Instructions to use ninja/frandy with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ninja/frandy with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="ninja/frandy")# Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("ninja/frandy") model = AutoModelForCTC.from_pretrained("ninja/frandy", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 7818db5a42043e4cad7623a2844750a926c9a45e8f610bc75540736ce931339e
- Size of remote file:
- 3.58 kB
- SHA256:
- f2e76db8d762e634f2a573b31533fd2da01a9c7f8eb488091a2ec9d8d3c88886
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