Example for whisper in Node.js

Author: xenova

whisper-node/README.md

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+# whisper-node
+
+This project demonstrates how to use OpenAI Whisper in a Node.js environment.
+
+## Instructions
+
+1. Clone the repository:
+   ```sh
+   git clone https://github.com/huggingface/transformers.js-examples.git
+   ```
+2. Change directory to the `whisper-node` project:
+   ```sh
+   cd transformers.js-examples/whisper-node
+   ```
+3. Install the dependencies:
+   ```sh
+   npm install
+   ```
+4. Run the example:
+   ```sh
+   node index.js
+   ```

whisper-node/index.js

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+import { pipeline } from "@huggingface/transformers";
+import wavefile from "wavefile";
+
+// Load model
+const transcriber = await pipeline(
+  "automatic-speech-recognition",
+  "Xenova/whisper-tiny.en",
+);
+
+// Load audio data
+const url =
+  "https://huggingface.co/datasets/Xenova/transformers.js-docs/resolve/main/jfk.wav";
+const buffer = Buffer.from(await fetch(url).then((x) => x.arrayBuffer()));
+
+// Read .wav file and convert it to required format
+const 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
+const start = performance.now();
+const output = await transcriber(audioData);
+const end = performance.now();
+console.log(`Execution duration: ${(end - start) / 1000} seconds`);
+console.log(output);
+// { text: ' And so my fellow Americans ask not what your country can do for you, ask what you can do for your country.' }