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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- c7117d3c742eec4a8583c955c9eef79280aaa75670566a6e69ae55510e1fa772
- Size of remote file:
- 66 kB
- SHA256:
- 8fda40ec4e0426275cf5eecadae9429592f7aac87b7eb5f619c0d07a9e6132b5
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