README.md

metadata

license: apache-2.0
base_model: Bisher/wav2vec2_ASV_deepfake_audio_detection
tags:
  - generated_from_trainer
metrics:
  - accuracy
  - precision
  - recall
  - f1
model-index:
  - name: wav2vec2_ASV_deepfake_audio_detection_DF_finetune_frozen
    results: []

wav2vec2_ASV_deepfake_audio_detection_DF_finetune_frozen

This model is a fine-tuned version of Bisher/wav2vec2_ASV_deepfake_audio_detection on an unknown dataset. It achieves the following results on the evaluation set:

• Loss: 0.4076
• Accuracy: 0.9115
• Precision: 0.9131
• Recall: 0.9115
• F1: 0.8803
• Tp: 265
• Tn: 17893
• Fn: 1742
• Fp: 20
• Eer: 0.0588
• Min Tdcf: 0.0271
• Auc Roc: 0.9849

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

• learning_rate: 3e-05
• train_batch_size: 152
• eval_batch_size: 152
• seed: 42
• gradient_accumulation_steps: 4
• total_train_batch_size: 608
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• lr_scheduler_warmup_ratio: 0.1
• num_epochs: 3
• mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	Precision	Recall	F1	Tp	Tn	Fn	Fp	Eer	Min Tdcf	Auc Roc
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
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
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
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
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
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
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
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
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
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

Framework versions

• Transformers 4.44.0
• Pytorch 2.4.0
• Datasets 2.21.0
• Tokenizers 0.19.1