Worked on the livekit/agents repository to deliver custom ONNX model file support for the Silero Voice Activity Detection (VAD) system. This feature enabled users to specify a custom model file path for the inference session, allowing seamless loading and switching between different Silero VAD model versions. By decoupling the model version from code and configuration, the implementation improved model governance and facilitated environment-specific deployments. The work focused on maintainability, providing centralized management and clear documentation for model files. Python was used throughout, leveraging skills in audio processing and machine learning to enhance flexibility and rollback capabilities within the VAD subsystem.