| --- |
| license: apache-2.0 |
| base_model: Flova/omr_transformer |
| pipeline_tag: image-to-text |
| tags: |
| - optical-music-recognition |
| - omr |
| - turkish-makam-music |
| - onnx |
| library_name: onnx |
| --- |
| |
| # omr-weights — Turkish (makam) optical music recognition |
|
|
| ONNX graphs for [KomaVision](https://komavision.netlify.app), an optical music recognition model |
| for **Classical Turkish (makam) music**: a photo or screenshot of sheet music in, notes out — |
| including the microtonal accidentals (koma, küçük mücennep, bakiye, büyük mücennep) that Western |
| OMR models have no vocabulary for. |
|
|
| Source code: <https://github.com/EmreDikimen/Turkish_note_to_solfeggio_converter> |
|
|
| ## What is here |
|
|
| Int8-quantized ONNX exports of a Donut-style vision-encoder-decoder (~143M parameters): |
|
|
| | File | Role | |
| |---|---| |
| | `encoder_model.onnx` | image encoder | |
| | `decoder_model.onnx` | first decode step | |
| | `decoder_with_past_model.onnx` | subsequent steps, with KV cache | |
|
|
| Input is a **409×583** grayscale strip — one staff line's worth of music, not a whole page. The |
| application slices a page into strips before decoding. Output is a LilyPond-flavoured token stream |
| with AEU accidental tokens. |
|
|
| The same graphs run in two places: in the browser via `onnxruntime-web` (the offline fallback) and |
| on a CPU server via `onnxruntime-node`. There is one decode implementation, shared. |
|
|
| ## Licence and attribution |
|
|
| **Apache-2.0**, inherited from the base model. |
|
|
| Fine-tuned from **[`Flova/omr_transformer`](https://huggingface.co/Flova/omr_transformer)** |
| (Apache-2.0) — a pretrained OMR transformer. That model is the reason this project did not need to |
| train an OMR system from scratch, and its licence and attribution travel with these weights as |
| Apache-2.0 §4 requires. |
|
|
| ## Training data |
|
|
| - **Self-rendered synthetic strips.** Turkish scores engraved by the project's own VexFlow |
| renderer, with augmentation aimed at what users actually upload (screenshots more than photos). |
| The pixels and the labels come from one code path, so a label can never disagree with its image. |
| - **Real printed pages**, hand-labelled, from freely-published Turkish score archives. These are |
| used as training and evaluation data locally and are **not redistributed** here or anywhere. |
| - Score metadata for the synthetic renders derives from |
| **[SymbTr](https://github.com/MTG/SymbTr)** (Karaosmanoğlu et al.), which is licensed |
| **CC BY-NC-SA 4.0** — attributed here accordingly. |
|
|
| No Western rehearsal data was used in fine-tuning; coverage comes from self-rendered Turkish |
| strips. |
|
|
| ## Intended use and limits |
|
|
| Intended for reading Classical Turkish music notation. It is **not** a general-purpose OMR model — |
| it was fine-tuned on a Turkish token vocabulary and will not do anything sensible with orchestral |
| or piano scores. |
|
|
| Known limits, stated plainly: |
|
|
| - Accuracy on **clean synthetic** strips is effectively solved; accuracy on **real printed pages** |
| is the open problem and is substantially lower. Treat every decode as a draft to be corrected — |
| the application ships an editor for exactly that reason. |
| - The microtonal accidentals are the hard part, and the koma/küçük mücennep distinction is where |
| the remaining errors concentrate. |
| - Long or dense staff lines can overrun the decoder's token budget. |
| - Handwritten manuscript is out of scope. |
|
|
| ## Citation |
|
|
| If the base model is useful to you, cite that first — |
| [`Flova/omr_transformer`](https://huggingface.co/Flova/omr_transformer). For SymbTr: |
|
|
| > M. K. Karaosmanoğlu, "A Turkish makam music symbolic database for music information retrieval: |
| > SymbTr", ISMIR 2012. |
|
|
