Add dataset README

README.md

@@ -1,33 +1,115 @@
 ---
-dataset_info:
-  features:
-  - name: name
-    dtype: string
-  - name: genre
-    list: string
-  - name: chroma
-    dtype:
-      array2_d:
-        shape:
-        - 128
-        - 12
-        dtype: float32
-  - name: text
-    dtype: string
-  splits:
-  - name: train
-    num_bytes: 28678416.937062938
-    num_examples: 4247
-  - name: test
-    num_bytes: 3187241.062937063
-    num_examples: 472
-  download_size: 9048600
-  dataset_size: 31865658.0
-configs:
-- config_name: default
-  data_files:
-  - split: train
-    path: data/train-*
-  - split: test
-    path: data/test-*
+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
+
+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.
+
+## Acknowledgments
+
+- Original MIDI dataset: foldl
+- Chroma extraction: pretty_midi library
+- Geometric VAE architecture: AbstractPhil/GeoVocab2