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# MusicMamba
This is the official implementation of MusicMamba.
*Checkout our demo and paper* : [Demo](https://moersxm.github.io/MusicMamba_Demo/) | [arXiv](https://arxiv.org/abs/2409.02421)
## Environment
* Clone this Repo
```bash
git clone https://github.com/Wietc/MusicMamba.git
```
* using python version 3.11.5
* using pytorch version 2.2.1
* install python dependencies
`pip install -r requirements.txt`
* Mamba needs to be downloaded separately
`pip install mamba_ssm`
* install checkpoints from Huggingface
## To train the model with GPU
We currently do not offer fine-tuning functionality.
## To generate music
`python generate.py`
## Details of the files in this repo
```
`
├── data Stores train, test and val data.
│ └── FolkDB
│ ├── train
│ ├── test
│ └── val
├── dataset.py Progress datasets.
├── generate.py For generating music. (Detailed usage are written in the file)
├── model.py The MusicMamba Architecture.
├── midi_tokenize Remi-M tokenize.
├── utilities Tools for generating music.
│ ├── argument_funcs.py Some arguments for generating.
│ ├── constants.py
│ └── device.py
└── README.md Readme
```
## Citation
If you find this work helpful and use our code in your research, please kindly cite our paper:
```
@article{MusicMamba,
title={MusicMamba: A Dual-Feature Modeling Approach for Generating Chinese Traditional Music with Modal Precision},
author={Jiatao Chen and Xing Tang and Tianming Xie and Jing Wang and Wenjing Dong and Bing Shi}, year={2024},
eprint={2409.02421},
archivePrefix={arXiv},
}
```
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