audio worker fix

package.json

@@ -1,6 +1,6 @@
 {
 	"name": "@ztimson/ai-utils",
-	"version": "0.6.7-rc.1",
+	"version": "0.6.9",
 	"description": "AI Utility library",
 	"author": "Zak Timson",
 	"license": "MIT",

src/audio.ts

@@ -1,19 +1,19 @@
+import {fileURLToPath} from 'url';
 import {Worker} from 'worker_threads';
-import Path from 'node:path';
 import {AbortablePromise, Ai} from './ai.ts';
 import {canDiarization} from './asr.ts';
+import {dirname, join} from 'path';
 
 export class Audio {
 	constructor(private ai: Ai) {}
 
 	asr(file: string, options: { model?: string; speaker?: boolean } = {}): AbortablePromise<string | null> {
-		console.log('audio', file);
 		const { model = this.ai.options.asr || 'whisper-base', speaker = false } = options;
 		let aborted = false;
 		const abort = () => { aborted = true; };
 
 		const p = new Promise<string | null>((resolve, reject) => {
-			const worker = new Worker(Path.join(import.meta.dirname, 'asr.js'));
+			const worker = new Worker(join(dirname(fileURLToPath(import.meta.url)), 'asr.js'));
 			const handleMessage = ({ text, warning, error }: any) => {
 				worker.terminate();
 				if(aborted) return;

src/embedder.ts

@@ -3,9 +3,9 @@ import { parentPort } from 'worker_threads';
 
 let embedder: any;
 
-parentPort?.on('message', async ({ id, text, model, modelDir }) => {
+parentPort?.on('message', async ({text, model, modelDir }) => {
 	if(!embedder) 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({ id, embedding });
+	parentPort?.postMessage({embedding});
 });

src/llm.ts

@@ -271,7 +271,7 @@ class LLM {
 				worker.on('exit', (code) => {
 					if(code !== 0) reject(new Error(`Worker exited with code ${code}`));
 				});
-				worker.postMessage({text, model: this.ai.options?.embedder || 'bge-small-en-v1.5', path: this.ai.options.path});
+				worker.postMessage({text, model: this.ai.options?.embedder || 'bge-small-en-v1.5', modelDir: this.ai.options.path});
 			});
 		};
 		const chunks = this.chunk(target, maxTokens, overlapTokens);