Instructions to use CLiC-UB/Casper with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use CLiC-UB/Casper with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="CLiC-UB/Casper")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("CLiC-UB/Casper") model = AutoModelForSpeechSeq2Seq.from_pretrained("CLiC-UB/Casper") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- f104525bbd5690fee385ebf2e0ded1efe348eff1b7808f6561dfd564fb2689f8
- Size of remote file:
- 967 MB
- SHA256:
- 499e09fdd3c75091ed68890e3b3e8bfbf6fd62fb0042542cfe28aa8426d485ce
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