Instructions to use Kuray107/RATS_clean with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Kuray107/RATS_clean with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="Kuray107/RATS_clean")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("Kuray107/RATS_clean") model = AutoModelForCTC.from_pretrained("Kuray107/RATS_clean", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from Kuray107/RATS_clean: direct link, hf CLI and curl.
- Browser
- Download file 3.5 kB
-
https://huggingface.co/Kuray107/RATS_clean/resolve/main/training_args.bin
- Command line
-
hf download hf://Kuray107/RATS_clean/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/Kuray107/RATS_clean/resolve/main/training_args.bin
3.5 kB
- Xet hash:
- b45dfd97231948eea461437ad77f29e1a406581c0fde760d93566673cc929f2b
- Size of remote file:
- 3.5 kB
- SHA256:
- d2ef52e015169509fd2f340b7d6c581afc917fa56f97cf281d72a3703b2a2a36
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