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metadata
license: apache-2.0
tags:
  - automatic-speech-recognition
  - kresnik/zeroth_korean
  - generated_from_trainer
datasets:
  - zeroth_korean_asr
model-index:
  - name: ''
    results: []

This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the KRESNIK/ZEROTH_KOREAN - CLEAN dataset. It achieves the following results on the evaluation set:

  • Loss: 0.2089
  • Wer: 0.2954
  • Cer: 0.0953

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: 2000
  • num_epochs: 50.0
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Wer Cer
19.7138 0.72 500 19.6427 1.0 1.0
4.8039 1.44 1000 4.7842 1.0 1.0
4.5619 2.16 1500 4.5608 0.9992 0.9598
4.254 2.88 2000 4.2729 0.9955 0.9063
4.1905 3.6 2500 4.2257 0.9903 0.8758
4.0683 4.32 3000 3.9294 0.9937 0.7911
3.486 5.04 3500 2.7045 1.0012 0.5934
2.946 5.75 4000 1.9691 0.9425 0.4634
2.634 6.47 4500 1.5212 0.8807 0.3850
2.4066 7.19 5000 1.2551 0.8177 0.3601
2.2651 7.91 5500 1.0423 0.7650 0.3039
2.1828 8.63 6000 0.9599 0.7273 0.3106
2.1023 9.35 6500 0.9482 0.7161 0.3063
2.0536 10.07 7000 0.8242 0.6767 0.2860
1.9803 10.79 7500 0.7643 0.6563 0.2637
1.9468 11.51 8000 0.7319 0.6441 0.2505
1.9178 12.23 8500 0.6937 0.6320 0.2489
1.8515 12.95 9000 0.6443 0.6053 0.2196
1.8083 13.67 9500 0.6286 0.6122 0.2148
1.819 14.39 10000 0.6015 0.5986 0.2074
1.7684 15.11 10500 0.5682 0.5741 0.1982
1.7195 15.83 11000 0.5385 0.5592 0.2007
1.7044 16.55 11500 0.5362 0.5524 0.2097
1.6879 17.27 12000 0.5119 0.5489 0.2083
1.656 17.98 12500 0.4990 0.5362 0.1968
1.6122 18.7 13000 0.4561 0.5092 0.1900
1.5919 19.42 13500 0.4778 0.5225 0.1975
1.5896 20.14 14000 0.4563 0.5098 0.1859
1.5589 20.86 14500 0.4362 0.4940 0.1725
1.5353 21.58 15000 0.4140 0.4826 0.1580
1.5441 22.3 15500 0.4031 0.4742 0.1550
1.5116 23.02 16000 0.3916 0.4748 0.1545
1.4731 23.74 16500 0.3841 0.4810 0.1542
1.4647 24.46 17000 0.3752 0.4524 0.1475
1.4328 25.18 17500 0.3587 0.4476 0.1461
1.4129 25.9 18000 0.3429 0.4242 0.1366
1.4062 26.62 18500 0.3450 0.4251 0.1355
1.3928 27.34 19000 0.3297 0.4145 0.1322
1.3906 28.06 19500 0.3210 0.4185 0.1336
1.358 28.78 20000 0.3131 0.3970 0.1275
1.3445 29.5 20500 0.3069 0.3920 0.1276
1.3159 30.22 21000 0.3035 0.3961 0.1255
1.3044 30.93 21500 0.2952 0.3854 0.1242
1.3034 31.65 22000 0.2966 0.3772 0.1227
1.2963 32.37 22500 0.2844 0.3706 0.1208
1.2765 33.09 23000 0.2841 0.3567 0.1173
1.2438 33.81 23500 0.2734 0.3552 0.1137
1.2487 34.53 24000 0.2703 0.3502 0.1118
1.2249 35.25 24500 0.2650 0.3484 0.1142
1.2229 35.97 25000 0.2584 0.3374 0.1097
1.2374 36.69 25500 0.2568 0.3337 0.1095
1.2153 37.41 26000 0.2494 0.3327 0.1071
1.1925 38.13 26500 0.2518 0.3366 0.1077
1.1908 38.85 27000 0.2437 0.3272 0.1057
1.1858 39.57 27500 0.2396 0.3265 0.1044
1.1808 40.29 28000 0.2373 0.3156 0.1028
1.1842 41.01 28500 0.2356 0.3152 0.1026
1.1668 41.73 29000 0.2319 0.3188 0.1025
1.1448 42.45 29500 0.2293 0.3099 0.0995
1.1327 43.17 30000 0.2265 0.3047 0.0979
1.1307 43.88 30500 0.2222 0.3078 0.0989
1.1419 44.6 31000 0.2215 0.3038 0.0981
1.1231 45.32 31500 0.2193 0.3013 0.0972
1.139 46.04 32000 0.2162 0.3007 0.0968
1.1114 46.76 32500 0.2122 0.2982 0.0960
1.111 47.48 33000 0.2125 0.2946 0.0948
1.0982 48.2 33500 0.2099 0.2957 0.0953
1.109 48.92 34000 0.2092 0.2955 0.0955
1.0905 49.64 34500 0.2088 0.2954 0.0953

Framework versions

  • Transformers 4.17.0.dev0
  • Pytorch 1.10.2+cu102
  • Datasets 1.18.2.dev0
  • Tokenizers 0.10.3