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--- |
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license: apache-2.0 |
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tags: |
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- code |
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- music |
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--- |
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# From Generality to Mastery: Composer-Style Conditioned Music Generation |
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Trained model weights and training datasets for the paper: |
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* Mingyang Yao and Ke Chen |
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"[From Generality to Mastery: Composer-Style Symbolic Music Generation via Large-Scale Pre-training](https://arxiv.org/abs/2506.17497)." |
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_Conference of AI Music Creativity (AIMC)_, 2025 |
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**Note:** Please find project details and usage at our [Github repo](https://github.com/AndyWeasley2004/Generality-to-Mastery) |
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## Model Architecture |
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### "Generality" Stage |
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The model learns **general** music patterns and knowledge from diverse genres of music |
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- Model backbone: 12-layer Transformer with relative positional encoding |
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- Num trainable params: 39.6M |
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### "Mastery" Stage |
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The model adapts its knowledge to specific composers' characteristics |
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- Model backbone: 12-layer Transformer with relative positional encoding plus adapter modules inserted after every two transformer layers |
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- Num trainable params: 46M |
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## Citation |
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If you find this project useful, please cite our paper: |
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``` |
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@inproceedings{generalitymastery2025, |
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author = {Mingyang Yao and Ke Chen}, |
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title = {From Generality to Mastery: Composer-Style Symbolic Music Generation via Large-Scale Pre-training}, |
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booktitle={Proceedings of the AI Music Creativity, {AIMC}}, |
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year = {2025} |
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} |
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``` |
