Instructions to use voidful/hubert-tiny-100-pr with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use voidful/hubert-tiny-100-pr with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="voidful/hubert-tiny-100-pr")# Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("voidful/hubert-tiny-100-pr") model = AutoModelForCTC.from_pretrained("voidful/hubert-tiny-100-pr", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 5e4dc40e3d4e5f3fff3fb8a004462a34786098cb80e1a9cd986246d78f92278d
- Size of remote file:
- 3.58 kB
- SHA256:
- 405d72302305eacc8c69fd718d216ba9d67eb763afe0a3a9de87eba35d115e86
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