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:
- df75814958fd79a4e200000d56f3ac36c8f09d319f2bc46a12da2cbbc70ef441
- Size of remote file:
- 57.4 kB
- SHA256:
- bb8aa8bbc498886302e917ce6acfe3d8f651cc09eb4f6f0cce0c0dac30ab46cf
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