Instructions to use ppparkker/for_test25 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ppparkker/for_test25 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="ppparkker/for_test25", trust_remote_code=True)# Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("ppparkker/for_test25", trust_remote_code=True) model = AutoModelForCTC.from_pretrained("ppparkker/for_test25", trust_remote_code=True) - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- a119e97044db3e2c68cf10528821478cc87be6cc3c8206643f2c6ad640cd1fe7
- Size of remote file:
- 378 MB
- SHA256:
- 330b843313ed3aa1ce4232535b473c7b75a21ca61a7b1912ca5c78285b29c2c9
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