audiosronnx-metadenoiser

ONNX exports of the Facebook/Meta Research denoiser (Defossez et al.) for the metadenoiser engine in audiosronnx.

A causal Demucs working directly on the waveform — a convolutional encoder/decoder around an LSTM bottleneck, with no spectral front-end. It is the only time-domain denoiser in the library; every other one masks or predicts a spectrum.

noisy[1, 1, 160000]  ->  enhanced[1, 1, 160000]

Amplitude normalisation and the internal length padding live inside the model's own forward, so the graph is self-contained.

File	Params	Size
dns64.onnx	33.5 M	33.9 MB
dns48.onnx	18.9 M	18.9 MB

Reproduces upstream to correlation 1.00000000 (max abs err 6.6e-07, 129.4 dB SNR).

Fixed window

Demucs computes its padding from the input length with Python arithmetic, which the tracer bakes in — a dynamic-length export is correct only at the length it was traced at (verified: 3.6e-07 at the traced length, ~1.0 elsewhere). These graphs therefore take a fixed 10 s window; audiosronnx slides that window with a crossfaded overlap.

License

CC-BY-NC-4.0 — research and non-commercial use only. That covers the model, not audio processed with it. Every other denoiser in audiosronnx is MIT or Apache-2.0, so choose this one deliberately.

Usage

from audiosronnx import load_denoise

dn = load_denoise("metadenoiser")                  # dns64
dn = load_denoise("metadenoiser", model="dns48")   # roughly half the size