learning_rate: 0.0001
train_batch_size: 8
eval_batch_size: 8
seed: 42
optimizer: Use 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: 1000
num_epochs: 10
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Wer
11.0853	0.1781	500	3.1694	1.0
3.1605	0.3563	1000	3.1995	1.0
3.0661	0.5344	1500	2.8477	1.0
1.6835	0.7125	2000	0.7489	0.6972
0.7354	0.8906	2500	0.4461	0.5712
0.5237	1.0688	3000	0.3285	0.5233
0.4167	1.2469	3500	0.2644	0.4881
0.3423	1.4250	4000	0.2429	0.4500
0.3162	1.6031	4500	0.2356	0.4322
0.267	1.7813	5000	0.2025	0.4306
0.2795	1.9594	5500	0.1766	0.4198
0.2181	2.1375	6000	0.1932	0.3943
0.2166	2.3156	6500	0.1578	0.3817
0.2236	2.4938	7000	0.2271	0.3955
0.2142	2.6719	7500	0.1487	0.3796
0.2025	2.8500	8000	0.1558	0.3731
0.1914	3.0281	8500	0.1298	0.3728
0.1623	3.2063	9000	0.3313	0.4452
0.188	3.3844	9500	0.1381	0.3709
0.1783	3.5625	10000	0.1177	0.3601
0.1365	3.7406	10500	0.1307	0.3693
0.146	3.9188	11000	0.1203	0.3645
0.1393	4.0969	11500	0.1096	0.3572
0.139	4.2750	12000	0.1065	0.3531
0.1271	4.4532	12500	0.0969	0.3553
0.1522	4.6313	13000	0.1120	0.3679
0.1441	4.8094	13500	0.1129	0.3570
0.1113	4.9875	14000	0.1033	0.3525
0.1115	5.1657	14500	0.0960	0.3489
0.1171	5.3438	15000	0.0851	0.3479
0.1064	5.5219	15500	0.0855	0.3446
0.085	5.7000	16000	0.0913	0.3434
0.0817	5.8782	16500	0.0836	0.3456
0.0973	6.0563	17000	0.0693	0.3388
0.0854	6.2344	17500	0.0769	0.3396
0.0846	6.4125	18000	0.0846	0.3404
0.076	6.5907	18500	0.0895	0.3368
0.08	6.7688	19000	0.0813	0.3396
0.0735	6.9469	19500	0.0790	0.3369
0.0634	7.1250	20000	0.0795	0.3351
0.0714	7.3032	20500	0.0739	0.3361
0.0776	7.4813	21000	0.0801	0.3335
0.0695	7.6594	21500	0.0687	0.3353
0.0556	7.8375	22000	0.0689	0.3345
0.0567	8.0157	22500	0.0634	0.3310
0.0566	8.1938	23000	0.0631	0.3322
0.0453	8.3719	23500	0.0605	0.3279
0.0583	8.5501	24000	0.0599	0.3281
0.0475	8.7282	24500	0.0672	0.3299
0.0518	8.9063	25000	0.0621	0.3273
0.0402	9.0844	25500	0.0654	0.3270
0.0418	9.2626	26000	0.0605	0.3260
0.0451	9.4407	26500	0.0589	0.3279
0.0473	9.6188	27000	0.0600	0.3271
0.0456	9.7969	27500	0.0579	0.3250
0.036	9.9751	28000	0.0564	0.3246

Framework versions

Transformers 4.46.3
Pytorch 2.5.1+cu121
Datasets 3.1.0
Tokenizers 0.20.3

tiffany101/wav2vec2-base-twi-demo

wav2vec2-base-twi-demo

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

tiffany101/
wav2vec2-base-twi-demo