Instructions to use dima806/music_genres_classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dima806/music_genres_classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="dima806/music_genres_classification")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForAudioClassification processor = AutoProcessor.from_pretrained("dima806/music_genres_classification") model = AutoModelForAudioClassification.from_pretrained("dima806/music_genres_classification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from dima806/music_genres_classification: direct link, hf CLI and curl.
- Browser
- Download file 3.57 kB
-
https://huggingface.co/dima806/music_genres_classification/resolve/main/training_args.bin
- Command line
-
hf download hf://dima806/music_genres_classification/training_args.bin
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curl -L -o training_args.bin https://huggingface.co/dima806/music_genres_classification/resolve/main/training_args.bin
3.57 kB
- Xet hash:
- 03554a2d52889e261f2b22278b2af9d3d8547c5ebb3b903953a0e529ea7ed89f
- Size of remote file:
- 3.57 kB
- SHA256:
- 19a7b8a791db16485108725a93c291741023d2fe95e5a842e3860116709d6e65
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