Instructions to use Kibalama/urban_sounds_classification_Model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Kibalama/urban_sounds_classification_Model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="Kibalama/urban_sounds_classification_Model")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForAudioClassification processor = AutoProcessor.from_pretrained("Kibalama/urban_sounds_classification_Model") model = AutoModelForAudioClassification.from_pretrained("Kibalama/urban_sounds_classification_Model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from Kibalama/urban_sounds_classification_Model: direct link, hf CLI and curl.
- Browser
- Download file 5.37 kB
-
https://huggingface.co/Kibalama/urban_sounds_classification_Model/resolve/main/training_args.bin
- Command line
-
hf download hf://Kibalama/urban_sounds_classification_Model/training_args.bin
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curl -L -o training_args.bin https://huggingface.co/Kibalama/urban_sounds_classification_Model/resolve/main/training_args.bin
5.37 kB
- Xet hash:
- eac37cf61c788abbbbd3d5214d9092d16d31ecf082b462b7a9f7688fe77c0ffe
- Size of remote file:
- 5.37 kB
- SHA256:
- 3e0ad0730af55132b27951c6170bf90438e4fd1f81c559cd28c5cb9284e6dbf5
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