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:
- e60d9f0c80bdd108ee12bdce1148bd9ed66cad79cf599368bf7733ed5f4d588c
- Size of remote file:
- 3.45 kB
- SHA256:
- 3f98d013c51f35f4d53a8d98592ee976a90c988ee21faeb8dde4e03692849522
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