Pyannote Segmentation 3.0 — GGUF
Native GGUF port of pyannote/segmentation-3.0 for speaker diarization.
Model details
| Property | Value |
|---|---|
| Architecture | SincNet + 4× biLSTM + Linear + LogSoftmax |
| Format | GGUF (F32) |
| Size | 5.7 MB |
| Tensors | 41 |
| Output classes | 7 (powerset mapping → 3 speakers) |
| Input | 10 s mono 16 kHz audio frames |
The model performs joint voice-activity detection, speaker segmentation, and overlapped-speech detection on short audio chunks. Downstream clustering then produces full-file speaker diarization.
Usage with CrispASR
crispasr \
--diarize-method pyannote \
--sherpa-segment-model pyannote-seg-3.0.gguf \
audio.wav
Provenance
Weights were exported directly from the original PyTorch checkpoint (pyannote/segmentation-3.0) into GGUF format, preserving full F32 precision across all 41 tensors.
License
MIT — same as the original pyannote-audio segmentation-3.0 model.
Provenance and EU AI Act Art. 53 note
- Upstream model: pyannote/segmentation-3.0 — published by
pyannote. - Upstream licence:
mit. This repository redistributes under the same terms; it grants no rights the upstream licence does not. - What was done here: format conversion and/or quantisation only (GGUF/GGML). No training, no fine-tuning, no merging, no distillation, no change to architecture, vocabulary or capability. Only the numeric representation of the upstream weights differs.
- Training data: documented — where it is documented at all — by the upstream provider; see the upstream model card. No training data was used, added or selected by this repository.
- Provider status: under Regulation (EU) 2024/1689 the upstream authors remain the provider of this model. Converting the serialisation format does not make this repository the provider of a new general-purpose AI model, and no such claim is made. Questions about training content, copyright policy or model capability belong upstream.