openai-edge-tts

OpenAI-Compatible Edge-TTS API 🗣️

This project provides a local, OpenAI-compatible text-to-speech (TTS) API using edge-tts. It emulates the OpenAI TTS endpoint (/v1/audio/speech), enabling users to generate speech from text with various voice options and playback speeds, just like the OpenAI API.

edge-tts uses Microsoft Edge's online text-to-speech service, so it is completely free.

View this project on Docker Hub

Please ⭐️ star this repo if you find it helpful

Features

• OpenAI-Compatible Endpoint: /v1/audio/speech with similar request structure and behavior.
• SSE Streaming Support: Real-time audio streaming via Server-Sent Events when stream_format: "sse" is specified.
• Supported Voices: Maps OpenAI voices (alloy, echo, fable, onyx, nova, shimmer) to edge-tts equivalents.
• Flexible Formats: Supports multiple audio formats (mp3, opus, aac, flac, wav, pcm).
• Adjustable Speed: Option to modify playback speed (0.25x to 4.0x).
• Optional Direct Edge-TTS Voice Selection: Use either OpenAI voice mappings or specify any edge-tts voice directly.

⚡️ Quick start

The simplest way to get started without having to configure anything is to run the command below

docker run -d -p 5050:5050 travisvn/openai-edge-tts:latest

This will run the service at port 5050 with all the default configs

(Docker required, obviously)

Setup

Prerequisites

• Docker (recommended): Docker and Docker Compose for containerized setup.
• Python (optional): For local development, install dependencies in requirements.txt.
• ffmpeg (optional): Required for audio format conversion. Optional if sticking to mp3.

Installation

1. Clone the Repository:

git clone https://github.com/travisvn/openai-edge-tts.git
cd openai-edge-tts

2. Environment Variables: Create a .env file in the root directory with the following variables:

API_KEY=your_api_key_here
PORT=5050

DEFAULT_VOICE=en-US-AvaNeural
DEFAULT_RESPONSE_FORMAT=mp3
DEFAULT_SPEED=1.0

DEFAULT_LANGUAGE=en-US

REQUIRE_API_KEY=True
REMOVE_FILTER=False
EXPAND_API=True
DETAILED_ERROR_LOGGING=True

Or, copy the default .env.example with the following:

cp .env.example .env

3. Run with Docker Compose (recommended):

docker compose up --build

Run with -d to run docker compose in "detached mode", meaning it will run in the background and free up your terminal.

docker compose up -d

Building Locally with FFmpeg using Docker Compose

By default, docker compose up --build creates a minimal image without ffmpeg. If you're building locally (after cloning this repository) and need ffmpeg for audio format conversions (beyond MP3), you can include it in the build.

This is controlled by the INSTALL_FFMPEG_ARG build argument. Set this environment variable to true in one of these ways:

1. Prefixing the command:

   INSTALL_FFMPEG_ARG=true docker compose up --build
   

2. Adding to your .env file: Add this line to the .env file in the project root:

   INSTALL_FFMPEG_ARG=true
   

   Then, run docker compose up --build.
3. Exporting in your shell environment: Add export INSTALL_FFMPEG_ARG=true to your shell configuration (e.g., ~/.zshrc, ~/.bashrc) and reload your shell. Then docker compose up --build will use it.

This is for local builds. For pre-built Docker Hub images, add the latest-ffmpeg tag to the version

docker run -d -p 5050:5050 -e API_KEY=your_api_key_here -e PORT=5050 travisvn/openai-edge-tts:latest-ffmpeg

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Alternatively, run directly with Docker:

docker build -t openai-edge-tts .
docker run -p 5050:5050 --env-file .env openai-edge-tts

To run the container in the background, add -d after the docker run command:

docker run -d -p 5050:5050 --env-file .env openai-edge-tts

4. Access the API: Your server will be accessible at http://localhost:5050.

Running with Python

If you prefer to run this project directly with Python, follow these steps to set up a virtual environment, install dependencies, and start the server.

1. Clone the Repository

git clone https://github.com/travisvn/openai-edge-tts.git
cd openai-edge-tts

2. Set Up a Virtual Environment

Create and activate a virtual environment to isolate dependencies:

# For macOS/Linux
python3 -m venv venv
source venv/bin/activate

# For Windows
python -m venv venv
venv\Scripts\activate

3. Install Dependencies

Use pip to install the required packages listed in requirements.txt:

pip install -r requirements.txt

4. Configure Environment Variables

Create a .env file in the root directory and set the following variables:

API_KEY=your_api_key_here
PORT=5050

DEFAULT_VOICE=en-US-AvaNeural
DEFAULT_RESPONSE_FORMAT=mp3
DEFAULT_SPEED=1.0

DEFAULT_LANGUAGE=en-US

REQUIRE_API_KEY=True
REMOVE_FILTER=False
EXPAND_API=True
DETAILED_ERROR_LOGGING=True

5. Run the Server

Once configured, start the server with:

python app/server.py

The server will start running at http://localhost:5050.

6. Test the API

You can now interact with the API at http://localhost:5050/v1/audio/speech and other available endpoints. See the Usage section for request examples.

Usage

Endpoint: /v1/audio/speech

Generates audio from the input text. Available parameters:

Required Parameter:

• input (string): The text to be converted to audio (up to 4096 characters).

Optional Parameters:

• model (string): Set to "tts-1" or "tts-1-hd" (default: "tts-1").
• voice (string): One of the OpenAI-compatible voices (alloy, echo, fable, onyx, nova, shimmer) or any valid edge-tts voice (default: "en-US-AvaNeural").
• response_format (string): Audio format. Options: mp3, opus, aac, flac, wav, pcm (default: mp3).
• speed (number): Playback speed (0.25 to 4.0). Default is 1.0.
• stream_format (string): Response format. Options: "audio" (raw audio data, default) or "sse" (Server-Sent Events streaming with JSON events).

Note: The API is fully compatible with OpenAI's TTS API specification. The instructions parameter (for fine-tuning voice characteristics) is not currently supported, but all other parameters work identically to OpenAI's implementation.

Standard Audio Generation

Example request with curl and saving the output to an mp3 file:

curl -X POST http://localhost:5050/v1/audio/speech \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer your_api_key_here" \
  -d '{
    "input": "Hello, I am your AI assistant! Just let me know how I can help bring your ideas to life.",
    "voice": "echo",
    "response_format": "mp3",
    "speed": 1.1
  }' \
  --output speech.mp3

Direct Audio Playback (like OpenAI)

You can pipe the audio directly to ffplay for immediate playback, just like OpenAI's API:

curl -X POST http://localhost:5050/v1/audio/speech \
  -H "Authorization: Bearer your_api_key_here" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "tts-1",
    "input": "Today is a wonderful day to build something people love!",
    "voice": "alloy",
    "response_format": "mp3"
  }' | ffplay -i -

Or for immediate playback without saving to file:

curl -X POST http://localhost:5050/v1/audio/speech \
  -H "Authorization: Bearer your_api_key_here" \
  -H "Content-Type: application/json" \
  -d '{
    "input": "This will play immediately without saving to disk!",
    "voice": "shimmer"
  }' | ffplay -autoexit -nodisp -i -

Or, to be in line with the OpenAI API endpoint parameters:

curl -X POST http://localhost:5050/v1/audio/speech \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer your_api_key_here" \
  -d '{
    "model": "tts-1",
    "input": "Hello, I am your AI assistant! Just let me know how I can help bring your ideas to life.",
    "voice": "alloy"
  }' \
  --output speech.mp3

Server-Sent Events (SSE) Streaming

For applications that need structured streaming events (like web applications), use SSE format:

curl -X POST http://localhost:5050/v1/audio/speech \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer your_api_key_here" \
  -d '{
    "model": "tts-1",
    "input": "This will stream as Server-Sent Events with JSON data containing base64-encoded audio chunks.",
    "voice": "alloy",
    "stream_format": "sse"
  }'

SSE Response Format:

data: {"type": "speech.audio.delta", "audio": "base64-encoded-audio-chunk"}

data: {"type": "speech.audio.delta", "audio": "base64-encoded-audio-chunk"}

data: {"type": "speech.audio.done", "usage": {"input_tokens": 12, "output_tokens": 0, "total_tokens": 12}}

JavaScript/Web Usage

Example using fetch API for SSE streaming:

async function streamTTSWithSSE(text) {
  const response = await fetch('http://localhost:5050/v1/audio/speech', {
    method: 'POST',
    headers: {
      'Content-Type': 'application/json',
      Authorization: 'Bearer your_api_key_here',
    },
    body: JSON.stringify({
      input: text,
      voice: 'alloy',
      stream_format: 'sse',
    }),
  });

  const reader = response.body.getReader();
  const decoder = new TextDecoder();
  const audioChunks = [];

  while (true) {
    const { done, value } = await reader.read();
    if (done) break;

    const chunk = decoder.decode(value);
    const lines = chunk.split('\n');

    for (const line of lines) {
      if (line.startsWith('data: ')) {
        const data = JSON.parse(line.slice(6));

        if (data.type === 'speech.audio.delta') {
          // Decode base64 audio chunk
          const audioData = atob(data.audio);
          const audioArray = new Uint8Array(audioData.length);
          for (let i = 0; i < audioData.length; i++) {
            audioArray[i] = audioData.charCodeAt(i);
          }
          audioChunks.push(audioArray);
        } else if (data.type === 'speech.audio.done') {
          console.log('Speech synthesis complete:', data.usage);

          // Combine all chunks and play
          const totalLength = audioChunks.reduce(
            (sum, chunk) => sum + chunk.length,
            0
          );
          const combinedArray = new Uint8Array(totalLength);
          let offset = 0;
          for (const chunk of audioChunks) {
            combinedArray.set(chunk, offset);
            offset += chunk.length;
          }

          const audioBlob = new Blob([combinedArray], { type: 'audio/mpeg' });
          const audioUrl = URL.createObjectURL(audioBlob);
          const audio = new Audio(audioUrl);
          audio.play();
          return;
        }
      }
    }
  }
}

// Usage
streamTTSWithSSE('Hello from SSE streaming!');

International Language Example

And an example of a language other than English:

curl -X POST http://localhost:5050/v1/audio/speech \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer your_api_key_here" \
  -d '{
    "model": "tts-1",
    "input": "じゃあ、行く。電車の時間、調べておくよ。",
    "voice": "ja-JP-KeitaNeural"
  }' \
  --output speech.mp3

JavaScript/Web Usage

Example using fetch API for SSE streaming:

async function streamTTSWithSSE(text) {
  const response = await fetch('http://localhost:5050/v1/audio/speech', {
    method: 'POST',
    headers: {
      'Content-Type': 'application/json',
      Authorization: 'Bearer your_api_key_here',
    },
    body: JSON.stringify({
      input: text,
      voice: 'alloy',
      stream_format: 'sse',
    }),
  });

  const reader = response.body.getReader();
  const decoder = new TextDecoder();
  const audioChunks = [];

  while (true) {
    const { done, value } = await reader.read();
    if (done) break;

    const chunk = decoder.decode(value);
    const lines = chunk.split('\n');

    for (const line of lines) {
      if (line.startsWith('data: ')) {
        const data = JSON.parse(line.slice(6));

        if (data.type === 'speech.audio.delta') {
          // Decode base64 audio chunk
          const audioData = atob(data.audio);
          const audioArray = new Uint8Array(audioData.length);
          for (let i = 0; i < audioData.length; i++) {
            audioArray[i] = audioData.charCodeAt(i);
          }
          audioChunks.push(audioArray);
        } else if (data.type === 'speech.audio.done') {
          console.log('Speech synthesis complete:', data.usage);

          // Combine all chunks and play
          const totalLength = audioChunks.reduce(
            (sum, chunk) => sum + chunk.length,
            0
          );
          const combinedArray = new Uint8Array(totalLength);
          let offset = 0;
          for (const chunk of audioChunks) {
            combinedArray.set(chunk, offset);
            offset += chunk.length;
          }

          const audioBlob = new Blob([combinedArray], { type: 'audio/mpeg' });
          const audioUrl = URL.createObjectURL(audioBlob);
          const audio = new Audio(audioUrl);
          audio.play();
          return;
        }
      }
    }
  }
}

// Usage
streamTTSWithSSE('Hello from SSE streaming!');

Additional Endpoints

• POST/GET /v1/models: Lists available TTS models.
• POST/GET /v1/voices: Lists edge-tts voices for a given language / locale.
• POST/GET /v1/voices/all: Lists all edge-tts voices, with language support information.

Contributing

Contributions are welcome! Please fork the repository and create a pull request for any improvements.

License

This project is licensed under GNU General Public License v3.0 (GPL-3.0), and the acceptable use-case is intended to be personal use. For enterprise or non-personal use of openai-edge-tts, contact me at [email protected]

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Example Use Case

[!TIP] Swap localhost to your local IP (ex. 192.168.0.1) if you have issues

It may be the case that, when accessing this endpoint on a different server / computer or when the call is made from another source (like Open WebUI), you need to change the URL from localhost to your local IP (something like 192.168.0.1 or similar)

Open WebUI

Open up the Admin Panel and go to Settings -> Audio

Below, you can see a screenshot of the correct configuration for using this project to substitute the OpenAI endpoint

If you're running both Open WebUI and this project in Docker, the API endpoint URL is probably http://host.docker.internal:5050/v1

[!NOTE] View the official docs for Open WebUI integration with OpenAI Edge TTS

AnythingLLM

In version 1.6.8, AnythingLLM added support for "generic OpenAI TTS providers" — meaning we can use this project as the TTS provider in AnythingLLM

Open up settings and go to Voice & Speech (Under AI Providers)

Below, you can see a screenshot of the correct configuration for using this project to substitute the OpenAI endpoint

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Quick Info

• your_api_key_here never needs to be replaced — No "real" API key is required. Use whichever string you'd like.
• The quickest way to get this up and running is to install docker and run the command below:

docker run -d -p 5050:5050 -e API_KEY=your_api_key_here -e PORT=5050 travisvn/openai-edge-tts:latest

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Voice Samples 🎙️

Play voice samples and see all available Edge TTS voices