Instructions to use taengk/audio_cls with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use taengk/audio_cls with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="taengk/audio_cls")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForAudioClassification processor = AutoProcessor.from_pretrained("taengk/audio_cls") model = AutoModelForAudioClassification.from_pretrained("taengk/audio_cls", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from taengk/audio_cls: direct link, hf CLI and curl.
- Browser
- Download file 5.78 kB
-
https://huggingface.co/taengk/audio_cls/resolve/main/training_args.bin
- Command line
-
hf download hf://taengk/audio_cls/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/taengk/audio_cls/resolve/main/training_args.bin
5.78 kB
- Xet hash:
- ccba63881f91f05b7b5964be6fbf482c4754b4182a9997153cb6322d5728062d
- Size of remote file:
- 5.78 kB
- SHA256:
- 1be7b5a86acfc11329a8530daf6ce0b68543cf8850771bb1f666d29f9dceaa5f
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