Instructions to use pnparam/switch_hyp22 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use pnparam/switch_hyp22 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="pnparam/switch_hyp22")# Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("pnparam/switch_hyp22") model = AutoModelForCTC.from_pretrained("pnparam/switch_hyp22", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 38ca98b5517e5dac4c289ba2390f31c5215fcdce58b12c650178026f067234cb
- Size of remote file:
- 1.26 GB
- SHA256:
- 5a3f5cd33a811bc0bca6e1c9901b1a93b6ee8f36a48fbb1af4b73211fe27d346
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