Instructions to use DunnBC22/Is_Vinyl_Scratched_Or_Not with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use DunnBC22/Is_Vinyl_Scratched_Or_Not with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="DunnBC22/Is_Vinyl_Scratched_Or_Not")# Load model directly from transformers import AutoProcessor, AutoModelForAudioClassification processor = AutoProcessor.from_pretrained("DunnBC22/Is_Vinyl_Scratched_Or_Not") model = AutoModelForAudioClassification.from_pretrained("DunnBC22/Is_Vinyl_Scratched_Or_Not") - Notebooks
- Google Colab
- Kaggle
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- Transformers 4.26.0
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- Pytorch 1.12.1
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- Datasets 2.8.0
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- Tokenizers 0.12.1
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- Transformers 4.26.0
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- Pytorch 1.12.1
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- Datasets 2.8.0
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- Tokenizers 0.12.1
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## License Notice
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This model is a fine-tuned derivative of a pretrained model.
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Users must comply with the original model license.
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## Dataset Notice
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This model was fine-tuned on third-party datasets which may have separate licenses or usage restrictions.
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