Instructions to use midoiv/Audio_Class_CREMA with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use midoiv/Audio_Class_CREMA with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="midoiv/Audio_Class_CREMA")# Load model directly from transformers import AutoProcessor, AutoModelForAudioClassification processor = AutoProcessor.from_pretrained("midoiv/Audio_Class_CREMA") model = AutoModelForAudioClassification.from_pretrained("midoiv/Audio_Class_CREMA", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- c43bed078b5dd62ee802cac47f2bb2eeee0d1f8fe607ce3cf72bb89f0bebe9a9
- Size of remote file:
- 378 MB
- SHA256:
- 853f9f7d90a882b3e989d3783af1c5695b9ed5ebe657f7915a2482ae844070d1
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