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WeSpeaker-ResNet34-LM-ONNX
soniqo/WeSpeaker-ResNet34-LM-ONNX
WeSpeaker-ResNet34-LM-ONNX is a machine learning model from soniqo. Use it for the machine learning task on the model card, and read the license before you ship it in a product. It is set up for onnx. The card lists the license as cc-by-4.0.
Speaker embeddings for the server-side diarization pipeline (ONNX Runtime, CUDA EP with CPU fallback). Exported by models/wespeaker/export/convertonnx.py in soniqo/speech-models.
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At a glance
- Library
- onnx
- License
- cc-by-4.0
- Access
- Public
- Created
- Jul 14, 2026
- Updated
- Jul 14, 2026
- SHA
- 9df39b49
Base models
Model card
From the Hugging Face model README
WeSpeaker ResNet34-LM — ONNX
Speaker embeddings for the server-side diarization pipeline (ONNX Runtime, CUDA EP with CPU fallback). Exported by models/wespeaker/export/convert_onnx.py in soniqo/speech-models.
| input | fbank [1, T, 80] f32 — precomputed kaldi log-mel, T dynamic |
| output | embedding [1, 256] f32 |
Takes precomputed fbank, not raw audio. pyannote compute_fbank uses torch.fft.rfft, and aten::fft_rfft has no ONNX lowering — an in-graph frontend cannot be exported at opset 18. The caller computes kaldi fbank (80 mels, 25 ms / 10 ms, dither 0, waveform scaled by 1<<15); per-utterance mean centering is applied inside the graph. Same contract as the LiteRT bundle, so swapping runtimes needs no feature-code change.
Verified against PyTorch on real speech: cosine similarity 1.000000 (benchmarks/parity_diarization_onnx.py).
- Library
- onnx
- License
- cc-by-4.0
- Created
- Jul 14, 2026
- Updated
- Jul 14, 2026