Instructions to use be2be2/audio_cls with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use be2be2/audio_cls with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="be2be2/audio_cls")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForAudioClassification processor = AutoProcessor.from_pretrained("be2be2/audio_cls") model = AutoModelForAudioClassification.from_pretrained("be2be2/audio_cls", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from be2be2/audio_cls: direct link, hf CLI and curl.
- Browser
- Download file 5.78 kB
-
https://huggingface.co/be2be2/audio_cls/resolve/main/training_args.bin
- Command line
-
hf download hf://be2be2/audio_cls/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/be2be2/audio_cls/resolve/main/training_args.bin
5.78 kB
- Xet hash:
- c210b2d201b275974953670d5358151b38b5572e3c1fbc74dbd53bf8a3665080
- Size of remote file:
- 5.78 kB
- SHA256:
- dd2afe83033885803858d5976e892084ff0510124bf8d948a71aadb4c578d9e2
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