Server-side Audio Processing in Node.js

A major benefit of writing code for the web is that you can access the multitude of APIs that are available in modern browsers. Unfortunately, when writing server-side code, we are not afforded such luxury, so we have to find another way. In this tutorial, we will design a simple Node.js application that uses Transformers.js for speech recognition with Whisper, and in the process, learn how to process audio on the server.

The main problem we need to solve is that the Web Audio API is not available in Node.js, meaning we can’t use the AudioContext class to process audio. So, we will need to install third-party libraries to obtain the raw audio data. For this example, we will only consider .wav files, but the same principles apply to other audio formats.

This tutorial will be written as an ES module, but you can easily adapt it to use CommonJS instead. For more information, see the node tutorial.

Useful links:

Prerequisites

Node.js version 18+
npm version 9+

Getting started

Let’s start by creating a new Node.js project and installing Transformers.js via NPM:

npm init -y
npm i @huggingface/transformers

Remember to add "type": "module" to your package.json to indicate that your project uses ECMAScript modules.

Next, let’s install the wavefile package, which we will use for loading .wav files:

npm i wavefile

Creating the application

Start by creating a new file called index.js, which will be the entry point for our application. Let’s also import the necessary modules:

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

For this tutorial, we will use the Xenova/whisper-tiny.en model, but feel free to choose one of the other whisper models from the Hugging Face Hub. Let’s create our pipeline with:

let transcriber = await pipeline('automatic-speech-recognition', 'Xenova/whisper-tiny.en');

Next, let’s load an audio file and convert it to the format required by Transformers.js:

// Load audio data
let url = 'https://huggingface.co/datasets/Xenova/transformers.js-docs/resolve/main/jfk.wav';
let buffer = Buffer.from(await fetch(url).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];
}

Finally, let’s run the model and measure execution duration.

let start = performance.now();
let output = await transcriber(audioData);
let end = performance.now();
console.log(`Execution duration: ${(end - start) / 1000} seconds`);
console.log(output);

You can now run the application with node index.js. Note that when running the script for the first time, it may take a while to download and cache the model. Subsequent requests will use the cached model, and model loading will be much faster.

You should see output similar to:

Execution duration: 0.6460317999720574 seconds
{
  text: ' And so my fellow Americans ask not what your country can do for you. Ask what you can do for your country.'
}

That’s it! You’ve successfully created a Node.js application that uses Transformers.js for speech recognition with Whisper. You can now use this as a starting point for your own applications.

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