initial commit
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559
nodejs/helper.js
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559
nodejs/helper.js
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import fs from 'fs';
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import path from 'path';
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import { fileURLToPath } from 'url';
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import * as ort from 'onnxruntime-node';
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const __filename = fileURLToPath(import.meta.url);
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const AVAILABLE_LANGS = ["en", "ko", "es", "pt", "fr"];
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/**
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* Unicode text processor
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*/
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class UnicodeProcessor {
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constructor(unicodeIndexerJsonPath) {
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this.indexer = JSON.parse(fs.readFileSync(unicodeIndexerJsonPath, 'utf8'));
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}
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_preprocessText(text, lang) {
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// TODO: Need advanced normalizer for better performance
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text = text.normalize('NFKD');
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// Remove emojis (wide Unicode range)
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const emojiPattern = /[\u{1F600}-\u{1F64F}\u{1F300}-\u{1F5FF}\u{1F680}-\u{1F6FF}\u{1F700}-\u{1F77F}\u{1F780}-\u{1F7FF}\u{1F800}-\u{1F8FF}\u{1F900}-\u{1F9FF}\u{1FA00}-\u{1FA6F}\u{1FA70}-\u{1FAFF}\u{2600}-\u{26FF}\u{2700}-\u{27BF}\u{1F1E6}-\u{1F1FF}]+/gu;
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text = text.replace(emojiPattern, '');
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// Replace various dashes and symbols
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const replacements = {
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'–': '-',
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'‑': '-',
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'—': '-',
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'_': ' ',
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'\u201C': '"', // left double quote "
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'\u201D': '"', // right double quote "
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'\u2018': "'", // left single quote '
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'\u2019': "'", // right single quote '
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'´': "'",
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'`': "'",
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'[': ' ',
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']': ' ',
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'|': ' ',
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'/': ' ',
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'#': ' ',
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'→': ' ',
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'←': ' ',
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};
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for (const [k, v] of Object.entries(replacements)) {
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text = text.replaceAll(k, v);
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}
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// Remove special symbols
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text = text.replace(/[♥☆♡©\\]/g, '');
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// Replace known expressions
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const exprReplacements = {
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'@': ' at ',
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'e.g.,': 'for example, ',
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'i.e.,': 'that is, ',
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};
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for (const [k, v] of Object.entries(exprReplacements)) {
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text = text.replaceAll(k, v);
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}
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// Fix spacing around punctuation
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text = text.replace(/ ,/g, ',');
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text = text.replace(/ \./g, '.');
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text = text.replace(/ !/g, '!');
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text = text.replace(/ \?/g, '?');
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text = text.replace(/ ;/g, ';');
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text = text.replace(/ :/g, ':');
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text = text.replace(/ '/g, "'");
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// Remove duplicate quotes
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while (text.includes('""')) {
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text = text.replace('""', '"');
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}
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while (text.includes("''")) {
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text = text.replace("''", "'");
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}
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while (text.includes('``')) {
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text = text.replace('``', '`');
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}
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// Remove extra spaces
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text = text.replace(/\s+/g, ' ').trim();
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// If text doesn't end with punctuation, quotes, or closing brackets, add a period
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if (!/[.!?;:,'\"')\]}…。」』】〉》›»]$/.test(text)) {
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text += '.';
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}
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// Validate language
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if (!AVAILABLE_LANGS.includes(lang)) {
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throw new Error(`Invalid language: ${lang}. Available: ${AVAILABLE_LANGS.join(', ')}`);
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}
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// Wrap text with language tags
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text = `<${lang}>` + text + `</${lang}>`;
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return text;
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}
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_textToUnicodeValues(text) {
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return Array.from(text).map(char => char.charCodeAt(0));
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}
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_getTextMask(textIdsLengths) {
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return lengthToMask(textIdsLengths);
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}
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call(textList, langList) {
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const processedTexts = textList.map((t, i) => this._preprocessText(t, langList[i]));
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const textIdsLengths = processedTexts.map(t => t.length);
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const maxLen = Math.max(...textIdsLengths);
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const textIds = [];
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for (let i = 0; i < processedTexts.length; i++) {
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const row = new Array(maxLen).fill(0);
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const unicodeVals = this._textToUnicodeValues(processedTexts[i]);
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for (let j = 0; j < unicodeVals.length; j++) {
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row[j] = this.indexer[unicodeVals[j]];
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}
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textIds.push(row);
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}
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const textMask = this._getTextMask(textIdsLengths);
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return { textIds, textMask };
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}
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}
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/**
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* Style class
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*/
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class Style {
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constructor(styleTtlOnnx, styleDpOnnx) {
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this.ttl = styleTtlOnnx;
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this.dp = styleDpOnnx;
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}
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}
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/**
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* TextToSpeech class
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*/
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class TextToSpeech {
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constructor(cfgs, textProcessor, dpOrt, textEncOrt, vectorEstOrt, vocoderOrt) {
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this.cfgs = cfgs;
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this.textProcessor = textProcessor;
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this.dpOrt = dpOrt;
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this.textEncOrt = textEncOrt;
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this.vectorEstOrt = vectorEstOrt;
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this.vocoderOrt = vocoderOrt;
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this.sampleRate = cfgs.ae.sample_rate;
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this.baseChunkSize = cfgs.ae.base_chunk_size;
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this.chunkCompressFactor = cfgs.ttl.chunk_compress_factor;
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this.ldim = cfgs.ttl.latent_dim;
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}
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sampleNoisyLatent(duration) {
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const wavLenMax = Math.max(...duration) * this.sampleRate;
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const wavLengths = duration.map(d => Math.floor(d * this.sampleRate));
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const chunkSize = this.baseChunkSize * this.chunkCompressFactor;
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const latentLen = Math.floor((wavLenMax + chunkSize - 1) / chunkSize);
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const latentDim = this.ldim * this.chunkCompressFactor;
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// Generate random noise
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const noisyLatent = [];
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for (let b = 0; b < duration.length; b++) {
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const batch = [];
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for (let d = 0; d < latentDim; d++) {
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const row = [];
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for (let t = 0; t < latentLen; t++) {
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// Box-Muller transform for normal distribution
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// Add epsilon to avoid log(0)
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const eps = 1e-10;
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const u1 = Math.max(eps, Math.random());
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const u2 = Math.random();
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const randNormal = Math.sqrt(-2.0 * Math.log(u1)) * Math.cos(2.0 * Math.PI * u2);
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row.push(randNormal);
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}
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batch.push(row);
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}
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noisyLatent.push(batch);
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}
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const latentMask = getLatentMask(wavLengths, this.baseChunkSize, this.chunkCompressFactor);
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// Apply mask
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for (let b = 0; b < noisyLatent.length; b++) {
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for (let d = 0; d < noisyLatent[b].length; d++) {
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for (let t = 0; t < noisyLatent[b][d].length; t++) {
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noisyLatent[b][d][t] *= latentMask[b][0][t];
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}
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}
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}
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return { noisyLatent, latentMask };
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}
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async _infer(textList, langList, style, totalStep, speed = 1.05) {
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if (textList.length !== style.ttl.dims[0]) {
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throw new Error('Number of texts must match number of style vectors');
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}
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const bsz = textList.length;
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const { textIds, textMask } = this.textProcessor.call(textList, langList);
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const textIdsShape = [bsz, textIds[0].length];
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const textMaskShape = [bsz, 1, textMask[0][0].length];
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const textMaskTensor = arrayToTensor(textMask, textMaskShape);
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const dpResult = await this.dpOrt.run({
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text_ids: intArrayToTensor(textIds, textIdsShape),
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style_dp: style.dp,
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text_mask: textMaskTensor
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});
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const durOnnx = Array.from(dpResult.duration.data);
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// Apply speed factor to duration
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for (let i = 0; i < durOnnx.length; i++) {
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durOnnx[i] /= speed;
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}
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const textEncResult = await this.textEncOrt.run({
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text_ids: intArrayToTensor(textIds, textIdsShape),
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style_ttl: style.ttl,
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text_mask: textMaskTensor
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});
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const textEmbTensor = textEncResult.text_emb;
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let { noisyLatent, latentMask } = this.sampleNoisyLatent(durOnnx);
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const latentShape = [bsz, noisyLatent[0].length, noisyLatent[0][0].length];
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const latentMaskShape = [bsz, 1, latentMask[0][0].length];
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const latentMaskTensor = arrayToTensor(latentMask, latentMaskShape);
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const totalStepArray = new Array(bsz).fill(totalStep);
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const scalarShape = [bsz];
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const totalStepTensor = arrayToTensor(totalStepArray, scalarShape);
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for (let step = 0; step < totalStep; step++) {
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const currentStepArray = new Array(bsz).fill(step);
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const vectorEstResult = await this.vectorEstOrt.run({
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noisy_latent: arrayToTensor(noisyLatent, latentShape),
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text_emb: textEmbTensor,
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style_ttl: style.ttl,
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text_mask: textMaskTensor,
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latent_mask: latentMaskTensor,
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total_step: totalStepTensor,
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current_step: arrayToTensor(currentStepArray, scalarShape)
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});
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const denoisedLatent = Array.from(vectorEstResult.denoised_latent.data);
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// Update latent with the denoised output
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let idx = 0;
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for (let b = 0; b < noisyLatent.length; b++) {
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for (let d = 0; d < noisyLatent[b].length; d++) {
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for (let t = 0; t < noisyLatent[b][d].length; t++) {
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noisyLatent[b][d][t] = denoisedLatent[idx++];
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}
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}
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}
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}
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const vocoderResult = await this.vocoderOrt.run({
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latent: arrayToTensor(noisyLatent, latentShape)
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});
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const wav = Array.from(vocoderResult.wav_tts.data);
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return { wav, duration: durOnnx };
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}
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async call(text, lang, style, totalStep, speed = 1.05, silenceDuration = 0.3) {
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if (style.ttl.dims[0] !== 1) {
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throw new Error('Single speaker text to speech only supports single style');
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}
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const maxLen = lang === 'ko' ? 120 : 300;
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const textList = chunkText(text, maxLen);
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let wavCat = null;
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let durCat = 0;
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for (const chunk of textList) {
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const { wav, duration } = await this._infer([chunk], [lang], style, totalStep, speed);
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if (wavCat === null) {
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wavCat = wav;
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durCat = duration[0];
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} else {
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const silenceLen = Math.floor(silenceDuration * this.sampleRate);
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const silence = new Array(silenceLen).fill(0);
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wavCat = [...wavCat, ...silence, ...wav];
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durCat += duration[0] + silenceDuration;
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}
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}
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return { wav: wavCat, duration: [durCat] };
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}
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async batch(textList, langList, style, totalStep, speed = 1.05) {
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return await this._infer(textList, langList, style, totalStep, speed);
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}
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}
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/**
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* Convert lengths to binary mask
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*/
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function lengthToMask(lengths, maxLen = null) {
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maxLen = maxLen || Math.max(...lengths);
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const mask = [];
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for (let i = 0; i < lengths.length; i++) {
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const row = [];
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for (let j = 0; j < maxLen; j++) {
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row.push(j < lengths[i] ? 1.0 : 0.0);
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}
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mask.push([row]); // [B, 1, maxLen]
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}
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return mask;
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}
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/**
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* Get latent mask from wav lengths
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*/
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function getLatentMask(wavLengths, baseChunkSize, chunkCompressFactor) {
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const latentSize = baseChunkSize * chunkCompressFactor;
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const latentLengths = wavLengths.map(len =>
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Math.floor((len + latentSize - 1) / latentSize)
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);
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return lengthToMask(latentLengths);
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}
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/**
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* Load ONNX model
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*/
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async function loadOnnx(onnxPath, opts) {
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return await ort.InferenceSession.create(onnxPath, opts);
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}
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/**
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* Load all ONNX models for TTS
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*/
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async function loadOnnxAll(onnxDir, opts) {
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const dpPath = path.join(onnxDir, 'duration_predictor.onnx');
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const textEncPath = path.join(onnxDir, 'text_encoder.onnx');
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const vectorEstPath = path.join(onnxDir, 'vector_estimator.onnx');
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const vocoderPath = path.join(onnxDir, 'vocoder.onnx');
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const [dpOrt, textEncOrt, vectorEstOrt, vocoderOrt] = await Promise.all([
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loadOnnx(dpPath, opts),
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loadOnnx(textEncPath, opts),
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loadOnnx(vectorEstPath, opts),
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loadOnnx(vocoderPath, opts)
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]);
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return { dpOrt, textEncOrt, vectorEstOrt, vocoderOrt };
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}
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/**
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* Load configuration
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*/
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function loadCfgs(onnxDir) {
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const cfgPath = path.join(onnxDir, 'tts.json');
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const cfgs = JSON.parse(fs.readFileSync(cfgPath, 'utf8'));
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return cfgs;
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}
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/**
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* Load text processor
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*/
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function loadTextProcessor(onnxDir) {
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const unicodeIndexerPath = path.join(onnxDir, 'unicode_indexer.json');
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const textProcessor = new UnicodeProcessor(unicodeIndexerPath);
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return textProcessor;
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}
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/**
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* Load voice style from JSON file
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*/
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export function loadVoiceStyle(voiceStylePaths, verbose = false) {
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const bsz = voiceStylePaths.length;
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// Read first file to get dimensions
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const firstStyle = JSON.parse(fs.readFileSync(voiceStylePaths[0], 'utf8'));
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const ttlDims = firstStyle.style_ttl.dims;
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const dpDims = firstStyle.style_dp.dims;
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const ttlDim1 = ttlDims[1];
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const ttlDim2 = ttlDims[2];
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const dpDim1 = dpDims[1];
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const dpDim2 = dpDims[2];
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// Pre-allocate arrays with full batch size
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const ttlSize = bsz * ttlDim1 * ttlDim2;
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const dpSize = bsz * dpDim1 * dpDim2;
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const ttlFlat = new Float32Array(ttlSize);
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const dpFlat = new Float32Array(dpSize);
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// Fill in the data
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for (let i = 0; i < bsz; i++) {
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const voiceStyle = JSON.parse(fs.readFileSync(voiceStylePaths[i], 'utf8'));
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const ttlData = voiceStyle.style_ttl.data.flat(Infinity);
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const ttlOffset = i * ttlDim1 * ttlDim2;
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ttlFlat.set(ttlData, ttlOffset);
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const dpData = voiceStyle.style_dp.data.flat(Infinity);
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const dpOffset = i * dpDim1 * dpDim2;
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dpFlat.set(dpData, dpOffset);
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}
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const ttlStyle = new ort.Tensor('float32', ttlFlat, [bsz, ttlDim1, ttlDim2]);
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const dpStyle = new ort.Tensor('float32', dpFlat, [bsz, dpDim1, dpDim2]);
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if (verbose) {
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console.log(`Loaded ${bsz} voice styles`);
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}
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return new Style(ttlStyle, dpStyle);
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}
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/**
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* Load text to speech components
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*/
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export async function loadTextToSpeech(onnxDir, useGpu = false) {
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const opts = {};
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if (useGpu) {
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throw new Error('GPU mode is not supported yet');
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} else {
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console.log('Using CPU for inference');
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}
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const cfgs = loadCfgs(onnxDir);
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const { dpOrt, textEncOrt, vectorEstOrt, vocoderOrt } = await loadOnnxAll(onnxDir, opts);
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const textProcessor = loadTextProcessor(onnxDir);
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const textToSpeech = new TextToSpeech(cfgs, textProcessor, dpOrt, textEncOrt, vectorEstOrt, vocoderOrt);
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return textToSpeech;
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}
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/**
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* Convert 3D array to ONNX tensor
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*/
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function arrayToTensor(array, dims) {
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// Flatten the array
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const flat = array.flat(Infinity);
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return new ort.Tensor('float32', Float32Array.from(flat), dims);
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}
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/**
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* Convert 2D int array to ONNX tensor
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*/
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function intArrayToTensor(array, dims) {
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const flat = array.flat(Infinity);
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return new ort.Tensor('int64', BigInt64Array.from(flat.map(x => BigInt(x))), dims);
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}
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/**
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* Write WAV file
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*/
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export function writeWavFile(filename, audioData, sampleRate) {
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const numChannels = 1;
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const bitsPerSample = 16;
|
||||
const byteRate = sampleRate * numChannels * bitsPerSample / 8;
|
||||
const blockAlign = numChannels * bitsPerSample / 8;
|
||||
const dataSize = audioData.length * bitsPerSample / 8;
|
||||
|
||||
const buffer = Buffer.alloc(44 + dataSize);
|
||||
|
||||
// RIFF header
|
||||
buffer.write('RIFF', 0);
|
||||
buffer.writeUInt32LE(36 + dataSize, 4);
|
||||
buffer.write('WAVE', 8);
|
||||
|
||||
// fmt chunk
|
||||
buffer.write('fmt ', 12);
|
||||
buffer.writeUInt32LE(16, 16); // fmt chunk size
|
||||
buffer.writeUInt16LE(1, 20); // audio format (PCM)
|
||||
buffer.writeUInt16LE(numChannels, 22);
|
||||
buffer.writeUInt32LE(sampleRate, 24);
|
||||
buffer.writeUInt32LE(byteRate, 28);
|
||||
buffer.writeUInt16LE(blockAlign, 32);
|
||||
buffer.writeUInt16LE(bitsPerSample, 34);
|
||||
|
||||
// data chunk
|
||||
buffer.write('data', 36);
|
||||
buffer.writeUInt32LE(dataSize, 40);
|
||||
|
||||
// Write audio data
|
||||
for (let i = 0; i < audioData.length; i++) {
|
||||
const sample = Math.max(-1, Math.min(1, audioData[i]));
|
||||
const intSample = Math.floor(sample * 32767);
|
||||
buffer.writeInt16LE(intSample, 44 + i * 2);
|
||||
}
|
||||
|
||||
fs.writeFileSync(filename, buffer);
|
||||
}
|
||||
|
||||
/**
|
||||
* Timer utility for measuring execution time
|
||||
*/
|
||||
export async function timer(name, fn) {
|
||||
const start = Date.now();
|
||||
console.log(`${name}...`);
|
||||
const result = await fn();
|
||||
const elapsed = ((Date.now() - start) / 1000).toFixed(2);
|
||||
console.log(` -> ${name} completed in ${elapsed} sec`);
|
||||
return result;
|
||||
}
|
||||
|
||||
/**
|
||||
* Sanitize filename by replacing non-alphanumeric characters with underscores (supports Unicode)
|
||||
*/
|
||||
export function sanitizeFilename(text, maxLen) {
|
||||
const prefix = text.substring(0, maxLen);
|
||||
// \p{L} matches any Unicode letter, \p{N} matches any Unicode number
|
||||
return prefix.replace(/[^\p{L}\p{N}_]/gu, '_');
|
||||
}
|
||||
|
||||
/**
|
||||
* Chunk text into manageable segments
|
||||
*/
|
||||
function chunkText(text, maxLen = 300) {
|
||||
if (typeof text !== 'string') {
|
||||
throw new Error(`chunkText expects a string, got ${typeof text}`);
|
||||
}
|
||||
|
||||
// Split by paragraph (two or more newlines)
|
||||
const paragraphs = text.trim().split(/\n\s*\n+/).filter(p => p.trim());
|
||||
|
||||
const chunks = [];
|
||||
|
||||
for (let paragraph of paragraphs) {
|
||||
paragraph = paragraph.trim();
|
||||
if (!paragraph) continue;
|
||||
|
||||
// Split by sentence boundaries (period, question mark, exclamation mark followed by space)
|
||||
// But exclude common abbreviations like Mr., Mrs., Dr., etc. and single capital letters like F.
|
||||
const sentences = paragraph.split(/(?<!Mr\.|Mrs\.|Ms\.|Dr\.|Prof\.|Sr\.|Jr\.|Ph\.D\.|etc\.|e\.g\.|i\.e\.|vs\.|Inc\.|Ltd\.|Co\.|Corp\.|St\.|Ave\.|Blvd\.)(?<!\b[A-Z]\.)(?<=[.!?])\s+/);
|
||||
|
||||
let currentChunk = "";
|
||||
|
||||
for (let sentence of sentences) {
|
||||
if (currentChunk.length + sentence.length + 1 <= maxLen) {
|
||||
currentChunk += (currentChunk ? " " : "") + sentence;
|
||||
} else {
|
||||
if (currentChunk) {
|
||||
chunks.push(currentChunk.trim());
|
||||
}
|
||||
currentChunk = sentence;
|
||||
}
|
||||
}
|
||||
|
||||
if (currentChunk) {
|
||||
chunks.push(currentChunk.trim());
|
||||
}
|
||||
}
|
||||
|
||||
return chunks;
|
||||
}
|
||||
Reference in New Issue
Block a user