Instructions to use voidful/hubert-tiny-v2-unit-beamnorm with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use voidful/hubert-tiny-v2-unit-beamnorm 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-beamnorm")# Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("voidful/hubert-tiny-v2-unit-beamnorm") model = AutoModelForCTC.from_pretrained("voidful/hubert-tiny-v2-unit-beamnorm", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 6af337a047245cf9726de860b783ab4855c58262b4fde2a9393e4d892a6a36ae
- Size of remote file:
- 3.58 kB
- SHA256:
- 19743ab1be9b8152315867d009827ed32053734047610c71af043d4fd87ca2a6
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