Instructions to use BenShermaister/whisper-base-he2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use BenShermaister/whisper-base-he2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="BenShermaister/whisper-base-he2")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("BenShermaister/whisper-base-he2") model = AutoModelForSpeechSeq2Seq.from_pretrained("BenShermaister/whisper-base-he2") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- b0b79b1cbea0a0e4801eca2bc9f817437111a4ab79d0ecbf655b10d8eec15ba7
- Size of remote file:
- 290 MB
- SHA256:
- ce2660528dd108ba8da8f306d3da02faf103a0a188ccb3f32f572382b7ca2107
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