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metadata
license: cc-by-sa-4.0
task_categories:
  - image-to-text
language:
  - en
tags:
  - music
  - optical-music-recognition
  - omr
  - sheet-music
  - musicxml
  - lilypond
size_categories:
  - 10K<n<100K
configs:
  - config_name: pages
    data_files:
      - split: dev
        path: pages/dev-*
      - split: test
        path: pages/test-*
      - split: train
        path: pages/train-*
  - config_name: pages-lieder
    data_files:
      - split: train
        path: pages-lieder/train-*
      - split: dev
        path: pages-lieder/dev-*
      - split: test
        path: pages-lieder/test-*
  - config_name: pages_transcribed
    data_files:
      - split: dev
        path: pages_transcribed/dev-*
      - split: test
        path: pages_transcribed/test-*
      - split: train
        path: pages_transcribed/train-*
  - config_name: scores
    data_files:
      - split: train
        path: scores/train-*
      - split: test
        path: scores/test-*
      - split: dev
        path: scores/dev-*
dataset_info:
  - config_name: pages
    features:
      - name: image
        dtype: image
      - name: score_id
        dtype: string
      - name: corpus
        dtype: string
      - name: page
        dtype: int64
      - name: n_pages
        dtype: int64
      - name: composer
        dtype: string
      - name: opus
        dtype: string
      - name: title
        dtype: string
    splits:
      - name: dev
        num_bytes: 89590116
        num_examples: 331
      - name: test
        num_bytes: 100755142
        num_examples: 475
      - name: train
        num_bytes: 3981286117
        num_examples: 16165
    download_size: 3176298223
    dataset_size: 4171631375
  - config_name: pages-lieder
    features:
      - name: score_id
        dtype: string
      - name: corpus
        dtype: string
      - name: page
        dtype: int64
      - name: n_pages
        dtype: int64
      - name: bar_start
        dtype: int64
      - name: bar_end
        dtype: int64
      - name: musicxml
        dtype: string
    splits:
      - name: train
        num_bytes: 511960929
        num_examples: 3415
      - name: dev
        num_bytes: 28971937
        num_examples: 195
      - name: test
        num_bytes: 31504305
        num_examples: 218
    download_size: 53578761
    dataset_size: 572437171
  - config_name: pages_transcribed
    features:
      - name: score_id
        dtype: string
      - name: corpus
        dtype: string
      - name: page
        dtype: int64
      - name: n_pages
        dtype: int64
      - name: bar_start
        dtype: int64
      - name: bar_end
        dtype: int64
      - name: musicxml
        dtype: string
      - name: image
        dtype: image
      - name: composer
        dtype: string
      - name: opus
        dtype: string
      - name: title
        dtype: string
    splits:
      - name: dev
        num_bytes: 140682244
        num_examples: 327
      - name: test
        num_bytes: 156874375
        num_examples: 455
      - name: train
        num_bytes: 5585319264
        num_examples: 14111
    download_size: 3207002375
    dataset_size: 5882875883
  - config_name: scores
    features:
      - name: score_id
        dtype: string
      - name: composer
        dtype: string
      - name: opus
        dtype: string
      - name: title
        dtype: string
      - name: corpus
        dtype: string
      - name: instruments
        list: string
      - name: page
        dtype: int64
      - name: n_pages
        dtype: int64
      - name: musicxml
        dtype: string
    splits:
      - name: train
        num_bytes: 1468576392
        num_examples: 1424
      - name: test
        num_bytes: 54739890
        num_examples: 79
      - name: dev
        num_bytes: 54992179
        num_examples: 79
    download_size: 154745773
    dataset_size: 1578308461

zzsi/openscore — OpenScore Sheet Music Pages

Rendered sheet music pages from three open-score corpora, paired with per-page MusicXML ground truth. Intended for optical music recognition (OMR) research and supervised fine-tuning of vision-language models.

Images are rendered from source MusicXML via LilyPond (Emmentaler font). Per-page MusicXML is extracted by parsing bar numbers from the rendered SVGs and slicing the source score with music21.


Corpora

Corpus Scores Instrumentation Source
lieder ~1,460 Voice + piano (3 staves) OpenScore/Lieder
quartets ~122 String quartet (4 staves) OpenScore/StringQuartets
orchestra ~94 movements Full orchestra (10–20+ staves) MarkGotham/Hauptstimme

⚠️ Known issue — broken musicxml labels in the quartets corpus

In the pages_transcribed config, the per-page musicxml slicing fails on a large fraction of quartets pages: the page image shows real music, but the musicxml label is an empty stub (one measure per part, <attributes> + end barline, no notes). Verified 2026-05-18 by a full scan of all splits.

Corpus Scores w/ empty pages Empty pages Genuine failed slices
quartets 97 / 109 3,746 / 6,410 (58.4%) 3,578
lieder 13 / 1,018 70 / 3,392 (2.1%) 13
orchestra 14 / 91 251 / 5,111 (4.9%) 0 (spurious bar_start > bar_end pages)

Do not train on the quartets portion of pages_transcribed until the slicer is fixed — empty labels teach a model "page of music → empty output". lieder and orchestra are usable (lieder has 13 stray failed-slice pages, listed below; orchestra has zero). The pages config (images only) and scores config are unaffected. Key signatures in non-empty labels are correct.

Broken quartet scores — score_id (empty_pages/total_pages) (97)

sq10307350 (14/26), sq10313029 (53/88), sq10328092 (8/16), sq10372717 (12/25), sq10381459 (9/20), sq10406164 (53/128), sq10414906 (13/22), sq10490761 (5/10), sq10517302 (116/198), sq10527526 (67/115), sq10675759 (74/101), sq11154985 (79/119), sq11164006 (134/204), sq11539384 (23/61), sq12113164 (21/34), sq12536479 (50/115), sq12701461 (9/25), sq12772795 (9/25), sq14720995 (12/25), sq15049456 (18/26), sq15230467 (14/19), sq15624112 (38/60), sq15730717 (16/22), sq16138966 (49/79), sq7103818 (46/62), sq7108150 (65/129), sq7114183 (9/21), sq7123582 (52/88), sq7127785 (44/54), sq7224846 (54/68), sq7249986 (21/34), sq7284122 (12/20), sq7294793 (33/48), sq7295726 (63/81), sq7300376 (9/18), sq7302602 (47/59), sq7302710 (33/65), sq7330550 (52/77), sq7353137 (16/28), sq7354505 (166/299), sq7358579 (95/125), sq7358708 (22/40), sq7383977 (14/38), sq7384409 (80/139), sq7397765 (63/161), sq7434431 (11/17), sq7471661 (14/36), sq7483523 (118/156), sq7524617 (15/33), sq7541288 (39/68), sq7551068 (17/26), sq7556360 (81/118), sq7577795 (44/60), sq7588853 (25/38), sq7872392 (79/127), sq8071278 (23/41), sq8075304 (9/18), sq8088531 (10/22), sq8437280 (5/14), sq8437358 (4/11), sq8438840 (2/9), sq8438915 (1/5), sq8438999 (2/10), sq8454356 (42/68), sq8455808 (15/26), sq8461409 (9/13), sq8509238 (17/26), sq8556926 (6/12), sq8561633 (42/62), sq8623643 (64/104), sq8630159 (83/92), sq8796660 (13/20), sq8806134 (10/14), sq8806746 (7/15), sq8806881 (9/13), sq8807040 (13/23), sq8807667 (11/17), sq8811375 (74/102), sq8818128 (68/109), sq8823783 (187/314), sq8853405 (12/31), sq8885439 (79/175), sq8885571 (72/129), sq8907120 (18/33), sq8913219 (46/61), sq8938822 (107/151), sq8940236 (21/57), sq9010547 (50/62), sq9094235 (78/162), sq9137469 (26/52), sq9146376 (9/17), sq9199617 (6/14), sq9396439 (16/24), sq9529900 (70/80), sq9608209 (19/43), sq9719026 (41/55), sq9961690 (15/28).

The 12 unaffected quartet scores: sq10502527, sq14387632, sq7070319, sq7070781, sq7075297, sq7078259, sq7082029, sq7093885, sq7095930, sq7236909, sq7648382, sq8812200.

Lieder scores with one stray failed-slice page each: lc6197282, lc6486038, lc6593095, lc6613436, lc6613481, lc6614760, lc6624112, lc6625925, lc6667483, lc6669339, lc6670960, lc6764425, lc8873154.


Configs

pages_transcribed — image + per-page MusicXML (SFT-ready)

Each row is one rendered page paired with the MusicXML for the bars on that page. Suitable for supervised fine-tuning of OMR models.

Split Rows
train 14,129
test 455
dev 329

Fields:

Field Type Description
image PIL.Image Full-page score render
score_id str Score identifier (e.g. lc6583477)
corpus str lieder, quartets, or orchestra
composer str
opus str
title str
page int 1-indexed page number
n_pages int Total pages in the score
bar_start int First bar number on this page
bar_end int Last bar number on this page (inclusive)
musicxml str MusicXML for bar_startbar_end

pages — image only, all corpora

Same rows as pages_transcribed but without the musicxml, bar_start, and bar_end fields. Useful for unsupervised pre-training or image-only tasks.

Split Rows
train 16,225
test 478
dev 339

scores — full MusicXML per score

One row per score (not per page). Contains the complete MusicXML for the entire piece plus metadata.

Split Rows
train 1,424
test 79
dev 79

Fields: score_id, composer, opus, title, corpus, instruments (list), page (total pages), n_pages, musicxml (full score).


Usage

Load pages_transcribed

from datasets import load_dataset

ds = load_dataset("zzsi/openscore", "pages_transcribed")
example = ds["train"][0]
example["image"].show()
print(example["musicxml"][:500])

Filter by corpus (streaming)

The dataset is sorted by corpus within each split, so row groups in the parquet files are corpus-homogeneous. This means streaming with a corpus filter is efficient: non-matching row groups are skipped without being downloaded.

from datasets import load_dataset

# Lieder only
ds = load_dataset("zzsi/openscore", "pages_transcribed",
                  streaming=True, split="train")
ds = ds.filter(lambda r: r["corpus"] == "lieder")

# Lieder + quartets (no orchestra)
ds = ds.filter(lambda r: r["corpus"] in {"lieder", "quartets"})

Quick subset for testing

# First 100 rows (any corpus)
ds = load_dataset("zzsi/openscore", "pages_transcribed",
                  streaming=True, split="train")
sample = list(ds.take(100))

Fine-tuning example (Qwen-VL style)

from datasets import load_dataset

ds = load_dataset("zzsi/openscore", "pages_transcribed", split="train")

def to_chat(row):
    return {
        "messages": [
            {"role": "user", "content": [
                {"type": "image", "image": row["image"]},
                {"type": "text",  "text": "Transcribe this sheet music page to MusicXML."},
            ]},
            {"role": "assistant", "content": row["musicxml"]},
        ]
    }

ds = ds.map(to_chat)

Construction

  1. Render: Source MusicXML is converted to LilyPond (.ly) format and rendered to SVG pages using a Docker image containing LilyPond 2.24. Bar numbers are made visible on every bar (all-bar-numbers-visible).
  2. Align: Bar numbers are parsed from each SVG page to determine which bars appear on each page.
  3. Slice: music21 slices the source MusicXML to the bar range for each page and re-exports it as a self-contained MusicXML fragment.

Pages whose bar numbers could not be reliably parsed (e.g. continuation pages with no bar number printed) are excluded.


Known Limitations

  • Pickup bars: Scores with a pickup bar (anacrusis) have an implicit measure 0 that is accounted for in bar_start/bar_end.
  • Orchestra page alignment: Orchestra scores frequently render to a different page count than the original due to \RemoveEmptyStaves in LilyPond. Alignment is based on bar numbers embedded in the rendered SVG, not on page index.
  • MusicXML slice quality: Sliced MusicXML may be missing some cross-page spanners (slurs, hairpins). Inexpressible rhythms (rare) cause individual pages to be dropped.
  • Render failures: ~6% of lieder scores, 3% of quartet scores, and 2 orchestra movements failed to render and are absent from the dataset.

License

Source scores are released under CC BY-SA 4.0. LilyPond renders and derived MusicXML slices carry the same license.


Attribution

Rendering pipeline uses LilyPond and music21. Dataset construction code: https://github.com/zhudotexe/CVlization