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---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: wav2vec2-large-xlsr-coraa-exp-9
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-large-xlsr-coraa-exp-9
This model is a fine-tuned version of [Edresson/wav2vec2-large-xlsr-coraa-portuguese](https://huggingface.co/Edresson/wav2vec2-large-xlsr-coraa-portuguese) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5553
- Wer: 0.3466
- Cer: 0.1788
## 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: 3e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 150
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer | Cer |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|
| 37.5508 | 1.0 | 14 | 23.1376 | 1.0 | 0.9619 |
| 37.5508 | 2.0 | 28 | 6.5036 | 1.0 | 0.9619 |
| 37.5508 | 3.0 | 42 | 4.3919 | 1.0 | 0.9619 |
| 37.5508 | 4.0 | 56 | 3.9441 | 1.0 | 0.9619 |
| 37.5508 | 5.0 | 70 | 3.7306 | 1.0 | 0.9619 |
| 37.5508 | 6.0 | 84 | 3.5762 | 1.0 | 0.9619 |
| 37.5508 | 7.0 | 98 | 3.4129 | 1.0 | 0.9619 |
| 8.6902 | 8.0 | 112 | 3.2859 | 1.0 | 0.9619 |
| 8.6902 | 9.0 | 126 | 3.2192 | 1.0 | 0.9619 |
| 8.6902 | 10.0 | 140 | 3.1479 | 1.0 | 0.9619 |
| 8.6902 | 11.0 | 154 | 3.1063 | 1.0 | 0.9619 |
| 8.6902 | 12.0 | 168 | 3.0897 | 1.0 | 0.9619 |
| 8.6902 | 13.0 | 182 | 3.0849 | 1.0 | 0.9619 |
| 8.6902 | 14.0 | 196 | 3.0485 | 1.0 | 0.9619 |
| 3.059 | 15.0 | 210 | 3.0496 | 1.0 | 0.9619 |
| 3.059 | 16.0 | 224 | 3.0510 | 1.0 | 0.9619 |
| 3.059 | 17.0 | 238 | 3.0428 | 1.0 | 0.9619 |
| 3.059 | 18.0 | 252 | 3.0331 | 1.0 | 0.9619 |
| 3.059 | 19.0 | 266 | 3.0353 | 1.0 | 0.9619 |
| 3.059 | 20.0 | 280 | 3.0217 | 1.0 | 0.9619 |
| 3.059 | 21.0 | 294 | 3.0107 | 1.0 | 0.9619 |
| 2.9492 | 22.0 | 308 | 3.0068 | 1.0 | 0.9619 |
| 2.9492 | 23.0 | 322 | 2.9950 | 1.0 | 0.9619 |
| 2.9492 | 24.0 | 336 | 2.9896 | 1.0 | 0.9619 |
| 2.9492 | 25.0 | 350 | 2.9687 | 1.0 | 0.9619 |
| 2.9492 | 26.0 | 364 | 2.9474 | 1.0 | 0.9619 |
| 2.9492 | 27.0 | 378 | 2.9414 | 1.0 | 0.9619 |
| 2.9492 | 28.0 | 392 | 2.8425 | 1.0 | 0.9619 |
| 2.8892 | 29.0 | 406 | 2.7813 | 1.0 | 0.9619 |
| 2.8892 | 30.0 | 420 | 2.7270 | 1.0 | 0.9619 |
| 2.8892 | 31.0 | 434 | 2.6645 | 1.0 | 0.9606 |
| 2.8892 | 32.0 | 448 | 2.5593 | 1.0 | 0.9139 |
| 2.8892 | 33.0 | 462 | 2.3230 | 1.0 | 0.7003 |
| 2.8892 | 34.0 | 476 | 1.9706 | 1.0 | 0.5358 |
| 2.8892 | 35.0 | 490 | 1.7085 | 0.9998 | 0.4548 |
| 2.3937 | 36.0 | 504 | 1.4494 | 1.0 | 0.4064 |
| 2.3937 | 37.0 | 518 | 1.2865 | 1.0 | 0.3847 |
| 2.3937 | 38.0 | 532 | 1.1509 | 0.9947 | 0.3659 |
| 2.3937 | 39.0 | 546 | 1.0467 | 0.9031 | 0.3183 |
| 2.3937 | 40.0 | 560 | 0.9832 | 0.5961 | 0.2404 |
| 2.3937 | 41.0 | 574 | 0.8921 | 0.5049 | 0.2223 |
| 2.3937 | 42.0 | 588 | 0.8306 | 0.4687 | 0.2123 |
| 1.0877 | 43.0 | 602 | 0.8017 | 0.4563 | 0.2088 |
| 1.0877 | 44.0 | 616 | 0.7716 | 0.4405 | 0.2046 |
| 1.0877 | 45.0 | 630 | 0.7694 | 0.4407 | 0.2054 |
| 1.0877 | 46.0 | 644 | 0.7451 | 0.4315 | 0.2037 |
| 1.0877 | 47.0 | 658 | 0.7112 | 0.4250 | 0.1996 |
| 1.0877 | 48.0 | 672 | 0.7008 | 0.4116 | 0.1958 |
| 1.0877 | 49.0 | 686 | 0.7140 | 0.4057 | 0.1980 |
| 0.6292 | 50.0 | 700 | 0.7208 | 0.4114 | 0.1988 |
| 0.6292 | 51.0 | 714 | 0.6675 | 0.4033 | 0.1937 |
| 0.6292 | 52.0 | 728 | 0.6650 | 0.4015 | 0.1938 |
| 0.6292 | 53.0 | 742 | 0.6550 | 0.4013 | 0.1938 |
| 0.6292 | 54.0 | 756 | 0.6477 | 0.3990 | 0.1932 |
| 0.6292 | 55.0 | 770 | 0.6362 | 0.3960 | 0.1932 |
| 0.6292 | 56.0 | 784 | 0.6323 | 0.3919 | 0.1930 |
| 0.6292 | 57.0 | 798 | 0.6264 | 0.3870 | 0.1921 |
| 0.4739 | 58.0 | 812 | 0.6290 | 0.3872 | 0.1921 |
| 0.4739 | 59.0 | 826 | 0.6207 | 0.3864 | 0.1925 |
| 0.4739 | 60.0 | 840 | 0.6178 | 0.3858 | 0.1918 |
| 0.4739 | 61.0 | 854 | 0.6217 | 0.3860 | 0.1918 |
| 0.4739 | 62.0 | 868 | 0.6078 | 0.3799 | 0.1900 |
| 0.4739 | 63.0 | 882 | 0.6072 | 0.3781 | 0.1889 |
| 0.4739 | 64.0 | 896 | 0.6068 | 0.3761 | 0.1883 |
| 0.3855 | 65.0 | 910 | 0.5945 | 0.3748 | 0.1870 |
| 0.3855 | 66.0 | 924 | 0.6194 | 0.3799 | 0.1900 |
| 0.3855 | 67.0 | 938 | 0.6044 | 0.3793 | 0.1885 |
| 0.3855 | 68.0 | 952 | 0.5946 | 0.3751 | 0.1880 |
| 0.3855 | 69.0 | 966 | 0.6116 | 0.3714 | 0.1880 |
| 0.3855 | 70.0 | 980 | 0.5877 | 0.3679 | 0.1861 |
| 0.3855 | 71.0 | 994 | 0.5861 | 0.3679 | 0.1863 |
| 0.3302 | 72.0 | 1008 | 0.5805 | 0.3685 | 0.1856 |
| 0.3302 | 73.0 | 1022 | 0.5862 | 0.3714 | 0.1862 |
| 0.3302 | 74.0 | 1036 | 0.5921 | 0.3720 | 0.1866 |
| 0.3302 | 75.0 | 1050 | 0.5692 | 0.3683 | 0.1854 |
| 0.3302 | 76.0 | 1064 | 0.5922 | 0.3702 | 0.1878 |
| 0.3302 | 77.0 | 1078 | 0.6105 | 0.3710 | 0.1883 |
| 0.3302 | 78.0 | 1092 | 0.5873 | 0.3683 | 0.1856 |
| 0.3046 | 79.0 | 1106 | 0.5826 | 0.3681 | 0.1859 |
| 0.3046 | 80.0 | 1120 | 0.5792 | 0.3633 | 0.1845 |
| 0.3046 | 81.0 | 1134 | 0.5738 | 0.3610 | 0.1835 |
| 0.3046 | 82.0 | 1148 | 0.5794 | 0.3625 | 0.1843 |
| 0.3046 | 83.0 | 1162 | 0.5766 | 0.3564 | 0.1829 |
| 0.3046 | 84.0 | 1176 | 0.5745 | 0.3578 | 0.1830 |
| 0.3046 | 85.0 | 1190 | 0.5615 | 0.3555 | 0.1814 |
| 0.2927 | 86.0 | 1204 | 0.5854 | 0.3614 | 0.1828 |
| 0.2927 | 87.0 | 1218 | 0.5818 | 0.3625 | 0.1835 |
| 0.2927 | 88.0 | 1232 | 0.5613 | 0.3578 | 0.1815 |
| 0.2927 | 89.0 | 1246 | 0.5661 | 0.3549 | 0.1813 |
| 0.2927 | 90.0 | 1260 | 0.5795 | 0.3604 | 0.1820 |
| 0.2927 | 91.0 | 1274 | 0.5604 | 0.3521 | 0.1802 |
| 0.2927 | 92.0 | 1288 | 0.5738 | 0.3590 | 0.1822 |
| 0.2576 | 93.0 | 1302 | 0.5658 | 0.3574 | 0.1814 |
| 0.2576 | 94.0 | 1316 | 0.5620 | 0.3511 | 0.1808 |
| 0.2576 | 95.0 | 1330 | 0.5709 | 0.3541 | 0.1810 |
| 0.2576 | 96.0 | 1344 | 0.5675 | 0.3503 | 0.1799 |
| 0.2576 | 97.0 | 1358 | 0.5788 | 0.3549 | 0.1815 |
| 0.2576 | 98.0 | 1372 | 0.5730 | 0.3525 | 0.1810 |
| 0.2576 | 99.0 | 1386 | 0.5694 | 0.3511 | 0.1803 |
| 0.2273 | 100.0 | 1400 | 0.5748 | 0.3527 | 0.1807 |
| 0.2273 | 101.0 | 1414 | 0.5688 | 0.3513 | 0.1797 |
| 0.2273 | 102.0 | 1428 | 0.5767 | 0.3553 | 0.1805 |
| 0.2273 | 103.0 | 1442 | 0.5758 | 0.3529 | 0.1812 |
| 0.2273 | 104.0 | 1456 | 0.5641 | 0.3507 | 0.1793 |
| 0.2273 | 105.0 | 1470 | 0.5628 | 0.3495 | 0.1789 |
| 0.2273 | 106.0 | 1484 | 0.5729 | 0.3466 | 0.1789 |
| 0.2273 | 107.0 | 1498 | 0.5722 | 0.3497 | 0.1798 |
| 0.2181 | 108.0 | 1512 | 0.5553 | 0.3466 | 0.1788 |
| 0.2181 | 109.0 | 1526 | 0.5582 | 0.3484 | 0.1792 |
| 0.2181 | 110.0 | 1540 | 0.5702 | 0.3521 | 0.1802 |
| 0.2181 | 111.0 | 1554 | 0.5691 | 0.3505 | 0.1798 |
| 0.2181 | 112.0 | 1568 | 0.5604 | 0.3470 | 0.1786 |
| 0.2181 | 113.0 | 1582 | 0.5661 | 0.3482 | 0.1795 |
| 0.2181 | 114.0 | 1596 | 0.5683 | 0.3511 | 0.1796 |
| 0.2171 | 115.0 | 1610 | 0.5738 | 0.3509 | 0.1798 |
| 0.2171 | 116.0 | 1624 | 0.5730 | 0.3458 | 0.1793 |
| 0.2171 | 117.0 | 1638 | 0.5705 | 0.3456 | 0.1789 |
| 0.2171 | 118.0 | 1652 | 0.5814 | 0.3466 | 0.1796 |
| 0.2171 | 119.0 | 1666 | 0.5715 | 0.3442 | 0.1791 |
| 0.2171 | 120.0 | 1680 | 0.5720 | 0.3470 | 0.1798 |
| 0.2171 | 121.0 | 1694 | 0.5769 | 0.3470 | 0.1797 |
| 0.1986 | 122.0 | 1708 | 0.5711 | 0.3464 | 0.1792 |
| 0.1986 | 123.0 | 1722 | 0.5728 | 0.3442 | 0.1790 |
| 0.1986 | 124.0 | 1736 | 0.5668 | 0.3450 | 0.1783 |
| 0.1986 | 125.0 | 1750 | 0.5855 | 0.3484 | 0.1797 |
| 0.1986 | 126.0 | 1764 | 0.5667 | 0.3427 | 0.1783 |
| 0.1986 | 127.0 | 1778 | 0.5711 | 0.3460 | 0.1789 |
| 0.1986 | 128.0 | 1792 | 0.5682 | 0.3444 | 0.1781 |
### Framework versions
- Transformers 4.28.0
- Pytorch 2.4.1+cu121
- Datasets 3.2.0
- Tokenizers 0.13.3
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