Update README.md

README.md

@@ -47,4 +47,95 @@ configs:
     path: data/validation-*
   - split: test
     path: data/test-*
+license: apache-2.0
+task_categories:
+- audio-classification
+language:
+- en
+tags:
+- music
+- audio
+- fma
+- genre-classification
+- mel-spectrogram
+pretty_name: FMA-Small Log-Mel-Spectrograms
+size_categories:
+- 100K<n<1M
 ---
+# FMA - Small: Pre - computed Log - Mel - Spectrograms for Music Genre Classification
+
+Pre - processed [FMA - Small](https://github.com/mdeff/fma) dataset containing **155,153** log - mel - spectrogram segments ready for training audio genre classifiers. No audio decoding needed - load and train directly.
+
+## Dataset Details
+
+| Property | Value |
+|---|---|
+| Source | FMA-Small (8,000 tracks × 30s) |
+| Representation | Log - Mel - Spectrogram |
+| Sample Shape | `(128, 300)` - 128 mel bins × 300 time frames |
+| Sample Rate | 32,000 Hz |
+| Segment Duration | 3 seconds (1.5s overlap → ~19 segments/track) |
+| Classes | 8 genres |
+| Split Strategy | `StratifiedGroupKFold` on `artist_id` (zero artist leakage) |
+
+## Features
+
+| Column | Type | Description |
+|---|---|---|
+| `mel` | `Array2D(float32)` `(128, 300)` | Log - mel - spectrogram segment |
+| `label` | `ClassLabel` | Genre label `[0–7]` |
+| `track_id` | `int64` | FMA track identifier |
+| `artist_id` | `int64` | FMA artist identifier |
+| `genre` | `string` | Human - readable genre name |
+
+## Labels
+
+`0` Electronic · `1` Experimental · `2` Folk · `3` Hip - Hop · `4` Instrumental · `5` International · `6` Pop · `7` Rock
+
+## Splits
+
+| Split | Samples | Ratio |
+|---|---|---|
+| `train` | 99,140 | ~64% |
+| `validation` | 24,807 | ~16% |
+| `test` | 31,206 | ~20% |
+
+**No artist appears in more than one split** - enforced via `StratifiedGroupKFold` on `artist_id` to prevent data leakage.
+
+## Quick Start
+
+```python
+from datasets import load_dataset
+
+dd = load_dataset("minhqng/fma-small")
+dd.set_format("torch", columns=["mel", "label"])
+
+sample = dd["train"][0]
+mel = sample["mel"]      # (128, 300) float32
+label = sample["label"]  # int, 0–7
+```
+
+## Audio Processing Pipeline
+
+Each 30 - second MP3 track was processed as follows:
+
+```
+MP3 → decode (PyAV) → mono → resample 32kHz → segment 3s (50% overlap)
+    → MelSpectrogram (n_fft=1024, hop=320, 128 bins, Slaney norm)
+    → log(mel + 1e-9) → truncate to 300 frames → (128, 300)
+```
+
+Silent/corrupt tracks and segments were removed before dataset creation.
+
+## Citation
+
+If you use this dataset, please cite the original FMA paper:
+
+```bibtex
+@inproceedings{defferrard2017fma,
+  title = {{FMA}: A Dataset for Music Analysis},
+  author = {Defferrard, Micha\"el and Benzi, Kirell and Vandergheynst, Pierre and Bresson, Xavier},
+  booktitle = {18th International Society for Music Information Retrieval Conference (ISMIR)},
+  year = {2017},
+}
+```