wav2vec2-xls-r-1b-dv

This model is a fine-tuned version of facebook/wav2vec2-xls-r-1b on the common_voice dataset. It achieves the following results on the evaluation set:

• Loss: 0.1702
• Wer: 0.2123

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: 4.5e-05
• train_batch_size: 8
• eval_batch_size: 8
• seed: 42
• gradient_accumulation_steps: 4
• 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: 500
• num_epochs: 30
• mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Wer
3.8412	0.66	400	0.7160	0.7913
0.6832	1.33	800	0.3401	0.5268
0.4624	1.99	1200	0.2671	0.4683
0.3832	2.65	1600	0.2395	0.4410
0.3443	3.32	2000	0.2410	0.4296
0.324	3.98	2400	0.2302	0.4143
0.2934	4.64	2800	0.2402	0.4136
0.2773	5.31	3200	0.2134	0.4088
0.2638	5.97	3600	0.2072	0.4037
0.2479	6.63	4000	0.2036	0.3876
0.2424	7.3	4400	0.2037	0.3767
0.2249	7.96	4800	0.1959	0.3802
0.2169	8.62	5200	0.1943	0.3813
0.2109	9.29	5600	0.1944	0.3691
0.1991	9.95	6000	0.1870	0.3589
0.1917	10.61	6400	0.1834	0.3485
0.1862	11.28	6800	0.1857	0.3486
0.1744	11.94	7200	0.1812	0.3330
0.171	12.6	7600	0.1797	0.3436
0.1599	13.27	8000	0.1839	0.3319
0.1597	13.93	8400	0.1737	0.3385
0.1494	14.59	8800	0.1807	0.3239
0.1444	15.26	9200	0.1750	0.3155
0.1382	15.92	9600	0.1705	0.3084
0.1299	16.58	10000	0.1777	0.2999
0.1306	17.25	10400	0.1765	0.3056
0.1239	17.91	10800	0.1676	0.2864
0.1149	18.57	11200	0.1774	0.2861
0.1134	19.24	11600	0.1654	0.2699
0.1101	19.9	12000	0.1621	0.2651
0.1038	20.56	12400	0.1686	0.2610
0.1038	21.23	12800	0.1722	0.2559
0.0988	21.89	13200	0.1708	0.2486
0.0949	22.55	13600	0.1696	0.2453
0.0913	23.22	14000	0.1677	0.2424
0.0879	23.88	14400	0.1640	0.2359
0.0888	24.54	14800	0.1697	0.2347
0.0826	25.21	15200	0.1709	0.2314
0.0819	25.87	15600	0.1679	0.2256
0.0793	26.53	16000	0.1701	0.2214
0.0773	27.2	16400	0.1682	0.2176
0.0783	27.86	16800	0.1685	0.2165
0.074	28.52	17200	0.1688	0.2155
0.0753	29.19	17600	0.1695	0.2110
0.0699	29.85	18000	0.1702	0.2123

Framework versions

• Transformers 4.17.0.dev0
• Pytorch 1.10.2+cu102
• Datasets 1.18.3
• Tokenizers 0.11.0