Instructions to use JovialValley/model_broadclass_onSet1.1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use JovialValley/model_broadclass_onSet1.1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="JovialValley/model_broadclass_onSet1.1")# Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("JovialValley/model_broadclass_onSet1.1") model = AutoModelForCTC.from_pretrained("JovialValley/model_broadclass_onSet1.1") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 55bb40342caed1761e49ad771d9119df221d82545bc1930b5c8f9280ed127b38
- Size of remote file:
- 3.45 kB
- SHA256:
- 305f3311969006d51f05a218857aa7e7cf71be071c017246dff881ec5320bb26
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