Instructions to use Ahmeng/audio_classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Ahmeng/audio_classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="Ahmeng/audio_classification")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForAudioClassification processor = AutoProcessor.from_pretrained("Ahmeng/audio_classification") model = AutoModelForAudioClassification.from_pretrained("Ahmeng/audio_classification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from Ahmeng/audio_classification: direct link, hf CLI and curl.
- Browser
- Download file 3.96 kB
-
https://huggingface.co/Ahmeng/audio_classification/resolve/main/training_args.bin
- Command line
-
hf download hf://Ahmeng/audio_classification/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/Ahmeng/audio_classification/resolve/main/training_args.bin
3.96 kB
- Xet hash:
- 63b747f62cb411305b57798a814f32de41eec6742f6ed869db8301b92f218863
- Size of remote file:
- 3.96 kB
- SHA256:
- 29ec30f421963211985eaaf737ccd35156a57fea1b40a41f9d3717f64f2b2c27
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