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Open weightsSilero/10 trustVerified

Silero VAD

The default open-source voice activity detector: a ~2MB MIT-licensed neural model that scores a 30ms audio chunk in under a millisecond on a single CPU thread, trained on corpora spanning 6000+ languages.

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About

Silero VAD (snakers4/silero-vad, active since 2020, now at v5) is the open-source voice activity detector that quietly became the industry default — it sits inside faster-whisper, LiveKit, sherpa-onnx and countless voice pipelines. The recipe is deliberately boring in the best way: a small LSTM-based network (~309K params in v5, ~2MB JIT/ONNX) that takes 16kHz or 8kHz audio in 256/512-sample chunks and emits a speech probability per chunk, with hysteresis-style post-processing to turn probabilities into clean speech timestamps. The provider's own benchmarks show it comfortably ahead of the classic WebRTC VAD on noisy speech, and an independent 2026 comparison table reports 87.7% true-positive rate at 5% false-positive rate (WebRTC VAD: 50.0%). There are no strings attached — MIT license, no telemetry, no keys, runs anywhere PyTorch or ONNX runs. The main knobs are the decision threshold and minimum speech/silence durations; push the threshold down if you're clipping first words, up if background chatter leaks through.

Specs

Context window

Not applicable — streams audio in 30ms+ chunks (256 samples @8kHz, 512 @16kHz); LSTM state carried across chunks

Pricing

Free / open source — MIT

Free quota

Fully open — pip install silero-vad, torch.hub, or ONNX weights; no registration

API access

No hosted API — self-host via pip (silero-vad), torch.hub, or ONNX Runtime

Weights

Open weights

Released

Not disclosed

Benchmarks

  • <1ms per 30ms+ audio chunk on a single CPU thread (provider-reported)\n- 87.7% TPR at 5% FPR on LibriSpeech+DEMAND-style benchmark (third-party 2026 comparison; WebRTC VAD 50.0%, Cobra 98.9% on the same table)\n- Provider wiki publishes quality metrics vs other VAD solutions

Provider-reported unless a source says otherwise. Benchmarks are marketing-adjacent by nature — treat them as directional.

Use cases

voice-activity-detectionvoice-assistantscall-centersspeech-preprocessingedge-aidata-cleaning

Trust score — 9/10

/10 trustVerified

Scored 9/10 (TIER verified, metrics-v1). Evidence: official repo (snakers4/silero-vad, MIT) with published quality/performance wikis; adopted as the default VAD in faster-whisper, LiveKit, sherpa-onnx; v5 in wide production use. Strengths: stellar speed (<1ms/chunk CPU), 6000+ language training corpora, zero-dependency deployment, no keys or telemetry. Docked for: (1) accuracy claims on noisy data are mostly provider-published, with limited peer-reviewed evaluation; (2) chunk-state quirks (v5's 64-sample carry requirement) bite integrators who skip the docs. License: MIT.

How the trust score is built

Full methodology →
Adoption · stars/downloads, log-scaled vs peers
10.0/10
Maintenance · recency, releases, issue load
9.0/10
Openness · license + docs
10.0/10
Manual review · sanity read + red-flag check
8.0/10
  • · Default VAD in faster-whisper, LiveKit, sherpa-onnx and many voice pipelines.
  • · v5 line (~309K params, 512-sample chunks) in wide production use; repo pushed Sept 2026.
  • · Accuracy claims on noisy data are mostly provider-published; v5's 64-sample carry requirement bites integrators who skip docs.

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