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bitsydarel/
usef-tse-onnx

Name: usef-tse-onnx
Availability: InStock

onnx

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USEF-TSE ONNX exports (audio-only, 8 kHz)

ONNX exports of the USEF-TSE target speaker extraction models from github.com/ZBang/USEF-TSE.

License

CC BY-NC 4.0 — inherited from the upstream weights. Non-commercial use only.

Models

Two architectures × three training datasets = six exports. All inputs are 8 kHz float32 mono PCM.

File	Architecture	Training set	Size
`usef_tse_tfgridnet_wsj0-2mix.onnx`	TF-GridNet	WSJ0-2mix (clean)	60 MB
`usef_tse_tfgridnet_wham.onnx`	TF-GridNet	WHAM! (noisy)	60 MB
`usef_tse_tfgridnet_whamr.onnx`	TF-GridNet	WHAMR! (noisy+reverb)	60 MB
`usef_tse_sepformer_wsj0-2mix.onnx`	SepFormer	WSJ0-2mix (clean)	131 MB
`usef_tse_sepformer_wham.onnx`	SepFormer	WHAM! (noisy)	131 MB
`usef_tse_sepformer_whamr.onnx`	SepFormer	WHAMR! (noisy+reverb)	131 MB

Per-dataset manifests in manifest_*.json carry SHA-256s and the parity numbers PyTorch ↔ ONNX hit on real audio fixtures.

Inference contract

Inputs:
- mixture: [1, 16000] float32 — 2 seconds @ 8 kHz mono
- enrollment: [1, 64000] float32 — 8 seconds @ 8 kHz mono (zero-pad if shorter)
Output:
- extracted: [1, 16000] float32 — 2 seconds @ 8 kHz mono (same length as mixture)

The 2 s mixture window is fixed because TF-GridNet bakes unfold constants in its ONNX graph. Longer audio must be chunked into 2 s windows and the outputs concatenated.

Exporter

Generated by iOS/scripts/export_usef_tse_onnx.py via legacy TorchScript exporter at opset 17, with TF-GridNet's torch.stft/torch.istft replaced by conv1d/conv_transpose1d-based equivalents (the legacy exporter rejects complex-typed STFT outputs).

PyTorch ↔ ONNX parity on real 16 kHz audio fixtures (downsampled to 8 kHz for inference): cosine similarity = 1.000 across all 18 cells; max absolute difference ≤ 2.2e-3.

bitsydarel/usef-tse-onnx