Switching to processes and whisper.cpp to avoid transformers.js memory leaks
This commit is contained in:
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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}
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