---
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).