Instructions to use ppparkker/for_test13 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ppparkker/for_test13 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="ppparkker/for_test13", trust_remote_code=True)# Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("ppparkker/for_test13", trust_remote_code=True) model = AutoModelForCTC.from_pretrained("ppparkker/for_test13", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 769502d0390d8b7dde352025397afa74f91d902ccc89ffabb5c7018bf5e4a705
- Size of remote file:
- 5.24 kB
- SHA256:
- b8bda180d779a216d8d6970fe6011f6e2233e2c8b5f5621d73d6325251a38957
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