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This library is a browser-based utility for real-time voice activity detection. It monitors live microphone input to distinguish between background noise and human vocalizations, enabling applications to identify when a person begins or stops speaking.
The main features of ricky0123/vad are: Voice Activity Detection, Speech Boundary Detection, Real-Time Speech Processing, Speech Activity Detection, Speech Segment Extraction, Audio Digital Signal Processing, Raw Audio Captures, Real-Time Microphone Captures.
Projects with overlapping indexed features include: snakers4/silero-vad — Silero VAD is a voice activity detection model and deep learning speech classifier designed to distinguish human… k2-fsa/sherpa-ncnn — Sherpa-ncnn is an edge-based speech recognition and synthesis engine designed to run neural network models locally on… huggingface/speech-to-speech — This project is a framework for building local voice assistants and a real-time audio streaming server. It functions… koljab/realtimestt — RealtimeSTT is a local speech-to-text engine and real-time automatic speech recognition server. It utilizes… ggerganov/kbd-audio — This project is an acoustic keystroke recognition system and audio side-channel analysis tool. It identifies typed… julius-speech/julius — Julius is a high-performance, open-source speech recognition engine designed for large vocabulary continuous speech…
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
Sherpa-ncnn is an edge-based speech recognition and synthesis engine designed to run neural network models locally on mobile, embedded, and desktop hardware. It provides a cross-platform framework for offline speech-to-text transcription and text-to-speech synthesis, ensuring that all audio processing occurs on-device without requiring an internet connection or external cloud services. The project distinguishes itself through its use of the ncnn inference engine, which is optimized for low-latency execution on resource-constrained devices. It incorporates on-device model quantization to reduc
This project is a framework for building local voice assistants and a real-time audio streaming server. It functions as a containerized inference engine and a multilingual speech pipeline that orchestrates speech-to-text, language models, and text-to-speech components to convert spoken input into spoken output. The system is distinguished by its use of WebSocket-based bidirectional streaming for low-latency interactions. It features a voice activity detection system that manages speech boundaries and handles user barge-in interruptions during assistant playback. It also supports custom voice
RealtimeSTT is a local speech-to-text engine and real-time automatic speech recognition server. It utilizes transformer-based recognition and omnilingual pipelines to convert live audio streams into text, providing a WebSocket-based streaming API for raw PCM audio transmission. The project is distinguished by a dual-backend transcription pipeline that uses a lightweight engine for immediate partial suggestions and a heavier model for final high-accuracy results. It includes a wake word detection system to trigger recording and employs a shared-resource inference model to distribute heavy spee