Claude Desktop Real-time Audio MCP Server (Python Implementation…

@joelfuller2016

代码仓库

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MIT license

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stdio

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joelfuller2016

关于 Claude Desktop Real-time Audio MCP Server

Python-based Model Context Protocol (MCP) server for real-time microphone input to Claude Desktop on Windows. FastMCP + sounddevice + multiple STT engines for sub-500ms latency voice conversations.

#audio #mcp #real-time #python #speech-recognition

Claude Desktop Real-time Audio MCP Server 配置

使用下面的配置,将此服务器添加到你的 MCP 客户端。

{
  "mcpServers": {
    "claude-desktop-realtime-audio-mcp-python": {
      "command": "python",
      "args": [
        "-m",
        "venv",
        "venv"
      ]
    }
  }
}

Claude Desktop Real-time Audio MCP Server 工具

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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。

概览

What is Claude Desktop Real-time Audio MCP Server (Python Implementation)?

A Python-based Model Context Protocol (MCP) server that enables real-time microphone input for Claude Desktop on Windows. It leverages Python’s audio ecosystem (sounddevice, webrtcvad, WASAPI) and supports multiple speech-to-text engines (OpenAI Whisper, Azure Speech, Google Speech) for robust voice‑driven conversations with Claude.

How to use Claude Desktop Real-time Audio MCP Server (Python Implementation)?

Clone the repository, create a Python 3.8+ virtual environment, install dependencies with pip install -r requirements.txt, set required API keys as environment variables (e.g., OPENAI_API_KEY), test audio with python -m audio.test_setup, then start the server with python main.py. Add the server to Claude Desktop’s claude_desktop_config.json as an MCP server entry.

Key features of Claude Desktop Real-time Audio MCP Server (Python Implementation)

Real‑time microphone capture via Windows WASAPI
Multiple speech recognition engines: Whisper (local/API), Azure, Google
Voice activity detection (webrtcvad) with configurable sensitivity
Low‑latency, async architecture using FastMCP and asyncio
Comprehensive logging and performance monitoring
Easy JSON/YAML configuration with environment variable support

Use cases of Claude Desktop Real-time Audio MCP Server (Python Implementation)

Voice‑driven conversations with Claude Desktop on Windows
Real‑time transcription and hands‑free interaction
Testing and benchmarking different STT engines locally or in the cloud
Building custom audio‑based MCP tools for Claude

常见问题

What MCP tools are available?

The server provides tools for audio control (`start_recording`, `stop_recording`, `get_recording_status`, `test_audio_capture`), device management (`list_audio_devices`, `set_audio_device`, `configure_audio_settings`), and speech recognition (`set_stt_engine`). It also exposes resources like `audio://devices`, `audio://config`, and `stt://engines`.

What are the prerequisites?

Windows 10/11 (or Windows 7+ with WASAPI support), Python 3.8+, and the latest Claude Desktop application. A microphone and appropriate API keys for cloud STT services (optional for local Whisper models).

How do I switch between STT engines?

Use the `set_stt_engine` tool or configure the `stt.default_engine` setting in the configuration file. Supported engines are `whisper`, `azure`, and `google`.

Where does audio data go?

Audio is processed locally by default when using Whisper local models. Cloud STT services (Azure, Google) can be disabled for maximum privacy. No audio data is permanently stored.

How can I optimize for low latency?

Reduce `chunk_size` to 512, enable `low_latency`, and use a small Whisper model (e.g., `tiny`) with FP16. For high accuracy, increase `chunk_size` to 2048 and use larger models like `large` with `beam_size: 10`.

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