Instructions to use ppparkker/for_test with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ppparkker/for_test with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="ppparkker/for_test", trust_remote_code=True)# Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("ppparkker/for_test", trust_remote_code=True) model = AutoModelForCTC.from_pretrained("ppparkker/for_test", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- b3a52d6f7aa052e31cc4e42b482fd5f5f9efc4748cb743513629b7939489db5c
- Size of remote file:
- 5.11 kB
- SHA256:
- 4d7ebf4fee5cac7a1229d195422f37577810acf20f9cc6397d9217415776b86a
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