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

@joelfuller2016

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

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

Config

Add this server to your MCP-compatible client using the configuration below.

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

Tools

No tools detected

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Overview

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

FAQ from Claude Desktop Real-time Audio MCP Server (Python Implementation)

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.

Frequently asked questions

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?

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`.

Comments

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