Silero VAD is a pre-trained voice activity detector that identifies speech in audio streams using deep learning models.
The tool solves the problem of distinguishing voice activity from silence and background noise in audio. It works by running audio chunks through a lightweight neural network model available in both PyTorch and ONNX formats. The detector processes 30+ millisecond audio chunks in under 1 millisecond on a single CPU thread, making it suitable for real-time applications. It supports both 8000 Hz and 16000 Hz sampling rates and handles audio from diverse domains, languages, and noise conditions without requiring configuration tuning.
Teams should adopt this tool if they need reliable speech detection for voice command systems, transcription pipelines, or audio preprocessing. The project suits applications ranging from embedded systems to cloud services because the model is only two megabytes and runs on any platform supporting PyTorch or ONNX Runtime, including edge devices with limited resources. The tool requires Python 3.8 or higher, 1GB of RAM, and a CPU with AVX instruction sets for x86-64 systems, though ONNX Runtime alone can run on other architectures if you implement custom audio I/O. It operates under the MIT license with no telemetry, registration requirements, or vendor lock-in.
The project maintains active development with regular updates to model quality and performance. The maintainers provide comprehensive documentation including quality metrics, performance benchmarks, and detailed examples for different use cases. The codebase includes both PyTorch and ONNX implementations, giving users flexibility in deployment choices. Community engagement is evident through the project's wiki, which documents comparisons with alternative solutions and provides guidance on sampling rate selection and batch processing optimization.
