Instructions to use voidful/hubert-tiny-v2-unit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use voidful/hubert-tiny-v2-unit with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="voidful/hubert-tiny-v2-unit")# Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("voidful/hubert-tiny-v2-unit") model = AutoModelForCTC.from_pretrained("voidful/hubert-tiny-v2-unit", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- c26bbabe6308e98aa8cee50041f936c5e4b33af8e6c7b16f09c8674adeebd396
- Size of remote file:
- 51.5 MB
- SHA256:
- db60a08ea91d884c3244ccda41e37d380c87b367e7da94af185c79725d23238c
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