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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- e6447c6c3ed347a6600d4c1d8d6772212310f76331e5da60b7d2bc7d4467e765
- Size of remote file:
- 3.5 kB
- SHA256:
- 927d0bb229729674dbae7aefc77c4809c5bcf4137ebed69870112b4e184289b1
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