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---
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license: other
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license_name: nvidia-oneway-noncommercial-license
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---
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# PyTorch Implementation of Audio-to-Audio Schrodinger Bridges
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**Zhifeng Kong, Kevin J Shih, Weili Nie, Arash Vahdat, Sang-gil Lee, Joao Felipe Santos, Ante Jukic, Rafael Valle, Bryan Catanzaro**
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[[paper]](https://arxiv.org/abs/2501.11311) [[GitHub]](https://github.com/NVIDIA/diffusion-audio-restoration) [[Demo]](https://research.nvidia.com/labs/adlr/A2SB/)
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This repo contains the PyTorch implementation of [A2SB: Audio-to-Audio Schrodinger Bridges](https://arxiv.org/abs/2501.11311). A2SB is an audio restoration model tailored for high-res music at 44.1kHz. It is capable of both bandwidth extension (predicting high-frequency components) and inpainting (re-generating missing segments). Critically, A2SB is end-to-end without need of a vocoder to predict waveform outputs, and able to restore hour-long audio inputs. A2SB is capable of achieving state-of-the-art bandwidth extension and inpainting quality on several out-of-distribution music test sets.
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- We propose A2SB, a state-of-the-art, end-to-end, vocoder-free, and multi-task diffusion Schrodinger Bridge model for 44.1kHz high-res music restoration, using an effective factorized audio representation.
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- A2SB is the first long audio restoration model that could restore hour-long audio without
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boundary artifacts
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## License
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The model is provided under the NVIDIA OneWay NonCommercial License.
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## Citation
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```
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@article{kong2025a2sb,
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title={A2SB: Audio-to-Audio Schrodinger Bridges},
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author={Kong, Zhifeng and Shih, Kevin J and Nie, Weili and Vahdat, Arash and Lee, Sang-gil and Santos, Joao Felipe and Jukic, Ante and Valle, Rafael and Catanzaro, Bryan},
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journal={arXiv preprint arXiv:2501.11311},
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year={2025}
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}
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``` |
