smt-grandstaff

Model description

The SMT model consists of a vision encoder (ConvNext) and a text decoder (classic Transformer). Given an image of a music system, the encoder first encodes the image into a tensor of embeddings (of shape batch_size, seq_len, hidden_size), after which the decoder autoregressively generates text, conditioned on the encoding of the encoder.

Intended uses & limitations

This model is fine-tuned on the GrandStaff dataset, its use is limited to transcribe pianoform images only.

BibTeX entry and citation info

@misc{RiosVila2024,
      title={Sheet Music Transformer: End-To-End Optical Music Recognition Beyond Monophonic Transcription},
      author={Antonio Ríos-Vila and Jorge Calvo-Zaragoza and Thierry Paquet},
      year={2024},
      eprint={2402.07596},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2402.07596},
}