What Silero VAD is
Silero VAD detects where speech occurs in an audio recording. VAD means voice activity detection. Its output can identify speech regions so that another program knows which parts of a recording to process; it does not convert the speech into written words.
This is a building block for developers, not a complete transcription app. It can help organize audio before recognition or support a voice interface that needs to distinguish speaking from silence. It does not establish who is speaking or what they mean.
Read the official documentationHow it fits into a workflow
A pipeline supplies audio in the expected format, runs the detector and receives speech-region timestamps. Those regions can be passed to a transcription model or used to manage recording segments. Listen to examples around the boundaries: clipping quiet speech is a different failure from passing background noise through.
Examples described as illustrative or proposed trials are editorial suggestions. Documented real builds, when available, are linked separately below.
Requirements, cost and limitations
The project provides PyTorch and ONNX routes under MIT terms. The selected runtime, input sampling and detection thresholds must match the documented setup. No universal recording quality or detection accuracy is established by this profile.
When to choose it and what to compare
Use Silero VAD when your task is finding speech, and pair it with speech recognition when you need text. Compare py-webrtcvad for a narrower frame-based interface. The useful test is whether important speech survives your pipeline, rather than whether the detector removes the largest amount of audio.
Practice a related technique
These tutorials use fictional samples and are separate from the real implementations linked on this site.
PRACTICE WORKSHOPTurn a recording into editable captionsTechnical details and documented capabilities
These details come from the linked sources, checked 2026-10-27. Open the current documentation before planning a deployment or migration.
- Type
- Speech & audio
- Runs on / through
- Python, ONNX-compatible platforms
- Execution choices
- local, self-hosted
- License or access terms
- MIT
- Speech timestamps are a useful building block for audio pipelines.
- PyTorch and ONNX options allow different deployment approaches.
Focused source review : Documented runtime, licensing and deployment conditions. Documentation review, not hands-on product testing.
Sources and evidence
Product facts are based on the following published sources, checked 2026-10-27. Workflow suggestions and comparisons are editorial interpretation; PrisAIm has not independently benchmarked this product.
Official project Documentation Examples and dependencies