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This is the official Hugging Face model repository for the paper **"[Frequency-Aware Self-Supervised Music Representation Learning](https://arxiv.org/abs/2606.25713)"**.
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- **Project Page:** https://www.yichenggu.com/
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- **GitHub Repository:** https://github.com/sizigi/
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## Model Checkpoints
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| Model Name | Directory | Description |
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| ----------------- | ---------------------------- | ------------------------------------------------- |
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## How to use
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You need to put the pretrained models in:
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```bash
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```
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of our official repository.
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## Citation
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```bibtex
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@article{
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title = {Frequency-Aware Self-Supervised Music Representation Learning},
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author = {Yicheng Gu and Junan Zhang and Jerry Li and Zhizheng Wu and Lauri Juvela},
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year = {2026},
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This is the official Hugging Face model repository for the paper **"[Frequency-Aware Self-Supervised Music Representation Learning](https://arxiv.org/abs/2606.25713)"**.
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- **Project Page:** https://www.yichenggu.com/PupuM2D/
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- **GitHub Repository:** https://github.com/sizigi/PupuM2D/
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## Model Checkpoints
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| Model Name | Directory | Description |
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| ----------------- | ---------------------------- | ------------------------------------------------- |
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| **PupuM2D-Tiny** | `./PupuM2DV2_25Hz_tiny/*` | 5M parameter tiny version of PupuM2D. |
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| **PupuM2D-Small** | `./PupuM2DV2_25Hz_small/*` | 22M parameter small version of PupuM2D. |
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| **PupuM2D-Base** | `./PupuM2DV2_25Hz_base/*` | 86M parameter base version of PupuM2D. |
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| **PupuM2D-Large** | `./PupuM2DV2_25Hz_large/*` | 307M parameter large version of PupuM2D. |
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| **PupuM2D-Huge** | `./PupuM2DV2_25Hz_huge/*` | 632M parameter huge version of PupuM2D. |
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| **PupuM2D-giant** | `./PupuM2DV2_25Hz_giant/*` | 1.1B parameter giant version of PupuM2D. |
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## How to use
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You need to put the pretrained models in:
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```bash
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PupuM2D/experiments
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```
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of our official repository.
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## Citation
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```bibtex
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@article{PupuM2D,
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title = {Frequency-Aware Self-Supervised Music Representation Learning},
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author = {Yicheng Gu and Junan Zhang and Jerry Li and Zhizheng Wu and Lauri Juvela},
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year = {2026},
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