[](mailto:hello@silero.ai) [](https://t.me/silero_speech) [](https://github.com/snakers4/silero-vad/blob/master/LICENSE)
[](https://colab.research.google.com/github/snakers4/silero-vad/blob/master/silero-vad.ipynb)

Silero VAD
**Silero VAD** - pre-trained enterprise-grade [Voice Activity Detector](https://en.wikipedia.org/wiki/Voice_activity_detection) (also see our [STT models](https://github.com/snakers4/silero-models)).
Real Time Example
https://user-images.githubusercontent.com/36505480/144874384-95f80f6d-a4f1-42cc-9be7-004c891dd481.mp4
Key Features
- **Stellar accuracy**
Silero VAD has [excellent results](https://github.com/snakers4/silero-vad/wiki/Quality-Metrics#vs-other-available-solutions) on speech detection tasks.
- **Fast**
One audio chunk (30+ ms) [takes](https://github.com/snakers4/silero-vad/wiki/Performance-Metrics#silero-vad-performance-metrics) 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](https://github.com/snakers4/silero-vad/wiki/Quality-Metrics#sample-rate-comparison) **8000 Hz** and **16000 Hz** [sampling rates](https://en.wikipedia.org/wiki/Sampling_(signal_processing)#Sampling_rate).
- **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.
Fast start
```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)
```
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](https://github.com/snakers4/silero-vad/wiki/Examples-and-Dependencies#dependencies)
- [Quality Metrics](https://github.com/snakers4/silero-vad/wiki/Quality-Metrics)
- [Performance Metrics](https://github.com/snakers4/silero-vad/wiki/Performance-Metrics)
- [Versions and Available Models](https://github.com/snakers4/silero-vad/wiki/Version-history-and-Available-Models)
- [Further reading](https://github.com/snakers4/silero-models#further-reading)
- [FAQ](https://github.com/snakers4/silero-vad/wiki/FAQ)
Get In Touch
Try our models, create an [issue](https://github.com/snakers4/silero-vad/issues/new), start a [discussion](https://github.com/snakers4/silero-vad/discussions/new), join our telegram [chat](https://t.me/silero_speech), [email](mailto:hello@silero.ai) us, read our [news](https://t.me/silero_news).
Please see our [wiki](https://github.com/snakers4/silero-models/wiki) and [tiers](https://github.com/snakers4/silero-models/wiki/Licensing-and-Tiers) for relevant information and [email](mailto:hello@silero.ai) 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}
}
```
Examples and VAD-based Community Apps
- Example of VAD ONNX Runtime model usage in [C++](https://github.com/snakers4/silero-vad/tree/master/examples/cpp)
- Voice activity detection for the [browser](https://github.com/ricky0123/vad) using ONNX Runtime Web