Silero VAD - pre-trained enterprise-grade Voice Activity Detector (also see our STT models).
Real Time Example
real-time-example.mp4
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Stellar accuracy
Silero VAD has excellent results on speech detection tasks.
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Fast
One audio chunk (30+ ms) takes less than 1ms to be processed on a single CPU thread. Using batching or GPU can also improve performance considerably. Under certain conditions ONNX may even run up to 4-5x faster.
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Lightweight
JIT model is around 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 30 ms. Longer chunks are supported directly, others may work as well.
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Highly Portable
Silero VAD reaps benefits from the rich ecosystems built around PyTorch and ONNX running everywhere where these runtimes are available.
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No Strings Attached
Published under permissive license (MIT) Silero VAD has zero strings attached - no telemetry, no keys, no registration, no built-in expiration, no keys or vendor lock.
- 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
- Versions and Available Models
- Further reading
- FAQ
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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 = {[email protected]}
}