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Contin-U · Full PDF piano score to one continuous performance
Contin-U turns the U-MusT image-to-audio model, trained on windows of 1–3 systems, into a full-score
renderer without retraining. The PDF is rasterized, every musical system is detected in reading order, and the
model slides over consecutive system pairs (j, j+1): the decoder is primed with the audio already generated
for system j, the cross-attention of a late decoder layer onto the tokens after the [SEP] separator marks
where system j’s audio ends, the stream is spliced there, and the tokens that follow prime the next window.
The result is a single continuous performance, decoded once from the joined audio tokens.
Contin-U was the MALerLab entry to RenCon 2025, the expressive piano performance rendering contest revived as a MIREX task at ISMIR 2025 (Daejeon). Paper: Contin-U: Full-Score to Performance Audio with Cross-Attentive System-Continuation Inference (Jung, Kim, Lee, Cho, Soh, Bukey, Donahue, Jeong) — https://futuremirex.com/portal/wp-content/uploads/2025/rencon/Contin-U.pdf
Inputs
| name | description |
|---|---|
score |
PDF of a piano score (engraved or scanned). |
first_page, last_page |
Page range (1-based; last_page = 0 = to the end). |
dpi |
Rasterization resolution, 150–300. |
max_systems |
Stop after this many systems; 0 = all. |
attention_threshold |
Cross-attention mass on the second system that marks the boundary (default 0.5). |
seed |
Random seed. |
Outputs
A list of files (order may vary): contin-u.wav (the whole performance), meta.json (duration_sec, n_pages,
n_systems, notes), systems.png (all system crops stacked in playback order) and pPP_sSS.png (each crop,
labelled with its position, page and system).
Notes
- One window (two systems) takes about 30 s of GPU time on an L40S; a 6-system page is 5 windows.
- Piano only; the model expects grand-staff systems. Title pages without music are skipped automatically.
- Research use under CC BY-NC-SA 4.0.
Code: https://github.com/MALerLab/U-MusT · U-MusT paper: https://doi.org/10.1109/TASLPRO.2025.3648794