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:
- 797a92dbd3c48916bbc5f0ebef4875be5d207f52e39d419bfbc030fd1c019c7a
- Size of remote file:
- 3.06 kB
- SHA256:
- 85a1c4334679944b24dc0aac497af1454a8e02a047c3ac55f2e7c45f5ee3b6d3
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