| --- |
| 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](https://github.com/wx83/MetaScore_Official) and developed to evaluate [DAMN](https://github.com/naivecynics/MIREX2026-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`](test.jsonl) | 100 entries with CID, genre, source, and prompt/reference note sequences | |
| | [`challenge_test/`](challenge_test) | Fixed prompt/reference JSON, companion MIDIs, and window manifest | |
| | `midi/`, `mscz/`, `mp3/` | Complete-piece MIDI, MuseScore scores, and existing source previews | |
| | [`label/`](label) | Genre annotations | |
| | `manifest.csv`, `summary.json` | Source selection metadata and statistics | |
| | [`attribution.json`](attribution.json) | Per-piece attribution and source license records | |
| | [`checksums.sha256`](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](LICENSE.md). The existing source-license conflict is documented in [rights_conflicts.json](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](RECONSTRUCTION.md) for reproduction instructions and [DETAILS.md](DETAILS.md) for full selection, timing, and provenance details. |
|
|
| ## Usage |
|
|
| ```python |
| 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: |
|
|
| ```bibtex |
| @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](https://github.com/naivecynics/MIREX2026-DAMN) · [Model weights](https://huggingface.co/naivecynics/MIREX2026-DAMN) · [MIREX 2026 results](https://music-ir.org/mirex/wiki/2026:Symbolic_Music_Generation_Results) · [Listening demo](https://futuremirex.com/demos/symbolic-music-generation-2026/) |
|
|
| Maintainer: Langchu Huang ([langchuhuang@gmail.com](mailto:langchuhuang@gmail.com)). |
|
|
