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---
license: apache-2.0
base_model: facebook/wav2vec2-xls-r-300m
tags:
- generated_from_trainer
model-index:
- name: ft_0112_korean
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. -->
# ft_0112_korean
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6163
- Cer: 0.1655
## 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: 0.0001
- train_batch_size: 4
- eval_batch_size: 8
- 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: 4
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Cer |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 66.0473 | 0.03 | 100 | 126.2500 | 1.0 |
| 39.2751 | 0.05 | 200 | 76.4439 | 1.0 |
| 24.2617 | 0.07 | 300 | 36.6274 | 1.0 |
| 10.2253 | 0.1 | 400 | 7.8025 | 1.0 |
| 4.9219 | 0.12 | 500 | 5.8257 | 1.0 |
| 4.7709 | 0.15 | 600 | 5.2597 | 1.0 |
| 4.7545 | 0.17 | 700 | 5.3516 | 1.0 |
| 4.701 | 0.2 | 800 | 5.2238 | 1.0 |
| 4.6753 | 0.23 | 900 | 5.1713 | 1.0 |
| 4.6339 | 0.25 | 1000 | 5.1546 | 1.0 |
| 4.6107 | 0.28 | 1100 | 5.0488 | 1.0 |
| 4.6086 | 0.3 | 1200 | 4.8149 | 1.0 |
| 4.5324 | 0.33 | 1300 | 4.7533 | 1.0 |
| 4.4797 | 0.35 | 1400 | 4.6892 | 1.0 |
| 4.4485 | 0.38 | 1500 | 4.5327 | 1.0 |
| 4.3794 | 0.4 | 1600 | 4.3797 | 0.9999 |
| 4.1549 | 0.42 | 1700 | 4.2075 | 0.9838 |
| 3.9647 | 0.45 | 1800 | 3.8729 | 0.9647 |
| 3.621 | 0.47 | 1900 | 3.3229 | 0.6854 |
| 3.3163 | 0.5 | 2000 | 2.9646 | 0.5646 |
| 3.0668 | 0.53 | 2100 | 2.7178 | 0.5608 |
| 2.8248 | 0.55 | 2200 | 2.4843 | 0.4937 |
| 2.7238 | 0.57 | 2300 | 2.3321 | 0.4736 |
| 2.614 | 0.6 | 2400 | 2.2513 | 0.4650 |
| 2.4994 | 0.62 | 2500 | 2.1655 | 0.4538 |
| 2.4431 | 0.65 | 2600 | 2.0785 | 0.4355 |
| 2.3307 | 0.68 | 2700 | 1.9603 | 0.4169 |
| 2.2495 | 0.7 | 2800 | 1.9026 | 0.4134 |
| 2.1647 | 0.72 | 2900 | 1.8152 | 0.4009 |
| 2.1075 | 0.75 | 3000 | 1.7521 | 0.3849 |
| 2.0577 | 0.78 | 3100 | 1.7004 | 0.3781 |
| 1.9935 | 0.8 | 3200 | 1.6226 | 0.3666 |
| 1.9391 | 0.82 | 3300 | 1.6097 | 0.3604 |
| 1.9295 | 0.85 | 3400 | 1.5416 | 0.3526 |
| 1.8759 | 0.88 | 3500 | 1.5227 | 0.3583 |
| 1.8316 | 0.9 | 3600 | 1.4791 | 0.3484 |
| 1.7531 | 0.93 | 3700 | 1.4472 | 0.3415 |
| 1.7413 | 0.95 | 3800 | 1.4178 | 0.3363 |
| 1.6609 | 0.97 | 3900 | 1.3587 | 0.3256 |
| 1.6986 | 1.0 | 4000 | 1.3396 | 0.3208 |
| 1.6189 | 1.02 | 4100 | 1.3253 | 0.3187 |
| 1.5853 | 1.05 | 4200 | 1.2929 | 0.3109 |
| 1.5153 | 1.07 | 4300 | 1.2691 | 0.3106 |
| 1.5259 | 1.1 | 4400 | 1.2500 | 0.3012 |
| 1.4916 | 1.12 | 4500 | 1.2151 | 0.2977 |
| 1.4113 | 1.15 | 4600 | 1.1796 | 0.2930 |
| 1.452 | 1.18 | 4700 | 1.1857 | 0.2928 |
| 1.3879 | 1.2 | 4800 | 1.1830 | 0.2915 |
| 1.4164 | 1.23 | 4900 | 1.1725 | 0.2920 |
| 1.4692 | 1.25 | 5000 | 1.1171 | 0.2794 |
| 1.346 | 1.27 | 5100 | 1.0858 | 0.2745 |
| 1.3964 | 1.3 | 5200 | 1.0644 | 0.2712 |
| 1.3359 | 1.32 | 5300 | 1.0585 | 0.2694 |
| 1.2769 | 1.35 | 5400 | 1.0290 | 0.2614 |
| 1.2741 | 1.38 | 5500 | 1.0356 | 0.2604 |
| 1.2257 | 1.4 | 5600 | 1.0167 | 0.2607 |
| 1.2416 | 1.43 | 5700 | 1.0074 | 0.2558 |
| 1.2376 | 1.45 | 5800 | 0.9889 | 0.2524 |
| 1.2048 | 1.48 | 5900 | 0.9649 | 0.2464 |
| 1.1335 | 1.5 | 6000 | 0.9580 | 0.2488 |
| 1.1946 | 1.52 | 6100 | 0.9503 | 0.2471 |
| 1.1926 | 1.55 | 6200 | 0.9467 | 0.2494 |
| 1.1451 | 1.57 | 6300 | 0.9202 | 0.2408 |
| 1.1426 | 1.6 | 6400 | 0.9018 | 0.2359 |
| 1.1569 | 1.62 | 6500 | 0.9216 | 0.2362 |
| 1.1093 | 1.65 | 6600 | 0.9433 | 0.2414 |
| 1.1258 | 1.68 | 6700 | 0.8986 | 0.2291 |
| 1.1024 | 1.7 | 6800 | 0.8838 | 0.2305 |
| 1.0567 | 1.73 | 6900 | 0.8916 | 0.2298 |
| 1.0928 | 1.75 | 7000 | 0.8855 | 0.2294 |
| 1.0526 | 1.77 | 7100 | 0.8592 | 0.2237 |
| 1.0236 | 1.8 | 7200 | 0.8433 | 0.2209 |
| 1.0454 | 1.82 | 7300 | 0.8382 | 0.2214 |
| 1.0252 | 1.85 | 7400 | 0.8252 | 0.2173 |
| 1.0404 | 1.88 | 7500 | 0.8190 | 0.2148 |
| 1.0326 | 1.9 | 7600 | 0.8067 | 0.2155 |
| 1.0008 | 1.93 | 7700 | 0.8081 | 0.2161 |
| 0.9814 | 1.95 | 7800 | 0.8061 | 0.2152 |
| 0.9664 | 1.98 | 7900 | 0.8147 | 0.2155 |
| 1.0032 | 2.0 | 8000 | 0.8232 | 0.2128 |
| 0.9274 | 2.02 | 8100 | 0.7951 | 0.2118 |
| 0.9115 | 2.05 | 8200 | 0.7857 | 0.2105 |
| 0.9339 | 2.08 | 8300 | 0.7722 | 0.2069 |
| 0.8553 | 2.1 | 8400 | 0.7603 | 0.2070 |
| 0.8671 | 2.12 | 8500 | 0.7927 | 0.2099 |
| 0.9067 | 2.15 | 8600 | 0.7511 | 0.2013 |
| 0.8507 | 2.17 | 8700 | 0.7763 | 0.2029 |
| 0.899 | 2.2 | 8800 | 0.7579 | 0.2026 |
| 0.8061 | 2.23 | 8900 | 0.7561 | 0.2014 |
| 0.8191 | 2.25 | 9000 | 0.7590 | 0.2024 |
| 0.8084 | 2.27 | 9100 | 0.7394 | 0.1972 |
| 0.8163 | 2.3 | 9200 | 0.7404 | 0.1941 |
| 0.8189 | 2.33 | 9300 | 0.7340 | 0.1955 |
| 0.8639 | 2.35 | 9400 | 0.7331 | 0.1950 |
| 0.8218 | 2.38 | 9500 | 0.7347 | 0.1959 |
| 0.8221 | 2.4 | 9600 | 0.7098 | 0.1922 |
| 0.7725 | 2.42 | 9700 | 0.7264 | 0.1923 |
| 0.7882 | 2.45 | 9800 | 0.7079 | 0.1875 |
| 0.7786 | 2.48 | 9900 | 0.7131 | 0.1913 |
| 0.7734 | 2.5 | 10000 | 0.7079 | 0.1912 |
| 0.7834 | 2.52 | 10100 | 0.6944 | 0.1896 |
| 0.78 | 2.55 | 10200 | 0.6980 | 0.1879 |
| 0.7602 | 2.58 | 10300 | 0.7076 | 0.1894 |
| 0.7415 | 2.6 | 10400 | 0.6946 | 0.1857 |
| 0.7791 | 2.62 | 10500 | 0.7025 | 0.1887 |
| 0.7357 | 2.65 | 10600 | 0.6949 | 0.1885 |
| 0.7102 | 2.67 | 10700 | 0.6978 | 0.1895 |
| 0.7395 | 2.7 | 10800 | 0.6893 | 0.1859 |
| 0.7301 | 2.73 | 10900 | 0.6847 | 0.1857 |
| 0.7492 | 2.75 | 11000 | 0.7063 | 0.1863 |
| 0.7372 | 2.77 | 11100 | 0.6917 | 0.1857 |
| 0.7474 | 2.8 | 11200 | 0.6843 | 0.1845 |
| 0.6727 | 2.83 | 11300 | 0.6628 | 0.1775 |
| 0.7342 | 2.85 | 11400 | 0.6729 | 0.1797 |
| 0.6599 | 2.88 | 11500 | 0.6631 | 0.1797 |
| 0.7209 | 2.9 | 11600 | 0.6658 | 0.1795 |
| 0.7222 | 2.92 | 11700 | 0.6741 | 0.1807 |
| 0.7124 | 2.95 | 11800 | 0.6722 | 0.1828 |
| 0.7304 | 2.98 | 11900 | 0.6606 | 0.1782 |
| 0.7234 | 3.0 | 12000 | 0.6499 | 0.1753 |
| 0.6857 | 3.02 | 12100 | 0.6547 | 0.1751 |
| 0.6238 | 3.05 | 12200 | 0.6615 | 0.1771 |
| 0.6495 | 3.08 | 12300 | 0.6499 | 0.1764 |
| 0.6219 | 3.1 | 12400 | 0.6558 | 0.1752 |
| 0.6684 | 3.12 | 12500 | 0.6479 | 0.1752 |
| 0.6455 | 3.15 | 12600 | 0.6574 | 0.1741 |
| 0.6414 | 3.17 | 12700 | 0.6489 | 0.1755 |
| 0.6619 | 3.2 | 12800 | 0.6527 | 0.1754 |
| 0.6303 | 3.23 | 12900 | 0.6462 | 0.1743 |
| 0.6525 | 3.25 | 13000 | 0.6505 | 0.1731 |
| 0.6347 | 3.27 | 13100 | 0.6432 | 0.1713 |
| 0.6206 | 3.3 | 13200 | 0.6495 | 0.1746 |
| 0.6445 | 3.33 | 13300 | 0.6328 | 0.1706 |
| 0.6097 | 3.35 | 13400 | 0.6329 | 0.1689 |
| 0.6151 | 3.38 | 13500 | 0.6473 | 0.1730 |
| 0.5948 | 3.4 | 13600 | 0.6413 | 0.1714 |
| 0.5949 | 3.42 | 13700 | 0.6377 | 0.1712 |
| 0.6402 | 3.45 | 13800 | 0.6295 | 0.1692 |
| 0.6607 | 3.48 | 13900 | 0.6287 | 0.1694 |
| 0.6219 | 3.5 | 14000 | 0.6357 | 0.1704 |
| 0.61 | 3.52 | 14100 | 0.6392 | 0.1715 |
| 0.5974 | 3.55 | 14200 | 0.6315 | 0.1687 |
| 0.5839 | 3.58 | 14300 | 0.6359 | 0.1689 |
| 0.6017 | 3.6 | 14400 | 0.6316 | 0.1673 |
| 0.6091 | 3.62 | 14500 | 0.6284 | 0.1686 |
| 0.6565 | 3.65 | 14600 | 0.6304 | 0.1684 |
| 0.6179 | 3.67 | 14700 | 0.6259 | 0.1661 |
| 0.5813 | 3.7 | 14800 | 0.6310 | 0.1672 |
| 0.5802 | 3.73 | 14900 | 0.6250 | 0.1667 |
| 0.6035 | 3.75 | 15000 | 0.6284 | 0.1666 |
| 0.5569 | 3.77 | 15100 | 0.6203 | 0.1651 |
| 0.5712 | 3.8 | 15200 | 0.6207 | 0.1660 |
| 0.546 | 3.83 | 15300 | 0.6246 | 0.1661 |
| 0.5602 | 3.85 | 15400 | 0.6206 | 0.1656 |
| 0.591 | 3.88 | 15500 | 0.6179 | 0.1650 |
| 0.5972 | 3.9 | 15600 | 0.6164 | 0.1653 |
| 0.6168 | 3.92 | 15700 | 0.6174 | 0.1660 |
| 0.5957 | 3.95 | 15800 | 0.6164 | 0.1657 |
| 0.5754 | 3.98 | 15900 | 0.6163 | 0.1657 |
| 0.5686 | 4.0 | 16000 | 0.6163 | 0.1655 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.1+cu121
- Datasets 2.13.0
- Tokenizers 0.15.0
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