speech-mcp 0.1.0

Speech MCP Server with command-line interface

Speech MCP

A Goose MCP extension for voice interaction with audio visualization.

Overview

Speech MCP provides a voice interface for Goose, allowing users to interact through speech rather than text. It includes:

• Real-time audio processing for speech recognition
• Local speech-to-text using OpenAI's Whisper model
• Text-to-speech capabilities
• Simple command-line interface for voice interaction

Features

• Voice Input: Capture and transcribe user speech using Whisper
• Voice Output: Convert agent responses to speech
• Continuous Conversation: Automatically listen for user input after agent responses
• Silence Detection: Automatically stops recording when the user stops speaking

Installation

Option 1: Quick Install (One-Click)

Click the link below if you have Goose installed:

goose://extension?cmd=uvx&arg=speech-mcp&id=speech_mcp&name=Speech%20Interface&description=Voice%20interaction%20with%20audio%20visualization%20for%20Goose

Option 2: Using Goose CLI (recommended)

Start Goose with your extension enabled:

# If you installed via PyPI
goose session --with-extension "uvx speech-mcp"

# Or if you want to use a local development version
goose session --with-extension "python -m speech_mcp"

Option 3: Manual setup in Goose

1. Run goose configure
2. Select "Add Extension" from the menu
3. Choose "Command-line Extension"
4. Enter a name (e.g., "Speech Interface")
5. For the command, enter: uvx speech-mcp
6. Follow the prompts to complete the setup

Option 4: Manual Installation

1. Clone this repository
2. Install dependencies:

   pip install -e .
   

Dependencies

• Python 3.10+
• PyAudio (for audio capture)
• OpenAI Whisper (for speech-to-text)
• NumPy (for audio processing)
• Pydub (for audio processing)

Usage

To use this MCP with Goose, you can:

1. Start the voice mode:

   start_voice_mode()
   

2. Listen for user input:

   transcript = listen()
   

3. Respond with speech:

   speak("Your response text")
   

4. Get the current state:

   get_speech_state()
   

Typical Workflow

# Start the voice interface
start_voice_mode()

# Listen for user input
transcript = listen()

# Process the transcript and generate a response
# ...

# Speak the response
speak("Here is my response")

# Automatically listen again
transcript = listen()

Technical Details

Speech-to-Text

The MCP uses OpenAI's Whisper model for speech recognition:

• Uses the "base" model for a good balance of accuracy and speed
• Processes audio locally without sending data to external services
• Automatically detects when the user has finished speaking

License

MIT License