https://github.com/iahispano/audio-upscaler

Versatile AI-driven audio upscaler to enhance the quality of any audio.
https://github.com/iahispano/audio-upscaler

ai audio audio-upscaler ml python

Last synced: 6 days ago
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Versatile AI-driven audio upscaler to enhance the quality of any audio.

• Host: GitHub
• URL: https://github.com/iahispano/audio-upscaler
• Owner: IAHispano
• License: mit
• Created: 2024-04-27T09:03:16.000Z (7 months ago)
• Default Branch: main
• Last Pushed: 2024-09-12T18:28:10.000Z (2 months ago)
• Last Synced: 2024-10-12T22:41:49.134Z (about 1 month ago)
• Topics: ai, audio, audio-upscaler, ml, python
• Language: Python
• Homepage:
• Size: 1.72 MB
• Stars: 54
• Watchers: 3
• Forks: 3
• Open Issues: 1
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# Audio Upscaler (AudioSR)

[![arXiv](https://img.shields.io/badge/arXiv-2309.07314-brightgreen.svg?style=flat-square)](https://arxiv.org/abs/2309.07314)  [![githubio](https://img.shields.io/badge/GitHub.io-Audio_Samples-blue?logo=Github&style=flat-square)](https://audioldm.github.io/audiosr)

## Overview

AudioSR is a powerful tool designed to enhance the fidelity of your audio files, regardless of their type (e.g., music, speech, ambient sounds) or sampling rates. It leverages cutting-edge super-resolution techniques to upscale audio signals, resulting in superior quality output.

## Key Features

- **Versatility**: Works seamlessly with all types of audio content, including music, speech, environmental sounds, and more.

- **Scale**: Handles audio files of various sampling rates, ensuring compatibility with a wide range of sources.

- **High Fidelity**: Produces high-quality output with enhanced clarity and detail.

- **Ease of Use**: Simple and intuitive interface makes it easy to enhance your audio files with just a few clicks.

## Acknowledgments

Based on the work of https://github.com/haoheliu/versatile_audio_super_resolution/

```bibtex

@article{liu2023audiosr,

  title={{AudioSR}: Versatile Audio Super-resolution at Scale},

  author={Liu, Haohe and Chen, Ke and Tian, Qiao and Wang, Wenwu and Plumbley, Mark D},

  journal={arXiv preprint arXiv:2309.07314},

  year={2023}

}

```