Instructions to use universalml/CA with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use universalml/CA with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="universalml/CA")# Load model directly from transformers import AutoProcessor, AutoModelForAudioClassification processor = AutoProcessor.from_pretrained("universalml/CA") model = AutoModelForAudioClassification.from_pretrained("universalml/CA") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- f3285a8af6393f1e29afb6a6f3eba58806c136a8f0e118dd01aeaf32947fe8f7
- Size of remote file:
- 378 MB
- SHA256:
- 7ed7b454627fb9d6f04f8037b0de0f451565be967e47c1616a8c2ad6f86f56a1
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