Datasets:

PRAIG
/

Solesmes_staffLevel

tags:
  - music
  - documents
  - end-to-end
  - amnlt
  - staff-level
annotations_creators:
  - manually expert-generated
pretty_name: Solesmes Staff-Level
size_categories:
  - 1K<n<10K
task_categories:
  - image-to-text
subtasks:
  - lyrics and music alignment
dataset_info:
  features:
    - name: image
      dtype: image
    - name: transcription
      dtype: string
  splits:
    - name: train
      num_bytes: 517750630
      num_examples: 597
    - name: validation
      num_bytes: 138725758
      num_examples: 170
    - name: test
      num_bytes: 80281305
      num_examples: 87
  download_size: 736650757
  dataset_size: 736757693
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train-*
      - split: validation
        path: data/validation-*
      - split: test
        path: data/test-*
extra_gated_fields:
  Affiliation: text

Solesmes Staff-Level

License

About • How to Use • Citations • License

📚 About

This dataset is used in the AMNLT (Aligned Music Notation and Lyrics Transcription) challenge. To see the rest of the datasets within this task, access to PRAIG/AMNLT.

⚙️ How to Use

To use this dataset, you must first request access. Once access is granted, load the dataset using:

from datasets import load_dataset

ds = load_dataset("PRAIG/Solesmes_staffLevel")

ds["train"][0]["image"].save("example_image.png")
print(ds["train"][0]["transcription"])

📖 Citations

If you use this work in your research, please cite us:

@article{FUENTESMARTINEZ2026112094,
  title     = {Aligned music notation and lyrics transcription},
  author    = {Eliseo Fuentes-Martínez and Antonio Ríos-Vila and Juan C. Martinez-Sevilla and David Rizo and Jorge Calvo-Zaragoza},
  journal   = {Pattern Recognition},
  volume    = {170},
  pages     = {112094},
  year      = {2026},
  issn      = {0031-3203},
  doi       = {https://doi.org/10.1016/j.patcog.2025.112094},
}

📝 License

These datasets are under the cc-by-nc-4.0 license, which permits use, distribution, and adaptation for non-commercial purposes, provided proper attribution is given.