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") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- d8f36bd7c168a3412b3e5cc008a126c1483a258d00f41bf285e2f61a0acfe364
- Size of remote file:
- 51.5 MB
- SHA256:
- 1099139e5207e55025214087c39fa1d28d94aff44d6bcb0124434bdb03e5927e
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