Instructions to use Flova/omr_transformer with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Flova/omr_transformer with Transformers:
# Use a pipeline as a high-level helper # Warning: Pipeline type "image-to-text" is no longer supported in transformers v5. # You must load the model directly (see below) or downgrade to v4.x with: # pip install "transformers<5.0.0" from transformers import pipeline pipe = pipeline("image-to-text", model="Flova/omr_transformer")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMultimodalLM tokenizer = AutoTokenizer.from_pretrained("Flova/omr_transformer") model = AutoModelForMultimodalLM.from_pretrained("Flova/omr_transformer", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Add demo to README
Browse files
README.md
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<!-- Provide a quick summary of what the model is/does. [Optional] -->
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Image-To-Text model for optical music recognition. The model is trained to predict simple notes in the lilypond format from a given image. Training data consists of artificial, handwritten and white board images.
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Repo: https://github.com/UHHRobotics22-23/robot_project/tree/main/marimbabot_vision
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<!-- Provide a quick summary of what the model is/does. [Optional] -->
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Image-To-Text model for optical music recognition. The model is trained to predict simple notes in the lilypond format from a given image. Training data consists of artificial, handwritten and white board images.
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## Demo
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Prediction: `c'2 a''8 c''8 r4 c'1 e'8 c'8 c'8 a''8 f'4 a'8 c'8`
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Prediction: `d'8 g'8 c''8 a'8 d'2 c'8 f''8 d'4 c''4 e'8 r8 g'8 b'8 e'8 g'8 d'2`
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Repo: https://github.com/UHHRobotics22-23/robot_project/tree/main/marimbabot_vision
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