What are the runtime dependencies?
Python 3.10+, faster-whisper>=0.9.0, PyTorch and torchaudio (CUDA or CPU version), and mcp[cli]>=1.2.0.
@BigUncle
基本信息
A high-performance speech recognition MCP server based on Faster Whisper, providing efficient audio transcription capabilities.
使用下面的配置,将此服务器添加到你的 MCP 客户端。
{
"mcpServers": {
"Fast-Whisper-MCP-Server": {
"command": "python",
"args": [
"whisper_server.py"
]
}
}
}工具列表暂未提供
当前收录暂时没有可展示的工具列表,不代表服务没有工具。请查阅项目文档了解支持的能力。
工具是从 README 中自动提取的。维护者可以在 ## Tools 标题下列出工具,即可填充这部分内容。
A high-performance speech recognition MCP server based on Faster Whisper, providing efficient audio transcription capabilities. It is designed for developers who want to integrate speech-to-text into AI tools like Claude Desktop.
Install dependencies (Python 3.10+, Faster Whisper, PyTorch, MCP), then run start_server.bat on Windows or python whisper_server.py on other platforms. Configure the server in Claude Desktop by adding its path to claude_desktop_config.json. Three tools are available: get_model_info, transcribe, and batch_transcribe.
transcribe toolbatch_transcribePython 3.10+, faster-whisper>=0.9.0, PyTorch and torchaudio (CUDA or CPU version), and mcp[cli]>=1.2.0.
On Windows, run `start_server.bat`. On other platforms, run `python whisper_server.py` from the project directory.
Edit `claude_desktop_config.json` (located in `%APPDATA%\Claude` on Windows or `~/Library/Application Support/Claude` on macOS) and add the server configuration with the command `python` and the path to `whisper_server.py`.
The server supports model sizes from tiny up to large-v3, allowing a trade-off between speed and accuracy.
Transcriptions can be produced in VTT (subtitles), SRT (subtitles), or JSON formats.

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