audiosronnx-cmgan

audiosronnx-cmgan

ONNX export of the CMGAN generator (Cao et al., MIT) for the cmgan denoise engine in audiosronnx.

CMGAN is a conformer-based metric GAN: a dense encoder feeds four two-stage conformer blocks attending along time and frequency in turn, and two decoders emit a magnitude mask plus a complex residual, so it corrects phase rather than reusing the noisy phase.

spec[1, 2, T, F]  ->  enhanced[1, 2, T, F]     (real, imag)

Only the generator is exported; the discriminator supplies the metric-GAN training loss and has no inference role.

Reproduces the upstream pipeline to correlation 1.00000000 (max abs err 2.0e-06).

Measured behaviour

CMGAN optimises PESQ, not waveform fidelity, and the two diverge. On speech at 11 dB input SNR it gains +0.76 PESQ while losing 1.5 dB SNR — it makes audio sound better and match the reference waveform less. Engines trained on an SNR-shaped objective gain on both.

Choose it when perceptual quality is the target and waveform fidelity is not.

Export note

TSCNet.forward derives the noisy phase via torch.angle(torch.complex(re, im)), and torch.complex has no ONNX operator. The export rewrites it as the identical torch.atan2(im, re) — bit-for-bit equal to the original.

License

MIT, inherited from the upstream CMGAN release.