readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  model-index:
  - name: wav2vec2-large-xlsr-korean-demo-test
    results: []
  ---
  
  <!-- 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. -->
  
  # wav2vec2-large-xlsr-korean-demo-test
  
  This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the None dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.9829
  - Wer: 0.5580
  
  ## 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: 0.0003
  - train_batch_size: 4
  - eval_batch_size: 8
  - seed: 42
  - gradient_accumulation_steps: 2
  - total_train_batch_size: 8
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - lr_scheduler_warmup_steps: 500
  - num_epochs: 30
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch | Step  | Validation Loss | Wer    |
  |:-------------:|:-----:|:-----:|:---------------:|:------:|
  | 8.1603        | 0.4   | 400   | 5.0560          | 1.0    |
  | 3.0513        | 0.79  | 800   | 2.1226          | 0.9984 |
  | 1.7673        | 1.19  | 1200  | 1.2358          | 0.9273 |
  | 1.4577        | 1.59  | 1600  | 1.0198          | 0.8512 |
  | 1.3308        | 1.98  | 2000  | 0.9258          | 0.8325 |
  | 1.1798        | 2.38  | 2400  | 0.8587          | 0.7933 |
  | 1.1268        | 2.77  | 2800  | 0.8166          | 0.7677 |
  | 1.0664        | 3.17  | 3200  | 0.7911          | 0.7428 |
  | 0.9923        | 3.57  | 3600  | 0.7964          | 0.7481 |
  | 1.0059        | 3.96  | 4000  | 0.7617          | 0.7163 |
  | 0.9141        | 4.36  | 4400  | 0.7854          | 0.7280 |
  | 0.8939        | 4.76  | 4800  | 0.7364          | 0.7160 |
  | 0.8689        | 5.15  | 5200  | 0.7895          | 0.6996 |
  | 0.8236        | 5.55  | 5600  | 0.7756          | 0.7100 |
  | 0.8409        | 5.95  | 6000  | 0.7433          | 0.6915 |
  | 0.7643        | 6.34  | 6400  | 0.7566          | 0.6993 |
  | 0.7601        | 6.74  | 6800  | 0.7873          | 0.6836 |
  | 0.7367        | 7.14  | 7200  | 0.7353          | 0.6640 |
  | 0.7099        | 7.53  | 7600  | 0.7421          | 0.6766 |
  | 0.7084        | 7.93  | 8000  | 0.7396          | 0.6740 |
  | 0.6837        | 8.32  | 8400  | 0.7717          | 0.6647 |
  | 0.6513        | 8.72  | 8800  | 0.7763          | 0.6798 |
  | 0.6458        | 9.12  | 9200  | 0.7659          | 0.6494 |
  | 0.6132        | 9.51  | 9600  | 0.7693          | 0.6511 |
  | 0.6287        | 9.91  | 10000 | 0.7555          | 0.6469 |
  | 0.6008        | 10.31 | 10400 | 0.7606          | 0.6408 |
  | 0.5796        | 10.7  | 10800 | 0.7622          | 0.6397 |
  | 0.5753        | 11.1  | 11200 | 0.7816          | 0.6510 |
  | 0.5531        | 11.5  | 11600 | 0.8351          | 0.6658 |
  | 0.5215        | 11.89 | 12000 | 0.7843          | 0.6416 |
  | 0.5205        | 12.29 | 12400 | 0.7674          | 0.6256 |
  | 0.5219        | 12.69 | 12800 | 0.7594          | 0.6287 |
  | 0.5186        | 13.08 | 13200 | 0.7863          | 0.6243 |
  | 0.473         | 13.48 | 13600 | 0.8209          | 0.6469 |
  | 0.4938        | 13.87 | 14000 | 0.8002          | 0.6241 |
  | 0.474         | 14.27 | 14400 | 0.8008          | 0.6122 |
  | 0.442         | 14.67 | 14800 | 0.8047          | 0.6089 |
  | 0.4521        | 15.06 | 15200 | 0.8341          | 0.6123 |
  | 0.4289        | 15.46 | 15600 | 0.8217          | 0.6122 |
  | 0.4278        | 15.86 | 16000 | 0.8400          | 0.6152 |
  | 0.4051        | 16.25 | 16400 | 0.8634          | 0.6182 |
  | 0.4063        | 16.65 | 16800 | 0.8486          | 0.6097 |
  | 0.4101        | 17.05 | 17200 | 0.8825          | 0.6002 |
  | 0.3896        | 17.44 | 17600 | 0.9575          | 0.6205 |
  | 0.3833        | 17.84 | 18000 | 0.8946          | 0.6216 |
  | 0.3678        | 18.24 | 18400 | 0.8905          | 0.5952 |
  | 0.3715        | 18.63 | 18800 | 0.8918          | 0.5994 |
  | 0.3748        | 19.03 | 19200 | 0.8856          | 0.5953 |
  | 0.3485        | 19.42 | 19600 | 0.9326          | 0.5906 |
  | 0.3522        | 19.82 | 20000 | 0.9237          | 0.5932 |
  | 0.3551        | 20.22 | 20400 | 0.9274          | 0.5932 |
  | 0.3339        | 20.61 | 20800 | 0.9075          | 0.5883 |
  | 0.3354        | 21.01 | 21200 | 0.9306          | 0.5861 |
  | 0.318         | 21.41 | 21600 | 0.8994          | 0.5854 |
  | 0.3235        | 21.8  | 22000 | 0.9114          | 0.5831 |
  | 0.3201        | 22.2  | 22400 | 0.9415          | 0.5867 |
  | 0.308         | 22.6  | 22800 | 0.9695          | 0.5807 |
  | 0.3049        | 22.99 | 23200 | 0.9166          | 0.5765 |
  | 0.2858        | 23.39 | 23600 | 0.9643          | 0.5746 |
  | 0.2938        | 23.79 | 24000 | 0.9461          | 0.5724 |
  | 0.2856    