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snakers4
GitHub Repository: snakers4/silero-vad
 Path: blob/master/README.md
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Mailing list : test Mailing list : test License: CC BY-NC 4.0 downloads

Open In Colab Test Package

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Silero VAD


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


Real Time Example

https://user-images.githubusercontent.com/36505480/144874384-95f80f6d-a4f1-42cc-9be7-004c891dd481.mp4

Please note, that video loads only if you are logged in your GitHub account.


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



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 = {hello@silero.ai}
}

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