Instructions to use FriedParrot/tmp_trainer with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use FriedParrot/tmp_trainer with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="FriedParrot/tmp_trainer")# Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("FriedParrot/tmp_trainer") model = AutoModelForCTC.from_pretrained("FriedParrot/tmp_trainer") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 09de61e1f94bcc5bc25edc013c90162775df7875657991730fb7afc59c31de79
- Size of remote file:
- 378 MB
- SHA256:
- f76edd1421dabc2d43df53d60f929e1bc072d382bd5e1add3f347d102f04cb70
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