Baybars/wav2vec2-xls-r-1b-turkish

This model is a fine-tuned version of ./checkpoint-10500 on the COMMON_VOICE - TR dataset. It achieves the following results on the evaluation set:

• Loss: 0.7540
• Wer: 0.4647
• Cer: 0.1318

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: 0.0002
• train_batch_size: 32
• eval_batch_size: 8
• seed: 42
• optimizer: Adam with betas=(0.999,0.9999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 120.0
• mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Cer	Validation Loss	Wer
1.0779	4.59	500	0.2354	0.8260	0.7395
0.7573	9.17	1000	0.2100	0.7544	0.6960
0.8225	13.76	1500	0.2021	0.6867	0.6672
0.621	18.35	2000	0.1874	0.6824	0.6209
0.6362	22.94	2500	0.1904	0.6712	0.6286
0.624	27.52	3000	0.1820	0.6940	0.6116
0.4781	32.11	3500	0.1735	0.6966	0.5989
0.5685	36.7	4000	0.1769	0.6742	0.5971
0.4384	41.28	4500	0.1767	0.6904	0.5999
0.5509	45.87	5000	0.1692	0.6734	0.5641
0.3665	50.46	5500	0.1680	0.7018	0.5662
0.3914	55.05	6000	0.1631	0.7121	0.5552
0.2467	59.63	6500	0.1563	0.6657	0.5374
0.2576	64.22	7000	0.1554	0.6920	0.5316
0.2711	68.81	7500	0.1495	0.6900	0.5176
0.2626	73.39	8000	0.1454	0.6843	0.5043
0.1377	77.98	8500	0.1470	0.7383	0.5101
0.2005	82.57	9000	0.1430	0.7228	0.5045
0.1355	87.16	9500	0.1375	0.7231	0.4869
0.0431	91.74	10000	0.1350	0.7397	0.4749
0.0586	96.33	10500	0.1339	0.7360	0.4754
0.0896	100.92	11000	0.7187	0.4885	0.1398
0.183	105.5	11500	0.7310	0.4838	0.1392
0.0963	110.09	12000	0.7643	0.4759	0.1362
0.0437	114.68	12500	0.7525	0.4641	0.1328
0.1122	119.27	13000	0.7535	0.4651	0.1317

Framework versions

• Transformers 4.17.0.dev0
• Pytorch 1.10.2+cu102
• Datasets 1.18.2.dev0
• Tokenizers 0.11.0