minhqng/fma-small

FMA - Small: Pre - computed Log - Mel - Spectrograms for Music Genre Classification

Pre - processed FMA - Small 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

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:

@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},
}