readme: ---
  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 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. -->
  
  # 
  
  This model is a fine-tuned version of [facebook/wav2vec2-xls-r-1b](https://huggingface.co/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 | 