Instructions to use Wseop/AST with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Wseop/AST with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="Wseop/AST")# Load model directly from transformers import AutoFeatureExtractor, AutoModelForAudioClassification extractor = AutoFeatureExtractor.from_pretrained("Wseop/AST") model = AutoModelForAudioClassification.from_pretrained("Wseop/AST", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 335cc78d5a9f24e4293b60d0f349b26ad5e40f9fd6436161d5a80d2499633d8b
- Size of remote file:
- 358 MB
- SHA256:
- 2bf7a358bc5375beb7da1098c85e37624b111abd03d090e8baa168f028b7865c
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