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:
- a296a461fa7a1f3d5fdad14b5c416dd05875d01a3f8ee03192a3f5a96acb1068
- Size of remote file:
- 66 kB
- SHA256:
- e4ba0c590d723764d0c2aba0203d8f28eb5b53a06d6e4b5f487db39a455b23ce
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