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Specialized detection of spoken numbers within audio streams.
Distinct from Automatic Speech Recognition: Focuses specifically on numeric patterns rather than general speech-to-text transcription
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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
Recognizes specific segments of audio where numbers are being spoken by the user.