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woongzip1/universr-audio
universr-audio is a machine learning model from woongzip1. 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 pytorch. The card lists the license as cc-by-4.0.
Vocoder-free audio super-resolution model that upsamples 8/12/16/24 kHz → 48 kHz audio using flow matching in the complex STFT domain. Trained on speech, music, and sound effects.
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From the Hugging Face model README
Vocoder-free audio super-resolution model that upsamples 8/12/16/24 kHz → 48 kHz audio using flow matching in the complex STFT domain. Trained on speech, music, and sound effects.
This is the recommended model for general use. For speech-only evaluation (e.g. VCTK benchmark), see universr-speech.
Paper: arXiv:2510.00771 | Demo: woongzip1.github.io/universr-demo | Code: github.com/woongzip1/UniverSR
import torchaudio
from universr import UniverSR
model = UniverSR.from_pretrained("woongzip1/universr-audio", device="cuda")
output = model.enhance("low_res.wav", input_sr=8000)
torchaudio.save("output_48k.wav", output.cpu(), 48000)
@inproceedings{choi2026universr,
title = {{UniverSR}: Unified and Versatile Audio Super-Resolution via Vocoder-Free Flow Matching},
author = {Choi, Woongjib and Lee, Sangmin and Lim, Hyungseob and Kang, Hong-Goo},
booktitle = {IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)},
year = {2026}
}