Instructions to use dima806/music_genres_classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dima806/music_genres_classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="dima806/music_genres_classification")# Load model directly from transformers import AutoProcessor, AutoModelForAudioClassification processor = AutoProcessor.from_pretrained("dima806/music_genres_classification") model = AutoModelForAudioClassification.from_pretrained("dima806/music_genres_classification") - Notebooks
- Google Colab
- Kaggle
Update README.md
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README.md
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metrics:
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- accuracy
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- roc_auc
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---
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[Music genre](https://en.wikipedia.org/wiki/Music_genre) classification is a fundamental and versatile application in many various domains. Some possible use cases for music genre classification include:
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metrics:
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- accuracy
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- roc_auc
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base_model:
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- facebook/wav2vec2-base-960h
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---
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[Music genre](https://en.wikipedia.org/wiki/Music_genre) classification is a fundamental and versatile application in many various domains. Some possible use cases for music genre classification include:
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