metadata

pretty_name: Meter2800
language:
  - en
tags:
  - audio
  - music-classification
  - meter-classification
  - multi-class-classification
  - multi-label-classification
license: mit
task_categories:
  - audio-classification
dataset_info:
  size_categories:
    - 1K<n<10K
  source_datasets:
    - gtzan
    - mag
    - own
    - fma
configs:
  - config_name: 2_classes
    default: true
    data_files:
      - split: train
        path: data_train_2_classes.csv
      - split: validation
        path: data_val_2_classes.csv
      - split: test
        path: data_test_2_classes.csv
  - config_name: 4_classes
    data_files:
      - split: train
        path: data_train_4_classes.csv
      - split: validation
        path: data_val_4_classes.csv
      - split: test
        path: data_test_4_classes.csv

Meter2800

Dataset for music time signature/ meter (rhythm) classification, combining tracks from GTZAN, MAG, OWN, and FMA.

Dataset Description

Meter2800 is a curated collection of 2,800 .wav music audio samples, each annotated with meter (and optionally alt_meter). It supports both:

4-class classification (e.g., 4 genres),
2-class classification (binary meter labeling).

Split into train/val/test sets with clear metadata in CSV.

Intended for music information retrieval tasks like rhythmic / structural analysis and genre prediction.

Supported Tasks and Usage

Load the dataset via the datasets library with automatic audio decoding:

from datasets import load_dataset, Audio
dataset = load_dataset(
  "pianistprogrammer/Meter2800",
  data_files={
    "train_4": "data_train_4_classes.csv",
    "val_4": "data_val_4_classes.csv",
    "test_4": "data_test_4_classes.csv",
    "train_2": "data_train_2_classes.csv",
    "val_2": "data_val_2_classes.csv",
    "test_2": "data_test_2_classes.csv"
  }
)


Each entry in the dataset contains:

- **filename**: Path to the audio file.
- **label**: Genre label (multi-class or binary, depending on split).
- **meter**: Primary meter annotation (e.g., 4/4, 3/4).
- **alt_meter**: Optional alternative meter annotation.
- **audio**: Audio data as a NumPy array and its sampling rate.

The dataset is organized into the following splits:

- `train_4`, `val_4`, `test_4`: For 4-class meter classification.
- `train_2`, `val_2`, `test_2`: For 2-class (binary) meter classification.

All splits are provided as CSV files referencing the audio files in the corresponding folders (`GTZAN/`, `MAG/`, `OWN/`, `FMA/`).

Example row in a CSV file:

| filename                | label   | meter | alt_meter | 
|-------------------------|---------|-------|-----------|
| GTZAN/blues.00000.wav   | three   |   3   |    6      |

Meter2800/
├── GTZAN/
├── MAG/
├── OWN/
├── FMA/
├── data_train_4_classes.csv
├── data_val_4_classes.csv
├── data_test_4_classes.csv
├── data_train_2_classes.csv
├── data_val_2_classes.csv
├── data_test_2_classes.csv
└── README.md


@misc{meter2800_dataset,
  author       = {PianistProgrammer},
  title        = {{Meter2800}: A Dataset for Music time signature detection / Meter Classification},
  year         = {2025},
  publisher    = {Hugging Face},
  url          = {https://huggingface.co/datasets/pianistprogrammer/Meter2800}
}

license: "CC0 1.0 Public Domain"