docs: 更新README文件以包含致谢部分和英文文档
在README-CN.md中添加了致谢部分，感谢开发过程中使用的AI工具和模型。同时新增了README.md文件，提供项目的英文文档，包括功能、安装、使用说明、性能优化等内容。
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BigUncleHomePC
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README-CN.md
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README-CN.md
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MIT
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## 致谢
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本项目在开发过程中得到了以下优秀AI工具和模型的帮助：
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- [GitHub Copilot](https://github.com/features/copilot) - AI结对编程助手
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- [Trae](https://trae.ai/) - 智能AI编码助手
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- [Cline](https://cline.ai/) - AI驱动的终端
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- [DeepSeek](https://www.deepseek.com/) - 先进的AI模型
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- [Claude-3.7-Sonnet](https://www.anthropic.com/claude) - Anthropic强大的AI助手
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- [Gemini-2.0-Flash](https://ai.google/gemini/) - Google的多模态AI模型
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- [VS Code](https://code.visualstudio.com/) - 强大的代码编辑器
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- [Whisper](https://github.com/openai/whisper) - OpenAI的语音识别模型
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- [Faster Whisper](https://github.com/guillaumekln/faster-whisper) - 优化的Whisper实现
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特别感谢这些出色的工具和背后的团队。
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---
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# Whisper 语音识别 MCP 服务器（cline claude sonnet 3.7 完成所有任务后的说明）
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# Whisper Speech Recognition MCP Server
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---
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[中文文档](README-CN.md)
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---
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A high-performance speech recognition MCP server based on Faster Whisper, providing efficient audio transcription capabilities.
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## Features
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- Integrated with Faster Whisper for efficient speech recognition
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- Batch processing acceleration for improved transcription speed
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- Automatic CUDA acceleration (if available)
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- Support for multiple model sizes (tiny to large-v3)
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- Output formats include VTT subtitles, SRT, and JSON
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- Support for batch transcription of audio files in a folder
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- Model instance caching to avoid repeated loading
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- Dynamic batch size adjustment based on GPU memory
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## Installation
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### Dependencies
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- Python 3.10+
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- faster-whisper>=0.9.0
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- torch==2.6.0+cu126
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- torchaudio==2.6.0+cu126
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- mcp[cli]>=1.2.0
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### Installation Steps
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1. Clone or download this repository
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2. Create and activate a virtual environment (recommended)
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3. Install dependencies:
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```bash
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pip install -r requirements.txt
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```
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### PyTorch Installation Guide
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Install the appropriate version of PyTorch based on your CUDA version:
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- CUDA 12.6:
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```bash
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pip install torch==2.6.0 torchvision==0.21.0 torchaudio==2.6.0 --index-url https://download.pytorch.org/whl/cu126
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```
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- CUDA 12.1:
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```bash
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pip install torch==2.5.1 torchvision==0.20.1 torchaudio==2.5.1 --index-url https://download.pytorch.org/whl/cu121
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```
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- CPU version:
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```bash
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pip install torch==2.6.0 torchvision==0.21.0 torchaudio==2.6.0 --index-url https://download.pytorch.org/whl/cpu
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```
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You can check your CUDA version with `nvcc --version` or `nvidia-smi`.
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## Usage
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### Starting the Server
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On Windows, simply run `start_server.bat`.
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On other platforms, run:
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```bash
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python whisper_server.py
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```
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### Configuring Claude Desktop
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1. Open the Claude Desktop configuration file:
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- Windows: `%APPDATA%\Claude\claude_desktop_config.json`
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- macOS: `~/Library/Application Support/Claude/claude_desktop_config.json`
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2. Add the Whisper server configuration:
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```json
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{
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"mcpServers": {
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"whisper": {
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"command": "python",
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"args": ["D:/path/to/whisper_server.py"],
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"env": {}
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}
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}
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}
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```
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3. Restart Claude Desktop
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### Available Tools
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The server provides the following tools:
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1. **get_model_info** - Get information about available Whisper models
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2. **transcribe** - Transcribe a single audio file
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3. **batch_transcribe** - Batch transcribe audio files in a folder
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## Performance Optimization Tips
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- Using CUDA acceleration significantly improves transcription speed
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- Batch processing mode is more efficient for large numbers of short audio files
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- Batch size is automatically adjusted based on GPU memory size
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- Using VAD (Voice Activity Detection) filtering improves accuracy for long audio
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- Specifying the correct language can improve transcription quality
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## Local Testing Methods
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1. Use MCP Inspector for quick testing:
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```bash
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mcp dev whisper_server.py
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```
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2. Use Claude Desktop for integration testing
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3. Use command line direct invocation (requires mcp[cli]):
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```bash
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mcp run whisper_server.py
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```
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## Error Handling
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The server implements the following error handling mechanisms:
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- Audio file existence check
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- Model loading failure handling
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- Transcription process exception catching
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- GPU memory management
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- Batch processing parameter adaptive adjustment
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## Project Structure
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- `whisper_server.py`: Main server code
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- `model_manager.py`: Whisper model loading and caching
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- `audio_processor.py`: Audio file validation and preprocessing
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- `formatters.py`: Output formatting (VTT, SRT, JSON)
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- `transcriber.py`: Core transcription logic
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- `start_server.bat`: Windows startup script
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## License
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MIT
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## Acknowledgements
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This project was developed with the assistance of these amazing AI tools and models:
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- [GitHub Copilot](https://github.com/features/copilot) - AI pair programmer
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- [Trae](https://trae.ai/) - Agentic AI coding assistant
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- [Cline](https://cline.ai/) - AI-powered terminal
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- [DeepSeek](https://www.deepseek.com/) - Advanced AI model
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- [Claude-3.7-Sonnet](https://www.anthropic.com/claude) - Anthropic's powerful AI assistant
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- [Gemini-2.0-Flash](https://ai.google/gemini/) - Google's multimodal AI model
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- [VS Code](https://code.visualstudio.com/) - Powerful code editor
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- [Whisper](https://github.com/openai/whisper) - OpenAI's speech recognition model
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- [Faster Whisper](https://github.com/guillaumekln/faster-whisper) - Optimized Whisper implementation
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Special thanks to these incredible tools and the teams behind them.
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