Instructions to use voidful/hubert-tiny-unit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use voidful/hubert-tiny-unit with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="voidful/hubert-tiny-unit")# Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("voidful/hubert-tiny-unit") model = AutoModelForCTC.from_pretrained("voidful/hubert-tiny-unit", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 32e0df51a2f20fbd47ae5f6733d757c7aba29169519ca7f6ba8afe0670356702
- Size of remote file:
- 627 Bytes
- SHA256:
- c98e017550dbdc9130ffa260d6288275167b481ac3b4494c9891ccca50a1f809
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.