---
title: Whisper-large-v3-turbo with Cloudflare Workers AI · Cloudflare Workers AI docs
description: In this tutorial you will learn how to:
source_url:
  html: https://0585d5ca.preview.developers.cloudflare.com/workers-ai/guides/tutorials/build-a-workers-ai-whisper-with-chunking/index.md
  md: https://0585d5ca.preview.developers.cloudflare.com/workers-ai/guides/tutorials/build-a-workers-ai-whisper-with-chunking/
---

In this tutorial you will learn how to:

* **Transcribe large audio files:** Use the [Whisper-large-v3-turbo](https://developers.cloudflare.com/workers-ai/models/whisper-large-v3-turbo/) model from Cloudflare Workers AI to perform automatic speech recognition (ASR) or translation.
* **Handle large files:** Split large audio files into smaller chunks for processing, which helps overcome memory and execution time limitations.
* **Deploy using Cloudflare Workers:** Create a scalable, low‑latency transcription pipeline in a serverless environment.

## 1: Create a new Cloudflare Worker project

1. Sign up for a [Cloudflare account](https://dash.cloudflare.com/sign-up/workers-and-pages).
2. Install [`Node.js`](https://docs.npmjs.com/downloading-and-installing-node-js-and-npm).

You will create a new Worker project using the `create-cloudflare` CLI (C3). [C3](https://github.com/cloudflare/workers-sdk/tree/main/packages/create-cloudflare) is a command-line tool designed to help you set up and deploy new applications to Cloudflare.

Create a new project named `whisper-tutorial` by running:

* npm

  ```sh
  npm create cloudflare@latest -- whisper-tutorial
  ```

* pnpm

  ```sh
  pnpm create cloudflare@latest whisper-tutorial
  ```

* yarn

  ```sh
  yarn create cloudflare whisper-tutorial
  ```

Running `npm create cloudflare@latest` will prompt you to install the [`create-cloudflare` package](https://www.npmjs.com/package/create-cloudflare), and lead you through setup. C3 will also install [Wrangler](https://developers.cloudflare.com/workers/wrangler/), the Cloudflare Developer Platform CLI.

For setup, select the following options:

* For *What would you like to start with?*, choose `Hello World Starter`.
* For *Which template would you like to use?*, choose `Worker only`.
* For *Which language do you want to use?*, choose `TypeScript`.
* For *Do you want to use git for version control?*, choose `Yes`.
* For *Do you want to deploy your application?*, choose `No` (we will be making some changes before deploying).

This will create a new `whisper-tutorial` directory. Your new `whisper-tutorial` directory will include:

* A `"Hello World"` [Worker](https://developers.cloudflare.com/workers/get-started/guide/#3-write-code) at `src/index.ts`.
* A [`wrangler.jsonc`](https://developers.cloudflare.com/workers/wrangler/configuration/) configuration file.

Go to your application directory:

```sh
cd whisper-tutorial
```

## 2. Connect your Worker to Workers AI

You must create an AI binding for your Worker to connect to Workers AI. [Bindings](https://developers.cloudflare.com/workers/runtime-apis/bindings/) allow your Workers to interact with resources, like Workers AI, on the Cloudflare Developer Platform.

To bind Workers AI to your Worker, add the following to the end of your `wrangler.toml` file:

* wrangler.jsonc

  ```jsonc
  {
    "ai": {
      "binding": "AI"
    }
  }
  ```

* wrangler.toml

  ```toml
  [ai]
  binding = "AI"
  ```

Your binding is [available in your Worker code](https://developers.cloudflare.com/workers/reference/migrate-to-module-workers/#bindings-in-es-modules-format) on [`env.AI`](https://developers.cloudflare.com/workers/runtime-apis/handlers/fetch/).

## 3. Configure Wrangler

In your wrangler file, add or update the following settings to enable Node.js APIs and polyfills (with a compatibility date of 2024‑09‑23 or later):

* wrangler.jsonc

  ```jsonc
  {
    "compatibility_flags": [
      "nodejs_compat"
    ],
    "compatibility_date": "2024-09-23"
  }
  ```

* wrangler.toml

  ```toml
  compatibility_flags = [ "nodejs_compat" ]
  compatibility_date = "2024-09-23"
  ```

## 4. Handle large audio files with chunking

Replace the contents of your `src/index.ts` file with the following integrated code. This sample demonstrates how to:

(1) Extract an audio file URL from the query parameters.

(2) Fetch the audio file while explicitly following redirects.

(3) Split the audio file into smaller chunks (such as, 1 MB chunks).

(4) Transcribe each chunk using the Whisper-large-v3-turbo model via the Cloudflare AI binding.

(5) Return the aggregated transcription as plain text.

```ts
import { Buffer } from "node:buffer";
import type { Ai } from "workers-ai";


export interface Env {
  AI: Ai;
  // If needed, add your KV namespace for storing transcripts.
  // MY_KV_NAMESPACE: KVNamespace;
}


/**
 * Fetches the audio file from the provided URL and splits it into chunks.
 * This function explicitly follows redirects.
 *
 * @param audioUrl - The URL of the audio file.
 * @returns An array of ArrayBuffers, each representing a chunk of the audio.
 */
async function getAudioChunks(audioUrl: string): Promise<ArrayBuffer[]> {
  const response = await fetch(audioUrl, { redirect: "follow" });
  if (!response.ok) {
    throw new Error(`Failed to fetch audio: ${response.status}`);
  }
  const arrayBuffer = await response.arrayBuffer();


  // Example: Split the audio into 1MB chunks.
  const chunkSize = 1024 * 1024; // 1MB
  const chunks: ArrayBuffer[] = [];
  for (let i = 0; i < arrayBuffer.byteLength; i += chunkSize) {
    const chunk = arrayBuffer.slice(i, i + chunkSize);
    chunks.push(chunk);
  }
  return chunks;
}


/**
 * Transcribes a single audio chunk using the Whisper‑large‑v3‑turbo model.
 * The function converts the audio chunk to a Base64-encoded string and
 * sends it to the model via the AI binding.
 *
 * @param chunkBuffer - The audio chunk as an ArrayBuffer.
 * @param env - The Cloudflare Worker environment, including the AI binding.
 * @returns The transcription text from the model.
 */
async function transcribeChunk(
  chunkBuffer: ArrayBuffer,
  env: Env,
): Promise<string> {
  const base64 = Buffer.from(chunkBuffer, "binary").toString("base64");
  const res = await env.AI.run("@cf/openai/whisper-large-v3-turbo", {
    audio: base64,
    // Optional parameters (uncomment and set if needed):
    // task: "transcribe",   // or "translate"
    // language: "en",
    // vad_filter: "false",
    // initial_prompt: "Provide context if needed.",
    // prefix: "Transcription:",
  });
  return res.text; // Assumes the transcription result includes a "text" property.
}


/**
 * The main fetch handler. It extracts the 'url' query parameter, fetches the audio,
 * processes it in chunks, and returns the full transcription.
 */
export default {
  async fetch(
    request: Request,
    env: Env,
    ctx: ExecutionContext,
  ): Promise<Response> {
    // Extract the audio URL from the query parameters.
    const { searchParams } = new URL(request.url);
    const audioUrl = searchParams.get("url");


    if (!audioUrl) {
      return new Response("Missing 'url' query parameter", { status: 400 });
    }


    // Get the audio chunks.
    const audioChunks: ArrayBuffer[] = await getAudioChunks(audioUrl);
    let fullTranscript = "";


    // Process each chunk and build the full transcript.
    for (const chunk of audioChunks) {
      try {
        const transcript = await transcribeChunk(chunk, env);
        fullTranscript += transcript + "\n";
      } catch (error) {
        fullTranscript += "[Error transcribing chunk]\n";
      }
    }


    return new Response(fullTranscript, {
      headers: { "Content-Type": "text/plain" },
    });
  },
} satisfies ExportedHandler<Env>;
```

## 5. Deploy your Worker

1. **Run the Worker locally:**

   Use wrangler's development mode to test your Worker locally:

```sh
npx wrangler dev
```

Open your browser and go to <http://localhost:8787>, or use curl:

```sh
curl "http://localhost:8787?url=https://raw.githubusercontent.com/your-username/your-repo/main/your-audio-file.mp3"
```

Replace the URL query parameter with the direct link to your audio file. (For GitHub-hosted files, ensure you use the raw file URL.)

1. **Deploy the Worker:**

   Once testing is complete, deploy your Worker with:

```sh
npx wrangler deploy
```

1. **Test the deployed Worker:**

   After deployment, test your Worker by passing the audio URL as a query parameter:

```sh
curl "https://<your-worker-subdomain>.workers.dev?url=https://raw.githubusercontent.com/your-username/your-repo/main/your-audio-file.mp3"
```

Make sure to replace `<your-worker-subdomain>`, `your-username`, `your-repo`, and `your-audio-file.mp3` with your actual details.

If successful, the Worker will return a transcript of the audio file:

```sh
This is the transcript of the audio...
```
