Instructions to use dangkhoadl/AudioResNet with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dangkhoadl/AudioResNet with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoImageProcessor, ResNetForAudioClassification processor = AutoImageProcessor.from_pretrained("dangkhoadl/AudioResNet") model = ResNetForAudioClassification.from_pretrained("dangkhoadl/AudioResNet", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from dangkhoadl/AudioResNet: direct link, hf CLI and curl.
- Browser
- Download file 233 MB
-
https://huggingface.co/dangkhoadl/AudioResNet/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://dangkhoadl/AudioResNet/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/dangkhoadl/AudioResNet/resolve/main/pytorch_model.bin
233 MB
- Xet hash:
- c2c547ccf8cde400dc223b5c3e678f35c2354de4e9e548ebbf9bd171764a9dee
- Size of remote file:
- 233 MB
- SHA256:
- 67dd9a4adb296de7e5c4420fcd6f7fb08d0015e74f2009f889ad0666501d5bd7
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