Instructions to use yaboidimsum/audio_classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use yaboidimsum/audio_classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="yaboidimsum/audio_classification")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForAudioClassification processor = AutoProcessor.from_pretrained("yaboidimsum/audio_classification") model = AutoModelForAudioClassification.from_pretrained("yaboidimsum/audio_classification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from yaboidimsum/audio_classification: direct link, hf CLI and curl.
- Browser
- Download file 378 MB
-
https://huggingface.co/yaboidimsum/audio_classification/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://yaboidimsum/audio_classification/pytorch_model.bin
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curl -L -o pytorch_model.bin https://huggingface.co/yaboidimsum/audio_classification/resolve/main/pytorch_model.bin
378 MB
- Xet hash:
- 50795001c306ceadefb553e19cb5bde7fb7a8b3a05c7787336aaebe112e54fbb
- Size of remote file:
- 378 MB
- SHA256:
- 7193871f8d0e16ce588feff5c938b5e2e8c01933b3252e372d2e9641d914a6ef
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