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ANLGBOY
396
web/helper.js
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396
web/helper.js
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import * as ort from 'onnxruntime-web';
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/**
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* Unicode Text Processor
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*/
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export class UnicodeProcessor {
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constructor(indexer) {
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this.indexer = indexer;
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}
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call(textList) {
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const processedTexts = textList.map(text => this.preprocessText(text));
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const textIdsLengths = processedTexts.map(text => text.length);
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const maxLen = Math.max(...textIdsLengths);
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const textIds = processedTexts.map(text => {
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const row = new Array(maxLen).fill(0);
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for (let j = 0; j < text.length; j++) {
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const codePoint = text.codePointAt(j);
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row[j] = (codePoint < this.indexer.length) ? this.indexer[codePoint] : -1;
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}
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return 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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preprocessText(text) {
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return text.normalize('NFKC');
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}
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getTextMask(textIdsLengths) {
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const maxLen = Math.max(...textIdsLengths);
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return this.lengthToMask(textIdsLengths, maxLen);
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}
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lengthToMask(lengths, maxLen = null) {
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const actualMaxLen = maxLen || Math.max(...lengths);
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return lengths.map(len => {
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const row = new Array(actualMaxLen).fill(0.0);
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for (let j = 0; j < Math.min(len, actualMaxLen); j++) {
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row[j] = 1.0;
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}
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return [row];
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});
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}
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}
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/**
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* Style class to hold TTL and DP tensors
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*/
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export class Style {
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constructor(ttlTensor, dpTensor) {
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this.ttl = ttlTensor;
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this.dp = dpTensor;
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}
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}
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/**
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* Text-to-Speech class
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*/
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export 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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}
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async call(textList, style, totalStep, progressCallback = null) {
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const bsz = textList.length;
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// Process text
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const { textIds, textMask } = this.textProcessor.call(textList);
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const textIdsFlat = new BigInt64Array(textIds.flat().map(x => BigInt(x)));
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const textIdsShape = [bsz, textIds[0].length];
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const textIdsTensor = new ort.Tensor('int64', textIdsFlat, textIdsShape);
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const textMaskFlat = new Float32Array(textMask.flat(2));
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const textMaskShape = [bsz, 1, textMask[0][0].length];
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const textMaskTensor = new ort.Tensor('float32', textMaskFlat, textMaskShape);
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// Predict duration
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const dpOutputs = await this.dpOrt.run({
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text_ids: textIdsTensor,
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style_dp: style.dp,
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text_mask: textMaskTensor
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});
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const duration = Array.from(dpOutputs.duration.data);
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// Encode text
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const textEncOutputs = await this.textEncOrt.run({
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text_ids: textIdsTensor,
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style_ttl: style.ttl,
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text_mask: textMaskTensor
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});
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const textEmb = textEncOutputs.text_emb;
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// Sample noisy latent
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let { xt, latentMask } = this.sampleNoisyLatent(
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duration,
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this.sampleRate,
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this.cfgs.ae.base_chunk_size,
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this.cfgs.ttl.chunk_compress_factor,
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this.cfgs.ttl.latent_dim
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);
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const latentMaskFlat = new Float32Array(latentMask.flat(2));
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const latentMaskShape = [bsz, 1, latentMask[0][0].length];
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const latentMaskTensor = new ort.Tensor('float32', latentMaskFlat, latentMaskShape);
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// Prepare constant arrays
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const totalStepArray = new Float32Array(bsz).fill(totalStep);
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const totalStepTensor = new ort.Tensor('float32', totalStepArray, [bsz]);
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// Denoising loop
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for (let step = 0; step < totalStep; step++) {
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if (progressCallback) {
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progressCallback(step + 1, totalStep);
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}
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const currentStepArray = new Float32Array(bsz).fill(step);
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const currentStepTensor = new ort.Tensor('float32', currentStepArray, [bsz]);
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const xtFlat = new Float32Array(xt.flat(2));
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const xtShape = [bsz, xt[0].length, xt[0][0].length];
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const xtTensor = new ort.Tensor('float32', xtFlat, xtShape);
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const vectorEstOutputs = await this.vectorEstOrt.run({
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noisy_latent: xtTensor,
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text_emb: textEmb,
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style_ttl: style.ttl,
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latent_mask: latentMaskTensor,
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text_mask: textMaskTensor,
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current_step: currentStepTensor,
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total_step: totalStepTensor
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});
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const denoised = Array.from(vectorEstOutputs.denoised_latent.data);
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// Reshape to 3D
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const latentDim = xt[0].length;
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const latentLen = xt[0][0].length;
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xt = [];
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let idx = 0;
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for (let b = 0; b < bsz; 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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row.push(denoised[idx++]);
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}
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batch.push(row);
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}
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xt.push(batch);
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}
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}
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// Generate waveform
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const finalXtFlat = new Float32Array(xt.flat(2));
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const finalXtShape = [bsz, xt[0].length, xt[0][0].length];
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const finalXtTensor = new ort.Tensor('float32', finalXtFlat, finalXtShape);
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const vocoderOutputs = await this.vocoderOrt.run({
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latent: finalXtTensor
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});
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const wav = Array.from(vocoderOutputs.wav_tts.data);
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return { wav, duration };
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}
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sampleNoisyLatent(duration, sampleRate, baseChunkSize, chunkCompress, latentDim) {
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const bsz = duration.length;
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const maxDur = Math.max(...duration);
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const wavLenMax = Math.floor(maxDur * sampleRate);
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const wavLengths = duration.map(d => Math.floor(d * sampleRate));
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const chunkSize = baseChunkSize * chunkCompress;
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const latentLen = Math.floor((wavLenMax + chunkSize - 1) / chunkSize);
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const latentDimVal = latentDim * chunkCompress;
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const xt = [];
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for (let b = 0; b < bsz; b++) {
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const batch = [];
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for (let d = 0; d < latentDimVal; 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
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const u1 = Math.max(0.0001, Math.random());
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const u2 = Math.random();
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const val = Math.sqrt(-2.0 * Math.log(u1)) * Math.cos(2.0 * Math.PI * u2);
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row.push(val);
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}
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batch.push(row);
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}
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xt.push(batch);
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}
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const latentLengths = wavLengths.map(len => Math.floor((len + chunkSize - 1) / chunkSize));
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const latentMask = this.lengthToMask(latentLengths, latentLen);
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// Apply mask
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for (let b = 0; b < bsz; b++) {
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for (let d = 0; d < latentDimVal; d++) {
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for (let t = 0; t < latentLen; t++) {
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xt[b][d][t] *= latentMask[b][0][t];
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}
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}
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}
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return { xt, latentMask };
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}
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lengthToMask(lengths, maxLen = null) {
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const actualMaxLen = maxLen || Math.max(...lengths);
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return lengths.map(len => {
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const row = new Array(actualMaxLen).fill(0.0);
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for (let j = 0; j < Math.min(len, actualMaxLen); j++) {
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row[j] = 1.0;
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}
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return [row];
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});
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}
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}
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/**
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* Load voice style from JSON files
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*/
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export async 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 firstResponse = await fetch(voiceStylePaths[0]);
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const firstStyle = await firstResponse.json();
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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 response = await fetch(voiceStylePaths[i]);
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const voiceStyle = await response.json();
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// Flatten TTL data
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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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// Flatten DP data
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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 ttlShape = [bsz, ttlDim1, ttlDim2];
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const dpShape = [bsz, dpDim1, dpDim2];
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const ttlTensor = new ort.Tensor('float32', ttlFlat, ttlShape);
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const dpTensor = new ort.Tensor('float32', dpFlat, dpShape);
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if (verbose) {
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console.log(`Loaded ${bsz} voice styles`);
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}
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return new Style(ttlTensor, dpTensor);
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}
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/**
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* Load configuration from JSON
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*/
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export async function loadCfgs(onnxDir) {
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const response = await fetch(`${onnxDir}/tts.json`);
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const cfgs = await response.json();
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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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export async function loadTextProcessor(onnxDir) {
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const response = await fetch(`${onnxDir}/unicode_indexer.json`);
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const indexer = await response.json();
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return new UnicodeProcessor(indexer);
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}
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/**
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* Load ONNX model
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*/
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export async function loadOnnx(onnxPath, options) {
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const session = await ort.InferenceSession.create(onnxPath, options);
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return session;
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}
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/**
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* Load all TTS components
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*/
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export async function loadTextToSpeech(onnxDir, sessionOptions = {}, progressCallback = null) {
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console.log('Using WebAssembly/WebGPU for inference');
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const cfgs = await loadCfgs(onnxDir);
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const dpPath = `${onnxDir}/duration_predictor.onnx`;
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const textEncPath = `${onnxDir}/text_encoder.onnx`;
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const vectorEstPath = `${onnxDir}/vector_estimator.onnx`;
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const vocoderPath = `${onnxDir}/vocoder.onnx`;
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const modelPaths = [
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{ name: 'Duration Predictor', path: dpPath },
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{ name: 'Text Encoder', path: textEncPath },
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{ name: 'Vector Estimator', path: vectorEstPath },
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{ name: 'Vocoder', path: vocoderPath }
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];
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const sessions = [];
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for (let i = 0; i < modelPaths.length; i++) {
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if (progressCallback) {
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progressCallback(modelPaths[i].name, i + 1, modelPaths.length);
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}
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const session = await loadOnnx(modelPaths[i].path, sessionOptions);
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sessions.push(session);
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}
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const [dpOrt, textEncOrt, vectorEstOrt, vocoderOrt] = sessions;
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const textProcessor = await loadTextProcessor(onnxDir);
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const textToSpeech = new TextToSpeech(cfgs, textProcessor, dpOrt, textEncOrt, vectorEstOrt, vocoderOrt);
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return { textToSpeech, cfgs };
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}
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/**
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* Write WAV file to ArrayBuffer
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*/
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export function writeWavFile(audioData, sampleRate) {
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const numChannels = 1;
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const bitsPerSample = 16;
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const byteRate = sampleRate * numChannels * bitsPerSample / 8;
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const blockAlign = numChannels * bitsPerSample / 8;
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const dataSize = audioData.length * 2;
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// Create ArrayBuffer
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const buffer = new ArrayBuffer(44 + dataSize);
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const view = new DataView(buffer);
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// Write WAV header
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const writeString = (offset, string) => {
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for (let i = 0; i < string.length; i++) {
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view.setUint8(offset + i, string.charCodeAt(i));
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}
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};
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writeString(0, 'RIFF');
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view.setUint32(4, 36 + dataSize, true);
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writeString(8, 'WAVE');
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writeString(12, 'fmt ');
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view.setUint32(16, 16, true);
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view.setUint16(20, 1, true); // PCM
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view.setUint16(22, numChannels, true);
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view.setUint32(24, sampleRate, true);
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view.setUint32(28, byteRate, true);
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view.setUint16(32, blockAlign, true);
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view.setUint16(34, bitsPerSample, true);
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writeString(36, 'data');
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view.setUint32(40, dataSize, true);
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// Write audio data
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const int16Data = new Int16Array(audioData.length);
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for (let i = 0; i < audioData.length; i++) {
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const clamped = Math.max(-1.0, Math.min(1.0, audioData[i]));
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int16Data[i] = Math.floor(clamped * 32767);
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}
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const dataView = new Uint8Array(buffer, 44);
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dataView.set(new Uint8Array(int16Data.buffer));
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return buffer;
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}
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