Instructions to use hf-internal-testing/tiny-random-ASTForAudioClassification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-ASTForAudioClassification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="hf-internal-testing/tiny-random-ASTForAudioClassification")# Load model directly from transformers import AutoFeatureExtractor, AutoModelForAudioClassification extractor = AutoFeatureExtractor.from_pretrained("hf-internal-testing/tiny-random-ASTForAudioClassification") model = AutoModelForAudioClassification.from_pretrained("hf-internal-testing/tiny-random-ASTForAudioClassification") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 233615c6ad9d4cbf99953990a13b13675bb755606e4962ab4a8d4d05816b23c2
- Size of remote file:
- 162 kB
- SHA256:
- ce9a8fc2ee93e60efcf5f072bb86cdc75c04c52f23fde409f1cd5ceb7e775440
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