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 pytorch_model.bin from dima806/music_genres_classification: direct link, hf CLI and curl.
- Browser
- Download file 378 MB
-
https://huggingface.co/dima806/music_genres_classification/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://dima806/music_genres_classification/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/dima806/music_genres_classification/resolve/main/pytorch_model.bin
378 MB
- Xet hash:
- cc54c7515b9c6881ce62a4f471893ebf7d6c39a812b078a9817c655a7fe49865
- Size of remote file:
- 378 MB
- SHA256:
- a36e8d7542c20a973b2295cd2aa4d3aa469720aaf2ef1bece1f80720e51d7122
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.