Instructions to use junbeom2/audio_cls with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use junbeom2/audio_cls with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="junbeom2/audio_cls")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForAudioClassification processor = AutoProcessor.from_pretrained("junbeom2/audio_cls") model = AutoModelForAudioClassification.from_pretrained("junbeom2/audio_cls", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from junbeom2/audio_cls: direct link, hf CLI and curl.
- Browser
- Download file 5.84 kB
-
https://huggingface.co/junbeom2/audio_cls/resolve/main/training_args.bin
- Command line
-
hf download hf://junbeom2/audio_cls/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/junbeom2/audio_cls/resolve/main/training_args.bin
5.84 kB
- Xet hash:
- 2f4d0592bb380fa8480c290ffa684bf10e2a679c7839ee918d25db53e6946aee
- Size of remote file:
- 5.84 kB
- SHA256:
- 7181bed0b8fe29fecccaed720fd094fd5bc00e6474d5a51bcc15e21abfad9f7b
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