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
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README.md
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## Training and evaluation data
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* The model is trained on multi
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* The input audio dataset is about 16KHz.
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## Training procedure
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## Training and evaluation data
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* The model is trained on multi-ethnic english dataset.
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* The input audio dataset is about 16KHz.
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## Training procedure
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