Working speaker detection with advanced LLM identifying. Improved LLM json function

src/asr.ts

@@ -1,14 +1,17 @@
 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';
+import { execSync } from 'node:child_process';
+import { mkdtempSync, rmSync, readFileSync } from 'node:fs';
+import { join } from 'node:path';
+import { tmpdir } from 'node:os';
+import wavefile from 'wavefile';
 
 let whisperPipeline: any;
 
 export async function canDiarization(): Promise<boolean> {
 	return new Promise((resolve) => {
-		const proc = spawn('python3', ['-c', 'import pyannote.audio']);
+		const proc = spawn('python', ['-c', 'import pyannote.audio']);
 		proc.on('close', (code: number) => resolve(code === 0));
 		proc.on('error', () => resolve(false));
 	});
@@ -21,25 +24,20 @@ import json
 import os
 from pyannote.audio import Pipeline
 
-os.environ['TORCH_HOME'] = "${dir}"
-os.environ['HF_TOKEN'] = "${token}"
-pipeline = Pipeline.from_pretrained("pyannote/speaker-diarization-3.1")
-diarization = pipeline(sys.argv[1])
+os.environ['TORCH_HOME'] = r"${dir}"
+pipeline = Pipeline.from_pretrained("pyannote/speaker-diarization-3.1", token="${token}")
+output = pipeline(sys.argv[1])
 
 segments = []
-for turn, _, speaker in diarization.itertracks(yield_label=True):
-    segments.append({
-        "start": turn.start,
-        "end": turn.end,
-        "speaker": speaker
-    })
+for turn, speaker in output.speaker_diarization:
+    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]);
+		const proc = spawn('python', ['-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) => {
@@ -72,55 +70,65 @@ function combineSpeakerTranscript(chunks: any[], speakers: any[]): string {
 		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()}`);
+			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()}`);
+	if(currentText) lines.push(`[Speaker ${currentSpeaker}]: ${currentText.trim()}`);
 	return lines.join('\n');
 }
 
+function prepareAudioBuffer(file: string): [string, Float32Array] {
+	let wav: any, tmp;
+	try {
+		wav = new wavefile.WaveFile(readFileSync(file));
+	} catch(err) {
+		tmp = join(mkdtempSync(join(tmpdir(), 'audio-')), 'converted.wav');
+		execSync(`ffmpeg -i "${file}" -ar 16000 -ac 1 -f wav "${tmp}"`, { stdio: 'ignore' });
+		wav = new wavefile.WaveFile(readFileSync(tmp));
+	} finally {
+		wav.toBitDepth('32f');
+		wav.toSampleRate(16000);
+		const samples = wav.getSamples();
+		if(Array.isArray(samples)) {
+			const left = samples[0];
+			const right = samples[1];
+			const buffer = new Float32Array(left.length);
+			for (let i = 0; i < left.length; i++) buffer[i] = (left[i] + right[i]) / 2;
+			return [tmp || file, buffer];
+		}
+		return [tmp || file, samples];
+	}
+}
+
 parentPort?.on('message', async ({ file, speaker, model, modelDir, token }) => {
 	try {
-		console.log('worker', file);
 		if(!whisperPipeline) whisperPipeline = await pipeline('automatic-speech-recognition', `Xenova/${model}`, {cache_dir: modelDir, quantized: true});
 
-		// Prepare audio file (convert to mono channel wave)
-		const wav = new wavefile.WaveFile(fs.readFileSync(file));
-		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;
-		}
+		// Prepare audio file
+		const [f, buffer] = prepareAudioBuffer(file);
 
-		// Transcribe
-		const transcriptResult = await whisperPipeline(buffer, {return_timestamps: speaker ? 'word' : false});
-		if(!speaker) {
-			parentPort?.postMessage({ text: transcriptResult.text?.trim() || null });
-			return;
-		}
+		// Fetch transcript and speakers
+		const hasDiarization = speaker && await canDiarization();
+		const [transcript, speakers] = await Promise.all([
+			whisperPipeline(buffer, {return_timestamps: speaker ? 'word' : false}),
+			(!speaker || !token || !hasDiarization) ? Promise.resolve(): runDiarization(f, modelDir, token),
+		]);
+		if(file != f) rmSync(f, { recursive: true, force: true });
 
-		// Speaker Diarization
-		const hasDiarization = await canDiarization();
-		if(!token || !hasDiarization) {
-			parentPort?.postMessage({ text: transcriptResult.text?.trim() || null, error: 'Speaker diarization unavailable' });
-			return;
-		}
+		// Return any results / errors if no more processing required
+		const text = transcript.text?.trim() || null;
+		if(!speaker) return parentPort?.postMessage({ text });
+		if(!token) return parentPort?.postMessage({ text, error: 'HuggingFace token required' });
+		if(!hasDiarization) return parentPort?.postMessage({ text, error: 'Speaker diarization unavailable' });
 
-		const speakers = await runDiarization(file, modelDir, token);
-		const combined = combineSpeakerTranscript(transcriptResult.chunks || [], speakers);
+		// Combine transcript and speakers
+		const combined = combineSpeakerTranscript(transcript.chunks || [], speakers || []);
 		parentPort?.postMessage({ text: combined });
-	} catch (err) {
-		parentPort?.postMessage({ error: (err as Error).message });
+	} catch (err: any) {
+		parentPort?.postMessage({ error: err.stack || err.message });
 	}
 });