Hugging Face's logo

Datasets:
naivecynics
/
MetaScore-SFT-100

Modalities:
Audio
Tabular
Text
Formats:
json
Size:
< 1K
Tags:
music
symbolic-music
midi
piano
music-generation
mirex
Libraries:
Datasets
pandas
Polars
License:
Dataset card Data Studio Files
xet
 Community
1
MetaScore-SFT-100 / README.md
naivecynics's picture
naivecynics
Prepare complete 100-piece benchmark for public access
f700aa1 verified
preview code
|
Raw History Blame Contribute Delete
4.96 kB
metadata 
pretty_name: MetaScore-SFT-100
license: other
license_name: per-piece-source-licenses
license_link: LICENSE.md
size_categories:
  - n<1K
tags:
  - music
  - symbolic-music
  - midi
  - piano
  - music-generation
  - mirex
configs:
  - config_name: default
    data_files:
      - split: test
        path: test.jsonl

MetaScore-SFT-100

Overview

MetaScore-SFT-100 is a fixed, 100-piece benchmark for symbolic piano continuation, derived from MetaScore and developed to evaluate DAMN.

Each example pairs a 4-bar piano prompt (with an optional pickup) with a 12-bar reference continuation, following the MIREX 2026 Symbolic Music Generation format. This is an independently curated development benchmark, not the official MIREX test set.

Dataset

The benchmark contains 100 complete examples across 15 genre labels, with fixed prompts, references, and source assets.

Resource Contents
test.jsonl 100 entries with CID, genre, source, and prompt/reference note sequences
challenge_test/ Fixed prompt/reference JSON, companion MIDIs, and window manifest
midi/, mscz/, mp3/ Complete-piece MIDI, MuseScore scores, and existing source previews
label/ Genre annotations
manifest.csv, summary.json Source selection metadata and statistics
attribution.json Per-piece attribution and source license records
checksums.sha256 File integrity checksums

Notes use integer (start, pitch, duration) attributes on a sixteenth-note grid. Steps 0-15 are reserved for an optional pickup, prompt starts are in 0-79, and reference starts are in 80-271. Durations are positive and notes end no later than step 272.

Source permissions apply per piece; see LICENSE.md. The existing source-license conflict is documented in rights_conflicts.json.

Construction

We selected piano scores from a genre-stratified sample of MetaScore using these criteria:

  • A single piano part, 4/4 meter, at least 16 measures, and no lyrics.
  • The upstream best-unique-arrangement filter.
  • A user rating of at least 4.5 from at least three ratings.

Automatic filtering retained 106 candidates; manual listening removed six clear quality-control failures, leaving 100 pieces. Genre counts are not balanced.

Deterministic extraction selects the first complete, protocol-compatible 16-bar window: 99 pieces use the opening window, and one uses a later window. Fixed-window MIDIs use 480 ticks per quarter note, 120 BPM, and a fixed piano program. Complete-piece source assets retain their original contents; their MP3s are not renderings of the extracted windows.

See RECONSTRUCTION.md for reproduction instructions and DETAILS.md for full selection, timing, and provenance details.

Usage 

from datasets import load_dataset

benchmark = load_dataset("naivecynics/MetaScore-SFT-100", split="test")
assert len(benchmark) == 100

example = benchmark[0]
prompt = {"prompt": example["prompt"]}
reference = {"generation": example["reference"]}

For reproducible experiments, pin the dataset revision and keep generation settings fixed across systems.

DAMN evaluation protocol: generate eight continuations per prompt, compute each continuation's CLaMP3 cosine similarity to its reference, average across the eight candidates, then average across the 100 prompts. The 95% confidence intervals use bootstrap resampling over prompts. This is mean-of-eight, not best-of-eight. Reference similarity is a development proxy, not a substitute for listening evaluation.

Citation

Please cite the original MetaScore work and this benchmark:

@inproceedings{xu2025metascore,
  title={Generating Symbolic Music from Natural Language Prompts Using an LLM-Enhanced Dataset},
  author={Xu, Weihan and McAuley, Julian and Berg-Kirkpatrick, Taylor and Dubnov, Shlomo and Dong, Hao-Wen},
  booktitle={Proceedings of the International Society for Music Information Retrieval Conference},
  year={2025}
}

@misc{huang2026metascoresft100,
  title={MetaScore-SFT-100: A Fixed Piano Continuation Development Benchmark},
  author={Huang, Langchu and Xu, Yang},
  year={2026},
  howpublished={Hugging Face dataset},
  url={https://huggingface.co/datasets/naivecynics/MetaScore-SFT-100}
}

Related resources: DAMN code · Model weights · MIREX 2026 results · Listening demo

Maintainer: Langchu Huang (langchuhuang@gmail.com).