`vad_sherpa_onnx` model guide¶

Overview¶

vad_sherpa_onnx is a VoiceHub voice activity detection integration. This page is generated from the model registry and its executable data and training contracts, so the documented support stays aligned with code. Open the vad_sherpa_onnx Colab notebook.

Quickstart¶

python -m pip install voicehub

Install VoiceHub and the provider extra shown above.
Choose a checkpoint that matches this integration.
Place a supported recording at speech.wav.
Run detection and tune the threshold against labeled validation audio.

from voicehub import AutoModelForVoiceActivityDetection

model = AutoModelForVoiceActivityDetection.from_pretrained(
    'safestack/silero-vad',
    model_type='vad_sherpa_onnx',
    device="cpu",
    lazy_load=True,
)
output = model.detect("speech.wav", threshold=0.5)
for segment in output.segments:
    print(segment.start, segment.end, segment.score)

Use only authorized recordings for reference voice, transcription, detection, or evaluation. The example selects a concrete device; verify checkpoint-specific hardware needs and pin an immutable revision before production use.

Supported tasks and capabilities¶

Property	Value
Task	Voice activity detection
Architecture	`native-vad-dispatch`
Runtime	`VoiceHub-native`
Capabilities	`voice-activity-detection`, `voicehub-native`, `safetensors`, `explicit-onnx-weight-conversion`, `fine-tuning`, `streaming`, `sherpa-compatible-segmentation`, `silero`, `ten-vad`
Reusable components	—

Data contract¶

Property	Value
Label boundary	Clip-, frame-, or segment-level labels
Required training inputs	`labels`

Use authorized audio and preserve annotation provenance. Follow the ASR and VAD data workflow for supported audio forms, timestamp labels, frame targets, leakage-safe splits, and evaluation.

Checkpoints, provenance, and license¶

Property	Value
Default checkpoint	`safestack/silero-vad`
Checkpoint status	Registry default; pin an immutable revision for production and reproducible evidence
Implementation	`voicehub.models.vad_sherpa_onnx.modeling_vad_sherpa_onnx.SherpaONNXVADForVoiceActivityDetection`
Configuration	`voicehub.models.vad_sherpa_onnx.configuration_vad_sherpa_onnx.SherpaONNXVADConfig`
Source provenance	`voicehub/architectures/ten_vad/SOURCE.json`
License	LicenseRef-TEN-VAD-Open-Source-License

The provider's optional TEN family is governed by a non-standard license with additional deployment restrictions, including limits on competing with Agora. Review the bundled THIRD_PARTY_LICENSE before conversion, fine-tuning, distribution, or deployment. The default Silero family retains its own checkpoint terms. Commercial use: review required.

The default checkpoint identifies the expected family, not every compatible variant. Confirm the selected checkpoint's revision, access terms, provenance, and license before downloading or redistributing it.

Optimization and training support¶

All public optimizations enter this model through the shared BaseSpeechModel lifecycle. Use available_optimization_passes() to discover the public pass registry, then apply, inspect, serialize, or restore a plan through the common model API. Application remains fail-closed when the active runtime or hardware cannot satisfy a pass.

Training contract¶

Property	Value
Support	`native`
Family	`frame-classification`
Recipe	`single-phase`
Default phase	`voice_activity_detection`
Training checkpoint	`safestack/silero-vad`
Native training graph	`yes`

Phase	Kind	Components	Required inputs	Loss keys
`voice_activity_detection`	objective	`model`	`labels`	`loss`

The integration accepts its declared source or prepared contract directly. Call model.validate_training_support() before constructing a trainer. Follow the shared training workflow for a one-step smoke test, validation, checkpoint resume, optimization, and portable export.

Public API¶

Purpose	Public object
Discover	`get_model_spec('vad_sherpa_onnx')`
Load and run	`AutoModelForVoiceActivityDetection`
Configure	`SherpaONNXVADConfig`
Model implementation	`SherpaONNXVADForVoiceActivityDetection`
Normalized output	`VADOutput`
Training contract	`get_training_spec('vad_sherpa_onnx')`
Optimization lifecycle	`available_optimization_passes`, `apply_optimization_plan`, `optimization_manifest`, `restore_optimization_plan`

vad_sherpa_onnx model guide¶