Instructions to use hf-tiny-model-private/tiny-random-ASTForAudioClassification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-tiny-model-private/tiny-random-ASTForAudioClassification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="hf-tiny-model-private/tiny-random-ASTForAudioClassification")# Load model directly from transformers import AutoFeatureExtractor, AutoModelForAudioClassification extractor = AutoFeatureExtractor.from_pretrained("hf-tiny-model-private/tiny-random-ASTForAudioClassification") model = AutoModelForAudioClassification.from_pretrained("hf-tiny-model-private/tiny-random-ASTForAudioClassification") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 1e5707e3a51f40815eda0c23dcc29e0d536fc425681878fb7de01d6a40df7a06
- Size of remote file:
- 181 kB
- SHA256:
- 7a497bc13a373636765b18b8c3243147d5af0ca9de80812f0fb31ecc258a1200
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