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---
license: mit
---
<style>
td {
vertical-align: middle !important;
}
</style>
# EMelodyGen
The model weights for generating ABC melodies by emotions.
## Demo (inference code)
<https://huggingface.co/spaces/monetjoe/EMelodyGen>
## 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
<https://github.com/monetjoe/EMelodyGen>
### Fine-tuning results
| Dataset | Loss curve | Min eval loss |
| :-----: | :---------------------------------------------------------------------------------------: | :-------------------: |
| VGMIDI |  | `0.23854530873296725` |
| EMOPIA |  | `0.26802811984950936` |
| Rough4Q |  | `0.2299637847539768` |
## Mirror
<https://www.modelscope.cn/models/monetjoe/EMelodyGen>
## 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}
}
``` |