Instructions to use aditira/audio_classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use aditira/audio_classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="aditira/audio_classification")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForAudioClassification processor = AutoProcessor.from_pretrained("aditira/audio_classification") model = AutoModelForAudioClassification.from_pretrained("aditira/audio_classification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from aditira/audio_classification: direct link, hf CLI and curl.
- Browser
- Download file 4.09 kB
-
https://huggingface.co/aditira/audio_classification/resolve/main/training_args.bin
- Command line
-
hf download hf://aditira/audio_classification/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/aditira/audio_classification/resolve/main/training_args.bin
4.09 kB
- Xet hash:
- 054c89b2341c5f5dbfdbd7e934224181ae04ee5b6237594d7d669dfd35e073c4
- Size of remote file:
- 4.09 kB
- SHA256:
- eb173777bc9a2595fa8f8e342660debc2072ef2b4d0a483d625e62e15ac359f1
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