Apollo-Enhancement/README.md

---
license: cc-by-sa-4.0
datasets:
- sebchw/musdb18
pipeline_tag: audio-to-audio
tags:
- music
---

<p align="center">
  <img src="https://cslikai.cn/Apollo/asserts/apollo-logo.png" alt="Logo" width="150"/>
</p>

<p align="center">
  <strong>Kai Li<sup>1,2</sup>, Yi Luo<sup>2</sup></strong><br>
    <strong><sup>1</sup>Tsinghua University, Beijing, China</strong><br>
    <strong><sup>2</sup>Tencent AI Lab, Shenzhen, China</strong><br>
  <a href="#">ArXiv</a> | <a href="https://cslikai.cn/Apollo/">Demo</a>

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  <img src="https://img.shields.io/github/stars/JusperLee/Apollo?style=social" alt="GitHub stars" />
  <img alt="Static Badge" src="https://img.shields.io/badge/license-CC%20BY--SA%204.0-blue">
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<p align="center">

# Apollo: Band-sequence Modeling for High-Quality Music Restoration in Compressed Audio

## 📖 Abstract

Apollo is a novel music restoration method designed to address distortions and artefacts caused by audio codecs, especially at low bitrates. Operating in the frequency domain, Apollo uses a frequency band-split module, band-sequence modeling, and frequency band reconstruction to restore the audio quality of **MP3-compressed music**. It divides the spectrogram into sub-bands, extracts gain-shape representations, and models both sub-band and temporal information for high-quality audio recovery. Trained with a Generative Adversarial Network (GAN), Apollo outperforms existing SR-GAN models on the **MUSDB18-HQ and MoisesDB** datasets, excelling in complex multi-instrument and vocal scenarios, while maintaining efficiency.

## 🔥 News

- [2024.09.10] Apollo is now available on [ArXiv](#) and [Demo](https://cslikai.cn/Apollo/).
- [2024.09.106] Apollo checkpoints and pre-trained models are available for download.

## ⚡️ Installation

clone the repository

```bash
git clone https://github.com/JusperLee/Apollo.git && cd Apollo
conda create --name look2hear --file look2hear.yml
conda activate look2hear
```

## 🖥️ Usage

### 🗂️ Datasets

Apollo is trained on the MUSDB18-HQ and MoisesDB datasets. To download the datasets, run the following commands:

```bash
wget https://zenodo.org/records/3338373/files/musdb18hq.zip?download=1
wget https://ds-website-downloads.55c2710389d9da776875002a7d018e59.r2.cloudflarestorage.com/moisesdb.zip
```
During data preprocessing, we drew inspiration from music separation techniques and implemented the following steps:

1. **Source Activity Detection (SAD):**  
   We used a Source Activity Detector (SAD) to remove silent regions from the audio tracks, retaining only the significant portions for training.

2. **Data Augmentation:**  
   We performed real-time data augmentation by mixing tracks from different songs. For each mix, we randomly selected between 1 and 8 stems from the 11 available tracks, extracting 3-second clips from each selected stem. These clips were scaled in energy by a random factor within the range of [-10, 10] dB relative to their original levels. The selected clips were then summed together to create simulated mixed music.

3. **Simulating Dynamic Bitrate Compression:**  
   We simulated various bitrate scenarios by applying MP3 codecs with bitrates of [24000, 32000, 48000, 64000, 96000, 128000]. 

4. **Rescaling:**  
   To ensure consistency across all samples, we rescaled both the target and the encoded audio based on their maximum absolute values.

5. **Saving as HDF5:**  
   After preprocessing, all data (including the source stems, mixed tracks, and compressed audio) was saved in HDF5 format, making it easy to load for training and evaluation purposes.

### 🚀 Training
To train the Apollo model, run the following command:

```bash
python train.py --conf_dir=configs/apollo.yml
```

### 🎨 Evaluation
To evaluate the Apollo model, run the following command:

```bash
python inference.py --in_wav=assets/input.wav --out_wav=assets/output.wav
```

## 📊 Results

*Here, you can include a brief overview of the performance metrics or results that Apollo achieves using different bitrates*

![](./https://cslikai.cn/Apollo/asserts/bitrates.png)


*Different methods' SDR/SI-SNR/VISQOL scores for various types of music, as well as the number of model parameters and GPU inference time. For the GPU inference time test, a music signal with a sampling rate of 44.1 kHz and a length of 1 second was used.*
![](./https://cslikai.cn/Apollo/asserts/types.png)

## License

<a rel="license" href="http://creativecommons.org/licenses/by-sa/4.0/"><img alt="Creative Commons License" style="border-width:0" src="https://i.creativecommons.org/l/by-sa/4.0/88x31.png" /></a><br />This work is licensed under a <a rel="license" href="http://creativecommons.org/licenses/by-sa/4.0/">Creative Commons Attribution-ShareAlike 4.0 International License</a>.

## Acknowledgements

Apollo is developed by the **Look2Hear** at Tsinghua University.

## Citation

If you use Apollo in your research or project, please cite the following paper:

```
@article{li2024apollo,
  title={Apollo: Band-sequence Modeling for High-Quality Music Restoration in Compressed Audio},
  author={Li, Kai and Luo, Yi},
  journal={xxxxxx},
  year={2024}
}
```

## Contact

For any questions or feedback regarding Apollo, feel free to reach out to us via email: `tsinghua.kaili@gmail.com`