Silero VAD is a voice activity detection model and deep learning speech classifier designed to distinguish human speech from silence across diverse languages and noisy environments. It functions as a pre-trained neural network capable of identifying speech segments within both static audio recordings and real-time data streams.
The project includes a language identification tool for classifying spoken languages and a framework for fine-tuning audio models. It provides utilities for optimizing detection thresholds using validation datasets and retraining the model with custom labeled audio to improve accuracy.
The system covers audio analysis capabilities such as speech probability estimation, temporal timestamp identification, and audio segment extraction. It also handles automated preprocessing by isolating and merging speech chunks to remove silence.
