Blaizzy/mlx-audio
May 2026 – May 2026
1 Month active
Languages Used
Python
Technical Skills
WebSocket programmingbackend developmentreal-time audio processingunit testing
Yoann Vanitou
PROFILE
Yoann Vanitou
Developed and integrated server-side Voice Activity Detection (VAD) for real-time audio processing in the Blaizzy/mlx-audio repository, shifting speech-turn management from the client to the backend to reduce client load and improve latency. Leveraged Python for backend development, implementing a TurnDetector state machine and StreamingVad framing to enable OpenAI-compatible turn detection over the /v1/realtime WebSocket endpoint. The solution introduced a configurable VAD model and automated turn commits, simplifying client integration and enhancing real-time responsiveness. Comprehensive unit testing and documentation updates ensured robust endpoint behavior, reflecting a focused approach to real-time audio and WebSocket programming within the project.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
1Total
Bugs
0
Commits
1
Features
1
Lines of code
767
Activity Months1
Your Network
77 peopleWork History
May 2026
1 Commits • 1 Features
May 1, 2026May 2026: Delivered server-side Voice Activity Detection (VAD) for real-time audio in Blaizzy/mlx-audio, shifting speech-turn management from client to server to reduce client load and improve latency. Implemented OpenAI-compatible turn_detection on the /v1/realtime WebSocket, with a configurable VAD model (default Silero) via a --vad-model flag. The server emits input_audio_buffer.speech_started/speech_stopped and auto-commits each turn, removing the need for client input_audio_buffer.commit. Core implementation includes a TurnDetector state machine and StreamingVad framing (realtime_vad.py), server wiring (server.py) and endpoint enhancements, plus documentation and tests. Commit reference: 20190a69b000267b4124fffd80837b532866c222
1 Commits • 1 Features
May 1, 2026May 2026: Delivered server-side Voice Activity Detection (VAD) for real-time audio in Blaizzy/mlx-audio, shifting speech-turn management from client to server to reduce client load and improve latency. Implemented OpenAI-compatible turn_detection on the /v1/realtime WebSocket, with a configurable VAD model (default Silero) via a --vad-model flag. The server emits input_audio_buffer.speech_started/speech_stopped and auto-commits each turn, removing the need for client input_audio_buffer.commit. Core implementation includes a TurnDetector state machine and StreamingVad framing (realtime_vad.py), server wiring (server.py) and endpoint enhancements, plus documentation and tests. Commit reference: 20190a69b000267b4124fffd80837b532866c222
May 2026
Activity
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Quality Metrics
Correctness100.0%
Maintainability80.0%
Architecture100.0%
Performance80.0%
AI Usage60.0%
Skills & Technologies
Programming Languages
Python
Technical Skills
WebSocket programmingbackend developmentreal-time audio processingunit testing
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