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

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.

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

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

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

pip install -r requirements.txt

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

Once configured, start the server with:

python app/server.py

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

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.

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.

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

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

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}}

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!');

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

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!');

• 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.