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--- |
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language: |
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- en |
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task_categories: |
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- text-to-audio |
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- audio-to-audio |
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tags: |
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- music |
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- midi |
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- chroma |
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- music-generation |
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- geometric-deep-learning |
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size_categories: |
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- 1K<n<10K |
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--- |
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# MIDI Chroma Dataset |
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This version requires the genres to be fixed and a restructure before it's compatible with training. |
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Pre-processed version of [foldl/midi](https://huggingface.co/datasets/foldl/midi) with chroma features extracted directly from MIDI note events. |
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## Dataset Description |
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This dataset contains **4719 songs** with pre-computed chroma features for efficient music generation training. |
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### Features |
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- **name**: Song title (string) |
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- **genre**: List of genres (list of strings) |
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- **chroma**: Pre-computed chroma features `[128, 12]` (float32 array) |
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- 12 pitch classes (C, C#, D, D#, E, F, F#, G, G#, A, A#, B) |
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- 128 time steps |
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- Values normalized to sum to 1.0 per timestep |
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- **text**: Text description for conditioning (string) |
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### Extraction Method |
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Chroma features are extracted **directly from MIDI note events** without audio synthesis: |
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- Notes are mapped to their pitch class (0-11) |
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- Velocity is used for intensity weighting |
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- Temporal resolution: ~10 FPS |
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- Much faster than audio-based extraction |
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### Usage |
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```python |
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from datasets import load_dataset |
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import torch |
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# Load dataset |
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dataset = load_dataset("AbstractPhil/foldl-midi") |
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# Access samples |
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sample = dataset['train'][0] |
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chroma = torch.tensor(sample['chroma']) # [128, 12] |
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text = sample['text'] # "rock, pop: Genesis - The Light Dies Down" |
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print(f"Text: {text}") |
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print(f"Chroma shape: {chroma.shape}") |
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``` |
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### Training ChromaLyra |
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This dataset is designed for training **ChromaLyra**, a geometric VAE for music generation: |
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```python |
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from geovocab2.train.model.chroma.chroma_lyra import ChromaLyra, ChromaLyraConfig |
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config = ChromaLyraConfig( |
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n_chroma=12, |
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seq_len=128, |
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latent_dim=256, |
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hidden_dim=384 |
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) |
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model = ChromaLyra(config) |
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# Train with text conditioning... |
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``` |
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## Dataset Creation |
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Created by extracting chroma from valid MIDI files in foldl/midi dataset: |
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- Filtered songs: 1s - 3min duration |
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- Skipped empty/drum-only tracks |
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- Original: ~20K MIDI files → This dataset: ~4719 valid samples |
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## Citation |
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Original dataset: |
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```bibtex |
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@misc{foldl-midi, |
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author = {foldl}, |
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title = {MIDI Dataset}, |
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year = {2023}, |
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publisher = {Hugging Face}, |
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url = {https://huggingface.co/datasets/foldl/midi} |
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} |
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``` |
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Geometric approach: |
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```bibtex |
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@misc{abstract-phil-geovocab, |
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author = {AbstractPhil}, |
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title = {GeoVocab: Geometric Deep Learning for Music Generation}, |
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year = {2025}, |
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url = {https://github.com/AbstractPhil/geovocab2} |
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} |
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``` |
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## License |
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Same as original foldl/midi dataset; |
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https://huggingface.co/datasets/foldl/midi |
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## Acknowledgments |
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- Original MIDI dataset: foldl |
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- Chroma extraction: pretty_midi library |
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- Geometric VAE architecture: AbstractPhil/GeoVocab2 |
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