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") - Notebooks
- Google Colab
- Kaggle
Adding `safetensors` variant of this model
#1
by SFconvertbot - opened
- model.safetensors +3 -0
model.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:72390891f781871f1ae45875d5288acde69b80bb21b0cf2c9db1bd9eaa426bc7
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size 51522352
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