Instructions to use hf-internal-testing/tiny-random-WhisperForAudioClassification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-WhisperForAudioClassification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="hf-internal-testing/tiny-random-WhisperForAudioClassification")# Load model directly from transformers import AutoProcessor, AutoModelForAudioClassification processor = AutoProcessor.from_pretrained("hf-internal-testing/tiny-random-WhisperForAudioClassification") model = AutoModelForAudioClassification.from_pretrained("hf-internal-testing/tiny-random-WhisperForAudioClassification") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- f8a6e81061ca78e2e7836d4ee00de2a5c3d753bd8ce58e9392a1bd923bb62cef
- Size of remote file:
- 66 kB
- SHA256:
- 455ae3872985873b6e1eae0e0d5c5f829ca16d14b5945b3666e286e45fb1d883
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