DeepFake-Audio-Rangers/DeepfakeDetect_wav2vec2

ourData_train

This model is a fine-tuned version of Bisher/wav2vec2_ASV_deepfake_audio_detection_DF_finetune_frozen on the audiofolder dataset. It achieves the following results on the evaluation set:

• Loss: 0.0655
• Accuracy: 0.9790
• Precision: 0.9806
• Recall: 0.9790
• F1: 0.9795
• Tp: 518
• Tn: 3313
• Fn: 14
• Fp: 68
• Eer: 0.0216
• Min Tdcf: 0.0069
• Auc Roc: 0.9984

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: 64
• eval_batch_size: 64
• seed: 42
• gradient_accumulation_steps: 4
• total_train_batch_size: 256
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• lr_scheduler_warmup_ratio: 0.1
• num_epochs: 2
• 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
2.0925	0.0816	5	2.4908	0.3095	0.7455	0.3095	0.3596	376	835	156	2546	0.5167	0.0499	0.4827
1.8849	0.1633	10	1.8190	0.4613	0.7686	0.4613	0.5381	303	1502	229	1879	0.4874	0.05	0.5170
1.1783	0.2449	15	0.9851	0.3330	0.8128	0.3330	0.3755	455	848	77	2533	0.3856	0.0499	0.6498
0.661	0.3265	20	0.5800	0.8684	0.8702	0.8684	0.8693	283	3115	249	266	0.2256	0.0446	0.8570
0.5967	0.4082	25	0.4366	0.8433	0.9017	0.8433	0.8609	448	2852	84	529	0.1559	0.0338	0.9265
0.3993	0.4898	30	0.2574	0.8919	0.9281	0.8919	0.9020	488	3002	44	379	0.0973	0.0228	0.9650
0.25	0.5714	35	0.1732	0.9422	0.9512	0.9422	0.9449	487	3200	45	181	0.0636	0.0147	0.9811
0.2353	0.6531	40	0.3041	0.9540	0.9526	0.9540	0.9529	413	3320	119	61	0.0769	0.0177	0.9787
0.1748	0.7347	45	0.1481	0.9601	0.9645	0.9601	0.9614	499	3258	33	123	0.0515	0.0126	0.9904
0.1273	0.8163	50	0.1373	0.9698	0.9702	0.9698	0.9700	479	3316	53	65	0.0451	0.0117	0.9939
0.143	0.8980	55	0.2027	0.9640	0.9635	0.9640	0.9637	453	3319	79	62	0.0545	0.0130	0.9910
0.1021	0.9796	60	0.1321	0.9701	0.9710	0.9701	0.9704	488	3308	44	73	0.0494	0.0101	0.9939
0.0694	1.0612	65	0.1845	0.9612	0.9626	0.9612	0.9617	475	3286	57	95	0.0564	0.0123	0.9906
0.0665	1.1429	70	0.1669	0.9681	0.9682	0.9681	0.9681	473	3315	59	66	0.0447	0.0116	0.9940
0.069	1.2245	75	0.1528	0.9691	0.9698	0.9691	0.9694	483	3309	49	72	0.0429	0.0114	0.9950
0.042	1.3061	80	0.1797	0.9693	0.9701	0.9693	0.9696	485	3308	47	73	0.0438	0.0103	0.9946
0.0689	1.3878	85	0.1625	0.9711	0.9718	0.9711	0.9714	488	3312	44	69	0.0399	0.0106	0.9956
0.0739	1.4694	90	0.0861	0.9750	0.9768	0.9750	0.9755	512	3303	20	78	0.0282	0.0080	0.9976
0.0556	1.5510	95	0.1952	0.9778	0.9775	0.9778	0.9775	474	3352	58	29	0.0358	0.0095	0.9963
0.056	1.6327	100	0.1888	0.9767	0.9764	0.9767	0.9765	472	3350	60	31	0.0364	0.0087	0.9959
0.0312	1.7143	105	0.1572	0.9783	0.9781	0.9783	0.9781	482	3346	50	35	0.0376	0.0088	0.9962
0.0532	1.7959	110	0.1333	0.9775	0.9778	0.9775	0.9776	494	3331	38	50	0.0338	0.0088	0.9967
0.0575	1.8776	115	0.0958	0.9798	0.9805	0.9798	0.9800	507	3327	25	54	0.0301	0.0080	0.9976
0.0624	1.9592	120	0.0655	0.9790	0.9806	0.9790	0.9795	518	3313	14	68	0.0216	0.0069	0.9984

Framework versions

• Transformers 4.42.4
• Pytorch 2.3.1+cu121
• Datasets 2.21.0
• Tokenizers 0.19.1