learning_rate: 0.001
train_batch_size: 8
eval_batch_size: 8
seed: 42
optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: linear
lr_scheduler_warmup_steps: 100
num_epochs: 5
mixed_precision_training: Native AMP

Training Loss	Epoch	Step	Validation Loss	Wer
0.7968	0.1871	200	0.4521	0.7382
0.6536	0.3742	400	0.3965	0.7499
0.5989	0.5613	600	0.3850	0.7644
0.5212	0.7484	800	0.3473	0.6373
0.5649	0.9355	1000	0.2951	0.5633
0.4996	1.1225	1200	0.2771	0.4806
0.5247	1.3096	1400	0.2553	0.4592
0.4559	1.4967	1600	0.2537	0.4396
0.5153	1.6838	1800	0.2479	0.4364
0.4622	1.8709	2000	0.2363	0.4251
0.4533	2.0580	2200	0.2280	0.4091
0.4529	2.2451	2400	0.2182	0.4066
0.4453	2.4322	2600	0.2191	0.3902
0.4339	2.6193	2800	0.2135	0.3815
0.4179	2.8064	3000	0.2151	0.3906
0.3983	2.9935	3200	0.2059	0.3725
0.3868	3.1805	3400	0.2058	0.3614
0.3804	3.3676	3600	0.1959	0.3558
0.3934	3.5547	3800	0.2015	0.3555
0.3693	3.7418	4000	0.1995	0.3587
0.3678	3.9289	4200	0.2017	0.3876
0.4097	4.1160	4400	0.1893	0.3355
0.34	4.3031	4600	0.1877	0.3291
0.3553	4.4902	4800	0.1857	0.3222
0.3133	4.6773	5000	0.1839	0.3224
0.3077	4.8644	5200	0.1829	0.3182

MatricariaV/kbd-asr-colab2

kbd-asr-colab2