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wav2vec2-xls-r-1b-korean

Automatic Speech Recognition
Transformers
PyTorch
Korean
wav2vec2
generated_from_trainer
hf-asr-leaderboard
robust-speech-event
Eval Results
Inference Endpoints
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wav2vec2-xls-r-1b-korean / README.md
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metadata
language: ko
license: apache-2.0
tags:
  - automatic-speech-recognition
  - generated_from_trainer
  - hf-asr-leaderboard
  - robust-speech-event
datasets:
  - kresnik/zeroth_korean
model-index:
  - name: Wav2Vec2 XLS-R 1B Korean
    results:
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: Robust Speech Event - Dev Data
          type: speech-recognition-community-v2/dev_data
          args: ko
        metrics:
          - name: Test WER
            type: wer
            value: 82.07
          - name: Test CER
            type: cer
            value: 42.12
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: Robust Speech Event - Test Data
          type: speech-recognition-community-v2/eval_data
          args: ko
        metrics:
          - name: Test WER
            type: wer
            value: 82.09

This model is a fine-tuned version of facebook/wav2vec2-xls-r-1b on the KRESNIK/ZEROTH_KOREAN - CLEAN dataset. It achieves the following results on the evaluation set:

  • Loss: 0.0639
  • Wer: 0.0449

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
4.603 0.72 500 4.6572 0.9985
2.6314 1.44 1000 2.0424 0.9256
2.2708 2.16 1500 0.9889 0.6989
2.1769 2.88 2000 0.8366 0.6312
2.1142 3.6 2500 0.7555 0.5998
2.0084 4.32 3000 0.7144 0.6003
1.9272 5.04 3500 0.6311 0.5461
1.8687 5.75 4000 0.6252 0.5430
1.8186 6.47 4500 0.5491 0.4988
1.7364 7.19 5000 0.5463 0.4959
1.6809 7.91 5500 0.4724 0.4484
1.641 8.63 6000 0.4679 0.4461
1.572 9.35 6500 0.4387 0.4236
1.5256 10.07 7000 0.3970 0.4003
1.5044 10.79 7500 0.3690 0.3893
1.4563 11.51 8000 0.3752 0.3875
1.394 12.23 8500 0.3386 0.3567
1.3641 12.95 9000 0.3290 0.3467
1.2878 13.67 9500 0.2893 0.3135
1.2602 14.39 10000 0.2723 0.3029
1.2302 15.11 10500 0.2603 0.2989
1.1865 15.83 11000 0.2440 0.2794
1.1491 16.55 11500 0.2500 0.2788
1.093 17.27 12000 0.2279 0.2629
1.0367 17.98 12500 0.2076 0.2443
0.9954 18.7 13000 0.1844 0.2259
0.99 19.42 13500 0.1794 0.2179
0.9385 20.14 14000 0.1765 0.2122
0.8952 20.86 14500 0.1706 0.1974
0.8841 21.58 15000 0.1791 0.1969
0.847 22.3 15500 0.1780 0.2060
0.8669 23.02 16000 0.1608 0.1862
0.8066 23.74 16500 0.1447 0.1626
0.7908 24.46 17000 0.1457 0.1655
0.7459 25.18 17500 0.1350 0.1445
0.7218 25.9 18000 0.1276 0.1421
0.703 26.62 18500 0.1177 0.1302
0.685 27.34 19000 0.1147 0.1305
0.6811 28.06 19500 0.1128 0.1244
0.6444 28.78 20000 0.1120 0.1213
0.6323 29.5 20500 0.1137 0.1166
0.5998 30.22 21000 0.1051 0.1107
0.5706 30.93 21500 0.1035 0.1037
0.5555 31.65 22000 0.1031 0.0927
0.5389 32.37 22500 0.0997 0.0900
0.5201 33.09 23000 0.0920 0.0912
0.5146 33.81 23500 0.0929 0.0947
0.515 34.53 24000 0.1000 0.0953
0.4743 35.25 24500 0.0922 0.0892
0.4707 35.97 25000 0.0852 0.0808
0.4456 36.69 25500 0.0855 0.0779
0.443 37.41 26000 0.0843 0.0738
0.4388 38.13 26500 0.0816 0.0699
0.4162 38.85 27000 0.0752 0.0645
0.3979 39.57 27500 0.0761 0.0621
0.3889 40.29 28000 0.0771 0.0625
0.3923 41.01 28500 0.0755 0.0598
0.3693 41.73 29000 0.0730 0.0578
0.3642 42.45 29500 0.0739 0.0598
0.3532 43.17 30000 0.0712 0.0553
0.3513 43.88 30500 0.0762 0.0516
0.3349 44.6 31000 0.0731 0.0504
0.3305 45.32 31500 0.0725 0.0507
0.3285 46.04 32000 0.0709 0.0489
0.3179 46.76 32500 0.0667 0.0467
0.3158 47.48 33000 0.0653 0.0494
0.3033 48.2 33500 0.0638 0.0456
0.3023 48.92 34000 0.0644 0.0464
0.2975 49.64 34500 0.0643 0.0455

Framework versions

  • Transformers 4.17.0.dev0
  • Pytorch 1.10.2+cu102
  • Datasets 1.18.3.dev0
  • Tokenizers 0.11.0