Instructions to use jadasdn/output2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jadasdn/output2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="jadasdn/output2")# Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("jadasdn/output2") model = AutoModelForCTC.from_pretrained("jadasdn/output2") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- c6f8e8303c14cc191592a3e331f62f94953a97379e5a29554a85a4caec2fb929
- Size of remote file:
- 4.54 kB
- SHA256:
- a9c1a80c2b8fc65fe7b9d54430501e3d0f66a7286840987f08f417a2c724991a
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