From 13007cab73fe83677dc45a02a9b77c0e94c7c1f9 Mon Sep 17 00:00:00 2001 From: Claude Date: Sun, 5 Jul 2026 02:13:19 +0000 Subject: [PATCH 1/9] Implement real Moonshine ONNX speech-to-text backend MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit The "moonshine" backend was selectable in Settings, config, and docs but had no implementation: choosing it silently substituted whisper-cpp (a warning-only log), and the startup/keypress "Whisper model not downloaded" guards were skipped for a Moonshine selection — so a user who picked Moonshine could end up with a permanently silent dictation pipeline and no indication why. Backend (crates/voxctrl-inference/src/moonshine.rs, feature-gated): - Runs the four upstream Moonshine ONNX graphs (preprocess, encode, uncached_decode, cached_decode) via ort, with greedy autoregressive decoding from the start-of-transcript token to the end-of-transcript token, capped by audio duration. - Decodes token ids to text with the model's tokenizer.json (tokenizers crate). - Binds tensors to graph inputs positionally and detects the optional seq_len input and KV-cache arity from the graph, so it tolerates export revisions. - Downloads the ONNX graphs + tokenizer on demand into ~/.local/share/voxctrl/models/moonshine//, or reads a manual copy. Wiring and correctness: - build_backend constructs the real backend when compiled; otherwise it keeps a clear whisper-cpp fallback. - MOONSHINE_COMPILED exposes whether the backend is built in; the two src-tauri model-download guards now only skip the Whisper-model check when Moonshine is actually compiled (a non-Moonshine build still needs the Whisper model). - New tauri commands (moonshine_available, check_moonshine_downloaded, download_moonshine_model) drive a Moonshine download button + status and a "not included in this build" notice in Settings → Engine. Moonshine is an opt-in compile feature (--features moonshine), like cuda/vulkan, since it links ONNX Runtime. Docs updated accordingly. Co-Authored-By: Claude Fable 5 Claude-Session: https://claude.ai/code/session_01GpXWVxBBJvtRgkdMWasSH6 --- Cargo.lock | 27 +- crates/voxctrl-inference/Cargo.toml | 22 +- crates/voxctrl-inference/src/lib.rs | 27 +- crates/voxctrl-inference/src/moonshine.rs | 651 ++++++++++++++++++++++ dist/assets/index-DlOLKceu.js | 23 - dist/assets/index-DtLMwI2N.js | 25 + dist/index.html | 2 +- docs/configuration.md | 15 +- docs/speech-recognition.md | 25 + package-lock.json | 4 +- src-tauri/Cargo.toml | 4 + src-tauri/src/commands.rs | 36 ++ src-tauri/src/lib.rs | 17 +- src/lib/Settings/EngineTab.svelte | 98 +++- 14 files changed, 916 insertions(+), 60 deletions(-) create mode 100644 crates/voxctrl-inference/src/moonshine.rs delete mode 100644 dist/assets/index-DlOLKceu.js create mode 100644 dist/assets/index-DtLMwI2N.js diff --git a/Cargo.lock b/Cargo.lock index 0d2d889..cf9c4f1 100644 --- a/Cargo.lock +++ b/Cargo.lock @@ -4583,9 +4583,9 @@ dependencies = [ [[package]] name = "lzma-rust2" -version = "0.15.7" +version = "0.15.8" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "1670343e58806300d87950e3401e820b519b9384281bbabfb15e3636689ffd69" +checksum = "e20f57f9918e5bd7bc58c22cdd70a6afc7375d4dd9683af5f2b34bd3d2bba619" [[package]] name = "mac-notification-sys" @@ -4819,21 +4819,6 @@ dependencies = [ "tempfile", ] -[[package]] -name = "ndarray" -version = "0.16.1" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "882ed72dce9365842bf196bdeedf5055305f11fc8c03dee7bb0194a6cad34841" -dependencies = [ - "matrixmultiply", - "num-complex", - "num-integer", - "num-traits", - "portable-atomic", - "portable-atomic-util", - "rawpointer", -] - [[package]] name = "ndarray" version = "0.17.2" @@ -5709,7 +5694,7 @@ version = "2.0.0-rc.12" source = "registry+https://github.com/rust-lang/crates.io-index" checksum = "d7de3af33d24a745ffb8fab904b13478438d1cd52868e6f17735ef6e1f8bf133" dependencies = [ - "ndarray 0.17.2", + "ndarray", "ort-sys", "smallvec", "tracing", @@ -9493,7 +9478,6 @@ dependencies = [ "anyhow", "crossbeam-channel", "dirs 5.0.1", - "ndarray 0.16.1", "ort", "regex", "reqwest 0.12.28", @@ -9501,6 +9485,7 @@ dependencies = [ "serde_json", "tempfile", "thiserror 2.0.18", + "tokenizers", "tokio", "tracing", "voxctrl-config", @@ -10024,9 +10009,9 @@ dependencies = [ [[package]] name = "webpki-root-certs" -version = "1.0.7" +version = "1.0.8" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "f31141ce3fc3e300ae89b78c0dd67f9708061d1d2eda54b8209346fd6be9a92c" +checksum = "0d46a5a140e6f7afeccd8eae97eff335163939eac8b929834875168b29b3d267" dependencies = [ "rustls-pki-types", ] diff --git a/crates/voxctrl-inference/Cargo.toml b/crates/voxctrl-inference/Cargo.toml index dc63ab0..c69380b 100644 --- a/crates/voxctrl-inference/Cargo.toml +++ b/crates/voxctrl-inference/Cargo.toml @@ -30,15 +30,27 @@ cuda = ["whisper-rs/cuda"] # CUDA's heavy runtime. Falls back to CPU when no Vulkan device is present. vulkan = ["whisper-rs/vulkan"] -# Moonshine ONNX backend via onnxruntime -moonshine = ["dep:ort", "dep:ndarray"] +# Moonshine ONNX backend via onnxruntime. `ort` keeps its default features, +# which include `download-binaries`: ONNX Runtime is fetched at build time and +# linked into the binary, so the shipped app is self-contained and needs no +# system `libonnxruntime` at runtime (nothing else in the workspace provides +# one). The tokenizer for decoding is loaded from the model's `tokenizer.json`. +moonshine = ["dep:ort", "dep:tokenizers"] [dependencies.ort] version = "2.0.0-rc.12" optional = true -features = ["download-binaries"] +# Default features include `download-binaries`, which fetches a prebuilt ONNX +# Runtime at build time and links it in, so the resulting binary is +# self-contained (no system libonnxruntime needed at runtime). Building the +# `moonshine` feature therefore requires network access to the ONNX Runtime +# binary host at compile time. To build fully offline instead, switch to +# `default-features = false, features = ["load-dynamic", "std", "api-24"]` and +# ship a `libonnxruntime` alongside the app. -[dependencies.ndarray] -version = "0.16" +[dependencies.tokenizers] +version = "0.21" optional = true +default-features = false +features = ["onig"] diff --git a/crates/voxctrl-inference/src/lib.rs b/crates/voxctrl-inference/src/lib.rs index edf0774..0b5d8d5 100644 --- a/crates/voxctrl-inference/src/lib.rs +++ b/crates/voxctrl-inference/src/lib.rs @@ -1,12 +1,20 @@ pub mod backend; +#[cfg(feature = "moonshine")] +pub mod moonshine; pub mod postprocess; pub mod whisper_cpp; +/// Whether the Moonshine ONNX backend was compiled into this build. When false, +/// selecting Moonshine transparently falls back to whisper-cpp, and callers +/// (e.g. the "model not downloaded" UI checks) must treat a Moonshine selection +/// as effectively whisper-cpp. +pub const MOONSHINE_COMPILED: bool = cfg!(feature = "moonshine"); + use std::sync::Arc; use anyhow::Result; use crossbeam_channel::{Receiver, Sender}; -use tracing::{error, info, warn}; +use tracing::{error, info}; use voxctrl_config::{AppConfig, BackendChoice}; use backend::{TranscribeRequest, TranscriptionBackend}; @@ -310,9 +318,20 @@ fn build_backend(config: &AppConfig) -> Box { Box::new(WhisperCppBackend::new(config.engine.whisper_cpp.clone())) } BackendChoice::Moonshine => { - // Moonshine feature not compiled — fall back to whisper-cpp - warn!("Moonshine backend selected but not compiled; using whisper-cpp"); - Box::new(WhisperCppBackend::new(config.engine.whisper_cpp.clone())) + #[cfg(feature = "moonshine")] + { + info!( + "Using Moonshine backend ({} model)", + config.engine.moonshine.model_size + ); + Box::new(moonshine::MoonshineBackend::new(config.engine.moonshine.clone())) + } + #[cfg(not(feature = "moonshine"))] + { + // Moonshine feature not compiled — fall back to whisper-cpp. + tracing::warn!("Moonshine backend selected but not compiled in this build; using whisper-cpp"); + Box::new(WhisperCppBackend::new(config.engine.whisper_cpp.clone())) + } } BackendChoice::Auto => { let selected = auto_select(config); diff --git a/crates/voxctrl-inference/src/moonshine.rs b/crates/voxctrl-inference/src/moonshine.rs new file mode 100644 index 0000000..fcc7b3c --- /dev/null +++ b/crates/voxctrl-inference/src/moonshine.rs @@ -0,0 +1,651 @@ +//! Moonshine speech-to-text backend (ONNX Runtime). +//! +//! Moonshine () is a compact +//! encoder–decoder ASR model designed for on-device use. Unlike Whisper it +//! consumes the raw 16 kHz waveform directly (no 30-second padding), which makes +//! it fast and low-latency on short utterances — a good fit for push-to-talk +//! dictation. +//! +//! The upstream ONNX release splits the model into four graphs that we run in +//! sequence: +//! +//! 1. `preprocess` — raw audio `[1, samples]` → features `[1, frames, dim]` +//! 2. `encode` — features → encoder context `[1, frames, dim]` +//! 3. `uncached_decode` — first decoder step: start token + context → logits + KV cache +//! 4. `cached_decode` — subsequent steps: one token + context + KV cache → logits + new cache +//! +//! Decoding is greedy autoregression: start from the start-of-transcript token, +//! take the arg-max of each step's logits, feed it back until the +//! end-of-transcript token appears (or a length cap derived from the audio +//! duration is hit). Token ids are turned back into text with the model's +//! `tokenizer.json`. +//! +//! To stay robust against small differences between exported model revisions, +//! tensors are bound to graph inputs **by position** using each session's +//! declared input list rather than by hard-coded names, and the presence of an +//! optional `seq_len` input is detected from the graph arity instead of assumed. + +use std::{ + path::{Path, PathBuf}, + sync::Mutex, + time::Instant, +}; + +use anyhow::{anyhow, bail, Context, Result}; +use ort::{ + session::{builder::GraphOptimizationLevel, Session, SessionInputValue, SessionOutputs}, + value::Tensor, +}; +use tokenizers::Tokenizer; +use tracing::info; +use voxctrl_config::MoonshineConfig; + +use crate::backend::{TranscribeRequest, TranscriptionBackend, TranscriptionResult}; + +// ── Model constants ─────────────────────────────────────────────────────────── + +/// Start-of-transcript token id (first token fed to the decoder). +const SOT_TOKEN: i32 = 1; +/// End-of-transcript token id (decoding stops once this is produced). +const EOT_TOKEN: i32 = 2; +/// Moonshine operates on 16 kHz mono audio. +const SAMPLE_RATE: usize = 16_000; +/// Upper bound on decoded tokens, scaled by audio length. Six tokens per second +/// comfortably exceeds natural speech rates while capping runaway generation if +/// the model never emits the end token. +const MAX_TOKENS_PER_SECOND: usize = 6; +/// Never fewer than this many tokens, so very short clips still get a few steps. +const MIN_MAX_TOKENS: usize = 8; + +/// The ONNX graph files that make up a Moonshine model, in load order. +const MODEL_FILES: [&str; 4] = [ + "preprocess.onnx", + "encode.onnx", + "uncached_decode.onnx", + "cached_decode.onnx", +]; +const TOKENIZER_FILE: &str = "tokenizer.json"; + +/// Base URL for the upstream ONNX weights on the Hugging Face hub. The float +/// (non-quantized) graphs live under `onnx/merged/{size}/float/`. Kept as a +/// single constant so a hosting change is a one-line edit; users can also point +/// `model_dir` at a local copy to bypass downloading entirely. +const HF_BASE_URL: &str = "https://huggingface.co/UsefulSensors/moonshine/resolve/main/onnx/merged"; + +fn valid_model_size(size: &str) -> bool { + matches!(size, "tiny" | "base") +} + +// ── Filesystem layout ───────────────────────────────────────────────────────── + +/// Default parent directory for all Moonshine models. Mirrors the whisper +/// backend's `~/.local/share/voxctrl/models` convention with a `moonshine` +/// subfolder so the two backends never collide. +pub fn default_model_dir() -> PathBuf { + dirs::data_local_dir() + .unwrap_or_else(|| PathBuf::from(".")) + .join("voxctrl") + .join("models") + .join("moonshine") +} + +fn expand_tilde(path: &str) -> PathBuf { + let home = std::env::var("HOME") + .map(PathBuf::from) + .ok() + .or_else(dirs::home_dir); + if path == "~" { + return home.unwrap_or_else(|| PathBuf::from("~")); + } + if let Some(rest) = path.strip_prefix("~/") { + if let Some(h) = home { + return h.join(rest); + } + } + PathBuf::from(path) +} + +/// Directory holding one model's files: `//`. +fn model_size_dir(model_dir: &str, size: &str) -> PathBuf { + let base = if model_dir.is_empty() { + default_model_dir() + } else { + expand_tilde(model_dir) + }; + base.join(size) +} + +/// True when every ONNX graph and the tokenizer for `size` are present on disk. +pub fn is_model_downloaded(size: &str, model_dir: &str) -> bool { + if !valid_model_size(size) { + return false; + } + let dir = model_size_dir(model_dir, size); + MODEL_FILES.iter().all(|f| dir.join(f).exists()) && dir.join(TOKENIZER_FILE).exists() +} + +// ── Download ────────────────────────────────────────────────────────────────── + +/// Serializes downloads so two triggers (e.g. a Settings click and an on-demand +/// load) can't fight over the same files. +static DOWNLOAD_LOCK: tokio::sync::Mutex<()> = tokio::sync::Mutex::const_new(()); + +/// Fetch every model file for `size` into `//`. Files already +/// present are skipped, so this is safe to call repeatedly. +pub async fn download_model(size: &str, model_dir: &str) -> Result<()> { + if !valid_model_size(size) { + bail!("Unknown Moonshine model size '{size}' (expected 'tiny' or 'base')"); + } + + let dir = model_size_dir(model_dir, size); + tokio::fs::create_dir_all(&dir).await?; + + let _guard = DOWNLOAD_LOCK.lock().await; + + // ONNX graphs live under `/float/`; the tokenizer is shared at + // `/tokenizer.json`. + let mut jobs: Vec<(String, PathBuf)> = MODEL_FILES + .iter() + .map(|f| { + ( + format!("{HF_BASE_URL}/{size}/float/{f}"), + dir.join(f), + ) + }) + .collect(); + jobs.push(( + format!("{HF_BASE_URL}/{size}/{TOKENIZER_FILE}"), + dir.join(TOKENIZER_FILE), + )); + + for (url, path) in jobs { + if path.exists() { + continue; + } + info!("Downloading Moonshine file: {url}"); + let response = reqwest::get(&url) + .await + .with_context(|| format!("request {url}"))? + .error_for_status() + .with_context(|| format!("fetch {url}"))?; + let bytes = response.bytes().await.with_context(|| format!("read {url}"))?; + // Write to a temp path then rename so an interrupted download never + // leaves a half-written file that later looks "present". + let tmp = path.with_extension("part"); + tokio::fs::write(&tmp, &bytes) + .await + .with_context(|| format!("write {}", tmp.display()))?; + tokio::fs::rename(&tmp, &path) + .await + .with_context(|| format!("finalize {}", path.display()))?; + } + + info!("Moonshine '{size}' model ready in {}", dir.display()); + Ok(()) +} + +// ── Loaded state ────────────────────────────────────────────────────────────── + +/// One Moonshine session (a graph) plus the number of KV-cache tensors it +/// produces, discovered from the graph, so we bind cache in/out without relying +/// on tensor names. +struct Loaded { + preprocess: Session, + encode: Session, + uncached_decode: Session, + cached_decode: Session, + tokenizer: Tokenizer, + /// Cache tensors emitted by `uncached_decode` (outputs after the logits). + num_cache: usize, + /// Non-cache inputs the decoders take: 2 = `[tokens, context]`, + /// 3 = `[tokens, context, seq_len]`. + decode_fixed_inputs: usize, + /// Whether `encode` takes a trailing `seq_len` input in addition to features. + encode_takes_seq_len: bool, +} + +// ── Backend ─────────────────────────────────────────────────────────────────── + +pub struct MoonshineBackend { + cfg: MoonshineConfig, + /// Serialized behind a mutex: `Session::run` needs `&mut`, and the inference + /// worker is single-threaded so there is never real contention. + state: Mutex>, + loaded: bool, +} + +impl MoonshineBackend { + pub fn new(cfg: MoonshineConfig) -> Self { + Self { + cfg, + state: Mutex::new(None), + loaded: false, + } + } + + fn build_session(path: &Path) -> Result { + // The builder methods return `ort::Error` (the error + // carries the builder back for recovery), which is not a `Send + Sync` + // `std::error::Error`, so it can't flow through `anyhow::Context`. Format + // it via Display instead. `commit_from_file` returns a plain `ort::Error` + // that anyhow can absorb directly. + Session::builder() + .map_err(|e| anyhow!("ort session builder: {e}"))? + .with_optimization_level(GraphOptimizationLevel::Level3) + .map_err(|e| anyhow!("set optimization level: {e}"))? + .with_intra_threads(threads()) + .map_err(|e| anyhow!("set intra threads: {e}"))? + .commit_from_file(path) + .with_context(|| format!("load ONNX graph {}", path.display())) + } +} + +impl TranscriptionBackend for MoonshineBackend { + fn name(&self) -> &str { + "moonshine" + } + + fn load(&mut self) -> Result<()> { + let size = &self.cfg.model_size; + if !valid_model_size(size) { + bail!("Unknown Moonshine model size '{size}' (expected 'tiny' or 'base')"); + } + + let dir = model_size_dir("", size); + // model_dir override: MoonshineConfig has no dir field, so honor an + // explicit path only through the default location. If files are missing, + // point the user at exactly where to put them. + if !is_model_downloaded(size, "") { + bail!( + "Moonshine '{size}' model is not downloaded (expected {} plus {} in {}). \ + Open Settings → Engine and download it, or place the files there manually.", + MODEL_FILES.join(", "), + TOKENIZER_FILE, + dir.display() + ); + } + + info!("Loading Moonshine '{size}' model from {}", dir.display()); + + let preprocess = Self::build_session(&dir.join("preprocess.onnx"))?; + let encode = Self::build_session(&dir.join("encode.onnx"))?; + let uncached_decode = Self::build_session(&dir.join("uncached_decode.onnx"))?; + let cached_decode = Self::build_session(&dir.join("cached_decode.onnx"))?; + + let tokenizer = Tokenizer::from_file(dir.join(TOKENIZER_FILE)) + .map_err(|e| anyhow!("load tokenizer.json: {e}"))?; + + // Discover the KV-cache shape of this export. + // uncached_decode outputs = [logits, cache_0, cache_1, ...] + // cached_decode inputs = [tokens, context, (seq_len), cache_0, ...] + let num_cache = uncached_decode + .outputs() + .len() + .checked_sub(1) + .ok_or_else(|| anyhow!("uncached_decode has no outputs"))?; + let cached_inputs = cached_decode.inputs().len(); + let decode_fixed_inputs = cached_inputs + .checked_sub(num_cache) + .ok_or_else(|| anyhow!("cached_decode has fewer inputs ({cached_inputs}) than cache tensors ({num_cache})"))?; + if !(2..=3).contains(&decode_fixed_inputs) { + bail!( + "Unexpected Moonshine decoder signature: {decode_fixed_inputs} non-cache inputs \ + (expected 2 or 3). The model export may be incompatible." + ); + } + let encode_takes_seq_len = encode.inputs().len() >= 2; + + info!( + "Moonshine graph shape: {num_cache} cache tensors, {decode_fixed_inputs} fixed decoder inputs, \ + encode_seq_len={encode_takes_seq_len}" + ); + + *self.state.lock().unwrap() = Some(Loaded { + preprocess, + encode, + uncached_decode, + cached_decode, + tokenizer, + num_cache, + decode_fixed_inputs, + encode_takes_seq_len, + }); + self.loaded = true; + Ok(()) + } + + fn transcribe(&self, req: &TranscribeRequest) -> Result { + if !self.loaded { + bail!("Model not loaded"); + } + let mut guard = self.state.lock().unwrap(); + let state = guard.as_mut().context("Moonshine state not initialised")?; + + let n_samples = req.audio.len(); + if n_samples == 0 { + return Ok(empty_result(&self.cfg.language)); + } + + let t0 = Instant::now(); + let text = run_inference(state, &req.audio)?; + let inference_ms = t0.elapsed().as_millis() as u32; + + Ok(TranscriptionResult { + text: text.trim().to_string(), + language: self.cfg.language.clone(), + language_probability: 1.0, + duration_ms: (n_samples / (SAMPLE_RATE / 1000)) as u32, + inference_ms, + word_timestamps: None, + }) + } + + fn unload(&mut self) { + *self.state.lock().unwrap() = None; + self.loaded = false; + } + + fn is_loaded(&self) -> bool { + self.loaded + } +} + +// ── Inference pipeline ──────────────────────────────────────────────────────── + +fn run_inference(state: &mut Loaded, audio: &[f32]) -> Result { + let n_samples = audio.len(); + + // 1. Preprocess: raw waveform [1, samples] → features [1, frames, dim]. + let audio_tensor = Tensor::from_array(([1_usize, n_samples], audio.to_vec())) + .context("build audio tensor")?; + let (feat_shape, feat_data) = { + let outputs = run_positional(&mut state.preprocess, vec![audio_tensor.into()])?; + let (shape, data) = outputs[0] + .try_extract_tensor::() + .context("extract preprocess output")?; + (shape.to_vec(), data.to_vec()) + }; + + // Number of encoder frames = second-to-last dimension of the features. + let frames = *feat_shape + .iter() + .rev() + .nth(1) + .ok_or_else(|| anyhow!("preprocess output has too few dimensions"))? as i32; + + // 2. Encode: features → context [1, frames, dim]. + let (ctx_shape, ctx_data) = { + let feat_tensor = Tensor::from_array((dims(&feat_shape), feat_data)) + .context("build features tensor")?; + let mut inputs: Vec = vec![feat_tensor.into()]; + if state.encode_takes_seq_len { + let seq = Tensor::from_array(([1_usize], vec![frames])).context("build encode seq_len")?; + inputs.push(seq.into()); + } + let outputs = run_positional(&mut state.encode, inputs)?; + let (shape, data) = outputs[0] + .try_extract_tensor::() + .context("extract encode output")?; + (shape.to_vec(), data.to_vec()) + }; + + // Context is fed unchanged into every decoder step; build it once and pass by + // reference so it is not re-copied each iteration. + let context = Tensor::from_array((dims(&ctx_shape), ctx_data)).context("build context tensor")?; + + // 3. First decoder step (no cache yet): start token → logits + KV cache. + let max_tokens = ((n_samples / SAMPLE_RATE) * MAX_TOKENS_PER_SECOND).max(MIN_MAX_TOKENS); + let mut tokens: Vec = Vec::with_capacity(max_tokens + 1); + + let mut token_count: i32 = 1; + let first_ids = Tensor::from_array(([1_usize, 1], vec![SOT_TOKEN])).context("build start token")?; + let (mut logits, mut cache) = { + let inputs = decoder_fixed_inputs(state, first_ids, &context, token_count)?; + let outputs = run_positional(&mut state.uncached_decode, inputs)?; + extract_logits_and_cache(&outputs, state.num_cache)? + }; + + // 4. Greedy autoregressive loop over cached_decode. + for _ in 0..max_tokens { + let next = argmax_last_row(&logits.0, &logits.1); + if next == EOT_TOKEN { + break; + } + tokens.push(next); + token_count += 1; + + let ids = Tensor::from_array(([1_usize, 1], vec![next])).context("build next token")?; + let mut inputs = decoder_fixed_inputs(state, ids, &context, token_count)?; + for (shape, data) in cache.drain(..) { + let t = Tensor::from_array((dims(&shape), data)).context("build cache tensor")?; + inputs.push(t.into()); + } + let outputs = run_positional(&mut state.cached_decode, inputs)?; + let (new_logits, new_cache) = extract_logits_and_cache(&outputs, state.num_cache)?; + logits = new_logits; + cache = new_cache; + } + + // 5. Decode token ids to text, dropping the special tokens. + let ids_u32: Vec = tokens.iter().map(|&t| t as u32).collect(); + let text = state + .tokenizer + .decode(&ids_u32, true) + .map_err(|e| anyhow!("tokenizer decode: {e}"))?; + Ok(text) +} + +/// Build the fixed (non-cache) decoder inputs: `[tokens, context]` and, when the +/// export declares it, a trailing `seq_len` scalar holding the running token +/// count. +fn decoder_fixed_inputs<'a>( + state: &Loaded, + ids: Tensor, + context: &'a Tensor, + token_count: i32, +) -> Result>> { + let mut inputs: Vec = Vec::with_capacity(state.decode_fixed_inputs + state.num_cache); + inputs.push(ids.into()); + inputs.push(context.into()); + if state.decode_fixed_inputs == 3 { + let seq = Tensor::from_array(([1_usize], vec![token_count])).context("build decode seq_len")?; + inputs.push(seq.into()); + } + Ok(inputs) +} + +/// Run a session binding `values` to its inputs positionally (input `i` gets +/// `values[i]`), which avoids depending on the export's tensor names. +fn run_positional<'s>( + session: &'s mut Session, + values: Vec>, +) -> Result> { + let names: Vec = session.inputs().iter().map(|i| i.name().to_string()).collect(); + if names.len() != values.len() { + bail!( + "graph expects {} inputs but {} were supplied", + names.len(), + values.len() + ); + } + let feed: Vec<(String, SessionInputValue)> = names.into_iter().zip(values).collect(); + session.run(feed).context("ort session run") +} + +/// Split a decoder step's outputs into `(logits shape, logits data)` and the +/// owned KV-cache tensors, copying out of the borrowed session outputs so the +/// session can be run again. +#[allow(clippy::type_complexity)] +fn extract_logits_and_cache( + outputs: &SessionOutputs, + num_cache: usize, +) -> Result<((Vec, Vec), Vec<(Vec, Vec)>)> { + let (lshape, ldata) = outputs[0] + .try_extract_tensor::() + .context("extract decoder logits")?; + let logits = (lshape.to_vec(), ldata.to_vec()); + + let mut cache = Vec::with_capacity(num_cache); + for i in 0..num_cache { + let (shape, data) = outputs[i + 1] + .try_extract_tensor::() + .with_context(|| format!("extract KV cache tensor {i}"))?; + cache.push((shape.to_vec(), data.to_vec())); + } + Ok((logits, cache)) +} + +/// Arg-max over the final timestep of a `[1, seq, vocab]` (or `[1, vocab]`) +/// logits tensor, returning the winning token id. +fn argmax_last_row(shape: &[i64], data: &[f32]) -> i32 { + let vocab = *shape.last().unwrap_or(&1) as usize; + if vocab == 0 || data.is_empty() { + return EOT_TOKEN; + } + // The last `vocab` values are the distribution for the most recent timestep. + let start = data.len().saturating_sub(vocab); + let row = &data[start..]; + let mut best = 0usize; + let mut best_val = f32::NEG_INFINITY; + for (i, &v) in row.iter().enumerate() { + if v > best_val { + best_val = v; + best = i; + } + } + best as i32 +} + +/// Convert an `i64` ONNX shape into the `usize` dims `Tensor::from_array` wants. +fn dims(shape: &[i64]) -> Vec { + shape.iter().map(|&d| d.max(0) as usize).collect() +} + +fn empty_result(language: &str) -> TranscriptionResult { + TranscriptionResult { + text: String::new(), + language: language.to_string(), + language_probability: 1.0, + duration_ms: 0, + inference_ms: 0, + word_timestamps: None, + } +} + +fn threads() -> usize { + std::thread::available_parallelism() + .map(|n| (n.get() / 2).max(1)) + .unwrap_or(2) +} + +#[cfg(test)] +mod tests { + use super::*; + + #[test] + fn test_valid_model_size() { + assert!(valid_model_size("tiny")); + assert!(valid_model_size("base")); + assert!(!valid_model_size("small")); + assert!(!valid_model_size("")); + } + + #[test] + fn test_default_model_dir_has_moonshine_segment() { + let dir = default_model_dir(); + assert!(dir.ends_with("moonshine")); + } + + #[test] + fn test_model_size_dir_layout() { + let dir = model_size_dir("/tmp/models", "base"); + assert_eq!(dir, PathBuf::from("/tmp/models/base")); + } + + #[test] + fn test_is_model_downloaded_unknown_size() { + assert!(!is_model_downloaded("nonexistent", "")); + assert!(!is_model_downloaded("nonexistent", "/tmp")); + } + + #[test] + fn test_is_model_downloaded_missing_files() { + let dir = tempfile::tempdir().unwrap(); + // Empty directory: nothing downloaded. + assert!(!is_model_downloaded("base", dir.path().to_str().unwrap())); + } + + #[test] + fn test_is_model_downloaded_complete_set() { + use std::io::Write; + let root = tempfile::tempdir().unwrap(); + let size_dir = root.path().join("base"); + std::fs::create_dir_all(&size_dir).unwrap(); + for f in MODEL_FILES.iter().chain([&TOKENIZER_FILE]) { + std::fs::File::create(size_dir.join(f)) + .unwrap() + .write_all(b"x") + .unwrap(); + } + assert!(is_model_downloaded("base", root.path().to_str().unwrap())); + + // Remove one graph → no longer considered downloaded. + std::fs::remove_file(size_dir.join("encode.onnx")).unwrap(); + assert!(!is_model_downloaded("base", root.path().to_str().unwrap())); + } + + #[test] + fn test_argmax_last_row_multistep() { + // Two timesteps, vocab of 4. Arg-max must come from the LAST row. + let shape = [1_i64, 2, 4]; + let data = [ + 0.1, 0.9, 0.2, 0.3, // step 0 (ignored) + 0.5, 0.4, 0.8, 0.1, // step 1 → index 2 wins + ]; + assert_eq!(argmax_last_row(&shape, &data), 2); + } + + #[test] + fn test_argmax_last_row_single_step() { + let shape = [1_i64, 5]; + let data = [0.0, 0.0, 0.0, 7.0, 1.0]; + assert_eq!(argmax_last_row(&shape, &data), 3); + } + + #[test] + fn test_argmax_last_row_empty_is_eot() { + assert_eq!(argmax_last_row(&[1, 0], &[]), EOT_TOKEN); + } + + #[test] + fn test_dims_conversion() { + assert_eq!(dims(&[1, 40, 288]), vec![1_usize, 40, 288]); + } + + #[test] + fn test_new_backend_reports_name_and_unloaded() { + let cfg = MoonshineConfig { + model_size: "base".into(), + language: "en".into(), + }; + let b = MoonshineBackend::new(cfg); + assert_eq!(b.name(), "moonshine"); + assert!(!b.is_loaded()); + } + + #[test] + fn test_transcribe_before_load_errors() { + let cfg = MoonshineConfig { + model_size: "base".into(), + language: "en".into(), + }; + let b = MoonshineBackend::new(cfg); + let req = TranscribeRequest { + audio: vec![0.0; 1600], + language: None, + word_timestamps: false, + initial_prompt: None, + }; + assert!(b.transcribe(&req).is_err()); + } +} diff --git a/dist/assets/index-DlOLKceu.js b/dist/assets/index-DlOLKceu.js deleted file mode 100644 index 437e024..0000000 --- a/dist/assets/index-DlOLKceu.js +++ /dev/null @@ -1,23 +0,0 @@ -var cl=Object.defineProperty;var Fi=s=>{throw TypeError(s)};var ul=(s,t,e)=>t in s?cl(s,t,{enumerable:!0,configurable:!0,writable:!0,value:e}):s[t]=e;var Lt=(s,t,e)=>ul(s,typeof t!="symbol"?t+"":t,e),Un=(s,t,e)=>t.has(s)||Fi("Cannot "+e);var m=(s,t,e)=>(Un(s,t,"read from private field"),e?e.call(s):t.get(s)),Te=(s,t,e)=>t.has(s)?Fi("Cannot add the same private member more than once"):t instanceof WeakSet?t.add(s):t.set(s,e),xe=(s,t,e,n)=>(Un(s,t,"write to private field"),n?n.call(s,e):t.set(s,e),e),Re=(s,t,e)=>(Un(s,t,"access private 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oc={engine:{backend:"auto",inference_mode:"Balanced",whisper_cpp:{model_dir:"",model_size:"tiny",device:"auto",threads:0},moonshine:{model_size:"base",language:"en"}},audio:{vad_threshold:.5,min_silence_duration_ms:500,input_device_index:null,evdev_device:null,noise_suppression:!1,gain:1,dynamic_stream:!0},ui:{show_overlay:!0,overlay_style:"mono_bars",overlay_position:"center",overlay_monitor:"primary",auto_show_settings:!0,show_notification:!1,history_enabled:!1},features:{remove_fillers:!0,custom_vocabulary:[],spoken_punctuation:!0,auto_format_lists:!0,quiet_mode:!1,snippets:{}},openai:{enabled:!1,model:"llama3.2:1b",mode:"clean",custom_prompt:null,system_prompt:"Fix grammar and punctuation only. 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General

History

When enabled, completed transcripts are saved to the History log. Disabled by default.

MCP Server

Socket: /tmp/voxctrl-mcp.sock (Linux) / \\\\.\\pipe\\voxctrl-mcp (Windows)

AT-SPI2 (Linux)

');function fc(s,t){$e(t,!0);let e=qe(t,"cfg",15);function n(){Es.set(!0)}var i=pc(),l=r(o(i),2),c=r(o(l),2),u=r(o(c),2),d=r(l,2),p=r(o(d),2),_=r(o(p),2),h=r(p,2),v=r(o(h),2),x=r(h,2),y=r(o(x),2),C=r(d,2),k=r(o(C),2),S=r(o(k),2),D=r(k,2),V=r(o(D),2),$=r(D,2),F=r(o($),2);K("change",u,n),Xe(u,()=>e().ui.history_enabled,b=>e(e().ui.history_enabled=b,!0)),K("change",_,n),Xe(_,()=>e().mcp.server_enabled,b=>e(e().mcp.server_enabled=b,!0)),K("change",v,n),Xe(v,()=>e().mcp.visual_feedback,b=>e(e().mcp.visual_feedback=b,!0)),K("change",y,n),ge(y,()=>e().mcp.record_timeout,b=>e(e().mcp.record_timeout=b,!0)),K("change",S,n),Xe(S,()=>e().atspi.injection,b=>e(e().atspi.injection=b,!0)),K("change",V,n),Xe(V,()=>e().atspi.context_prompt,b=>e(e().atspi.context_prompt=b,!0)),K("change",F,n),Xe(F,()=>e().atspi.auto_code_mode,b=>e(e().atspi.auto_code_mode=b,!0)),g(s,i),Ye()}Wt(["change"]);var hc=E(''),_c=E('
'),gc=E('
');function bt(s,t){$e(t,!0);let e=qe(t,"value",15),n=qe(t,"options",19,()=>[]),i=qe(t,"disabled",3,!1),l=B(!1),c=B(null),u=he(()=>n().map(C=>typeof C=="string"?{value:C,label:C,disabled:!1}:{value:C.value,label:C.label,disabled:!!C.disabled})),d=he(()=>{const C=a(u).find(k=>k.value===e());return C?C.label:e()!==void 0&&e()!==null?String(e()):""});ka(()=>{if(!a(l))return;const C=k=>{a(c)&&!a(c).contains(k.target)&&f(l,!1)};return document.addEventListener("click",C),()=>{document.removeEventListener("click",C)}});function p(C){e(C),f(l,!1),t.onchange&&t.onchange(C)}var _=gc(),h=o(_),v=o(h),x=r(h,2);{var y=C=>{var k=_c();ht(k,21,()=>a(u),ft,(S,D)=>{var V=hc();let $;var F=o(V);G(()=>{$=ye(V,1,"custom-dropdown-item svelte-1heock4",null,$,{selected:a(D).value===e()}),V.disabled=a(D).disabled,X(F,a(D).label)}),K("click",V,b=>{b.preventDefault(),b.stopPropagation(),p(a(D).value)}),g(S,V)}),g(C,k)};U(x,C=>{a(l)&&!i()&&C(y)})}sl(_,C=>f(c,C),()=>a(c)),G(()=>{h.disabled=i(),X(v,a(d))}),K("click",h,()=>f(l,!a(l))),g(s,_),Ye()}Wt(["click"]);var bc=E('
'),mc=E('

Visual & Feedback

Overlay & HUD

Alerts & OS Integration

Application Window

');function yc(s,t){$e(t,!0);let e=qe(t,"cfg",15),n=B(Be([])),i=B(Be([])),l=he(()=>[{value:"voice_card",label:"Voice Card"},{value:"waveform",label:"Waveform"},{value:"pulse",label:"Pulse Ring"},{value:"blue_wave",label:"Ocean Wave"},{value:"mono_bars",label:"Mono Bars"},{value:"spectrum",label:"Neon Spectrum"},{value:"terminal",label:"Retro Terminal"},{value:"vinyl",label:"Analog VU"},...a(n).map(w=>({value:w.name,label:w.name}))]);const c=[{value:"top",label:"Top of screen"},{value:"center",label:"Center of screen"},{value:"bottom",label:"Bottom of screen"}];let u=he(()=>[{value:"primary",label:"Primary Monitor"},...a(i).filter(w=>w.name).map(w=>({value:w.name,label:`${w.name} (${w.width}x${w.height})${w.is_primary?" [Primary]":""}`}))]);rt(async()=>{try{f(n,await O("get_custom_overlays"),!0)}catch(w){console.error("Failed to fetch custom overlays:",w)}try{f(i,await O("get_available_monitors"),!0)}catch(w){console.error("Failed to fetch available monitors:",w)}});function d(){Es.set(!0)}var p=mc(),_=r(o(p),2),h=r(o(_),2),v=r(o(h),2),x=r(h,2),y=r(o(x),2);bt(y,{get options(){return a(l)},onchange:d,get value(){return e().ui.overlay_style},set value(w){e(e().ui.overlay_style=w,!0)}});var C=r(x,2),k=r(o(C),2);bt(k,{get options(){return c},onchange:d,get value(){return e().ui.overlay_position},set value(w){e(e().ui.overlay_position=w,!0)}});var S=r(C,2),D=r(o(S),2);bt(D,{get options(){return a(u)},onchange:d,get value(){return e().ui.overlay_monitor},set value(w){e(e().ui.overlay_monitor=w,!0)}});var V=r(S,2);{var $=w=>{var J=bc(),q=o(J),N=o(q);G(()=>{var M;return X(N,`⚠️ Configured monitor "${(M=e().ui.overlay_monitor)!=null?M:""}" is disconnected. Using Primary Monitor.`)}),g(w,J)},F=he(()=>e().ui.overlay_monitor!=="primary"&&a(i).length>0&&!a(i).some(w=>w.name===e().ui.overlay_monitor));U(V,w=>{a(F)&&w($)})}var b=r(_,2),L=r(o(b),2),H=r(o(L),2),se=r(b,2),oe=r(o(se),2),ue=r(o(oe),2);K("change",v,d),Xe(v,()=>e().ui.show_overlay,w=>e(e().ui.show_overlay=w,!0)),K("change",H,d),Xe(H,()=>e().ui.show_notification,w=>e(e().ui.show_notification=w,!0)),K("change",ue,d),Xe(ue,()=>e().ui.auto_show_settings,w=>e(e().ui.auto_show_settings=w,!0)),g(s,p),Ye()}Wt(["change"]);var wc=E(`
⚠️
Voice Model Not Downloaded

The configuration specifies a voice model ( ) that is not currently downloaded. Please select the model size to - use and download it below, or choose another model.

`),xc=E('⏳ Checking local model files...'),kc=E(' '),Tc=E('✔ Model downloaded and ready'),Ec=E('
❌ Model file missing
'),Sc=E('

'),Ac=E('

Whisper.cpp Settings

Model directory (leave blank for default)

Default model directory: ~/.local/share/voxctrl/models/

'),Cc=E('

Moonshine Settings

'),Pc=E('

Inference Engine

Backend

');function Mc(s,t){$e(t,!0);let e=qe(t,"cfg",15);function n(){Es.set(!0)}const i=["tiny","tiny.en","base","base.en","small","small.en","medium","medium.en","large-v2","large-v3","large-v3-turbo"],l=[{value:"auto",label:"Auto-detect"},{value:"whisper-cpp",label:"Whisper.cpp"},{value:"moonshine",label:"Moonshine (CPU only)"}],c=[{value:"Balanced",label:"Balanced"},{value:"Aggressive",label:"Aggressive (shorter silence)"}];let u=he(()=>i.map(M=>({value:M,label:`${M}${a(h)[M]?" ✔":""}`}))),d=B(!1),p=he(()=>[{value:"auto",label:"Auto"},...a(d)?[{value:"cuda",label:"CUDA (NVIDIA)"}]:[],{value:"vulkan",label:"Vulkan (AMD/Intel)"},{value:"cpu",label:"CPU"}]);const _=[{value:"base",label:"Base"},{value:"tiny",label:"Tiny"}];let h=B(Be({})),v=B(!1),x=B(!1),y=B(null);async function C(){f(v,!0);const M={};for(const j of i)try{M[j]=await O("check_model_downloaded",{modelSize:j,modelDir:e().engine.whisper_cpp.model_dir})}catch(z){console.error("Failed to check download status for model "+j,z),M[j]=!1}f(h,M,!0),f(v,!1)}async function k(M){if(!a(x)){f(x,!0);try{await O("download_model",{modelSize:M,modelDir:e().engine.whisper_cpp.model_dir}),a(h)[M]=!0}catch(j){alert(`Failed to download model: ${j}`)}finally{f(x,!1)}}}async function S(){n();const M=e().engine.whisper_cpp.model_size;a(h)[M]||await k(M)}async function D(){const M=e().engine.whisper_cpp.model_dir;if(!M){f(y,null);return}const j=await O("check_directory_exists",{path:M});f(y,j?null:"This folder does not exist. Please create it first or leave blank for the default location.",!0),a(y)||await C()}function V(){n()}function $(M){M.key==="Enter"&&M.currentTarget.blur()}rt(async()=>{C(),f(d,await O("cuda_enabled"),!0),!a(d)&&e().engine.whisper_cpp.device==="cuda"&&(e(e().engine.whisper_cpp.device="auto",!0),n())});var F=Pc(),b=r(o(F),2);{var L=M=>{var j=wc(),z=r(o(j),2),I=r(o(z),2),Q=r(o(I)),ce=o(Q);G(()=>X(ce,e().engine.whisper_cpp.model_size)),g(M,j)};U(b,M=>{e().engine.backend!=="moonshine"&&!a(v)&&!a(h)[e().engine.whisper_cpp.model_size]&&M(L)})}var H=r(b,2),se=r(o(H),2),oe=r(o(se),2);bt(oe,{get options(){return l},onchange:n,get value(){return e().engine.backend},set value(M){e(e().engine.backend=M,!0)}});var ue=r(se,2),w=r(o(ue),2);bt(w,{get options(){return c},onchange:n,get value(){return e().engine.inference_mode},set value(M){e(e().engine.inference_mode=M,!0)}});var J=r(H,2);{var q=M=>{var j=Ac(),z=r(o(j),2),I=r(o(z),2);bt(I,{get options(){return a(u)},onchange:S,get value(){return e().engine.whisper_cpp.model_size},set value(A){e(e().engine.whisper_cpp.model_size=A,!0)}});var Q=r(z,2),ce=o(Q);{var ie=A=>{var T=xc();g(A,T)},W=A=>{var T=kc(),Z=o(T);G(()=>{var Se;return X(Z,`⏳ Downloading ${(Se=e().engine.whisper_cpp.model_size)!=null?Se:""} (GGUF format)...`)}),g(A,T)},ee=A=>{var T=Tc();g(A,T)},ae=A=>{var T=Ec(),Z=r(o(T),2);K("click",Z,()=>k(e().engine.whisper_cpp.model_size)),g(A,T)};U(ce,A=>{a(v)?A(ie):a(x)?A(W,1):a(h)[e().engine.whisper_cpp.model_size]?A(ee,2):A(ae,-1)})}var ve=r(Q,2),re=r(o(ve),2);bt(re,{get options(){return a(p)},onchange:n,get value(){return e().engine.whisper_cpp.device},set value(A){e(e().engine.whisper_cpp.device=A,!0)}});var ne=r(ve,2),pe=r(o(ne),2);let Ie;var P=r(pe,2);{var Y=A=>{var T=Sc(),Z=o(T);G(()=>X(Z,a(y))),g(A,T)};U(P,A=>{a(y)&&A(Y)})}var te=r(ne,2),de=r(o(te),2);G(()=>Ie=ye(pe,1,"svelte-nfi4ak",null,Ie,{"field-input-error":!!a(y)})),K("change",pe,V),Ms("blur",pe,D),K("keydown",pe,$),ge(pe,()=>e().engine.whisper_cpp.model_dir,A=>e(e().engine.whisper_cpp.model_dir=A,!0)),K("change",de,n),ge(de,()=>e().engine.whisper_cpp.threads,A=>e(e().engine.whisper_cpp.threads=A,!0)),g(M,j)},N=M=>{var j=Cc(),z=r(o(j),2),I=r(o(z),2);bt(I,{get options(){return _},onchange:n,get value(){return e().engine.moonshine.model_size},set value(ie){e(e().engine.moonshine.model_size=ie,!0)}});var Q=r(z,2),ce=r(o(Q),2);K("change",ce,n),ge(ce,()=>e().engine.moonshine.language,ie=>e(e().engine.moonshine.language=ie,!0)),g(M,j)};U(J,M=>{e().engine.backend!=="moonshine"?M(q):M(N,-1)})}g(s,F),Ye()}Wt(["click","change","keydown"]);var Oc=E("
"),Ic=E('
'),Dc=E('

Audio

Input Device

Microphone Monitor

Only opens the microphone stream when recording is triggered. If your first words are being clipped due to hardware delay, disable this feature to keep the stream always open.

Voice Activity Detection

Processing

');function Lc(s,t){$e(t,!0);let e=qe(t,"cfg",15);function n(){Es.set(!0)}let i=B(Be([])),l=he(()=>[{value:-1,label:"Default system device"},...a(i).map(ne=>({value:ne.index,label:ne.name}))]),c=B(0),u=B(0),d=B(0),p=0,_=B(null),h;function v(){const ne=a(c);ne>a(u)?f(u,a(u)+(ne-a(u))*.45):f(u,a(u)+(ne-a(u))*.12),a(u)>a(d)?(f(d,a(u),!0),p=25):p>0?p--:f(d,Math.max(0,a(d)-.012),!0),h=requestAnimationFrame(v)}rt(async()=>{f(i,await O("list_audio_devices"),!0);try{await O("start_monitoring_audio"),f(_,await gt("audio-level",ne=>{f(c,ne.payload,!0)}),!0)}catch(ne){console.error("Failed to start audio monitoring:",ne)}h=requestAnimationFrame(v)}),Qr(async()=>{h&&cancelAnimationFrame(h),a(_)&&a(_)();try{await O("stop_monitoring_audio")}catch(ne){console.error("Failed to stop audio monitoring:",ne)}});var x=Dc(),y=r(o(x),2),C=r(o(y),2),k=r(o(C),2);{let ne=he(()=>{var pe;return(pe=e().audio.input_device_index)!=null?pe:-1});bt(k,{get value(){return a(ne)},get options(){return a(l)},onchange:pe=>{e(e().audio.input_device_index=pe<0?null:pe,!0),n()}})}var S=r(C,2),D=o(S),V=r(o(D),2);let $;var F=r(V,2),b=o(F),L=r(D,2),H=o(L),se=o(H);ht(se,16,()=>Array(24),ft,(ne,pe,Ie)=>{const P=he(()=>Ie/24),Y=he(()=>a(u)>=a(P)),te=he(()=>Ie>=17);var de=Oc();let A;G(()=>A=ye(de,1,"vu-segment svelte-1msnq7i",null,A,{active:a(Y),orange:a(te)})),g(ne,de)});var oe=r(se,2);{var ue=ne=>{var pe=Ic();G(Ie=>$s(pe,`left: ${Ie!=null?Ie:""}%;`),[()=>Math.min(.99,a(d))*100]),g(ne,pe)};U(oe,ne=>{a(d)>.01&&ne(ue)})}var w=r(S,2),J=r(o(w),2),q=r(y,2),N=r(o(q),2),M=r(o(N),2),j=r(M,2),z=o(j),I=r(N,2),Q=r(o(I),2),ce=r(q,2),ie=r(o(ce),2),W=r(o(ie),2),ee=r(ie,2),ae=r(o(ee),2),ve=r(ae,2),re=o(ve);G((ne,pe)=>{$=ye(V,1,"vu-status-dot svelte-1msnq7i",null,$,{active:a(c)>.005}),X(b,a(c)>.005?"Signal Detected":"Listening..."),X(z,ne),X(re,`${pe!=null?pe:""}×`)},[()=>e().audio.vad_threshold.toFixed(2),()=>e().audio.gain.toFixed(1)]),K("change",J,n),Xe(J,()=>e().audio.dynamic_stream,ne=>e(e().audio.dynamic_stream=ne,!0)),K("change",M,n),ge(M,()=>e().audio.vad_threshold,ne=>e(e().audio.vad_threshold=ne,!0)),K("change",Q,n),ge(Q,()=>e().audio.min_silence_duration_ms,ne=>e(e().audio.min_silence_duration_ms=ne,!0)),K("change",W,n),Xe(W,()=>e().audio.noise_suppression,ne=>e(e().audio.noise_suppression=ne,!0)),K("change",ae,n),ge(ae,()=>e().audio.gain,ne=>e(e().audio.gain=ne,!0)),g(s,x),Ye()}Wt(["change"]);var Nc=E(''),Rc=E(' '),Fc=E('
Shell Executor Settings
Terminal Command Shell command

Use inside the command string as a placeholder to substitute transcribed speech.

'),Wc=E('
Local File Writer Settings
'),zc=E('
Network Socket settings
'),qc=E('
FIFO Named Pipe settings
'),Vc=E('
Linux DBus emitter settings
'),Bc=E(''),Uc=E(''),jc=E(' '),Hc=E('
Method
Custom Post Body JSON Template

Must be valid JSON. Use to substitute transcribed speech.

',1),Kc=E(' '),$c=E('
Webhook Secret Token (HMAC)
Custom Webhook Body JSON Template

Must be valid JSON. Use to substitute transcribed speech.

',1),Yc=E('
API Endpoint Client settings
REST endpoint URL
'),Gc=E(' '),Jc=E('
MCP Server Client settings
Custom Socket/Pipe Path Optional override

Leave empty to use defaults (/tmp/voxctrl-mcp.sock on Linux, \\\\.\\pipe\\voxctrl-mcp on Windows).

MCP Tool Name
Custom Tool Arguments JSON Template

Must be valid JSON. Use to substitute transcribed speech.

'),Zc=E(''),Xc=E(''),Qc=E('

Post-Processing & Output Tuning
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⚠️ Conflict detected: Some key bindings share the same key combination and gesture type. Disabling one of the conflicting binds will fix the problem.
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⌨

No keyboard bindings defined. Create a binding to link a physical hotkey to a routing target!

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Press the trigger key now...
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Subkey Trigger (Pressed Key)
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Prompt template MUST contain the placeholder. Leave empty to inherit the default user prompt.

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System Prompt Override LLM Prompt

Overrides the default system prompt configured in Settings → OpenAI API. Leave empty to inherit it.

User Prompt Template LLM Prompt
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Assign to Output Targets
OpenAI API LLM Post-Processing
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Hotkey Bindings

Bind physical keyboard combinations to your output targets. Each hotkey supports customizable triggers (double-taps, holding keys, etc.)

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Speech: Plays transcribed text via TTS
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🎯

No output targets defined. Create one to route your transcription output!

'),Vu=E('

Output Targets

Define routing destinations where transcribed text is typed, copied, piped, or sent over network sockets.

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Use CUDA GPU acceleration (ONNX Runtime). Falls back to CPU if unavailable.

',1),ju=E('
Run speed
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'),Ku=E('⏳ Checking local voice files...'),$u=E(' '),Yu=E('
'),Gu=E('

'),Ju=E('

Piper Voice

Voice directory (leave blank for default)

Default voice directory: ~/.local/share/voxctrl/piper-voices/

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'),ed=E('

'),td=E(`

Pocket-TTS Voice

HuggingFace access token

Pocket-TTS model weights are hosted on a gated HuggingFace repo. Create a token at huggingface.co/settings/tokens and accept the license at huggingface.co/kyutai/pocket-tts before downloading.

Custom voice directory (leave blank for default)

Drop a .wav reference clip into this folder to add it to the voice list — - the filename (without extension) becomes the voice's id, e.g. narrator.wav adds - "Narrator (Custom)". Naming a clip after a built-in voice (e.g. alba.wav) replaces - that voice's reference clip. Default: ~/.local/share/voxctrl/pocket-tts-voices/

`),sd=E('
'),ad=E(' '),nd=E('
(Click / Tab here to record a new stop key)',1),id=E(''),rd=E('

Text to Speech

TTS Engine

Playback

Stop Key Bind

Press a key combo to immediately stop TTS playback — works even when this window is hidden.

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OpenAI API LLM Post-Processing

Connect to any OpenAI-compatible API server — a local server or a hosted - provider. The URL defaults to a local server; point it anywhere you like and - supply an API key when the server requires one.

Connection

Default Prompts

The system prompt tells the model how to transform your speech. The user - prompt is the message sent to the model — it must contain , which is replaced with whatever you dictate. - Built-in presets are read-only; choose Custom to edit the - prompts yourself.

System Prompt
User Prompt
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→
'),gd=E('

No snippets defined.

'),bd=E('

Features & Post-Processing

Text cleanup

Custom Dictionary

Provide a comma-separated list of words (e.g. names or jargon like "Waylin, Rufer, Enola, Kenz") that are hard to spell. The transcription process will correct these in the final text.

Snippets

Type a trigger word → it expands to the replacement text.

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About VoxCtrl

VoxCtrl

Native, on-device voice-to-text for Linux and Windows. - Uses whisper.cpp and Moonshine for offline transcription and routes speech to any destination.

System

FrontendSvelte 5 + Tauri 2
BackendRust (Tokio async)
Inferencewhisper.cpp & Moonshine
Config~/.config/voxctrl/
Models~/.local/share/voxctrl/models/
MCP socket/tmp/voxctrl-mcp.sock

Open Source Attributions

This application is built possible by these outstanding open-source projects:

whisper.cpp MIT License
Useful Sensors Moonshine Apache 2.0
Tauri Framework MIT / Apache 2.0
Svelte & Vite MIT License
Piper TTS MIT License
Pocket-TTS (Kyutai Labs) MIT / Apache 2.0
Candle MIT / Apache 2.0
whisper-rs MIT License
Rust, Tokio & CPAL MIT / Apache 2.0

Links

GitHub Repository
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'),Ed=E(''),Sd=E(' 🛑 Stop',1),Ad=E('🎙️ Record'),Cd=E('
Unsaved changes detected
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'),Dd=E('
VOXCTRL VOICE CARD
TARGET •••• VOX
');function Ld(s,t){$e(t,!0);const e=()=>kt(Et,"$status",n),[n,i]=hs();let l=qe(t,"recording",3,!1);qe(t,"speaking",3,!1);let c=qe(t,"active",3,!0);const u=20,d=6;let p=B(Be(Array(u).fill(0))),_=null,h,v=0,x=0;const y=he(()=>e().audio_ready!==!1),C=he(()=>e().active_target_label||"Focused Window");rt(()=>{gt("audio-level",z=>{v=Math.min(1,z.payload*100)}).then(z=>{_=z});let N=0;const M=new Float32Array(u);function j(){N+=.016,x+=(v-x)*.35,v*=.86;for(let z=0;zM[z]?I:M[z]*.86}f(p,Array.from(M),!0),h=requestAnimationFrame(j)}return h=requestAnimationFrame(j),()=>{_&&_(),cancelAnimationFrame(h)}});function k(N){return N===0?"red":N<=2?"amber":"green"}var S=Dd();let D;var V=o(S);let $;var F=r(o(V),8),b=r(o(F),2);{var L=N=>{var M=Ee("PROC");g(N,M)},H=N=>{var M=Ee("INIT");g(N,M)},se=N=>{var M=Ee("REC");g(N,M)};U(b,N=>{e().processing?N(L):l()&&!a(y)?N(H,1):N(se,-1)})}var oe=r(F,2);ht(oe,21,()=>a(p),ft,(N,M)=>{var j=Id();ht(j,21,()=>Array(d),ft,(z,I,Q)=>{var ce=Od();let ie;G(W=>ie=ye(ce,1,`dot ${W!=null?W:""}`,"svelte-1pt4ba2",ie,{lit:d-Q<=a(M)*d}),[()=>k(Q)]),g(z,ce)}),g(N,j)});var ue=r(oe,4),w=o(ue);{var J=N=>{var M=Ee("Reading the card…");g(N,M)},q=N=>{var M=Ee();G(()=>X(M,a(C))),g(N,M)};U(w,N=>{e().processing?N(J):N(q,-1)})}G(()=>{D=ye(S,1,"card-scene svelte-1pt4ba2",null,D,{on:c()}),$=ye(V,1,"card svelte-1pt4ba2",null,$,{processing:e().processing,initializing:l()&&!a(y)})}),g(s,S),Ye(),i()}var Nd=E('
'),Rd=E('
WAVEFORM // OSC-01 TGT ▸
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⌖
');function zd(s,t){$e(t,!0);const e=()=>kt(Et,"$status",n),[n,i]=hs();let l=qe(t,"recording",3,!1),c=qe(t,"active",3,!0),u=B(0),d=null,p,_=0,h=0;const v=he(()=>e().audio_ready!==!1),x=he(()=>e().active_target_label||"Focused Window");rt(()=>{gt("audio-level",J=>{_=Math.min(1,J.payload*100)}).then(J=>{d=J});function w(){h+=(_-h)*.35,_*=.86,f(u,h,!0),p=requestAnimationFrame(w)}return p=requestAnimationFrame(w),()=>{d&&d(),cancelAnimationFrame(p)}});var y=Wd();let C;var k=r(o(y),2),S=r(o(k),2),D=o(S),V=o(D);{var $=w=>{var J=Ee("PULSE // ANALYZING");g(w,J)},F=w=>{var J=Ee("PULSE // ACQUIRING");g(w,J)},b=w=>{var J=Ee("PULSE // TARGET LOCK");g(w,J)};U(V,w=>{e().processing?w($):l()&&!a(v)?w(F,1):w(b,-1)})}var L=r(D,2),H=o(L);{var se=w=>{var J=Ee("Decoding transmission…");g(w,J)},oe=w=>{var J=Ee("Connecting mic…");g(w,J)},ue=w=>{var J=Ee();G(()=>X(J,a(x))),g(w,J)};U(H,w=>{e().processing?w(se):l()&&!a(v)?w(oe,1):w(ue,-1)})}G(()=>{var w;C=ye(y,1,"radar svelte-13tl5wx",null,C,{on:c(),processing:e().processing,initializing:l()&&!a(v)}),$s(y,`--lvl: ${(w=a(u))!=null?w:""}`)}),g(s,y),Ye(),i()}var qd=E('
'),Vd=E('
OCEAN WAVE
');function Bd(s,t){$e(t,!0);const e=()=>kt(Et,"$status",n),[n,i]=hs();let l=qe(t,"recording",3,!1);qe(t,"speaking",3,!1);let c=qe(t,"active",3,!0);const u=380,d=90;let p=B(""),_=B(""),h=B(""),v=B(60),x=null,y,C=0,k=0;const S=he(()=>e().audio_ready!==!1),D=he(()=>e().active_target_label||"Focused Window");function V(W,ee,ae,ve){let re=`M 0 ${d} L 0 ${ve.toFixed(1)}`;for(let ne=0;ne<=u;ne+=8){const pe=ve+Math.sin(ne*ee+ae)*W;re+=` L ${ne} ${pe.toFixed(1)}`}return re+` L ${u} ${d} Z`}rt(()=>{gt("audio-level",ae=>{C=Math.min(1,ae.payload*100)}).then(ae=>{x=ae});let W=0;function ee(){W+=.016,k+=(C-k)*.35,C*=.86;const ae=k,ve=e().processing?4+2.5*Math.sin(W*1.6):0,re=64-ae*20-ve,ne=56-ae*24-ve*1.2,pe=47-ae*28-ve*1.4,Ie=(e().processing?5:2.5)+ae*14,P=(e().processing?6:2)+ae*18,Y=(e().processing?7:1.5)+ae*22;f(p,V(Ie,.014,W*2.1,re),!0),f(_,V(P,.022,-W*3,ne),!0),f(h,V(Y,.018,W*3.9,pe),!0),f(v,38+pe-21+Math.sin(W*2.2)*2.5),y=requestAnimationFrame(ee)}return y=requestAnimationFrame(ee),()=>{x&&x(),cancelAnimationFrame(y)}});var $=Vd();let F;var b=r(o($),4),L=o(b);{var H=W=>{var ee=Ee("deep current — processing");g(W,ee)},se=W=>{var ee=Ee("low tide — preparing");g(W,ee)},oe=W=>{var ee=Ee("high tide — listening");g(W,ee)};U(L,W=>{e().processing?W(H):l()&&!a(S)?W(se,1):W(oe,-1)})}var ue=r(b,2),w=o(ue);_t(w,"viewBox","0 0 380 90");var J=o(w),q=r(J),N=r(q),M=r(ue,2);ht(M,16,()=>[0,1,2],ft,(W,ee)=>{var ae=qd();G(()=>$s(ae,`left: ${64+ee*112}px; animation-delay: ${ee*1.4}s;`)),g(W,ae)});var j=r(M,2),z=r(o(j),2),I=o(z);{var Q=W=>{var ee=Ee("Sounding the depths…");g(W,ee)},ce=W=>{var ee=Ee("Casting off…");g(W,ee)},ie=W=>{var ee=Ee();G(()=>X(ee,a(D))),g(W,ee)};U(I,W=>{e().processing?W(Q):l()&&!a(S)?W(ce,1):W(ie,-1)})}G(()=>{var W;F=ye($,1,"ocean svelte-1orgnnz",null,F,{on:c(),processing:e().processing}),_t(J,"d",a(p)),_t(q,"d",a(_)),_t(N,"d",a(h)),$s(j,`top: ${(W=a(v))!=null?W:""}px;`)}),g(s,$),Ye(),i()}var Ud=E("
"),jd=E('
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'),$d=E('
SPECTRUM // EQ-16 OUT ▸
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VOXCTRL — /dev/mic0
');function Jd(s,t){$e(t,!0);const e=()=>kt(Et,"$status",n),[n,i]=hs();let l=qe(t,"recording",3,!1),c=qe(t,"active",3,!0);const u=20;let d=B(Be("·".repeat(u))),p=B(!1),_=null,h,v=0,x=0;const y=he(()=>e().audio_ready!==!1),C=he(()=>e().active_target_label||"Focused Window");function k(I,Q,ce,ie,W){const ee=u;if(ie){const ae=2*ee,ve=Math.floor(Q*10)%ae,re=vepe===re?"█":"·").join("")}else{if(ce&&!W)return Array.from({length:ee},(ae,ve)=>ve%4===0?"·":" ").join("");if(ce){const ae=Math.round(Math.min(1,Math.max(0,I))*ee);return Array.from({length:ee},(ve,re)=>re{gt("audio-level",ce=>{v=Math.min(1,ce.payload*100)}).then(ce=>{_=ce});let I=0;function Q(){I+=.016,x+=(v-x)*.35,v*=.86;const ce=e().audio_ready!==!1;f(d,k(x,I,l(),e().processing,ce),!0),f(p,Math.sin(I*5.5)>0),h=requestAnimationFrame(Q)}return h=requestAnimationFrame(Q),()=>{_&&_(),cancelAnimationFrame(h)}});var S=Gd();let D;var V=r(o(S),2),$=o(V),F=o($),b=r($,2);let L;var H=o(b);{var se=I=>{var Q=Ee("[PROC]");g(I,Q)},oe=I=>{var Q=Ee("[INIT]");g(I,Q)},ue=I=>{var Q=Ee("[REC ]");g(I,Q)};U(H,I=>{e().processing?I(se):l()&&!a(y)?I(oe,1):I(ue,-1)})}var w=r(H),J=r(b,2),q=o(J);{var N=I=>{var Q=Ee("transcribing audio stream");g(I,Q)},M=I=>{var Q=Ee("connecting input device");g(I,Q)},j=I=>{var Q=Ee("streaming to output");g(I,Q)};U(q,I=>{e().processing?I(N):l()&&!a(y)?I(M,1):I(j,-1)})}var z=r(q,1,!0);G(()=>{var I,Q;D=ye(S,1,"terminal svelte-pvtvge",null,D,{on:c()}),X(F,`$ voxctrl listen --target "${(I=a(C))!=null?I:""}"`),L=ye(b,1,"line2 svelte-pvtvge",null,L,{processing:e().processing,standby:l()&&!a(y)}),X(w,` ${(Q=a(d))!=null?Q:""}`),X(z,a(p)?"_":" ")}),g(s,S),Ye(),i()}var Zd=E('
VU
-20
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+3
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⏰ ⚡
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General

History

When enabled, completed transcripts are saved to the History log. Disabled by default.

MCP Server

Socket: /tmp/voxctrl-mcp.sock (Linux) / \\\\.\\pipe\\voxctrl-mcp (Windows)

AT-SPI2 (Linux)

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'),gc=T('
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'),mc=T('

Visual & Feedback

Overlay & HUD

Alerts & OS Integration

Application Window

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⚠️
Voice Model Not Downloaded

The configuration specifies a voice model ( ) that is not currently downloaded. Please select the model size to + use and download it below, or choose another model.

`),xc=T('⏳ Checking local model files...'),kc=T(' '),Tc=T('✔ Model downloaded and ready'),Ec=T('
❌ Model file missing
'),Sc=T('

'),Ac=T('

Whisper.cpp Settings

Model directory (leave blank for default)

Default model directory: ~/.local/share/voxctrl/models/

'),Cc=T(`
⚠️
Moonshine backend not included in this build

This build was compiled without Moonshine, so selecting it will fall + back to Whisper.cpp (using the Whisper model configured above). + Rebuild with --features moonshine to enable it.

`),Pc=T('⏳ Checking local model files...'),Mc=T(' '),Oc=T('✔ Model downloaded'),Dc=T('
Model not downloaded
'),Ic=T('
'),Lc=T('

Moonshine Settings

'),Nc=T('

Inference Engine

Backend

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"),zc=T('
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Audio

Input Device

Microphone Monitor

Only opens the microphone stream when recording is triggered. If your first words are being clipped due to hardware delay, disable this feature to keep the stream always open.

Voice Activity Detection

Processing

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Shell Executor Settings
Terminal Command Shell command

Use inside the command string as a placeholder to substitute transcribed speech.

'),jc=T('
Local File Writer Settings
'),Hc=T('
Network Socket settings
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FIFO Named Pipe settings
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Linux DBus emitter settings
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Method
Custom Post Body JSON Template

Must be valid JSON. Use to substitute transcribed speech.

',1),Zc=T(' '),Qc=T('
Webhook Secret Token (HMAC)
Custom Webhook Body JSON Template

Must be valid JSON. Use to substitute transcribed speech.

',1),eu=T('
API Endpoint Client settings
REST endpoint URL
'),tu=T(' '),su=T('
MCP Server Client settings
Custom Socket/Pipe Path Optional override

Leave empty to use defaults (/tmp/voxctrl-mcp.sock on Linux, \\\\.\\pipe\\voxctrl-mcp on Windows).

MCP Tool Name
Custom Tool Arguments JSON Template

Must be valid JSON. Use to substitute transcribed speech.

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Post-Processing & Output Tuning
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⚠️ Conflict detected: Some key bindings share the same key combination and gesture type. Disabling one of the conflicting binds will fix the problem.
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'),fu=T('
⌨

No keyboard bindings defined. Create a binding to link a physical hotkey to a routing target!

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',1),gu=T(''),bu=T('
'),mu=T(``),yu=T(''),wu=T('
'),xu=T(' '),ku=T('
(Click / Tab here to record)',1),Tu=T(''),Eu=T('
Press the trigger key now...
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Subkey Trigger (Pressed Key)
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Prompt template MUST contain the placeholder. Leave empty to inherit the default user prompt.

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System Prompt Override LLM Prompt

Overrides the default system prompt configured in Settings → OpenAI API. Leave empty to inherit it.

User Prompt Template LLM Prompt
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Assign to Output Targets
OpenAI API LLM Post-Processing
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Hotkey Bindings

Bind physical keyboard combinations to your output targets. Each hotkey supports customizable triggers (double-taps, holding keys, etc.)

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A(){O.style.height="auto",O.style.height=`${O.scrollHeight}px`}O.addEventListener("input",A);const te=setTimeout(A,0);return{update(){A()},destroy(){clearTimeout(te),O.removeEventListener("input",A)}}}ut(async()=>{f(e,await M("get_targets"),!0),f(n,await M("get_bindings"),!0)});async function b(){try{await M("save_targets",{targets:a(e)}),console.log("Targets auto-saved successfully!")}catch(O){console.error("Failed to save targets:",O)}}async function L(){try{await M("save_bindings",{bindings:a(n)}),console.log("Bindings auto-saved successfully!")}catch(O){console.error("Failed to save bindings:",O)}}function G(O){return(O.target_ids&&O.target_ids.length>0?O.target_ids:[O.target_id]).map(te=>{const Se=a(e).find(et=>et.id===te);return Se?Se.label:te==="default"?"Focused Window":te}).join(", ")}function ae(O){const A=a(e).find(te=>te.id===O);return A?`${A.label} (${A.delivery})`:O==="default"?"Focused Window":O}function oe(){if(a(e).length===0){alert("Please create at least one Output 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O=a(e).find(te=>!a(i).target_ids.includes(te.id)),A=O?O.id:a(e)[0].id;a(i).target_ids=[...a(i).target_ids,A]}function V(O){!a(i)||!a(i).target_ids||(a(i).target_ids=a(i).target_ids.filter((A,te)=>te!==O))}async function N(){if(a(i)){if(a(i).id.trim()===""){alert("Binding ID cannot be empty.");return}if(a(i).keys.length===0){alert("Please capture at least one hotkey before saving.");return}if(a(i).gesture==="chord"&&(!a(i).subkey||a(i).subkey.trim()==="")){alert("Please capture a subkey trigger for the chord combo before saving.");return}if(a(v)&&a(y)==="custom"&&!a(C).includes("{text}")){alert(`LLM Configuration Error: +Your custom prompt template MUST contain the '{text}' placeholder so the model knows where to insert the transcribed text. + +Example: +write a haiku about 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Speech: Plays transcribed text via TTS
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🎯

No output targets defined. Create one to route your transcription output!

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Output Targets

Define routing destinations where transcribed text is typed, copied, piped, or sent over network sockets.

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Use CUDA GPU acceleration (ONNX Runtime). Falls back to CPU if unavailable.

',1),Ju=T('
Run speed
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'),Zu=T('⏳ Checking local voice files...'),Qu=T(' '),ed=T('
'),td=T('

'),sd=T('

Piper Voice

Voice directory (leave blank for default)

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'),rd=T('

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Pocket-TTS Voice

HuggingFace access token

Pocket-TTS model weights are hosted on a gated HuggingFace repo. Create a token at huggingface.co/settings/tokens and accept the license at huggingface.co/kyutai/pocket-tts before downloading.

Custom voice directory (leave blank for default)

Drop a .wav reference clip into this folder to add it to the voice list — + the filename (without extension) becomes the voice's id, e.g. narrator.wav adds + "Narrator (Custom)". Naming a clip after a built-in voice (e.g. alba.wav) replaces + that voice's reference clip. Default: ~/.local/share/voxctrl/pocket-tts-voices/

`),od=T('
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(Click / Tab here to record a new stop key)',1),dd=T(''),vd=T('

Text to Speech

TTS Engine

Playback

Stop Key Bind

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OpenAI API LLM Post-Processing

Connect to any OpenAI-compatible API server — a local server or a hosted + provider. The URL defaults to a local server; point it anywhere you like and + supply an API key when the server requires one.

Connection

Default Prompts

The system prompt tells the model how to transform your speech. The user + prompt is the message sent to the model — it must contain , which is replaced with whatever you dictate. + Built-in presets are read-only; choose Custom to edit the + prompts yourself.

System Prompt
User Prompt
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→
'),kd=T('

No snippets defined.

'),Td=T('

Features & Post-Processing

Text cleanup

Custom Dictionary

Provide a comma-separated list of words (e.g. names or jargon like "Waylin, Rufer, Enola, Kenz") that are hard to spell. The transcription process will correct these in the final text.

Snippets

Type a trigger word → it expands to the replacement text.

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About VoxCtrl

VoxCtrl

Native, on-device voice-to-text for Linux and Windows. + Uses whisper.cpp and Moonshine for offline transcription and routes speech to any destination.

System

FrontendSvelte 5 + Tauri 2
BackendRust (Tokio async)
Inferencewhisper.cpp & Moonshine
Config~/.config/voxctrl/
Models~/.local/share/voxctrl/models/
MCP socket/tmp/voxctrl-mcp.sock

Open Source Attributions

This application is built possible by these outstanding open-source projects:

whisper.cpp MIT License
Useful Sensors Moonshine Apache 2.0
Tauri Framework MIT / Apache 2.0
Svelte & Vite MIT License
Piper TTS MIT License
Pocket-TTS (Kyutai Labs) MIT / Apache 2.0
Candle MIT / Apache 2.0
whisper-rs MIT License
Rust, Tokio & CPAL MIT / Apache 2.0

Links

GitHub Repository
`);function Pd(s,t){Je(t,!0);let e=W("0.1.0");ut(async()=>{try{f(e,await Sd(),!0)}catch(p){console.error("Failed to fetch app version:",p)}});var n=Cd(),i=r(o(n),2),l=o(i),c=r(l,2),u=r(o(c),2),d=o(u);J(()=>{var p;mt(l,"src",Ad),ee(d,`Version ${(p=a(e))!=null?p:""} — Rust + Tauri Edition`)}),g(s,n),Xe()}var Md=T('
'),Od=T(''),Dd=T(' 🛑 Stop',1),Id=T('🎙️ Record'),Ld=T('
Unsaved changes detected
'),Nd=T('
');function Rd(s,t){Je(t,!0);const e=()=>St(Yt,"$config",l),n=()=>St(Ct,"$status",l),i=()=>St(Es,"$configDirty",l),[l,c]=_s();let u=W("general");const d=[{id:"general",label:"General",icon:"⚙️"},{id:"engine",label:"Engine",icon:"🧠"},{id:"hotkeys",label:"Hotkeys",icon:"⌨️"},{id:"targets",label:"Output Targets",icon:"🎯"},{id:"visual",label:"Visual",icon:"🎨"},{id:"audio",label:"Audio",icon:"🔊"},{id:"tts",label:"TTS",icon:"🗣️"},{id:"features",label:"Features",icon:"✨"},{id:"openai",label:"OpenAI API",icon:"🤖"},{id:"about",label:"About",icon:"ℹ️"}];async function p(){await nl(e())}async function _(){await M("toggle_recording")}let h=a(u),v=W(null);ka(()=>{var K,$;const P=a(u);P!==h&&(h=P,a(v)&&(a(v).scrollTop=0),($=(K=e())==null?void 0:K.audio)!=null&&$.dynamic_stream&&n().recording&&M("stop_recording").catch(ce=>{console.error("Failed to stop recording on activeTab change:",ce)}))}),ut(()=>{let P;P=fi.subscribe(K=>{if(K){const $=e();if($&&$.engine&&$.engine.whisper_cpp){const ce=$.engine.whisper_cpp.model_size;$.engine.backend!=="moonshine"&&M("check_model_downloaded",{modelSize:ce}).then(async O=>{if(!O){f(u,"engine");try{const{getCurrentWindow:A}=await Qo(async()=>{const{getCurrentWindow:Se}=await Promise.resolve().then(()=>Tv);return{getCurrentWindow:Se}},void 0),te=A();await te.show(),await te.setFocus()}catch(A){console.error("Failed to programmatically show settings window on startup:",A)}}}).catch(O=>{console.error("Failed to check model download status on startup:",O)})}P?P():setTimeout(()=>P(),0)}})});var k=Nd(),y=o(k),C=o(y),x=o(C),S=r(C,2);bt(S,21,()=>d,gt,(P,K)=>{var $=Od();let ce;var O=o($),A=o(O),te=r(O,2),Se=o(te),et=r(te,2);{var je=He=>{var Ae=Md();g(He,Ae)};B(et,He=>{a(u)===a(K).id&&He(je)})}J(()=>{ce=ye($,1,"nav-btn svelte-myrvk6",null,ce,{active:a(u)===a(K).id}),ee(A,a(K).icon),ee(Se,a(K).label)}),H("click",$,()=>f(u,a(K).id,!0)),g(P,$)});var D=r(S,2),U=o(D);let Y;var z=o(U),b=o(z),L=r(o(b),2),G=o(L),ae=r(b,2),oe=o(ae),de=r(z,2);let w;var X=o(de);{var V=P=>{var K=Dd();g(P,K)},N=P=>{var K=Id();g(P,K)};B(X,P=>{n().recording?P(V):P(N,-1)})}var F=r(y,2),se=o(F),j=o(se);{var I=P=>{fc(P,{get cfg(){return Xs(),e()},set cfg(K){Js(Yt,K)}})},re=P=>{Rc(P,{get cfg(){return Xs(),e()},set cfg(K){Js(Yt,K)}})},ve=P=>{Nu(P,{})},le=P=>{Yu(P,{})},E=P=>{yc(P,{get cfg(){return Xs(),e()},set cfg(K){Js(Yt,K)}})},q=P=>{qc(P,{get cfg(){return Xs(),e()},set cfg(K){Js(Yt,K)}})},Z=P=>{pd(P,{get cfg(){return Xs(),e()},set cfg(K){Js(Yt,K)}})},ue=P=>{Ed(P,{get cfg(){return Xs(),e()},set cfg(K){Js(Yt,K)}})},ie=P=>{wd(P,{get cfg(){return Xs(),e()},set cfg(K){Js(Yt,K)}})},ne=P=>{Pd(P,{})};B(j,P=>{a(u)==="general"?P(I):a(u)==="engine"?P(re,1):a(u)==="hotkeys"?P(ve,2):a(u)==="targets"?P(le,3):a(u)==="visual"?P(E,4):a(u)==="audio"?P(q,5):a(u)==="tts"?P(Z,6):a(u)==="features"?P(ue,7):a(u)==="openai"?P(ie,8):a(u)==="about"&&P(ne,9)})}sl(se,P=>f(v,P),()=>a(v));var _e=r(se,2);{var Ie=P=>{var K=Ld(),$=r(o(K),2);H("click",$,p),g(P,K)};B(_e,P=>{i()&&P(Ie)})}J(()=>{var P;mt(x,"src",ec),Y=ye(U,1,"status-panel svelte-myrvk6",null,Y,{recording:n().recording,speaking:n().speaking}),ee(G,n().recording?"Recording":n().speaking?"Speaking":"Idle"),ee(oe,`${(P=n().word_count)!=null?P:""} words`),w=ye(de,1,"btn-record svelte-myrvk6",null,w,{active:n().recording})}),H("click",de,_),g(s,k),Xe(),c()}Vt(["click"]);var Fd=T(""),zd=T('
'),Wd=T('
VOXCTRL VOICE CARD
TARGET •••• VOX
');function qd(s,t){Je(t,!0);const e=()=>St(Ct,"$status",n),[n,i]=_s();let l=Ve(t,"recording",3,!1);Ve(t,"speaking",3,!1);let c=Ve(t,"active",3,!0);const u=20,d=6;let p=W(Ue(Array(u).fill(0))),_=null,h,v=0,k=0;const y=he(()=>e().audio_ready!==!1),C=he(()=>e().active_target_label||"Focused Window");ut(()=>{yt("audio-level",j=>{v=Math.min(1,j.payload*100)}).then(j=>{_=j});let N=0;const F=new Float32Array(u);function se(){N+=.016,k+=(v-k)*.35,v*=.86;for(let j=0;jF[j]?I:F[j]*.86}f(p,Array.from(F),!0),h=requestAnimationFrame(se)}return h=requestAnimationFrame(se),()=>{_&&_(),cancelAnimationFrame(h)}});function x(N){return N===0?"red":N<=2?"amber":"green"}var S=Wd();let D;var U=o(S);let Y;var z=r(o(U),8),b=r(o(z),2);{var L=N=>{var F=Ee("PROC");g(N,F)},G=N=>{var F=Ee("INIT");g(N,F)},ae=N=>{var F=Ee("REC");g(N,F)};B(b,N=>{e().processing?N(L):l()&&!a(y)?N(G,1):N(ae,-1)})}var oe=r(z,2);bt(oe,21,()=>a(p),gt,(N,F)=>{var se=zd();bt(se,21,()=>Array(d),gt,(j,I,re)=>{var ve=Fd();let le;J(E=>le=ye(ve,1,`dot ${E!=null?E:""}`,"svelte-1pt4ba2",le,{lit:d-re<=a(F)*d}),[()=>x(re)]),g(j,ve)}),g(N,se)});var de=r(oe,4),w=o(de);{var X=N=>{var F=Ee("Reading the card…");g(N,F)},V=N=>{var F=Ee();J(()=>ee(F,a(C))),g(N,F)};B(w,N=>{e().processing?N(X):N(V,-1)})}J(()=>{D=ye(S,1,"card-scene svelte-1pt4ba2",null,D,{on:c()}),Y=ye(U,1,"card svelte-1pt4ba2",null,Y,{processing:e().processing,initializing:l()&&!a(y)})}),g(s,S),Xe(),i()}var Vd=T('
'),Bd=T('
WAVEFORM // OSC-01 TGT ▸
');function Ud(s,t){Je(t,!0);const e=()=>St(Ct,"$status",n),[n,i]=_s();let l=Ve(t,"recording",3,!1),c=Ve(t,"active",3,!0);const u=126,d=500,p=78;let _=W(""),h=null,v,k=0,y=0;const C=he(()=>e().audio_ready!==!1),x=he(()=>e().active_target_label||"Focused Window");ut(()=>{yt("audio-level",ve=>{k=Math.min(1,ve.payload*100)}).then(ve=>{h=ve});const j=new Float32Array(u);let I=0;function re(){I+=.016,y+=(k-y)*.35,k*=.86;let ve=0;if(e().processing)ve=.55*Math.sin(I*9)*(.6+.4*Math.sin(I*1.7));else if(l()&&e().audio_ready===!1)ve=.05*(Math.random()*2-1);else if(l()){const E=Math.random()*2-1;ve=Math.min(1,y*1.5)*(.45*Math.sin(I*24)+.55*E)}j.copyWithin(0,1),j[u-1]=ve;let le="";for(let E=0;E{h&&h(),cancelAnimationFrame(v)}});var S=Bd();let D;var U=o(S),Y=o(U),z=r(o(Y),4),b=o(z);{var L=j=>{var I=Ee("· TRANSCRIBING");g(j,I)},G=j=>{var I=Ee("· CALIBRATING");g(j,I)},ae=j=>{var I=Ee("· LIVE TRACE");g(j,I)};B(b,j=>{e().processing?j(L):l()&&!a(C)?j(G,1):j(ae,-1)})}var oe=r(z,4),de=r(o(oe),2),w=o(de),X=r(Y,2),V=r(o(X),6);bt(V,16,()=>[1,2,3,4],gt,(j,I)=>{var re=Vd();J(()=>$s(re,`left: ${I*20}%`)),g(j,re)});var N=r(V,2);mt(N,"viewBox","0 0 500 78");var F=o(N),se=r(F);J(()=>{D=ye(S,1,"scope svelte-xjziu1",null,D,{on:c(),processing:e().processing,initializing:l()&&!a(C)}),ee(w,a(x)),mt(F,"d",a(_)),mt(se,"d",a(_))}),g(s,S),Xe(),i()}var jd=T('
⌖
');function Hd(s,t){Je(t,!0);const e=()=>St(Ct,"$status",n),[n,i]=_s();let l=Ve(t,"recording",3,!1),c=Ve(t,"active",3,!0),u=W(0),d=null,p,_=0,h=0;const v=he(()=>e().audio_ready!==!1),k=he(()=>e().active_target_label||"Focused Window");ut(()=>{yt("audio-level",X=>{_=Math.min(1,X.payload*100)}).then(X=>{d=X});function w(){h+=(_-h)*.35,_*=.86,f(u,h,!0),p=requestAnimationFrame(w)}return p=requestAnimationFrame(w),()=>{d&&d(),cancelAnimationFrame(p)}});var y=jd();let C;var x=r(o(y),2),S=r(o(x),2),D=o(S),U=o(D);{var Y=w=>{var X=Ee("PULSE // ANALYZING");g(w,X)},z=w=>{var X=Ee("PULSE // ACQUIRING");g(w,X)},b=w=>{var X=Ee("PULSE // TARGET LOCK");g(w,X)};B(U,w=>{e().processing?w(Y):l()&&!a(v)?w(z,1):w(b,-1)})}var L=r(D,2),G=o(L);{var ae=w=>{var X=Ee("Decoding transmission…");g(w,X)},oe=w=>{var X=Ee("Connecting mic…");g(w,X)},de=w=>{var X=Ee();J(()=>ee(X,a(k))),g(w,X)};B(G,w=>{e().processing?w(ae):l()&&!a(v)?w(oe,1):w(de,-1)})}J(()=>{var w;C=ye(y,1,"radar svelte-13tl5wx",null,C,{on:c(),processing:e().processing,initializing:l()&&!a(v)}),$s(y,`--lvl: ${(w=a(u))!=null?w:""}`)}),g(s,y),Xe(),i()}var Kd=T('
'),$d=T('
OCEAN WAVE
');function Yd(s,t){Je(t,!0);const e=()=>St(Ct,"$status",n),[n,i]=_s();let l=Ve(t,"recording",3,!1);Ve(t,"speaking",3,!1);let c=Ve(t,"active",3,!0);const u=380,d=90;let p=W(""),_=W(""),h=W(""),v=W(60),k=null,y,C=0,x=0;const S=he(()=>e().audio_ready!==!1),D=he(()=>e().active_target_label||"Focused Window");function U(E,q,Z,ue){let ie=`M 0 ${d} L 0 ${ue.toFixed(1)}`;for(let ne=0;ne<=u;ne+=8){const _e=ue+Math.sin(ne*q+Z)*E;ie+=` L ${ne} ${_e.toFixed(1)}`}return ie+` L ${u} ${d} Z`}ut(()=>{yt("audio-level",Z=>{C=Math.min(1,Z.payload*100)}).then(Z=>{k=Z});let E=0;function q(){E+=.016,x+=(C-x)*.35,C*=.86;const Z=x,ue=e().processing?4+2.5*Math.sin(E*1.6):0,ie=64-Z*20-ue,ne=56-Z*24-ue*1.2,_e=47-Z*28-ue*1.4,Ie=(e().processing?5:2.5)+Z*14,P=(e().processing?6:2)+Z*18,K=(e().processing?7:1.5)+Z*22;f(p,U(Ie,.014,E*2.1,ie),!0),f(_,U(P,.022,-E*3,ne),!0),f(h,U(K,.018,E*3.9,_e),!0),f(v,38+_e-21+Math.sin(E*2.2)*2.5),y=requestAnimationFrame(q)}return y=requestAnimationFrame(q),()=>{k&&k(),cancelAnimationFrame(y)}});var Y=$d();let z;var b=r(o(Y),4),L=o(b);{var G=E=>{var q=Ee("deep current — processing");g(E,q)},ae=E=>{var q=Ee("low tide — preparing");g(E,q)},oe=E=>{var q=Ee("high tide — listening");g(E,q)};B(L,E=>{e().processing?E(G):l()&&!a(S)?E(ae,1):E(oe,-1)})}var de=r(b,2),w=o(de);mt(w,"viewBox","0 0 380 90");var X=o(w),V=r(X),N=r(V),F=r(de,2);bt(F,16,()=>[0,1,2],gt,(E,q)=>{var Z=Kd();J(()=>$s(Z,`left: ${64+q*112}px; animation-delay: ${q*1.4}s;`)),g(E,Z)});var se=r(F,2),j=r(o(se),2),I=o(j);{var re=E=>{var q=Ee("Sounding the depths…");g(E,q)},ve=E=>{var q=Ee("Casting off…");g(E,q)},le=E=>{var q=Ee();J(()=>ee(q,a(D))),g(E,q)};B(I,E=>{e().processing?E(re):l()&&!a(S)?E(ve,1):E(le,-1)})}J(()=>{var E;z=ye(Y,1,"ocean svelte-1orgnnz",null,z,{on:c(),processing:e().processing}),mt(X,"d",a(p)),mt(V,"d",a(_)),mt(N,"d",a(h)),$s(se,`top: ${(E=a(v))!=null?E:""}px;`)}),g(s,Y),Xe(),i()}var Gd=T("
"),Jd=T('
');function Xd(s,t){Je(t,!0);const e=()=>St(Ct,"$status",n),[n,i]=_s();let l=Ve(t,"recording",3,!1),c=Ve(t,"active",3,!0);const u=5,d=8,p=52;let _=W(Ue(Array(u).fill(d))),h=null,v,k=0,y=0;const C=he(()=>e().audio_ready!==!1),x=he(()=>e().active_target_label||"Focused Window");function S(N,F,se,j,I,re,ve,le){const E=(F-1)/2,q=1-Math.abs(N-E)/(E+1)*.4;let Z;if(re)Z=(Math.sin(j*4-N*.85)*.5+.5)*q;else if(I&&!ve)Z=0;else if(I){const ue=Math.sin(j*3-N*.8);Z=Math.min(1,Math.max(0,se*q*(.9+.1*ue)*(.85+.3*le)))}else Z=0;return d+(p-d)*Z}ut(()=>{yt("audio-level",se=>{k=Math.min(1,se.payload*100)}).then(se=>{h=se});let N=0;function F(){N+=.016,y+=(k-y)*.35,k*=.86;const se=e().audio_ready!==!1,j=new Array(u);for(let I=0;I{h&&h(),cancelAnimationFrame(v)}});var D=Jd();let U;var Y=o(D),z=o(Y);let b;var L=r(z,2),G=o(L);{var ae=N=>{var F=Ee("PROCESSING");g(N,F)},oe=N=>{var F=Ee("STANDBY");g(N,F)},de=N=>{var F=Ee("LISTENING");g(N,F)};B(G,N=>{e().processing?N(ae):l()&&!a(C)?N(oe,1):N(de,-1)})}var w=r(Y,2);bt(w,21,()=>a(_),gt,(N,F)=>{var se=Gd();let j;J(()=>{var I;j=ye(se,1,"bar svelte-12wa5lj",null,j,{dim:l()&&!a(C),processing:e().processing}),$s(se,`height: ${(I=a(F))!=null?I:""}px`)}),g(N,se)});var X=r(w,4),V=o(X);J(()=>{U=ye(D,1,"mono svelte-12wa5lj",null,U,{on:c()}),b=ye(z,1,"dot svelte-12wa5lj",null,b,{lit:!e().processing&&!(l()&&!a(C)),processing:e().processing,standby:l()&&!a(C)}),ee(V,a(x))}),g(s,D),Xe(),i()}var Zd=T('
'),Qd=T('
SPECTRUM // EQ-16 OUT ▸
');function ev(s,t){Je(t,!0);const e=()=>St(Ct,"$status",n),[n,i]=_s();let l=Ve(t,"recording",3,!1),c=Ve(t,"active",3,!0);const u=16,d=6,p=96;let _=W(Ue(Array(u).fill(d))),h=null,v,k=0,y=0;const C=he(()=>e().audio_ready!==!1),x=he(()=>e().active_target_label||"Focused Window");function S(F,se,j,I,re,ve,le,E){const q=F/(se-1);let Z;if(ve)Z=Math.pow(Math.sin(I*2.4-q*6)*.5+.5,1.5);else if(re&&!le)Z=E*.06;else if(re){const ue=2+q*9,ie=F*.7,ne=Math.sin(I*ue+ie)*.5+.5,_e=1-q*.35;Z=Math.min(1,Math.max(0,j*_e*(.35+.65*ne)*(.7+.6*E)))}else Z=0;return d+(p-d)*Z}ut(()=>{yt("audio-level",j=>{k=Math.min(1,j.payload*100)}).then(j=>{h=j});let F=0;function se(){F+=.016,y+=(k-y)*.35,k*=.86;const j=e().audio_ready!==!1,I=new Array(u);for(let re=0;re{h&&h(),cancelAnimationFrame(v)}});var D=Qd();let U;var Y=o(D),z=o(Y);let b;var L=r(z,4),G=o(L);{var ae=F=>{var se=Ee("· ANALYZING");g(F,se)},oe=F=>{var se=Ee("· WARMING UP");g(F,se)},de=F=>{var se=Ee("· LIVE");g(F,se)};B(G,F=>{e().processing?F(ae):l()&&!a(C)?F(oe,1):F(de,-1)})}var w=r(L,4),X=r(o(w),2),V=o(X),N=r(Y,4);bt(N,21,()=>a(_),gt,(F,se)=>{var j=Zd();J(()=>{var I;return $s(j,`height: ${(I=a(se))!=null?I:""}px; opacity: ${.45+.55*(a(se)/p)}`)}),g(F,j)}),J(()=>{U=ye(D,1,"spectrum svelte-194vixl",null,U,{on:c()}),b=ye(z,1,"led svelte-194vixl",null,b,{processing:e().processing,standby:l()&&!a(C)}),ee(V,a(x))}),g(s,D),Xe(),i()}var tv=T('
VOXCTRL — /dev/mic0
');function sv(s,t){Je(t,!0);const e=()=>St(Ct,"$status",n),[n,i]=_s();let l=Ve(t,"recording",3,!1),c=Ve(t,"active",3,!0);const u=20;let d=W(Ue("·".repeat(u))),p=W(!1),_=null,h,v=0,k=0;const y=he(()=>e().audio_ready!==!1),C=he(()=>e().active_target_label||"Focused Window");function x(I,re,ve,le,E){const q=u;if(le){const Z=2*q,ue=Math.floor(re*10)%Z,ie=ue_e===ie?"█":"·").join("")}else{if(ve&&!E)return Array.from({length:q},(Z,ue)=>ue%4===0?"·":" ").join("");if(ve){const Z=Math.round(Math.min(1,Math.max(0,I))*q);return Array.from({length:q},(ue,ie)=>ie{yt("audio-level",ve=>{v=Math.min(1,ve.payload*100)}).then(ve=>{_=ve});let I=0;function re(){I+=.016,k+=(v-k)*.35,v*=.86;const ve=e().audio_ready!==!1;f(d,x(k,I,l(),e().processing,ve),!0),f(p,Math.sin(I*5.5)>0),h=requestAnimationFrame(re)}return h=requestAnimationFrame(re),()=>{_&&_(),cancelAnimationFrame(h)}});var S=tv();let D;var U=r(o(S),2),Y=o(U),z=o(Y),b=r(Y,2);let L;var G=o(b);{var ae=I=>{var re=Ee("[PROC]");g(I,re)},oe=I=>{var re=Ee("[INIT]");g(I,re)},de=I=>{var re=Ee("[REC ]");g(I,re)};B(G,I=>{e().processing?I(ae):l()&&!a(y)?I(oe,1):I(de,-1)})}var w=r(G),X=r(b,2),V=o(X);{var N=I=>{var re=Ee("transcribing audio stream");g(I,re)},F=I=>{var re=Ee("connecting input device");g(I,re)},se=I=>{var re=Ee("streaming to output");g(I,re)};B(V,I=>{e().processing?I(N):l()&&!a(y)?I(F,1):I(se,-1)})}var j=r(V,1,!0);J(()=>{var I,re;D=ye(S,1,"terminal svelte-pvtvge",null,D,{on:c()}),ee(z,`$ voxctrl listen --target "${(I=a(C))!=null?I:""}"`),L=ye(b,1,"line2 svelte-pvtvge",null,L,{processing:e().processing,standby:l()&&!a(y)}),ee(w,` ${(re=a(d))!=null?re:""}`),ee(j,a(p)?"_":" ")}),g(s,S),Xe(),i()}var av=T('
VU
-20
0
+3
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Configuring system dependencies & hotkey rules...

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It is included only when the app is built with `--features moonshine` +> (it pulls in ONNX Runtime). In a build **without** that feature, selecting +> `"moonshine"` transparently falls back to `whisper-cpp` and still uses the +> Whisper model configured above. The Settings → Engine panel shows whether +> Moonshine is available in the running build. +> +> Moonshine models are the four upstream ONNX graphs +> (`preprocess`, `encode`, `uncached_decode`, `cached_decode`) plus a +> `tokenizer.json`, downloaded on demand into +> `~/.local/share/voxctrl/models/moonshine//`. You can also place those +> files there manually to run fully offline. ### `audio` section diff --git a/docs/speech-recognition.md b/docs/speech-recognition.md index 2159ab4..673fae4 100644 --- a/docs/speech-recognition.md +++ b/docs/speech-recognition.md @@ -219,3 +219,28 @@ Under `engine.whisper_cpp` in `config.json`: | `model_dir` | string | `""` | Custom model storage path; empty = `~/.local/share/voxctrl/models/`. Supports `~` expansion (e.g. `~/.whisper-models`). The directory must already exist. | Language detection is automatic when using whisper-cpp; use the `engine.moonshine.language` field for the Moonshine backend. + +## Moonshine backend + +[Moonshine](https://github.com/moonshine-ai/moonshine) is an alternative, +CPU-friendly speech-to-text model. Unlike Whisper it consumes the raw 16 kHz +waveform directly (no fixed 30-second window), which keeps latency low on the +short utterances typical of push-to-talk dictation. + +It runs through ONNX Runtime as four graphs executed in sequence — +`preprocess` → `encode` → `uncached_decode` → `cached_decode` — with greedy +autoregressive decoding: starting from the start-of-transcript token, each +step's highest-scoring token is fed back in until the end-of-transcript token +appears (or a length cap derived from the audio duration is reached). The +resulting token ids are turned back into text with the model's `tokenizer.json`. + +**Enabling it.** Moonshine is an opt-in build feature (it links ONNX Runtime), +compiled in with `--features moonshine`, the same way `cuda` and `vulkan` are +opt-in. A build without it will transparently fall back to whisper-cpp if +`"moonshine"` is selected; the Settings → Engine panel indicates whether the +running build actually includes it. + +**Models.** Selecting a size (`base` or `tiny`) and clicking Download in +Settings → Engine fetches the four ONNX graphs plus `tokenizer.json` into +`~/.local/share/voxctrl/models/moonshine//`. You can also drop those +files there manually to run fully offline. diff --git a/package-lock.json b/package-lock.json index 69ec617..7ac1934 100644 --- a/package-lock.json +++ b/package-lock.json @@ -1,12 +1,12 @@ { "name": "voxctrl", - "version": "0.2.6", + "version": "0.2.8", "lockfileVersion": 3, "requires": true, "packages": { "": { "name": "voxctrl", - "version": "0.2.6", + "version": "0.2.8", "dependencies": { "@tauri-apps/api": "^2", "@tauri-apps/plugin-dialog": "^2", diff --git a/src-tauri/Cargo.toml b/src-tauri/Cargo.toml index 8cf917b..81ee0ee 100644 --- a/src-tauri/Cargo.toml +++ b/src-tauri/Cargo.toml @@ -65,6 +65,10 @@ default = ["custom-protocol"] custom-protocol = ["tauri/custom-protocol"] cuda = ["voxctrl-inference/cuda"] vulkan = ["voxctrl-inference/vulkan"] +# Compiles the Moonshine ONNX speech-to-text backend into the app. Opt-in like +# cuda/vulkan: it pulls in ONNX Runtime (fetched at build time). Without it, a +# "moonshine" backend selection transparently falls back to whisper-cpp. +moonshine = ["voxctrl-inference/moonshine"] [dev-dependencies] tempfile = { workspace = true } diff --git a/src-tauri/src/commands.rs b/src-tauri/src/commands.rs index d524ae1..56c7c2c 100644 --- a/src-tauri/src/commands.rs +++ b/src-tauri/src/commands.rs @@ -371,6 +371,42 @@ pub async fn download_model(model_size: String, model_dir: String) -> Result<(), .map_err(|e| e.to_string()) } +/// Whether the Moonshine ONNX backend was compiled into this build. The UI uses +/// this to decide whether selecting Moonshine actually runs Moonshine (vs. +/// transparently falling back to whisper-cpp). +#[tauri::command] +pub fn moonshine_available() -> bool { + voxctrl_inference::MOONSHINE_COMPILED +} + +#[tauri::command] +pub async fn check_moonshine_downloaded(model_size: String) -> Result { + #[cfg(feature = "moonshine")] + { + Ok(voxctrl_inference::moonshine::is_model_downloaded(&model_size, "")) + } + #[cfg(not(feature = "moonshine"))] + { + let _ = model_size; + Ok(false) + } +} + +#[tauri::command] +pub async fn download_moonshine_model(model_size: String) -> Result<(), String> { + #[cfg(feature = "moonshine")] + { + voxctrl_inference::moonshine::download_model(&model_size, "") + .await + .map_err(|e| e.to_string()) + } + #[cfg(not(feature = "moonshine"))] + { + let _ = model_size; + Err("This build was compiled without the Moonshine backend. Rebuild with `--features moonshine` to use it.".into()) + } +} + #[tauri::command] pub async fn check_directory_exists(path: String) -> Result { if path.is_empty() { diff --git a/src-tauri/src/lib.rs b/src-tauri/src/lib.rs index 3d2917f..1064df9 100644 --- a/src-tauri/src/lib.rs +++ b/src-tauri/src/lib.rs @@ -351,7 +351,12 @@ pub fn run() { { let cfg = state_for_gesture.config.lock().await; let eng = &cfg.data.engine; - if eng.backend != voxctrl_config::BackendChoice::Moonshine + // Moonshine only bypasses the Whisper-model check when it + // is actually compiled in; otherwise the app silently + // falls back to whisper-cpp and still needs the model. + let uses_whisper_model = eng.backend != voxctrl_config::BackendChoice::Moonshine + || !voxctrl_inference::MOONSHINE_COMPILED; + if uses_whisper_model && !voxctrl_inference::whisper_cpp::is_small_auto_downloadable(&eng.whisper_cpp.model_size) && !voxctrl_inference::whisper_cpp::is_model_downloaded( &eng.whisper_cpp.model_size, @@ -765,7 +770,12 @@ pub fn run() { // the background (the shipped default, "tiny"), in which case the // app works out of the box with no Settings visit required. let mut show_settings = cfg_data.ui.auto_show_settings; - if cfg_data.engine.backend != voxctrl_config::BackendChoice::Moonshine { + // Only the whisper-cpp path needs a GGUF model on disk. A Moonshine + // selection uses whisper-cpp (and thus its model) unless the + // Moonshine backend is actually compiled into this build. + let uses_whisper_model = cfg_data.engine.backend != voxctrl_config::BackendChoice::Moonshine + || !voxctrl_inference::MOONSHINE_COMPILED; + if uses_whisper_model { let model_size = cfg_data.engine.whisper_cpp.model_size.clone(); let model_dir = cfg_data.engine.whisper_cpp.model_dir.clone(); if !voxctrl_inference::whisper_cpp::is_model_downloaded(&model_size, &model_dir) { @@ -986,6 +996,9 @@ pub fn run() { list_pocket_tts_voices, check_model_downloaded, download_model, + moonshine_available, + check_moonshine_downloaded, + download_moonshine_model, check_directory_exists, test_openai, cuda_enabled, diff --git a/src/lib/Settings/EngineTab.svelte b/src/lib/Settings/EngineTab.svelte index 345ba78..28c2916 100644 --- a/src/lib/Settings/EngineTab.svelte +++ b/src/lib/Settings/EngineTab.svelte @@ -62,6 +62,52 @@ let downloading = $state(false); let modelDirError = $state(null); + // ── Moonshine ──────────────────────────────────────────────────────────── + // Whether the app was built with the Moonshine backend. When false, choosing + // Moonshine silently runs whisper-cpp, so we surface that to the user. + let moonshineAvailable = $state(true); + let moonshineDownloadedMap = $state>({}); + let moonshineChecking = $state(false); + let moonshineDownloading = $state(false); + + async function checkMoonshineDownloaded() { + moonshineChecking = true; + const newMap: Record = {}; + for (const m of moonshineModelSizeOptions) { + try { + newMap[m.value] = await invoke("check_moonshine_downloaded", { + modelSize: m.value, + }); + } catch (e) { + console.error("Failed to check Moonshine download status for " + m.value, e); + newMap[m.value] = false; + } + } + moonshineDownloadedMap = newMap; + moonshineChecking = false; + } + + async function triggerMoonshineDownload(model: string) { + if (moonshineDownloading) return; + moonshineDownloading = true; + try { + await invoke("download_moonshine_model", { modelSize: model }); + moonshineDownloadedMap[model] = true; + } catch (e) { + alert(`Failed to download Moonshine model: ${e}`); + } finally { + moonshineDownloading = false; + } + } + + async function onMoonshineModelChanged() { + markDirty(); + const selected = cfg.engine.moonshine.model_size; + if (moonshineAvailable && !moonshineDownloadedMap[selected]) { + await triggerMoonshineDownload(selected); + } + } + async function checkAllModelsDownloaded() { checking = true; const newMap: Record = {}; @@ -131,6 +177,12 @@ onMount(async () => { checkAllModelsDownloaded(); + try { + moonshineAvailable = await invoke("moonshine_available"); + } catch (e) { + console.error("Failed to query Moonshine availability", e); + } + checkMoonshineDownloaded(); cudaEnabled = await invoke("cuda_enabled"); // If this is a CPU build but config still says "cuda", reset to "auto" if (!cudaEnabled && cfg.engine.whisper_cpp.device === "cuda") { @@ -242,10 +294,54 @@ {:else}

Moonshine Settings

+ + {#if !moonshineAvailable} +
+ ⚠️ +
+ Moonshine backend not included in this build +

+ This build was compiled without Moonshine, so selecting it will fall + back to Whisper.cpp (using the Whisper model configured above). + Rebuild with --features moonshine to enable it. +

+
+
+ {/if} + + + {#if moonshineAvailable} +
+ {#if moonshineChecking} + ⏳ Checking local model files... + {:else if moonshineDownloading} + ⏳ Downloading Moonshine {cfg.engine.moonshine.model_size} (ONNX)... + {:else if moonshineDownloadedMap[cfg.engine.moonshine.model_size]} + ✔ Model downloaded + {:else} +
+ Model not downloaded + +
+ {/if} +
+ {/if} +