Spoof_detection

This model is a fine-tuned version of facebook/wav2vec2-base on an unknown dataset. It achieves the following results on the evaluation set:

• Loss: 1.7448
• Wer: 0.1090

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: 0.0001
• train_batch_size: 4
• eval_batch_size: 8
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• lr_scheduler_warmup_steps: 1000
• num_epochs: 30
• mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Wer
95.9046	0.66	500	992.2993	0.6180
14.0322	1.33	1000	1.8873	0.1090
1.8659	1.99	1500	1.7827	0.1090
1.851	2.65	2000	1.8489	0.1090
1.8218	3.32	2500	1.8943	0.1090
1.8108	3.98	3000	1.9250	0.1090
1.8228	4.64	3500	1.7555	0.1090
1.832	5.31	4000	1.7837	0.1090
1.8403	5.97	4500	1.6644	0.1090
1.8292	6.63	5000	1.6906	0.1090
1.8223	7.29	5500	1.6966	0.1090
1.8007	7.96	6000	1.6951	0.1090
1.7986	8.62	6500	1.7436	0.1090
1.7933	9.28	7000	1.8169	0.1090
1.7861	9.95	7500	1.7209	0.1090
1.7843	10.61	8000	1.9379	0.1090
1.7743	11.27	8500	1.9834	0.1090
1.7721	11.94	9000	1.9279	0.1090
1.7719	12.6	9500	1.8187	0.1090
1.7616	13.26	10000	1.7804	0.1090
1.7638	13.93	10500	1.7884	0.1090
1.7651	14.59	11000	1.7476	0.1090
1.7603	15.25	11500	1.7570	0.1090
1.7543	15.92	12000	1.7356	0.1090
1.7556	16.58	12500	1.7140	0.1090
1.751	17.24	13000	1.7453	0.1090
1.75	17.9	13500	1.7648	0.1090
1.7492	18.57	14000	1.7338	0.1090
1.7484	19.23	14500	1.7093	0.1090
1.7461	19.89	15000	1.7393	0.1090
1.7429	20.56	15500	1.7605	0.1090
1.7446	21.22	16000	1.7782	0.1090
1.7435	21.88	16500	1.6749	0.1090
1.7392	22.55	17000	1.7468	0.1090
1.741	23.21	17500	1.7406	0.1090
1.7394	23.87	18000	1.7787	0.1090
1.739	24.54	18500	1.7969	0.1090
1.7341	25.2	19000	1.7490	0.1090
1.7371	25.86	19500	1.7783	0.1090
1.735	26.53	20000	1.7540	0.1090
1.7353	27.19	20500	1.7735	0.1090
1.7331	27.85	21000	1.7188	0.1090
1.7308	28.51	21500	1.7349	0.1090
1.7341	29.18	22000	1.7531	0.1090
1.7305	29.84	22500	1.7448	0.1090

Framework versions

• Transformers 4.17.0
• Pytorch 1.10.0+cu102
• Datasets 1.16.1
• Tokenizers 0.12.1