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--- |
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pipeline_tag: other |
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language: en |
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library_name: pytorch |
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license: apache-2.0 |
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tags: |
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- music |
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- midi |
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- mir |
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- deduplication |
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- caugbert |
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model-index: |
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- name: LMD Deduplication - CAugBERT |
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results: |
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- task: |
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type: representation-learning |
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name: symbolic music representation learning |
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dataset: |
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type: midi |
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name: Lakh MIDI Dataset |
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metrics: |
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- type: F1 |
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value: 0.493 |
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--- |
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# LMD Deduplication Supplements |
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This repository provides the pre-trained CAugBERT model checkpoint used in: |
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**"On the De-duplication of the Lakh MIDI Dataset" (ISMIR 2025)** |
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[[Paper]](https://ismir2025program.ismir.net/poster_188.html) | [[GitHub Code]](https://github.com/jech2/LMD_Deduplication) |
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--- |
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# Usage |
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You can either integrate this checkpoint into the main repository for inference, or load it directly: |
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```bash |
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# Option 1: Run inference in the main repo |
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poetry run python inference.py # make sure yamls/inference.yaml paths are correct |
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``` |
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```python |
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# Option 2: Load checkpoint manually |
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import torch |
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from contrastive_musicbert.model.BERT import BERT_Lightning |
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model = BERT_Lightning(...).to(device) # see .hydra/config.yaml for arguments |
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checkpoint = torch.load(checkpoint_path, map_location="cpu") |
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model.load_state_dict(checkpoint['state_dict']) |
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``` |
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# Note |
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If you have any questions regarding the checkpoint, please contact: |
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Eunjin Choi ([email protected]) |
