Instructions to use lsb/tironiculum with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use lsb/tironiculum with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="lsb/tironiculum")# Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("lsb/tironiculum") model = AutoModelForCTC.from_pretrained("lsb/tironiculum", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 7cc70fd05a696ff4bcbca3b21084577d74837875f9e1e60961d448fe9ae88421
- Size of remote file:
- 378 MB
- SHA256:
- afbae07d3dc1654d6bd24db9a3b78043722f1d7f028a175c8548fb91307a5f16
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