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ft_0112_korean

Automatic Speech Recognition
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wav2vec2
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metadata
license: apache-2.0
base_model: facebook/wav2vec2-xls-r-300m
tags:
  - generated_from_trainer
model-index:
  - name: ft_0112_korean
    results: []

ft_0112_korean

This model is a fine-tuned version of 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