Instructions to use hf-tiny-model-private/tiny-random-WhisperForAudioClassification 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-WhisperForAudioClassification 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-WhisperForAudioClassification")# Load model directly from transformers import AutoProcessor, AutoModelForAudioClassification processor = AutoProcessor.from_pretrained("hf-tiny-model-private/tiny-random-WhisperForAudioClassification") model = AutoModelForAudioClassification.from_pretrained("hf-tiny-model-private/tiny-random-WhisperForAudioClassification") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 9139ec092d1ed64d31119999a15c03b544c712da17404ca8d3896cbb52eea94f
- Size of remote file:
- 57.4 kB
- SHA256:
- 2fb7b02488fc9d1c1dac18de17adb1614d3d46c30120429ded6e441c09a92a08
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