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  license: mit
 
 
 
 
 
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  license: mit
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+ datasets:
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+ - M2UGen/MUCaps
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+ - M2UGen/MUEdit
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+ - M2UGen/MUImage
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+ - M2UGen/MUVideo
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  ---
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+ # M<sup>2</sup>UGen Model with MusicGen-medium
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+
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+ The M<sup>2</sup>UGen model is a Music Understanding and Generation model that is capable of Music Question Answering and also Music Generation
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+ from texts, images, videos and audios, as well as Music Editing. The model utilizes encoders such as MERT for music understanding, ViT for image understanding
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+ and ViViT for video understanding and the MusicGen/AudioLDM2 model as the music generation model (music decoder), coupled with adapters and the LLaMA 2 model
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+ to make the model possible for multiple abilities.
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+
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+ M<sup>2</sup>UGen was published in [M<sup>2</sup>UGen: Multi-modal Music Understanding and Generation with the Power of Large Language Models](https://arxiv.org/abs/2311.11255) by *Atin Sakkeer Hussain, Shansong Liu, Chenshuo Sun and Ying Shan*.
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+
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+ The code repository for the model is published in [crypto-code/M2UGen](https://github.com/crypto-code/M2UGen). Clone the repository, download the checkpoint and run the following for a model demo:
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+ ```bash
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+ python gradio_app.py --model ./ckpts/M2UGen-MusicGen-medium/checkpoint.pth --llama_dir ./ckpts/LLaMA-2 --music_decoder musicgen --music_decoder_path facebook/musicgen-medium
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+ ```
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+
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+ ## Citation
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+
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+ If you find this model useful, please consider citing:
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+
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+ ```bibtex
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+ @article{hussain2023m,
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+ title={{M$^{2}$UGen: Multi-modal Music Understanding and Generation with the Power of Large Language Models}},
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+ author={Hussain, Atin Sakkeer and Liu, Shansong and Sun, Chenshuo and Shan, Ying},
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+ journal={arXiv preprint arXiv:2311.11255},
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+ year={2023}
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+ }
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+ ```