Instructions to use hansin91/audio_classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hansin91/audio_classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="hansin91/audio_classification")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForAudioClassification processor = AutoProcessor.from_pretrained("hansin91/audio_classification") model = AutoModelForAudioClassification.from_pretrained("hansin91/audio_classification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from hansin91/audio_classification: direct link, hf CLI and curl.
- Browser
- Download file 378 MB
-
https://huggingface.co/hansin91/audio_classification/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://hansin91/audio_classification/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/hansin91/audio_classification/resolve/main/pytorch_model.bin
378 MB
- Xet hash:
- 725630ea4e8a72c215eab070a312c672935af15e3e91f147dc72d971ccea60bc
- Size of remote file:
- 378 MB
- SHA256:
- c3f0e9339711a7d24d221296d463d34693d4e56692f351244bfda15f098bc602
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