node-audio-processing/index.js

import { pipeline } from "@huggingface/transformers";
import wavefile from "wavefile";

// Load model
const transcriber = await pipeline(
  "automatic-speech-recognition",
  "onnx-community/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.' }