feat: local offline voice models (sherpa-onnx) with model manager and wake word (v2.1.0)

- electron/voice: model catalog (Whisper base/small/turbo, SenseVoice, Kokoro v1.0,
  Piper fr), streaming tar.bz2 downloader with progress, sherpa-onnx worker running in
  an Electron utilityProcess (transcribe / synthesize / status / unload), IPC + bridge.
- Renderer: 'local' providers for STT and TTS, Settings → Modèles locaux (download,
  progress, delete, activate), speaker selection, hands-free wake word with a tolerant
  matcher (accents, punctuation, edit distance) and attention window after a bare wake word.
- Packaging: native addon and worker unpacked from the asar; release 2.1.0.

Validated on Linux: Kokoro (fr) → Whisper base round trip, Piper (fr) → Whisper round trip.

Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_017Wn5VX9HNbJ7N54hR24u9Y

electron/voice/worker.ts

@@ -0,0 +1,273 @@
+/**
+ * Voice worker: runs sherpa-onnx (speech recognition and synthesis) in an Electron utility
+ * process so heavy inference never blocks the main process. Also runnable with
+ * `child_process.fork` (advanced serialization) for local tests.
+ */
+import os from 'node:os';
+import path from 'node:path';
+import type { VoiceEngineKind } from '../../shared/voice';
+
+interface ModelRef {
+  id: string;
+  engine: VoiceEngineKind;
+  dir: string;
+  files: string[];
+}
+
+type Request =
+  | { id: number; type: 'status' }
+  | { id: number; type: 'transcribe'; model: ModelRef; wav: Uint8Array; language: string }
+  | { id: number; type: 'synthesize'; model: ModelRef; text: string; speaker: number; speed: number }
+  | { id: number; type: 'unload'; modelId?: string };
+
+type Response = { id: number; ok: true; result: unknown } | { id: number; ok: false; error: string };
+
+// ── sherpa-onnx loading (lazy, so a missing native package is reported, not fatal) ──
+type Sherpa = {
+  OfflineRecognizer: new (config: unknown) => {
+    createStream: () => { acceptWaveform: (w: { sampleRate: number; samples: Float32Array }) => void };
+    decode: (s: unknown) => void;
+    getResult: (s: unknown) => { text: string; lang?: string };
+  };
+  OfflineTts: new (config: unknown) => {
+    numSpeakers: number;
+    sampleRate: number;
+    generate: (req: { text: string; sid: number; speed: number }) => { samples: Float32Array; sampleRate: number };
+  };
+  version: string;
+};
+
+let sherpa: Sherpa | null = null;
+let loadError: string | null = null;
+
+function loadSherpa(): Sherpa {
+  if (sherpa) return sherpa;
+  if (loadError) throw new Error(loadError);
+  try {
+    // eslint-disable-next-line @typescript-eslint/no-require-imports
+    sherpa = require('sherpa-onnx-node') as Sherpa;
+    return sherpa;
+  } catch (err) {
+    loadError = `Module natif sherpa-onnx indisponible : ${(err as Error).message}`;
+    throw new Error(loadError);
+  }
+}
+
+const threads = Math.max(2, Math.min(6, Math.floor(os.cpus().length / 2)));
+
+// ── caches ────────────────────────────────────────────────────────────────
+const recognizers = new Map<string, InstanceType<Sherpa['OfflineRecognizer']>>();
+const synthesizers = new Map<string, InstanceType<Sherpa['OfflineTts']>>();
+
+function whisperPrefix(model: ModelRef): string {
+  const encoder = model.files.find((f) => f.includes('-encoder'));
+  return encoder ? encoder.slice(0, encoder.indexOf('-encoder')) : 'base';
+}
+
+function getRecognizer(model: ModelRef, language: string) {
+  const lang = language === 'auto' ? '' : language.split('-')[0].toLowerCase();
+  const key = `${model.id}:${lang}`;
+  const cached = recognizers.get(key);
+  if (cached) return cached;
+  const s = loadSherpa();
+  const p = (f: string) => path.join(model.dir, f);
+  let modelConfig: Record<string, unknown>;
+  switch (model.engine) {
+    case 'whisper': {
+      const prefix = whisperPrefix(model);
+      modelConfig = {
+        whisper: { encoder: p(`${prefix}-encoder.int8.onnx`), decoder: p(`${prefix}-decoder.int8.onnx`), language: lang, task: 'transcribe', tailPaddings: -1 },
+        tokens: p(`${prefix}-tokens.txt`)
+      };
+      break;
+    }
+    case 'sense-voice':
+      modelConfig = { senseVoice: { model: p('model.int8.onnx'), language: lang || 'auto', useInverseTextNormalization: 1 }, tokens: p('tokens.txt') };
+      break;
+    case 'nemo-transducer':
+      modelConfig = {
+        transducer: { encoder: p('encoder.int8.onnx'), decoder: p('decoder.int8.onnx'), joiner: p('joiner.int8.onnx') },
+        tokens: p('tokens.txt'),
+        modelType: 'nemo_transducer'
+      };
+      break;
+    default:
+      throw new Error(`Moteur STT non supporté : ${model.engine}`);
+  }
+  for (const [k, v] of Object.entries({ numThreads: threads, provider: 'cpu', debug: 0 })) modelConfig[k] = v;
+  // Whisper keeps one recognizer per language; other engines ignore the language key.
+  for (const [k, r] of recognizers) if (k.startsWith(`${model.id}:`)) recognizers.delete(k) && void r;
+  const recognizer = new s.OfflineRecognizer({ featConfig: { sampleRate: 16000, featureDim: 80 }, modelConfig, decodingMethod: 'greedy_search' });
+  recognizers.set(key, recognizer);
+  return recognizer;
+}
+
+function getSynthesizer(model: ModelRef) {
+  const cached = synthesizers.get(model.id);
+  if (cached) return cached;
+  const s = loadSherpa();
+  const p = (f: string) => path.join(model.dir, f);
+  let ttsModel: Record<string, unknown>;
+  switch (model.engine) {
+    case 'kokoro':
+      ttsModel = {
+        kokoro: {
+          model: p('model.onnx'),
+          voices: p('voices.bin'),
+          tokens: p('tokens.txt'),
+          dataDir: p('espeak-ng-data'),
+          lexicon: [p('lexicon-us-en.txt'), p('lexicon-zh.txt')].join(',')
+        }
+      };
+      break;
+    case 'piper': {
+      const onnx = model.files.find((f) => f.endsWith('.onnx')) ?? 'model.onnx';
+      ttsModel = { vits: { model: p(onnx), tokens: p('tokens.txt'), dataDir: p('espeak-ng-data') } };
+      break;
+    }
+    default:
+      throw new Error(`Moteur TTS non supporté : ${model.engine}`);
+  }
+  const tts = new s.OfflineTts({ model: { ...ttsModel, numThreads: threads, provider: 'cpu', debug: 0 }, maxNumSentences: 1 });
+  synthesizers.set(model.id, tts);
+  return tts;
+}
+
+// ── audio helpers ──────────────────────────────────────────────────────────
+function decodeWav(bytes: Uint8Array): { samples: Float32Array; sampleRate: number } {
+  const view = new DataView(bytes.buffer, bytes.byteOffset, bytes.byteLength);
+  if (String.fromCharCode(bytes[0], bytes[1], bytes[2], bytes[3]) !== 'RIFF') throw new Error('WAV invalide');
+  let offset = 12;
+  let sampleRate = 16000;
+  let channels = 1;
+  let bits = 16;
+  let data: { start: number; length: number } | null = null;
+  while (offset + 8 <= bytes.byteLength) {
+    const id = String.fromCharCode(bytes[offset], bytes[offset + 1], bytes[offset + 2], bytes[offset + 3]);
+    const size = view.getUint32(offset + 4, true);
+    if (id === 'fmt ') {
+      channels = view.getUint16(offset + 10, true);
+      sampleRate = view.getUint32(offset + 12, true);
+      bits = view.getUint16(offset + 22, true);
+    } else if (id === 'data') {
+      data = { start: offset + 8, length: Math.min(size, bytes.byteLength - offset - 8) };
+      break;
+    }
+    offset += 8 + size + (size % 2);
+  }
+  if (!data) throw new Error('WAV sans données');
+  const bytesPerSample = bits / 8;
+  const frames = Math.floor(data.length / bytesPerSample / channels);
+  const samples = new Float32Array(frames);
+  for (let i = 0; i < frames; i++) {
+    let sum = 0;
+    for (let c = 0; c < channels; c++) {
+      const pos = data.start + (i * channels + c) * bytesPerSample;
+      sum += bits === 16 ? view.getInt16(pos, true) / 32768 : bits === 32 ? view.getInt32(pos, true) / 2147483648 : (bytes[pos] - 128) / 128;
+    }
+    samples[i] = sum / channels;
+  }
+  return { samples, sampleRate };
+}
+
+function resampleTo16k(samples: Float32Array, rate: number): Float32Array {
+  if (rate === 16000) return samples;
+  const ratio = rate / 16000;
+  const out = new Float32Array(Math.round(samples.length / ratio));
+  for (let i = 0; i < out.length; i++) {
+    const pos = i * ratio;
+    const idx = Math.floor(pos);
+    const frac = pos - idx;
+    const a = samples[Math.min(idx, samples.length - 1)];
+    const b = samples[Math.min(idx + 1, samples.length - 1)];
+    out[i] = a + (b - a) * frac;
+  }
+  return out;
+}
+
+function encodeWav(samples: Float32Array, sampleRate: number): Uint8Array {
+  const buffer = new ArrayBuffer(44 + samples.length * 2);
+  const view = new DataView(buffer);
+  const str = (o: number, s: string) => {
+    for (let i = 0; i < s.length; i++) view.setUint8(o + i, s.charCodeAt(i));
+  };
+  str(0, 'RIFF');
+  view.setUint32(4, 36 + samples.length * 2, true);
+  str(8, 'WAVE');
+  str(12, 'fmt ');
+  view.setUint32(16, 16, true);
+  view.setUint16(20, 1, true);
+  view.setUint16(22, 1, true);
+  view.setUint32(24, sampleRate, true);
+  view.setUint32(28, sampleRate * 2, true);
+  view.setUint16(32, 2, true);
+  view.setUint16(34, 16, true);
+  str(36, 'data');
+  view.setUint32(40, samples.length * 2, true);
+  let o = 44;
+  for (let i = 0; i < samples.length; i++, o += 2) {
+    const s = Math.max(-1, Math.min(1, samples[i]));
+    view.setInt16(o, s < 0 ? s * 0x8000 : s * 0x7fff, true);
+  }
+  return new Uint8Array(buffer);
+}
+
+// ── request handling ───────────────────────────────────────────────────────
+function handle(req: Request): unknown {
+  switch (req.type) {
+    case 'status': {
+      try {
+        const s = loadSherpa();
+        return { available: true, version: s.version, loaded: [...recognizers.keys(), ...synthesizers.keys()] };
+      } catch (err) {
+        return { available: false, error: (err as Error).message, loaded: [] };
+      }
+    }
+    case 'transcribe': {
+      const started = Date.now();
+      const { samples, sampleRate } = decodeWav(req.wav);
+      const pcm = resampleTo16k(samples, sampleRate);
+      const recognizer = getRecognizer(req.model, req.language);
+      const stream = recognizer.createStream();
+      stream.acceptWaveform({ sampleRate: 16000, samples: pcm });
+      recognizer.decode(stream);
+      const result = recognizer.getResult(stream);
+      return { text: (result.text ?? '').trim(), language: result.lang, durationMs: Date.now() - started, audioSec: pcm.length / 16000 };
+    }
+    case 'synthesize': {
+      const started = Date.now();
+      const tts = getSynthesizer(req.model);
+      const sid = Math.max(0, Math.min(tts.numSpeakers - 1, Math.floor(req.speaker)));
+      const audio = tts.generate({ text: req.text, sid, speed: Math.max(0.5, Math.min(2, req.speed || 1)) });
+      return { wav: encodeWav(audio.samples, audio.sampleRate), sampleRate: audio.sampleRate, durationMs: Date.now() - started, audioSec: audio.samples.length / audio.sampleRate };
+    }
+    case 'unload': {
+      if (req.modelId) {
+        for (const k of [...recognizers.keys()]) if (k.startsWith(`${req.modelId}:`)) recognizers.delete(k);
+        synthesizers.delete(req.modelId);
+      } else {
+        recognizers.clear();
+        synthesizers.clear();
+      }
+      return { ok: true };
+    }
+    default:
+      throw new Error('requête inconnue');
+  }
+}
+
+function respond(req: Request): Response {
+  try {
+    return { id: req.id, ok: true, result: handle(req) };
+  } catch (err) {
+    return { id: req.id, ok: false, error: (err as Error).message || String(err) };
+  }
+}
+
+// Electron utility process transport, with a child_process fallback for tests.
+const parentPort = (process as unknown as { parentPort?: { on: (ev: 'message', cb: (e: { data: Request }) => void) => void; postMessage: (m: unknown) => void } }).parentPort;
+if (parentPort) {
+  parentPort.on('message', (event) => parentPort.postMessage(respond(event.data)));
+} else if (process.send) {
+  process.on('message', (msg: Request) => process.send!(respond(msg)));
+}