Instructions to use Bgeorge/model_dialect with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Bgeorge/model_dialect with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="Bgeorge/model_dialect")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForAudioClassification processor = AutoProcessor.from_pretrained("Bgeorge/model_dialect") model = AutoModelForAudioClassification.from_pretrained("Bgeorge/model_dialect", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from Bgeorge/model_dialect: direct link, hf CLI and curl.
- Browser
- Download file 5.3 kB
-
https://huggingface.co/Bgeorge/model_dialect/resolve/c85e6c423419b6823cf073a7853e09613328a9c3/training_args.bin
- Command line
-
hf download hf://Bgeorge/model_dialect@c85e6c423419b6823cf073a7853e09613328a9c3/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/Bgeorge/model_dialect/resolve/c85e6c423419b6823cf073a7853e09613328a9c3/training_args.bin
5.3 kB
- Xet hash:
- b671ecc961654c3abda593361d0aaf26a206719c0ade1eabc2fbf0998409f20f
- Size of remote file:
- 5.3 kB
- SHA256:
- 54703caae75304554797aeb55a49b6748a8a274df122ef1cffa45b7ef9262baa
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