Audio WebSocket Handler for Radio Dicator

RF Quantum SCYTHE Integration Summary of https://github.com/theckid/RadioTranscriptor

an integrated solution that combines the functionality of radiodictator_v4.py with the WebSocket server and leverages the gemma_data_preprocessor.py features.

Key Components

1. Audio WebSocket Handler (audio_websocket_handler.py):
   • Combines real-time audio capture from radiodictator_v4.py with WebSocket broadcasting
   • Uses OpenAI’s Whisper model for real-time speech-to-text
   • Implements Voice Activity Detection (VAD) to detect when someone is speaking
   • Maintains a rolling buffer to capture pre-speech audio
   • Transcribes detected speech segments and logs them
   • Has placeholder for integration with Gemma for advanced analysis
2. WebSocket Server Patch (patch_websocket_server.py):
   • Modifies the existing WebSocket server to add audio transcription capabilities
   • Adds a new /audio WebSocket endpoint
   • Integrates the audio handler at server startup
   • Broadcasts transcriptions to connected clients
   • Updates the admin status to include audio system information
3. Demo UI (audio_transcription_demo.html):
   • Provides a user-friendly interface for viewing real-time transcriptions
   • Shows system status and connection information
   • Has controls for restarting the audio system
   • Displays Gemma analysis results when available
4. Startup Script (start_audio_websocket_demo.sh):
   • Automates the setup and startup of the audio WebSocket demo
   • Checks and installs required packages
   • Deploys the demo HTML to the static directory
   • Starts the WebSocket server with audio capabilities

Integration with Gemma

The system is designed with integration points for the Gemma functionality from gemma_data_preprocessor.py. The _process_with_gemma method in the AudioWebSocketHandler class serves as a placeholder where you can add code to:

1. Format the transcription data for Gemma processing
2. Send it for analysis using the feature extraction techniques from gemma_data_preprocessor.py
3. Broadcast the analysis results back to WebSocket clients

Future Enhancements

1. Full Gemma Integration: Complete the _process_with_gemma method to perform advanced analysis of transcribed text.
2. Multi-Source Support: Add the ability to monitor multiple audio sources simultaneously.
3. User Authentication: Implement proper authentication for the WebSocket endpoint.
4. Real-time Signal Classification: Combine audio transcription with signal classification for a more comprehensive analysis.
5. Mobile Support: Adapt the demo UI for mobile devices to enable field monitoring.

This integrated solution combines real-time audio processing with WebSocket-based data distribution, creating a powerful system for monitoring and analyzing radio communications in real-time.