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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 53f703b1a36199152b08e9c7f961cab776831ce555509ce75c70413b224bf907
- Size of remote file:
- 66 kB
- SHA256:
- 14b5a42b36ef4cb008238ad2cbfbdbee6ed7cad5a76f6f2f597b21b5d762e6f5
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