--- language: - uk license: apache-2.0 tags: - automatic-speech-recognition - common_voice - generated_from_trainer datasets: - common_voice model-index: - name: wav2vec2-xls-r-300m-uk results: [] --- # wav2vec2-xls-r-300m-uk This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the Common Voice 7.0 dataset. It achieves the following results on the evaluation set: - Loss: 0.4754 - Wer: 0.3159 - Cer: 0.0739 ## 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: 0.0005 - train_batch_size: 8 - eval_batch_size: 4 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 180.0 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Cer | Validation Loss | Wer | |:-------------:|:------:|:------:|:------:|:---------------:|:------:| | 3.0162 | 0.12 | 500 | 1.0 | 3.1486 | 1.0 | | 1.6532 | 0.24 | 1000 | 0.4583 | 1.3737 | 0.9951 | | 1.3941 | 0.37 | 1500 | 0.3709 | 1.1033 | 0.9866 | | 1.3275 | 0.49 | 2000 | 0.3487 | 1.0937 | 0.9540 | | 1.2648 | 0.61 | 2500 | 0.3137 | 0.9403 | 0.9450 | | 1.3085 | 0.73 | 3000 | 0.3090 | 0.9275 | 0.9288 | | 1.1934 | 0.85 | 3500 | 0.2816 | 0.8737 | 0.8882 | | 1.1909 | 0.98 | 4000 | 0.2780 | 0.8657 | 0.8698 | | 1.0647 | 1.1 | 4500 | 0.2660 | 0.8246 | 0.8817 | | 1.1362 | 1.22 | 5000 | 0.2711 | 0.8032 | 0.9086 | | 1.0994 | 1.34 | 5500 | 0.2462 | 0.7719 | 0.8306 | | 1.1 | 1.46 | 6000 | 0.2561 | 0.7853 | 0.8401 | | 1.0629 | 1.59 | 6500 | 0.2459 | 0.7809 | 0.8245 | | 1.1032 | 1.71 | 7000 | 0.2427 | 0.7638 | 0.8227 | | 1.0171 | 1.83 | 7500 | 0.2332 | 0.7411 | 0.8087 | | 1.0591 | 1.95 | 8000 | 0.2362 | 0.7332 | 0.8274 | | 0.9725 | 2.07 | 8500 | 0.2217 | 0.7190 | 0.7847 | | 1.03 | 2.2 | 9000 | 0.2356 | 0.7176 | 0.8255 | | 0.9939 | 2.32 | 9500 | 0.2471 | 0.7189 | 0.8653 | | 0.9564 | 2.44 | 10000 | 0.2270 | 0.7050 | 0.7984 | | 0.966 | 2.56 | 10500 | 0.2200 | 0.6984 | 0.7738 | | 0.9858 | 2.68 | 11000 | 0.2255 | 0.6885 | 0.8050 | | 0.9484 | 2.81 | 11500 | 0.2183 | 0.6879 | 0.7646 | | 0.9244 | 2.93 | 12000 | 0.2166 | 0.6590 | 0.7744 | | 0.9224 | 3.05 | 12500 | 0.2035 | 0.6523 | 0.7477 | | 0.9148 | 3.17 | 13000 | 0.2054 | 0.6522 | 0.7507 | | 0.9227 | 3.29 | 13500 | 0.2037 | 0.6420 | 0.7541 | | 0.8935 | 3.42 | 14000 | 0.2014 | 0.6442 | 0.7416 | | 0.9257 | 3.54 | 14500 | 0.1986 | 0.6285 | 0.7263 | | 0.9194 | 3.66 | 15000 | 0.1938 | 0.6117 | 0.72 | | 0.9158 | 3.78 | 15500 | 0.1942 | 0.6197 | 0.7234 | | 0.9079 | 3.9 | 16000 | 0.1939 | 0.6110 | 0.7187 | | 0.8748 | 4.03 | 16500 | 0.1924 | 0.6182 | 0.7096 | | 0.8646 | 4.15 | 17000 | 0.1894 | 0.6105 | 0.7057 | | 0.8455 | 4.27 | 17500 | 0.1912 | 0.6236 | 0.7036 | | 0.8922 | 4.39 | 18000 | 0.1921 | 0.5946 | 0.7341 | | 0.892 | 4.51 | 18500 | 0.1869 | 0.5912 | 0.7142 | | 0.8652 | 4.64 | 19000 | 0.1871 | 0.6005 | 0.6966 | | 0.899 | 4.76 | 19500 | 0.1828 | 0.5773 | 0.6981 | | 0.8552 | 4.88 | 20000 | 0.1805 | 0.5840 | 0.6875 | | 0.8581 | 5.0 | 20500 | 0.1900 | 0.5941 | 0.7327 | | 0.8571 | 5.12 | 21000 | 0.1846 | 0.5919 | 0.7049 | | 0.7979 | 5.25 | 21500 | 0.1748 | 0.5704 | 0.6698 | | 0.8348 | 5.37 | 22000 | 0.1789 | 0.5869 | 0.6766 | | 0.7843 | 5.49 | 22500 | 0.1750 | 0.5732 | 0.6732 | | 0.855 | 5.61 | 23000 | 0.1687 | 0.5448 | 0.6520 | | 0.7774 | 5.73 | 23500 | 0.1759 | 0.5685 | 0.6818 | | 0.8622 | 5.86 | 24000 | 0.1742 | 0.5598 | 0.6687 | | 0.7968 | 5.98 | 24500 | 0.1699 | 0.5589 | 0.6577 | | 0.8253 | 6.1 | 25000 | 0.1689 | 0.5601 | 0.6617 | | 0.7947 | 6.22 | 25500 | 0.1678 | 0.5527 | 0.6472 | | 0.8273 | 6.34 | 26000 | 0.1723 | 0.5426 | 0.6673 | | 0.8085 | 6.47 | 26500 | 0.1682 | 0.5464 | 0.6476 | | 0.8164 | 6.59 | 27000 | 0.1653 | 0.5460 | 0.6329 | | 0.755 | 6.71 | 27500 | 0.1694 | 0.5420 | 0.6614 | | 0.822 | 6.83 | 28000 | 0.1699 | 0.5540 | 0.6493 | | 0.7957 | 6.95 | 28500 | 0.1630 | 0.5358 | 0.6373 | | 0.7739 | 7.08 | 29000 | 0.1727 | 0.5662 | 0.6696 | | 0.7833 | 7.2 | 29500 | 0.1594 | 0.5323 | 0.6227 | | 0.7737 | 7.32 | 30000 | 0.1613 | 0.5349 | 0.6303 | | 0.7697 | 7.44 | 30500 | 0.1623 | 0.5315 | 0.6386 | | 0.7647 | 7.56 | 31000 | 0.1608 | 0.5346 | 0.6219 | | 0.7123 | 7.69 | 31500 | 0.1561 | 0.5195 | 0.6110 | | 0.7412 | 7.81 | 32000 | 0.1613 | 0.5385 | 0.6256 | | 0.7702 | 7.93 | 32500 | 0.1614 | 0.5291 | 0.6343 | | 0.7561 | 8.05 | 33000 | 0.1553 | 0.5044 | 0.6138 | | 0.6707 | 8.78 | 36000 | 0.1484 | 0.4949 | 0.5881 | | 0.719 | 9.52 | 39000 | 0.1508 | 0.5014 | 0.5959 | | 0.6563 | 10.25 | 42000 | 0.1442 | 0.4852 | 0.5691 | | 0.7166 | 10.98 | 45000 | 0.1437 | 0.4731 | 0.5718 | | 0.6627 | 11.71 | 48000 | 0.1421 | 0.4787 | 0.5595 | | 0.6642 | 12.45 | 51000 | 0.1353 | 0.4787 | 0.5417 | | 0.615 | 13.18 | 54000 | 0.1324 | 0.4704 | 0.5297 | | 0.6308 | 13.91 | 57000 | 0.1298 | 0.4570 | 0.5181 | | 0.6169 | 14.64 | 60000 | 0.1291 | 0.4514 | 0.5106 | | 0.5731 | 15.37 | 63000 | 0.1259 | 0.4462 | 0.5028 | | 0.5328 | 16.11 | 66000 | 0.1246 | 0.4535 | 0.5023 | | 0.5743 | 16.84 | 69000 | 0.1255 | 0.4555 | 0.5069 | | 0.5363 | 17.57 | 72000 | 0.1214 | 0.4389 | 0.4915 | | 0.5078 | 18.3 | 75000 | 0.1222 | 0.4525 | 0.4915 | | 0.5075 | 19.03 | 78000 | 0.1208 | 0.4532 | 0.4871 | | 0.5461 | 19.77 | 81000 | 0.1196 | 0.4401 | 0.4813 | | 0.5044 | 20.5 | 84000 | 0.1144 | 0.4268 | 0.4654 | | 0.4332 | 21.23 | 87000 | 0.1138 | 0.4383 | 0.4626 | | 0.4671 | 21.96 | 90000 | 0.1118 | 0.4198 | 0.4547 | | 0.4451 | 22.69 | 93000 | 0.1119 | 0.4426 | 0.4509 | | 0.4319 | 23.43 | 96000 | 0.1096 | 0.4272 | 0.4472 | | 0.3624 | 24.16 | 99000 | 0.1078 | 0.4347 | 0.4437 | | 0.4512 | 24.89 | 102000 | 0.1102 | 0.4271 | 0.4471 | | 0.4049 | 25.62 | 105000 | 0.1071 | 0.4207 | 0.4349 | | 0.4134 | 26.35 | 108000 | 0.1061 | 0.4302 | 0.4351 | | 0.4083 | 27.09 | 111000 | 0.1062 | 0.4583 | 0.4320 | | 0.4618 | 27.82 | 114000 | 0.1046 | 0.4229 | 0.4281 | | 0.4538 | 28.55 | 117000 | 0.1022 | 0.4060 | 0.42 | | 0.4378 | 29.28 | 120000 | 0.1030 | 0.4239 | 0.4161 | | 0.4062 | 30.01 | 123000 | 0.1012 | 0.4130 | 0.4171 | | 0.3903 | 30.75 | 126000 | 0.1006 | 0.4134 | 0.4124 | | 0.369 | 31.48 | 129000 | 0.0976 | 0.4163 | 0.4007 | | 0.3896 | 32.21 | 132000 | 0.0986 | 0.3985 | 0.4015 | | 0.3912 | 32.94 | 135000 | 0.0964 | 0.4103 | 0.3948 | | 0.3995 | 33.67 | 138000 | 0.0975 | 0.3962 | 0.4024 | | 0.4042 | 34.41 | 141000 | 0.0940 | 0.4196 | 0.3947 | | 0.4055 | 35.14 | 144000 | 0.0949 | 0.3956 | 0.3882 | | 0.3831 | 35.87 | 147000 | 0.0933 | 0.3962 | 0.3842 | | 0.408 | 36.6 | 150000 | 0.0914 | 0.4019 | 0.3781 | | 0.3632 | 37.34 | 153000 | 0.0917 | 0.4083 | 0.3814 | | 0.381 | 38.07 | 156000 | 0.0914 | 0.4063 | 0.3738 | | 0.3891 | 38.8 | 159000 | 0.0900 | 0.4060 | 0.3734 | | 0.3668 | 39.53 | 162000 | 0.0893 | 0.4087 | 0.3701 | | 0.3243 | 133.39 | 165000 | 0.3808 | 0.3460 | 0.0820 | | 0.2861 | 135.81 | 168000 | 0.3986 | 0.3321 | 0.0788 | | 0.2684 | 138.24 | 171000 | 0.4015 | 0.3299 | 0.0774 | | 0.3027 | 140.66 | 174000 | 0.4023 | 0.3272 | 0.0771 | | 0.2742 | 143.09 | 177000 | 0.4133 | 0.3273 | 0.0770 | | 0.2339 | 145.51 | 180000 | 0.4287 | 0.3268 | 0.0771 | | 0.2547 | 147.94 | 183000 | 0.4396 | 0.3254 | 0.0768 | | 0.2072 | 150.36 | 186000 | 0.4586 | 0.3289 | 0.0774 | | 0.2444 | 152.79 | 189000 | 0.4524 | 0.3239 | 0.0762 | | 0.2272 | 155.21 | 192000 | 0.4620 | 0.3222 | 0.0759 | | 0.2102 | 157.64 | 195000 | 0.4533 | 0.3212 | 0.0754 | | 0.2231 | 160.06 | 198000 | 0.4563 | 0.3183 | 0.0745 | | 0.2096 | 162.49 | 201000 | 0.4669 | 0.3183 | 0.0747 | | 0.2173 | 164.92 | 204000 | 0.4704 | 0.3180 | 0.0746 | | 0.1797 | 167.34 | 207000 | 0.4653 | 0.3169 | 0.0739 | | 0.1841 | 169.77 | 210000 | 0.4726 | 0.3164 | 0.0737 | | 0.1774 | 172.19 | 213000 | 0.4742 | 0.3162 | 0.0738 | | 0.1819 | 174.62 | 216000 | 0.4720 | 0.3149 | 0.0737 | | 0.1746 | 177.04 | 219000 | 0.4736 | 0.3153 | 0.0738 | | 0.2101 | 179.47 | 222000 | 0.4756 | 0.3161 | 0.0738 | ### Framework versions - Transformers 4.14.1 - Pytorch 1.10.0 - Datasets 1.16.1 - Tokenizers 0.10.3