Instructions to use nomsgadded/Audio_Classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use nomsgadded/Audio_Classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="nomsgadded/Audio_Classification")# Load model directly from transformers import AutoProcessor, AutoModelForAudioClassification processor = AutoProcessor.from_pretrained("nomsgadded/Audio_Classification") model = AutoModelForAudioClassification.from_pretrained("nomsgadded/Audio_Classification") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 95b3301c4658ffe131c2eefe6283275628245da97f83959afd986721f0f8c30e
- Size of remote file:
- 378 MB
- SHA256:
- ac6db06e884b8bb3fd0f1fd4e2c102f691dae979c07b3695d2f9d03145e83217
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