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https://github.com/snakers4/silero-vad
Silero VAD: pre-trained enterprise-grade Voice Activity Detector
https://github.com/snakers4/silero-vad
onnx onnx-runtime onnxruntime pytorch speech speech-processing vad voice-activity-detection voice-commands voice-control voice-detection voice-recognition
Last synced: 9 days ago
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Silero VAD: pre-trained enterprise-grade Voice Activity Detector
- Host: GitHub
- URL: https://github.com/snakers4/silero-vad
- Owner: snakers4
- License: mit
- Created: 2020-11-23T09:54:16.000Z (almost 4 years ago)
- Default Branch: master
- Last Pushed: 2024-10-21T07:22:03.000Z (19 days ago)
- Last Synced: 2024-10-21T10:28:25.119Z (18 days ago)
- Topics: onnx, onnx-runtime, onnxruntime, pytorch, speech, speech-processing, vad, voice-activity-detection, voice-commands, voice-control, voice-detection, voice-recognition
- Language: Python
- Homepage:
- Size: 94.2 MB
- Stars: 4,206
- Watchers: 49
- Forks: 412
- Open Issues: 9
-
Metadata Files:
- Readme: README.md
- License: LICENSE
- Code of conduct: CODE_OF_CONDUCT.md
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README
[](mailto:[email protected]) [](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
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:[email protected]) 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:[email protected]) 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++](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