Instructions to use monideep2255/batch_size_2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use monideep2255/batch_size_2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="monideep2255/batch_size_2")# Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("monideep2255/batch_size_2") model = AutoModelForCTC.from_pretrained("monideep2255/batch_size_2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- fd93fcf99a1d0ae0c55f07961ba536f9382f3633f15af483cb0ac676fca95d74
- Size of remote file:
- 1.26 GB
- SHA256:
- 02ff9b8a1d60234e1717dd3f80caad0aa2601632816eea6bcce19be44dc2b530
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