Instructions to use pepppper/audio_cls with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use pepppper/audio_cls with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="pepppper/audio_cls")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForAudioClassification processor = AutoProcessor.from_pretrained("pepppper/audio_cls") model = AutoModelForAudioClassification.from_pretrained("pepppper/audio_cls", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from pepppper/audio_cls: direct link, hf CLI and curl.
- Browser
- Download file 5.78 kB
-
https://huggingface.co/pepppper/audio_cls/resolve/main/training_args.bin
- Command line
-
hf download hf://pepppper/audio_cls/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/pepppper/audio_cls/resolve/main/training_args.bin
5.78 kB
- Xet hash:
- 5032fa8a812fff6629eab7c790f16c1aa66a77fb8315f0feb7eaa791c5ecc36f
- Size of remote file:
- 5.78 kB
- SHA256:
- 35bd951e58ea9f2d050b6befe6567d40fc99fcb26aeec8d1a6c12e238d2c8eea
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