README.md

metadata

license: cc-by-nc-sa-4.0
pretty_name: FGMCaps Benchmark
language:
  - en
tags:
  - music
  - music-retrieval
  - audio-text-retrieval
  - fine-grained
  - contrastive-learning
size_categories:
  - 10K<n<100K
configs:
  - config_name: default
    data_files:
      - split: test
        path: data/test-*

FIGMA: Towards FIne-Grained Music retrievAl (ACL 2026)

Accepted to ACL 2026.

FGMCaps-Test is a benchmark for fine-grained music retrieval: retrieving music from natural-language descriptions that specify precise musical attributes, introduced in the paper 'FIGMA: Towards FIne-Grained Music retrievAl'. This benchmark provides 10,000 music clips, each paired with a single-sentence caption that describes fine-grained musical characteristics — tempo (BPM), musical key, chord progression, and beat / time signature — alongside higher-level descriptive content.

• Paper: https://arxiv.org/abs/2606.06615
• Project page: https://nishitanand.github.io/figma-website
• Model: https://huggingface.co/nishitanand/FIGMA
• Code: https://github.com/nishitanand/FIGMA

Contents

Column	Type	Description
id	string	Unique clip identifier (UUID)
caption	string	Fine-grained natural-language description of the clip
audio	audio	24 kHz mono audio clip

Split: test (10,000 examples). Audio renders directly in the dataset viewer.

Usage

from datasets import load_dataset

ds = load_dataset("nishitanand/FGMCaps-benchmark", split="test")
ex = ds[0]
print(ex["caption"])
audio = ex["audio"]          # {'array': np.ndarray, 'sampling_rate': 24000, 'path': ...}

Typical evaluation: encode every caption and every clip with a music–text retrieval model, then measure Recall@K for text→audio and audio→text retrieval.

Intended use

Intended for non-commercial academic research on fine-grained music retrieval and music–language alignment.

License

Released under CC BY-NC 4.0-ShareAlike (CC BY-NC-SA 4.0) for non-commercial research use. Redistribution and derivatives must remain non-commercial and be shared under the same terms, with attribution.

Citation

@inproceedings{figma2026,
  title     = {FIGMA: Towards FIne-Grained Music retrievAl},
  author    = {Anand, Nishit and Seth, Ashish and Ghosh, Sreyan and Manocha, Dinesh and Duraiswami, Ramani},
  booktitle = {Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics},
  year      = {2026},
  url       = {https://arxiv.org/abs/2606.06615}
}