# Whisper Speech Recognition MCP Server
---
[中文文档](README-CN.md)
---
A high-performance speech recognition MCP server based on Faster Whisper, providing efficient audio transcription capabilities.
## Features
- Integrated with Faster Whisper for efficient speech recognition
- Batch processing acceleration for improved transcription speed
- Automatic CUDA acceleration (if available)
- Support for multiple model sizes (tiny to large-v3)
- Output formats include VTT subtitles, SRT, and JSON
- Support for batch transcription of audio files in a folder
- Model instance caching to avoid repeated loading
- Dynamic batch size adjustment based on GPU memory
## Installation
### Dependencies
- Python 3.10+
- faster-whisper>=0.9.0
- torch==2.6.0+cu126
- torchaudio==2.6.0+cu126
- mcp[cli]>=1.2.0
### Installation Steps
1. Clone or download this repository
2. Create and activate a virtual environment (recommended)
3. Install dependencies:
```bash
pip install -r requirements.txt
```
### PyTorch Installation Guide
Install the appropriate version of PyTorch based on your CUDA version:
- CUDA 12.6:
```bash
pip install torch==2.6.0 torchvision==0.21.0 torchaudio==2.6.0 --index-url https://download.pytorch.org/whl/cu126
```
- CUDA 12.1:
```bash
pip install torch==2.5.1 torchvision==0.20.1 torchaudio==2.5.1 --index-url https://download.pytorch.org/whl/cu121
```
- CPU version:
```bash
pip install torch==2.6.0 torchvision==0.21.0 torchaudio==2.6.0 --index-url https://download.pytorch.org/whl/cpu
```
You can check your CUDA version with `nvcc --version` or `nvidia-smi`.
## Usage
### Starting the Server
On Windows, simply run `start_server.bat`.
On other platforms, run:
```bash
python whisper_server.py
```
### Configuring Claude Desktop
1. Open the Claude Desktop configuration file:
- Windows: `%APPDATA%\Claude\claude_desktop_config.json`
- macOS: `~/Library/Application Support/Claude/claude_desktop_config.json`
2. Add the Whisper server configuration:
```json
{
"mcpServers": {
"whisper": {
"command": "python",
"args": ["D:/path/to/whisper_server.py"],
"env": {}
}
}
}
```
3. Restart Claude Desktop
### Available Tools
The server provides the following tools:
1. **get_model_info** - Get information about available Whisper models
2. **transcribe** - Transcribe a single audio file
3. **batch_transcribe** - Batch transcribe audio files in a folder
## Performance Optimization Tips
- Using CUDA acceleration significantly improves transcription speed
- Batch processing mode is more efficient for large numbers of short audio files
- Batch size is automatically adjusted based on GPU memory size
- Using VAD (Voice Activity Detection) filtering improves accuracy for long audio
- Specifying the correct language can improve transcription quality
## Local Testing Methods
1. Use MCP Inspector for quick testing:
```bash
mcp dev whisper_server.py
```
2. Use Claude Desktop for integration testing
3. Use command line direct invocation (requires mcp[cli]):
```bash
mcp run whisper_server.py
```
## Error Handling
The server implements the following error handling mechanisms:
- Audio file existence check
- Model loading failure handling
- Transcription process exception catching
- GPU memory management
- Batch processing parameter adaptive adjustment
## Project Structure
- `whisper_server.py`: Main server code
- `model_manager.py`: Whisper model loading and caching
- `audio_processor.py`: Audio file validation and preprocessing
- `formatters.py`: Output formatting (VTT, SRT, JSON)
- `transcriber.py`: Core transcription logic
- `start_server.bat`: Windows startup script
## License
MIT
## Acknowledgements
This project was developed with the assistance of these amazing AI tools and models:
- [GitHub Copilot](https://github.com/features/copilot) - AI pair programmer
- [Trae](https://trae.ai/) - Agentic AI coding assistant
- [Cline](https://cline.ai/) - AI-powered terminal
- [DeepSeek](https://www.deepseek.com/) - Advanced AI model
- [Claude-3.7-Sonnet](https://www.anthropic.com/claude) - Anthropic's powerful AI assistant
- [Gemini-2.0-Flash](https://ai.google/gemini/) - Google's multimodal AI model
- [VS Code](https://code.visualstudio.com/) - Powerful code editor
- [Whisper](https://github.com/openai/whisper) - OpenAI's speech recognition model
- [Faster Whisper](https://github.com/guillaumekln/faster-whisper) - Optimized Whisper implementation
Special thanks to these incredible tools and the teams behind them.