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--- |
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license: apache-2.0 |
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base_model: Bisher/wav2vec2_ASV_deepfake_audio_detection |
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tags: |
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- generated_from_trainer |
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metrics: |
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- accuracy |
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- precision |
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- recall |
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- f1 |
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model-index: |
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- name: wav2vec2_ASV_deepfake_audio_detection_DF_finetune_frozen |
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results: [] |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# wav2vec2_ASV_deepfake_audio_detection_DF_finetune_frozen |
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This model is a fine-tuned version of [Bisher/wav2vec2_ASV_deepfake_audio_detection](https://huggingface.co/Bisher/wav2vec2_ASV_deepfake_audio_detection) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.4076 |
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- Accuracy: 0.9115 |
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- Precision: 0.9131 |
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- Recall: 0.9115 |
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- F1: 0.8803 |
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- Tp: 265 |
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- Tn: 17893 |
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- Fn: 1742 |
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- Fp: 20 |
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- Eer: 0.0588 |
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- Min Tdcf: 0.0271 |
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- Auc Roc: 0.9849 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 3e-05 |
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- train_batch_size: 152 |
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- eval_batch_size: 152 |
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- seed: 42 |
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- gradient_accumulation_steps: 4 |
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- total_train_batch_size: 608 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- lr_scheduler_warmup_ratio: 0.1 |
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- num_epochs: 3 |
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- mixed_precision_training: Native AMP |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | Tp | Tn | Fn | Fp | Eer | Min Tdcf | Auc Roc | |
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|:-------------:|:------:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|:---:|:-----:|:----:|:--:|:------:|:--------:|:-------:| |
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| 0.025 | 0.0490 | 10 | 0.3573 | 0.9139 | 0.9168 | 0.9139 | 0.8847 | 308 | 17896 | 1699 | 17 | 0.0826 | 0.0329 | 0.9732 | |
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| 0.0219 | 0.0979 | 20 | 0.3002 | 0.9152 | 0.9172 | 0.9152 | 0.8875 | 339 | 17891 | 1668 | 22 | 0.0757 | 0.0321 | 0.9759 | |
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| 0.0193 | 0.1469 | 30 | 0.3557 | 0.9159 | 0.9178 | 0.9159 | 0.8889 | 354 | 17890 | 1653 | 23 | 0.0710 | 0.0320 | 0.9541 | |
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| 0.0197 | 0.1958 | 40 | 0.3963 | 0.9191 | 0.9206 | 0.9191 | 0.8951 | 423 | 17885 | 1584 | 28 | 0.0802 | 0.0325 | 0.9453 | |
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| 0.0181 | 0.2448 | 50 | 0.3524 | 0.9155 | 0.9176 | 0.9155 | 0.8882 | 346 | 17891 | 1661 | 22 | 0.0673 | 0.0324 | 0.9794 | |
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| 0.0152 | 0.2938 | 60 | 0.5161 | 0.9050 | 0.9102 | 0.9050 | 0.8654 | 119 | 17908 | 1888 | 5 | 0.0857 | 0.0314 | 0.9582 | |
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| 0.0155 | 0.3427 | 70 | 0.6375 | 0.9037 | 0.9120 | 0.9037 | 0.8622 | 90 | 17912 | 1917 | 1 | 0.1395 | 0.0307 | 0.9384 | |
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| 0.0193 | 0.3917 | 80 | 0.3521 | 0.9119 | 0.9153 | 0.9119 | 0.8808 | 267 | 17899 | 1740 | 14 | 0.0643 | 0.0309 | 0.9816 | |
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| 0.0158 | 0.4406 | 90 | 0.3775 | 0.9066 | 0.9113 | 0.9066 | 0.8692 | 154 | 17906 | 1853 | 7 | 0.0613 | 0.0290 | 0.9839 | |
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| 0.017 | 0.4896 | 100 | 0.4076 | 0.9115 | 0.9131 | 0.9115 | 0.8803 | 265 | 17893 | 1742 | 20 | 0.0588 | 0.0271 | 0.9849 | |
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### Framework versions |
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- Transformers 4.44.0 |
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- Pytorch 2.4.0 |
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- Datasets 2.21.0 |
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- Tokenizers 0.19.1 |
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