Instructions to use voidful/hubert-base-100-pr with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use voidful/hubert-base-100-pr with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="voidful/hubert-base-100-pr")# Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("voidful/hubert-base-100-pr") model = AutoModelForCTC.from_pretrained("voidful/hubert-base-100-pr", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- b3e50bfc9ae791535090e6dd4f8a2e756c00eb650eacd9e1d36c0c563791eaa4
- Size of remote file:
- 756 MB
- SHA256:
- 84747a7ad66ba6dbdd1ee5ed0043a6a90fea7bcea479dce6173c72153de7de15
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