README.md

Silero VAD

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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Fast start

Dependencies

System requirements to run python examples on x86-64 systems:

• python 3.8+;
• 1G+ RAM;
• A modern CPU with AVX, AVX2, AVX-512 or AMX instruction sets.

Dependencies:

• torch>=1.12.0;
• torchaudio>=0.12.0 (for I/O only);
• onnxruntime>=1.16.1 (for ONNX model usage).

Silero VAD uses torchaudio library for audio I/O (torchaudio.info, torchaudio.load, and torchaudio.save), so a proper audio backend is required:

• Option №1 - FFmpeg backend. conda install -c conda-forge 'ffmpeg<7';
• Option №2 - sox_io backend. apt-get install sox, TorchAudio is tested on libsox 14.4.2;
• Option №3 - soundfile backend. pip install soundfile.

If you are planning to run the VAD using solely the onnx-runtime, it will run on any other system architectures where onnx-runtume is supported. In this case please note that:

• You will have to implement the I/O;
• You will have to adapt the existing wrappers / examples / post-processing for your use-case.

Using pip: pip install silero-vad

from silero_vad import load_silero_vad, read_audio, get_speech_timestamps
model = load_silero_vad()
wav = read_audio('path_to_audio_file')
speech_timestamps = get_speech_timestamps(
  wav,
  model,
  return_seconds=True,  # Return speech timestamps in seconds (default is samples)
)

Using torch.hub:

import torch
torch.set_num_threads(1)

model, utils = torch.hub.load(repo_or_dir='snakers4/silero-vad', model='silero_vad')
(get_speech_timestamps, _, read_audio, _, _) = utils

wav = read_audio('path_to_audio_file')
speech_timestamps = get_speech_timestamps(
  wav,
  model,
  return_seconds=True,  # Return speech timestamps in seconds (default is samples)
)

Key Features

• 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 two megabytes in size.

• General

  Silero VAD was trained on huge corpora that include over 6000 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.

• 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.

Typical Use Cases

• 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

Links

• Examples and Dependencies
• Quality Metrics
• Performance Metrics
• Versions and Available Models
• Further reading
• FAQ

Get In Touch

Try our models, create an issue, start a discussion, join our telegram chat, email us, read our news.

Please see our wiki 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 = {2024},
  publisher = {GitHub},
  journal = {GitHub repository},
  howpublished = {\url{https://github.com/snakers4/silero-vad}},
  commit = {insert_some_commit_here},
  email = {[email protected]}
}

Examples and VAD-based Community Apps

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

• Voice activity detection for the browser using ONNX Runtime Web

• Rust, Go, Java, C++, C# and other community examples