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This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - SV-SE dataset. It achieves the following results on the evaluation set:

  • Loss: 2.3347
  • Wer: 1.0286

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: 7.5e-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: 20
  • num_epochs: 5.0
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Wer
10.7838 0.01 5 14.5035 1.0
13.0582 0.03 10 13.6658 1.0
7.3034 0.04 15 9.7898 1.0
6.1847 0.05 20 6.9148 1.0
5.3371 0.07 25 5.3661 1.0
4.4274 0.08 30 4.6945 1.0
4.0918 0.1 35 4.3172 1.0
4.1734 0.11 40 4.0759 1.0
3.7332 0.12 45 3.9039 1.0
3.6871 0.14 50 3.7777 1.0
3.4428 0.15 55 3.6718 1.0
3.5514 0.16 60 3.5947 1.0
3.4307 0.18 65 3.5144 1.0
3.4102 0.19 70 3.4432 1.0
3.4964 0.21 75 3.3890 1.0
3.3936 0.22 80 3.3467 1.0
3.3051 0.23 85 3.3102 1.0
3.278 0.25 90 3.2801 1.0
3.2223 0.26 95 3.2440 1.0
3.1888 0.27 100 3.2900 1.0
3.218 0.29 105 3.2627 1.0
3.1308 0.3 110 3.2152 1.0
3.109 0.31 115 3.1686 1.0
3.1188 0.33 120 3.1734 1.0
3.1132 0.34 125 3.1431 1.0
3.0667 0.36 130 3.1686 1.0
3.1167 0.37 135 3.1885 1.0
3.0592 0.38 140 3.1100 1.0
3.0531 0.4 145 3.1149 1.0
3.1224 0.41 150 3.1205 1.0
3.0651 0.42 155 3.1101 1.0
3.0077 0.44 160 3.0980 1.0
3.0027 0.45 165 3.1132 1.0
3.0423 0.47 170 3.0886 1.0
3.0462 0.48 175 3.0865 1.0
3.0701 0.49 180 3.0863 1.0
3.0871 0.51 185 3.0825 1.0
3.0585 0.52 190 3.0720 1.0
3.0274 0.53 195 3.0736 1.0
3.0983 0.55 200 3.0658 1.0
3.0538 0.56 205 3.1241 1.0
3.0862 0.57 210 3.0573 1.0
3.0041 0.59 215 3.0608 1.0
3.027 0.6 220 3.0614 1.0
2.9916 0.62 225 3.0527 1.0
3.0157 0.63 230 3.0514 1.0
3.0429 0.64 235 3.0391 1.0
2.999 0.66 240 3.0462 1.0
3.0053 0.67 245 3.0438 1.0
2.9812 0.68 250 3.0447 1.0
3.0062 0.7 255 3.0660 1.0
3.0045 0.71 260 3.0103 1.0
2.9684 0.73 265 3.0106 1.0
2.9885 0.74 270 3.0014 1.0
3.0062 0.75 275 2.9885 1.0
2.9736 0.77 280 3.0330 1.0
2.9766 0.78 285 2.9910 1.0
2.9545 0.79 290 2.9972 1.0
2.9936 0.81 295 2.9872 1.0
3.0832 0.82 300 2.9978 1.0
2.974 0.83 305 2.9978 1.0
2.9846 0.85 310 2.9849 1.0
2.9554 0.86 315 2.9810 1.0
2.9524 0.88 320 2.9731 1.0
2.9426 0.89 325 2.9824 1.0
2.9416 0.9 330 2.9731 1.0
2.9705 0.92 335 2.9830 1.0
2.9502 0.93 340 2.9713 1.0
2.9393 0.94 345 2.9790 1.0
2.9336 0.96 350 2.9684 1.0
2.9542 0.97 355 2.9689 1.0
2.9408 0.98 360 2.9556 1.0
2.9544 1.0 365 2.9563 1.0
2.9187 1.01 370 2.9624 1.0
2.9935 1.03 375 2.9500 1.0
2.9803 1.04 380 2.9558 1.0
2.9867 1.05 385 2.9473 1.0
2.8925 1.07 390 2.9444 1.0
2.9633 1.08 395 2.9490 1.0
2.9191 1.1 400 2.9362 1.0
2.9081 1.11 405 2.9394 1.0
2.9381 1.12 410 2.9846 1.0
2.9271 1.14 415 2.9638 1.0
2.959 1.15 420 2.9835 1.0
2.9486 1.16 425 2.9361 1.0
2.9246 1.18 430 2.9615 1.0
2.923 1.19 435 2.9313 1.0
2.8908 1.21 440 2.9362 1.0
2.8976 1.22 445 2.9224 1.0
2.9278 1.23 450 2.9276 1.0
2.8429 1.25 455 2.9299 1.0
2.867 1.26 460 2.9258 1.0
2.9734 1.27 465 2.9281 1.0000
2.934 1.29 470 2.9229 1.0
2.9521 1.3 475 2.9134 1.0
2.9098 1.31 480 2.9051 0.9993
2.9112 1.33 485 2.9028 0.9999
2.8799 1.34 490 2.9101 0.9986
2.857 1.36 495 2.9005 0.9992
2.8525 1.37 500 2.8937 1.0
2.8682 1.38 505 2.8904 1.0000
2.8899 1.4 510 2.8914 0.9964
2.7475 1.41 515 2.8842 0.9950
2.9263 1.42 520 2.8852 0.9972
2.8603 1.44 525 2.8762 0.9966
2.864 1.45 530 2.8680 0.9978
2.8632 1.47 535 2.8602 0.9964
2.9289 1.48 540 2.8584 0.9952
2.8689 1.49 545 2.8587 0.9956
2.8304 1.51 550 2.8511 0.9993
2.8024 1.52 555 2.8460 1.0
2.7649 1.53 560 2.8460 1.0000
2.8756 1.55 565 2.8348 0.9987
2.8808 1.56 570 2.8539 0.9993
2.9027 1.57 575 2.8282 0.9975
2.8586 1.59 580 2.8288 0.9976
2.8193 1.6 585 2.8101 1.0051
2.811 1.62 590 2.7965 1.0014
2.7332 1.63 595 2.7884 1.0026
2.7717 1.64 600 2.7883 1.0060
2.6901 1.66 605 2.7801 0.9974
2.6905 1.67 610 2.8113 0.9968
2.7442 1.68 615 2.8113 1.0007
2.8431 1.7 620 2.8152 1.0343
2.8028 1.71 625 2.7790 1.0250
2.7151 1.73 630 2.7653 1.0287
2.7405 1.74 635 2.7714 1.1303
2.7566 1.75 640 2.7488 1.0312
2.7337 1.77 645 2.7498 1.0176
2.7486 1.78 650 2.7496 1.0760
2.6918 1.79 655 2.7391 1.0353
2.7142 1.81 660 2.7500 1.0283
2.7057 1.82 665 2.7612 1.0127
2.8348 1.83 670 2.7441 1.0056
2.705 1.85 675 2.7473 1.0519
2.7547 1.86 680 2.7216 1.0218
2.7045 1.88 685 2.7261 1.1414
2.7121 1.89 690 2.7223 1.0287
2.6877 1.9 695 2.7283 1.0274
2.6879 1.92 700 2.7451 1.1322
2.6958 1.93 705 2.7166 1.0364
2.6692 1.94 710 2.7148 1.0074
2.5786 1.96 715 2.7101 1.0504
2.6919 1.97 720 2.6963 1.0454
2.7256 1.98 725 2.7201 1.0349
2.6507 2.0 730 2.7099 1.1339
2.7833 2.01 735 2.7111 1.0124
2.7521 2.03 740 2.7024 1.0275
2.6732 2.04 745 2.7058 1.0647
2.719 2.05 750 2.7200 1.0211
2.701 2.07 755 2.7024 1.0808
2.6444 2.08 760 2.6813 1.0582
2.5592 2.1 765 2.6783 1.1010
2.6444 2.11 770 2.6707 1.0946
2.6944 2.12 775 2.7012 1.1315
2.6733 2.14 780 2.7072 1.1144
2.6998 2.15 785 2.7132 1.0206
2.796 2.16 790 2.7076 1.1262
2.6881 2.18 795 2.6953 1.0841
2.7382 2.19 800 2.6605 1.1234
2.5814 2.21 805 2.6814 1.1865
2.6695 2.22 810 2.6531 1.0985
2.6415 2.23 815 2.6590 1.0804
2.646 2.25 820 2.6514 1.0853
2.6028 2.26 825 2.6723 1.1411
2.6429 2.27 830 2.6729 1.0395
2.6736 2.29 835 2.7039 1.0355
2.6959 2.3 840 2.6510 1.0414
2.6426 2.31 845 2.6660 1.1591
2.7152 2.33 850 2.6361 1.0276
2.7148 2.34 855 2.6723 1.2461
2.6336 2.36 860 2.6332 1.0310
2.665 2.37 865 2.6365 1.1312
2.5607 2.38 870 2.6344 1.1301
2.5614 2.4 875 2.6437 1.1513
2.4899 2.41 880 2.6418 1.1532
2.6794 2.42 885 2.6403 1.0272
2.6814 2.44 890 2.6420 1.1323
2.6614 2.45 895 2.6183 1.0525
2.6629 2.47 900 2.6414 1.1569
2.6166 2.48 905 2.6167 1.0265
2.6374 2.49 910 2.6299 1.1720
2.6035 2.51 915 2.6139 1.1565
2.595 2.52 920 2.6126 1.0557
2.6416 2.53 925 2.6190 1.0414
2.6785 2.55 930 2.6352 1.0289
2.6986 2.56 935 2.6268 1.0077
2.6145 2.57 940 2.6166 1.0445
2.6961 2.59 945 2.6142 1.0185
2.6852 2.6 950 2.6072 1.0122
2.5792 2.62 955 2.6078 1.1165
2.6118 2.63 960 2.6177 1.1210
2.5472 2.64 965 2.6126 1.0044
2.577 2.66 970 2.6051 1.0881
2.5602 2.67 975 2.5992 1.0178
2.695 2.68 980 2.6023 1.0248
2.7017 2.7 985 2.6190 1.0041
2.6327 2.71 990 2.6024 1.0142
2.6193 2.73 995 2.5897 1.0148
2.5939 2.74 1000 2.5900 1.0329
2.5477 2.75 1005 2.5971 1.0338
2.6089 2.77 1010 2.5969 1.0064
2.5625 2.78 1015 2.5899 1.0648
2.5745 2.79 1020 2.5861 1.0627
2.5702 2.81 1025 2.5923 1.0526
2.645 2.82 1030 2.6053 1.0199
2.6869 2.83 1035 2.6227 1.0011
2.6678 2.85 1040 2.6094 1.0179
2.6787 2.86 1045 2.5978 1.0028
2.6246 2.88 1050 2.5965 1.0093
2.5676 2.89 1055 2.5927 1.0627
2.6773 2.9 1060 2.5907 1.0817
2.6114 2.92 1065 2.5932 1.1013
2.6227 2.93 1070 2.5840 1.0402
2.594 2.94 1075 2.5997 1.1371
2.751 2.96 1080 2.5909 1.0972
2.6366 2.97 1085 2.6081 1.0598
2.577 2.98 1090 2.5915 1.0410
2.579 3.0 1095 2.5953 1.1433
2.6706 3.01 1100 2.5913 1.0456
2.6161 3.03 1105 2.6079 1.1009
2.6397 3.04 1110 2.5951 1.1771
2.6246 3.05 1115 2.5730 1.0299
2.5637 3.07 1120 2.5622 1.0848
2.5692 3.08 1125 2.5561 1.1472
2.5948 3.1 1130 2.5568 1.0802
2.5372 3.11 1135 2.5638 1.1261
2.4995 3.12 1140 2.5727 1.1395
2.6304 3.14 1145 2.5671 1.0259
2.6395 3.15 1150 2.5778 1.0212
2.6127 3.16 1155 2.5609 1.0457
2.5919 3.18 1160 2.5604 1.0902
2.6111 3.19 1165 2.5463 1.0014
2.5971 3.21 1170 2.5429 1.0022
2.5887 3.22 1175 2.5394 1.0412
2.5644 3.23 1180 2.5342 1.0469
2.4805 3.25 1185 2.6066 1.2668
2.5324 3.26 1190 2.5395 1.0234
2.5491 3.27 1195 2.5431 1.0644
2.6302 3.29 1200 2.5558 1.0680
2.6139 3.3 1205 2.5711 1.0565
2.5607 3.31 1210 2.5635 1.0415
2.6535 3.33 1215 2.5505 1.0613
2.6129 3.34 1220 2.5403 1.0724
2.5157 3.36 1225 2.5294 1.0585
2.551 3.37 1230 2.5242 1.1599
2.5527 3.38 1235 2.5474 1.2327
2.4964 3.4 1240 2.5244 1.0857
2.5781 3.41 1245 2.5299 1.0470
2.6143 3.42 1250 2.5313 1.0019
2.6566 3.44 1255 2.5431 1.0488
2.5373 3.45 1260 2.5281 1.0901
2.6597 3.47 1265 2.5300 1.0610
2.5457 3.48 1270 2.5130 1.0420
2.5632 3.49 1275 2.5306 1.1418
2.5267 3.51 1280 2.5021 1.0293
2.507 3.52 1285 2.5013 1.0196
2.5713 3.53 1290 2.4978 1.0664
2.4783 3.55 1295 2.4958 1.0530
2.5874 3.56 1300 2.4968 1.0059
2.5744 3.57 1305 2.5078 1.0287
2.5701 3.59 1310 2.4971 1.0366
2.5366 3.6 1315 2.4897 1.0191
2.5679 3.62 1320 2.4830 1.0223
2.5239 3.63 1325 2.4833 1.0784
2.5411 3.64 1330 2.4851 1.1522
2.5037 3.66 1335 2.4792 1.0928
2.5907 3.67 1340 2.4750 1.0187
2.5107 3.68 1345 2.4805 1.0873
2.5908 3.7 1350 2.4753 1.0098
2.6274 3.71 1355 2.4765 1.0045
2.5708 3.73 1360 2.4597 1.0456
2.6039 3.74 1365 2.4503 1.0485
2.5305 3.75 1370 2.4439 1.0126
2.4878 3.77 1375 2.4407 1.0162
2.5055 3.78 1380 2.4421 1.0605
2.5249 3.79 1385 2.4499 1.1163
2.5508 3.81 1390 2.4654 1.1472
2.5827 3.82 1395 2.4510 1.0561
2.6148 3.83 1400 2.4496 0.9998
2.5763 3.85 1405 2.4417 1.0067
2.6077 3.86 1410 2.4458 1.0682
2.5388 3.88 1415 2.4352 1.0820
2.5235 3.89 1420 2.4277 1.0784
2.4996 3.9 1425 2.4245 1.0671
2.5601 3.92 1430 2.4202 1.0650
2.5805 3.93 1435 2.4199 1.0530
2.5841 3.94 1440 2.4228 1.0797
2.4877 3.96 1445 2.4284 1.1159
2.5542 3.97 1450 2.4190 1.0575
2.5961 3.98 1455 2.4162 1.0676
2.495 4.0 1460 2.4165 1.0821
2.6157 4.01 1465 2.4119 1.0117
2.5415 4.03 1470 2.4089 1.0110
2.4916 4.04 1475 2.4032 1.0498
2.5445 4.05 1480 2.3997 1.0429
2.4941 4.07 1485 2.4008 1.0141
2.5113 4.08 1490 2.3975 1.0357
2.4707 4.1 1495 2.3938 1.0288
2.4952 4.11 1500 2.3910 1.0300
2.5017 4.12 1505 2.3861 1.0813
2.5566 4.14 1510 2.3919 1.1082
2.5754 4.15 1515 2.3947 1.0074
2.6138 4.16 1520 2.4040 0.9989
2.5024 4.18 1525 2.3949 1.0039
2.5136 4.19 1530 2.3993 1.0496
2.5646 4.21 1535 2.3981 1.0729
2.4556 4.22 1540 2.3952 1.0494
2.5774 4.23 1545 2.3924 1.0345
2.5126 4.25 1550 2.3888 1.0306
2.4596 4.26 1555 2.3960 1.0775
2.521 4.27 1560 2.3978 1.1025
2.6304 4.29 1565 2.3885 1.0433
2.543 4.3 1570 2.3849 1.0072
2.5601 4.31 1575 2.3855 1.0110
2.6304 4.33 1580 2.3878 1.0369
2.4121 4.34 1585 2.3783 1.0366
2.4261 4.36 1590 2.3746 1.0307
2.5038 4.37 1595 2.3789 1.0611
2.5391 4.38 1600 2.3849 1.0738
2.4341 4.4 1605 2.3779 1.0573
2.5306 4.41 1610 2.3751 1.0460
2.5818 4.42 1615 2.3743 1.0251
2.5531 4.44 1620 2.3723 1.0209
2.51 4.45 1625 2.3755 1.0316
2.5788 4.47 1630 2.3725 1.0396
2.5701 4.48 1635 2.3663 1.0292
2.4194 4.49 1640 2.3641 1.0261
2.5439 4.51 1645 2.3629 1.0376
2.4527 4.52 1650 2.3629 1.0563
2.5705 4.53 1655 2.3654 1.0766
2.4552 4.55 1660 2.3708 1.0802
2.5657 4.56 1665 2.3638 1.0248
2.5371 4.57 1670 2.3639 1.0053
2.5365 4.59 1675 2.3626 1.0072
2.5383 4.6 1680 2.3584 1.0170
2.546 4.62 1685 2.3574 1.0469
2.6006 4.63 1690 2.3517 1.0509
2.4894 4.64 1695 2.3489 1.0452
2.4732 4.66 1700 2.3489 1.0586
2.4933 4.67 1705 2.3501 1.0694
2.4784 4.68 1710 2.3472 1.0647
2.5349 4.7 1715 2.3419 1.0299
2.553 4.71 1720 2.3420 1.0115
2.5035 4.73 1725 2.3415 1.0117
2.561 4.74 1730 2.3418 1.0242
2.4773 4.75 1735 2.3420 1.0325
2.4691 4.77 1740 2.3422 1.0394
2.4959 4.78 1745 2.3405 1.0418
2.4928 4.79 1750 2.3394 1.0449
2.5058 4.81 1755 2.3392 1.0489
2.5193 4.82 1760 2.3390 1.0506
2.5369 4.83 1765 2.3392 1.0384
2.4843 4.85 1770 2.3398 1.0236
2.5074 4.86 1775 2.3400 1.0150
2.4941 4.88 1780 2.3386 1.0150
2.4352 4.89 1785 2.3370 1.0172
2.4372 4.9 1790 2.3362 1.0208
2.4855 4.92 1795 2.3358 1.0238
2.4516 4.93 1800 2.3355 1.0276
2.5281 4.94 1805 2.3356 1.0312
2.5519 4.96 1810 2.3352 1.0318
2.4641 4.97 1815 2.3349 1.0294
2.4515 4.98 1820 2.3348 1.0284
2.553 5.0 1825 2.3347 1.0286

Framework versions

  • Transformers 4.17.0.dev0
  • Pytorch 1.10.2+cu102
  • Datasets 1.18.2.dev0
  • Tokenizers 0.11.0
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Dataset used to train masapasa/xls-r-300m-sv-cv8

Evaluation results

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