wav2vec2-large-voxrex-npsc

This model is a fine-tuned version of KBLab/wav2vec2-large-voxrex on the NBAILAB/NPSC - 16K_MP3 dataset. It achieves the following results on the evaluation set:

• Loss: nan
• Wer: 1.0

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: 16
• eval_batch_size: 16
• seed: 42
• gradient_accumulation_steps: 2
• total_train_batch_size: 32
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• lr_scheduler_warmup_steps: 2000
• num_epochs: 15.0
• mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Wer
2.9728	0.32	500	2.9449	1.0
2.5099	0.64	1000	1.8492	0.9910
0.7872	0.97	1500	0.4467	0.3774
0.5993	1.29	2000	0.3181	0.2819
0.5134	1.61	2500	0.2638	0.2401
0.4544	1.93	3000	0.2287	0.2091
0.4085	2.26	3500	0.2153	0.1918
0.3921	2.58	4000	0.2004	0.1804
0.4613	2.9	4500	0.1905	0.1732
0.3402	3.22	5000	0.1778	0.1659
0.3258	3.55	5500	0.1732	0.1571
0.3044	3.87	6000	0.1677	0.1497
0.2914	4.19	6500	0.1597	0.1420
0.278	4.51	7000	0.1574	0.1386
0.2858	4.84	7500	0.1552	0.1300
0.2585	5.16	8000	0.1523	0.1276
0.2827	5.48	8500	0.1448	0.1265
0.3365	5.8	9000	0.1411	0.1232
0.2488	6.13	9500	0.1456	0.1195
0.2406	6.45	10000	0.1414	0.1194
0.2488	6.77	10500	0.1393	0.1173
0.3084	7.09	11000	0.1379	0.1164
0.2365	7.41	11500	0.1387	0.1165
0.2217	7.74	12000	0.1381	0.1132
0.2381	8.06	12500	0.1360	0.1126
0.2329	8.38	13000	0.1357	0.1124
0.2103	8.7	13500	0.1335	0.1087
0.2366	9.03	14000	0.1388	0.1105
0.2289	9.35	14500	0.1383	0.1098
0.2486	9.67	15000	0.1386	0.1087
0.2772	9.99	15500	0.1598	0.1093
0.2728	10.32	16000	0.1814	0.1110
0.3437	10.64	16500	0.2505	0.1124
0.431	10.96	17000	0.2828	0.1143
0.3929	11.28	17500	0.2977	0.1149
0.4396	11.61	18000	0.3198	0.1170
0.59	11.93	18500	0.4158	0.1315
0.7813	12.25	19000	0.6123	0.2208
0.9345	12.57	19500	0.6815	0.2885
0.998	12.89	20000	0.7587	0.1991
1.0493	13.22	20500	0.7583	0.1996
1.438	13.54	21000	nan	1.0
0.0	13.86	21500	nan	1.0
0.0	14.18	22000	nan	1.0
0.0	14.51	22500	nan	1.0
0.0	14.83	23000	nan	1.0

Framework versions

• Transformers 4.17.0.dev0
• Pytorch 1.10.2+cu113
• Datasets 1.18.3.dev0
• Tokenizers 0.11.0