speech-mcp 0.2.1

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 faster-whisper (a faster implementation of OpenAI's Whisper model)
• Text-to-speech capabilities
• Simple command-line interface for voice interaction

Features

• Voice Input: Capture and transcribe user speech using faster-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
• Robust Error Handling: Graceful recovery from common failure modes

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 "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: speech-mcp
6. Follow the prompts to complete the setup

Option 4: Manual Installation

1. Clone this repository
2. Install dependencies:

   uv pip install -e .
   

Dependencies

• Python 3.10+
• PyAudio (for audio capture)
• faster-whisper (for speech-to-text)
• NumPy (for audio processing)
• Pydub (for audio processing)
• pyttsx3 (for text-to-speech)
• psutil (for process management)

Usage

To use this MCP with Goose, you can:

1. Start a conversation:

   user_input = start_conversation()
   

2. Reply to the user and get their response:

   user_response = reply("Your response text here")
   

Typical Workflow

# Start the conversation
user_input = start_conversation()

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

# Reply to the user and get their response
follow_up = reply("Here's my response to your question.")

# Process the follow-up and reply again
reply("I understand your follow-up question. Here's my answer.")

Troubleshooting

If you encounter issues with the extension freezing or not responding:

1. Check the logs: Look at the log files in src/speech_mcp/ for detailed error messages.
2. Reset the state: If the extension seems stuck, try deleting src/speech_mcp/speech_state.json or setting all states to false.
3. Use the direct command: Instead of uv run speech-mcp, use the installed package with speech-mcp directly.
4. Check audio devices: Ensure your microphone is properly configured and accessible to Python.
5. Verify dependencies: Make sure all required dependencies are installed correctly.

Recent Fixes

• Improved error handling: Better recovery from common failure modes
• Timeout management: Reduced timeouts and added fallback mechanisms
• Process management: Better handling of UI process startup and termination
• State consistency: Added state reset mechanisms to avoid getting stuck
• Fallback transcription: Added emergency transcription when UI process fails
• Debugging output: Enhanced logging and console output for troubleshooting

Technical Details

Speech-to-Text

The MCP uses faster-whisper 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
• Provides improved performance over the original Whisper implementation

License

MIT License