Instructions to use hf-tiny-model-private/tiny-random-WhisperForConditionalGeneration 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-WhisperForConditionalGeneration with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="hf-tiny-model-private/tiny-random-WhisperForConditionalGeneration")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("hf-tiny-model-private/tiny-random-WhisperForConditionalGeneration") model = AutoModelForSpeechSeq2Seq.from_pretrained("hf-tiny-model-private/tiny-random-WhisperForConditionalGeneration") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- dca22677807e3ca0831d6cbfeb3d171f305e414e2257960244c46885126297c8
- Size of remote file:
- 3.29 MB
- SHA256:
- e69d9987c72cc40a3bbc71a7d55df66b936726a241f44c9c3140c5eaf61a7801
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