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whisper-kor_noising_full

This model is a fine-tuned version of openai/whisper-small on the whisper-kor_noising_full dataset. It achieves the following results on the evaluation set:

  • Loss: 0.1966
  • Wer: 15.2377
  • Cer: 7.1689

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: 1e-05
  • train_batch_size: 16
  • eval_batch_size: 16
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_steps: 500
  • training_steps: 4000

Training results

Training Loss Epoch Step Validation Loss Wer Cer
0.1965 0.05 100 0.1848 13.3271 5.5988
0.2196 0.09 200 0.1880 13.7310 5.6454
0.2601 0.14 300 0.1932 14.8493 6.1532
0.2118 0.18 400 0.2005 15.8434 6.5002
0.2784 0.23 500 0.2088 16.0298 6.7160
0.2421 0.28 600 0.2105 16.0920 6.7160
0.2209 0.32 700 0.2159 16.9773 7.0884
0.2426 0.37 800 0.2157 17.2258 7.1096
0.2429 0.42 900 0.2166 16.7754 6.9403
0.258 0.46 1000 0.2158 17.2569 7.0673
0.2605 0.51 1100 0.2135 16.5113 6.9784
0.2196 0.55 1200 0.2120 16.7443 6.8261
0.2423 0.6 1300 0.2163 16.8841 7.0884
0.2389 0.65 1400 0.2138 16.6201 7.0419
0.2314 0.69 1500 0.2149 16.8531 6.8599
0.2509 0.74 1600 0.2126 17.2103 7.8206
0.2329 0.78 1700 0.2103 16.0764 6.7457
0.2504 0.83 1800 0.2092 15.8590 6.6526
0.2632 0.88 1900 0.2107 16.2783 6.8726
0.2374 0.92 2000 0.2091 16.3249 6.7245
0.2625 0.97 2100 0.2057 15.7658 6.5425
0.1471 1.02 2200 0.2052 15.8434 6.5129
0.1541 1.06 2300 0.2069 16.3249 6.7457
0.1301 1.11 2400 0.2042 15.9211 6.4917
0.1674 1.15 2500 0.2058 15.3153 6.4240
0.1435 1.2 2600 0.2060 15.6726 6.5044
0.1352 1.25 2700 0.2040 15.2998 6.3902
0.1258 1.29 2800 0.2019 15.1600 6.2971
0.1273 1.34 2900 0.2025 15.6881 6.4875
0.1527 1.39 3000 0.2031 15.7036 6.5044
0.1371 1.43 3100 0.2011 15.3308 6.3309
0.1247 1.48 3200 0.2003 15.2842 6.3521
0.1376 1.52 3300 0.1987 15.4551 7.2366
0.1194 1.57 3400 0.1999 15.5949 7.2704
0.144 1.62 3500 0.1983 14.9425 6.2886
0.1387 1.66 3600 0.1979 14.9425 6.2082
0.1372 1.71 3700 0.1979 15.3464 7.1435
0.1513 1.75 3800 0.1972 15.1445 7.0334
0.134 1.8 3900 0.1970 15.2377 7.1646
0.1165 1.85 4000 0.1966 15.2377 7.1689

Framework versions

  • Transformers 4.33.2
  • Pytorch 2.1.0+cu121
  • Datasets 2.14.5
  • Tokenizers 0.13.3
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