Use one-off workers to process requests without blocking
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This commit is contained in:
2026-02-12 19:45:17 -05:00
parent 3ed206923f
commit ad1ee48763
6 changed files with 168 additions and 140 deletions

124
src/asr.ts Normal file
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import { pipeline } from '@xenova/transformers';
import { parentPort } from 'worker_threads';
import * as fs from 'node:fs';
import wavefile from 'wavefile';
import { spawn } from 'node:child_process';
let whisperPipeline: any;
export async function canDiarization(): Promise<boolean> {
return new Promise((resolve) => {
const proc = spawn('python3', ['-c', 'import pyannote.audio']);
proc.on('close', (code: number) => resolve(code === 0));
proc.on('error', () => resolve(false));
});
}
async function runDiarization(audioPath: string, torchHome: string): Promise<any[]> {
const script = `
import sys
import json
import os
from pyannote.audio import Pipeline
os.environ['TORCH_HOME'] = "${torchHome}"
pipeline = Pipeline.from_pretrained("pyannote/speaker-diarization-3.1")
diarization = pipeline(sys.argv[1])
segments = []
for turn, _, speaker in diarization.itertracks(yield_label=True):
segments.append({
"start": turn.start,
"end": turn.end,
"speaker": speaker
})
print(json.dumps(segments))
`;
return new Promise((resolve, reject) => {
let output = '';
const proc = spawn('python3', ['-c', script, audioPath]);
proc.stdout.on('data', (data: Buffer) => output += data.toString());
proc.stderr.on('data', (data: Buffer) => console.error(data.toString()));
proc.on('close', (code: number) => {
if(code === 0) {
try {
resolve(JSON.parse(output));
} catch (err) {
reject(new Error('Failed to parse diarization output'));
}
} else {
reject(new Error(`Python process exited with code ${code}`));
}
});
proc.on('error', reject);
});
}
function combineSpeakerTranscript(chunks: any[], speakers: any[]): string {
const speakerMap = new Map();
let speakerCount = 0;
speakers.forEach((seg: any) => {
if(!speakerMap.has(seg.speaker)) speakerMap.set(seg.speaker, ++speakerCount);
});
const lines: string[] = [];
let currentSpeaker = -1;
let currentText = '';
chunks.forEach((chunk: any) => {
const time = chunk.timestamp[0];
const speaker = speakers.find((s: any) => time >= s.start && time <= s.end);
const speakerNum = speaker ? speakerMap.get(speaker.speaker) : 1;
if (speakerNum !== currentSpeaker) {
if(currentText) lines.push(`[speaker ${currentSpeaker}]: ${currentText.trim()}`);
currentSpeaker = speakerNum;
currentText = chunk.text;
} else {
currentText += chunk.text;
}
});
if(currentText) lines.push(`[speaker ${currentSpeaker}]: ${currentText.trim()}`);
return lines.join('\n');
}
parentPort?.on('message', async ({ path, model, speaker, torchHome }) => {
try {
if(!whisperPipeline) whisperPipeline = await pipeline('automatic-speech-recognition', `Xenova/${model}`, {cache_dir: torchHome, quantized: true});
// Prepare audio file (convert to mono channel wave)
const wav = new wavefile.WaveFile(fs.readFileSync(path));
wav.toBitDepth('32f');
wav.toSampleRate(16000);
const samples = wav.getSamples();
let buffer;
if(Array.isArray(samples)) { // stereo to mono - average the channels
const left = samples[0];
const right = samples[1];
buffer = new Float32Array(left.length);
for (let i = 0; i < left.length; i++) buffer[i] = (left[i] + right[i]) / 2;
} else {
buffer = samples;
}
// Transcribe
const transcriptResult = await whisperPipeline(buffer, {return_timestamps: speaker ? 'word' : false});
if(!speaker) {
parentPort?.postMessage({ text: transcriptResult.text?.trim() || null });
return;
}
// Speaker Diarization
const hasDiarization = await canDiarization();
if(!hasDiarization) {
parentPort?.postMessage({ text: transcriptResult.text?.trim() || null, warning: 'Speaker diarization unavailable' });
return;
}
const speakers = await runDiarization(path, torchHome);
const combined = combineSpeakerTranscript(transcriptResult.chunks || [], speakers);
parentPort?.postMessage({ text: combined });
} catch (err) {
parentPort?.postMessage({ error: (err as Error).message });
}
});