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") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 34509c05d0b1d15536c21401726040d197ea185f25731fe9aba23b8955fac0d5
- Size of remote file:
- 67.3 MB
- SHA256:
- 113ba68634e295a63c232fcd07b08caf5181b7252887f7763037732960060841
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.