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:
- 5c7408442a77674ceca3f8a79781c13dfe95c1a9055205b41e525cd3e3f985ab
- Size of remote file:
- 627 Bytes
- SHA256:
- 85ba977224f6e50749b21b89fe49ac2d043ba053465ad28397f862d8deb61316
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