Added hugging face token

src/asr.ts

@@ -14,14 +14,15 @@ export async function canDiarization(): Promise<boolean> {
 	});
 }
 
-async function runDiarization(audioPath: string, torchHome: string): Promise<any[]> {
+async function runDiarization(audioPath: string, dir: string, token: string): Promise<any[]> {
 	const script = `
 import sys
 import json
 import os
 from pyannote.audio import Pipeline
 
-os.environ['TORCH_HOME'] = "${torchHome}"
+os.environ['TORCH_HOME'] = "${dir}"
+os.environ['HF_TOKEN'] = "${token}"
 pipeline = Pipeline.from_pretrained("pyannote/speaker-diarization-3.1")
 diarization = pipeline(sys.argv[1])
 
@@ -82,7 +83,7 @@ function combineSpeakerTranscript(chunks: any[], speakers: any[]): string {
 	return lines.join('\n');
 }
 
-parentPort?.on('message', async ({ file, speaker, model, modelDir }) => {
+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});
@@ -111,12 +112,12 @@ parentPort?.on('message', async ({ file, speaker, model, modelDir }) => {
 
 		// Speaker Diarization
 		const hasDiarization = await canDiarization();
-		if(!hasDiarization) {
+		if(!token || !hasDiarization) {
 			parentPort?.postMessage({ text: transcriptResult.text?.trim() || null, error: 'Speaker diarization unavailable' });
 			return;
 		}
 
-		const speakers = await runDiarization(file, modelDir);
+		const speakers = await runDiarization(file, modelDir, token);
 		const combined = combineSpeakerTranscript(transcriptResult.chunks || [], speakers);
 		parentPort?.postMessage({ text: combined });
 	} catch (err) {