What is Mcp Elevenlab Scribe Asr

MCP-Elevenlab-Scribe-ASR is a Model Control Protocol (MCP) server implementation for ElevenLabs’ Scribe speech-to-text API, enabling real-time transcription with advanced context management and bidirectional streaming capabilities.

Use cases

Use cases include real-time transcription of meetings, batch processing of recorded interviews, automated captioning for videos, and enhancing accessibility for hearing-impaired individuals.

How to use

To use MCP-Elevenlab-Scribe-ASR, clone the repository, set up a virtual environment, install dependencies, and create a .env file with your ElevenLabs API key. Start the server and use the example client for file or microphone transcription.

Key features

Key features include real-time transcription, file-based transcription, MCP protocol support, WebSocket support for bidirectional communication, context management for improved accuracy, support for multiple audio formats, automatic language detection, and event detection.

Where to use

MCP-Elevenlab-Scribe-ASR can be used in various fields such as customer service for transcribing calls, content creation for generating text from audio, accessibility for providing captions, and education for transcribing lectures.

ElevenLabs Scribe MCP Server

A Model Control Protocol (MCP) server implementation for ElevenLabs’ Scribe speech-to-text API, providing real-time transcription capabilities with advanced context management and bidirectional streaming.

Features

• Real-time Transcription: Stream audio directly from your microphone and get instant transcriptions
• File-based Transcription: Upload audio files for batch processing
• MCP Protocol Support: Full implementation of the Model Control Protocol for better context management
• WebSocket Support: Real-time bidirectional communication
• Context Management: Maintain conversation context for improved transcription accuracy
• Multiple Audio Formats: Support for various audio formats with automatic conversion
• Language Detection: Automatic language detection and confidence scoring
• Event Detection: Identify speech and non-speech audio events

Installation

1. Clone the repository:

git clone https://github.com/aromanstatue/MCP-Elevenlab-Scribe-ASR.git
cd MCP-Elevenlab-Scribe-ASR

2. Create and activate a virtual environment:

python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

3. Install dependencies:

pip install -e .

4. Create a .env file with your ElevenLabs API key:

ELEVENLABS_API_KEY=your-api-key-here

Usage

Starting the Server

python -m elevenlabs_scribe_mcp_server.main

The server will start on port 8000 by default (or the next available port).

Using the Example Client

1. File Transcription:

python examples/client_example.py --file path/to/audio.wav

2. Microphone Transcription:

python examples/client_example.py --mic

API Endpoints

1. REST API:

• POST /transcribe: Upload an audio file for transcription
• GET /health: Health check endpoint

2. WebSocket API:

• ws://localhost:8000/ws/transcribe: Real-time audio transcription

MCP Protocol

The server implements the Model Control Protocol (MCP) with the following message types:

1. INIT: Initialize a new transcription session
2. START: Begin audio streaming
3. AUDIO: Send audio data
4. TRANSCRIPTION: Receive transcription results
5. ERROR: Error messages
6. STOP: End audio streaming
7. DONE: Complete session

Development

Running Tests

pytest tests/

Project Structure

elevenlabs-scribe-mcp-server/
├── elevenlabs_scribe_mcp_server/
│   ├── __init__.py
│   ├── main.py              # FastAPI server
│   └── mcp/
│       ├── __init__.py
│       ├── protocol.py      # MCP protocol handler
│       ├── types.py         # Protocol types
│       └── elevenlabs.py    # ElevenLabs implementation
├── examples/
│   └── client_example.py    # Example client
├── tests/
│   └── test_transcribe.py   # Test suite
├── pyproject.toml           # Project metadata
└── README.md

Requirements

• Python 3.8+
• FastAPI
• Uvicorn
• PyAudio (for microphone support)
• aiohttp
• python-dotenv
• pydantic

Contributing

1. Fork the repository
2. Create your feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'Add amazing feature')
4. Push to the branch (git push origin feature/amazing-feature)
5. Open a Pull Request

License

MIT License - see LICENSE file for details.

Acknowledgments

• ElevenLabs for their excellent Scribe API
• FastAPI for the modern web framework
• The Python community for the amazing tools and libraries