Instructions to use Hemgg/Deepfake-audio-detection with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Hemgg/Deepfake-audio-detection with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="Hemgg/Deepfake-audio-detection")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForAudioClassification processor = AutoProcessor.from_pretrained("Hemgg/Deepfake-audio-detection") model = AutoModelForAudioClassification.from_pretrained("Hemgg/Deepfake-audio-detection", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from Hemgg/Deepfake-audio-detection: direct link, hf CLI and curl.
- Browser
- Download file 4.92 kB
-
https://huggingface.co/Hemgg/Deepfake-audio-detection/resolve/main/training_args.bin
- Command line
-
hf download hf://Hemgg/Deepfake-audio-detection/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/Hemgg/Deepfake-audio-detection/resolve/main/training_args.bin
4.92 kB
- Xet hash:
- 3ae986c09ff53a71d5ca652cb22721785817fe05069577a05dab4c3c42f1a230
- Size of remote file:
- 4.92 kB
- SHA256:
- b6b6e6dc88a2ec888a63325d95fa4c42c4a71cd51faf388472852de6191e48c6
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