Instructions to use Kuenga/DzongkhaASR2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Kuenga/DzongkhaASR2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="Kuenga/DzongkhaASR2")# Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("Kuenga/DzongkhaASR2") model = AutoModelForCTC.from_pretrained("Kuenga/DzongkhaASR2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- b4c91ace8c9490fc321e58923cd5c11eea2106fa4f9fe667361c710d1ee6b19d
- Size of remote file:
- 5.3 kB
- SHA256:
- 5d6157f3b1d12012d2113b5625408f6850a828a9b97319632b51a30401768f7d
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