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smt-omr-models
AEmotionStudio/smt-omr-models
smt-omr-models is a machine learning model from AEmotionStudio. Use it for the machine learning task on the model card, and read the license before you ship it in a product. The card lists the license as mit.
Mirror of PRAIG's full-page Optical Music Recognition checkpoints, laid out one subdirectory per variant for MAESTRO's download-on-demand loader:
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Public
Updated Aug 25, 2026
Repo size
43.7 MB
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.safetensors43.7 MB · 100%
At a glance
- License
- mit
- Access
- Public
- Created
- Aug 25, 2026
- Updated
- Aug 25, 2026
- SHA
- 0f990df9
Model card
From the Hugging Face model README
SMT-fp — full-page Sheet Music Transformer (MAESTRO mirror)
Mirror of PRAIG's full-page Optical Music Recognition checkpoints, laid out one subdirectory per variant for MAESTRO's download-on-demand loader:
| subdir | upstream | material |
|---|---|---|
grandstaff/ | PRAIG/smt-fp-grandstaff | engraved piano (two-staff) scores |
polish-scores/ | PRAIG/smt-fp-polish-scores | historical printed scores (Polish digital libraries) |
mozarteum/ | PRAIG/smt-fp-mozarteum | Mozart-era engravings (Digital Mozart Edition) |
Each variant is config.json (vocabulary embedded) + model.safetensors,
byte-identical to upstream (sha256-verified at mirror time). Model class:
SMTModelForCausalLM — ConvNeXt encoder + autoregressive kern decoder from
antoniorv6/SMT (MIT).
License
MIT, as published by PRAIG (Pattern Recognition and Artificial Intelligence Group, University of Alicante). This mirror adds no restrictions.
Citation
@article{RiosVila2024SMT,
title = {Sheet Music Transformer: End-To-End Optical Music Recognition Beyond Monophonic Transcription},
author = {R{\'i}os-Vila, Antonio and Calvo-Zaragoza, Jorge and Paquet, Thierry},
journal = {International Conference on Document Analysis and Recognition (ICDAR)},
year = {2024}
}
Mirrored for MAESTRO by AEmotionStudio.
- License
- mit
- Created
- Aug 25, 2026
- Updated
- Aug 25, 2026