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:
- b602407f06f1d569e7439da2672ca9008b2547a5cb3b0d2c9b79f14cdc27cfca
- Size of remote file:
- 103 MB
- SHA256:
- 903c18d6d8b9be1c4948618e8be9fdf0211890dcb1a90ea48ee9ac1648327fe4
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