ONNX Runtime 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
A cross-platform library for running and training machine learning models quickly.
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 ONNX Runtime if you need…
- Developers who want to deploy a trained model inside an application or service
- Teams running the same model across Windows, Linux, macOS, or ARM devices
- Projects that need to speed up inference on available hardware accelerators
- Engineers with existing PyTorch training scripts who want to test the README's training acceleration
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
| ONNX Runtime | Silero VAD | |
|---|---|---|
| Type | Library | Library |
| Price | Free and open source | Free and open source |
| Licence | MIT | MIT |
| GitHub stars | 22.2k | 10.6k |
| Setup difficulty | Intermediate | Intermediate |
| How you get it | Install with a package manager | Install with a package manager |
| Main language | C++ | Python |
| Last code update | 4 days ago | 15 days ago |
What each one is
ONNX Runtime
ONNX Runtime is a library that runs machine learning models on many kinds of hardware and operating systems. It supports models built in frameworks such as PyTorch and TensorFlow/Keras, as well as classical libraries such as scikit-learn, LightGBM and XGBoost. According to the README, it aims for faster performance and lower costs by using hardware accelerators and graph optimizations, and it also offers a training feature for transformer models on NVIDIA GPUs.
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
ONNX Runtime
- Developers who want to deploy a trained model inside an application or service
- Teams running the same model across Windows, Linux, macOS, or ARM devices
- Projects that need to speed up inference on available hardware accelerators
- Engineers with existing PyTorch training scripts who want to test the README's training acceleration
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
ONNX Runtime
- Supports many hardware and operating system targets, according to the README
- Works with models from several major frameworks through the ONNX format
- Open source under the MIT license with active development and published releases
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
ONNX Runtime
- People who only want to train or experiment with models in a notebook, since the README focuses on production inference and training acceleration rather than beginner workflows
- Readers without some programming background, because the README assumes familiarity with ML frameworks and model formats
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
ONNX Runtime
- The README is truncated and gives few concrete setup details, so getting started requires reading the linked documentation
- Performance benefits depend on hardware and model, and the README does not give specific numbers
- Training acceleration as described targets NVIDIA GPUs and transformer models
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
ONNX Runtime
Clone the repository
git clone https://github.com/microsoft/onnxruntimeSilero 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 ONNX Runtime and Silero VAD?
ONNX Runtime: A cross-platform library for running and training machine learning models quickly. Silero VAD: Pre-trained voice activity detector that finds where speech occurs in audio, for Python, ONNX, and other runti. ONNX Runtime suits developers who want to deploy a trained model inside an application or service, while Silero VAD suits developers adding speech detection to python apps.
Are ONNX Runtime and Silero VAD free?
ONNX Runtime: Free and open source. Silero VAD: Free and open source.
Is ONNX Runtime or Silero VAD more popular?
ONNX Runtime has more GitHub stars (22.2k) 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 ONNX Runtime and Silero VAD?
Other options people compare them with include Petals, Weights & Biases, spaCy, PyG, Nano-vLLM.
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.