Update index.js

index.js

@@ -1,6 +1,6 @@
-class MyClassificationPipeline {
-  static task = 'text-classification';
-  static model = 'Xenova/distilbert-base-uncased-finetuned-sst-2-english';
+class MyPipeline {
+  static task = 'automatic-speech-recognition';
+  static model = 'Xenova/whisper-tiny.en';
   static instance = null;
 
   static async getInstance(progress_callback = null) {
@@ -17,22 +17,51 @@ class MyClassificationPipeline {
     return this.instance;
   }
 }
-//MyClassificationPipeline.getInstance();
+//MyPipeline.getInstance();
 
 
 const http = require('http');
 const url = require('url');
+const wavefile = require('wavefile');
 
 http.createServer(async (req, res) => {
   res.writeHead(200, {'Content-Type': 'text/html'});
   var u = url.parse(req.url, true);
 
   if (u.query.q) {
-    const classifier = await MyClassificationPipeline.getInstance();
-    const response = await classifier(u.query.q);
-    res.end(JSON.stringify(response));
+    const transcriber = await MyPipeline.getInstance();
+
+    //let url = 'https://huggingface.co/datasets/Xenova/transformers.js-docs/resolve/main/jfk.wav';
+    let buffer = Buffer.from(await fetch(u.query.q).then(x => x.arrayBuffer()))
+
+    // Read .wav file and convert it to required format
+    let wav = new wavefile.WaveFile(buffer);
+    wav.toBitDepth('32f'); // Pipeline expects input as a Float32Array
+    wav.toSampleRate(16000); // Whisper expects audio with a sampling rate of 16000
+    let audioData = wav.getSamples();
+    if (Array.isArray(audioData)) {
+        if (audioData.length > 1) {
+            const SCALING_FACTOR = Math.sqrt(2);
+
+            // Merge channels (into first channel to save memory)
+            for (let i = 0; i < audioData[0].length; ++i) {
+                audioData[0][i] = SCALING_FACTOR * (audioData[0][i] + audioData[1][i]) / 2;
+            }
+        }
+
+        // Select first channel
+        audioData = audioData[0];
+    }
+
+    // Run model
+    let start = performance.now();
+    let output = await transcriber(audioData);
+    let end = performance.now();
+    
+    res.write(`Execution duration: ${(end - start) / 1000} seconds <br/>`);
+    res.end(JSON.stringify(output));
   } else {
-    res.end("Empty query string");
+    res.end("Empty query");
   }
   
 }).listen(8080);