Instructions to use ScatterRaven/audio_cls_ko with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ScatterRaven/audio_cls_ko with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="ScatterRaven/audio_cls_ko")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForAudioClassification processor = AutoProcessor.from_pretrained("ScatterRaven/audio_cls_ko") model = AutoModelForAudioClassification.from_pretrained("ScatterRaven/audio_cls_ko", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from ScatterRaven/audio_cls_ko: direct link, hf CLI and curl.
- Browser
- Download file 5.78 kB
-
https://huggingface.co/ScatterRaven/audio_cls_ko/resolve/main/training_args.bin
- Command line
-
hf download hf://ScatterRaven/audio_cls_ko/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/ScatterRaven/audio_cls_ko/resolve/main/training_args.bin
5.78 kB
- Xet hash:
- 907b110cd4f12907f3fcf2353fbf64c05b0df8d5a6f8fb125298cca28b813a59
- Size of remote file:
- 5.78 kB
- SHA256:
- 2564b30d3ac2b28b73073cea68a08a37302d7e58ec760a00ff5cb04a76029858
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.