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packages/transformers/docs/source/guides/node-audio-processing.md

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+# 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](https://huggingface.co/Xenova/whisper-tiny.en), 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](https://developer.mozilla.org/en-US/docs/Web/API/Web_Audio_API) is not available in Node.js, meaning we can't use the [`AudioContext`](https://developer.mozilla.org/en-US/docs/Web/API/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.
+
+<Tip>
+
+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](https://huggingface.co/docs/transformers.js/tutorials/node).
+
+</Tip>
+
+**Useful links:**
+
+- [Source code](https://github.com/huggingface/transformers.js/tree/main/examples/node-audio-processing)
+- [Documentation](https://huggingface.co/docs/transformers.js)
+
+## Prerequisites
+
+- [Node.js](https://nodejs.org/en/) version 18+
+- [npm](https://www.npmjs.com/) version 9+
+
+## Getting started
+
+Let's start by creating a new Node.js project and installing Transformers.js via [NPM](https://www.npmjs.com/package/@huggingface/transformers):
+
+```bash
+npm init -y
+npm i @huggingface/transformers
+```
+
+<Tip>
+
+Remember to add `"type": "module"` to your `package.json` to indicate that your project uses ECMAScript modules.
+
+</Tip>
+
+Next, let's install the [`wavefile`](https://www.npmjs.com/package/wavefile) package, which we will use for loading `.wav` files:
+
+```bash
+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:
+
+```js
+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](https://huggingface.co/models?library=transformers.js&search=whisper). Let's create our pipeline with:
+
+```js
+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:
+
+```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.
+
+```js
+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.