pyannote vs Silero VAD
Which should you use? A side-by-side look at what each one does, who it suits, what it costs, how hard it is to set up, and where each falls short.
Open source · MIT
Python toolkit for figuring out who spoke when in an audio recording, using pretrained PyTorch models.
Open source · MIT
Pre-trained voice activity detector that finds where speech occurs in audio, for Python, ONNX, and other runti
The short answer
Choose pyannote if you need…
- Developers who want to add speaker separation to a Python audio workflow
- Researchers comparing or fine-tuning speaker diarization models
- Teams processing meeting or interview recordings who can run PyTorch code
- Users who want a pretrained pipeline without training their own model
Choose Silero VAD if you need…
- Developers adding speech detection to Python apps
- Teams building telephony, call-center, or voice bot pipelines
- Engineers preparing audio datasets by removing non-speech sections
- Edge or mobile projects that need a small model (the README says the JIT model is about two megabytes)
Side-by-side
| pyannote | Silero VAD | |
|---|---|---|
| Type | Library | Library |
| Price | Free and open source | Free and open source |
| Licence | MIT | MIT |
| GitHub stars | 10.8k | 10.6k |
| Setup difficulty | Intermediate | Intermediate |
| How you get it | Install with a package manager | Install with a package manager |
| Main language | Jupyter Notebook | Python |
| Last code update | 12 days ago | 15 days ago |
What each one is
pyannote
pyannote.audio is an open-source Python toolkit for speaker diarization, which means splitting a recording into segments and labeling which speaker is talking at each point. It is built on PyTorch and ships with pretrained models and pipelines that can be fine-tuned on your own data. The README also describes a paid hosted service, pyannoteAI, as an optional premium option.
Silero VAD
Silero VAD is a pre-trained model that listens to audio and marks the parts that contain human speech. Developers use it to cut silence out of recordings, decide when someone has started or stopped talking, or clean datasets before further processing. The README says it runs on PyTorch and ONNX and can be used from Python or other languages through ONNX Runtime.
Best for
pyannote
- Developers who want to add speaker separation to a Python audio workflow
- Researchers comparing or fine-tuning speaker diarization models
- Teams processing meeting or interview recordings who can run PyTorch code
- Users who want a pretrained pipeline without training their own model
Silero VAD
- Developers adding speech detection to Python apps
- Teams building telephony, call-center, or voice bot pipelines
- Engineers preparing audio datasets by removing non-speech sections
- Edge or mobile projects that need a small model (the README says the JIT model is about two megabytes)
Strengths
pyannote
- Built on PyTorch with pretrained pipelines available on the Hugging Face model hub
- Python-first API with a short example that produces readable speaker turns
- MIT licensed and actively maintained, with a latest release of 4.0.7 and a recent push
Silero VAD
- The README describes the model as pre-trained, so you do not need to train anything first
- The model is small and the README reports a single chunk takes under 1 ms on one CPU thread
- MIT license and no telemetry, keys, or registration, according to the README
Not ideal for
pyannote
- People who need a no-code app: the README only shows Python code samples
- Users who cannot accept the model conditions and Hugging Face token step, since the pretrained pipeline requires them
- Anyone with no Python experience, because setup involves ffmpeg, a package install, and an access token
Silero VAD
- People who need speech-to-text; the README points to separate STT models in another repository
- Non-programmers looking for a ready-made app, since this is a library with no GUI described
- Projects needing sample rates other than 8000 Hz or 16000 Hz, which the README lists as the supported rates
Trade-offs
pyannote
- Pretrained pipelines require accepting user conditions and creating a Hugging Face access token before use
- Benchmark numbers in the README come from the project itself and are not independently verified here
- Some features and the fastest version point to the paid pyannoteAI service
Silero VAD
- The README badge reads CC BY-NC 4.0 while the linked license is MIT, so check the LICENSE file before commercial use
- Requires a CPU with AVX-family instructions and at least 1G RAM for the Python examples
- Audio loading needs an extra backend such as torchaudio, torchcodec, or FFmpeg, which adds setup work
Getting started
pyannote
Install with uv (recommended)
uv add pyannote.audioInstall with pip
pip install pyannote.audioSilero VAD
Install with audio loading helpers
pip install silero-vad[audio]Install minimal package (if you load audio yourself)
pip install silero-vadFrequently asked questions
What is the difference between pyannote and Silero VAD?
pyannote: Python toolkit for figuring out who spoke when in an audio recording, using pretrained PyTorch models. Silero VAD: Pre-trained voice activity detector that finds where speech occurs in audio, for Python, ONNX, and other runti. pyannote suits developers who want to add speaker separation to a python audio workflow, while Silero VAD suits developers adding speech detection to python apps.
Are pyannote and Silero VAD free?
pyannote: Free and open source. Silero VAD: Free and open source.
Is pyannote or Silero VAD more popular?
pyannote has more GitHub stars (10.8k) than Silero VAD (10.6k). Popularity isn't the same as fit — check which one matches your use case above.
What are other alternatives to pyannote and Silero VAD?
Other options people compare them with include FunASR, Spleeter, ONNX Runtime.
Keep comparing
Built from our catalogue entries for both products: open-source details come from each project's README (AI-assisted, refreshed weekly) and GitHub data; AI-tool details are editorial. Spotted something wrong? Let us know from either product's page.