Switching to processes and whisper.cpp to avoid transformers.js memory leaks
This commit is contained in:
@@ -1,6 +1,6 @@
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{
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"name": "@ztimson/ai-utils",
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"version": "0.7.7",
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"version": "0.7.8",
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"description": "AI Utility library",
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"author": "Zak Timson",
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"license": "MIT",
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@@ -32,8 +32,7 @@
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"@ztimson/utils": "^0.28.13",
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"cheerio": "^1.2.0",
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"openai": "^6.22.0",
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"tesseract.js": "^7.0.0",
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"wavefile": "^11.0.0"
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"tesseract.js": "^7.0.0"
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},
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"devDependencies": {
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"@types/node": "^24.8.1",
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@@ -12,7 +12,7 @@ export type AiOptions = {
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hfToken?: string;
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/** Path to models */
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path?: string;
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/** ASR model: whisper-tiny, whisper-base */
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/** Whisper ASR model: ggml-tiny.en.bin, ggml-base.en.bin */
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asr?: string;
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/** Embedding model: all-MiniLM-L6-v2, bge-small-en-v1.5, bge-large-en-v1.5 */
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embedder?: string;
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@@ -22,6 +22,8 @@ export type AiOptions = {
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}
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/** OCR model: eng, eng_best, eng_fast */
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ocr?: string;
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/** Whisper binary */
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whisper?: string;
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}
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export class Ai {
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137
src/asr.ts
137
src/asr.ts
@@ -1,137 +0,0 @@
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import { pipeline } from '@xenova/transformers';
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import { parentPort } from 'worker_threads';
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import { spawn } from 'node:child_process';
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import { execSync } from 'node:child_process';
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import { mkdtempSync, rmSync, readFileSync } from 'node:fs';
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import { join } from 'node:path';
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import { tmpdir } from 'node:os';
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import wavefile from 'wavefile';
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let whisperPipeline: any;
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export async function canDiarization(): Promise<string | null> {
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const checkPython = (cmd: string) => {
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return new Promise<boolean>((resolve) => {
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const proc = spawn(cmd, ['-c', 'import pyannote.audio']);
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proc.on('close', (code: number) => resolve(code === 0));
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proc.on('error', () => resolve(false));
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});
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};
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if(await checkPython('python3')) return 'python3';
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if(await checkPython('python')) return 'python';
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return null;
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}
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async function runDiarization(binary: string, audioPath: string, dir: string, token: string): Promise<any[]> {
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const script = `
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import sys
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import json
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import os
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from pyannote.audio import Pipeline
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os.environ['TORCH_HOME'] = r"${dir}"
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pipeline = Pipeline.from_pretrained("pyannote/speaker-diarization-3.1", token="${token}")
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output = pipeline(sys.argv[1])
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segments = []
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for turn, speaker in output.speaker_diarization:
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segments.append({"start": turn.start, "end": turn.end, "speaker": speaker})
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print(json.dumps(segments))
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`;
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return new Promise((resolve, reject) => {
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let output = '';
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const proc = spawn(binary, ['-c', script, audioPath]);
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proc.stdout.on('data', (data: Buffer) => output += data.toString());
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proc.stderr.on('data', (data: Buffer) => console.error(data.toString()));
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proc.on('close', (code: number) => {
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if(code === 0) {
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try {
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resolve(JSON.parse(output));
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} catch (err) {
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reject(new Error('Failed to parse diarization output'));
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}
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} else {
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reject(new Error(`Python process exited with code ${code}`));
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}
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});
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proc.on('error', reject);
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});
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}
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function combineSpeakerTranscript(chunks: any[], speakers: any[]): string {
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const speakerMap = new Map();
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let speakerCount = 0;
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speakers.forEach((seg: any) => {
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if(!speakerMap.has(seg.speaker)) speakerMap.set(seg.speaker, ++speakerCount);
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});
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const lines: string[] = [];
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let currentSpeaker = -1;
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let currentText = '';
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chunks.forEach((chunk: any) => {
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const time = chunk.timestamp[0];
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const speaker = speakers.find((s: any) => time >= s.start && time <= s.end);
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const speakerNum = speaker ? speakerMap.get(speaker.speaker) : 1;
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if (speakerNum !== currentSpeaker) {
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if(currentText) lines.push(`[Speaker ${currentSpeaker}]: ${currentText.trim()}`);
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currentSpeaker = speakerNum;
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currentText = chunk.text;
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} else {
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currentText += chunk.text;
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}
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});
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if(currentText) lines.push(`[Speaker ${currentSpeaker}]: ${currentText.trim()}`);
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return lines.join('\n');
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}
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function prepareAudioBuffer(file: string): [string, Float32Array] {
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let wav: any, tmp;
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try {
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wav = new wavefile.WaveFile(readFileSync(file));
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} catch(err) {
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tmp = join(mkdtempSync(join(tmpdir(), 'audio-')), 'converted.wav');
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execSync(`ffmpeg -i "${file}" -ar 16000 -ac 1 -f wav "${tmp}"`, { stdio: 'ignore' });
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wav = new wavefile.WaveFile(readFileSync(tmp));
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} finally {
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wav.toBitDepth('32f');
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wav.toSampleRate(16000);
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const samples = wav.getSamples();
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if(Array.isArray(samples)) {
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const left = samples[0];
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const right = samples[1];
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const buffer = new Float32Array(left.length);
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for (let i = 0; i < left.length; i++) buffer[i] = (left[i] + right[i]) / 2;
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return [tmp || file, buffer];
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}
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return [tmp || file, samples];
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}
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}
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parentPort?.on('message', async ({ file, speaker, model, modelDir, token }) => {
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let tempFile = null;
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try {
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if(!whisperPipeline) whisperPipeline = await pipeline('automatic-speech-recognition', `Xenova/${model}`, {cache_dir: modelDir, quantized: true});
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const [f, buffer] = prepareAudioBuffer(file);
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tempFile = f !== file ? f : null;
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const hasDiarization = await canDiarization();
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const [transcript, speakers] = await Promise.all([
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whisperPipeline(buffer, {return_timestamps: speaker ? 'word' : false}),
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(!speaker || !token || !hasDiarization) ? Promise.resolve(): runDiarization(hasDiarization, f, modelDir, token),
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]);
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const text = transcript.text?.trim() || null;
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if(!speaker) return parentPort?.postMessage({ text });
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if(!token) return parentPort?.postMessage({ text, error: 'HuggingFace token required' });
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if(!hasDiarization) return parentPort?.postMessage({ text, error: 'Speaker diarization unavailable' });
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const combined = combineSpeakerTranscript(transcript.chunks || [], speakers || []);
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parentPort?.postMessage({ text: combined });
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} catch (err: any) {
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parentPort?.postMessage({ error: err.stack || err.message });
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} finally {
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if(tempFile) rmSync(tempFile, { recursive: true, force: true });
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}
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});
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216
src/audio.ts
216
src/audio.ts
@@ -1,82 +1,172 @@
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import {fileURLToPath} from 'url';
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import {Worker} from 'worker_threads';
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import {execSync, spawn} from 'node:child_process';
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import {mkdtempSync, rmSync} from 'node:fs';
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import fs from 'node:fs/promises';
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import {tmpdir} from 'node:os';
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import Path, {join} from 'node:path';
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import {AbortablePromise, Ai} from './ai.ts';
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import {canDiarization} from './asr.ts';
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import {dirname, join} from 'path';
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export class Audio {
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private busy = false;
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private currentJob: any;
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private queue: Array<{file: string, model: string, speaker: boolean | 'id', modelDir: string, token: string, resolve: any, reject: any}> = [];
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private worker: Worker | null = null;
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private downloads: {[key: string]: Promise<string>} = {};
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private pyannote!: string;
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private whisperModel!: string;
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constructor(private ai: Ai) {}
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private processQueue() {
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if(this.busy || !this.queue.length) return;
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this.busy = true;
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const job = this.queue.shift()!;
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if(!this.worker) {
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this.worker = new Worker(join(dirname(fileURLToPath(import.meta.url)), 'asr.js'));
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this.worker.on('message', this.handleMessage.bind(this));
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this.worker.on('error', this.handleError.bind(this));
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constructor(private ai: Ai) {
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if(ai.options.whisper) {
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this.whisperModel = ai.options.asr?.endsWith('.bin') ? ai.options.asr : ai.options.asr + '.bin';
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this.downloadAsrModel();
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}
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this.currentJob = job;
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this.worker.postMessage({file: job.file, model: job.model, speaker: job.speaker, modelDir: job.modelDir, token: job.token});
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this.pyannote = `
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import sys
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import json
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import os
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from pyannote.audio import Pipeline
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os.environ['TORCH_HOME'] = r"${ai.options.path}"
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pipeline = Pipeline.from_pretrained("pyannote/speaker-diarization-3.1", token="${ai.options.hfToken}")
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output = pipeline(sys.argv[1])
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segments = []
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for turn, speaker in output.speaker_diarization:
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segments.append({"start": turn.start, "end": turn.end, "speaker": speaker})
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print(json.dumps(segments))
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`;
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}
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private handleMessage({text, warning, error}: any) {
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const job = this.currentJob!;
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this.busy = false;
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if(error) job.reject(new Error(error));
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else {
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if(warning) console.warn(warning);
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job.resolve(text);
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}
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this.processQueue();
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}
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private handleError(err: Error) {
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if(this.currentJob) {
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this.currentJob.reject(err);
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this.busy = false;
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this.processQueue();
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}
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}
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asr(file: string, options: { model?: string; speaker?: boolean | 'id' } = {}): AbortablePromise<string | null> {
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const { model = this.ai.options.asr || 'whisper-base', speaker = false } = options;
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let aborted = false;
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const abort = () => { aborted = true; };
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let p = new Promise<string | null>((resolve, reject) => {
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this.queue.push({file, model, speaker, modelDir: <string>this.ai.options.path, token: <string>this.ai.options.hfToken,
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resolve: (text: string | null) => !aborted && resolve(text),
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reject: (err: Error) => !aborted && reject(err)
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private runAsr(file: string, opts: {model?: string, diarization?: boolean} = {}): AbortablePromise<any> {
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let proc: any;
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const p = new Promise<any>((resolve, reject) => {
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this.downloadAsrModel(opts.model).then(m => {
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let output = '';
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const args = [opts.diarization ? '-owts' : '-nt', '-m', m, '-f', file];
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proc = spawn(<string>this.ai.options.whisper, args, {stdio: ['ignore', 'pipe', 'ignore']});
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proc.on('error', (err: Error) => reject(err));
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proc.stdout.on('data', (data: Buffer) => output += data.toString());
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proc.on('close', (code: number) => {
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if(code === 0) {
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if(opts.diarization) {
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try { resolve(JSON.parse(output)); }
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catch(e) { reject(new Error('Failed to parse whisper JSON')); }
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} else {
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resolve(output.trim() || null);
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}
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} else {
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reject(new Error(`Exit code ${code}`));
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}
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});
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});
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this.processQueue();
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});
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return <any>Object.assign(p, {abort: () => proc?.kill('SIGTERM')});
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}
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private runDiarization(file: string): AbortablePromise<any> {
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let aborted = false, abort = () => { aborted = true; };
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const checkPython = (cmd: string) => {
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return new Promise<boolean>((resolve) => {
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const proc = spawn(cmd, ['-c', 'import pyannote.audio']);
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proc.on('close', (code: number) => resolve(code === 0));
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proc.on('error', () => resolve(false));
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});
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};
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const p = Promise.all<any>([
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checkPython('python'),
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checkPython('python3'),
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]).then(<any>(async ([p, p3]: [boolean, boolean]) => {
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if(aborted) return;
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if(!p && !p3) throw new Error('Pyannote is not installed: pip install pyannote.audio');
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const binary = p3 ? 'python3' : 'python';
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let tmp: string | null = null;
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return new Promise((resolve, reject) => {
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tmp = join(mkdtempSync(join(tmpdir(), 'audio-')), 'converted.wav');
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execSync(`ffmpeg -i "${file}" -ar 16000 -ac 1 -f wav "${tmp}"`, { stdio: 'ignore' });
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if(aborted) return;
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let output = '';
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const proc = spawn(binary, ['-c', this.pyannote, tmp]);
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proc.stdout.on('data', (data: Buffer) => output += data.toString());
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proc.stderr.on('data', (data: Buffer) => console.error(data.toString()));
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proc.on('close', (code: number) => {
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if(code === 0) {
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try { resolve(JSON.parse(output)); }
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catch (err) { reject(new Error('Failed to parse diarization output')); }
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} else {
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reject(new Error(`Python process exited with code ${code}`));
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}
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});
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proc.on('error', reject);
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abort = () => proc.kill('SIGTERM');
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}).finally(() => { if(tmp) rmSync(Path.dirname(tmp), { recursive: true, force: true }); });
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}));
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return <any>Object.assign(p, {abort});
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}
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private combineSpeakerTranscript(transcript: any, speakers: any[]): string {
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const speakerMap = new Map();
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let speakerCount = 0;
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speakers.forEach((seg: any) => {
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if(!speakerMap.has(seg.speaker)) speakerMap.set(seg.speaker, ++speakerCount);
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});
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if(options.speaker == 'id') {
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if(!this.ai.language.defaultModel) throw new Error('Configure an LLM for advanced ASR speaker detection');
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p = p.then(async transcript => {
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if(!transcript) return transcript;
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let chunks = this.ai.language.chunk(transcript, 500, 0);
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const lines: string[] = [];
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let currentSpeaker = -1;
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let currentText = '';
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transcript.transcription.forEach((word: any) => {
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const time = word.offsets.from / 1000; // Convert ms to seconds
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const speaker = speakers.find((s: any) => time >= s.start && time <= s.end);
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const speakerNum = speaker ? speakerMap.get(speaker.speaker) : 1;
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if (speakerNum !== currentSpeaker) {
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if(currentText) lines.push(`[Speaker ${currentSpeaker}]: ${currentText.trim()}`);
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currentSpeaker = speakerNum;
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currentText = word.text;
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} else {
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currentText += ' ' + word.text;
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}
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});
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if(currentText) lines.push(`[Speaker ${currentSpeaker}]: ${currentText.trim()}`);
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return lines.join('\n');
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}
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asr(file: string, options: { model?: string; diarization?: boolean | 'id' } = {}): AbortablePromise<string | null> {
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if(!this.ai.options.whisper) throw new Error('Whisper not configured');
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const transcript = this.runAsr(file, {model: options.model, diarization: !!options.diarization});
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const diarization: any = options.diarization ? this.runDiarization(file) : Promise.resolve(null);
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const abort = () => {
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transcript.abort();
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diarization?.abort?.();
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};
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const response = Promise.all([transcript, diarization]).then(async ([t, d]) => {
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if(!options.diarization) return t;
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t = this.combineSpeakerTranscript(t, d);
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if(options.diarization === 'id') {
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if(!this.ai.language.defaultModel) throw new Error('Configure an LLM for advanced ASR speaker detection');
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let chunks = this.ai.language.chunk(t, 500, 0);
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if(chunks.length > 4) chunks = [...chunks.slice(0, 3), <string>chunks.at(-1)];
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const names = await this.ai.language.json(chunks.join('\n'), '{1: "Detected Name", 2: "Second Name"}', {
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system: 'Use the following transcript to identify speakers. Only identify speakers you are positive about, dont mention speakers you are unsure about in your response',
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temperature: 0.1,
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});
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Object.entries(names).forEach(([speaker, name]) => {
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transcript = (<string>transcript).replaceAll(`[Speaker ${speaker}]`, `[${name}]`);
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});
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return transcript;
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})
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}
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return Object.assign(p, { abort });
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Object.entries(names).forEach(([speaker, name]) => t = t.replaceAll(`[Speaker ${speaker}]`, `[${name}]`));
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}
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return t;
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});
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return <any>Object.assign(response, {abort});
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}
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canDiarization = () => canDiarization().then(resp => !!resp);
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async downloadAsrModel(model: string = this.whisperModel): Promise<string> {
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if(!this.ai.options.whisper) throw new Error('Whisper not configured');
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if(!model.endsWith('.bin')) model += '.bin';
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const p = Path.join(<string>this.ai.options.path, model);
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if(await fs.stat(p).then(() => true).catch(() => false)) return p;
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if(!!this.downloads[model]) return this.downloads[model];
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this.downloads[model] = fetch(`https://huggingface.co/ggerganov/whisper.cpp/resolve/main/${model}`)
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.then(resp => resp.arrayBuffer())
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.then(arr => Buffer.from(arr)).then(async buffer => {
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await fs.writeFile(p, buffer);
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delete this.downloads[model];
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return p;
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});
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return this.downloads[model];
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}
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}
|
||||
|
||||
@@ -1,11 +1,13 @@
|
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import { pipeline } from '@xenova/transformers';
|
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import { parentPort } from 'worker_threads';
|
||||
|
||||
let embedder: any;
|
||||
const [modelDir, model] = process.argv.slice(2);
|
||||
|
||||
parentPort?.on('message', async ({text, model, modelDir }) => {
|
||||
if(!embedder) embedder = await pipeline('feature-extraction', 'Xenova/' + model, {quantized: true, cache_dir: modelDir});
|
||||
let text = '';
|
||||
process.stdin.on('data', chunk => text += chunk);
|
||||
process.stdin.on('end', async () => {
|
||||
const embedder = await pipeline('feature-extraction', 'Xenova/' + model, {quantized: true, cache_dir: modelDir});
|
||||
const output = await embedder(text, { pooling: 'mean', normalize: true });
|
||||
const embedding = Array.from(output.data);
|
||||
parentPort?.postMessage({embedding});
|
||||
console.log(JSON.stringify({embedding}));
|
||||
process.exit();
|
||||
});
|
||||
|
||||
@@ -1,6 +1,5 @@
|
||||
export * from './ai';
|
||||
export * from './antrhopic';
|
||||
export * from './asr';
|
||||
export * from './audio';
|
||||
export * from './embedder'
|
||||
export * from './llm';
|
||||
|
||||
66
src/llm.ts
66
src/llm.ts
@@ -4,9 +4,9 @@ import {Anthropic} from './antrhopic.ts';
|
||||
import {OpenAi} from './open-ai.ts';
|
||||
import {LLMProvider} from './provider.ts';
|
||||
import {AiTool} from './tools.ts';
|
||||
import {Worker} from 'worker_threads';
|
||||
import {fileURLToPath} from 'url';
|
||||
import {dirname, join} from 'path';
|
||||
import { spawn } from 'node:child_process';
|
||||
|
||||
export type AnthropicConfig = {proto: 'anthropic', token: string};
|
||||
export type OllamaConfig = {proto: 'ollama', host: string};
|
||||
@@ -258,34 +258,54 @@ class LLM {
|
||||
* @param {maxTokens?: number, overlapTokens?: number} opts Options for embedding such as chunk sizes
|
||||
* @returns {Promise<Awaited<{index: number, embedding: number[], text: string, tokens: number}>[]>} Chunked embeddings
|
||||
*/
|
||||
async embedding(target: object | string, opts: {maxTokens?: number, overlapTokens?: number} = {}) {
|
||||
embedding(target: object | string, opts: {maxTokens?: number, overlapTokens?: number} = {}): AbortablePromise<any[]> {
|
||||
let {maxTokens = 500, overlapTokens = 50} = opts;
|
||||
let aborted = false;
|
||||
const abort = () => { aborted = true; };
|
||||
|
||||
const embed = (text: string): Promise<number[]> => {
|
||||
return new Promise((resolve, reject) => {
|
||||
const worker = new Worker(join(dirname(fileURLToPath(import.meta.url)), 'embedder.js'));
|
||||
const handleMessage = ({ embedding }: any) => {
|
||||
worker.terminate();
|
||||
resolve(embedding);
|
||||
};
|
||||
const handleError = (err: Error) => {
|
||||
worker.terminate();
|
||||
reject(err);
|
||||
};
|
||||
worker.on('message', handleMessage);
|
||||
worker.on('error', handleError);
|
||||
worker.on('exit', (code) => {
|
||||
if(code !== 0) reject(new Error(`Worker exited with code ${code}`));
|
||||
if(aborted) return reject(new Error('Aborted'));
|
||||
|
||||
const args: string[] = [
|
||||
join(dirname(fileURLToPath(import.meta.url)), 'embedder.js'),
|
||||
<string>this.ai.options.path,
|
||||
this.ai.options?.embedder || 'bge-small-en-v1.5'
|
||||
];
|
||||
const proc = spawn('node', args, {stdio: ['pipe', 'pipe', 'ignore']});
|
||||
proc.stdin.write(text);
|
||||
proc.stdin.end();
|
||||
|
||||
let output = '';
|
||||
proc.stdout.on('data', (data: Buffer) => output += data.toString());
|
||||
proc.on('close', (code: number) => {
|
||||
if(aborted) return reject(new Error('Aborted'));
|
||||
if(code === 0) {
|
||||
try {
|
||||
const result = JSON.parse(output);
|
||||
resolve(result.embedding);
|
||||
} catch(err) {
|
||||
reject(new Error('Failed to parse embedding output'));
|
||||
}
|
||||
} else {
|
||||
reject(new Error(`Embedder process exited with code ${code}`));
|
||||
}
|
||||
});
|
||||
worker.postMessage({text, model: this.ai.options?.embedder || 'bge-small-en-v1.5', modelDir: this.ai.options.path});
|
||||
proc.on('error', reject);
|
||||
});
|
||||
};
|
||||
const chunks = this.chunk(target, maxTokens, overlapTokens), results: any[] = [];
|
||||
for(let i = 0; i < chunks.length; i++) {
|
||||
const text= chunks[i];
|
||||
const embedding = await embed(text);
|
||||
results.push({index: i, embedding, text, tokens: this.estimateTokens(text)});
|
||||
}
|
||||
return results;
|
||||
|
||||
const p = (async () => {
|
||||
const chunks = this.chunk(target, maxTokens, overlapTokens), results: any[] = [];
|
||||
for(let i = 0; i < chunks.length; i++) {
|
||||
if(aborted) break;
|
||||
const text = chunks[i];
|
||||
const embedding = await embed(text);
|
||||
results.push({index: i, embedding, text, tokens: this.estimateTokens(text)});
|
||||
}
|
||||
return results;
|
||||
})();
|
||||
return Object.assign(p, { abort });
|
||||
}
|
||||
|
||||
/**
|
||||
|
||||
@@ -2,43 +2,22 @@ import {createWorker} from 'tesseract.js';
|
||||
import {AbortablePromise, Ai} from './ai.ts';
|
||||
|
||||
export class Vision {
|
||||
private worker: any = null;
|
||||
private queue: Array<{ path: string, resolve: any, reject: any }> = [];
|
||||
private busy = false;
|
||||
|
||||
constructor(private ai: Ai) {}
|
||||
|
||||
private async processQueue() {
|
||||
if(this.busy || !this.queue.length) return;
|
||||
this.busy = true;
|
||||
const job = this.queue.shift()!;
|
||||
if(!this.worker) this.worker = await createWorker(this.ai.options.ocr || 'eng', 2, {cachePath: this.ai.options.path});
|
||||
try {
|
||||
const {data} = await this.worker.recognize(job.path);
|
||||
job.resolve(data.text.trim() || null);
|
||||
} catch(err) {
|
||||
job.reject(err);
|
||||
}
|
||||
this.busy = false;
|
||||
this.processQueue();
|
||||
}
|
||||
|
||||
/**
|
||||
* Convert image to text using Optical Character Recognition
|
||||
* @param {string} path Path to image
|
||||
* @returns {AbortablePromise<string | null>} Promise of extracted text with abort method
|
||||
*/
|
||||
ocr(path: string): AbortablePromise<string | null> {
|
||||
let aborted = false;
|
||||
const abort = () => { aborted = true; };
|
||||
const p = new Promise<string | null>((resolve, reject) => {
|
||||
this.queue.push({
|
||||
path,
|
||||
resolve: (text: string | null) => !aborted && resolve(text),
|
||||
reject: (err: Error) => !aborted && reject(err)
|
||||
});
|
||||
this.processQueue();
|
||||
let worker: any;
|
||||
const p = new Promise<string | null>(async res => {
|
||||
worker = await createWorker(this.ai.options.ocr || 'eng', 2, {cachePath: this.ai.options.path});
|
||||
const {data} = await worker.recognize(path);
|
||||
await worker.terminate();
|
||||
res(data.text.trim() || null);
|
||||
});
|
||||
return Object.assign(p, {abort});
|
||||
return Object.assign(p, {abort: () => worker?.terminate()});
|
||||
}
|
||||
}
|
||||
|
||||
@@ -5,7 +5,6 @@ export default defineConfig({
|
||||
build: {
|
||||
lib: {
|
||||
entry: {
|
||||
asr: './src/asr.ts',
|
||||
index: './src/index.ts',
|
||||
embedder: './src/embedder.ts',
|
||||
},
|
||||
|
||||
Reference in New Issue
Block a user