Silero VAD - pre-trained enterprise-grade Voice Activity Detector (also see our STT models).

• Stellar accuracy

  Silero VAD has excellent results on speech detection tasks.

• 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.

• Lightweight

  JIT model is around one megabyte in size.

• 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.

• Flexible sampling rate

  Silero VAD supports 8000 Hz and 16000 Hz sampling rates.

• Flexible chunk size

  Model was trained on 30 ms. Longer chunks are supported directly, others may work as well.

• Highly Portable

  Silero VAD reaps benefits from the rich ecosystems built around PyTorch and ONNX running everywhere where these runtimes are available.

• 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 = {hello@silero.ai}
}

• Example of VAD ONNX Runtime model usage in C++

• Voice activity detection for the browser using ONNX Runtime Web