Instructions to use monideep2255/batch_size_4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use monideep2255/batch_size_4 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="monideep2255/batch_size_4")# Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("monideep2255/batch_size_4") model = AutoModelForCTC.from_pretrained("monideep2255/batch_size_4", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 2099c1dae679751b16dacb0d04248e381d4c602903e60e23be83bfd6816003a7
- Size of remote file:
- 1.26 GB
- SHA256:
- bd0ca15d1ddac4c83a5a017ee56fde33bf68d7aaab57cf63651f9c2b57ef37fd
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