segmentation-3.0

Use cases

• Voice activity detection to identify speech vs. non-speech regions
• Speaker change detection as preprocessing for downstream diarization
• Overlapping speech detection in multi-party conversations
• Audio preprocessing to remove silence before ASR
• Component in pyannote diarization pipeline

Pros

• MIT license
• Handles voice activity, speaker change, and overlapping speech in a single model
• Can run standalone for VAD without the full diarization stack
• State-of-the-art segmentation performance on pyannote benchmarks
• Integrates directly with speaker-diarization-3.1

Cons

• Requires HuggingFace token acceptance for download
• Frame-level model output requires post-processing for usable timestamps
• Overlapping speech detection accuracy degrades with more than 2 simultaneous speakers
• Not designed for keyword spotting or speech content analysis
• Performance varies with recording quality and background noise level

FAQ

What is segmentation-3.0 used for?

Voice activity detection to identify speech vs. non-speech regions. Speaker change detection as preprocessing for downstream diarization. Overlapping speech detection in multi-party conversations. Audio preprocessing to remove silence before ASR. Component in pyannote diarization pipeline.

Is segmentation-3.0 free to use?

segmentation-3.0 is an open-source model published on HuggingFace. License terms vary by model — check the model card for the specific license.

How do I run segmentation-3.0 locally?

Most HuggingFace models can be loaded with transformers or the appropriate framework library. See the model card for framework-specific instructions and hardware requirements.

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