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