Pyannote Segmentation 3.0 — ONNX
Speaker segmentation for the server-side diarization pipeline (ONNX Runtime, CUDA EP with CPU fallback). Exported by models/pyannote-vad/export/convert_onnx.py in soniqo/speech-models.
| input | audio [1, 1, 160000] f32 — one 10 s window @ 16 kHz |
| output | posteriors [1, 589, 7] f32 — per-frame powerset probabilities |
Frame geometry differs from the LiteRT bundle. This graph runs the LSTM internally over the whole 10 s window and emits 589 frames (17.0 ms/frame). The LiteRT bundle decomposes the same model into 1 s chunks with explicit LSTM state (56 frames/chunk, 560 per window). A decoder ported from the LiteRT path must re-derive its frame-to-time mapping — reusing the old one shifts every segment.
Verified against PyTorch on real speech: powerset class agreement 1.0000, max abs diff ~1e-4 (benchmarks/parity_diarization_onnx.py).