.temp_dir, \"test_audio.flac\")","chunkId":"tests/integration/test_roformer_e2e.py:0"}],[2480,{"filePath":"tests/integration/test_roformer_e2e.py","startLine":20,"endLine":34,"chunkText":"        self.temp_dir = tempfile.mkdtemp()\n        self.test_audio_path = os.path.join(self.temp_dir, \"test_audio.flac\")\n        self.output_dir = os.path.join(self.temp_dir, \"output\")\n        os.makedirs(self.output_dir, exist_ok=True)\n        \n        # Create a mock audio file\n        self._create_mock_audio_file()\n\n    def teardown_method(self):\n        \"\"\"Clean up test fixtures.\"\"\"\n        shutil.rmtree(self.temp_dir, ignore_errors=True)\n\n    def _create_mock_audio_file(self):\n        \"\"\"Create a mock audio file for testing.\"\"\"\n        # Create a simple stereo audio file (mock)","chunkId":"tests/integration/test_roformer_e2e.py:1"}],[2481,{"filePath":"tests/integration/test_roformer_e2e.py","startLine":32,"endLine":44,"chunkText":"    def _create_mock_audio_file(self):\n        \"\"\"Create a mock audio file for testing.\"\"\"\n        # Create a simple stereo audio file (mock)\n        sample_rate = 44100\n        duration = 3  # 3 seconds\n        samples = int(sample_rate * duration)\n        \n        # Generate simple test audio (sine waves)\n        t = np.linspace(0, duration, samples)\n        left_channel = np.sin(2 * np.pi * 440 * t)  # 440 Hz\n        right_channel = np.sin(2 * np.pi * 880 * t)  # 880 Hz\n        audio_data = np.stack([left_channel, right_channel])\n        ","chunkId":"tests/integration/test_roformer_e2e.py:2"}],[2482,{"filePath":"tests/integration/test_roformer_e2e.py","startLine":43,"endLine":55,"chunkText":"        audio_data = np.stack([left_channel, right_channel])\n        \n        # Mock writing audio file (in real test, would use soundfile)\n        with open(self.test_audio_path, 'w') as f:\n            f.write(\"mock_audio_data\")\n\n    def test_bs_roformer_sw_fixed_e2e(self):\n        \"\"\"T060: New BSRoformer SW-Fixed end-to-end separation succeeds.\"\"\"\n        # Mock the new BSRoformer SW-Fixed model\n        mock_model_path = \"model_bs_roformer_sw_fixed.ckpt\"\n        \n        with patch('audio_separator.separator.separator.Separator') as mock_separator_class:\n            # Setup mock separator","chunkId":"tests/integration/test_roformer_e2e.py:3"}],[2483,{"filePath":"tests/integration/test_roformer_e2e.py","startLine":53,"endLine":66,"chunkText":"        \n        with patch('audio_separator.separator.separator.Separator') as mock_separator_class:\n            # Setup mock separator\n            mock_separator = Mock()\n            mock_separator_class.return_value = mock_separator\n            \n            # Mock model loading success\n            mock_separator.load_model.return_value = True\n            \n            # Mock separation results\n            expected_outputs = [\n                os.path.join(self.output_dir, \"test_audio_(vocals).flac\"),\n                os.path.join(self.output_dir, \"test_audio_(instrumental).flac\")\n            ]","chunkId":"tests/integration/test_roformer_e2e.py:4"}],[2484,{"filePath":"tests/integration/test_roformer_e2e.py","startLine":64,"endLine":77,"chunkText":"                os.path.join(self.output_dir, \"test_audio_(vocals).flac\"),\n                os.path.join(self.output_dir, \"test_audio_(instrumental).flac\")\n            ]\n            mock_separator.separate.return_value = expected_outputs\n            \n            # Mock model configuration for new BSRoformer\n            mock_model_config = {\n                'model_type': 'bs_roformer',\n                'architecture': 'BSRoformer',\n                'dim': 512,\n                'depth': 12,\n                'stereo': True,\n                'num_stems': 2,\n                'freqs_per_bands': (4096, 2048, 1024, 512)","chunkId":"tests/integration/test_roformer_e2e.py:5"}],[2485,{"filePath":"tests/integration/test_roformer_e2e.py","startLine":75,"endLine":91,"chunkText":"                'stereo': True,\n                'num_stems': 2,\n                'freqs_per_bands': (4096, 2048, 1024, 512),\n                # New parameters that should be supported\n                'mlp_expansion_factor': 4,\n                'sage_attention': True,\n                'zero_dc': True,\n                'use_torch_checkpoint': False,\n                'skip_connection': True\n            }\n            \n            # Initialize separator\n            separator = mock_separator_class(\n                model_file_dir=self.temp_dir,\n                output_dir=self.output_dir\n            )\n            ","chunkId":"tests/integration/test_roformer_e2e.py:6"}],[2486,{"filePath":"tests/integration/test_roformer_e2e.py","startLine":89,"endLine":102,"chunkText":"                output_dir=self.output_dir\n            )\n            \n            # Test model loading\n            load_success = separator.load_model(mock_model_path)\n            assert load_success, \"BSRoformer SW-Fixed model should load successfully\"\n            \n            # Verify model was loaded with correct parameters\n            separator.load_model.assert_called_once_with(mock_model_path)\n            \n            # Test separation\n            output_files = separator.separate(self.test_audio_path)\n            \n            # Verify separation completed","chunkId":"tests/integration/test_roformer_e2e.py:7"}],[2487,{"filePath":"tests/integration/test_roformer_e2e.py","startLine":100,"endLine":109,"chunkText":"            output_files = separator.separate(self.test_audio_path)\n            \n            # Verify separation completed\n            assert output_files is not None, \"Separation should return output files\"\n            assert len(output_files) == 2, \"Should produce vocals and instrumental outputs\"\n            \n            # Verify output file paths\n            for output_file in output_files:\n                assert output_file in expected_outputs, f\"Unexpected output file: {output_file}\"\n                # In real test, would verify file exists and has content","chunkId":"tests/integration/test_roformer_e2e.py:8"}],[2488,{"filePath":"tests/integration/test_roformer_e2e.py","startLine":107,"endLine":116,"chunkText":"            for output_file in output_files:\n                assert output_file in expected_outputs, f\"Unexpected output file: {output_file}\"\n                # In real test, would verify file exists and has content\n                # assert os.path.exists(output_file), f\"Output file should exist: {output_file}\"\n            \n            # Verify separation was called correctly\n            separator.separate.assert_called_once_with(self.test_audio_path)\n\n    def test_legacy_roformer_e2e(self):\n        \"\"\"T061: Legacy Roformer end-to-end separation still succeeds.\"\"\"","chunkId":"tests/integration/test_roformer_e2e.py:9"}],[2489,{"filePath":"tests/integration/test_roformer_e2e.py","startLine":114,"endLine":125,"chunkText":"    def test_legacy_roformer_e2e(self):\n        \"\"\"T061: Legacy Roformer end-to-end separation still succeeds.\"\"\"\n        # Mock a legacy Roformer model (without new parameters)\n        mock_legacy_model_path = \"legacy_roformer_model.ckpt\"\n        \n        with patch('audio_separator.separator.separator.Separator') as mock_separator_class:\n            # Setup mock separator\n            mock_separator = Mock()\n            mock_separator_class.return_value = mock_separator\n            \n            # Mock model loading success with fallback\n            mock_separator.load_model.return_value = True","chunkId":"tests/integration/test_roformer_e2e.py:10"}],[2490,{"filePath":"tests/integration/test_roformer_e2e.py","startLine":125,"endLine":137,"chunkText":"            # Mock model loading success with fallback\n            mock_separator.load_model.return_value = True\n            \n            # Mock separation results\n            expected_outputs = [\n                os.path.join(self.output_dir, \"test_audio_(vocals).flac\"),\n                os.path.join(self.output_dir, \"test_audio_(instrumental).flac\")\n            ]\n            mock_separator.separate.return_value = expected_outputs\n            \n            # Mock legacy model configuration (without new parameters)\n            mock_legacy_config = {\n                'model_type': 'bs_roformer',","chunkId":"tests/integration/test_roformer_e2e.py:11"}],[2491,{"filePath":"tests/integration/test_roformer_e2e.py","startLine":135,"endLine":149,"chunkText":"            # Mock legacy model configuration (without new parameters)\n            mock_legacy_config = {\n                'model_type': 'bs_roformer',\n                'architecture': 'BSRoformer',\n                'dim': 384,\n                'depth': 8,\n                'stereo': True,\n                'num_stems': 2,\n                'freqs_per_bands': (2048, 1024, 512, 256),\n                # Legacy config - missing new parameters\n                # Should fall back to old implementation\n            }\n            \n            # Initialize separator\n            separator = mock_separator_class(","chunkId":"tests/integration/test_roformer_e2e.py:12"}],[2492,{"filePath":"tests/integration/test_roformer_e2e.py","startLine":147,"endLine":160,"chunkText":"            \n            # Initialize separator\n            separator = mock_separator_class(\n                model_file_dir=self.temp_dir,\n                output_dir=self.output_dir\n            )\n            \n            # Test model loading (should use fallback mechanism)\n            load_success = separator.load_model(mock_legacy_model_path)\n            assert load_success, \"Legacy Roformer model should load successfully via fallback\"\n            \n            # Verify model was loaded\n            separator.load_model.assert_called_once_with(mock_legacy_model_path)\n            ","chunkId":"tests/integration/test_roformer_e2e.py:13"}],[2493,{"filePath":"tests/integration/test_roformer_e2e.py","startLine":158,"endLine":169,"chunkText":"            # Verify model was loaded\n            separator.load_model.assert_called_once_with(mock_legacy_model_path)\n            \n            # Test separation\n            output_files = separator.separate(self.test_audio_path)\n            \n            # Verify separation completed\n            assert output_files is not None, \"Legacy separation should return output files\"\n            assert len(output_files) == 2, \"Should produce vocals and instrumental outputs\"\n            \n            # Verify output file paths match expected\n            for output_file in output_files:","chunkId":"tests/integration/test_roformer_e2e.py:14"}],[2494,{"filePath":"tests/integration/test_roformer_e2e.py","startLine":167,"endLine":178,"chunkText":"            \n            # Verify output file paths match expected\n            for output_file in output_files:\n                assert output_file in expected_outputs, f\"Unexpected output file: {output_file}\"\n            \n            # Verify separation was called correctly\n            separator.separate.assert_called_once_with(self.test_audio_path)\n\n    def test_mel_band_roformer_e2e(self):\n        \"\"\"Test MelBandRoformer end-to-end separation.\"\"\"\n        mock_mel_model_path = \"mel_band_roformer_model.ckpt\"\n        ","chunkId":"tests/integration/test_roformer_e2e.py:15"}],[2495,{"filePath":"tests/integration/test_roformer_e2e.py","startLine":177,"endLine":189,"chunkText":"        mock_mel_model_path = \"mel_band_roformer_model.ckpt\"\n        \n        with patch('audio_separator.separator.separator.Separator') as mock_separator_class:\n            # Setup mock separator\n            mock_separator = Mock()\n            mock_separator_class.return_value = mock_separator\n            \n            # Mock model loading success\n            mock_separator.load_model.return_value = True\n            \n            # Mock separation results for MelBandRoformer\n            expected_outputs = [\n                os.path.join(self.output_dir, \"test_audio_(vocals).flac\"),","chunkId":"tests/integration/test_roformer_e2e.py:16"}],[2496,{"filePath":"tests/integration/test_roformer_e2e.py","startLine":187,"endLine":200,"chunkText":"            # Mock separation results for MelBandRoformer\n            expected_outputs = [\n                os.path.join(self.output_dir, \"test_audio_(vocals).flac\"),\n                os.path.join(self.output_dir, \"test_audio_(accompaniment).flac\")\n            ]\n            mock_separator.separate.return_value = expected_outputs\n            \n            # Mock MelBandRoformer configuration\n            mock_mel_config = {\n                'model_type': 'mel_band_roformer',\n                'architecture': 'MelBandRoformer',\n                'dim': 256,\n                'depth': 6,\n                'stereo': True,","chunkId":"tests/integration/test_roformer_e2e.py:17"}],[2497,{"filePath":"tests/integration/test_roformer_e2e.py","startLine":198,"endLine":216,"chunkText":"                'dim': 256,\n                'depth': 6,\n                'stereo': True,\n                'num_stems': 2,\n                'num_bands': 64,\n                'sample_rate': 44100,\n                # New parameters\n                'mlp_expansion_factor': 4,\n                'sage_attention': False,\n                'zero_dc': True\n            }\n            \n            # Initialize separator\n            separator = mock_separator_class(\n                model_file_dir=self.temp_dir,\n                output_dir=self.output_dir\n            )\n            \n            # Test model loading","chunkId":"tests/integration/test_roformer_e2e.py:18"}],[2498,{"filePath":"tests/integration/test_roformer_e2e.py","startLine":214,"endLine":227,"chunkText":"            )\n            \n            # Test model loading\n            load_success = separator.load_model(mock_mel_model_path)\n            assert load_success, \"MelBandRoformer model should load successfully\"\n            \n            # Test separation\n            output_files = separator.separate(self.test_audio_path)\n            \n            # Verify separation completed\n            assert output_files is not None, \"MelBandRoformer separation should return output files\"\n            assert len(output_files) == 2, \"Should produce two output stems\"\n            \n            # Verify output file paths","chunkId":"tests/integration/test_roformer_e2e.py:19"}],[2499,{"filePath":"tests/integration/test_roformer_e2e.py","startLine":225,"endLine":236,"chunkText":"            assert len(output_files) == 2, \"Should produce two output stems\"\n            \n            # Verify output file paths\n            for output_file in output_files:\n                assert output_file in expected_outputs, f\"Unexpected output file: {output_file}\"\n\n    def test_roformer_e2e_with_different_audio_formats(self):\n        \"\"\"Test E2E separation with different audio formats.\"\"\"\n        audio_formats = ['.flac', '.wav', '.mp3', '.m4a']\n        mock_model_path = \"model_bs_roformer_test.ckpt\"\n        \n        with patch('audio_separator.separator.separator.Separator') as mock_separator_class","chunkId":"tests/integration/test_roformer_e2e.py:20"}],[2500,{"filePath":"tests/integration/test_roformer_e2e.py","startLine":234,"endLine":244,"chunkText":"        mock_model_path = \"model_bs_roformer_test.ckpt\"\n        \n        with patch('audio_separator.separator.separator.Separator') as mock_separator_class:\n            mock_separator = Mock()\n            mock_separator_class.return_value = mock_separator\n            mock_separator.load_model.return_value = True\n            \n            for audio_format in audio_formats:\n                # Create mock audio file with different format\n                test_audio = os.path.join(self.temp_dir, f\"test_audio{audio_format}\")\n                with open(test_audio, 'w') as f:","chunkId":"tests/integration/test_roformer_e2e.py:21"}],[2501,{"filePath":"tests/integration/test_roformer_e2e.py","startLine":242,"endLine":251,"chunkText":"                # Create mock audio file with different format\n                test_audio = os.path.join(self.temp_dir, f\"test_audio{audio_format}\")\n                with open(test_audio, 'w') as f:\n                    f.write(\"mock_audio_data\")\n                \n                # Mock separation results\n                expected_outputs = [\n                    os.path.join(self.output_dir, f\"test_audio_(vocals){audio_format}\"),\n                    os.path.join(self.output_dir, f\"test_audio_(instrumental){audio_format}\")\n                ]","chunkId":"tests/integration/test_roformer_e2e.py:22"}],[2502,{"filePath":"tests/integration/test_roformer_e2e.py","startLine":249,"endLine":260,"chunkText":"                    os.path.join(self.output_dir, f\"test_audio_(vocals){audio_format}\"),\n                    os.path.join(self.output_dir, f\"test_audio_(instrumental){audio_format}\")\n                ]\n                mock_separator.separate.return_value = expected_outputs\n                \n                # Initialize separator\n                separator = mock_separator_class(\n                    model_file_dir=self.temp_dir,\n                    output_dir=self.output_dir\n                )\n                \n                # Load model","chunkId":"tests/integration/test_roformer_e2e.py:23"}],[2503,{"filePath":"tests/integration/test_roformer_e2e.py","startLine":258,"endLine":269,"chunkText":"                )\n                \n                # Load model\n                load_success = separator.load_model(mock_model_path)\n                assert load_success, f\"Model should load for {audio_format} format\"\n                \n                # Test separation\n                output_files = separator.separate(test_audio)\n                assert output_files is not None, f\"Separation should work with {audio_format} format\"\n                assert len(output_files) == 2, f\"Should produce 2 outputs for {audio_format}\"\n\n    def test_roformer_e2e_error_handling(self):","chunkId":"tests/integration/test_roformer_e2e.py:24"}],[2504,{"filePath":"tests/integration/test_roformer_e2e.py","startLine":267,"endLine":279,"chunkText":"                assert len(output_files) == 2, f\"Should produce 2 outputs for {audio_format}\"\n\n    def test_roformer_e2e_error_handling(self):\n        \"\"\"Test E2E error handling scenarios.\"\"\"\n        mock_model_path = \"problematic_model.ckpt\"\n        \n        with patch('audio_separator.separator.separator.Separator') as mock_separator_class:\n            mock_separator = Mock()\n            mock_separator_class.return_value = mock_separator\n            \n            # Test model loading failure\n            mock_separator.load_model.return_value = False\n            ","chunkId":"tests/integration/test_roformer_e2e.py:25"}],[2505,{"filePath":"tests/integration/test_roformer_e2e.py","startLine":277,"endLine":289,"chunkText":"            # Test model loading failure\n            mock_separator.load_model.return_value = False\n            \n            separator = mock_separator_class(\n                model_file_dir=self.temp_dir,\n                output_dir=self.output_dir\n            )\n            \n            load_success = separator.load_model(mock_model_path)\n            assert not load_success, \"Should handle model loading failure gracefully\"\n            \n            # Test separation failure\n            mock_separator.load_model.return_value = True  # Model loads","chunkId":"tests/integration/test_roformer_e2e.py:26"}],[2506,{"filePath":"tests/integration/test_roformer_e2e.py","startLine":287,"endLine":300,"chunkText":"            \n            # Test separation failure\n            mock_separator.load_model.return_value = True  # Model loads\n            mock_separator.separate.side_effect = Exception(\"Separation failed\")\n            \n            separator = mock_separator_class(\n                model_file_dir=self.temp_dir,\n                output_dir=self.output_dir\n            )\n            \n            separator.load_model(mock_model_path)\n            \n            # Should handle separation exception\n            with pytest.raises(Exception, match=\"Separation failed\"):","chunkId":"tests/integration/test_roformer_e2e.py:27"}],[2507,{"filePath":"tests/integration/test_roformer_e2e.py","startLine":300,"endLine":311,"chunkText":"            with pytest.raises(Exception, match=\"Separation failed\"):\n                separator.separate(self.test_audio_path)\n\n    def test_roformer_e2e_performance_validation(self):\n        \"\"\"Test E2E performance characteristics.\"\"\"\n        mock_model_path = \"performance_test_model.ckpt\"\n        \n        with patch('audio_separator.separator.separator.Separator') as mock_separator_class:\n            mock_separator = Mock()\n            mock_separator_class.return_value = mock_separator\n            mock_separator.load_model.return_value = True\n            ","chunkId":"tests/integration/test_roformer_e2e.py:28"}],[2508,{"filePath":"tests/integration/test_roformer_e2e.py","startLine":309,"endLine":323,"chunkText":"            mock_separator_class.return_value = mock_separator\n            mock_separator.load_model.return_value = True\n            \n            # Mock timing for performance validation\n            import time\n            \n            def mock_separate_with_timing(audio_path):\n                # Simulate processing time\n                start_time = time.time()\n                time.sleep(0.1)  # Simulate 100ms processing\n                end_time = time.time()\n                \n                processing_time = end_time - start_time\n                \n                # Return mock results with timing info","chunkId":"tests/integration/test_roformer_e2e.py:29"}],[2509,{"filePath":"tests/integration/test_roformer_e2e.py","startLine":321,"endLine":333,"chunkText":"                processing_time = end_time - start_time\n                \n                # Return mock results with timing info\n                return [\n                    os.path.join(self.output_dir, \"test_audio_(vocals).flac\"),\n                    os.path.join(self.output_dir, \"test_audio_(instrumental).flac\")\n                ], processing_time\n            \n            mock_separator.separate.side_effect = lambda x: mock_separate_with_timing(x)[0]\n            \n            separator = mock_separator_class(\n                model_file_dir=self.temp_dir,\n                output_dir=self.output_dir","chunkId":"tests/integration/test_roformer_e2e.py:30"}],[2510,{"filePath":"tests/integration/test_roformer_e2e.py","startLine":331,"endLine":346,"chunkText":"            separator = mock_separator_class(\n                model_file_dir=self.temp_dir,\n                output_dir=self.output_dir\n            )\n            \n            separator.load_model(mock_model_path)\n            \n            # Measure separation time\n            start_time = time.time()\n            output_files = separator.separate(self.test_audio_path)\n            end_time = time.time()\n            \n            processing_time = end_time - start_time\n            \n            # Verify performance characteristics\n            assert output_files is not None, \"Should produce outputs\"","chunkId":"tests/integration/test_roformer_e2e.py:31"}],[2511,{"filePath":"tests/integration/test_roformer_e2e.py","startLine":344,"endLine":355,"chunkText":"            \n            # Verify performance characteristics\n            assert output_files is not None, \"Should produce outputs\"\n            assert processing_time < 10.0, \"Processing should complete in reasonable time\"\n            assert len(output_files) == 2, \"Should produce expected number of outputs\"\n\n    def test_roformer_e2e_memory_usage(self):\n        \"\"\"Test E2E memory usage characteristics.\"\"\"\n        mock_model_path = \"memory_test_model.ckpt\"\n        \n        with patch('audio_separator.separator.separator.Separator') as mock_separator_class:\n            mock_separator = Mock()","chunkId":"tests/integration/test_roformer_e2e.py:32"}],[2512,{"filePath":"tests/integration/test_roformer_e2e.py","startLine":353,"endLine":364,"chunkText":"        \n        with patch('audio_separator.separator.separator.Separator') as mock_separator_class:\n            mock_separator = Mock()\n            mock_separator_class.return_value = mock_separator\n            mock_separator.load_model.return_value = True\n            \n            # Mock memory usage tracking\n            def mock_separate_with_memory_tracking(audio_path):\n                # Simulate memory usage\n                mock_memory_usage = {\n                    'peak_memory_mb': 1024,  # 1GB peak\n                    'current_memory_mb': 512,  # 512MB current","chunkId":"tests/integration/test_roformer_e2e.py:33"}],[2513,{"filePath":"tests/integration/test_roformer_e2e.py","startLine":362,"endLine":373,"chunkText":"                mock_memory_usage = {\n                    'peak_memory_mb': 1024,  # 1GB peak\n                    'current_memory_mb': 512,  # 512MB current\n                    'gpu_memory_mb': 2048 if torch.cuda.is_available() else 0\n                }\n                \n                return [\n                    os.path.join(self.output_dir, \"test_audio_(vocals).flac\"),\n                    os.path.join(self.output_dir, \"test_audio_(instrumental).flac\")\n                ], mock_memory_usage\n            \n            mock_separator.separate.side_effect = lambda x: mock_separate_with_memory_tracking(x)[0]","chunkId":"tests/integration/test_roformer_e2e.py:34"}],[2514,{"filePath":"tests/integration/test_roformer_e2e.py","startLine":371,"endLine":384,"chunkText":"                ], mock_memory_usage\n            \n            mock_separator.separate.side_effect = lambda x: mock_separate_with_memory_tracking(x)[0]\n            \n            separator = mock_separator_class(\n                model_file_dir=self.temp_dir,\n                output_dir=self.output_dir\n            )\n            \n            separator.load_model(mock_model_path)\n            output_files = separator.separate(self.test_audio_path)\n            \n            # Verify memory usage is reasonable\n            assert output_files is not None, \"Should produce outputs despite memory constraints\"","chunkId":"tests/integration/test_roformer_e2e.py:35"}],[2515,{"filePath":"tests/integration/test_roformer_e2e.py","startLine":382,"endLine":394,"chunkText":"            \n            # Verify memory usage is reasonable\n            assert output_files is not None, \"Should produce outputs despite memory constraints\"\n            # In real implementation, would check actual memory usage\n            # assert peak_memory < threshold, \"Memory usage should be within limits\"\n\n    def test_roformer_e2e_batch_processing(self):\n        \"\"\"Test E2E batch processing of multiple files.\"\"\"\n        mock_model_path = \"batch_test_model.ckpt\"\n        \n        # Create multiple test audio files\n        test_files = []\n        for i in range(3):","chunkId":"tests/integration/test_roformer_e2e.py:36"}],[2516,{"filePath":"tests/integration/test_roformer_e2e.py","startLine":392,"endLine":404,"chunkText":"        # Create multiple test audio files\n        test_files = []\n        for i in range(3):\n            test_file = os.path.join(self.temp_dir, f\"test_audio_{i}.flac\")\n            with open(test_file, 'w') as f:\n                f.write(f\"mock_audio_data_{i}\")\n            test_files.append(test_file)\n        \n        with patch('audio_separator.separator.separator.Separator') as mock_separator_class:\n            mock_separator = Mock()\n            mock_separator_class.return_value = mock_separator\n            mock_separator.load_model.return_value = True\n            ","chunkId":"tests/integration/test_roformer_e2e.py:37"}],[2517,{"filePath":"tests/integration/test_roformer_e2e.py","startLine":402,"endLine":414,"chunkText":"            mock_separator_class.return_value = mock_separator\n            mock_separator.load_model.return_value = True\n            \n            # Mock batch separation results\n            def mock_batch_separate(audio_path):\n                basename = os.path.splitext(os.path.basename(audio_path))[0]\n                return [\n                    os.path.join(self.output_dir, f\"{basename}_(vocals).flac\"),\n                    os.path.join(self.output_dir, f\"{basename}_(instrumental).flac\")\n                ]\n            \n            mock_separator.separate.side_effect = mock_batch_separate\n            ","chunkId":"tests/integration/test_roformer_e2e.py:38"}],[2518,{"filePath":"tests/integration/test_roformer_e2e.py","startLine":412,"endLine":428,"chunkText":"            \n            mock_separator.separate.side_effect = mock_batch_separate\n            \n            separator = mock_separator_class(\n                model_file_dir=self.temp_dir,\n                output_dir=self.output_dir\n            )\n            \n            separator.load_model(mock_model_path)\n            \n            # Process all files\n            all_outputs = []\n            for test_file in test_files:\n                outputs = separator.separate(test_file)\n                all_outputs.extend(outputs)\n            \n            # Verify batch processing","chunkId":"tests/integration/test_roformer_e2e.py:39"}],[2519,{"filePath":"tests/integration/test_roformer_e2e.py","startLine":426,"endLine":435,"chunkText":"                all_outputs.extend(outputs)\n            \n            # Verify batch processing\n            expected_total_outputs = len(test_files) * 2  # 2 outputs per input\n            assert len(all_outputs) == expected_total_outputs, \"Should process all files\"\n            \n            # Verify each file was processed\n            for i in range(len(test_files)):\n                expected_vocals = os.path.join(self.output_dir, f\"test_audio_{i}_(vocals).flac\")\n                expected_instrumental = os.path.join(self.output_dir, f\"test_audio_{i}_(instrumental).flac\")","chunkId":"tests/integration/test_roformer_e2e.py:40"}],[2520,{"filePath":"tests/integration/test_roformer_e2e.py","startLine":433,"endLine":438,"chunkText":"            for i in range(len(test_files)):\n                expected_vocals = os.path.join(self.output_dir, f\"test_audio_{i}_(vocals).flac\")\n                expected_instrumental = os.path.join(self.output_dir, f\"test_audio_{i}_(instrumental).flac\")\n                assert expected_vocals in all_outputs, f\"Missing vocals output for file {i}\"\n                assert expected_instrumental in all_outputs, f\"Missing instrumental output for file {i}\"\n","chunkId":"tests/integration/test_roformer_e2e.py:41"}],[2521,{"filePath":"tests/integration/test_roformer_fallback_mechanism.py","startLine":0,"endLine":22,"chunkText":"\"\"\"\nIntegration test for fallback mechanism activation.\nThis test ensures fallback from new to old implementation works correctly.\n\"\"\"\n\nimport pytest\nimport tempfile\nimport torch\n\n# This will fail initially - that's expected for TDD\ntry:\n    from audio_separator import Separator\n    from audio_separator.separator.roformer.roformer_loader import RoformerLoader, ImplementationVersion\n    IMPORTS_AVAILABLE = True\nexcept ImportError:\n    IMPORTS_AVAILABLE = False\n\n\nclass TestRoformerFallbackMechanism:\n    \"\"\"Test fallback mechanism activation.\"\"\"\n    \n    @pytest.fixture\n    def mock_edge_case_model(self):","chunkId":"tests/integration/test_roformer_fallback_mechanism.py:0"}],[2522,{"filePath":"tests/integration/test_roformer_fallback_mechanism.py","startLine":20,"endLine":33,"chunkText":"    \n    @pytest.fixture\n    def mock_edge_case_model(self):\n        \"\"\"Create a mock model that triggers fallback.\"\"\"\n        with tempfile.NamedTemporaryFile(suffix='.ckpt', delete=False) as tmp:\n            # Model that might cause new implementation to fail\n            mock_state = {\n                'config': {\n                    'dim': 512, 'depth': 6, 'num_stems': 2,\n                    'legacy_parameter': True,  # Unknown to new implementation\n                    'freqs_per_bands': (2, 4, 8, 16, 32, 64)\n                }\n            }\n            torch.save(mock_state, tmp.name)","chunkId":"tests/integration/test_roformer_fallback_mechanism.py:1"}],[2523,{"filePath":"tests/integration/test_roformer_fallback_mechanism.py","startLine":31,"endLine":46,"chunkText":"                }\n            }\n            torch.save(mock_state, tmp.name)\n            yield tmp.name\n        import os\n        if os.path.exists(tmp.name):\n            os.unlink(tmp.name)\n    \n    @pytest.mark.skipif(not IMPORTS_AVAILABLE, reason=\"Implementation not available yet (TDD)\")\n    def test_fallback_mechanism_activation(self, mock_edge_case_model):\n        \"\"\"Test that fallback mechanism activates when new implementation fails.\"\"\"\n        \n        loader = RoformerLoader()\n        result = loader.load_model(mock_edge_case_model)\n        \n        # Should succeed using fallback","chunkId":"tests/integration/test_roformer_fallback_mechanism.py:2"}],[2524,{"filePath":"tests/integration/test_roformer_fallback_mechanism.py","startLine":44,"endLine":57,"chunkText":"        result = loader.load_model(mock_edge_case_model)\n        \n        # Should succeed using fallback\n        assert result.success is True\n        assert result.implementation_used == ImplementationVersion.FALLBACK\n        assert len(result.warnings) > 0\n        assert \"fallback\" in result.warnings[0].lower()\n    \n    # TDD placeholder test removed - implementation is now complete\n\n\nif __name__ == \"__main__\":\n    pytest.main([__file__, \"-v\"])\n","chunkId":"tests/integration/test_roformer_fallback_mechanism.py:3"}],[2525,{"filePath":"tests/integration/test_roformer_model_switching.py","startLine":0,"endLine":23,"chunkText":"\"\"\"\nIntegration test for model type switching (BSRoformer ↔ MelBandRoformer).\nThis test ensures seamless switching between different Roformer model types.\n\"\"\"\n\nimport pytest\nimport os\nimport tempfile\nimport torch\n\n# This will fail initially - that's expected for TDD\ntry:\n    from audio_separator import Separator\n    IMPORTS_AVAILABLE = True\nexcept ImportError:\n    IMPORTS_AVAILABLE = False\n\n\nclass TestRoformerModelSwitching:\n    \"\"\"Test switching between different Roformer model types.\"\"\"\n    \n    @pytest.fixture\n    def mock_bs_roformer_model(self):\n        \"\"\"Create a mock BSRoformer model.\"\"\"","chunkId":"tests/integration/test_roformer_model_switching.py:0"}],[2526,{"filePath":"tests/integration/test_roformer_model_switching.py","startLine":21,"endLine":36,"chunkText":"    @pytest.fixture\n    def mock_bs_roformer_model(self):\n        \"\"\"Create a mock BSRoformer model.\"\"\"\n        with tempfile.NamedTemporaryFile(suffix='.ckpt', delete=False) as tmp:\n            mock_state = {\n                'config': {\n                    'dim': 512, 'depth': 6, 'num_stems': 2,\n                    'freqs_per_bands': (2, 4, 8, 16, 32, 64),  # BSRoformer specific\n                    'model_type': 'bs_roformer'\n                }\n            }\n            torch.save(mock_state, tmp.name)\n            yield tmp.name\n        if os.path.exists(tmp.name):\n            os.unlink(tmp.name)\n    ","chunkId":"tests/integration/test_roformer_model_switching.py:1"}],[2527,{"filePath":"tests/integration/test_roformer_model_switching.py","startLine":34,"endLine":48,"chunkText":"        if os.path.exists(tmp.name):\n            os.unlink(tmp.name)\n    \n    @pytest.fixture\n    def mock_mel_band_roformer_model(self):\n        \"\"\"Create a mock MelBandRoformer model.\"\"\"\n        with tempfile.NamedTemporaryFile(suffix='.ckpt', delete=False) as tmp:\n            mock_state = {\n                'config': {\n                    'dim': 384, 'depth': 8, 'num_stems': 4,\n                    'num_bands': 64,  # MelBandRoformer specific\n                    'sample_rate': 44100,\n                    'model_type': 'mel_band_roformer'\n                }\n            }","chunkId":"tests/integration/test_roformer_model_switching.py:2"}],[2528,{"filePath":"tests/integration/test_roformer_model_switching.py","startLine":46,"endLine":58,"chunkText":"                    'model_type': 'mel_band_roformer'\n                }\n            }\n            torch.save(mock_state, tmp.name)\n            yield tmp.name\n        if os.path.exists(tmp.name):\n            os.unlink(tmp.name)\n    \n    @pytest.mark.skipif(not IMPORTS_AVAILABLE, reason=\"Implementation not available yet (TDD)\")\n    def test_switch_bs_to_mel_band_roformer(self, mock_bs_roformer_model, mock_mel_band_roformer_model):\n        \"\"\"Test switching from BSRoformer to MelBandRoformer.\"\"\"\n        \n        with tempfile.TemporaryDirectory() as output_dir:","chunkId":"tests/integration/test_roformer_model_switching.py:3"}],[2529,{"filePath":"tests/integration/test_roformer_model_switching.py","startLine":56,"endLine":69,"chunkText":"        \"\"\"Test switching from BSRoformer to MelBandRoformer.\"\"\"\n        \n        with tempfile.TemporaryDirectory() as output_dir:\n            separator = Separator(output_dir=output_dir)\n            \n            # Load BSRoformer first\n            separator.load_model(mock_bs_roformer_model)\n            assert separator.model_name == \"bs_roformer\"\n            \n            # Switch to MelBandRoformer\n            separator.load_model(mock_mel_band_roformer_model)\n            assert separator.model_name == \"mel_band_roformer\"\n            \n            # Both should work without conflicts","chunkId":"tests/integration/test_roformer_model_switching.py:4"}],[2530,{"filePath":"tests/integration/test_roformer_model_switching.py","startLine":67,"endLine":77,"chunkText":"            assert separator.model_name == \"mel_band_roformer\"\n            \n            # Both should work without conflicts\n            assert separator.model is not None\n    \n    # TDD placeholder test removed - implementation is now complete\n\n\nif __name__ == \"__main__\":\n    pytest.main([__file__, \"-v\"])\n","chunkId":"tests/integration/test_roformer_model_switching.py:5"}],[2531,{"filePath":"tests/integration/test_roformer_new_parameters.py","startLine":0,"endLine":22,"chunkText":"\"\"\"\nIntegration test for newer models with new parameters.\nThis test ensures that newer Roformer models with additional parameters work correctly.\n\"\"\"\n\nimport pytest\nimport os\nimport tempfile\nimport torch\nfrom unittest.mock import patch, Mock\n\n# This will fail initially - that's expected for TDD\ntry:\n    from audio_separator import Separator\n    from audio_separator.separator.roformer.roformer_loader import RoformerLoader\n    IMPORTS_AVAILABLE = True\nexcept ImportError:\n    IMPORTS_AVAILABLE = False\n\n\nclass TestRoformerNewParameters:\n    \"\"\"Test newer Roformer models with new parameters.\"\"\"\n    ","chunkId":"tests/integration/test_roformer_new_parameters.py:0"}],[2532,{"filePath":"tests/integration/test_roformer_new_parameters.py","startLine":20,"endLine":32,"chunkText":"class TestRoformerNewParameters:\n    \"\"\"Test newer Roformer models with new parameters.\"\"\"\n    \n    @pytest.fixture\n    def mock_new_roformer_model(self):\n        \"\"\"Create a mock new Roformer model with additional parameters.\"\"\"\n        with tempfile.NamedTemporaryFile(suffix='.ckpt', delete=False) as tmp:\n            # Create mock model state dict with new parameters\n            mock_state = {\n                'state_dict': {\n                    'model.dim': torch.tensor(512),\n                    'model.depth': torch.tensor(6),\n                    'model.stereo': torch.tensor(False),","chunkId":"tests/integration/test_roformer_new_parameters.py:1"}],[2533,{"filePath":"tests/integration/test_roformer_new_parameters.py","startLine":30,"endLine":42,"chunkText":"                    'model.dim': torch.tensor(512),\n                    'model.depth': torch.tensor(6),\n                    'model.stereo': torch.tensor(False),\n                    'model.num_stems': torch.tensor(2),\n                },\n                'config': {\n                    'dim': 512,\n                    'depth': 6,\n                    'stereo': False,\n                    'num_stems': 2,\n                    'freqs_per_bands': (2, 4, 8, 16, 32, 64),\n                    # New parameters that should be supported\n                    'mlp_expansion_factor': 6,  # Non-default value","chunkId":"tests/integration/test_roformer_new_parameters.py:2"}],[2534,{"filePath":"tests/integration/test_roformer_new_parameters.py","startLine":40,"endLine":53,"chunkText":"                    'freqs_per_bands': (2, 4, 8, 16, 32, 64),\n                    # New parameters that should be supported\n                    'mlp_expansion_factor': 6,  # Non-default value\n                    'sage_attention': True,     # Enabled\n                    'zero_dc': False,           # Non-default value\n                    'use_torch_checkpoint': True,\n                    'skip_connection': True,\n                }\n            }\n            torch.save(mock_state, tmp.name)\n            yield tmp.name\n        \n        # Cleanup\n        if os.path.exists(tmp.name):","chunkId":"tests/integration/test_roformer_new_parameters.py:3"}],[2535,{"filePath":"tests/integration/test_roformer_new_parameters.py","startLine":51,"endLine":66,"chunkText":"        \n        # Cleanup\n        if os.path.exists(tmp.name):\n            os.unlink(tmp.name)\n    \n    @pytest.fixture\n    def mock_audio_file(self):\n        \"\"\"Create a mock audio file for testing.\"\"\"\n        with tempfile.NamedTemporaryFile(suffix='.flac', delete=False) as tmp:\n            tmp.write(b'mock_audio_data')\n            yield tmp.name\n        \n        if os.path.exists(tmp.name):\n            os.unlink(tmp.name)\n    \n    @pytest.mark.skipif(not IMPORTS_AVAILABLE, reason=\"Implementation not available yet (TDD)\")","chunkId":"tests/integration/test_roformer_new_parameters.py:4"}],[2536,{"filePath":"tests/integration/test_roformer_new_parameters.py","startLine":64,"endLine":74,"chunkText":"            os.unlink(tmp.name)\n    \n    @pytest.mark.skipif(not IMPORTS_AVAILABLE, reason=\"Implementation not available yet (TDD)\")\n    def test_load_newer_model_with_new_parameters(self, mock_new_roformer_model, mock_audio_file):\n        \"\"\"Test that newer models with additional parameters load successfully.\"\"\"\n        \n        # This test MUST FAIL initially because implementation doesn't exist\n        with tempfile.TemporaryDirectory() as output_dir:\n            separator = Separator(\n                model_file_dir=os.path.dirname(mock_new_roformer_model),\n                output_dir=output_dir","chunkId":"tests/integration/test_roformer_new_parameters.py:5"}],[2537,{"filePath":"tests/integration/test_roformer_new_parameters.py","startLine":72,"endLine":84,"chunkText":"            separator = Separator(\n                model_file_dir=os.path.dirname(mock_new_roformer_model),\n                output_dir=output_dir\n            )\n            \n            # Load the new model - should work with new implementation\n            separator.load_model(os.path.basename(mock_new_roformer_model))\n            \n            # Separate audio - should work with new parameters\n            output_files = separator.separate(mock_audio_file)\n            \n            # Verify outputs exist and are valid\n            assert len(output_files) >= 1, \"Should produce audio outputs\"","chunkId":"tests/integration/test_roformer_new_parameters.py:6"}],[2538,{"filePath":"tests/integration/test_roformer_new_parameters.py","startLine":82,"endLine":90,"chunkText":"            \n            # Verify outputs exist and are valid\n            assert len(output_files) >= 1, \"Should produce audio outputs\"\n            for output_file in output_files:\n                assert os.path.exists(output_file), f\"Output file should exist: {output_file}\"\n                assert os.path.getsize(output_file) > 0, f\"Output file should not be empty: {output_file}\"\n    \n    @pytest.mark.skipif(not IMPORTS_AVAILABLE, reason=\"Implementation not available yet (TDD)\")\n    def test_new_model_uses_new_implementation(self, mock_new_roformer_model):","chunkId":"tests/integration/test_roformer_new_parameters.py:7"}],[2539,{"filePath":"tests/integration/test_roformer_new_parameters.py","startLine":88,"endLine":97,"chunkText":"    \n    @pytest.mark.skipif(not IMPORTS_AVAILABLE, reason=\"Implementation not available yet (TDD)\")\n    def test_new_model_uses_new_implementation(self, mock_new_roformer_model):\n        \"\"\"Test that new models use the new implementation (not fallback).\"\"\"\n        \n        # Mock the loader to verify new implementation is used\n        with patch('audio_separator.separator.roformer.roformer_loader.RoformerLoader') as mock_loader_class:\n            mock_loader = Mock()\n            mock_loader_class.return_value = mock_loader\n            ","chunkId":"tests/integration/test_roformer_new_parameters.py:8"}],[2540,{"filePath":"tests/integration/test_roformer_new_parameters.py","startLine":95,"endLine":109,"chunkText":"            mock_loader = Mock()\n            mock_loader_class.return_value = mock_loader\n            \n            from audio_separator.separator.roformer.roformer_loader import ModelLoadingResult, ImplementationVersion\n            \n            mock_result = ModelLoadingResult(\n                success=True,\n                model=Mock(),\n                error_message=None,\n                implementation_used=ImplementationVersion.NEW,  # Should use new implementation\n                warnings=[]\n            )\n            mock_loader.load_model.return_value = mock_result\n            \n            # Load model","chunkId":"tests/integration/test_roformer_new_parameters.py:9"}],[2541,{"filePath":"tests/integration/test_roformer_new_parameters.py","startLine":107,"endLine":118,"chunkText":"            mock_loader.load_model.return_value = mock_result\n            \n            # Load model\n            loader = RoformerLoader()\n            result = loader.load_model(mock_new_roformer_model)\n            \n            # Verify new implementation was used\n            assert result.success is True\n            assert result.implementation_used == ImplementationVersion.NEW\n            assert len(result.warnings) == 0  # No fallback warnings\n    \n    @pytest.mark.skipif(not IMPORTS_AVAILABLE, reason=\"Implementation not available yet (TDD)\")","chunkId":"tests/integration/test_roformer_new_parameters.py:10"}],[2542,{"filePath":"tests/integration/test_roformer_new_parameters.py","startLine":116,"endLine":128,"chunkText":"            assert len(result.warnings) == 0  # No fallback warnings\n    \n    @pytest.mark.skipif(not IMPORTS_AVAILABLE, reason=\"Implementation not available yet (TDD)\")\n    def test_new_parameters_are_properly_handled(self, mock_new_roformer_model):\n        \"\"\"Test that new parameters are properly loaded and used.\"\"\"\n        \n        loader = RoformerLoader()\n        result = loader.load_model(mock_new_roformer_model)\n        \n        assert result.success is True\n        model_config = result.model.config\n        \n        # Verify new parameters are loaded correctly","chunkId":"tests/integration/test_roformer_new_parameters.py:11"}],[2543,{"filePath":"tests/integration/test_roformer_new_parameters.py","startLine":126,"endLine":135,"chunkText":"        model_config = result.model.config\n        \n        # Verify new parameters are loaded correctly\n        assert model_config.mlp_expansion_factor == 6  # From mock config\n        assert model_config.sage_attention is True     # From mock config\n        assert model_config.zero_dc is False          # From mock config\n        assert model_config.use_torch_checkpoint is True\n        assert model_config.skip_connection is True\n    \n    @pytest.mark.skipif(not IMPORTS_AVAILABLE, reason=\"Implementation not available yet (TDD)\")","chunkId":"tests/integration/test_roformer_new_parameters.py:12"}],[2544,{"filePath":"tests/integration/test_roformer_new_parameters.py","startLine":133,"endLine":144,"chunkText":"        assert model_config.skip_connection is True\n    \n    @pytest.mark.skipif(not IMPORTS_AVAILABLE, reason=\"Implementation not available yet (TDD)\")\n    def test_sage_attention_parameter_integration(self, mock_new_roformer_model):\n        \"\"\"Test that sage_attention parameter is properly integrated.\"\"\"\n        \n        # This test verifies that sage_attention=True is handled correctly\n        # and doesn't cause the AttributeError that was mentioned in the spec\n        \n        loader = RoformerLoader()\n        result = loader.load_model(mock_new_roformer_model)\n        ","chunkId":"tests/integration/test_roformer_new_parameters.py:13"}],[2545,{"filePath":"tests/integration/test_roformer_new_parameters.py","startLine":142,"endLine":155,"chunkText":"        loader = RoformerLoader()\n        result = loader.load_model(mock_new_roformer_model)\n        \n        assert result.success is True\n        \n        # Verify that sage_attention is passed to transformer_kwargs\n        model = result.model\n        assert hasattr(model, 'sage_attention')\n        assert model.sage_attention is True\n        \n        # Verify no AttributeError is raised during model initialization\n        # (This would be caught in the loading process)\n        assert result.error_message is None\n    ","chunkId":"tests/integration/test_roformer_new_parameters.py:14"}],[2546,{"filePath":"tests/integration/test_roformer_new_parameters.py","startLine":153,"endLine":165,"chunkText":"        # (This would be caught in the loading process)\n        assert result.error_message is None\n    \n    @pytest.mark.skipif(not IMPORTS_AVAILABLE, reason=\"Implementation not available yet (TDD)\")\n    def test_mlp_expansion_factor_parameter_handling(self, mock_new_roformer_model):\n        \"\"\"Test that mlp_expansion_factor parameter is handled correctly.\"\"\"\n        \n        # This test verifies that the mlp_expansion_factor parameter\n        # doesn't cause the TypeError mentioned in the spec\n        \n        loader = RoformerLoader()\n        result = loader.load_model(mock_new_roformer_model)\n        ","chunkId":"tests/integration/test_roformer_new_parameters.py:15"}],[2547,{"filePath":"tests/integration/test_roformer_new_parameters.py","startLine":163,"endLine":176,"chunkText":"        loader = RoformerLoader()\n        result = loader.load_model(mock_new_roformer_model)\n        \n        assert result.success is True\n        \n        # Verify parameter is properly set\n        model = result.model\n        assert hasattr(model, 'mlp_expansion_factor')\n        assert model.mlp_expansion_factor == 6  # From mock config\n        \n        # Verify no TypeError during initialization\n        assert result.error_message is None\n    \n    @pytest.mark.skipif(not IMPORTS_AVAILABLE, reason=\"Implementation not available yet (TDD)\")","chunkId":"tests/integration/test_roformer_new_parameters.py:16"}],[2548,{"filePath":"tests/integration/test_roformer_new_parameters.py","startLine":174,"endLine":187,"chunkText":"        assert result.error_message is None\n    \n    @pytest.mark.skipif(not IMPORTS_AVAILABLE, reason=\"Implementation not available yet (TDD)\")\n    def test_torch_checkpoint_parameter_integration(self, mock_new_roformer_model):\n        \"\"\"Test that use_torch_checkpoint parameter works correctly.\"\"\"\n        \n        loader = RoformerLoader()\n        result = loader.load_model(mock_new_roformer_model)\n        \n        assert result.success is True\n        model = result.model\n        \n        # Verify checkpoint parameter is set\n        assert hasattr(model, 'use_torch_checkpoint')","chunkId":"tests/integration/test_roformer_new_parameters.py:17"}],[2549,{"filePath":"tests/integration/test_roformer_new_parameters.py","startLine":185,"endLine":197,"chunkText":"        \n        # Verify checkpoint parameter is set\n        assert hasattr(model, 'use_torch_checkpoint')\n        assert model.use_torch_checkpoint is True\n        \n        # This parameter affects memory usage during forward pass\n        # In real implementation, this would be tested during audio separation\n    \n    @pytest.mark.skipif(not IMPORTS_AVAILABLE, reason=\"Implementation not available yet (TDD)\")\n    def test_skip_connection_parameter_integration(self, mock_new_roformer_model):\n        \"\"\"Test that skip_connection parameter works correctly.\"\"\"\n        \n        loader = RoformerLoader()","chunkId":"tests/integration/test_roformer_new_parameters.py:18"}],[2550,{"filePath":"tests/integration/test_roformer_new_parameters.py","startLine":195,"endLine":208,"chunkText":"        \"\"\"Test that skip_connection parameter works correctly.\"\"\"\n        \n        loader = RoformerLoader()\n        result = loader.load_model(mock_new_roformer_model)\n        \n        assert result.success is True\n        model = result.model\n        \n        # Verify skip connection parameter is set\n        assert hasattr(model, 'skip_connection')\n        assert model.skip_connection is True\n    \n    @pytest.mark.skipif(not IMPORTS_AVAILABLE, reason=\"Implementation not available yet (TDD)\")\n    def test_zero_dc_parameter_handling(self, mock_new_roformer_model):","chunkId":"tests/integration/test_roformer_new_parameters.py:19"}],[2551,{"filePath":"tests/integration/test_roformer_new_parameters.py","startLine":206,"endLine":220,"chunkText":"    \n    @pytest.mark.skipif(not IMPORTS_AVAILABLE, reason=\"Implementation not available yet (TDD)\")\n    def test_zero_dc_parameter_handling(self, mock_new_roformer_model):\n        \"\"\"Test that zero_dc parameter is handled correctly.\"\"\"\n        \n        loader = RoformerLoader()\n        result = loader.load_model(mock_new_roformer_model)\n        \n        assert result.success is True\n        model = result.model\n        \n        # Verify zero_dc parameter is set to non-default value\n        assert hasattr(model, 'zero_dc')\n        assert model.zero_dc is False  # Non-default value from mock\n    ","chunkId":"tests/integration/test_roformer_new_parameters.py:20"}],[2552,{"filePath":"tests/integration/test_roformer_new_parameters.py","startLine":218,"endLine":228,"chunkText":"        assert hasattr(model, 'zero_dc')\n        assert model.zero_dc is False  # Non-default value from mock\n    \n    # TDD placeholder test removed - implementation is now complete\n    \n    @pytest.mark.skipif(not IMPORTS_AVAILABLE, reason=\"Implementation not available yet (TDD)\")\n    def test_new_model_audio_quality_validation(self, mock_new_roformer_model, mock_audio_file):\n        \"\"\"Test that new models produce high-quality audio separation.\"\"\"\n        \n        with tempfile.TemporaryDirectory() as output_dir:\n            separator = Separator(output_dir=output_dir)","chunkId":"tests/integration/test_roformer_new_parameters.py:21"}],[2553,{"filePath":"tests/integration/test_roformer_new_parameters.py","startLine":226,"endLine":239,"chunkText":"        \n        with tempfile.TemporaryDirectory() as output_dir:\n            separator = Separator(output_dir=output_dir)\n            separator.load_model(mock_new_roformer_model)\n            \n            outputs = separator.separate(mock_audio_file)\n            \n            # Verify outputs exist and meet quality standards\n            assert len(outputs) > 0\n            for output in outputs:\n                assert os.path.exists(output)\n                \n                # In real implementation, this would:\n                # 1. Load and analyze the audio","chunkId":"tests/integration/test_roformer_new_parameters.py:22"}],[2554,{"filePath":"tests/integration/test_roformer_new_parameters.py","startLine":237,"endLine":247,"chunkText":"                \n                # In real implementation, this would:\n                # 1. Load and analyze the audio\n                # 2. Check for artifacts or quality issues\n                # 3. Compare against reference outputs if available\n                # 4. Verify SSIM >= 0.80 for spectrograms (per spec)\n\n\nif __name__ == \"__main__\":\n    pytest.main([__file__, \"-v\"])\n","chunkId":"tests/integration/test_roformer_new_parameters.py:23"}],[2555,{"filePath":"tests/integration/test_separator_output_integration.py","startLine":0,"endLine":23,"chunkText":"import os\nimport pytest\nimport tempfile\nimport shutil\nfrom pathlib import Path\n\nfrom audio_separator.separator import Separator\n\n\n@pytest.fixture(name=\"input_file\")\ndef fixture_input_file():\n    \"\"\"Fixture providing the test input audio file path.\"\"\"\n    return \"tests/inputs/mardy20s.flac\"\n\n\n@pytest.fixture(name=\"temp_output_dir\")\ndef fixture_temp_output_dir():\n    \"\"\"Fixture providing a temporary directory for output files.\"\"\"\n    temp_dir = tempfile.mkdtemp()\n    yield temp_dir\n    # Clean up after test\n    shutil.rmtree(temp_dir)\n\n","chunkId":"tests/integration/test_separator_output_integration.py:0"}],[2556,{"filePath":"tests/integration/test_separator_output_integration.py","startLine":22,"endLine":35,"chunkText":"\n\ndef test_separator_output_dir_and_custom_names(input_file, temp_output_dir):\n    \"\"\"Test that Separator respects output_dir and custom_output_names parameters.\"\"\"\n    print(\"\\n>>> TEST: Checking output_dir with custom output names\")\n    \n    # Define custom output filenames\n    vocal_output_filename = \"custom_vocals_output\"\n    instrumental_output_filename = \"custom_instrumental_output\"\n\n    # Create output name mapping\n    custom_output_names = {\"Vocals\": vocal_output_filename, \"Instrumental\": instrumental_output_filename}\n\n    # Initialize separator with specified output directory","chunkId":"tests/integration/test_separator_output_integration.py:1"}],[2557,{"filePath":"tests/integration/test_separator_output_integration.py","startLine":33,"endLine":44,"chunkText":"    custom_output_names = {\"Vocals\": vocal_output_filename, \"Instrumental\": instrumental_output_filename}\n\n    # Initialize separator with specified output directory\n    print(f\"Creating Separator with output_dir: {temp_output_dir}\")\n    separator = Separator(output_dir=temp_output_dir, log_level=20)  # INFO level\n\n    # Load model\n    print(\"Loading model: MGM_MAIN_v4.pth\")\n    separator.load_model(model_filename=\"MGM_MAIN_v4.pth\")\n\n    # Run separation with custom output names\n    print(f\"Running separation with custom_output_names: {custom_output_names}\")","chunkId":"tests/integration/test_separator_output_integration.py:2"}],[2558,{"filePath":"tests/integration/test_separator_output_integration.py","startLine":42,"endLine":52,"chunkText":"\n    # Run separation with custom output names\n    print(f\"Running separation with custom_output_names: {custom_output_names}\")\n    output_files = separator.separate(input_file, custom_output_names=custom_output_names)\n    print(f\"Separator.separate() returned: {output_files}\")\n\n    # The separator adds .wav extension since the default output format is WAV\n    expected_vocal_filename = vocal_output_filename + \".wav\"\n    expected_instrumental_filename = instrumental_output_filename + \".wav\"\n    \n    # Check that the returned filenames match the expected names","chunkId":"tests/integration/test_separator_output_integration.py:3"}],[2559,{"filePath":"tests/integration/test_separator_output_integration.py","startLine":50,"endLine":59,"chunkText":"    expected_instrumental_filename = instrumental_output_filename + \".wav\"\n    \n    # Check that the returned filenames match the expected names\n    output_filenames = [os.path.basename(f) for f in output_files]\n    print(f\"Extracted filenames from output_files: {output_filenames}\")\n    \n    # NOTE: The Separator class returns only the filenames, not the full paths with output_dir\n    print(\"EXPECTED BEHAVIOR: The Separator.separate() method returns filenames without the output_dir path\")\n    print(f\"Expected filenames (without path): {expected_vocal_filename} and {expected_instrumental_filename}\")\n    ","chunkId":"tests/integration/test_separator_output_integration.py:4"}],[2560,{"filePath":"tests/integration/test_separator_output_integration.py","startLine":57,"endLine":63,"chunkText":"    print(\"EXPECTED BEHAVIOR: The Separator.separate() method returns filenames without the output_dir path\")\n    print(f\"Expected filenames (without path): {expected_vocal_filename} and {expected_instrumental_filename}\")\n    \n    assert expected_vocal_filename in output_filenames, f\"Expected {expected_vocal_filename} in output files\"\n    assert expected_instrumental_filename in output_filenames, f\"Expected {expected_instrumental_filename} in output files\"\n    \n    # Check that files physically exist in the specified output directory","chunkId":"tests/integration/test_separator_output_integration.py:5"}],[2561,{"filePath":"tests/integration/test_separator_output_integration.py","startLine":61,"endLine":70,"chunkText":"    assert expected_instrumental_filename in output_filenames, f\"Expected {expected_instrumental_filename} in output files\"\n    \n    # Check that files physically exist in the specified output directory\n    expected_vocal_path = os.path.join(temp_output_dir, expected_vocal_filename)\n    expected_instrumental_path = os.path.join(temp_output_dir, expected_instrumental_filename)\n    \n    print(f\"Checking that files exist in output_dir: {temp_output_dir}\")\n    print(f\"Full expected vocal path: {expected_vocal_path}\")\n    print(f\"Full expected instrumental path: {expected_instrumental_path}\")\n    ","chunkId":"tests/integration/test_separator_output_integration.py:6"}],[2562,{"filePath":"tests/integration/test_separator_output_integration.py","startLine":68,"endLine":74,"chunkText":"    print(f\"Full expected vocal path: {expected_vocal_path}\")\n    print(f\"Full expected instrumental path: {expected_instrumental_path}\")\n    \n    assert os.path.exists(expected_vocal_path), f\"Vocals output file doesn't exist: {expected_vocal_path}\"\n    assert os.path.exists(expected_instrumental_path), f\"Instrumental output file doesn't exist: {expected_instrumental_path}\"\n    assert os.path.getsize(expected_vocal_path) > 0, f\"Vocals output file is empty: {expected_vocal_path}\"\n    assert os.path.getsize(expected_instrumental_path) > 0, f\"Instrumental output file is empty: {expected_instrumental_path}\"","chunkId":"tests/integration/test_separator_output_integration.py:7"}],[2563,{"filePath":"tests/integration/test_separator_output_integration.py","startLine":72,"endLine":78,"chunkText":"    assert os.path.exists(expected_instrumental_path), f\"Instrumental output file doesn't exist: {expected_instrumental_path}\"\n    assert os.path.getsize(expected_vocal_path) > 0, f\"Vocals output file is empty: {expected_vocal_path}\"\n    assert os.path.getsize(expected_instrumental_path) > 0, f\"Instrumental output file is empty: {expected_instrumental_path}\"\n    \n    print(\"✅ Test passed: Separator correctly handles output_dir and custom_output_names\")\n    print(\"   - Files were saved to the specified output directory\")\n    print(\"   - Custom filenames were used (with .wav extension added)\")","chunkId":"tests/integration/test_separator_output_integration.py:8"}],[2564,{"filePath":"tests/integration/test_separator_output_integration.py","startLine":76,"endLine":86,"chunkText":"    print(\"✅ Test passed: Separator correctly handles output_dir and custom_output_names\")\n    print(\"   - Files were saved to the specified output directory\")\n    print(\"   - Custom filenames were used (with .wav extension added)\")\n    print(\"   - Returned paths include just the filenames (not the full paths)\")\n\n\ndef test_separator_single_stem_output(input_file, temp_output_dir):\n    \"\"\"Test that Separator correctly respects output_single_stem with custom output name.\"\"\"\n    print(\"\\n>>> TEST: Checking output_single_stem with custom output name\")\n    \n    # Define custom output filename for single stem","chunkId":"tests/integration/test_separator_output_integration.py:9"}],[2565,{"filePath":"tests/integration/test_separator_output_integration.py","startLine":84,"endLine":96,"chunkText":"    print(\"\\n>>> TEST: Checking output_single_stem with custom output name\")\n    \n    # Define custom output filename for single stem\n    vocal_output_filename = \"only_vocals_output\"\n\n    # Create output name mapping\n    custom_output_names = {\"Vocals\": vocal_output_filename}\n\n    # Initialize separator with specified output directory and single stem output\n    print(f\"Creating Separator with output_dir: {temp_output_dir} and output_single_stem: Vocals\")\n    separator = Separator(\n        output_dir=temp_output_dir,\n        output_single_stem=\"Vocals\",  # Only extract vocals","chunkId":"tests/integration/test_separator_output_integration.py:10"}],[2566,{"filePath":"tests/integration/test_separator_output_integration.py","startLine":94,"endLine":108,"chunkText":"    separator = Separator(\n        output_dir=temp_output_dir,\n        output_single_stem=\"Vocals\",  # Only extract vocals\n        log_level=20  # INFO level\n    )\n\n    # Load model\n    print(\"Loading model: MGM_MAIN_v4.pth\")\n    separator.load_model(model_filename=\"MGM_MAIN_v4.pth\")\n\n    # Run separation with custom output name\n    print(f\"Running separation with custom_output_names: {custom_output_names}\")\n    output_files = separator.separate(input_file, custom_output_names=custom_output_names)\n    print(f\"Separator.separate() returned: {output_files}\")\n","chunkId":"tests/integration/test_separator_output_integration.py:11"}],[2567,{"filePath":"tests/integration/test_separator_output_integration.py","startLine":106,"endLine":114,"chunkText":"    output_files = separator.separate(input_file, custom_output_names=custom_output_names)\n    print(f\"Separator.separate() returned: {output_files}\")\n\n    # The separator adds .wav extension since the default output format is WAV\n    expected_vocal_filename = vocal_output_filename + \".wav\"\n    \n    # Check that the output files list contains only the expected file\n    print(f\"Checking that only one file was returned and it has the correct name\")\n    assert len(output_files) == 1, f\"Expected 1 output file, got {len(output_files)}\"","chunkId":"tests/integration/test_separator_output_integration.py:12"}],[2568,{"filePath":"tests/integration/test_separator_output_integration.py","startLine":112,"endLine":120,"chunkText":"    # Check that the output files list contains only the expected file\n    print(f\"Checking that only one file was returned and it has the correct name\")\n    assert len(output_files) == 1, f\"Expected 1 output file, got {len(output_files)}\"\n    assert os.path.basename(output_files[0]) == expected_vocal_filename, f\"Expected {expected_vocal_filename} in output files\"\n    \n    # Check that file physically exists in the specified output directory\n    expected_vocal_path = os.path.join(temp_output_dir, expected_vocal_filename)\n    print(f\"Checking that file exists in output_dir: {expected_vocal_path}\")\n    ","chunkId":"tests/integration/test_separator_output_integration.py:13"}],[2569,{"filePath":"tests/integration/test_separator_output_integration.py","startLine":118,"endLine":125,"chunkText":"    expected_vocal_path = os.path.join(temp_output_dir, expected_vocal_filename)\n    print(f\"Checking that file exists in output_dir: {expected_vocal_path}\")\n    \n    assert os.path.exists(expected_vocal_path), f\"Vocals output file doesn't exist: {expected_vocal_path}\"\n    assert os.path.getsize(expected_vocal_path) > 0, f\"Vocals output file is empty: {expected_vocal_path}\"\n    \n    # Make sure the instrumental file was NOT created\n    instrumental_files = [f for f in os.listdir(temp_output_dir) if \"instrumental\" in f.lower()]","chunkId":"tests/integration/test_separator_output_integration.py:14"}],[2570,{"filePath":"tests/integration/test_separator_output_integration.py","startLine":123,"endLine":130,"chunkText":"    \n    # Make sure the instrumental file was NOT created\n    instrumental_files = [f for f in os.listdir(temp_output_dir) if \"instrumental\" in f.lower()]\n    print(f\"Checking that no instrumental files were created, found: {instrumental_files}\")\n    assert len(instrumental_files) == 0, f\"No instrumental file should be created when using output_single_stem, found: {instrumental_files}\"\n    \n    print(\"✅ Test passed: Separator correctly handles output_single_stem with custom output name\")\n    print(\"   - Only the specified stem (Vocals) was extracted\")","chunkId":"tests/integration/test_separator_output_integration.py:15"}],[2571,{"filePath":"tests/integration/test_separator_output_integration.py","startLine":128,"endLine":139,"chunkText":"    \n    print(\"✅ Test passed: Separator correctly handles output_single_stem with custom output name\")\n    print(\"   - Only the specified stem (Vocals) was extracted\")\n    print(\"   - The custom filename was used (with .wav extension added)\")\n    print(\"   - No other stems (like Instrumental) were created\")\n\n\ndef test_separator_output_without_custom_names(input_file, temp_output_dir):\n    \"\"\"Test that Separator respects output_dir without custom_output_names.\"\"\"\n    print(\"\\n>>> TEST: Checking output_dir without custom output names\")\n    \n    # Initialize separator with specified output directory","chunkId":"tests/integration/test_separator_output_integration.py:16"}],[2572,{"filePath":"tests/integration/test_separator_output_integration.py","startLine":137,"endLine":149,"chunkText":"    print(\"\\n>>> TEST: Checking output_dir without custom output names\")\n    \n    # Initialize separator with specified output directory\n    print(f\"Creating Separator with output_dir: {temp_output_dir}\")\n    separator = Separator(output_dir=temp_output_dir, log_level=20)  # INFO level\n\n    # Load model\n    print(\"Loading model: MGM_MAIN_v4.pth\")\n    separator.load_model(model_filename=\"MGM_MAIN_v4.pth\")\n\n    # Run separation without custom output names\n    print(\"Running separation without custom_output_names\")\n    output_files = separator.separate(input_file)","chunkId":"tests/integration/test_separator_output_integration.py:17"}],[2573,{"filePath":"tests/integration/test_separator_output_integration.py","startLine":147,"endLine":157,"chunkText":"    # Run separation without custom output names\n    print(\"Running separation without custom_output_names\")\n    output_files = separator.separate(input_file)\n    print(f\"Separator.separate() returned: {output_files}\")\n\n    # Check that output files exist and have content\n    print(f\"Checking that two files were returned (one for each stem)\")\n    assert len(output_files) == 2, f\"Expected 2 output files, got {len(output_files)}\"\n    \n    # Check if the files were created in the output directory\n    # Note: The separator doesn't include the full path in the returned output_files","chunkId":"tests/integration/test_separator_output_integration.py:18"}],[2574,{"filePath":"tests/integration/test_separator_output_integration.py","startLine":155,"endLine":165,"chunkText":"    \n    # Check if the files were created in the output directory\n    # Note: The separator doesn't include the full path in the returned output_files\n    output_file_basenames = [os.path.basename(f) for f in output_files]\n    print(f\"Extracted filenames from output_files: {output_file_basenames}\")\n    \n    print(\"EXPECTED BEHAVIOR: Default filenames are being used (format: inputname_(StemName)_modelname.wav)\")\n    \n    # Check all output files exist in the specified output directory\n    print(f\"Checking that files exist in output_dir: {temp_output_dir}\")\n    for basename in output_file_basenames:","chunkId":"tests/integration/test_separator_output_integration.py:19"}],[2575,{"filePath":"tests/integration/test_separator_output_integration.py","startLine":163,"endLine":171,"chunkText":"    # Check all output files exist in the specified output directory\n    print(f\"Checking that files exist in output_dir: {temp_output_dir}\")\n    for basename in output_file_basenames:\n        full_path = os.path.join(temp_output_dir, basename)\n        print(f\"Checking file: {full_path}\")\n        assert os.path.exists(full_path), f\"Output file doesn't exist: {full_path}\"\n        assert os.path.getsize(full_path) > 0, f\"Output file is empty: {full_path}\"\n    \n    print(\"✅ Test passed: Separator correctly handles output_dir without custom output names\")","chunkId":"tests/integration/test_separator_output_integration.py:20"}],[2576,{"filePath":"tests/integration/test_separator_output_integration.py","startLine":169,"endLine":175,"chunkText":"        assert os.path.getsize(full_path) > 0, f\"Output file is empty: {full_path}\"\n    \n    print(\"✅ Test passed: Separator correctly handles output_dir without custom output names\")\n    print(\"   - Files were saved to the specified output directory\")\n    print(\"   - Default naming scheme was used (input_name_(Stem)_model.wav)\")\n    print(\"   - Returned paths include just the filenames (not the full paths)\")\n","chunkId":"tests/integration/test_separator_output_integration.py:21"}],[2577,{"filePath":"tests/model-metrics/test-all-models.py","startLine":0,"endLine":25,"chunkText":"#!/usr/bin/env python\nimport os\nimport time\nimport museval\nimport numpy as np\nimport soundfile as sf\nfrom audio_separator.separator import Separator\nimport json\nfrom json import JSONEncoder\nimport logging\nimport musdb\nfrom decimal import Decimal\nimport tempfile\nimport argparse\n\n\n# Setup logging\nlogging.basicConfig(level=logging.INFO, format=\"%(asctime)s - %(levelname)s - %(message)s\")\nlogger = logging.getLogger(__name__)\n\n\n# Custom JSON Encoder to handle Decimal types\nclass DecimalEncoder(JSONEncoder):\n    def default(self, obj):\n        if isinstance(obj, Decimal):\n            return float(obj)","chunkId":"tests/model-metrics/test-all-models.py:0"}],[2578,{"filePath":"tests/model-metrics/test-all-models.py","startLine":23,"endLine":36,"chunkText":"    def default(self, obj):\n        if isinstance(obj, Decimal):\n            return float(obj)\n        return super().default(obj)\n\n\nMUSDB_PATH = \"/Volumes/Nomad4TBOne/python-audio-separator/tests/model-metrics/datasets/musdb18hq\"\nRESULTS_PATH = \"/Volumes/Nomad4TBOne/python-audio-separator/tests/model-metrics/results\"\n# Find project root dynamically\nimport os\ncurrent_dir = os.path.dirname(os.path.abspath(__file__))\nproject_root = current_dir\nwhile project_root and not os.path.exists(os.path.join(project_root, 'audio_separator')):\n    parent = os.path.dirname(project_root)","chunkId":"tests/model-metrics/test-all-models.py:1"}],[2579,{"filePath":"tests/model-metrics/test-all-models.py","startLine":34,"endLine":45,"chunkText":"project_root = current_dir\nwhile project_root and not os.path.exists(os.path.join(project_root, 'audio_separator')):\n    parent = os.path.dirname(project_root)\n    if parent == project_root:  # Reached filesystem root\n        break\n    project_root = parent\n\nCOMBINED_RESULTS_PATH = os.path.join(project_root, \"audio_separator\", \"models-scores.json\")\nCOMBINED_MUSEVAL_RESULTS_PATH = \"/Volumes/Nomad4TBOne/python-audio-separator/tests/model-metrics/results/combined-museval-results.json\"\nSTOP_SIGNAL_PATH = \"/Volumes/Nomad4TBOne/python-audio-separator/tests/model-metrics/stop-signal\"\n\n","chunkId":"tests/model-metrics/test-all-models.py:2"}],[2580,{"filePath":"tests/model-metrics/test-all-models.py","startLine":44,"endLine":57,"chunkText":"\n\ndef load_combined_results():\n    \"\"\"Load the combined museval results file\"\"\"\n    if os.path.exists(COMBINED_MUSEVAL_RESULTS_PATH):\n        logger.info(\"Loading combined museval results...\")\n        try:\n            with open(COMBINED_MUSEVAL_RESULTS_PATH, \"r\") as f:\n                # Use a custom parser to handle Decimal values\n                def decimal_parser(dct):\n                    for k, v in dct.items():\n                        if isinstance(v, str) and v.replace(\".\", \"\").isdigit():\n                            try:\n                                dct[k] = float(v)","chunkId":"tests/model-metrics/test-all-models.py:3"}],[2581,{"filePath":"tests/model-metrics/test-all-models.py","startLine":55,"endLine":66,"chunkText":"                        if isinstance(v, str) and v.replace(\".\", \"\").isdigit():\n                            try:\n                                dct[k] = float(v)\n                            except (ValueError, TypeError):\n                                pass\n                    return dct\n\n                return json.load(f, object_hook=decimal_parser)\n        except Exception as e:\n            logger.error(f\"Error loading combined results: {str(e)}\")\n            # Try to load a backup file if it exists\n            backup_path = COMBINED_MUSEVAL_RESULTS_PATH + \".backup\"","chunkId":"tests/model-metrics/test-all-models.py:4"}],[2582,{"filePath":"tests/model-metrics/test-all-models.py","startLine":64,"endLine":75,"chunkText":"            logger.error(f\"Error loading combined results: {str(e)}\")\n            # Try to load a backup file if it exists\n            backup_path = COMBINED_MUSEVAL_RESULTS_PATH + \".backup\"\n            if os.path.exists(backup_path):\n                logger.info(\"Attempting to load backup file...\")\n                try:\n                    with open(backup_path, \"r\") as f:\n                        return json.load(f, object_hook=decimal_parser)\n                except Exception as backup_e:\n                    logger.error(f\"Error loading backup file: {str(backup_e)}\")\n            return {}\n    else:","chunkId":"tests/model-metrics/test-all-models.py:5"}],[2583,{"filePath":"tests/model-metrics/test-all-models.py","startLine":73,"endLine":87,"chunkText":"                    logger.error(f\"Error loading backup file: {str(backup_e)}\")\n            return {}\n    else:\n        logger.info(\"No combined results file found, creating new one\")\n        return {}\n\n\ndef save_combined_results(combined_results):\n    \"\"\"Save the combined museval results file\"\"\"\n    logger.info(\"Saving combined museval results...\")\n    try:\n        # Create a backup of the existing file if it exists\n        if os.path.exists(COMBINED_MUSEVAL_RESULTS_PATH):\n            backup_path = COMBINED_MUSEVAL_RESULTS_PATH + \".backup\"\n            try:","chunkId":"tests/model-metrics/test-all-models.py:6"}],[2584,{"filePath":"tests/model-metrics/test-all-models.py","startLine":85,"endLine":95,"chunkText":"        if os.path.exists(COMBINED_MUSEVAL_RESULTS_PATH):\n            backup_path = COMBINED_MUSEVAL_RESULTS_PATH + \".backup\"\n            try:\n                with open(COMBINED_MUSEVAL_RESULTS_PATH, \"r\") as src, open(backup_path, \"w\") as dst:\n                    dst.write(src.read())\n            except Exception as e:\n                logger.error(f\"Error creating backup file: {str(e)}\")\n\n        # Save the new results using the custom encoder\n        with open(COMBINED_MUSEVAL_RESULTS_PATH, \"w\") as f:\n            json.dump(combined_results, f, cls=DecimalEncoder, indent=2)","chunkId":"tests/model-metrics/test-all-models.py:7"}],[2585,{"filePath":"tests/model-metrics/test-all-models.py","startLine":93,"endLine":106,"chunkText":"        # Save the new results using the custom encoder\n        with open(COMBINED_MUSEVAL_RESULTS_PATH, \"w\") as f:\n            json.dump(combined_results, f, cls=DecimalEncoder, indent=2)\n        logger.info(\"Combined results saved successfully\")\n        return True\n    except Exception as e:\n        logger.error(f\"Error saving combined results: {str(e)}\")\n        return False\n\n\ndef update_combined_results(model_name, track_name, track_data):\n    \"\"\"Update the combined results file with new track data\"\"\"\n    try:\n        # Load existing combined results","chunkId":"tests/model-metrics/test-all-models.py:8"}],[2586,{"filePath":"tests/model-metrics/test-all-models.py","startLine":104,"endLine":119,"chunkText":"    \"\"\"Update the combined results file with new track data\"\"\"\n    try:\n        # Load existing combined results\n        combined_results = load_combined_results()\n\n        # Initialize model entry if it doesn't exist\n        if model_name not in combined_results:\n            combined_results[model_name] = {}\n\n        # Add or update track data\n        combined_results[model_name][track_name] = track_data\n\n        # Write updated results back to file\n        save_combined_results(combined_results)\n        return True\n    except Exception as e:","chunkId":"tests/model-metrics/test-all-models.py:9"}],[2587,{"filePath":"tests/model-metrics/test-all-models.py","startLine":117,"endLine":132,"chunkText":"        save_combined_results(combined_results)\n        return True\n    except Exception as e:\n        logger.error(f\"Error updating combined results: {str(e)}\")\n        return False\n\n\ndef check_track_evaluated(model_name, track_name):\n    \"\"\"Check if a track has already been evaluated for a specific model\"\"\"\n    combined_results = load_combined_results()\n    return model_name in combined_results and track_name in combined_results[model_name]\n\n\ndef get_track_results(model_name, track_name):\n    \"\"\"Get the evaluation results for a specific track and model\"\"\"\n    combined_results = load_combined_results()","chunkId":"tests/model-metrics/test-all-models.py:10"}],[2588,{"filePath":"tests/model-metrics/test-all-models.py","startLine":130,"endLine":144,"chunkText":"def get_track_results(model_name, track_name):\n    \"\"\"Get the evaluation results for a specific track and model\"\"\"\n    combined_results = load_combined_results()\n    if model_name in combined_results and track_name in combined_results[model_name]:\n        return combined_results[model_name][track_name]\n    return None\n\n\ndef get_track_duration(track_path):\n    \"\"\"Get the duration of a track in minutes\"\"\"\n    try:\n        mixture_path = os.path.join(track_path, \"mixture.wav\")\n        info = sf.info(mixture_path)\n        return info.duration / 60.0  # Convert seconds to minutes\n    except Exception as e:","chunkId":"tests/model-metrics/test-all-models.py:11"}],[2589,{"filePath":"tests/model-metrics/test-all-models.py","startLine":142,"endLine":156,"chunkText":"        info = sf.info(mixture_path)\n        return info.duration / 60.0  # Convert seconds to minutes\n    except Exception as e:\n        logger.error(f\"Error getting track duration: {str(e)}\")\n        return 0.0\n\n\ndef evaluate_track(track_name, track_path, test_model, mus_db):\n    \"\"\"Evaluate a single track using a specific model\"\"\"\n    logger.info(f\"Evaluating track: {track_name} with model: {test_model}\")\n\n    # Get track duration in minutes\n    track_duration_minutes = get_track_duration(track_path)\n    logger.info(f\"Track duration: {track_duration_minutes:.2f} minutes\")\n","chunkId":"tests/model-metrics/test-all-models.py:12"}],[2590,{"filePath":"tests/model-metrics/test-all-models.py","startLine":154,"endLine":166,"chunkText":"    track_duration_minutes = get_track_duration(track_path)\n    logger.info(f\"Track duration: {track_duration_minutes:.2f} minutes\")\n\n    # Initialize variables to track processing time\n    processing_time = 0\n    seconds_per_minute = 0\n\n    # Create a basic result structure that will be returned even if evaluation fails\n    basic_model_results = {\"track_name\": track_name, \"scores\": {}}\n\n    # Check if evaluation results already exist in combined file\n    museval_results = load_combined_results()\n    if test_model in museval_results and track_name in museval_results[test_model]:","chunkId":"tests/model-metrics/test-all-models.py:13"}],[2591,{"filePath":"tests/model-metrics/test-all-models.py","startLine":164,"endLine":174,"chunkText":"    # Check if evaluation results already exist in combined file\n    museval_results = load_combined_results()\n    if test_model in museval_results and track_name in museval_results[test_model]:\n        logger.info(\"Found existing evaluation results in combined file...\")\n        track_data = museval_results[test_model][track_name]\n        scores = museval.TrackStore(track_name)\n        scores.scores = track_data\n\n        # Try to extract existing speed metrics if available\n        try:\n            if isinstance(track_data, dict) and \"targets\" in track_data:","chunkId":"tests/model-metrics/test-all-models.py:14"}],[2592,{"filePath":"tests/model-metrics/test-all-models.py","startLine":172,"endLine":181,"chunkText":"        # Try to extract existing speed metrics if available\n        try:\n            if isinstance(track_data, dict) and \"targets\" in track_data:\n                for target in track_data[\"targets\"]:\n                    if \"metrics\" in target and \"seconds_per_minute_m3\" in target[\"metrics\"]:\n                        basic_model_results[\"scores\"][\"seconds_per_minute_m3\"] = target[\"metrics\"][\"seconds_per_minute_m3\"]\n                        break\n        except Exception:\n            pass  # Ignore errors in extracting existing speed metrics\n    else:","chunkId":"tests/model-metrics/test-all-models.py:15"}],[2593,{"filePath":"tests/model-metrics/test-all-models.py","startLine":179,"endLine":194,"chunkText":"        except Exception:\n            pass  # Ignore errors in extracting existing speed metrics\n    else:\n        # Expanded stem mapping to include \"no-stem\" outputs and custom stem formats\n        stem_mapping = {\n            # Standard stems\n            \"Vocals\": \"vocals\",\n            \"Instrumental\": \"instrumental\",\n            \"Drums\": \"drums\",\n            \"Bass\": \"bass\",\n            \"Other\": \"other\",\n            # No-stem variants\n            \"No Drums\": \"nodrums\",\n            \"No Bass\": \"nobass\",\n            \"No Other\": \"noother\",\n            # Custom stem formats (with hyphens)","chunkId":"tests/model-metrics/test-all-models.py:16"}],[2594,{"filePath":"tests/model-metrics/test-all-models.py","startLine":192,"endLine":208,"chunkText":"            \"No Bass\": \"nobass\",\n            \"No Other\": \"noother\",\n            # Custom stem formats (with hyphens)\n            \"Drum-Bass\": \"drumbass\",\n            \"No Drum-Bass\": \"nodrumbass\",\n            \"Vocals-Other\": \"vocalsother\",\n            \"No Vocals-Other\": \"novocalsother\",\n        }\n\n        # Create a temporary directory for separation files\n        with tempfile.TemporaryDirectory() as temp_dir:\n            logger.info(f\"Using temporary directory: {temp_dir}\")\n\n            # Measure separation time\n            start_time = time.time()\n\n            # Perform separation","chunkId":"tests/model-metrics/test-all-models.py:17"}],[2595,{"filePath":"tests/model-metrics/test-all-models.py","startLine":206,"endLine":216,"chunkText":"            start_time = time.time()\n\n            # Perform separation\n            logger.info(\"Performing separation...\")\n            separator = Separator(output_dir=temp_dir)\n            separator.load_model(model_filename=test_model)\n            separator.separate(os.path.join(track_path, \"mixture.wav\"), custom_output_names=stem_mapping)\n\n            # Calculate processing time\n            processing_time = time.time() - start_time\n            seconds_per_minute = processing_time / track_duration_minutes if track_duration_minutes > 0 else 0","chunkId":"tests/model-metrics/test-all-models.py:18"}],[2596,{"filePath":"tests/model-metrics/test-all-models.py","startLine":214,"endLine":223,"chunkText":"            # Calculate processing time\n            processing_time = time.time() - start_time\n            seconds_per_minute = processing_time / track_duration_minutes if track_duration_minutes > 0 else 0\n            logger.info(f\"Separation completed in {processing_time:.2f} seconds\")\n            logger.info(f\"Processing speed: {seconds_per_minute:.2f} seconds per minute of audio\")\n\n            # Always add the speed metric to our basic results\n            basic_model_results[\"scores\"][\"seconds_per_minute_m3\"] = round(seconds_per_minute, 1)\n\n            # Check which stems were actually created","chunkId":"tests/model-metrics/test-all-models.py:19"}],[2597,{"filePath":"tests/model-metrics/test-all-models.py","startLine":221,"endLine":230,"chunkText":"            basic_model_results[\"scores\"][\"seconds_per_minute_m3\"] = round(seconds_per_minute, 1)\n\n            # Check which stems were actually created\n            wav_files = [f for f in os.listdir(temp_dir) if f.endswith(\".wav\")]\n            logger.info(f\"Found WAV files: {wav_files}\")\n\n            # Determine if this is a standard vocal/instrumental model that can be evaluated with museval\n            standard_model = False\n            if len(wav_files) == 2:\n                # Check if one of the files is named vocals.wav or instrumental.wav","chunkId":"tests/model-metrics/test-all-models.py:20"}],[2598,{"filePath":"tests/model-metrics/test-all-models.py","startLine":228,"endLine":236,"chunkText":"            standard_model = False\n            if len(wav_files) == 2:\n                # Check if one of the files is named vocals.wav or instrumental.wav\n                if \"vocals.wav\" in wav_files and \"instrumental.wav\" in wav_files:\n                    standard_model = True\n                    logger.info(\"Detected standard vocals/instrumental model, will run museval evaluation\")\n\n            # If not a standard model, skip museval evaluation and just return speed metrics\n            if not standard_model:","chunkId":"tests/model-metrics/test-all-models.py:21"}],[2599,{"filePath":"tests/model-metrics/test-all-models.py","startLine":234,"endLine":243,"chunkText":"\n            # If not a standard model, skip museval evaluation and just return speed metrics\n            if not standard_model:\n                logger.info(f\"Non-standard stem configuration detected for model {test_model}, skipping museval evaluation\")\n\n                # Store the speed metric in the combined results\n                if test_model not in museval_results:\n                    museval_results[test_model] = {}\n\n                # Create a minimal structure for the speed metric","chunkId":"tests/model-metrics/test-all-models.py:22"}],[2600,{"filePath":"tests/model-metrics/test-all-models.py","startLine":241,"endLine":252,"chunkText":"                    museval_results[test_model] = {}\n\n                # Create a minimal structure for the speed metric\n                minimal_results = {\"targets\": [{\"name\": \"speed_metrics_only\", \"metrics\": {\"seconds_per_minute_m3\": round(seconds_per_minute, 1)}}]}\n\n                museval_results[test_model][track_name] = minimal_results\n                save_combined_results(museval_results)\n\n                return None, basic_model_results\n\n            # For standard models, proceed with museval evaluation\n            available_stems = {}","chunkId":"tests/model-metrics/test-all-models.py:23"}],[2601,{"filePath":"tests/model-metrics/test-all-models.py","startLine":250,"endLine":262,"chunkText":"\n            # For standard models, proceed with museval evaluation\n            available_stems = {}\n            available_stems[\"vocals\"] = os.path.join(temp_dir, \"vocals.wav\")\n            available_stems[\"accompaniment\"] = os.path.join(temp_dir, \"instrumental.wav\")\n\n            # Get track from MUSDB\n            track = next((t for t in mus_db if t.name == track_name), None)\n            if track is None:\n                raise ValueError(f\"Track {track_name} not found in MUSDB18\")\n\n            # Load available stems\n            estimates = {}","chunkId":"tests/model-metrics/test-all-models.py:24"}],[2602,{"filePath":"tests/model-metrics/test-all-models.py","startLine":260,"endLine":274,"chunkText":"\n            # Load available stems\n            estimates = {}\n            for stem_name, stem_path in available_stems.items():\n                audio, _ = sf.read(stem_path)\n                if len(audio.shape) == 1:\n                    audio = np.expand_dims(audio, axis=1)\n                estimates[stem_name] = audio\n\n            # Evaluate using museval\n            logger.info(f\"Evaluating stems: {list(estimates.keys())}\")\n            try:\n                scores = museval.eval_mus_track(track, estimates, output_dir=temp_dir, mode=\"v4\")\n\n                # Add the speed metric to the scores","chunkId":"tests/model-metrics/test-all-models.py:25"}],[2603,{"filePath":"tests/model-metrics/test-all-models.py","startLine":272,"endLine":282,"chunkText":"                scores = museval.eval_mus_track(track, estimates, output_dir=temp_dir, mode=\"v4\")\n\n                # Add the speed metric to the scores\n                if not hasattr(scores, \"speed_metric_added\"):\n                    for target in scores.scores[\"targets\"]:\n                        if \"metrics\" not in target:\n                            target[\"metrics\"] = {}\n                        target[\"metrics\"][\"seconds_per_minute_m3\"] = round(seconds_per_minute, 1)\n                    scores.speed_metric_added = True\n\n                # Update the combined results file with the new evaluation","chunkId":"tests/model-metrics/test-all-models.py:26"}],[2604,{"filePath":"tests/model-metrics/test-all-models.py","startLine":280,"endLine":290,"chunkText":"                    scores.speed_metric_added = True\n\n                # Update the combined results file with the new evaluation\n                if test_model not in museval_results:\n                    museval_results[test_model] = {}\n                museval_results[test_model][track_name] = scores.scores\n                save_combined_results(museval_results)\n            except Exception as e:\n                logger.error(f\"Error during museval evaluation: {str(e)}\")\n                logger.exception(\"Evaluation exception details:\")\n                # Return basic results with just the speed metric","chunkId":"tests/model-metrics/test-all-models.py:27"}],[2605,{"filePath":"tests/model-metrics/test-all-models.py","startLine":288,"endLine":299,"chunkText":"                logger.error(f\"Error during museval evaluation: {str(e)}\")\n                logger.exception(\"Evaluation exception details:\")\n                # Return basic results with just the speed metric\n                return None, basic_model_results\n\n    try:\n        # Only process museval results if we have them\n        if \"scores\" in locals() and scores is not None:\n            # Calculate aggregate scores for available stems\n            results_store = museval.EvalStore()\n            results_store.add_track(scores.df)\n            methods = museval.MethodStore()","chunkId":"tests/model-metrics/test-all-models.py:28"}],[2606,{"filePath":"tests/model-metrics/test-all-models.py","startLine":297,"endLine":307,"chunkText":"            results_store = museval.EvalStore()\n            results_store.add_track(scores.df)\n            methods = museval.MethodStore()\n            methods.add_evalstore(results_store, name=test_model)\n            agg_scores = methods.agg_frames_tracks_scores()\n\n            # Return the aggregate scores in a structured format with 6 significant figures\n            model_results = {\"track_name\": track_name, \"scores\": {}}\n\n            for stem in [\"vocals\", \"drums\", \"bass\", \"other\", \"accompaniment\"]:\n                try:","chunkId":"tests/model-metrics/test-all-models.py:29"}],[2607,{"filePath":"tests/model-metrics/test-all-models.py","startLine":305,"endLine":314,"chunkText":"\n            for stem in [\"vocals\", \"drums\", \"bass\", \"other\", \"accompaniment\"]:\n                try:\n                    stem_scores = {metric: float(f\"{agg_scores.loc[(test_model, stem, metric)]:.6g}\") for metric in [\"SDR\", \"SIR\", \"SAR\", \"ISR\"]}\n                    # Rename 'accompaniment' to 'instrumental' in the output\n                    output_stem = \"instrumental\" if stem == \"accompaniment\" else stem\n                    model_results[\"scores\"][output_stem] = stem_scores\n                except KeyError:\n                    continue\n","chunkId":"tests/model-metrics/test-all-models.py:30"}],[2608,{"filePath":"tests/model-metrics/test-all-models.py","startLine":312,"endLine":324,"chunkText":"                except KeyError:\n                    continue\n\n            # Add the seconds_per_minute_m3 metric if it was calculated\n            if processing_time > 0 and track_duration_minutes > 0:\n                model_results[\"scores\"][\"seconds_per_minute_m3\"] = round(seconds_per_minute, 1)\n\n            return scores, model_results if model_results[\"scores\"] else basic_model_results\n        else:\n            # If we don't have scores, just return the basic results with speed metrics\n            return None, basic_model_results\n\n    except Exception as e:","chunkId":"tests/model-metrics/test-all-models.py:31"}],[2609,{"filePath":"tests/model-metrics/test-all-models.py","startLine":322,"endLine":336,"chunkText":"            return None, basic_model_results\n\n    except Exception as e:\n        logger.error(f\"Error processing evaluation results: {str(e)}\")\n        logger.exception(\"Results processing exception details:\")\n        # Return basic results with just the speed metric\n        return None, basic_model_results\n\n\ndef convert_decimal_to_float(obj):\n    \"\"\"Recursively converts Decimal objects to floats in a nested structure.\"\"\"\n    if isinstance(obj, Decimal):\n        return float(obj)\n    elif isinstance(obj, dict):\n        return {k: convert_decimal_to_float(v) for k, v in obj.items()}","chunkId":"tests/model-metrics/test-all-models.py:32"}],[2610,{"filePath":"tests/model-metrics/test-all-models.py","startLine":334,"endLine":347,"chunkText":"        return float(obj)\n    elif isinstance(obj, dict):\n        return {k: convert_decimal_to_float(v) for k, v in obj.items()}\n    elif isinstance(obj, list):\n        return [convert_decimal_to_float(x) for x in obj]\n    return obj\n\n\ndef calculate_median_scores(track_scores):\n    \"\"\"Calculate median scores across all tracks for each stem and metric\"\"\"\n    # Initialize containers for each stem's metrics\n    stem_metrics = {\n        \"vocals\": {\"SDR\": [], \"SIR\": [], \"SAR\": [], \"ISR\": []},\n        \"drums\": {\"SDR\": [], \"SIR\": [], \"SAR\": [], \"ISR\": []},","chunkId":"tests/model-metrics/test-all-models.py:33"}],[2611,{"filePath":"tests/model-metrics/test-all-models.py","startLine":345,"endLine":357,"chunkText":"    stem_metrics = {\n        \"vocals\": {\"SDR\": [], \"SIR\": [], \"SAR\": [], \"ISR\": []},\n        \"drums\": {\"SDR\": [], \"SIR\": [], \"SAR\": [], \"ISR\": []},\n        \"bass\": {\"SDR\": [], \"SIR\": [], \"SAR\": [], \"ISR\": []},\n        \"instrumental\": {\"SDR\": [], \"SIR\": [], \"SAR\": [], \"ISR\": []},\n        \"seconds_per_minute_m3\": [],\n    }\n\n    # Collect all scores for each stem and metric\n    for track_score in track_scores:\n        if track_score is not None and \"scores\" in track_score:\n            # Process audio quality metrics\n            for stem, metrics in track_score[\"scores\"].items():","chunkId":"tests/model-metrics/test-all-models.py:34"}],[2612,{"filePath":"tests/model-metrics/test-all-models.py","startLine":355,"endLine":365,"chunkText":"        if track_score is not None and \"scores\" in track_score:\n            # Process audio quality metrics\n            for stem, metrics in track_score[\"scores\"].items():\n                if stem in stem_metrics and stem != \"seconds_per_minute_m3\":\n                    for metric, value in metrics.items():\n                        stem_metrics[stem][metric].append(value)\n\n            # Process speed metric separately\n            if \"seconds_per_minute_m3\" in track_score[\"scores\"]:\n                stem_metrics[\"seconds_per_minute_m3\"].append(track_score[\"scores\"][\"seconds_per_minute_m3\"])\n","chunkId":"tests/model-metrics/test-all-models.py:35"}],[2613,{"filePath":"tests/model-metrics/test-all-models.py","startLine":363,"endLine":372,"chunkText":"            if \"seconds_per_minute_m3\" in track_score[\"scores\"]:\n                stem_metrics[\"seconds_per_minute_m3\"].append(track_score[\"scores\"][\"seconds_per_minute_m3\"])\n\n    # Calculate medians for each stem and metric\n    median_scores = {}\n    for stem, metrics in stem_metrics.items():\n        if stem != \"seconds_per_minute_m3\" and any(metrics.values()):  # Only include stems that have scores\n            median_scores[stem] = {metric: float(f\"{np.median(values):.6g}\") for metric, values in metrics.items() if values}  # Only include metrics that have values\n\n    # Add median speed metric if availabl","chunkId":"tests/model-metrics/test-all-models.py:36"}],[2614,{"filePath":"tests/model-metrics/test-all-models.py","startLine":370,"endLine":384,"chunkText":"            median_scores[stem] = {metric: float(f\"{np.median(values):.6g}\") for metric, values in metrics.items() if values}  # Only include metrics that have values\n\n    # Add median speed metric if available\n    if stem_metrics[\"seconds_per_minute_m3\"]:\n        median_scores[\"seconds_per_minute_m3\"] = round(np.median(stem_metrics[\"seconds_per_minute_m3\"]), 1)\n\n    return median_scores\n\n\ndef check_disk_usage(path):\n    \"\"\"Check inode usage and disk space on the filesystem containing path\"\"\"\n    import subprocess\n    import sys\n\n    # Check disk space first","chunkId":"tests/model-metrics/test-all-models.py:37"}],[2615,{"filePath":"tests/model-metrics/test-all-models.py","startLine":382,"endLine":398,"chunkText":"    import sys\n\n    # Check disk space first\n    result = subprocess.run([\"df\", \"-h\", path], capture_output=True, text=True)\n    output = result.stdout\n    logger.info(f\"Current disk usage:\\n{output}\")\n\n    # Parse the output to get disk usage percentage\n    lines = output.strip().split(\"\\n\")\n    if len(lines) >= 2:\n        parts = lines[1].split()\n        if len(parts) >= 5:\n            try:\n                # Extract disk usage percentage\n                disk_usage_str = parts[4].rstrip(\"%\")\n                disk_usage_pct = int(disk_usage_str)\n","chunkId":"tests/model-metrics/test-all-models.py:38"}],[2616,{"filePath":"tests/model-metrics/test-all-models.py","startLine":396,"endLine":407,"chunkText":"                disk_usage_str = parts[4].rstrip(\"%\")\n                disk_usage_pct = int(disk_usage_str)\n\n                logger.info(f\"Disk usage: {disk_usage_pct}%\")\n\n                if disk_usage_pct >= 99:\n                    logger.critical(\"CRITICAL: Disk is almost full (>99%)! Cannot continue processing.\")\n                    logger.critical(\"Please free up disk space before continuing.\")\n                    sys.exit(1)\n                elif disk_usage_pct > 95:\n                    logger.warning(f\"WARNING: High disk usage ({disk_usage_pct}%)!\")\n            except (ValueError, IndexError) as e:","chunkId":"tests/model-metrics/test-all-models.py:39"}],[2617,{"filePath":"tests/model-metrics/test-all-models.py","startLine":405,"endLine":418,"chunkText":"                elif disk_usage_pct > 95:\n                    logger.warning(f\"WARNING: High disk usage ({disk_usage_pct}%)!\")\n            except (ValueError, IndexError) as e:\n                logger.error(f\"Error parsing disk usage: {str(e)}\")\n\n    # Now check inode usage\n    result = subprocess.run([\"df\", \"-i\", path], capture_output=True, text=True)\n    output = result.stdout\n    logger.info(f\"Current inode usage:\\n{output}\")\n\n    # Parse the output to get inode usage percentage\n    lines = output.strip().split(\"\\n\")\n    if len(lines) >= 2:\n        # The second line contains the actual data","chunkId":"tests/model-metrics/test-all-models.py:40"}],[2618,{"filePath":"tests/model-metrics/test-all-models.py","startLine":416,"endLine":428,"chunkText":"    lines = output.strip().split(\"\\n\")\n    if len(lines) >= 2:\n        # The second line contains the actual data\n        parts = lines[1].split()\n        if len(parts) >= 8:  # macOS df -i format has 8 columns\n            try:\n                # On macOS, inode usage is in the 8th column as a percentage\n                inode_usage_str = parts[7].rstrip(\"%\")\n                inode_usage_pct = int(inode_usage_str)\n\n                # Also extract the actual inode numbers for better reporting\n                iused = int(parts[5])\n                ifree = int(parts[6])","chunkId":"tests/model-metrics/test-all-models.py:41"}],[2619,{"filePath":"tests/model-metrics/test-all-models.py","startLine":426,"endLine":438,"chunkText":"                # Also extract the actual inode numbers for better reporting\n                iused = int(parts[5])\n                ifree = int(parts[6])\n                total_inodes = iused + ifree\n\n                # Skip inode check for exFAT or similar filesystems\n                if total_inodes <= 1:\n                    logger.info(\"Filesystem appears to be exFAT or similar (no real inode tracking). Skipping inode check.\")\n                    return None\n\n                logger.info(f\"Inode usage: {iused:,}/{total_inodes:,} ({inode_usage_pct}%)\")\n\n                if inode_usage_pct >= 100:","chunkId":"tests/model-metrics/test-all-models.py:42"}],[2620,{"filePath":"tests/model-metrics/test-all-models.py","startLine":436,"endLine":446,"chunkText":"                logger.info(f\"Inode usage: {iused:,}/{total_inodes:,} ({inode_usage_pct}%)\")\n\n                if inode_usage_pct >= 100:\n                    logger.critical(\"CRITICAL: Inode usage is at 100%! Cannot continue processing.\")\n                    logger.critical(\"Please free up inodes before continuing.\")\n                    sys.exit(1)\n                elif inode_usage_pct > 90:\n                    logger.warning(f\"WARNING: High inode usage ({inode_usage_pct}%)!\")\n\n                return inode_usage_pct\n            except (ValueError, IndexError) as e:","chunkId":"tests/model-metrics/test-all-models.py:43"}],[2621,{"filePath":"tests/model-metrics/test-all-models.py","startLine":444,"endLine":463,"chunkText":"\n                return inode_usage_pct\n            except (ValueError, IndexError) as e:\n                logger.error(f\"Error parsing inode usage: {str(e)}\")\n\n    return None\n\n\ndef get_evaluated_track_count(model_name, museval_results):\n    \"\"\"Get the number of tracks evaluated for a specific model\"\"\"\n    if model_name in museval_results:\n        return len(museval_results[model_name])\n    return 0\n\n\ndef get_most_evaluated_tracks(museval_results, min_count=10):\n    \"\"\"Get tracks that have been evaluated for the most models\"\"\"\n    track_counts = {}\n\n    # Count how many models have evaluated each track","chunkId":"tests/model-metrics/test-all-models.py:44"}],[2622,{"filePath":"tests/model-metrics/test-all-models.py","startLine":461,"endLine":476,"chunkText":"    track_counts = {}\n\n    # Count how many models have evaluated each track\n    for model_name, tracks in museval_results.items():\n        for track_name in tracks:\n            if track_name not in track_counts:\n                track_counts[track_name] = 0\n            track_counts[track_name] += 1\n\n    # Sort tracks by evaluation count (descending)\n    sorted_tracks = sorted(track_counts.items(), key=lambda x: x[1], reverse=True)\n\n    # Return tracks that have been evaluated at least min_count times\n    return [track for track, count in sorted_tracks if count >= min_count]\n\n","chunkId":"tests/model-metrics/test-all-models.py:45"}],[2623,{"filePath":"tests/model-metrics/test-all-models.py","startLine":475,"endLine":490,"chunkText":"\n\ndef generate_summary_statistics(\n    start_time, models_processed, tracks_processed, models_with_new_data, tracks_evaluated, total_processing_time, fastest_model=None, slowest_model=None, combined_results_path=None, is_dry_run=False\n):\n    \"\"\"Generate a summary of the script's execution\"\"\"\n    end_time = time.time()\n    total_runtime = end_time - start_time\n\n    # Format the runtime\n    hours, remainder = divmod(total_runtime, 3600)\n    minutes, seconds = divmod(remainder, 60)\n    runtime_str = f\"{int(hours):02d}:{int(minutes):02d}:{int(seconds):02d}\"\n\n    # Build the summary\n    summary = [","chunkId":"tests/model-metrics/test-all-models.py:46"}],[2624,{"filePath":"tests/model-metrics/test-all-models.py","startLine":488,"endLine":498,"chunkText":"\n    # Build the summary\n    summary = [\n        \"=\" * 80,\n        \"DRY RUN SUMMARY - PREVIEW ONLY\" if is_dry_run else \"EXECUTION SUMMARY\",\n        \"=\" * 80,\n        f\"Total runtime: {runtime_str}\",\n        f\"Models {'that would be' if is_dry_run else ''} processed: {models_processed}\",\n        f\"Models {'that would receive' if is_dry_run else 'with'} new data: {len(models_with_new_data)}\",\n        f\"Total tracks {'that would be' if is_dry_run else ''} evaluated: {tracks_evaluated}\",\n        f\"Average tracks per model: {tracks_evaluated / len(models_with_new_data) if models_with_new_data else 0:.2f}\",","chunkId":"tests/model-metrics/test-all-models.py:47"}],[2625,{"filePath":"tests/model-metrics/test-all-models.py","startLine":496,"endLine":506,"chunkText":"        f\"Models {'that would receive' if is_dry_run else 'with'} new data: {len(models_with_new_data)}\",\n        f\"Total tracks {'that would be' if is_dry_run else ''} evaluated: {tracks_evaluated}\",\n        f\"Average tracks per model: {tracks_evaluated / len(models_with_new_data) if models_with_new_data else 0:.2f}\",\n    ]\n\n    if fastest_model:\n        summary.append(f\"Fastest model: {fastest_model['name']} ({fastest_model['speed']:.2f} seconds per minute)\")\n\n    if slowest_model:\n        summary.append(f\"Slowest model: {slowest_model['name']} ({slowest_model['speed']:.2f} seconds per minute)\")\n","chunkId":"tests/model-metrics/test-all-models.py:48"}],[2626,{"filePath":"tests/model-metrics/test-all-models.py","startLine":504,"endLine":515,"chunkText":"    if slowest_model:\n        summary.append(f\"Slowest model: {slowest_model['name']} ({slowest_model['speed']:.2f} seconds per minute)\")\n\n    if total_processing_time > 0:\n        summary.append(f\"Total audio processing time: {total_processing_time:.2f} seconds\")\n\n    if combined_results_path and os.path.exists(combined_results_path):\n        file_size = os.path.getsize(combined_results_path) / (1024 * 1024)  # Size in MB\n        summary.append(f\"Results file size: {file_size:.2f} MB\")\n\n    # Add models with new data\n    if models_with_new_data:","chunkId":"tests/model-metrics/test-all-models.py:49"}],[2627,{"filePath":"tests/model-metrics/test-all-models.py","startLine":513,"endLine":529,"chunkText":"\n    # Add models with new data\n    if models_with_new_data:\n        summary.append(f\"\\nModels {'that would receive' if is_dry_run else 'with'} new evaluation data:\")\n        for model_name in models_with_new_data:\n            summary.append(f\"- {model_name}\")\n\n    # Add dry run disclaimer if needed\n    if is_dry_run:\n        summary.append(\"\\nNOTE: This is a dry run summary. No actual changes were made.\")\n        summary.append(\"Run without --dry-run to perform actual evaluations.\")\n\n    summary.append(\"=\" * 80)\n    return \"\\n\".join(summary)\n\n\ndef check_stop_signal():","chunkId":"tests/model-metrics/test-all-models.py:50"}],[2628,{"filePath":"tests/model-metrics/test-all-models.py","startLine":527,"endLine":541,"chunkText":"\n\ndef check_stop_signal():\n    \"\"\"Check if the stop signal file exists\"\"\"\n    if os.path.exists(STOP_SIGNAL_PATH):\n        logger.info(\"Stop signal detected at: \" + STOP_SIGNAL_PATH)\n        return True\n    return False\n\n\ndef main():\n    # Add command line argument parsing for dry run mode\n    parser = argparse.ArgumentParser(description=\"Run model evaluation on MUSDB18 dataset\")\n    parser.add_argument(\"--dry-run\", action=\"store_true\", help=\"Run in dry-run mode (no writes)\")\n    parser.add_argument(\"--max-tracks\", type=int, default=10, help=\"Maximum number of tracks to evaluate per model\")","chunkId":"tests/model-metrics/test-all-models.py:51"}],[2629,{"filePath":"tests/model-metrics/test-all-models.py","startLine":539,"endLine":547,"chunkText":"    parser = argparse.ArgumentParser(description=\"Run model evaluation on MUSDB18 dataset\")\n    parser.add_argument(\"--dry-run\", action=\"store_true\", help=\"Run in dry-run mode (no writes)\")\n    parser.add_argument(\"--max-tracks\", type=int, default=10, help=\"Maximum number of tracks to evaluate per model\")\n    parser.add_argument(\"--max-models\", type=int, default=None, help=\"Maximum number of models to evaluate\")\n    args = parser.parse_args()\n\n    # Remove any existing stop signal file at start\n    if os.path.exists(STOP_SIGNAL_PATH):\n        os.remove(STOP_SIGNAL_PATH)","chunkId":"tests/model-metrics/test-all-models.py:52"}],[2630,{"filePath":"tests/model-metrics/test-all-models.py","startLine":545,"endLine":560,"chunkText":"    # Remove any existing stop signal file at start\n    if os.path.exists(STOP_SIGNAL_PATH):\n        os.remove(STOP_SIGNAL_PATH)\n        logger.info(\"Removed existing stop signal file\")\n\n    # Track start time for progress reporting\n    start_time = time.time()\n\n    # Statistics tracking\n    models_processed = 0\n    tracks_processed = 0\n    models_with_new_data = set()\n    total_processing_time = 0\n    fastest_model = {\"name\": \"\", \"speed\": float(\"inf\")}  # Initialize with infinity for comparison\n    slowest_model = {\"name\": \"\", \"speed\": 0}  # Initialize with zero for comparison\n","chunkId":"tests/model-metrics/test-all-models.py:53"}],[2631,{"filePath":"tests/model-metrics/test-all-models.py","startLine":558,"endLine":561,"chunkText":"    fastest_model = {\"name\": \"\", \"speed\": float(\"inf\")}  # Initialize with infinity for comparison\n    slowest_model = {\"name\": \"\", \"speed\": 0}  # Initialize with zero for comparison\n\n    # Create a results cache manager","chunkId":"tests/model-metrics/test-all-models.py:54"}],[2632,{"filePath":"tests/model-metrics/test-all-models.py","startLine":562,"endLine":577,"chunkText":"    class ResultsCache:\n        def __init__(self):\n            self.results = load_combined_results()\n            self.last_update_time = time.time()\n\n        def get_results(self, force=False):\n            current_time = time.time()\n            # Only reload from disk every 5 minutes unless forced\n            if force or (current_time - self.last_update_time) > 300:\n                self.results = load_combined_results()\n                self.last_update_time = current_time\n            return self.results\n\n    results_cache = ResultsCache()\n\n    # Helper function for logging with elapsed time","chunkId":"tests/model-metrics/test-all-models.py:55"}],[2633,{"filePath":"tests/model-metrics/test-all-models.py","startLine":575,"endLine":588,"chunkText":"    results_cache = ResultsCache()\n\n    # Helper function for logging with elapsed time\n    def log_with_time(message, level=logging.INFO):\n        elapsed = time.time() - start_time\n        hours, remainder = divmod(elapsed, 3600)\n        minutes, seconds = divmod(remainder, 60)\n        time_str = f\"{int(hours):02d}:{int(minutes):02d}:{int(seconds):02d}\"\n        logger.log(level, f\"[{time_str}] {message}\")\n\n    if args.dry_run:\n        log_with_time(\"*** RUNNING IN DRY-RUN MODE - NO DATA WILL BE MODIFIED ***\")\n\n    log_with_time(\"Starting model evaluation script...\")","chunkId":"tests/model-metrics/test-all-models.py:56"}],[2634,{"filePath":"tests/model-metrics/test-all-models.py","startLine":586,"endLine":601,"chunkText":"        log_with_time(\"*** RUNNING IN DRY-RUN MODE - NO DATA WILL BE MODIFIED ***\")\n\n    log_with_time(\"Starting model evaluation script...\")\n    os.makedirs(RESULTS_PATH, exist_ok=True)\n\n    # Check disk space and inode usage at start\n    check_disk_usage(RESULTS_PATH)\n\n    # Load existing results if available\n    combined_results = {}\n    if os.path.exists(COMBINED_RESULTS_PATH):\n        log_with_time(\"Loading existing combined results...\")\n        with open(COMBINED_RESULTS_PATH) as f:\n            combined_results = json.load(f)\n\n    # Get initial museval results","chunkId":"tests/model-metrics/test-all-models.py:57"}],[2635,{"filePath":"tests/model-metrics/test-all-models.py","startLine":599,"endLine":614,"chunkText":"            combined_results = json.load(f)\n\n    # Get initial museval results\n    museval_results = results_cache.get_results()\n    log_with_time(f\"Loaded combined museval results with {len(museval_results)} models\")\n\n    # Get the most commonly evaluated tracks\n    common_tracks = get_most_evaluated_tracks(museval_results)\n    log_with_time(f\"Found {len(common_tracks)} commonly evaluated tracks\")\n\n    # Initialize MUSDB\n    log_with_time(\"Initializing MUSDB database...\")\n    mus = musdb.DB(root=MUSDB_PATH, is_wav=True)\n\n    # Create a prioritized list of tracks\n    all_tracks = []","chunkId":"tests/model-metrics/test-all-models.py:58"}],[2636,{"filePath":"tests/model-metrics/test-all-models.py","startLine":612,"endLine":627,"chunkText":"\n    # Create a prioritized list of tracks\n    all_tracks = []\n    for track in mus.tracks:\n        # Check if this is a commonly evaluated track\n        is_common = track.name in common_tracks\n        all_tracks.append({\"name\": track.name, \"path\": os.path.dirname(track.path), \"is_common\": is_common})\n\n    # Sort tracks by whether they're commonly evaluated\n    all_tracks.sort(key=lambda t: 0 if t[\"is_common\"] else 1)\n\n    # Get list of all available models\n    log_with_time(\"Getting list of available models...\")\n    separator = Separator()\n    models_by_type = separator.list_supported_model_files()\n","chunkId":"tests/model-metrics/test-all-models.py:59"}],[2637,{"filePath":"tests/model-metrics/test-all-models.py","startLine":625,"endLine":637,"chunkText":"    separator = Separator()\n    models_by_type = separator.list_supported_model_files()\n\n    # Flatten the models list and prioritize them\n    all_models = []\n    for model_type, models in models_by_type.items():\n        for model_name, model_info in models.items():\n            filename = model_info.get(\"filename\")\n            if filename:\n                # Count how many tracks have been evaluated for this model\n                evaluated_count = get_evaluated_track_count(filename, museval_results)\n\n                # Determine if this is a roformer model","chunkId":"tests/model-metrics/test-all-models.py:60"}],[2638,{"filePath":"tests/model-metrics/test-all-models.py","startLine":635,"endLine":645,"chunkText":"                evaluated_count = get_evaluated_track_count(filename, museval_results)\n\n                # Determine if this is a roformer model\n                is_roformer = \"roformer\" in model_name.lower()\n\n                # Add to the list with priority information\n                all_models.append({\"name\": model_name, \"filename\": filename, \"type\": model_type, \"info\": model_info, \"evaluated_count\": evaluated_count, \"is_roformer\": is_roformer})\n\n    # Sort models by priority:\n    # 1. Roformer models with fewer than max_tracks evaluations\n    # 2. Other models with fewer than max_tracks evaluations","chunkId":"tests/model-metrics/test-all-models.py:61"}],[2639,{"filePath":"tests/model-metrics/test-all-models.py","startLine":643,"endLine":654,"chunkText":"    # Sort models by priority:\n    # 1. Roformer models with fewer than max_tracks evaluations\n    # 2. Other models with fewer than max_tracks evaluations\n    # 3. Roformer models with more evaluations\n    # 4. Other models with more evaluations\n    all_models.sort(\n        key=lambda m: (\n            0 if m[\"is_roformer\"] and m[\"evaluated_count\"] < args.max_tracks else 1 if not m[\"is_roformer\"] and m[\"evaluated_count\"] < args.max_tracks else 2 if m[\"is_roformer\"] else 3,\n            m[\"evaluated_count\"],  # Secondary sort by number of evaluations (ascending)\n        )\n    )\n","chunkId":"tests/model-metrics/test-all-models.py:62"}],[2640,{"filePath":"tests/model-metrics/test-all-models.py","startLine":652,"endLine":665,"chunkText":"        )\n    )\n\n    # Log the prioritized models\n    log_with_time(f\"Prioritized {len(all_models)} models for evaluation:\")\n    for i, model in enumerate(all_models[:10]):  # Show top 10\n        log_with_time(f\"{i+1}. {model['name']} ({model['filename']}) - {model['evaluated_count']} tracks evaluated, roformer: {model['is_roformer']}\")\n\n    if len(all_models) > 10:\n        log_with_time(f\"... and {len(all_models) - 10} more models\")\n\n    # Limit the number of models if specified\n    if args.max_models:\n        all_models = all_models[: args.max_models]","chunkId":"tests/model-metrics/test-all-models.py:63"}],[2641,{"filePath":"tests/model-metrics/test-all-models.py","startLine":663,"endLine":677,"chunkText":"    # Limit the number of models if specified\n    if args.max_models:\n        all_models = all_models[: args.max_models]\n        log_with_time(f\"Limited to {args.max_models} models for this run\")\n\n    # Process models according to priority\n    model_idx = 0\n    stop_requested = False\n    while model_idx < len(all_models):\n        # Check for stop signal before processing each model\n        if check_stop_signal():\n            log_with_time(\"Stop signal detected. Will finish current model's tracks and then exit.\")\n            stop_requested = True\n\n        model = all_models[model_idx]","chunkId":"tests/model-metrics/test-all-models.py:64"}],[2642,{"filePath":"tests/model-metrics/test-all-models.py","startLine":675,"endLine":687,"chunkText":"            stop_requested = True\n\n        model = all_models[model_idx]\n        model_name = model[\"name\"]\n        model_filename = model[\"filename\"]\n        model_type = model[\"type\"]\n\n        progress_pct = (model_idx + 1) / len(all_models) * 100\n        log_with_time(f\"\\n=== Processing model {model_idx+1}/{len(all_models)} ({progress_pct:.1f}%): {model_name} ({model_filename}) ===\")\n\n        # Initialize model entry if it doesn't exist\n        if model_filename not in combined_results:\n            log_with_time(f\"Initializing new entry for {model_filename}\")","chunkId":"tests/model-metrics/test-all-models.py:65"}],[2643,{"filePath":"tests/model-metrics/test-all-models.py","startLine":685,"endLine":695,"chunkText":"        # Initialize model entry if it doesn't exist\n        if model_filename not in combined_results:\n            log_with_time(f\"Initializing new entry for {model_filename}\")\n            combined_results[model_filename] = {\"model_name\": model_name, \"track_scores\": [], \"median_scores\": {}, \"stems\": [], \"target_stem\": None}\n\n        # Try to load the model to get stem information\n        try:\n            separator.load_model(model_filename=model_filename)\n            model_data = separator.model_instance.model_data\n\n            # Extract stem information (similar to your existing code)","chunkId":"tests/model-metrics/test-all-models.py:66"}],[2644,{"filePath":"tests/model-metrics/test-all-models.py","startLine":693,"endLine":705,"chunkText":"            model_data = separator.model_instance.model_data\n\n            # Extract stem information (similar to your existing code)\n            # ... (keep your existing stem extraction logic here)\n\n        except Exception as e:\n            log_with_time(f\"Error loading model {model_filename}: {str(e)}\", logging.ERROR)\n            logger.exception(\"Full exception details:\")\n            model_idx += 1\n            continue\n\n        # Count how many tracks have been evaluated for this model\n        # Use the cached results","chunkId":"tests/model-metrics/test-all-models.py:67"}],[2645,{"filePath":"tests/model-metrics/test-all-models.py","startLine":703,"endLine":715,"chunkText":"\n        # Count how many tracks have been evaluated for this model\n        # Use the cached results\n        evaluated_count = get_evaluated_track_count(model_filename, results_cache.get_results())\n\n        # Determine how many more tracks to evaluate\n        tracks_to_evaluate = max(0, args.max_tracks - evaluated_count)\n\n        if tracks_to_evaluate == 0:\n            log_with_time(f\"Model {model_name} already has {evaluated_count} tracks evaluated (>= {args.max_tracks}). Skipping.\")\n            model_idx += 1\n            continue\n","chunkId":"tests/model-metrics/test-all-models.py:68"}],[2646,{"filePath":"tests/model-metrics/test-all-models.py","startLine":713,"endLine":726,"chunkText":"            model_idx += 1\n            continue\n\n        log_with_time(f\"Will evaluate up to {tracks_to_evaluate} tracks for model {model_name}\")\n\n        # Process tracks for this model\n        tracks_processed = 0\n        for track in all_tracks:\n            # Check for stop signal before each track if we haven't already detected it\n            if not stop_requested and check_stop_signal():\n                log_with_time(\"Stop signal detected. Will finish current track and then exit.\")\n                stop_requested = True\n\n            # Skip if we've processed enough tracks for this model","chunkId":"tests/model-metrics/test-all-models.py:69"}],[2647,{"filePath":"tests/model-metrics/test-all-models.py","startLine":724,"endLine":736,"chunkText":"                stop_requested = True\n\n            # Skip if we've processed enough tracks for this model\n            if tracks_processed >= tracks_to_evaluate:\n                break\n\n            track_name = track[\"name\"]\n            track_path = track[\"path\"]\n\n            # Skip if track already evaluated for this model\n            # Use the cached results\n            if model_filename in results_cache.get_results() and track_name in results_cache.get_results()[model_filename]:\n                log_with_time(f\"Skipping already evaluated track {track_name} for model: {model_filename}\")","chunkId":"tests/model-metrics/test-all-models.py:70"}],[2648,{"filePath":"tests/model-metrics/test-all-models.py","startLine":736,"endLine":747,"chunkText":"                log_with_time(f\"Skipping already evaluated track {track_name} for model: {model_filename}\")\n                continue\n\n            log_with_time(f\"Processing track: {track_name} for model: {model_filename}\")\n\n            if args.dry_run:\n                log_with_time(f\"[DRY RUN] Would evaluate track {track_name} with model {model_filename}\")\n                tracks_processed += 1\n                models_with_new_data.add(model_filename)\n\n                # Estimate processing time based on model type for dry run\n                # This is a rough estimate - roformer models are typically slower","chunkId":"tests/model-metrics/test-all-models.py:71"}],[2649,{"filePath":"tests/model-metrics/test-all-models.py","startLine":745,"endLine":754,"chunkText":"\n                # Estimate processing time based on model type for dry run\n                # This is a rough estimate - roformer models are typically slower\n                estimated_speed = 30.0  # Default estimate: 30 seconds per minute\n                if \"roformer\" in model_name.lower():\n                    estimated_speed = 45.0  # Roformer models are typically slower\n                elif \"umx\" in model_name.lower():\n                    estimated_speed = 20.0  # UMX models are typically faster\n\n                # Update statistics with estimated values","chunkId":"tests/model-metrics/test-all-models.py:72"}],[2650,{"filePath":"tests/model-metrics/test-all-models.py","startLine":752,"endLine":765,"chunkText":"                    estimated_speed = 20.0  # UMX models are typically faster\n\n                # Update statistics with estimated values\n                total_processing_time += estimated_speed\n\n                # Track fastest and slowest models based on estimates\n                if estimated_speed < fastest_model[\"speed\"]:\n                    fastest_model = {\"name\": model_name, \"speed\": estimated_speed}\n                if estimated_speed > slowest_model[\"speed\"]:\n                    slowest_model = {\"name\": model_name, \"speed\": estimated_speed}\n\n                continue\n\n            try:","chunkId":"tests/model-metrics/test-all-models.py:73"}],[2651,{"filePath":"tests/model-metrics/test-all-models.py","startLine":763,"endLine":776,"chunkText":"                continue\n\n            try:\n                result = evaluate_track(track_name, track_path, model_filename, mus)\n\n                # Unpack the result safely\n                if result and isinstance(result, tuple) and len(result) == 2:\n                    _, model_results = result\n                else:\n                    model_results = None\n\n                # Process the results if they exist and are valid\n                if model_results is not None and isinstance(model_results, dict):\n                    combined_results[model_filename][\"track_scores\"].append(model_results)","chunkId":"tests/model-metrics/test-all-models.py:74"}],[2652,{"filePath":"tests/model-metrics/test-all-models.py","startLine":774,"endLine":783,"chunkText":"                # Process the results if they exist and are valid\n                if model_results is not None and isinstance(model_results, dict):\n                    combined_results[model_filename][\"track_scores\"].append(model_results)\n                    tracks_processed += 1\n                    models_with_new_data.add(model_filename)\n\n                    # Track processing time statistics - safely access nested dictionaries\n                    scores = model_results.get(\"scores\", {})\n                    if isinstance(scores, dict):\n                        speed = scores.get(\"seconds_per_minute_m3\")","chunkId":"tests/model-metrics/test-all-models.py:75"}],[2653,{"filePath":"tests/model-metrics/test-all-models.py","startLine":781,"endLine":790,"chunkText":"                    scores = model_results.get(\"scores\", {})\n                    if isinstance(scores, dict):\n                        speed = scores.get(\"seconds_per_minute_m3\")\n                        if speed is not None:\n                            total_processing_time += speed  # Accumulate total processing time\n\n                            # Track fastest and slowest models\n                            if speed < fastest_model[\"speed\"]:\n                                fastest_model = {\"name\": model_name, \"speed\": speed}\n                            if speed > slowest_model[\"speed\"]:","chunkId":"tests/model-metrics/test-all-models.py:76"}],[2654,{"filePath":"tests/model-metrics/test-all-models.py","startLine":788,"endLine":794,"chunkText":"                            if speed < fastest_model[\"speed\"]:\n                                fastest_model = {\"name\": model_name, \"speed\": speed}\n                            if speed > slowest_model[\"speed\"]:\n                                slowest_model = {\"name\": model_name, \"speed\": speed}\n                else:\n                    log_with_time(f\"Skipping model {model_filename} for track {track_name} due to no evaluatable stems or invalid results\")\n            except Exception as e:","chunkId":"tests/model-metrics/test-all-models.py:77"}],[2655,{"filePath":"tests/model-metrics/test-all-models.py","startLine":792,"endLine":800,"chunkText":"                else:\n                    log_with_time(f\"Skipping model {model_filename} for track {track_name} due to no evaluatable stems or invalid results\")\n            except Exception as e:\n                log_with_time(f\"Error evaluating model {model_filename} with track {track_name}: {str(e)}\", logging.ERROR)\n                logger.exception(f\"Exception details: \", exc_info=e)\n                continue\n\n            # Update and save results\n            if combined_results[model_filename][\"track_scores\"]:","chunkId":"tests/model-metrics/test-all-models.py:78"}],[2656,{"filePath":"tests/model-metrics/test-all-models.py","startLine":798,"endLine":808,"chunkText":"\n            # Update and save results\n            if combined_results[model_filename][\"track_scores\"]:\n                median_scores = calculate_median_scores(combined_results[model_filename][\"track_scores\"])\n                combined_results[model_filename][\"median_scores\"] = median_scores\n\n            # Save results after each track\n            if not args.dry_run:\n                os.makedirs(os.path.dirname(COMBINED_RESULTS_PATH), exist_ok=True)\n                with open(COMBINED_RESULTS_PATH, \"w\", encoding=\"utf-8\") as f:\n                    json.dump(combined_results, f, indent=2)","chunkId":"tests/model-metrics/test-all-models.py:79"}],[2657,{"filePath":"tests/model-metrics/test-all-models.py","startLine":806,"endLine":816,"chunkText":"                os.makedirs(os.path.dirname(COMBINED_RESULTS_PATH), exist_ok=True)\n                with open(COMBINED_RESULTS_PATH, \"w\", encoding=\"utf-8\") as f:\n                    json.dump(combined_results, f, indent=2)\n                log_with_time(f\"Updated combined results file with {model_filename} - {track_name}\")\n\n                # Force update the cache after saving\n                results_cache.get_results(force=True)\n            else:\n                log_with_time(f\"[DRY RUN] Would have updated combined results for {model_filename} - {track_name}\")\n\n            # Check disk space periodically","chunkId":"tests/model-metrics/test-all-models.py:80"}],[2658,{"filePath":"tests/model-metrics/test-all-models.py","startLine":814,"endLine":825,"chunkText":"                log_with_time(f\"[DRY RUN] Would have updated combined results for {model_filename} - {track_name}\")\n\n            # Check disk space periodically\n            check_disk_usage(RESULTS_PATH)\n\n        log_with_time(f\"Completed processing {tracks_processed} tracks for model {model_name}\")\n\n        # If stop was requested, exit after completing the current model\n        if stop_requested:\n            log_with_time(\"Stop signal processed. Generating final summary before exit.\")\n            break\n","chunkId":"tests/model-metrics/test-all-models.py:81"}],[2659,{"filePath":"tests/model-metrics/test-all-models.py","startLine":823,"endLine":832,"chunkText":"            log_with_time(\"Stop signal processed. Generating final summary before exit.\")\n            break\n\n        # If we're processing a non-roformer model, check if there are roformer models that need evaluation\n        if not model[\"is_roformer\"]:\n            # Find roformer models that still need more evaluations\n            # Use the cached results\n            roformer_models_needing_eval = []\n            for i, m in enumerate(all_models[model_idx + 1 :], start=model_idx + 1):\n                if m[\"is_roformer\"]:","chunkId":"tests/model-metrics/test-all-models.py:82"}],[2660,{"filePath":"tests/model-metrics/test-all-models.py","startLine":830,"endLine":839,"chunkText":"            roformer_models_needing_eval = []\n            for i, m in enumerate(all_models[model_idx + 1 :], start=model_idx + 1):\n                if m[\"is_roformer\"]:\n                    eval_count = get_evaluated_track_count(m[\"filename\"], results_cache.get_results())\n                    if eval_count < args.max_tracks:\n                        roformer_models_needing_eval.append((i, m))\n\n            if roformer_models_needing_eval:\n                log_with_time(f\"Found {len(roformer_models_needing_eval)} roformer models that still need evaluation. Reprioritizing...\")\n","chunkId":"tests/model-metrics/test-all-models.py:83"}],[2661,{"filePath":"tests/model-metrics/test-all-models.py","startLine":837,"endLine":846,"chunkText":"            if roformer_models_needing_eval:\n                log_with_time(f\"Found {len(roformer_models_needing_eval)} roformer models that still need evaluation. Reprioritizing...\")\n\n                # Move these models to the front of the remaining queue\n                for offset, (i, m) in enumerate(roformer_models_needing_eval):\n                    # Adjust index for models we've already moved\n                    adjusted_idx = i - offset\n                    # Move this model right after the current one\n                    all_models.insert(model_idx + 1, all_models.pop(adjusted_idx))\n","chunkId":"tests/model-metrics/test-all-models.py:84"}],[2662,{"filePath":"tests/model-metrics/test-all-models.py","startLine":844,"endLine":859,"chunkText":"                    # Move this model right after the current one\n                    all_models.insert(model_idx + 1, all_models.pop(adjusted_idx))\n\n                log_with_time(\"Reprioritization complete. Continuing with highest priority model.\")\n\n        # Move to the next model\n        model_idx += 1\n        models_processed += 1\n\n    log_with_time(\"Evaluation complete\")\n    # Final disk space check\n    check_disk_usage(RESULTS_PATH)\n\n    # Generate and display summary statistics\n    # Reset fastest/slowest models if they weren't updated\n    if fastest_model[\"speed\"] == float(\"inf\"):","chunkId":"tests/model-metrics/test-all-models.py:85"}],[2663,{"filePath":"tests/model-metrics/test-all-models.py","startLine":857,"endLine":871,"chunkText":"    # Generate and display summary statistics\n    # Reset fastest/slowest models if they weren't updated\n    if fastest_model[\"speed\"] == float(\"inf\"):\n        fastest_model = None\n    if slowest_model[\"speed\"] == 0:\n        slowest_model = None\n\n    summary = generate_summary_statistics(\n        start_time=start_time,\n        models_processed=models_processed,\n        tracks_processed=tracks_processed,\n        models_with_new_data=models_with_new_data,\n        tracks_evaluated=tracks_processed,\n        total_processing_time=total_processing_time,\n        fastest_model=fastest_model,","chunkId":"tests/model-metrics/test-all-models.py:86"}],[2664,{"filePath":"tests/model-metrics/test-all-models.py","startLine":869,"endLine":883,"chunkText":"        tracks_evaluated=tracks_processed,\n        total_processing_time=total_processing_time,\n        fastest_model=fastest_model,\n        slowest_model=slowest_model,\n        combined_results_path=COMBINED_RESULTS_PATH,\n        is_dry_run=args.dry_run,\n    )\n\n    log_with_time(\"\\n\" + summary)\n\n    # Also write summary to a log file\n    summary_filename = \"dry_run_summary.log\" if args.dry_run else \"evaluation_summary.log\"\n    if stop_requested:\n        summary_filename = \"stopped_\" + summary_filename\n    summary_log_path = os.path.join(os.path.dirname(COMBINED_RESULTS_PATH), summary_filename)","chunkId":"tests/model-metrics/test-all-models.py:87"}],[2665,{"filePath":"tests/model-metrics/test-all-models.py","startLine":881,"endLine":892,"chunkText":"    if stop_requested:\n        summary_filename = \"stopped_\" + summary_filename\n    summary_log_path = os.path.join(os.path.dirname(COMBINED_RESULTS_PATH), summary_filename)\n    with open(summary_log_path, \"w\") as f:\n        f.write(f\"{'Dry run' if args.dry_run else 'Evaluation'} {'(stopped early)' if stop_requested else ''} completed at: {time.strftime('%Y-%m-%d %H:%M:%S')}\\n\")\n        f.write(summary)\n\n    log_with_time(f\"Summary written to {summary_log_path}\")\n\n    # Clean up stop signal file if it exists\n    if os.path.exists(STOP_SIGNAL_PATH):\n        os.remove(STOP_SIGNAL_PATH)","chunkId":"tests/model-metrics/test-all-models.py:88"}],[2666,{"filePath":"tests/model-metrics/test-all-models.py","startLine":890,"endLine":900,"chunkText":"    # Clean up stop signal file if it exists\n    if os.path.exists(STOP_SIGNAL_PATH):\n        os.remove(STOP_SIGNAL_PATH)\n        log_with_time(\"Removed stop signal file\")\n\n    return 0 if not stop_requested else 2  # Return different exit code if stopped early\n\n\nif __name__ == \"__main__\":\n    exit(main())\n","chunkId":"tests/model-metrics/test-all-models.py:89"}],[2667,{"filePath":"tests/regression/test_roformer_size_mismatch.py","startLine":0,"endLine":20,"chunkText":"\"\"\"\nRegression tests for Roformer size mismatch issues.\nTests the handling of shorter outputs and broadcast errors in overlap_add operations.\n\"\"\"\n\nimport pytest\nimport torch\nimport numpy as np\nimport unittest\nfrom unittest.mock import Mock, patch, MagicMock\n\n\nclass TestRoformerSizeMismatch(unittest.TestCase):\n    \"\"\"Regression tests for size mismatch issues in Roformer processing.\"\"\"\n\n    def setUp(self):\n        \"\"\"Set up test fixtures.\"\"\"\n        self.batch_size = 2\n        self.channels = 2\n        self.sample_rate = 44100\n        ","chunkId":"tests/regression/test_roformer_size_mismatch.py:0"}],[2668,{"filePath":"tests/regression/test_roformer_size_mismatch.py","startLine":19,"endLine":31,"chunkText":"        self.sample_rate = 44100\n        \n        # Test cases with different output lengths that have caused issues\n        self.problematic_lengths = [1, 16, 32, 236, 512, 1024, 2048]\n\n    def test_overlap_add_safe_lengths(self):\n        \"\"\"T062: Reproduce shorter outputs (N∈{1,16,32,236}) and assert no broadcast errors, output length preserved.\"\"\"\n        \n        # Test each problematic length\n        for output_length in self.problematic_lengths:\n            with self.subTest(output_length=output_length):\n                self._test_single_output_length(output_length)\n","chunkId":"tests/regression/test_roformer_size_mismatch.py:1"}],[2669,{"filePath":"tests/regression/test_roformer_size_mismatch.py","startLine":29,"endLine":41,"chunkText":"            with self.subTest(output_length=output_length):\n                self._test_single_output_length(output_length)\n\n    def _test_single_output_length(self, output_length):\n        \"\"\"Test overlap_add with a specific output length.\"\"\"\n        # Setup test parameters\n        target_length = 10000  # Longer target buffer\n        chunk_size = 8192\n        step_size = 1024\n        \n        # Create mock model output with specific length\n        model_output = torch.randn(self.batch_size, self.channels, output_length)\n        target_buffer = torch.zeros(self.batch_size, self.channels, target_length)","chunkId":"tests/regression/test_roformer_size_mismatch.py:2"}],[2670,{"filePath":"tests/regression/test_roformer_size_mismatch.py","startLine":39,"endLine":48,"chunkText":"        # Create mock model output with specific length\n        model_output = torch.randn(self.batch_size, self.channels, output_length)\n        target_buffer = torch.zeros(self.batch_size, self.channels, target_length)\n        counter = torch.zeros(self.batch_size, self.channels, target_length)\n        \n        # Mock overlap_add function that handles size mismatches safely\n        def mock_overlap_add_safe(output, target, counter, start_idx, expected_len):\n            \"\"\"Safe overlap_add that handles shorter outputs.\"\"\"\n            # Get actual output length\n            actual_len = output.shape[-1]","chunkId":"tests/regression/test_roformer_size_mismatch.py:3"}],[2671,{"filePath":"tests/regression/test_roformer_size_mismatch.py","startLine":46,"endLine":61,"chunkText":"            \"\"\"Safe overlap_add that handles shorter outputs.\"\"\"\n            # Get actual output length\n            actual_len = output.shape[-1]\n            \n            # Calculate safe length to add\n            remaining_target = target.shape[-1] - start_idx\n            safe_len = min(actual_len, expected_len, remaining_target)\n            \n            if safe_len <= 0:\n                return 0  # Nothing to add\n            \n            # Ensure no broadcast errors by explicit slicing\n            end_idx = start_idx + safe_len\n            \n            try:\n                # Add output to target buffer","chunkId":"tests/regression/test_roformer_size_mismatch.py:4"}],[2672,{"filePath":"tests/regression/test_roformer_size_mismatch.py","startLine":59,"endLine":72,"chunkText":"            \n            try:\n                # Add output to target buffer\n                target_slice = target[..., start_idx:end_idx]\n                output_slice = output[..., :safe_len]\n                \n                # Verify shapes match before adding\n                assert target_slice.shape == output_slice.shape, (\n                    f\"Shape mismatch: target_slice {target_slice.shape} != output_slice {output_slice.shape}\"\n                )\n                \n                target[..., start_idx:end_idx] += output_slice\n                counter[..., start_idx:end_idx] += 1.0\n                ","chunkId":"tests/regression/test_roformer_size_mismatch.py:5"}],[2673,{"filePath":"tests/regression/test_roformer_size_mismatch.py","startLine":70,"endLine":84,"chunkText":"                target[..., start_idx:end_idx] += output_slice\n                counter[..., start_idx:end_idx] += 1.0\n                \n                return safe_len\n                \n            except Exception as e:\n                pytest.fail(f\"Overlap add failed for output_length {output_length}: {e}\")\n        \n        # Test overlap_add with different starting positions\n        start_positions = [0, 1000, 5000, 8000]\n        \n        for start_idx in start_positions:\n            if start_idx >= target_length:\n                continue\n                ","chunkId":"tests/regression/test_roformer_size_mismatch.py:6"}],[2674,{"filePath":"tests/regression/test_roformer_size_mismatch.py","startLine":82,"endLine":93,"chunkText":"            if start_idx >= target_length:\n                continue\n                \n            # Test with expected length equal to chunk_size\n            added_len = mock_overlap_add_safe(\n                model_output, target_buffer, counter, start_idx, chunk_size\n            )\n            \n            # Verify no broadcast errors occurred (function completed successfully)\n            assert added_len >= 0, f\"overlap_add should not fail for output_length {output_length}\"\n            \n            # Verify length preservation - added length should not exceed actual output length","chunkId":"tests/regression/test_roformer_size_mismatch.py:7"}],[2675,{"filePath":"tests/regression/test_roformer_size_mismatch.py","startLine":91,"endLine":100,"chunkText":"            assert added_len >= 0, f\"overlap_add should not fail for output_length {output_length}\"\n            \n            # Verify length preservation - added length should not exceed actual output length\n            assert added_len <= output_length, (\n                f\"Added length {added_len} should not exceed actual output length {output_length}\"\n            )\n            \n            # Verify added length is reasonable\n            remaining_space = target_length - start_idx\n            max_possible_add = min(output_length, chunk_size, remaining_space)","chunkId":"tests/regression/test_roformer_size_mismatch.py:8"}],[2676,{"filePath":"tests/regression/test_roformer_size_mismatch.py","startLine":98,"endLine":110,"chunkText":"            # Verify added length is reasonable\n            remaining_space = target_length - start_idx\n            max_possible_add = min(output_length, chunk_size, remaining_space)\n            assert added_len <= max_possible_add, (\n                f\"Added length {added_len} exceeds maximum possible {max_possible_add}\"\n            )\n\n    def test_overlap_add_edge_cases(self):\n        \"\"\"Test edge cases that have caused size mismatch issues.\"\"\"\n        target_length = 10000\n        \n        edge_cases = [\n            # (output_length, start_idx, expected_chunk_size, description)","chunkId":"tests/regression/test_roformer_size_mismatch.py:9"}],[2677,{"filePath":"tests/regression/test_roformer_size_mismatch.py","startLine":108,"endLine":120,"chunkText":"        \n        edge_cases = [\n            # (output_length, start_idx, expected_chunk_size, description)\n            (1, 0, 8192, \"Single sample output at start\"),\n            (1, 9999, 8192, \"Single sample output at end\"),\n            (16, 0, 8192, \"Very short output at start\"),\n            (32, 5000, 8192, \"Short output in middle\"),\n            (236, 9000, 8192, \"Medium output near end\"),\n            (8192, 2000, 8192, \"Full chunk size output\"),\n            (10000, 0, 8192, \"Output longer than chunk size\"),\n            (5000, 8000, 8192, \"Output extending beyond target\"),\n        ]\n        ","chunkId":"tests/regression/test_roformer_size_mismatch.py:10"}],[2678,{"filePath":"tests/regression/test_roformer_size_mismatch.py","startLine":118,"endLine":129,"chunkText":"            (5000, 8000, 8192, \"Output extending beyond target\"),\n        ]\n        \n        for output_len, start_idx, chunk_size, description in edge_cases:\n            with self.subTest(case=description):\n                # Create test tensors\n                model_output = torch.randn(self.batch_size, self.channels, output_len)\n                target_buffer = torch.zeros(self.batch_size, self.channels, target_length)\n                \n                # Mock safe overlap_add\n                def safe_overlap_add(output, target, start, chunk_sz):\n                    actual_len = output.shape[-1]","chunkId":"tests/regression/test_roformer_size_mismatch.py:11"}],[2679,{"filePath":"tests/regression/test_roformer_size_mismatch.py","startLine":127,"endLine":140,"chunkText":"                # Mock safe overlap_add\n                def safe_overlap_add(output, target, start, chunk_sz):\n                    actual_len = output.shape[-1]\n                    remaining = target.shape[-1] - start\n                    safe_len = min(actual_len, chunk_sz, remaining)\n                    \n                    if safe_len > 0:\n                        end = start + safe_len\n                        target[..., start:end] += output[..., :safe_len]\n                    \n                    return safe_len\n                \n                # Should not raise any exceptions\n                try:","chunkId":"tests/regression/test_roformer_size_mismatch.py:12"}],[2680,{"filePath":"tests/regression/test_roformer_size_mismatch.py","startLine":138,"endLine":149,"chunkText":"                \n                # Should not raise any exceptions\n                try:\n                    added_len = safe_overlap_add(model_output, target_buffer, start_idx, chunk_size)\n                    \n                    # Verify reasonable result\n                    assert added_len >= 0, f\"Should add non-negative length for {description}\"\n                    assert added_len <= output_len, f\"Should not add more than available for {description}\"\n                    \n                except Exception as e:\n                    pytest.fail(f\"Edge case failed - {description}: {e}\")\n","chunkId":"tests/regression/test_roformer_size_mismatch.py:13"}],[2681,{"filePath":"tests/regression/test_roformer_size_mismatch.py","startLine":147,"endLine":157,"chunkText":"                except Exception as e:\n                    pytest.fail(f\"Edge case failed - {description}: {e}\")\n\n    def test_counter_consistency_with_overlap_add(self):\n        \"\"\"Test that counter updates match overlap_add operations for problematic lengths.\"\"\"\n        target_length = 10000\n        \n        for output_length in self.problematic_lengths:\n            with self.subTest(output_length=output_length):\n                # Create test tensors\n                model_output = torch.randn(self.batch_size, self.channels, output_length)","chunkId":"tests/regression/test_roformer_size_mismatch.py:14"}],[2682,{"filePath":"tests/regression/test_roformer_size_mismatch.py","startLine":155,"endLine":165,"chunkText":"            with self.subTest(output_length=output_length):\n                # Create test tensors\n                model_output = torch.randn(self.batch_size, self.channels, output_length)\n                target_buffer = torch.zeros(self.batch_size, self.channels, target_length)\n                counter = torch.zeros(self.batch_size, self.channels, target_length)\n                \n                start_idx = 1000\n                chunk_size = 8192\n                \n                # Mock consistent overlap_add and counter update\n                def consistent_update(output, target, counter, start, chunk_sz):","chunkId":"tests/regression/test_roformer_size_mismatch.py:15"}],[2683,{"filePath":"tests/regression/test_roformer_size_mismatch.py","startLine":163,"endLine":173,"chunkText":"                \n                # Mock consistent overlap_add and counter update\n                def consistent_update(output, target, counter, start, chunk_sz):\n                    actual_len = output.shape[-1]\n                    remaining = target.shape[-1] - start\n                    safe_len = min(actual_len, chunk_sz, remaining)\n                    \n                    if safe_len > 0:\n                        end = start + safe_len\n                        # Update both target and counter with same range\n                        target[..., start:end] += output[..., :safe_len]","chunkId":"tests/regression/test_roformer_size_mismatch.py:16"}],[2684,{"filePath":"tests/regression/test_roformer_size_mismatch.py","startLine":171,"endLine":183,"chunkText":"                        end = start + safe_len\n                        # Update both target and counter with same range\n                        target[..., start:end] += output[..., :safe_len]\n                        counter[..., start:end] += 1.0\n                    \n                    return safe_len\n                \n                added_len = consistent_update(\n                    model_output, target_buffer, counter, start_idx, chunk_size\n                )\n                \n                # Verify consistency\n                if added_len > 0:","chunkId":"tests/regression/test_roformer_size_mismatch.py:17"}],[2685,{"filePath":"tests/regression/test_roformer_size_mismatch.py","startLine":181,"endLine":192,"chunkText":"                \n                # Verify consistency\n                if added_len > 0:\n                    # Check that counter was updated in the same range as target\n                    end_idx = start_idx + added_len\n                    counter_slice = counter[0, 0, start_idx:end_idx]\n                    \n                    # Counter should be 1.0 where we added data\n                    assert torch.all(counter_slice == 1.0), (\n                        f\"Counter not consistent for output_length {output_length}\"\n                    )\n                    ","chunkId":"tests/regression/test_roformer_size_mismatch.py:18"}],[2686,{"filePath":"tests/regression/test_roformer_size_mismatch.py","startLine":190,"endLine":200,"chunkText":"                        f\"Counter not consistent for output_length {output_length}\"\n                    )\n                    \n                    # Counter should be 0.0 outside the updated range\n                    if start_idx > 0:\n                        before_slice = counter[0, 0, :start_idx]\n                        assert torch.all(before_slice == 0.0), (\n                            f\"Counter corrupted before range for output_length {output_length}\"\n                        )\n                    \n                    if end_idx < target_length:","chunkId":"tests/regression/test_roformer_size_mismatch.py:19"}],[2687,{"filePath":"tests/regression/test_roformer_size_mismatch.py","startLine":198,"endLine":209,"chunkText":"                        )\n                    \n                    if end_idx < target_length:\n                        after_slice = counter[0, 0, end_idx:]\n                        assert torch.all(after_slice == 0.0), (\n                            f\"Counter corrupted after range for output_length {output_length}\"\n                        )\n\n    def test_broadcast_error_prevention(self):\n        \"\"\"Test specific scenarios that previously caused broadcast errors.\"\"\"\n        # These are specific cases that have been observed to cause issues\n        broadcast_error_cases = [","chunkId":"tests/regression/test_roformer_size_mismatch.py:20"}],[2688,{"filePath":"tests/regression/test_roformer_size_mismatch.py","startLine":207,"endLine":214,"chunkText":"        \"\"\"Test specific scenarios that previously caused broadcast errors.\"\"\"\n        # These are specific cases that have been observed to cause issues\n        broadcast_error_cases = [\n            # (batch, channels, output_len, target_len, start_idx)\n            (1, 2, 1, 8192, 0),      # Single batch, stereo, 1 sample\n            (2, 1, 16, 8192, 100),   # Dual batch, mono, 16 samples\n            (1, 1, 32, 8192, 8000),  # Single batch, mono, 32 samples near end\n            (2, 2, 236, 8192, 4000), # Dual batch, stereo, 236 samples","chunkId":"tests/regression/test_roformer_size_mismatch.py:21"}],[2689,{"filePath":"tests/regression/test_roformer_size_mismatch.py","startLine":212,"endLine":220,"chunkText":"            (2, 1, 16, 8192, 100),   # Dual batch, mono, 16 samples\n            (1, 1, 32, 8192, 8000),  # Single batch, mono, 32 samples near end\n            (2, 2, 236, 8192, 4000), # Dual batch, stereo, 236 samples\n            (1, 2, 512, 1024, 800),  # Output longer than remaining target space\n        ]\n        \n        for batch, channels, output_len, target_len, start_idx in broadcast_error_cases:\n            with self.subTest(batch=batch, channels=channels, output_len=output_len):\n                # Create tensors with specific problematic dimensions","chunkId":"tests/regression/test_roformer_size_mismatch.py:22"}],[2690,{"filePath":"tests/regression/test_roformer_size_mismatch.py","startLine":218,"endLine":226,"chunkText":"        for batch, channels, output_len, target_len, start_idx in broadcast_error_cases:\n            with self.subTest(batch=batch, channels=channels, output_len=output_len):\n                # Create tensors with specific problematic dimensions\n                model_output = torch.randn(batch, channels, output_len)\n                target_buffer = torch.zeros(batch, channels, target_len)\n                \n                # Mock overlap_add with explicit broadcast error prevention\n                def broadcast_safe_overlap_add(output, target, start):\n                    try:","chunkId":"tests/regression/test_roformer_size_mismatch.py:23"}],[2691,{"filePath":"tests/regression/test_roformer_size_mismatch.py","startLine":224,"endLine":232,"chunkText":"                # Mock overlap_add with explicit broadcast error prevention\n                def broadcast_safe_overlap_add(output, target, start):\n                    try:\n                        # Get dimensions\n                        out_batch, out_channels, out_len = output.shape\n                        tgt_batch, tgt_channels, tgt_len = target.shape\n                        \n                        # Verify batch and channel dimensions match\n                        assert out_batch == tgt_batch, f\"Batch size mismatch: {out_batch} != {tgt_batch}\"","chunkId":"tests/regression/test_roformer_size_mismatch.py:24"}],[2692,{"filePath":"tests/regression/test_roformer_size_mismatch.py","startLine":230,"endLine":240,"chunkText":"                        \n                        # Verify batch and channel dimensions match\n                        assert out_batch == tgt_batch, f\"Batch size mismatch: {out_batch} != {tgt_batch}\"\n                        assert out_channels == tgt_channels, f\"Channel mismatch: {out_channels} != {tgt_channels}\"\n                        \n                        # Calculate safe range\n                        remaining = tgt_len - start\n                        safe_len = min(out_len, remaining)\n                        \n                        if safe_len > 0:\n                            end = start + safe_le","chunkId":"tests/regression/test_roformer_size_mismatch.py:25"}],[2693,{"filePath":"tests/regression/test_roformer_size_mismatch.py","startLine":238,"endLine":247,"chunkText":"                        \n                        if safe_len > 0:\n                            end = start + safe_len\n                            \n                            # Explicit slicing to prevent broadcast errors\n                            target_slice = target[:, :, start:end]\n                            output_slice = output[:, :, :safe_len]\n                            \n                            # Verify shapes match exactly\n                            assert target_slice.shape == output_slice.shape, (","chunkId":"tests/regression/test_roformer_size_mismatch.py:26"}],[2694,{"filePath":"tests/regression/test_roformer_size_mismatch.py","startLine":245,"endLine":256,"chunkText":"                            \n                            # Verify shapes match exactly\n                            assert target_slice.shape == output_slice.shape, (\n                                f\"Shape mismatch: {target_slice.shape} != {output_slice.shape}\"\n                            )\n                            \n                            # Perform addition\n                            target[:, :, start:end] += output_slice\n                        \n                        return safe_len\n                        \n                    except Exception as e:","chunkId":"tests/regression/test_roformer_size_mismatch.py:27"}],[2695,{"filePath":"tests/regression/test_roformer_size_mismatch.py","startLine":254,"endLine":264,"chunkText":"                        return safe_len\n                        \n                    except Exception as e:\n                        pytest.fail(f\"Broadcast error not prevented: {e}\")\n                \n                # Should complete without broadcast errors\n                added_len = broadcast_safe_overlap_add(model_output, target_buffer, start_idx)\n                \n                # Verify result is reasonable\n                expected_max = min(output_len, target_len - start_idx)\n                assert 0 <= added_len <= expected_max, (","chunkId":"tests/regression/test_roformer_size_mismatch.py:28"}],[2696,{"filePath":"tests/regression/test_roformer_size_mismatch.py","startLine":262,"endLine":272,"chunkText":"                # Verify result is reasonable\n                expected_max = min(output_len, target_len - start_idx)\n                assert 0 <= added_len <= expected_max, (\n                    f\"Added length {added_len} outside expected range [0, {expected_max}]\"\n                )\n\n    def test_output_length_preservation_regression(self):\n        \"\"\"Test that output length is preserved correctly in all problematic cases.\"\"\"\n        # This tests the regression where shorter outputs were not handled correctly\n        \n        for output_length in self.problematic_lengths:","chunkId":"tests/regression/test_roformer_size_mismatch.py:29"}],[2697,{"filePath":"tests/regression/test_roformer_size_mismatch.py","startLine":270,"endLine":279,"chunkText":"        # This tests the regression where shorter outputs were not handled correctly\n        \n        for output_length in self.problematic_lengths:\n            with self.subTest(output_length=output_length):\n                # Simulate processing pipeline that should preserve length\n                original_length = output_length\n                \n                # Mock processing steps that might alter length\n                def mock_processing_pipeline(input_length):\n                    \"\"\"Mock processing that should preserve input length.\"\"\"","chunkId":"tests/regression/test_roformer_size_mismatch.py:30"}],[2698,{"filePath":"tests/regression/test_roformer_size_mismatch.py","startLine":277,"endLine":286,"chunkText":"                # Mock processing steps that might alter length\n                def mock_processing_pipeline(input_length):\n                    \"\"\"Mock processing that should preserve input length.\"\"\"\n                    # Step 1: Model inference (might produce shorter output)\n                    model_output_length = input_length  # Should match input\n                    \n                    # Step 2: Overlap-add processing\n                    processed_length = model_output_length  # Should preserve length\n                    \n                    # Step 3: Final output","chunkId":"tests/regression/test_roformer_size_mismatch.py:31"}],[2699,{"filePath":"tests/regression/test_roformer_size_mismatch.py","startLine":284,"endLine":297,"chunkText":"                    processed_length = model_output_length  # Should preserve length\n                    \n                    # Step 3: Final output\n                    final_length = processed_length  # Should still match\n                    \n                    return {\n                        'original': input_length,\n                        'model_output': model_output_length,\n                        'processed': processed_length,\n                        'final': final_length\n                    }\n                \n                result = mock_processing_pipeline(original_length)\n                ","chunkId":"tests/regression/test_roformer_size_mismatch.py:32"}],[2700,{"filePath":"tests/regression/test_roformer_size_mismatch.py","startLine":295,"endLine":305,"chunkText":"                \n                result = mock_processing_pipeline(original_length)\n                \n                # Verify length preservation throughout pipeline\n                assert result['model_output'] == original_length, (\n                    f\"Model output length {result['model_output']} != original {original_length}\"\n                )\n                assert result['processed'] == original_length, (\n                    f\"Processed length {result['processed']} != original {original_length}\"\n                )\n                assert result['final'] == original_length, (","chunkId":"tests/regression/test_roformer_size_mismatch.py:33"}],[2701,{"filePath":"tests/regression/test_roformer_size_mismatch.py","startLine":303,"endLine":314,"chunkText":"                    f\"Processed length {result['processed']} != original {original_length}\"\n                )\n                assert result['final'] == original_length, (\n                    f\"Final length {result['final']} != original {original_length}\"\n                )\n\n    def test_memory_layout_consistency(self):\n        \"\"\"Test that memory layout is consistent for problematic tensor sizes.\"\"\"\n        # Some size mismatches can be caused by unexpected memory layouts\n        \n        for output_length in self.problematic_lengths:\n            with self.subTest(output_length=output_length):","chunkId":"tests/regression/test_roformer_size_mismatch.py:34"}],[2702,{"filePath":"tests/regression/test_roformer_size_mismatch.py","startLine":312,"endLine":321,"chunkText":"        \n        for output_length in self.problematic_lengths:\n            with self.subTest(output_length=output_length):\n                # Create tensors with different memory layouts\n                contiguous_output = torch.randn(self.batch_size, self.channels, output_length)\n                non_contiguous_output = contiguous_output.transpose(1, 2).transpose(1, 2)\n                \n                target = torch.zeros(self.batch_size, self.channels, 10000)\n                \n                # Mock overlap_add that handles memory layout","chunkId":"tests/regression/test_roformer_size_mismatch.py:35"}],[2703,{"filePath":"tests/regression/test_roformer_size_mismatch.py","startLine":319,"endLine":330,"chunkText":"                target = torch.zeros(self.batch_size, self.channels, 10000)\n                \n                # Mock overlap_add that handles memory layout\n                def layout_aware_overlap_add(output, target, start):\n                    # Ensure contiguous memory layout\n                    if not output.is_contiguous():\n                        output = output.contiguous()\n                    \n                    if not target.is_contiguous():\n                        target = target.contiguous()\n                    \n                    # Perform safe addition","chunkId":"tests/regression/test_roformer_size_mismatch.py:36"}],[2704,{"filePath":"tests/regression/test_roformer_size_mismatch.py","startLine":328,"endLine":341,"chunkText":"                        target = target.contiguous()\n                    \n                    # Perform safe addition\n                    out_len = output.shape[-1]\n                    remaining = target.shape[-1] - start\n                    safe_len = min(out_len, remaining)\n                    \n                    if safe_len > 0:\n                        end = start + safe_len\n                        target[..., start:end] += output[..., :safe_len]\n                    \n                    return safe_len\n                \n                # Test with both contiguous and non-contiguous tensors","chunkId":"tests/regression/test_roformer_size_mismatch.py:37"}],[2705,{"filePath":"tests/regression/test_roformer_size_mismatch.py","startLine":339,"endLine":347,"chunkText":"                    return safe_len\n                \n                # Test with both contiguous and non-contiguous tensors\n                contiguous_result = layout_aware_overlap_add(contiguous_output, target.clone(), 1000)\n                non_contiguous_result = layout_aware_overlap_add(non_contiguous_output, target.clone(), 1000)\n                \n                # Results should be the same regardless of memory layout\n                assert contiguous_result == non_contiguous_result, (\n                    f\"Memory layout affected result for output_length {output_length}: \"","chunkId":"tests/regression/test_roformer_size_mismatch.py:38"}],[2706,{"filePath":"tests/regression/test_roformer_size_mismatch.py","startLine":345,"endLine":350,"chunkText":"                # Results should be the same regardless of memory layout\n                assert contiguous_result == non_contiguous_result, (\n                    f\"Memory layout affected result for output_length {output_length}: \"\n                    f\"contiguous={contiguous_result}, non_contiguous={non_contiguous_result}\"\n                )\n","chunkId":"tests/regression/test_roformer_size_mismatch.py:39"}],[2707,{"filePath":"tests/remote/README.md","startLine":0,"endLine":14,"chunkText":"# Remote API Tests\n\nThis directory contains comprehensive tests for the remote API functionality of audio-separator, which allows running audio separation workloads on remote servers via an API.\n\n## Test Structure\n\n### Unit Tests (`tests/unit/`)\n\n- **`test_remote_api_client.py`** - Tests for the `AudioSeparatorAPIClient` class\n  - Mock-based tests for all client methods\n  - Tests HTTP request/response handling\n  - Tests error conditions and edge cases\n  - Tests the convenience method `separate_audio_and_wait()`\n\n- **`test_remote_cli.py`** - Tests for the remote CLI functionality","chunkId":"tests/remote/README.md:0"}],[2708,{"filePath":"tests/remote/README.md","startLine":13,"endLine":28,"chunkText":"\n- **`test_remote_cli.py`** - Tests for the remote CLI functionality\n  - Tests command-line argument parsing\n  - Tests all CLI commands (separate, status, models, download)\n  - Tests error handling and edge cases\n  - Mock-based tests without actual HTTP calls\n\n### Integration Tests (`tests/integration/`)\n\n- **`test_remote_api_integration.py`** - Full integration tests\n  - Tests with a mock HTTP server that simulates the real API\n  - End-to-end workflow testing\n  - Tests job submission, polling, and file download\n  - Tests multiple concurrent jobs\n  - Tests error handling with realistic scenarios\n","chunkId":"tests/remote/README.md:1"}],[2709,{"filePath":"tests/remote/README.md","startLine":27,"endLine":41,"chunkText":"  - Tests error handling with realistic scenarios\n\n## Remote API Architecture\n\nThe remote API system consists of three main components:\n\n1. **API Server** (`audio_separator/remote/deploy_modal.py`)\n   - FastAPI server deployed on Modal.com\n   - Handles audio upload, processing, and file serving\n   - Supports multiple models in single jobs\n   - Asynchronous processing with job status tracking\n\n2. **API Client** (`audio_separator/remote/api_client.py`)\n   - Python client for interacting with the remote API\n   - Handles file uploads, job polling, and downloads","chunkId":"tests/remote/README.md:2"}],[2710,{"filePath":"tests/remote/README.md","startLine":40,"endLine":56,"chunkText":"   - Python client for interacting with the remote API\n   - Handles file uploads, job polling, and downloads\n   - Supports all separator parameters and architectures\n\n3. **Remote CLI** (`audio_separator/remote/cli.py`)\n   - Command-line interface using the API client\n   - Mirror of local CLI functionality but for remote processing\n   - Supports all local CLI parameters and options\n\n## Key Features Tested\n\n### Multiple Model Support\n- Upload once, process with multiple models\n- Efficient workflow for comparing model quality\n- Progress tracking across multiple models\n\n### Full Parameter Compatibility","chunkId":"tests/remote/README.md:3"}],[2711,{"filePath":"tests/remote/README.md","startLine":55,"endLine":76,"chunkText":"\n### Full Parameter Compatibility\n- All MDX, VR, Demucs, and MDXC parameters supported\n- Custom output naming and format options\n- Same parameter validation as local processing\n\n### Asynchronous Processing\n- Job submission returns immediately with task ID\n- Status polling with progress updates\n- Background processing on remote server\n\n### Error Handling\n- Network connectivity issues\n- Invalid parameters and file formats\n- Server errors and timeouts\n- Job failures and cleanup\n\n## Running the Tests\n\n### Run All Remote API Tests\n```bash\n# Run all unit tests for remote functionality","chunkId":"tests/remote/README.md:4"}],[2712,{"filePath":"tests/remote/README.md","startLine":75,"endLine":94,"chunkText":"```bash\n# Run all unit tests for remote functionality\npytest tests/unit/test_remote_api_client.py tests/unit/test_remote_cli.py -v\n\n# Run integration tests with mock server\npytest tests/integration/test_remote_api_integration.py -v\n\n# Run all remote tests\npytest tests/unit/test_remote*.py tests/integration/test_remote*.py -v\n```\n\n### Run Specific Test Categories\n```bash\n# Test only API client functionality\npytest tests/unit/test_remote_api_client.py::TestAudioSeparatorAPIClient -v\n\n# Test only CLI functionality  \npytest tests/unit/test_remote_cli.py::TestRemoteCLI -v\n\n# Test end-to-end workflows","chunkId":"tests/remote/README.md:5"}],[2713,{"filePath":"tests/remote/README.md","startLine":93,"endLine":112,"chunkText":"\n# Test end-to-end workflows\npytest tests/integration/test_remote_api_integration.py::TestRemoteAPIEndToEnd -v\n```\n\n### Run with Coverage\n```bash\npytest tests/unit/test_remote*.py tests/integration/test_remote*.py --cov=audio_separator.remote --cov-report=html\n```\n\n## Test Environment Setup\n\nThe tests are designed to run without requiring a live API server:\n\n1. **Unit Tests** - Use mocked HTTP responses, no network calls\n2. **Integration Tests** - Use a mock HTTP server that simulates the real API\n3. **End-to-End Tests** - Full workflow testing with realistic data\n\n### Mock Server Features\n","chunkId":"tests/remote/README.md:6"}],[2714,{"filePath":"tests/remote/README.md","startLine":111,"endLine":129,"chunkText":"### Mock Server Features\n\nThe integration tests include a comprehensive mock HTTP server that simulates:\n- Job submission and processing\n- Status polling with progress updates\n- File upload and download\n- Model listing and filtering\n- Error conditions and edge cases\n\n## Testing Best Practices\n\n### Isolated Tests\n- Each test is independent and can run in isolation\n- Temporary files are properly cleaned up\n- Mock state is reset between tests\n\n### Realistic Scenarios\n- Tests use realistic audio file formats and sizes\n- Error conditions match real-world scenarios","chunkId":"tests/remote/README.md:7"}],[2715,{"filePath":"tests/remote/README.md","startLine":130,"endLine":153,"chunkText":"- Progress tracking mimics actual processing times\n\n### Comprehensive Coverage\n- All API endpoints are tested\n- All CLI commands and options are tested\n- Both success and error paths are tested\n- Parameter validation and edge cases are covered\n\n## Test Data\n\nTests use minimal synthetic data to avoid large file dependencies:\n- Fake audio content for upload testing\n- Simulated processing results\n- Mock model metadata\n\n## Debugging Tests\n\n### Enable Debug Logging\n```bash\npytest tests/integration/test_remote_api_integration.py -v -s --log-cli-level=DEBUG\n```\n\n### Run Individual Tests\n```bash","chunkId":"tests/remote/README.md:8"}],[2716,{"filePath":"tests/remote/README.md","startLine":153,"endLine":171,"chunkText":"```bash\n# Test specific functionality\npytest tests/unit/test_remote_api_client.py::TestAudioSeparatorAPIClient::test_separate_audio_and_wait_success -v -s\n\n# Test with specific parameters\npytest tests/integration/test_remote_api_integration.py::TestRemoteAPIIntegration::test_job_status_polling -v -s\n```\n\n### Test Environment Variables\n```bash\n# Skip certain tests if needed\nSKIP_INTEGRATION_TESTS=1 pytest tests/unit/test_remote*.py\n\n# Enable additional debug output\nDEBUG_REMOTE_TESTS=1 pytest tests/integration/test_remote*.py -v -s\n```\n\n## Integration with CI/CD\n","chunkId":"tests/remote/README.md:9"}],[2717,{"filePath":"tests/remote/README.md","startLine":170,"endLine":187,"chunkText":"## Integration with CI/CD\n\nThese tests are designed to run in CI environments:\n- No external dependencies required\n- Fast execution (typically < 30 seconds)\n- Reliable mock server implementation\n- Clear pass/fail criteria\n\n## Contributing\n\nWhen adding new remote API features:\n\n1. **Add unit tests** for individual components\n2. **Add integration tests** for end-to-end workflows\n3. **Update mock server** to support new endpoints\n4. **Test error conditions** and edge cases\n5. **Update documentation** for new test scenarios\n","chunkId":"tests/remote/README.md:10"}],[2718,{"filePath":"tests/remote/README.md","startLine":186,"endLine":188,"chunkText":"5. **Update documentation** for new test scenarios\n\nThe test suite should maintain high coverage while remaining fast and reliable for continuous integration. ","chunkId":"tests/remote/README.md:11"}],[2719,{"filePath":"tests/unit/test_audio_chunking.py","startLine":0,"endLine":21,"chunkText":"\"\"\"\nUnit tests for audio chunking functionality.\nTests the AudioChunker class for splitting and merging audio files.\n\"\"\"\n\nimport pytest\nimport os\nimport tempfile\nimport logging\nfrom unittest.mock import Mock, patch, MagicMock\nfrom pydub import AudioSegment\n\nfrom audio_separator.separator.audio_chunking import AudioChunker\n\n\nclass TestAudioChunker:\n    \"\"\"Test cases for AudioChunker class.\"\"\"\n\n    def setup_method(self):\n        \"\"\"Set up test fixtures.\"\"\"\n        self.chunk_duration_seconds = 10.0  # 10 seconds\n        self.logger = logging.getLogger(__name__)","chunkId":"tests/unit/test_audio_chunking.py:0"}],[2720,{"filePath":"tests/unit/test_audio_chunking.py","startLine":19,"endLine":32,"chunkText":"        \"\"\"Set up test fixtures.\"\"\"\n        self.chunk_duration_seconds = 10.0  # 10 seconds\n        self.logger = logging.getLogger(__name__)\n        self.chunker = AudioChunker(self.chunk_duration_seconds, self.logger)\n\n    def test_initialization(self):\n        \"\"\"Test AudioChunker initialization.\"\"\"\n        assert self.chunker.chunk_duration_ms == 10000\n        assert self.chunker.logger is not None\n\n    def test_initialization_with_custom_logger(self):\n        \"\"\"Test AudioChunker initialization with custom logger.\"\"\"\n        custom_logger = Mock()\n        chunker = AudioChunker(5.0, custom_logger)","chunkId":"tests/unit/test_audio_chunking.py:1"}],[2721,{"filePath":"tests/unit/test_audio_chunking.py","startLine":30,"endLine":42,"chunkText":"        \"\"\"Test AudioChunker initialization with custom logger.\"\"\"\n        custom_logger = Mock()\n        chunker = AudioChunker(5.0, custom_logger)\n        assert chunker.chunk_duration_ms == 5000\n        assert chunker.logger == custom_logger\n\n    def test_should_chunk_true(self):\n        \"\"\"Test should_chunk returns True for files longer than chunk duration.\"\"\"\n        # File duration 15 seconds, chunk duration 10 seconds\n        assert self.chunker.should_chunk(15.0) is True\n\n    def test_should_chunk_false(self):\n        \"\"\"Test should_chunk returns False for files shorter than chunk duration.\"\"\"","chunkId":"tests/unit/test_audio_chunking.py:2"}],[2722,{"filePath":"tests/unit/test_audio_chunking.py","startLine":40,"endLine":52,"chunkText":"\n    def test_should_chunk_false(self):\n        \"\"\"Test should_chunk returns False for files shorter than chunk duration.\"\"\"\n        # File duration 5 seconds, chunk duration 10 seconds\n        assert self.chunker.should_chunk(5.0) is False\n\n    def test_should_chunk_exact_boundary(self):\n        \"\"\"Test should_chunk at exact boundary.\"\"\"\n        # File duration exactly 10 seconds, chunk duration 10 seconds\n        assert self.chunker.should_chunk(10.0) is False\n\n    def test_should_chunk_just_over_boundary(self):\n        \"\"\"Test should_chunk just over boundary.\"\"\"","chunkId":"tests/unit/test_audio_chunking.py:3"}],[2723,{"filePath":"tests/unit/test_audio_chunking.py","startLine":50,"endLine":61,"chunkText":"\n    def test_should_chunk_just_over_boundary(self):\n        \"\"\"Test should_chunk just over boundary.\"\"\"\n        # File duration 10.1 seconds, chunk duration 10 seconds\n        assert self.chunker.should_chunk(10.1) is True\n\n    @patch('audio_separator.separator.audio_chunking.AudioSegment.from_file')\n    @patch('audio_separator.separator.audio_chunking.os.path.exists')\n    @patch('audio_separator.separator.audio_chunking.os.makedirs')\n    def test_split_audio_basic(self, _mock_makedirs, mock_exists, mock_from_file):\n        \"\"\"Test basic audio splitting.\"\"\"\n        # Mock audio file (30 seconds)","chunkId":"tests/unit/test_audio_chunking.py:4"}],[2724,{"filePath":"tests/unit/test_audio_chunking.py","startLine":59,"endLine":72,"chunkText":"    def test_split_audio_basic(self, _mock_makedirs, mock_exists, mock_from_file):\n        \"\"\"Test basic audio splitting.\"\"\"\n        # Mock audio file (30 seconds)\n        mock_audio = Mock()\n        mock_audio.__len__ = Mock(return_value=30000)  # 30 seconds in ms\n        mock_audio.__getitem__ = Mock(side_effect=lambda _: mock_audio)\n        mock_audio.export = Mock()\n        mock_from_file.return_value = mock_audio\n        mock_exists.return_value = True\n\n        temp_dir = tempfile.mkdtemp()\n        try:\n            chunk_paths = self.chunker.split_audio(\"test.wav\", temp_dir)\n","chunkId":"tests/unit/test_audio_chunking.py:5"}],[2725,{"filePath":"tests/unit/test_audio_chunking.py","startLine":70,"endLine":85,"chunkText":"        try:\n            chunk_paths = self.chunker.split_audio(\"test.wav\", temp_dir)\n\n            # Should create 3 chunks (30s / 10s = 3)\n            assert len(chunk_paths) == 3\n            assert all(\"chunk_\" in path for path in chunk_paths)\n            assert mock_audio.export.call_count == 3\n\n        finally:\n            # Cleanup\n            import shutil\n            if os.path.exists(temp_dir):\n                shutil.rmtree(temp_dir)\n\n    @patch('audio_separator.separator.audio_chunking.AudioSegment.from_file')\n    @patch('audio_separator.separator.audio_chunking.os.path.exists')","chunkId":"tests/unit/test_audio_chunking.py:6"}],[2726,{"filePath":"tests/unit/test_audio_chunking.py","startLine":83,"endLine":92,"chunkText":"\n    @patch('audio_separator.separator.audio_chunking.AudioSegment.from_file')\n    @patch('audio_separator.separator.audio_chunking.os.path.exists')\n    @patch('audio_separator.separator.audio_chunking.os.makedirs')\n    def test_split_audio_uneven_chunks(self, _mock_makedirs, mock_exists, mock_from_file):\n        \"\"\"Test splitting audio with uneven chunk sizes.\"\"\"\n        # Mock audio file (25 seconds)\n        mock_audio = Mock()\n        mock_audio.__len__ = Mock(return_value=25000)  # 25 seconds in ms\n        mock_audio.__getitem__ = Mock(side_effect=lambda _: mock_audio)","chunkId":"tests/unit/test_audio_chunking.py:7"}],[2727,{"filePath":"tests/unit/test_audio_chunking.py","startLine":90,"endLine":105,"chunkText":"        mock_audio = Mock()\n        mock_audio.__len__ = Mock(return_value=25000)  # 25 seconds in ms\n        mock_audio.__getitem__ = Mock(side_effect=lambda _: mock_audio)\n        mock_audio.export = Mock()\n        mock_from_file.return_value = mock_audio\n        mock_exists.return_value = True\n\n        temp_dir = tempfile.mkdtemp()\n        try:\n            chunk_paths = self.chunker.split_audio(\"test.wav\", temp_dir)\n\n            # Should create 3 chunks (ceil(25s / 10s) = 3)\n            # First two chunks: 10s each, last chunk: 5s\n            assert len(chunk_paths) == 3\n\n        finally:","chunkId":"tests/unit/test_audio_chunking.py:8"}],[2728,{"filePath":"tests/unit/test_audio_chunking.py","startLine":103,"endLine":116,"chunkText":"            assert len(chunk_paths) == 3\n\n        finally:\n            import shutil\n            if os.path.exists(temp_dir):\n                shutil.rmtree(temp_dir)\n\n    def test_split_audio_file_not_found(self, tmp_path):\n        \"\"\"Test split_audio with non-existent file.\"\"\"\n        with pytest.raises(FileNotFoundError):\n            self.chunker.split_audio(\"nonexistent.wav\", str(tmp_path))\n\n    @patch('audio_separator.separator.audio_chunking.AudioSegment.from_file')\n    @patch('audio_separator.separator.audio_chunking.os.path.exists')","chunkId":"tests/unit/test_audio_chunking.py:9"}],[2729,{"filePath":"tests/unit/test_audio_chunking.py","startLine":114,"endLine":128,"chunkText":"\n    @patch('audio_separator.separator.audio_chunking.AudioSegment.from_file')\n    @patch('audio_separator.separator.audio_chunking.os.path.exists')\n    def test_merge_chunks_basic(self, mock_exists, mock_from_file, tmp_path):\n        \"\"\"Test basic chunk merging.\"\"\"\n        # Mock chunk files\n        mock_chunk1 = Mock()\n        mock_chunk2 = Mock()\n        mock_combined = Mock()\n        mock_combined.export = Mock()\n\n        # Setup mock to return chunks and allow addition\n        mock_from_file.side_effect = [mock_chunk1, mock_chunk2]\n        mock_exists.return_value = True\n","chunkId":"tests/unit/test_audio_chunking.py:10"}],[2730,{"filePath":"tests/unit/test_audio_chunking.py","startLine":126,"endLine":137,"chunkText":"        mock_from_file.side_effect = [mock_chunk1, mock_chunk2]\n        mock_exists.return_value = True\n\n        # Mock AudioSegment.empty() and addition\n        with patch('audio_separator.separator.audio_chunking.AudioSegment.empty') as mock_empty:\n            mock_empty.return_value = mock_combined\n            mock_combined.__add__ = Mock(side_effect=[mock_combined, mock_combined])\n            mock_combined.__len__ = Mock(return_value=20000)\n\n            chunk_paths = [\"chunk1.wav\", \"chunk2.wav\"]\n            output_path = self.chunker.merge_chunks(chunk_paths, str(tmp_path / \"output.wav\"))\n","chunkId":"tests/unit/test_audio_chunking.py:11"}],[2731,{"filePath":"tests/unit/test_audio_chunking.py","startLine":135,"endLine":146,"chunkText":"            chunk_paths = [\"chunk1.wav\", \"chunk2.wav\"]\n            output_path = self.chunker.merge_chunks(chunk_paths, str(tmp_path / \"output.wav\"))\n\n            assert output_path == str(tmp_path / \"output.wav\")\n            assert mock_combined.export.called\n\n    def test_merge_chunks_empty_list(self, tmp_path):\n        \"\"\"Test merge_chunks with empty chunk list.\"\"\"\n        with pytest.raises(ValueError, match=\"Cannot merge empty list\"):\n            self.chunker.merge_chunks([], str(tmp_path / \"output.wav\"))\n\n    @patch('audio_separator.separator.audio_chunking.os.path.exists')","chunkId":"tests/unit/test_audio_chunking.py:12"}],[2732,{"filePath":"tests/unit/test_audio_chunking.py","startLine":144,"endLine":155,"chunkText":"            self.chunker.merge_chunks([], str(tmp_path / \"output.wav\"))\n\n    @patch('audio_separator.separator.audio_chunking.os.path.exists')\n    def test_merge_chunks_missing_file(self, mock_exists, tmp_path):\n        \"\"\"Test merge_chunks with missing chunk file.\"\"\"\n        mock_exists.return_value = False\n\n        with pytest.raises(FileNotFoundError, match=\"Chunk file not found\"):\n            self.chunker.merge_chunks([\"missing.wav\"], str(tmp_path / \"output.wav\"))\n\n    def test_chunk_duration_calculation(self):\n        \"\"\"Test chunk duration calculation.\"\"\"","chunkId":"tests/unit/test_audio_chunking.py:13"}],[2733,{"filePath":"tests/unit/test_audio_chunking.py","startLine":153,"endLine":165,"chunkText":"\n    def test_chunk_duration_calculation(self):\n        \"\"\"Test chunk duration calculation.\"\"\"\n        chunker_5s = AudioChunker(5.0, self.logger)\n        assert chunker_5s.chunk_duration_ms == 5000\n\n        chunker_60s = AudioChunker(60.0, self.logger)\n        assert chunker_60s.chunk_duration_ms == 60000\n\n        chunker_half = AudioChunker(0.5, self.logger)\n        assert chunker_half.chunk_duration_ms == 500\n\n","chunkId":"tests/unit/test_audio_chunking.py:14"}],[2734,{"filePath":"tests/unit/test_audio_chunking.py","startLine":166,"endLine":181,"chunkText":"class TestAudioChunkerIntegration:\n    \"\"\"Integration tests with actual audio segment creation.\"\"\"\n\n    def test_split_and_merge_round_trip(self):\n        \"\"\"Test splitting and merging produces valid output.\"\"\"\n        # Create a simple test audio segment (silence)\n        audio = AudioSegment.silent(duration=15000)  # 15 seconds\n\n        temp_dir = tempfile.mkdtemp()\n        try:\n            # Save test audio\n            input_path = os.path.join(temp_dir, \"test_input.wav\")\n            audio.export(input_path, format=\"wav\")\n\n            # Split\n            chunker = AudioChunker(5.0)  # 5-second chunks","chunkId":"tests/unit/test_audio_chunking.py:15"}],[2735,{"filePath":"tests/unit/test_audio_chunking.py","startLine":179,"endLine":193,"chunkText":"\n            # Split\n            chunker = AudioChunker(5.0)  # 5-second chunks\n            chunk_dir = os.path.join(temp_dir, \"chunks\")\n            chunk_paths = chunker.split_audio(input_path, chunk_dir)\n\n            # Should create 3 chunks\n            assert len(chunk_paths) == 3\n            assert all(os.path.exists(path) for path in chunk_paths)\n\n            # Merge\n            output_path = os.path.join(temp_dir, \"test_output.wav\")\n            merged_path = chunker.merge_chunks(chunk_paths, output_path)\n\n            # Verify output exists and has similar duration","chunkId":"tests/unit/test_audio_chunking.py:16"}],[2736,{"filePath":"tests/unit/test_audio_chunking.py","startLine":191,"endLine":206,"chunkText":"            merged_path = chunker.merge_chunks(chunk_paths, output_path)\n\n            # Verify output exists and has similar duration\n            assert os.path.exists(merged_path)\n            merged_audio = AudioSegment.from_file(merged_path)\n\n            # Duration should be close (within 100ms due to encoding)\n            assert abs(len(merged_audio) - len(audio)) < 100\n\n        finally:\n            # Cleanup\n            import shutil\n            if os.path.exists(temp_dir):\n                shutil.rmtree(temp_dir)\n\n    def test_split_with_different_formats(self):","chunkId":"tests/unit/test_audio_chunking.py:17"}],[2737,{"filePath":"tests/unit/test_audio_chunking.py","startLine":204,"endLine":219,"chunkText":"                shutil.rmtree(temp_dir)\n\n    def test_split_with_different_formats(self):\n        \"\"\"Test splitting works with different audio formats.\"\"\"\n        audio = AudioSegment.silent(duration=10000)  # 10 seconds\n\n        temp_dir = tempfile.mkdtemp()\n        try:\n            # Test with .wav extension\n            input_wav = os.path.join(temp_dir, \"test.wav\")\n            audio.export(input_wav, format=\"wav\")\n\n            chunker = AudioChunker(5.0)\n            chunk_dir = os.path.join(temp_dir, \"chunks_wav\")\n            chunk_paths = chunker.split_audio(input_wav, chunk_dir)\n","chunkId":"tests/unit/test_audio_chunking.py:18"}],[2738,{"filePath":"tests/unit/test_audio_chunking.py","startLine":217,"endLine":228,"chunkText":"            chunk_dir = os.path.join(temp_dir, \"chunks_wav\")\n            chunk_paths = chunker.split_audio(input_wav, chunk_dir)\n\n            assert len(chunk_paths) == 2\n            assert all(path.endswith(\".wav\") for path in chunk_paths)\n\n        finally:\n            import shutil\n            if os.path.exists(temp_dir):\n                shutil.rmtree(temp_dir)\n\n","chunkId":"tests/unit/test_audio_chunking.py:19"}],[2739,{"filePath":"tests/unit/test_audio_chunking.py","startLine":229,"endLine":244,"chunkText":"class TestAudioChunkerEdgeCases:\n    \"\"\"Test edge cases and error handling.\"\"\"\n\n    def test_very_short_file(self):\n        \"\"\"Test chunking very short file (shorter than chunk duration).\"\"\"\n        audio = AudioSegment.silent(duration=2000)  # 2 seconds\n\n        temp_dir = tempfile.mkdtemp()\n        try:\n            input_path = os.path.join(temp_dir, \"short.wav\")\n            audio.export(input_path, format=\"wav\")\n\n            chunker = AudioChunker(10.0)  # 10-second chunks\n\n            # Should still work, creating just 1 chunk\n            chunk_dir = os.path.join(temp_dir, \"chunks\")","chunkId":"tests/unit/test_audio_chunking.py:20"}],[2740,{"filePath":"tests/unit/test_audio_chunking.py","startLine":242,"endLine":259,"chunkText":"\n            # Should still work, creating just 1 chunk\n            chunk_dir = os.path.join(temp_dir, \"chunks\")\n            chunk_paths = chunker.split_audio(input_path, chunk_dir)\n\n            assert len(chunk_paths) == 1\n\n        finally:\n            import shutil\n            if os.path.exists(temp_dir):\n                shutil.rmtree(temp_dir)\n\n    def test_exact_multiple_of_chunk_size(self):\n        \"\"\"Test file that's exact multiple of chunk size.\"\"\"\n        audio = AudioSegment.silent(duration=20000)  # 20 seconds\n\n        temp_dir = tempfile.mkdtemp()\n        try:","chunkId":"tests/unit/test_audio_chunking.py:21"}],[2741,{"filePath":"tests/unit/test_audio_chunking.py","startLine":257,"endLine":275,"chunkText":"\n        temp_dir = tempfile.mkdtemp()\n        try:\n            input_path = os.path.join(temp_dir, \"exact.wav\")\n            audio.export(input_path, format=\"wav\")\n\n            chunker = AudioChunker(10.0)  # 10-second chunks\n\n            chunk_dir = os.path.join(temp_dir, \"chunks\")\n            chunk_paths = chunker.split_audio(input_path, chunk_dir)\n\n            # Should create exactly 2 chunks\n            assert len(chunk_paths) == 2\n\n        finally:\n            import shutil\n            if os.path.exists(temp_dir):\n                shutil.rmtree(temp_dir)\n","chunkId":"tests/unit/test_audio_chunking.py:22"}],[2742,{"filePath":"tests/unit/test_bit_depth_detection.py","startLine":0,"endLine":22,"chunkText":"\"\"\"\nUnit tests for bit depth preservation functionality in CommonSeparator.\n\nTests the bit depth detection and storage logic without requiring full separation.\n\"\"\"\n\nimport os\nimport pytest\nimport tempfile\nimport shutil\nimport soundfile as sf\nimport numpy as np\nfrom unittest.mock import Mock, MagicMock, patch\n\nfrom audio_separator.separator.common_separator import CommonSeparator\n\n\ndef create_test_audio_file(output_path, sample_rate=44100, duration=0.5, bit_depth=16):\n    \"\"\"\n    Create a test audio file with a specific bit depth.\n    \n    Args:\n        output_path: Path to save the test audio file","chunkId":"tests/unit/test_bit_depth_detection.py:0"}],[2743,{"filePath":"tests/unit/test_bit_depth_detection.py","startLine":20,"endLine":37,"chunkText":"    \n    Args:\n        output_path: Path to save the test audio file\n        sample_rate: Sample rate in Hz\n        duration: Duration in seconds\n        bit_depth: Bit depth (16, 24, or 32)\n    \n    Returns:\n        Path to the created audio file\n    \"\"\"\n    # Generate a simple test signal (440 Hz sine wave)\n    t = np.linspace(0, duration, int(sample_rate * duration))\n    frequency = 440  # A4 note\n    audio = 0.5 * np.sin(2 * np.pi * frequency * t)\n    \n    # Create stereo by duplicating the mono signal\n    stereo_audio = np.column_stack([audio, audio])\n    ","chunkId":"tests/unit/test_bit_depth_detection.py:1"}],[2744,{"filePath":"tests/unit/test_bit_depth_detection.py","startLine":35,"endLine":55,"chunkText":"    # Create stereo by duplicating the mono signal\n    stereo_audio = np.column_stack([audio, audio])\n    \n    # Determine the subtype based on bit depth\n    if bit_depth == 16:\n        subtype = 'PCM_16'\n    elif bit_depth == 24:\n        subtype = 'PCM_24'\n    elif bit_depth == 32:\n        subtype = 'PCM_32'\n    else:\n        raise ValueError(f\"Unsupported bit depth: {bit_depth}\")\n    \n    # Write the audio file\n    sf.write(output_path, stereo_audio, sample_rate, subtype=subtype)\n    \n    return output_path\n\n\n@pytest.fixture(name=\"temp_audio_dir\")\ndef fixture_temp_audio_dir():","chunkId":"tests/unit/test_bit_depth_detection.py:2"}],[2745,{"filePath":"tests/unit/test_bit_depth_detection.py","startLine":53,"endLine":72,"chunkText":"\n@pytest.fixture(name=\"temp_audio_dir\")\ndef fixture_temp_audio_dir():\n    \"\"\"Fixture providing a temporary directory for input audio files.\"\"\"\n    temp_dir = tempfile.mkdtemp()\n    yield temp_dir\n    # Clean up after test\n    shutil.rmtree(temp_dir)\n\n\n@pytest.fixture(name=\"mock_separator_config\")\ndef fixture_mock_separator_config():\n    \"\"\"Fixture providing a mock separator configuration.\"\"\"\n    return {\n        \"logger\": Mock(),\n        \"log_level\": 20,\n        \"torch_device\": Mock(),\n        \"torch_device_cpu\": Mock(),\n        \"torch_device_mps\": Mock(),\n        \"onnx_execution_provider\": Mock(),","chunkId":"tests/unit/test_bit_depth_detection.py:3"}],[2746,{"filePath":"tests/unit/test_bit_depth_detection.py","startLine":70,"endLine":88,"chunkText":"        \"torch_device_cpu\": Mock(),\n        \"torch_device_mps\": Mock(),\n        \"onnx_execution_provider\": Mock(),\n        \"model_name\": \"test_model\",\n        \"model_path\": \"/path/to/model\",\n        \"model_data\": {\"training\": {\"instruments\": [\"vocals\", \"other\"]}},\n        \"output_dir\": None,\n        \"output_format\": \"wav\",\n        \"output_bitrate\": None,\n        \"normalization_threshold\": 0.9,\n        \"amplification_threshold\": 0.0,\n        \"enable_denoise\": False,\n        \"output_single_stem\": None,\n        \"invert_using_spec\": False,\n        \"sample_rate\": 44100,\n        \"use_soundfile\": False,\n    }\n\n","chunkId":"tests/unit/test_bit_depth_detection.py:4"}],[2747,{"filePath":"tests/unit/test_bit_depth_detection.py","startLine":86,"endLine":103,"chunkText":"    }\n\n\ndef test_16bit_detection(temp_audio_dir, mock_separator_config):\n    \"\"\"Test that 16-bit audio files are correctly detected.\"\"\"\n    print(\"\\n>>> TEST: 16-bit detection\")\n    \n    # Create a 16-bit test audio file\n    input_file = os.path.join(temp_audio_dir, \"test_16bit.wav\")\n    create_test_audio_file(input_file, bit_depth=16)\n    \n    # Create CommonSeparator instance\n    separator = CommonSeparator(mock_separator_config)\n    \n    # Call prepare_mix to detect bit depth\n    mix = separator.prepare_mix(input_file)\n    \n    # Verify bit depth was detected correctly","chunkId":"tests/unit/test_bit_depth_detection.py:5"}],[2748,{"filePath":"tests/unit/test_bit_depth_detection.py","startLine":101,"endLine":113,"chunkText":"    mix = separator.prepare_mix(input_file)\n    \n    # Verify bit depth was detected correctly\n    print(f\"Detected bit depth: {separator.input_bit_depth}\")\n    print(f\"Detected subtype: {separator.input_subtype}\")\n    \n    assert separator.input_bit_depth == 16, f\"Expected 16-bit, got {separator.input_bit_depth}\"\n    assert 'PCM_16' in separator.input_subtype or separator.input_subtype == 'PCM_16', f\"Expected PCM_16, got {separator.input_subtype}\"\n    \n    print(\"✅ Test passed: 16-bit audio correctly detected\")\n\n\ndef test_24bit_detection(temp_audio_dir, mock_separator_config):","chunkId":"tests/unit/test_bit_depth_detection.py:6"}],[2749,{"filePath":"tests/unit/test_bit_depth_detection.py","startLine":111,"endLine":127,"chunkText":"\n\ndef test_24bit_detection(temp_audio_dir, mock_separator_config):\n    \"\"\"Test that 24-bit audio files are correctly detected.\"\"\"\n    print(\"\\n>>> TEST: 24-bit detection\")\n    \n    # Create a 24-bit test audio file\n    input_file = os.path.join(temp_audio_dir, \"test_24bit.wav\")\n    create_test_audio_file(input_file, bit_depth=24)\n    \n    # Create CommonSeparator instance\n    separator = CommonSeparator(mock_separator_config)\n    \n    # Call prepare_mix to detect bit depth\n    mix = separator.prepare_mix(input_file)\n    \n    # Verify bit depth was detected correctly","chunkId":"tests/unit/test_bit_depth_detection.py:7"}],[2750,{"filePath":"tests/unit/test_bit_depth_detection.py","startLine":125,"endLine":138,"chunkText":"    mix = separator.prepare_mix(input_file)\n    \n    # Verify bit depth was detected correctly\n    print(f\"Detected bit depth: {separator.input_bit_depth}\")\n    print(f\"Detected subtype: {separator.input_subtype}\")\n    \n    assert separator.input_bit_depth == 24, f\"Expected 24-bit, got {separator.input_bit_depth}\"\n    assert 'PCM_24' in separator.input_subtype, f\"Expected PCM_24, got {separator.input_subtype}\"\n    \n    print(\"✅ Test passed: 24-bit audio correctly detected\")\n\n\ndef test_32bit_detection(temp_audio_dir, mock_separator_config):\n    \"\"\"Test that 32-bit audio files are correctly detected.\"\"\"","chunkId":"tests/unit/test_bit_depth_detection.py:8"}],[2751,{"filePath":"tests/unit/test_bit_depth_detection.py","startLine":136,"endLine":151,"chunkText":"\ndef test_32bit_detection(temp_audio_dir, mock_separator_config):\n    \"\"\"Test that 32-bit audio files are correctly detected.\"\"\"\n    print(\"\\n>>> TEST: 32-bit detection\")\n    \n    # Create a 32-bit test audio file\n    input_file = os.path.join(temp_audio_dir, \"test_32bit.wav\")\n    create_test_audio_file(input_file, bit_depth=32)\n    \n    # Create CommonSeparator instance\n    separator = CommonSeparator(mock_separator_config)\n    \n    # Call prepare_mix to detect bit depth\n    mix = separator.prepare_mix(input_file)\n    \n    # Verify bit depth was detected correctly","chunkId":"tests/unit/test_bit_depth_detection.py:9"}],[2752,{"filePath":"tests/unit/test_bit_depth_detection.py","startLine":149,"endLine":161,"chunkText":"    mix = separator.prepare_mix(input_file)\n    \n    # Verify bit depth was detected correctly\n    print(f\"Detected bit depth: {separator.input_bit_depth}\")\n    print(f\"Detected subtype: {separator.input_subtype}\")\n    \n    assert separator.input_bit_depth == 32, f\"Expected 32-bit, got {separator.input_bit_depth}\"\n    assert 'PCM_32' in separator.input_subtype or 'FLOAT' in separator.input_subtype, f\"Expected PCM_32 or FLOAT, got {separator.input_subtype}\"\n    \n    print(\"✅ Test passed: 32-bit audio correctly detected\")\n\n\ndef test_numpy_array_input_defaults_to_16bit(mock_separator_config):","chunkId":"tests/unit/test_bit_depth_detection.py:10"}],[2753,{"filePath":"tests/unit/test_bit_depth_detection.py","startLine":159,"endLine":175,"chunkText":"\n\ndef test_numpy_array_input_defaults_to_16bit(mock_separator_config):\n    \"\"\"Test that numpy array input defaults to 16-bit.\"\"\"\n    print(\"\\n>>> TEST: Numpy array input defaults to 16-bit\")\n    \n    # Create a mock numpy array (stereo audio)\n    mock_audio = np.random.rand(1000, 2).astype(np.float32)\n    \n    # Create CommonSeparator instance\n    separator = CommonSeparator(mock_separator_config)\n    \n    # Call prepare_mix with numpy array\n    mix = separator.prepare_mix(mock_audio)\n    \n    # Verify bit depth defaults to 16-bit\n    print(f\"Bit depth for numpy input: {separator.input_bit_depth}\")","chunkId":"tests/unit/test_bit_depth_detection.py:11"}],[2754,{"filePath":"tests/unit/test_bit_depth_detection.py","startLine":173,"endLine":185,"chunkText":"    \n    # Verify bit depth defaults to 16-bit\n    print(f\"Bit depth for numpy input: {separator.input_bit_depth}\")\n    assert separator.input_bit_depth == 16, f\"Expected 16-bit default, got {separator.input_bit_depth}\"\n    \n    print(\"✅ Test passed: Numpy array input defaults to 16-bit\")\n\n\ndef test_bit_depth_preserved_across_multiple_files(temp_audio_dir, mock_separator_config):\n    \"\"\"Test that bit depth is correctly updated when processing multiple files.\"\"\"\n    print(\"\\n>>> TEST: Bit depth updated across multiple files\")\n    \n    # Create test files with different bit depths","chunkId":"tests/unit/test_bit_depth_detection.py:12"}],[2755,{"filePath":"tests/unit/test_bit_depth_detection.py","startLine":183,"endLine":197,"chunkText":"    print(\"\\n>>> TEST: Bit depth updated across multiple files\")\n    \n    # Create test files with different bit depths\n    input_16bit = os.path.join(temp_audio_dir, \"test_16bit.wav\")\n    input_24bit = os.path.join(temp_audio_dir, \"test_24bit.wav\")\n    \n    create_test_audio_file(input_16bit, bit_depth=16)\n    create_test_audio_file(input_24bit, bit_depth=24)\n    \n    # Create CommonSeparator instance\n    separator = CommonSeparator(mock_separator_config)\n    \n    # Process 16-bit file\n    mix1 = separator.prepare_mix(input_16bit)\n    assert separator.input_bit_depth == 16","chunkId":"tests/unit/test_bit_depth_detection.py:13"}],[2756,{"filePath":"tests/unit/test_bit_depth_detection.py","startLine":195,"endLine":209,"chunkText":"    # Process 16-bit file\n    mix1 = separator.prepare_mix(input_16bit)\n    assert separator.input_bit_depth == 16\n    print(f\"After 16-bit file: bit depth = {separator.input_bit_depth}\")\n    \n    # Process 24-bit file\n    mix2 = separator.prepare_mix(input_24bit)\n    assert separator.input_bit_depth == 24\n    print(f\"After 24-bit file: bit depth = {separator.input_bit_depth}\")\n    \n    # Process 16-bit file again\n    mix3 = separator.prepare_mix(input_16bit)\n    assert separator.input_bit_depth == 16\n    print(f\"After 16-bit file again: bit depth = {separator.input_bit_depth}\")\n    ","chunkId":"tests/unit/test_bit_depth_detection.py:14"}],[2757,{"filePath":"tests/unit/test_bit_depth_detection.py","startLine":207,"endLine":217,"chunkText":"    assert separator.input_bit_depth == 16\n    print(f\"After 16-bit file again: bit depth = {separator.input_bit_depth}\")\n    \n    print(\"✅ Test passed: Bit depth correctly updated across multiple files\")\n\n\nif __name__ == \"__main__\":\n    # Run tests with pytest\n    pytest.main([__file__, \"-v\", \"-s\"])\n\n","chunkId":"tests/unit/test_bit_depth_detection.py:15"}],[2758,{"filePath":"tests/unit/test_bit_depth_writing.py","startLine":0,"endLine":24,"chunkText":"\"\"\"\nUnit tests for bit depth preservation in audio writing functions.\n\nTests that the write_audio functions preserve the input bit depth.\n\"\"\"\n\nimport os\nimport pytest\nimport tempfile\nimport shutil\nimport soundfile as sf\nimport numpy as np\nfrom unittest.mock import Mock\n\nfrom audio_separator.separator.common_separator import CommonSeparator\n\n\n# Check if FFmpeg is available (required for pydub tests)\ndef is_ffmpeg_available():\n    \"\"\"Check if FFmpeg is available in the system.\"\"\"\n    import subprocess\n    try:\n        subprocess.run(\n            [\"ffmpeg\", \"-version\"],\n            stdout=subprocess.DEVNULL,","chunkId":"tests/unit/test_bit_depth_writing.py:0"}],[2759,{"filePath":"tests/unit/test_bit_depth_writing.py","startLine":22,"endLine":41,"chunkText":"        subprocess.run(\n            [\"ffmpeg\", \"-version\"],\n            stdout=subprocess.DEVNULL,\n            stderr=subprocess.DEVNULL,\n            check=True\n        )\n        return True\n    except (subprocess.CalledProcessError, FileNotFoundError):\n        return False\n\n\nFFMPEG_AVAILABLE = is_ffmpeg_available()\nrequires_ffmpeg = pytest.mark.skipif(\n    not FFMPEG_AVAILABLE,\n    reason=\"FFmpeg not available (required for pydub tests)\"\n)\n\n\ndef create_test_audio_file(output_path, sample_rate=44100, duration=0.5, bit_depth=16):\n    \"\"\"Create a test audio file with a specific bit depth.\"\"\"","chunkId":"tests/unit/test_bit_depth_writing.py:1"}],[2760,{"filePath":"tests/unit/test_bit_depth_writing.py","startLine":39,"endLine":55,"chunkText":"\ndef create_test_audio_file(output_path, sample_rate=44100, duration=0.5, bit_depth=16):\n    \"\"\"Create a test audio file with a specific bit depth.\"\"\"\n    t = np.linspace(0, duration, int(sample_rate * duration))\n    frequency = 440  # A4 note\n    audio = 0.5 * np.sin(2 * np.pi * frequency * t)\n    stereo_audio = np.column_stack([audio, audio])\n    \n    if bit_depth == 16:\n        subtype = 'PCM_16'\n    elif bit_depth == 24:\n        subtype = 'PCM_24'\n    elif bit_depth == 32:\n        subtype = 'PCM_32'\n    else:\n        raise ValueError(f\"Unsupported bit depth: {bit_depth}\")\n    ","chunkId":"tests/unit/test_bit_depth_writing.py:2"}],[2761,{"filePath":"tests/unit/test_bit_depth_writing.py","startLine":53,"endLine":75,"chunkText":"    else:\n        raise ValueError(f\"Unsupported bit depth: {bit_depth}\")\n    \n    sf.write(output_path, stereo_audio, sample_rate, subtype=subtype)\n    return output_path\n\n\ndef get_audio_bit_depth(file_path):\n    \"\"\"Get the bit depth of an audio file.\"\"\"\n    info = sf.info(file_path)\n    subtype = info.subtype\n    \n    if 'PCM_16' in subtype or subtype == 'PCM_S8':\n        return 16\n    elif 'PCM_24' in subtype:\n        return 24\n    elif 'PCM_32' in subtype or 'FLOAT' in subtype or 'DOUBLE' in subtype:\n        return 32\n    else:\n        return None\n\n\n@pytest.fixture(name=\"temp_dir\")","chunkId":"tests/unit/test_bit_depth_writing.py:3"}],[2762,{"filePath":"tests/unit/test_bit_depth_writing.py","startLine":74,"endLine":93,"chunkText":"\n@pytest.fixture(name=\"temp_dir\")\ndef fixture_temp_dir():\n    \"\"\"Fixture providing a temporary directory.\"\"\"\n    temp_dir = tempfile.mkdtemp()\n    yield temp_dir\n    shutil.rmtree(temp_dir)\n\n\n@pytest.fixture(name=\"mock_separator_config\")\ndef fixture_mock_separator_config(temp_dir):\n    \"\"\"Fixture providing a mock separator configuration.\"\"\"\n    return {\n        \"logger\": Mock(),\n        \"log_level\": 20,\n        \"torch_device\": Mock(),\n        \"torch_device_cpu\": Mock(),\n        \"torch_device_mps\": Mock(),\n        \"onnx_execution_provider\": Mock(),\n        \"model_name\": \"test_model\",","chunkId":"tests/unit/test_bit_depth_writing.py:4"}],[2763,{"filePath":"tests/unit/test_bit_depth_writing.py","startLine":91,"endLine":109,"chunkText":"        \"torch_device_mps\": Mock(),\n        \"onnx_execution_provider\": Mock(),\n        \"model_name\": \"test_model\",\n        \"model_path\": \"/path/to/model\",\n        \"model_data\": {\"training\": {\"instruments\": [\"vocals\", \"other\"]}},\n        \"output_dir\": temp_dir,\n        \"output_format\": \"wav\",\n        \"output_bitrate\": None,\n        \"normalization_threshold\": 0.9,\n        \"amplification_threshold\": 0.0,\n        \"enable_denoise\": False,\n        \"output_single_stem\": None,\n        \"invert_using_spec\": False,\n        \"sample_rate\": 44100,\n        \"use_soundfile\": False,\n    }\n\n\n@requires_ffmpeg","chunkId":"tests/unit/test_bit_depth_writing.py:5"}],[2764,{"filePath":"tests/unit/test_bit_depth_writing.py","startLine":107,"endLine":122,"chunkText":"\n\n@requires_ffmpeg\ndef test_write_16bit_with_pydub(temp_dir, mock_separator_config):\n    \"\"\"Test that 16-bit audio is written correctly with pydub.\"\"\"\n    print(\"\\n>>> TEST: Write 16-bit audio with pydub\")\n    \n    # Create a 16-bit test input file\n    input_file = os.path.join(temp_dir, \"input_16bit.wav\")\n    create_test_audio_file(input_file, bit_depth=16)\n    \n    # Create CommonSeparator and prepare mix\n    separator = CommonSeparator(mock_separator_config)\n    separator.audio_file_path = input_file\n    mix = separator.prepare_mix(input_file)\n    ","chunkId":"tests/unit/test_bit_depth_writing.py:6"}],[2765,{"filePath":"tests/unit/test_bit_depth_writing.py","startLine":120,"endLine":134,"chunkText":"    separator.audio_file_path = input_file\n    mix = separator.prepare_mix(input_file)\n    \n    print(f\"Input bit depth detected: {separator.input_bit_depth}\")\n    \n    # Create output audio data (simulated separation output)\n    # The mix is in format [channels, samples], we need [samples, channels] for writing\n    output_audio = mix.T\n    \n    # Write audio using pydub\n    output_file = \"test_output_16bit.wav\"\n    separator.write_audio_pydub(output_file, output_audio)\n    \n    # Check the output file bit depth\n    full_output_path = os.path.join(temp_dir, output_file)","chunkId":"tests/unit/test_bit_depth_writing.py:7"}],[2766,{"filePath":"tests/unit/test_bit_depth_writing.py","startLine":132,"endLine":145,"chunkText":"    \n    # Check the output file bit depth\n    full_output_path = os.path.join(temp_dir, output_file)\n    assert os.path.exists(full_output_path), f\"Output file not created: {full_output_path}\"\n    \n    output_bit_depth = get_audio_bit_depth(full_output_path)\n    print(f\"Output bit depth: {output_bit_depth}\")\n    \n    assert output_bit_depth == 16, f\"Expected 16-bit output, got {output_bit_depth}\"\n    print(\"✅ Test passed: 16-bit audio written correctly with pydub\")\n\n\n@requires_ffmpeg\ndef test_write_24bit_with_pydub(temp_dir, mock_separator_config):","chunkId":"tests/unit/test_bit_depth_writing.py:8"}],[2767,{"filePath":"tests/unit/test_bit_depth_writing.py","startLine":143,"endLine":157,"chunkText":"\n@requires_ffmpeg\ndef test_write_24bit_with_pydub(temp_dir, mock_separator_config):\n    \"\"\"Test that 24-bit audio is written correctly with pydub.\"\"\"\n    print(\"\\n>>> TEST: Write 24-bit audio with pydub\")\n    \n    # Create a 24-bit test input file\n    input_file = os.path.join(temp_dir, \"input_24bit.wav\")\n    create_test_audio_file(input_file, bit_depth=24)\n    \n    # Create CommonSeparator and prepare mix\n    separator = CommonSeparator(mock_separator_config)\n    separator.audio_file_path = input_file\n    mix = separator.prepare_mix(input_file)\n    ","chunkId":"tests/unit/test_bit_depth_writing.py:9"}],[2768,{"filePath":"tests/unit/test_bit_depth_writing.py","startLine":155,"endLine":170,"chunkText":"    separator.audio_file_path = input_file\n    mix = separator.prepare_mix(input_file)\n    \n    print(f\"Input bit depth detected: {separator.input_bit_depth}\")\n    \n    # Create output audio data\n    output_audio = mix.T\n    \n    # Write audio using pydub\n    output_file = \"test_output_24bit.wav\"\n    separator.write_audio_pydub(output_file, output_audio)\n    \n    # Check the output file bit depth\n    full_output_path = os.path.join(temp_dir, output_file)\n    assert os.path.exists(full_output_path), f\"Output file not created: {full_output_path}\"\n    ","chunkId":"tests/unit/test_bit_depth_writing.py:10"}],[2769,{"filePath":"tests/unit/test_bit_depth_writing.py","startLine":168,"endLine":180,"chunkText":"    full_output_path = os.path.join(temp_dir, output_file)\n    assert os.path.exists(full_output_path), f\"Output file not created: {full_output_path}\"\n    \n    output_bit_depth = get_audio_bit_depth(full_output_path)\n    print(f\"Output bit depth: {output_bit_depth}\")\n    \n    assert output_bit_depth == 24, f\"Expected 24-bit output, got {output_bit_depth}\"\n    print(\"✅ Test passed: 24-bit audio written correctly with pydub\")\n\n\n@requires_ffmpeg\ndef test_write_32bit_with_pydub(temp_dir, mock_separator_config):\n    \"\"\"Test that 32-bit audio is written correctly with pydub.\"\"\"","chunkId":"tests/unit/test_bit_depth_writing.py:11"}],[2770,{"filePath":"tests/unit/test_bit_depth_writing.py","startLine":178,"endLine":191,"chunkText":"@requires_ffmpeg\ndef test_write_32bit_with_pydub(temp_dir, mock_separator_config):\n    \"\"\"Test that 32-bit audio is written correctly with pydub.\"\"\"\n    print(\"\\n>>> TEST: Write 32-bit audio with pydub\")\n    \n    # Create a 32-bit test input file\n    input_file = os.path.join(temp_dir, \"input_32bit.wav\")\n    create_test_audio_file(input_file, bit_depth=32)\n    \n    # Create CommonSeparator and prepare mix\n    separator = CommonSeparator(mock_separator_config)\n    separator.audio_file_path = input_file\n    mix = separator.prepare_mix(input_file)\n    ","chunkId":"tests/unit/test_bit_depth_writing.py:12"}],[2771,{"filePath":"tests/unit/test_bit_depth_writing.py","startLine":189,"endLine":204,"chunkText":"    separator.audio_file_path = input_file\n    mix = separator.prepare_mix(input_file)\n    \n    print(f\"Input bit depth detected: {separator.input_bit_depth}\")\n    \n    # Create output audio data\n    output_audio = mix.T\n    \n    # Write audio using pydub\n    output_file = \"test_output_32bit.wav\"\n    separator.write_audio_pydub(output_file, output_audio)\n    \n    # Check the output file bit depth\n    full_output_path = os.path.join(temp_dir, output_file)\n    assert os.path.exists(full_output_path), f\"Output file not created: {full_output_path}\"\n    ","chunkId":"tests/unit/test_bit_depth_writing.py:13"}],[2772,{"filePath":"tests/unit/test_bit_depth_writing.py","startLine":202,"endLine":213,"chunkText":"    full_output_path = os.path.join(temp_dir, output_file)\n    assert os.path.exists(full_output_path), f\"Output file not created: {full_output_path}\"\n    \n    output_bit_depth = get_audio_bit_depth(full_output_path)\n    print(f\"Output bit depth: {output_bit_depth}\")\n    \n    assert output_bit_depth == 32, f\"Expected 32-bit output, got {output_bit_depth}\"\n    print(\"✅ Test passed: 32-bit audio written correctly with pydub\")\n\n\ndef test_write_24bit_with_soundfile(temp_dir, mock_separator_config):\n    \"\"\"Test that 24-bit audio is written correctly with soundfile.\"\"\"","chunkId":"tests/unit/test_bit_depth_writing.py:14"}],[2773,{"filePath":"tests/unit/test_bit_depth_writing.py","startLine":211,"endLine":225,"chunkText":"\ndef test_write_24bit_with_soundfile(temp_dir, mock_separator_config):\n    \"\"\"Test that 24-bit audio is written correctly with soundfile.\"\"\"\n    print(\"\\n>>> TEST: Write 24-bit audio with soundfile\")\n    \n    # Update config to use soundfile\n    mock_separator_config[\"use_soundfile\"] = True\n    \n    # Create a 24-bit test input file\n    input_file = os.path.join(temp_dir, \"input_24bit.wav\")\n    create_test_audio_file(input_file, bit_depth=24)\n    \n    # Create CommonSeparator and prepare mix\n    separator = CommonSeparator(mock_separator_config)\n    separator.audio_file_path = input_file","chunkId":"tests/unit/test_bit_depth_writing.py:15"}],[2774,{"filePath":"tests/unit/test_bit_depth_writing.py","startLine":223,"endLine":238,"chunkText":"    # Create CommonSeparator and prepare mix\n    separator = CommonSeparator(mock_separator_config)\n    separator.audio_file_path = input_file\n    mix = separator.prepare_mix(input_file)\n    \n    print(f\"Input bit depth detected: {separator.input_bit_depth}\")\n    \n    # Create output audio data\n    output_audio = mix.T\n    \n    # Write audio using soundfile\n    output_file = \"test_output_24bit_sf.wav\"\n    separator.write_audio_soundfile(output_file, output_audio)\n    \n    # Check the output file bit depth\n    full_output_path = os.path.join(temp_dir, output_file)","chunkId":"tests/unit/test_bit_depth_writing.py:16"}],[2775,{"filePath":"tests/unit/test_bit_depth_writing.py","startLine":236,"endLine":248,"chunkText":"    \n    # Check the output file bit depth\n    full_output_path = os.path.join(temp_dir, output_file)\n    assert os.path.exists(full_output_path), f\"Output file not created: {full_output_path}\"\n    \n    output_bit_depth = get_audio_bit_depth(full_output_path)\n    print(f\"Output bit depth: {output_bit_depth}\")\n    \n    assert output_bit_depth == 24, f\"Expected 24-bit output, got {output_bit_depth}\"\n    print(\"✅ Test passed: 24-bit audio written correctly with soundfile\")\n\n\ndef test_write_16bit_with_soundfile(temp_dir, mock_separator_config):","chunkId":"tests/unit/test_bit_depth_writing.py:17"}],[2776,{"filePath":"tests/unit/test_bit_depth_writing.py","startLine":246,"endLine":261,"chunkText":"\n\ndef test_write_16bit_with_soundfile(temp_dir, mock_separator_config):\n    \"\"\"Test that 16-bit audio is written correctly with soundfile.\"\"\"\n    print(\"\\n>>> TEST: Write 16-bit audio with soundfile\")\n    \n    # Update config to use soundfile\n    mock_separator_config[\"use_soundfile\"] = True\n    \n    # Create a 16-bit test input file\n    input_file = os.path.join(temp_dir, \"input_16bit.wav\")\n    create_test_audio_file(input_file, bit_depth=16)\n    \n    # Create CommonSeparator and prepare mix\n    separator = CommonSeparator(mock_separator_config)\n    separator.audio_file_path = input_file","chunkId":"tests/unit/test_bit_depth_writing.py:18"}],[2777,{"filePath":"tests/unit/test_bit_depth_writing.py","startLine":259,"endLine":274,"chunkText":"    # Create CommonSeparator and prepare mix\n    separator = CommonSeparator(mock_separator_config)\n    separator.audio_file_path = input_file\n    mix = separator.prepare_mix(input_file)\n    \n    print(f\"Input bit depth detected: {separator.input_bit_depth}\")\n    \n    # Create output audio data\n    output_audio = mix.T\n    \n    # Write audio using soundfile\n    output_file = \"test_output_16bit_sf.wav\"\n    separator.write_audio_soundfile(output_file, output_audio)\n    \n    # Check the output file bit depth\n    full_output_path = os.path.join(temp_dir, output_file)","chunkId":"tests/unit/test_bit_depth_writing.py:19"}],[2778,{"filePath":"tests/unit/test_bit_depth_writing.py","startLine":272,"endLine":288,"chunkText":"    \n    # Check the output file bit depth\n    full_output_path = os.path.join(temp_dir, output_file)\n    assert os.path.exists(full_output_path), f\"Output file not created: {full_output_path}\"\n    \n    output_bit_depth = get_audio_bit_depth(full_output_path)\n    print(f\"Output bit depth: {output_bit_depth}\")\n    \n    assert output_bit_depth == 16, f\"Expected 16-bit output, got {output_bit_depth}\"\n    print(\"✅ Test passed: 16-bit audio written correctly with soundfile\")\n\n\nif __name__ == \"__main__\":\n    # Run tests with pytest\n    pytest.main([__file__, \"-v\", \"-s\"])\n\n","chunkId":"tests/unit/test_bit_depth_writing.py:20"}],[2779,{"filePath":"tests/unit/test_cli.py","startLine":0,"endLine":20,"chunkText":"import json\nimport pytest\nimport logging\nfrom audio_separator.utils.cli import main\nimport subprocess\nfrom unittest import mock\nfrom unittest.mock import patch, MagicMock, mock_open\n\n\n# Common fixture for expected arguments\n@pytest.fixture\ndef common_expected_args():\n    return {\n        \"log_formatter\": mock.ANY,\n        \"log_level\": logging.INFO,\n        \"model_file_dir\": \"/tmp/audio-separator-models/\",\n        \"output_dir\": None,\n        \"output_format\": \"FLAC\",\n        \"output_bitrate\": None,\n        \"normalization_threshold\": 0.9,\n        \"amplification_threshold\": 0.0,","chunkId":"tests/unit/test_cli.py:0"}],[2780,{"filePath":"tests/unit/test_cli.py","startLine":19,"endLine":28,"chunkText":"        \"normalization_threshold\": 0.9,\n        \"amplification_threshold\": 0.0,\n        \"output_single_stem\": None,\n        \"invert_using_spec\": False,\n        \"sample_rate\": 44100,\n        \"use_soundfile\": False,\n        \"use_autocast\": False,\n        \"chunk_duration\": None,\n        \"mdx_params\": {\"hop_length\": 1024, \"segment_size\": 256, \"overlap\": 0.25, \"batch_size\": 1, \"enable_denoise\": False},\n        \"vr_params\": {\"batch_size\": 1, \"window_size\": 512, \"aggression\": 5, \"enable_tta\": False, \"enable_post_process\": False, \"post_process_threshold\": 0.2, \"high_end_process\": False},","chunkId":"tests/unit/test_cli.py:1"}],[2781,{"filePath":"tests/unit/test_cli.py","startLine":26,"endLine":33,"chunkText":"        \"chunk_duration\": None,\n        \"mdx_params\": {\"hop_length\": 1024, \"segment_size\": 256, \"overlap\": 0.25, \"batch_size\": 1, \"enable_denoise\": False},\n        \"vr_params\": {\"batch_size\": 1, \"window_size\": 512, \"aggression\": 5, \"enable_tta\": False, \"enable_post_process\": False, \"post_process_threshold\": 0.2, \"high_end_process\": False},\n        \"demucs_params\": {\"segment_size\": \"Default\", \"shifts\": 2, \"overlap\": 0.25, \"segments_enabled\": True},\n        \"mdxc_params\": {\"segment_size\": 256, \"batch_size\": 1, \"overlap\": 8, \"override_model_segment_size\": False, \"pitch_shift\": 0},\n    }\n\n","chunkId":"tests/unit/test_cli.py:2"}],[2782,{"filePath":"tests/unit/test_cli.py","startLine":31,"endLine":49,"chunkText":"    }\n\n\n# Test the CLI with version argument using subprocess\ndef test_cli_version_subprocess():\n    # Skip subprocess CLI tests - require proper CLI installation\n    pytest.skip(\"CLI subprocess tests require proper installation\")\n\n\n# Test the CLI with no arguments\ndef test_cli_no_args(capsys):\n    # Skip subprocess CLI tests - require proper CLI installation  \n    pytest.skip(\"CLI subprocess tests require proper installation\")\n\n\n# Test with multiple filename arguments\ndef test_cli_multiple_filenames():\n    test_args = [\"cli.py\", \"test1.mp3\", \"test2.mp3\"]\n","chunkId":"tests/unit/test_cli.py:3"}],[2783,{"filePath":"tests/unit/test_cli.py","startLine":47,"endLine":60,"chunkText":"def test_cli_multiple_filenames():\n    test_args = [\"cli.py\", \"test1.mp3\", \"test2.mp3\"]\n\n    # Mock the open function to prevent actual file operations\n    mock_file = mock_open()\n\n    # Create a mock logger\n    mock_logger = MagicMock()\n\n    # Patch multiple functions to prevent actual file operations and separations\n    with patch(\"sys.argv\", test_args), patch(\"builtins.open\", mock_file), patch(\"audio_separator.separator.Separator.separate\") as mock_separate, patch(\n        \"audio_separator.separator.Separator.load_model\"\n    ), patch(\"logging.getLogger\", return_value=mock_logger):\n","chunkId":"tests/unit/test_cli.py:4"}],[2784,{"filePath":"tests/unit/test_cli.py","startLine":58,"endLine":72,"chunkText":"        \"audio_separator.separator.Separator.load_model\"\n    ), patch(\"logging.getLogger\", return_value=mock_logger):\n\n        # Mock the separate method to return some dummy output\n        mock_separate.return_value = [\"output_file1.mp3\", \"output_file2.mp3\"]\n\n        # Call the main function\n        main()\n\n        mock_separate.assert_called_once()\n        args, kwargs = mock_separate.call_args\n        assert args[0] == [\"test1.mp3\", \"test2.mp3\"]\n\n        # Check if the logger captured information about both files\n        log_messages = [call[0][0] for call in mock_logger.info.call_args_list]","chunkId":"tests/unit/test_cli.py:5"}],[2785,{"filePath":"tests/unit/test_cli.py","startLine":70,"endLine":80,"chunkText":"\n        # Check if the logger captured information about both files\n        log_messages = [call[0][0] for call in mock_logger.info.call_args_list]\n        assert any(\"test1.mp3\" in msg and \"test2.mp3\" in msg for msg in log_messages)\n        assert any(\"Separation complete\" in msg for msg in log_messages)\n\n\n# Test the CLI with a specific audio file\ndef test_cli_with_audio_file(capsys, common_expected_args):\n    test_args = [\"cli.py\", \"test_audio.mp3\", \"--model_filename=UVR-MDX-NET-Inst_HQ_4.onnx\"]\n    with patch(\"audio_separator.separator.Separator.separate\") as mock_separate:","chunkId":"tests/unit/test_cli.py:6"}],[2786,{"filePath":"tests/unit/test_cli.py","startLine":78,"endLine":89,"chunkText":"def test_cli_with_audio_file(capsys, common_expected_args):\n    test_args = [\"cli.py\", \"test_audio.mp3\", \"--model_filename=UVR-MDX-NET-Inst_HQ_4.onnx\"]\n    with patch(\"audio_separator.separator.Separator.separate\") as mock_separate:\n        mock_separate.return_value = [\"output_file.mp3\"]\n        with patch(\"sys.argv\", test_args):\n            # Call the main function in cli.py\n            main()\n\n    # Update expected args for this specific test\n    common_expected_args[\"model_file_dir\"] = \"/tmp/audio-separator-models/\"\n\n    # Check if the separate method was called with the correct arguments","chunkId":"tests/unit/test_cli.py:7"}],[2787,{"filePath":"tests/unit/test_cli.py","startLine":87,"endLine":105,"chunkText":"    common_expected_args[\"model_file_dir\"] = \"/tmp/audio-separator-models/\"\n\n    # Check if the separate method was called with the correct arguments\n    mock_separate.assert_called_once()\n\n    # Assertions\n    assert mock_separate.called\n\n\n# Test the CLI with invalid log level\ndef test_cli_invalid_log_level():\n    test_args = [\"cli.py\", \"test_audio.mp3\", \"--log_level=invalid\"]\n    with patch(\"sys.argv\", test_args):\n        # Assert an attribute error is raised due to the invalid LogLevel\n        with pytest.raises(AttributeError):\n            # Call the main function in cli.py\n            main()\n\n","chunkId":"tests/unit/test_cli.py:8"}],[2788,{"filePath":"tests/unit/test_cli.py","startLine":103,"endLine":116,"chunkText":"            main()\n\n\n# Test using model name argument\ndef test_cli_model_filename_argument(common_expected_args):\n    test_args = [\"cli.py\", \"test_audio.mp3\", \"--model_filename=Custom_Model.onnx\"]\n    with patch(\"sys.argv\", test_args):\n        with patch(\"audio_separator.separator.Separator\") as mock_separator:\n            mock_separator_instance = mock_separator.return_value\n            mock_separator_instance.separate.return_value = [\"output_file.mp3\"]\n            main()\n\n            # Assertions\n            mock_separator.assert_called_once_with(**common_expected_args)","chunkId":"tests/unit/test_cli.py:9"}],[2789,{"filePath":"tests/unit/test_cli.py","startLine":114,"endLine":125,"chunkText":"\n            # Assertions\n            mock_separator.assert_called_once_with(**common_expected_args)\n            mock_separator_instance.load_model.assert_called_once_with(model_filename=\"Custom_Model.onnx\")\n\n\n# Test using output directory argument\ndef test_cli_output_dir_argument(common_expected_args):\n    test_args = [\"cli.py\", \"test_audio.mp3\", \"--output_dir=/custom/output/dir\"]\n    with patch(\"sys.argv\", test_args):\n        with patch(\"audio_separator.separator.Separator\") as mock_separator:\n            mock_separator_instance = mock_separator.return_value","chunkId":"tests/unit/test_cli.py:10"}],[2790,{"filePath":"tests/unit/test_cli.py","startLine":123,"endLine":137,"chunkText":"    with patch(\"sys.argv\", test_args):\n        with patch(\"audio_separator.separator.Separator\") as mock_separator:\n            mock_separator_instance = mock_separator.return_value\n            mock_separator_instance.separate.return_value = [\"output_file.mp3\"]\n            main()\n\n            # Update expected args for this specific test\n            expected_args = common_expected_args.copy()\n            expected_args[\"output_dir\"] = \"/custom/output/dir\"\n\n            # Assertions\n            mock_separator.assert_called_once_with(**expected_args)\n\n\n# Test using output format argument","chunkId":"tests/unit/test_cli.py:11"}],[2791,{"filePath":"tests/unit/test_cli.py","startLine":135,"endLine":147,"chunkText":"\n\n# Test using output format argument\ndef test_cli_output_format_argument(common_expected_args):\n    test_args = [\"cli.py\", \"test_audio.mp3\", \"--output_format=MP3\"]\n    with patch(\"sys.argv\", test_args):\n        with patch(\"audio_separator.separator.Separator\") as mock_separator:\n            mock_separator_instance = mock_separator.return_value\n            mock_separator_instance.separate.return_value = [\"output_file.mp3\"]\n            main()\n\n            # Update expected args for this specific test\n            expected_args = common_expected_args.copy()","chunkId":"tests/unit/test_cli.py:12"}],[2792,{"filePath":"tests/unit/test_cli.py","startLine":145,"endLine":158,"chunkText":"\n            # Update expected args for this specific test\n            expected_args = common_expected_args.copy()\n            expected_args[\"output_format\"] = \"MP3\"\n\n            # Assertions\n            mock_separator.assert_called_once_with(**expected_args)\n\n\n# Test using normalization_threshold argument\ndef test_cli_normalization_threshold_argument(common_expected_args):\n    test_args = [\"cli.py\", \"test_audio.mp3\", \"--normalization=0.75\"]\n    with patch(\"sys.argv\", test_args):\n        with patch(\"audio_separator.separator.Separator\") as mock_separator:","chunkId":"tests/unit/test_cli.py:13"}],[2793,{"filePath":"tests/unit/test_cli.py","startLine":156,"endLine":167,"chunkText":"    test_args = [\"cli.py\", \"test_audio.mp3\", \"--normalization=0.75\"]\n    with patch(\"sys.argv\", test_args):\n        with patch(\"audio_separator.separator.Separator\") as mock_separator:\n            mock_separator_instance = mock_separator.return_value\n            mock_separator_instance.separate.return_value = [\"output_file.mp3\"]\n            main()\n\n            # Update expected args for this specific test\n            expected_args = common_expected_args.copy()\n            expected_args[\"normalization_threshold\"] = 0.75\n\n            # Assertions","chunkId":"tests/unit/test_cli.py:14"}],[2794,{"filePath":"tests/unit/test_cli.py","startLine":165,"endLine":177,"chunkText":"            expected_args[\"normalization_threshold\"] = 0.75\n\n            # Assertions\n            mock_separator.assert_called_once_with(**expected_args)\n\n\n# Test using amplification_threshold argument\ndef test_cli_amplification_threshold_argument(common_expected_args):\n    test_args = [\"cli.py\", \"test_audio.mp3\", \"--amplification=0.75\"]\n    with patch(\"sys.argv\", test_args):\n        with patch(\"audio_separator.separator.Separator\") as mock_separator:\n            mock_separator_instance = mock_separator.return_value\n            mock_separator_instance.separate.return_value = [\"output_file.mp3\"]","chunkId":"tests/unit/test_cli.py:15"}],[2795,{"filePath":"tests/unit/test_cli.py","startLine":175,"endLine":189,"chunkText":"        with patch(\"audio_separator.separator.Separator\") as mock_separator:\n            mock_separator_instance = mock_separator.return_value\n            mock_separator_instance.separate.return_value = [\"output_file.mp3\"]\n            main()\n\n            # Update expected args for this specific test\n            expected_args = common_expected_args.copy()\n            expected_args[\"amplification_threshold\"] = 0.75\n\n            # Assertions\n            mock_separator.assert_called_once_with(**expected_args)\n\n\n# Test using single stem argument\ndef test_cli_single_stem_argument(common_expected_args):","chunkId":"tests/unit/test_cli.py:16"}],[2796,{"filePath":"tests/unit/test_cli.py","startLine":187,"endLine":198,"chunkText":"\n# Test using single stem argument\ndef test_cli_single_stem_argument(common_expected_args):\n    test_args = [\"cli.py\", \"test_audio.mp3\", \"--single_stem=instrumental\"]\n    with patch(\"sys.argv\", test_args):\n        with patch(\"audio_separator.separator.Separator\") as mock_separator:\n            mock_separator_instance = mock_separator.return_value\n            mock_separator_instance.separate.return_value = [\"output_file.mp3\"]\n            main()\n\n            # Update expected args for this specific test\n            expected_args = common_expected_args.copy()","chunkId":"tests/unit/test_cli.py:17"}],[2797,{"filePath":"tests/unit/test_cli.py","startLine":196,"endLine":209,"chunkText":"\n            # Update expected args for this specific test\n            expected_args = common_expected_args.copy()\n            expected_args[\"output_single_stem\"] = \"instrumental\"\n\n            # Assertions\n            mock_separator.assert_called_once_with(**expected_args)\n\n\n# Test using invert spectrogram argument\ndef test_cli_invert_spectrogram_argument(common_expected_args):\n    test_args = [\"cli.py\", \"test_audio.mp3\", \"--invert_spect\"]\n    with patch(\"sys.argv\", test_args):\n        with patch(\"audio_separator.separator.Separator\") as mock_separator:","chunkId":"tests/unit/test_cli.py:18"}],[2798,{"filePath":"tests/unit/test_cli.py","startLine":207,"endLine":221,"chunkText":"    test_args = [\"cli.py\", \"test_audio.mp3\", \"--invert_spect\"]\n    with patch(\"sys.argv\", test_args):\n        with patch(\"audio_separator.separator.Separator\") as mock_separator:\n            mock_separator_instance = mock_separator.return_value\n            mock_separator_instance.separate.return_value = [\"output_file.mp3\"]\n            main()\n\n            # Update expected args for this specific test\n            expected_args = common_expected_args.copy()\n            expected_args[\"invert_using_spec\"] = True\n\n            # Assertions\n            mock_separator.assert_called_once_with(**expected_args)\n\n","chunkId":"tests/unit/test_cli.py:19"}],[2799,{"filePath":"tests/unit/test_cli.py","startLine":219,"endLine":231,"chunkText":"            mock_separator.assert_called_once_with(**expected_args)\n\n\n# Test using use_autocast argument\ndef test_cli_use_autocast_argument(common_expected_args):\n    test_args = [\"cli.py\", \"test_audio.mp3\", \"--use_autocast\"]\n    with patch(\"sys.argv\", test_args):\n        with patch(\"audio_separator.separator.Separator\") as mock_separator:\n            mock_separator_instance = mock_separator.return_value\n            mock_separator_instance.separate.return_value = [\"output_file.mp3\"]\n            main()\n\n            # Update expected args for this specific test","chunkId":"tests/unit/test_cli.py:20"}],[2800,{"filePath":"tests/unit/test_cli.py","startLine":229,"endLine":245,"chunkText":"            main()\n\n            # Update expected args for this specific test\n            expected_args = common_expected_args.copy()\n            expected_args[\"use_autocast\"] = True\n\n            # Assertions\n            mock_separator.assert_called_once_with(**expected_args)\n\n\n# Test using custom_output_names arguments\ndef test_cli_custom_output_names_argument(common_expected_args):\n    custom_names = {\n        \"Vocals\": \"vocals_output\",\n        \"Instrumental\": \"instrumental_output\",\n    }\n    test_args = [\"cli.py\", \"test_audio.mp3\", f\"--custom_output_names={json.dumps(custom_names)}\"]","chunkId":"tests/unit/test_cli.py:21"}],[2801,{"filePath":"tests/unit/test_cli.py","startLine":243,"endLine":253,"chunkText":"        \"Instrumental\": \"instrumental_output\",\n    }\n    test_args = [\"cli.py\", \"test_audio.mp3\", f\"--custom_output_names={json.dumps(custom_names)}\"]\n    with patch(\"sys.argv\", test_args):\n        with patch(\"audio_separator.separator.Separator\") as mock_separator:\n            mock_separator_instance = mock_separator.return_value\n            mock_separator_instance.separate.return_value = [\"output_file.mp3\"]\n            main()\n\n            # Assertions\n            mock_separator.assert_called_once_with(**common_expected_args)","chunkId":"tests/unit/test_cli.py:22"}],[2802,{"filePath":"tests/unit/test_cli.py","startLine":251,"endLine":266,"chunkText":"\n            # Assertions\n            mock_separator.assert_called_once_with(**common_expected_args)\n            mock_separator_instance.separate.assert_called_once_with([\"test_audio.mp3\"], custom_output_names=custom_names)\n\n\n# Test using custom_output_names arguments\ndef test_cli_demucs_output_names_argument(common_expected_args):\n    demucs_output_names = {\n        \"Vocals\": \"vocals_output\",\n        \"Drums\": \"drums_output\",\n        \"Bass\": \"bass_output\",\n        \"Other\": \"other_output\",\n        \"Guitar\": \"guitar_output\",\n        \"Piano\": \"piano_output\"\n    }","chunkId":"tests/unit/test_cli.py:23"}],[2803,{"filePath":"tests/unit/test_cli.py","startLine":264,"endLine":275,"chunkText":"        \"Guitar\": \"guitar_output\",\n        \"Piano\": \"piano_output\"\n    }\n    test_args = [\"cli.py\", \"test_audio.mp3\", f\"--custom_output_names={json.dumps(demucs_output_names)}\", \"--model_filename=htdemucs_6s.yaml\"]\n    with patch(\"sys.argv\", test_args):\n        with patch(\"audio_separator.separator.Separator\") as mock_separator:\n            mock_separator_instance = mock_separator.return_value\n            mock_separator_instance.separate.return_value = [\"output_file.mp3\"]\n            main()\n\n            # Assertions\n            mock_separator.assert_called_once_with(**common_expected_args)","chunkId":"tests/unit/test_cli.py:24"}],[2804,{"filePath":"tests/unit/test_cli.py","startLine":273,"endLine":277,"chunkText":"\n            # Assertions\n            mock_separator.assert_called_once_with(**common_expected_args)\n            mock_separator_instance.separate.assert_called_once_with([\"test_audio.mp3\"], custom_output_names=demucs_output_names)\n","chunkId":"tests/unit/test_cli.py:25"}],[2805,{"filePath":"tests/unit/test_configuration_normalizer.py","startLine":0,"endLine":16,"chunkText":"\"\"\"\nUnit tests for ConfigurationNormalizer methods.\nTests the configuration normalization and validation logic.\n\"\"\"\n\nimport pytest\nfrom unittest.mock import Mock, patch\n\n# Add the roformer module to path for imports\nimport sys\nimport os\n# Find project root dynamically\ncurrent_dir = os.path.dirname(os.path.abspath(__file__))\nproject_root = current_dir\n# Go up until we find the project root (contains audio_separator/ directory)\nwhile project_root and not os.path.exists(os.path.join(project_root, 'audio_separator')):\n    parent = os.path.dirname(project_root)","chunkId":"tests/unit/test_configuration_normalizer.py:0"}],[2806,{"filePath":"tests/unit/test_configuration_normalizer.py","startLine":14,"endLine":28,"chunkText":"# Go up until we find the project root (contains audio_separator/ directory)\nwhile project_root and not os.path.exists(os.path.join(project_root, 'audio_separator')):\n    parent = os.path.dirname(project_root)\n    if parent == project_root:  # Reached filesystem root\n        break\n    project_root = parent\n\nif project_root:\n    sys.path.append(project_root)\n\nfrom audio_separator.separator.roformer.configuration_normalizer import ConfigurationNormalizer\nfrom audio_separator.separator.roformer.parameter_validation_error import ParameterValidationError\n\n\nclass TestConfigurationNormalizer:","chunkId":"tests/unit/test_configuration_normalizer.py:1"}],[2807,{"filePath":"tests/unit/test_configuration_normalizer.py","startLine":26,"endLine":45,"chunkText":"\n\nclass TestConfigurationNormalizer:\n    \"\"\"Test cases for ConfigurationNormalizer class.\"\"\"\n    \n    def setup_method(self):\n        \"\"\"Set up test fixtures.\"\"\"\n        self.normalizer = ConfigurationNormalizer()\n    \n    def test_normalize_config_basic(self):\n        \"\"\"Test basic configuration normalization.\"\"\"\n        config = {\n            'dim': 512,\n            'depth': 12,\n            'freqs_per_bands': (2, 4, 8, 16, 32, 64)\n        }\n        \n        result = self.normalizer.normalize_config(config, \"bs_roformer\", apply_defaults=False, validate=False)\n        \n        assert result['dim'] == 512","chunkId":"tests/unit/test_configuration_normalizer.py:2"}],[2808,{"filePath":"tests/unit/test_configuration_normalizer.py","startLine":43,"endLine":56,"chunkText":"        result = self.normalizer.normalize_config(config, \"bs_roformer\", apply_defaults=False, validate=False)\n        \n        assert result['dim'] == 512\n        assert result['depth'] == 12\n        assert result['freqs_per_bands'] == (2, 4, 8, 16, 32, 64)\n    \n    def test_normalize_config_with_defaults(self):\n        \"\"\"Test configuration normalization with defaults applied.\"\"\"\n        config = {\n            'dim': 512,\n            'depth': 12,\n            'freqs_per_bands': (2, 4, 8, 16, 32, 64)\n        }\n        ","chunkId":"tests/unit/test_configuration_normalizer.py:3"}],[2809,{"filePath":"tests/unit/test_configuration_normalizer.py","startLine":54,"endLine":69,"chunkText":"            'freqs_per_bands': (2, 4, 8, 16, 32, 64)\n        }\n        \n        result = self.normalizer.normalize_config(config, \"bs_roformer\", apply_defaults=True, validate=False)\n        \n        # Original values preserved\n        assert result['dim'] == 512\n        assert result['depth'] == 12\n        assert result['freqs_per_bands'] == (2, 4, 8, 16, 32, 64)\n        \n        # Defaults applied\n        assert result['stereo'] is False\n        assert result['num_stems'] == 2\n        assert result['flash_attn'] is True\n        assert result['mlp_expansion_factor'] == 4\n    ","chunkId":"tests/unit/test_configuration_normalizer.py:4"}],[2810,{"filePath":"tests/unit/test_configuration_normalizer.py","startLine":67,"endLine":81,"chunkText":"        assert result['flash_attn'] is True\n        assert result['mlp_expansion_factor'] == 4\n    \n    def test_normalize_config_with_validation_valid(self):\n        \"\"\"Test configuration normalization with validation - valid config.\"\"\"\n        config = {\n            'dim': 512,\n            'depth': 12,\n            'freqs_per_bands': (2, 4, 8, 16, 32, 64)\n        }\n        \n        # Should not raise any exception\n        result = self.normalizer.normalize_config(config, \"bs_roformer\", apply_defaults=True, validate=True)\n        assert result is not None\n    ","chunkId":"tests/unit/test_configuration_normalizer.py:5"}],[2811,{"filePath":"tests/unit/test_configuration_normalizer.py","startLine":79,"endLine":92,"chunkText":"        result = self.normalizer.normalize_config(config, \"bs_roformer\", apply_defaults=True, validate=True)\n        assert result is not None\n    \n    def test_normalize_config_with_validation_invalid(self):\n        \"\"\"Test configuration normalization with validation - invalid config.\"\"\"\n        config = {\n            'dim': \"invalid\",  # Invalid type\n            'depth': 12\n            # Missing required 'freqs_per_bands'\n        }\n        \n        with pytest.raises(ParameterValidationError):\n            self.normalizer.normalize_config(config, \"bs_roformer\", apply_defaults=True, validate=True)\n    ","chunkId":"tests/unit/test_configuration_normalizer.py:6"}],[2812,{"filePath":"tests/unit/test_configuration_normalizer.py","startLine":90,"endLine":106,"chunkText":"        with pytest.raises(ParameterValidationError):\n            self.normalizer.normalize_config(config, \"bs_roformer\", apply_defaults=True, validate=True)\n    \n    def test_normalize_structure_flat_config(self):\n        \"\"\"Test structure normalization with flat configuration.\"\"\"\n        config = {\n            'dim': 512,\n            'depth': 12,\n            'sample_rate': 44100\n        }\n        \n        result = self.normalizer._normalize_structure(config, \"bs_roformer\")\n        \n        assert result['dim'] == 512\n        assert result['depth'] == 12\n        assert result['sample_rate'] == 44100\n    ","chunkId":"tests/unit/test_configuration_normalizer.py:7"}],[2813,{"filePath":"tests/unit/test_configuration_normalizer.py","startLine":104,"endLine":123,"chunkText":"        assert result['depth'] == 12\n        assert result['sample_rate'] == 44100\n    \n    def test_normalize_structure_nested_config(self):\n        \"\"\"Test structure normalization with nested configuration.\"\"\"\n        config = {\n            'model': {\n                'dim': 512,\n                'depth': 12\n            },\n            'training': {\n                'sample_rate': 44100,\n                'hop_length': 512\n            },\n            'inference': {\n                'dim_t': 1024,\n                'n_fft': 2048\n            }\n        }\n        ","chunkId":"tests/unit/test_configuration_normalizer.py:8"}],[2814,{"filePath":"tests/unit/test_configuration_normalizer.py","startLine":121,"endLine":138,"chunkText":"            }\n        }\n        \n        result = self.normalizer._normalize_structure(config, \"bs_roformer\")\n        \n        # Flattened model parameters\n        assert result['dim'] == 512\n        assert result['depth'] == 12\n        \n        # Extracted training/inference parameters\n        assert result['sample_rate'] == 44100\n        assert result['hop_length'] == 512\n        assert result['dim_t'] == 1024\n        assert result['n_fft'] == 2048\n    \n    def test_normalize_parameter_names_aliases(self):\n        \"\"\"Test parameter name normalization with aliases.\"\"\"\n        config = {","chunkId":"tests/unit/test_configuration_normalizer.py:9"}],[2815,{"filePath":"tests/unit/test_configuration_normalizer.py","startLine":136,"endLine":148,"chunkText":"    def test_normalize_parameter_names_aliases(self):\n        \"\"\"Test parameter name normalization with aliases.\"\"\"\n        config = {\n            'n_fft': 2048,  # Should become 'stft_n_fft'\n            'hop_length': 512,  # Should become 'stft_hop_length'\n            'n_heads': 8,  # Should become 'heads'\n            'expansion_factor': 4,  # Should become 'mlp_expansion_factor'\n            'freq_bands': (2, 4, 8, 16),  # Should become 'freqs_per_bands'\n            'n_mels': 64  # Should become 'num_bands'\n        }\n        \n        result = self.normalizer._normalize_parameter_names(config)\n        ","chunkId":"tests/unit/test_configuration_normalizer.py:10"}],[2816,{"filePath":"tests/unit/test_configuration_normalizer.py","startLine":146,"endLine":161,"chunkText":"        \n        result = self.normalizer._normalize_parameter_names(config)\n        \n        assert result['stft_n_fft'] == 2048\n        assert result['stft_hop_length'] == 512\n        assert result['heads'] == 8\n        assert result['mlp_expansion_factor'] == 4\n        assert result['freqs_per_bands'] == (2, 4, 8, 16)\n        assert result['num_bands'] == 64\n        \n        # Original names should not be present\n        assert 'n_fft' not in result\n        assert 'hop_length' not in result\n        assert 'n_heads' not in result\n    \n    def test_normalize_parameter_values_booleans(self):","chunkId":"tests/unit/test_configuration_normalizer.py:11"}],[2817,{"filePath":"tests/unit/test_configuration_normalizer.py","startLine":159,"endLine":175,"chunkText":"        assert 'n_heads' not in result\n    \n    def test_normalize_parameter_values_booleans(self):\n        \"\"\"Test parameter value normalization for booleans.\"\"\"\n        config = {\n            'stereo': 'true',\n            'flash_attn': 'false',\n            'sage_attention': '1',\n            'zero_dc': 'yes',\n            'use_torch_checkpoint': 'on',\n            'skip_connection': '0'\n        }\n        \n        result = self.normalizer._normalize_parameter_values(config, \"bs_roformer\")\n        \n        assert result['stereo'] is True\n        assert result['flash_attn'] is False","chunkId":"tests/unit/test_configuration_normalizer.py:12"}],[2818,{"filePath":"tests/unit/test_configuration_normalizer.py","startLine":173,"endLine":187,"chunkText":"        \n        assert result['stereo'] is True\n        assert result['flash_attn'] is False\n        assert result['sage_attention'] is True\n        assert result['zero_dc'] is True\n        assert result['use_torch_checkpoint'] is True\n        assert result['skip_connection'] is False\n    \n    def test_normalize_parameter_values_numbers(self):\n        \"\"\"Test parameter value normalization for numbers.\"\"\"\n        config = {\n            'dim': '512',\n            'depth': '12.0',  # Float string to int\n            'sample_rate': 44100.0,  # Float to int\n            'attn_dropout': '0.1',","chunkId":"tests/unit/test_configuration_normalizer.py:13"}],[2819,{"filePath":"tests/unit/test_configuration_normalizer.py","startLine":185,"endLine":201,"chunkText":"            'depth': '12.0',  # Float string to int\n            'sample_rate': 44100.0,  # Float to int\n            'attn_dropout': '0.1',\n            'ff_dropout': 0.2\n        }\n        \n        result = self.normalizer._normalize_parameter_values(config, \"bs_roformer\")\n        \n        assert result['dim'] == 512\n        assert result['depth'] == 12\n        assert result['sample_rate'] == 44100\n        assert result['attn_dropout'] == 0.1\n        assert result['ff_dropout'] == 0.2\n        \n        # Check types\n        assert isinstance(result['dim'], int)\n        assert isinstance(result['depth'], int)","chunkId":"tests/unit/test_configuration_normalizer.py:14"}],[2820,{"filePath":"tests/unit/test_configuration_normalizer.py","startLine":199,"endLine":210,"chunkText":"        # Check types\n        assert isinstance(result['dim'], int)\n        assert isinstance(result['depth'], int)\n        assert isinstance(result['sample_rate'], int)\n        assert isinstance(result['attn_dropout'], float)\n        assert isinstance(result['ff_dropout'], float)\n    \n    def test_normalize_parameter_values_tuples(self):\n        \"\"\"Test parameter value normalization for tuples/lists.\"\"\"\n        config = {\n            'freqs_per_bands': '[2, 4, 8, 16]',  # String representation\n            'freqs_per_bands_2': [2, 4, 8, 16],  # List to tuple","chunkId":"tests/unit/test_configuration_normalizer.py:15"}],[2821,{"filePath":"tests/unit/test_configuration_normalizer.py","startLine":208,"endLine":221,"chunkText":"        config = {\n            'freqs_per_bands': '[2, 4, 8, 16]',  # String representation\n            'freqs_per_bands_2': [2, 4, 8, 16],  # List to tuple\n            'freqs_per_bands_3': '(2,4,8,16)'  # String tuple\n        }\n        \n        result = self.normalizer._normalize_parameter_values(config, \"bs_roformer\")\n        \n        assert result['freqs_per_bands'] == (2, 4, 8, 16)\n        assert result['freqs_per_bands_2'] == (2, 4, 8, 16)\n        assert result['freqs_per_bands_3'] == (2, 4, 8, 16)\n        \n        # Check types are tuples\n        assert isinstance(result['freqs_per_bands'], tuple)","chunkId":"tests/unit/test_configuration_normalizer.py:16"}],[2822,{"filePath":"tests/unit/test_configuration_normalizer.py","startLine":219,"endLine":232,"chunkText":"        \n        # Check types are tuples\n        assert isinstance(result['freqs_per_bands'], tuple)\n        assert isinstance(result['freqs_per_bands_2'], tuple)\n        assert isinstance(result['freqs_per_bands_3'], tuple)\n    \n    def test_normalize_parameter_values_strings(self):\n        \"\"\"Test parameter value normalization for strings.\"\"\"\n        config = {\n            'norm': 'LAYER_NORM',  # Should become lowercase\n            'act': 'GELU',  # Should become lowercase\n            'mel_scale': 'HTK'  # Should become lowercase\n        }\n        ","chunkId":"tests/unit/test_configuration_normalizer.py:17"}],[2823,{"filePath":"tests/unit/test_configuration_normalizer.py","startLine":230,"endLine":243,"chunkText":"            'mel_scale': 'HTK'  # Should become lowercase\n        }\n        \n        result = self.normalizer._normalize_parameter_values(config, \"mel_band_roformer\")\n        \n        assert result['norm'] == 'layer_norm'\n        assert result['act'] == 'gelu'\n        assert result['mel_scale'] == 'htk'\n    \n    def test_detect_model_type_bs_roformer(self):\n        \"\"\"Test model type detection for BSRoformer.\"\"\"\n        configs = [\n            {'freqs_per_bands': (2, 4, 8, 16)},  # Direct indicator\n            {'model_type': 'bs_roformer'},  # Explicit type","chunkId":"tests/unit/test_configuration_normalizer.py:18"}],[2824,{"filePath":"tests/unit/test_configuration_normalizer.py","startLine":241,"endLine":254,"chunkText":"        configs = [\n            {'freqs_per_bands': (2, 4, 8, 16)},  # Direct indicator\n            {'model_type': 'bs_roformer'},  # Explicit type\n            {'type': 'BSRoformer'},  # Explicit type variant\n            {'architecture': 'bs-roformer'}  # Architecture field\n        ]\n        \n        for config in configs:\n            result = self.normalizer.detect_model_type(config)\n            assert result == \"bs_roformer\", f\"Failed for config: {config}\"\n    \n    def test_detect_model_type_mel_band_roformer(self):\n        \"\"\"Test model type detection for MelBandRoformer.\"\"\"\n        configs = [","chunkId":"tests/unit/test_configuration_normalizer.py:19"}],[2825,{"filePath":"tests/unit/test_configuration_normalizer.py","startLine":252,"endLine":264,"chunkText":"    def test_detect_model_type_mel_band_roformer(self):\n        \"\"\"Test model type detection for MelBandRoformer.\"\"\"\n        configs = [\n            {'num_bands': 64},  # Direct indicator\n            {'n_mels': 64},  # Alias\n            {'mel_bands': 64},  # Alias\n            {'model_type': 'mel_band_roformer'},  # Explicit type\n            {'type': 'MelBandRoformer'},  # Explicit type variant\n            {'architecture': 'mel-roformer'}  # Architecture field\n        ]\n        \n        for config in configs:\n            result = self.normalizer.detect_model_type(config)","chunkId":"tests/unit/test_configuration_normalizer.py:20"}],[2826,{"filePath":"tests/unit/test_configuration_normalizer.py","startLine":262,"endLine":276,"chunkText":"        \n        for config in configs:\n            result = self.normalizer.detect_model_type(config)\n            assert result == \"mel_band_roformer\", f\"Failed for config: {config}\"\n    \n    def test_detect_model_type_unknown(self):\n        \"\"\"Test model type detection for unknown configurations.\"\"\"\n        configs = [\n            {},  # Empty config\n            {'dim': 512, 'depth': 12},  # No specific indicators\n            {'model_type': 'unknown'}  # Unknown type\n        ]\n        \n        for config in configs:\n            result = self.normalizer.detect_model_type(config)","chunkId":"tests/unit/test_configuration_normalizer.py:21"}],[2827,{"filePath":"tests/unit/test_configuration_normalizer.py","startLine":274,"endLine":291,"chunkText":"        \n        for config in configs:\n            result = self.normalizer.detect_model_type(config)\n            assert result is None, f\"Should return None for config: {config}\"\n    \n    def test_normalize_from_file_path_bs_roformer(self):\n        \"\"\"Test normalization with file path detection - BSRoformer.\"\"\"\n        config = {\n            'dim': 512,\n            'depth': 12\n        }\n        \n        file_paths = [\n            '/path/to/bs_roformer_model.ckpt',\n            '/path/to/BS-Roformer-model.pth',\n            '/path/to/model_bs_roformer.bin'\n        ]\n        ","chunkId":"tests/unit/test_configuration_normalizer.py:22"}],[2828,{"filePath":"tests/unit/test_configuration_normalizer.py","startLine":289,"endLine":301,"chunkText":"            '/path/to/model_bs_roformer.bin'\n        ]\n        \n        for file_path in file_paths:\n            result = self.normalizer.normalize_from_file_path(\n                config, file_path, apply_defaults=True, validate=False\n            )\n            \n            # Should have BSRoformer defaults\n            assert 'freqs_per_bands' in result, f\"Failed for path: {file_path}\"\n            assert 'mask_estimator_depth' in result, f\"Failed for path: {file_path}\"\n    \n    def test_normalize_from_file_path_mel_band_roformer(self):","chunkId":"tests/unit/test_configuration_normalizer.py:23"}],[2829,{"filePath":"tests/unit/test_configuration_normalizer.py","startLine":299,"endLine":315,"chunkText":"            assert 'mask_estimator_depth' in result, f\"Failed for path: {file_path}\"\n    \n    def test_normalize_from_file_path_mel_band_roformer(self):\n        \"\"\"Test normalization with file path detection - MelBandRoformer.\"\"\"\n        config = {\n            'dim': 512,\n            'depth': 12\n        }\n        \n        file_paths = [\n            '/path/to/mel_band_roformer_model.ckpt',\n            '/path/to/MelBand-Roformer-model.pth',\n            '/path/to/model_mel_roformer.bin'\n        ]\n        \n        for file_path in file_paths:\n            result = self.normalizer.normalize_from_file_path(","chunkId":"tests/unit/test_configuration_normalizer.py:24"}],[2830,{"filePath":"tests/unit/test_configuration_normalizer.py","startLine":313,"endLine":328,"chunkText":"        \n        for file_path in file_paths:\n            result = self.normalizer.normalize_from_file_path(\n                config, file_path, apply_defaults=True, validate=False\n            )\n            \n            # Should have MelBandRoformer defaults\n            assert result['num_bands'] == 64, f\"Failed for path: {file_path}\"\n    \n    def test_normalize_from_file_path_default_fallback(self):\n        \"\"\"Test normalization with file path detection - default fallback.\"\"\"\n        config = {\n            'dim': 512,\n            'depth': 12\n        }\n        ","chunkId":"tests/unit/test_configuration_normalizer.py:25"}],[2831,{"filePath":"tests/unit/test_configuration_normalizer.py","startLine":326,"endLine":341,"chunkText":"            'depth': 12\n        }\n        \n        file_path = '/path/to/unknown_model.ckpt'\n        \n        with patch.object(self.normalizer, 'detect_model_type', return_value=None):\n            result = self.normalizer.normalize_from_file_path(\n                config, file_path, apply_defaults=True, validate=False\n            )\n            \n            # Should default to BSRoformer\n            assert 'freqs_per_bands' in result\n    \n    def test_normalization_preserves_original_config(self):\n        \"\"\"Test that normalization doesn't modify the original configuration.\"\"\"\n        original_config = {","chunkId":"tests/unit/test_configuration_normalizer.py:26"}],[2832,{"filePath":"tests/unit/test_configuration_normalizer.py","startLine":339,"endLine":353,"chunkText":"    def test_normalization_preserves_original_config(self):\n        \"\"\"Test that normalization doesn't modify the original configuration.\"\"\"\n        original_config = {\n            'dim': 512,\n            'depth': 12,\n            'n_fft': 2048  # Will be renamed to stft_n_fft\n        }\n        \n        # Keep a copy to compare\n        config_copy = original_config.copy()\n        \n        result = self.normalizer.normalize_config(original_config, \"bs_roformer\")\n        \n        # Original config should be unchanged\n        assert original_config == config_copy","chunkId":"tests/unit/test_configuration_normalizer.py:27"}],[2833,{"filePath":"tests/unit/test_configuration_normalizer.py","startLine":351,"endLine":365,"chunkText":"        \n        # Original config should be unchanged\n        assert original_config == config_copy\n        assert 'n_fft' in original_config  # Original name preserved\n        assert 'stft_n_fft' not in original_config\n        \n        # Result should have normalized names\n        assert 'stft_n_fft' in result\n        assert result['stft_n_fft'] == 2048\n    \n    def test_normalization_error_handling(self):\n        \"\"\"Test error handling during normalization.\"\"\"\n        # Test with invalid string values that can't be converted\n        config = {\n            'dim': 'not_a_number',","chunkId":"tests/unit/test_configuration_normalizer.py:28"}],[2834,{"filePath":"tests/unit/test_configuration_normalizer.py","startLine":363,"endLine":374,"chunkText":"        # Test with invalid string values that can't be converted\n        config = {\n            'dim': 'not_a_number',\n            'depth': 12,\n            'freqs_per_bands': 'invalid_tuple_string'\n        }\n        \n        # Should not crash, invalid values should be passed through for validator to catch\n        result = self.normalizer.normalize_config(config, \"bs_roformer\", validate=False)\n        \n        assert result['dim'] == 'not_a_number'  # Passed through unchanged\n        assert result['depth'] == 12  # Valid value normalized","chunkId":"tests/unit/test_configuration_normalizer.py:29"}],[2835,{"filePath":"tests/unit/test_configuration_normalizer.py","startLine":372,"endLine":386,"chunkText":"        \n        assert result['dim'] == 'not_a_number'  # Passed through unchanged\n        assert result['depth'] == 12  # Valid value normalized\n        assert result['freqs_per_bands'] == 'invalid_tuple_string'  # Passed through unchanged\n    \n    def test_comprehensive_normalization_workflow(self):\n        \"\"\"Test complete normalization workflow with complex configuration.\"\"\"\n        config = {\n            'model': {\n                'dim': '512',\n                'depth': '12.0',\n                'n_heads': '8'\n            },\n            'training': {\n                'sample_rate': 44100,","chunkId":"tests/unit/test_configuration_normalizer.py:30"}],[2836,{"filePath":"tests/unit/test_configuration_normalizer.py","startLine":384,"endLine":402,"chunkText":"            },\n            'training': {\n                'sample_rate': 44100,\n                'n_fft': 2048\n            },\n            'stereo': 'true',\n            'flash_attn': 'false',\n            'freq_bands': '[2, 4, 8, 16, 32, 64]',\n            'norm': 'LAYER_NORM'\n        }\n        \n        result = self.normalizer.normalize_config(config, \"bs_roformer\", apply_defaults=True, validate=False)\n        \n        # Structure flattened\n        assert result['dim'] == 512\n        assert result['depth'] == 12\n        assert result['sample_rate'] == 44100\n        \n        # Names normalized","chunkId":"tests/unit/test_configuration_normalizer.py:31"}],[2837,{"filePath":"tests/unit/test_configuration_normalizer.py","startLine":400,"endLine":419,"chunkText":"        assert result['sample_rate'] == 44100\n        \n        # Names normalized\n        assert result['heads'] == 8\n        assert result['stft_n_fft'] == 2048\n        assert result['freqs_per_bands'] == (2, 4, 8, 16, 32, 64)\n        \n        # Values normalized\n        assert result['stereo'] is True\n        assert result['flash_attn'] is False\n        assert result['norm'] == 'layer_norm'\n        \n        # Defaults applied\n        assert result['num_stems'] == 2\n        assert result['mlp_expansion_factor'] == 4\n\n\nif __name__ == \"__main__\":\n    pytest.main([__file__])\n","chunkId":"tests/unit/test_configuration_normalizer.py:32"}],[2838,{"filePath":"tests/unit/test_mdxc_roformer_chunking.py","startLine":0,"endLine":22,"chunkText":"\"\"\"\nUnit tests for MDXC Roformer chunking and overlap logic.\nTests the chunking mechanism, overlap handling, and edge cases.\n\"\"\"\n\nimport pytest\nimport numpy as np\nimport torch\nfrom unittest.mock import Mock, MagicMock, patch\nimport logging\n\n\nclass TestMDXCRoformerChunking:\n    \"\"\"Test cases for MDXC Roformer chunking and overlap functionality.\"\"\"\n\n    def setup_method(self):\n        \"\"\"Set up test fixtures.\"\"\"\n        self.sample_rate = 44100\n        self.audio_length = 132300  # 3 seconds\n        self.chunk_size = 8192\n        self.hop_length = 1024\n        \n        # Mock model with stft_hop_length","chunkId":"tests/unit/test_mdxc_roformer_chunking.py:0"}],[2839,{"filePath":"tests/unit/test_mdxc_roformer_chunking.py","startLine":20,"endLine":34,"chunkText":"        self.hop_length = 1024\n        \n        # Mock model with stft_hop_length\n        self.mock_model = Mock()\n        self.mock_model.stft_hop_length = self.hop_length\n        \n        # Mock audio object\n        self.mock_audio = Mock()\n        self.mock_audio.hop_length = 512  # Different from model for fallback test\n\n    def test_chunk_size_uses_model_stft_hop_length(self):\n        \"\"\"T051: Assert chunk_size uses model.stft_hop_length.\"\"\"\n        # Test implementation for chunking optimization - placeholder for future implementation\n        pytest.skip(\"Chunking optimization not yet implemented\")\n","chunkId":"tests/unit/test_mdxc_roformer_chunking.py:1"}],[2840,{"filePath":"tests/unit/test_mdxc_roformer_chunking.py","startLine":32,"endLine":41,"chunkText":"        # Test implementation for chunking optimization - placeholder for future implementation\n        pytest.skip(\"Chunking optimization not yet implemented\")\n\n    def test_chunk_size_falls_back_to_audio_hop_length(self):\n        \"\"\"T052: Fallback to audio.hop_length if model.stft_hop_length missing.\"\"\"\n        # Test implementation for chunking optimization - placeholder for future implementation\n        pytest.skip(\"Chunking optimization not yet implemented\")\n\n    def test_step_clamped_to_chunk_size(self):\n        \"\"\"T053: Step clamped to chunk_size (desired_step > chunk_size or ≤ 0).\"\"\"","chunkId":"tests/unit/test_mdxc_roformer_chunking.py:2"}],[2841,{"filePath":"tests/unit/test_mdxc_roformer_chunking.py","startLine":39,"endLine":53,"chunkText":"\n    def test_step_clamped_to_chunk_size(self):\n        \"\"\"T053: Step clamped to chunk_size (desired_step > chunk_size or ≤ 0).\"\"\"\n        chunk_size = 8192\n        \n        # Test case 1: desired_step > chunk_size\n        desired_step_too_large = 10000\n        actual_step = min(desired_step_too_large, chunk_size)\n        assert actual_step == chunk_size\n        \n        # Test case 2: desired_step ≤ 0\n        desired_step_zero = 0\n        actual_step = max(desired_step_zero, chunk_size // 4)  # Use quarter chunk as minimum\n        assert actual_step == chunk_size // 4\n        ","chunkId":"tests/unit/test_mdxc_roformer_chunking.py:3"}],[2842,{"filePath":"tests/unit/test_mdxc_roformer_chunking.py","startLine":51,"endLine":64,"chunkText":"        actual_step = max(desired_step_zero, chunk_size // 4)  # Use quarter chunk as minimum\n        assert actual_step == chunk_size // 4\n        \n        # Test case 3: desired_step negative\n        desired_step_negative = -100\n        actual_step = max(desired_step_negative, chunk_size // 4)\n        assert actual_step == chunk_size // 4\n        \n        # Test case 4: valid desired_step\n        desired_step_valid = 4096\n        actual_step = min(max(desired_step_valid, 1), chunk_size)\n        assert actual_step == desired_step_valid\n\n    def test_overlap_add_short_output_safe(self):","chunkId":"tests/unit/test_mdxc_roformer_chunking.py:4"}],[2843,{"filePath":"tests/unit/test_mdxc_roformer_chunking.py","startLine":62,"endLine":75,"chunkText":"        assert actual_step == desired_step_valid\n\n    def test_overlap_add_short_output_safe(self):\n        \"\"\"T054: overlap_add handles shorter model output safely (safe_len).\"\"\"\n        # Create mock tensors\n        chunk_size = 8192\n        model_output_length = 6000  # Shorter than expected\n        \n        # Mock overlap_add logic\n        def mock_overlap_add_safe(output, target, start_idx, safe_len):\n            \"\"\"Mock overlap add with safe length handling.\"\"\"\n            actual_len = min(output.shape[-1], safe_len)\n            end_idx = start_idx + actual_len\n            ","chunkId":"tests/unit/test_mdxc_roformer_chunking.py:5"}],[2844,{"filePath":"tests/unit/test_mdxc_roformer_chunking.py","startLine":73,"endLine":87,"chunkText":"            actual_len = min(output.shape[-1], safe_len)\n            end_idx = start_idx + actual_len\n            \n            # Ensure we don't go beyond target bounds\n            if end_idx > target.shape[-1]:\n                end_idx = target.shape[-1]\n                actual_len = end_idx - start_idx\n            \n            if actual_len > 0:\n                target[..., start_idx:end_idx] += output[..., :actual_len]\n            \n            return actual_len\n        \n        # Test with shorter output\n        output = torch.randn(2, model_output_length)  # Shorter than chunk_size","chunkId":"tests/unit/test_mdxc_roformer_chunking.py:6"}],[2845,{"filePath":"tests/unit/test_mdxc_roformer_chunking.py","startLine":85,"endLine":101,"chunkText":"        \n        # Test with shorter output\n        output = torch.randn(2, model_output_length)  # Shorter than chunk_size\n        target = torch.zeros(2, 20000)\n        start_idx = 1000\n        safe_len = chunk_size\n        \n        actual_added = mock_overlap_add_safe(output, target, start_idx, safe_len)\n        \n        # Should handle shorter output gracefully\n        assert actual_added == model_output_length\n        assert actual_added < safe_len\n        \n        # Verify no out-of-bounds access\n        assert start_idx + actual_added <= target.shape[-1]\n\n    def test_counter_updates_safe_len(self)","chunkId":"tests/unit/test_mdxc_roformer_chunking.py:7"}],[2846,{"filePath":"tests/unit/test_mdxc_roformer_chunking.py","startLine":99,"endLine":112,"chunkText":"        assert start_idx + actual_added <= target.shape[-1]\n\n    def test_counter_updates_safe_len(self):\n        \"\"\"T055: Counter increments match overlap_add safe span.\"\"\"\n        # Mock counter and overlap_add logic\n        counter = torch.zeros(2, 20000)\n        chunk_size = 8192\n        safe_len = 6000  # Shorter than chunk_size\n        start_idx = 1000\n        \n        def mock_update_counter_safe(counter, start_idx, safe_len):\n            \"\"\"Mock counter update that matches overlap_add safe span.\"\"\"\n            end_idx = start_idx + safe_len\n            if end_idx > counter.shape[-1]:","chunkId":"tests/unit/test_mdxc_roformer_chunking.py:8"}],[2847,{"filePath":"tests/unit/test_mdxc_roformer_chunking.py","startLine":110,"endLine":125,"chunkText":"            \"\"\"Mock counter update that matches overlap_add safe span.\"\"\"\n            end_idx = start_idx + safe_len\n            if end_idx > counter.shape[-1]:\n                end_idx = counter.shape[-1]\n                safe_len = end_idx - start_idx\n            \n            if safe_len > 0:\n                counter[..., start_idx:end_idx] += 1.0\n            \n            return safe_len\n        \n        actual_updated = mock_update_counter_safe(counter, start_idx, safe_len)\n        \n        # Counter increment should match the safe length used\n        assert actual_updated == safe_len\n        ","chunkId":"tests/unit/test_mdxc_roformer_chunking.py:9"}],[2848,{"filePath":"tests/unit/test_mdxc_roformer_chunking.py","startLine":123,"endLine":136,"chunkText":"        # Counter increment should match the safe length used\n        assert actual_updated == safe_len\n        \n        # Verify counter was updated correctly\n        expected_ones = counter[0, start_idx:start_idx + actual_updated]\n        assert torch.all(expected_ones == 1.0)\n        \n        # Verify areas outside weren't touched\n        before_area = counter[0, :start_idx]\n        after_area = counter[0, start_idx + actual_updated:]\n        assert torch.all(before_area == 0.0)\n        assert torch.all(after_area == 0.0)\n\n    def test_counter_clamp_no_nan(self):","chunkId":"tests/unit/test_mdxc_roformer_chunking.py:10"}],[2849,{"filePath":"tests/unit/test_mdxc_roformer_chunking.py","startLine":134,"endLine":144,"chunkText":"        assert torch.all(after_area == 0.0)\n\n    def test_counter_clamp_no_nan(self):\n        \"\"\"T056: No NaN/inf on normalization (counter clamp).\"\"\"\n        # Create counter with some zero values (potential division by zero)\n        counter = torch.tensor([[0.0, 1.0, 2.0, 0.0, 3.0, 0.0]], dtype=torch.float32)\n        output = torch.tensor([[1.0, 2.0, 4.0, 8.0, 6.0, 16.0]], dtype=torch.float32)\n        \n        # Mock normalization with counter clamping\n        def mock_normalize_with_clamp(output, counter, min_clamp=1e-8):\n            \"\"\"Mock normalization that clamps counter to avoid NaN/inf.\"\"\"","chunkId":"tests/unit/test_mdxc_roformer_chunking.py:11"}],[2850,{"filePath":"tests/unit/test_mdxc_roformer_chunking.py","startLine":142,"endLine":153,"chunkText":"        # Mock normalization with counter clamping\n        def mock_normalize_with_clamp(output, counter, min_clamp=1e-8):\n            \"\"\"Mock normalization that clamps counter to avoid NaN/inf.\"\"\"\n            clamped_counter = torch.clamp(counter, min=min_clamp)\n            normalized = output / clamped_counter\n            return normalized, clamped_counter\n        \n        normalized, clamped_counter = mock_normalize_with_clamp(output, counter)\n        \n        # Verify no NaN or inf values\n        assert not torch.any(torch.isnan(normalized))\n        assert not torch.any(torch.isinf(normalized))","chunkId":"tests/unit/test_mdxc_roformer_chunking.py:12"}],[2851,{"filePath":"tests/unit/test_mdxc_roformer_chunking.py","startLine":151,"endLine":164,"chunkText":"        # Verify no NaN or inf values\n        assert not torch.any(torch.isnan(normalized))\n        assert not torch.any(torch.isinf(normalized))\n        \n        # Verify clamping worked\n        assert torch.all(clamped_counter >= 1e-8)\n        \n        # Verify normalization is reasonable\n        assert torch.all(normalized >= 0)  # Should be positive\n        \n        # Test with all-zero counter (extreme case)\n        zero_counter = torch.zeros_like(counter)\n        normalized_zero, clamped_zero = mock_normalize_with_clamp(output, zero_counter)\n        ","chunkId":"tests/unit/test_mdxc_roformer_chunking.py:13"}],[2852,{"filePath":"tests/unit/test_mdxc_roformer_chunking.py","startLine":162,"endLine":174,"chunkText":"        zero_counter = torch.zeros_like(counter)\n        normalized_zero, clamped_zero = mock_normalize_with_clamp(output, zero_counter)\n        \n        assert not torch.any(torch.isnan(normalized_zero))\n        assert not torch.any(torch.isinf(normalized_zero))\n        assert torch.all(clamped_zero >= 1e-8)\n\n    def test_short_audio_last_block(self):\n        \"\"\"T057: Short-audio last-block path works and preserves length.\"\"\"\n        # Test with audio shorter than one chunk\n        short_audio_length = 4000  # Less than chunk_size (8192)\n        chunk_size = 8192\n        ","chunkId":"tests/unit/test_mdxc_roformer_chunking.py:14"}],[2853,{"filePath":"tests/unit/test_mdxc_roformer_chunking.py","startLine":172,"endLine":185,"chunkText":"        short_audio_length = 4000  # Less than chunk_size (8192)\n        chunk_size = 8192\n        \n        # Mock processing of short audio\n        def mock_process_short_audio(audio_length, chunk_size):\n            \"\"\"Mock processing that handles short audio specially.\"\"\"\n            if audio_length < chunk_size:\n                # Last block path: process entire audio as one chunk\n                return {\n                    'processed_length': audio_length,\n                    'num_chunks': 1,\n                    'last_block': True,\n                    'preserved_length': audio_length\n                }","chunkId":"tests/unit/test_mdxc_roformer_chunking.py:15"}],[2854,{"filePath":"tests/unit/test_mdxc_roformer_chunking.py","startLine":183,"endLine":197,"chunkText":"                    'last_block': True,\n                    'preserved_length': audio_length\n                }\n            else:\n                # Normal chunking path\n                num_chunks = (audio_length + chunk_size - 1) // chunk_size\n                return {\n                    'processed_length': audio_length,\n                    'num_chunks': num_chunks,\n                    'last_block': False,\n                    'preserved_length': audio_length\n                }\n        \n        result = mock_process_short_audio(short_audio_length, chunk_size)\n        ","chunkId":"tests/unit/test_mdxc_roformer_chunking.py:16"}],[2855,{"filePath":"tests/unit/test_mdxc_roformer_chunking.py","startLine":195,"endLine":209,"chunkText":"        \n        result = mock_process_short_audio(short_audio_length, chunk_size)\n        \n        # Verify last block path was taken\n        assert result['last_block'] is True\n        assert result['num_chunks'] == 1\n        \n        # Verify length preservation\n        assert result['processed_length'] == short_audio_length\n        assert result['preserved_length'] == short_audio_length\n        \n        # Test with normal-length audio for comparison\n        normal_audio_length = 20000\n        normal_result = mock_process_short_audio(normal_audio_length, chunk_size)\n        ","chunkId":"tests/unit/test_mdxc_roformer_chunking.py:17"}],[2856,{"filePath":"tests/unit/test_mdxc_roformer_chunking.py","startLine":207,"endLine":222,"chunkText":"        normal_audio_length = 20000\n        normal_result = mock_process_short_audio(normal_audio_length, chunk_size)\n        \n        assert normal_result['last_block'] is False\n        assert normal_result['num_chunks'] > 1\n        assert normal_result['preserved_length'] == normal_audio_length\n\n    @pytest.mark.parametrize(\"dim_t,hop_length\", [\n        (256, 512),\n        (512, 1024), \n        (1024, 2048),\n        (128, 256)\n    ])\n    def test_parametrized_shape_invariants(self, dim_t, hop_length):\n        \"\"\"T058: Parametrized invariants across dim_t and hop configs.\"\"\"\n        batch_size = 2","chunkId":"tests/unit/test_mdxc_roformer_chunking.py:18"}],[2857,{"filePath":"tests/unit/test_mdxc_roformer_chunking.py","startLine":220,"endLine":233,"chunkText":"    def test_parametrized_shape_invariants(self, dim_t, hop_length):\n        \"\"\"T058: Parametrized invariants across dim_t and hop configs.\"\"\"\n        batch_size = 2\n        audio_length = 44100  # 1 second\n        \n        # Mock model with parametrized config\n        mock_model = Mock()\n        mock_model.dim_t = dim_t\n        mock_model.stft_hop_length = hop_length\n        \n        # Mock chunking calculation\n        def mock_calculate_chunks(audio_length, dim_t, hop_length):\n            \"\"\"Calculate number of chunks needed.\"\"\"\n            chunk_size = dim_t * 8  # Example chunk size calculation","chunkId":"tests/unit/test_mdxc_roformer_chunking.py:19"}],[2858,{"filePath":"tests/unit/test_mdxc_roformer_chunking.py","startLine":231,"endLine":243,"chunkText":"        def mock_calculate_chunks(audio_length, dim_t, hop_length):\n            \"\"\"Calculate number of chunks needed.\"\"\"\n            chunk_size = dim_t * 8  # Example chunk size calculation\n            step_size = hop_length\n            \n            if audio_length <= chunk_size:\n                return 1\n            \n            return (audio_length - chunk_size + step_size - 1) // step_size + 1\n        \n        num_chunks = mock_calculate_chunks(audio_length, dim_t, hop_length)\n        \n        # Invariants that should hold regardless of parameters","chunkId":"tests/unit/test_mdxc_roformer_chunking.py:20"}],[2859,{"filePath":"tests/unit/test_mdxc_roformer_chunking.py","startLine":241,"endLine":250,"chunkText":"        num_chunks = mock_calculate_chunks(audio_length, dim_t, hop_length)\n        \n        # Invariants that should hold regardless of parameters\n        assert num_chunks >= 1, f\"Should always have at least 1 chunk for dim_t={dim_t}, hop={hop_length}\"\n        assert num_chunks <= audio_length // hop_length + 2, f\"Chunks should be reasonable for dim_t={dim_t}, hop={hop_length}\"\n        \n        # Test output shape consistency\n        mock_output_shape = (batch_size, 2, dim_t)  # (batch, channels, time)\n        assert mock_output_shape[0] == batch_size\n        assert mock_output_shape[2] == dim_t","chunkId":"tests/unit/test_mdxc_roformer_chunking.py:21"}],[2860,{"filePath":"tests/unit/test_mdxc_roformer_chunking.py","startLine":248,"endLine":260,"chunkText":"        mock_output_shape = (batch_size, 2, dim_t)  # (batch, channels, time)\n        assert mock_output_shape[0] == batch_size\n        assert mock_output_shape[2] == dim_t\n        \n        # Test that chunk_size scales with dim_t\n        chunk_size = dim_t * 8\n        assert chunk_size > 0\n        assert chunk_size >= dim_t\n\n    def test_logging_for_hop_and_step(self, caplog):\n        \"\"\"T063: Logs include hop/step sources (stft_hop_length, dim_t, desired vs actual step).\"\"\"\n        with caplog.at_level(logging.DEBUG):\n            # Mock logging during chunking setup","chunkId":"tests/unit/test_mdxc_roformer_chunking.py:22"}],[2861,{"filePath":"tests/unit/test_mdxc_roformer_chunking.py","startLine":258,"endLine":266,"chunkText":"        \"\"\"T063: Logs include hop/step sources (stft_hop_length, dim_t, desired vs actual step).\"\"\"\n        with caplog.at_level(logging.DEBUG):\n            # Mock logging during chunking setup\n            def mock_setup_chunking_with_logging(model, audio):\n                \"\"\"Mock chunking setup that logs parameter sources.\"\"\"\n                stft_hop = getattr(model, 'stft_hop_length', None)\n                dim_t = getattr(model, 'dim_t', None)\n                audio_hop = getattr(audio, 'hop_length', None)\n                ","chunkId":"tests/unit/test_mdxc_roformer_chunking.py:23"}],[2862,{"filePath":"tests/unit/test_mdxc_roformer_chunking.py","startLine":264,"endLine":273,"chunkText":"                dim_t = getattr(model, 'dim_t', None)\n                audio_hop = getattr(audio, 'hop_length', None)\n                \n                logging.debug(f\"Chunking setup: stft_hop_length={stft_hop}, dim_t={dim_t}\")\n                logging.debug(f\"Audio hop_length={audio_hop}\")\n                \n                desired_step = stft_hop if stft_hop else audio_hop\n                chunk_size = dim_t * 8 if dim_t else 8192\n                actual_step = min(desired_step, chunk_size) if desired_step else chunk_size // 4\n                ","chunkId":"tests/unit/test_mdxc_roformer_chunking.py:24"}],[2863,{"filePath":"tests/unit/test_mdxc_roformer_chunking.py","startLine":271,"endLine":283,"chunkText":"                chunk_size = dim_t * 8 if dim_t else 8192\n                actual_step = min(desired_step, chunk_size) if desired_step else chunk_size // 4\n                \n                logging.debug(f\"Step calculation: desired={desired_step}, actual={actual_step}, chunk_size={chunk_size}\")\n                \n                return {\n                    'chunk_size': chunk_size,\n                    'step_size': actual_step,\n                    'stft_hop_source': stft_hop is not None\n                }\n            \n            # Test with model having stft_hop_length\n            model_with_stft = Mock()","chunkId":"tests/unit/test_mdxc_roformer_chunking.py:25"}],[2864,{"filePath":"tests/unit/test_mdxc_roformer_chunking.py","startLine":281,"endLine":296,"chunkText":"            \n            # Test with model having stft_hop_length\n            model_with_stft = Mock()\n            model_with_stft.stft_hop_length = 1024\n            model_with_stft.dim_t = 256\n            \n            audio = Mock()\n            audio.hop_length = 512\n            \n            result = mock_setup_chunking_with_logging(model_with_stft, audio)\n            \n            # Verify logging occurred\n            assert \"stft_hop_length=1024\" in caplog.text\n            assert \"dim_t=256\" in caplog.text\n            assert \"desired=1024\" in caplog.text\n            assert \"actual=\" in caplog.text","chunkId":"tests/unit/test_mdxc_roformer_chunking.py:26"}],[2865,{"filePath":"tests/unit/test_mdxc_roformer_chunking.py","startLine":294,"endLine":306,"chunkText":"            assert \"dim_t=256\" in caplog.text\n            assert \"desired=1024\" in caplog.text\n            assert \"actual=\" in caplog.text\n            assert \"chunk_size=\" in caplog.text\n\n    def test_iteration_count_reasonable(self):\n        \"\"\"T064: Iteration count reasonable (ceil calculation within ±1).\"\"\"\n        import math\n        \n        # Test various audio lengths and chunk configurations\n        test_cases = [\n            (44100, 8192, 1024),   # 1 second, normal chunk\n            (88200, 4096, 512),    # 2 seconds, smaller chunk  ","chunkId":"tests/unit/test_mdxc_roformer_chunking.py:27"}],[2866,{"filePath":"tests/unit/test_mdxc_roformer_chunking.py","startLine":304,"endLine":316,"chunkText":"        test_cases = [\n            (44100, 8192, 1024),   # 1 second, normal chunk\n            (88200, 4096, 512),    # 2 seconds, smaller chunk  \n            (22050, 16384, 2048),  # 0.5 seconds, large chunk\n            (132300, 8192, 1024),  # 3 seconds, normal chunk\n        ]\n        \n        for audio_length, chunk_size, step_size in test_cases:\n            # Calculate expected iterations using ceiling division\n            if audio_length <= chunk_size:\n                expected_iterations = 1\n            else:\n                remaining_length = audio_length - chunk_size","chunkId":"tests/unit/test_mdxc_roformer_chunking.py:28"}],[2867,{"filePath":"tests/unit/test_mdxc_roformer_chunking.py","startLine":314,"endLine":328,"chunkText":"                expected_iterations = 1\n            else:\n                remaining_length = audio_length - chunk_size\n                expected_iterations = 1 + math.ceil(remaining_length / step_size)\n            \n            # Mock actual iteration calculation\n            def mock_calculate_iterations(audio_len, chunk_sz, step_sz):\n                if audio_len <= chunk_sz:\n                    return 1\n                \n                iterations = 0\n                pos = 0\n                while pos < audio_len:\n                    iterations += 1\n                    if pos + chunk_sz >= audio_len:","chunkId":"tests/unit/test_mdxc_roformer_chunking.py:29"}],[2868,{"filePath":"tests/unit/test_mdxc_roformer_chunking.py","startLine":326,"endLine":338,"chunkText":"                while pos < audio_len:\n                    iterations += 1\n                    if pos + chunk_sz >= audio_len:\n                        break\n                    pos += step_sz\n                \n                return iterations\n            \n            actual_iterations = mock_calculate_iterations(audio_length, chunk_size, step_size)\n            \n            # Verify iteration count is reasonable (within ±1 of expected)\n            diff = abs(actual_iterations - expected_iterations)\n            assert diff <= 1, (","chunkId":"tests/unit/test_mdxc_roformer_chunking.py:30"}],[2869,{"filePath":"tests/unit/test_mdxc_roformer_chunking.py","startLine":336,"endLine":346,"chunkText":"            # Verify iteration count is reasonable (within ±1 of expected)\n            diff = abs(actual_iterations - expected_iterations)\n            assert diff <= 1, (\n                f\"Iteration count {actual_iterations} differs too much from expected {expected_iterations} \"\n                f\"for audio_len={audio_length}, chunk={chunk_size}, step={step_size}\"\n            )\n            \n            # Verify minimum iterations\n            assert actual_iterations >= 1, f\"Should always have at least 1 iteration\"\n            \n            # Verify maximum reasonable iterations","chunkId":"tests/unit/test_mdxc_roformer_chunking.py:31"}],[2870,{"filePath":"tests/unit/test_mdxc_roformer_chunking.py","startLine":344,"endLine":351,"chunkText":"            assert actual_iterations >= 1, f\"Should always have at least 1 iteration\"\n            \n            # Verify maximum reasonable iterations\n            max_reasonable = (audio_length // step_size) + 2\n            assert actual_iterations <= max_reasonable, (\n                f\"Too many iterations {actual_iterations} for audio_len={audio_length}\"\n            )\n","chunkId":"tests/unit/test_mdxc_roformer_chunking.py:32"}],[2871,{"filePath":"tests/unit/test_model_configuration.py","startLine":0,"endLine":16,"chunkText":"\"\"\"\nUnit tests for ModelConfiguration validation.\nTests the ModelConfiguration dataclass and its validation logic.\n\"\"\"\n\nimport pytest\nfrom dataclasses import FrozenInstanceError\n\n# Add the roformer module to path for imports\nimport sys\nimport os\n# Find project root dynamically\ncurrent_dir = os.path.dirname(os.path.abspath(__file__))\nproject_root = current_dir\n# Go up until we find the project root (contains audio_separator/ directory)\nwhile project_root and not os.path.exists(os.path.join(project_root, 'audio_separator')):\n    parent = os.path.dirname(project_root)","chunkId":"tests/unit/test_model_configuration.py:0"}],[2872,{"filePath":"tests/unit/test_model_configuration.py","startLine":14,"endLine":30,"chunkText":"# Go up until we find the project root (contains audio_separator/ directory)\nwhile project_root and not os.path.exists(os.path.join(project_root, 'audio_separator')):\n    parent = os.path.dirname(project_root)\n    if parent == project_root:  # Reached filesystem root\n        break\n    project_root = parent\n\nif project_root:\n    sys.path.append(project_root)\n\nfrom audio_separator.separator.roformer.model_configuration import ModelConfiguration\n\n\nclass TestModelConfiguration:\n    \"\"\"Test cases for ModelConfiguration dataclass.\"\"\"\n    \n    def test_model_configuration_creation_valid(self):","chunkId":"tests/unit/test_model_configuration.py:1"}],[2873,{"filePath":"tests/unit/test_model_configuration.py","startLine":28,"endLine":46,"chunkText":"    \"\"\"Test cases for ModelConfiguration dataclass.\"\"\"\n    \n    def test_model_configuration_creation_valid(self):\n        \"\"\"Test creating a valid ModelConfiguration.\"\"\"\n        config = ModelConfiguration(\n            dim=512,\n            depth=12,\n            stereo=False,\n            num_stems=2,\n            time_transformer_depth=2,\n            freq_transformer_depth=2,\n            dim_head=64,\n            heads=8,\n            attn_dropout=0.1,\n            ff_dropout=0.1,\n            flash_attn=True,\n            mlp_expansion_factor=4,\n            sage_attention=False,\n            zero_dc=True,","chunkId":"tests/unit/test_model_configuration.py:2"}],[2874,{"filePath":"tests/unit/test_model_configuration.py","startLine":44,"endLine":61,"chunkText":"            mlp_expansion_factor=4,\n            sage_attention=False,\n            zero_dc=True,\n            use_torch_checkpoint=False,\n            skip_connection=False\n        )\n        \n        assert config.dim == 512\n        assert config.depth == 12\n        assert config.stereo is False\n        assert config.num_stems == 2\n        assert config.mlp_expansion_factor == 4\n        assert config.sage_attention is False\n    \n    def test_model_configuration_defaults(self):\n        \"\"\"Test ModelConfiguration with minimal required parameters.\"\"\"\n        config = ModelConfiguration(\n            dim=256,","chunkId":"tests/unit/test_model_configuration.py:3"}],[2875,{"filePath":"tests/unit/test_model_configuration.py","startLine":59,"endLine":73,"chunkText":"        \"\"\"Test ModelConfiguration with minimal required parameters.\"\"\"\n        config = ModelConfiguration(\n            dim=256,\n            depth=6\n        )\n        \n        # Check defaults are applied\n        assert config.dim == 256\n        assert config.depth == 6\n        assert config.stereo is False  # Default\n        assert config.num_stems == 1  # Default\n        assert config.time_transformer_depth == 2  # Default\n        assert config.freq_transformer_depth == 2  # Default\n        assert config.dim_head == 64  # Default\n        assert config.heads == 8  # Default","chunkId":"tests/unit/test_model_configuration.py:4"}],[2876,{"filePath":"tests/unit/test_model_configuration.py","startLine":71,"endLine":82,"chunkText":"        assert config.freq_transformer_depth == 2  # Default\n        assert config.dim_head == 64  # Default\n        assert config.heads == 8  # Default\n        assert config.attn_dropout == 0.0  # Default\n        assert config.ff_dropout == 0.0  # Default\n        assert config.flash_attn is True  # Default\n        assert config.mlp_expansion_factor == 4  # Default\n        assert config.sage_attention is False  # Default\n        assert config.zero_dc is True  # Default\n        assert config.use_torch_checkpoint is False  # Default\n        assert config.skip_connection is False  # Default\n    ","chunkId":"tests/unit/test_model_configuration.py:5"}],[2877,{"filePath":"tests/unit/test_model_configuration.py","startLine":80,"endLine":94,"chunkText":"        assert config.use_torch_checkpoint is False  # Default\n        assert config.skip_connection is False  # Default\n    \n    def test_model_configuration_immutable(self):\n        \"\"\"Test that ModelConfiguration is immutable (frozen).\"\"\"\n        config = ModelConfiguration(dim=512, depth=12)\n        \n        with pytest.raises(FrozenInstanceError):\n            config.dim = 1024\n        \n        with pytest.raises(FrozenInstanceError):\n            config.depth = 24\n    \n    def test_model_configuration_type_validation(self):\n        \"\"\"Test type validation in ModelConfiguration.\"\"\"","chunkId":"tests/unit/test_model_configuration.py:6"}],[2878,{"filePath":"tests/unit/test_model_configuration.py","startLine":92,"endLine":109,"chunkText":"    \n    def test_model_configuration_type_validation(self):\n        \"\"\"Test type validation in ModelConfiguration.\"\"\"\n        # Valid types should work\n        config = ModelConfiguration(\n            dim=512,\n            depth=12,\n            stereo=True,\n            attn_dropout=0.1,\n            flash_attn=False\n        )\n        assert isinstance(config.dim, int)\n        assert isinstance(config.stereo, bool)\n        assert isinstance(config.attn_dropout, float)\n    \n    def test_model_configuration_edge_values(self):\n        \"\"\"Test edge values for ModelConfiguration.\"\"\"\n        # Test minimum values","chunkId":"tests/unit/test_model_configuration.py:7"}],[2879,{"filePath":"tests/unit/test_model_configuration.py","startLine":107,"endLine":126,"chunkText":"    def test_model_configuration_edge_values(self):\n        \"\"\"Test edge values for ModelConfiguration.\"\"\"\n        # Test minimum values\n        config_min = ModelConfiguration(\n            dim=1,\n            depth=1,\n            num_stems=1,\n            heads=1,\n            attn_dropout=0.0,\n            ff_dropout=0.0\n        )\n        assert config_min.dim == 1\n        assert config_min.depth == 1\n        assert config_min.num_stems == 1\n        \n        # Test larger values\n        config_max = ModelConfiguration(\n            dim=8192,\n            depth=64,\n            num_stems=16,","chunkId":"tests/unit/test_model_configuration.py:8"}],[2880,{"filePath":"tests/unit/test_model_configuration.py","startLine":124,"endLine":143,"chunkText":"            dim=8192,\n            depth=64,\n            num_stems=16,\n            heads=64,\n            attn_dropout=1.0,\n            ff_dropout=1.0,\n            mlp_expansion_factor=16\n        )\n        assert config_max.dim == 8192\n        assert config_max.depth == 64\n        assert config_max.mlp_expansion_factor == 16\n    \n    def test_model_configuration_boolean_parameters(self):\n        \"\"\"Test boolean parameter handling.\"\"\"\n        config = ModelConfiguration(\n            dim=512,\n            depth=12,\n            stereo=True,\n            flash_attn=False,\n            sage_attention=True,","chunkId":"tests/unit/test_model_configuration.py:9"}],[2881,{"filePath":"tests/unit/test_model_configuration.py","startLine":141,"endLine":157,"chunkText":"            stereo=True,\n            flash_attn=False,\n            sage_attention=True,\n            zero_dc=False,\n            use_torch_checkpoint=True,\n            skip_connection=True\n        )\n        \n        assert config.stereo is True\n        assert config.flash_attn is False\n        assert config.sage_attention is True\n        assert config.zero_dc is False\n        assert config.use_torch_checkpoint is True\n        assert config.skip_connection is True\n    \n    def test_model_configuration_new_parameters(self):\n        \"\"\"Test the new parameters added for updated Roformer implementation.\"\"\"","chunkId":"tests/unit/test_model_configuration.py:10"}],[2882,{"filePath":"tests/unit/test_model_configuration.py","startLine":155,"endLine":171,"chunkText":"    \n    def test_model_configuration_new_parameters(self):\n        \"\"\"Test the new parameters added for updated Roformer implementation.\"\"\"\n        config = ModelConfiguration(\n            dim=512,\n            depth=12,\n            mlp_expansion_factor=8,\n            sage_attention=True,\n            zero_dc=False,\n            use_torch_checkpoint=True,\n            skip_connection=True\n        )\n        \n        # Verify new parameters are stored correctly\n        assert config.mlp_expansion_factor == 8\n        assert config.sage_attention is True\n        assert config.zero_dc is False","chunkId":"tests/unit/test_model_configuration.py:11"}],[2883,{"filePath":"tests/unit/test_model_configuration.py","startLine":169,"endLine":183,"chunkText":"        assert config.mlp_expansion_factor == 8\n        assert config.sage_attention is True\n        assert config.zero_dc is False\n        assert config.use_torch_checkpoint is True\n        assert config.skip_connection is True\n    \n    def test_model_configuration_repr(self):\n        \"\"\"Test string representation of ModelConfiguration.\"\"\"\n        config = ModelConfiguration(dim=512, depth=12)\n        repr_str = repr(config)\n        \n        assert \"ModelConfiguration\" in repr_str\n        assert \"dim=512\" in repr_str\n        assert \"depth=12\" in repr_str\n    ","chunkId":"tests/unit/test_model_configuration.py:12"}],[2884,{"filePath":"tests/unit/test_model_configuration.py","startLine":181,"endLine":194,"chunkText":"        assert \"dim=512\" in repr_str\n        assert \"depth=12\" in repr_str\n    \n    def test_model_configuration_equality(self):\n        \"\"\"Test equality comparison of ModelConfiguration instances.\"\"\"\n        config1 = ModelConfiguration(dim=512, depth=12, stereo=True)\n        config2 = ModelConfiguration(dim=512, depth=12, stereo=True)\n        config3 = ModelConfiguration(dim=512, depth=12, stereo=False)\n        \n        assert config1 == config2\n        assert config1 != config3\n    \n    def test_model_configuration_hash(self):\n        \"\"\"Test that ModelConfiguration is hashable.\"\"\"","chunkId":"tests/unit/test_model_configuration.py:13"}],[2885,{"filePath":"tests/unit/test_model_configuration.py","startLine":192,"endLine":206,"chunkText":"    \n    def test_model_configuration_hash(self):\n        \"\"\"Test that ModelConfiguration is hashable.\"\"\"\n        config1 = ModelConfiguration(dim=512, depth=12)\n        config2 = ModelConfiguration(dim=512, depth=12)\n        config3 = ModelConfiguration(dim=256, depth=6)\n        \n        # Same configurations should have same hash\n        assert hash(config1) == hash(config2)\n        \n        # Different configurations should have different hashes\n        assert hash(config1) != hash(config3)\n        \n        # Should be usable as dict keys\n        config_dict = {config1: \"first\", config3: \"second\"}","chunkId":"tests/unit/test_model_configuration.py:14"}],[2886,{"filePath":"tests/unit/test_model_configuration.py","startLine":204,"endLine":221,"chunkText":"        \n        # Should be usable as dict keys\n        config_dict = {config1: \"first\", config3: \"second\"}\n        assert config_dict[config2] == \"first\"  # config2 == config1\n    \n    def test_model_configuration_from_dict(self):\n        \"\"\"Test creating ModelConfiguration from dictionary-like data.\"\"\"\n        data = {\n            'dim': 512,\n            'depth': 12,\n            'stereo': True,\n            'mlp_expansion_factor': 8,\n            'sage_attention': True\n        }\n        \n        config = ModelConfiguration(**data)\n        \n        assert config.dim == 512","chunkId":"tests/unit/test_model_configuration.py:15"}],[2887,{"filePath":"tests/unit/test_model_configuration.py","startLine":219,"endLine":234,"chunkText":"        config = ModelConfiguration(**data)\n        \n        assert config.dim == 512\n        assert config.depth == 12\n        assert config.stereo is True\n        assert config.mlp_expansion_factor == 8\n        assert config.sage_attention is True\n    \n    def test_model_configuration_with_extra_kwargs(self):\n        \"\"\"Test ModelConfiguration ignores extra unknown parameters.\"\"\"\n        # This should not raise an error, unknown params should be ignored\n        # due to the dataclass design\n        try:\n            config = ModelConfiguration(\n                dim=512,\n                depth=12,","chunkId":"tests/unit/test_model_configuration.py:16"}],[2888,{"filePath":"tests/unit/test_model_configuration.py","startLine":232,"endLine":246,"chunkText":"            config = ModelConfiguration(\n                dim=512,\n                depth=12,\n                unknown_param=\"should_be_ignored\"  # This will cause TypeError\n            )\n            # If we get here, the dataclass accepted unknown params (unexpected)\n            assert False, \"Expected TypeError for unknown parameter\"\n        except TypeError as e:\n            # This is expected - dataclasses don't accept unknown parameters\n            assert \"unknown_param\" in str(e)\n\n\nif __name__ == \"__main__\":\n    pytest.main([__file__])\n","chunkId":"tests/unit/test_model_configuration.py:17"}],[2889,{"filePath":"tests/unit/test_parameter_validator.py","startLine":0,"endLine":16,"chunkText":"\"\"\"\nUnit tests for ParameterValidator methods.\nTests the core parameter validation logic for Roformer models.\n\"\"\"\n\nimport pytest\nfrom unittest.mock import Mock, patch\n\n# Add the roformer module to path for imports\nimport sys\nimport os\n# Find project root dynamically\ncurrent_dir = os.path.dirname(os.path.abspath(__file__))\nproject_root = current_dir\n# Go up until we find the project root (contains audio_separator/ directory)\nwhile project_root and not os.path.exists(os.path.join(project_root, 'audio_separator')):\n    parent = os.path.dirname(project_root)","chunkId":"tests/unit/test_parameter_validator.py:0"}],[2890,{"filePath":"tests/unit/test_parameter_validator.py","startLine":14,"endLine":28,"chunkText":"# Go up until we find the project root (contains audio_separator/ directory)\nwhile project_root and not os.path.exists(os.path.join(project_root, 'audio_separator')):\n    parent = os.path.dirname(project_root)\n    if parent == project_root:  # Reached filesystem root\n        break\n    project_root = parent\n\nif project_root:\n    sys.path.append(project_root)\n\nfrom audio_separator.separator.roformer.parameter_validator import ParameterValidator, ValidationSeverity\nfrom audio_separator.separator.roformer.parameter_validation_error import ParameterValidationError\n\n\nclass TestParameterValidator:","chunkId":"tests/unit/test_parameter_validator.py:1"}],[2891,{"filePath":"tests/unit/test_parameter_validator.py","startLine":26,"endLine":45,"chunkText":"\n\nclass TestParameterValidator:\n    \"\"\"Test cases for ParameterValidator class.\"\"\"\n    \n    def setup_method(self):\n        \"\"\"Set up test fixtures.\"\"\"\n        self.validator = ParameterValidator()\n    \n    def test_validate_required_parameters_bs_roformer_valid(self):\n        \"\"\"Test validation of required parameters for BSRoformer - valid case.\"\"\"\n        config = {\n            'dim': 512,\n            'depth': 12,\n            'freqs_per_bands': (2, 4, 8, 16, 32, 64)\n        }\n        \n        issues = self.validator.validate_required_parameters(config, \"bs_roformer\")\n        assert len(issues) == 0\n    ","chunkId":"tests/unit/test_parameter_validator.py:2"}],[2892,{"filePath":"tests/unit/test_parameter_validator.py","startLine":43,"endLine":56,"chunkText":"        issues = self.validator.validate_required_parameters(config, \"bs_roformer\")\n        assert len(issues) == 0\n    \n    def test_validate_required_parameters_bs_roformer_missing(self):\n        \"\"\"Test validation of required parameters for BSRoformer - missing parameters.\"\"\"\n        config = {\n            'dim': 512\n            # Missing 'depth' and 'freqs_per_bands'\n        }\n        \n        issues = self.validator.validate_required_parameters(config, \"bs_roformer\")\n        assert len(issues) == 2\n        \n        # Check that both missing parameters are reported","chunkId":"tests/unit/test_parameter_validator.py:3"}],[2893,{"filePath":"tests/unit/test_parameter_validator.py","startLine":54,"endLine":68,"chunkText":"        assert len(issues) == 2\n        \n        # Check that both missing parameters are reported\n        missing_params = [issue.parameter_name for issue in issues]\n        assert 'depth' in missing_params\n        assert 'freqs_per_bands' in missing_params\n        \n        # Check error severity\n        for issue in issues:\n            assert issue.severity == ValidationSeverity.ERROR\n    \n    def test_validate_required_parameters_mel_band_roformer_valid(self):\n        \"\"\"Test validation of required parameters for MelBandRoformer - valid case.\"\"\"\n        config = {\n            'dim': 512,","chunkId":"tests/unit/test_parameter_validator.py:4"}],[2894,{"filePath":"tests/unit/test_parameter_validator.py","startLine":66,"endLine":82,"chunkText":"        \"\"\"Test validation of required parameters for MelBandRoformer - valid case.\"\"\"\n        config = {\n            'dim': 512,\n            'depth': 12,\n            'num_bands': 64\n        }\n        \n        issues = self.validator.validate_required_parameters(config, \"mel_band_roformer\")\n        assert len(issues) == 0\n    \n    def test_validate_required_parameters_mel_band_roformer_missing(self):\n        \"\"\"Test validation of required parameters for MelBandRoformer - missing parameters.\"\"\"\n        config = {\n            'dim': 512\n            # Missing 'depth' and 'num_bands'\n        }\n        ","chunkId":"tests/unit/test_parameter_validator.py:5"}],[2895,{"filePath":"tests/unit/test_parameter_validator.py","startLine":80,"endLine":96,"chunkText":"            # Missing 'depth' and 'num_bands'\n        }\n        \n        issues = self.validator.validate_required_parameters(config, \"mel_band_roformer\")\n        assert len(issues) == 2\n        \n        missing_params = [issue.parameter_name for issue in issues]\n        assert 'depth' in missing_params\n        assert 'num_bands' in missing_params\n    \n    def test_validate_parameter_types_valid(self):\n        \"\"\"Test parameter type validation - valid types.\"\"\"\n        config = {\n            'dim': 512,\n            'depth': 12,\n            'stereo': False,\n            'attn_dropout': 0.1,","chunkId":"tests/unit/test_parameter_validator.py:6"}],[2896,{"filePath":"tests/unit/test_parameter_validator.py","startLine":94,"endLine":109,"chunkText":"            'depth': 12,\n            'stereo': False,\n            'attn_dropout': 0.1,\n            'freqs_per_bands': (2, 4, 8, 16),\n            'sample_rate': 44100\n        }\n        \n        issues = self.validator.validate_parameter_types(config)\n        assert len(issues) == 0\n    \n    def test_validate_parameter_types_invalid(self):\n        \"\"\"Test parameter type validation - invalid types.\"\"\"\n        config = {\n            'dim': \"512\",  # Should be int\n            'stereo': \"false\",  # Should be bool\n            'attn_dropout': \"0.1\",  # Should be float","chunkId":"tests/unit/test_parameter_validator.py:7"}],[2897,{"filePath":"tests/unit/test_parameter_validator.py","startLine":107,"endLine":120,"chunkText":"            'dim': \"512\",  # Should be int\n            'stereo': \"false\",  # Should be bool\n            'attn_dropout': \"0.1\",  # Should be float\n            'sample_rate': 44100.5  # Should be int\n        }\n        \n        issues = self.validator.validate_parameter_types(config)\n        assert len(issues) == 4\n        \n        # Check that all type errors are reported\n        invalid_params = [issue.parameter_name for issue in issues]\n        assert 'dim' in invalid_params\n        assert 'stereo' in invalid_params\n        assert 'attn_dropout' in invalid_params","chunkId":"tests/unit/test_parameter_validator.py:8"}],[2898,{"filePath":"tests/unit/test_parameter_validator.py","startLine":118,"endLine":135,"chunkText":"        assert 'dim' in invalid_params\n        assert 'stereo' in invalid_params\n        assert 'attn_dropout' in invalid_params\n        assert 'sample_rate' in invalid_params\n    \n    def test_validate_parameter_ranges_valid(self):\n        \"\"\"Test parameter range validation - valid ranges.\"\"\"\n        config = {\n            'dim': 512,\n            'depth': 12,\n            'heads': 8,\n            'attn_dropout': 0.1,\n            'sample_rate': 44100\n        }\n        \n        issues = self.validator.validate_parameter_ranges(config)\n        assert len(issues) == 0\n    ","chunkId":"tests/unit/test_parameter_validator.py:9"}],[2899,{"filePath":"tests/unit/test_parameter_validator.py","startLine":133,"endLine":147,"chunkText":"        issues = self.validator.validate_parameter_ranges(config)\n        assert len(issues) == 0\n    \n    def test_validate_parameter_ranges_invalid(self):\n        \"\"\"Test parameter range validation - invalid ranges.\"\"\"\n        config = {\n            'dim': 0,  # Below minimum (1)\n            'depth': 100,  # Above maximum (64)\n            'attn_dropout': 1.5,  # Above maximum (1.0)\n            'sample_rate': 5000  # Below minimum (8000)\n        }\n        \n        issues = self.validator.validate_parameter_ranges(config)\n        assert len(issues) == 4\n        ","chunkId":"tests/unit/test_parameter_validator.py:10"}],[2900,{"filePath":"tests/unit/test_parameter_validator.py","startLine":145,"endLine":157,"chunkText":"        issues = self.validator.validate_parameter_ranges(config)\n        assert len(issues) == 4\n        \n        # Check that all range errors are reported\n        invalid_params = [issue.parameter_name for issue in issues]\n        assert 'dim' in invalid_params\n        assert 'depth' in invalid_params\n        assert 'attn_dropout' in invalid_params\n        assert 'sample_rate' in invalid_params\n    \n    def test_validate_parameter_compatibility_sage_flash_conflict(self):\n        \"\"\"Test parameter compatibility - sage_attention and flash_attn conflict.\"\"\"\n        config = {","chunkId":"tests/unit/test_parameter_validator.py:11"}],[2901,{"filePath":"tests/unit/test_parameter_validator.py","startLine":155,"endLine":167,"chunkText":"    def test_validate_parameter_compatibility_sage_flash_conflict(self):\n        \"\"\"Test parameter compatibility - sage_attention and flash_attn conflict.\"\"\"\n        config = {\n            'sage_attention': True,\n            'flash_attn': True\n        }\n        \n        issues = self.validator.validate_parameter_compatibility(config)\n        assert len(issues) == 1\n        assert issues[0].severity == ValidationSeverity.WARNING\n        assert \"sage_attention, flash_attn\" in issues[0].parameter_name\n    \n    def test_validate_parameter_compatibility_freqs_per_bands_warning(self):","chunkId":"tests/unit/test_parameter_validator.py:12"}],[2902,{"filePath":"tests/unit/test_parameter_validator.py","startLine":165,"endLine":177,"chunkText":"        assert \"sage_attention, flash_attn\" in issues[0].parameter_name\n    \n    def test_validate_parameter_compatibility_freqs_per_bands_warning(self):\n        \"\"\"Test parameter compatibility - freqs_per_bands sum warning.\"\"\"\n        config = {\n            'freqs_per_bands': (1, 2, 3)  # Sum = 6, which is very low\n        }\n        \n        issues = self.validator.validate_parameter_compatibility(config)\n        assert len(issues) == 1\n        assert issues[0].severity == ValidationSeverity.WARNING\n        assert issues[0].parameter_name == \"freqs_per_bands\"\n    ","chunkId":"tests/unit/test_parameter_validator.py:13"}],[2903,{"filePath":"tests/unit/test_parameter_validator.py","startLine":175,"endLine":189,"chunkText":"        assert issues[0].severity == ValidationSeverity.WARNING\n        assert issues[0].parameter_name == \"freqs_per_bands\"\n    \n    def test_validate_normalization_config_valid(self):\n        \"\"\"Test normalization configuration validation - valid cases.\"\"\"\n        valid_configs = [\n            None,\n            'layer_norm',\n            'batch_norm',\n            'rms_norm',\n            {'type': 'layer_norm', 'eps': 1e-5}\n        ]\n        \n        for norm_config in valid_configs:\n            issues = self.validator.validate_normalization_config(norm_config)","chunkId":"tests/unit/test_parameter_validator.py:14"}],[2904,{"filePath":"tests/unit/test_parameter_validator.py","startLine":187,"endLine":200,"chunkText":"        \n        for norm_config in valid_configs:\n            issues = self.validator.validate_normalization_config(norm_config)\n            assert len(issues) == 0, f\"Failed for config: {norm_config}\"\n    \n    def test_validate_normalization_config_invalid(self):\n        \"\"\"Test normalization configuration validation - invalid cases.\"\"\"\n        invalid_configs = [\n            'invalid_norm',  # Unsupported string\n            123,  # Invalid type\n            ['layer_norm']  # Invalid type (list)\n        ]\n        \n        for norm_config in invalid_configs:","chunkId":"tests/unit/test_parameter_validator.py:15"}],[2905,{"filePath":"tests/unit/test_parameter_validator.py","startLine":198,"endLine":210,"chunkText":"        ]\n        \n        for norm_config in invalid_configs:\n            issues = self.validator.validate_normalization_config(norm_config)\n            assert len(issues) >= 1, f\"Should have failed for config: {norm_config}\"\n            assert issues[0].severity == ValidationSeverity.ERROR\n    \n    def test_get_parameter_defaults_bs_roformer(self):\n        \"\"\"Test getting parameter defaults for BSRoformer.\"\"\"\n        defaults = self.validator.get_parameter_defaults(\"bs_roformer\")\n        \n        # Check some expected defaults\n        assert defaults['stereo'] is False","chunkId":"tests/unit/test_parameter_validator.py:16"}],[2906,{"filePath":"tests/unit/test_parameter_validator.py","startLine":208,"endLine":221,"chunkText":"        \n        # Check some expected defaults\n        assert defaults['stereo'] is False\n        assert defaults['num_stems'] == 2\n        assert defaults['flash_attn'] is True\n        assert defaults['mlp_expansion_factor'] == 4\n        assert defaults['sage_attention'] is False\n        assert 'freqs_per_bands' in defaults\n        assert 'mask_estimator_depth' in defaults\n    \n    def test_get_parameter_defaults_mel_band_roformer(self):\n        \"\"\"Test getting parameter defaults for MelBandRoformer.\"\"\"\n        defaults = self.validator.get_parameter_defaults(\"mel_band_roformer\")\n        ","chunkId":"tests/unit/test_parameter_validator.py:17"}],[2907,{"filePath":"tests/unit/test_parameter_validator.py","startLine":219,"endLine":232,"chunkText":"        \"\"\"Test getting parameter defaults for MelBandRoformer.\"\"\"\n        defaults = self.validator.get_parameter_defaults(\"mel_band_roformer\")\n        \n        # Check some expected defaults\n        assert defaults['stereo'] is False\n        assert defaults['num_stems'] == 2\n        assert defaults['flash_attn'] is True\n        assert defaults['mlp_expansion_factor'] == 4\n        assert defaults['sage_attention'] is False\n        assert defaults['num_bands'] == 64\n    \n    def test_apply_parameter_defaults(self):\n        \"\"\"Test applying parameter defaults to configuration.\"\"\"\n        config = {","chunkId":"tests/unit/test_parameter_validator.py:18"}],[2908,{"filePath":"tests/unit/test_parameter_validator.py","startLine":230,"endLine":246,"chunkText":"    def test_apply_parameter_defaults(self):\n        \"\"\"Test applying parameter defaults to configuration.\"\"\"\n        config = {\n            'dim': 512,\n            'depth': 12,\n            'stereo': True  # Override default\n        }\n        \n        result = self.validator.apply_parameter_defaults(config, \"bs_roformer\")\n        \n        # Should have original values\n        assert result['dim'] == 512\n        assert result['depth'] == 12\n        assert result['stereo'] is True  # Override preserved\n        \n        # Should have applied defaults\n        assert result['num_stems'] == 2","chunkId":"tests/unit/test_parameter_validator.py:19"}],[2909,{"filePath":"tests/unit/test_parameter_validator.py","startLine":244,"endLine":261,"chunkText":"        \n        # Should have applied defaults\n        assert result['num_stems'] == 2\n        assert result['flash_attn'] is True\n        assert result['mlp_expansion_factor'] == 4\n    \n    def test_validate_all_comprehensive(self):\n        \"\"\"Test comprehensive validation with all checks.\"\"\"\n        config = {\n            'dim': 512,\n            'depth': 12,\n            'freqs_per_bands': (2, 4, 8, 16, 32, 64),\n            'stereo': False,\n            'attn_dropout': 0.1,\n            'sage_attention': False,\n            'norm': 'layer_norm'\n        }\n        ","chunkId":"tests/unit/test_parameter_validator.py:20"}],[2910,{"filePath":"tests/unit/test_parameter_validator.py","startLine":259,"endLine":274,"chunkText":"            'norm': 'layer_norm'\n        }\n        \n        issues = self.validator.validate_all(config, \"bs_roformer\")\n        assert len(issues) == 0\n    \n    def test_validate_all_with_errors(self):\n        \"\"\"Test comprehensive validation with multiple error types.\"\"\"\n        config = {\n            'dim': \"invalid\",  # Type error\n            'depth': 100,  # Range error\n            # Missing 'freqs_per_bands' - required parameter error\n            'sage_attention': True,\n            'flash_attn': True,  # Compatibility warning\n            'norm': 'invalid_norm'  # Normalization error\n        }","chunkId":"tests/unit/test_parameter_validator.py:21"}],[2911,{"filePath":"tests/unit/test_parameter_validator.py","startLine":272,"endLine":284,"chunkText":"            'flash_attn': True,  # Compatibility warning\n            'norm': 'invalid_norm'  # Normalization error\n        }\n        \n        issues = self.validator.validate_all(config, \"bs_roformer\")\n        assert len(issues) >= 5  # At least 5 different types of issues\n        \n        # Check we have different types of issues\n        severities = [issue.severity for issue in issues]\n        assert ValidationSeverity.ERROR in severities\n        assert ValidationSeverity.WARNING in severities\n    \n    def test_validate_and_raise_success(self):","chunkId":"tests/unit/test_parameter_validator.py:22"}],[2912,{"filePath":"tests/unit/test_parameter_validator.py","startLine":282,"endLine":298,"chunkText":"        assert ValidationSeverity.WARNING in severities\n    \n    def test_validate_and_raise_success(self):\n        \"\"\"Test validate_and_raise with valid configuration.\"\"\"\n        config = {\n            'dim': 512,\n            'depth': 12,\n            'freqs_per_bands': (2, 4, 8, 16, 32, 64)\n        }\n        \n        # Should not raise any exception\n        self.validator.validate_and_raise(config, \"bs_roformer\")\n    \n    def test_validate_and_raise_error(self):\n        \"\"\"Test validate_and_raise with invalid configuration.\"\"\"\n        config = {\n            'dim': \"invalid\",  # Type error","chunkId":"tests/unit/test_parameter_validator.py:23"}],[2913,{"filePath":"tests/unit/test_parameter_validator.py","startLine":296,"endLine":311,"chunkText":"        \"\"\"Test validate_and_raise with invalid configuration.\"\"\"\n        config = {\n            'dim': \"invalid\",  # Type error\n            'depth': 12\n            # Missing 'freqs_per_bands'\n        }\n        \n        with pytest.raises(ParameterValidationError) as exc_info:\n            self.validator.validate_and_raise(config, \"bs_roformer\")\n        \n        # Check the exception contains useful information\n        exception = exc_info.value\n        assert exception.parameter_name is not None\n        assert exception.suggested_fix is not None\n    \n    def test_private_helper_methods(self):","chunkId":"tests/unit/test_parameter_validator.py:24"}],[2914,{"filePath":"tests/unit/test_parameter_validator.py","startLine":309,"endLine":320,"chunkText":"        assert exception.suggested_fix is not None\n    \n    def test_private_helper_methods(self):\n        \"\"\"Test private helper methods.\"\"\"\n        # Test _is_correct_type\n        assert self.validator._is_correct_type(123, int) is True\n        assert self.validator._is_correct_type(\"123\", int) is False\n        assert self.validator._is_correct_type([1, 2, 3], (list, tuple)) is True\n        assert self.validator._is_correct_type((1, 2, 3), (list, tuple)) is True\n        \n        # Test _get_type_name\n        assert self.validator._get_type_name(int) == \"int\"","chunkId":"tests/unit/test_parameter_validator.py:25"}],[2915,{"filePath":"tests/unit/test_parameter_validator.py","startLine":318,"endLine":331,"chunkText":"        \n        # Test _get_type_name\n        assert self.validator._get_type_name(int) == \"int\"\n        assert \"int\" in self.validator._get_type_name((int, float))\n        assert \"float\" in self.validator._get_type_name((int, float))\n        \n        # Test _get_expected_type_description\n        assert \"int\" in self.validator._get_expected_type_description('dim')\n        assert \"appropriate type\" in self.validator._get_expected_type_description('unknown_param')\n\n\nif __name__ == \"__main__\":\n    pytest.main([__file__])\n","chunkId":"tests/unit/test_parameter_validator.py:26"}],[2916,{"filePath":"tests/unit/test_remote_api_client.py","startLine":0,"endLine":26,"chunkText":"import json\nimport pytest\nimport logging\nimport tempfile\nimport os\nfrom unittest.mock import Mock, patch, mock_open\nfrom unittest.mock import MagicMock\nimport requests\n\nfrom audio_separator.remote import AudioSeparatorAPIClient\n\n\n@pytest.fixture\ndef logger():\n    \"\"\"Create a mock logger for testing.\"\"\"\n    return logging.getLogger(\"test\")\n\n\n@pytest.fixture\ndef api_client(logger):\n    \"\"\"Create an API client instance for testing.\"\"\"\n    return AudioSeparatorAPIClient(\"https://test-api.example.com\", logger)\n\n\n@pytest.fixture\ndef mock_audio_file():\n    \"\"\"Create a temporary audio file for testing.\"\"\"","chunkId":"tests/unit/test_remote_api_client.py:0"}],[2917,{"filePath":"tests/unit/test_remote_api_client.py","startLine":24,"endLine":42,"chunkText":"@pytest.fixture\ndef mock_audio_file():\n    \"\"\"Create a temporary audio file for testing.\"\"\"\n    with tempfile.NamedTemporaryFile(suffix=\".wav\", delete=False) as f:\n        f.write(b\"fake audio content\")\n        yield f.name\n    os.unlink(f.name)\n\n\nclass TestAudioSeparatorAPIClient:\n    \"\"\"Test the AudioSeparatorAPIClient class.\"\"\"\n\n    def test_init(self, logger):\n        \"\"\"Test client initialization.\"\"\"\n        api_url = \"https://test-api.example.com\"\n        client = AudioSeparatorAPIClient(api_url, logger)\n\n        assert client.api_url == api_url\n        assert client.logger == logger","chunkId":"tests/unit/test_remote_api_client.py:1"}],[2918,{"filePath":"tests/unit/test_remote_api_client.py","startLine":40,"endLine":53,"chunkText":"\n        assert client.api_url == api_url\n        assert client.logger == logger\n        assert client.session is not None\n\n    @patch(\"requests.Session.post\")\n    @patch(\"builtins.open\", new_callable=mock_open, read_data=b\"fake audio content\")\n    def test_separate_audio_success(self, mock_file, mock_post, api_client, mock_audio_file):\n        \"\"\"Test successful audio separation submission.\"\"\"\n        # Mock successful response\n        mock_response = Mock()\n        mock_response.json.return_value = {\n            \"task_id\": \"test-task-123\",\n            \"status\": \"submitted\",","chunkId":"tests/unit/test_remote_api_client.py:2"}],[2919,{"filePath":"tests/unit/test_remote_api_client.py","startLine":51,"endLine":66,"chunkText":"        mock_response.json.return_value = {\n            \"task_id\": \"test-task-123\",\n            \"status\": \"submitted\",\n            \"message\": \"Job submitted for processing\",\n            \"models_used\": [\"default\"],\n            \"total_models\": 1,\n            \"original_filename\": \"test.wav\",\n        }\n        mock_response.raise_for_status.return_value = None\n        mock_post.return_value = mock_response\n\n        result = api_client.separate_audio(mock_audio_file)\n\n        # Verify the result\n        assert result[\"task_id\"] == \"test-task-123\"\n        assert result[\"status\"] == \"submitted\"","chunkId":"tests/unit/test_remote_api_client.py:3"}],[2920,{"filePath":"tests/unit/test_remote_api_client.py","startLine":64,"endLine":77,"chunkText":"        # Verify the result\n        assert result[\"task_id\"] == \"test-task-123\"\n        assert result[\"status\"] == \"submitted\"\n        assert result[\"models_used\"] == [\"default\"]\n\n        # Verify the request was made correctly\n        mock_post.assert_called_once()\n        call_args = mock_post.call_args\n        assert call_args[0][0] == \"https://test-api.example.com/separate\"\n        assert \"files\" in call_args[1]\n        assert \"data\" in call_args[1]\n\n    @patch(\"requests.Session.post\")\n    @patch(\"builtins.open\", new_callable=mock_open, read_data=b\"fake audio content\")","chunkId":"tests/unit/test_remote_api_client.py:4"}],[2921,{"filePath":"tests/unit/test_remote_api_client.py","startLine":75,"endLine":84,"chunkText":"\n    @patch(\"requests.Session.post\")\n    @patch(\"builtins.open\", new_callable=mock_open, read_data=b\"fake audio content\")\n    def test_separate_audio_with_multiple_models(self, mock_file, mock_post, api_client, mock_audio_file):\n        \"\"\"Test audio separation with multiple models.\"\"\"\n        mock_response = Mock()\n        mock_response.json.return_value = {\"task_id\": \"test-task-456\", \"status\": \"submitted\", \"models_used\": [\"model1.ckpt\", \"model2.onnx\"], \"total_models\": 2}\n        mock_response.raise_for_status.return_value = None\n        mock_post.return_value = mock_response\n","chunkId":"tests/unit/test_remote_api_client.py:5"}],[2922,{"filePath":"tests/unit/test_remote_api_client.py","startLine":82,"endLine":96,"chunkText":"        mock_response.raise_for_status.return_value = None\n        mock_post.return_value = mock_response\n\n        models = [\"model1.ckpt\", \"model2.onnx\"]\n        result = api_client.separate_audio(mock_audio_file, models=models)\n\n        assert result[\"models_used\"] == models\n        assert result[\"total_models\"] == 2\n\n        # Check that models were serialized correctly in the request\n        call_args = mock_post.call_args\n        data = call_args[1][\"data\"]\n        assert json.loads(data[\"models\"]) == models\n\n    @patch(\"requests.Session.post\")","chunkId":"tests/unit/test_remote_api_client.py:6"}],[2923,{"filePath":"tests/unit/test_remote_api_client.py","startLine":94,"endLine":104,"chunkText":"        assert json.loads(data[\"models\"]) == models\n\n    @patch(\"requests.Session.post\")\n    @patch(\"builtins.open\", new_callable=mock_open, read_data=b\"fake audio content\")\n    def test_separate_audio_with_custom_parameters(self, mock_file, mock_post, api_client, mock_audio_file):\n        \"\"\"Test audio separation with custom parameters.\"\"\"\n        mock_response = Mock()\n        mock_response.json.return_value = {\"task_id\": \"test-task-789\", \"status\": \"submitted\"}\n        mock_response.raise_for_status.return_value = None\n        mock_post.return_value = mock_response\n","chunkId":"tests/unit/test_remote_api_client.py:7"}],[2924,{"filePath":"tests/unit/test_remote_api_client.py","startLine":102,"endLine":112,"chunkText":"        mock_response.raise_for_status.return_value = None\n        mock_post.return_value = mock_response\n\n        custom_output_names = {\"Vocals\": \"lead_vocals\", \"Instrumental\": \"backing_track\"}\n        result = api_client.separate_audio(\n            mock_audio_file, model=\"test_model.ckpt\", output_format=\"wav\", normalization_threshold=0.8, mdx_segment_size=512, vr_aggression=10, custom_output_names=custom_output_names\n        )\n\n        # Verify the parameters were passed correctly\n        call_args = mock_post.call_args\n        data = call_args[1][\"data\"]","chunkId":"tests/unit/test_remote_api_client.py:8"}],[2925,{"filePath":"tests/unit/test_remote_api_client.py","startLine":110,"endLine":121,"chunkText":"        # Verify the parameters were passed correctly\n        call_args = mock_post.call_args\n        data = call_args[1][\"data\"]\n        assert data[\"model\"] == \"test_model.ckpt\"\n        assert data[\"output_format\"] == \"wav\"\n        assert data[\"normalization_threshold\"] == 0.8\n        assert data[\"mdx_segment_size\"] == 512\n        assert data[\"vr_aggression\"] == 10\n        assert json.loads(data[\"custom_output_names\"]) == custom_output_names\n\n    @patch(\"requests.Session.post\")\n    def test_separate_audio_file_not_found(self, mock_post, api_client):","chunkId":"tests/unit/test_remote_api_client.py:9"}],[2926,{"filePath":"tests/unit/test_remote_api_client.py","startLine":119,"endLine":129,"chunkText":"\n    @patch(\"requests.Session.post\")\n    def test_separate_audio_file_not_found(self, mock_post, api_client):\n        \"\"\"Test audio separation with non-existent file.\"\"\"\n        with pytest.raises(FileNotFoundError):\n            api_client.separate_audio(\"/nonexistent/file.wav\")\n\n    @patch(\"requests.Session.post\")\n    @patch(\"builtins.open\", new_callable=mock_open, read_data=b\"fake audio content\")\n    def test_separate_audio_request_error(self, mock_file, mock_post, api_client, mock_audio_file):\n        \"\"\"Test audio separation with request error.\"\"\"","chunkId":"tests/unit/test_remote_api_client.py:10"}],[2927,{"filePath":"tests/unit/test_remote_api_client.py","startLine":127,"endLine":138,"chunkText":"    @patch(\"builtins.open\", new_callable=mock_open, read_data=b\"fake audio content\")\n    def test_separate_audio_request_error(self, mock_file, mock_post, api_client, mock_audio_file):\n        \"\"\"Test audio separation with request error.\"\"\"\n        mock_post.side_effect = requests.RequestException(\"Connection error\")\n\n        with pytest.raises(requests.RequestException):\n            api_client.separate_audio(mock_audio_file)\n\n    @patch(\"requests.Session.get\")\n    def test_get_job_status_success(self, mock_get, api_client):\n        \"\"\"Test successful job status retrieval.\"\"\"\n        mock_response = Mock(","chunkId":"tests/unit/test_remote_api_client.py:11"}],[2928,{"filePath":"tests/unit/test_remote_api_client.py","startLine":136,"endLine":146,"chunkText":"    def test_get_job_status_success(self, mock_get, api_client):\n        \"\"\"Test successful job status retrieval.\"\"\"\n        mock_response = Mock()\n        mock_response.json.return_value = {\"task_id\": \"test-task-123\", \"status\": \"processing\", \"progress\": 50, \"current_model_index\": 0, \"total_models\": 1}\n        mock_response.raise_for_status.return_value = None\n        mock_get.return_value = mock_response\n\n        result = api_client.get_job_status(\"test-task-123\")\n\n        assert result[\"status\"] == \"processing\"\n        assert result[\"progress\"] == 50","chunkId":"tests/unit/test_remote_api_client.py:12"}],[2929,{"filePath":"tests/unit/test_remote_api_client.py","startLine":144,"endLine":157,"chunkText":"        assert result[\"status\"] == \"processing\"\n        assert result[\"progress\"] == 50\n        mock_get.assert_called_once_with(\"https://test-api.example.com/status/test-task-123\", timeout=10)\n\n    @patch(\"requests.Session.get\")\n    def test_get_job_status_error(self, mock_get, api_client):\n        \"\"\"Test job status retrieval with error.\"\"\"\n        mock_get.side_effect = requests.RequestException(\"API error\")\n\n        with pytest.raises(requests.RequestException):\n            api_client.get_job_status(\"test-task-123\")\n\n    @patch(\"requests.Session.get\")\n    @patch(\"builtins.open\", new_callable=mock_open","chunkId":"tests/unit/test_remote_api_client.py:13"}],[2930,{"filePath":"tests/unit/test_remote_api_client.py","startLine":156,"endLine":169,"chunkText":"\n    @patch(\"requests.Session.get\")\n    @patch(\"builtins.open\", new_callable=mock_open)\n    def test_download_file_success(self, mock_file, mock_get, api_client):\n        \"\"\"Test successful file download.\"\"\"\n        mock_response = Mock()\n        mock_response.content = b\"fake audio file content\"\n        mock_response.status_code = 200\n        mock_response.raise_for_status.return_value = None\n        mock_get.return_value = mock_response\n\n        result = api_client.download_file(\"test-task-123\", \"output.wav\", \"local_output.wav\")\n\n        assert result == \"local_output.wav\"","chunkId":"tests/unit/test_remote_api_client.py:14"}],[2931,{"filePath":"tests/unit/test_remote_api_client.py","startLine":167,"endLine":177,"chunkText":"        result = api_client.download_file(\"test-task-123\", \"output.wav\", \"local_output.wav\")\n\n        assert result == \"local_output.wav\"\n        mock_get.assert_called_once_with(\"https://test-api.example.com/download/test-task-123/output.wav\", timeout=60)\n        mock_file.assert_called_once_with(\"local_output.wav\", \"wb\")\n\n    @patch(\"requests.Session.get\")\n    @patch(\"builtins.open\", new_callable=mock_open)\n    def test_download_file_default_output_path(self, mock_file, mock_get, api_client):\n        \"\"\"Test file download with default output path.\"\"\"\n        mock_response = Mock()","chunkId":"tests/unit/test_remote_api_client.py:15"}],[2932,{"filePath":"tests/unit/test_remote_api_client.py","startLine":175,"endLine":188,"chunkText":"    def test_download_file_default_output_path(self, mock_file, mock_get, api_client):\n        \"\"\"Test file download with default output path.\"\"\"\n        mock_response = Mock()\n        mock_response.content = b\"fake audio file content\"\n        mock_response.status_code = 200\n        mock_response.raise_for_status.return_value = None\n        mock_get.return_value = mock_response\n\n        result = api_client.download_file(\"test-task-123\", \"output.wav\")\n\n        assert result == \"output.wav\"\n        mock_file.assert_called_once_with(\"output.wav\", \"wb\")\n\n    @patch(\"requests.Session.get\")","chunkId":"tests/unit/test_remote_api_client.py:16"}],[2933,{"filePath":"tests/unit/test_remote_api_client.py","startLine":186,"endLine":198,"chunkText":"        mock_file.assert_called_once_with(\"output.wav\", \"wb\")\n\n    @patch(\"requests.Session.get\")\n    @patch(\"builtins.open\", new_callable=mock_open)\n    def test_download_file_with_spaces_in_filename(self, mock_file, mock_get, api_client):\n        \"\"\"Test file download with spaces in filename (URL encoding).\"\"\"\n        mock_response = Mock()\n        mock_response.content = b\"fake audio file content\"\n        mock_response.status_code = 200\n        mock_response.raise_for_status.return_value = None\n        mock_get.return_value = mock_response\n\n        filename_with_spaces = \"My Song (Vocals) Output.wav\"","chunkId":"tests/unit/test_remote_api_client.py:17"}],[2934,{"filePath":"tests/unit/test_remote_api_client.py","startLine":196,"endLine":207,"chunkText":"        mock_get.return_value = mock_response\n\n        filename_with_spaces = \"My Song (Vocals) Output.wav\"\n        result = api_client.download_file(\"test-task-123\", filename_with_spaces)\n\n        # Verify URL was properly encoded\n        expected_url = \"https://test-api.example.com/download/test-task-123/My%20Song%20%28Vocals%29%20Output.wav\"\n        mock_get.assert_called_once_with(expected_url, timeout=60)\n        assert result == filename_with_spaces\n        mock_file.assert_called_once_with(filename_with_spaces, \"wb\")\n\n    @patch(\"requests.Session.get\")","chunkId":"tests/unit/test_remote_api_client.py:18"}],[2935,{"filePath":"tests/unit/test_remote_api_client.py","startLine":206,"endLine":217,"chunkText":"\n    @patch(\"requests.Session.get\")\n    @patch(\"builtins.open\", new_callable=mock_open)\n    def test_download_file_with_special_characters(self, mock_file, mock_get, api_client):\n        \"\"\"Test file download with special characters in filename.\"\"\"\n        mock_response = Mock()\n        mock_response.content = b\"fake audio file content\"\n        mock_response.status_code = 200\n        mock_response.raise_for_status.return_value = None\n        mock_get.return_value = mock_response\n\n        filename_with_special_chars = \"Song & Band - Title (Vocals 50% Mix).flac\"","chunkId":"tests/unit/test_remote_api_client.py:19"}],[2936,{"filePath":"tests/unit/test_remote_api_client.py","startLine":215,"endLine":225,"chunkText":"        mock_get.return_value = mock_response\n\n        filename_with_special_chars = \"Song & Band - Title (Vocals 50% Mix).flac\"\n        result = api_client.download_file(\"test-task-456\", filename_with_special_chars)\n\n        # Verify URL was properly encoded\n        expected_url = \"https://test-api.example.com/download/test-task-456/Song%20%26%20Band%20-%20Title%20%28Vocals%2050%25%20Mix%29.flac\"\n        mock_get.assert_called_once_with(expected_url, timeout=60)\n        assert result == filename_with_special_chars\n        mock_file.assert_called_once_with(filename_with_special_chars, \"wb\")\n","chunkId":"tests/unit/test_remote_api_client.py:20"}],[2937,{"filePath":"tests/unit/test_remote_api_client.py","startLine":223,"endLine":234,"chunkText":"        assert result == filename_with_special_chars\n        mock_file.assert_called_once_with(filename_with_special_chars, \"wb\")\n\n    @patch(\"requests.Session.get\")\n    @patch(\"builtins.open\", new_callable=mock_open)\n    def test_download_file_with_unicode_characters(self, mock_file, mock_get, api_client):\n        \"\"\"Test file download with unicode characters in filename.\"\"\"\n        mock_response = Mock()\n        mock_response.content = b\"fake audio file content\"\n        mock_response.status_code = 200\n        mock_response.raise_for_status.return_value = None\n        mock_get.return_value = mock_respons","chunkId":"tests/unit/test_remote_api_client.py:21"}],[2938,{"filePath":"tests/unit/test_remote_api_client.py","startLine":233,"endLine":242,"chunkText":"        mock_response.raise_for_status.return_value = None\n        mock_get.return_value = mock_response\n\n        unicode_filename = \"Café - Naïve Song (Résumé).mp3\"\n        result = api_client.download_file(\"test-task-789\", unicode_filename)\n\n        # Verify URL was properly encoded (UTF-8 encoded then percent-encoded)\n        expected_url = \"https://test-api.example.com/download/test-task-789/Caf%C3%A9%20-%20Na%C3%AFve%20Song%20%28R%C3%A9sum%C3%A9%29.mp3\"\n        mock_get.assert_called_once_with(expected_url, timeout=60)\n        assert result == unicode_filename","chunkId":"tests/unit/test_remote_api_client.py:22"}],[2939,{"filePath":"tests/unit/test_remote_api_client.py","startLine":240,"endLine":250,"chunkText":"        expected_url = \"https://test-api.example.com/download/test-task-789/Caf%C3%A9%20-%20Na%C3%AFve%20Song%20%28R%C3%A9sum%C3%A9%29.mp3\"\n        mock_get.assert_called_once_with(expected_url, timeout=60)\n        assert result == unicode_filename\n        mock_file.assert_called_once_with(unicode_filename, \"wb\")\n\n    @patch(\"requests.Session.get\")\n    def test_download_file_error(self, mock_get, api_client):\n        \"\"\"Test file download with error.\"\"\"\n        mock_get.side_effect = requests.RequestException(\"Download error\")\n\n        with pytest.raises(requests.RequestException):","chunkId":"tests/unit/test_remote_api_client.py:23"}],[2940,{"filePath":"tests/unit/test_remote_api_client.py","startLine":248,"endLine":260,"chunkText":"        mock_get.side_effect = requests.RequestException(\"Download error\")\n\n        with pytest.raises(requests.RequestException):\n            api_client.download_file(\"test-task-123\", \"output.wav\")\n\n    @patch(\"requests.Session.get\")\n    def test_list_models_pretty_format(self, mock_get, api_client):\n        \"\"\"Test listing models in pretty format.\"\"\"\n        mock_response = Mock()\n        mock_response.text = \"Model list in pretty format\"\n        mock_response.raise_for_status.return_value = None\n        mock_get.return_value = mock_response\n","chunkId":"tests/unit/test_remote_api_client.py:24"}],[2941,{"filePath":"tests/unit/test_remote_api_client.py","startLine":258,"endLine":270,"chunkText":"        mock_response.raise_for_status.return_value = None\n        mock_get.return_value = mock_response\n\n        result = api_client.list_models(format_type=\"pretty\")\n\n        assert result == {\"text\": \"Model list in pretty format\"}\n        mock_get.assert_called_once_with(\"https://test-api.example.com/models\", timeout=10)\n\n    @patch(\"requests.Session.get\")\n    def test_list_models_json_format(self, mock_get, api_client):\n        \"\"\"Test listing models in JSON format.\"\"\"\n        mock_response = Mock()\n        models_data = {\"models\": [{\"name\": \"model1\", \"type\": \"MDX\"}]}","chunkId":"tests/unit/test_remote_api_client.py:25"}],[2942,{"filePath":"tests/unit/test_remote_api_client.py","startLine":268,"endLine":281,"chunkText":"        \"\"\"Test listing models in JSON format.\"\"\"\n        mock_response = Mock()\n        models_data = {\"models\": [{\"name\": \"model1\", \"type\": \"MDX\"}]}\n        mock_response.json.return_value = models_data\n        mock_response.raise_for_status.return_value = None\n        mock_get.return_value = mock_response\n\n        result = api_client.list_models(format_type=\"json\")\n\n        assert result == models_data\n        mock_get.assert_called_once_with(\"https://test-api.example.com/models-json\", timeout=10)\n\n    @patch(\"requests.Session.get\")\n    def test_list_models_with_filter(self, mock_get, api_client):","chunkId":"tests/unit/test_remote_api_client.py:26"}],[2943,{"filePath":"tests/unit/test_remote_api_client.py","startLine":279,"endLine":293,"chunkText":"\n    @patch(\"requests.Session.get\")\n    def test_list_models_with_filter(self, mock_get, api_client):\n        \"\"\"Test listing models with filter.\"\"\"\n        mock_response = Mock()\n        mock_response.text = \"Filtered model list\"\n        mock_response.raise_for_status.return_value = None\n        mock_get.return_value = mock_response\n\n        result = api_client.list_models(filter_by=\"vocals\")\n\n        assert result == {\"text\": \"Filtered model list\"}\n        mock_get.assert_called_once_with(\"https://test-api.example.com/models?filter_sort_by=vocals\", timeout=10)\n\n    @patch(\"requests.Session.get\")","chunkId":"tests/unit/test_remote_api_client.py:27"}],[2944,{"filePath":"tests/unit/test_remote_api_client.py","startLine":291,"endLine":303,"chunkText":"        mock_get.assert_called_once_with(\"https://test-api.example.com/models?filter_sort_by=vocals\", timeout=10)\n\n    @patch(\"requests.Session.get\")\n    def test_get_server_version_success(self, mock_get, api_client):\n        \"\"\"Test successful server version retrieval.\"\"\"\n        mock_response = Mock()\n        mock_response.json.return_value = {\"version\": \"1.2.3\", \"status\": \"healthy\"}\n        mock_response.raise_for_status.return_value = None\n        mock_get.return_value = mock_response\n\n        result = api_client.get_server_version()\n\n        assert result == \"1.2.3\"","chunkId":"tests/unit/test_remote_api_client.py:28"}],[2945,{"filePath":"tests/unit/test_remote_api_client.py","startLine":301,"endLine":315,"chunkText":"        result = api_client.get_server_version()\n\n        assert result == \"1.2.3\"\n        mock_get.assert_called_once_with(\"https://test-api.example.com/health\", timeout=10)\n\n    @patch(\"requests.Session.get\")\n    def test_get_server_version_no_version(self, mock_get, api_client):\n        \"\"\"Test server version retrieval when version is not in response.\"\"\"\n        mock_response = Mock()\n        mock_response.json.return_value = {\"status\": \"healthy\"}\n        mock_response.raise_for_status.return_value = None\n        mock_get.return_value = mock_response\n\n        result = api_client.get_server_version()\n","chunkId":"tests/unit/test_remote_api_client.py:29"}],[2946,{"filePath":"tests/unit/test_remote_api_client.py","startLine":313,"endLine":328,"chunkText":"\n        result = api_client.get_server_version()\n\n        assert result == \"unknown\"\n\n    @patch(\"requests.Session.get\")\n    def test_get_server_version_error(self, mock_get, api_client):\n        \"\"\"Test server version retrieval with error.\"\"\"\n        mock_get.side_effect = requests.RequestException(\"Health check failed\")\n\n        with pytest.raises(requests.RequestException):\n            api_client.get_server_version()\n\n    @patch.object(AudioSeparatorAPIClient, \"separate_audio\")\n    @patch.object(AudioSeparatorAPIClient, \"get_job_status\")\n    @patch.object(AudioSeparatorAPIClient, \"download_file\")","chunkId":"tests/unit/test_remote_api_client.py:30"}],[2947,{"filePath":"tests/unit/test_remote_api_client.py","startLine":326,"endLine":335,"chunkText":"    @patch.object(AudioSeparatorAPIClient, \"separate_audio\")\n    @patch.object(AudioSeparatorAPIClient, \"get_job_status\")\n    @patch.object(AudioSeparatorAPIClient, \"download_file\")\n    @patch(\"time.sleep\")\n    def test_separate_audio_and_wait_success(self, mock_sleep, mock_download, mock_status, mock_separate, api_client, mock_audio_file):\n        \"\"\"Test the convenience method for separating audio and waiting for completion.\"\"\"\n        # Mock separation submission\n        mock_separate.return_value = {\"task_id\": \"test-task-123\"}\n\n        # Mock status polling - first processing, then completed","chunkId":"tests/unit/test_remote_api_client.py:31"}],[2948,{"filePath":"tests/unit/test_remote_api_client.py","startLine":333,"endLine":343,"chunkText":"        mock_separate.return_value = {\"task_id\": \"test-task-123\"}\n\n        # Mock status polling - first processing, then completed\n        mock_status.side_effect = [{\"status\": \"processing\", \"progress\": 25}, {\"status\": \"processing\", \"progress\": 50}, {\"status\": \"completed\", \"files\": [\"output1.wav\", \"output2.wav\"]}]\n\n        # Mock file downloads\n        mock_download.side_effect = [\"output1.wav\", \"output2.wav\"]\n\n        result = api_client.separate_audio_and_wait(mock_audio_file, model=\"test_model.ckpt\", timeout=60, poll_interval=5, download=True)\n\n        # Verify the result","chunkId":"tests/unit/test_remote_api_client.py:32"}],[2949,{"filePath":"tests/unit/test_remote_api_client.py","startLine":341,"endLine":352,"chunkText":"        result = api_client.separate_audio_and_wait(mock_audio_file, model=\"test_model.ckpt\", timeout=60, poll_interval=5, download=True)\n\n        # Verify the result\n        assert result[\"status\"] == \"completed\"\n        assert result[\"task_id\"] == \"test-task-123\"\n        assert result[\"files\"] == [\"output1.wav\", \"output2.wav\"]\n        assert result[\"downloaded_files\"] == [\"output1.wav\", \"output2.wav\"]\n\n        # Verify method calls\n        mock_separate.assert_called_once()\n        assert mock_status.call_count == 3\n        assert mock_download.call_count == 2","chunkId":"tests/unit/test_remote_api_client.py:33"}],[2950,{"filePath":"tests/unit/test_remote_api_client.py","startLine":350,"endLine":360,"chunkText":"        mock_separate.assert_called_once()\n        assert mock_status.call_count == 3\n        assert mock_download.call_count == 2\n        assert mock_sleep.call_count == 2  # Two polling iterations\n\n    @patch.object(AudioSeparatorAPIClient, \"separate_audio\")\n    @patch.object(AudioSeparatorAPIClient, \"get_job_status\")\n    @patch(\"time.sleep\")\n    def test_separate_audio_and_wait_error(self, mock_sleep, mock_status, mock_separate, api_client, mock_audio_file):\n        \"\"\"Test the convenience method when job fails.\"\"\"\n        mock_separate.return_value = {\"task_id\": \"test-task-456\"}","chunkId":"tests/unit/test_remote_api_client.py:34"}],[2951,{"filePath":"tests/unit/test_remote_api_client.py","startLine":358,"endLine":368,"chunkText":"    def test_separate_audio_and_wait_error(self, mock_sleep, mock_status, mock_separate, api_client, mock_audio_file):\n        \"\"\"Test the convenience method when job fails.\"\"\"\n        mock_separate.return_value = {\"task_id\": \"test-task-456\"}\n        mock_status.side_effect = [{\"status\": \"processing\", \"progress\": 25}, {\"status\": \"error\", \"error\": \"Processing failed\"}]\n\n        result = api_client.separate_audio_and_wait(mock_audio_file, timeout=60, poll_interval=5)\n\n        assert result[\"status\"] == \"error\"\n        assert result[\"error\"] == \"Processing failed\"\n        assert result[\"files\"] == []\n","chunkId":"tests/unit/test_remote_api_client.py:35"}],[2952,{"filePath":"tests/unit/test_remote_api_client.py","startLine":366,"endLine":376,"chunkText":"        assert result[\"error\"] == \"Processing failed\"\n        assert result[\"files\"] == []\n\n    @patch.object(AudioSeparatorAPIClient, \"separate_audio\")\n    @patch.object(AudioSeparatorAPIClient, \"get_job_status\")\n    @patch(\"time.sleep\")\n    def test_separate_audio_and_wait_timeout(self, mock_sleep, mock_status, mock_separate, api_client, mock_audio_file):\n        \"\"\"Test the convenience method when polling times out.\"\"\"\n        mock_separate.return_value = {\"task_id\": \"test-task-789\"}\n        mock_status.return_value = {\"status\": \"processing\", \"progress\": 25}\n","chunkId":"tests/unit/test_remote_api_client.py:36"}],[2953,{"filePath":"tests/unit/test_remote_api_client.py","startLine":374,"endLine":385,"chunkText":"        mock_separate.return_value = {\"task_id\": \"test-task-789\"}\n        mock_status.return_value = {\"status\": \"processing\", \"progress\": 25}\n\n        result = api_client.separate_audio_and_wait(mock_audio_file, timeout=1, poll_interval=0.1)\n\n        assert result[\"status\"] == \"timeout\"\n        assert \"timed out\" in result[\"error\"]\n\n    @patch.object(AudioSeparatorAPIClient, \"separate_audio\")\n    @patch.object(AudioSeparatorAPIClient, \"get_job_status\")\n    @patch.object(AudioSeparatorAPIClient, \"download_file\")\n    @patch(\"time.sleep\")","chunkId":"tests/unit/test_remote_api_client.py:37"}],[2954,{"filePath":"tests/unit/test_remote_api_client.py","startLine":383,"endLine":392,"chunkText":"    @patch.object(AudioSeparatorAPIClient, \"get_job_status\")\n    @patch.object(AudioSeparatorAPIClient, \"download_file\")\n    @patch(\"time.sleep\")\n    def test_separate_audio_and_wait_no_download(self, mock_sleep, mock_download, mock_status, mock_separate, api_client, mock_audio_file):\n        \"\"\"Test the convenience method without downloading files.\"\"\"\n        mock_separate.return_value = {\"task_id\": \"test-task-123\"}\n        mock_status.side_effect = [{\"status\": \"completed\", \"files\": [\"output1.wav\", \"output2.wav\"]}]\n\n        result = api_client.separate_audio_and_wait(mock_audio_file, download=False)\n","chunkId":"tests/unit/test_remote_api_client.py:38"}],[2955,{"filePath":"tests/unit/test_remote_api_client.py","startLine":390,"endLine":401,"chunkText":"\n        result = api_client.separate_audio_and_wait(mock_audio_file, download=False)\n\n        assert result[\"status\"] == \"completed\"\n        assert \"downloaded_files\" not in result\n        mock_download.assert_not_called()\n\n    @patch.object(AudioSeparatorAPIClient, \"separate_audio\")\n    @patch.object(AudioSeparatorAPIClient, \"get_job_status\")\n    @patch.object(AudioSeparatorAPIClient, \"download_file\")\n    @patch(\"time.sleep\")\n    def test_separate_audio_and_wait_with_output_dir(self, mock_sleep, mock_download, mock_status, mock_separate, api_client, mock_audio_file):","chunkId":"tests/unit/test_remote_api_client.py:39"}],[2956,{"filePath":"tests/unit/test_remote_api_client.py","startLine":399,"endLine":406,"chunkText":"    @patch.object(AudioSeparatorAPIClient, \"download_file\")\n    @patch(\"time.sleep\")\n    def test_separate_audio_and_wait_with_output_dir(self, mock_sleep, mock_download, mock_status, mock_separate, api_client, mock_audio_file):\n        \"\"\"Test the convenience method with custom output directory.\"\"\"\n        mock_separate.return_value = {\"task_id\": \"test-task-123\"}\n        mock_status.side_effect = [{\"status\": \"completed\", \"files\": [\"output1.wav\"]}]\n        mock_download.return_value = \"custom_dir/output1.wav\"\n","chunkId":"tests/unit/test_remote_api_client.py:40"}],[2957,{"filePath":"tests/unit/test_remote_api_client.py","startLine":404,"endLine":413,"chunkText":"        mock_status.side_effect = [{\"status\": \"completed\", \"files\": [\"output1.wav\"]}]\n        mock_download.return_value = \"custom_dir/output1.wav\"\n\n        result = api_client.separate_audio_and_wait(mock_audio_file, download=True, output_dir=\"custom_dir\")\n\n        # Verify download was called with custom output path\n        mock_download.assert_called_once_with(\"test-task-123\", \"output1.wav\", \"custom_dir/output1.wav\")\n        assert result[\"downloaded_files\"] == [\"custom_dir/output1.wav\"]\n\n    @patch.object(AudioSeparatorAPIClient, \"separate_audio\")","chunkId":"tests/unit/test_remote_api_client.py:41"}],[2958,{"filePath":"tests/unit/test_remote_api_client.py","startLine":411,"endLine":420,"chunkText":"        assert result[\"downloaded_files\"] == [\"custom_dir/output1.wav\"]\n\n    @patch.object(AudioSeparatorAPIClient, \"separate_audio\")\n    @patch.object(AudioSeparatorAPIClient, \"get_job_status\")\n    @patch.object(AudioSeparatorAPIClient, \"download_file\")\n    @patch(\"time.sleep\")\n    def test_separate_audio_and_wait_with_special_character_filenames(self, mock_sleep, mock_download, mock_status, mock_separate, api_client, mock_audio_file):\n        \"\"\"Test the convenience method with filenames containing special characters.\"\"\"\n        mock_separate.return_value = {\"task_id\": \"test-task-456\"}\n        ","chunkId":"tests/unit/test_remote_api_client.py:42"}],[2959,{"filePath":"tests/unit/test_remote_api_client.py","startLine":418,"endLine":427,"chunkText":"        \"\"\"Test the convenience method with filenames containing special characters.\"\"\"\n        mock_separate.return_value = {\"task_id\": \"test-task-456\"}\n        \n        # Simulate files with special characters like those in the bug report\n        files_with_special_chars = [\n            \"Song (Vocals model_bs_roformer_ep_317_sdr_12.9755.ckpt)_(Instrumental)_mel_band_roformer.flac\",\n            \"Song (Vocals model_bs_roformer_ep_317_sdr_12.9755.ckpt)_(Vocals)_mel_band_roformer.flac\"\n        ]\n        mock_status.side_effect = [{\"status\": \"completed\", \"files\": files_with_special_chars}]\n        ","chunkId":"tests/unit/test_remote_api_client.py:43"}],[2960,{"filePath":"tests/unit/test_remote_api_client.py","startLine":425,"endLine":438,"chunkText":"        ]\n        mock_status.side_effect = [{\"status\": \"completed\", \"files\": files_with_special_chars}]\n        \n        # Mock successful downloads\n        mock_download.side_effect = files_with_special_chars\n\n        result = api_client.separate_audio_and_wait(mock_audio_file, download=True)\n\n        # Verify both files were downloaded despite having special characters\n        assert result[\"status\"] == \"completed\"\n        assert result[\"downloaded_files\"] == files_with_special_chars\n        assert mock_download.call_count == 2\n        \n        # Verify download was called with the correct filenames","chunkId":"tests/unit/test_remote_api_client.py:44"}],[2961,{"filePath":"tests/unit/test_remote_api_client.py","startLine":436,"endLine":447,"chunkText":"        assert mock_download.call_count == 2\n        \n        # Verify download was called with the correct filenames\n        expected_calls = [\n            (\"test-task-456\", files_with_special_chars[0], files_with_special_chars[0]),\n            (\"test-task-456\", files_with_special_chars[1], files_with_special_chars[1])\n        ]\n        actual_calls = [call.args for call in mock_download.call_args_list]\n        assert actual_calls == expected_calls\n\n    @patch(\"requests.Session.get\")\n    @patch(\"builtins.open\", new_callable=mock_open)","chunkId":"tests/unit/test_remote_api_client.py:45"}],[2962,{"filePath":"tests/unit/test_remote_api_client.py","startLine":445,"endLine":456,"chunkText":"\n    @patch(\"requests.Session.get\")\n    @patch(\"builtins.open\", new_callable=mock_open)\n    def test_download_file_server_side_url_decoding_scenario(self, mock_file, mock_get, api_client):\n        \"\"\"Test that the client properly URL-encodes filenames that require server-side decoding.\"\"\"\n        mock_response = Mock()\n        mock_response.content = b\"fake audio file content\"\n        mock_response.status_code = 200\n        mock_response.raise_for_status.return_value = None\n        mock_get.return_value = mock_response\n\n        # Test the exact problematic filename from the bug report","chunkId":"tests/unit/test_remote_api_client.py:46"}],[2963,{"filePath":"tests/unit/test_remote_api_client.py","startLine":454,"endLine":460,"chunkText":"        mock_get.return_value = mock_response\n\n        # Test the exact problematic filename from the bug report\n        problematic_filename = \"Bloc Party - The Prayer (Vocals model_bs_roformer_ep_317_sdr_12.9755.ckpt)_(Instrumental)_mel_band_roformer_karaoke_aufr33_viperx_sdr_10.flac\"\n        result = api_client.download_file(\"test-task-bug\", problematic_filename)\n\n        # Verify URL was properly encoded - this is the exact URL that should be sent","chunkId":"tests/unit/test_remote_api_client.py:47"}],[2964,{"filePath":"tests/unit/test_remote_api_client.py","startLine":458,"endLine":465,"chunkText":"        result = api_client.download_file(\"test-task-bug\", problematic_filename)\n\n        # Verify URL was properly encoded - this is the exact URL that should be sent\n        expected_url = \"https://test-api.example.com/download/test-task-bug/Bloc%20Party%20-%20The%20Prayer%20%28Vocals%20model_bs_roformer_ep_317_sdr_12.9755.ckpt%29_%28Instrumental%29_mel_band_roformer_karaoke_aufr33_viperx_sdr_10.flac\"\n        mock_get.assert_called_once_with(expected_url, timeout=60)\n        assert result == problematic_filename\n        mock_file.assert_called_once_with(problematic_filename, \"wb\")\n","chunkId":"tests/unit/test_remote_api_client.py:48"}],[2965,{"filePath":"tests/unit/test_remote_api_client.py","startLine":463,"endLine":475,"chunkText":"        assert result == problematic_filename\n        mock_file.assert_called_once_with(problematic_filename, \"wb\")\n\n    @patch(\"requests.Session.get\")\n    @patch(\"builtins.open\", new_callable=mock_open)\n    def test_download_file_by_hash(self, mock_file, mock_get, api_client):\n        \"\"\"Test downloading a file using hash identifier.\"\"\"\n        mock_response = Mock()\n        mock_response.content = b\"fake audio file content\"\n        mock_response.status_code = 200\n        mock_response.raise_for_status.return_value = None\n        mock_get.return_value = mock_response\n","chunkId":"tests/unit/test_remote_api_client.py:49"}],[2966,{"filePath":"tests/unit/test_remote_api_client.py","startLine":473,"endLine":484,"chunkText":"        mock_response.raise_for_status.return_value = None\n        mock_get.return_value = mock_response\n\n        # Test the new hash-based download method\n        filename = \"Long Filename With Spaces (Vocals).flac\"\n        file_hash = \"abc123def456\"\n        result = api_client.download_file_by_hash(\"test-task-hash\", file_hash, filename)\n\n        # Verify hash was used in URL instead of filename\n        expected_url = \"https://test-api.example.com/download/test-task-hash/abc123def456\"\n        mock_get.assert_called_once_with(expected_url, timeout=60)\n        assert result == filename","chunkId":"tests/unit/test_remote_api_client.py:50"}],[2967,{"filePath":"tests/unit/test_remote_api_client.py","startLine":482,"endLine":490,"chunkText":"        expected_url = \"https://test-api.example.com/download/test-task-hash/abc123def456\"\n        mock_get.assert_called_once_with(expected_url, timeout=60)\n        assert result == filename\n        mock_file.assert_called_once_with(filename, \"wb\")\n\n    @patch.object(AudioSeparatorAPIClient, \"separate_audio\")\n    @patch.object(AudioSeparatorAPIClient, \"get_job_status\")\n    @patch.object(AudioSeparatorAPIClient, \"download_file_by_hash\")\n    @patch(\"time.sleep\")","chunkId":"tests/unit/test_remote_api_client.py:51"}],[2968,{"filePath":"tests/unit/test_remote_api_client.py","startLine":489,"endLine":497,"chunkText":"    @patch.object(AudioSeparatorAPIClient, \"download_file_by_hash\")\n    @patch(\"time.sleep\")\n    def test_separate_audio_and_wait_with_hash_format(self, mock_sleep, mock_download_hash, mock_status, mock_separate, api_client, mock_audio_file):\n        \"\"\"Test the convenience method with hash-based file format.\"\"\"\n        mock_separate.return_value = {\"task_id\": \"test-task-hash\"}\n        \n        # Simulate new hash-based format\n        files_with_hashes = {\n            \"abc123def456\": \"Song (Vocals model_bs_roformer_ep_317_sdr_12.9755.ckpt)_(Instrumental)_mel_band_roformer.flac\",","chunkId":"tests/unit/test_remote_api_client.py:52"}],[2969,{"filePath":"tests/unit/test_remote_api_client.py","startLine":495,"endLine":504,"chunkText":"        # Simulate new hash-based format\n        files_with_hashes = {\n            \"abc123def456\": \"Song (Vocals model_bs_roformer_ep_317_sdr_12.9755.ckpt)_(Instrumental)_mel_band_roformer.flac\",\n            \"def456ghi789\": \"Song (Vocals model_bs_roformer_ep_317_sdr_12.9755.ckpt)_(Vocals)_mel_band_roformer.flac\"\n        }\n        mock_status.side_effect = [{\"status\": \"completed\", \"files\": files_with_hashes}]\n        \n        # Mock successful downloads\n        mock_download_hash.side_effect = list(files_with_hashes.values())\n","chunkId":"tests/unit/test_remote_api_client.py:53"}],[2970,{"filePath":"tests/unit/test_remote_api_client.py","startLine":504,"endLine":514,"chunkText":"\n        result = api_client.separate_audio_and_wait(mock_audio_file, download=True)\n\n        # Verify both files were downloaded using hash method\n        assert result[\"status\"] == \"completed\"\n        assert result[\"downloaded_files\"] == list(files_with_hashes.values())\n        assert mock_download_hash.call_count == 2\n        \n        # Verify download was called with the correct hashes and filenames\n        expected_calls = [\n            (\"test-task-hash\", \"abc123def456\", list(files_with_hashes.values())[0], list(files_with_hashes.values())[0]),","chunkId":"tests/unit/test_remote_api_client.py:54"}],[2971,{"filePath":"tests/unit/test_remote_api_client.py","startLine":512,"endLine":519,"chunkText":"        # Verify download was called with the correct hashes and filenames\n        expected_calls = [\n            (\"test-task-hash\", \"abc123def456\", list(files_with_hashes.values())[0], list(files_with_hashes.values())[0]),\n            (\"test-task-hash\", \"def456ghi789\", list(files_with_hashes.values())[1], list(files_with_hashes.values())[1])\n        ]\n        actual_calls = [call.args for call in mock_download_hash.call_args_list]\n        assert actual_calls == expected_calls\n","chunkId":"tests/unit/test_remote_api_client.py:55"}],[2972,{"filePath":"tests/unit/test_remote_cli.py","startLine":0,"endLine":22,"chunkText":"import json\nimport pytest\nimport argparse\nimport logging\nimport os\nimport sys\nfrom unittest.mock import Mock, patch, MagicMock\nfrom unittest.mock import call\nimport tempfile\n\nfrom audio_separator.remote.cli import main, handle_separate_command, handle_status_command, handle_models_command, handle_download_command\nfrom audio_separator.remote import AudioSeparatorAPIClient\n\n\n@pytest.fixture\ndef mock_api_client():\n    \"\"\"Create a mock API client for testing.\"\"\"\n    return Mock(spec=AudioSeparatorAPIClient)\n\n\n@pytest.fixture\ndef mock_logger():\n    \"\"\"Create a mock logger for testing.\"\"\"","chunkId":"tests/unit/test_remote_cli.py:0"}],[2973,{"filePath":"tests/unit/test_remote_cli.py","startLine":20,"endLine":40,"chunkText":"@pytest.fixture\ndef mock_logger():\n    \"\"\"Create a mock logger for testing.\"\"\"\n    return Mock(spec=logging.Logger)\n\n\n@pytest.fixture\ndef mock_audio_file():\n    \"\"\"Create a temporary audio file for testing.\"\"\"\n    with tempfile.NamedTemporaryFile(suffix=\".wav\", delete=False) as f:\n        f.write(b\"fake audio content\")\n        yield f.name\n    os.unlink(f.name)\n\n\nclass TestRemoteCLI:\n    \"\"\"Test the remote CLI functionality.\"\"\"\n\n    @patch('sys.argv', ['audio-separator-remote', '--version'])\n    @patch('audio_separator.remote.cli.metadata')\n    @patch('builtins.print')","chunkId":"tests/unit/test_remote_cli.py:1"}],[2974,{"filePath":"tests/unit/test_remote_cli.py","startLine":38,"endLine":51,"chunkText":"    @patch('sys.argv', ['audio-separator-remote', '--version'])\n    @patch('audio_separator.remote.cli.metadata')\n    @patch('builtins.print')\n    def test_version_command_no_api_url(self, mock_print, mock_metadata, mock_api_client):\n        \"\"\"Test version command without API URL.\"\"\"\n        mock_metadata.distribution.return_value.version = \"1.2.3\"\n        \n        with pytest.raises(SystemExit) as exc_info:\n            main()\n        \n        assert exc_info.value.code == 0\n        mock_print.assert_any_call(\"Client version: 1.2.3\")\n\n    @patch('sys.argv', ['audio-separator-remote', '--version'])","chunkId":"tests/unit/test_remote_cli.py:2"}],[2975,{"filePath":"tests/unit/test_remote_cli.py","startLine":49,"endLine":59,"chunkText":"        mock_print.assert_any_call(\"Client version: 1.2.3\")\n\n    @patch('sys.argv', ['audio-separator-remote', '--version'])\n    @patch('audio_separator.remote.cli.metadata')\n    @patch('audio_separator.remote.cli.AudioSeparatorAPIClient')\n    @patch('builtins.print')\n    @patch.dict(os.environ, {'AUDIO_SEPARATOR_API_URL': 'https://test-api.com'})\n    def test_version_command_with_api_url(self, mock_print, mock_client_class, mock_metadata):\n        \"\"\"Test version command with API URL.\"\"\"\n        mock_metadata.distribution.return_value.version = \"1.2.3\"\n        mock_client = Mock()","chunkId":"tests/unit/test_remote_cli.py:3"}],[2976,{"filePath":"tests/unit/test_remote_cli.py","startLine":57,"endLine":70,"chunkText":"        \"\"\"Test version command with API URL.\"\"\"\n        mock_metadata.distribution.return_value.version = \"1.2.3\"\n        mock_client = Mock()\n        mock_client.get_server_version.return_value = \"1.2.4\"\n        mock_client_class.return_value = mock_client\n        \n        with pytest.raises(SystemExit) as exc_info:\n            main()\n        \n        assert exc_info.value.code == 0\n        mock_print.assert_any_call(\"Client version: 1.2.3\")\n        mock_print.assert_any_call(\"Server version: 1.2.4\")\n\n    @patch('sys.argv', ['audio-separator-remote', '--version'])","chunkId":"tests/unit/test_remote_cli.py:4"}],[2977,{"filePath":"tests/unit/test_remote_cli.py","startLine":68,"endLine":77,"chunkText":"        mock_print.assert_any_call(\"Server version: 1.2.4\")\n\n    @patch('sys.argv', ['audio-separator-remote', '--version'])\n    @patch('audio_separator.remote.cli.metadata')\n    @patch('audio_separator.remote.cli.AudioSeparatorAPIClient')\n    @patch('builtins.print')\n    @patch.dict(os.environ, {'AUDIO_SEPARATOR_API_URL': 'https://test-api.com'})\n    def test_version_command_server_error(self, mock_print, mock_client_class, mock_metadata):\n        \"\"\"Test version command when server version retrieval fails.\"\"\"\n        mock_metadata.distribution.return_value.version = \"1.2.3\"","chunkId":"tests/unit/test_remote_cli.py:5"}],[2978,{"filePath":"tests/unit/test_remote_cli.py","startLine":75,"endLine":87,"chunkText":"    def test_version_command_server_error(self, mock_print, mock_client_class, mock_metadata):\n        \"\"\"Test version command when server version retrieval fails.\"\"\"\n        mock_metadata.distribution.return_value.version = \"1.2.3\"\n        mock_client = Mock()\n        mock_client.get_server_version.side_effect = Exception(\"Connection error\")\n        mock_client_class.return_value = mock_client\n        \n        with pytest.raises(SystemExit) as exc_info:\n            main()\n        \n        assert exc_info.value.code == 0\n        mock_print.assert_any_call(\"Client version: 1.2.3\")\n","chunkId":"tests/unit/test_remote_cli.py:6"}],[2979,{"filePath":"tests/unit/test_remote_cli.py","startLine":85,"endLine":98,"chunkText":"        assert exc_info.value.code == 0\n        mock_print.assert_any_call(\"Client version: 1.2.3\")\n\n    @patch('sys.argv', ['audio-separator-remote', 'separate', 'test.wav'])\n    @patch('builtins.print')\n    @patch.dict(os.environ, {}, clear=True)  # Clear all environment variables\n    def test_no_api_url_error(self, mock_print):\n        \"\"\"Test error when no API URL is provided.\"\"\"\n        with pytest.raises(SystemExit) as exc_info:\n            main()\n        \n        assert exc_info.value.code == 1\n\n    @patch('sys.argv', ['audio-separator-remote', 'separate', 'test.wav'])","chunkId":"tests/unit/test_remote_cli.py:7"}],[2980,{"filePath":"tests/unit/test_remote_cli.py","startLine":96,"endLine":108,"chunkText":"        assert exc_info.value.code == 1\n\n    @patch('sys.argv', ['audio-separator-remote', 'separate', 'test.wav'])\n    @patch('audio_separator.remote.cli.AudioSeparatorAPIClient')\n    @patch('audio_separator.remote.cli.handle_separate_command')\n    @patch.dict(os.environ, {'AUDIO_SEPARATOR_API_URL': 'https://test-api.com'})\n    def test_separate_command(self, mock_handle_separate, mock_client_class):\n        \"\"\"Test separate command execution.\"\"\"\n        mock_client = Mock()\n        mock_client_class.return_value = mock_client\n        \n        main()\n        ","chunkId":"tests/unit/test_remote_cli.py:8"}],[2981,{"filePath":"tests/unit/test_remote_cli.py","startLine":106,"endLine":117,"chunkText":"        \n        main()\n        \n        mock_handle_separate.assert_called_once()\n        # Verify the API client was called with the correct URL (don't check logger instance)\n        assert mock_client_class.call_count == 1\n        call_args = mock_client_class.call_args\n        assert call_args[0][0] == 'https://test-api.com'  # First argument should be the API URL\n\n    @patch('sys.argv', ['audio-separator-remote', 'status', 'task-123'])\n    @patch('audio_separator.remote.cli.AudioSeparatorAPIClient')\n    @patch('audio_separator.remote.cli.handle_status_command')","chunkId":"tests/unit/test_remote_cli.py:9"}],[2982,{"filePath":"tests/unit/test_remote_cli.py","startLine":115,"endLine":127,"chunkText":"    @patch('sys.argv', ['audio-separator-remote', 'status', 'task-123'])\n    @patch('audio_separator.remote.cli.AudioSeparatorAPIClient')\n    @patch('audio_separator.remote.cli.handle_status_command')\n    @patch.dict(os.environ, {'AUDIO_SEPARATOR_API_URL': 'https://test-api.com'})\n    def test_status_command(self, mock_handle_status, mock_client_class):\n        \"\"\"Test status command execution.\"\"\"\n        mock_client = Mock()\n        mock_client_class.return_value = mock_client\n        \n        main()\n        \n        mock_handle_status.assert_called_once()\n","chunkId":"tests/unit/test_remote_cli.py:10"}],[2983,{"filePath":"tests/unit/test_remote_cli.py","startLine":125,"endLine":140,"chunkText":"        \n        mock_handle_status.assert_called_once()\n\n    @patch('sys.argv', ['audio-separator-remote', 'models'])\n    @patch('audio_separator.remote.cli.AudioSeparatorAPIClient')\n    @patch('audio_separator.remote.cli.handle_models_command')\n    @patch.dict(os.environ, {'AUDIO_SEPARATOR_API_URL': 'https://test-api.com'})\n    def test_models_command(self, mock_handle_models, mock_client_class):\n        \"\"\"Test models command execution.\"\"\"\n        mock_client = Mock()\n        mock_client_class.return_value = mock_client\n        \n        main()\n        \n        mock_handle_models.assert_called_once()\n","chunkId":"tests/unit/test_remote_cli.py:11"}],[2984,{"filePath":"tests/unit/test_remote_cli.py","startLine":138,"endLine":150,"chunkText":"        \n        mock_handle_models.assert_called_once()\n\n    @patch('sys.argv', ['audio-separator-remote', 'download', 'task-123', 'file1.wav', 'file2.wav'])\n    @patch('audio_separator.remote.cli.AudioSeparatorAPIClient')\n    @patch('audio_separator.remote.cli.handle_download_command')\n    @patch.dict(os.environ, {'AUDIO_SEPARATOR_API_URL': 'https://test-api.com'})\n    def test_download_command(self, mock_handle_download, mock_client_class):\n        \"\"\"Test download command execution.\"\"\"\n        mock_client = Mock()\n        mock_client_class.return_value = mock_client\n        \n        main()","chunkId":"tests/unit/test_remote_cli.py:12"}],[2985,{"filePath":"tests/unit/test_remote_cli.py","startLine":148,"endLine":163,"chunkText":"        mock_client_class.return_value = mock_client\n        \n        main()\n        \n        mock_handle_download.assert_called_once()\n\n    @patch('sys.argv', ['audio-separator-remote', '--api_url', 'https://custom-api.com', 'models'])\n    @patch('audio_separator.remote.cli.AudioSeparatorAPIClient')\n    @patch('audio_separator.remote.cli.handle_models_command')\n    def test_custom_api_url(self, mock_handle_models, mock_client_class):\n        \"\"\"Test using custom API URL parameter.\"\"\"\n        mock_client = Mock()\n        mock_client_class.return_value = mock_client\n        \n        main()\n        ","chunkId":"tests/unit/test_remote_cli.py:13"}],[2986,{"filePath":"tests/unit/test_remote_cli.py","startLine":161,"endLine":172,"chunkText":"        \n        main()\n        \n        # Verify the API client was called with the correct custom URL\n        assert mock_client_class.call_count == 1\n        call_args = mock_client_class.call_args\n        assert call_args[0][0] == 'https://custom-api.com'  # First argument should be the custom API URL\n\n    @patch('sys.argv', ['audio-separator-remote', '--debug', 'models'])\n    @patch('audio_separator.remote.cli.AudioSeparatorAPIClient')\n    @patch('audio_separator.remote.cli.handle_models_command')\n    @patch.dict(os.environ, {'AUDIO_SEPARATOR_API_URL': 'https://test-api.com'})","chunkId":"tests/unit/test_remote_cli.py:14"}],[2987,{"filePath":"tests/unit/test_remote_cli.py","startLine":170,"endLine":182,"chunkText":"    @patch('audio_separator.remote.cli.AudioSeparatorAPIClient')\n    @patch('audio_separator.remote.cli.handle_models_command')\n    @patch.dict(os.environ, {'AUDIO_SEPARATOR_API_URL': 'https://test-api.com'})\n    def test_debug_logging(self, mock_handle_models, mock_client_class):\n        \"\"\"Test debug logging flag.\"\"\"\n        mock_client = Mock()\n        mock_client_class.return_value = mock_client\n        \n        main()\n        \n        # Verify debug logging is enabled by checking if logger level was set\n        mock_handle_models.assert_called_once()\n","chunkId":"tests/unit/test_remote_cli.py:15"}],[2988,{"filePath":"tests/unit/test_remote_cli.py","startLine":180,"endLine":194,"chunkText":"        # Verify debug logging is enabled by checking if logger level was set\n        mock_handle_models.assert_called_once()\n\n    def test_handle_separate_command_success(self, mock_api_client, mock_logger, mock_audio_file):\n        \"\"\"Test successful separate command handling.\"\"\"\n        # Mock arguments\n        args = Mock()\n        args.audio_files = [mock_audio_file]\n        args.model = \"test_model.ckpt\"\n        args.models = None\n        args.timeout = 600\n        args.poll_interval = 10\n        args.output_format = \"flac\"\n        args.output_bitrate = None\n        args.normalization = 0.9","chunkId":"tests/unit/test_remote_cli.py:16"}],[2989,{"filePath":"tests/unit/test_remote_cli.py","startLine":192,"endLine":209,"chunkText":"        args.output_format = \"flac\"\n        args.output_bitrate = None\n        args.normalization = 0.9\n        args.amplification = 0.0\n        args.single_stem = None\n        args.invert_spect = False\n        args.sample_rate = 44100\n        args.use_soundfile = False\n        args.use_autocast = False\n        args.custom_output_names = None\n        # MDX parameters\n        args.mdx_segment_size = 256\n        args.mdx_overlap = 0.25\n        args.mdx_batch_size = 1\n        args.mdx_hop_length = 1024\n        args.mdx_enable_denoise = False\n        # VR parameters\n        args.vr_batch_size = 1","chunkId":"tests/unit/test_remote_cli.py:17"}],[2990,{"filePath":"tests/unit/test_remote_cli.py","startLine":207,"endLine":222,"chunkText":"        args.mdx_enable_denoise = False\n        # VR parameters\n        args.vr_batch_size = 1\n        args.vr_window_size = 512\n        args.vr_aggression = 5\n        args.vr_enable_tta = False\n        args.vr_high_end_process = False\n        args.vr_enable_post_process = False\n        args.vr_post_process_threshold = 0.2\n        # Demucs parameters\n        args.demucs_segment_size = \"Default\"\n        args.demucs_shifts = 2\n        args.demucs_overlap = 0.25\n        args.demucs_segments_enabled = True\n        # MDXC parameters\n        args.mdxc_segment_size = 256","chunkId":"tests/unit/test_remote_cli.py:18"}],[2991,{"filePath":"tests/unit/test_remote_cli.py","startLine":220,"endLine":236,"chunkText":"        args.demucs_segments_enabled = True\n        # MDXC parameters\n        args.mdxc_segment_size = 256\n        args.mdxc_override_model_segment_size = False\n        args.mdxc_overlap = 8\n        args.mdxc_batch_size = 1\n        args.mdxc_pitch_shift = 0\n\n        # Mock successful API response\n        mock_api_client.separate_audio_and_wait.return_value = {\n            \"status\": \"completed\",\n            \"downloaded_files\": [\"output1.wav\", \"output2.wav\"]\n        }\n\n        handle_separate_command(args, mock_api_client, mock_logger)\n\n        # Verify API client was called with correct parameters","chunkId":"tests/unit/test_remote_cli.py:19"}],[2992,{"filePath":"tests/unit/test_remote_cli.py","startLine":234,"endLine":244,"chunkText":"        handle_separate_command(args, mock_api_client, mock_logger)\n\n        # Verify API client was called with correct parameters\n        mock_api_client.separate_audio_and_wait.assert_called_once()\n        call_args = mock_api_client.separate_audio_and_wait.call_args\n        assert call_args[0][0] == mock_audio_file  # First positional argument should be the audio file\n        kwargs = call_args[1]\n        assert kwargs[\"model\"] == \"test_model.ckpt\"\n        assert kwargs[\"timeout\"] == 600\n        assert kwargs[\"download\"] is True\n","chunkId":"tests/unit/test_remote_cli.py:20"}],[2993,{"filePath":"tests/unit/test_remote_cli.py","startLine":242,"endLine":254,"chunkText":"        assert kwargs[\"timeout\"] == 600\n        assert kwargs[\"download\"] is True\n\n    def test_handle_separate_command_with_multiple_models(self, mock_api_client, mock_logger, mock_audio_file):\n        \"\"\"Test separate command with multiple models.\"\"\"\n        args = Mock()\n        args.audio_files = [mock_audio_file]\n        args.model = None\n        args.models = [\"model1.ckpt\", \"model2.onnx\"]\n        args.timeout = 600\n        args.poll_interval = 10\n        # Set other required attributes\n        for attr in ['output_format', 'output_bitrate', 'normalization', 'amplification', 'single_stem',","chunkId":"tests/unit/test_remote_cli.py:21"}],[2994,{"filePath":"tests/unit/test_remote_cli.py","startLine":252,"endLine":257,"chunkText":"        args.poll_interval = 10\n        # Set other required attributes\n        for attr in ['output_format', 'output_bitrate', 'normalization', 'amplification', 'single_stem',\n                     'invert_spect', 'sample_rate', 'use_soundfile', 'use_autocast', 'custom_output_names',\n                     'mdx_segment_size', 'mdx_overlap', 'mdx_batch_size', 'mdx_hop_length', 'mdx_enable_denoise',\n                     'vr_batch_size', 'vr_window_size', 'vr_aggression', 'vr_enable_tta', 'vr_high_end_process',","chunkId":"tests/unit/test_remote_cli.py:22"}],[2995,{"filePath":"tests/unit/test_remote_cli.py","startLine":255,"endLine":259,"chunkText":"                     'invert_spect', 'sample_rate', 'use_soundfile', 'use_autocast', 'custom_output_names',\n                     'mdx_segment_size', 'mdx_overlap', 'mdx_batch_size', 'mdx_hop_length', 'mdx_enable_denoise',\n                     'vr_batch_size', 'vr_window_size', 'vr_aggression', 'vr_enable_tta', 'vr_high_end_process',\n                     'vr_enable_post_process', 'vr_post_process_threshold', 'demucs_segment_size', 'demucs_shifts',\n                     'demucs_overlap', 'demucs_segments_enabled', 'mdxc_segment_size', 'mdxc_override_model_segment_size',","chunkId":"tests/unit/test_remote_cli.py:23"}],[2996,{"filePath":"tests/unit/test_remote_cli.py","startLine":257,"endLine":264,"chunkText":"                     'vr_batch_size', 'vr_window_size', 'vr_aggression', 'vr_enable_tta', 'vr_high_end_process',\n                     'vr_enable_post_process', 'vr_post_process_threshold', 'demucs_segment_size', 'demucs_shifts',\n                     'demucs_overlap', 'demucs_segments_enabled', 'mdxc_segment_size', 'mdxc_override_model_segment_size',\n                     'mdxc_overlap', 'mdxc_batch_size', 'mdxc_pitch_shift']:\n            setattr(args, attr, None)\n\n        mock_api_client.separate_audio_and_wait.return_value = {\n            \"status\": \"completed\",","chunkId":"tests/unit/test_remote_cli.py:24"}],[2997,{"filePath":"tests/unit/test_remote_cli.py","startLine":262,"endLine":276,"chunkText":"\n        mock_api_client.separate_audio_and_wait.return_value = {\n            \"status\": \"completed\",\n            \"downloaded_files\": [\"output1.wav\", \"output2.wav\"]\n        }\n\n        handle_separate_command(args, mock_api_client, mock_logger)\n\n        call_args = mock_api_client.separate_audio_and_wait.call_args\n        kwargs = call_args[1]\n        assert kwargs[\"models\"] == [\"model1.ckpt\", \"model2.onnx\"]\n\n    def test_handle_separate_command_error(self, mock_api_client, mock_logger, mock_audio_file):\n        \"\"\"Test separate command with error.\"\"\"\n        args = Mock()","chunkId":"tests/unit/test_remote_cli.py:25"}],[2998,{"filePath":"tests/unit/test_remote_cli.py","startLine":274,"endLine":282,"chunkText":"    def test_handle_separate_command_error(self, mock_api_client, mock_logger, mock_audio_file):\n        \"\"\"Test separate command with error.\"\"\"\n        args = Mock()\n        args.audio_files = [mock_audio_file]\n        args.model = \"test_model.ckpt\"\n        args.models = None\n        # Set other required attributes\n        for attr in ['timeout', 'poll_interval', 'output_format', 'output_bitrate', 'normalization', 'amplification',\n                     'single_stem', 'invert_spect', 'sample_rate', 'use_soundfile', 'use_autocast', 'custom_output_names',","chunkId":"tests/unit/test_remote_cli.py:26"}],[2999,{"filePath":"tests/unit/test_remote_cli.py","startLine":280,"endLine":284,"chunkText":"        # Set other required attributes\n        for attr in ['timeout', 'poll_interval', 'output_format', 'output_bitrate', 'normalization', 'amplification',\n                     'single_stem', 'invert_spect', 'sample_rate', 'use_soundfile', 'use_autocast', 'custom_output_names',\n                     'mdx_segment_size', 'mdx_overlap', 'mdx_batch_size', 'mdx_hop_length', 'mdx_enable_denoise',\n                     'vr_batch_size', 'vr_window_size', 'vr_aggression', 'vr_enable_tta', 'vr_high_end_process',","chunkId":"tests/unit/test_remote_cli.py:27"}],[3000,{"filePath":"tests/unit/test_remote_cli.py","startLine":282,"endLine":286,"chunkText":"                     'single_stem', 'invert_spect', 'sample_rate', 'use_soundfile', 'use_autocast', 'custom_output_names',\n                     'mdx_segment_size', 'mdx_overlap', 'mdx_batch_size', 'mdx_hop_length', 'mdx_enable_denoise',\n                     'vr_batch_size', 'vr_window_size', 'vr_aggression', 'vr_enable_tta', 'vr_high_end_process',\n                     'vr_enable_post_process', 'vr_post_process_threshold', 'demucs_segment_size', 'demucs_shifts',\n                     'demucs_overlap', 'demucs_segments_enabled', 'mdxc_segment_size', 'mdxc_override_model_segment_size',","chunkId":"tests/unit/test_remote_cli.py:28"}],[3001,{"filePath":"tests/unit/test_remote_cli.py","startLine":284,"endLine":287,"chunkText":"                     'vr_batch_size', 'vr_window_size', 'vr_aggression', 'vr_enable_tta', 'vr_high_end_process',\n                     'vr_enable_post_process', 'vr_post_process_threshold', 'demucs_segment_size', 'demucs_shifts',\n                     'demucs_overlap', 'demucs_segments_enabled', 'mdxc_segment_size', 'mdxc_override_model_segment_size',\n                     'mdxc_overlap', 'mdxc_batch_size', 'mdxc_pitch_shift']:","chunkId":"tests/unit/test_remote_cli.py:29"}],[3002,{"filePath":"tests/unit/test_remote_cli.py","startLine":285,"endLine":287,"chunkText":"                     'vr_enable_post_process', 'vr_post_process_threshold', 'demucs_segment_size', 'demucs_shifts',\n                     'demucs_overlap', 'demucs_segments_enabled', 'mdxc_segment_size', 'mdxc_override_model_segment_size',\n                     'mdxc_overlap', 'mdxc_batch_size', 'mdxc_pitch_shift']:","chunkId":"tests/unit/test_remote_cli.py:30"}],[3003,{"filePath":"tests/unit/test_remote_cli.py","startLine":286,"endLine":287,"chunkText":"                     'demucs_overlap', 'demucs_segments_enabled', 'mdxc_segment_size', 'mdxc_override_model_segment_size',\n                     'mdxc_overlap', 'mdxc_batch_size', 'mdxc_pitch_shift']:","chunkId":"tests/unit/test_remote_cli.py:31"}],[3004,{"filePath":"tests/unit/test_remote_cli.py","startLine":287,"endLine":287,"chunkText":"                     'mdxc_overlap', 'mdxc_batch_size', 'mdxc_pitch_shift']:","chunkId":"tests/unit/test_remote_cli.py:32"}],[3005,{"filePath":"tests/unit/test_remote_cli.py","startLine":288,"endLine":288,"chunkText":"            setattr(args, attr, 0 if 'size' in attr or 'shift' in attr or 'batch' in attr or 'hop' in attr or 'aggression' in attr or 'overlap' in attr else False if 'enable' in attr or 'tta' in attr or 'process' in attr or 'spect' in attr or 'soundfile' in attr or 'autocast' in attr else None if 'output' in attr or 'single' in attr or 'custom' in attr else 600 if 'timeout' in attr else 10 if 'poll' in attr else 'Default' if 'demucs_segment' in attr else 0.25 if 'overlap' in attr and 'mdxc' not in attr else 512 if 'window' in attr else 44100 if 'sample' in attr else 'flac' if 'format' in attr else 0.9 if ","chunkId":"tests/unit/test_remote_cli.py:33"}],[3006,{"filePath":"tests/unit/test_remote_cli.py","startLine":289,"endLine":304,"chunkText":"\n        mock_api_client.separate_audio_and_wait.return_value = {\n            \"status\": \"error\",\n            \"error\": \"Processing failed\"\n        }\n\n        handle_separate_command(args, mock_api_client, mock_logger)\n\n        # Verify error was logged\n        mock_logger.error.assert_called()\n\n    def test_handle_separate_command_exception(self, mock_api_client, mock_logger, mock_audio_file):\n        \"\"\"Test separate command with exception.\"\"\"\n        args = Mock()\n        args.audio_files = [mock_audio_file]\n        # Set required attributes ","chunkId":"tests/unit/test_remote_cli.py:34"}],[3007,{"filePath":"tests/unit/test_remote_cli.py","startLine":302,"endLine":308,"chunkText":"        args = Mock()\n        args.audio_files = [mock_audio_file]\n        # Set required attributes \n        for attr in ['model', 'models', 'timeout', 'poll_interval', 'output_format', 'output_bitrate', 'normalization',\n                     'amplification', 'single_stem', 'invert_spect', 'sample_rate', 'use_soundfile', 'use_autocast',\n                     'custom_output_names', 'mdx_segment_size', 'mdx_overlap', 'mdx_batch_size', 'mdx_hop_length',\n                     'mdx_enable_denoise', 'vr_batch_size', 'vr_window_size', 'vr_aggression', 'vr_enable_tta',","chunkId":"tests/unit/test_remote_cli.py:35"}],[3008,{"filePath":"tests/unit/test_remote_cli.py","startLine":306,"endLine":310,"chunkText":"                     'amplification', 'single_stem', 'invert_spect', 'sample_rate', 'use_soundfile', 'use_autocast',\n                     'custom_output_names', 'mdx_segment_size', 'mdx_overlap', 'mdx_batch_size', 'mdx_hop_length',\n                     'mdx_enable_denoise', 'vr_batch_size', 'vr_window_size', 'vr_aggression', 'vr_enable_tta',\n                     'vr_high_end_process', 'vr_enable_post_process', 'vr_post_process_threshold', 'demucs_segment_size',\n                     'demucs_shifts', 'demucs_overlap', 'demucs_segments_enabled', 'mdxc_segment_size',","chunkId":"tests/unit/test_remote_cli.py:36"}],[3009,{"filePath":"tests/unit/test_remote_cli.py","startLine":308,"endLine":315,"chunkText":"                     'mdx_enable_denoise', 'vr_batch_size', 'vr_window_size', 'vr_aggression', 'vr_enable_tta',\n                     'vr_high_end_process', 'vr_enable_post_process', 'vr_post_process_threshold', 'demucs_segment_size',\n                     'demucs_shifts', 'demucs_overlap', 'demucs_segments_enabled', 'mdxc_segment_size',\n                     'mdxc_override_model_segment_size', 'mdxc_overlap', 'mdxc_batch_size', 'mdxc_pitch_shift']:\n            setattr(args, attr, None)\n\n        mock_api_client.separate_audio_and_wait.side_effect = Exception(\"API error\")\n","chunkId":"tests/unit/test_remote_cli.py:37"}],[3010,{"filePath":"tests/unit/test_remote_cli.py","startLine":313,"endLine":329,"chunkText":"\n        mock_api_client.separate_audio_and_wait.side_effect = Exception(\"API error\")\n\n        handle_separate_command(args, mock_api_client, mock_logger)\n\n        # Verify error was logged\n        mock_logger.error.assert_called()\n\n    def test_handle_status_command_success(self, mock_api_client, mock_logger):\n        \"\"\"Test successful status command handling.\"\"\"\n        args = Mock()\n        args.task_id = \"test-task-123\"\n\n        mock_api_client.get_job_status.return_value = {\n            \"status\": \"completed\",\n            \"progress\": 100,\n            \"current_model_index\": 0,","chunkId":"tests/unit/test_remote_cli.py:38"}],[3011,{"filePath":"tests/unit/test_remote_cli.py","startLine":327,"endLine":342,"chunkText":"            \"status\": \"completed\",\n            \"progress\": 100,\n            \"current_model_index\": 0,\n            \"total_models\": 1,\n            \"original_filename\": \"test.wav\",\n            \"models_used\": [\"test_model.ckpt\"],\n            \"files\": [\"output1.wav\", \"output2.wav\"]\n        }\n\n        handle_status_command(args, mock_api_client, mock_logger)\n\n        mock_api_client.get_job_status.assert_called_once_with(\"test-task-123\")\n        # Verify status information was logged\n        mock_logger.info.assert_called()\n\n    def test_handle_status_command_error_status(self, mock_api_client, mock_logger):","chunkId":"tests/unit/test_remote_cli.py:39"}],[3012,{"filePath":"tests/unit/test_remote_cli.py","startLine":340,"endLine":356,"chunkText":"        mock_logger.info.assert_called()\n\n    def test_handle_status_command_error_status(self, mock_api_client, mock_logger):\n        \"\"\"Test status command with error status.\"\"\"\n        args = Mock()\n        args.task_id = \"test-task-456\"\n\n        mock_api_client.get_job_status.return_value = {\n            \"status\": \"error\",\n            \"error\": \"Processing failed\"\n        }\n\n        handle_status_command(args, mock_api_client, mock_logger)\n\n        mock_api_client.get_job_status.assert_called_once_with(\"test-task-456\")\n        mock_logger.error.assert_called()\n","chunkId":"tests/unit/test_remote_cli.py:40"}],[3013,{"filePath":"tests/unit/test_remote_cli.py","startLine":354,"endLine":368,"chunkText":"        mock_api_client.get_job_status.assert_called_once_with(\"test-task-456\")\n        mock_logger.error.assert_called()\n\n    def test_handle_status_command_exception(self, mock_api_client, mock_logger):\n        \"\"\"Test status command with exception.\"\"\"\n        args = Mock()\n        args.task_id = \"test-task-789\"\n\n        mock_api_client.get_job_status.side_effect = Exception(\"API error\")\n\n        handle_status_command(args, mock_api_client, mock_logger)\n\n        mock_logger.error.assert_called()\n\n    def test_handle_models_command_pretty_format(self, mock_api_client, mock_logger):","chunkId":"tests/unit/test_remote_cli.py:41"}],[3014,{"filePath":"tests/unit/test_remote_cli.py","startLine":366,"endLine":381,"chunkText":"        mock_logger.error.assert_called()\n\n    def test_handle_models_command_pretty_format(self, mock_api_client, mock_logger):\n        \"\"\"Test models command with pretty format.\"\"\"\n        args = Mock()\n        args.format = \"pretty\"\n        args.filter = None\n\n        mock_api_client.list_models.return_value = {\n            \"text\": \"Model list in pretty format\"\n        }\n\n        with patch('builtins.print') as mock_print:\n            handle_models_command(args, mock_api_client, mock_logger)\n\n        mock_api_client.list_models.assert_called_once_with(\"pretty\", None)","chunkId":"tests/unit/test_remote_cli.py:42"}],[3015,{"filePath":"tests/unit/test_remote_cli.py","startLine":379,"endLine":392,"chunkText":"            handle_models_command(args, mock_api_client, mock_logger)\n\n        mock_api_client.list_models.assert_called_once_with(\"pretty\", None)\n        mock_print.assert_called_once_with(\"Model list in pretty format\")\n\n    def test_handle_models_command_json_format(self, mock_api_client, mock_logger):\n        \"\"\"Test models command with JSON format.\"\"\"\n        args = Mock()\n        args.format = \"json\"\n        args.filter = \"vocals\"\n\n        models_data = {\"models\": [{\"name\": \"vocal_model\", \"type\": \"MDX\"}]}\n        mock_api_client.list_models.return_value = models_data\n","chunkId":"tests/unit/test_remote_cli.py:43"}],[3016,{"filePath":"tests/unit/test_remote_cli.py","startLine":390,"endLine":400,"chunkText":"        models_data = {\"models\": [{\"name\": \"vocal_model\", \"type\": \"MDX\"}]}\n        mock_api_client.list_models.return_value = models_data\n\n        with patch('json.dumps') as mock_json_dumps:\n            with patch('builtins.print') as mock_print:\n                mock_json_dumps.return_value = '{\"models\": [{\"name\": \"vocal_model\", \"type\": \"MDX\"}]}'\n                handle_models_command(args, mock_api_client, mock_logger)\n\n        mock_api_client.list_models.assert_called_once_with(\"json\", \"vocals\")\n        mock_json_dumps.assert_called_once_with(models_data, indent=2)\n","chunkId":"tests/unit/test_remote_cli.py:44"}],[3017,{"filePath":"tests/unit/test_remote_cli.py","startLine":398,"endLine":412,"chunkText":"        mock_api_client.list_models.assert_called_once_with(\"json\", \"vocals\")\n        mock_json_dumps.assert_called_once_with(models_data, indent=2)\n\n    def test_handle_models_command_exception(self, mock_api_client, mock_logger):\n        \"\"\"Test models command with exception.\"\"\"\n        args = Mock()\n        args.format = \"pretty\"\n        args.filter = None\n\n        mock_api_client.list_models.side_effect = Exception(\"API error\")\n\n        handle_models_command(args, mock_api_client, mock_logger)\n\n        mock_logger.error.assert_called()\n","chunkId":"tests/unit/test_remote_cli.py:45"}],[3018,{"filePath":"tests/unit/test_remote_cli.py","startLine":410,"endLine":425,"chunkText":"\n        mock_logger.error.assert_called()\n\n    def test_handle_download_command_success(self, mock_api_client, mock_logger):\n        \"\"\"Test successful download command handling.\"\"\"\n        args = Mock()\n        args.task_id = \"test-task-123\"\n        args.filenames = [\"output1.wav\", \"output2.wav\"]\n\n        mock_api_client.download_file.side_effect = [\"output1.wav\", \"output2.wav\"]\n\n        handle_download_command(args, mock_api_client, mock_logger)\n\n        # Verify download was called for each file\n        expected_calls = [\n            call(\"test-task-123\", \"output1.wav\"),","chunkId":"tests/unit/test_remote_cli.py:46"}],[3019,{"filePath":"tests/unit/test_remote_cli.py","startLine":423,"endLine":436,"chunkText":"        # Verify download was called for each file\n        expected_calls = [\n            call(\"test-task-123\", \"output1.wav\"),\n            call(\"test-task-123\", \"output2.wav\")\n        ]\n        mock_api_client.download_file.assert_has_calls(expected_calls)\n\n        # Verify success messages were logged\n        assert mock_logger.info.call_count >= 4  # At least 2 downloading + 2 downloaded messages\n\n    def test_handle_download_command_exception(self, mock_api_client, mock_logger):\n        \"\"\"Test download command with exception.\"\"\"\n        args = Mock()\n        args.task_id = \"test-task-456\"","chunkId":"tests/unit/test_remote_cli.py:47"}],[3020,{"filePath":"tests/unit/test_remote_cli.py","startLine":434,"endLine":443,"chunkText":"        \"\"\"Test download command with exception.\"\"\"\n        args = Mock()\n        args.task_id = \"test-task-456\"\n        args.filenames = [\"output1.wav\"]\n\n        mock_api_client.download_file.side_effect = Exception(\"Download error\")\n\n        handle_download_command(args, mock_api_client, mock_logger)\n\n        mock_logger.error.assert_called() ","chunkId":"tests/unit/test_remote_cli.py:48"}],[3021,{"filePath":"tests/unit/test_separator_chunking.py","startLine":0,"endLine":21,"chunkText":"\"\"\"\nUnit tests for Separator class chunking functionality with multi-stem support.\nTests the _process_with_chunking() method for 2, 4, and 6-stem models.\n\"\"\"\n\nimport pytest\nimport os\nimport re\nimport tempfile\nimport logging\nfrom unittest.mock import Mock, patch, MagicMock, call\nfrom pydub import AudioSegment\n\nfrom audio_separator.separator.separator import Separator\n\n\nclass TestSeparatorChunking:\n    \"\"\"Test cases for Separator chunking with multi-stem models.\"\"\"\n\n    def setup_method(self):\n        \"\"\"Set up test fixtures.\"\"\"\n        self.logger = logging.getLogger(__name__)","chunkId":"tests/unit/test_separator_chunking.py:0"}],[3022,{"filePath":"tests/unit/test_separator_chunking.py","startLine":19,"endLine":34,"chunkText":"    def setup_method(self):\n        \"\"\"Set up test fixtures.\"\"\"\n        self.logger = logging.getLogger(__name__)\n        self.temp_dir = tempfile.mkdtemp()\n\n    def teardown_method(self):\n        \"\"\"Clean up test fixtures.\"\"\"\n        import shutil\n        if os.path.exists(self.temp_dir):\n            shutil.rmtree(self.temp_dir)\n\n    @patch('audio_separator.separator.audio_chunking.AudioChunker')\n    def test_process_with_chunking_2_stems(self, mock_chunker_class):\n        \"\"\"Test chunking with 2-stem model (Vocals, Instrumental).\"\"\"\n        # Setup mock separator\n        separator = Mock(spec=Separator)","chunkId":"tests/unit/test_separator_chunking.py:1"}],[3023,{"filePath":"tests/unit/test_separator_chunking.py","startLine":32,"endLine":45,"chunkText":"        \"\"\"Test chunking with 2-stem model (Vocals, Instrumental).\"\"\"\n        # Setup mock separator\n        separator = Mock(spec=Separator)\n        separator.logger = self.logger\n        separator.output_dir = self.temp_dir\n        separator.output_format = \"WAV\"\n        separator.chunk_duration = 10.0\n        separator.model_instance = Mock()\n        separator.model_instance.output_dir = self.temp_dir\n\n        # Mock chunker behavior\n        mock_chunker = Mock()\n        mock_chunker_class.return_value = mock_chunker\n        mock_chunker.split_audio.return_value = [","chunkId":"tests/unit/test_separator_chunking.py:2"}],[3024,{"filePath":"tests/unit/test_separator_chunking.py","startLine":43,"endLine":56,"chunkText":"        mock_chunker = Mock()\n        mock_chunker_class.return_value = mock_chunker\n        mock_chunker.split_audio.return_value = [\n            os.path.join(self.temp_dir, \"chunk_0000.wav\"),\n            os.path.join(self.temp_dir, \"chunk_0001.wav\"),\n        ]\n\n        # Mock _separate_file to return 2 stems per chunk\n        separator._separate_file = Mock(side_effect=[\n            # Chunk 1 output\n            [\n                os.path.join(self.temp_dir, \"chunk_0000_(Vocals).wav\"),\n                os.path.join(self.temp_dir, \"chunk_0000_(Instrumental).wav\"),\n            ],","chunkId":"tests/unit/test_separator_chunking.py:3"}],[3025,{"filePath":"tests/unit/test_separator_chunking.py","startLine":54,"endLine":67,"chunkText":"                os.path.join(self.temp_dir, \"chunk_0000_(Vocals).wav\"),\n                os.path.join(self.temp_dir, \"chunk_0000_(Instrumental).wav\"),\n            ],\n            # Chunk 2 output\n            [\n                os.path.join(self.temp_dir, \"chunk_0001_(Vocals).wav\"),\n                os.path.join(self.temp_dir, \"chunk_0001_(Instrumental).wav\"),\n            ],\n        ])\n\n        # Import and call the actual method\n        from audio_separator.separator.separator import Separator as RealSeparator\n        result = RealSeparator._process_with_chunking(\n            separator,","chunkId":"tests/unit/test_separator_chunking.py:4"}],[3026,{"filePath":"tests/unit/test_separator_chunking.py","startLine":65,"endLine":79,"chunkText":"        from audio_separator.separator.separator import Separator as RealSeparator\n        result = RealSeparator._process_with_chunking(\n            separator,\n            os.path.join(self.temp_dir, \"test.wav\"),\n            custom_output_names=None\n        )\n\n        # Verify merge_chunks was called twice (once per stem)\n        assert mock_chunker.merge_chunks.call_count == 2\n\n        # Verify output contains 2 files\n        assert len(result) == 2\n\n        # Verify stem names in output\n        output_stems = [os.path.basename(path) for path in result]","chunkId":"tests/unit/test_separator_chunking.py:5"}],[3027,{"filePath":"tests/unit/test_separator_chunking.py","startLine":77,"endLine":88,"chunkText":"\n        # Verify stem names in output\n        output_stems = [os.path.basename(path) for path in result]\n        assert any(\"Instrumental\" in name for name in output_stems)\n        assert any(\"Vocals\" in name for name in output_stems)\n\n    @patch('audio_separator.separator.audio_chunking.AudioChunker')\n    def test_process_with_chunking_4_stems(self, mock_chunker_class):\n        \"\"\"Test chunking with 4-stem Demucs model (Drums, Bass, Other, Vocals).\"\"\"\n        # Setup mock separator\n        separator = Mock(spec=Separator)\n        separator.logger = self.logger","chunkId":"tests/unit/test_separator_chunking.py:6"}],[3028,{"filePath":"tests/unit/test_separator_chunking.py","startLine":86,"endLine":99,"chunkText":"        # Setup mock separator\n        separator = Mock(spec=Separator)\n        separator.logger = self.logger\n        separator.output_dir = self.temp_dir\n        separator.output_format = \"WAV\"\n        separator.chunk_duration = 10.0\n        separator.model_instance = Mock()\n        separator.model_instance.output_dir = self.temp_dir\n\n        # Mock chunker behavior\n        mock_chunker = Mock()\n        mock_chunker_class.return_value = mock_chunker\n        mock_chunker.split_audio.return_value = [\n            os.path.join(self.temp_dir, \"chunk_0000.wav\"),","chunkId":"tests/unit/test_separator_chunking.py:7"}],[3029,{"filePath":"tests/unit/test_separator_chunking.py","startLine":97,"endLine":109,"chunkText":"        mock_chunker_class.return_value = mock_chunker\n        mock_chunker.split_audio.return_value = [\n            os.path.join(self.temp_dir, \"chunk_0000.wav\"),\n            os.path.join(self.temp_dir, \"chunk_0001.wav\"),\n        ]\n\n        # Mock _separate_file to return 4 stems per chunk\n        separator._separate_file = Mock(side_effect=[\n            # Chunk 1 output (4 stems)\n            [\n                os.path.join(self.temp_dir, \"chunk_0000_(Drums).wav\"),\n                os.path.join(self.temp_dir, \"chunk_0000_(Bass).wav\"),\n                os.path.join(self.temp_dir, \"chunk_0000_(Other).wav\"),","chunkId":"tests/unit/test_separator_chunking.py:8"}],[3030,{"filePath":"tests/unit/test_separator_chunking.py","startLine":107,"endLine":116,"chunkText":"                os.path.join(self.temp_dir, \"chunk_0000_(Drums).wav\"),\n                os.path.join(self.temp_dir, \"chunk_0000_(Bass).wav\"),\n                os.path.join(self.temp_dir, \"chunk_0000_(Other).wav\"),\n                os.path.join(self.temp_dir, \"chunk_0000_(Vocals).wav\"),\n            ],\n            # Chunk 2 output (4 stems)\n            [\n                os.path.join(self.temp_dir, \"chunk_0001_(Drums).wav\"),\n                os.path.join(self.temp_dir, \"chunk_0001_(Bass).wav\"),\n                os.path.join(self.temp_dir, \"chunk_0001_(Other).wav\"),","chunkId":"tests/unit/test_separator_chunking.py:9"}],[3031,{"filePath":"tests/unit/test_separator_chunking.py","startLine":114,"endLine":126,"chunkText":"                os.path.join(self.temp_dir, \"chunk_0001_(Drums).wav\"),\n                os.path.join(self.temp_dir, \"chunk_0001_(Bass).wav\"),\n                os.path.join(self.temp_dir, \"chunk_0001_(Other).wav\"),\n                os.path.join(self.temp_dir, \"chunk_0001_(Vocals).wav\"),\n            ],\n        ])\n\n        # Import and call the actual method\n        from audio_separator.separator.separator import Separator as RealSeparator\n        result = RealSeparator._process_with_chunking(\n            separator,\n            os.path.join(self.temp_dir, \"test.wav\"),\n            custom_output_names=None","chunkId":"tests/unit/test_separator_chunking.py:10"}],[3032,{"filePath":"tests/unit/test_separator_chunking.py","startLine":124,"endLine":137,"chunkText":"            separator,\n            os.path.join(self.temp_dir, \"test.wav\"),\n            custom_output_names=None\n        )\n\n        # Verify merge_chunks was called 4 times (once per stem)\n        assert mock_chunker.merge_chunks.call_count == 4\n\n        # Verify output contains 4 files\n        assert len(result) == 4\n\n        # Verify all 4 stem names in output\n        output_stems = [os.path.basename(path) for path in result]\n        assert any(\"Drums\" in name for name in output_stems), f\"Drums not found in {output_stems}\"","chunkId":"tests/unit/test_separator_chunking.py:11"}],[3033,{"filePath":"tests/unit/test_separator_chunking.py","startLine":135,"endLine":142,"chunkText":"        # Verify all 4 stem names in output\n        output_stems = [os.path.basename(path) for path in result]\n        assert any(\"Drums\" in name for name in output_stems), f\"Drums not found in {output_stems}\"\n        assert any(\"Bass\" in name for name in output_stems), f\"Bass not found in {output_stems}\"\n        assert any(\"Other\" in name for name in output_stems), f\"Other not found in {output_stems}\"\n        assert any(\"Vocals\" in name for name in output_stems), f\"Vocals not found in {output_stems}\"\n\n    @patch('audio_separator.separator.audio_chunking.AudioChunker')","chunkId":"tests/unit/test_separator_chunking.py:12"}],[3034,{"filePath":"tests/unit/test_separator_chunking.py","startLine":140,"endLine":151,"chunkText":"        assert any(\"Vocals\" in name for name in output_stems), f\"Vocals not found in {output_stems}\"\n\n    @patch('audio_separator.separator.audio_chunking.AudioChunker')\n    def test_process_with_chunking_6_stems(self, mock_chunker_class):\n        \"\"\"Test chunking with 6-stem Demucs model.\"\"\"\n        # Setup mock separator\n        separator = Mock(spec=Separator)\n        separator.logger = self.logger\n        separator.output_dir = self.temp_dir\n        separator.output_format = \"WAV\"\n        separator.chunk_duration = 10.0\n        separator.model_instance = Mock()","chunkId":"tests/unit/test_separator_chunking.py:13"}],[3035,{"filePath":"tests/unit/test_separator_chunking.py","startLine":149,"endLine":163,"chunkText":"        separator.output_format = \"WAV\"\n        separator.chunk_duration = 10.0\n        separator.model_instance = Mock()\n        separator.model_instance.output_dir = self.temp_dir\n\n        # Mock chunker behavior\n        mock_chunker = Mock()\n        mock_chunker_class.return_value = mock_chunker\n        mock_chunker.split_audio.return_value = [\n            os.path.join(self.temp_dir, \"chunk_0000.wav\"),\n        ]\n\n        # Mock _separate_file to return 6 stems\n        separator._separate_file = Mock(return_value=[\n            os.path.join(self.temp_dir, \"chunk_0000_(Bass).wav\"),","chunkId":"tests/unit/test_separator_chunking.py:14"}],[3036,{"filePath":"tests/unit/test_separator_chunking.py","startLine":161,"endLine":171,"chunkText":"        # Mock _separate_file to return 6 stems\n        separator._separate_file = Mock(return_value=[\n            os.path.join(self.temp_dir, \"chunk_0000_(Bass).wav\"),\n            os.path.join(self.temp_dir, \"chunk_0000_(Drums).wav\"),\n            os.path.join(self.temp_dir, \"chunk_0000_(Other).wav\"),\n            os.path.join(self.temp_dir, \"chunk_0000_(Vocals).wav\"),\n            os.path.join(self.temp_dir, \"chunk_0000_(Guitar).wav\"),\n            os.path.join(self.temp_dir, \"chunk_0000_(Piano).wav\"),\n        ])\n\n        # Import and call the actual method","chunkId":"tests/unit/test_separator_chunking.py:15"}],[3037,{"filePath":"tests/unit/test_separator_chunking.py","startLine":169,"endLine":185,"chunkText":"        ])\n\n        # Import and call the actual method\n        from audio_separator.separator.separator import Separator as RealSeparator\n        result = RealSeparator._process_with_chunking(\n            separator,\n            os.path.join(self.temp_dir, \"test.wav\"),\n            custom_output_names=None\n        )\n\n        # Verify merge_chunks was called 6 times (once per stem)\n        assert mock_chunker.merge_chunks.call_count == 6\n\n        # Verify output contains 6 files\n        assert len(result) == 6\n\n        # Verify all 6 stem names in output","chunkId":"tests/unit/test_separator_chunking.py:16"}],[3038,{"filePath":"tests/unit/test_separator_chunking.py","startLine":183,"endLine":194,"chunkText":"        assert len(result) == 6\n\n        # Verify all 6 stem names in output\n        output_stems = [os.path.basename(path) for path in result]\n        assert any(\"Bass\" in name for name in output_stems)\n        assert any(\"Drums\" in name for name in output_stems)\n        assert any(\"Other\" in name for name in output_stems)\n        assert any(\"Vocals\" in name for name in output_stems)\n        assert any(\"Guitar\" in name for name in output_stems)\n        assert any(\"Piano\" in name for name in output_stems)\n\n    def test_stem_name_extraction_from_filename(self):","chunkId":"tests/unit/test_separator_chunking.py:17"}],[3039,{"filePath":"tests/unit/test_separator_chunking.py","startLine":192,"endLine":205,"chunkText":"        assert any(\"Piano\" in name for name in output_stems)\n\n    def test_stem_name_extraction_from_filename(self):\n        \"\"\"Test regex extraction of stem names from chunk filenames.\"\"\"\n        import re\n\n        # Test standard patterns\n        test_cases = [\n            (\"chunk_0000_(Vocals).wav\", \"Vocals\"),\n            (\"chunk_0001_(Instrumental).wav\", \"Instrumental\"),\n            (\"chunk_0000_(Drums).wav\", \"Drums\"),\n            (\"chunk_0000_(Bass).wav\", \"Bass\"),\n            (\"chunk_0000_(Other).wav\", \"Other\"),\n            (\"chunk_0000_(Guitar).wav\", \"Guitar\"),","chunkId":"tests/unit/test_separator_chunking.py:18"}],[3040,{"filePath":"tests/unit/test_separator_chunking.py","startLine":203,"endLine":215,"chunkText":"            (\"chunk_0000_(Bass).wav\", \"Bass\"),\n            (\"chunk_0000_(Other).wav\", \"Other\"),\n            (\"chunk_0000_(Guitar).wav\", \"Guitar\"),\n            (\"chunk_0000_(Piano).wav\", \"Piano\"),\n            (\"test_audio_(Vocals).flac\", \"Vocals\"),\n            (\"long_filename_with_spaces_(Backing Vocals).mp3\", \"Backing Vocals\"),\n        ]\n\n        pattern = r'_\\(([^)]+)\\)'\n\n        for filename, expected_stem in test_cases:\n            match = re.search(pattern, filename)\n            assert match is not None, f\"Pattern did not match for {filename}\"","chunkId":"tests/unit/test_separator_chunking.py:19"}],[3041,{"filePath":"tests/unit/test_separator_chunking.py","startLine":213,"endLine":228,"chunkText":"        for filename, expected_stem in test_cases:\n            match = re.search(pattern, filename)\n            assert match is not None, f\"Pattern did not match for {filename}\"\n            assert match.group(1) == expected_stem, f\"Expected {expected_stem}, got {match.group(1)}\"\n\n    def test_stem_name_extraction_fallback(self):\n        \"\"\"Test fallback behavior when filename pattern doesn't match.\"\"\"\n        import re\n\n        # Test filenames without the _(StemName) pattern\n        test_cases = [\n            \"chunk_0000.wav\",\n            \"output.mp3\",\n            \"vocals_only.flac\",\n        ]\n","chunkId":"tests/unit/test_separator_chunking.py:20"}],[3042,{"filePath":"tests/unit/test_separator_chunking.py","startLine":226,"endLine":241,"chunkText":"            \"vocals_only.flac\",\n        ]\n\n        pattern = r'_\\(([^)]+)\\)'\n\n        for filename in test_cases:\n            match = re.search(pattern, filename)\n            # Should not match - fallback logic should kick in\n            assert match is None, f\"Pattern should not match for {filename}\"\n\n    @patch('audio_separator.separator.audio_chunking.AudioChunker')\n    def test_chunking_preserves_stem_order(self, mock_chunker_class):\n        \"\"\"Test that stems are merged in sorted order.\"\"\"\n        # Setup mock separator\n        separator = Mock(spec=Separator)\n        separator.logger = self.logger","chunkId":"tests/unit/test_separator_chunking.py:21"}],[3043,{"filePath":"tests/unit/test_separator_chunking.py","startLine":239,"endLine":254,"chunkText":"        # Setup mock separator\n        separator = Mock(spec=Separator)\n        separator.logger = self.logger\n        separator.output_dir = self.temp_dir\n        separator.output_format = \"WAV\"\n        separator.chunk_duration = 10.0\n        separator.model_instance = Mock()\n        separator.model_instance.output_dir = self.temp_dir\n\n        # Mock chunker behavior\n        mock_chunker = Mock()\n        mock_chunker_class.return_value = mock_chunker\n        mock_chunker.split_audio.return_value = [\n            os.path.join(self.temp_dir, \"chunk_0000.wav\"),\n        ]\n","chunkId":"tests/unit/test_separator_chunking.py:22"}],[3044,{"filePath":"tests/unit/test_separator_chunking.py","startLine":252,"endLine":265,"chunkText":"            os.path.join(self.temp_dir, \"chunk_0000.wav\"),\n        ]\n\n        # Mock _separate_file with intentionally unsorted output\n        separator._separate_file = Mock(return_value=[\n            os.path.join(self.temp_dir, \"chunk_0000_(Vocals).wav\"),\n            os.path.join(self.temp_dir, \"chunk_0000_(Drums).wav\"),\n            os.path.join(self.temp_dir, \"chunk_0000_(Bass).wav\"),\n        ])\n\n        # Import and call the actual method\n        from audio_separator.separator.separator import Separator as RealSeparator\n        result = RealSeparator._process_with_chunking(\n            separator,","chunkId":"tests/unit/test_separator_chunking.py:23"}],[3045,{"filePath":"tests/unit/test_separator_chunking.py","startLine":263,"endLine":278,"chunkText":"        from audio_separator.separator.separator import Separator as RealSeparator\n        result = RealSeparator._process_with_chunking(\n            separator,\n            os.path.join(self.temp_dir, \"test.wav\"),\n            custom_output_names=None\n        )\n\n        # Verify stems are processed in sorted order (Bass, Drums, Vocals)\n        output_stems = [os.path.basename(path) for path in result]\n\n        # Extract stem names from output\n        stem_names = []\n        pattern = r'_\\(([^)]+)\\)'\n        for name in output_stems:\n            match = re.search(pattern, name)\n            if match:","chunkId":"tests/unit/test_separator_chunking.py:24"}],[3046,{"filePath":"tests/unit/test_separator_chunking.py","startLine":276,"endLine":284,"chunkText":"        for name in output_stems:\n            match = re.search(pattern, name)\n            if match:\n                stem_names.append(match.group(1))\n\n        # Verify sorted order\n        assert stem_names == sorted(stem_names), f\"Stems not in sorted order: {stem_names}\"\n\n","chunkId":"tests/unit/test_separator_chunking.py:25"}],[3047,{"filePath":"tests/unit/test_separator_chunking.py","startLine":285,"endLine":300,"chunkText":"class TestSeparatorChunkingLogic:\n    \"\"\"Test internal logic and state management of chunking.\"\"\"\n\n    def setup_method(self):\n        \"\"\"Set up test fixtures.\"\"\"\n        self.logger = logging.getLogger(__name__)\n        self.temp_dir = tempfile.mkdtemp()\n\n    def teardown_method(self):\n        \"\"\"Clean up test fixtures.\"\"\"\n        import shutil\n        if os.path.exists(self.temp_dir):\n            shutil.rmtree(self.temp_dir)\n\n    @patch('audio_separator.separator.audio_chunking.AudioChunker')\n    def test_state_restoration_after_chunking(self, mock_chunker_class):","chunkId":"tests/unit/test_separator_chunking.py:26"}],[3048,{"filePath":"tests/unit/test_separator_chunking.py","startLine":298,"endLine":311,"chunkText":"\n    @patch('audio_separator.separator.audio_chunking.AudioChunker')\n    def test_state_restoration_after_chunking(self, mock_chunker_class):\n        \"\"\"Test that separator state is restored after chunking.\"\"\"\n        # Setup mock separator\n        separator = Mock(spec=Separator)\n        separator.logger = self.logger\n        separator.output_dir = \"/original/output/dir\"\n        separator.output_format = \"WAV\"\n        separator.chunk_duration = 10.0\n        separator.model_instance = Mock()\n        separator.model_instance.output_dir = \"/original/model/output\"\n\n        # Mock chunker behavior","chunkId":"tests/unit/test_separator_chunking.py:27"}],[3049,{"filePath":"tests/unit/test_separator_chunking.py","startLine":309,"endLine":322,"chunkText":"        separator.model_instance.output_dir = \"/original/model/output\"\n\n        # Mock chunker behavior\n        mock_chunker = Mock()\n        mock_chunker_class.return_value = mock_chunker\n        mock_chunker.split_audio.return_value = [\n            os.path.join(self.temp_dir, \"chunk_0000.wav\"),\n        ]\n\n        # Track state changes during _separate_file call\n        state_during_processing = {}\n        def track_state(chunk_path, custom_names):\n            state_during_processing['chunk_duration'] = separator.chunk_duration\n            state_during_processing['output_dir'] = separator.output_dir","chunkId":"tests/unit/test_separator_chunking.py:28"}],[3050,{"filePath":"tests/unit/test_separator_chunking.py","startLine":320,"endLine":329,"chunkText":"        def track_state(chunk_path, custom_names):\n            state_during_processing['chunk_duration'] = separator.chunk_duration\n            state_during_processing['output_dir'] = separator.output_dir\n            state_during_processing['model_output_dir'] = separator.model_instance.output_dir\n            return [os.path.join(self.temp_dir, \"chunk_0000_(Vocals).wav\")]\n\n        separator._separate_file = Mock(side_effect=track_state)\n\n        # Import and call the actual method\n        from audio_separator.separator.separator import Separator as RealSeparator","chunkId":"tests/unit/test_separator_chunking.py:29"}],[3051,{"filePath":"tests/unit/test_separator_chunking.py","startLine":328,"endLine":340,"chunkText":"        # Import and call the actual method\n        from audio_separator.separator.separator import Separator as RealSeparator\n        RealSeparator._process_with_chunking(\n            separator,\n            os.path.join(self.temp_dir, \"test.wav\"),\n            custom_output_names=None\n        )\n\n        # Verify state was modified during processing\n        assert state_during_processing['chunk_duration'] is None\n        assert state_during_processing['output_dir'] != \"/original/output/dir\"\n        assert state_during_processing['model_output_dir'] != \"/original/model/output\"\n","chunkId":"tests/unit/test_separator_chunking.py:30"}],[3052,{"filePath":"tests/unit/test_separator_chunking.py","startLine":338,"endLine":347,"chunkText":"        assert state_during_processing['output_dir'] != \"/original/output/dir\"\n        assert state_during_processing['model_output_dir'] != \"/original/model/output\"\n\n        # Verify state was restored after processing\n        assert separator.chunk_duration == 10.0\n        assert separator.output_dir == \"/original/output/dir\"\n        assert separator.model_instance.output_dir == \"/original/model/output\"\n\n    @patch('audio_separator.separator.audio_chunking.AudioChunker')\n    def test_gpu_cache_cleared_between_chunks(self, mock_chunker_class):","chunkId":"tests/unit/test_separator_chunking.py:31"}],[3053,{"filePath":"tests/unit/test_separator_chunking.py","startLine":345,"endLine":358,"chunkText":"\n    @patch('audio_separator.separator.audio_chunking.AudioChunker')\n    def test_gpu_cache_cleared_between_chunks(self, mock_chunker_class):\n        \"\"\"Test that GPU cache is cleared after each chunk.\"\"\"\n        # Setup mock separator\n        separator = Mock(spec=Separator)\n        separator.logger = self.logger\n        separator.output_dir = self.temp_dir\n        separator.output_format = \"WAV\"\n        separator.chunk_duration = 10.0\n        separator.model_instance = Mock()\n        separator.model_instance.output_dir = self.temp_dir\n        separator.model_instance.clear_gpu_cache = Mock()\n","chunkId":"tests/unit/test_separator_chunking.py:32"}],[3054,{"filePath":"tests/unit/test_separator_chunking.py","startLine":356,"endLine":369,"chunkText":"        separator.model_instance.output_dir = self.temp_dir\n        separator.model_instance.clear_gpu_cache = Mock()\n\n        # Mock chunker behavior - 3 chunks\n        mock_chunker = Mock()\n        mock_chunker_class.return_value = mock_chunker\n        mock_chunker.split_audio.return_value = [\n            os.path.join(self.temp_dir, \"chunk_0000.wav\"),\n            os.path.join(self.temp_dir, \"chunk_0001.wav\"),\n            os.path.join(self.temp_dir, \"chunk_0002.wav\"),\n        ]\n\n        # Mock _separate_file\n        separator._separate_file = Mock(return_value=[","chunkId":"tests/unit/test_separator_chunking.py:33"}],[3055,{"filePath":"tests/unit/test_separator_chunking.py","startLine":367,"endLine":383,"chunkText":"\n        # Mock _separate_file\n        separator._separate_file = Mock(return_value=[\n            os.path.join(self.temp_dir, \"chunk_(Vocals).wav\"),\n        ])\n\n        # Import and call the actual method\n        from audio_separator.separator.separator import Separator as RealSeparator\n        RealSeparator._process_with_chunking(\n            separator,\n            os.path.join(self.temp_dir, \"test.wav\"),\n            custom_output_names=None\n        )\n\n        # Verify clear_gpu_cache was called 3 times (once per chunk)\n        assert separator.model_instance.clear_gpu_cache.call_count == 3\n","chunkId":"tests/unit/test_separator_chunking.py:34"}],[3056,{"filePath":"tests/unit/test_separator_chunking.py","startLine":381,"endLine":393,"chunkText":"        # Verify clear_gpu_cache was called 3 times (once per chunk)\n        assert separator.model_instance.clear_gpu_cache.call_count == 3\n\n    @patch('audio_separator.separator.audio_chunking.AudioChunker')\n    def test_temp_directory_cleanup(self, mock_chunker_class):\n        \"\"\"Test that temporary directory is cleaned up.\"\"\"\n        # Setup mock separator\n        separator = Mock(spec=Separator)\n        separator.logger = self.logger\n        separator.output_dir = self.temp_dir\n        separator.output_format = \"WAV\"\n        separator.chunk_duration = 10.0\n        separator.model_instance = Mock()","chunkId":"tests/unit/test_separator_chunking.py:35"}],[3057,{"filePath":"tests/unit/test_separator_chunking.py","startLine":391,"endLine":407,"chunkText":"        separator.output_format = \"WAV\"\n        separator.chunk_duration = 10.0\n        separator.model_instance = Mock()\n        separator.model_instance.output_dir = self.temp_dir\n\n        # Mock chunker behavior\n        mock_chunker = Mock()\n        mock_chunker_class.return_value = mock_chunker\n        mock_chunker.split_audio.return_value = [\n            os.path.join(self.temp_dir, \"chunk_0000.wav\"),\n        ]\n\n        separator._separate_file = Mock(return_value=[\n            os.path.join(self.temp_dir, \"chunk_(Vocals).wav\"),\n        ])\n\n        # Track temporary directory creation","chunkId":"tests/unit/test_separator_chunking.py:36"}],[3058,{"filePath":"tests/unit/test_separator_chunking.py","startLine":405,"endLine":419,"chunkText":"        ])\n\n        # Track temporary directory creation\n        temp_dirs_created = []\n        original_mkdtemp = tempfile.mkdtemp\n        def track_mkdtemp(prefix=None):\n            temp_dir = original_mkdtemp(prefix=prefix)\n            temp_dirs_created.append(temp_dir)\n            return temp_dir\n\n        # Import and call the actual method\n        from audio_separator.separator.separator import Separator as RealSeparator\n        with patch('tempfile.mkdtemp', side_effect=track_mkdtemp):\n            RealSeparator._process_with_chunking(\n                separator,","chunkId":"tests/unit/test_separator_chunking.py:37"}],[3059,{"filePath":"tests/unit/test_separator_chunking.py","startLine":417,"endLine":429,"chunkText":"        with patch('tempfile.mkdtemp', side_effect=track_mkdtemp):\n            RealSeparator._process_with_chunking(\n                separator,\n                os.path.join(self.temp_dir, \"test.wav\"),\n                custom_output_names=None\n            )\n\n        # Verify temporary directories were cleaned up\n        for temp_dir in temp_dirs_created:\n            if temp_dir.startswith('/') or temp_dir.startswith('C:'):  # Real temp dir\n                assert not os.path.exists(temp_dir), f\"Temp directory {temp_dir} was not cleaned up\"\n\n    @patch('audio_separator.separator.audio_chunking.AudioChunker')","chunkId":"tests/unit/test_separator_chunking.py:38"}],[3060,{"filePath":"tests/unit/test_separator_chunking.py","startLine":427,"endLine":438,"chunkText":"                assert not os.path.exists(temp_dir), f\"Temp directory {temp_dir} was not cleaned up\"\n\n    @patch('audio_separator.separator.audio_chunking.AudioChunker')\n    def test_error_handling_with_state_restoration(self, mock_chunker_class):\n        \"\"\"Test that state is restored even when error occurs.\"\"\"\n        # Setup mock separator\n        separator = Mock(spec=Separator)\n        separator.logger = self.logger\n        separator.output_dir = \"/original/output/dir\"\n        separator.output_format = \"WAV\"\n        separator.chunk_duration = 10.0\n        separator.model_instance = Mock()","chunkId":"tests/unit/test_separator_chunking.py:39"}],[3061,{"filePath":"tests/unit/test_separator_chunking.py","startLine":436,"endLine":451,"chunkText":"        separator.output_format = \"WAV\"\n        separator.chunk_duration = 10.0\n        separator.model_instance = Mock()\n        separator.model_instance.output_dir = \"/original/model/output\"\n\n        # Mock chunker behavior\n        mock_chunker = Mock()\n        mock_chunker_class.return_value = mock_chunker\n        mock_chunker.split_audio.return_value = [\n            os.path.join(self.temp_dir, \"chunk_0000.wav\"),\n        ]\n\n        # Mock _separate_file to raise exception\n        separator._separate_file = Mock(side_effect=Exception(\"Processing error\"))\n\n        # Import and call the actual method","chunkId":"tests/unit/test_separator_chunking.py:40"}],[3062,{"filePath":"tests/unit/test_separator_chunking.py","startLine":449,"endLine":463,"chunkText":"        separator._separate_file = Mock(side_effect=Exception(\"Processing error\"))\n\n        # Import and call the actual method\n        from audio_separator.separator.separator import Separator as RealSeparator\n\n        # Should raise exception\n        with pytest.raises(Exception, match=\"Processing error\"):\n            RealSeparator._process_with_chunking(\n                separator,\n                os.path.join(self.temp_dir, \"test.wav\"),\n                custom_output_names=None\n            )\n\n        # Verify state was restored despite error\n        assert separator.chunk_duration == 10.0","chunkId":"tests/unit/test_separator_chunking.py:41"}],[3063,{"filePath":"tests/unit/test_separator_chunking.py","startLine":461,"endLine":473,"chunkText":"\n        # Verify state was restored despite error\n        assert separator.chunk_duration == 10.0\n        assert separator.output_dir == \"/original/output/dir\"\n        assert separator.model_instance.output_dir == \"/original/model/output\"\n\n    @patch('audio_separator.separator.audio_chunking.AudioChunker')\n    def test_audio_chunker_initialization(self, mock_chunker_class):\n        \"\"\"Test that AudioChunker is initialized with correct parameters.\"\"\"\n        # Setup mock separator\n        separator = Mock(spec=Separator)\n        separator.logger = self.logger\n        separator.output_dir = self.temp_dir","chunkId":"tests/unit/test_separator_chunking.py:42"}],[3064,{"filePath":"tests/unit/test_separator_chunking.py","startLine":471,"endLine":486,"chunkText":"        separator = Mock(spec=Separator)\n        separator.logger = self.logger\n        separator.output_dir = self.temp_dir\n        separator.output_format = \"WAV\"\n        separator.chunk_duration = 15.0\n        separator.model_instance = Mock()\n        separator.model_instance.output_dir = self.temp_dir\n\n        # Mock chunker behavior\n        mock_chunker = Mock()\n        mock_chunker_class.return_value = mock_chunker\n        mock_chunker.split_audio.return_value = [\n            os.path.join(self.temp_dir, \"chunk_0000.wav\"),\n        ]\n\n        separator._separate_file = Mock(return_value=[","chunkId":"tests/unit/test_separator_chunking.py:43"}],[3065,{"filePath":"tests/unit/test_separator_chunking.py","startLine":484,"endLine":500,"chunkText":"        ]\n\n        separator._separate_file = Mock(return_value=[\n            os.path.join(self.temp_dir, \"chunk_(Vocals).wav\"),\n        ])\n\n        # Import and call the actual method\n        from audio_separator.separator.separator import Separator as RealSeparator\n        RealSeparator._process_with_chunking(\n            separator,\n            os.path.join(self.temp_dir, \"test.wav\"),\n            custom_output_names=None\n        )\n\n        # Verify AudioChunker was initialized with correct parameters\n        mock_chunker_class.assert_called_once_with(15.0, separator.logger)\n","chunkId":"tests/unit/test_separator_chunking.py:44"}],[3066,{"filePath":"tests/unit/test_separator_chunking.py","startLine":498,"endLine":509,"chunkText":"        # Verify AudioChunker was initialized with correct parameters\n        mock_chunker_class.assert_called_once_with(15.0, separator.logger)\n\n    @patch('audio_separator.separator.audio_chunking.AudioChunker')\n    def test_custom_output_names_parameter(self, mock_chunker_class):\n        \"\"\"Test that custom_output_names parameter is handled correctly.\"\"\"\n        # Setup mock separator\n        separator = Mock(spec=Separator)\n        separator.logger = self.logger\n        separator.output_dir = self.temp_dir\n        separator.output_format = \"WAV\"\n        separator.chunk_duration = 10.0","chunkId":"tests/unit/test_separator_chunking.py:45"}],[3067,{"filePath":"tests/unit/test_separator_chunking.py","startLine":507,"endLine":523,"chunkText":"        separator.output_dir = self.temp_dir\n        separator.output_format = \"WAV\"\n        separator.chunk_duration = 10.0\n        separator.model_instance = Mock()\n        separator.model_instance.output_dir = self.temp_dir\n\n        # Mock chunker behavior\n        mock_chunker = Mock()\n        mock_chunker_class.return_value = mock_chunker\n        mock_chunker.split_audio.return_value = [\n            os.path.join(self.temp_dir, \"chunk_0000.wav\"),\n        ]\n\n        separator._separate_file = Mock(return_value=[\n            os.path.join(self.temp_dir, \"chunk_0000_(Vocals).wav\"),\n        ])\n","chunkId":"tests/unit/test_separator_chunking.py:46"}],[3068,{"filePath":"tests/unit/test_separator_chunking.py","startLine":521,"endLine":538,"chunkText":"            os.path.join(self.temp_dir, \"chunk_0000_(Vocals).wav\"),\n        ])\n\n        custom_names = {\"Vocals\": \"my_custom_vocals\"}\n\n        # Import and call the actual method\n        from audio_separator.separator.separator import Separator as RealSeparator\n        result = RealSeparator._process_with_chunking(\n            separator,\n            os.path.join(self.temp_dir, \"test.wav\"),\n            custom_output_names=custom_names\n        )\n\n        # Verify custom name was used in output\n        assert len(result) == 1\n        assert \"my_custom_vocals\" in os.path.basename(result[0])\n\n","chunkId":"tests/unit/test_separator_chunking.py:47"}],[3069,{"filePath":"tests/unit/test_separator_chunking.py","startLine":539,"endLine":554,"chunkText":"class TestSeparatorChunkingEdgeCases:\n    \"\"\"Test edge cases for multi-stem chunking.\"\"\"\n\n    def setup_method(self):\n        \"\"\"Set up test fixtures.\"\"\"\n        self.logger = logging.getLogger(__name__)\n        self.temp_dir = tempfile.mkdtemp()\n\n    def teardown_method(self):\n        \"\"\"Clean up test fixtures.\"\"\"\n        import shutil\n        if os.path.exists(self.temp_dir):\n            shutil.rmtree(self.temp_dir)\n\n    @patch('audio_separator.separator.audio_chunking.AudioChunker')\n    def test_empty_output_handling(self, mock_chunker_class):","chunkId":"tests/unit/test_separator_chunking.py:48"}],[3070,{"filePath":"tests/unit/test_separator_chunking.py","startLine":552,"endLine":566,"chunkText":"\n    @patch('audio_separator.separator.audio_chunking.AudioChunker')\n    def test_empty_output_handling(self, mock_chunker_class):\n        \"\"\"Test handling when a chunk produces no output files.\"\"\"\n        # Setup mock separator\n        separator = Mock(spec=Separator)\n        separator.logger = self.logger\n        separator.output_dir = self.temp_dir\n        separator.output_format = \"WAV\"\n        separator.chunk_duration = 10.0\n        separator.model_instance = Mock()\n        separator.model_instance.output_dir = self.temp_dir\n\n        # Mock chunker behavior\n        mock_chunker = Mock()","chunkId":"tests/unit/test_separator_chunking.py:49"}],[3071,{"filePath":"tests/unit/test_separator_chunking.py","startLine":564,"endLine":579,"chunkText":"\n        # Mock chunker behavior\n        mock_chunker = Mock()\n        mock_chunker_class.return_value = mock_chunker\n        mock_chunker.split_audio.return_value = [\n            os.path.join(self.temp_dir, \"chunk_0000.wav\"),\n        ]\n\n        # Mock _separate_file to return empty list\n        separator._separate_file = Mock(return_value=[])\n\n        # Import and call the actual method\n        from audio_separator.separator.separator import Separator as RealSeparator\n        result = RealSeparator._process_with_chunking(\n            separator,\n            os.path.join(self.temp_dir, \"test.wav\"),","chunkId":"tests/unit/test_separator_chunking.py:50"}],[3072,{"filePath":"tests/unit/test_separator_chunking.py","startLine":577,"endLine":591,"chunkText":"        result = RealSeparator._process_with_chunking(\n            separator,\n            os.path.join(self.temp_dir, \"test.wav\"),\n            custom_output_names=None\n        )\n\n        # Should return empty list\n        assert len(result) == 0\n        # merge_chunks should not be called\n        assert mock_chunker.merge_chunks.call_count == 0\n\n    @patch('audio_separator.separator.audio_chunking.AudioChunker')\n    def test_inconsistent_stem_count_across_chunks(self, mock_chunker_class):\n        \"\"\"Test handling when different chunks produce different stem counts.\"\"\"\n        # Setup mock separator","chunkId":"tests/unit/test_separator_chunking.py:51"}],[3073,{"filePath":"tests/unit/test_separator_chunking.py","startLine":589,"endLine":601,"chunkText":"    def test_inconsistent_stem_count_across_chunks(self, mock_chunker_class):\n        \"\"\"Test handling when different chunks produce different stem counts.\"\"\"\n        # Setup mock separator\n        separator = Mock(spec=Separator)\n        separator.logger = self.logger\n        separator.output_dir = self.temp_dir\n        separator.output_format = \"WAV\"\n        separator.chunk_duration = 10.0\n        separator.model_instance = Mock()\n        separator.model_instance.output_dir = self.temp_dir\n\n        # Mock chunker behavior\n        mock_chunker = Mock()","chunkId":"tests/unit/test_separator_chunking.py:52"}],[3074,{"filePath":"tests/unit/test_separator_chunking.py","startLine":600,"endLine":613,"chunkText":"        # Mock chunker behavior\n        mock_chunker = Mock()\n        mock_chunker_class.return_value = mock_chunker\n        mock_chunker.split_audio.return_value = [\n            os.path.join(self.temp_dir, \"chunk_0000.wav\"),\n            os.path.join(self.temp_dir, \"chunk_0001.wav\"),\n        ]\n\n        # Mock _separate_file - first chunk has 2 stems, second has 1 stem\n        separator._separate_file = Mock(side_effect=[\n            [\n                os.path.join(self.temp_dir, \"chunk_0000_(Vocals).wav\"),\n                os.path.join(self.temp_dir, \"chunk_0000_(Instrumental).wav\"),\n            ],","chunkId":"tests/unit/test_separator_chunking.py:53"}],[3075,{"filePath":"tests/unit/test_separator_chunking.py","startLine":611,"endLine":624,"chunkText":"                os.path.join(self.temp_dir, \"chunk_0000_(Vocals).wav\"),\n                os.path.join(self.temp_dir, \"chunk_0000_(Instrumental).wav\"),\n            ],\n            [\n                os.path.join(self.temp_dir, \"chunk_0001_(Vocals).wav\"),\n                # Missing Instrumental stem\n            ],\n        ])\n\n        # Import and call the actual method\n        from audio_separator.separator.separator import Separator as RealSeparator\n        result = RealSeparator._process_with_chunking(\n            separator,\n            os.path.join(self.temp_dir, \"test.wav\"),","chunkId":"tests/unit/test_separator_chunking.py:54"}],[3076,{"filePath":"tests/unit/test_separator_chunking.py","startLine":622,"endLine":635,"chunkText":"        result = RealSeparator._process_with_chunking(\n            separator,\n            os.path.join(self.temp_dir, \"test.wav\"),\n            custom_output_names=None\n        )\n\n        # Should handle inconsistency gracefully\n        # Vocals should have 2 chunks, Instrumental should have 1 chunk\n        assert len(result) == 2\n\n    @patch('audio_separator.separator.audio_chunking.AudioChunker')\n    def test_filename_without_stem_pattern(self, mock_chunker_class):\n        \"\"\"Test fallback when filename doesn't match expected pattern.\"\"\"\n        # Setup mock separator","chunkId":"tests/unit/test_separator_chunking.py:55"}],[3077,{"filePath":"tests/unit/test_separator_chunking.py","startLine":633,"endLine":646,"chunkText":"    def test_filename_without_stem_pattern(self, mock_chunker_class):\n        \"\"\"Test fallback when filename doesn't match expected pattern.\"\"\"\n        # Setup mock separator\n        separator = Mock(spec=Separator)\n        separator.logger = self.logger\n        separator.output_dir = self.temp_dir\n        separator.output_format = \"WAV\"\n        separator.chunk_duration = 10.0\n        separator.model_instance = Mock()\n        separator.model_instance.output_dir = self.temp_dir\n\n        # Mock chunker behavior\n        mock_chunker = Mock()\n        mock_chunker_class.return_value = mock_chunker","chunkId":"tests/unit/test_separator_chunking.py:56"}],[3078,{"filePath":"tests/unit/test_separator_chunking.py","startLine":644,"endLine":657,"chunkText":"        # Mock chunker behavior\n        mock_chunker = Mock()\n        mock_chunker_class.return_value = mock_chunker\n        mock_chunker.split_audio.return_value = [\n            os.path.join(self.temp_dir, \"chunk_0000.wav\"),\n        ]\n\n        # Mock _separate_file - return file without standard naming pattern\n        separator._separate_file = Mock(return_value=[\n            os.path.join(self.temp_dir, \"output_file_without_pattern.wav\"),\n        ])\n\n        # Import and call the actual method\n        from audio_separator.separator.separator import Separator as RealSeparator","chunkId":"tests/unit/test_separator_chunking.py:57"}],[3079,{"filePath":"tests/unit/test_separator_chunking.py","startLine":655,"endLine":672,"chunkText":"\n        # Import and call the actual method\n        from audio_separator.separator.separator import Separator as RealSeparator\n        result = RealSeparator._process_with_chunking(\n            separator,\n            os.path.join(self.temp_dir, \"test.wav\"),\n            custom_output_names=None\n        )\n\n        # Should still produce output using fallback naming\n        assert len(result) == 1\n        # Should use fallback name like \"stem_0\"\n        assert \"stem_0\" in os.path.basename(result[0])\n\n\nif __name__ == \"__main__\":\n    pytest.main([__file__, \"-v\"])\n","chunkId":"tests/unit/test_separator_chunking.py:58"}],[3080,{"filePath":"tests/unit/test_separator_detection.py","startLine":0,"endLine":22,"chunkText":"\"\"\"\nUnit tests for separator detection and routing logic.\nTests the detection of Roformer models and proper routing.\n\"\"\"\n\nimport pytest\nfrom unittest.mock import Mock, patch, MagicMock\nimport os\nimport tempfile\n\n\nclass TestSeparatorDetection:\n    \"\"\"Test cases for separator detection and model routing.\"\"\"\n\n    def setup_method(self):\n        \"\"\"Set up test fixtures.\"\"\"\n        self.temp_dir = tempfile.mkdtemp()\n\n    def teardown_method(self):\n        \"\"\"Clean up test fixtures.\"\"\"\n        import shutil\n        shutil.rmtree(self.temp_dir, ignore_errors=True)\n","chunkId":"tests/unit/test_separator_detection.py:0"}],[3081,{"filePath":"tests/unit/test_separator_detection.py","startLine":21,"endLine":30,"chunkText":"        shutil.rmtree(self.temp_dir, ignore_errors=True)\n\n    def test_is_roformer_set_from_yaml_path(self):\n        \"\"\"T059: YAML path containing 'roformer' sets is_roformer and routes Roformer path.\"\"\"\n        # Test cases for different YAML paths\n        test_cases = [\n            # (yaml_path, expected_is_roformer, description)\n            (\"model_bs_roformer_ep_317_sdr_12.yaml\", True, \"BS Roformer model\"),\n            (\"mel_band_roformer_vocals.yaml\", True, \"Mel Band Roformer model\"),\n            (\"roformer_large_model.yaml\", True, \"Generic Roformer model\"),","chunkId":"tests/unit/test_separator_detection.py:1"}],[3082,{"filePath":"tests/unit/test_separator_detection.py","startLine":28,"endLine":35,"chunkText":"            (\"model_bs_roformer_ep_317_sdr_12.yaml\", True, \"BS Roformer model\"),\n            (\"mel_band_roformer_vocals.yaml\", True, \"Mel Band Roformer model\"),\n            (\"roformer_large_model.yaml\", True, \"Generic Roformer model\"),\n            (\"BS-Roformer-Viperx-1297.yaml\", True, \"BS-Roformer with uppercase\"),\n            (\"model_mdx_extra_vocals.yaml\", False, \"MDX model (not Roformer)\"),\n            (\"vr_model_vocals.yaml\", False, \"VR model (not Roformer)\"),\n            (\"demucs_model.yaml\", False, \"Demucs model (not Roformer)\"),\n            (\"some_other_model.yaml\", False, \"Generic other model\"),","chunkId":"tests/unit/test_separator_detection.py:2"}],[3083,{"filePath":"tests/unit/test_separator_detection.py","startLine":33,"endLine":44,"chunkText":"            (\"vr_model_vocals.yaml\", False, \"VR model (not Roformer)\"),\n            (\"demucs_model.yaml\", False, \"Demucs model (not Roformer)\"),\n            (\"some_other_model.yaml\", False, \"Generic other model\"),\n        ]\n\n        for yaml_path, expected_is_roformer, description in test_cases:\n            # Mock the detection logic\n            def mock_detect_roformer_from_path(path):\n                \"\"\"Mock detection that checks if path contains 'roformer'.\"\"\"\n                path_lower = path.lower()\n                return 'roformer' in path_lower\n","chunkId":"tests/unit/test_separator_detection.py:3"}],[3084,{"filePath":"tests/unit/test_separator_detection.py","startLine":42,"endLine":54,"chunkText":"                path_lower = path.lower()\n                return 'roformer' in path_lower\n\n            is_roformer = mock_detect_roformer_from_path(yaml_path)\n            \n            assert is_roformer == expected_is_roformer, (\n                f\"Failed for {description}: path '{yaml_path}' should \"\n                f\"{'be' if expected_is_roformer else 'not be'} detected as Roformer\"\n            )\n\n        # Test routing logic based on detection\n        def mock_route_separator(yaml_path):\n            \"\"\"Mock routing logic that selects separator based on detection.\"\"\"","chunkId":"tests/unit/test_separator_detection.py:4"}],[3085,{"filePath":"tests/unit/test_separator_detection.py","startLine":52,"endLine":63,"chunkText":"        # Test routing logic based on detection\n        def mock_route_separator(yaml_path):\n            \"\"\"Mock routing logic that selects separator based on detection.\"\"\"\n            is_roformer = mock_detect_roformer_from_path(yaml_path)\n            \n            if is_roformer:\n                # In current routing, Roformer models are handled via MDXCSeparator path\n                return \"MDXCSeparator\"\n            elif 'mdx' in yaml_path.lower():\n                return \"MDXSeparator\"\n            elif 'vr' in yaml_path.lower():\n                return \"VRSeparator\"","chunkId":"tests/unit/test_separator_detection.py:5"}],[3086,{"filePath":"tests/unit/test_separator_detection.py","startLine":61,"endLine":77,"chunkText":"                return \"MDXSeparator\"\n            elif 'vr' in yaml_path.lower():\n                return \"VRSeparator\"\n            elif 'demucs' in yaml_path.lower():\n                return \"DemucsSeparator\"\n            else:\n                return \"DefaultSeparator\"\n\n        # Test routing for Roformer models\n        roformer_paths = [\n            \"model_bs_roformer_ep_317_sdr_12.yaml\",\n            \"mel_band_roformer_vocals.yaml\",\n            \"BS-Roformer-Viperx-1297.yaml\"\n        ]\n        \n        for path in roformer_paths:\n            separator_type = mock_route_separator(path)","chunkId":"tests/unit/test_separator_detection.py:6"}],[3087,{"filePath":"tests/unit/test_separator_detection.py","startLine":75,"endLine":89,"chunkText":"        \n        for path in roformer_paths:\n            separator_type = mock_route_separator(path)\n            assert separator_type == \"MDXCSeparator\", (\n                f\"Roformer model '{path}' should route to MDXCSeparator, got {separator_type}\"\n            )\n\n        # Test routing for non-Roformer models\n        non_roformer_cases = [\n            (\"model_mdx_extra_vocals.yaml\", \"MDXSeparator\"),\n            (\"vr_model_vocals.yaml\", \"VRSeparator\"),\n            (\"demucs_model.yaml\", \"DemucsSeparator\"),\n            (\"some_other_model.yaml\", \"DefaultSeparator\"),\n        ]\n        ","chunkId":"tests/unit/test_separator_detection.py:7"}],[3088,{"filePath":"tests/unit/test_separator_detection.py","startLine":87,"endLine":100,"chunkText":"            (\"some_other_model.yaml\", \"DefaultSeparator\"),\n        ]\n        \n        for path, expected_separator in non_roformer_cases:\n            separator_type = mock_route_separator(path)\n            assert separator_type == expected_separator, (\n                f\"Non-Roformer model '{path}' should route to {expected_separator}, got {separator_type}\"\n            )\n\n    def test_roformer_detection_case_insensitive(self):\n        \"\"\"Test that Roformer detection is case-insensitive.\"\"\"\n        case_variations = [\n            \"ROFORMER_MODEL.yaml\",\n            \"roformer_model.yaml\", ","chunkId":"tests/unit/test_separator_detection.py:8"}],[3089,{"filePath":"tests/unit/test_separator_detection.py","startLine":98,"endLine":114,"chunkText":"        case_variations = [\n            \"ROFORMER_MODEL.yaml\",\n            \"roformer_model.yaml\", \n            \"RoFormer_Model.yaml\",\n            \"BS_ROFORMER.yaml\",\n            \"bs_roformer.yaml\",\n            \"Bs_Roformer.yaml\",\n            \"MEL_BAND_ROFORMER.yaml\",\n            \"mel_band_roformer.yaml\",\n            \"Mel_Band_Roformer.yaml\"\n        ]\n\n        def mock_detect_roformer_case_insensitive(path):\n            \"\"\"Mock detection with case-insensitive matching.\"\"\"\n            return 'roformer' in path.lower()\n\n        for path in case_variations:","chunkId":"tests/unit/test_separator_detection.py:9"}],[3090,{"filePath":"tests/unit/test_separator_detection.py","startLine":112,"endLine":123,"chunkText":"            return 'roformer' in path.lower()\n\n        for path in case_variations:\n            is_roformer = mock_detect_roformer_case_insensitive(path)\n            assert is_roformer, f\"Case variation '{path}' should be detected as Roformer\"\n\n    def test_roformer_detection_with_full_paths(self):\n        \"\"\"Test Roformer detection works with full file paths.\"\"\"\n        full_path_cases = [\n            (\"/models/roformer/model_bs_roformer_ep_317.yaml\", True),\n            (\"/path/to/models/mel_band_roformer_vocals.yaml\", True),\n            (\"/home/user/models/mdx_extra_vocals.yaml\", False),","chunkId":"tests/unit/test_separator_detection.py:10"}],[3091,{"filePath":"tests/unit/test_separator_detection.py","startLine":121,"endLine":132,"chunkText":"            (\"/models/roformer/model_bs_roformer_ep_317.yaml\", True),\n            (\"/path/to/models/mel_band_roformer_vocals.yaml\", True),\n            (\"/home/user/models/mdx_extra_vocals.yaml\", False),\n            (\"C:\\\\Models\\\\BS-Roformer-Viperx-1297.yaml\", True),\n            (\"./local/models/vr_model_vocals.yaml\", False),\n            (\"../models/roformer_large_model.yaml\", True),\n        ]\n\n        def mock_detect_roformer_full_path(full_path):\n            \"\"\"Mock detection that works with full paths.\"\"\"\n            filename = os.path.basename(full_path)\n            return 'roformer' in filename.lower(","chunkId":"tests/unit/test_separator_detection.py:11"}],[3092,{"filePath":"tests/unit/test_separator_detection.py","startLine":131,"endLine":143,"chunkText":"            filename = os.path.basename(full_path)\n            return 'roformer' in filename.lower()\n\n        for full_path, expected_result in full_path_cases:\n            is_roformer = mock_detect_roformer_full_path(full_path)\n            assert is_roformer == expected_result, (\n                f\"Full path '{full_path}' detection failed: expected {expected_result}, got {is_roformer}\"\n            )\n\n    def test_roformer_detection_with_config_content(self):\n        \"\"\"Test Roformer detection based on YAML configuration content.\"\"\"\n        # Mock YAML configurations\n        roformer_configs = [","chunkId":"tests/unit/test_separator_detection.py:12"}],[3093,{"filePath":"tests/unit/test_separator_detection.py","startLine":141,"endLine":152,"chunkText":"        \"\"\"Test Roformer detection based on YAML configuration content.\"\"\"\n        # Mock YAML configurations\n        roformer_configs = [\n            {\"model_type\": \"bs_roformer\", \"architecture\": \"BSRoformer\"},\n            {\"model_type\": \"mel_band_roformer\", \"architecture\": \"MelBandRoformer\"},\n            {\"architecture\": \"roformer\", \"variant\": \"large\"},\n            {\"separator_class\": \"RoformerSeparator\", \"model\": \"bs_roformer\"},\n        ]\n\n        non_roformer_configs = [\n            {\"model_type\": \"mdx\", \"architecture\": \"MDX\"},\n            {\"model_type\": \"vr\", \"architecture\": \"VR\"},","chunkId":"tests/unit/test_separator_detection.py:13"}],[3094,{"filePath":"tests/unit/test_separator_detection.py","startLine":150,"endLine":163,"chunkText":"        non_roformer_configs = [\n            {\"model_type\": \"mdx\", \"architecture\": \"MDX\"},\n            {\"model_type\": \"vr\", \"architecture\": \"VR\"},\n            {\"architecture\": \"demucs\", \"variant\": \"v4\"},\n            {\"separator_class\": \"MDXSeparator\", \"model\": \"mdx_extra\"},\n        ]\n\n        def mock_detect_roformer_from_config(config):\n            \"\"\"Mock detection based on configuration content.\"\"\"\n            config_str = str(config).lower()\n            return 'roformer' in config_str\n\n        # Test Roformer configs\n        for config in roformer_configs:","chunkId":"tests/unit/test_separator_detection.py:14"}],[3095,{"filePath":"tests/unit/test_separator_detection.py","startLine":161,"endLine":173,"chunkText":"\n        # Test Roformer configs\n        for config in roformer_configs:\n            is_roformer = mock_detect_roformer_from_config(config)\n            assert is_roformer, f\"Roformer config should be detected: {config}\"\n\n        # Test non-Roformer configs  \n        for config in non_roformer_configs:\n            is_roformer = mock_detect_roformer_from_config(config)\n            assert not is_roformer, f\"Non-Roformer config should not be detected: {config}\"\n\n    def test_roformer_routing_integration(self):\n        \"\"\"Test integration between detection and routing.\"\"\"","chunkId":"tests/unit/test_separator_detection.py:15"}],[3096,{"filePath":"tests/unit/test_separator_detection.py","startLine":171,"endLine":182,"chunkText":"\n    def test_roformer_routing_integration(self):\n        \"\"\"Test integration between detection and routing.\"\"\"\n        with patch('audio_separator.separator.separator.Separator') as mock_separator_class:\n            mock_instance = Mock()\n            mock_separator_class.return_value = mock_instance\n            \n            # Mock the routing logic\n            def mock_load_model_with_routing(model_path):\n                \"\"\"Mock model loading that routes based on detection.\"\"\"\n                filename = os.path.basename(model_path)\n                is_roformer = 'roformer' in filename.lower()","chunkId":"tests/unit/test_separator_detection.py:16"}],[3097,{"filePath":"tests/unit/test_separator_detection.py","startLine":180,"endLine":192,"chunkText":"                \"\"\"Mock model loading that routes based on detection.\"\"\"\n                filename = os.path.basename(model_path)\n                is_roformer = 'roformer' in filename.lower()\n                \n                if is_roformer:\n                    # Should use Roformer-specific loading\n                    return {\n                        'separator_type': 'RoformerSeparator',\n                        'is_roformer': True,\n                        'routing_path': 'roformer_path'\n                    }\n                else:\n                    # Should use default loading","chunkId":"tests/unit/test_separator_detection.py:17"}],[3098,{"filePath":"tests/unit/test_separator_detection.py","startLine":190,"endLine":202,"chunkText":"                    }\n                else:\n                    # Should use default loading\n                    return {\n                        'separator_type': 'DefaultSeparator', \n                        'is_roformer': False,\n                        'routing_path': 'default_path'\n                    }\n\n            # Test Roformer model routing\n            roformer_result = mock_load_model_with_routing(\"model_bs_roformer_ep_317.ckpt\")\n            assert roformer_result['is_roformer'] is True\n            assert roformer_result['separator_type'] == 'RoformerSeparator'","chunkId":"tests/unit/test_separator_detection.py:18"}],[3099,{"filePath":"tests/unit/test_separator_detection.py","startLine":200,"endLine":208,"chunkText":"            roformer_result = mock_load_model_with_routing(\"model_bs_roformer_ep_317.ckpt\")\n            assert roformer_result['is_roformer'] is True\n            assert roformer_result['separator_type'] == 'RoformerSeparator'\n            assert roformer_result['routing_path'] == 'roformer_path'\n\n            # Test non-Roformer model routing\n            mdx_result = mock_load_model_with_routing(\"model_mdx_extra_vocals.ckpt\")\n            assert mdx_result['is_roformer'] is False\n            assert mdx_result['separator_type'] == 'DefaultSeparator'","chunkId":"tests/unit/test_separator_detection.py:19"}],[3100,{"filePath":"tests/unit/test_separator_detection.py","startLine":208,"endLine":219,"chunkText":"            assert mdx_result['separator_type'] == 'DefaultSeparator'\n            assert mdx_result['routing_path'] == 'default_path'\n\n    def test_roformer_detection_edge_cases(self):\n        \"\"\"Test edge cases in Roformer detection.\"\"\"\n        edge_cases = [\n            # (path, expected_result, description)\n            (\"\", False, \"Empty string\"),\n            (\"model.yaml\", False, \"No roformer in name\"),\n            (\"roformer\", True, \"Just 'roformer'\"),\n            (\"notroformer.yaml\", True, \"Contains 'roformer' as substring\"),\n            (\"roformer.txt\", True, \"Different extension\"),","chunkId":"tests/unit/test_separator_detection.py:20"}],[3101,{"filePath":"tests/unit/test_separator_detection.py","startLine":217,"endLine":228,"chunkText":"            (\"roformer\", True, \"Just 'roformer'\"),\n            (\"notroformer.yaml\", True, \"Contains 'roformer' as substring\"),\n            (\"roformer.txt\", True, \"Different extension\"),\n            (\"ROFORMER.YAML\", True, \"All uppercase\"),\n            (\"r0f0rmer.yaml\", False, \"Similar but not exact\"),\n            (\"roformermodel.ckpt\", True, \"No separator between roformer and model\"),\n            (\"model_roformer_v2.pth\", True, \"Roformer in middle of filename\"),\n        ]\n\n        def mock_detect_roformer_edge_cases(path):\n            \"\"\"Mock detection handling edge cases.\"\"\"\n            if not path:","chunkId":"tests/unit/test_separator_detection.py:21"}],[3102,{"filePath":"tests/unit/test_separator_detection.py","startLine":226,"endLine":238,"chunkText":"        def mock_detect_roformer_edge_cases(path):\n            \"\"\"Mock detection handling edge cases.\"\"\"\n            if not path:\n                return False\n            return 'roformer' in path.lower()\n\n        for path, expected_result, description in edge_cases:\n            is_roformer = mock_detect_roformer_edge_cases(path)\n            assert is_roformer == expected_result, (\n                f\"Edge case failed - {description}: path '{path}' should \"\n                f\"{'be' if expected_result else 'not be'} detected as Roformer\"\n            )\n","chunkId":"tests/unit/test_separator_detection.py:22"}],[3103,{"filePath":"tests/unit/test_separator_detection.py","startLine":236,"endLine":246,"chunkText":"                f\"{'be' if expected_result else 'not be'} detected as Roformer\"\n            )\n\n    def test_roformer_detection_with_model_extensions(self):\n        \"\"\"Test Roformer detection works with various model file extensions.\"\"\"\n        model_extensions = ['.ckpt', '.pth', '.pt', '.onnx', '.yaml', '.yml']\n        base_names = ['bs_roformer_model', 'mel_band_roformer', 'roformer_large']\n\n        def mock_detect_roformer_with_extension(path):\n            \"\"\"Mock detection that ignores file extension.\"\"\"\n            filename = os.path.basename(path)","chunkId":"tests/unit/test_separator_detection.py:23"}],[3104,{"filePath":"tests/unit/test_separator_detection.py","startLine":244,"endLine":254,"chunkText":"        def mock_detect_roformer_with_extension(path):\n            \"\"\"Mock detection that ignores file extension.\"\"\"\n            filename = os.path.basename(path)\n            name_without_ext = os.path.splitext(filename)[0]\n            return 'roformer' in name_without_ext.lower()\n\n        for base_name in base_names:\n            for ext in model_extensions:\n                full_filename = f\"{base_name}{ext}\"\n                is_roformer = mock_detect_roformer_with_extension(full_filename)\n                assert is_roformer, (","chunkId":"tests/unit/test_separator_detection.py:24"}],[3105,{"filePath":"tests/unit/test_separator_detection.py","startLine":252,"endLine":262,"chunkText":"                full_filename = f\"{base_name}{ext}\"\n                is_roformer = mock_detect_roformer_with_extension(full_filename)\n                assert is_roformer, (\n                    f\"Roformer model '{full_filename}' should be detected regardless of extension\"\n                )\n\n        # Test non-Roformer models with same extensions\n        non_roformer_bases = ['mdx_model', 'vr_vocals', 'demucs_v4']\n        for base_name in non_roformer_bases:\n            for ext in model_extensions:\n                full_filename = f\"{base_name}{ext}\"","chunkId":"tests/unit/test_separator_detection.py:25"}],[3106,{"filePath":"tests/unit/test_separator_detection.py","startLine":260,"endLine":267,"chunkText":"        for base_name in non_roformer_bases:\n            for ext in model_extensions:\n                full_filename = f\"{base_name}{ext}\"\n                is_roformer = mock_detect_roformer_with_extension(full_filename)\n                assert not is_roformer, (\n                    f\"Non-Roformer model '{full_filename}' should not be detected\"\n                )\n","chunkId":"tests/unit/test_separator_detection.py:26"}],[3107,{"filePath":"tests/unit/test_stft.py","startLine":0,"endLine":11,"chunkText":"import unittest\nimport numpy as np\nimport torch\nfrom unittest.mock import Mock\nfrom audio_separator.separator.uvr_lib_v5.stft import STFT\n\n# Short-Time Fourier Transform (STFT) Process Overview:\n#\n# STFT transforms a time-domain signal into a frequency-domain representation.\n#   This transformation is achieved by dividing the signal into short frames (or segments) and applying the Fourier Transform to each frame.\n#\n# n_fft: The number of points used in the Fourier Transform, which determines the resolution of the frequency domain representation.","chunkId":"tests/unit/test_stft.py:0"}],[3108,{"filePath":"tests/unit/test_stft.py","startLine":9,"endLine":16,"chunkText":"#   This transformation is achieved by dividing the signal into short frames (or segments) and applying the Fourier Transform to each frame.\n#\n# n_fft: The number of points used in the Fourier Transform, which determines the resolution of the frequency domain representation.\n#   Essentially, it dictates how many frequency bins we get in our STFT.\n#\n# hop_length: The number of samples by which we shift each frame of the signal.\n#   It affects the overlap between consecutive frames. If the hop_length is less than n_fft, we get overlapping frames.\n#","chunkId":"tests/unit/test_stft.py:1"}],[3109,{"filePath":"tests/unit/test_stft.py","startLine":14,"endLine":26,"chunkText":"# hop_length: The number of samples by which we shift each frame of the signal.\n#   It affects the overlap between consecutive frames. If the hop_length is less than n_fft, we get overlapping frames.\n#\n# Windowing: Each frame of the signal is multiplied by a window function (e.g. Hann window) before applying the Fourier Transform.\n#   This is done to minimize discontinuities at the borders of each frame.\n\n\nclass TestSTFT(unittest.TestCase):\n    def setUp(self):\n        self.n_fft = 2048\n        self.hop_length = 512\n        self.dim_f = 1025\n        self.device = torch.device(\"cpu\")","chunkId":"tests/unit/test_stft.py:2"}],[3110,{"filePath":"tests/unit/test_stft.py","startLine":24,"endLine":38,"chunkText":"        self.hop_length = 512\n        self.dim_f = 1025\n        self.device = torch.device(\"cpu\")\n        self.stft = STFT(logger=Mock(), n_fft=self.n_fft, hop_length=self.hop_length, dim_f=self.dim_f, device=self.device)\n\n    def create_mock_tensor(self, shape, device=None):\n        tensor = torch.rand(shape)\n        if device:\n            tensor = tensor.to(device)\n        return tensor\n\n    def test_stft_initialization(self):\n        self.assertEqual(self.stft.n_fft, self.n_fft)\n        self.assertEqual(self.stft.hop_length, self.hop_length)\n        self.assertEqual(self.stft.dim_f, self.dim_f)","chunkId":"tests/unit/test_stft.py:3"}],[3111,{"filePath":"tests/unit/test_stft.py","startLine":36,"endLine":51,"chunkText":"        self.assertEqual(self.stft.n_fft, self.n_fft)\n        self.assertEqual(self.stft.hop_length, self.hop_length)\n        self.assertEqual(self.stft.dim_f, self.dim_f)\n        self.assertEqual(self.stft.device.type, \"cpu\")\n        self.assertIsInstance(self.stft.hann_window, torch.Tensor)\n\n    def test_stft_call(self):\n        input_tensor = self.create_mock_tensor((1, 16000))\n\n        # Apply STFT\n        stft_result = self.stft(input_tensor)\n\n        # Test conditions\n        self.assertIsNotNone(stft_result)\n        self.assertIsInstance(stft_result, torch.Tensor)\n","chunkId":"tests/unit/test_stft.py:4"}],[3112,{"filePath":"tests/unit/test_stft.py","startLine":49,"endLine":57,"chunkText":"        self.assertIsNotNone(stft_result)\n        self.assertIsInstance(stft_result, torch.Tensor)\n\n        # Calculate the expected shape based on input parameters:\n\n        # Frequency Dimension (dim_f): This corresponds to the number of frequency bins in the STFT output.\n        #   In the case of a real-valued input signal (like audio), the Fourier Transform produces a symmetric output.\n        #   Hence, for an n_fft of 2048, we would typically get 2049 frequency bins (from 0 Hz to the Nyquist frequency).\n        #   However, we often don't need the full symmetric spectrum.","chunkId":"tests/unit/test_stft.py:5"}],[3113,{"filePath":"tests/unit/test_stft.py","startLine":55,"endLine":60,"chunkText":"        #   In the case of a real-valued input signal (like audio), the Fourier Transform produces a symmetric output.\n        #   Hence, for an n_fft of 2048, we would typically get 2049 frequency bins (from 0 Hz to the Nyquist frequency).\n        #   However, we often don't need the full symmetric spectrum.\n        #   So, dim_f is used to specify how many frequency bins we are interested in.\n        #   In this test, it's set to 1025, which is about half of n_fft + 1 (as the Fourier Transform of a real-valued signal is symmetric).\n","chunkId":"tests/unit/test_stft.py:6"}],[3114,{"filePath":"tests/unit/test_stft.py","startLine":58,"endLine":63,"chunkText":"        #   So, dim_f is used to specify how many frequency bins we are interested in.\n        #   In this test, it's set to 1025, which is about half of n_fft + 1 (as the Fourier Transform of a real-valued signal is symmetric).\n\n        # Time Dimension: This corresponds to how many frames (or segments) the input signal has been divided into.\n        #   It depends on the length of the input signal and the hop_length.\n        #   The formula for calculating the number of frames is derived from how we stride the window across the signal:","chunkId":"tests/unit/test_stft.py:7"}],[3115,{"filePath":"tests/unit/test_stft.py","startLine":61,"endLine":64,"chunkText":"        # Time Dimension: This corresponds to how many frames (or segments) the input signal has been divided into.\n        #   It depends on the length of the input signal and the hop_length.\n        #   The formula for calculating the number of frames is derived from how we stride the window across the signal:\n        #     Length of Input Signal: Let's denote it as L. In this test, the input tensor has a shape of [1, 16000], so L is 16000 (ignoring the batch dimension for simplicity).","chunkId":"tests/unit/test_stft.py:8"}],[3116,{"filePath":"tests/unit/test_stft.py","startLine":62,"endLine":65,"chunkText":"        #   It depends on the length of the input signal and the hop_length.\n        #   The formula for calculating the number of frames is derived from how we stride the window across the signal:\n        #     Length of Input Signal: Let's denote it as L. In this test, the input tensor has a shape of [1, 16000], so L is 16000 (ignoring the batch dimension for simplicity).\n        #     Number of Frames: The number of frames depends on how we stride the window across the signal. For each frame, we move the window by hop_length samples.","chunkId":"tests/unit/test_stft.py:9"}],[3117,{"filePath":"tests/unit/test_stft.py","startLine":63,"endLine":66,"chunkText":"        #   The formula for calculating the number of frames is derived from how we stride the window across the signal:\n        #     Length of Input Signal: Let's denote it as L. In this test, the input tensor has a shape of [1, 16000], so L is 16000 (ignoring the batch dimension for simplicity).\n        #     Number of Frames: The number of frames depends on how we stride the window across the signal. For each frame, we move the window by hop_length samples.\n        #     Therefore, the number of frames N_frames can be roughly estimated by dividing the length of the signal by the hop_length.","chunkId":"tests/unit/test_stft.py:10"}],[3118,{"filePath":"tests/unit/test_stft.py","startLine":64,"endLine":66,"chunkText":"        #     Length of Input Signal: Let's denote it as L. In this test, the input tensor has a shape of [1, 16000], so L is 16000 (ignoring the batch dimension for simplicity).\n        #     Number of Frames: The number of frames depends on how we stride the window across the signal. For each frame, we move the window by hop_length samples.\n        #     Therefore, the number of frames N_frames can be roughly estimated by dividing the length of the signal by the hop_length.","chunkId":"tests/unit/test_stft.py:11"}],[3119,{"filePath":"tests/unit/test_stft.py","startLine":65,"endLine":69,"chunkText":"        #     Number of Frames: The number of frames depends on how we stride the window across the signal. For each frame, we move the window by hop_length samples.\n        #     Therefore, the number of frames N_frames can be roughly estimated by dividing the length of the signal by the hop_length.\n        #     However, since the window overlaps the signal, we add an extra frame to account for the last segment of the signal. This gives us N_frames = (L // hop_length) + 1.\n\n        # Putting It All Together","chunkId":"tests/unit/test_stft.py:12"}],[3120,{"filePath":"tests/unit/test_stft.py","startLine":67,"endLine":77,"chunkText":"        #     However, since the window overlaps the signal, we add an extra frame to account for the last segment of the signal. This gives us N_frames = (L // hop_length) + 1.\n\n        # Putting It All Together\n        #   expected_shape thus becomes (dim_f, N_frames), which is (1025, (16000 // 512) + 1) in this test case.\n\n        expected_shape = (self.dim_f, (input_tensor.shape[1] // self.hop_length) + 1)\n\n        self.assertEqual(stft_result.shape[-2:], expected_shape)\n\n    def test_calculate_inverse_dimensions(self):\n        # Create a sample input tensor","chunkId":"tests/unit/test_stft.py:13"}],[3121,{"filePath":"tests/unit/test_stft.py","startLine":75,"endLine":88,"chunkText":"\n    def test_calculate_inverse_dimensions(self):\n        # Create a sample input tensor\n        sample_input = torch.randn(1, 2, 500, 32)  # Batch, Channel, Frequency, Time dimensions\n        batch_dims, channel_dim, freq_dim, time_dim, num_freq_bins = self.stft.calculate_inverse_dimensions(sample_input)\n\n        # Expected values\n        expected_num_freq_bins = self.n_fft // 2 + 1\n\n        # Assertions\n        self.assertEqual(batch_dims, sample_input.shape[:-3])\n        self.assertEqual(channel_dim, 2)\n        self.assertEqual(freq_dim, 500)\n        self.assertEqual(time_dim, 32)","chunkId":"tests/unit/test_stft.py:14"}],[3122,{"filePath":"tests/unit/test_stft.py","startLine":86,"endLine":96,"chunkText":"        self.assertEqual(channel_dim, 2)\n        self.assertEqual(freq_dim, 500)\n        self.assertEqual(time_dim, 32)\n        self.assertEqual(num_freq_bins, expected_num_freq_bins)\n\n    def test_pad_frequency_dimension(self):\n        # Create a sample input tensor\n        sample_input = torch.randn(1, 2, 500, 32)  # Batch, Channel, Frequency, Time dimensions\n        batch_dims, channel_dim, freq_dim, time_dim, num_freq_bins = self.stft.calculate_inverse_dimensions(sample_input)\n\n        # Apply padding","chunkId":"tests/unit/test_stft.py:15"}],[3123,{"filePath":"tests/unit/test_stft.py","startLine":94,"endLine":106,"chunkText":"        batch_dims, channel_dim, freq_dim, time_dim, num_freq_bins = self.stft.calculate_inverse_dimensions(sample_input)\n\n        # Apply padding\n        padded_output = self.stft.pad_frequency_dimension(sample_input, batch_dims, channel_dim, freq_dim, time_dim, num_freq_bins)\n\n        # Expected frequency dimension after padding\n        expected_freq_dim = num_freq_bins\n\n        # Assertions\n        self.assertEqual(padded_output.shape[-2], expected_freq_dim)\n\n    def test_prepare_for_istft(self):\n        # Create a sample input tensor","chunkId":"tests/unit/test_stft.py:16"}],[3124,{"filePath":"tests/unit/test_stft.py","startLine":104,"endLine":113,"chunkText":"\n    def test_prepare_for_istft(self):\n        # Create a sample input tensor\n        sample_input = torch.randn(1, 2, 500, 32)  # Batch, Channel, Frequency, Time dimensions\n        batch_dims, channel_dim, freq_dim, time_dim, num_freq_bins = self.stft.calculate_inverse_dimensions(sample_input)\n        padded_output = self.stft.pad_frequency_dimension(sample_input, batch_dims, channel_dim, freq_dim, time_dim, num_freq_bins)\n\n        # Apply prepare_for_istft\n        complex_tensor = self.stft.prepare_for_istft(padded_output, batch_dims, channel_dim, num_freq_bins, time_dim)\n","chunkId":"tests/unit/test_stft.py:17"}],[3125,{"filePath":"tests/unit/test_stft.py","startLine":111,"endLine":123,"chunkText":"        # Apply prepare_for_istft\n        complex_tensor = self.stft.prepare_for_istft(padded_output, batch_dims, channel_dim, num_freq_bins, time_dim)\n\n        # Calculate the expected flattened batch size (flattening batch and channel dimensions)\n        expected_flattened_batch_size = batch_dims[0] * (channel_dim // 2)\n\n        # Expected shape of the complex tensor\n        expected_shape = (expected_flattened_batch_size, num_freq_bins, time_dim)\n\n        # Assertions\n        self.assertEqual(complex_tensor.shape, expected_shape)\n\n    def test_inverse_stft(self):","chunkId":"tests/unit/test_stft.py:18"}],[3126,{"filePath":"tests/unit/test_stft.py","startLine":121,"endLine":135,"chunkText":"        self.assertEqual(complex_tensor.shape, expected_shape)\n\n    def test_inverse_stft(self):\n        # Create a mock tensor with the correct input shape\n        input_tensor = torch.rand(1, 2, 1025, 32)  # shape matching output of STFT\n\n        # Apply inverse STFT\n        output_tensor = self.stft.inverse(input_tensor)\n\n        # Check if the output tensor is on the CPU\n        self.assertEqual(output_tensor.device.type, \"cpu\")\n\n        # Expected output shape: (Batch size, Channel dimension, Time dimension)\n        expected_shape = (1, 2, 7936)  # Calculated based on STFT parameters\n","chunkId":"tests/unit/test_stft.py:19"}],[3127,{"filePath":"tests/unit/test_stft.py","startLine":133,"endLine":144,"chunkText":"        # Expected output shape: (Batch size, Channel dimension, Time dimension)\n        expected_shape = (1, 2, 7936)  # Calculated based on STFT parameters\n\n        # Check if the output tensor has the expected shape\n        self.assertEqual(output_tensor.shape, expected_shape)\n\n    @unittest.skipIf(not torch.backends.mps.is_available(), \"MPS not available\")\n    def test_stft_with_mps_device(self):\n        mps_device = torch.device(\"mps\")\n        self.stft.device = mps_device\n        input_tensor = self.create_mock_tensor((1, 16000), device=mps_device)\n        stft_result = self.stft(input_tensor)","chunkId":"tests/unit/test_stft.py:20"}],[3128,{"filePath":"tests/unit/test_stft.py","startLine":142,"endLine":153,"chunkText":"        self.stft.device = mps_device\n        input_tensor = self.create_mock_tensor((1, 16000), device=mps_device)\n        stft_result = self.stft(input_tensor)\n        self.assertIsNotNone(stft_result)\n        self.assertIsInstance(stft_result, torch.Tensor)\n\n    @unittest.skipIf(not torch.backends.mps.is_available(), \"MPS not available\")\n    def test_inverse_with_mps_device(self):\n        mps_device = torch.device(\"mps\")\n        self.stft.device = mps_device\n        input_tensor = self.create_mock_tensor((1, 2, 1025, 32), device=mps_device)\n        istft_result = self.stft.inverse(input_tensor)","chunkId":"tests/unit/test_stft.py:21"}],[3129,{"filePath":"tests/unit/test_stft.py","startLine":151,"endLine":158,"chunkText":"        self.stft.device = mps_device\n        input_tensor = self.create_mock_tensor((1, 2, 1025, 32), device=mps_device)\n        istft_result = self.stft.inverse(input_tensor)\n        self.assertIsNotNone(istft_result)\n        self.assertIsInstance(istft_result, torch.Tensor)\n\n\n# Mock logger to use in tests","chunkId":"tests/unit/test_stft.py:22"}],[3130,{"filePath":"tests/unit/test_stft.py","startLine":159,"endLine":166,"chunkText":"class MockLogger:\n    def debug(self, message):\n        pass\n\n\nif __name__ == \"__main__\":\n    unittest.main()\n","chunkId":"tests/unit/test_stft.py:23"}],[3131,{"filePath":"tests/utils.py","startLine":0,"endLine":20,"chunkText":"import os\nimport numpy as np\nimport librosa\nimport librosa.display\nimport matplotlib.pyplot as plt\nfrom PIL import Image\nfrom io import BytesIO\nimport soundfile as sf\nfrom pathlib import Path\nfrom skimage.metrics import structural_similarity as ssim\n\n\ndef generate_waveform_image(audio_path, output_path=None, fig_size=(10, 4)):\n    \"\"\"Generate a waveform image from an audio file.\n    \n    Args:\n        audio_path: Path to the audio file\n        output_path: Path to save the generated image (optional)\n        fig_size: Size of the figure (width, height)\n        \n    Returns:","chunkId":"tests/utils.py:0"}],[3132,{"filePath":"tests/utils.py","startLine":18,"endLine":35,"chunkText":"        fig_size: Size of the figure (width, height)\n        \n    Returns:\n        BytesIO object containing the image if output_path is None, otherwise saves to output_path\n    \"\"\"\n    # Load audio file\n    y, sr = librosa.load(audio_path, sr=None, mono=False)\n    \n    # If mono, convert to stereo-like format for consistent plotting\n    if y.ndim == 1:\n        y = np.array([y, y])\n    \n    plt.figure(figsize=fig_size)\n    \n    # Plot waveform for each channel with fixed Y-axis scale\n    plt.subplot(2, 1, 1)\n    plt.plot(y[0])\n    plt.title('Channel 1')","chunkId":"tests/utils.py:1"}],[3133,{"filePath":"tests/utils.py","startLine":33,"endLine":56,"chunkText":"    plt.subplot(2, 1, 1)\n    plt.plot(y[0])\n    plt.title('Channel 1')\n    plt.ylim([-1.0, 1.0])  # Fixed Y-axis scale for all waveforms\n    \n    plt.subplot(2, 1, 2)\n    plt.plot(y[1])\n    plt.title('Channel 2')\n    plt.ylim([-1.0, 1.0])  # Fixed Y-axis scale for all waveforms\n    \n    plt.tight_layout()\n    \n    if output_path:\n        plt.savefig(output_path)\n        plt.close()\n        return output_path\n    else:\n        buf = BytesIO()\n        plt.savefig(buf, format='png')\n        plt.close()\n        buf.seek(0)\n        return buf\n\n","chunkId":"tests/utils.py:2"}],[3134,{"filePath":"tests/utils.py","startLine":54,"endLine":70,"chunkText":"        return buf\n\n\ndef generate_spectrogram_image(audio_path, output_path=None, fig_size=(10, 8)):\n    \"\"\"Generate a spectrogram image from an audio file.\n    \n    Args:\n        audio_path: Path to the audio file\n        output_path: Path to save the generated image (optional)\n        fig_size: Size of the figure (width, height)\n        \n    Returns:\n        BytesIO object containing the image if output_path is None, otherwise saves to output_path\n    \"\"\"\n    # Load audio file\n    y, sr = librosa.load(audio_path, sr=None, mono=False)\n    ","chunkId":"tests/utils.py:3"}],[3135,{"filePath":"tests/utils.py","startLine":68,"endLine":86,"chunkText":"    # Load audio file\n    y, sr = librosa.load(audio_path, sr=None, mono=False)\n    \n    # If mono, convert to stereo-like format for consistent plotting\n    if y.ndim == 1:\n        y = np.array([y, y])\n    \n    plt.figure(figsize=fig_size)\n    \n    # Set fixed min and max values for spectrogram color scale\n    vmin = -80  # dB\n    vmax = 0    # dB\n    \n    # Generate spectrograms for each channel\n    for i in range(2):\n        # Compute spectrogram\n        S = librosa.amplitude_to_db(np.abs(librosa.stft(y[i])), ref=np.max)\n        \n        plt.subplot(2, 1, i+1)","chunkId":"tests/utils.py:4"}],[3136,{"filePath":"tests/utils.py","startLine":84,"endLine":101,"chunkText":"        S = librosa.amplitude_to_db(np.abs(librosa.stft(y[i])), ref=np.max)\n        \n        plt.subplot(2, 1, i+1)\n        # Use fixed frequency range and consistent color scaling\n        librosa.display.specshow(\n            S, \n            sr=sr, \n            x_axis='time', \n            y_axis='log',\n            vmin=vmin,\n            vmax=vmax\n        )\n        plt.colorbar(format='%+2.0f dB')\n        plt.title(f'Channel {i+1} Spectrogram')\n        \n        # Set frequency range (y-axis) - typically up to Nyquist frequency (sr/2)\n        plt.ylim([20, sr/2])  # From 20Hz to Nyquist frequency\n    ","chunkId":"tests/utils.py:5"}],[3137,{"filePath":"tests/utils.py","startLine":99,"endLine":119,"chunkText":"        # Set frequency range (y-axis) - typically up to Nyquist frequency (sr/2)\n        plt.ylim([20, sr/2])  # From 20Hz to Nyquist frequency\n    \n    plt.tight_layout()\n    \n    if output_path:\n        plt.savefig(output_path)\n        plt.close()\n        return output_path\n    else:\n        buf = BytesIO()\n        plt.savefig(buf, format='png')\n        plt.close()\n        buf.seek(0)\n        return buf\n\n\ndef compare_images(image1_path, image2_path, min_similarity_threshold=0.999):\n    \"\"\"Compare two images using Structural Similarity Index (SSIM) which is robust to small shifts.\n    \n    Args:","chunkId":"tests/utils.py:6"}],[3138,{"filePath":"tests/utils.py","startLine":117,"endLine":128,"chunkText":"    \"\"\"Compare two images using Structural Similarity Index (SSIM) which is robust to small shifts.\n    \n    Args:\n        image1_path: Path to the first image\n        image2_path: Path to the second image\n        min_similarity_threshold: Minimum similarity required for images to be considered matching (0.0-1.0)\n            - Higher values (closer to 1.0) require images to be more similar\n            - Lower values (closer to 0.0) are more permissive\n            - A value of 0.99 requires 99% similarity between images\n            - A value of 0.0 would consider any images to match\n        \n    Returns:","chunkId":"tests/utils.py:7"}],[3139,{"filePath":"tests/utils.py","startLine":126,"endLine":142,"chunkText":"            - A value of 0.0 would consider any images to match\n        \n    Returns:\n        Tuple of (similarity_score, is_match)\n        - similarity_score: Value between 0.0 and 1.0, where 1.0 means identical images\n        - is_match: Boolean indicating if similarity_score >= min_similarity_threshold\n    \"\"\"\n    # Open images\n    img1 = Image.open(image1_path).convert('RGB')\n    img2 = Image.open(image2_path).convert('RGB')\n    \n    # Ensure same size for comparison\n    if img1.size != img2.size:\n        img2 = img2.resize(img1.size)\n    \n    # Convert to numpy arrays\n    arr1 = np.array(img1)","chunkId":"tests/utils.py:8"}],[3140,{"filePath":"tests/utils.py","startLine":140,"endLine":159,"chunkText":"    \n    # Convert to numpy arrays\n    arr1 = np.array(img1)\n    arr2 = np.array(img2)\n    \n    # Calculate SSIM for each color channel\n    similarity_scores = []\n    for channel in range(3):  # RGB channels\n        score = ssim(arr1[:,:,channel], arr2[:,:,channel], data_range=255)\n        similarity_scores.append(score)\n    \n    # Calculate average SSIM across channels\n    similarity_score = np.mean(similarity_scores)\n    \n    # Determine if images match by comparing similarity to threshold\n    is_match = similarity_score >= min_similarity_threshold\n    \n    return (similarity_score, is_match)\n\n","chunkId":"tests/utils.py:9"}],[3141,{"filePath":"tests/utils.py","startLine":157,"endLine":174,"chunkText":"    return (similarity_score, is_match)\n\n\ndef generate_reference_images(input_path, output_dir=None, prefix=\"\"):\n    \"\"\"Generate reference waveform and spectrogram images for an audio file.\n    \n    Args:\n        input_path: Path to the audio file\n        output_dir: Directory to save the generated images (optional)\n        prefix: Prefix to add to the output image filenames\n        \n    Returns:\n        Tuple of (waveform_path, spectrogram_path)\n    \"\"\"\n    if output_dir is None:\n        output_dir = os.path.dirname(input_path)\n    \n    # Create output directory if it doesn't exist","chunkId":"tests/utils.py:10"}],[3142,{"filePath":"tests/utils.py","startLine":172,"endLine":185,"chunkText":"        output_dir = os.path.dirname(input_path)\n    \n    # Create output directory if it doesn't exist\n    os.makedirs(output_dir, exist_ok=True)\n    \n    input_filename = os.path.basename(input_path)\n    name_without_ext = os.path.splitext(input_filename)[0]\n    \n    # Generate waveform image\n    waveform_path = os.path.join(output_dir, f\"{prefix}{name_without_ext}_waveform.png\")\n    generate_waveform_image(input_path, waveform_path)\n    \n    # Generate spectrogram image\n    spectrogram_path = os.path.join(output_dir, f\"{prefix}{name_without_ext}_spectrogram.png\")","chunkId":"tests/utils.py:11"}],[3143,{"filePath":"tests/utils.py","startLine":183,"endLine":188,"chunkText":"    \n    # Generate spectrogram image\n    spectrogram_path = os.path.join(output_dir, f\"{prefix}{name_without_ext}_spectrogram.png\")\n    generate_spectrogram_image(input_path, spectrogram_path)\n    \n    return (waveform_path, spectrogram_path) ","chunkId":"tests/utils.py:12"}],[3144,{"filePath":"tools/calculate-model-hashes.py","startLine":0,"endLine":18,"chunkText":"#!/usr/bin/env python3\n\nimport os\nimport sys\nimport json\nimport hashlib\nimport requests\n\nMODEL_CACHE_PATH = \"/tmp/audio-separator-models\"\nVR_MODEL_DATA_LOCAL_PATH = f\"{MODEL_CACHE_PATH}/vr_model_data.json\"\nMDX_MODEL_DATA_LOCAL_PATH = f\"{MODEL_CACHE_PATH}/mdx_model_data.json\"\n\nMODEL_DATA_URL_PREFIX = \"https://raw.githubusercontent.com/TRvlvr/application_data/main\"\nVR_MODEL_DATA_URL = f\"{MODEL_DATA_URL_PREFIX}/vr_model_data/model_data_new.json\"\nMDX_MODEL_DATA_URL = f\"{MODEL_DATA_URL_PREFIX}/mdx_model_data/model_data_new.json\"\n\nOUTPUT_PATH = f\"{MODEL_CACHE_PATH}/model_hashes.json\"\n\n","chunkId":"tools/calculate-model-hashes.py:0"}],[3145,{"filePath":"tools/calculate-model-hashes.py","startLine":16,"endLine":32,"chunkText":"OUTPUT_PATH = f\"{MODEL_CACHE_PATH}/model_hashes.json\"\n\n\ndef get_model_hash(model_path):\n    \"\"\"\n    Get the hash of a model file\n    \"\"\"\n    # print(f\"Getting hash for model at {model_path}\")\n    try:\n        with open(model_path, \"rb\") as f:\n            f.seek(-10000 * 1024, 2)  # Move the file pointer 10MB before the end of the file\n            hash_result = hashlib.md5(f.read()).hexdigest()\n            # print(f\"Hash for {model_path}: {hash_result}\")\n            return hash_result\n    except IOError:\n        with open(model_path, \"rb\") as f:\n            hash_result = hashlib.md5(f.read()).hexdigest()","chunkId":"tools/calculate-model-hashes.py:1"}],[3146,{"filePath":"tools/calculate-model-hashes.py","startLine":30,"endLine":44,"chunkText":"    except IOError:\n        with open(model_path, \"rb\") as f:\n            hash_result = hashlib.md5(f.read()).hexdigest()\n            # print(f\"IOError encountered, hash for {model_path}: {hash_result}\")\n            return hash_result\n\n\ndef download_file_if_missing(url, local_path):\n    \"\"\"\n    Download a file from a URL if it doesn't exist locally\n    \"\"\"\n    print(f\"Checking if {local_path} needs to be downloaded from {url}\")\n    if not os.path.exists(local_path):\n        print(f\"Downloading {url} to {local_path}\")\n        with requests.get(url, stream=True, timeout=10) as r:","chunkId":"tools/calculate-model-hashes.py:2"}],[3147,{"filePath":"tools/calculate-model-hashes.py","startLine":42,"endLine":59,"chunkText":"    if not os.path.exists(local_path):\n        print(f\"Downloading {url} to {local_path}\")\n        with requests.get(url, stream=True, timeout=10) as r:\n            r.raise_for_status()\n            with open(local_path, \"wb\") as f:\n                for chunk in r.iter_content(chunk_size=8192):\n                    f.write(chunk)\n        print(f\"Downloaded {url} to {local_path}\")\n    else:\n        print(f\"{local_path} already exists. Skipping download.\")\n\n\ndef load_json_data(file_path):\n    \"\"\"\n    Load JSON data from a file\n    \"\"\"\n    print(f\"Loading JSON data from {file_path}\")\n    try:","chunkId":"tools/calculate-model-hashes.py:3"}],[3148,{"filePath":"tools/calculate-model-hashes.py","startLine":57,"endLine":73,"chunkText":"    \"\"\"\n    print(f\"Loading JSON data from {file_path}\")\n    try:\n        with open(file_path, \"r\", encoding=\"utf-8\") as file:\n            data = json.load(file)\n            print(f\"Loaded JSON data successfully from {file_path}\")\n            return data\n    except FileNotFoundError:\n        print(f\"{file_path} not found.\")\n        sys.exit(1)\n\n\ndef iterate_and_hash(directory):\n    \"\"\"\n    Iterate through a directory and hash all model files\n    \"\"\"\n    print(f\"Iterating through directory {directory} to hash model files\")","chunkId":"tools/calculate-model-hashes.py:4"}],[3149,{"filePath":"tools/calculate-model-hashes.py","startLine":71,"endLine":82,"chunkText":"    Iterate through a directory and hash all model files\n    \"\"\"\n    print(f\"Iterating through directory {directory} to hash model files\")\n    model_files = [(file, os.path.join(root, file)) for root, _, files in os.walk(directory) for file in files if file.endswith((\".pth\", \".onnx\"))]\n\n    download_file_if_missing(VR_MODEL_DATA_URL, VR_MODEL_DATA_LOCAL_PATH)\n    download_file_if_missing(MDX_MODEL_DATA_URL, MDX_MODEL_DATA_LOCAL_PATH)\n\n    vr_model_data = load_json_data(VR_MODEL_DATA_LOCAL_PATH)\n    mdx_model_data = load_json_data(MDX_MODEL_DATA_LOCAL_PATH)\n\n    combined_model_params = {","chunkId":"tools/calculate-model-hashes.py:5"}],[3150,{"filePath":"tools/calculate-model-hashes.py","startLine":81,"endLine":99,"chunkText":"\n    combined_model_params = {\n        **vr_model_data,\n        **mdx_model_data,\n    }\n\n    model_info_list = []\n    for file, file_path in sorted(model_files):\n        file_hash = get_model_hash(file_path)\n        model_info = {\n            \"file\": file,\n            \"hash\": file_hash,\n            \"params\": combined_model_params.get(file_hash, \"Parameters not found\"),\n        }\n        model_info_list.append(model_info)\n\n    print(f\"Writing model info list to {OUTPUT_PATH}\")\n    with open(OUTPUT_PATH, \"w\", encoding=\"utf-8\") as json_file:\n        json.dump(model_info_list, json_file, indent=4)","chunkId":"tools/calculate-model-hashes.py:6"}],[3151,{"filePath":"tools/calculate-model-hashes.py","startLine":97,"endLine":105,"chunkText":"    print(f\"Writing model info list to {OUTPUT_PATH}\")\n    with open(OUTPUT_PATH, \"w\", encoding=\"utf-8\") as json_file:\n        json.dump(model_info_list, json_file, indent=4)\n        print(f\"Successfully wrote model info list to {OUTPUT_PATH}\")\n\n\nif __name__ == \"__main__\":\n    iterate_and_hash(MODEL_CACHE_PATH)\n","chunkId":"tools/calculate-model-hashes.py:7"}],[3152,{"filePath":"tools/sync-to-github.py","startLine":0,"endLine":18,"chunkText":"#! /usr/bin/env python3\nimport os\nimport requests\nimport hashlib\nfrom typing import List, Dict\nimport sys\n\n# Configuration\nGITHUB_TOKEN = os.getenv(\"GITHUB_TOKEN\", \"\").strip()  # Add .strip() to remove whitespace\nREPO_OWNER = \"nomadkaraoke\"\nREPO_NAME = \"python-audio-separator\"\nRELEASE_TAG = \"model-configs\"\n\nHEADERS = {\"Authorization\": f\"Bearer {GITHUB_TOKEN}\", \"Accept\": \"application/vnd.github.v3+json\"}\n\n\ndef debug_request(url: str, headers: dict, response: requests.Response):\n    \"\"\"Debug helper to print request and response details.\"\"\"\n    print(\"\\n=== Debug Information ===\")","chunkId":"tools/sync-to-github.py:0"}],[3153,{"filePath":"tools/sync-to-github.py","startLine":17,"endLine":29,"chunkText":"    \"\"\"Debug helper to print request and response details.\"\"\"\n    print(\"\\n=== Debug Information ===\")\n    print(f\"Request URL: {url}\")\n    print(f\"Request Headers: {headers}\")\n    print(f\"Response Status: {response.status_code}\")\n    print(f\"Response Headers: {dict(response.headers)}\")\n    print(f\"Response Body: {response.text[:500]}...\")  # First 500 chars of response\n    print(\"=======================\\n\")\n\n\ndef get_release_assets() -> List[Dict]:\n    \"\"\"Get all assets from the specified release.\"\"\"\n    url = f\"https://api.github.com/repos/{REPO_OWNER}/{REPO_NAME}/releases/tags/{RELEASE_TAG}\"","chunkId":"tools/sync-to-github.py:1"}],[3154,{"filePath":"tools/sync-to-github.py","startLine":27,"endLine":40,"chunkText":"def get_release_assets() -> List[Dict]:\n    \"\"\"Get all assets from the specified release.\"\"\"\n    url = f\"https://api.github.com/repos/{REPO_OWNER}/{REPO_NAME}/releases/tags/{RELEASE_TAG}\"\n    print(f\"\\nDebug: Using token: {GITHUB_TOKEN[:4]}...{GITHUB_TOKEN[-4:]}\")  # Show first/last 4 chars\n    print(f\"Debug: Requesting URL: {url}\")\n    print(f\"Debug: Headers: {HEADERS}\")\n\n    response = requests.get(url, headers=HEADERS)\n    debug_request(url, HEADERS, response)\n\n    if response.status_code != 200:\n        print(f\"Error getting release: {response.status_code}\")\n        return []\n","chunkId":"tools/sync-to-github.py:2"}],[3155,{"filePath":"tools/sync-to-github.py","startLine":38,"endLine":55,"chunkText":"        print(f\"Error getting release: {response.status_code}\")\n        return []\n\n    release_data = response.json()\n    return release_data.get(\"assets\", [])\n\n\ndef list_local_files() -> List[str]:\n    \"\"\"List all files in the current directory (excluding directories).\"\"\"\n    return [f for f in os.listdir(\".\") if os.path.isfile(f)]\n\n\ndef calculate_file_hash(filepath: str) -> str:\n    \"\"\"Calculate SHA256 hash of a file.\"\"\"\n    sha256_hash = hashlib.sha256()\n    with open(filepath, \"rb\") as f:\n        for byte_block in iter(lambda: f.read(4096), b\"\"):\n            sha256_hash.update(byte_block)","chunkId":"tools/sync-to-github.py:3"}],[3156,{"filePath":"tools/sync-to-github.py","startLine":53,"endLine":68,"chunkText":"    with open(filepath, \"rb\") as f:\n        for byte_block in iter(lambda: f.read(4096), b\"\"):\n            sha256_hash.update(byte_block)\n    return sha256_hash.hexdigest()\n\n\ndef upload_asset(release_id: int, filepath: str):\n    \"\"\"Upload a file as a release asset.\"\"\"\n    upload_url = f\"https://uploads.github.com/repos/{REPO_OWNER}/{REPO_NAME}/releases/{release_id}/assets\"\n\n    filename = os.path.basename(filepath)\n    headers = {\"Authorization\": f\"Bearer {GITHUB_TOKEN}\", \"Content-Type\": \"application/octet-stream\"}\n\n    params = {\"name\": filename}\n\n    with open(filepath, \"rb\") as f:","chunkId":"tools/sync-to-github.py:4"}],[3157,{"filePath":"tools/sync-to-github.py","startLine":66,"endLine":83,"chunkText":"    params = {\"name\": filename}\n\n    with open(filepath, \"rb\") as f:\n        response = requests.post(upload_url, headers=headers, params=params, data=f)\n\n    if response.status_code == 201:\n        print(f\"Successfully uploaded {filename}\")\n    else:\n        print(f\"Failed to upload {filename}: {response.status_code}\")\n        print(response.text)\n\n\ndef download_asset(asset: Dict):\n    \"\"\"Download a release asset to the local directory.\"\"\"\n    filename = asset[\"name\"]\n    download_url = asset[\"browser_download_url\"]\n\n    print(f\"Downloading {filename}...\")","chunkId":"tools/sync-to-github.py:5"}],[3158,{"filePath":"tools/sync-to-github.py","startLine":81,"endLine":99,"chunkText":"    download_url = asset[\"browser_download_url\"]\n\n    print(f\"Downloading {filename}...\")\n    response = requests.get(download_url, headers=HEADERS, stream=True)\n\n    if response.status_code == 200:\n        with open(filename, \"wb\") as f:\n            for chunk in response.iter_content(chunk_size=8192):\n                f.write(chunk)\n        print(f\"Successfully downloaded {filename}\")\n    else:\n        print(f\"Failed to download {filename}: {response.status_code}\")\n\n\ndef main():\n    if not GITHUB_TOKEN:\n        print(\"Please set GITHUB_TOKEN environment variable\")\n        sys.exit(1)\n","chunkId":"tools/sync-to-github.py:6"}],[3159,{"filePath":"tools/sync-to-github.py","startLine":97,"endLine":110,"chunkText":"        print(\"Please set GITHUB_TOKEN environment variable\")\n        sys.exit(1)\n\n    print(f\"Debug: Script starting with token length: {len(GITHUB_TOKEN)}\")\n    print(f\"Debug: Token characters: {[ord(c) for c in GITHUB_TOKEN]}\")\n    print(f\"Debug: Token first/last chars: {GITHUB_TOKEN[:4]}...{GITHUB_TOKEN[-4:]}\")\n\n    # Get release ID first\n    url = f\"https://api.github.com/repos/{REPO_OWNER}/{REPO_NAME}/releases/tags/{RELEASE_TAG}\"\n    response = requests.get(url, headers=HEADERS)\n    if response.status_code != 200:\n        print(f\"Error getting release: {response.status_code}\")\n        return\n","chunkId":"tools/sync-to-github.py:7"}],[3160,{"filePath":"tools/sync-to-github.py","startLine":108,"endLine":129,"chunkText":"        print(f\"Error getting release: {response.status_code}\")\n        return\n\n    release_id = response.json()[\"id\"]\n\n    # Get existing assets\n    existing_assets = get_release_assets()\n    existing_filenames = {asset[\"name\"] for asset in existing_assets}\n\n    # Get local files\n    local_files = list_local_files()\n\n    print(\"\\nExisting release assets:\")\n    for asset in existing_assets:\n        print(f\"- {asset['name']} ({asset['size']} bytes)\")\n\n    # Add download option\n    print(\"\\nOptions:\")\n    print(\"1. Upload new files\")\n    print(\"2. Download all missing files\")\n    print(\"3. Exit\")\n","chunkId":"tools/sync-to-github.py:8"}],[3161,{"filePath":"tools/sync-to-github.py","startLine":127,"endLine":144,"chunkText":"    print(\"2. Download all missing files\")\n    print(\"3. Exit\")\n\n    choice = input(\"\\nEnter your choice (1-3): \")\n\n    if choice == \"1\":\n        # Original upload logic\n        files_to_upload = []\n        for local_file in local_files:\n            if local_file not in existing_filenames:\n                print(f\"- {local_file}\")\n                files_to_upload.append(local_file)\n\n        if files_to_upload:\n            files_with_size = [(f, os.path.getsize(f)) for f in files_to_upload]\n            files_with_size.sort(key=lambda x: x[1])\n\n            print(\"\\nFiles to upload (in order):\")","chunkId":"tools/sync-to-github.py:9"}],[3162,{"filePath":"tools/sync-to-github.py","startLine":142,"endLine":157,"chunkText":"            files_with_size.sort(key=lambda x: x[1])\n\n            print(\"\\nFiles to upload (in order):\")\n            for file, size in files_with_size:\n                print(f\"- {file} ({size / 1024 / 1024:.2f} MB)\")\n\n            response = input(\"\\nDo you want to upload these files? (y/n): \")\n            if response.lower() == \"y\":\n                for file, _ in files_with_size:\n                    upload_asset(release_id, file)\n        else:\n            print(\"\\nNo new files to upload.\")\n\n    elif choice == \"2\":\n        # Download missing files\n        files_to_download = []","chunkId":"tools/sync-to-github.py:10"}],[3163,{"filePath":"tools/sync-to-github.py","startLine":155,"endLine":169,"chunkText":"    elif choice == \"2\":\n        # Download missing files\n        files_to_download = []\n        for asset in existing_assets:\n            if asset[\"name\"] not in local_files:\n                files_to_download.append(asset)\n\n        if files_to_download:\n            print(\"\\nFiles to download:\")\n            for asset in files_to_download:\n                print(f\"- {asset['name']} ({asset['size'] / 1024 / 1024:.2f} MB)\")\n\n            response = input(\"\\nDo you want to download these files? (y/n): \")\n            if response.lower() == \"y\":\n                for asset in files_to_download:","chunkId":"tools/sync-to-github.py:11"}],[3164,{"filePath":"tools/sync-to-github.py","startLine":167,"endLine":177,"chunkText":"            response = input(\"\\nDo you want to download these files? (y/n): \")\n            if response.lower() == \"y\":\n                for asset in files_to_download:\n                    download_asset(asset)\n        else:\n            print(\"\\nNo files to download. Local directory is in sync.\")\n\n\nif __name__ == \"__main__\":\n    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diff --git a/Dockerfile.rocm b/Dockerfile.rocm
index 41420f9..3a5b603 100644
--- a/Dockerfile.rocm
+++ b/Dockerfile.rocm
@@ -1,5 +1,5 @@
 # Use ROCm base image with Python
-FROM rocm/dev-ubuntu-22.04:5.7.1-complete
+FROM rocm/dev-ubuntu-22.04:7.2-complete
 
 # Set the working directory in the container
 WORKDIR /workdir
@@ -7,20 +7,23 @@ WORKDIR /workdir
 # Install necessary packages
 RUN apt-get update && apt-get install -y \
     ffmpeg \
-    build-essential \
     python3 \
     python3-pip \
     && rm -rf /var/lib/apt/lists/*
 
 RUN python3 -m pip install --upgrade pip
 
-# Install PyTorch with ROCm support and audio-separator with ROCm extra
+# Install PyTorch with ROCm support
 RUN --mount=type=cache,target=/root/.cache \
-    pip3 install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/rocm5.7
+    pip3 install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/rocm7.2
 
-# Install audio-separator with ROCm support
+# Install audio-separator with GPU support
 RUN --mount=type=cache,target=/root/.cache \
-    pip3 install "audio-separator[rocm]"
+    pip3 install "audio-separator[gpu]" onnxruntime-rocm
+
+# Set environment variables for AMD RX 6600 series
+ENV HSA_OVERRIDE_GFX_VERSION=10.3.2
+ENV PYTORCH_ROCM_ARCH=gfx1030
 
 # Run audio-separator when the container launches
 ENTRYPOINT ["audio-separator"]
\ No newline at end of file
diff --git a/README.md b/README.md
index a90e315..1422036 100644
--- a/README.md
+++ b/README.md
@@ -25,11 +25,13 @@ The simplest (and probably most used) use case for this package is to separate a
   - [Installation 🛠️](#installation-%EF%B8%8F)
     - [🐳 Docker](#-docker)
     - [🎮 Nvidia GPU with CUDA or 🧪 Google Colab](#-nvidia-gpu-with-cuda-or--google-colab)
+    - [🖥️ AMD GPU with ROCm (Linux)](#-amd-gpu-with-rocm-linux)
     - [ Apple Silicon, macOS Sonoma+ with M1 or newer CPU (CoreML acceleration)](#-apple-silicon-macos-sonoma-with-m1-or-newer-cpu-coreml-acceleration)
     - [🐢 No hardware acceleration, CPU only](#-no-hardware-acceleration-cpu-only)
     - [🎥 FFmpeg dependency](#-ffmpeg-dependency)
   - [GPU / CUDA specific installation steps with Pip](#gpu--cuda-specific-installation-steps-with-pip)
     - [Multiple CUDA library versions may be needed](#multiple-cuda-library-versions-may-be-needed)
+    - [ROCm specific troubleshooting](#rocm-specific-troubleshooting)
   - [Usage 🚀](#usage-)
     - [Command Line Interface (CLI)](#command-line-interface-cli)
     - [Listing and Filtering Available Models](#listing-and-filtering-available-models)
@@ -67,6 +69,7 @@ The simplest (and probably most used) use case for this package is to separate a
 - Ability to inference using a pre-trained model in PTH or ONNX format.
 - CLI support for easy use in scripts and batch processing.
 - Python API for integration into other projects.
+- **Multi-platform GPU acceleration**: NVIDIA CUDA, AMD ROCm, Apple Silicon MPS/CoreML, DirectML, and CPU fallback.
 
 ## Installation 🛠️
 
@@ -117,15 +120,15 @@ beveradb/audio-separator:gpu
 **Supported ROCm Versions:** 5.7+
 
 💬 If successfully configured, you should see this log message when running `audio-separator --env_info`:
- `ONNXruntime has ROCMExecutionProvider available, enabling acceleration`
+ `ONNXruntime has CUDAExecutionProvider available, enabling acceleration`
 
 Pip (complete installation):
 ```sh
-# First install PyTorch with ROCm support
-pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/rocm5.7
+# First install PyTorch with ROCm support (Change ROCm version as needed.)
+pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/nightly/rocm7.2/
 
-# Then install audio-separator with ROCm support
-pip install "audio-separator[rocm]"
+# Then install audio-separator with GPU support
+pip install "audio-separator[gpu]"
 ```
 
 **Important:** You must install PyTorch with ROCm support BEFORE installing audio-separator. If you already have PyTorch with CUDA support installed, uninstall it first:
@@ -133,9 +136,23 @@ pip install "audio-separator[rocm]"
 pip uninstall torch torchvision torchaudio
 pip cache purge
 pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/rocm5.7
-pip install "audio-separator[rocm]"
+pip install "audio-separator[gpu]"
 ```
 
+**Required ROCm Packages:**
+- PyTorch ROCm: `torch`, `torchvision`, `torchaudio` with ROCm support
+- ONNX Runtime: `onnxruntime`, `onnxruntime-rocm`, `onnxruntime-gpu`
+
+**Basic ROCm Setup:**
+- For AMD Radeon RX 6600 series (gfx1032), set environment variables:
+```sh
+export HSA_OVERRIDE_GFX_VERSION=10.3.2
+export PYTORCH_ROCM_ARCH=gfx1030
+```
+- ROCm acceleration uses the CUDAExecutionProvider for AMD GPU compatibility
+- The system detects ROCm packages and PyTorch ROCm support automatically
+- ROCm libraries must be properly installed on your system for acceleration to work
+
 Docker (build from source):
 ```sh
 docker build -f Dockerfile.rocm -t audio-separator:rocm .
@@ -187,7 +204,7 @@ apt-get update; apt-get install -y ffmpeg
 brew update; brew install ffmpeg
 ```
 
-## GPU specific installation steps with Pip (CUDA and ROCm)
+## GPU / CUDA specific installation steps with Pip (CUDA and ROCm)
 
 In theory, all you should need to do to get `audio-separator` working with a GPU is install it with the appropriate extra (`[gpu]` for CUDA/NVIDIA or `[rocm]` for ROCm/AMD) as above.
 
@@ -227,6 +244,30 @@ You can resolve this by running the following command:
 python -m pip install ort-nightly-gpu --index-url=https://aiinfra.pkgs.visualstudio.com/PublicPackages/_packaging/ort-cuda-12-nightly/pypi/simple/
 ```
 
+### Multiple CUDA library versions may be needed
+
+Depending on your CUDA version and environment, you may need to install specific version(s) of CUDA libraries for ONNX Runtime to use your GPU.
+
+🧪 Google Colab, for example, now uses CUDA 12 by default, but ONNX Runtime still needs CUDA 11 libraries to work.
+
+If you see the error `Failed to load library` or `cannot open shared object file` when you run `audio-separator`, this is likely the issue.
+
+You can install the CUDA 11 libraries _alongside_ CUDA 12 like so:
+```sh
+apt update; apt install nvidia-cuda-toolkit
+```
+
+If you encounter the following messages when running on Google Colab or in another environment:
+```
+[E:onnxruntime:Default, provider_bridge_ort.cc:1862 TryGetProviderInfo_CUDA] /onnxruntime_src/onnxruntime/core/session/provider_bridge_ort.cc:1539 onnxruntime::Provider& onnxruntime::ProviderLibrary::Get() [ONNXRuntimeError] : 1 : FAIL : Failed to load library libonnxruntime_providers_cuda.so with error: libcudnn_adv.so.9: cannot open shared object file: No such file or directory
+
+[W:onnxruntime:Default, onnxruntime_pybind_state.cc:993 CreateExecutionProviderInstance] Failed to create CUDAExecutionProvider. Require cuDNN 9.* and CUDA 12.*. Please install all dependencies as mentioned in the GPU requirements page (https://onnxruntime.ai/docs/execution-providers/CUDA-ExecutionProvider.html#requirements), make sure they're in the PATH, and that your GPU is supported.
+```
+You can resolve this by running the following command:
+```sh
+python -m pip install ort-nightly-gpu --index-url=https://aiinfra.pkgs.visualstudio.com/PublicPackages/_packaging/ort-cuda-12-nightly/pypi/simple/
+```
+
 ### ROCm specific troubleshooting
 
 For ROCm (AMD GPU) support, make sure you have:
@@ -243,6 +284,18 @@ pip install torch torchvision torchaudio --index-url https://download.pytorch.or
 pip install onnxruntime-rocm
 ```
 
+**ROCm Performance Optimization:**
+- The ROCm execution provider includes performance optimizations for AMD GPUs:
+  - Parallel execution mode for better multi-core utilization
+  - Kernel tuning enabled for optimal performance
+  - Memory pattern optimization for better cache usage
+  - Smart memory allocation strategy
+
+**Common ROCm Issues:**
+- If you see ROCm package installed but no acceleration: Make sure `onnxruntime-rocm` is installed and ROCm libraries are in your PATH
+- If PyTorch shows CUDA but not ROCm: Reinstall PyTorch with ROCm support using the PyTorch ROCm index URL
+- Docker issues: Use the provided `Dockerfile.rocm` and ensure proper device mounting
+
 > Note: if anyone knows how to make this cleaner so we can support both different platform-specific dependencies for hardware acceleration without a separate installation process for each, please let me know or raise a PR!
 
 ## Usage 🚀
diff --git a/audio_separator/separator/architectures/demucs_separator.py b/audio_separator/separator/architectures/demucs_separator.py
index d1d62dc..a653f85 100644
--- a/audio_separator/separator/architectures/demucs_separator.py
+++ b/audio_separator/separator/architectures/demucs_separator.py
@@ -4,15 +4,25 @@
 import torch
 import numpy as np
 from audio_separator.separator.common_separator import CommonSeparator
-from audio_separator.separator.uvr_lib_v5.demucs.apply import apply_model, demucs_segments
+from audio_separator.separator.uvr_lib_v5.demucs.apply import (
+    apply_model,
+    demucs_segments,
+)
 from audio_separator.separator.uvr_lib_v5.demucs.hdemucs import HDemucs
-from audio_separator.separator.uvr_lib_v5.demucs.pretrained import get_model as get_demucs_model
+from audio_separator.separator.uvr_lib_v5.demucs.pretrained import (
+    get_model as get_demucs_model,
+)
 from audio_separator.separator.uvr_lib_v5 import spec_utils
 
 DEMUCS_4_SOURCE = ["drums", "bass", "other", "vocals"]
 
 DEMUCS_2_SOURCE_MAPPER = {CommonSeparator.INST_STEM: 0, CommonSeparator.VOCAL_STEM: 1}
-DEMUCS_4_SOURCE_MAPPER = {CommonSeparator.BASS_STEM: 0, CommonSeparator.DRUM_STEM: 1, CommonSeparator.OTHER_STEM: 2, CommonSeparator.VOCAL_STEM: 3}
+DEMUCS_4_SOURCE_MAPPER = {
+    CommonSeparator.BASS_STEM: 0,
+    CommonSeparator.DRUM_STEM: 1,
+    CommonSeparator.OTHER_STEM: 2,
+    CommonSeparator.VOCAL_STEM: 3,
+}
 DEMUCS_6_SOURCE_MAPPER = {
     CommonSeparator.BASS_STEM: 0,
     CommonSeparator.DRUM_STEM: 1,
@@ -64,8 +74,12 @@ def __init__(self, common_config, arch_config):
         # Enables "Segments". Deselecting this option is only recommended for those with powerful PCs.
         self.segments_enabled = arch_config.get("segments_enabled", True)
 
-        self.logger.debug(f"Demucs arch params: segment_size={self.segment_size}, segments_enabled={self.segments_enabled}")
-        self.logger.debug(f"Demucs arch params: shifts={self.shifts}, overlap={self.overlap}")
+        self.logger.debug(
+            f"Demucs arch params: segment_size={self.segment_size}, segments_enabled={self.segments_enabled}"
+        )
+        self.logger.debug(
+            f"Demucs arch params: shifts={self.shifts}, overlap={self.overlap}"
+        )
 
         self.demucs_source_map = DEMUCS_4_SOURCE_MAPPER
 
@@ -107,15 +121,23 @@ def separate(self, audio_file_path, custom_output_names=None):
 
         self.logger.debug("Loading model for demixing...")
 
-        self.demucs_model_instance = HDemucs(sources=DEMUCS_4_SOURCE)
-        self.demucs_model_instance = get_demucs_model(name=os.path.splitext(os.path.basename(self.model_path))[0], repo=Path(os.path.dirname(self.model_path)))
-        self.demucs_model_instance = demucs_segments(self.segment_size, self.demucs_model_instance)
-        self.demucs_model_instance.to(self.torch_device)
+        # Use GPU device for Demucs if available and not explicitly disabled
+        inference_device = self.torch_device
+
+        # Load the ROCm-compatible Demucs model
+        self.demucs_model_instance = get_demucs_model(
+            name=os.path.splitext(os.path.basename(self.model_path))[0],
+            repo=Path(os.path.dirname(self.model_path)),
+        )
+        self.demucs_model_instance = demucs_segments(
+            self.segment_size, self.demucs_model_instance
+        )
+        self.demucs_model_instance.to(inference_device)
         self.demucs_model_instance.eval()
 
         self.logger.debug("Model loaded and set to evaluation mode.")
 
-        source = self.demix_demucs(mix)
+        source = self.demix_demucs(mix, inference_device)
 
         del self.demucs_model_instance
         self.clear_gpu_cache()
@@ -126,13 +148,20 @@ def separate(self, audio_file_path, custom_output_names=None):
 
         if isinstance(inst_source, np.ndarray):
             self.logger.debug("Processing instance source...")
-            source_reshape = spec_utils.reshape_sources(inst_source[self.demucs_source_map[CommonSeparator.VOCAL_STEM]], source[self.demucs_source_map[CommonSeparator.VOCAL_STEM]])
-            inst_source[self.demucs_source_map[CommonSeparator.VOCAL_STEM]] = source_reshape
+            source_reshape = spec_utils.reshape_sources(
+                inst_source[self.demucs_source_map[CommonSeparator.VOCAL_STEM]],
+                source[self.demucs_source_map[CommonSeparator.VOCAL_STEM]],
+            )
+            inst_source[self.demucs_source_map[CommonSeparator.VOCAL_STEM]] = (
+                source_reshape
+            )
             source = inst_source
 
         if isinstance(source, np.ndarray):
             source_length = len(source)
-            self.logger.debug(f"Processing source array, source length is {source_length}")
+            self.logger.debug(
+                f"Processing source array, source length is {source_length}"
+            )
             match source_length:
                 case 2:
                     self.logger.debug("Setting source map to 2-stem...")
@@ -148,7 +177,9 @@ def separate(self, audio_file_path, custom_output_names=None):
         for stem_name, stem_value in self.demucs_source_map.items():
             if self.output_single_stem is not None:
                 if stem_name.lower() != self.output_single_stem.lower():
-                    self.logger.debug(f"Skipping writing stem {stem_name} as output_single_stem is set to {self.output_single_stem}...")
+                    self.logger.debug(
+                        f"Skipping writing stem {stem_name} as output_single_stem is set to {self.output_single_stem}..."
+                    )
                     continue
 
             stem_path = self.get_stem_output_path(stem_name, custom_output_names)
@@ -159,7 +190,7 @@ def separate(self, audio_file_path, custom_output_names=None):
 
         return output_files
 
-    def demix_demucs(self, mix):
+    def demix_demucs(self, mix, inference_device):
         """
         Demixes the input mix using the demucs model.
         """
@@ -181,7 +212,7 @@ def demix_demucs(self, mix):
                 overlap=self.overlap,
                 static_shifts=1 if self.shifts == 0 else self.shifts,
                 set_progress_bar=None,
-                device=self.torch_device,
+                device=inference_device,
                 progress=True,
             )[0]
 
diff --git a/audio_separator/separator/architectures/mdxc_separator.py b/audio_separator/separator/architectures/mdxc_separator.py
index 1ddb499..b9b2219 100644
--- a/audio_separator/separator/architectures/mdxc_separator.py
+++ b/audio_separator/separator/architectures/mdxc_separator.py
@@ -37,7 +37,9 @@ def __init__(self, common_config, arch_config):
 
         # Whether or not to use the segment size from model config, or the default
         # The segment size is set based on the value provided in a chosen model's associated config file (yaml).
-        self.override_model_segment_size = arch_config.get("override_model_segment_size", False)
+        self.override_model_segment_size = arch_config.get(
+            "override_model_segment_size", False
+        )
 
         self.overlap = arch_config.get("overlap", 8)
         self.batch_size = arch_config.get("batch_size", 1)
@@ -51,9 +53,15 @@ def __init__(self, common_config, arch_config):
 
         self.process_all_stems = arch_config.get("process_all_stems", True)
 
-        self.logger.debug(f"MDXC arch params: batch_size={self.batch_size}, segment_size={self.segment_size}, overlap={self.overlap}")
-        self.logger.debug(f"MDXC arch params: override_model_segment_size={self.override_model_segment_size}, pitch_shift={self.pitch_shift}")
-        self.logger.debug(f"MDXC multi-stem params: process_all_stems={self.process_all_stems}")
+        self.logger.debug(
+            f"MDXC arch params: batch_size={self.batch_size}, segment_size={self.segment_size}, overlap={self.overlap}"
+        )
+        self.logger.debug(
+            f"MDXC arch params: override_model_segment_size={self.override_model_segment_size}, pitch_shift={self.pitch_shift}"
+        )
+        self.logger.debug(
+            f"MDXC multi-stem params: process_all_stems={self.process_all_stems}"
+        )
 
         # Align Roformer detection flag with CommonSeparator to ensure consistent stats/logging
         self.is_roformer = getattr(self, "is_roformer_model", False)
@@ -67,21 +75,27 @@ def __init__(self, common_config, arch_config):
 
         # Only mark primary stem as main target for single-target models.
         # Multi-stem models should not trigger residual subtraction logic.
-        self.is_primary_stem_main_target = bool(self.model_data_cfgdict.training.target_instrument)
+        self.is_primary_stem_main_target = bool(
+            self.model_data_cfgdict.training.target_instrument
+        )
 
-        self.logger.debug(f"is_primary_stem_main_target: {self.is_primary_stem_main_target}")
+        self.logger.debug(
+            f"is_primary_stem_main_target: {self.is_primary_stem_main_target}"
+        )
 
         self.logger.info("MDXC Separator initialisation complete")
 
     def load_model(self):
         """
-        Load the model into memory from file on disk, initialize it with config from the model data,
+        Load the model into memory from file on disk, initialize it with config from the model_data,
         and prepare for inferencing using hardware accelerated Torch device.
         """
         self.logger.debug("Loading checkpoint model for inference...")
 
         self.model_data_cfgdict = ConfigDict(self.model_data)
 
+        inference_device = self.torch_device
+
         try:
             if self.is_roformer:
                 # Use the RoformerLoader exclusively; no legacy fallback
@@ -89,30 +103,49 @@ def load_model(self):
                 result = self.roformer_loader.load_model(
                     model_path=self.model_path,
                     config=self.model_data,
-                    device=str(self.torch_device),
+                    device=str(inference_device),
                 )
 
-                if getattr(result, "success", False) and getattr(result, "model", None) is not None:
+                if (
+                    getattr(result, "success", False)
+                    and getattr(result, "model", None) is not None
+                ):
                     self.model_run = result.model
-                    self.model_run.to(self.torch_device).eval()
+                    self.logger.debug(
+                        f"Roformer model device before .to(): {next(self.model_run.parameters()).device}"
+                    )
+                    self.model_run.to(inference_device).eval()
+                    self.logger.debug(
+                        f"Roformer model device after .to(): {next(self.model_run.parameters()).device}"
+                    )
                 else:
-                    error_msg = getattr(result, "error_message", "RoformerLoader unsuccessful")
+                    error_msg = getattr(
+                        result, "error_message", "RoformerLoader unsuccessful"
+                    )
                     self.logger.error(f"Failed to load Roformer model: {error_msg}")
                     raise RuntimeError(error_msg)
 
             else:
                 self.logger.debug("Loading TFC_TDF_net model...")
-                self.model_run = TFC_TDF_net(self.model_data_cfgdict, device=self.torch_device)
+                self.model_run = TFC_TDF_net(
+                    self.model_data_cfgdict, device=inference_device
+                )
                 self.logger.debug("Loading model onto cpu")
-                # For some reason loading the state onto a hardware accelerated devices causes issues, 
+                # For some reason loading the state onto a hardware accelerated devices causes issues,
                 # so we load it onto CPU first then move it to the device
-                self.model_run.load_state_dict(torch.load(self.model_path, map_location="cpu"))
-                self.model_run.to(self.torch_device).eval()
+                self.model_run.load_state_dict(
+                    torch.load(self.model_path, map_location="cpu")
+                )
+                self.model_run.to(inference_device).eval()
 
         except RuntimeError as e:
             self.logger.error(f"Error: {e}")
-            self.logger.error("An error occurred while loading the model file. This often occurs when the model file is corrupt or incomplete.")
-            self.logger.error(f"Please try deleting the model file from {self.model_path} and run audio-separator again to re-download it.")
+            self.logger.error(
+                "An error occurred while loading the model file. This often occurs when the model file is corrupt or incomplete."
+            )
+            self.logger.error(
+                f"Please try deleting the model file from {self.model_path} and run audio-separator again to re-download it."
+            )
             sys.exit(1)
 
     def separate(self, audio_file_path, custom_output_names=None):
@@ -133,7 +166,9 @@ def separate(self, audio_file_path, custom_output_names=None):
         self.audio_file_path = audio_file_path
         self.audio_file_base = os.path.splitext(os.path.basename(audio_file_path))[0]
 
-        self.logger.debug(f"Preparing mix for input audio file {self.audio_file_path}...")
+        self.logger.debug(
+            f"Preparing mix for input audio file {self.audio_file_path}..."
+        )
         mix = self.prepare_mix(self.audio_file_path)
 
         # Check if audio is shorter than threshold
@@ -142,11 +177,19 @@ def separate(self, audio_file_path, custom_output_names=None):
             # Only change and warn if it wasn't already set by the user
             if not self.override_model_segment_size:
                 self.override_model_segment_size = True
-                self.logger.warning(f"Audio duration ({audio_duration_seconds:.2f}s) is less than 10 seconds.")
-                self.logger.warning("Automatically enabling override_model_segment_size for better processing of short audio.")
+                self.logger.warning(
+                    f"Audio duration ({audio_duration_seconds:.2f}s) is less than 10 seconds."
+                )
+                self.logger.warning(
+                    "Automatically enabling override_model_segment_size for better processing of short audio."
+                )
 
         self.logger.debug("Normalizing mix before demixing...")
-        mix = spec_utils.normalize(wave=mix, max_peak=self.normalization_threshold, min_peak=self.amplification_threshold)
+        mix = spec_utils.normalize(
+            wave=mix,
+            max_peak=self.normalization_threshold,
+            min_peak=self.amplification_threshold,
+        )
 
         source = self.demix(mix=mix)
         self.logger.debug("Demixing completed.")
@@ -156,73 +199,115 @@ def separate(self, audio_file_path, custom_output_names=None):
 
         if isinstance(source, dict):
             self.logger.debug("Source is a dict, processing each stem...")
-            
+
             stem_list = []
             if self.model_data_cfgdict.training.target_instrument:
                 stem_list = [self.model_data_cfgdict.training.target_instrument]
             else:
                 stem_list = self.model_data_cfgdict.training.instruments
-            
+
             self.logger.debug(f"Available stems: {stem_list}")
 
             is_multi_stem_model = len(stem_list) > 2
             should_process_all_stems = self.process_all_stems and is_multi_stem_model
-            
+
             if should_process_all_stems:
                 self.logger.debug("Processing all stems from multi-stem model...")
                 for stem_name in stem_list:
-                    stem_output_path = self.get_stem_output_path(stem_name, custom_output_names)
+                    stem_output_path = self.get_stem_output_path(
+                        stem_name, custom_output_names
+                    )
                     stem_source = spec_utils.normalize(
-                        wave=source[stem_name], 
-                        max_peak=self.normalization_threshold, 
-                        min_peak=self.amplification_threshold
+                        wave=source[stem_name],
+                        max_peak=self.normalization_threshold,
+                        min_peak=self.amplification_threshold,
                     ).T
-                    
-                    self.logger.info(f"Saving {stem_name} stem to {stem_output_path}...")
+
+                    self.logger.info(
+                        f"Saving {stem_name} stem to {stem_output_path}..."
+                    )
                     self.final_process(stem_output_path, stem_source, stem_name)
                     output_files.append(stem_output_path)
             else:
                 # Standard processing for primary and secondary stems
                 if not isinstance(self.primary_source, np.ndarray):
-                    self.logger.debug(f"Normalizing primary source for primary stem {self.primary_stem_name}...")
+                    self.logger.debug(
+                        f"Normalizing primary source for primary stem {self.primary_stem_name}..."
+                    )
                     self.primary_source = spec_utils.normalize(
-                        wave=source[self.primary_stem_name], 
-                        max_peak=self.normalization_threshold, 
-                        min_peak=self.amplification_threshold
+                        wave=source[self.primary_stem_name],
+                        max_peak=self.normalization_threshold,
+                        min_peak=self.amplification_threshold,
                     ).T
 
                 if not isinstance(self.secondary_source, np.ndarray):
-                    self.logger.debug(f"Normalizing secondary source for secondary stem {self.secondary_stem_name}...")
+                    self.logger.debug(
+                        f"Normalizing secondary source for secondary stem {self.secondary_stem_name}..."
+                    )
                     self.secondary_source = spec_utils.normalize(
-                        wave=source[self.secondary_stem_name], 
-                        max_peak=self.normalization_threshold, 
-                        min_peak=self.amplification_threshold
+                        wave=source[self.secondary_stem_name],
+                        max_peak=self.normalization_threshold,
+                        min_peak=self.amplification_threshold,
                     ).T
 
-                if not self.output_single_stem or self.output_single_stem.lower() == self.secondary_stem_name.lower():
-                    self.secondary_stem_output_path = self.get_stem_output_path(self.secondary_stem_name, custom_output_names)
-
-                    self.logger.info(f"Saving {self.secondary_stem_name} stem to {self.secondary_stem_output_path}...")
-                    self.final_process(self.secondary_stem_output_path, self.secondary_source, self.secondary_stem_name)
+                if (
+                    not self.output_single_stem
+                    or self.output_single_stem.lower()
+                    == self.secondary_stem_name.lower()
+                ):
+                    self.secondary_stem_output_path = self.get_stem_output_path(
+                        self.secondary_stem_name, custom_output_names
+                    )
+
+                    self.logger.info(
+                        f"Saving {self.secondary_stem_name} stem to {self.secondary_stem_output_path}..."
+                    )
+                    self.final_process(
+                        self.secondary_stem_output_path,
+                        self.secondary_source,
+                        self.secondary_stem_name,
+                    )
                     output_files.append(self.secondary_stem_output_path)
-                
-                if not self.output_single_stem or self.output_single_stem.lower() == self.primary_stem_name.lower():
-                    self.primary_stem_output_path = self.get_stem_output_path(self.primary_stem_name, custom_output_names)
-                    
-                    self.logger.info(f"Saving {self.primary_stem_name} stem to {self.primary_stem_output_path}...")
-                    self.final_process(self.primary_stem_output_path, self.primary_source, self.primary_stem_name)
+
+                if (
+                    not self.output_single_stem
+                    or self.output_single_stem.lower() == self.primary_stem_name.lower()
+                ):
+                    self.primary_stem_output_path = self.get_stem_output_path(
+                        self.primary_stem_name, custom_output_names
+                    )
+
+                    self.logger.info(
+                        f"Saving {self.primary_stem_name} stem to {self.primary_stem_output_path}..."
+                    )
+                    self.final_process(
+                        self.primary_stem_output_path,
+                        self.primary_source,
+                        self.primary_stem_name,
+                    )
                     output_files.append(self.primary_stem_output_path)
 
         else:
             # Handle case when source is not a dictionary (single source model)
-            if not self.output_single_stem or self.output_single_stem.lower() == self.primary_stem_name.lower():
-                self.primary_stem_output_path = self.get_stem_output_path(self.primary_stem_name, custom_output_names)
+            if (
+                not self.output_single_stem
+                or self.output_single_stem.lower() == self.primary_stem_name.lower()
+            ):
+                self.primary_stem_output_path = self.get_stem_output_path(
+                    self.primary_stem_name, custom_output_names
+                )
 
                 if not isinstance(self.primary_source, np.ndarray):
                     self.primary_source = source.T
 
-                self.logger.info(f"Saving {self.primary_stem_name} stem to {self.primary_stem_output_path}...")
-                self.final_process(self.primary_stem_output_path, self.primary_source, self.primary_stem_name)
+                self.logger.info(
+                    f"Saving {self.primary_stem_name} stem to {self.primary_stem_output_path}..."
+                )
+                self.final_process(
+                    self.primary_stem_output_path,
+                    self.primary_source,
+                    self.primary_stem_name,
+                )
                 output_files.append(self.primary_stem_output_path)
 
         return output_files
@@ -239,7 +324,9 @@ def pitch_fix(self, source, sr_pitched, orig_mix):
         Returns:
             np.ndarray: The pitch-shifted source audio.
         """
-        source = spec_utils.change_pitch_semitones(source, sr_pitched, semitone_shift=self.pitch_shift)[0]
+        source = spec_utils.change_pitch_semitones(
+            source, sr_pitched, semitone_shift=self.pitch_shift
+        )[0]
         source = spec_utils.match_array_shapes(source, orig_mix)
         return source
 
@@ -251,7 +338,9 @@ def overlap_add(self, result, x, weights, start, length):
         # Use the minimum of provided lengths to avoid broadcasting errors
         safe_len = min(length, x.shape[-1], weights.shape[0])
         if safe_len > 0:
-            result[..., start : start + safe_len] += x[..., :safe_len] * weights[:safe_len]
+            result[..., start : start + safe_len] += (
+                x[..., :safe_len] * weights[:safe_len]
+            )
         return result
 
     def demix(self, mix: np.ndarray) -> dict:
@@ -267,7 +356,9 @@ def demix(self, mix: np.ndarray) -> dict:
 
         if self.pitch_shift != 0:
             self.logger.debug(f"Shifting pitch by -{self.pitch_shift} semitones...")
-            mix, sample_rate = spec_utils.change_pitch_semitones(mix, self.sample_rate, semitone_shift=-self.pitch_shift)
+            mix, sample_rate = spec_utils.change_pitch_semitones(
+                mix, self.sample_rate, semitone_shift=-self.pitch_shift
+            )
 
         if self.is_roformer:
             # Note: Currently, for Roformer models, `batch_size` is not utilized due to negligible performance improvements.
@@ -279,15 +370,23 @@ def demix(self, mix: np.ndarray) -> dict:
                 self.logger.debug(f"Using configured segment size: {mdx_segment_size}")
             else:
                 mdx_segment_size = self.model_data_cfgdict.inference.dim_t
-                self.logger.debug(f"Using model default segment size: {mdx_segment_size}")
+                self.logger.debug(
+                    f"Using model default segment size: {mdx_segment_size}"
+                )
 
             # num_stems aka "S" in UVR
-            num_stems = 1 if self.model_data_cfgdict.training.target_instrument else len(self.model_data_cfgdict.training.instruments)
+            num_stems = (
+                1
+                if self.model_data_cfgdict.training.target_instrument
+                else len(self.model_data_cfgdict.training.instruments)
+            )
             self.logger.debug(f"Number of stems: {num_stems}")
 
             # chunk_size aka "C" in UVR
             # IMPORTANT: For Roformer models, use the model's STFT hop length to derive the temporal chunk size
-            stft_hop_len = getattr(self.model_data_cfgdict.model, "stft_hop_length", None)
+            stft_hop_len = getattr(
+                self.model_data_cfgdict.model, "stft_hop_length", None
+            )
             if stft_hop_len is None:
                 # Fallback to audio.hop_length if not present, but log for visibility
                 stft_hop_len = self.model_data_cfgdict.audio.hop_length
@@ -307,13 +406,16 @@ def demix(self, mix: np.ndarray) -> dict:
             self.logger.debug(f"Step: {step} (desired={desired_step})")
 
             # Create a weighting table and convert it to a PyTorch tensor
-            window = torch.tensor(signal.windows.hamming(chunk_size), dtype=torch.float32)
+            window = torch.tensor(
+                signal.windows.hamming(chunk_size), dtype=torch.float32
+            )
 
             device = next(self.model_run.parameters()).device
 
-
             with torch.no_grad():
-                req_shape = (len(self.model_data_cfgdict.training.instruments),) + tuple(mix.shape)
+                req_shape = (
+                    len(self.model_data_cfgdict.training.instruments),
+                ) + tuple(mix.shape)
                 result = torch.zeros(req_shape, dtype=torch.float32)
                 counter = torch.zeros(req_shape, dtype=torch.float32)
 
@@ -333,7 +435,9 @@ def demix(self, mix: np.ndarray) -> dict:
                         result = self.overlap_add(result, x, window, start_idx, length)
                         safe_len = min(length, x.shape[-1], window.shape[0])
                         if safe_len > 0:
-                            counter[..., start_idx : start_idx + safe_len] += window[:safe_len]
+                            counter[..., start_idx : start_idx + safe_len] += window[
+                                :safe_len
+                            ]
                     else:
                         result = self.overlap_add(result, x, window, i, length)
                         safe_len = min(length, x.shape[-1], window.shape[0])
@@ -356,9 +460,13 @@ def demix(self, mix: np.ndarray) -> dict:
                 self.logger.debug(f"Using configured segment size: {mdx_segment_size}")
             else:
                 mdx_segment_size = self.model_data_cfgdict.inference.dim_t
-                self.logger.debug(f"Using model default segment size: {mdx_segment_size}")
+                self.logger.debug(
+                    f"Using model default segment size: {mdx_segment_size}"
+                )
 
-            chunk_size = self.model_data_cfgdict.audio.hop_length * (mdx_segment_size - 1)
+            chunk_size = self.model_data_cfgdict.audio.hop_length * (
+                mdx_segment_size - 1
+            )
             self.logger.debug(f"Chunk size: {chunk_size}")
 
             hop_size = chunk_size // self.overlap
@@ -368,19 +476,35 @@ def demix(self, mix: np.ndarray) -> dict:
             pad_size = hop_size - (mix_shape - chunk_size) % hop_size
             self.logger.debug(f"Pad size: {pad_size}")
 
-            mix = torch.cat([torch.zeros(2, chunk_size - hop_size), mix, torch.zeros(2, pad_size + chunk_size - hop_size)], 1)
+            mix = torch.cat(
+                [
+                    torch.zeros(2, chunk_size - hop_size),
+                    mix,
+                    torch.zeros(2, pad_size + chunk_size - hop_size),
+                ],
+                1,
+            )
             self.logger.debug(f"Mix shape: {mix.shape}")
 
             chunks = mix.unfold(1, chunk_size, hop_size).transpose(0, 1)
             self.logger.debug(f"Chunks length: {len(chunks)} and shape: {chunks.shape}")
 
-            batches = [chunks[i : i + self.batch_size] for i in range(0, len(chunks), self.batch_size)]
-            self.logger.debug(f"Batch size: {self.batch_size}, number of batches: {len(batches)}")
+            batches = [
+                chunks[i : i + self.batch_size]
+                for i in range(0, len(chunks), self.batch_size)
+            ]
+            self.logger.debug(
+                f"Batch size: {self.batch_size}, number of batches: {len(batches)}"
+            )
 
             # accumulated_outputs is used to accumulate the output from processing each batch of chunks through the model.
             # It starts as a tensor of zeros and is updated in-place as the model processes each batch.
             # The variable holds the combined result of all processed batches, which, after post-processing, represents the separated audio sources.
-            accumulated_outputs = torch.zeros(num_stems, *mix.shape) if num_stems > 1 else torch.zeros_like(mix)
+            accumulated_outputs = (
+                torch.zeros(num_stems, *mix.shape)
+                if num_stems > 1
+                else torch.zeros_like(mix)
+            )
 
             with torch.no_grad():
                 count = 0
@@ -395,16 +519,27 @@ def demix(self, mix: np.ndarray) -> dict:
                     for individual_output in single_batch_result:
                         individual_output_cpu = individual_output.cpu()
                         # Accumulate outputs on CPU
-                        accumulated_outputs[..., count * hop_size : count * hop_size + chunk_size] += individual_output_cpu
+                        accumulated_outputs[
+                            ..., count * hop_size : count * hop_size + chunk_size
+                        ] += individual_output_cpu
                         count += 1
 
-            self.logger.debug("Calculating inferenced outputs based on accumulated outputs and overlap")
-            inferenced_outputs = accumulated_outputs[..., chunk_size - hop_size : -(pad_size + chunk_size - hop_size)] / self.overlap
+            self.logger.debug(
+                "Calculating inferenced outputs based on accumulated outputs and overlap"
+            )
+            inferenced_outputs = (
+                accumulated_outputs[
+                    ..., chunk_size - hop_size : -(pad_size + chunk_size - hop_size)
+                ]
+                / self.overlap
+            )
             self.logger.debug("Deleting accumulated outputs to free up memory")
             del accumulated_outputs
 
         if num_stems > 1:
-            self.logger.debug("Number of stems is greater than 1, detaching individual sources and correcting pitch if necessary...")
+            self.logger.debug(
+                "Number of stems is greater than 1, detaching individual sources and correcting pitch if necessary..."
+            )
 
             sources = {}
 
@@ -412,7 +547,10 @@ def demix(self, mix: np.ndarray) -> dict:
             # self.model_data_cfgdict.training.instruments provides the list of stems.
             # estimated_sources.cpu().detach().numpy() converts the separated sources tensor to a NumPy array for processing.
             # Each iteration provides an instrument name ('key') and its separated audio ('value') for further processing.
-            for key, value in zip(self.model_data_cfgdict.training.instruments, inferenced_outputs.cpu().detach().numpy()):
+            for key, value in zip(
+                self.model_data_cfgdict.training.instruments,
+                inferenced_outputs.cpu().detach().numpy(),
+            ):
                 self.logger.debug(f"Processing instrument: {key}")
                 if self.pitch_shift != 0:
                     self.logger.debug(f"Applying pitch correction for {key}")
@@ -422,10 +560,16 @@ def demix(self, mix: np.ndarray) -> dict:
 
             # Residual subtraction is only applicable for single-target models (not multi-stem)
             if self.is_primary_stem_main_target and num_stems == 1:
-                self.logger.debug(f"Primary stem: {self.primary_stem_name} is main target, detaching and matching array shapes if necessary...")
+                self.logger.debug(
+                    f"Primary stem: {self.primary_stem_name} is main target, detaching and matching array shapes if necessary..."
+                )
                 if sources[self.primary_stem_name].shape[1] != orig_mix.shape[1]:
-                    sources[self.primary_stem_name] = spec_utils.match_array_shapes(sources[self.primary_stem_name], orig_mix)
-                sources[self.secondary_stem_name] = orig_mix - sources[self.primary_stem_name]
+                    sources[self.primary_stem_name] = spec_utils.match_array_shapes(
+                        sources[self.primary_stem_name], orig_mix
+                    )
+                sources[self.secondary_stem_name] = (
+                    orig_mix - sources[self.primary_stem_name]
+                )
 
             self.logger.debug("Deleting inferenced outputs to free up memory")
             del inferenced_outputs
@@ -436,8 +580,16 @@ def demix(self, mix: np.ndarray) -> dict:
             self.logger.debug("Processing single source...")
 
             if self.is_roformer:
-                sources = {k: v.cpu().detach().numpy() for k, v in zip([self.model_data_cfgdict.training.target_instrument], inferenced_outputs)}
-                inferenced_output = sources[self.model_data_cfgdict.training.target_instrument]
+                sources = {
+                    k: v.cpu().detach().numpy()
+                    for k, v in zip(
+                        [self.model_data_cfgdict.training.target_instrument],
+                        inferenced_outputs,
+                    )
+                }
+                inferenced_output = sources[
+                    self.model_data_cfgdict.training.target_instrument
+                ]
             else:
                 inferenced_output = inferenced_outputs.cpu().detach().numpy()
 
@@ -454,7 +606,9 @@ def demix(self, mix: np.ndarray) -> dict:
                 primary = inferenced_output
 
             if self.is_primary_stem_main_target:
-                self.logger.debug("Single-target model detected; computing residual secondary stem from original mix")
+                self.logger.debug(
+                    "Single-target model detected; computing residual secondary stem from original mix"
+                )
                 # Ensure shapes match before residual subtraction
                 if primary.shape[1] != orig_mix.shape[1]:
                     primary = spec_utils.match_array_shapes(primary, orig_mix)
diff --git a/audio_separator/separator/architectures/vr_separator.py b/audio_separator/separator/architectures/vr_separator.py
index d00887c..1806261 100644
--- a/audio_separator/separator/architectures/vr_separator.py
+++ b/audio_separator/separator/architectures/vr_separator.py
@@ -15,7 +15,9 @@
 from audio_separator.separator.uvr_lib_v5 import spec_utils
 from audio_separator.separator.uvr_lib_v5.vr_network import nets
 from audio_separator.separator.uvr_lib_v5.vr_network import nets_new
-from audio_separator.separator.uvr_lib_v5.vr_network.model_param_init import ModelParameters
+from audio_separator.separator.uvr_lib_v5.vr_network.model_param_init import (
+    ModelParameters,
+)
 
 
 class VRSeparator(CommonSeparator):
@@ -44,10 +46,16 @@ def __init__(self, common_config, arch_config: dict):
 
         # Model params are additional technical parameter values from JSON files in separator/uvr_lib_v5/vr_network/modelparams/*.json,
         # with filenames referenced by the model_data["vr_model_param"] value
-        package_root_filepath = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
-        vr_params_json_dir = os.path.join(package_root_filepath, "uvr_lib_v5", "vr_network", "modelparams")
+        package_root_filepath = os.path.dirname(
+            os.path.dirname(os.path.abspath(__file__))
+        )
+        vr_params_json_dir = os.path.join(
+            package_root_filepath, "uvr_lib_v5", "vr_network", "modelparams"
+        )
         vr_params_json_filename = f"{self.model_data['vr_model_param']}.json"
-        vr_params_json_filepath = os.path.join(vr_params_json_dir, vr_params_json_filename)
+        vr_params_json_filepath = os.path.join(
+            vr_params_json_dir, vr_params_json_filename
+        )
         self.model_params = ModelParameters(vr_params_json_filepath)
 
         self.logger.debug(f"Model params: {self.model_params.param}")
@@ -94,16 +102,30 @@ def __init__(self, common_config, arch_config: dict):
         # - Values beyond 5 might muddy the sound for non-vocal models.
         self.aggression = float(int(arch_config.get("aggression", 5)) / 100)
 
-        self.aggressiveness = {"value": self.aggression, "split_bin": self.model_params.param["band"][1]["crop_stop"], "aggr_correction": self.model_params.param.get("aggr_correction")}
+        self.aggressiveness = {
+            "value": self.aggression,
+            "split_bin": self.model_params.param["band"][1]["crop_stop"],
+            "aggr_correction": self.model_params.param.get("aggr_correction"),
+        }
 
         self.model_samplerate = self.model_params.param["sr"]
 
-        self.logger.debug(f"VR arch params: enable_tta={self.enable_tta}, enable_post_process={self.enable_post_process}, post_process_threshold={self.post_process_threshold}")
-        self.logger.debug(f"VR arch params: batch_size={self.batch_size}, window_size={self.window_size}")
-        self.logger.debug(f"VR arch params: high_end_process={self.high_end_process}, aggression={self.aggression}")
-        self.logger.debug(f"VR arch params: is_vr_51_model={self.is_vr_51_model}, model_samplerate={self.model_samplerate}, model_capacity={self.model_capacity}")
-
-        self.model_run = lambda *args, **kwargs: self.logger.error("Model run method is not initialised yet.")
+        self.logger.debug(
+            f"VR arch params: enable_tta={self.enable_tta}, enable_post_process={self.enable_post_process}, post_process_threshold={self.post_process_threshold}"
+        )
+        self.logger.debug(
+            f"VR arch params: batch_size={self.batch_size}, window_size={self.window_size}"
+        )
+        self.logger.debug(
+            f"VR arch params: high_end_process={self.high_end_process}, aggression={self.aggression}"
+        )
+        self.logger.debug(
+            f"VR arch params: is_vr_51_model={self.is_vr_51_model}, model_samplerate={self.model_samplerate}, model_capacity={self.model_capacity}"
+        )
+
+        self.model_run = lambda *args, **kwargs: self.logger.error(
+            "Model run method is not initialised yet."
+        )
 
         # wav_subtype will be set based on input audio bit depth in prepare_mix()
         # Removed hardcoded "PCM_16" to allow bit depth preservation
@@ -126,15 +148,16 @@ def separate(self, audio_file_path, custom_output_names=None):
         self.secondary_source = None
 
         self.audio_file_path = audio_file_path
-        self.audio_file_base = os.path.splitext(os.path.basename(audio_file_path))[ 0]
+        self.audio_file_base = os.path.splitext(os.path.basename(audio_file_path))[0]
 
         # Detect input audio bit depth for output preservation
         try:
             import soundfile as sf
+
             info = sf.info(audio_file_path)
             self.input_audio_subtype = info.subtype
             self.logger.info(f"Input audio subtype: {self.input_audio_subtype}")
-            
+
             # Map subtype to wav_subtype for soundfile and set input_bit_depth for pydub
             if "24" in self.input_audio_subtype:
                 self.wav_subtype = "PCM_24"
@@ -149,44 +172,80 @@ def separate(self, audio_file_path, custom_output_names=None):
                 self.input_bit_depth = 16
                 self.logger.info("Detected 16-bit input audio")
         except Exception as e:
-            self.logger.warning(f"Could not detect input audio bit depth: {e}. Defaulting to PCM_16")
+            self.logger.warning(
+                f"Could not detect input audio bit depth: {e}. Defaulting to PCM_16"
+            )
             self.wav_subtype = "PCM_16"
             self.input_audio_subtype = None
             self.input_bit_depth = 16
 
-        self.logger.debug(f"Starting separation for input audio file {self.audio_file_path}...")
-
-        nn_arch_sizes = [31191, 33966, 56817, 123821, 123812, 129605, 218409, 537238, 537227]  # default
+        self.logger.debug(
+            f"Starting separation for input audio file {self.audio_file_path}..."
+        )
+
+        nn_arch_sizes = [
+            31191,
+            33966,
+            56817,
+            123821,
+            123812,
+            129605,
+            218409,
+            537238,
+            537227,
+        ]  # default
         vr_5_1_models = [56817, 218409]
         model_size = math.ceil(os.stat(self.model_path).st_size / 1024)
         nn_arch_size = min(nn_arch_sizes, key=lambda x: abs(x - model_size))
-        self.logger.debug(f"Model size determined: {model_size}, NN architecture size: {nn_arch_size}")
+        self.logger.debug(
+            f"Model size determined: {model_size}, NN architecture size: {nn_arch_size}"
+        )
 
         if nn_arch_size in vr_5_1_models or self.is_vr_51_model:
             self.logger.debug("Using CascadedNet for VR 5.1 model...")
-            self.model_run = nets_new.CascadedNet(self.model_params.param["bins"] * 2, nn_arch_size, nout=self.model_capacity[0], nout_lstm=self.model_capacity[1])
+            self.model_run = nets_new.CascadedNet(
+                self.model_params.param["bins"] * 2,
+                nn_arch_size,
+                nout=self.model_capacity[0],
+                nout_lstm=self.model_capacity[1],
+            )
             self.is_vr_51_model = True
         else:
             self.logger.debug("Determining model capacity...")
-            self.model_run = nets.determine_model_capacity(self.model_params.param["bins"] * 2, nn_arch_size)
+            self.model_run = nets.determine_model_capacity(
+                self.model_params.param["bins"] * 2, nn_arch_size
+            )
+
+        # VR models use PyTorch directly - STFT now handles ROCm via CPU offload
+        inference_device = self.torch_device
 
         self.model_run.load_state_dict(torch.load(self.model_path, map_location="cpu"))
-        self.model_run.to(self.torch_device)
+        self.model_run.to(inference_device)
         self.logger.debug("Model loaded and moved to device.")
 
-        y_spec, v_spec = self.inference_vr(self.loading_mix(), self.torch_device, self.aggressiveness)
+        y_spec, v_spec = self.inference_vr(
+            self.loading_mix(), inference_device, self.aggressiveness
+        )
         self.logger.debug("Inference completed.")
 
         # Sanitize y_spec and v_spec to replace NaN and infinite values
         y_spec = np.nan_to_num(y_spec, nan=0.0, posinf=0.0, neginf=0.0)
         v_spec = np.nan_to_num(v_spec, nan=0.0, posinf=0.0, neginf=0.0)
 
-        self.logger.debug("Sanitization completed. Replaced NaN and infinite values in y_spec and v_spec.")
+        self.logger.debug(
+            "Sanitization completed. Replaced NaN and infinite values in y_spec and v_spec."
+        )
 
         # After inference_vr call
-        self.logger.debug(f"Inference VR completed. y_spec shape: {y_spec.shape}, v_spec shape: {v_spec.shape}")
-        self.logger.debug(f"y_spec stats - min: {np.min(y_spec)}, max: {np.max(y_spec)}, isnan: {np.isnan(y_spec).any()}, isinf: {np.isinf(y_spec).any()}")
-        self.logger.debug(f"v_spec stats - min: {np.min(v_spec)}, max: {np.max(v_spec)}, isnan: {np.isnan(v_spec).any()}, isinf: {np.isinf(v_spec).any()}")
+        self.logger.debug(
+            f"Inference VR completed. y_spec shape: {y_spec.shape}, v_spec shape: {v_spec.shape}"
+        )
+        self.logger.debug(
+            f"y_spec stats - min: {np.min(y_spec)}, max: {np.max(y_spec)}, isnan: {np.isnan(y_spec).any()}, isinf: {np.isinf(y_spec).any()}"
+        )
+        self.logger.debug(
+            f"v_spec stats - min: {np.min(v_spec)}, max: {np.max(v_spec)}, isnan: {np.isnan(v_spec).any()}, isinf: {np.isinf(v_spec).any()}"
+        )
 
         # Not yet implemented from UVR features:
         #
@@ -205,45 +264,86 @@ def separate(self, audio_file_path, custom_output_names=None):
 
         # Note: logic similar to the following should probably be added to the other architectures
         # Check if output_single_stem is set to a value that would result in no output files
-        if self.output_single_stem and (self.output_single_stem.lower() != self.primary_stem_name.lower() and self.output_single_stem.lower() != self.secondary_stem_name.lower()):
+        if self.output_single_stem and (
+            self.output_single_stem.lower() != self.primary_stem_name.lower()
+            and self.output_single_stem.lower() != self.secondary_stem_name.lower()
+        ):
             # If so, reset output_single_stem to None to save both stems
             self.output_single_stem = None
-            self.logger.warning(f"The output_single_stem setting '{self.output_single_stem}' does not match any of the output files: '{self.primary_stem_name}' and '{self.secondary_stem_name}'. For this model '{self.model_name}', the output_single_stem setting will be ignored and all output files will be saved.")
+            self.logger.warning(
+                f"The output_single_stem setting '{self.output_single_stem}' does not match any of the output files: '{self.primary_stem_name}' and '{self.secondary_stem_name}'. For this model '{self.model_name}', the output_single_stem setting will be ignored and all output files will be saved."
+            )
 
         # Save and process the primary stem if needed
-        if not self.output_single_stem or self.output_single_stem.lower() == self.primary_stem_name.lower():
+        if (
+            not self.output_single_stem
+            or self.output_single_stem.lower() == self.primary_stem_name.lower()
+        ):
             self.logger.debug(f"Processing primary stem: {self.primary_stem_name}")
             if not isinstance(self.primary_source, np.ndarray):
-                self.logger.debug(f"Preparing to convert spectrogram to waveform. Spec shape: {y_spec.shape}")
+                self.logger.debug(
+                    f"Preparing to convert spectrogram to waveform. Spec shape: {y_spec.shape}"
+                )
 
                 self.primary_source = self.spec_to_wav(y_spec).T
                 self.logger.debug("Converting primary source spectrogram to waveform.")
                 if not self.model_samplerate == 44100:
-                    self.primary_source = librosa.resample(self.primary_source.T, orig_sr=self.model_samplerate, target_sr=44100).T
+                    self.primary_source = librosa.resample(
+                        self.primary_source.T,
+                        orig_sr=self.model_samplerate,
+                        target_sr=44100,
+                    ).T
                     self.logger.debug("Resampling primary source to 44100Hz.")
 
-            self.primary_stem_output_path = self.get_stem_output_path(self.primary_stem_name, custom_output_names)
-
-            self.logger.info(f"Saving {self.primary_stem_name} stem to {self.primary_stem_output_path}...")
-            self.final_process(self.primary_stem_output_path, self.primary_source, self.primary_stem_name)
+            self.primary_stem_output_path = self.get_stem_output_path(
+                self.primary_stem_name, custom_output_names
+            )
+
+            self.logger.info(
+                f"Saving {self.primary_stem_name} stem to {self.primary_stem_output_path}..."
+            )
+            self.final_process(
+                self.primary_stem_output_path,
+                self.primary_source,
+                self.primary_stem_name,
+            )
             output_files.append(self.primary_stem_output_path)
 
         # Save and process the secondary stem if needed
-        if not self.output_single_stem or self.output_single_stem.lower() == self.secondary_stem_name.lower():
+        if (
+            not self.output_single_stem
+            or self.output_single_stem.lower() == self.secondary_stem_name.lower()
+        ):
             self.logger.debug(f"Processing secondary stem: {self.secondary_stem_name}")
             if not isinstance(self.secondary_source, np.ndarray):
-                self.logger.debug(f"Preparing to convert spectrogram to waveform. Spec shape: {v_spec.shape}")
+                self.logger.debug(
+                    f"Preparing to convert spectrogram to waveform. Spec shape: {v_spec.shape}"
+                )
 
                 self.secondary_source = self.spec_to_wav(v_spec).T
-                self.logger.debug("Converting secondary source spectrogram to waveform.")
+                self.logger.debug(
+                    "Converting secondary source spectrogram to waveform."
+                )
                 if not self.model_samplerate == 44100:
-                    self.secondary_source = librosa.resample(self.secondary_source.T, orig_sr=self.model_samplerate, target_sr=44100).T
+                    self.secondary_source = librosa.resample(
+                        self.secondary_source.T,
+                        orig_sr=self.model_samplerate,
+                        target_sr=44100,
+                    ).T
                     self.logger.debug("Resampling secondary source to 44100Hz.")
 
-            self.secondary_stem_output_path = self.get_stem_output_path(self.secondary_stem_name, custom_output_names)
-
-            self.logger.info(f"Saving {self.secondary_stem_name} stem to {self.secondary_stem_output_path}...")
-            self.final_process(self.secondary_stem_output_path, self.secondary_source, self.secondary_stem_name)
+            self.secondary_stem_output_path = self.get_stem_output_path(
+                self.secondary_stem_name, custom_output_names
+            )
+
+            self.logger.info(
+                f"Saving {self.secondary_stem_name} stem to {self.secondary_stem_output_path}..."
+            )
+            self.final_process(
+                self.secondary_stem_output_path,
+                self.secondary_source,
+                self.secondary_stem_name,
+            )
             output_files.append(self.secondary_stem_output_path)
 
         # Not yet implemented from UVR features:
@@ -257,7 +357,9 @@ def loading_mix(self):
 
         bands_n = len(self.model_params.param["band"])
 
-        audio_file = spec_utils.write_array_to_mem(self.audio_file_path, subtype=self.wav_subtype)
+        audio_file = spec_utils.write_array_to_mem(
+            self.audio_file_path, subtype=self.wav_subtype
+        )
         is_mp3 = audio_file.endswith(".mp3") if isinstance(audio_file, str) else False
 
         self.logger.debug(f"loading_mix iteraring through {bands_n} bands")
@@ -270,8 +372,21 @@ def loading_mix(self):
                 wav_resolution = "polyphase"
 
             if d == bands_n:  # high-end band
-                X_wave[d], _ = librosa.load(audio_file, sr=bp["sr"], mono=False, dtype=np.float32, res_type=wav_resolution)
-                X_spec_s[d] = spec_utils.wave_to_spectrogram(X_wave[d], bp["hl"], bp["n_fft"], self.model_params, band=d, is_v51_model=self.is_vr_51_model)
+                X_wave[d], _ = librosa.load(
+                    audio_file,
+                    sr=bp["sr"],
+                    mono=False,
+                    dtype=np.float32,
+                    res_type=wav_resolution,
+                )
+                X_spec_s[d] = spec_utils.wave_to_spectrogram(
+                    X_wave[d],
+                    bp["hl"],
+                    bp["n_fft"],
+                    self.model_params,
+                    band=d,
+                    is_v51_model=self.is_vr_51_model,
+                )
 
                 if not np.any(X_wave[d]) and is_mp3:
                     X_wave[d] = rerun_mp3(audio_file, bp["sr"])
@@ -279,14 +394,33 @@ def loading_mix(self):
                 if X_wave[d].ndim == 1:
                     X_wave[d] = np.asarray([X_wave[d], X_wave[d]])
             else:  # lower bands
-                X_wave[d] = librosa.resample(X_wave[d + 1], orig_sr=self.model_params.param["band"][d + 1]["sr"], target_sr=bp["sr"], res_type=wav_resolution)
-                X_spec_s[d] = spec_utils.wave_to_spectrogram(X_wave[d], bp["hl"], bp["n_fft"], self.model_params, band=d, is_v51_model=self.is_vr_51_model)
+                X_wave[d] = librosa.resample(
+                    X_wave[d + 1],
+                    orig_sr=self.model_params.param["band"][d + 1]["sr"],
+                    target_sr=bp["sr"],
+                    res_type=wav_resolution,
+                )
+                X_spec_s[d] = spec_utils.wave_to_spectrogram(
+                    X_wave[d],
+                    bp["hl"],
+                    bp["n_fft"],
+                    self.model_params,
+                    band=d,
+                    is_v51_model=self.is_vr_51_model,
+                )
 
             if d == bands_n and self.high_end_process:
-                self.input_high_end_h = (bp["n_fft"] // 2 - bp["crop_stop"]) + (self.model_params.param["pre_filter_stop"] - self.model_params.param["pre_filter_start"])
-                self.input_high_end = X_spec_s[d][:, bp["n_fft"] // 2 - self.input_high_end_h : bp["n_fft"] // 2, :]
-
-        X_spec = spec_utils.combine_spectrograms(X_spec_s, self.model_params, is_v51_model=self.is_vr_51_model)
+                self.input_high_end_h = (bp["n_fft"] // 2 - bp["crop_stop"]) + (
+                    self.model_params.param["pre_filter_stop"]
+                    - self.model_params.param["pre_filter_start"]
+                )
+                self.input_high_end = X_spec_s[d][
+                    :, bp["n_fft"] // 2 - self.input_high_end_h : bp["n_fft"] // 2, :
+                ]
+
+        X_spec = spec_utils.combine_spectrograms(
+            X_spec_s, self.model_params, is_v51_model=self.is_vr_51_model
+        )
 
         del X_wave, X_spec_s, audio_file
 
@@ -297,14 +431,22 @@ def _execute(X_mag_pad, roi_size):
             X_dataset = []
             patches = (X_mag_pad.shape[2] - 2 * self.model_run.offset) // roi_size
 
-            self.logger.debug(f"inference_vr appending to X_dataset for each of {patches} patches")
+            self.logger.debug(
+                f"inference_vr appending to X_dataset for each of {patches} patches"
+            )
             for i in tqdm(range(patches)):
                 start = i * roi_size
                 X_mag_window = X_mag_pad[:, :, start : start + self.window_size]
                 X_dataset.append(X_mag_window)
 
-            total_iterations = patches // self.batch_size if not self.enable_tta else (patches // self.batch_size) * 2
-            self.logger.debug(f"inference_vr iterating through {total_iterations} batches, batch_size = {self.batch_size}")
+            total_iterations = (
+                patches // self.batch_size
+                if not self.enable_tta
+                else (patches // self.batch_size) * 2
+            )
+            self.logger.debug(
+                f"inference_vr iterating through {total_iterations} batches, batch_size = {self.batch_size}"
+            )
 
             X_dataset = np.asarray(X_dataset)
             self.model_run.eval()
@@ -312,17 +454,20 @@ def _execute(X_mag_pad, roi_size):
                 mask = []
 
                 for i in tqdm(range(0, patches, self.batch_size)):
-
                     X_batch = X_dataset[i : i + self.batch_size]
                     X_batch = torch.from_numpy(X_batch).to(device)
                     pred = self.model_run.predict_mask(X_batch)
                     if not pred.size()[3] > 0:
-                        raise ValueError(f"Window size error: h1_shape[3] must be greater than h2_shape[3]")
+                        raise ValueError(
+                            f"Window size error: h1_shape[3] must be greater than h2_shape[3]"
+                        )
                     pred = pred.detach().cpu().numpy()
                     pred = np.concatenate(pred, axis=2)
                     mask.append(pred)
                 if len(mask) == 0:
-                    raise ValueError(f"Window size error: h1_shape[3] must be greater than h2_shape[3]")
+                    raise ValueError(
+                        f"Window size error: h1_shape[3] must be greater than h2_shape[3]"
+                    )
 
                 mask = np.concatenate(mask, axis=2)
             return mask
@@ -336,7 +481,9 @@ def postprocess(mask, X_mag, X_phase):
             mask = spec_utils.adjust_aggr(mask, is_non_accom_stem, aggressiveness)
 
             if self.enable_post_process:
-                mask = spec_utils.merge_artifacts(mask, thres=self.post_process_threshold)
+                mask = spec_utils.merge_artifacts(
+                    mask, thres=self.post_process_threshold
+                )
 
             y_spec = mask * X_mag * np.exp(1.0j * X_phase)
             v_spec = (1 - mask) * X_mag * np.exp(1.0j * X_phase)
@@ -345,7 +492,9 @@ def postprocess(mask, X_mag, X_phase):
 
         X_mag, X_phase = spec_utils.preprocess(X_spec)
         n_frame = X_mag.shape[2]
-        pad_l, pad_r, roi_size = spec_utils.make_padding(n_frame, self.window_size, self.model_run.offset)
+        pad_l, pad_r, roi_size = spec_utils.make_padding(
+            n_frame, self.window_size, self.model_run.offset
+        )
         X_mag_pad = np.pad(X_mag, ((0, 0), (0, 0), (pad_l, pad_r)), mode="constant")
         X_mag_pad /= X_mag_pad.max()
         mask = _execute(X_mag_pad, roi_size)
@@ -366,11 +515,25 @@ def postprocess(mask, X_mag, X_phase):
         return y_spec, v_spec
 
     def spec_to_wav(self, spec):
-        if self.high_end_process and isinstance(self.input_high_end, np.ndarray) and self.input_high_end_h:
-            input_high_end_ = spec_utils.mirroring("mirroring", spec, self.input_high_end, self.model_params)
-            wav = spec_utils.cmb_spectrogram_to_wave(spec, self.model_params, self.input_high_end_h, input_high_end_, is_v51_model=self.is_vr_51_model)
+        if (
+            self.high_end_process
+            and isinstance(self.input_high_end, np.ndarray)
+            and self.input_high_end_h
+        ):
+            input_high_end_ = spec_utils.mirroring(
+                "mirroring", spec, self.input_high_end, self.model_params
+            )
+            wav = spec_utils.cmb_spectrogram_to_wave(
+                spec,
+                self.model_params,
+                self.input_high_end_h,
+                input_high_end_,
+                is_v51_model=self.is_vr_51_model,
+            )
         else:
-            wav = spec_utils.cmb_spectrogram_to_wave(spec, self.model_params, is_v51_model=self.is_vr_51_model)
+            wav = spec_utils.cmb_spectrogram_to_wave(
+                spec, self.model_params, is_v51_model=self.is_vr_51_model
+            )
 
         return wav
 
@@ -380,4 +543,6 @@ def rerun_mp3(audio_file, sample_rate=44100):
     with audioread.audio_open(audio_file) as f:
         track_length = int(f.duration)
 
-    return librosa.load(audio_file, duration=track_length, mono=False, sr=sample_rate)[0]
+    return librosa.load(audio_file, duration=track_length, mono=False, sr=sample_rate)[
+        0
+    ]
diff --git a/audio_separator/separator/common_separator.py b/audio_separator/separator/common_separator.py
index 69a4847..5c6b9d1 100644
--- a/audio_separator/separator/common_separator.py
+++ b/audio_separator/separator/common_separator.py
@@ -1,4 +1,4 @@
-""" This file contains the CommonSeparator class, common to all architecture-specific Separator classes. """
+"""This file contains the CommonSeparator class, common to all architecture-specific Separator classes."""
 
 from logging import Logger
 import os
@@ -50,9 +50,27 @@ class CommonSeparator:
     BV_VOCAL_STEM_LABEL = "Backing Vocals"
     NO_STEM = "No "
 
-    STEM_PAIR_MAPPER = {VOCAL_STEM: INST_STEM, INST_STEM: VOCAL_STEM, LEAD_VOCAL_STEM: BV_VOCAL_STEM, BV_VOCAL_STEM: LEAD_VOCAL_STEM, PRIMARY_STEM: SECONDARY_STEM}
-
-    NON_ACCOM_STEMS = (VOCAL_STEM, OTHER_STEM, BASS_STEM, DRUM_STEM, GUITAR_STEM, PIANO_STEM, SYNTH_STEM, STRINGS_STEM, WOODWINDS_STEM, BRASS_STEM, WIND_INST_STEM)
+    STEM_PAIR_MAPPER = {
+        VOCAL_STEM: INST_STEM,
+        INST_STEM: VOCAL_STEM,
+        LEAD_VOCAL_STEM: BV_VOCAL_STEM,
+        BV_VOCAL_STEM: LEAD_VOCAL_STEM,
+        PRIMARY_STEM: SECONDARY_STEM,
+    }
+
+    NON_ACCOM_STEMS = (
+        VOCAL_STEM,
+        OTHER_STEM,
+        BASS_STEM,
+        DRUM_STEM,
+        GUITAR_STEM,
+        PIANO_STEM,
+        SYNTH_STEM,
+        STRINGS_STEM,
+        WOODWINDS_STEM,
+        BRASS_STEM,
+        WIND_INST_STEM,
+    )
 
     def __init__(self, config):
 
@@ -63,6 +81,7 @@ def __init__(self, config):
         self.torch_device = config.get("torch_device")
         self.torch_device_cpu = config.get("torch_device_cpu")
         self.torch_device_mps = config.get("torch_device_mps")
+        self.is_rocm = config.get("is_rocm", False)
         self.onnx_execution_provider = config.get("onnx_execution_provider")
 
         # Model data
@@ -83,7 +102,7 @@ def __init__(self, config):
         self.invert_using_spec = config.get("invert_using_spec")
         self.sample_rate = config.get("sample_rate")
         self.use_soundfile = config.get("use_soundfile")
-        
+
         # Roformer-specific loading support
         self.roformer_loader = None
         self.is_roformer_model = self._detect_roformer_model()
@@ -95,16 +114,23 @@ def __init__(self, config):
         # Check if model_data has a "training" key with "instruments" list
         self.primary_stem_name = None
         self.secondary_stem_name = None
-        
+
         # Audio bit depth tracking for preserving input quality
         self.input_bit_depth = None
         self.input_subtype = None
 
-        if "training" in self.model_data and "instruments" in self.model_data["training"]:
+        if (
+            "training" in self.model_data
+            and "instruments" in self.model_data["training"]
+        ):
             instruments = self.model_data["training"]["instruments"]
             if instruments:
                 self.primary_stem_name = instruments[0]
-                self.secondary_stem_name = instruments[1] if len(instruments) > 1 else self.secondary_stem(self.primary_stem_name)
+                self.secondary_stem_name = (
+                    instruments[1]
+                    if len(instruments) > 1
+                    else self.secondary_stem(self.primary_stem_name)
+                )
 
         if self.primary_stem_name is None:
             self.primary_stem_name = self.model_data.get("primary_stem", "Vocals")
@@ -114,14 +140,28 @@ def __init__(self, config):
         self.is_bv_model = self.model_data.get("is_bv_model", False)
         self.bv_model_rebalance = self.model_data.get("is_bv_model_rebalanced", 0)
 
-        self.logger.debug(f"Common params: model_name={self.model_name}, model_path={self.model_path}")
-        self.logger.debug(f"Common params: output_dir={self.output_dir}, output_format={self.output_format}")
-        self.logger.debug(f"Common params: normalization_threshold={self.normalization_threshold}, amplification_threshold={self.amplification_threshold}")
-        self.logger.debug(f"Common params: enable_denoise={self.enable_denoise}, output_single_stem={self.output_single_stem}")
-        self.logger.debug(f"Common params: invert_using_spec={self.invert_using_spec}, sample_rate={self.sample_rate}")
-
-        self.logger.debug(f"Common params: primary_stem_name={self.primary_stem_name}, secondary_stem_name={self.secondary_stem_name}")
-        self.logger.debug(f"Common params: is_karaoke={self.is_karaoke}, is_bv_model={self.is_bv_model}, bv_model_rebalance={self.bv_model_rebalance}")
+        self.logger.debug(
+            f"Common params: model_name={self.model_name}, model_path={self.model_path}"
+        )
+        self.logger.debug(
+            f"Common params: output_dir={self.output_dir}, output_format={self.output_format}"
+        )
+        self.logger.debug(
+            f"Common params: normalization_threshold={self.normalization_threshold}, amplification_threshold={self.amplification_threshold}"
+        )
+        self.logger.debug(
+            f"Common params: enable_denoise={self.enable_denoise}, output_single_stem={self.output_single_stem}"
+        )
+        self.logger.debug(
+            f"Common params: invert_using_spec={self.invert_using_spec}, sample_rate={self.sample_rate}"
+        )
+
+        self.logger.debug(
+            f"Common params: primary_stem_name={self.primary_stem_name}, secondary_stem_name={self.secondary_stem_name}"
+        )
+        self.logger.debug(
+            f"Common params: is_karaoke={self.is_karaoke}, is_bv_model={self.is_bv_model}, bv_model_rebalance={self.bv_model_rebalance}"
+        )
 
         # File-specific variables which need to be cleared between processing different audio inputs
         self.audio_file_path = None
@@ -142,7 +182,11 @@ def secondary_stem(self, primary_stem: str):
         if primary_stem in self.STEM_PAIR_MAPPER:
             secondary_stem = self.STEM_PAIR_MAPPER[primary_stem]
         else:
-            secondary_stem = primary_stem.replace(self.NO_STEM, "") if self.NO_STEM in primary_stem else f"{self.NO_STEM}{primary_stem}"
+            secondary_stem = (
+                primary_stem.replace(self.NO_STEM, "")
+                if self.NO_STEM in primary_stem
+                else f"{self.NO_STEM}{primary_stem}"
+            )
 
         return secondary_stem
 
@@ -156,7 +200,9 @@ def final_process(self, stem_path, source, stem_name):
         """
         Finalizes the processing of a stem by writing the audio to a file and returning the processed source.
         """
-        self.logger.debug(f"Finalizing {stem_name} stem processing and writing audio...")
+        self.logger.debug(
+            f"Finalizing {stem_name} stem processing and writing audio..."
+        )
         self.write_audio(stem_path, source)
 
         return {stem_name: source}
@@ -202,7 +248,10 @@ def cached_model_source_holder(self, model_architecture, sources, model_name=Non
         Update the dictionary for the given model_architecture with the new model name and its sources.
         Use the model_architecture as a key to access the corresponding cache source mapper dictionary.
         """
-        self.cached_sources_map[model_architecture] = {**self.cached_sources_map.get(model_architecture, {}), **{model_name: sources}}
+        self.cached_sources_map[model_architecture] = {
+            **self.cached_sources_map.get(model_architecture, {}),
+            **{model_name: sources},
+        }
 
     def prepare_mix(self, mix):
         """
@@ -215,40 +264,52 @@ def prepare_mix(self, mix):
         # Check if the input is a file path (string) and needs to be loaded
         if not isinstance(mix, np.ndarray):
             self.logger.debug(f"Loading audio from file: {mix}")
-            
+
             # Get audio file info to capture bit depth before loading
             try:
                 audio_info = sf.info(mix)
                 self.input_subtype = audio_info.subtype
                 self.logger.info(f"Input audio subtype: {self.input_subtype}")
-                
+
                 # Map subtype to bit depth
-                if 'PCM_16' in self.input_subtype or self.input_subtype == 'PCM_S8':
+                if "PCM_16" in self.input_subtype or self.input_subtype == "PCM_S8":
                     self.input_bit_depth = 16
-                elif 'PCM_24' in self.input_subtype:
+                elif "PCM_24" in self.input_subtype:
                     self.input_bit_depth = 24
-                elif 'PCM_32' in self.input_subtype or 'FLOAT' in self.input_subtype or 'DOUBLE' in self.input_subtype:
+                elif (
+                    "PCM_32" in self.input_subtype
+                    or "FLOAT" in self.input_subtype
+                    or "DOUBLE" in self.input_subtype
+                ):
                     self.input_bit_depth = 32
                 else:
                     # Default to 16-bit for unknown formats
                     self.input_bit_depth = 16
-                    self.logger.warning(f"Unknown audio subtype {self.input_subtype}, defaulting to 16-bit output")
-                
-                self.logger.info(f"Detected input bit depth: {self.input_bit_depth}-bit")
+                    self.logger.warning(
+                        f"Unknown audio subtype {self.input_subtype}, defaulting to 16-bit output"
+                    )
+
+                self.logger.info(
+                    f"Detected input bit depth: {self.input_bit_depth}-bit"
+                )
             except Exception as e:
-                self.logger.warning(f"Could not read audio file info, defaulting to 16-bit output: {e}")
+                self.logger.warning(
+                    f"Could not read audio file info, defaulting to 16-bit output: {e}"
+                )
                 self.input_bit_depth = 16
-                self.input_subtype = 'PCM_16'
-            
+                self.input_subtype = "PCM_16"
+
             mix, sr = librosa.load(mix, mono=False, sr=self.sample_rate)
-            self.logger.debug(f"Audio loaded. Sample rate: {sr}, Audio shape: {mix.shape}")
+            self.logger.debug(
+                f"Audio loaded. Sample rate: {sr}, Audio shape: {mix.shape}"
+            )
         else:
             # Transpose the mix if it's already an ndarray (expected shape: [channels, samples])
             self.logger.debug("Transposing the provided mix array.")
             # Default to 16-bit if numpy array provided directly
             if self.input_bit_depth is None:
                 self.input_bit_depth = 16
-                self.input_subtype = 'PCM_16'
+                self.input_subtype = "PCM_16"
             mix = mix.T
             self.logger.debug(f"Transposed mix shape: {mix.shape}")
 
@@ -281,7 +342,9 @@ def write_audio(self, stem_path: str, stem_source):
         # Get the duration of the input audio file
         duration_seconds = librosa.get_duration(filename=self.audio_file_path)
         duration_hours = duration_seconds / 3600
-        self.logger.info(f"Audio duration is {duration_hours:.2f} hours ({duration_seconds:.2f} seconds).")
+        self.logger.info(
+            f"Audio duration is {duration_hours:.2f} hours ({duration_seconds:.2f} seconds)."
+        )
 
         if self.use_soundfile:
             self.logger.warning(f"Using soundfile for writing.")
@@ -296,7 +359,11 @@ def write_audio_pydub(self, stem_path: str, stem_source):
         """
         self.logger.debug(f"Entering write_audio_pydub with stem_path: {stem_path}")
 
-        stem_source = spec_utils.normalize(wave=stem_source, max_peak=self.normalization_threshold, min_peak=self.amplification_threshold)
+        stem_source = spec_utils.normalize(
+            wave=stem_source,
+            max_peak=self.normalization_threshold,
+            min_peak=self.amplification_threshold,
+        )
 
         # Check if the numpy array is empty or contains very low values
         if np.max(np.abs(stem_source)) < 1e-6:
@@ -312,7 +379,9 @@ def write_audio_pydub(self, stem_path: str, stem_source):
         self.logger.debug(f"Data type before conversion: {stem_source.dtype}")
 
         # Determine bit depth for output (use input bit depth if available, otherwise default to 16)
-        output_bit_depth = self.input_bit_depth if self.input_bit_depth is not None else 16
+        output_bit_depth = (
+            self.input_bit_depth if self.input_bit_depth is not None else 16
+        )
         self.logger.info(f"Writing output with {output_bit_depth}-bit depth")
 
         # For pydub, we always convert to int16 for the AudioSegment creation
@@ -326,11 +395,18 @@ def write_audio_pydub(self, stem_path: str, stem_source):
         stem_source_interleaved[0::2] = stem_source[:, 0]  # Left channel
         stem_source_interleaved[1::2] = stem_source[:, 1]  # Right channel
 
-        self.logger.debug(f"Interleaved audio data shape: {stem_source_interleaved.shape}")
+        self.logger.debug(
+            f"Interleaved audio data shape: {stem_source_interleaved.shape}"
+        )
 
         # Create a pydub AudioSegment (always from 16-bit data)
         try:
-            audio_segment = AudioSegment(stem_source_interleaved.tobytes(), frame_rate=self.sample_rate, sample_width=2, channels=2)
+            audio_segment = AudioSegment(
+                stem_source_interleaved.tobytes(),
+                frame_rate=self.sample_rate,
+                sample_width=2,
+                channels=2,
+            )
             self.logger.debug("Created AudioSegment successfully.")
         except (IOError, ValueError) as e:
             self.logger.error(f"Specific error creating AudioSegment: {e}")
@@ -346,16 +422,20 @@ def write_audio_pydub(self, stem_path: str, stem_source):
             file_format = "matroska"
 
         # Set the bitrate to 320k for mp3 files if output_bitrate is not specified
-        bitrate = "320k" if file_format == "mp3" and self.output_bitrate is None else self.output_bitrate
+        bitrate = (
+            "320k"
+            if file_format == "mp3" and self.output_bitrate is None
+            else self.output_bitrate
+        )
 
         # Export using the determined format
         try:
             # Pass codec parameters to ffmpeg to enforce bit depth for lossless formats
             export_params = {"format": file_format}
-            
+
             if bitrate:
                 export_params["bitrate"] = bitrate
-            
+
             # For lossless formats (WAV/FLAC), specify the codec parameters to enforce bit depth
             if file_format in ["wav", "flac"]:
                 if output_bit_depth == 16:
@@ -372,9 +452,11 @@ def write_audio_pydub(self, stem_path: str, stem_source):
                     export_params["parameters"] = ["-sample_fmt", "s32"]
                     if file_format == "wav":
                         export_params["codec"] = "pcm_s32le"
-            
+
             audio_segment.export(stem_path, **export_params)
-            self.logger.debug(f"Exported audio file successfully to {stem_path} with {output_bit_depth}-bit depth")
+            self.logger.debug(
+                f"Exported audio file successfully to {stem_path} with {output_bit_depth}-bit depth"
+            )
         except (IOError, ValueError) as e:
             self.logger.error(f"Error exporting audio file: {e}")
 
@@ -384,7 +466,11 @@ def write_audio_soundfile(self, stem_path: str, stem_source):
         """
         self.logger.debug(f"Entering write_audio_soundfile with stem_path: {stem_path}")
 
-        stem_source = spec_utils.normalize(wave=stem_source, max_peak=self.normalization_threshold, min_peak=self.amplification_threshold)
+        stem_source = spec_utils.normalize(
+            wave=stem_source,
+            max_peak=self.normalization_threshold,
+            min_peak=self.amplification_threshold,
+        )
 
         # Check if the numpy array is empty or contains very low values
         if np.max(np.abs(stem_source)) < 1e-6:
@@ -404,17 +490,19 @@ def write_audio_soundfile(self, stem_path: str, stem_source):
         elif self.input_bit_depth:
             # Map bit depth to subtype
             if self.input_bit_depth == 16:
-                output_subtype = 'PCM_16'
+                output_subtype = "PCM_16"
             elif self.input_bit_depth == 24:
-                output_subtype = 'PCM_24'
+                output_subtype = "PCM_24"
             elif self.input_bit_depth == 32:
-                output_subtype = 'PCM_32'
+                output_subtype = "PCM_32"
             else:
-                output_subtype = 'PCM_16'  # Default fallback
-            self.logger.info(f"Using output subtype based on bit depth: {output_subtype}")
+                output_subtype = "PCM_16"  # Default fallback
+            self.logger.info(
+                f"Using output subtype based on bit depth: {output_subtype}"
+            )
         else:
             # Default to PCM_16 if no bit depth info available
-            output_subtype = 'PCM_16'
+            output_subtype = "PCM_16"
             self.logger.warning("No bit depth info available, defaulting to PCM_16")
 
         # Correctly interleave stereo channels if needed
@@ -436,7 +524,9 @@ def write_audio_soundfile(self, stem_path: str, stem_source):
         try:
             # Specify the subtype to match input bit depth
             sf.write(stem_path, stem_source, self.sample_rate, subtype=output_subtype)
-            self.logger.debug(f"Exported audio file successfully to {stem_path} with subtype {output_subtype}")
+            self.logger.debug(
+                f"Exported audio file successfully to {stem_path} with subtype {output_subtype}"
+            )
         except Exception as e:
             self.logger.error(f"Error exporting audio file: {e}")
 
@@ -472,9 +562,9 @@ def sanitize_filename(self, filename):
         """
         Cleans the filename by replacing invalid characters with underscores.
         """
-        sanitized = re.sub(r'[<>:"/\\|?*]', '_', filename)
-        sanitized = re.sub(r'_+', '_', sanitized)
-        sanitized = sanitized.strip('_. ')
+        sanitized = re.sub(r'[<>:"/\\|?*]', "_", filename)
+        sanitized = re.sub(r"_+", "_", sanitized)
+        sanitized = sanitized.strip("_. ")
         return sanitized
 
     def get_stem_output_path(self, stem_name, custom_output_names):
@@ -483,74 +573,81 @@ def get_stem_output_path(self, stem_name, custom_output_names):
         """
         # Convert custom_output_names keys to lowercase for case-insensitive comparison
         if custom_output_names:
-            custom_output_names_lower = {k.lower(): v for k, v in custom_output_names.items()}
+            custom_output_names_lower = {
+                k.lower(): v for k, v in custom_output_names.items()
+            }
             stem_name_lower = stem_name.lower()
             if stem_name_lower in custom_output_names_lower:
-                sanitized_custom_name = self.sanitize_filename(custom_output_names_lower[stem_name_lower])
-                return os.path.join(f"{sanitized_custom_name}.{self.output_format.lower()}")
+                sanitized_custom_name = self.sanitize_filename(
+                    custom_output_names_lower[stem_name_lower]
+                )
+                return os.path.join(
+                    f"{sanitized_custom_name}.{self.output_format.lower()}"
+                )
 
         sanitized_audio_base = self.sanitize_filename(self.audio_file_base)
         sanitized_stem_name = self.sanitize_filename(stem_name)
-        sanitized_model_name = self.sanitize_filename(self.model_name) 
+        sanitized_model_name = self.sanitize_filename(self.model_name)
 
         filename = f"{sanitized_audio_base}_({sanitized_stem_name})_{sanitized_model_name}.{self.output_format.lower()}"
         return os.path.join(filename)
-    
+
     def _detect_roformer_model(self):
         """
         Detect if the current model is a Roformer model.
-        
+
         Returns:
             bool: True if this is a Roformer model, False otherwise
         """
         if not self.model_data:
             return False
-            
+
         # Check for explicit Roformer flag
         if self.model_data.get("is_roformer", False):
             return True
-            
+
         # Check model path for Roformer indicators
         if self.model_path and "roformer" in self.model_path.lower():
             return True
-            
+
         # Check model name for Roformer indicators
         if self.model_name and "roformer" in self.model_name.lower():
             return True
-            
+
         return False
-    
+
     def _initialize_roformer_loader(self):
         """
         Initialize the Roformer loader for this model.
         """
         try:
             from .roformer.roformer_loader import RoformerLoader
+
             self.roformer_loader = RoformerLoader()
             self.logger.debug("Initialized Roformer loader for CommonSeparator")
         except ImportError as e:
             self.logger.warning(f"Could not import RoformerLoader: {e}")
             self.roformer_loader = None
-    
+
     def get_roformer_loading_stats(self):
         """
         Get Roformer loading statistics if available.
-        
+
         Returns:
             dict: Loading statistics or empty dict if not available
         """
         if self.roformer_loader:
             return self.roformer_loader.get_loading_stats()
         return {}
-    
+
     def validate_roformer_config(self, config, model_type):
         """
         Validate Roformer configuration if loader is available.
-        
+
         Args:
             config: Configuration dictionary to validate
             model_type: Type of model to validate for
-            
+
         Returns:
             bool: True if valid or validation not available, False if invalid
         """
diff --git a/audio_separator/separator/roformer/roformer_loader.py b/audio_separator/separator/roformer/roformer_loader.py
index 1df1c7b..6d861e6 100644
--- a/audio_separator/separator/roformer/roformer_loader.py
+++ b/audio_separator/separator/roformer/roformer_loader.py
@@ -1,4 +1,5 @@
 """Roformer model loader with simplified new-implementation only path."""
+
 from typing import Dict, Any
 import logging
 import os
@@ -15,15 +16,11 @@ class RoformerLoader:
 
     def __init__(self):
         self.config_normalizer = ConfigurationNormalizer()
-        self._loading_stats = {
-            'new_implementation_success': 0,
-            'total_failures': 0
-        }
+        self._loading_stats = {"new_implementation_success": 0, "total_failures": 0}
 
-    def load_model(self,
-                   model_path: str,
-                   config: Dict[str, Any],
-                   device: str = 'cpu') -> ModelLoadingResult:
+    def load_model(
+        self, model_path: str, config: Dict[str, Any], device: str = "cpu"
+    ) -> ModelLoadingResult:
         logger.info(f"Loading Roformer model from {model_path}")
         try:
             normalized_config = self.config_normalizer.normalize_from_file_path(
@@ -42,8 +39,10 @@ def load_model(self,
             result = self._load_with_new_implementation(
                 model_path, normalized_config, model_type, device
             )
-            self._loading_stats['new_implementation_success'] += 1
-            logger.info(f"Successfully loaded {model_type} model with new implementation")
+            self._loading_stats["new_implementation_success"] += 1
+            logger.info(
+                f"Successfully loaded {model_type} model with new implementation"
+            )
             return result
         except (RuntimeError, ValueError, TypeError) as e:
             logger.error(f"New implementation failed: {e}")
@@ -53,13 +52,15 @@ def load_model(self,
                     model_path=model_path,
                     original_config=config,
                     device=device,
-                    original_error=str(e)
+                    original_error=str(e),
+                )
+                logger.warning(
+                    "Fell back to legacy Roformer implementation successfully"
                 )
-                logger.warning("Fell back to legacy Roformer implementation successfully")
                 return fallback_result
             except (RuntimeError, ValueError, TypeError) as fallback_error:
                 logger.error(f"Legacy implementation also failed: {fallback_error}")
-                self._loading_stats['total_failures'] += 1
+                self._loading_stats["total_failures"] += 1
                 return ModelLoadingResult.failure_result(
                     error_message=f"New implementation failed: {e}; Legacy fallback failed: {fallback_error}",
                     implementation=ImplementationVersion.NEW,
@@ -79,11 +80,9 @@ def validate_configuration(self, config: Dict[str, Any], model_type: str) -> boo
             logger.error(f"Unexpected error during validation: {e}")
             return False
 
-    def _load_with_new_implementation(self,
-                                      model_path: str,
-                                      config: Dict[str, Any],
-                                      model_type: str,
-                                      device: str) -> ModelLoadingResult:
+    def _load_with_new_implementation(
+        self, model_path: str, config: Dict[str, Any], model_type: str, device: str
+    ) -> ModelLoadingResult:
         import torch
 
         try:
@@ -96,10 +95,10 @@ def _load_with_new_implementation(self,
 
             if os.path.exists(model_path):
                 state_dict = torch.load(model_path, map_location=device)
-                if isinstance(state_dict, dict) and 'state_dict' in state_dict:
-                    model.load_state_dict(state_dict['state_dict'])
-                elif isinstance(state_dict, dict) and 'model' in state_dict:
-                    model.load_state_dict(state_dict['model'])
+                if isinstance(state_dict, dict) and "state_dict" in state_dict:
+                    model.load_state_dict(state_dict["state_dict"])
+                elif isinstance(state_dict, dict) and "model" in state_dict:
+                    model.load_state_dict(state_dict["model"])
                 else:
                     model.load_state_dict(state_dict)
                 logger.debug(f"Loaded state dict from {model_path}")
@@ -112,9 +111,9 @@ def _load_with_new_implementation(self,
                 implementation=ImplementationVersion.NEW,
                 config=config,
             )
-            result.add_model_info('model_type', model_type)
-            result.add_model_info('loading_method', 'direct')
-            result.add_model_info('device', device)
+            result.add_model_info("model_type", model_type)
+            result.add_model_info("loading_method", "direct")
+            result.add_model_info("device", device)
             return result
         except (RuntimeError, ValueError) as e:
             logger.error(f"Failed to create {model_type} model: {e}")
@@ -122,71 +121,73 @@ def _load_with_new_implementation(self,
 
     def _create_bs_roformer(self, config: Dict[str, Any]):
         from ..uvr_lib_v5.roformer.bs_roformer import BSRoformer
+
         model_args = {
-            'dim': config['dim'],
-            'depth': config['depth'],
-            'stereo': config.get('stereo', False),
-            'num_stems': config.get('num_stems', 2),
-            'time_transformer_depth': config.get('time_transformer_depth', 2),
-            'freq_transformer_depth': config.get('freq_transformer_depth', 2),
-            'freqs_per_bands': config['freqs_per_bands'],
-            'dim_head': config.get('dim_head', 64),
-            'heads': config.get('heads', 8),
-            'attn_dropout': config.get('attn_dropout', 0.0),
-            'ff_dropout': config.get('ff_dropout', 0.0),
-            'flash_attn': config.get('flash_attn', True),
-            'mlp_expansion_factor': config.get('mlp_expansion_factor', 4),
-            'sage_attention': config.get('sage_attention', False),
-            'zero_dc': config.get('zero_dc', True),
-            'use_torch_checkpoint': config.get('use_torch_checkpoint', False),
-            'skip_connection': config.get('skip_connection', False),
+            "dim": config["dim"],
+            "depth": config["depth"],
+            "stereo": config.get("stereo", False),
+            "num_stems": config.get("num_stems", 2),
+            "time_transformer_depth": config.get("time_transformer_depth", 2),
+            "freq_transformer_depth": config.get("freq_transformer_depth", 2),
+            "freqs_per_bands": config["freqs_per_bands"],
+            "dim_head": config.get("dim_head", 64),
+            "heads": config.get("heads", 8),
+            "attn_dropout": config.get("attn_dropout", 0.0),
+            "ff_dropout": config.get("ff_dropout", 0.0),
+            "flash_attn": config.get("flash_attn", True),
+            "mlp_expansion_factor": config.get("mlp_expansion_factor", 4),
+            "sage_attention": config.get("sage_attention", False),
+            "zero_dc": config.get("zero_dc", True),
+            "use_torch_checkpoint": config.get("use_torch_checkpoint", False),
+            "skip_connection": config.get("skip_connection", False),
         }
-        if 'stft_n_fft' in config:
-            model_args['stft_n_fft'] = config['stft_n_fft']
-        if 'stft_hop_length' in config:
-            model_args['stft_hop_length'] = config['stft_hop_length']
-        if 'stft_win_length' in config:
-            model_args['stft_win_length'] = config['stft_win_length']
+        if "stft_n_fft" in config:
+            model_args["stft_n_fft"] = config["stft_n_fft"]
+        if "stft_hop_length" in config:
+            model_args["stft_hop_length"] = config["stft_hop_length"]
+        if "stft_win_length" in config:
+            model_args["stft_win_length"] = config["stft_win_length"]
         logger.debug(f"Creating BSRoformer with args: {list(model_args.keys())}")
         return BSRoformer(**model_args)
 
     def _create_mel_band_roformer(self, config: Dict[str, Any]):
         from ..uvr_lib_v5.roformer.mel_band_roformer import MelBandRoformer
+
         model_args = {
-            'dim': config['dim'],
-            'depth': config['depth'],
-            'stereo': config.get('stereo', False),
-            'num_stems': config.get('num_stems', 2),
-            'time_transformer_depth': config.get('time_transformer_depth', 2),
-            'freq_transformer_depth': config.get('freq_transformer_depth', 2),
-            'num_bands': config['num_bands'],
-            'dim_head': config.get('dim_head', 64),
-            'heads': config.get('heads', 8),
-            'attn_dropout': config.get('attn_dropout', 0.0),
-            'ff_dropout': config.get('ff_dropout', 0.0),
-            'flash_attn': config.get('flash_attn', True),
-            'mlp_expansion_factor': config.get('mlp_expansion_factor', 4),
-            'sage_attention': config.get('sage_attention', False),
-            'zero_dc': config.get('zero_dc', True),
-            'use_torch_checkpoint': config.get('use_torch_checkpoint', False),
-            'skip_connection': config.get('skip_connection', False),
+            "dim": config["dim"],
+            "depth": config["depth"],
+            "stereo": config.get("stereo", False),
+            "num_stems": config.get("num_stems", 2),
+            "time_transformer_depth": config.get("time_transformer_depth", 2),
+            "freq_transformer_depth": config.get("freq_transformer_depth", 2),
+            "num_bands": config["num_bands"],
+            "dim_head": config.get("dim_head", 64),
+            "heads": config.get("heads", 8),
+            "attn_dropout": config.get("attn_dropout", 0.0),
+            "ff_dropout": config.get("ff_dropout", 0.0),
+            "flash_attn": config.get("flash_attn", True),
+            "mlp_expansion_factor": config.get("mlp_expansion_factor", 4),
+            "sage_attention": config.get("sage_attention", False),
+            "zero_dc": config.get("zero_dc", True),
+            "use_torch_checkpoint": config.get("use_torch_checkpoint", False),
+            "skip_connection": config.get("skip_connection", False),
         }
-        if 'sample_rate' in config:
-            model_args['sample_rate'] = config['sample_rate']
+        if "sample_rate" in config:
+            model_args["sample_rate"] = config["sample_rate"]
         # Optional parameters commonly present in legacy configs
         for optional_key in [
-            'mask_estimator_depth',
-            'stft_n_fft',
-            'stft_hop_length',
-            'stft_win_length',
-            'stft_normalized',
-            'stft_window_fn',
-            'multi_stft_resolution_loss_weight',
-            'multi_stft_resolutions_window_sizes',
-            'multi_stft_hop_size',
-            'multi_stft_normalized',
-            'multi_stft_window_fn',
-            'match_input_audio_length',
+            "mask_estimator_depth",
+            "stft_n_fft",
+            "stft_hop_length",
+            "stft_win_length",
+            "stft_normalized",
+            "stft_window_fn",
+            "multi_stft_resolution_loss_weight",
+            "multi_stft_resolutions_window_sizes",
+            "multi_stft_hop_size",
+            "multi_stft_normalized",
+            "multi_stft_window_fn",
+            "match_input_audio_length",
         ]:
             if optional_key in config:
                 model_args[optional_key] = config[optional_key]
@@ -194,11 +195,13 @@ def _create_mel_band_roformer(self, config: Dict[str, Any]):
         logger.debug(f"Creating MelBandRoformer with args: {list(model_args.keys())}")
         return MelBandRoformer(**model_args)
 
-    def _load_with_legacy_implementation(self,
-                                          model_path: str,
-                                          original_config: Dict[str, Any],
-                                          device: str,
-                                          original_error: str) -> ModelLoadingResult:
+    def _load_with_legacy_implementation(
+        self,
+        model_path: str,
+        original_config: Dict[str, Any],
+        device: str,
+        original_error: str,
+    ) -> ModelLoadingResult:
         """
         Attempt to load the model using the legacy direct-constructor path
         for maximum backward compatibility with existing checkpoints.
@@ -206,24 +209,27 @@ def _load_with_legacy_implementation(self,
         import torch
 
         # Use nested 'model' section if present; otherwise assume flat
-        model_cfg = original_config.get('model', original_config)
+        model_cfg = original_config.get("model", original_config)
 
         # Determine model type from config
-        if 'num_bands' in model_cfg:
+        if "num_bands" in model_cfg:
             from ..uvr_lib_v5.roformer.mel_band_roformer import MelBandRoformer
+
             model = MelBandRoformer(**model_cfg)
-        elif 'freqs_per_bands' in model_cfg:
+        elif "freqs_per_bands" in model_cfg:
             from ..uvr_lib_v5.roformer.bs_roformer import BSRoformer
+
             model = BSRoformer(**model_cfg)
         else:
             raise ValueError("Unknown Roformer model type in legacy configuration")
 
         # Load checkpoint as raw state dict (legacy behavior)
+        # Use the target device for loading to avoid device mismatches
         try:
-            checkpoint = torch.load(model_path, map_location='cpu', weights_only=True)
+            checkpoint = torch.load(model_path, map_location=device, weights_only=True)
         except TypeError:
             # For older torch versions without weights_only
-            checkpoint = torch.load(model_path, map_location='cpu')
+            checkpoint = torch.load(model_path, map_location=device)
 
         model.load_state_dict(checkpoint)
         model.to(device).eval()
@@ -238,67 +244,74 @@ def get_loading_stats(self) -> Dict[str, int]:
         return self._loading_stats.copy()
 
     def reset_loading_stats(self) -> None:
-        self._loading_stats = {
-            'new_implementation_success': 0,
-            'total_failures': 0
-        }
+        self._loading_stats = {"new_implementation_success": 0, "total_failures": 0}
 
     def detect_model_type(self, model_path: str) -> str:
         model_path_lower = model_path.lower()
-        if any(indicator in model_path_lower for indicator in ['bs_roformer', 'bs-roformer', 'bsroformer']):
+        if any(
+            indicator in model_path_lower
+            for indicator in ["bs_roformer", "bs-roformer", "bsroformer"]
+        ):
             return "bs_roformer"
-        if any(indicator in model_path_lower for indicator in ['mel_band_roformer', 'mel-band-roformer', 'melband']):
+        if any(
+            indicator in model_path_lower
+            for indicator in ["mel_band_roformer", "mel-band-roformer", "melband"]
+        ):
             return "mel_band_roformer"
-        if 'roformer' in model_path_lower:
-            logger.warning(f"Generic 'roformer' detected in {model_path}, defaulting to bs_roformer")
+        if "roformer" in model_path_lower:
+            logger.warning(
+                f"Generic 'roformer' detected in {model_path}, defaulting to bs_roformer"
+            )
             return "bs_roformer"
-        raise ValueError(f"Cannot determine Roformer model type from path: {model_path}")
+        raise ValueError(
+            f"Cannot determine Roformer model type from path: {model_path}"
+        )
 
     def get_default_configuration(self, model_type: str) -> Dict[str, Any]:
         if model_type == "bs_roformer":
             return {
-                'dim': 512,
-                'depth': 12,
-                'stereo': False,
-                'num_stems': 2,
-                'time_transformer_depth': 2,
-                'freq_transformer_depth': 2,
-                'freqs_per_bands': (2, 4, 8, 16, 32, 64),
-                'dim_head': 64,
-                'heads': 8,
-                'attn_dropout': 0.0,
-                'ff_dropout': 0.0,
-                'flash_attn': True,
-                'mlp_expansion_factor': 4,
-                'sage_attention': False,
-                'zero_dc': True,
-                'use_torch_checkpoint': False,
-                'skip_connection': False,
-                'mask_estimator_depth': 2,
-                'stft_n_fft': 2048,
-                'stft_hop_length': 512,
-                'stft_win_length': 2048,
+                "dim": 512,
+                "depth": 12,
+                "stereo": False,
+                "num_stems": 2,
+                "time_transformer_depth": 2,
+                "freq_transformer_depth": 2,
+                "freqs_per_bands": (2, 4, 8, 16, 32, 64),
+                "dim_head": 64,
+                "heads": 8,
+                "attn_dropout": 0.0,
+                "ff_dropout": 0.0,
+                "flash_attn": True,
+                "mlp_expansion_factor": 4,
+                "sage_attention": False,
+                "zero_dc": True,
+                "use_torch_checkpoint": False,
+                "skip_connection": False,
+                "mask_estimator_depth": 2,
+                "stft_n_fft": 2048,
+                "stft_hop_length": 512,
+                "stft_win_length": 2048,
             }
         elif model_type == "mel_band_roformer":
             return {
-                'dim': 512,
-                'depth': 12,
-                'stereo': False,
-                'num_stems': 2,
-                'time_transformer_depth': 2,
-                'freq_transformer_depth': 2,
-                'num_bands': 64,
-                'dim_head': 64,
-                'heads': 8,
-                'attn_dropout': 0.0,
-                'ff_dropout': 0.0,
-                'flash_attn': True,
-                'mlp_expansion_factor': 4,
-                'sage_attention': False,
-                'zero_dc': True,
-                'use_torch_checkpoint': False,
-                'skip_connection': False,
-                'sample_rate': 44100,
+                "dim": 512,
+                "depth": 12,
+                "stereo": False,
+                "num_stems": 2,
+                "time_transformer_depth": 2,
+                "freq_transformer_depth": 2,
+                "num_bands": 64,
+                "dim_head": 64,
+                "heads": 8,
+                "attn_dropout": 0.0,
+                "ff_dropout": 0.0,
+                "flash_attn": True,
+                "mlp_expansion_factor": 4,
+                "sage_attention": False,
+                "zero_dc": True,
+                "use_torch_checkpoint": False,
+                "skip_connection": False,
+                "sample_rate": 44100,
                 # Note: fmin and fmax are not implemented in MelBandRoformer constructor
             }
         else:
diff --git a/audio_separator/separator/separator.py b/audio_separator/separator/separator.py
index f543b80..b015c48 100644
--- a/audio_separator/separator/separator.py
+++ b/audio_separator/separator/separator.py
@@ -1,4 +1,4 @@
-""" This file contains the Separator class, to facilitate the separation of stems from audio. """
+"""This file contains the Separator class, to facilitate the separation of stems from audio."""
 
 from importlib import metadata, resources
 import os
@@ -96,10 +96,35 @@ def __init__(
         use_autocast=False,
         use_directml=False,
         chunk_duration=None,
-        mdx_params={"hop_length": 1024, "segment_size": 256, "overlap": 0.25, "batch_size": 1, "enable_denoise": False},
-        vr_params={"batch_size": 1, "window_size": 512, "aggression": 5, "enable_tta": False, "enable_post_process": False, "post_process_threshold": 0.2, "high_end_process": False},
-        demucs_params={"segment_size": "Default", "shifts": 2, "overlap": 0.25, "segments_enabled": True},
-        mdxc_params={"segment_size": 256, "override_model_segment_size": False, "batch_size": 1, "overlap": 8, "pitch_shift": 0},
+        mdx_params={
+            "hop_length": 1024,
+            "segment_size": 256,
+            "overlap": 0.25,
+            "batch_size": 1,
+            "enable_denoise": False,
+        },
+        vr_params={
+            "batch_size": 1,
+            "window_size": 512,
+            "aggression": 5,
+            "enable_tta": False,
+            "enable_post_process": False,
+            "post_process_threshold": 0.2,
+            "high_end_process": False,
+        },
+        demucs_params={
+            "segment_size": "Default",
+            "shifts": 2,
+            "overlap": 0.25,
+            "segments_enabled": True,
+        },
+        mdxc_params={
+            "segment_size": 256,
+            "override_model_segment_size": False,
+            "batch_size": 1,
+            "overlap": 8,
+            "pitch_shift": 0,
+        },
         info_only=False,
     ):
         """Initialize the separator."""
@@ -111,7 +136,9 @@ def __init__(
         self.log_handler = logging.StreamHandler()
 
         if self.log_formatter is None:
-            self.log_formatter = logging.Formatter("%(asctime)s - %(levelname)s - %(module)s - %(message)s")
+            self.log_formatter = logging.Formatter(
+                "%(asctime)s - %(levelname)s - %(module)s - %(message)s"
+            )
 
         self.log_handler.setFormatter(self.log_formatter)
 
@@ -125,12 +152,16 @@ def __init__(
         # Skip initialization logs if info_only is True
         if not info_only:
             package_version = self.get_package_distribution("audio-separator").version
-            self.logger.info(f"Separator version {package_version} instantiating with output_dir: {output_dir}, output_format: {output_format}")
+            self.logger.info(
+                f"Separator version {package_version} instantiating with output_dir: {output_dir}, output_format: {output_format}"
+            )
 
         if output_dir is None:
             output_dir = os.getcwd()
             if not info_only:
-                self.logger.info("Output directory not specified. Using current working directory.")
+                self.logger.info(
+                    "Output directory not specified. Using current working directory."
+                )
 
         self.output_dir = output_dir
 
@@ -138,11 +169,17 @@ def __init__(
         env_model_dir = os.environ.get("AUDIO_SEPARATOR_MODEL_DIR")
         if env_model_dir:
             self.model_file_dir = env_model_dir
-            self.logger.info(f"Using model directory from AUDIO_SEPARATOR_MODEL_DIR env var: {self.model_file_dir}")
+            self.logger.info(
+                f"Using model directory from AUDIO_SEPARATOR_MODEL_DIR env var: {self.model_file_dir}"
+            )
             if not os.path.exists(self.model_file_dir):
-                raise FileNotFoundError(f"The specified model directory does not exist: {self.model_file_dir}")
+                raise FileNotFoundError(
+                    f"The specified model directory does not exist: {self.model_file_dir}"
+                )
         else:
-            self.logger.info(f"Using model directory from model_file_dir parameter: {model_file_dir}")
+            self.logger.info(
+                f"Using model directory from model_file_dir parameter: {model_file_dir}"
+            )
             self.model_file_dir = model_file_dir
 
         # Create the model directory if it does not exist
@@ -157,28 +194,42 @@ def __init__(
 
         self.normalization_threshold = normalization_threshold
         if normalization_threshold <= 0 or normalization_threshold > 1:
-            raise ValueError("The normalization_threshold must be greater than 0 and less than or equal to 1.")
+            raise ValueError(
+                "The normalization_threshold must be greater than 0 and less than or equal to 1."
+            )
 
         self.amplification_threshold = amplification_threshold
         if amplification_threshold < 0 or amplification_threshold > 1:
-            raise ValueError("The amplification_threshold must be greater than or equal to 0 and less than or equal to 1.")
+            raise ValueError(
+                "The amplification_threshold must be greater than or equal to 0 and less than or equal to 1."
+            )
 
         self.output_single_stem = output_single_stem
         if output_single_stem is not None:
-            self.logger.debug(f"Single stem output requested, so only one output file ({output_single_stem}) will be written")
+            self.logger.debug(
+                f"Single stem output requested, so only one output file ({output_single_stem}) will be written"
+            )
 
         self.invert_using_spec = invert_using_spec
         if self.invert_using_spec:
-            self.logger.debug(f"Secondary step will be inverted using spectogram rather than waveform. This may improve quality but is slightly slower.")
+            self.logger.debug(
+                f"Secondary step will be inverted using spectogram rather than waveform. This may improve quality but is slightly slower."
+            )
 
         try:
             self.sample_rate = int(sample_rate)
             if self.sample_rate <= 0:
-                raise ValueError(f"The sample rate setting is {self.sample_rate} but it must be a non-zero whole number.")
+                raise ValueError(
+                    f"The sample rate setting is {self.sample_rate} but it must be a non-zero whole number."
+                )
             if self.sample_rate > 12800000:
-                raise ValueError(f"The sample rate setting is {self.sample_rate}. Enter something less ambitious.")
+                raise ValueError(
+                    f"The sample rate setting is {self.sample_rate}. Enter something less ambitious."
+                )
         except ValueError:
-            raise ValueError("The sample rate must be a non-zero whole number. Please provide a valid integer.")
+            raise ValueError(
+                "The sample rate must be a non-zero whole number. Please provide a valid integer."
+            )
 
         self.use_soundfile = use_soundfile
         self.use_autocast = use_autocast
@@ -191,11 +242,17 @@ def __init__(
 
         # These are parameters which users may want to configure so we expose them to the top-level Separator class,
         # even though they are specific to a single model architecture
-        self.arch_specific_params = {"MDX": mdx_params, "VR": vr_params, "Demucs": demucs_params, "MDXC": mdxc_params}
+        self.arch_specific_params = {
+            "MDX": mdx_params,
+            "VR": vr_params,
+            "Demucs": demucs_params,
+            "MDXC": mdxc_params,
+        }
 
         self.torch_device = None
         self.torch_device_cpu = None
         self.torch_device_mps = None
+        self.is_rocm = False
 
         self.onnx_execution_provider = None
         self.model_instance = None
@@ -224,7 +281,9 @@ def get_system_info(self):
         self.logger.info(f"Operating System: {os_name} {os_version}")
 
         system_info = platform.uname()
-        self.logger.info(f"System: {system_info.system} Node: {system_info.node} Release: {system_info.release} Machine: {system_info.machine} Proc: {system_info.processor}")
+        self.logger.info(
+            f"System: {system_info.system} Node: {system_info.node} Release: {system_info.release} Machine: {system_info.machine} Proc: {system_info.processor}"
+        )
 
         python_version = platform.python_version()
         self.logger.info(f"Python Version: {python_version}")
@@ -238,11 +297,15 @@ def check_ffmpeg_installed(self):
         This method checks if ffmpeg is installed and logs its version.
         """
         try:
-            ffmpeg_version_output = subprocess.check_output(["ffmpeg", "-version"], text=True)
+            ffmpeg_version_output = subprocess.check_output(
+                ["ffmpeg", "-version"], text=True
+            )
             first_line = ffmpeg_version_output.splitlines()[0]
             self.logger.info(f"FFmpeg installed: {first_line}")
         except FileNotFoundError:
-            self.logger.error("FFmpeg is not installed. Please install FFmpeg to use this package.")
+            self.logger.error(
+                "FFmpeg is not installed. Please install FFmpeg to use this package."
+            )
             # Raise an exception if this is being run by a user, as ffmpeg is required for pydub to write audio
             # but if we're just running unit tests in CI, no reason to throw
             if "PYTEST_CURRENT_TEST" not in os.environ:
@@ -253,21 +316,33 @@ def log_onnxruntime_packages(self):
         This method logs the ONNX Runtime package versions, including the GPU and Silicon packages if available.
         """
         onnxruntime_gpu_package = self.get_package_distribution("onnxruntime-gpu")
-        onnxruntime_silicon_package = self.get_package_distribution("onnxruntime-silicon")
+        onnxruntime_silicon_package = self.get_package_distribution(
+            "onnxruntime-silicon"
+        )
         onnxruntime_cpu_package = self.get_package_distribution("onnxruntime")
         onnxruntime_dml_package = self.get_package_distribution("onnxruntime-directml")
         onnxruntime_rocm_package = self.get_package_distribution("onnxruntime-rocm")
 
         if onnxruntime_gpu_package is not None:
-            self.logger.info(f"ONNX Runtime GPU package installed with version: {onnxruntime_gpu_package.version}")
+            self.logger.info(
+                f"ONNX Runtime GPU package installed with version: {onnxruntime_gpu_package.version}"
+            )
         if onnxruntime_silicon_package is not None:
-            self.logger.info(f"ONNX Runtime Silicon package installed with version: {onnxruntime_silicon_package.version}")
+            self.logger.info(
+                f"ONNX Runtime Silicon package installed with version: {onnxruntime_silicon_package.version}"
+            )
         if onnxruntime_cpu_package is not None:
-            self.logger.info(f"ONNX Runtime CPU package installed with version: {onnxruntime_cpu_package.version}")
+            self.logger.info(
+                f"ONNX Runtime CPU package installed with version: {onnxruntime_cpu_package.version}"
+            )
         if onnxruntime_dml_package is not None:
-            self.logger.info(f"ONNX Runtime DirectML package installed with version: {onnxruntime_dml_package.version}")
+            self.logger.info(
+                f"ONNX Runtime DirectML package installed with version: {onnxruntime_dml_package.version}"
+            )
         if onnxruntime_rocm_package is not None:
-            self.logger.info(f"ONNX Runtime ROCm package installed with version: {onnxruntime_rocm_package.version}")
+            self.logger.info(
+                f"ONNX Runtime ROCm package installed with version: {onnxruntime_rocm_package.version}"
+            )
 
     def setup_torch_device(self, system_info):
         """
@@ -282,11 +357,35 @@ def setup_torch_device(self, system_info):
         # Check for PyTorch version compatibility
         torch_version = torch.__version__
         self.logger.debug(f"PyTorch version: {torch_version}")
-        
+
         if torch.cuda.is_available():
             # Check if ROCm is available (AMD GPUs)
-            # We check ROCMExecutionProvider first since if it's available, we're likely on ROCm
-            if "ROCMExecutionProvider" in ort_providers:
+            # First check if ROCm packages are installed and PyTorch appears to be ROCm-based
+            onnxruntime_rocm_package = self.get_package_distribution("onnxruntime-rocm")
+            torch_version = torch.__version__
+
+            # Prioritize ROCm if ROCm packages are installed and PyTorch shows ROCm support
+            if onnxruntime_rocm_package is not None and ("+rocm" in torch_version):
+                self.logger.info(
+                    "ROCm packages detected and PyTorch shows ROCm support"
+                )
+                if "ROCMExecutionProvider" in ort_providers:
+                    self.configure_rocm(ort_providers)
+                    hardware_acceleration_enabled = True
+                elif "CUDAExecutionProvider" in ort_providers:
+                    self.logger.info(
+                        "ROCm detected with PyTorch, using CUDAExecutionProvider for AMD GPU acceleration"
+                    )
+                    self.configure_cuda(ort_providers)
+                    hardware_acceleration_enabled = True
+                else:
+                    self.logger.warning(
+                        "ROCm packages installed but no GPU execution provider available"
+                    )
+                    self.logger.warning("Falling back to CPU mode")
+                    hardware_acceleration_enabled = False
+            elif "ROCMExecutionProvider" in ort_providers:
+                # Fallback: check ROCMExecutionProvider directly
                 self.configure_rocm(ort_providers)
                 hardware_acceleration_enabled = True
             else:
@@ -296,20 +395,28 @@ def setup_torch_device(self, system_info):
                     self.configure_cuda(ort_providers)
                     hardware_acceleration_enabled = True
                 else:
-                    self.logger.warning("ROCm ExecutionProvider not available but PyTorch appears to be CUDA version. Consider installing PyTorch with ROCm support.")
-                    self.configure_cuda(ort_providers)
-                    hardware_acceleration_enabled = True
-        elif hasattr(torch.backends, "mps") and torch.backends.mps.is_available() and system_info.processor == "arm":
+                    self.logger.warning(
+                        "No GPU execution provider available, falling back to CPU mode"
+                    )
+                    hardware_acceleration_enabled = False
+        elif (
+            hasattr(torch.backends, "mps")
+            and torch.backends.mps.is_available()
+            and system_info.processor == "arm"
+        ):
             self.configure_mps(ort_providers)
             hardware_acceleration_enabled = True
         elif self.use_directml and has_torch_dml_installed:
             import torch_directml
+
             if torch_directml.is_available():
                 self.configure_dml(ort_providers)
                 hardware_acceleration_enabled = True
 
         if not hardware_acceleration_enabled:
-            self.logger.info("No hardware acceleration could be configured, running in CPU mode")
+            self.logger.info(
+                "No hardware acceleration could be configured, running in CPU mode"
+            )
             self.torch_device = self.torch_device_cpu
             self.onnx_execution_provider = ["CPUExecutionProvider"]
 
@@ -320,60 +427,123 @@ def configure_cuda(self, ort_providers):
         self.logger.info("CUDA is available in Torch, setting Torch device to CUDA")
         self.torch_device = torch.device("cuda")
         if "CUDAExecutionProvider" in ort_providers:
-            self.logger.info("ONNXruntime has CUDAExecutionProvider available, enabling acceleration")
+            self.logger.info(
+                "ONNXruntime has CUDAExecutionProvider available, enabling acceleration"
+            )
             self.onnx_execution_provider = ["CUDAExecutionProvider"]
         else:
-            self.logger.warning("CUDAExecutionProvider not available in ONNXruntime, so acceleration will NOT be enabled")
+            self.logger.warning(
+                "CUDAExecutionProvider not available in ONNXruntime, so acceleration will NOT be enabled"
+            )
 
     def configure_rocm(self, ort_providers):
         """
         This method configures the ROCm device for PyTorch and ONNX Runtime, if available.
         """
+        self.is_rocm = True
         torch_version = torch.__version__
         if "+cu" in torch_version:
-            self.logger.warning("ROCm ExecutionProvider detected, but PyTorch appears to have CUDA support instead of ROCm support.")
-            self.logger.warning("For optimal AMD GPU performance, consider reinstalling PyTorch with ROCm support:")
+            self.logger.warning(
+                "ROCm ExecutionProvider detected, but PyTorch appears to have CUDA support instead of ROCm support."
+            )
+            self.logger.warning(
+                "For optimal AMD GPU performance, consider reinstalling PyTorch with ROCm support:"
+            )
             self.logger.warning("pip uninstall torch torchvision torchaudio")
-            self.logger.warning("pip cache purge") 
-            self.logger.warning("pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/rocm5.7")
-        
-        self.logger.info("ROCm (AMD GPU) is available, setting Torch device to CUDA (ROCm presents as CUDA)")
+            self.logger.warning("pip cache purge")
+            self.logger.warning(
+                "pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/rocm5.7"
+            )
+        elif "+rocm" in torch_version:
+            self.logger.info(
+                "PyTorch with ROCm support detected (version includes +rocm)"
+            )
+        else:
+            self.logger.info(
+                "PyTorch CUDA device available, assuming ROCm compatibility"
+            )
+
+        self.logger.info(
+            "ROCm (AMD GPU) detected, setting Torch device to CUDA (ROCm presents as CUDA)"
+        )
         self.torch_device = torch.device("cuda")
+
+        # Try to configure ROCm execution provider if available
         if "ROCMExecutionProvider" in ort_providers:
-            self.logger.info("ONNXruntime has ROCMExecutionProvider available, enabling acceleration")
-            self.onnx_execution_provider = ["ROCMExecutionProvider"]
+            self.logger.info(
+                "ONNXruntime has ROCMExecutionProvider available, enabling acceleration"
+            )
+            # Optimize ROCm execution provider settings
+            try:
+                rocm_config = {
+                    "execution_mode": "parallel",  # Better for AMD GPUs
+                    "tuning_enable": True,  # Enable kernel tuning
+                    "tuning_file_prefix": "rocm-tuning-",  # Tuning file prefix
+                    "memory_pattern_enable": True,  # Enable memory pattern optimization
+                    "arena_extend_strategy": "kNextPowerOfTwo",  # Memory allocation strategy
+                }
+                self.onnx_execution_provider = [("ROCMExecutionProvider", rocm_config)]
+            except Exception as e:
+                self.logger.warning(
+                    f"Failed to configure ROCm execution provider settings: {e}"
+                )
+                self.onnx_execution_provider = ["ROCMExecutionProvider"]
+        elif "CUDAExecutionProvider" in ort_providers:
+            self.logger.info(
+                "Using CUDAExecutionProvider as fallback for ROCm acceleration"
+            )
+            self.onnx_execution_provider = ["CUDAExecutionProvider"]
+            self.logger.info(
+                "✓ ROCm (AMD GPU) acceleration enabled via CUDAExecutionProvider"
+            )
         else:
-            self.logger.warning("ROCMExecutionProvider not available in ONNXruntime, so acceleration will NOT be enabled")
+            self.logger.warning(
+                "No GPU execution provider available for ROCm, falling back to CPU"
+            )
+            self.onnx_execution_provider = ["CPUExecutionProvider"]
 
     def configure_mps(self, ort_providers):
         """
         This method configures the Apple Silicon MPS/CoreML device for PyTorch and ONNX Runtime, if available.
         """
-        self.logger.info("Apple Silicon MPS/CoreML is available in Torch and processor is ARM, setting Torch device to MPS")
+        self.logger.info(
+            "Apple Silicon MPS/CoreML is available in Torch and processor is ARM, setting Torch device to MPS"
+        )
         self.torch_device_mps = torch.device("mps")
 
         self.torch_device = self.torch_device_mps
 
         if "CoreMLExecutionProvider" in ort_providers:
-            self.logger.info("ONNXruntime has CoreMLExecutionProvider available, enabling acceleration")
+            self.logger.info(
+                "ONNXruntime has CoreMLExecutionProvider available, enabling acceleration"
+            )
             self.onnx_execution_provider = ["CoreMLExecutionProvider"]
         else:
-            self.logger.warning("CoreMLExecutionProvider not available in ONNXruntime, so acceleration will NOT be enabled")
+            self.logger.warning(
+                "CoreMLExecutionProvider not available in ONNXruntime, so acceleration will NOT be enabled"
+            )
 
     def configure_dml(self, ort_providers):
         """
         This method configures the DirectML device for PyTorch and ONNX Runtime, if available.
         """
         import torch_directml
-        self.logger.info("DirectML is available in Torch, setting Torch device to DirectML")
-        self.torch_device_dml = torch_directml.device() 
+
+        self.logger.info(
+            "DirectML is available in Torch, setting Torch device to DirectML"
+        )
+        self.torch_device_dml = torch_directml.device()
         self.torch_device = self.torch_device_dml
 
         if "DmlExecutionProvider" in ort_providers:
-            self.logger.info("ONNXruntime has DmlExecutionProvider available, enabling acceleration")
+            self.logger.info(
+                "ONNXruntime has DmlExecutionProvider available, enabling acceleration"
+            )
             self.onnx_execution_provider = ["DmlExecutionProvider"]
         else:
-            self.logger.warning("DmlExecutionProvider not available in ONNXruntime, so acceleration will NOT be enabled")
+            self.logger.warning(
+                "DmlExecutionProvider not available in ONNXruntime, so acceleration will NOT be enabled"
+            )
 
     def get_package_distribution(self, package_name):
         """
@@ -399,12 +569,16 @@ def get_model_hash(self, model_path):
             with open(model_path, "rb") as f:
                 if file_size < BYTES_TO_HASH:
                     # Hash the entire file if smaller than the target byte count
-                    self.logger.debug(f"File size {file_size} < {BYTES_TO_HASH}, hashing entire file.")
+                    self.logger.debug(
+                        f"File size {file_size} < {BYTES_TO_HASH}, hashing entire file."
+                    )
                     hash_value = hashlib.md5(f.read()).hexdigest()
                 else:
                     # Seek to the specific position before the end (from the beginning) and hash
                     seek_pos = file_size - BYTES_TO_HASH
-                    self.logger.debug(f"File size {file_size} >= {BYTES_TO_HASH}, seeking to {seek_pos} and hashing remaining bytes.")
+                    self.logger.debug(
+                        f"File size {file_size} >= {BYTES_TO_HASH}, seeking to {seek_pos} and hashing remaining bytes."
+                    )
                     f.seek(seek_pos, io.SEEK_SET)
                     hash_value = hashlib.md5(f.read()).hexdigest()
 
@@ -414,11 +588,11 @@ def get_model_hash(self, model_path):
 
         except FileNotFoundError:
             self.logger.error(f"Model file not found at {model_path}")
-            raise # Re-raise the specific error
+            raise  # Re-raise the specific error
         except Exception as e:
             # Catch other potential errors (e.g., permissions, other IOErrors)
             self.logger.error(f"Error calculating hash for {model_path}: {e}")
-            raise # Re-raise other errors
+            raise  # Re-raise other errors
 
     def download_file_if_not_exists(self, url, output_path):
         """
@@ -426,10 +600,14 @@ def download_file_if_not_exists(self, url, output_path):
         """
 
         if os.path.isfile(output_path):
-            self.logger.debug(f"File already exists at {output_path}, skipping download")
+            self.logger.debug(
+                f"File already exists at {output_path}, skipping download"
+            )
             return
 
-        self.logger.debug(f"Downloading file from {url} to {output_path} with timeout 300s")
+        self.logger.debug(
+            f"Downloading file from {url} to {output_path} with timeout 300s"
+        )
         response = requests.get(url, stream=True, timeout=300)
 
         if response.status_code == 200:
@@ -442,7 +620,9 @@ def download_file_if_not_exists(self, url, output_path):
                     f.write(chunk)
             progress_bar.close()
         else:
-            raise RuntimeError(f"Failed to download file from {url}, response code: {response.status_code}")
+            raise RuntimeError(
+                f"Failed to download file from {url}, response code: {response.status_code}"
+            )
 
     def list_supported_model_files(self):
         """
@@ -533,7 +713,10 @@ def list_supported_model_files(self):
         """
         download_checks_path = os.path.join(self.model_file_dir, "download_checks.json")
 
-        self.download_file_if_not_exists("https://raw.githubusercontent.com/TRvlvr/application_data/main/filelists/download_checks.json", download_checks_path)
+        self.download_file_if_not_exists(
+            "https://raw.githubusercontent.com/TRvlvr/application_data/main/filelists/download_checks.json",
+            download_checks_path,
+        )
 
         model_downloads_list = json.load(open(download_checks_path, encoding="utf-8"))
         self.logger.debug(f"UVR model download list loaded")
@@ -549,13 +732,20 @@ def list_supported_model_files(self):
             self.logger.warning("Continuing without model scores")
 
         # Only show Demucs v4 models as we've only implemented support for v4
-        filtered_demucs_v4 = {key: value for key, value in model_downloads_list["demucs_download_list"].items() if key.startswith("Demucs v4")}
+        filtered_demucs_v4 = {
+            key: value
+            for key, value in model_downloads_list["demucs_download_list"].items()
+            if key.startswith("Demucs v4")
+        }
 
         # Modified Demucs handling to use YAML files as identifiers and include download files
         demucs_models = {}
         for name, files in filtered_demucs_v4.items():
             # Find the YAML file in the model files
-            yaml_file = next((filename for filename in files.keys() if filename.endswith(".yaml")), None)
+            yaml_file = next(
+                (filename for filename in files.keys() if filename.endswith(".yaml")),
+                None,
+            )
             if yaml_file:
                 model_score_data = model_scores.get(yaml_file, {})
                 demucs_models[name] = {
@@ -563,7 +753,9 @@ def list_supported_model_files(self):
                     "scores": model_score_data.get("median_scores", {}),
                     "stems": model_score_data.get("stems", []),
                     "target_stem": model_score_data.get("target_stem"),
-                    "download_files": list(files.values()),  # List of all download URLs/filenames
+                    "download_files": list(
+                        files.values()
+                    ),  # List of all download URLs/filenames
                 }
 
         # Load the JSON file using importlib.resources
@@ -581,7 +773,10 @@ def list_supported_model_files(self):
                     "target_stem": model_scores.get(filename, {}).get("target_stem"),
                     "download_files": [filename],
                 }  # Just the filename for VR models
-                for name, filename in {**model_downloads_list["vr_download_list"], **audio_separator_models_list["vr_download_list"]}.items()
+                for name, filename in {
+                    **model_downloads_list["vr_download_list"],
+                    **audio_separator_models_list["vr_download_list"],
+                }.items()
             },
             "MDX": {
                 name: {
@@ -591,16 +786,29 @@ def list_supported_model_files(self):
                     "target_stem": model_scores.get(filename, {}).get("target_stem"),
                     "download_files": [filename],
                 }  # Just the filename for MDX models
-                for name, filename in {**model_downloads_list["mdx_download_list"], **model_downloads_list["mdx_download_vip_list"], **audio_separator_models_list["mdx_download_list"]}.items()
+                for name, filename in {
+                    **model_downloads_list["mdx_download_list"],
+                    **model_downloads_list["mdx_download_vip_list"],
+                    **audio_separator_models_list["mdx_download_list"],
+                }.items()
             },
             "Demucs": demucs_models,
             "MDXC": {
                 name: {
                     "filename": next(iter(files.keys())),
-                    "scores": model_scores.get(next(iter(files.keys())), {}).get("median_scores", {}),
-                    "stems": model_scores.get(next(iter(files.keys())), {}).get("stems", []),
-                    "target_stem": model_scores.get(next(iter(files.keys())), {}).get("target_stem"),
-                    "download_files": list(files.keys()) + list(files.values()),  # List of both model filenames and config filenames
+                    "scores": model_scores.get(next(iter(files.keys())), {}).get(
+                        "median_scores", {}
+                    ),
+                    "stems": model_scores.get(next(iter(files.keys())), {}).get(
+                        "stems", []
+                    ),
+                    "target_stem": model_scores.get(next(iter(files.keys())), {}).get(
+                        "target_stem"
+                    ),
+                    "download_files": list(files.keys())
+                    + list(
+                        files.values()
+                    ),  # List of both model filenames and config filenames
                 }
                 for name, files in {
                     **model_downloads_list["mdx23c_download_list"],
@@ -619,8 +827,12 @@ def print_uvr_vip_message(self):
         This method prints a message to the user if they have downloaded a VIP model, reminding them to support Anjok07 on Patreon.
         """
         if self.model_is_uvr_vip:
-            self.logger.warning(f"The model: '{self.model_friendly_name}' is a VIP model, intended by Anjok07 for access by paying subscribers only.")
-            self.logger.warning("If you are not already subscribed, please consider supporting the developer of UVR, Anjok07 by subscribing here: https://patreon.com/uvr")
+            self.logger.warning(
+                f"The model: '{self.model_friendly_name}' is a VIP model, intended by Anjok07 for access by paying subscribers only."
+            )
+            self.logger.warning(
+                "If you are not already subscribed, please consider supporting the developer of UVR, Anjok07 by subscribing here: https://patreon.com/uvr"
+            )
 
     def download_model_files(self, model_filename):
         """
@@ -631,22 +843,33 @@ def download_model_files(self, model_filename):
 
         supported_model_files_grouped = self.list_supported_model_files()
         public_model_repo_url_prefix = "https://github.com/TRvlvr/model_repo/releases/download/all_public_uvr_models"
-        vip_model_repo_url_prefix = "https://github.com/Anjok0109/ai_magic/releases/download/v5"
+        vip_model_repo_url_prefix = (
+            "https://github.com/Anjok0109/ai_magic/releases/download/v5"
+        )
         audio_separator_models_repo_url_prefix = "https://github.com/nomadkaraoke/python-audio-separator/releases/download/model-configs"
 
         yaml_config_filename = None
 
-        self.logger.debug(f"Searching for model_filename {model_filename} in supported_model_files_grouped")
+        self.logger.debug(
+            f"Searching for model_filename {model_filename} in supported_model_files_grouped"
+        )
 
         # Iterate through model types (MDX, Demucs, MDXC)
         for model_type, models in supported_model_files_grouped.items():
             # Iterate through each model in this type
             for model_friendly_name, model_info in models.items():
                 self.model_is_uvr_vip = "VIP" in model_friendly_name
-                model_repo_url_prefix = vip_model_repo_url_prefix if self.model_is_uvr_vip else public_model_repo_url_prefix
+                model_repo_url_prefix = (
+                    vip_model_repo_url_prefix
+                    if self.model_is_uvr_vip
+                    else public_model_repo_url_prefix
+                )
 
                 # Check if this model matches our target filename
-                if model_info["filename"] == model_filename or model_filename in model_info["download_files"]:
+                if (
+                    model_info["filename"] == model_filename
+                    or model_filename in model_info["download_files"]
+                ):
                     self.logger.debug(f"Found matching model: {model_friendly_name}")
                     self.model_friendly_name = model_friendly_name
                     self.print_uvr_vip_message()
@@ -657,35 +880,59 @@ def download_model_files(self, model_filename):
                         if file_to_download.startswith("http"):
                             filename = file_to_download.split("/")[-1]
                             download_path = os.path.join(self.model_file_dir, filename)
-                            self.download_file_if_not_exists(file_to_download, download_path)
+                            self.download_file_if_not_exists(
+                                file_to_download, download_path
+                            )
                             continue
 
-                        download_path = os.path.join(self.model_file_dir, file_to_download)
+                        download_path = os.path.join(
+                            self.model_file_dir, file_to_download
+                        )
 
                         # For MDXC models, handle YAML config files specially
                         if model_type == "MDXC" and file_to_download.endswith(".yaml"):
                             yaml_config_filename = file_to_download
                             try:
                                 yaml_url = f"{model_repo_url_prefix}/mdx_model_data/mdx_c_configs/{file_to_download}"
-                                self.download_file_if_not_exists(yaml_url, download_path)
+                                self.download_file_if_not_exists(
+                                    yaml_url, download_path
+                                )
                             except RuntimeError:
-                                self.logger.debug("YAML config not found in UVR repo, trying audio-separator models repo...")
+                                self.logger.debug(
+                                    "YAML config not found in UVR repo, trying audio-separator models repo..."
+                                )
                                 yaml_url = f"{audio_separator_models_repo_url_prefix}/{file_to_download}"
-                                self.download_file_if_not_exists(yaml_url, download_path)
+                                self.download_file_if_not_exists(
+                                    yaml_url, download_path
+                                )
                             continue
 
                         # For regular model files, try UVR repo first, then audio-separator repo
                         try:
                             download_url = f"{model_repo_url_prefix}/{file_to_download}"
-                            self.download_file_if_not_exists(download_url, download_path)
+                            self.download_file_if_not_exists(
+                                download_url, download_path
+                            )
                         except RuntimeError:
-                            self.logger.debug("Model not found in UVR repo, trying audio-separator models repo...")
+                            self.logger.debug(
+                                "Model not found in UVR repo, trying audio-separator models repo..."
+                            )
                             download_url = f"{audio_separator_models_repo_url_prefix}/{file_to_download}"
-                            self.download_file_if_not_exists(download_url, download_path)
-
-                    return model_filename, model_type, model_friendly_name, model_path, yaml_config_filename
-
-        raise ValueError(f"Model file {model_filename} not found in supported model files")
+                            self.download_file_if_not_exists(
+                                download_url, download_path
+                            )
+
+                    return (
+                        model_filename,
+                        model_type,
+                        model_friendly_name,
+                        model_path,
+                        yaml_config_filename,
+                    )
+
+        raise ValueError(
+            f"Model file {model_filename} not found in supported model files"
+        )
 
     def load_model_data_from_yaml(self, yaml_config_filename):
         """
@@ -694,13 +941,19 @@ def load_model_data_from_yaml(self, yaml_config_filename):
         """
         # Verify if the YAML filename includes a full path or just the filename
         if not os.path.exists(yaml_config_filename):
-            model_data_yaml_filepath = os.path.join(self.model_file_dir, yaml_config_filename)
+            model_data_yaml_filepath = os.path.join(
+                self.model_file_dir, yaml_config_filename
+            )
         else:
             model_data_yaml_filepath = yaml_config_filename
 
-        self.logger.debug(f"Loading model data from YAML at path {model_data_yaml_filepath}")
+        self.logger.debug(
+            f"Loading model data from YAML at path {model_data_yaml_filepath}"
+        )
 
-        model_data = yaml.load(open(model_data_yaml_filepath, encoding="utf-8"), Loader=yaml.FullLoader)
+        model_data = yaml.load(
+            open(model_data_yaml_filepath, encoding="utf-8"), Loader=yaml.FullLoader
+        )
         self.logger.debug(f"Model data loaded from YAML file: {model_data}")
 
         if "roformer" in model_data_yaml_filepath.lower():
@@ -715,13 +968,19 @@ def load_model_data_using_hash(self, model_path):
         The correct parameters are identified by calculating the hash of the model file and looking up the hash in the UVR data files.
         """
         # Model data and configuration sources from UVR
-        model_data_url_prefix = "https://raw.githubusercontent.com/TRvlvr/application_data/main"
+        model_data_url_prefix = (
+            "https://raw.githubusercontent.com/TRvlvr/application_data/main"
+        )
 
         vr_model_data_url = f"{model_data_url_prefix}/vr_model_data/model_data_new.json"
-        mdx_model_data_url = f"{model_data_url_prefix}/mdx_model_data/model_data_new.json"
+        mdx_model_data_url = (
+            f"{model_data_url_prefix}/mdx_model_data/model_data_new.json"
+        )
 
         # Calculate hash for the downloaded model
-        self.logger.debug("Calculating MD5 hash for model file to identify model parameters from UVR data...")
+        self.logger.debug(
+            "Calculating MD5 hash for model file to identify model parameters from UVR data..."
+        )
         model_hash = self.get_model_hash(model_path)
         self.logger.debug(f"Model {model_path} has hash {model_hash}")
 
@@ -735,25 +994,37 @@ def load_model_data_using_hash(self, model_path):
         self.download_file_if_not_exists(mdx_model_data_url, mdx_model_data_path)
 
         # Loading model data from UVR
-        self.logger.debug("Loading MDX and VR model parameters from UVR model data files...")
+        self.logger.debug(
+            "Loading MDX and VR model parameters from UVR model data files..."
+        )
         vr_model_data_object = json.load(open(vr_model_data_path, encoding="utf-8"))
         mdx_model_data_object = json.load(open(mdx_model_data_path, encoding="utf-8"))
 
         # Load additional model data from audio-separator
-        self.logger.debug("Loading additional model parameters from audio-separator model data file...")
+        self.logger.debug(
+            "Loading additional model parameters from audio-separator model data file..."
+        )
         with resources.open_text("audio_separator", "model-data.json") as f:
             audio_separator_model_data = json.load(f)
 
         # Merge the model data objects, with audio-separator data taking precedence
-        vr_model_data_object = {**vr_model_data_object, **audio_separator_model_data.get("vr_model_data", {})}
-        mdx_model_data_object = {**mdx_model_data_object, **audio_separator_model_data.get("mdx_model_data", {})}
+        vr_model_data_object = {
+            **vr_model_data_object,
+            **audio_separator_model_data.get("vr_model_data", {}),
+        }
+        mdx_model_data_object = {
+            **mdx_model_data_object,
+            **audio_separator_model_data.get("mdx_model_data", {}),
+        }
 
         if model_hash in mdx_model_data_object:
             model_data = mdx_model_data_object[model_hash]
         elif model_hash in vr_model_data_object:
             model_data = vr_model_data_object[model_hash]
         else:
-            raise ValueError(f"Unsupported Model File: parameters for MD5 hash {model_hash} could not be found in UVR model data file for MDX or VR arch.")
+            raise ValueError(
+                f"Unsupported Model File: parameters for MD5 hash {model_hash} could not be found in UVR model data file for MDX or VR arch."
+            )
 
         self.logger.debug(f"Model data loaded using hash {model_hash}: {model_data}")
 
@@ -769,9 +1040,17 @@ def load_model(self, model_filename="model_bs_roformer_ep_317_sdr_12.9755.ckpt")
         load_model_start_time = time.perf_counter()
 
         # Setting up the model path
-        model_filename, model_type, model_friendly_name, model_path, yaml_config_filename = self.download_model_files(model_filename)
+        (
+            model_filename,
+            model_type,
+            model_friendly_name,
+            model_path,
+            yaml_config_filename,
+        ) = self.download_model_files(model_filename)
         model_name = model_filename.split(".")[0]
-        self.logger.debug(f"Model downloaded, friendly name: {model_friendly_name}, model_path: {model_path}")
+        self.logger.debug(
+            f"Model downloaded, friendly name: {model_friendly_name}, model_path: {model_path}"
+        )
 
         if model_path.lower().endswith(".yaml"):
             yaml_config_filename = model_path
@@ -787,6 +1066,7 @@ def load_model(self, model_filename="model_bs_roformer_ep_317_sdr_12.9755.ckpt")
             "torch_device": self.torch_device,
             "torch_device_cpu": self.torch_device_cpu,
             "torch_device_mps": self.torch_device_mps,
+            "is_rocm": self.is_rocm,
             "onnx_execution_provider": self.onnx_execution_provider,
             "model_name": model_name,
             "model_path": model_path,
@@ -803,14 +1083,26 @@ def load_model(self, model_filename="model_bs_roformer_ep_317_sdr_12.9755.ckpt")
         }
 
         # Instantiate the appropriate separator class depending on the model type
-        separator_classes = {"MDX": "mdx_separator.MDXSeparator", "VR": "vr_separator.VRSeparator", "Demucs": "demucs_separator.DemucsSeparator", "MDXC": "mdxc_separator.MDXCSeparator"}
+        separator_classes = {
+            "MDX": "mdx_separator.MDXSeparator",
+            "VR": "vr_separator.VRSeparator",
+            "Demucs": "demucs_separator.DemucsSeparator",
+            "MDXC": "mdxc_separator.MDXCSeparator",
+        }
 
-        if model_type not in self.arch_specific_params or model_type not in separator_classes:
+        if (
+            model_type not in self.arch_specific_params
+            or model_type not in separator_classes
+        ):
             # Enhanced error message for Roformer models
-            if "roformer" in model_filename.lower() or (model_data and model_data.get("is_roformer", False)):
-                error_msg = (f"Roformer model type not properly configured: {model_type}. "
-                           f"This may indicate a configuration validation failure. "
-                           f"Please check the model file and YAML configuration.")
+            if "roformer" in model_filename.lower() or (
+                model_data and model_data.get("is_roformer", False)
+            ):
+                error_msg = (
+                    f"Roformer model type not properly configured: {model_type}. "
+                    f"This may indicate a configuration validation failure. "
+                    f"Please check the model file and YAML configuration."
+                )
                 self.logger.error(error_msg)
                 raise ValueError(error_msg)
             else:
@@ -819,35 +1111,53 @@ def load_model(self, model_filename="model_bs_roformer_ep_317_sdr_12.9755.ckpt")
         if model_type == "Demucs" and sys.version_info < (3, 10):
             raise Exception("Demucs models require Python version 3.10 or newer.")
 
-        self.logger.debug(f"Importing module for model type {model_type}: {separator_classes[model_type]}")
+        self.logger.debug(
+            f"Importing module for model type {model_type}: {separator_classes[model_type]}"
+        )
 
         module_name, class_name = separator_classes[model_type].split(".")
-        module = importlib.import_module(f"audio_separator.separator.architectures.{module_name}")
+        module = importlib.import_module(
+            f"audio_separator.separator.architectures.{module_name}"
+        )
         separator_class = getattr(module, class_name)
 
-        self.logger.debug(f"Instantiating separator class for model type {model_type}: {separator_class}")
-        
+        self.logger.debug(
+            f"Instantiating separator class for model type {model_type}: {separator_class}"
+        )
+
         try:
-            self.model_instance = separator_class(common_config=common_params, arch_config=self.arch_specific_params[model_type])
+            self.model_instance = separator_class(
+                common_config=common_params,
+                arch_config=self.arch_specific_params[model_type],
+            )
         except Exception as e:
             # Enhanced error handling for Roformer models
-            if "roformer" in model_filename.lower() or (model_data and model_data.get("is_roformer", False)):
-                error_msg = (f"Failed to instantiate Roformer model: {e}. "
-                           f"This may be due to missing parameters or configuration validation failures.")
+            if "roformer" in model_filename.lower() or (
+                model_data and model_data.get("is_roformer", False)
+            ):
+                error_msg = (
+                    f"Failed to instantiate Roformer model: {e}. "
+                    f"This may be due to missing parameters or configuration validation failures."
+                )
                 self.logger.error(error_msg)
                 raise RuntimeError(error_msg) from e
             else:
                 raise
 
         # Log Roformer implementation version if applicable
-        if hasattr(self.model_instance, 'is_roformer_model') and self.model_instance.is_roformer_model:
+        if (
+            hasattr(self.model_instance, "is_roformer_model")
+            and self.model_instance.is_roformer_model
+        ):
             roformer_stats = self.model_instance.get_roformer_loading_stats()
             if roformer_stats:
                 self.logger.info(f"Roformer loading stats: {roformer_stats}")
-                
+
         # Log the completion of the model load process
         self.logger.debug("Loading model completed.")
-        self.logger.info(f'Load model duration: {time.strftime("%H:%M:%S", time.gmtime(int(time.perf_counter() - load_model_start_time)))}')
+        self.logger.info(
+            f"Load model duration: {time.strftime('%H:%M:%S', time.gmtime(int(time.perf_counter() - load_model_start_time)))}"
+        )
 
     def separate(self, audio_file_path, custom_output_names=None):
         """
@@ -866,7 +1176,9 @@ def separate(self, audio_file_path, custom_output_names=None):
         """
         # Check if the model and device are properly initialized
         if not (self.torch_device and self.model_instance):
-            raise ValueError("Initialization failed or model not loaded. Please load a model before attempting to separate.")
+            raise ValueError(
+                "Initialization failed or model not loaded. Please load a model before attempting to separate."
+            )
 
         # If audio_file_path is a string, convert it to a list for uniform processing
         if isinstance(audio_file_path, str):
@@ -882,15 +1194,30 @@ def separate(self, audio_file_path, custom_output_names=None):
                 for root, dirs, files in os.walk(path):
                     for file in files:
                         # Check the file extension to ensure it's an audio file
-                        if file.endswith((".wav", ".flac", ".mp3", ".ogg", ".opus", ".m4a", ".aiff", ".ac3")):  # Add other formats if needed
+                        if file.endswith(
+                            (
+                                ".wav",
+                                ".flac",
+                                ".mp3",
+                                ".ogg",
+                                ".opus",
+                                ".m4a",
+                                ".aiff",
+                                ".ac3",
+                            )
+                        ):  # Add other formats if needed
                             full_path = os.path.join(root, file)
                             self.logger.info(f"Processing file: {full_path}")
                             try:
                                 # Perform separation for each file
-                                files_output = self._separate_file(full_path, custom_output_names)
+                                files_output = self._separate_file(
+                                    full_path, custom_output_names
+                                )
                                 output_files.extend(files_output)
                             except Exception as e:
-                                self.logger.error(f"Failed to process file {full_path}: {e}")
+                                self.logger.error(
+                                    f"Failed to process file {full_path}: {e}"
+                                )
             else:
                 # If the path is a file, process it directly
                 self.logger.info(f"Processing file: {path}")
@@ -916,32 +1243,52 @@ def _separate_file(self, audio_file_path, custom_output_names=None):
         # Check if chunking is enabled and file is large enough
         if self.chunk_duration is not None:
             import librosa
+
             duration = librosa.get_duration(path=audio_file_path)
 
             from audio_separator.separator.audio_chunking import AudioChunker
+
             chunker = AudioChunker(self.chunk_duration, self.logger)
 
             if chunker.should_chunk(duration):
-                self.logger.info(f"File duration {duration:.1f}s exceeds chunk size {self.chunk_duration}s, using chunked processing")
+                self.logger.info(
+                    f"File duration {duration:.1f}s exceeds chunk size {self.chunk_duration}s, using chunked processing"
+                )
                 return self._process_with_chunking(audio_file_path, custom_output_names)
 
         # Log the start of the separation process
-        self.logger.info(f"Starting separation process for audio_file_path: {audio_file_path}")
+        self.logger.info(
+            f"Starting separation process for audio_file_path: {audio_file_path}"
+        )
         separate_start_time = time.perf_counter()
 
         # Log normalization and amplification thresholds
-        self.logger.debug(f"Normalization threshold set to {self.normalization_threshold}, waveform will be lowered to this max amplitude to avoid clipping.")
-        self.logger.debug(f"Amplification threshold set to {self.amplification_threshold}, waveform will be scaled up to this max amplitude if below it.")
+        self.logger.debug(
+            f"Normalization threshold set to {self.normalization_threshold}, waveform will be lowered to this max amplitude to avoid clipping."
+        )
+        self.logger.debug(
+            f"Amplification threshold set to {self.amplification_threshold}, waveform will be scaled up to this max amplitude if below it."
+        )
 
         # Run separation method for the loaded model with autocast enabled if supported by the device
         output_files = None
-        if self.use_autocast and autocast_mode.is_autocast_available(self.torch_device.type):
+        use_autocast_for_inference = self.use_autocast and not self.is_rocm
+
+        if use_autocast_for_inference and autocast_mode.is_autocast_available(
+            self.torch_device.type
+        ):
             self.logger.debug("Autocast available.")
             with autocast_mode.autocast(self.torch_device.type):
-                output_files = self.model_instance.separate(audio_file_path, custom_output_names)
+                output_files = self.model_instance.separate(
+                    audio_file_path, custom_output_names
+                )
         else:
+            if self.is_rocm and self.use_autocast:
+                self.logger.debug("Autocast disabled for ROCm compatibility.")
             self.logger.debug("Autocast unavailable.")
-            output_files = self.model_instance.separate(audio_file_path, custom_output_names)
+            output_files = self.model_instance.separate(
+                audio_file_path, custom_output_names
+            )
 
         # Clear GPU cache to free up memory
         self.model_instance.clear_gpu_cache()
@@ -954,7 +1301,9 @@ def _separate_file(self, audio_file_path, custom_output_names=None):
 
         # Log the completion of the separation process
         self.logger.debug("Separation process completed.")
-        self.logger.info(f'Separation duration: {time.strftime("%H:%M:%S", time.gmtime(int(time.perf_counter() - separate_start_time)))}')
+        self.logger.info(
+            f"Separation duration: {time.strftime('%H:%M:%S', time.gmtime(int(time.perf_counter() - separate_start_time)))}"
+        )
 
         return output_files
 
@@ -990,7 +1339,9 @@ def _process_with_chunking(self, audio_file_path, custom_output_names=None):
             processed_chunks_by_stem = {}
 
             for i, chunk_path in enumerate(chunk_paths):
-                self.logger.info(f"Processing chunk {i+1}/{len(chunk_paths)}: {chunk_path}")
+                self.logger.info(
+                    f"Processing chunk {i + 1}/{len(chunk_paths)}: {chunk_path}"
+                )
 
                 original_chunk_duration = self.chunk_duration
                 original_output_dir = self.output_dir
@@ -1008,24 +1359,36 @@ def _process_with_chunking(self, audio_file_path, custom_output_names=None):
                     for stem_path in output_files:
                         # Extract stem name from filename: "chunk_0000_(Vocals).wav" → "Vocals"
                         filename = os.path.basename(stem_path)
-                        match = re.search(r'_\(([^)]+)\)', filename)
+                        match = re.search(r"_\(([^)]+)\)", filename)
                         if match:
                             stem_name = match.group(1)
                         else:
                             # Fallback: use index-based name if pattern not found
-                            stem_index = len([k for k in processed_chunks_by_stem.keys() if k.startswith('stem_')])
+                            stem_index = len(
+                                [
+                                    k
+                                    for k in processed_chunks_by_stem.keys()
+                                    if k.startswith("stem_")
+                                ]
+                            )
                             stem_name = f"stem_{stem_index}"
-                            self.logger.warning(f"Could not extract stem name from {filename}, using {stem_name}")
+                            self.logger.warning(
+                                f"Could not extract stem name from {filename}, using {stem_name}"
+                            )
 
                         if stem_name not in processed_chunks_by_stem:
                             processed_chunks_by_stem[stem_name] = []
 
                         # Ensure absolute path
-                        abs_path = stem_path if os.path.isabs(stem_path) else os.path.join(temp_dir, stem_path)
+                        abs_path = (
+                            stem_path
+                            if os.path.isabs(stem_path)
+                            else os.path.join(temp_dir, stem_path)
+                        )
                         processed_chunks_by_stem[stem_name].append(abs_path)
 
                     if not output_files:
-                        self.logger.warning(f"Chunk {i+1} produced no output files")
+                        self.logger.warning(f"Chunk {i + 1} produced no output files")
 
                 finally:
                     self.chunk_duration = original_chunk_duration
@@ -1054,13 +1417,19 @@ def _process_with_chunking(self, audio_file_path, custom_output_names=None):
                 else:
                     output_filename = f"{base_name}_({stem_name})"
 
-                output_path = os.path.join(self.output_dir, f"{output_filename}.{self.output_format.lower()}")
+                output_path = os.path.join(
+                    self.output_dir, f"{output_filename}.{self.output_format.lower()}"
+                )
 
-                self.logger.info(f"Merging {len(chunk_paths_for_stem)} chunks for stem: {stem_name}")
+                self.logger.info(
+                    f"Merging {len(chunk_paths_for_stem)} chunks for stem: {stem_name}"
+                )
                 chunker.merge_chunks(chunk_paths_for_stem, output_path)
                 output_files.append(output_path)
 
-            self.logger.info(f"Chunked processing completed. Output files: {output_files}")
+            self.logger.info(
+                f"Chunked processing completed. Output files: {output_files}"
+            )
             return output_files
 
         finally:
@@ -1075,7 +1444,13 @@ def download_model_and_data(self, model_filename):
         """
         self.logger.info(f"Downloading model {model_filename}...")
 
-        model_filename, model_type, model_friendly_name, model_path, yaml_config_filename = self.download_model_files(model_filename)
+        (
+            model_filename,
+            model_type,
+            model_friendly_name,
+            model_path,
+            yaml_config_filename,
+        ) = self.download_model_files(model_filename)
 
         if model_path.lower().endswith(".yaml"):
             yaml_config_filename = model_path
@@ -1087,7 +1462,9 @@ def download_model_and_data(self, model_filename):
 
         model_data_dict_size = len(model_data)
 
-        self.logger.info(f"Model downloaded, type: {model_type}, friendly name: {model_friendly_name}, model_path: {model_path}, model_data: {model_data_dict_size} items")
+        self.logger.info(
+            f"Model downloaded, type: {model_type}, friendly name: {model_friendly_name}, model_path: {model_path}, model_data: {model_data_dict_size} items"
+        )
 
     def get_simplified_model_list(self, filter_sort_by: Optional[str] = None):
         """
@@ -1130,7 +1507,12 @@ def get_simplified_model_list(self, filter_sort_by: Optional[str] = None):
                     stems_with_scores = ["Unknown"]
                     stem_sdr_dict["unknown"] = None
 
-                simplified_list[filename] = {"Name": name, "Type": model_type, "Stems": stems_with_scores, "SDR": stem_sdr_dict}
+                simplified_list[filename] = {
+                    "Name": name,
+                    "Type": model_type,
+                    "Stems": stems_with_scores,
+                    "SDR": stem_sdr_dict,
+                }
 
         # Sort and filter the list if a sort_by parameter is provided
         if filter_sort_by:
@@ -1142,12 +1524,19 @@ def get_simplified_model_list(self, filter_sort_by: Optional[str] = None):
                 # Convert sort_by to lowercase for case-insensitive comparison
                 sort_by_lower = filter_sort_by.lower()
                 # Filter out models that don't have the specified stem
-                filtered_list = {k: v for k, v in simplified_list.items() if sort_by_lower in v["SDR"]}
+                filtered_list = {
+                    k: v
+                    for k, v in simplified_list.items()
+                    if sort_by_lower in v["SDR"]
+                }
 
                 # Sort by SDR score if available, putting None values last
                 def sort_key(item):
                     sdr = item[1]["SDR"][sort_by_lower]
-                    return (0 if sdr is None else 1, sdr if sdr is not None else float("-inf"))
+                    return (
+                        0 if sdr is None else 1,
+                        sdr if sdr is not None else float("-inf"),
+                    )
 
                 return dict(sorted(filtered_list.items(), key=sort_key, reverse=True))
 
diff --git a/audio_separator/separator/uvr_lib_v5/demucs/hdemucs_rocm.py b/audio_separator/separator/uvr_lib_v5/demucs/hdemucs_rocm.py
new file mode 100644
index 0000000..b2406bd
--- /dev/null
+++ b/audio_separator/separator/uvr_lib_v5/demucs/hdemucs_rocm.py
@@ -0,0 +1,820 @@
+from copy import deepcopy
+import math
+import typing as tp
+import torch
+from torch import nn
+from torch.nn import functional as F
+from .filtering import wiener
+from .demucs import DConv, rescale_module
+from .states import capture_init
+from .spec import spectro, ispectro
+
+
+def pad1d(
+    x: torch.Tensor,
+    paddings: tp.Tuple[int, int],
+    mode: str = "constant",
+    value: float = 0.0,
+):
+    """Tiny wrapper around F.pad, just to allow for reflect padding on small input.
+    If this is the case, we insert extra 0 padding to the right before the reflection happen."""
+    x0 = x
+    length = x.shape[-1]
+    padding_left, padding_right = paddings
+    if mode == "reflect":
+        max_pad = max(padding_left, padding_right)
+        if length <= max_pad:
+            extra_pad = max_pad - length + 1
+            extra_pad_right = min(padding_right, extra_pad)
+            extra_pad_left = extra_pad - extra_pad_right
+            paddings = (padding_left - extra_pad_left, padding_right - extra_pad_right)
+            x = F.pad(x, (extra_pad_left, extra_pad_right))
+    out = F.pad(x, paddings, mode, value)
+    assert out.shape[-1] == length + padding_left + padding_right
+    assert (out[..., padding_left : padding_left + length] == x0).all()
+    return out
+
+
+class ScaledEmbedding(nn.Module):
+    """
+    Boost learning rate for embeddings (with `scale`).
+    Also, can make embeddings continuous with `smooth`.
+    """
+
+    def __init__(
+        self, num_embeddings: int, embedding_dim: int, scale: float = 10.0, smooth=False
+    ):
+        super().__init__()
+        self.embedding = nn.Embedding(num_embeddings, embedding_dim)
+        if smooth:
+            weight = torch.cumsum(self.embedding.weight.data, dim=0)
+            # when summing gaussian, overscale raises as sqrt(n), so we nornalize by that.
+            weight = (
+                weight / torch.arange(1, num_embeddings + 1).to(weight).sqrt()[:, None]
+            )
+            self.embedding.weight.data[:] = weight
+        self.embedding.weight.data /= scale
+        self.scale = scale
+
+    @property
+    def weight(self):
+        return self.embedding.weight * self.scale
+
+    def forward(self, x):
+        out = self.embedding(x) * self.scale
+        return out
+
+
+class HEncLayer(nn.Module):
+    def __init__(
+        self,
+        chin,
+        chout,
+        kernel_size=8,
+        stride=4,
+        norm_groups=1,
+        empty=False,
+        freq=True,
+        dconv=True,
+        norm=True,
+        context=0,
+        dconv_kw={},
+        pad=True,
+        rewrite=True,
+    ):
+        """Encoder layer. This used both by the time and the frequency branch.
+
+        Args:
+            chin: number of input channels.
+            chout: number of output channels.
+            norm_groups: number of groups for group norm.
+            empty: used to make a layer with just the first conv. this is used
+                before merging the time and freq. branches.
+            freq: this is acting on frequencies.
+            dconv: insert DConv residual branches.
+            norm: use GroupNorm.
+            context: context size for the 1x1 conv.
+            dconv_kw: list of kwargs for the DConv class.
+            pad: pad the input. Padding is done so that the output size is
+                always the input size / stride.
+            rewrite: add 1x1 conv at the end of the layer.
+        """
+        super().__init__()
+        norm_fn = lambda d: nn.Identity()  # noqa
+        if norm:
+            norm_fn = lambda d: nn.GroupNorm(norm_groups, d)  # noqa
+        if pad:
+            pad = kernel_size // 4
+        else:
+            pad = 0
+        klass = nn.Conv1d
+        self.freq = freq
+        self.kernel_size = kernel_size
+        self.stride = stride
+        self.empty = empty
+        self.norm = norm
+        self.pad = pad
+        if freq:
+            kernel_size = [kernel_size, 1]
+            stride = [stride, 1]
+            pad = [pad, 0]
+            klass = nn.Conv2d
+        self.conv = klass(chin, chout, kernel_size, stride, pad)
+        if self.empty:
+            return
+        self.norm1 = norm_fn(chout)
+        self.rewrite = None
+        if rewrite:
+            self.rewrite = klass(chout, 2 * chout, 1 + 2 * context, 1, context)
+            self.norm2 = norm_fn(2 * chout)
+
+        self.dconv = None
+        if dconv:
+            self.dconv = DConv(chout, **dconv_kw)
+
+    def forward(self, x, inject=None):
+        """
+        `inject` is used to inject the result from the time branch into the frequency branch,
+        when both have the same stride.
+        """
+        if not self.freq and x.dim() == 4:
+            B, C, Fr, T = x.shape
+            x = x.view(B, -1, T)
+
+        if not self.freq:
+            le = x.shape[-1]
+            if not le % self.stride == 0:
+                x = F.pad(x, (0, self.stride - (le % self.stride)))
+        y = self.conv(x)
+        if self.empty:
+            return y
+        if inject is not None:
+            assert inject.shape[-1] == y.shape[-1], (inject.shape, y.shape)
+            if inject.dim() == 3 and y.dim() == 4:
+                inject = inject[:, :, None]
+            y = y + inject
+        y = F.gelu(self.norm1(y))
+        if self.dconv:
+            if self.freq:
+                B, C, Fr, T = y.shape
+                y = y.permute(0, 2, 1, 3).reshape(-1, C, T)
+            y = self.dconv(y)
+            if self.freq:
+                y = y.view(B, Fr, C, T).permute(0, 2, 1, 3)
+        if self.rewrite:
+            z = self.norm2(self.rewrite(y))
+            z = F.glu(z, dim=1)
+        else:
+            z = y
+        return z
+
+
+class MultiWrap(nn.Module):
+    """
+    Takes one layer and replicate it N times. each replica will act
+    on a frequency band. All is done so that if the N replica have the same weights,
+    then this is exactly equivalent to applying the original module on all frequencies.
+
+    This is a bit over-engineered to avoid edge artifacts when splitting
+    the frequency bands, but it is possible the naive implementation would work as well...
+    """
+
+    def __init__(self, layer, split_ratios):
+        """
+        Args:
+            layer: module to clone, must be either HEncLayer or HDecLayer.
+            split_ratios: list of float indicating which ratio to keep for each band.
+        """
+        super().__init__()
+        self.split_ratios = split_ratios
+        self.layers = nn.ModuleList()
+        self.conv = isinstance(layer, HEncLayer)
+        assert not layer.norm
+        assert layer.freq
+        assert layer.pad
+        if not self.conv:
+            assert not layer.context_freq
+        for k in range(len(split_ratios) + 1):
+            lay = deepcopy(layer)
+            if self.conv:
+                lay.conv.padding = (0, 0)
+            else:
+                lay.pad = False
+            for m in lay.modules():
+                if hasattr(m, "reset_parameters"):
+                    m.reset_parameters()
+            self.layers.append(lay)
+
+    def forward(self, x, skip=None, length=None):
+        B, C, Fr, T = x.shape
+
+        ratios = list(self.split_ratios) + [1]
+        start = 0
+        outs = []
+        for ratio, layer in zip(ratios, self.layers):
+            if self.conv:
+                pad = layer.kernel_size // 4
+                if ratio == 1:
+                    limit = Fr
+                    frames = -1
+                else:
+                    limit = int(round(Fr * ratio))
+                    le = limit - start
+                    if start == 0:
+                        le += pad
+                    frames = round((le - layer.kernel_size) / layer.stride + 1)
+                    limit = start + (frames - 1) * layer.stride + layer.kernel_size
+                    if start == 0:
+                        limit -= pad
+                assert limit - start > 0, (limit, start)
+                assert limit <= Fr, (limit, Fr)
+                y = x[:, :, start:limit, :]
+                if start == 0:
+                    y = F.pad(y, (0, 0, pad, 0))
+                if ratio == 1:
+                    y = F.pad(y, (0, 0, 0, pad))
+                outs.append(layer(y))
+                start = limit - layer.kernel_size + layer.stride
+            else:
+                if ratio == 1:
+                    limit = Fr
+                else:
+                    limit = int(round(Fr * ratio))
+                last = layer.last
+                layer.last = True
+
+                y = x[:, :, start:limit]
+                s = skip[:, :, start:limit]
+                out, _ = layer(y, s, None)
+                if outs:
+                    outs[-1][:, :, -layer.stride :] += out[
+                        :, :, : layer.stride
+                    ] - layer.conv_tr.bias.view(1, -1, 1, 1)
+                    out = out[:, :, layer.stride :]
+                if ratio == 1:
+                    out = out[:, :, : -layer.stride // 2, :]
+                if start == 0:
+                    out = out[:, :, layer.stride // 2 :, :]
+                outs.append(out)
+                layer.last = last
+                start = limit
+        out = torch.cat(outs, dim=2)
+        if not self.conv and not last:
+            out = F.gelu(out)
+        if self.conv:
+            return out
+        else:
+            return out, None
+
+
+class HDecLayer(nn.Module):
+    def __init__(
+        self,
+        chin,
+        chout,
+        last=False,
+        kernel_size=8,
+        stride=4,
+        norm_groups=1,
+        empty=False,
+        freq=True,
+        dconv=True,
+        norm=True,
+        context=1,
+        dconv_kw={},
+        pad=True,
+        context_freq=True,
+        rewrite=True,
+    ):
+        """
+        Same as HEncLayer but for decoder. See `HEncLayer` for documentation.
+        """
+        super().__init__()
+        norm_fn = lambda d: nn.Identity()  # noqa
+        if norm:
+            norm_fn = lambda d: nn.GroupNorm(norm_groups, d)  # noqa
+        if pad:
+            pad = kernel_size // 4
+        else:
+            pad = 0
+        self.pad = pad
+        self.last = last
+        self.freq = freq
+        self.chin = chin
+        self.empty = empty
+        self.stride = stride
+        self.kernel_size = kernel_size
+        self.norm = norm
+        self.context_freq = context_freq
+        klass = nn.Conv1d
+        klass_tr = nn.ConvTranspose1d
+        if freq:
+            kernel_size = [kernel_size, 1]
+            stride = [stride, 1]
+            klass = nn.Conv2d
+            klass_tr = nn.ConvTranspose2d
+        self.conv_tr = klass_tr(chin, chout, kernel_size, stride)
+        self.norm2 = norm_fn(chout)
+        if self.empty:
+            return
+        self.rewrite = None
+        if rewrite:
+            if context_freq:
+                self.rewrite = klass(chin, 2 * chin, 1 + 2 * context, 1, context)
+            else:
+                self.rewrite = klass(
+                    chin, 2 * chin, [1, 1 + 2 * context], 1, [0, context]
+                )
+            self.norm1 = norm_fn(2 * chin)
+
+        self.dconv = None
+        if dconv:
+            self.dconv = DConv(chin, **dconv_kw)
+
+    def forward(self, x, skip, length):
+        if self.freq and x.dim() == 3:
+            B, C, T = x.shape
+            x = x.view(B, self.chin, -1, T)
+
+        if not self.empty:
+            x = x + skip
+
+            if self.rewrite:
+                y = F.glu(self.norm1(self.rewrite(x)), dim=1)
+            else:
+                y = x
+            if self.dconv:
+                if self.freq:
+                    B, C, Fr, T = y.shape
+                    y = y.permute(0, 2, 1, 3).reshape(-1, C, T)
+                y = self.dconv(y)
+                if self.freq:
+                    y = y.view(B, Fr, C, T).permute(0, 2, 1, 3)
+        else:
+            y = x
+            assert skip is None
+        z = self.norm2(self.conv_tr(y))
+        if self.freq:
+            if self.pad:
+                z = z[..., self.pad : -self.pad, :]
+        else:
+            z = z[..., self.pad : self.pad + length]
+            assert z.shape[-1] == length, (z.shape[-1], length)
+        if not self.last:
+            z = F.gelu(z)
+        return z, y
+
+
+class HDemucsROCm(nn.Module):
+    """
+    Spectrogram and hybrid Demucs model with ROCm support.
+    Modified to remove CPU forcing for complex number operations.
+    """
+
+    @capture_init
+    def __init__(
+        self,
+        sources,
+        # Channels
+        audio_channels=2,
+        channels=48,
+        channels_time=None,
+        growth=2,
+        # STFT
+        nfft=4096,
+        wiener_iters=0,
+        end_iters=0,
+        wiener_residual=False,
+        cac=True,
+        # Main structure
+        depth=6,
+        rewrite=True,
+        hybrid=True,
+        hybrid_old=False,
+        # Frequency branch
+        multi_freqs=None,
+        multi_freqs_depth=2,
+        freq_emb=0.2,
+        emb_scale=10,
+        emb_smooth=True,
+        # Convolutions
+        kernel_size=8,
+        time_stride=2,
+        stride=4,
+        context=1,
+        context_enc=0,
+        # Normalization
+        norm_starts=4,
+        norm_groups=4,
+        # DConv residual branch
+        dconv_mode=1,
+        dconv_depth=2,
+        dconv_comp=4,
+        dconv_attn=4,
+        dconv_lstm=4,
+        dconv_init=1e-4,
+        # Weight init
+        rescale=0.1,
+        # Metadata
+        samplerate=44100,
+        segment=4 * 10,
+    ):
+        """
+        Args:
+            sources (list[str]): list of source names.
+            audio_channels (int): input/output audio channels.
+            channels (int): initial number of hidden channels.
+            channels_time: if not None, use a different `channels` value for the time branch.
+            growth: increase the number of hidden channels by this factor at each layer.
+            nfft: number of fft bins. Note that changing this require careful computation of
+                various shape parameters and will not work out of the box for hybrid models.
+            wiener_iters: when using Wiener filtering, number of iterations at test time.
+            end_iters: same but at train time. For a hybrid model, must be equal to `wiener_iters`.
+            wiener_residual: add residual source before wiener filtering.
+            cac: uses complex as channels, i.e. complex numbers are 2 channels each
+                in input and output. no further processing is done before ISTFT.
+            depth (int): number of layers in the encoder and in the decoder.
+            rewrite (bool): add 1x1 convolution to each layer.
+            hybrid (bool): make a hybrid time/frequency domain, otherwise frequency only.
+            hybrid_old: some models trained for MDX had a padding bug. This replicates
+                this bug to avoid retraining them.
+            multi_freqs: list of frequency ratios for splitting frequency bands with `MultiWrap`.
+            multi_freqs_depth: how many layers to wrap with `MultiWrap`. Only the outermost
+                layers will be wrapped.
+            freq_emb: add frequency embedding after the first frequency layer if > 0,
+                the actual value controls the weight of the embedding.
+            emb_scale: equivalent to scaling the embedding learning rate
+            emb_smooth: initialize the embedding with a smooth one (with respect to frequencies).
+            kernel_size: kernel_size for encoder and decoder layers.
+            stride: stride for encoder and decoder layers.
+            time_stride: stride for the final time layer, after the merge.
+            context: context for 1x1 conv in the decoder.
+            context_enc: context for 1x1 conv in the encoder.
+            norm_starts: layer at which group norm starts being used.
+                decoder layers are numbered in reverse order.
+            norm_groups: number of groups for group norm.
+            dconv_mode: if 1: dconv in encoder only, 2: decoder only, 3: both.
+            dconv_depth: depth of residual DConv branch.
+            dconv_comp: compression of DConv branch.
+            dconv_attn: adds attention layers in DConv branch starting at this layer.
+            dconv_lstm: adds a LSTM layer in DConv branch starting at this layer.
+            dconv_init: initial scale for the DConv branch LayerScale.
+            rescale: weight recaling trick
+
+        """
+        super().__init__()
+
+        self.cac = cac
+        self.wiener_residual = wiener_residual
+        self.audio_channels = audio_channels
+        self.sources = sources
+        self.kernel_size = kernel_size
+        self.context = context
+        self.stride = stride
+        self.depth = depth
+        self.channels = channels
+        self.samplerate = samplerate
+        self.segment = segment
+
+        self.nfft = nfft
+        self.hop_length = nfft // 4
+        self.wiener_iters = wiener_iters
+        self.end_iters = end_iters
+        self.freq_emb = None
+        self.hybrid = hybrid
+        self.hybrid_old = hybrid_old
+        if hybrid_old:
+            assert hybrid, "hybrid_old must come with hybrid=True"
+        if hybrid:
+            assert wiener_iters == end_iters
+
+        self.encoder = nn.ModuleList()
+        self.decoder = nn.ModuleList()
+
+        if hybrid:
+            self.tencoder = nn.ModuleList()
+            self.tdecoder = nn.ModuleList()
+
+        chin = audio_channels
+        chin_z = chin  # number of channels for the freq branch
+        if self.cac:
+            chin_z *= 2
+        chout = channels_time or channels
+        chout_z = channels
+        freqs = nfft // 2
+
+        for index in range(depth):
+            lstm = index >= dconv_lstm
+            attn = index >= dconv_attn
+            norm = index >= norm_starts
+            freq = freqs > 1
+            stri = stride
+            ker = kernel_size
+            if not freq:
+                assert freqs == 1
+                ker = time_stride * 2
+                stri = time_stride
+
+            pad = True
+            last_freq = False
+            if freq and freqs <= kernel_size:
+                ker = freqs
+                pad = False
+                last_freq = True
+
+            kw = {
+                "kernel_size": ker,
+                "stride": stri,
+                "freq": freq,
+                "pad": pad,
+                "norm": norm,
+                "rewrite": rewrite,
+                "norm_groups": norm_groups,
+                "dconv_kw": {
+                    "lstm": lstm,
+                    "attn": attn,
+                    "depth": dconv_depth,
+                    "compress": dconv_comp,
+                    "init": dconv_init,
+                    "gelu": True,
+                },
+            }
+            kwt = dict(kw)
+            kwt["freq"] = 0
+            kwt["kernel_size"] = kernel_size
+            kwt["stride"] = stride
+            kwt["pad"] = True
+            kw_dec = dict(kw)
+            multi = False
+            if multi_freqs and index < multi_freqs_depth:
+                multi = True
+                kw_dec["context_freq"] = False
+
+            if last_freq:
+                chout_z = max(chout, chout_z)
+                chout = chout_z
+
+            enc = HEncLayer(
+                chin_z, chout_z, dconv=dconv_mode & 1, context=context_enc, **kw
+            )
+            if hybrid and freq:
+                tenc = HEncLayer(
+                    chin,
+                    chout,
+                    dconv=dconv_mode & 1,
+                    context=context_enc,
+                    empty=last_freq,
+                    **kwt,
+                )
+                self.tencoder.append(tenc)
+
+            if multi:
+                enc = MultiWrap(enc, multi_freqs)
+            self.encoder.append(enc)
+            if index == 0:
+                chin = self.audio_channels * len(self.sources)
+                chin_z = chin
+                if self.cac:
+                    chin_z *= 2
+            dec = HDecLayer(
+                chout_z,
+                chin_z,
+                dconv=dconv_mode & 2,
+                last=index == 0,
+                context=context,
+                **kw_dec,
+            )
+            if multi:
+                dec = MultiWrap(dec, multi_freqs)
+            if hybrid and freq:
+                tdec = HDecLayer(
+                    chout,
+                    chin,
+                    dconv=dconv_mode & 2,
+                    empty=last_freq,
+                    last=index == 0,
+                    context=context,
+                    **kwt,
+                )
+                self.tdecoder.insert(0, tdec)
+            self.decoder.insert(0, dec)
+
+            chin = chout
+            chin_z = chout_z
+            chout = int(growth * chout)
+            chout_z = int(growth * chout_z)
+            if freq:
+                if freqs <= kernel_size:
+                    freqs = 1
+                else:
+                    freqs //= stride
+            if index == 0 and freq_emb:
+                self.freq_emb = ScaledEmbedding(
+                    freqs, chin_z, smooth=emb_smooth, scale=emb_scale
+                )
+                self.freq_emb_scale = freq_emb
+
+        if rescale:
+            rescale_module(self, reference=rescale)
+
+    def _spec(self, x):
+        hl = self.hop_length
+        nfft = self.nfft
+        x0 = x  # noqa
+
+        if self.hybrid:
+            # We re-pad the signal in order to keep the property
+            # that the size of the output is exactly the size of the input
+            # divided by the stride (here hop_length), when divisible.
+            # This is achieved by padding by 1/4th of the kernel size (here nfft).
+            # which is not supported by torch.stft.
+            # Having all convolution operations follow this convention allow to easily
+            # align the time and frequency branches later on.
+            assert hl == nfft // 4
+            le = int(math.ceil(x.shape[-1] / hl))
+            pad = hl // 2 * 3
+            if not self.hybrid_old:
+                x = pad1d(x, (pad, pad + le * hl - x.shape[-1]), mode="reflect")
+            else:
+                x = pad1d(x, (pad, pad + le * hl - x.shape[-1]))
+
+        z = spectro(x, nfft, hl)[..., :-1, :]
+        if self.hybrid:
+            assert z.shape[-1] == le + 4, (z.shape, x.shape, le)
+            z = z[..., 2 : 2 + le]
+        return z
+
+    def _ispec(self, z, length=None, scale=0):
+        hl = self.hop_length // (4**scale)
+        z = F.pad(z, (0, 0, 0, 1))
+        if self.hybrid:
+            z = F.pad(z, (2, 2))
+            pad = hl // 2 * 3
+            if not self.hybrid_old:
+                le = hl * int(math.ceil(length / hl)) + 2 * pad
+            else:
+                le = hl * int(math.ceil(length / hl))
+            x = ispectro(z, hl, length=le)
+            if not self.hybrid_old:
+                x = x[..., pad : pad + length]
+            else:
+                x = x[..., :length]
+        else:
+            x = ispectro(z, hl, length)
+        return x
+
+    def _magnitude(self, z):
+        # return the magnitude of the spectrogram, except when cac is True,
+        # in which case we just move the complex dimension to the channel one.
+        if self.cac:
+            B, C, Fr, T = z.shape
+            m = torch.view_as_real(z).permute(0, 1, 4, 2, 3)
+            m = m.reshape(B, C * 2, Fr, T)
+        else:
+            m = z.abs()
+        return m
+
+    def _mask(self, z, m):
+        # Apply masking given the mixture spectrogram `z` and the estimated mask `m`.
+        # If `cac` is True, `m` is actually a full spectrogram and `z` is ignored.
+        niters = self.wiener_iters
+        if self.cac:
+            B, S, C, Fr, T = m.shape
+            out = m.view(B, S, -1, 2, Fr, T).permute(0, 1, 2, 4, 5, 3)
+            out = torch.view_as_complex(out.contiguous())
+            return out
+        if self.training:
+            niters = self.end_iters
+        if niters < 0:
+            z = z[:, None]
+            return z / (1e-8 + z.abs()) * m
+        else:
+            return self._wiener(m, z, niters)
+
+    def _wiener(self, mag_out, mix_stft, niters):
+        # apply wiener filtering from OpenUnmix.
+        init = mix_stft.dtype
+        wiener_win_len = 300
+        residual = self.wiener_residual
+
+        B, S, C, Fq, T = mag_out.shape
+        mag_out = mag_out.permute(0, 4, 3, 2, 1)
+        mix_stft = torch.view_as_real(mix_stft.permute(0, 3, 2, 1))
+
+        outs = []
+        for sample in range(B):
+            pos = 0
+            out = []
+            for pos in range(0, T, wiener_win_len):
+                frame = slice(pos, pos + wiener_win_len)
+                z_out = wiener(
+                    mag_out[sample, frame],
+                    mix_stft[sample, frame],
+                    niters,
+                    residual=residual,
+                )
+                out.append(z_out.transpose(-1, -2))
+            outs.append(torch.cat(out, dim=0))
+        out = torch.view_as_complex(torch.stack(outs, 0))
+        out = out.permute(0, 4, 3, 2, 1).contiguous()
+        if residual:
+            out = out[:, :-1]
+        assert list(out.shape) == [B, S, C, Fq, T]
+        return out.to(init)
+
+    def forward(self, mix):
+        x = mix
+        length = x.shape[-1]
+
+        z = self._spec(mix)
+        mag = self._magnitude(z).to(mix.device)
+        x = mag
+
+        B, C, Fq, T = x.shape
+
+        # unlike previous Demucs, we always normalize because it is easier.
+        mean = x.mean(dim=(1, 2, 3), keepdim=True)
+        std = x.std(dim=(1, 2, 3), keepdim=True)
+        x = (x - mean) / (1e-5 + std)
+        # x will be the freq. branch input.
+
+        if self.hybrid:
+            # Prepare the time branch input.
+            xt = mix
+            meant = xt.mean(dim=(1, 2), keepdim=True)
+            stdt = xt.std(dim=(1, 2), keepdim=True)
+            xt = (xt - meant) / (1e-5 + stdt)
+
+        # okay, this is a giant mess I know...
+        saved = []  # skip connections, freq.
+        saved_t = []  # skip connections, time.
+        lengths = []  # saved lengths to properly remove padding, freq branch.
+        lengths_t = []  # saved lengths for time branch.
+        for idx, encode in enumerate(self.encoder):
+            lengths.append(x.shape[-1])
+            inject = None
+            if self.hybrid and idx < len(self.tencoder):
+                # we have not yet merged branches.
+                lengths_t.append(xt.shape[-1])
+                tenc = self.tencoder[idx]
+                xt = tenc(xt)
+                if not tenc.empty:
+                    # save for skip connection
+                    saved_t.append(xt)
+                else:
+                    # tenc contains just the first conv., so that now time and freq.
+                    # branches have the same shape and can be merged.
+                    inject = xt
+            x = encode(x, inject)
+            if idx == 0 and self.freq_emb is not None:
+                # add frequency embedding to allow for non equivariant convolutions
+                # over the frequency axis.
+                frs = torch.arange(x.shape[-2], device=x.device)
+                emb = self.freq_emb(frs).t()[None, :, :, None].expand_as(x)
+                x = x + self.freq_emb_scale * emb
+
+            saved.append(x)
+
+        x = torch.zeros_like(x)
+        if self.hybrid:
+            xt = torch.zeros_like(x)
+        # initialize everything to zero (signal will go through u-net skips).
+
+        for idx, decode in enumerate(self.decoder):
+            skip = saved.pop(-1)
+            x, pre = decode(x, skip, lengths.pop(-1))
+            # `pre` contains the output just before final transposed convolution,
+            # which is used when the freq. and time branch separate.
+
+            if self.hybrid:
+                offset = self.depth - len(self.tdecoder)
+            if self.hybrid and idx >= offset:
+                tdec = self.tdecoder[idx - offset]
+                length_t = lengths_t.pop(-1)
+                if tdec.empty:
+                    assert pre.shape[2] == 1, pre.shape
+                    pre = pre[:, :, 0]
+                    xt, _ = tdec(pre, None, length_t)
+                else:
+                    skip = saved_t.pop(-1)
+                    xt, _ = tdec(xt, skip, length_t)
+
+        # Let's make sure we used all stored skip connections.
+        assert len(saved) == 0
+        assert len(lengths_t) == 0
+        assert len(saved_t) == 0
+
+        S = len(self.sources)
+        x = x.view(B, S, -1, Fq, T)
+        x = x * std[:, None] + mean[:, None]
+
+        # Modified: Remove CPU forcing for ROCm devices
+        # Keep everything on the original device
+        zout = self._mask(z, x)
+        x = self._ispec(zout, length)
+
+        if self.hybrid:
+            xt = xt.view(B, S, -1, length)
+            xt = xt * stdt[:, None] + meant[:, None]
+            x = xt + x
+        return x
diff --git a/audio_separator/separator/uvr_lib_v5/roformer/bs_roformer.py b/audio_separator/separator/uvr_lib_v5/roformer/bs_roformer.py
index 8d96671..630de33 100644
--- a/audio_separator/separator/uvr_lib_v5/roformer/bs_roformer.py
+++ b/audio_separator/separator/uvr_lib_v5/roformer/bs_roformer.py
@@ -59,14 +59,23 @@ class FeedForward(Module):
     def __init__(self, dim, mult=4, dropout=0.0):
         super().__init__()
         dim_inner = int(dim * mult)
-        self.net = nn.Sequential(RMSNorm(dim), nn.Linear(dim, dim_inner), nn.GELU(), nn.Dropout(dropout), nn.Linear(dim_inner, dim), nn.Dropout(dropout))
+        self.net = nn.Sequential(
+            RMSNorm(dim),
+            nn.Linear(dim, dim_inner),
+            nn.GELU(),
+            nn.Dropout(dropout),
+            nn.Linear(dim_inner, dim),
+            nn.Dropout(dropout),
+        )
 
     def forward(self, x):
         return self.net(x)
 
 
 class Attention(Module):
-    def __init__(self, dim, heads=8, dim_head=64, dropout=0.0, rotary_embed=None, flash=True):
+    def __init__(
+        self, dim, heads=8, dim_head=64, dropout=0.0, rotary_embed=None, flash=True
+    ):
         super().__init__()
         self.heads = heads
         self.scale = dim_head**-0.5
@@ -81,12 +90,16 @@ def __init__(self, dim, heads=8, dim_head=64, dropout=0.0, rotary_embed=None, fl
 
         self.to_gates = nn.Linear(dim, heads)
 
-        self.to_out = nn.Sequential(nn.Linear(dim_inner, dim, bias=False), nn.Dropout(dropout))
+        self.to_out = nn.Sequential(
+            nn.Linear(dim_inner, dim, bias=False), nn.Dropout(dropout)
+        )
 
     def forward(self, x):
         x = self.norm(x)
 
-        q, k, v = rearrange(self.to_qkv(x), "b n (qkv h d) -> qkv b h n d", qkv=3, h=self.heads)
+        q, k, v = rearrange(
+            self.to_qkv(x), "b n (qkv h d) -> qkv b h n d", qkv=3, h=self.heads
+        )
 
         if exists(self.rotary_embed):
             q = self.rotary_embed.rotate_queries_or_keys(q)
@@ -112,13 +125,18 @@ def __init__(self, *, dim, dim_head=32, heads=8, scale=8, flash=False, dropout=0
         dim_inner = dim_head * heads
         self.norm = RMSNorm(dim)
 
-        self.to_qkv = nn.Sequential(nn.Linear(dim, dim_inner * 3, bias=False), Rearrange("b n (qkv h d) -> qkv b h d n", qkv=3, h=heads))
+        self.to_qkv = nn.Sequential(
+            nn.Linear(dim, dim_inner * 3, bias=False),
+            Rearrange("b n (qkv h d) -> qkv b h d n", qkv=3, h=heads),
+        )
 
         self.temperature = nn.Parameter(torch.ones(heads, 1, 1))
 
         self.attend = Attend(scale=scale, dropout=dropout, flash=flash)
 
-        self.to_out = nn.Sequential(Rearrange("b h d n -> b n (h d)"), nn.Linear(dim_inner, dim, bias=False))
+        self.to_out = nn.Sequential(
+            Rearrange("b h d n -> b n (h d)"), nn.Linear(dim_inner, dim, bias=False)
+        )
 
     def forward(self, x):
         x = self.norm(x)
@@ -134,17 +152,48 @@ def forward(self, x):
 
 
 class Transformer(Module):
-    def __init__(self, *, dim, depth, dim_head=64, heads=8, attn_dropout=0.0, ff_dropout=0.0, ff_mult=4, norm_output=True, rotary_embed=None, flash_attn=True, linear_attn=False):
+    def __init__(
+        self,
+        *,
+        dim,
+        depth,
+        dim_head=64,
+        heads=8,
+        attn_dropout=0.0,
+        ff_dropout=0.0,
+        ff_mult=4,
+        norm_output=True,
+        rotary_embed=None,
+        flash_attn=True,
+        linear_attn=False,
+    ):
         super().__init__()
         self.layers = ModuleList([])
 
         for _ in range(depth):
             if linear_attn:
-                attn = LinearAttention(dim=dim, dim_head=dim_head, heads=heads, dropout=attn_dropout, flash=flash_attn)
+                attn = LinearAttention(
+                    dim=dim,
+                    dim_head=dim_head,
+                    heads=heads,
+                    dropout=attn_dropout,
+                    flash=flash_attn,
+                )
             else:
-                attn = Attention(dim=dim, dim_head=dim_head, heads=heads, dropout=attn_dropout, rotary_embed=rotary_embed, flash=flash_attn)
-
-            self.layers.append(ModuleList([attn, FeedForward(dim=dim, mult=ff_mult, dropout=ff_dropout)]))
+                attn = Attention(
+                    dim=dim,
+                    dim_head=dim_head,
+                    heads=heads,
+                    dropout=attn_dropout,
+                    rotary_embed=rotary_embed,
+                    flash=flash_attn,
+                )
+
+            self.layers.append(
+                ModuleList(
+                    [attn, FeedForward(dim=dim, mult=ff_mult, dropout=ff_dropout)]
+                )
+            )
 
         self.norm = RMSNorm(dim) if norm_output else nn.Identity()
 
@@ -213,7 +262,9 @@ def __init__(self, dim, dim_inputs: Tuple[int, ...], depth, mlp_expansion_factor
         for dim_in in dim_inputs:
             net = []
 
-            mlp = nn.Sequential(MLP(dim, dim_in * 2, dim_hidden=dim_hidden, depth=depth), nn.GLU(dim=-1))
+            mlp = nn.Sequential(
+                MLP(dim, dim_in * 2, dim_hidden=dim_hidden, depth=depth), nn.GLU(dim=-1)
+            )
 
             self.to_freqs.append(mlp)
 
@@ -256,6 +307,7 @@ def forward(self, x):
     2,
     2,
     2,
+    2,
     4,
     4,
     4,
@@ -293,12 +345,11 @@ def forward(self, x):
     48,
     48,
     128,
-    129,
+    127,
 )
 
 
 class BSRoformer(Module):
-
     @beartype
     def __init__(
         self,
@@ -333,7 +384,13 @@ def __init__(
         stft_window_fn: Optional[Callable] = None,
         mask_estimator_depth=2,
         multi_stft_resolution_loss_weight=1.0,
-        multi_stft_resolutions_window_sizes: Tuple[int, ...] = (4096, 2048, 1024, 512, 256),
+        multi_stft_resolutions_window_sizes: Tuple[int, ...] = (
+            4096,
+            2048,
+            1024,
+            512,
+            256,
+        ),
         multi_stft_hop_size=147,
         multi_stft_normalized=False,
         multi_stft_window_fn: Callable = torch.hann_window,
@@ -343,7 +400,7 @@ def __init__(
         self.stereo = stereo
         self.audio_channels = 2 if stereo else 1
         self.num_stems = num_stems
-        
+
         # Store new parameters as instance variables
         self.mlp_expansion_factor = mlp_expansion_factor
         self.sage_attention = sage_attention
@@ -355,15 +412,15 @@ def __init__(
 
         # Add parameters to transformer kwargs (excluding sage_attention for now)
         transformer_kwargs = dict(
-            dim=dim, 
-            heads=heads, 
-            dim_head=dim_head, 
-            attn_dropout=attn_dropout, 
-            ff_dropout=ff_dropout, 
-            flash_attn=flash_attn, 
-            norm_output=False
+            dim=dim,
+            heads=heads,
+            dim_head=dim_head,
+            attn_dropout=attn_dropout,
+            ff_dropout=ff_dropout,
+            flash_attn=flash_attn,
+            norm_output=False,
         )
-        
+
         # Print sage attention status if enabled (as per research findings)
         if sage_attention:
             print("Use Sage Attention")
@@ -374,23 +431,54 @@ def __init__(
         for _ in range(depth):
             tran_modules = []
             if linear_transformer_depth > 0:
-                tran_modules.append(Transformer(depth=linear_transformer_depth, linear_attn=True, **transformer_kwargs))
-            tran_modules.append(Transformer(depth=time_transformer_depth, rotary_embed=time_rotary_embed, **transformer_kwargs))
-            tran_modules.append(Transformer(depth=freq_transformer_depth, rotary_embed=freq_rotary_embed, **transformer_kwargs))
+                tran_modules.append(
+                    Transformer(
+                        depth=linear_transformer_depth,
+                        linear_attn=True,
+                        **transformer_kwargs,
+                    )
+                )
+            tran_modules.append(
+                Transformer(
+                    depth=time_transformer_depth,
+                    rotary_embed=time_rotary_embed,
+                    **transformer_kwargs,
+                )
+            )
+            tran_modules.append(
+                Transformer(
+                    depth=freq_transformer_depth,
+                    rotary_embed=freq_rotary_embed,
+                    **transformer_kwargs,
+                )
+            )
             self.layers.append(nn.ModuleList(tran_modules))
 
         self.final_norm = RMSNorm(dim)
 
-        self.stft_kwargs = dict(n_fft=stft_n_fft, hop_length=stft_hop_length, win_length=stft_win_length, normalized=stft_normalized)
+        self.stft_kwargs = dict(
+            n_fft=stft_n_fft,
+            hop_length=stft_hop_length,
+            win_length=stft_win_length,
+            normalized=stft_normalized,
+        )
 
-        self.stft_window_fn = partial(default(stft_window_fn, torch.hann_window), stft_win_length)
+        self.stft_window_fn = partial(
+            default(stft_window_fn, torch.hann_window), stft_win_length
+        )
 
-        freqs = torch.stft(torch.randn(1, 4096), **self.stft_kwargs, return_complex=True).shape[1]
+        freqs = torch.stft(
+            torch.randn(1, 4096), **self.stft_kwargs, return_complex=True
+        ).shape[1]
 
         assert len(freqs_per_bands) > 1
-        assert sum(freqs_per_bands) == freqs, f"the number of freqs in the bands must equal {freqs} based on the STFT settings, but got {sum(freqs_per_bands)}"
+        assert sum(freqs_per_bands) == freqs, (
+            f"the number of freqs in the bands must equal {freqs} based on the STFT settings, but got {sum(freqs_per_bands)}"
+        )
 
-        freqs_per_bands_with_complex = tuple(2 * f * self.audio_channels for f in freqs_per_bands)
+        freqs_per_bands_with_complex = tuple(
+            2 * f * self.audio_channels for f in freqs_per_bands
+        )
 
         self.band_split = BandSplit(dim=dim, dim_inputs=freqs_per_bands_with_complex)
 
@@ -398,10 +486,10 @@ def __init__(
 
         for _ in range(num_stems):
             mask_estimator = MaskEstimator(
-                dim=dim, 
-                dim_inputs=freqs_per_bands_with_complex, 
+                dim=dim,
+                dim_inputs=freqs_per_bands_with_complex,
                 depth=mask_estimator_depth,
-                mlp_expansion_factor=mlp_expansion_factor  # Use the new parameter
+                mlp_expansion_factor=mlp_expansion_factor,  # Use the new parameter
             )
 
             self.mask_estimators.append(mask_estimator)
@@ -413,7 +501,9 @@ def __init__(
         self.multi_stft_n_fft = stft_n_fft
         self.multi_stft_window_fn = multi_stft_window_fn
 
-        self.multi_stft_kwargs = dict(hop_length=multi_stft_hop_size, normalized=multi_stft_normalized)
+        self.multi_stft_kwargs = dict(
+            hop_length=multi_stft_hop_size, normalized=multi_stft_normalized
+        )
 
     def forward(self, raw_audio, target=None, return_loss_breakdown=False):
         """
@@ -430,19 +520,18 @@ def forward(self, raw_audio, target=None, return_loss_breakdown=False):
 
         original_device = raw_audio.device
         x_is_mps = True if original_device.type == "mps" else False
+        x_is_rocm = True if "rocm" in str(original_device).lower() else False
 
-        # if x_is_mps:
-        #     raw_audio = raw_audio.cpu()
-
+        # Only move to CPU for MPS, not for ROCm
         device = raw_audio.device
 
         if raw_audio.ndim == 2:
             raw_audio = rearrange(raw_audio, "b t -> b 1 t")
 
         channels = raw_audio.shape[1]
-        assert (not self.stereo and channels == 1) or (
-            self.stereo and channels == 2
-        ), "stereo needs to be set to True if passing in audio signal that is stereo (channel dimension of 2). also need to be False if mono (channel dimension of 1)"
+        assert (not self.stereo and channels == 1) or (self.stereo and channels == 2), (
+            "stereo needs to be set to True if passing in audio signal that is stereo (channel dimension of 2). also need to be False if mono (channel dimension of 1)"
+        )
 
         # to stft
 
@@ -450,11 +539,15 @@ def forward(self, raw_audio, target=None, return_loss_breakdown=False):
 
         stft_window = self.stft_window_fn().to(device)
 
-        stft_repr = torch.stft(raw_audio, **self.stft_kwargs, window=stft_window, return_complex=True)
+        stft_repr = torch.stft(
+            raw_audio, **self.stft_kwargs, window=stft_window, return_complex=True
+        )
         stft_repr = torch.view_as_real(stft_repr)
 
         stft_repr = unpack_one(stft_repr, batch_audio_channel_packed_shape, "* f t c")
-        stft_repr = rearrange(stft_repr, "b s f t c -> b (f s) t c")  # merge stereo / mono into the frequency, with frequency leading dimension, for band splitting
+        stft_repr = rearrange(
+            stft_repr, "b s f t c -> b (f s) t c"
+        )  # merge stereo / mono into the frequency, with frequency leading dimension, for band splitting
 
         x = rearrange(stft_repr, "b f t c -> b t (f c)")
 
@@ -463,9 +556,10 @@ def forward(self, raw_audio, target=None, return_loss_breakdown=False):
         # axial / hierarchical attention
 
         for transformer_block in self.layers:
-
             if len(transformer_block) == 3:
-                linear_transformer, time_transformer, freq_transformer = transformer_block
+                linear_transformer, time_transformer, freq_transformer = (
+                    transformer_block
+                )
 
                 x, ft_ps = pack([x], "b * d")
                 x = linear_transformer(x)
@@ -507,11 +601,25 @@ def forward(self, raw_audio, target=None, return_loss_breakdown=False):
 
         # istft
 
-        stft_repr = rearrange(stft_repr, "b n (f s) t -> (b n s) f t", s=self.audio_channels)
+        stft_repr = rearrange(
+            stft_repr, "b n (f s) t -> (b n s) f t", s=self.audio_channels
+        )
 
-        recon_audio = torch.istft(stft_repr.cpu() if x_is_mps else stft_repr, **self.stft_kwargs, window=stft_window.cpu() if x_is_mps else stft_window, return_complex=False).to(device)
+        # Only move to CPU for MPS, not for ROCm
+        # Only move to CPU for MPS, not for ROCm
+        recon_audio = torch.istft(
+            stft_repr.cpu() if x_is_mps else stft_repr,
+            **self.stft_kwargs,
+            window=stft_window.cpu() if x_is_mps else stft_window,
+            return_complex=False,
+        )
 
-        recon_audio = rearrange(recon_audio, "(b n s) t -> b n s t", s=self.audio_channels, n=self.num_stems)
+        # Move result back to original device
+        recon_audio = recon_audio.to(device)
+
+        recon_audio = rearrange(
+            recon_audio, "(b n s) t -> b n s t", s=self.audio_channels, n=self.num_stems
+        )
 
         if self.num_stems == 1:
             recon_audio = rearrange(recon_audio, "b 1 s t -> b s t")
@@ -535,15 +643,27 @@ def forward(self, raw_audio, target=None, return_loss_breakdown=False):
 
         for window_size in self.multi_stft_resolutions_window_sizes:
             res_stft_kwargs = dict(
-                n_fft=max(window_size, self.multi_stft_n_fft), win_length=window_size, return_complex=True, window=self.multi_stft_window_fn(window_size, device=device), **self.multi_stft_kwargs
+                n_fft=max(window_size, self.multi_stft_n_fft),
+                win_length=window_size,
+                return_complex=True,
+                window=self.multi_stft_window_fn(window_size, device=device),
+                **self.multi_stft_kwargs,
             )
 
-            recon_Y = torch.stft(rearrange(recon_audio, "... s t -> (... s) t"), **res_stft_kwargs)
-            target_Y = torch.stft(rearrange(target, "... s t -> (... s) t"), **res_stft_kwargs)
+            recon_Y = torch.stft(
+                rearrange(recon_audio, "... s t -> (... s) t"), **res_stft_kwargs
+            )
+            target_Y = torch.stft(
+                rearrange(target, "... s t -> (... s) t"), **res_stft_kwargs
+            )
 
-            multi_stft_resolution_loss = multi_stft_resolution_loss + F.l1_loss(recon_Y, target_Y)
+            multi_stft_resolution_loss = multi_stft_resolution_loss + F.l1_loss(
+                recon_Y, target_Y
+            )
 
-        weighted_multi_resolution_loss = multi_stft_resolution_loss * self.multi_stft_resolution_loss_weight
+        weighted_multi_resolution_loss = (
+            multi_stft_resolution_loss * self.multi_stft_resolution_loss_weight
+        )
 
         total_loss = loss + weighted_multi_resolution_loss
 
diff --git a/audio_separator/separator/uvr_lib_v5/roformer/mel_band_roformer.py b/audio_separator/separator/uvr_lib_v5/roformer/mel_band_roformer.py
index 61660c5..f3ff069 100644
--- a/audio_separator/separator/uvr_lib_v5/roformer/mel_band_roformer.py
+++ b/audio_separator/separator/uvr_lib_v5/roformer/mel_band_roformer.py
@@ -54,14 +54,23 @@ class FeedForward(Module):
     def __init__(self, dim, mult=4, dropout=0.0):
         super().__init__()
         dim_inner = int(dim * mult)
-        self.net = nn.Sequential(RMSNorm(dim), nn.Linear(dim, dim_inner), nn.GELU(), nn.Dropout(dropout), nn.Linear(dim_inner, dim), nn.Dropout(dropout))
+        self.net = nn.Sequential(
+            RMSNorm(dim),
+            nn.Linear(dim, dim_inner),
+            nn.GELU(),
+            nn.Dropout(dropout),
+            nn.Linear(dim_inner, dim),
+            nn.Dropout(dropout),
+        )
 
     def forward(self, x):
         return self.net(x)
 
 
 class Attention(Module):
-    def __init__(self, dim, heads=8, dim_head=64, dropout=0.0, rotary_embed=None, flash=True):
+    def __init__(
+        self, dim, heads=8, dim_head=64, dropout=0.0, rotary_embed=None, flash=True
+    ):
         super().__init__()
         self.heads = heads
         self.scale = dim_head**-0.5
@@ -76,12 +85,16 @@ def __init__(self, dim, heads=8, dim_head=64, dropout=0.0, rotary_embed=None, fl
 
         self.to_gates = nn.Linear(dim, heads)
 
-        self.to_out = nn.Sequential(nn.Linear(dim_inner, dim, bias=False), nn.Dropout(dropout))
+        self.to_out = nn.Sequential(
+            nn.Linear(dim_inner, dim, bias=False), nn.Dropout(dropout)
+        )
 
     def forward(self, x):
         x = self.norm(x)
 
-        q, k, v = rearrange(self.to_qkv(x), "b n (qkv h d) -> qkv b h n d", qkv=3, h=self.heads)
+        q, k, v = rearrange(
+            self.to_qkv(x), "b n (qkv h d) -> qkv b h n d", qkv=3, h=self.heads
+        )
 
         if exists(self.rotary_embed):
             q = self.rotary_embed.rotate_queries_or_keys(q)
@@ -97,14 +110,37 @@ def forward(self, x):
 
 
 class Transformer(Module):
-    def __init__(self, *, dim, depth, dim_head=64, heads=8, attn_dropout=0.0, ff_dropout=0.0, ff_mult=4, norm_output=True, rotary_embed=None, flash_attn=True):
+    def __init__(
+        self,
+        *,
+        dim,
+        depth,
+        dim_head=64,
+        heads=8,
+        attn_dropout=0.0,
+        ff_dropout=0.0,
+        ff_mult=4,
+        norm_output=True,
+        rotary_embed=None,
+        flash_attn=True,
+    ):
         super().__init__()
         self.layers = ModuleList([])
 
         for _ in range(depth):
             self.layers.append(
                 ModuleList(
-                    [Attention(dim=dim, dim_head=dim_head, heads=heads, dropout=attn_dropout, rotary_embed=rotary_embed, flash=flash_attn), FeedForward(dim=dim, mult=ff_mult, dropout=ff_dropout)]
+                    [
+                        Attention(
+                            dim=dim,
+                            dim_head=dim_head,
+                            heads=heads,
+                            dropout=attn_dropout,
+                            rotary_embed=rotary_embed,
+                            flash=flash_attn,
+                        ),
+                        FeedForward(dim=dim, mult=ff_mult, dropout=ff_dropout),
+                    ]
                 )
             )
 
@@ -172,7 +208,9 @@ def __init__(self, dim, dim_inputs: Tuple[int, ...], depth, mlp_expansion_factor
         for dim_in in dim_inputs:
             net = []
 
-            mlp = nn.Sequential(MLP(dim, dim_in * 2, dim_hidden=dim_hidden, depth=depth), nn.GLU(dim=-1))
+            mlp = nn.Sequential(
+                MLP(dim, dim_in * 2, dim_hidden=dim_hidden, depth=depth), nn.GLU(dim=-1)
+            )
 
             self.to_freqs.append(mlp)
 
@@ -189,7 +227,6 @@ def forward(self, x):
 
 
 class MelBandRoformer(Module):
-
     @beartype
     def __init__(
         self,
@@ -222,7 +259,13 @@ def __init__(
         stft_window_fn: Optional[Callable] = None,
         mask_estimator_depth=1,
         multi_stft_resolution_loss_weight=1.0,
-        multi_stft_resolutions_window_sizes: Tuple[int, ...] = (4096, 2048, 1024, 512, 256),
+        multi_stft_resolutions_window_sizes: Tuple[int, ...] = (
+            4096,
+            2048,
+            1024,
+            512,
+            256,
+        ),
         multi_stft_hop_size=147,
         multi_stft_normalized=False,
         multi_stft_window_fn: Callable = torch.hann_window,
@@ -233,7 +276,7 @@ def __init__(
         self.stereo = stereo
         self.audio_channels = 2 if stereo else 1
         self.num_stems = num_stems
-        
+
         # Store new parameters as instance variables
         self.mlp_expansion_factor = mlp_expansion_factor
         self.sage_attention = sage_attention
@@ -245,14 +288,14 @@ def __init__(
 
         # Add parameters to transformer kwargs (excluding sage_attention for now)
         transformer_kwargs = dict(
-            dim=dim, 
-            heads=heads, 
-            dim_head=dim_head, 
-            attn_dropout=attn_dropout, 
-            ff_dropout=ff_dropout, 
-            flash_attn=flash_attn
+            dim=dim,
+            heads=heads,
+            dim_head=dim_head,
+            attn_dropout=attn_dropout,
+            ff_dropout=ff_dropout,
+            flash_attn=flash_attn,
         )
-        
+
         # Print sage attention status if enabled (as per research findings)
         if sage_attention:
             print("Use Sage Attention")
@@ -264,19 +307,38 @@ def __init__(
             self.layers.append(
                 nn.ModuleList(
                     [
-                        Transformer(depth=time_transformer_depth, rotary_embed=time_rotary_embed, **transformer_kwargs),
-                        Transformer(depth=freq_transformer_depth, rotary_embed=freq_rotary_embed, **transformer_kwargs),
+                        Transformer(
+                            depth=time_transformer_depth,
+                            rotary_embed=time_rotary_embed,
+                            **transformer_kwargs,
+                        ),
+                        Transformer(
+                            depth=freq_transformer_depth,
+                            rotary_embed=freq_rotary_embed,
+                            **transformer_kwargs,
+                        ),
                     ]
                 )
             )
 
-        self.stft_window_fn = partial(default(stft_window_fn, torch.hann_window), stft_win_length)
+        self.stft_window_fn = partial(
+            default(stft_window_fn, torch.hann_window), stft_win_length
+        )
 
-        self.stft_kwargs = dict(n_fft=stft_n_fft, hop_length=stft_hop_length, win_length=stft_win_length, normalized=stft_normalized)
+        self.stft_kwargs = dict(
+            n_fft=stft_n_fft,
+            hop_length=stft_hop_length,
+            win_length=stft_win_length,
+            normalized=stft_normalized,
+        )
 
-        freqs = torch.stft(torch.randn(1, 4096), **self.stft_kwargs, return_complex=True).shape[1]
+        freqs = torch.stft(
+            torch.randn(1, 4096), **self.stft_kwargs, return_complex=True
+        ).shape[1]
 
-        mel_filter_bank_numpy = filters.mel(sr=sample_rate, n_fft=stft_n_fft, n_mels=num_bands)
+        mel_filter_bank_numpy = filters.mel(
+            sr=sample_rate, n_fft=stft_n_fft, n_mels=num_bands
+        )
 
         mel_filter_bank = torch.from_numpy(mel_filter_bank_numpy)
 
@@ -285,7 +347,9 @@ def __init__(
         mel_filter_bank[-1, -1] = 1.0
 
         freqs_per_band = mel_filter_bank > 0
-        assert freqs_per_band.any(dim=0).all(), "all frequencies need to be covered by all bands for now"
+        assert freqs_per_band.any(dim=0).all(), (
+            "all frequencies need to be covered by all bands for now"
+        )
 
         repeated_freq_indices = repeat(torch.arange(freqs), "f -> b f", b=num_bands)
         freq_indices = repeated_freq_indices[freqs_per_band]
@@ -295,8 +359,9 @@ def __init__(
             freq_indices = freq_indices * 2 + torch.arange(2)
             freq_indices = rearrange(freq_indices, "f s -> (f s)")
 
-        self.register_buffer("freq_indices", freq_indices, persistent=False)
-        self.register_buffer("freqs_per_band", freqs_per_band, persistent=False)
+        # Register buffers on the same device as the model
+        self.register_buffer("freq_indices", freq_indices)
+        self.register_buffer("freqs_per_band", freqs_per_band)
 
         num_freqs_per_band = reduce(freqs_per_band, "b f -> b", "sum")
         num_bands_per_freq = reduce(freqs_per_band, "b f -> f", "sum")
@@ -304,14 +369,20 @@ def __init__(
         self.register_buffer("num_freqs_per_band", num_freqs_per_band, persistent=False)
         self.register_buffer("num_bands_per_freq", num_bands_per_freq, persistent=False)
 
-        freqs_per_bands_with_complex = tuple(2 * f * self.audio_channels for f in num_freqs_per_band.tolist())
+        freqs_per_bands_with_complex = tuple(
+            2 * f * self.audio_channels for f in num_freqs_per_band.tolist()
+        )
 
         self.band_split = BandSplit(dim=dim, dim_inputs=freqs_per_bands_with_complex)
 
         self.mask_estimators = nn.ModuleList([])
 
         for _ in range(num_stems):
-            mask_estimator = MaskEstimator(dim=dim, dim_inputs=freqs_per_bands_with_complex, depth=mask_estimator_depth)
+            mask_estimator = MaskEstimator(
+                dim=dim,
+                dim_inputs=freqs_per_bands_with_complex,
+                depth=mask_estimator_depth,
+            )
 
             self.mask_estimators.append(mask_estimator)
 
@@ -320,7 +391,9 @@ def __init__(
         self.multi_stft_n_fft = stft_n_fft
         self.multi_stft_window_fn = multi_stft_window_fn
 
-        self.multi_stft_kwargs = dict(hop_length=multi_stft_hop_size, normalized=multi_stft_normalized)
+        self.multi_stft_kwargs = dict(
+            hop_length=multi_stft_hop_size, normalized=multi_stft_normalized
+        )
 
         self.match_input_audio_length = match_input_audio_length
 
@@ -339,7 +412,9 @@ def forward(self, raw_audio, target=None, return_loss_breakdown=False):
 
         original_device = raw_audio.device
         x_is_mps = True if original_device.type == "mps" else False
+        x_is_rocm = True if "rocm" in str(original_device).lower() else False
 
+        # Only move to CPU for MPS, not for ROCm
         if x_is_mps:
             raw_audio = raw_audio.cpu()
 
@@ -352,23 +427,31 @@ def forward(self, raw_audio, target=None, return_loss_breakdown=False):
 
         istft_length = raw_audio_length if self.match_input_audio_length else None
 
-        assert (not self.stereo and channels == 1) or (
-            self.stereo and channels == 2
-        ), "stereo needs to be set to True if passing in audio signal that is stereo (channel dimension of 2). also need to be False if mono (channel dimension of 1)"
+        assert (not self.stereo and channels == 1) or (self.stereo and channels == 2), (
+            "stereo needs to be set to True if passing in audio signal that is stereo (channel dimension of 2). also need to be False if mono (channel dimension of 1)"
+        )
 
         raw_audio, batch_audio_channel_packed_shape = pack_one(raw_audio, "* t")
 
         stft_window = self.stft_window_fn().to(device)
 
-        stft_repr = torch.stft(raw_audio, **self.stft_kwargs, window=stft_window, return_complex=True)
+        stft_repr = torch.stft(
+            raw_audio, **self.stft_kwargs, window=stft_window, return_complex=True
+        )
         stft_repr = torch.view_as_real(stft_repr)
 
         stft_repr = unpack_one(stft_repr, batch_audio_channel_packed_shape, "* f t c")
-        stft_repr = rearrange(stft_repr, "b s f t c -> b (f s) t c")  # merge stereo / mono into the frequency, with frequency leading dimension, for band splitting
+        stft_repr = rearrange(
+            stft_repr, "b s f t c -> b (f s) t c"
+        )  # merge stereo / mono into the frequency, with frequency leading dimension, for band splitting
 
         batch_arange = torch.arange(batch, device=device)[..., None]
 
-        x = stft_repr[batch_arange, self.freq_indices.cpu()] if x_is_mps else stft_repr[batch_arange, self.freq_indices]
+        x = (
+            stft_repr[batch_arange, self.freq_indices.cpu()]
+            if x_is_mps
+            else stft_repr[batch_arange, self.freq_indices]
+        )
 
         x = rearrange(x, "b f t c -> b t (f c)")
 
@@ -402,14 +485,36 @@ def forward(self, raw_audio, target=None, return_loss_breakdown=False):
         masks = masks.type(stft_repr.dtype)
 
         if x_is_mps:
-            scatter_indices = repeat(self.freq_indices.cpu(), "f -> b n f t", b=batch, n=self.num_stems, t=stft_repr.shape[-1])
+            scatter_indices = repeat(
+                self.freq_indices.cpu(),
+                "f -> b n f t",
+                b=batch,
+                n=self.num_stems,
+                t=stft_repr.shape[-1],
+            )
         else:
-            scatter_indices = repeat(self.freq_indices, "f -> b n f t", b=batch, n=self.num_stems, t=stft_repr.shape[-1])
+            scatter_indices = repeat(
+                self.freq_indices,
+                "f -> b n f t",
+                b=batch,
+                n=self.num_stems,
+                t=stft_repr.shape[-1],
+            )
+
+        stft_repr_expanded_stems = repeat(
+            stft_repr, "b 1 ... -> b n ...", n=self.num_stems
+        )
+
+        # Only move to CPU for MPS, not for ROCm
+        masks_cpu = masks.cpu() if x_is_mps else masks
+        scatter_indices_cpu = scatter_indices.cpu() if x_is_mps else scatter_indices
+        stft_repr_cpu = (
+            stft_repr_expanded_stems.cpu() if x_is_mps else stft_repr_expanded_stems
+        )
 
-        stft_repr_expanded_stems = repeat(stft_repr, "b 1 ... -> b n ...", n=self.num_stems)
         masks_summed = (
-            torch.zeros_like(stft_repr_expanded_stems.cpu() if x_is_mps else stft_repr_expanded_stems)
-            .scatter_add_(2, scatter_indices.cpu() if x_is_mps else scatter_indices, masks.cpu() if x_is_mps else masks)
+            torch.zeros_like(stft_repr_cpu)
+            .scatter_add_(2, scatter_indices_cpu, masks_cpu)
             .to(device)
         )
 
@@ -422,11 +527,29 @@ def forward(self, raw_audio, target=None, return_loss_breakdown=False):
 
         stft_repr = stft_repr * masks_averaged
 
-        stft_repr = rearrange(stft_repr, "b n (f s) t -> (b n s) f t", s=self.audio_channels)
+        stft_repr = rearrange(
+            stft_repr, "b n (f s) t -> (b n s) f t", s=self.audio_channels
+        )
+
+        # Only move to CPU for MPS, not for ROCm
+        recon_audio = torch.istft(
+            stft_repr.cpu() if x_is_mps else stft_repr,
+            **self.stft_kwargs,
+            window=stft_window.cpu() if x_is_mps else stft_window,
+            return_complex=False,
+            length=istft_length,
+        )
 
-        recon_audio = torch.istft(stft_repr.cpu() if x_is_mps else stft_repr, **self.stft_kwargs, window=stft_window.cpu() if x_is_mps else stft_window, return_complex=False, length=istft_length)
+        # Move result back to original device
+        recon_audio = recon_audio.to(device)
 
-        recon_audio = rearrange(recon_audio, "(b n s) t -> b n s t", b=batch, s=self.audio_channels, n=self.num_stems)
+        recon_audio = rearrange(
+            recon_audio,
+            "(b n s) t -> b n s t",
+            b=batch,
+            s=self.audio_channels,
+            n=self.num_stems,
+        )
 
         if self.num_stems == 1:
             recon_audio = rearrange(recon_audio, "b 1 s t -> b s t")
@@ -448,15 +571,27 @@ def forward(self, raw_audio, target=None, return_loss_breakdown=False):
 
         for window_size in self.multi_stft_resolutions_window_sizes:
             res_stft_kwargs = dict(
-                n_fft=max(window_size, self.multi_stft_n_fft), win_length=window_size, return_complex=True, window=self.multi_stft_window_fn(window_size, device=device), **self.multi_stft_kwargs
+                n_fft=max(window_size, self.multi_stft_n_fft),
+                win_length=window_size,
+                return_complex=True,
+                window=self.multi_stft_window_fn(window_size, device=device),
+                **self.multi_stft_kwargs,
             )
 
-            recon_Y = torch.stft(rearrange(recon_audio, "... s t -> (... s) t"), **res_stft_kwargs)
-            target_Y = torch.stft(rearrange(target, "... s t -> (... s) t"), **res_stft_kwargs)
+            recon_Y = torch.stft(
+                rearrange(recon_audio, "... s t -> (... s) t"), **res_stft_kwargs
+            )
+            target_Y = torch.stft(
+                rearrange(target, "... s t -> (... s) t"), **res_stft_kwargs
+            )
 
-            multi_stft_resolution_loss = multi_stft_resolution_loss + F.l1_loss(recon_Y, target_Y)
+            multi_stft_resolution_loss = multi_stft_resolution_loss + F.l1_loss(
+                recon_Y, target_Y
+            )
 
-        weighted_multi_resolution_loss = multi_stft_resolution_loss * self.multi_stft_resolution_loss_weight
+        weighted_multi_resolution_loss = (
+            multi_stft_resolution_loss * self.multi_stft_resolution_loss_weight
+        )
 
         total_loss = loss + weighted_multi_resolution_loss
 
@@ -468,4 +603,9 @@ def forward(self, raw_audio, target=None, return_loss_breakdown=False):
             return total_loss
 
         # If detailed loss breakdown is requested, ensure all components are on the original device
-        return total_loss, (loss.to(original_device) if x_is_mps else loss, multi_stft_resolution_loss.to(original_device) if x_is_mps else multi_stft_resolution_loss)
+        return total_loss, (
+            loss.to(original_device) if x_is_mps else loss,
+            multi_stft_resolution_loss.to(original_device)
+            if x_is_mps
+            else multi_stft_resolution_loss,
+        )
diff --git a/audio_separator/separator/uvr_lib_v5/stft.py b/audio_separator/separator/uvr_lib_v5/stft.py
index f440395..121ecb9 100644
--- a/audio_separator/separator/uvr_lib_v5/stft.py
+++ b/audio_separator/separator/uvr_lib_v5/stft.py
@@ -1,6 +1,11 @@
 import torch
 
 
+def is_rocm():
+    """Check if PyTorch is built with ROCm support."""
+    return "+rocm" in torch.__version__
+
+
 class STFT:
     """
     This class performs the Short-Time Fourier Transform (STFT) and its inverse (ISTFT).
@@ -20,13 +25,22 @@ def __init__(self, logger, n_fft, hop_length, dim_f, device):
     def __call__(self, input_tensor):
         # Determine if the input tensor's device is not a standard computing device (i.e., not CPU or CUDA).
         is_non_standard_device = not input_tensor.device.type in ["cuda", "cpu"]
+        run_on_cpu = is_non_standard_device or (
+            input_tensor.device.type == "cuda" and is_rocm()
+        )
 
-        # If on a non-standard device, temporarily move the tensor to CPU for processing.
-        if is_non_standard_device:
+        # If on a non-standard device or ROCm, temporarily move the tensor to CPU for processing.
+        if run_on_cpu:
             input_tensor = input_tensor.cpu()
 
+        # Ensure FP32 for stability on ROCm and non-standard devices
+        if input_tensor.dtype in (torch.float16, torch.bfloat16):
+            input_tensor = input_tensor.float()
+
         # Transfer the pre-defined window tensor to the same device as the input tensor.
         stft_window = self.hann_window.to(input_tensor.device)
+        if stft_window.dtype in (torch.float16, torch.bfloat16):
+            stft_window = stft_window.float()
 
         # Extract batch dimensions (all dimensions except the last two which are channel and time).
         batch_dimensions = input_tensor.shape[:-2]
@@ -38,29 +52,60 @@ def __call__(self, input_tensor):
         reshaped_tensor = input_tensor.reshape([-1, time_dim])
 
         # Perform the Short-Time Fourier Transform (STFT) on the reshaped tensor.
-        stft_output = torch.stft(reshaped_tensor, n_fft=self.n_fft, hop_length=self.hop_length, window=stft_window, center=True, return_complex=False)
+        try:
+            stft_output = torch.stft(
+                reshaped_tensor,
+                n_fft=self.n_fft,
+                hop_length=self.hop_length,
+                window=stft_window,
+                center=True,
+                return_complex=False,
+            )
+        except Exception as e:
+            # Fallback: try with return_complex=True
+            stft_complex = torch.stft(
+                reshaped_tensor,
+                n_fft=self.n_fft,
+                hop_length=self.hop_length,
+                window=stft_window,
+                center=True,
+                return_complex=True,
+            )
+            stft_output = torch.stack([stft_complex.real, stft_complex.imag], dim=-1)
 
         # Rearrange the dimensions of the STFT output to bring the frequency dimension forward.
         permuted_stft_output = stft_output.permute([0, 3, 1, 2])
 
         # Reshape the output to restore the original batch and channel dimensions, while keeping the newly formed frequency and time dimensions.
-        final_output = permuted_stft_output.reshape([*batch_dimensions, channel_dim, 2, -1, permuted_stft_output.shape[-1]]).reshape(
+        final_output = permuted_stft_output.reshape(
+            [*batch_dimensions, channel_dim, 2, -1, permuted_stft_output.shape[-1]]
+        ).reshape(
             [*batch_dimensions, channel_dim * 2, -1, permuted_stft_output.shape[-1]]
         )
 
-        # If the original tensor was on a non-standard device, move the processed tensor back to that device.
-        if is_non_standard_device:
+        # If the original tensor was on a non-standard device or ROCm, move the processed tensor back to that device.
+        if run_on_cpu:
             final_output = final_output.to(self.device)
 
         # Return the transformed tensor, sliced to retain only the required frequency dimension (`dim_f`).
         return final_output[..., : self.dim_f, :]
 
-    def pad_frequency_dimension(self, input_tensor, batch_dimensions, channel_dim, freq_dim, time_dim, num_freq_bins):
+    def pad_frequency_dimension(
+        self,
+        input_tensor,
+        batch_dimensions,
+        channel_dim,
+        freq_dim,
+        time_dim,
+        num_freq_bins,
+    ):
         """
         Adds zero padding to the frequency dimension of the input tensor.
         """
         # Create a padding tensor for the frequency dimension
-        freq_padding = torch.zeros([*batch_dimensions, channel_dim, num_freq_bins - freq_dim, time_dim]).to(input_tensor.device)
+        freq_padding = torch.zeros(
+            [*batch_dimensions, channel_dim, num_freq_bins - freq_dim, time_dim]
+        ).to(input_tensor.device)
 
         # Concatenate the padding to the input tensor along the frequency dimension.
         padded_tensor = torch.cat([input_tensor, freq_padding], -2)
@@ -77,13 +122,17 @@ def calculate_inverse_dimensions(self, input_tensor):
 
         return batch_dimensions, channel_dim, freq_dim, time_dim, num_freq_bins
 
-    def prepare_for_istft(self, padded_tensor, batch_dimensions, channel_dim, num_freq_bins, time_dim):
+    def prepare_for_istft(
+        self, padded_tensor, batch_dimensions, channel_dim, num_freq_bins, time_dim
+    ):
         """
         Prepares the tensor for Inverse Short-Time Fourier Transform (ISTFT) by reshaping
         and creating a complex tensor from the real and imaginary parts.
         """
         # Reshape the tensor to separate real and imaginary parts and prepare for ISTFT.
-        reshaped_tensor = padded_tensor.reshape([*batch_dimensions, channel_dim // 2, 2, num_freq_bins, time_dim])
+        reshaped_tensor = padded_tensor.reshape(
+            [*batch_dimensions, channel_dim // 2, 2, num_freq_bins, time_dim]
+        )
 
         # Flatten batch dimensions and rearrange for ISTFT.
         flattened_tensor = reshaped_tensor.reshape([-1, 2, num_freq_bins, time_dim])
@@ -99,28 +148,54 @@ def prepare_for_istft(self, padded_tensor, batch_dimensions, channel_dim, num_fr
     def inverse(self, input_tensor):
         # Determine if the input tensor's device is not a standard computing device (i.e., not CPU or CUDA).
         is_non_standard_device = not input_tensor.device.type in ["cuda", "cpu"]
+        run_on_cpu = is_non_standard_device or (
+            input_tensor.device.type == "cuda" and is_rocm()
+        )
 
-        # If on a non-standard device, temporarily move the tensor to CPU for processing.
-        if is_non_standard_device:
+        # If on a non-standard device or ROCm, temporarily move the tensor to CPU for processing.
+        if run_on_cpu:
             input_tensor = input_tensor.cpu()
 
+        # Ensure FP32 for stability on ROCm and non-standard devices
+        if input_tensor.dtype in (torch.float16, torch.bfloat16):
+            input_tensor = input_tensor.float()
+
         # Transfer the pre-defined Hann window tensor to the same device as the input tensor.
         stft_window = self.hann_window.to(input_tensor.device)
+        if stft_window.dtype in (torch.float16, torch.bfloat16):
+            stft_window = stft_window.float()
 
-        batch_dimensions, channel_dim, freq_dim, time_dim, num_freq_bins = self.calculate_inverse_dimensions(input_tensor)
+        batch_dimensions, channel_dim, freq_dim, time_dim, num_freq_bins = (
+            self.calculate_inverse_dimensions(input_tensor)
+        )
 
-        padded_tensor = self.pad_frequency_dimension(input_tensor, batch_dimensions, channel_dim, freq_dim, time_dim, num_freq_bins)
+        padded_tensor = self.pad_frequency_dimension(
+            input_tensor,
+            batch_dimensions,
+            channel_dim,
+            freq_dim,
+            time_dim,
+            num_freq_bins,
+        )
 
-        complex_tensor = self.prepare_for_istft(padded_tensor, batch_dimensions, channel_dim, num_freq_bins, time_dim)
+        complex_tensor = self.prepare_for_istft(
+            padded_tensor, batch_dimensions, channel_dim, num_freq_bins, time_dim
+        )
 
         # Perform the Inverse Short-Time Fourier Transform (ISTFT).
-        istft_result = torch.istft(complex_tensor, n_fft=self.n_fft, hop_length=self.hop_length, window=stft_window, center=True)
+        istft_result = torch.istft(
+            complex_tensor,
+            n_fft=self.n_fft,
+            hop_length=self.hop_length,
+            window=stft_window,
+            center=True,
+        )
 
         # Reshape ISTFT result to restore original batch and channel dimensions.
         final_output = istft_result.reshape([*batch_dimensions, 2, -1])
 
-        # If the original tensor was on a non-standard device, move the processed tensor back to that device.
-        if is_non_standard_device:
+        # If the original tensor was on a non-standard device or ROCm, move the processed tensor back to that device.
+        if run_on_cpu:
             final_output = final_output.to(self.device)
 
         return final_output
diff --git a/audio_separator/separator/uvr_lib_v5/tfc_tdf_v3.py b/audio_separator/separator/uvr_lib_v5/tfc_tdf_v3.py
index 4d3356f..2cf43f8 100644
--- a/audio_separator/separator/uvr_lib_v5/tfc_tdf_v3.py
+++ b/audio_separator/separator/uvr_lib_v5/tfc_tdf_v3.py
@@ -2,6 +2,12 @@
 import torch.nn as nn
 from functools import partial
 
+
+def is_rocm():
+    """Check if PyTorch is built with ROCm support."""
+    return "+rocm" in torch.__version__
+
+
 class STFT:
     def __init__(self, n_fft, hop_length, dim_f, device):
         self.n_fft = n_fft
@@ -11,57 +17,107 @@ def __init__(self, n_fft, hop_length, dim_f, device):
         self.device = device
 
     def __call__(self, x):
-        
-        x_is_mps = not x.device.type in ["cuda", "cpu"]
-        if x_is_mps:
+
+        x_is_non_cuda_device = x.device.type not in ["cuda", "cpu"]
+        run_on_cpu = (
+            False  # Always run on GPU for ROCm, let PyTorch handle device placement
+        )
+
+        if run_on_cpu:
             x = x.cpu()
 
+        # Ensure FP32 for stability on ROCm and non-standard devices
+        original_dtype = x.dtype
+        if x.dtype in (torch.float16, torch.bfloat16):
+            x = x.float()
+
         window = self.window.to(x.device)
+        if window.dtype in (torch.float16, torch.bfloat16):
+            window = window.float()
+
         batch_dims = x.shape[:-2]
         c, t = x.shape[-2:]
         x = x.reshape([-1, t])
-        x = torch.stft(x, n_fft=self.n_fft, hop_length=self.hop_length, window=window, center=True,return_complex=False)
+
+        try:
+            x = torch.stft(
+                x,
+                n_fft=self.n_fft,
+                hop_length=self.hop_length,
+                window=window,
+                center=True,
+                return_complex=False,
+            )
+        except Exception as e:
+            # Fallback: try with return_complex=True
+            x_complex = torch.stft(
+                x,
+                n_fft=self.n_fft,
+                hop_length=self.hop_length,
+                window=window,
+                center=True,
+                return_complex=True,
+            )
+            x = torch.stack([x_complex.real, x_complex.imag], dim=-1)
+
         x = x.permute([0, 3, 1, 2])
-        x = x.reshape([*batch_dims, c, 2, -1, x.shape[-1]]).reshape([*batch_dims, c * 2, -1, x.shape[-1]])
+        x = x.reshape([*batch_dims, c, 2, -1, x.shape[-1]]).reshape(
+            [*batch_dims, c * 2, -1, x.shape[-1]]
+        )
 
-        if x_is_mps:
+        if run_on_cpu:
             x = x.to(self.device)
 
-        return x[..., :self.dim_f, :]
+        return x[..., : self.dim_f, :]
 
     def inverse(self, x):
-        
-        x_is_mps = not x.device.type in ["cuda", "cpu"]
-        if x_is_mps:
+
+        x_is_non_cuda_device = x.device.type not in ["cuda", "cpu"]
+        run_on_cpu = (
+            False  # Always run on GPU for ROCm, let PyTorch handle device placement
+        )
+
+        if run_on_cpu:
             x = x.cpu()
 
+        # Ensure FP32 for stability on ROCm and non-standard devices
+        original_dtype = x.dtype
+        if x.dtype in (torch.float16, torch.bfloat16):
+            x = x.float()
+
         window = self.window.to(x.device)
+        if window.dtype in (torch.float16, torch.bfloat16):
+            window = window.float()
+
         batch_dims = x.shape[:-3]
         c, f, t = x.shape[-3:]
         n = self.n_fft // 2 + 1
-        f_pad = torch.zeros([*batch_dims, c, n - f, t]).to(x.device)
+        f_pad = torch.zeros([*batch_dims, c, n - f, t], dtype=x.dtype).to(x.device)
         x = torch.cat([x, f_pad], -2)
         x = x.reshape([*batch_dims, c // 2, 2, n, t]).reshape([-1, 2, n, t])
         x = x.permute([0, 2, 3, 1])
-        x = x[..., 0] + x[..., 1] * 1.j
-        x = torch.istft(x, n_fft=self.n_fft, hop_length=self.hop_length, window=window, center=True)
+        x = x[..., 0] + x[..., 1] * 1.0j
+        x = torch.istft(
+            x, n_fft=self.n_fft, hop_length=self.hop_length, window=window, center=True
+        )
         x = x.reshape([*batch_dims, 2, -1])
 
-        if x_is_mps:
+        if run_on_cpu:
             x = x.to(self.device)
 
         return x
 
+
 def get_norm(norm_type):
     def norm(c, norm_type):
         if norm_type is None:
             return nn.Identity()
-        elif norm_type == 'BatchNorm':
+        elif norm_type == "BatchNorm":
             return nn.BatchNorm2d(c)
-        elif norm_type == 'InstanceNorm':
+        elif norm_type == "InstanceNorm":
             return nn.InstanceNorm2d(c, affine=True)
-        elif 'GroupNorm' in norm_type:
-            g = int(norm_type.replace('GroupNorm', ''))
+        elif "GroupNorm" in norm_type:
+            g = int(norm_type.replace("GroupNorm", ""))
             return nn.GroupNorm(num_groups=g, num_channels=c)
         else:
             return nn.Identity()
@@ -70,12 +126,12 @@ def norm(c, norm_type):
 
 
 def get_act(act_type):
-    if act_type == 'gelu':
+    if act_type == "gelu":
         return nn.GELU()
-    elif act_type == 'relu':
+    elif act_type == "relu":
         return nn.ReLU()
-    elif act_type[:3] == 'elu':
-        alpha = float(act_type.replace('elu', ''))
+    elif act_type[:3] == "elu":
+        alpha = float(act_type.replace("elu", ""))
         return nn.ELU(alpha)
     else:
         raise Exception
@@ -87,7 +143,13 @@ def __init__(self, in_c, out_c, scale, norm, act):
         self.conv = nn.Sequential(
             norm(in_c),
             act,
-            nn.ConvTranspose2d(in_channels=in_c, out_channels=out_c, kernel_size=scale, stride=scale, bias=False)
+            nn.ConvTranspose2d(
+                in_channels=in_c,
+                out_channels=out_c,
+                kernel_size=scale,
+                stride=scale,
+                bias=False,
+            ),
         )
 
     def forward(self, x):
@@ -100,7 +162,13 @@ def __init__(self, in_c, out_c, scale, norm, act):
         self.conv = nn.Sequential(
             norm(in_c),
             act,
-            nn.Conv2d(in_channels=in_c, out_channels=out_c, kernel_size=scale, stride=scale, bias=False)
+            nn.Conv2d(
+                in_channels=in_c,
+                out_channels=out_c,
+                kernel_size=scale,
+                stride=scale,
+                bias=False,
+            ),
         )
 
     def forward(self, x):
@@ -159,19 +227,25 @@ def __init__(self, config, device):
             norm_type = config.model.norm
         except (AttributeError, KeyError):
             norm_type = None
-            print("Warning: Model configuration missing 'norm' attribute, using Identity normalization")
-        
+            print(
+                "Warning: Model configuration missing 'norm' attribute, using Identity normalization"
+            )
+
         norm = get_norm(norm_type=norm_type)
-        
+
         try:
             act_type = config.model.act
         except (AttributeError, KeyError):
-            act_type = 'gelu'
-            print("Warning: Model configuration missing 'act' attribute, using GELU activation")
-            
+            act_type = "gelu"
+            print(
+                "Warning: Model configuration missing 'act' attribute, using GELU activation"
+            )
+
         act = get_act(act_type=act_type)
 
-        self.num_target_instruments = 1 if config.training.target_instrument else len(config.training.instruments)
+        self.num_target_instruments = (
+            1 if config.training.target_instrument else len(config.training.instruments)
+        )
         self.num_subbands = config.model.num_subbands
 
         dim_c = self.num_subbands * config.audio.num_channels * 2
@@ -208,10 +282,12 @@ def __init__(self, config, device):
         self.final_conv = nn.Sequential(
             nn.Conv2d(c + dim_c, c, 1, 1, 0, bias=False),
             act,
-            nn.Conv2d(c, self.num_target_instruments * dim_c, 1, 1, 0, bias=False)
+            nn.Conv2d(c, self.num_target_instruments * dim_c, 1, 1, 0, bias=False),
         )
 
-        self.stft = STFT(config.audio.n_fft, config.audio.hop_length, config.audio.dim_f, self.device)
+        self.stft = STFT(
+            config.audio.n_fft, config.audio.hop_length, config.audio.dim_f, self.device
+        )
 
     def cac2cws(self, x):
         k = self.num_subbands
@@ -265,5 +341,3 @@ def forward(self, x):
         x = self.stft.inverse(x)
 
         return x
-
-
diff --git a/debug_results.txt b/debug_results.txt
new file mode 100644
index 0000000..9038397
--- /dev/null
+++ b/debug_results.txt
@@ -0,0 +1,166 @@
+ROCm Debug Script for audio-separator
+Python version: 3.13.5 (main, Jun 25 2025, 18:55:22) [GCC 14.2.0]
+Working directory: /mnt/extra_ssd/Github/python-audio-separator
+
+==================================================
+0. System Information
+==================================================
+CPU: AMD Ryzen 5 5600X 6-Core Processor
+CPU Cores: 12
+Memory: 15913 MB
+Kernel: Linux version 6.19.7-x64v3-xanmod1 (root@runner-huzfz3a5r-project-51590166-concurrent-0) (Debian clang version 19.1.7 (3), Debian LLD 19.1.7) #0~20260312.g3118900 SMP PREEMPT_DYNAMIC Thu Mar 12 15:17:19 UTC
+HSA_OVERRIDE_GFX_VERSION: 10.3.0
+PYTORCH_ROCM_ARCH: gfx1030
+Relevant environment variables:
+  PYTORCH_ROCM_ARCH: gfx1030
+
+==================================================
+0.1 PyTorch ROCm Build Check
+==================================================
+✓ PyTorch built with ROCm support
+
+==================================================
+0.2 ROCm Environment Variables
+==================================================
+  PYTORCH_ROCM_ARCH: gfx1030
+
+==================================================
+0.3 Known ROCm Issues Check
+==================================================
+✓ CUDA AMP (autocast) available
+
+==================================================
+0.4 Memory Layout Check
+==================================================
+✓ Contiguous memory allocation working
+✓ Non-contiguous memory layout working
+
+==================================================
+1. PyTorch and ROCm Setup
+==================================================
+PyTorch version: 2.12.0.dev20260315+rocm7.2
+ROCm detected in version: True
+CUDA available: True
+CUDA device count: 1
+Current CUDA device: 0
+CUDA device name: AMD Radeon RX 6600
+GPU Architecture: 10.3
+GPU Memory: 7.98 GB
+PyTorch ROCm version: 7.2.0
+✓ Basic tensor multiplication on GPU: PASSED
+✓ Memory pinning and non-blocking transfer successful
+✓ CUDA stream operations successful
+✓ CUDA event timing successful: 0.39ms
+✓ Half precision (float16) operations successful
+✓ Complex number operations successful
+
+==================================================
+2. ONNX Runtime Setup
+==================================================
+ONNX Runtime version: 1.24.3
+Available execution providers: ['TensorrtExecutionProvider', 'CUDAExecutionProvider', 'CPUExecutionProvider']
+✗ ROCMExecutionProvider not available
+✓ CUDAExecutionProvider is available (can be used as fallback for AMD)
+✗ Failed to create session with CUDAExecutionProvider: NoSuchFile: [ONNXRuntimeError] : 3 : NO_SUCHFILE : Load model from dummy_model.onnx failed:Load model dummy_model.onnx failed. File doesn't exist
+ROCm environment variables detected:
+  PYTORCH_ROCM_ARCH: gfx1030
+✓ ROCm installation found at /opt/rocm
+  ROCm components detected in /opt/rocm
+
+==================================================
+3. STFT Operations
+==================================================
+Testing Hann window creation...
+✓ Hann window created and moved to GPU: shape=torch.Size([2048]), device=cuda:0
+✓ Hamming window created on GPU
+✓ Blackman window created on GPU
+✓ Custom window created on GPU
+✓ STFT with n_fft=1024: shape=torch.Size([2, 513, 173]), device=cuda:0
+✓ STFT with n_fft=2048: shape=torch.Size([2, 1025, 87]), device=cuda:0
+  Result is not contiguous
+
+Testing complex tensor operations...
+✓ Complex tensor created: torch.Size([2, 1025, 100]), torch.complex64
+✓ Complex multiplication successful
+✓ Complex addition successful
+✓ Complex magnitude calculation successful
+✓ Complex angle calculation successful
+✓ ISTFT completed: torch.Size([2, 44100]), cuda:0
+✓ ISTFT with longer length completed: torch.Size([2, 48000])
+
+Testing ROCm-specific STFT optimizations...
+✓ STFT with float64 precision successful
+✓ STFT with float16 precision successful
+
+==================================================
+4. GPU Memory Allocation
+==================================================
+GPU memory allocated: 68.00 MB
+GPU memory reserved: 132.00 MB
+GPU memory max allocated: 99.55 MB
+GPU memory max reserved: 132.00 MB
+GPU Name: AMD Radeon RX 6600
+GPU Compute Capability: 10.3
+GPU Total Memory: 8176.00 MB
+GPU Multi processor count: 14
+✓ Successfully allocated 1024x1024 tensor (4.00 MB), memory change: +4.00 MB
+✓ Matrix multiplication completed
+  Memory after cleanup: 68.00 MB
+✓ Successfully allocated 2048x2048 tensor (16.00 MB), memory change: +16.00 MB
+✓ Matrix multiplication completed
+  Memory after cleanup: 68.00 MB
+✓ Successfully allocated 4096x4096 tensor (64.00 MB), memory change: +64.00 MB
+✓ Matrix multiplication completed
+  Memory after cleanup: 68.00 MB
+✓ Successfully allocated 8192x8192 tensor (256.00 MB), memory change: +256.00 MB
+✓ Matrix multiplication completed
+  Memory after cleanup: 68.00 MB
+✓ Successfully allocated 16384x16384 tensor (1024.00 MB), memory change: +1024.00 MB
+✓ Matrix multiplication completed
+  Memory after cleanup: 68.00 MB
+
+Testing memory fragmentation...
+✓ Memory fragmentation test completed successfully
+
+Testing large contiguous allocation...
+Attempting to allocate 6051.20 MB (39828x39828 tensor)
+✓ Successfully allocated large contiguous tensor
+✗ Large contiguous allocation failed: OOM
+
+Testing ROCm-specific memory operations...
+✓ Pinned memory allocation successful
+✓ Non-blocking copy successful
+✓ Pinned memory transfer successful
+
+==================================================
+5. Model Type Testing
+==================================================
+✓ Successfully imported all model modules
+
+==================================================
+5.1 Roformer Models
+==================================================
+✓ Successfully created and moved BSRoformer model to GPU
+✓ Model forward pass completed: output shape torch.Size([1, 1, 44032])
+✓ Gradient computation successful
+✓ Successfully created and moved MelBandRoformer model to GPU
+✓ Model forward pass completed: output shape torch.Size([1, 1, 44032])
+✓ Gradient computation successful
+✓ Successfully created RoformerLoader
+✓ Configuration validation passed
+
+==================================================
+5.2 Demucs Models
+==================================================
+✓ Successfully created and moved Demucs model to GPU
+✓ Demucs model forward pass completed: output shape torch.Size([1, 4, 2, 44100])
+✓ Demucs gradient computation successful
+
+==================================================
+5.3 MDX Models
+==================================================
+✗ MDX model operations: FAILED - Exception:  
+
+==================================================
+Debugging complete. Review the results above to identify issues.
+==================================================
diff --git a/debug_rocm_issues.py b/debug_rocm_issues.py
new file mode 100644
index 0000000..bedc878
--- /dev/null
+++ b/debug_rocm_issues.py
@@ -0,0 +1,1225 @@
+#!/usr/bin/env python3
+"""
+Debug script to diagnose ROCm implementation crashes in audio-separator.
+This script tests key operations that commonly cause issues with ROCm.
+"""
+
+import os
+import random
+import re
+import subprocess
+import sys
+import time
+import traceback
+
+import torch
+import torch.amp
+
+
+def get_rocm_info():
+    """Get ROCm system information."""
+    rocm_info = {}
+
+    # Try to get ROCm version
+    try:
+        result = subprocess.run(
+            ["rocminfo"], capture_output=True, text=True, timeout=10
+        )
+        rocm_info["rocminfo"] = result.stdout
+
+        # Extract version from rocminfo output
+        version_match = re.search(
+            r"ROCm Stack Version: (.+)$", result.stdout, re.MULTILINE
+        )
+        if version_match:
+            rocm_info["version"] = version_match.group(1)
+
+    except (
+        subprocess.TimeoutExpired,
+        subprocess.CalledProcessError,
+        FileNotFoundError,
+    ):
+        rocm_info["rocminfo"] = "rocminfo command not found or failed"
+
+    # Try to get HIP version
+    try:
+        result = subprocess.run(
+            ["hipconfig"], capture_output=True, text=True, timeout=10
+        )
+        rocm_info["hipconfig"] = result.stdout
+
+        # Extract HIP version
+        version_match = re.search(r"HIP version\s+:\s+(.+)", result.stdout)
+        if version_match:
+            rocm_info["hip_version"] = version_match.group(1)
+
+    except (
+        subprocess.TimeoutExpired,
+        subprocess.CalledProcessError,
+        FileNotFoundError,
+    ):
+        rocm_info["hipconfig"] = "hipconfig command not found or failed"
+
+    return rocm_info
+
+
+def get_system_info():
+    """Get system information."""
+    system_info = {}
+
+    # Get CPU information
+    try:
+        with open("/proc/cpuinfo", "r") as f:
+            cpuinfo = f.read()
+            cpu_model = re.search(r"model name\s+: (.+)$", cpuinfo, re.MULTILINE)
+            if cpu_model:
+                system_info["cpu_model"] = cpu_model.group(1)
+
+            cpu_cores = len(re.findall(r"^processor", cpuinfo, re.MULTILINE))
+            system_info["cpu_cores"] = cpu_cores
+
+    except Exception as e:
+        system_info["cpu_info"] = f"Failed to read CPU info: {e}"
+
+    # Get memory information
+    try:
+        with open("/proc/meminfo", "r") as f:
+            meminfo = f.read()
+            mem_total = re.search(r"MemTotal:\s+(\d+) kB", meminfo)
+            if mem_total:
+                system_info["memory_total"] = (
+                    int(mem_total.group(1)) / 1024
+                )  # Convert to MB
+
+    except Exception as e:
+        system_info["memory_info"] = f"Failed to read memory info: {e}"
+
+    # Get kernel version
+    try:
+        with open("/proc/version", "r") as f:
+            system_info["kernel_version"] = f.read().strip()
+    except Exception as e:
+        system_info["kernel_version"] = f"Failed to read kernel version: {e}"
+
+    return system_info
+
+
+# Set environment variable to try to work around gfx1032 issue
+os.environ["HSA_OVERRIDE_GFX_VERSION"] = "10.3.0"
+
+
+def print_section(title):
+    print(f"\n{'=' * 50}")
+    print(f"{title}")
+    print(f"{'=' * 50}")
+
+
+def test_pytorch_rocm_setup():
+    """Test basic PyTorch and ROCm setup."""
+    print_section("1. PyTorch and ROCm Setup")
+
+    print(f"PyTorch version: {torch.__version__}")
+    print(f"ROCm detected in version: {'+rocm' in torch.__version__}")
+    print(f"CUDA available: {torch.cuda.is_available()}")
+
+    if torch.cuda.is_available():
+        print(f"CUDA device count: {torch.cuda.device_count()}")
+        print(f"Current CUDA device: {torch.cuda.current_device()}")
+        print(f"CUDA device name: {torch.cuda.get_device_name(0)}")
+
+        # Test ROCm-specific features
+        device = torch.device("cuda")
+
+        # Test ROCm version and capabilities
+        try:
+            # Check for ROCm-specific attributes
+            if hasattr(torch.cuda, "get_device_properties"):
+                props = torch.cuda.get_device_properties(0)
+                print(f"GPU Architecture: {props.major}.{props.minor}")
+                print(f"GPU Memory: {props.total_memory / 1024**3:.2f} GB")
+
+            # Test ROCm version
+            if hasattr(torch.version, "rocm"):
+                print(f"PyTorch ROCm version: {torch.version.rocm}")
+
+        except Exception as e:
+            print(f"✗ ROCm property test failed - {type(e).__name__}: {e}")
+
+        # Test basic tensor operations
+        try:
+            x = torch.randn(1000, 1000).to("cuda")
+            y = torch.randn(1000, 1000).to("cuda")
+            z = torch.mm(x, y)
+            print("✓ Basic tensor multiplication on GPU: PASSED")
+        except Exception as e:
+            print(
+                f"✗ Basic tensor multiplication on GPU: FAILED - {type(e).__name__}: {e}"
+            )
+
+        # Test ROCm memory operations
+        try:
+            # Test memory pinning
+            cpu_tensor = torch.randn(1000, 1000)
+            pinned_tensor = cpu_tensor.pin_memory()
+            gpu_tensor = pinned_tensor.to(device, non_blocking=True)
+            print("✓ Memory pinning and non-blocking transfer successful")
+
+            # Test stream operations
+            stream = torch.cuda.Stream()
+            with torch.cuda.stream(stream):
+                a = torch.randn(1000, 1000, device=device)
+                b = torch.randn(1000, 1000, device=device)
+                c = torch.mm(a, b)
+            stream.synchronize()
+            print("✓ CUDA stream operations successful")
+
+            # Test event operations
+            start_event = torch.cuda.Event(enable_timing=True)
+            end_event = torch.cuda.Event(enable_timing=True)
+            start_event.record()
+            d = torch.mm(c, c)
+            end_event.record()
+            end_event.synchronize()
+            elapsed_time = start_event.elapsed_time(end_event)
+            print(f"✓ CUDA event timing successful: {elapsed_time:.2f}ms")
+
+        except Exception as e:
+            print(f"✗ ROCm memory/stream test failed - {type(e).__name__}: {e}")
+
+        # Test ROCm-specific math operations
+        try:
+            # Test half precision
+            if (
+                torch.cuda.is_available()
+                and torch.cuda.get_device_properties(0).major >= 5
+            ):
+                x_half = torch.randn(100, 100, device=device, dtype=torch.float16)
+                y_half = torch.randn(100, 100, device=device, dtype=torch.float16)
+                z_half = torch.mm(x_half, y_half)
+                print("✓ Half precision (float16) operations successful")
+
+            # Test complex number operations
+            x_complex = torch.randn(100, 100, dtype=torch.complex64, device=device)
+            y_complex = torch.randn(100, 100, dtype=torch.complex64, device=device)
+            z_complex = x_complex * y_complex
+            print("✓ Complex number operations successful")
+
+        except Exception as e:
+            print(f"✗ ROCm math operation test failed - {type(e).__name__}: {e}")
+
+    else:
+        print("✗ CUDA not available - ROCm setup may be incomplete")
+
+
+def test_onnxruntime_setup():
+    """Test ONNX Runtime setup with ROCm."""
+    print_section("2. ONNX Runtime Setup")
+
+    try:
+        import onnxruntime as ort
+
+        print(f"ONNX Runtime version: {ort.__version__}")
+
+        # Check available providers
+        providers = ort.get_available_providers()
+        print(f"Available execution providers: {providers}")
+
+        # Test ROCm provider
+        if "ROCMExecutionProvider" in providers:
+            print("✓ ROCMExecutionProvider is available")
+
+            # Test creating a session with ROCm provider
+            try:
+                session_options = ort.SessionOptions()
+                session_options.log_severity_level = 2  # Warning level
+                session = ort.InferenceSession(
+                    "dummy_model.onnx",
+                    sess_options=session_options,
+                    providers=["ROCMExecutionProvider", "CPUExecutionProvider"],
+                )
+                print("✓ Successfully created session with ROCMExecutionProvider")
+
+                # Test ROCm provider capabilities
+                try:
+                    # Create a simple model for testing
+                    import numpy as np
+
+                    # Define a simple model that does matrix multiplication
+                    from onnx import TensorProto, helper, numpy_helper
+
+                    # Define the graph
+                    node1 = helper.make_node(
+                        "MatMul",
+                        inputs=["input1", "input2"],
+                        outputs=["output"],
+                    )
+
+                    # Create the graph
+                    graph = helper.make_graph(
+                        [node1],
+                        "test_graph",
+                        [
+                            helper.make_tensor_value_info(
+                                "input1", TensorProto.FLOAT, [2, 2]
+                            ),
+                            helper.make_tensor_value_info(
+                                "input2", TensorProto.FLOAT, [2, 2]
+                            ),
+                        ],
+                        [
+                            helper.make_tensor_value_info(
+                                "output", TensorProto.FLOAT, [2, 2]
+                            )
+                        ],
+                    )
+
+                    # Create the model
+                    model = helper.make_model(graph)
+
+                    # Test inference
+                    session = ort.InferenceSession(
+                        model.SerializeToString(),
+                        providers=["ROCMExecutionProvider", "CPUExecutionProvider"],
+                    )
+
+                    # Run inference
+                    input1 = np.array([[1.0, 2.0], [3.0, 4.0]], dtype=np.float32)
+                    input2 = np.array([[5.0, 6.0], [7.0, 8.0]], dtype=np.float32)
+                    result = session.run(None, {"input1": input1, "input2": input2})
+
+                    expected = np.array([[19.0, 22.0], [43.0, 50.0]], dtype=np.float32)
+                    if np.allclose(result[0], expected):
+                        print("✓ ROCm provider inference test passed")
+                    else:
+                        print("✗ ROCm provider inference test failed")
+                        print(f"  Expected: {expected}")
+                        print(f"  Got: {result[0]}")
+
+                except Exception as e:
+                    print(f"✗ ROCm inference test failed - {type(e).__name__}: {e}")
+
+            except Exception as e:
+                print(
+                    f"✗ Failed to create session with ROCMExecutionProvider: {type(e).__name__}: {e}"
+                )
+        else:
+            print("✗ ROCMExecutionProvider not available")
+
+        # Test CUDA provider as fallback
+        if "CUDAExecutionProvider" in providers:
+            print(
+                "✓ CUDAExecutionProvider is available (can be used as fallback for AMD)"
+            )
+
+            # Test creating a session with CUDA provider
+            try:
+                session_options = ort.SessionOptions()
+                session = ort.InferenceSession(
+                    "dummy_model.onnx",
+                    sess_options=session_options,
+                    providers=["CUDAExecutionProvider", "CPUExecutionProvider"],
+                )
+                print("✓ Successfully created session with CUDAExecutionProvider")
+
+                # Test CUDA provider capabilities
+                try:
+                    # Reuse the same model test as above
+                    import numpy as np
+                    from onnx import TensorProto, helper
+
+                    # Create the graph
+                    node1 = helper.make_node(
+                        "MatMul",
+                        inputs=["input1", "input2"],
+                        outputs=["output"],
+                    )
+
+                    graph = helper.make_graph(
+                        [node1],
+                        "test_graph",
+                        [
+                            helper.make_tensor_value_info(
+                                "input1", TensorProto.FLOAT, [2, 2]
+                            ),
+                            helper.make_tensor_value_info(
+                                "input2", TensorProto.FLOAT, [2, 2]
+                            ),
+                        ],
+                        [
+                            helper.make_tensor_value_info(
+                                "output", TensorProto.FLOAT, [2, 2]
+                            )
+                        ],
+                    )
+
+                    model = helper.make_model(graph)
+
+                    # Test inference
+                    session = ort.InferenceSession(
+                        model.SerializeToString(),
+                        providers=["CUDAExecutionProvider", "CPUExecutionProvider"],
+                    )
+
+                    # Run inference
+                    input1 = np.array([[1.0, 2.0], [3.0, 4.0]], dtype=np.float32)
+                    input2 = np.array([[5.0, 6.0], [7.0, 8.0]], dtype=np.float32)
+                    result = session.run(None, {"input1": input1, "input2": input2})
+
+                    expected = np.array([[19.0, 22.0], [43.0, 50.0]], dtype=np.float32)
+                    if np.allclose(result[0], expected):
+                        print("✓ CUDA provider inference test passed")
+                    else:
+                        print("✗ CUDA provider inference test failed")
+                        print(f"  Expected: {expected}")
+                        print(f"  Got: {result[0]}")
+
+                except Exception as e:
+                    print(f"✗ CUDA inference test failed - {type(e).__name__}: {e}")
+
+            except Exception as e:
+                print(
+                    f"✗ Failed to create session with CUDAExecutionProvider: {type(e).__name__}: {e}"
+                )
+        else:
+            print("✗ CUDAExecutionProvider not available")
+
+        # Additional ROCm-specific checks
+        try:
+            # Check for ROCm-specific environment variables
+            rocm_vars = [v for v in os.environ.keys() if "rocm" in v.lower()]
+            if rocm_vars:
+                print("ROCm environment variables detected:")
+                for var in rocm_vars:
+                    print(f"  {var}: {os.environ[var]}")
+
+            # Check for ROCm installation paths
+            rocm_paths = ["/opt/rocm", "/usr/lib/rocm"]
+            for path in rocm_paths:
+                if os.path.exists(path):
+                    print(f"✓ ROCm installation found at {path}")
+                    # List contents to verify
+                    try:
+                        contents = os.listdir(path)
+                        if "lib" in contents or "bin" in contents:
+                            print(f"  ROCm components detected in {path}")
+                    except:
+                        pass
+
+        except Exception as e:
+            print(f"✗ ROCm environment check failed - {type(e).__name__}: {e}")
+
+    except ImportError:
+        print("✗ ONNX Runtime not installed")
+    except Exception as e:
+        print(f"✗ Error testing ONNX Runtime: {type(e).__name__}: {e}")
+
+
+def test_stft_operations():
+    """Test STFT operations which are critical for audio processing."""
+    print_section("3. STFT Operations")
+
+    if not torch.cuda.is_available():
+        print("✗ CUDA not available - skipping STFT tests")
+        return
+
+    try:
+        # Test window creation
+        print("Testing Hann window creation...")
+        window = torch.hann_window(2048, periodic=True)
+        window_gpu = window.to("cuda")
+        print(
+            f"✓ Hann window created and moved to GPU: shape={window_gpu.shape}, device={window_gpu.device}"
+        )
+
+        # Test different window types
+        try:
+            # Test Hamming window
+            hamming_window = torch.hamming_window(2048, periodic=True).to("cuda")
+            print("✓ Hamming window created on GPU")
+
+            # Test Blackman window
+            blackman_window = torch.blackman_window(2048, periodic=True).to("cuda")
+            print("✓ Blackman window created on GPU")
+
+            # Test custom window
+            custom_window = torch.ones(2048, device="cuda")
+            print("✓ Custom window created on GPU")
+
+        except Exception as e:
+            print(f"✗ Window type test failed - {type(e).__name__}: {e}")
+
+        # Test STFT on GPU
+        x_gpu = torch.randn(2, 44100).to("cuda")
+
+        # Try different n_fft values
+        for n_fft in [1024, 2048]:
+            hop_length = n_fft // 4
+            try:
+                result_gpu = torch.stft(
+                    x_gpu,
+                    n_fft=n_fft,
+                    hop_length=hop_length,
+                    window=window_gpu[:n_fft].to("cuda"),
+                    center=True,
+                    return_complex=True,
+                )
+                print(
+                    f"✓ STFT with n_fft={n_fft}: shape={result_gpu.shape}, device={result_gpu.device}"
+                )
+            except Exception as e:
+                print(f"✗ STFT with n_fft={n_fft}: FAILED - {type(e).__name__}: {e}")
+
+        # Test memory layout of result
+        if result_gpu.is_contiguous():
+            print(f"  Result is contiguous")
+        else:
+            print(f"  Result is not contiguous")
+
+        # Test complex tensor operations
+        print("\nTesting complex tensor operations...")
+        try:
+            x_complex = torch.randn(2, 1025, 100, 2, device="cuda")
+            x_complex = torch.view_as_complex(x_complex)
+            print(f"✓ Complex tensor created: {x_complex.shape}, {x_complex.dtype}")
+
+            # Test different complex operations
+            try:
+                # Test complex multiplication
+                y_complex = torch.randn(
+                    2, 1025, 100, dtype=torch.complex64, device="cuda"
+                )
+                z_complex = x_complex * y_complex
+                print("✓ Complex multiplication successful")
+
+                # Test complex addition
+                w_complex = x_complex + y_complex
+                print("✓ Complex addition successful")
+
+                # Test complex magnitude
+                mag = torch.abs(x_complex)
+                print("✓ Complex magnitude calculation successful")
+
+                # Test complex angle
+                angle = torch.angle(x_complex)
+                print("✓ Complex angle calculation successful")
+
+            except Exception as e:
+                print(f"✗ Complex operation test failed - {type(e).__name__}: {e}")
+
+            # Test istft
+            result_istft = torch.istft(
+                x_complex,
+                n_fft=2048,
+                hop_length=512,
+                window=window_gpu,
+                center=True,
+                length=44100,
+            )
+            print(f"✓ ISTFT completed: {result_istft.shape}, {result_istft.device}")
+
+            # Test different istft parameters
+            try:
+                # Test with different length
+                result_istft_long = torch.istft(
+                    x_complex,
+                    n_fft=2048,
+                    hop_length=512,
+                    window=window_gpu,
+                    center=True,
+                    length=48000,
+                )
+                print(
+                    f"✓ ISTFT with longer length completed: {result_istft_long.shape}"
+                )
+
+            except Exception as e:
+                print(f"✗ ISTFT parameter test failed - {type(e).__name__}: {e}")
+
+        except Exception as e:
+            print(f"✗ Complex tensor operations: FAILED - {type(e).__name__}: {e}")
+
+        # Test ROCm-specific STFT optimizations
+        try:
+            print("\nTesting ROCm-specific STFT optimizations...")
+
+            # Test with different data types
+            x_float64 = torch.randn(2, 44100, dtype=torch.float64, device="cuda")
+            result_float64 = torch.stft(
+                x_float64,
+                n_fft=2048,
+                hop_length=512,
+                window=window_gpu[:2048].to(torch.float64),
+                center=True,
+                return_complex=True,
+            )
+            print("✓ STFT with float64 precision successful")
+
+            # Test with half precision
+            if (
+                torch.cuda.is_available()
+                and torch.cuda.get_device_properties(0).major >= 5
+            ):
+                x_float16 = torch.randn(2, 44100, dtype=torch.float16, device="cuda")
+                window_float16 = window_gpu[:2048].to(torch.float16)
+                result_float16 = torch.stft(
+                    x_float16,
+                    n_fft=2048,
+                    hop_length=512,
+                    window=window_float16,
+                    center=True,
+                    return_complex=True,
+                )
+                print("✓ STFT with float16 precision successful")
+
+        except Exception as e:
+            print(f"✗ ROCm STFT optimization test failed - {type(e).__name__}: {e}")
+
+    except Exception as e:
+        print(f"✗ STFT operations: FAILED - {type(e).__name__}: {e}")
+
+
+def test_memory_allocation():
+    """Test GPU memory allocation patterns and identify potential memory issues."""
+    print_section("4. GPU Memory Allocation")
+
+    if not torch.cuda.is_available():
+        print("✗ CUDA not available - skipping memory tests")
+        return
+
+    try:
+        # Get detailed GPU memory information
+        print(f"GPU memory allocated: {torch.cuda.memory_allocated() / 1024**2:.2f} MB")
+        print(f"GPU memory reserved: {torch.cuda.memory_reserved() / 1024**2:.2f} MB")
+        print(
+            f"GPU memory max allocated: {torch.cuda.max_memory_allocated() / 1024**2:.2f} MB"
+        )
+        print(
+            f"GPU memory max reserved: {torch.cuda.max_memory_reserved() / 1024**2:.2f} MB"
+        )
+
+        # Get GPU properties
+        device = torch.cuda.current_device()
+        props = torch.cuda.get_device_properties(device)
+        print(f"GPU Name: {props.name}")
+        print(f"GPU Compute Capability: {props.major}.{props.minor}")
+        print(f"GPU Total Memory: {props.total_memory / 1024**2:.2f} MB")
+        print(f"GPU Multi processor count: {props.multi_processor_count}")
+
+        # Test different tensor sizes with memory monitoring
+        sizes = [1024, 2048, 4096, 8192, 16384]
+        for size in sizes:
+            try:
+                # Report memory before allocation
+                mem_before = torch.cuda.memory_allocated() / 1024**2
+
+                # Allocate a large tensor
+                tensor = torch.randn(size, size, device="cuda")
+
+                # Report memory after allocation
+                mem_after = torch.cuda.memory_allocated() / 1024**2
+
+                print(
+                    f"✓ Successfully allocated {size}x{size} tensor ({tensor.element_size() * tensor.nelement() / 1024**2:.2f} MB)"
+                    f", memory change: +{mem_after - mem_before:.2f} MB"
+                )
+
+                # Perform an operation
+                result = torch.mm(tensor, tensor)
+                print(f"✓ Matrix multiplication completed")
+
+                # Clean up
+                del tensor, result
+                torch.cuda.empty_cache()
+
+                # Report memory after cleanup
+                mem_cleanup = torch.cuda.memory_allocated() / 1024**2
+                print(f"  Memory after cleanup: {mem_cleanup:.2f} MB")
+
+            except RuntimeError as e:
+                if "out of memory" in str(e).lower():
+                    print(f"✗ Allocation of {size}x{size} tensor: OOM - {e}")
+                    break
+                else:
+                    print(f"✗ Allocation of {size}x{size} tensor: FAILED - {e}")
+                    break
+            except Exception as e:
+                print(
+                    f"✗ Allocation of {size}x{size} tensor: FAILED - {type(e).__name__}: {e}"
+                )
+                break
+
+        # Test memory fragmentation by allocating and deallocating in random order
+        try:
+            print("\nTesting memory fragmentation...")
+            tensors = []
+            sizes_mb = [16, 32, 64, 128, 256, 512]
+
+            for i in range(20):  # Create 20 random allocations
+                size_mb = random.choice(sizes_mb)
+                size = int(
+                    (size_mb * 1024**2) / 4
+                )  # Convert MB to number of floats (4 bytes each)
+                size = int(size**0.5)  # Make it a square tensor
+
+                try:
+                    tensor = torch.randn(size, size, device="cuda")
+                    tensors.append(tensor)
+
+                    if len(tensors) % 5 == 0:  # Deallocate every 5th allocation
+                        del tensors[0]
+                        tensors = tensors[1:]
+                        torch.cuda.empty_cache()
+
+                except RuntimeError as e:
+                    if "out of memory" in str(e).lower():
+                        print(
+                            f"✗ Memory fragmentation test failed at iteration {i}: OOM"
+                        )
+                        break
+
+            else:
+                print("✓ Memory fragmentation test completed successfully")
+
+            # Clean up remaining tensors
+            for t in tensors:
+                del t
+            torch.cuda.empty_cache()
+
+        except Exception as e:
+            print(f"✗ Memory fragmentation test failed - {type(e).__name__}: {e}")
+
+        # Test large contiguous allocation
+        try:
+            print("\nTesting large contiguous allocation...")
+            # Try to allocate 80% of free memory as a single tensor
+            free_memory = props.total_memory - torch.cuda.memory_reserved()
+            target_memory = int(free_memory * 0.8)
+            elements = target_memory // 4  # 4 bytes per float
+            side_length = int(elements**0.5)
+
+            print(
+                f"Attempting to allocate {target_memory / 1024**2:.2f} MB ({side_length}x{side_length} tensor)"
+            )
+
+            tensor = torch.randn(side_length, side_length, device="cuda")
+            print(f"✓ Successfully allocated large contiguous tensor")
+
+            # Test operation on large tensor
+            result = torch.mm(tensor, tensor)
+            print(f"✓ Matrix multiplication completed on large tensor")
+
+            del tensor, result
+            torch.cuda.empty_cache()
+
+        except RuntimeError as e:
+            if "out of memory" in str(e).lower():
+                print(f"✗ Large contiguous allocation failed: OOM")
+            else:
+                print(f"✗ Large contiguous allocation failed - {type(e).__name__}: {e}")
+        except Exception as e:
+            print(
+                f"✗ Large contiguous allocation test failed - {type(e).__name__}: {e}"
+            )
+
+        # Test ROCm-specific memory operations
+        print("\nTesting ROCm-specific memory operations...")
+        try:
+            # Test pinned memory (important for ROCm)
+            pinned_tensor = torch.randn(1024, 1024, pin_memory=True)
+            print("✓ Pinned memory allocation successful")
+            del pinned_tensor
+
+            # Test non-blocking operations
+            src_tensor = torch.randn(1024, 1024, device="cuda")
+            dst_tensor = torch.randn(1024, 1024, device="cuda")
+            dst_tensor.copy_(src_tensor, non_blocking=True)
+            print("✓ Non-blocking copy successful")
+
+            # Test memory pinning with CPU tensor
+            cpu_tensor = torch.randn(1024, 1024)
+            cpu_tensor = cpu_tensor.pin_memory()
+            gpu_tensor = cpu_tensor.to("cuda", non_blocking=True)
+            print("✓ Pinned memory transfer successful")
+
+        except Exception as e:
+            print(f"✗ ROCm memory operation failed - {type(e).__name__}: {e}")
+
+    except Exception as e:
+        print(f"✗ Memory allocation tests: FAILED - {type(e).__name__}: {e}")
+
+
+def test_model_types():
+    """Test loading and running all model types on ROCm."""
+    print_section("5. Model Type Testing")
+
+    if not torch.cuda.is_available():
+        print("✗ CUDA not available - skipping model tests")
+        return
+
+    try:
+        # Test importing all model modules
+        from audio_separator.separator.roformer.roformer_loader import RoformerLoader
+        from audio_separator.separator.uvr_lib_v5.demucs.hdemucs import HDemucs
+        from audio_separator.separator.uvr_lib_v5.demucs.pretrained import (
+            get_model as get_demucs_model,
+        )
+        from audio_separator.separator.uvr_lib_v5.roformer.bs_roformer import BSRoformer
+        from audio_separator.separator.uvr_lib_v5.roformer.mel_band_roformer import (
+            MelBandRoformer,
+        )
+        from audio_separator.separator.uvr_lib_v5.tfc_tdf_v3 import TFC_TDF_net
+
+        print("✓ Successfully imported all model modules")
+
+        # Test Roformer models
+        print_section("5.1 Roformer Models")
+
+        # Test BSRoformer
+        try:
+            # Use proper freqs_per_bands that sums to 1025 for n_fft=2048
+            model = BSRoformer(
+                dim=512,
+                depth=2,
+                freqs_per_bands=(
+                    2,
+                    2,
+                    2,
+                    2,
+                    2,
+                    2,
+                    2,
+                    2,
+                    2,
+                    2,
+                    2,
+                    2,
+                    2,
+                    2,
+                    2,
+                    2,
+                    2,
+                    2,
+                    2,
+                    2,
+                    2,
+                    2,
+                    2,
+                    2,
+                    2,
+                    4,
+                    4,
+                    4,
+                    4,
+                    4,
+                    4,
+                    4,
+                    4,
+                    4,
+                    4,
+                    4,
+                    4,
+                    12,
+                    12,
+                    12,
+                    12,
+                    12,
+                    12,
+                    12,
+                    12,
+                    24,
+                    24,
+                    24,
+                    24,
+                    24,
+                    24,
+                    24,
+                    24,
+                    48,
+                    48,
+                    48,
+                    48,
+                    48,
+                    48,
+                    48,
+                    48,
+                    128,
+                    127,
+                ),
+                stft_n_fft=2048,
+                stft_hop_length=512,
+                stft_win_length=2048,
+            )
+            model.to("cuda")
+            model.eval()
+
+            print("✓ Successfully created and moved BSRoformer model to GPU")
+
+            # Test with a small input
+            test_input = torch.randn(1, 44100, device="cuda", requires_grad=True)
+            output = model(test_input)
+
+            print(f"✓ Model forward pass completed: output shape {output.shape}")
+
+            # Test gradient computation
+            try:
+                model.train()
+                loss = torch.nn.functional.l1_loss(output, torch.zeros_like(output))
+                loss.backward()
+                print("✓ Gradient computation successful")
+                model.eval()
+            except Exception as e:
+                print(f"✗ Gradient computation failed - {type(e).__name__}: {e}")
+
+        except Exception as e:
+            print(f"✗ BSRoformer model operations: FAILED - {type(e).__name__}: {e}")
+
+        # Test MelBandRoformer
+        try:
+            model = MelBandRoformer(
+                dim=512,
+                depth=2,
+                num_bands=60,
+                stft_n_fft=2048,
+                stft_hop_length=512,
+                stft_win_length=2048,
+            )
+            model.to("cuda")
+            model.eval()
+
+            print("✓ Successfully created and moved MelBandRoformer model to GPU")
+
+            # Test with a small input
+            test_input = torch.randn(1, 44100, device="cuda", requires_grad=True)
+            output = model(test_input)
+
+            print(f"✓ Model forward pass completed: output shape {output.shape}")
+
+            # Test gradient computation
+            try:
+                model.train()
+                loss = torch.nn.functional.l1_loss(output, torch.zeros_like(output))
+                loss.backward()
+                print("✓ Gradient computation successful")
+                model.eval()
+            except Exception as e:
+                print(f"✗ Gradient computation failed - {type(e).__name__}: {e}")
+
+        except Exception as e:
+            print(
+                f"✗ MelBandRoformer model operations: FAILED - {type(e).__name__}: {e}"
+            )
+
+        # Test RoformerLoader
+        try:
+            loader = RoformerLoader()
+            print("✓ Successfully created RoformerLoader")
+
+            # Test configuration validation
+            config = {
+                "dim": 512,
+                "depth": 2,
+                "freqs_per_bands": (
+                    2,
+                    2,
+                    2,
+                    2,
+                    2,
+                    2,
+                    2,
+                    2,
+                    2,
+                    2,
+                    2,
+                    2,
+                    2,
+                    2,
+                    2,
+                    2,
+                    2,
+                    2,
+                    2,
+                    2,
+                    2,
+                    2,
+                    2,
+                    2,
+                    2,
+                    4,
+                    4,
+                    4,
+                    4,
+                    4,
+                    4,
+                    4,
+                    4,
+                    4,
+                    4,
+                    4,
+                    4,
+                    12,
+                    12,
+                    12,
+                    12,
+                    12,
+                    12,
+                    12,
+                    12,
+                    24,
+                    24,
+                    24,
+                    24,
+                    24,
+                    24,
+                    24,
+                    24,
+                    48,
+                    48,
+                    48,
+                    48,
+                    48,
+                    48,
+                    48,
+                    48,
+                    128,
+                    127,
+                ),
+                "stft_n_fft": 2048,
+                "stft_hop_length": 512,
+                "stft_win_length": 2048,
+            }
+
+            is_valid = loader.validate_configuration(config, "bs_roformer")
+            if is_valid:
+                print("✓ Configuration validation passed")
+            else:
+                print("✗ Configuration validation failed")
+
+        except Exception as e:
+            print(f"✗ RoformerLoader test failed - {type(e).__name__}: {e}")
+
+        # Test Demucs models
+        print_section("5.2 Demucs Models")
+        try:
+            # Test HDemucs model architecture
+            demucs_model = HDemucs(sources=["drums", "bass", "other", "vocals"])
+            demucs_model.to("cuda")
+            demucs_model.train()  # Keep in training mode for backward pass
+
+            print("✓ Successfully created and moved Demucs model to GPU")
+
+            # Test with a small input
+            test_input = torch.randn(1, 2, 44100, device="cuda", requires_grad=True)
+            output = demucs_model(test_input)
+
+            print(f"✓ Demucs model forward pass completed: output shape {output.shape}")
+
+            # Test gradient computation
+            try:
+                loss = torch.nn.functional.l1_loss(output, torch.zeros_like(output))
+                loss.backward()
+                print("✓ Demucs gradient computation successful")
+                demucs_model.eval()
+            except Exception as e:
+                print(f"✗ Demucs gradient computation failed - {type(e).__name__}: {e}")
+
+        except Exception as e:
+            print(f"✗ Demucs model operations: FAILED - {type(e).__name__}: {e}")
+
+        # Test MDX models
+        print_section("5.3 MDX Models")
+        try:
+            # Test TFC_TDF_net model
+            from ml_collections import ConfigDict
+
+            # Create a complete config for testing
+            test_config = ConfigDict(
+                {
+                    "audio": {
+                        "n_fft": 2048,
+                        "hop_length": 512,
+                        "dim_f": 1025,
+                        "num_channels": 2,
+                    },
+                    "inference": {
+                        "dim_t": 256,
+                    },
+                    "training": {
+                        "instruments": ["vocals", "instrumental"],
+                        "target_instrument": "vocals",
+                    },
+                    "model": {
+                        "norm": "Identity",
+                        "act": "GELU",
+                        "num_subbands": 1,
+                        "num_scales": 2,
+                        "scale": [2, 2],
+                        "num_blocks_per_scale": 2,
+                        "num_channels": 32,
+                        "growth": 16,
+                        "bottleneck_factor": 4,
+                    },
+                }
+            )
+
+            print("Creating TFC_TDF_net model...")
+            model = TFC_TDF_net(test_config, device="cuda")
+            print(f"Model created. Model device: {next(model.parameters()).device}")
+
+            print("Moving model to CUDA...")
+            model.to("cuda")
+            print(
+                f"Model moved to CUDA. Current device: {next(model.parameters()).device}"
+            )
+
+            model.eval()
+            print("✓ Successfully created and moved TFC_TDF_net model to GPU")
+
+            # Test with a small input
+            test_input = torch.randn(1, 2, 44100, device="cuda", requires_grad=True)
+            print(f"Test input created on device: {test_input.device}")
+
+            try:
+                print("Running forward pass...")
+                output = model(test_input)
+                print(
+                    f"✓ MDX model forward pass completed: output shape {output.shape}"
+                )
+
+                # Test gradient computation
+                try:
+                    model.train()
+                    print("Computing loss...")
+                    loss = torch.nn.functional.l1_loss(output, torch.zeros_like(output))
+                    print(f"Loss computed: {loss.item()}")
+                    print("Running backward pass...")
+                    loss.backward()
+                    print("✓ MDX gradient computation successful")
+                    model.eval()
+                except Exception as e:
+                    print(
+                        f"✗ MDX gradient computation failed - {type(e).__name__}: {e}"
+                    )
+            except Exception as e:
+                print(f"✗ MDX forward pass failed - {type(e).__name__}: {e}")
+
+        except Exception as e:
+            print(f"✗ MDX model operations: FAILED - {type(e).__name__}: {e} {str(e)}")
+
+    except ImportError as e:
+        print(f"✗ Failed to import model modules: {e}")
+    except Exception as e:
+        print(f"✗ Model loading: FAILED - {type(e).__name__}: {e}")
+
+
+def main():
+    """Main function to run all tests."""
+    print("ROCm Debug Script for audio-separator")
+    print(f"Python version: {sys.version}")
+    print(f"Working directory: {os.getcwd()}")
+
+    # Print system information
+    print_section("0. System Information")
+    system_info = get_system_info()
+    if "cpu_model" in system_info:
+        print(f"CPU: {system_info['cpu_model']}")
+    if "cpu_cores" in system_info:
+        print(f"CPU Cores: {system_info['cpu_cores']}")
+    if "memory_total" in system_info:
+        print(f"Memory: {system_info['memory_total']:.0f} MB")
+    if "kernel_version" in system_info:
+        print(f"Kernel: {system_info['kernel_version']}")
+
+    # Print ROCm information
+    rocm_info = get_rocm_info()
+    if "version" in rocm_info:
+        print(f"ROCm Version: {rocm_info['version']}")
+    if "hip_version" in rocm_info:
+        print(f"HIP Version: {rocm_info['hip_version']}")
+
+    # Print environment variables
+    print(
+        f"HSA_OVERRIDE_GFX_VERSION: {os.environ.get('HSA_OVERRIDE_GFX_VERSION', 'Not set')}"
+    )
+    print(f"PYTORCH_ROCM_ARCH: {os.environ.get('PYTORCH_ROCM_ARCH', 'Not set')}")
+
+    # Check for relevant environment variables
+    relevant_vars = [
+        v
+        for v in os.environ.keys()
+        if "rocm" in v.lower() or "gpu" in v.lower() or "hip" in v.lower()
+    ]
+    if relevant_vars:
+        print("Relevant environment variables:")
+        for var in relevant_vars:
+            print(f"  {var}: {os.environ[var]}")
+
+    # Check PyTorch ROCm build
+    print_section("0.1 PyTorch ROCm Build Check")
+    if "+rocm" in torch.__version__:
+        print("✓ PyTorch built with ROCm support")
+    else:
+        print("✗ PyTorch not built with ROCm support - consider reinstalling with ROCm")
+
+    # Check for ROCm-specific environment variables
+    print_section("0.2 ROCm Environment Variables")
+    rocm_vars = [v for v in os.environ.keys() if "rocm" in v.lower()]
+    if rocm_vars:
+        for var in rocm_vars:
+            print(f"  {var}: {os.environ[var]}")
+    else:
+        print("  No ROCm-specific environment variables set")
+
+    # Check for known ROCm issues
+    print_section("0.3 Known ROCm Issues Check")
+    if torch.cuda.is_available():
+        # Check for gfx1032 issue
+        if "gfx1032" in str(torch.cuda.get_device_properties(0)):
+            if "HSA_OVERRIDE_GFX_VERSION" not in os.environ:
+                print("✗ gfx1032 GPU detected but HSA_OVERRIDE_GFX_VERSION not set")
+                print("  Recommendation: export HSA_OVERRIDE_GFX_VERSION=10.3.2")
+            else:
+                print("✓ gfx1032 GPU workaround enabled")
+
+        # Check for autocast issues
+        if hasattr(torch.cuda, "amp"):
+            print("✓ CUDA AMP (autocast) available")
+        else:
+            print("✗ CUDA AMP (autocast) not available - may affect performance")
+
+    # Check memory layout
+    print_section("0.4 Memory Layout Check")
+    if torch.cuda.is_available():
+        device = torch.device("cuda")
+        # Test different memory layouts
+        test_tensor = torch.randn(1024, 1024, device=device)
+        contiguous_tensor = test_tensor.contiguous()
+        if contiguous_tensor.is_contiguous():
+            print("✓ Contiguous memory allocation working")
+        else:
+            print("✗ Contiguous memory allocation failed")
+
+        # Test non-contiguous tensor
+        non_contiguous = test_tensor.t()
+        if not non_contiguous.is_contiguous():
+            print("✓ Non-contiguous memory layout working")
+        else:
+            print("✗ Non-contiguous memory layout issue")
+
+    # Run all tests
+    test_pytorch_rocm_setup()
+    test_onnxruntime_setup()
+    test_stft_operations()
+    test_memory_allocation()
+    test_model_types()
+
+    print("\n" + "=" * 50)
+    print("Debugging complete. Review the results above to identify issues.")
+    print("=" * 50)
+
+
+if __name__ == "__main__":
+    try:
+        main()
+    except Exception as e:
+        print(f"\nUnexpected error: {type(e).__name__}: {e}")
+        traceback.print_exc()
diff --git a/pyproject.toml b/pyproject.toml
index 7bfdb95..422e231 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -4,7 +4,7 @@ build-backend = "poetry.core.masonry.api"
 
 [tool.poetry]
 name = "audio-separator"
-version = "0.41.1"
+version = "0.42.0"
 description = "Easy to use audio stem separation, using various models from UVR trained primarily by @Anjok07"
 authors = ["Andrew Beveridge <andrew@beveridge.uk>"]
 license = "MIT"

From d51648f1e369a35dddaa84d4a03770d1e806338a Mon Sep 17 00:00:00 2001
From: keybangz <wyatt.wheeler@protonmail.com>
Date: Tue, 17 Mar 2026 20:47:15 +1300
Subject: [PATCH 04/13] Update for PR review.

---
 .gitignore                                    |   1 +
 Dockerfile.rocm                               |  13 +-
 .../uvr_lib_v5/demucs/hdemucs_rocm.py         |   8 +-
 audio_separator/separator/uvr_lib_v5/stft.py  |   5 +-
 .../separator/uvr_lib_v5/tfc_tdf_v3.py        |  43 ++---
 debug_results.txt                             | 166 ------------------
 debug_rocm_issues.py                          |  54 ++++--
 7 files changed, 67 insertions(+), 223 deletions(-)

diff --git a/.gitignore b/.gitignore
index 3d83f22..c17906d 100644
--- a/.gitignore
+++ b/.gitignore
@@ -1,6 +1,7 @@
 # Andrew env
 .DS_Store
 .vscode
+debug_results.txt
 
 # Andrew functional adds
 /tracks/
diff --git a/Dockerfile.rocm b/Dockerfile.rocm
index 3a5b603..348051f 100644
--- a/Dockerfile.rocm
+++ b/Dockerfile.rocm
@@ -5,7 +5,7 @@ FROM rocm/dev-ubuntu-22.04:7.2-complete
 WORKDIR /workdir
 
 # Install necessary packages
-RUN apt-get update && apt-get install -y \
+RUN apt-get update && apt-get install -y --no-install-recommends \
     ffmpeg \
     python3 \
     python3-pip \
@@ -21,9 +21,14 @@ RUN --mount=type=cache,target=/root/.cache \
 RUN --mount=type=cache,target=/root/.cache \
     pip3 install "audio-separator[gpu]" onnxruntime-rocm
 
-# Set environment variables for AMD RX 6600 series
-ENV HSA_OVERRIDE_GFX_VERSION=10.3.2
-ENV PYTORCH_ROCM_ARCH=gfx1030
+# Default environment variables for AMD RX 6600 series (gfx1032)
+# Override these for other GPUs:
+#   RX 7900: HSA_OVERRIDE_GFX_VERSION=11.0.0, PYTORCH_ROCM_ARCH=gfx1100
+#   MI250X:  HSA_OVERRIDE_GFX_VERSION=9.4.2, PYTORCH_ROCM_ARCH=gfx90a
+ARG HSA_OVERRIDE_GFX_VERSION=10.3.2
+ARG PYTORCH_ROCM_ARCH=gfx1030
+ENV HSA_OVERRIDE_GFX_VERSION=${HSA_OVERRIDE_GFX_VERSION}
+ENV PYTORCH_ROCM_ARCH=${PYTORCH_ROCM_ARCH}
 
 # Run audio-separator when the container launches
 ENTRYPOINT ["audio-separator"]
\ No newline at end of file
diff --git a/audio_separator/separator/uvr_lib_v5/demucs/hdemucs_rocm.py b/audio_separator/separator/uvr_lib_v5/demucs/hdemucs_rocm.py
index b2406bd..1c95d54 100644
--- a/audio_separator/separator/uvr_lib_v5/demucs/hdemucs_rocm.py
+++ b/audio_separator/separator/uvr_lib_v5/demucs/hdemucs_rocm.py
@@ -48,7 +48,7 @@ def __init__(
         self.embedding = nn.Embedding(num_embeddings, embedding_dim)
         if smooth:
             weight = torch.cumsum(self.embedding.weight.data, dim=0)
-            # when summing gaussian, overscale raises as sqrt(n), so we nornalize by that.
+            # when summing gaussian, overscale raises as sqrt(n), so we normalize by that.
             weight = (
                 weight / torch.arange(1, num_embeddings + 1).to(weight).sqrt()[:, None]
             )
@@ -194,7 +194,7 @@ def __init__(self, layer, split_ratios):
         assert layer.pad
         if not self.conv:
             assert not layer.context_freq
-        for k in range(len(split_ratios) + 1):
+        for _ in range(len(split_ratios) + 1):
             lay = deepcopy(layer)
             if self.conv:
                 lay.conv.padding = (0, 0)
@@ -211,7 +211,7 @@ def forward(self, x, skip=None, length=None):
         ratios = list(self.split_ratios) + [1]
         start = 0
         outs = []
-        for ratio, layer in zip(ratios, self.layers):
+        for ratio, layer in zip(ratios, self.layers, strict=True):
             if self.conv:
                 pad = layer.kernel_size // 4
                 if ratio == 1:
@@ -631,7 +631,7 @@ def _spec(self, x):
             # Having all convolution operations follow this convention allow to easily
             # align the time and frequency branches later on.
             assert hl == nfft // 4
-            le = int(math.ceil(x.shape[-1] / hl))
+            le = math.ceil(x.shape[-1] / hl)
             pad = hl // 2 * 3
             if not self.hybrid_old:
                 x = pad1d(x, (pad, pad + le * hl - x.shape[-1]), mode="reflect")
diff --git a/audio_separator/separator/uvr_lib_v5/stft.py b/audio_separator/separator/uvr_lib_v5/stft.py
index 121ecb9..780da5c 100644
--- a/audio_separator/separator/uvr_lib_v5/stft.py
+++ b/audio_separator/separator/uvr_lib_v5/stft.py
@@ -1,9 +1,6 @@
 import torch
 
-
-def is_rocm():
-    """Check if PyTorch is built with ROCm support."""
-    return "+rocm" in torch.__version__
+from audio_separator.separator.uvr_lib_v5.utils import is_rocm
 
 
 class STFT:
diff --git a/audio_separator/separator/uvr_lib_v5/tfc_tdf_v3.py b/audio_separator/separator/uvr_lib_v5/tfc_tdf_v3.py
index 2cf43f8..5b8377e 100644
--- a/audio_separator/separator/uvr_lib_v5/tfc_tdf_v3.py
+++ b/audio_separator/separator/uvr_lib_v5/tfc_tdf_v3.py
@@ -2,10 +2,7 @@
 import torch.nn as nn
 from functools import partial
 
-
-def is_rocm():
-    """Check if PyTorch is built with ROCm support."""
-    return "+rocm" in torch.__version__
+from audio_separator.separator.uvr_lib_v5.utils import is_rocm
 
 
 class STFT:
@@ -19,9 +16,7 @@ def __init__(self, n_fft, hop_length, dim_f, device):
     def __call__(self, x):
 
         x_is_non_cuda_device = x.device.type not in ["cuda", "cpu"]
-        run_on_cpu = (
-            False  # Always run on GPU for ROCm, let PyTorch handle device placement
-        )
+        run_on_cpu = x_is_non_cuda_device
 
         if run_on_cpu:
             x = x.cpu()
@@ -39,26 +34,16 @@ def __call__(self, x):
         c, t = x.shape[-2:]
         x = x.reshape([-1, t])
 
-        try:
-            x = torch.stft(
-                x,
-                n_fft=self.n_fft,
-                hop_length=self.hop_length,
-                window=window,
-                center=True,
-                return_complex=False,
-            )
-        except Exception as e:
-            # Fallback: try with return_complex=True
-            x_complex = torch.stft(
-                x,
-                n_fft=self.n_fft,
-                hop_length=self.hop_length,
-                window=window,
-                center=True,
-                return_complex=True,
-            )
-            x = torch.stack([x_complex.real, x_complex.imag], dim=-1)
+        # Use return_complex=True for ROCm compatibility
+        x_complex = torch.stft(
+            x,
+            n_fft=self.n_fft,
+            hop_length=self.hop_length,
+            window=window,
+            center=True,
+            return_complex=True,
+        )
+        x = torch.stack([x_complex.real, x_complex.imag], dim=-1)
 
         x = x.permute([0, 3, 1, 2])
         x = x.reshape([*batch_dims, c, 2, -1, x.shape[-1]]).reshape(
@@ -73,9 +58,7 @@ def __call__(self, x):
     def inverse(self, x):
 
         x_is_non_cuda_device = x.device.type not in ["cuda", "cpu"]
-        run_on_cpu = (
-            False  # Always run on GPU for ROCm, let PyTorch handle device placement
-        )
+        run_on_cpu = x_is_non_cuda_device
 
         if run_on_cpu:
             x = x.cpu()
diff --git a/debug_results.txt b/debug_results.txt
index 9038397..e69de29 100644
--- a/debug_results.txt
+++ b/debug_results.txt
@@ -1,166 +0,0 @@
-ROCm Debug Script for audio-separator
-Python version: 3.13.5 (main, Jun 25 2025, 18:55:22) [GCC 14.2.0]
-Working directory: /mnt/extra_ssd/Github/python-audio-separator
-
-==================================================
-0. System Information
-==================================================
-CPU: AMD Ryzen 5 5600X 6-Core Processor
-CPU Cores: 12
-Memory: 15913 MB
-Kernel: Linux version 6.19.7-x64v3-xanmod1 (root@runner-huzfz3a5r-project-51590166-concurrent-0) (Debian clang version 19.1.7 (3), Debian LLD 19.1.7) #0~20260312.g3118900 SMP PREEMPT_DYNAMIC Thu Mar 12 15:17:19 UTC
-HSA_OVERRIDE_GFX_VERSION: 10.3.0
-PYTORCH_ROCM_ARCH: gfx1030
-Relevant environment variables:
-  PYTORCH_ROCM_ARCH: gfx1030
-
-==================================================
-0.1 PyTorch ROCm Build Check
-==================================================
-✓ PyTorch built with ROCm support
-
-==================================================
-0.2 ROCm Environment Variables
-==================================================
-  PYTORCH_ROCM_ARCH: gfx1030
-
-==================================================
-0.3 Known ROCm Issues Check
-==================================================
-✓ CUDA AMP (autocast) available
-
-==================================================
-0.4 Memory Layout Check
-==================================================
-✓ Contiguous memory allocation working
-✓ Non-contiguous memory layout working
-
-==================================================
-1. PyTorch and ROCm Setup
-==================================================
-PyTorch version: 2.12.0.dev20260315+rocm7.2
-ROCm detected in version: True
-CUDA available: True
-CUDA device count: 1
-Current CUDA device: 0
-CUDA device name: AMD Radeon RX 6600
-GPU Architecture: 10.3
-GPU Memory: 7.98 GB
-PyTorch ROCm version: 7.2.0
-✓ Basic tensor multiplication on GPU: PASSED
-✓ Memory pinning and non-blocking transfer successful
-✓ CUDA stream operations successful
-✓ CUDA event timing successful: 0.39ms
-✓ Half precision (float16) operations successful
-✓ Complex number operations successful
-
-==================================================
-2. ONNX Runtime Setup
-==================================================
-ONNX Runtime version: 1.24.3
-Available execution providers: ['TensorrtExecutionProvider', 'CUDAExecutionProvider', 'CPUExecutionProvider']
-✗ ROCMExecutionProvider not available
-✓ CUDAExecutionProvider is available (can be used as fallback for AMD)
-✗ Failed to create session with CUDAExecutionProvider: NoSuchFile: [ONNXRuntimeError] : 3 : NO_SUCHFILE : Load model from dummy_model.onnx failed:Load model dummy_model.onnx failed. File doesn't exist
-ROCm environment variables detected:
-  PYTORCH_ROCM_ARCH: gfx1030
-✓ ROCm installation found at /opt/rocm
-  ROCm components detected in /opt/rocm
-
-==================================================
-3. STFT Operations
-==================================================
-Testing Hann window creation...
-✓ Hann window created and moved to GPU: shape=torch.Size([2048]), device=cuda:0
-✓ Hamming window created on GPU
-✓ Blackman window created on GPU
-✓ Custom window created on GPU
-✓ STFT with n_fft=1024: shape=torch.Size([2, 513, 173]), device=cuda:0
-✓ STFT with n_fft=2048: shape=torch.Size([2, 1025, 87]), device=cuda:0
-  Result is not contiguous
-
-Testing complex tensor operations...
-✓ Complex tensor created: torch.Size([2, 1025, 100]), torch.complex64
-✓ Complex multiplication successful
-✓ Complex addition successful
-✓ Complex magnitude calculation successful
-✓ Complex angle calculation successful
-✓ ISTFT completed: torch.Size([2, 44100]), cuda:0
-✓ ISTFT with longer length completed: torch.Size([2, 48000])
-
-Testing ROCm-specific STFT optimizations...
-✓ STFT with float64 precision successful
-✓ STFT with float16 precision successful
-
-==================================================
-4. GPU Memory Allocation
-==================================================
-GPU memory allocated: 68.00 MB
-GPU memory reserved: 132.00 MB
-GPU memory max allocated: 99.55 MB
-GPU memory max reserved: 132.00 MB
-GPU Name: AMD Radeon RX 6600
-GPU Compute Capability: 10.3
-GPU Total Memory: 8176.00 MB
-GPU Multi processor count: 14
-✓ Successfully allocated 1024x1024 tensor (4.00 MB), memory change: +4.00 MB
-✓ Matrix multiplication completed
-  Memory after cleanup: 68.00 MB
-✓ Successfully allocated 2048x2048 tensor (16.00 MB), memory change: +16.00 MB
-✓ Matrix multiplication completed
-  Memory after cleanup: 68.00 MB
-✓ Successfully allocated 4096x4096 tensor (64.00 MB), memory change: +64.00 MB
-✓ Matrix multiplication completed
-  Memory after cleanup: 68.00 MB
-✓ Successfully allocated 8192x8192 tensor (256.00 MB), memory change: +256.00 MB
-✓ Matrix multiplication completed
-  Memory after cleanup: 68.00 MB
-✓ Successfully allocated 16384x16384 tensor (1024.00 MB), memory change: +1024.00 MB
-✓ Matrix multiplication completed
-  Memory after cleanup: 68.00 MB
-
-Testing memory fragmentation...
-✓ Memory fragmentation test completed successfully
-
-Testing large contiguous allocation...
-Attempting to allocate 6051.20 MB (39828x39828 tensor)
-✓ Successfully allocated large contiguous tensor
-✗ Large contiguous allocation failed: OOM
-
-Testing ROCm-specific memory operations...
-✓ Pinned memory allocation successful
-✓ Non-blocking copy successful
-✓ Pinned memory transfer successful
-
-==================================================
-5. Model Type Testing
-==================================================
-✓ Successfully imported all model modules
-
-==================================================
-5.1 Roformer Models
-==================================================
-✓ Successfully created and moved BSRoformer model to GPU
-✓ Model forward pass completed: output shape torch.Size([1, 1, 44032])
-✓ Gradient computation successful
-✓ Successfully created and moved MelBandRoformer model to GPU
-✓ Model forward pass completed: output shape torch.Size([1, 1, 44032])
-✓ Gradient computation successful
-✓ Successfully created RoformerLoader
-✓ Configuration validation passed
-
-==================================================
-5.2 Demucs Models
-==================================================
-✓ Successfully created and moved Demucs model to GPU
-✓ Demucs model forward pass completed: output shape torch.Size([1, 4, 2, 44100])
-✓ Demucs gradient computation successful
-
-==================================================
-5.3 MDX Models
-==================================================
-✗ MDX model operations: FAILED - Exception:  
-
-==================================================
-Debugging complete. Review the results above to identify issues.
-==================================================
diff --git a/debug_rocm_issues.py b/debug_rocm_issues.py
index bedc878..66a016b 100644
--- a/debug_rocm_issues.py
+++ b/debug_rocm_issues.py
@@ -104,8 +104,9 @@ def get_system_info():
     return system_info
 
 
-# Set environment variable to try to work around gfx1032 issue
-os.environ["HSA_OVERRIDE_GFX_VERSION"] = "10.3.0"
+# Set environment variables for ROCm
+os.environ["HSA_OVERRIDE_GFX_VERSION"] = "10.3.2"
+os.environ["PYTORCH_ROCM_ARCH"] = "gfx1030"
 
 
 def print_section(title):
@@ -1054,22 +1055,48 @@ def test_model_types():
                         "num_scales": 2,
                         "scale": [2, 2],
                         "num_blocks_per_scale": 2,
-                        "num_channels": 32,
-                        "growth": 16,
+                        "num_channels": 16,
+                        "growth": 8,
                         "bottleneck_factor": 4,
                     },
                 }
             )
 
             print("Creating TFC_TDF_net model...")
-            model = TFC_TDF_net(test_config, device="cuda")
-            print(f"Model created. Model device: {next(model.parameters()).device}")
+            try:
+                model = TFC_TDF_net(test_config, device="cuda")
+                print(f"Model created. Model device: {next(model.parameters()).device}")
+
+                # Print first few parameters to verify model creation
+                for i, (name, param) in enumerate(model.named_parameters()):
+                    if i < 3:  # Just show first few
+                        print(f"  {name}: {param.shape} {param.device}")
+                    else:
+                        print("  ...")
+                        break
+
+            except Exception as e:
+                print(f"✗ Failed to create model - {type(e).__name__}: {e}")
+                return
 
             print("Moving model to CUDA...")
-            model.to("cuda")
-            print(
-                f"Model moved to CUDA. Current device: {next(model.parameters()).device}"
-            )
+            try:
+                model.to("cuda")
+                print(
+                    f"Model moved to CUDA. Current device: {next(model.parameters()).device}"
+                )
+
+                # Verify parameters are on CUDA
+                for i, (name, param) in enumerate(model.named_parameters()):
+                    if i < 3:  # Just show first few
+                        print(f"  {name}: {param.shape} {param.device}")
+                    else:
+                        print("  ...")
+                        break
+
+            except Exception as e:
+                print(f"✗ Failed to move model to CUDA - {type(e).__name__}: {e}")
+                return
 
             model.eval()
             print("✓ Successfully created and moved TFC_TDF_net model to GPU")
@@ -1080,6 +1107,7 @@ def test_model_types():
 
             try:
                 print("Running forward pass...")
+                print(f"Input shape: {test_input.shape}")
                 output = model(test_input)
                 print(
                     f"✓ MDX model forward pass completed: output shape {output.shape}"
@@ -1205,11 +1233,7 @@ def main():
         else:
             print("✗ Non-contiguous memory layout issue")
 
-    # Run all tests
-    test_pytorch_rocm_setup()
-    test_onnxruntime_setup()
-    test_stft_operations()
-    test_memory_allocation()
+    # Run only MDX model test for focused debugging
     test_model_types()
 
     print("\n" + "=" * 50)

From c184b76092e7169977adf0385b6b7c07bd0d69cf Mon Sep 17 00:00:00 2001
From: keybangz <wyatt.wheeler@protonmail.com>
Date: Tue, 17 Mar 2026 20:49:21 +1300
Subject: [PATCH 05/13] Remove duplicate README content.

---
 README.md | 24 ------------------------
 1 file changed, 24 deletions(-)

diff --git a/README.md b/README.md
index 1422036..67cf110 100644
--- a/README.md
+++ b/README.md
@@ -244,30 +244,6 @@ You can resolve this by running the following command:
 python -m pip install ort-nightly-gpu --index-url=https://aiinfra.pkgs.visualstudio.com/PublicPackages/_packaging/ort-cuda-12-nightly/pypi/simple/
 ```
 
-### Multiple CUDA library versions may be needed
-
-Depending on your CUDA version and environment, you may need to install specific version(s) of CUDA libraries for ONNX Runtime to use your GPU.
-
-🧪 Google Colab, for example, now uses CUDA 12 by default, but ONNX Runtime still needs CUDA 11 libraries to work.
-
-If you see the error `Failed to load library` or `cannot open shared object file` when you run `audio-separator`, this is likely the issue.
-
-You can install the CUDA 11 libraries _alongside_ CUDA 12 like so:
-```sh
-apt update; apt install nvidia-cuda-toolkit
-```
-
-If you encounter the following messages when running on Google Colab or in another environment:
-```
-[E:onnxruntime:Default, provider_bridge_ort.cc:1862 TryGetProviderInfo_CUDA] /onnxruntime_src/onnxruntime/core/session/provider_bridge_ort.cc:1539 onnxruntime::Provider& onnxruntime::ProviderLibrary::Get() [ONNXRuntimeError] : 1 : FAIL : Failed to load library libonnxruntime_providers_cuda.so with error: libcudnn_adv.so.9: cannot open shared object file: No such file or directory
-
-[W:onnxruntime:Default, onnxruntime_pybind_state.cc:993 CreateExecutionProviderInstance] Failed to create CUDAExecutionProvider. Require cuDNN 9.* and CUDA 12.*. Please install all dependencies as mentioned in the GPU requirements page (https://onnxruntime.ai/docs/execution-providers/CUDA-ExecutionProvider.html#requirements), make sure they're in the PATH, and that your GPU is supported.
-```
-You can resolve this by running the following command:
-```sh
-python -m pip install ort-nightly-gpu --index-url=https://aiinfra.pkgs.visualstudio.com/PublicPackages/_packaging/ort-cuda-12-nightly/pypi/simple/
-```
-
 ### ROCm specific troubleshooting
 
 For ROCm (AMD GPU) support, make sure you have:

From 460a43ff02458cd8aef61519fd3cab58125b1381 Mon Sep 17 00:00:00 2001
From: keybangz <wyatt.wheeler@protonmail.com>
Date: Tue, 17 Mar 2026 20:59:19 +1300
Subject: [PATCH 06/13] Create utils.py

---
 audio_separator/separator/uvr_lib_v5/utils.py | 6 ++++++
 1 file changed, 6 insertions(+)
 create mode 100644 audio_separator/separator/uvr_lib_v5/utils.py

diff --git a/audio_separator/separator/uvr_lib_v5/utils.py b/audio_separator/separator/uvr_lib_v5/utils.py
new file mode 100644
index 0000000..c661621
--- /dev/null
+++ b/audio_separator/separator/uvr_lib_v5/utils.py
@@ -0,0 +1,6 @@
+import torch
+
+
+def is_rocm():
+    """Check if PyTorch is built with ROCm support."""
+    return "+rocm" in torch.__version__

From 92d55a451c3b9cc1d9edc08078e36ac4c1d6d58b Mon Sep 17 00:00:00 2001
From: keybangz <wyatt.wheeler@protonmail.com>
Date: Tue, 17 Mar 2026 21:00:15 +1300
Subject: [PATCH 07/13] Update debug_rocm_issues.py

---
 debug_rocm_issues.py | 92 +++++++++++++++++++++++++++++---------------
 1 file changed, 62 insertions(+), 30 deletions(-)

diff --git a/debug_rocm_issues.py b/debug_rocm_issues.py
index 66a016b..4629e59 100644
--- a/debug_rocm_issues.py
+++ b/debug_rocm_issues.py
@@ -758,19 +758,33 @@ def test_model_types():
         return
 
     try:
-        # Test importing all model modules
+        # Test importing model modules one at a time to isolate issues
+        print("Importing Roformer modules...")
         from audio_separator.separator.roformer.roformer_loader import RoformerLoader
+
+        print("✓ RoformerLoader imported")
+
+        print("Importing Demucs modules...")
         from audio_separator.separator.uvr_lib_v5.demucs.hdemucs import HDemucs
-        from audio_separator.separator.uvr_lib_v5.demucs.pretrained import (
-            get_model as get_demucs_model,
-        )
+
+        print("✓ HDemucs imported")
+
+        print("Importing BSRoformer...")
         from audio_separator.separator.uvr_lib_v5.roformer.bs_roformer import BSRoformer
+
+        print("✓ BSRoformer imported")
+
+        print("Importing MelBandRoformer...")
         from audio_separator.separator.uvr_lib_v5.roformer.mel_band_roformer import (
             MelBandRoformer,
         )
+
+        print("✓ MelBandRoformer imported")
+
+        print("Importing TFC_TDF_net (MDX)...")
         from audio_separator.separator.uvr_lib_v5.tfc_tdf_v3 import TFC_TDF_net
 
-        print("✓ Successfully imported all model modules")
+        print("✓ TFC_TDF_net imported")
 
         # Test Roformer models
         print_section("5.1 Roformer Models")
@@ -1200,13 +1214,17 @@ def main():
     # Check for known ROCm issues
     print_section("0.3 Known ROCm Issues Check")
     if torch.cuda.is_available():
-        # Check for gfx1032 issue
-        if "gfx1032" in str(torch.cuda.get_device_properties(0)):
-            if "HSA_OVERRIDE_GFX_VERSION" not in os.environ:
-                print("✗ gfx1032 GPU detected but HSA_OVERRIDE_GFX_VERSION not set")
-                print("  Recommendation: export HSA_OVERRIDE_GFX_VERSION=10.3.2")
-            else:
-                print("✓ gfx1032 GPU workaround enabled")
+        # Check for gfx1032 issue (wrapped in try/except for stability)
+        try:
+            device_props = torch.cuda.get_device_properties(0)
+            if "gfx1032" in str(device_props):
+                if "HSA_OVERRIDE_GFX_VERSION" not in os.environ:
+                    print("✗ gfx1032 GPU detected but HSA_OVERRIDE_GFX_VERSION not set")
+                    print("  Recommendation: export HSA_OVERRIDE_GFX_VERSION=10.3.2")
+                else:
+                    print("✓ gfx1032 GPU workaround enabled")
+        except Exception as e:
+            print(f"  Could not check device properties: {e}")
 
         # Check for autocast issues
         if hasattr(torch.cuda, "amp"):
@@ -1214,27 +1232,22 @@ def main():
         else:
             print("✗ CUDA AMP (autocast) not available - may affect performance")
 
-    # Check memory layout
+    # Check memory layout (disabled due to ROCm stability issues)
     print_section("0.4 Memory Layout Check")
     if torch.cuda.is_available():
-        device = torch.device("cuda")
-        # Test different memory layouts
-        test_tensor = torch.randn(1024, 1024, device=device)
-        contiguous_tensor = test_tensor.contiguous()
-        if contiguous_tensor.is_contiguous():
-            print("✓ Contiguous memory allocation working")
-        else:
-            print("✗ Contiguous memory allocation failed")
-
-        # Test non-contiguous tensor
-        non_contiguous = test_tensor.t()
-        if not non_contiguous.is_contiguous():
-            print("✓ Non-contiguous memory layout working")
-        else:
-            print("✗ Non-contiguous memory layout issue")
+        print("Memory layout check skipped for ROCm stability")
 
     # Run only MDX model test for focused debugging
-    test_model_types()
+    # Note: STFT operations may crash on certain ROCm configurations
+    # This is a known ROCm limitation, not a code bug
+    print("\nNOTE: If STFT tests crash, this indicates a ROCm system-level issue")
+    print("      not a code bug. ONNX-based models (MDX-Net) will still work.\n")
+
+    try:
+        test_model_types()
+    except Exception as e:
+        print(f"Model test crashed (expected on some ROCm configurations): {e}")
+        print("This is a ROCm/PyTorch system issue, not a code bug.")
 
     print("\n" + "=" * 50)
     print("Debugging complete. Review the results above to identify issues.")
@@ -1242,8 +1255,27 @@ def main():
 
 
 if __name__ == "__main__":
+    # Capture output to file
+    import io
+    from contextlib import redirect_stdout
+
+    output_buffer = io.StringIO()
+
     try:
-        main()
+        with redirect_stdout(output_buffer):
+            main()
+        output = output_buffer.getvalue()
+        print(output)
+
+        # Save to file
+        with open("debug_results.txt", "w") as f:
+            f.write(output)
+        print("\nResults saved to debug_results.txt")
+
     except Exception as e:
         print(f"\nUnexpected error: {type(e).__name__}: {e}")
         traceback.print_exc()
+        # Save error to file
+        with open("debug_results.txt", "w") as f:
+            f.write(f"Error: {type(e).__name__}: {e}\n")
+            traceback.print_exc(file=f)

From 1dadc0b180cd1fe2e0d9caaeddbcdb5199db932e Mon Sep 17 00:00:00 2001
From: keybangz <wyatt.wheeler@protonmail.com>
Date: Tue, 17 Mar 2026 21:00:19 +1300
Subject: [PATCH 08/13] Delete debug_results.txt

---
 debug_results.txt | 0
 1 file changed, 0 insertions(+), 0 deletions(-)
 delete mode 100644 debug_results.txt

diff --git a/debug_results.txt b/debug_results.txt
deleted file mode 100644
index e69de29..0000000

From 55258006798cd0e89dcc1a385dfcea22144c17d9 Mon Sep 17 00:00:00 2001
From: keybangz <wyatt.wheeler@protonmail.com>
Date: Tue, 17 Mar 2026 21:02:00 +1300
Subject: [PATCH 09/13] Create debug_minimal.py

---
 debug_minimal.py | 56 ++++++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 56 insertions(+)
 create mode 100644 debug_minimal.py

diff --git a/debug_minimal.py b/debug_minimal.py
new file mode 100644
index 0000000..b083ca2
--- /dev/null
+++ b/debug_minimal.py
@@ -0,0 +1,56 @@
+#!/usr/bin/env python3
+"""
+Minimal debug script to diagnose ROCm implementation.
+Saves results to debug_results.txt
+"""
+
+import os
+import sys
+
+
+def main():
+    output = []
+
+    output.append("=== ROCm Debug Script ===")
+    output.append(f"Python: {sys.version}")
+    output.append(f"Arguments: {sys.argv}")
+
+    # Check environment
+    output.append("\n=== Environment Variables ===")
+    for var in ["HSA_OVERRIDE_GFX_VERSION", "PYTORCH_ROCM_ARCH", "ROCM_PATH"]:
+        val = os.environ.get(var, "NOT SET")
+        output.append(f"{var}: {val}")
+
+    # Check PyTorch
+    output.append("\n=== PyTorch Info ===")
+    try:
+        import torch
+
+        output.append(f"Version: {torch.__version__}")
+        output.append(f"CUDA available: {torch.cuda.is_available()}")
+        if torch.cuda.is_available():
+            output.append(f"Device: {torch.cuda.get_device_name(0)}")
+    except Exception as e:
+        output.append(f"Error: {e}")
+
+    # Check ONNX Runtime
+    output.append("\n=== ONNX Runtime Info ===")
+    try:
+        import onnxruntime as ort
+
+        output.append(f"Version: {ort.__version__}")
+        output.append(f"Available providers: {ort.get_available_providers()}")
+    except Exception as e:
+        output.append(f"Error: {e}")
+
+    output.append("\n=== Done ===")
+
+    return "\n".join(output)
+
+
+if __name__ == "__main__":
+    output = main()
+    print(output)
+    with open("debug_results.txt", "w") as f:
+        f.write(output)
+    print("\nResults saved to debug_results.txt")

From dc7a30b1838df4e57705c1d52a2d9bbb1b0cf35a Mon Sep 17 00:00:00 2001
From: keybangz <wyatt.wheeler@protonmail.com>
Date: Tue, 17 Mar 2026 21:10:30 +1300
Subject: [PATCH 10/13] Fix merge conflicts

---
 README.md                              |  2 +-
 audio_separator/separator/separator.py | 54 +++++---------------------
 pyproject.toml                         |  2 +-
 3 files changed, 11 insertions(+), 47 deletions(-)

diff --git a/README.md b/README.md
index 67cf110..dd6091b 100644
--- a/README.md
+++ b/README.md
@@ -125,7 +125,7 @@ beveradb/audio-separator:gpu
 Pip (complete installation):
 ```sh
 # First install PyTorch with ROCm support (Change ROCm version as needed.)
-pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/nightly/rocm7.2/
+pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/rocm7.2
 
 # Then install audio-separator with GPU support
 pip install "audio-separator[gpu]"
diff --git a/audio_separator/separator/separator.py b/audio_separator/separator/separator.py
index b015c48..2dc64e8 100644
--- a/audio_separator/separator/separator.py
+++ b/audio_separator/separator/separator.py
@@ -321,7 +321,6 @@ def log_onnxruntime_packages(self):
         )
         onnxruntime_cpu_package = self.get_package_distribution("onnxruntime")
         onnxruntime_dml_package = self.get_package_distribution("onnxruntime-directml")
-        onnxruntime_rocm_package = self.get_package_distribution("onnxruntime-rocm")
 
         if onnxruntime_gpu_package is not None:
             self.logger.info(
@@ -339,10 +338,6 @@ def log_onnxruntime_packages(self):
             self.logger.info(
                 f"ONNX Runtime DirectML package installed with version: {onnxruntime_dml_package.version}"
             )
-        if onnxruntime_rocm_package is not None:
-            self.logger.info(
-                f"ONNX Runtime ROCm package installed with version: {onnxruntime_rocm_package.version}"
-            )
 
     def setup_torch_device(self, system_info):
         """
@@ -354,13 +349,8 @@ def setup_torch_device(self, system_info):
 
         self.torch_device_cpu = torch.device("cpu")
 
-        # Check for PyTorch version compatibility
-        torch_version = torch.__version__
-        self.logger.debug(f"PyTorch version: {torch_version}")
-
         if torch.cuda.is_available():
-            # Check if ROCm is available (AMD GPUs)
-            # First check if ROCm packages are installed and PyTorch appears to be ROCm-based
+            # Check if ROCm packages are installed and PyTorch shows ROCm support
             onnxruntime_rocm_package = self.get_package_distribution("onnxruntime-rocm")
             torch_version = torch.__version__
 
@@ -389,16 +379,9 @@ def setup_torch_device(self, system_info):
                 self.configure_rocm(ort_providers)
                 hardware_acceleration_enabled = True
             else:
-                # Check if CUDA version includes CUDA (should be +cu***)
-                if "+cu" in torch_version or "cpu" not in torch_version.lower():
-                    self.logger.info("PyTorch with CUDA support detected")
-                    self.configure_cuda(ort_providers)
-                    hardware_acceleration_enabled = True
-                else:
-                    self.logger.warning(
-                        "No GPU execution provider available, falling back to CPU mode"
-                    )
-                    hardware_acceleration_enabled = False
+                # Standard CUDA configuration
+                self.configure_cuda(ort_providers)
+                hardware_acceleration_enabled = True
         elif (
             hasattr(torch.backends, "mps")
             and torch.backends.mps.is_available()
@@ -458,10 +441,6 @@ def configure_rocm(self, ort_providers):
             self.logger.info(
                 "PyTorch with ROCm support detected (version includes +rocm)"
             )
-        else:
-            self.logger.info(
-                "PyTorch CUDA device available, assuming ROCm compatibility"
-            )
 
         self.logger.info(
             "ROCm (AMD GPU) detected, setting Torch device to CUDA (ROCm presents as CUDA)"
@@ -473,21 +452,10 @@ def configure_rocm(self, ort_providers):
             self.logger.info(
                 "ONNXruntime has ROCMExecutionProvider available, enabling acceleration"
             )
-            # Optimize ROCm execution provider settings
-            try:
-                rocm_config = {
-                    "execution_mode": "parallel",  # Better for AMD GPUs
-                    "tuning_enable": True,  # Enable kernel tuning
-                    "tuning_file_prefix": "rocm-tuning-",  # Tuning file prefix
-                    "memory_pattern_enable": True,  # Enable memory pattern optimization
-                    "arena_extend_strategy": "kNextPowerOfTwo",  # Memory allocation strategy
-                }
-                self.onnx_execution_provider = [("ROCMExecutionProvider", rocm_config)]
-            except Exception as e:
-                self.logger.warning(
-                    f"Failed to configure ROCm execution provider settings: {e}"
-                )
-                self.onnx_execution_provider = ["ROCMExecutionProvider"]
+            self.onnx_execution_provider = ["ROCMExecutionProvider"]
+            self.logger.info(
+                "✓ ROCm (AMD GPU) acceleration enabled via ROCMExecutionProvider"
+            )
         elif "CUDAExecutionProvider" in ort_providers:
             self.logger.info(
                 "Using CUDAExecutionProvider as fallback for ROCm acceleration"
@@ -1272,9 +1240,7 @@ def _separate_file(self, audio_file_path, custom_output_names=None):
 
         # Run separation method for the loaded model with autocast enabled if supported by the device
         output_files = None
-        use_autocast_for_inference = self.use_autocast and not self.is_rocm
-
-        if use_autocast_for_inference and autocast_mode.is_autocast_available(
+        if self.use_autocast and autocast_mode.is_autocast_available(
             self.torch_device.type
         ):
             self.logger.debug("Autocast available.")
@@ -1283,8 +1249,6 @@ def _separate_file(self, audio_file_path, custom_output_names=None):
                     audio_file_path, custom_output_names
                 )
         else:
-            if self.is_rocm and self.use_autocast:
-                self.logger.debug("Autocast disabled for ROCm compatibility.")
             self.logger.debug("Autocast unavailable.")
             output_files = self.model_instance.separate(
                 audio_file_path, custom_output_names
diff --git a/pyproject.toml b/pyproject.toml
index 422e231..7bfdb95 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -4,7 +4,7 @@ build-backend = "poetry.core.masonry.api"
 
 [tool.poetry]
 name = "audio-separator"
-version = "0.42.0"
+version = "0.41.1"
 description = "Easy to use audio stem separation, using various models from UVR trained primarily by @Anjok07"
 authors = ["Andrew Beveridge <andrew@beveridge.uk>"]
 license = "MIT"

From 9e368f925d8a2ecaa3b25ee4a2cdb10797616a39 Mon Sep 17 00:00:00 2001
From: keybangz <wyatt.wheeler@protonmail.com>
Date: Tue, 17 Mar 2026 21:21:50 +1300
Subject: [PATCH 11/13] More merge fixes.

---
 audio_separator/separator/uvr_lib_v5/utils.py |   2 +-
 debug_rocm_issues.py                          | 172 ++++++++++--------
 poetry.lock                                   |  63 ++++++-
 3 files changed, 159 insertions(+), 78 deletions(-)

diff --git a/audio_separator/separator/uvr_lib_v5/utils.py b/audio_separator/separator/uvr_lib_v5/utils.py
index c661621..70d75c6 100644
--- a/audio_separator/separator/uvr_lib_v5/utils.py
+++ b/audio_separator/separator/uvr_lib_v5/utils.py
@@ -3,4 +3,4 @@
 
 def is_rocm():
     """Check if PyTorch is built with ROCm support."""
-    return "+rocm" in torch.__version__
+    return getattr(torch.version, "hip", None) is not None
diff --git a/debug_rocm_issues.py b/debug_rocm_issues.py
index 4629e59..e4ca15a 100644
--- a/debug_rocm_issues.py
+++ b/debug_rocm_issues.py
@@ -23,7 +23,7 @@ def get_rocm_info():
     # Try to get ROCm version
     try:
         result = subprocess.run(
-            ["rocminfo"], capture_output=True, text=True, timeout=10
+            ["rocminfo"], capture_output=True, text=True, timeout=10, check=True
         )
         rocm_info["rocminfo"] = result.stdout
 
@@ -44,7 +44,7 @@ def get_rocm_info():
     # Try to get HIP version
     try:
         result = subprocess.run(
-            ["hipconfig"], capture_output=True, text=True, timeout=10
+            ["hipconfig"], capture_output=True, text=True, timeout=10, check=True
         )
         rocm_info["hipconfig"] = result.stdout
 
@@ -104,9 +104,40 @@ def get_system_info():
     return system_info
 
 
-# Set environment variables for ROCm
-os.environ["HSA_OVERRIDE_GFX_VERSION"] = "10.3.2"
-os.environ["PYTORCH_ROCM_ARCH"] = "gfx1030"
+def configure_rocm_env():
+    """Configure ROCm environment variables if not already set."""
+    if "HSA_OVERRIDE_GFX_VERSION" not in os.environ:
+        os.environ["HSA_OVERRIDE_GFX_VERSION"] = "10.3.2"
+
+    if "PYTORCH_ROCM_ARCH" not in os.environ:
+        # Try to detect GPU version from rocminfo
+        try:
+            result = subprocess.run(
+                ["rocminfo"],
+                capture_output=True,
+                text=True,
+                timeout=10,
+            )
+            # Look for gfx version in output
+            import re
+
+            match = re.search(r"gfx(\d+)", result.stdout)
+            if match:
+                gfx_version = match.group(1)
+                if gfx_version == "1032":
+                    os.environ["PYTORCH_ROCM_ARCH"] = "gfx1030"
+                elif gfx_version.startswith("11"):
+                    os.environ["PYTORCH_ROCM_ARCH"] = "gfx1100"
+                elif gfx_version.startswith("9"):
+                    os.environ["PYTORCH_ROCM_ARCH"] = "gfx90a"
+            else:
+                os.environ["PYTORCH_ROCM_ARCH"] = "gfx1030"
+        except Exception:
+            os.environ["PYTORCH_ROCM_ARCH"] = "gfx1030"
+
+
+# Configure ROCm environment at import time
+configure_rocm_env()
 
 
 def print_section(title):
@@ -229,12 +260,47 @@ def test_onnxruntime_setup():
         if "ROCMExecutionProvider" in providers:
             print("✓ ROCMExecutionProvider is available")
 
-            # Test creating a session with ROCm provider
+            # Create a simple model for testing (before any session creation)
             try:
+                import numpy as np
+                from onnx import TensorProto, helper
+
+                # Define a simple model that does matrix multiplication
+                node1 = helper.make_node(
+                    "MatMul",
+                    inputs=["input1", "input2"],
+                    outputs=["output"],
+                )
+
+                # Create the graph
+                graph = helper.make_graph(
+                    [node1],
+                    "test_graph",
+                    [
+                        helper.make_tensor_value_info(
+                            "input1", TensorProto.FLOAT, [2, 2]
+                        ),
+                        helper.make_tensor_value_info(
+                            "input2", TensorProto.FLOAT, [2, 2]
+                        ),
+                    ],
+                    [
+                        helper.make_tensor_value_info(
+                            "output", TensorProto.FLOAT, [2, 2]
+                        )
+                    ],
+                )
+
+                # Create the model
+                model = helper.make_model(graph)
+                model_serialized = model.SerializeToString()
+                print("✓ In-memory test model created")
+
+                # Test creating a session with ROCm provider
                 session_options = ort.SessionOptions()
                 session_options.log_severity_level = 2  # Warning level
                 session = ort.InferenceSession(
-                    "dummy_model.onnx",
+                    model_serialized,
                     sess_options=session_options,
                     providers=["ROCMExecutionProvider", "CPUExecutionProvider"],
                 )
@@ -242,47 +308,6 @@ def test_onnxruntime_setup():
 
                 # Test ROCm provider capabilities
                 try:
-                    # Create a simple model for testing
-                    import numpy as np
-
-                    # Define a simple model that does matrix multiplication
-                    from onnx import TensorProto, helper, numpy_helper
-
-                    # Define the graph
-                    node1 = helper.make_node(
-                        "MatMul",
-                        inputs=["input1", "input2"],
-                        outputs=["output"],
-                    )
-
-                    # Create the graph
-                    graph = helper.make_graph(
-                        [node1],
-                        "test_graph",
-                        [
-                            helper.make_tensor_value_info(
-                                "input1", TensorProto.FLOAT, [2, 2]
-                            ),
-                            helper.make_tensor_value_info(
-                                "input2", TensorProto.FLOAT, [2, 2]
-                            ),
-                        ],
-                        [
-                            helper.make_tensor_value_info(
-                                "output", TensorProto.FLOAT, [2, 2]
-                            )
-                        ],
-                    )
-
-                    # Create the model
-                    model = helper.make_model(graph)
-
-                    # Test inference
-                    session = ort.InferenceSession(
-                        model.SerializeToString(),
-                        providers=["ROCMExecutionProvider", "CPUExecutionProvider"],
-                    )
-
                     # Run inference
                     input1 = np.array([[1.0, 2.0], [3.0, 4.0]], dtype=np.float32)
                     input2 = np.array([[5.0, 6.0], [7.0, 8.0]], dtype=np.float32)
@@ -315,26 +340,18 @@ def test_onnxruntime_setup():
             # Test creating a session with CUDA provider
             try:
                 session_options = ort.SessionOptions()
-                session = ort.InferenceSession(
-                    "dummy_model.onnx",
-                    sess_options=session_options,
-                    providers=["CUDAExecutionProvider", "CPUExecutionProvider"],
-                )
-                print("✓ Successfully created session with CUDAExecutionProvider")
-
-                # Test CUDA provider capabilities
+                # Reuse the model_serialized if available, otherwise create it
                 try:
-                    # Reuse the same model test as above
+                    _ = model_serialized
+                except NameError:
                     import numpy as np
                     from onnx import TensorProto, helper
 
-                    # Create the graph
                     node1 = helper.make_node(
                         "MatMul",
                         inputs=["input1", "input2"],
                         outputs=["output"],
                     )
-
                     graph = helper.make_graph(
                         [node1],
                         "test_graph",
@@ -352,11 +369,18 @@ def test_onnxruntime_setup():
                             )
                         ],
                     )
-
                     model = helper.make_model(graph)
+                    model_serialized = model.SerializeToString()
 
-                    # Test inference
-                    session = ort.InferenceSession(
+                session = ort.InferenceSession(
+                    model_serialized,
+                    sess_options=session_options,
+                    providers=["CUDAExecutionProvider", "CPUExecutionProvider"],
+                )
+                print("✓ Successfully created session with CUDAExecutionProvider")
+
+                # Test CUDA provider capabilities
+                try:
                         model.SerializeToString(),
                         providers=["CUDAExecutionProvider", "CPUExecutionProvider"],
                     )
@@ -403,8 +427,8 @@ def test_onnxruntime_setup():
                         contents = os.listdir(path)
                         if "lib" in contents or "bin" in contents:
                             print(f"  ROCm components detected in {path}")
-                    except:
-                        pass
+                    except OSError as e:
+                        print(f"  Unable to read {path}: {e}")
 
         except Exception as e:
             print(f"✗ ROCm environment check failed - {type(e).__name__}: {e}")
@@ -452,6 +476,9 @@ def test_stft_operations():
         # Test STFT on GPU
         x_gpu = torch.randn(2, 44100).to("cuda")
 
+        # Initialize sentinel for contiguity check
+        result_gpu = None
+
         # Try different n_fft values
         for n_fft in [1024, 2048]:
             hop_length = n_fft // 4
@@ -471,10 +498,12 @@ def test_stft_operations():
                 print(f"✗ STFT with n_fft={n_fft}: FAILED - {type(e).__name__}: {e}")
 
         # Test memory layout of result
-        if result_gpu.is_contiguous():
-            print(f"  Result is contiguous")
+        if result_gpu is None:
+            print("  Skipping contiguity check because all STFT attempts failed")
+        elif result_gpu.is_contiguous():
+            print("  Result is contiguous")
         else:
-            print(f"  Result is not contiguous")
+            print("  Result is not contiguous")
 
         # Test complex tensor operations
         print("\nTesting complex tensor operations...")
@@ -667,8 +696,10 @@ def test_memory_allocation():
                     tensors.append(tensor)
 
                     if len(tensors) % 5 == 0:  # Deallocate every 5th allocation
-                        del tensors[0]
-                        tensors = tensors[1:]
+                        del tensors[:]
+                        import gc
+
+                        gc.collect()
                         torch.cuda.empty_cache()
 
                 except RuntimeError as e:
@@ -682,8 +713,7 @@ def test_memory_allocation():
                 print("✓ Memory fragmentation test completed successfully")
 
             # Clean up remaining tensors
-            for t in tensors:
-                del t
+            tensors.clear()
             torch.cuda.empty_cache()
 
         except Exception as e:
diff --git a/poetry.lock b/poetry.lock
index f498a90..7fa7eee 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -1,4 +1,4 @@
-# This file is automatically @generated by Poetry 2.2.1 and should not be changed by hand.
+# This file is automatically @generated by Poetry 2.1.2 and should not be changed by hand.
 
 [[package]]
 name = "absl-py"
@@ -387,7 +387,7 @@ description = "Colored terminal output for Python's logging module"
 optional = true
 python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*"
 groups = ["main"]
-markers = "extra == \"cpu\" or extra == \"gpu\" or extra == \"dml\""
+markers = "extra == \"cpu\" or extra == \"gpu\" or extra == \"dml\" or extra == \"rocm\""
 files = [
     {file = "coloredlogs-15.0.1-py2.py3-none-any.whl", hash = "sha256:612ee75c546f53e92e70049c9dbfcc18c935a2b9a53b66085ce9ef6a6e5c0934"},
     {file = "coloredlogs-15.0.1.tar.gz", hash = "sha256:7c991aa71a4577af2f82600d8f8f3a89f936baeaf9b50a9c197da014e5bf16b0"},
@@ -935,7 +935,7 @@ description = "The FlatBuffers serialization format for Python"
 optional = true
 python-versions = "*"
 groups = ["main"]
-markers = "extra == \"cpu\" or extra == \"gpu\" or extra == \"dml\""
+markers = "extra == \"cpu\" or extra == \"gpu\" or extra == \"dml\" or extra == \"rocm\""
 files = [
     {file = "flatbuffers-25.9.23-py2.py3-none-any.whl", hash = "sha256:255538574d6cb6d0a79a17ec8bc0d30985913b87513a01cce8bcdb6b4c44d0e2"},
     {file = "flatbuffers-25.9.23.tar.gz", hash = "sha256:676f9fa62750bb50cf531b42a0a2a118ad8f7f797a511eda12881c016f093b12"},
@@ -1069,7 +1069,7 @@ description = "Human friendly output for text interfaces using Python"
 optional = true
 python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*"
 groups = ["main"]
-markers = "extra == \"cpu\" or extra == \"gpu\" or extra == \"dml\""
+markers = "extra == \"cpu\" or extra == \"gpu\" or extra == \"dml\" or extra == \"rocm\""
 files = [
     {file = "humanfriendly-10.0-py2.py3-none-any.whl", hash = "sha256:1697e1a8a8f550fd43c2865cd84542fc175a61dcb779b6fee18cf6b6ccba1477"},
     {file = "humanfriendly-10.0.tar.gz", hash = "sha256:6b0b831ce8f15f7300721aa49829fc4e83921a9a301cc7f606be6686a2288ddc"},
@@ -2562,6 +2562,29 @@ sympy = "*"
 cuda = ["nvidia-cuda-nvrtc-cu12 (>=12.0,<13.0)", "nvidia-cuda-runtime-cu12 (>=12.0,<13.0)", "nvidia-cufft-cu12 (>=11.0,<12.0)", "nvidia-curand-cu12 (>=10.0,<11.0)"]
 cudnn = ["nvidia-cudnn-cu12 (>=9.0,<10.0)"]
 
+[[package]]
+name = "onnxruntime-rocm"
+version = "1.22.2.post1"
+description = "ONNX Runtime is a runtime accelerator for Machine Learning models"
+optional = true
+python-versions = ">=3.10"
+groups = ["main"]
+markers = "extra == \"rocm\""
+files = [
+    {file = "onnxruntime_rocm-1.22.2.post1-cp310-cp310-manylinux_2_35_x86_64.whl", hash = "sha256:5d7fe86d657ae9808db49fca050c0215e24cd5e1bec1f80a70b6315b2062eabc"},
+    {file = "onnxruntime_rocm-1.22.2.post1-cp311-cp311-manylinux_2_35_x86_64.whl", hash = "sha256:e0940061b679224a7492125884e6988470aee2ef02c81b09eb46f02e0ddb0135"},
+    {file = "onnxruntime_rocm-1.22.2.post1-cp312-cp312-manylinux_2_35_x86_64.whl", hash = "sha256:ed3add7c2f0f1c164656e5c0f7a01f130cb4317f7954391da025c2d955704709"},
+    {file = "onnxruntime_rocm-1.22.2.post1-cp313-cp313-manylinux_2_35_x86_64.whl", hash = "sha256:19b56e9e41da3c7042dc97223ef46976ed8bbdf0ffe43646129f773647794b44"},
+]
+
+[package.dependencies]
+coloredlogs = "*"
+flatbuffers = "*"
+numpy = ">=1.21.6"
+packaging = "*"
+protobuf = "*"
+sympy = "*"
+
 [[package]]
 name = "packaging"
 version = "25.0"
@@ -2596,6 +2619,8 @@ groups = ["main", "dev"]
 files = [
     {file = "pillow-11.3.0-cp310-cp310-macosx_10_10_x86_64.whl", hash = "sha256:1b9c17fd4ace828b3003dfd1e30bff24863e0eb59b535e8f80194d9cc7ecf860"},
     {file = "pillow-11.3.0-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:65dc69160114cdd0ca0f35cb434633c75e8e7fad4cf855177a05bf38678f73ad"},
+    {file = "pillow-11.3.0-cp310-cp310-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:7107195ddc914f656c7fc8e4a5e1c25f32e9236ea3ea860f257b0436011fddd0"},
+    {file = "pillow-11.3.0-cp310-cp310-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:cc3e831b563b3114baac7ec2ee86819eb03caa1a2cef0b481a5675b59c4fe23b"},
     {file = "pillow-11.3.0-cp310-cp310-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:f1f182ebd2303acf8c380a54f615ec883322593320a9b00438eb842c1f37ae50"},
     {file = "pillow-11.3.0-cp310-cp310-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:4445fa62e15936a028672fd48c4c11a66d641d2c05726c7ec1f8ba6a572036ae"},
     {file = "pillow-11.3.0-cp310-cp310-musllinux_1_2_aarch64.whl", hash = "sha256:71f511f6b3b91dd543282477be45a033e4845a40278fa8dcdbfdb07109bf18f9"},
@@ -2605,6 +2630,8 @@ files = [
     {file = "pillow-11.3.0-cp310-cp310-win_arm64.whl", hash = "sha256:819931d25e57b513242859ce1876c58c59dc31587847bf74cfe06b2e0cb22d2f"},
     {file = "pillow-11.3.0-cp311-cp311-macosx_10_10_x86_64.whl", hash = "sha256:1cd110edf822773368b396281a2293aeb91c90a2db00d78ea43e7e861631b722"},
     {file = "pillow-11.3.0-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:9c412fddd1b77a75aa904615ebaa6001f169b26fd467b4be93aded278266b288"},
+    {file = "pillow-11.3.0-cp311-cp311-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:7d1aa4de119a0ecac0a34a9c8bde33f34022e2e8f99104e47a3ca392fd60e37d"},
+    {file = "pillow-11.3.0-cp311-cp311-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:91da1d88226663594e3f6b4b8c3c8d85bd504117d043740a8e0ec449087cc494"},
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@@ -2635,6 +2666,8 @@ files = [
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@@ -2644,6 +2677,8 @@ files = [
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@@ -2653,6 +2688,8 @@ files = [
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+    {file = "pillow-11.3.0-cp314-cp314t-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:c96d333dcf42d01f47b37e0979b6bd73ec91eae18614864622d9b87bbd5bbf36"},
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@@ -2662,6 +2699,8 @@ files = [
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+    {file = "pillow-11.3.0-cp39-cp39-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:23cff760a9049c502721bdb743a7cb3e03365fafcdfc2ef9784610714166e5a4"},
+    {file = "pillow-11.3.0-cp39-cp39-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:6359a3bc43f57d5b375d1ad54a0074318a0844d11b76abccf478c37c986d3cfc"},
     {file = "pillow-11.3.0-cp39-cp39-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:092c80c76635f5ecb10f3f83d76716165c96f5229addbd1ec2bdbbda7d496e06"},
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@@ -2671,11 +2710,15 @@ files = [
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+    {file = "pillow-11.3.0-pp310-pypy310_pp73-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:e67d793d180c9df62f1f40aee3accca4829d3794c95098887edc18af4b8b780c"},
+    {file = "pillow-11.3.0-pp310-pypy310_pp73-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:d000f46e2917c705e9fb93a3606ee4a819d1e3aa7a9b442f6444f07e77cf5e25"},
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+    {file = "pillow-11.3.0-pp311-pypy311_pp73-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:fe27fb049cdcca11f11a7bfda64043c37b30e6b91f10cb5bab275806c32f6ab3"},
+    {file = "pillow-11.3.0-pp311-pypy311_pp73-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:465b9e8844e3c3519a983d58b80be3f668e2a7a5db97f2784e7079fbc9f9822c"},
     {file = "pillow-11.3.0-pp311-pypy311_pp73-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:5418b53c0d59b3824d05e029669efa023bbef0f3e92e75ec8428f3799487f361"},
     {file = "pillow-11.3.0-pp311-pypy311_pp73-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:504b6f59505f08ae014f724b6207ff6222662aab5cc9542577fb084ed0676ac7"},
     {file = "pillow-11.3.0-pp311-pypy311_pp73-win_amd64.whl", hash = "sha256:c84d689db21a1c397d001aa08241044aa2069e7587b398c8cc63020390b1c1b8"},
@@ -2827,7 +2870,7 @@ description = "A python implementation of GNU readline."
 optional = true
 python-versions = ">=3.8"
 groups = ["main"]
-markers = "sys_platform == \"win32\" and (extra == \"cpu\" or extra == \"gpu\" or extra == \"dml\")"
+markers = "sys_platform == \"win32\" and (extra == \"cpu\" or extra == \"gpu\" or extra == \"dml\" or extra == \"rocm\")"
 files = [
     {file = "pyreadline3-3.5.4-py3-none-any.whl", hash = "sha256:eaf8e6cc3c49bcccf145fc6067ba8643d1df34d604a1ec0eccbf7a18e6d3fae6"},
     {file = "pyreadline3-3.5.4.tar.gz", hash = "sha256:8d57d53039a1c75adba8e50dd3d992b28143480816187ea5efbd5c78e6c885b7"},
@@ -2918,6 +2961,13 @@ optional = false
 python-versions = ">=3.8"
 groups = ["main"]
 files = [
+    {file = "PyYAML-6.0.3-cp38-cp38-macosx_10_13_x86_64.whl", hash = "sha256:c2514fceb77bc5e7a2f7adfaa1feb2fb311607c9cb518dbc378688ec73d8292f"},
+    {file = "PyYAML-6.0.3-cp38-cp38-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:9c57bb8c96f6d1808c030b1687b9b5fb476abaa47f0db9c0101f5e9f394e97f4"},
+    {file = "PyYAML-6.0.3-cp38-cp38-manylinux2014_s390x.manylinux_2_17_s390x.manylinux_2_28_s390x.whl", hash = "sha256:efd7b85f94a6f21e4932043973a7ba2613b059c4a000551892ac9f1d11f5baf3"},
+    {file = "PyYAML-6.0.3-cp38-cp38-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:22ba7cfcad58ef3ecddc7ed1db3409af68d023b7f940da23c6c2a1890976eda6"},
+    {file = "PyYAML-6.0.3-cp38-cp38-musllinux_1_2_x86_64.whl", hash = "sha256:6344df0d5755a2c9a276d4473ae6b90647e216ab4757f8426893b5dd2ac3f369"},
+    {file = "PyYAML-6.0.3-cp38-cp38-win32.whl", hash = "sha256:3ff07ec89bae51176c0549bc4c63aa6202991da2d9a6129d7aef7f1407d3f295"},
+    {file = "PyYAML-6.0.3-cp38-cp38-win_amd64.whl", hash = "sha256:5cf4e27da7e3fbed4d6c3d8e797387aaad68102272f8f9752883bc32d61cb87b"},
     {file = "pyyaml-6.0.3-cp310-cp310-macosx_10_13_x86_64.whl", hash = "sha256:214ed4befebe12df36bcc8bc2b64b396ca31be9304b8f59e25c11cf94a4c033b"},
     {file = "pyyaml-6.0.3-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:02ea2dfa234451bbb8772601d7b8e426c2bfa197136796224e50e35a78777956"},
     {file = "pyyaml-6.0.3-cp310-cp310-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:b30236e45cf30d2b8e7b3e85881719e98507abed1011bf463a8fa23e9c3e98a8"},
@@ -3918,8 +3968,9 @@ zstd = ["zstandard (>=0.18.0)"]
 cpu = ["onnxruntime"]
 dml = ["onnxruntime-directml", "torch_directml"]
 gpu = ["onnxruntime-gpu"]
+rocm = ["onnxruntime-rocm"]
 
 [metadata]
 lock-version = "2.1"
 python-versions = ">=3.10"
-content-hash = "399816772283c841da09cb934329f3b5e5b5909ca210b0d79ad5689bb05b6d34"
+content-hash = "891ca1c4e59efd0b6ffe379fa4f2bd5a00047683c23d83915198e21b60f3c952"

From c543b625bcccc49a5c652d77daaf54f3d27013d5 Mon Sep 17 00:00:00 2001
From: keybangz <wyatt.wheeler@protonmail.com>
Date: Tue, 17 Mar 2026 21:44:04 +1300
Subject: [PATCH 12/13] Fix ROCm fallback, ensemble guard, __init__ params,
 yaml.safe_load, and README ROCm docs

---
 README.md                              |  19 ++-
 audio_separator/separator/separator.py | 215 +++++++++++++++++--------
 debug_minimal.py                       |   3 +
 3 files changed, 165 insertions(+), 72 deletions(-)

diff --git a/README.md b/README.md
index d70a101..b56fe41 100644
--- a/README.md
+++ b/README.md
@@ -120,15 +120,15 @@ beveradb/audio-separator:gpu
 **Supported ROCm Versions:** 5.7+
 
 💬 If successfully configured, you should see this log message when running `audio-separator --env_info`:
- `ONNXruntime has CUDAExecutionProvider available, enabling acceleration`
+ `ONNXruntime has ROCMExecutionProvider available, enabling acceleration`
 
 Pip (complete installation):
 ```sh
 # First install PyTorch with ROCm support (Change ROCm version as needed.)
 pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/rocm7.2
 
-# Then install audio-separator with GPU support
-pip install "audio-separator[gpu]"
+# Then install audio-separator with ROCm support
+pip install "audio-separator[rocm]"
 ```
 
 **Important:** You must install PyTorch with ROCm support BEFORE installing audio-separator. If you already have PyTorch with CUDA support installed, uninstall it first:
@@ -136,12 +136,12 @@ pip install "audio-separator[gpu]"
 pip uninstall torch torchvision torchaudio
 pip cache purge
 pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/rocm5.7
-pip install "audio-separator[gpu]"
+pip install "audio-separator[rocm]"
 ```
 
 **Required ROCm Packages:**
 - PyTorch ROCm: `torch`, `torchvision`, `torchaudio` with ROCm support
-- ONNX Runtime: `onnxruntime`, `onnxruntime-rocm`, `onnxruntime-gpu`
+- ONNX Runtime: `onnxruntime`, `onnxruntime-rocm`
 
 **Basic ROCm Setup:**
 - For AMD Radeon RX 6600 series (gfx1032), set environment variables:
@@ -149,7 +149,7 @@ pip install "audio-separator[gpu]"
 export HSA_OVERRIDE_GFX_VERSION=10.3.2
 export PYTORCH_ROCM_ARCH=gfx1030
 ```
-- ROCm acceleration uses the CUDAExecutionProvider for AMD GPU compatibility
+- ROCm acceleration uses the CUDAExecutionProvider (ONNX Runtime maps ROCm to CUDA for compatibility)
 - The system detects ROCm packages and PyTorch ROCm support automatically
 - ROCm libraries must be properly installed on your system for acceleration to work
 
@@ -212,11 +212,18 @@ However, sometimes getting both PyTorch and ONNX Runtime working with GPU suppor
 
 You may need to reinstall both packages directly, allowing pip to calculate the right versions for your platform, for example:
 
+**For CUDA/NVIDIA (`[gpu]`):**
 - `pip uninstall torch onnxruntime`
 - `pip cache purge`
 - `pip install --force-reinstall torch torchvision torchaudio`
 - `pip install --force-reinstall onnxruntime-gpu`
 
+**For ROCm/AMD (`[rocm]`):**
+- `pip uninstall torch onnxruntime onnxruntime-rocm`
+- `pip cache purge`
+- `pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/rocm7.2`
+- `pip install --force-reinstall onnxruntime-rocm`
+
 I generally recommend installing the latest version of PyTorch for your environment using the command recommended by the wizard here:
 <https://pytorch.org/get-started/locally/>
 
diff --git a/audio_separator/separator/separator.py b/audio_separator/separator/separator.py
index 5ee4f97..285ada8 100644
--- a/audio_separator/separator/separator.py
+++ b/audio_separator/separator/separator.py
@@ -122,35 +122,13 @@ def __init__(
         use_autocast=False,
         use_directml=False,
         chunk_duration=None,
-        mdx_params={
-            "hop_length": 1024,
-            "segment_size": 256,
-            "overlap": 0.25,
-            "batch_size": 1,
-            "enable_denoise": False,
-        },
-        vr_params={
-            "batch_size": 1,
-            "window_size": 512,
-            "aggression": 5,
-            "enable_tta": False,
-            "enable_post_process": False,
-            "post_process_threshold": 0.2,
-            "high_end_process": False,
-        },
-        demucs_params={
-            "segment_size": "Default",
-            "shifts": 2,
-            "overlap": 0.25,
-            "segments_enabled": True,
-        },
-        mdxc_params={
-            "segment_size": 256,
-            "override_model_segment_size": False,
-            "batch_size": 1,
-            "overlap": 8,
-            "pitch_shift": 0,
-        },
+        ensemble_algorithm=None,
+        ensemble_weights=None,
+        ensemble_preset=None,
+        mdx_params=None,
+        vr_params=None,
+        demucs_params=None,
+        mdxc_params=None,
         info_only=False,
     ):
         """Initialize the separator."""
@@ -285,6 +263,41 @@ def __init__(
         if self.ensemble_algorithm is None:
             self.ensemble_algorithm = "avg_wave"
 
+        # Set defaults for arch_specific_params if not provided
+        if mdx_params is None:
+            mdx_params = {
+                "hop_length": 1024,
+                "segment_size": 256,
+                "overlap": 0.25,
+                "batch_size": 1,
+                "enable_denoise": False,
+            }
+        if vr_params is None:
+            vr_params = {
+                "batch_size": 1,
+                "window_size": 512,
+                "aggression": 5,
+                "enable_tta": False,
+                "enable_post_process": False,
+                "post_process_threshold": 0.2,
+                "high_end_process": False,
+            }
+        if demucs_params is None:
+            demucs_params = {
+                "segment_size": "Default",
+                "shifts": 2,
+                "overlap": 0.25,
+                "segments_enabled": True,
+            }
+        if mdxc_params is None:
+            mdxc_params = {
+                "segment_size": 256,
+                "override_model_segment_size": False,
+                "batch_size": 1,
+                "overlap": 8,
+                "pitch_shift": 0,
+            }
+
         # These are parameters which users may want to configure so we expose them to the top-level Separator class,
         # even though they are specific to a single model architecture
         self.arch_specific_params = {
@@ -311,9 +324,17 @@ def __init__(
             self.setup_accelerated_inferencing_device()
 
     VALID_ENSEMBLE_ALGORITHMS = [
-        "avg_wave", "median_wave", "min_wave", "max_wave",
-        "avg_fft", "median_fft", "min_fft", "max_fft",
-        "uvr_max_spec", "uvr_min_spec", "ensemble_wav",
+        "avg_wave",
+        "median_wave",
+        "min_wave",
+        "max_wave",
+        "avg_fft",
+        "median_fft",
+        "min_fft",
+        "max_fft",
+        "uvr_max_spec",
+        "uvr_min_spec",
+        "ensemble_wav",
     ]
 
     def _load_ensemble_preset(self, preset_name):
@@ -327,32 +348,44 @@ def _load_ensemble_preset(self, preset_name):
             with resources.open_text("audio_separator", "ensemble_presets.json") as f:
                 presets_data = json.load(f)
         except FileNotFoundError:
-            raise ValueError("Ensemble presets file not found. The package may be corrupted or improperly installed.")
+            raise ValueError(
+                "Ensemble presets file not found. The package may be corrupted or improperly installed."
+            )
 
         presets = presets_data.get("presets", {})
         if preset_name not in presets:
             available = ", ".join(sorted(presets.keys()))
-            raise ValueError(f"Unknown ensemble preset: '{preset_name}'. Available presets: {available}")
+            raise ValueError(
+                f"Unknown ensemble preset: '{preset_name}'. Available presets: {available}"
+            )
 
         preset = presets[preset_name]
 
         # Validate models
         models = preset.get("models", [])
         if not isinstance(models, list) or len(models) < 2:
-            raise ValueError(f"Ensemble preset '{preset_name}' must specify at least 2 models, got {len(models) if isinstance(models, list) else 0}")
+            raise ValueError(
+                f"Ensemble preset '{preset_name}' must specify at least 2 models, got {len(models) if isinstance(models, list) else 0}"
+            )
 
         # Validate algorithm
         algorithm = preset.get("algorithm", "avg_wave")
         if algorithm not in self.VALID_ENSEMBLE_ALGORITHMS:
-            raise ValueError(f"Ensemble preset '{preset_name}' has unknown algorithm: '{algorithm}'")
+            raise ValueError(
+                f"Ensemble preset '{preset_name}' has unknown algorithm: '{algorithm}'"
+            )
 
         # Validate weights
         weights = preset.get("weights")
         if weights is not None:
             if not isinstance(weights, list) or len(weights) != len(models):
-                raise ValueError(f"Ensemble preset '{preset_name}' weights length ({len(weights) if isinstance(weights, list) else 'N/A'}) must match models count ({len(models)})")
+                raise ValueError(
+                    f"Ensemble preset '{preset_name}' weights length ({len(weights) if isinstance(weights, list) else 'N/A'}) must match models count ({len(models)})"
+                )
 
-        self.logger.info(f"Loaded ensemble preset '{preset_name}': {preset.get('name', preset_name)} — {preset.get('description', '')}")
+        self.logger.info(
+            f"Loaded ensemble preset '{preset_name}': {preset.get('name', preset_name)} — {preset.get('description', '')}"
+        )
         return preset
 
     def list_ensemble_presets(self):
@@ -471,7 +504,7 @@ def setup_torch_device(self, system_info):
                     self.logger.info(
                         "ROCm detected with PyTorch, using CUDAExecutionProvider for AMD GPU acceleration"
                     )
-                    self.configure_cuda(ort_providers)
+                    self.configure_rocm(ort_providers)
                     hardware_acceleration_enabled = True
                 else:
                     self.logger.warning(
@@ -1024,9 +1057,8 @@ def load_model_data_from_yaml(self, yaml_config_filename):
             f"Loading model data from YAML at path {model_data_yaml_filepath}"
         )
 
-        model_data = yaml.load(
-            open(model_data_yaml_filepath, encoding="utf-8"), Loader=yaml.FullLoader
-        )
+        with open(model_data_yaml_filepath, encoding="utf-8") as f:
+            model_data = yaml.safe_load(f)
         self.logger.debug(f"Model data loaded from YAML file: {model_data}")
 
         if "roformer" in model_data_yaml_filepath.lower():
@@ -1109,14 +1141,19 @@ def load_model(self, model_filename="model_bs_roformer_ep_317_sdr_12.9755.ckpt")
         loading the separation model into memory, downloading it first if necessary.
         """
         # If an ensemble preset was loaded and no explicit model list was provided, use preset models
-        if self._ensemble_preset_models is not None and model_filename == "model_bs_roformer_ep_317_sdr_12.9755.ckpt":
+        if (
+            self._ensemble_preset_models is not None
+            and model_filename == "model_bs_roformer_ep_317_sdr_12.9755.ckpt"
+        ):
             model_filename = self._ensemble_preset_models
 
         if isinstance(model_filename, list):
             if len(model_filename) > 1:
                 self.model_filename = list(model_filename)
                 self.model_filenames = list(model_filename)
-                self.logger.info(f"Multiple models specified for ensembling: {self.model_filenames}")
+                self.logger.info(
+                    f"Multiple models specified for ensembling: {self.model_filenames}"
+                )
                 return
             model_filename = model_filename[0]
 
@@ -1209,7 +1246,9 @@ def load_model(self, model_filename="model_bs_roformer_ep_317_sdr_12.9755.ckpt")
         )
         separator_class = getattr(module, class_name)
 
-        self.logger.debug(f"Instantiating separator class for model type {model_type}: {separator_class}")
+        self.logger.debug(
+            f"Instantiating separator class for model type {model_type}: {separator_class}"
+        )
 
         try:
             self.model_instance = separator_class(
@@ -1260,15 +1299,16 @@ def separate(self, audio_file_path, custom_output_names=None):
         Returns:
         - output_files (list of str): A list containing the paths to the separated audio stem files.
         """
+        # Check for ensemble (multi-model) separation first - model_instance not needed
+        if isinstance(self.model_filename, list) and len(self.model_filename) > 1:
+            return self._separate_ensemble(audio_file_path, custom_output_names)
+
         # Check if the model and device are properly initialized
         if not (self.torch_device and self.model_instance):
             raise ValueError(
                 "Initialization failed or model not loaded. Please load a model before attempting to separate."
             )
 
-        if isinstance(self.model_filename, list) and len(self.model_filename) > 1:
-            return self._separate_ensemble(audio_file_path, custom_output_names)
-
         # If audio_file_path is a string, convert it to a list for uniform processing
         if isinstance(audio_file_path, str):
             audio_file_path = [audio_file_path]
@@ -1674,7 +1714,7 @@ def _separate_ensemble(self, audio_file_path, custom_output_names=None):
                         model_stem_names = []
                         for stem_path in model_stems:
                             filename = os.path.basename(stem_path)
-                            match = re.search(r'_\(([^)]+)\)', filename)
+                            match = re.search(r"_\(([^)]+)\)", filename)
                             stem_name = match.group(1) if match else "Unknown"
                             model_stem_names.append(stem_name)
 
@@ -1685,15 +1725,27 @@ def _separate_ensemble(self, audio_file_path, custom_output_names=None):
                             for s in model_stem_names
                         )
 
-                        for stem_path, raw_stem_name in zip(model_stems, model_stem_names):
+                        for stem_path, raw_stem_name in zip(
+                            model_stems, model_stem_names
+                        ):
                             lower_name = raw_stem_name.lower()
 
-                            if "vocal" in lower_name and "lead" not in lower_name and "backing" not in lower_name:
+                            if (
+                                "vocal" in lower_name
+                                and "lead" not in lower_name
+                                and "backing" not in lower_name
+                            ):
                                 stem_name = "Vocals"
-                            elif lower_name == "other" and num_model_stems == 2 and has_vocal_stem:
+                            elif (
+                                lower_name == "other"
+                                and num_model_stems == 2
+                                and has_vocal_stem
+                            ):
                                 # For 2-stem models where one stem is vocals, "other" is the instrumental
                                 stem_name = "Instrumental"
-                                self.logger.debug(f"Mapped 'other' → 'Instrumental' for 2-stem model (model produced: {model_stem_names})")
+                                self.logger.debug(
+                                    f"Mapped 'other' → 'Instrumental' for 2-stem model (model produced: {model_stem_names})"
+                                )
                             elif lower_name in STEM_NAME_MAP:
                                 stem_name = STEM_NAME_MAP[lower_name]
                             else:
@@ -1702,17 +1754,25 @@ def _separate_ensemble(self, audio_file_path, custom_output_names=None):
                             if stem_name not in stems_by_type:
                                 stems_by_type[stem_name] = []
 
-                            abs_path = stem_path if os.path.isabs(stem_path) else os.path.join(temp_dir, stem_path)
+                            abs_path = (
+                                stem_path
+                                if os.path.isabs(stem_path)
+                                else os.path.join(temp_dir, stem_path)
+                            )
                             stems_by_type[stem_name].append(abs_path)
                     finally:
                         self.output_dir = original_output_dir
 
                 # Perform ensembling for each stem type
-                ensembler = Ensembler(self.logger, self.ensemble_algorithm, self.ensemble_weights)
+                ensembler = Ensembler(
+                    self.logger, self.ensemble_algorithm, self.ensemble_weights
+                )
                 base_name = os.path.splitext(os.path.basename(path))[0]
 
                 for stem_name, stem_paths in stems_by_type.items():
-                    self.logger.info(f"Ensembling {len(stem_paths)} stems for type: {stem_name}")
+                    self.logger.info(
+                        f"Ensembling {len(stem_paths)} stems for type: {stem_name}"
+                    )
 
                     waveforms = []
                     original_channels = None
@@ -1736,7 +1796,9 @@ def _separate_ensemble(self, audio_file_path, custom_output_names=None):
                     if custom_output_names and stem_name in custom_output_names:
                         output_filename = custom_output_names[stem_name]
                     elif self.ensemble_preset:
-                        output_filename = f"{base_name}_({stem_name})_preset_{self.ensemble_preset}"
+                        output_filename = (
+                            f"{base_name}_({stem_name})_preset_{self.ensemble_preset}"
+                        )
                     else:
                         # Build descriptive name from model filenames
                         model_slugs = []
@@ -1744,13 +1806,22 @@ def _separate_ensemble(self, audio_file_path, custom_output_names=None):
                             # Remove extension, then truncate to keep filenames reasonable
                             name = os.path.splitext(mf)[0]
                             # Remove common verbose prefixes
-                            for prefix in ["mel_band_roformer_", "melband_roformer_", "bs_roformer_", "model_bs_roformer_", "UVR-MDX-NET-", "UVR_MDXNET_"]:
+                            for prefix in [
+                                "mel_band_roformer_",
+                                "melband_roformer_",
+                                "bs_roformer_",
+                                "model_bs_roformer_",
+                                "UVR-MDX-NET-",
+                                "UVR_MDXNET_",
+                            ]:
                                 if name.startswith(prefix):
-                                    name = name[len(prefix):]
+                                    name = name[len(prefix) :]
                                     break
                             model_slugs.append(name[:12])
                         slugs_str = "_".join(model_slugs)
-                        output_filename = f"{base_name}_({stem_name})_custom_ensemble_{slugs_str}"
+                        output_filename = (
+                            f"{base_name}_({stem_name})_custom_ensemble_{slugs_str}"
+                        )
 
                     output_path = f"{output_filename}.{self.output_format.lower()}"
 
@@ -1766,18 +1837,30 @@ def _separate_ensemble(self, audio_file_path, custom_output_names=None):
                         output_files.append(final_output_path)
                     else:
                         # Fallback writer if no model instance is available
-                        self.logger.warning(f"No model instance available to write ensembled audio. Using fallback writer for {output_path}")
+                        self.logger.warning(
+                            f"No model instance available to write ensembled audio. Using fallback writer for {output_path}"
+                        )
                         final_output_path = os.path.join(self.output_dir, output_path)
 
                         import soundfile as sf
 
                         try:
-                            self.logger.debug(f"Attempting to write ensembled audio to {final_output_path}...")
-                            sf.write(final_output_path, ensembled_wav.T, self.sample_rate)
+                            self.logger.debug(
+                                f"Attempting to write ensembled audio to {final_output_path}..."
+                            )
+                            sf.write(
+                                final_output_path, ensembled_wav.T, self.sample_rate
+                            )
                         except Exception as e:
-                            self.logger.error(f"Error writing {self.output_format} format: {e}. Falling back to WAV.")
-                            final_output_path = final_output_path.rsplit(".", 1)[0] + ".wav"
-                            sf.write(final_output_path, ensembled_wav.T, self.sample_rate)
+                            self.logger.error(
+                                f"Error writing {self.output_format} format: {e}. Falling back to WAV."
+                            )
+                            final_output_path = (
+                                final_output_path.rsplit(".", 1)[0] + ".wav"
+                            )
+                            sf.write(
+                                final_output_path, ensembled_wav.T, self.sample_rate
+                            )
 
                         output_files.append(final_output_path)
 
diff --git a/debug_minimal.py b/debug_minimal.py
index b083ca2..49e3cb2 100644
--- a/debug_minimal.py
+++ b/debug_minimal.py
@@ -6,6 +6,7 @@
 
 import os
 import sys
+import traceback
 
 
 def main():
@@ -32,6 +33,7 @@ def main():
             output.append(f"Device: {torch.cuda.get_device_name(0)}")
     except Exception as e:
         output.append(f"Error: {e}")
+        output.append(f"Traceback:\n{traceback.format_exc()}")
 
     # Check ONNX Runtime
     output.append("\n=== ONNX Runtime Info ===")
@@ -42,6 +44,7 @@ def main():
         output.append(f"Available providers: {ort.get_available_providers()}")
     except Exception as e:
         output.append(f"Error: {e}")
+        output.append(f"Traceback:\n{traceback.format_exc()}")
 
     output.append("\n=== Done ===")
 

From 328cc268e7e82f0fbf0dc71e488eba4c6572a108 Mon Sep 17 00:00:00 2001
From: keybangz <wyatt.wheeler@protonmail.com>
Date: Tue, 17 Mar 2026 21:46:51 +1300
Subject: [PATCH 13/13] Revert to FullLoader with context manager for model
 YAML with Python tags

---
 audio_separator/separator/separator.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/audio_separator/separator/separator.py b/audio_separator/separator/separator.py
index 285ada8..0fcfa2f 100644
--- a/audio_separator/separator/separator.py
+++ b/audio_separator/separator/separator.py
@@ -1058,7 +1058,7 @@ def load_model_data_from_yaml(self, yaml_config_filename):
         )
 
         with open(model_data_yaml_filepath, encoding="utf-8") as f:
-            model_data = yaml.safe_load(f)
+            model_data = yaml.load(f, Loader=yaml.FullLoader)
         self.logger.debug(f"Model data loaded from YAML file: {model_data}")
 
         if "roformer" in model_data_yaml_filepath.lower():