Instructions to use Gustking/wav2vec2-large-xlsr-deepfake-audio-classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Gustking/wav2vec2-large-xlsr-deepfake-audio-classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="Gustking/wav2vec2-large-xlsr-deepfake-audio-classification")# Load model directly from transformers import AutoProcessor, AutoModelForAudioClassification processor = AutoProcessor.from_pretrained("Gustking/wav2vec2-large-xlsr-deepfake-audio-classification") model = AutoModelForAudioClassification.from_pretrained("Gustking/wav2vec2-large-xlsr-deepfake-audio-classification") - Notebooks
- Google Colab
- Kaggle
This model is a fine tuning for the deepfake audio classification task.
It achieves the following results on its evalutation data:
F1: 0.95
Loss: 0.4056
It achieves the following results on ASVspoof2019 evaluation subset:
Accuracy:0.9286
Precision:0.9999
Recall:0.9205
F1-Score:0.9363
Equal Error Rate (EER): 0.0401
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Model tree for Gustking/wav2vec2-large-xlsr-deepfake-audio-classification
Base model
facebook/wav2vec2-xls-r-300m