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:
- e805f1d4d1451eb0565d43759ac861b4df392c07105d4bb8fe94e21d80cc2c7b
- Size of remote file:
- 162 kB
- SHA256:
- fd4b2d75d91fb20b773f3118a4bcc5205c1d05fe65811da13ad04bac384bc887
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