Instructions to use Wing4/base_korean_audio_cls with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Wing4/base_korean_audio_cls with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="Wing4/base_korean_audio_cls")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForAudioClassification processor = AutoProcessor.from_pretrained("Wing4/base_korean_audio_cls") model = AutoModelForAudioClassification.from_pretrained("Wing4/base_korean_audio_cls", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from Wing4/base_korean_audio_cls: direct link, hf CLI and curl.
- Browser
- Download file 5.84 kB
-
https://huggingface.co/Wing4/base_korean_audio_cls/resolve/main/training_args.bin
- Command line
-
hf download hf://Wing4/base_korean_audio_cls/training_args.bin
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curl -L -o training_args.bin https://huggingface.co/Wing4/base_korean_audio_cls/resolve/main/training_args.bin
5.84 kB
- Xet hash:
- 347bf07bba0a14ca4669257496f39d55a211f91d8a12f017e100074c65fb2f99
- Size of remote file:
- 5.84 kB
- SHA256:
- 13866889aa53d4d715f7cdf167dc67d97a6f0d68fe1ecc7926927c9e92c01287
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