Welcome to MMGen!
This repository contains the implementation of the music generation model MMGen, the first novel approach using melody to guide the music generation that, despite a pretty simple method and extremely limited resources, achieves excellent performance.
Anyone can use this model to generate personalized background music for their short videos on platforms like TikTok, YouTube Shorts, and Meta Reels. Additionally, it is very cost-effective to fine-tune the model with your own private music dataset.
Check out our live demo at https://awesome-mmgen.github.io/.
Now you can try music generation with your own prompt on our Website.
Tips: To generate high-quality music using MMGen, you would want to craft detailed and descriptive prompts that provide rich context and specific musical elements.
Read our research paper on arXiv.
To get started with MMGen, follow the steps below:
git clone https://github.com/shaopengw/Awesome-Music-Generation.git
cd Awesome-Music-Generation
# Create and activate the environment from the provided environment file
conda env create -f environment.yml
conda activate MMGen_quickstart
Step 3: Download checkpoints from huggingface
# Ensure that the checkpoints are stored in the following directory structure
Awesome-Music-Generation/
└── data/
└── checkpoints/
# Update the paths to reflect your local environment setup
# Replace:
export PYTHONPATH=/mnt/sda/quick_start_demonstration/Awesome-Music-Generation:$PYTHONPATH
export PYTHONPATH=/mnt/sda/quick_start_demonstration/Awesome-Music-Generation/data:$PYTHONPATH
# With:
export PYTHONPATH=/your/local/path/Awesome-Music-Generation:$PYTHONPATH
export PYTHONPATH=/your/local/path/Awesome-Music-Generation/data:$PYTHONPATH
chmod +x quick_start.sh
bash quick_start.sh
Allow the script to run for several minutes. Upon completion, the results will be available in the following directory:
Awesome-Music-Generation/log/latent_diffusion/quick_start/quick_start
https://huggingface.co/ManzhenWei/MMGen
- Demo website
- Huggingface checkpoints
- Quick start (Inference)
- Training Datasets
- Training/fine-tuning code
- Online free generation service
- Checkpoints on larger datasets
Feel free to explore the repository and contribute!
@article{wei2024melodyneedmusicgeneration,
title={Melody Is All You Need For Music Generation},
author={Shaopeng Wei and Manzhen Wei and Haoyu Wang and Yu Zhao and Gang Kou},
year={2024},
eprint={2409.20196},
archivePrefix={arXiv},
primaryClass={cs.SD},
url={https://arxiv.org/abs/2409.20196},
}