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File size: 14,539 Bytes
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const VOICE_BASE = 'https://huggingface.co/kyutai/pocket-tts-without-voice-cloning/resolve/main';
let model = null;
let tokenizer = null;
let voiceIndices = {}; // name → voice_index
let activeVoiceIndex = -1;
function post(type, data = {}, transferables = []) {
self.postMessage({ type, ...data }, transferables);
}
// ---- Fetch with Cache API + progress ----
const CACHE_NAME = 'tts-model-v3';
async function cachedFetch(url, label) {
const cache = await caches.open(CACHE_NAME);
const cached = await cache.match(url);
if (cached) {
post('status', { text: `${label} (cached)` });
return await cached.arrayBuffer();
}
const resp = await fetch(url);
if (!resp.ok) throw new Error(`Failed to fetch ${url}: ${resp.status}`);
const contentLength = parseInt(resp.headers.get('Content-Length') || '0', 10);
const reader = resp.body.getReader();
const chunks = [];
let loaded = 0;
while (true) {
const { done, value } = await reader.read();
if (done) break;
chunks.push(value);
loaded += value.byteLength;
post('status', { text: label, progress: { loaded, total: contentLength } });
}
const buf = new Uint8Array(loaded);
let offset = 0;
for (const chunk of chunks) { buf.set(chunk, offset); offset += chunk.byteLength; }
try {
await cache.put(url, new Response(buf.buffer, { headers: { 'Content-Type': 'application/octet-stream' } }));
} catch (e) { console.warn('[worker] cache error:', e); }
return buf.buffer;
}
// ---- Minimal protobuf decoder for sentencepiece .model files ----
function decodeSentencepieceModel(buffer) {
const view = new DataView(buffer.buffer, buffer.byteOffset, buffer.byteLength);
let pos = 0;
function readVarint() {
let result = 0, shift = 0;
while (pos < buffer.length) {
const b = buffer[pos++];
result |= (b & 0x7f) << shift;
shift += 7;
if ((b & 0x80) === 0) return result;
}
return result;
}
function readBytes(n) {
const data = buffer.slice(pos, pos + n);
pos += n;
return data;
}
function decodePiece(data) {
let pPos = 0, piece = '', score = 0, type = 1;
const pView = new DataView(data.buffer, data.byteOffset, data.byteLength);
while (pPos < data.length) {
const key = readVarIntFrom(data, pPos);
pPos = key.pos;
const fieldNum = key.val >>> 3;
const wireType = key.val & 0x7;
if (fieldNum === 1 && wireType === 2) {
const len = readVarIntFrom(data, pPos);
pPos = len.pos;
piece = new TextDecoder().decode(data.slice(pPos, pPos + len.val));
pPos += len.val;
} else if (fieldNum === 2 && wireType === 5) {
score = pView.getFloat32(pPos, true);
pPos += 4;
} else if (fieldNum === 3 && wireType === 0) {
const v = readVarIntFrom(data, pPos);
type = v.val;
pPos = v.pos;
} else {
if (wireType === 0) { const v = readVarIntFrom(data, pPos); pPos = v.pos; }
else if (wireType === 1) { pPos += 8; }
else if (wireType === 2) { const len = readVarIntFrom(data, pPos); pPos = len.pos + len.val; }
else if (wireType === 5) { pPos += 4; }
else break;
}
}
return { piece, score, type };
}
function readVarIntFrom(buf, p) {
let result = 0, shift = 0;
while (p < buf.length) {
const b = buf[p++];
result |= (b & 0x7f) << shift;
shift += 7;
if ((b & 0x80) === 0) return { val: result, pos: p };
}
return { val: result, pos: p };
}
const pieces = [];
while (pos < buffer.length) {
const key = readVarint();
const fieldNum = key >>> 3;
const wireType = key & 0x7;
if (fieldNum === 1 && wireType === 2) {
const len = readVarint();
const data = readBytes(len);
const p = decodePiece(data);
pieces.push(p);
} else {
if (wireType === 0) { readVarint(); }
else if (wireType === 1) { pos += 8; }
else if (wireType === 2) { const len = readVarint(); pos += len; }
else if (wireType === 5) { pos += 4; }
else break;
}
}
return pieces;
}
// ---- Unigram tokenizer (Viterbi) ----
class UnigramTokenizer {
constructor(pieces) {
this.pieces = pieces;
this.vocab = new Map();
this.unkId = 0;
for (let i = 0; i < pieces.length; i++) {
const p = pieces[i];
if (p.type === 2) this.unkId = i;
if (p.type === 1 || p.type === 4) {
this.vocab.set(p.piece, { id: i, score: p.score });
}
if (p.type === 6) {
this.vocab.set(p.piece, { id: i, score: p.score });
}
}
}
encode(text) {
const normalized = '\u2581' + text.replace(/ /g, '\u2581');
return this._viterbi(normalized);
}
_viterbi(text) {
const n = text.length;
const best = new Array(n + 1);
best[0] = { score: 0, len: 0, id: -1 };
for (let i = 1; i <= n; i++) {
best[i] = { score: -Infinity, len: 0, id: -1 };
}
for (let i = 0; i < n; i++) {
if (best[i].score === -Infinity) continue;
for (let len = 1; len <= n - i && len <= 64; len++) {
const sub = text.substring(i, i + len);
const entry = this.vocab.get(sub);
if (entry) {
const newScore = best[i].score + entry.score;
if (newScore > best[i + len].score) {
best[i + len] = { score: newScore, len: len, id: entry.id };
}
}
}
if (best[i + 1].score === -Infinity) {
const ch = text.charCodeAt(i);
const byteStr = `<0x${ch.toString(16).toUpperCase().padStart(2, '0')}>`;
const byteEntry = this.vocab.get(byteStr);
const fallbackId = byteEntry ? byteEntry.id : this.unkId;
const fallbackScore = byteEntry ? byteEntry.score : -100;
best[i + 1] = { score: best[i].score + fallbackScore, len: 1, id: fallbackId };
}
}
const ids = [];
let p = n;
while (p > 0) {
ids.push(best[p].id);
p -= best[p].len;
}
ids.reverse();
return new Uint32Array(ids);
}
}
const MAX_TOKENS_PER_CHUNK = 50;
const SPACE_MARKER = '\u2581';
const SPACE_MARKER_ID = 260; // ▁ in the SentencePiece vocab; identified empirically
// Build a boundary-token set from a punctuation string, excluding the
// SentencePiece space marker (▁). The encoder prepends ▁ to its input, so
// encoding e.g. '.!...?' yields [..., 260, ...] where 260 is the bare ▁
// token. If we include 260 in the boundary set, every standalone ▁ in the
// tokenized sentence (which appears whenever the Viterbi tokenizer decides
// to split a ▁word into ▁ + word) gets treated as a sentence boundary —
// producing phantom splits that then feed the model 1-token segments.
function boundaryIds(text) {
return tokenizer.encode(text).filter(id => id !== SPACE_MARKER_ID);
}
function splitTextIntoSegments(text, temperature) {
const [processedText, defaultFramesAfterEos] = model.prepare_text(text);
const normalized = processedText.trim();
const tokenIds = tokenizer.encode(normalized);
const totalTokens = tokenIds.length;
if (totalTokens <= MAX_TOKENS_PER_CHUNK) {
return [{ text: normalized, framesAfterEos: defaultFramesAfterEos }];
}
// Find boundary token IDs for sentence punctuation
const eosSet = new Set(boundaryIds('.!...?'));
const commaSet = new Set(boundaryIds(',;:'));
// Split on sentence boundaries
const boundaries = [0];
let prevWasBoundary = false;
for (let i = 0; i < tokenIds.length; i++) {
if (eosSet.has(tokenIds[i])) {
prevWasBoundary = true;
} else {
if (prevWasBoundary) boundaries.push(i);
prevWasBoundary = false;
}
}
boundaries.push(tokenIds.length);
// Build segments from boundaries
const segments = [];
for (let i = 0; i < boundaries.length - 1; i++) {
const start = boundaries[i];
const end = boundaries[i + 1];
const segTokens = tokenIds.slice(start, end);
segments.push(segTokens);
}
// Sub-split oversized segments on commas
const refined = [];
for (const seg of segments) {
if (seg.length <= MAX_TOKENS_PER_CHUNK) {
refined.push(seg);
} else {
const subBounds = [0];
let prev = false;
for (let i = 0; i < seg.length; i++) {
if (commaSet.has(seg[i])) {
prev = true;
} else {
if (prev) subBounds.push(i);
prev = false;
}
}
subBounds.push(seg.length);
for (let i = 0; i < subBounds.length - 1; i++) {
const sub = seg.slice(subBounds[i], subBounds[i + 1]);
if (sub.length > 0) refined.push(sub);
}
}
}
// Final pass: merge tiny segments back together (under 10 tokens).
// NOTE: do NOT use `buffer.flatMap(x => x)` here. `refined` contains
// Uint32Arrays, and `flatMap` only flattens Array instances — not typed
// arrays. Using flatMap silently produces `[Uint32Array]` (a regular
// array wrapping one Uint32Array), and the downstream `new Uint32Array
// (tokenIds)` then sees a 1-element input and feeds the model a single
// token, which is why "audios" used to produce 2.24s of gibberish.
const merged = [];
let buffer = [];
let bufferLen = 0;
for (const seg of refined) {
buffer.push(seg);
bufferLen += seg.length;
if (bufferLen >= 10 || seg === refined[refined.length - 1]) {
const totalLen = buffer.reduce((sum, s) => sum + s.length, 0);
const combined = new Uint32Array(totalLen);
let off = 0;
for (const s of buffer) {
combined.set(s, off);
off += s.length;
}
merged.push(combined);
buffer = [];
bufferLen = 0;
}
}
return merged.map(tokens => ({
tokens,
framesAfterEos: 1,
}));
}
// ---- Handlers ----
async function handleLoad(config) {
const base = (config.baseUrl || '').replace(/\/+$/, '');
// 1. Import WASM
post('status', { text: 'Loading WASM module...' });
const wasmJsUrl = base ? (base + '/pkg/tts_wasm.js') : new URL('../pkg/tts_wasm.js', import.meta.url).href;
const wasmBgUrl = base ? (base + '/pkg/tts_wasm_bg.wasm') : new URL('../pkg/tts_wasm_bg.wasm', import.meta.url).href;
const wasmModule = await import(wasmJsUrl);
await wasmModule.default(wasmBgUrl);
// 2. Download and load tokenizer
const tokUrl = config.tokenizerUrl || `${HF_BASE}/tokenizer.model`;
const tokBuf = await cachedFetch(tokUrl, 'Downloading tokenizer');
const pieces = decodeSentencepieceModel(new Uint8Array(tokBuf));
tokenizer = new UnigramTokenizer(pieces);
post('status', { text: `Tokenizer loaded (${pieces.length} pieces)` });
// 3. Download and init model
const modelUrl = config.modelUrl || `${HF_BASE}/pocket-tts-q8_0.gguf`;
const modelBuf = await cachedFetch(modelUrl, 'Downloading model');
post('status', { text: 'Initializing model...' });
model = new wasmModule.Model(new Uint8Array(modelBuf));
// 4. Ready (voice loaded separately)
const sampleRate = model.sample_rate();
post('status', { text: 'Select a voice', ready: true });
post('loaded', { sampleRate });
}
async function handleLoadVoice(name) {
if (name in voiceIndices) {
activeVoiceIndex = voiceIndices[name];
post('voice_loaded', { name, voiceIndex: activeVoiceIndex });
return;
}
const url = `${VOICE_BASE}/embeddings_v2/${name}.safetensors`;
const voiceBuf = await cachedFetch(url, `Downloading voice: ${name}`);
post('status', { text: `Loading voice: ${name}...` });
const voiceIndex = model.add_voice(new Uint8Array(voiceBuf));
voiceIndices[name] = voiceIndex;
activeVoiceIndex = voiceIndex;
post('status', { text: 'Ready', ready: true });
post('voice_loaded', { name, voiceIndex });
}
async function handleGenerate(text, temperature) {
const segments = splitTextIntoSegments(text, temperature);
const totalTokens = segments.reduce((sum, s) => sum + (s.tokens ? s.tokens.length : 0), 0);
post('gen_start', { numTokens: totalTokens, totalSegments: segments.length });
let step = 0;
for (let segIdx = 0; segIdx < segments.length; segIdx++) {
const seg = segments[segIdx];
const tokenIds = seg.tokens || tokenizer.encode(seg.text);
const framesAfterEos = seg.framesAfterEos;
model.start_generation(activeVoiceIndex, new Uint32Array(tokenIds), framesAfterEos, temperature);
while (true) {
const chunk = model.generation_step();
if (!chunk) break;
post('chunk', { data: chunk, step, segment: segIdx }, [chunk.buffer]);
step++;
}
// Tiny pause between segments so the audio worklet doesn't glitch
if (segIdx < segments.length - 1) {
await new Promise(r => setTimeout(r, 20));
}
}
post('done', { totalSteps: step });
}
self.onmessage = async (e) => {
const { type, ...data } = e.data;
try {
if (type === 'load') {
await handleLoad(data.config || {});
} else if (type === 'load_voice') {
await handleLoadVoice(data.name);
} else if (type === 'generate') {
await handleGenerate(data.text, data.temperature || 0.7);
}
} catch (err) {
post('error', { message: err.message || String(err) });
console.error(err);
}
};
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