# silero-vad
**Repository Path**: mirrors/silero-vad
## Basic Information
- **Project Name**: silero-vad
- **Description**: Silero VAD 是预训练的企业级语音活动检测器（另请参阅我们的语音转文本 (STT) 模型）
- **Primary Language**: Python
- **License**: MIT
- **Default Branch**: master
- **Homepage**: https://www.oschina.net/p/silero-vad
- **GVP Project**: No
## Statistics
- **Stars**: 0
- **Forks**: 0
- **Created**: 2025-07-28
- **Last Updated**: 2026-01-03
## Categories & Tags
**Categories**: Uncategorized
**Tags**: None
## README
[](mailto:hello@silero.ai) [](https://t.me/silero_speech) [](https://github.com/snakers4/silero-vad/blob/master/LICENSE) [](https://pypi.org/project/silero-vad/)
[](https://colab.research.google.com/github/snakers4/silero-vad/blob/master/silero-vad.ipynb) [](https://github.com/snakers4/silero-vad/actions/workflows/test.yml) [](https://pypi.org/project/silero-vad/) [](https://pypi.org/project/silero-vad)
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
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**](https://www.ffmpeg.org/) backend. `conda install -c conda-forge 'ffmpeg<7'`;
- Option №2 - [**sox_io**](https://pypi.org/project/sox/) backend. `apt-get install sox`, TorchAudio is tested on libsox 14.4.2;
- Option №3 - [**soundfile**](https://pypi.org/project/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](https://onnxruntime.ai/getting-started). 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](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.
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) 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 = {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++](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
- [Rust](https://github.com/snakers4/silero-vad/tree/master/examples/rust-example), [Go](https://github.com/snakers4/silero-vad/tree/master/examples/go), [Java](https://github.com/snakers4/silero-vad/tree/master/examples/java-example), [C++](https://github.com/snakers4/silero-vad/tree/master/examples/cpp), [C#](https://github.com/snakers4/silero-vad/tree/master/examples/csharp) and [other](https://github.com/snakers4/silero-vad/tree/master/examples) community examples