Instructions to use JovialValley/model_broadclass_onSet1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use JovialValley/model_broadclass_onSet1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="JovialValley/model_broadclass_onSet1")# Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("JovialValley/model_broadclass_onSet1") model = AutoModelForCTC.from_pretrained("JovialValley/model_broadclass_onSet1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- fe732ba026f2ed655c74d1c8fe1f4586359470f9167d4554f56878bd7255f1b4
- Size of remote file:
- 1.26 GB
- SHA256:
- 369b769a95b34dd1a03cd616704598b8102646bad2e077c09ff013d62426e98e
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