Instructions to use dima806/classical_composer_classification-new with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dima806/classical_composer_classification-new with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="dima806/classical_composer_classification-new")# Load model directly from transformers import AutoProcessor, AutoModelForAudioClassification processor = AutoProcessor.from_pretrained("dima806/classical_composer_classification-new") model = AutoModelForAudioClassification.from_pretrained("dima806/classical_composer_classification-new") - Notebooks
- Google Colab
- Kaggle
Update README.md
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README.md
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- accuracy
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- roc_auc
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---
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More details in https://www.kaggle.com/code/dima806/speech-accent-multiclass-classification.
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- accuracy
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- roc_auc
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---
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Currently, the model returns the confidence score that the input audio is created by one of the following classical composers
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found in [MusicNet Dataset](https://www.kaggle.com/datasets/imsparsh/musicnet-dataset) - a curated collection of 330 freely-licensed
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labeled classical music recordings - used for the training of this model:
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- [Beethoven](https://en.wikipedia.org/wiki/Ludwig_van_Beethoven);
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- [Bach](https://en.wikipedia.org/wiki/Johann_Sebastian_Bach);
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- [Schubert](https://en.wikipedia.org/wiki/Franz_Schubert);
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- [Mozart](https://en.wikipedia.org/wiki/Wolfgang_Amadeus_Mozart);
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- [Brahms](https://en.wikipedia.org/wiki/Johannes_Brahms).
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More details in https://www.kaggle.com/code/dima806/speech-accent-multiclass-classification.
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