Instructions to use octava/audio_classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use octava/audio_classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="octava/audio_classification")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForAudioClassification processor = AutoProcessor.from_pretrained("octava/audio_classification") model = AutoModelForAudioClassification.from_pretrained("octava/audio_classification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from octava/audio_classification: direct link, hf CLI and curl.
- Browser
- Download file 378 MB
-
https://huggingface.co/octava/audio_classification/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://octava/audio_classification/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/octava/audio_classification/resolve/main/pytorch_model.bin
378 MB
- Xet hash:
- eea4f2f0339b9321b5e26fb276c653f468fde9c8233435d125ba52fc8e7b0763
- Size of remote file:
- 378 MB
- SHA256:
- 3ea5a46bc3dc1bab0aa6e10f19bd9a79dbc0f00f3015e3dd97613ec98e2ae583
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