--- license: mit --- # EMelodyGen The model weights for generating ABC melodies by emotions. ## Demo (inference code) ## Usage ```python from huggingface_hub import snapshot_download model_dir = snapshot_download("monetjoe/EMelodyGen") ``` ## Maintenance ```bash GIT_LFS_SKIP_SMUDGE=1 git clone git@hf.co:monetjoe/EMelodyGen cd EMelodyGen ``` ## Evaluation ### Fine-tuning results | Dataset | Loss curve | Min eval loss | | :-----: | :---------------------------------------------------------------------------------------: | :-------------------: | | VGMIDI | ![](https://www.modelscope.cn/models/monetjoe/EMelodyGen/resolve/master/vgmidi/loss.jpg) | `0.23854530873296725` | | EMOPIA | ![](https://www.modelscope.cn/models/monetjoe/EMelodyGen/resolve/master/emopia/loss.jpg) | `0.26802811984950936` | | Rough4Q | ![](https://www.modelscope.cn/models/monetjoe/EMelodyGen/resolve/master/rough4q/loss.jpg) | `0.2299637847539768` | ## Mirror ## Cite ### AIART ```bibtex @inproceedings{11152266, author = {Zhou, Monan and Li, Xiaobing and Yu, Feng and Li, Wei}, booktitle = {2025 IEEE International Conference on Multimedia and Expo Workshops (ICMEW)}, title = {EMelodyGen: Emotion-Conditioned Melody Generation in ABC Notation with the Musical Feature Template}, year = {2025}, pages = {1-6}, keywords = {Correlation;Codes;Conferences;Confusion matrices;Music;Psychology;Data augmentation;Complexity theory;Reliability;Melody generation;controllable music generation;ABC notation;emotional condition}, doi = {10.1109/ICMEW68306.2025.11152266} } ``` ### TAI ```bibtex @article{zhou_li_yu_li_2025, title = {EMelodyGen: Emotion-Conditioned Melody Generation in ABC Notation with Musical Feature Templates}, volume = {1}, issn = {2982-3439}, doi = {10.53941/tai.2025.100013}, number = {1}, journal = {Transactions on Artificial Intelligence}, publisher = {Scilight Press}, author = {Zhou, Monan and Li, Xiaobing and Yu, Feng and Li, Wei}, year = {2025}, pages = {199–211} } ```