Instructions to use aliyzd95/wavlm-deepmine-base-plus with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use aliyzd95/wavlm-deepmine-base-plus with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="aliyzd95/wavlm-deepmine-base-plus")# Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("aliyzd95/wavlm-deepmine-base-plus") model = AutoModelForCTC.from_pretrained("aliyzd95/wavlm-deepmine-base-plus") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 50d489137547870b195ac164ed0640b26af1be48bcff651c670d871d26a9411d
- Size of remote file:
- 5.24 kB
- SHA256:
- df80c0b619a17fed0609bba82bc3b67f65801b825d5f48bbefcea173cc797267
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