audiosronnx 0.9.0a2

Pure-ONNX multi-engine audio super-resolution / bandwidth extension (upscale speech to 48 kHz)

audiosronnx

Speech restoration, denoising and bandwidth extension in pure ONNX. Clean up noisy recordings, and upscale narrowband speech — 8 kHz telephony, muffled captures, low-fidelity TTS — to a full 48 kHz signal.

Inference runs entirely on onnxruntime. There is no torch at runtime: every STFT, filterbank and resampler lives in numpy/scipy, so the ONNX graphs stay portable and the front-ends stay inspectable.

pip install audiosronnx

from audiosronnx import load_denoise, load_sr

clean, rate = load_denoise("dpdfnet").denoise("noisy_call.wav")   # remove noise
wide,  _    = load_sr("lavasr").upscale(clean, rate)              # extend to 48 kHz

audiosronnx denoise noisy.wav clean.wav
audiosronnx upscale clean.wav wide_48k.wav

Runtime dependencies are numpy, onnxruntime, scipy, soundfile and huggingface_hub. Weights download on first use and cache under ~/.local/share/audiosronnx, pinned by revision.

Two jobs

Denoising removes background noise and keeps the sample rate. Bandwidth extension invents high-frequency content that was never in the input and always outputs 48 kHz. They have separate entry points — load_denoise() and load_sr() — which refuse each other's engines.

They compose, in that order: denoise first, then extend. Run them the other way round and the extender reconstructs a high band out of the noise.

Denoisers

Engine	Rate	Size	License	Reach for it when
dpdfnet (default)	8 / 16 / 48 kHz	8.7–14.9 MB	Apache-2.0	you have no other reason to choose
mossformer2	48 kHz	229 MB	Apache-2.0	the source is fullband and quality wins
frcrn	16 kHz	57.5 MB	Apache-2.0	reproducing published benchmarks
mpsenet	16 kHz	9.7 MB	MIT	you want a spectral model that stays small
gtcrn	16 kHz	0.54 MB	MIT	footprint is the binding constraint
cmgan	16 kHz	7.8 MB	MIT	you want a conformer metric-GAN specifically
metadenoiser	16 kHz	19–34 MB	CC-BY-NC-4.0	you want a time-domain model and NC is acceptable
mossformergan	16 kHz	17.7 MB	Apache-2.0	highest published PESQ (3.47)
voicefixer	44.1 kHz	415 MB	MIT	restoration — noise, reverb, clipping and band loss at once
deepfilternet	48 kHz	~2 MB	MIT	you already depend on libdf

SNR recovered on one clip at 19 / 11 / 5 dB input SNR: dpdfnet +4.9 / +10.3 / +13.7 dB, mossformer2 +5.9 / +10.4 / +13.4, mpsenet +4.8 / +8.9 / +11.7, frcrn +4.6 / +8.8 / +11.7, gtcrn +3.5 / +7.3 / +7.5. That is broadband Gaussian noise — a hostile synthetic case that ranks engines consistently but predicts little about babble or codec artefacts.

Recommendations: start with dpdfnet — no extra dependencies, 8/16/48 kHz, top-tier measured gain. Take mossformer2 for the best fullband quality, mossformergan for the best benchmark scores, gtcrn when footprint is the constraint, metadenoiser for a time-domain model. Engines are kept even when something else beats them, so published results stay reproducible — cmgan is dominated and stays for that reason.

Full detail in docs/denoising.md.

Bandwidth extension

Engine	Input	Size	CPU speed	License
lavasr (default)	8–48 kHz	~52 MB	~50× realtime	Apache-2.0
novasr	16 kHz	~0.2 MB	~1000× realtime	Apache-2.0
flowhigh	any	~200 MB	slow (4 ODE steps)	MIT
hifiganbwe	any	~4 MB	fast	MIT
apbwe	any (12 kHz band)	~120 MB	moderate	MIT
sidon	16 kHz	~410 MB	~0.6× realtime	MIT
callenhancer	8–16 kHz	~3 GB / ~1.3 GB int8	~0.3–0.4× realtime	CC-BY-NC-4.0

lavasr is the general-purpose choice. sidon and callenhancer do something different: they resynthesise speech rather than extend its band, repairing codec damage a bandwidth extender cannot, at a much higher cost. callenhancer is trained on telephony specifically, and its weights are non-commercial.

Full detail in docs/engines.md.

Choosing an engine

Three questions settle it.

1. What is wrong with the audio?

Problem	Reach for
background noise	a denoiser — dpdfnet
narrowband / muffled, but clean	a bandwidth extender — lavasr
noisy and narrowband	denoise then extend — dpdfnet → lavasr
telephony, codec-damaged	callenhancer (restoration, not extension)
damaged several ways at once	voicefixer (restoration)

2. What constrains you?

Constraint	Denoise	Extend
nothing in particular	dpdfnet	lavasr
footprint	gtcrn (0.54 MB)	novasr (0.2 MB)
best quality, fullband	mossformer2	apbwe
best benchmark scores	mossformergan	—
license must be permissive	anything but metadenoiser	anything but callenhancer

3. Do you mind invented detail?

sidon, callenhancer, flowhigh and voicefixer are generative — they resynthesise speech rather than filter it, so the detail they add is plausible rather than recovered. That is fine for listening and reasonable as an acoustic-model target; it is not a faithful reconstruction, and SNR against a clean reference is the wrong yardstick for them.

Engines are kept even when something else beats them, so published results stay reproducible and distinct architectures stay runnable. cmgan is the clearest example: it is dominated on both PESQ and SNR by gtcrn at a fourteenth of the size, and it stays.

Documentation

Page	Contents
docs/engines.md	Every bandwidth-extension engine, options, precision
docs/denoising.md	Every denoiser, and how to choose
docs/api.md	load_sr, load_denoise, the model contracts, the registry
docs/cli.md	Command-line reference
docs/custom-engines.md	Registering your own engine
docs/conversion.md	How the ONNX graphs are produced and validated
docs/not-shipped.md	Models evaluated and rejected, with reasons

Runnable scripts are in examples/.

API

sr = load_sr("lavasr")
out, rate = sr.upscale("telephone_8k.wav")   # path, int16 bytes, or numpy array
sr.upscale_file("in.wav", "out_48k.wav")
sr.upscale_dir("clips_in/", "clips_out/")

dn = load_denoise("dpdfnet")
clean, rate = dn.denoise(audio, in_rate)     # returns the ENGINE's rate
dn.denoise_file("in.wav", "clean.wav")
dn.denoise_dir("clips_in/", "clips_out/")

available_models(); available_denoisers(); get_engine(alias); register_engine(entry)

Output is always mono float32. Empty, silent and non-finite input produce finite output rather than raising. See docs/api.md.

Which engines ship

An engine ships when it exports to a single static ONNX graph, runs on CPU through onnxruntime, carries a permissive license, and is validated end-to-end against the upstream pipeline — not merely against the model, since reproducing a network but not its surrounding normalisation yields audio that sounds plausible and is quietly wrong.

That rules out diffusion and flow-matching models, location-variable convolutions, and unlicensed checkpoints. docs/not-shipped.md names every candidate considered and which condition it failed.

License

Apache-2.0.

Model weights carry their upstream licenses, listed per engine in the docs and reported by audiosronnx list. Most are MIT or Apache-2.0; callenhancer is CC-BY-NC-4.0, which covers the weights rather than audio processed with them.