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

python3

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:

python3

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

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

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++
Example of VAD ExecuTorch model usage in C++
Voice activity detection for the browser using ONNX Runtime Web
Rust, Go, Java, C++, C# and other community examples

huggingworld/silero-vad

Silero VAD

Fast start

Key Features

Typical Use Cases

Links

Get In Touch

Examples and VAD-based Community Apps

huggingworld/
silero-vad