Silero VAD - pre-trained enterprise-grade Voice Activity Detector (also see our STT models).
This repository also includes Number Detector and Language classifier models
Real Time Example
real-time-example.mp4
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High accuracy
Silero VAD has excellent results on speech detection tasks.
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Fast
One audio chunk (30+ ms) takes around 1ms to be processed on a single CPU thread. Using batching or GPU can also improve performance considerably.
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Lightweight
JIT model is less than one megabyte in size.
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General
Silero VAD was trained on huge corpora that include over 100 languages and it performs well on audios from different domains with various background noise and quality levels.
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Flexible sampling rate
Silero VAD supports 8000 Hz and 16000 Hz sampling rates.
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Flexible chunk size
Model was trained on audio chunks of different lengths. 30 ms, 60 ms and 100 ms long chunks are supported directly, others may work as well.
- Voice activity detection for IOT / edge / mobile use cases
- Data cleaning and preparation, voice detection in general
- Telephony and call-center automation, voice bots
- Voice interfaces
- Examples and Dependencies
- Quality Metrics
- Performance Metrics
- Number Detector and Language classifier models
- Versions and Available Models
- Further reading
Try our models, create an issue, start a discussion, join our telegram chat, email us, read our news.
Please see our wiki and tiers for relevant information and email us directly.
Citations
@misc{Silero VAD,
author = {Silero Team},
title = {Silero VAD: pre-trained enterprise-grade Voice Activity Detector (VAD), Number Detector and Language Classifier},
year = {2021},
publisher = {GitHub},
journal = {GitHub repository},
howpublished = {\url{https://github.com/snakers4/silero-vad}},
commit = {insert_some_commit_here},
email = {hello@silero.ai}
}