| license: mit | |
| library_name: onnx | |
| tags: [speaker-diarization, segmentation, onnx] | |
| base_model: pyannote/segmentation-3.0 | |
| # 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). | |