README.md

README.md

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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},
+}
+```