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Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Silero VAD: pre-trained enterprise-grade Voice Activity Detector
| Date | Stars |
|---|---|
| 2026-07-24 | 9660 |
| 2026-07-25 | 9673 |
| 2026-07-28 | 9673 |
| 2026-07-30 | 9673 |
| 2026-08-06 | 9673 |
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Momentum
5.0
growth rate 0.00%/day
[](mailto:[email protected]) [](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)  <br/> <h1 align="center">Silero VAD</h1> <br/> **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)). <br/> <p align="center"> <img src="https://github.com/user-attachments/assets/dfd29c4f-226a-4687-8bea-e8faaaee0287" /> </p> <details> <summary>Real Time Example</summary> 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. </details> <br/> <h2 align="center">Fast start</h2> <br/> <details> <summary>Dependencies</summary> 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. </details> **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) ) ``` <br/> <h2 align="center">Key Features</h2> <br/> - **Stellar accuracy** Silero VAD has [excellent results](https://github.com/snakers4/silero-vad/wiki/Quality-Metrics#vs-other-available-solu
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Dimitrii Voronin
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Alexander Veysov
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Zac Wang
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Daniel Müller
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Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:01e580f115afdc39, topic:onnx, topic:onnxruntime
matched fp:01e580f115afdc39, topic:pytorch