readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  model-index:
  - name: korean-aihub-learning-math-1-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. -->
  
  # korean-aihub-learning-math-1-test
  
  This model is a fine-tuned version of [kresnik/wav2vec2-large-xlsr-korean](https://huggingface.co/kresnik/wav2vec2-large-xlsr-korean) on the None dataset.
  It achieves the following results on the evaluation set:
  - Loss: 1.2537
  - Wer: 0.4765
  
  ## 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    |
  |:-------------:|:-----:|:----:|:---------------:|:------:|
  | No log        | 1.0   | 35   | 29.8031         | 1.0    |
  | No log        | 2.0   | 70   | 5.7158          | 1.0    |
  | 19.8789       | 3.0   | 105  | 4.5005          | 1.0    |
  | 19.8789       | 4.0   | 140  | 4.3677          | 0.9984 |
  | 19.8789       | 5.0   | 175  | 3.8013          | 0.9882 |
  | 3.9785        | 6.0   | 210  | 2.4132          | 0.8730 |
  | 3.9785        | 7.0   | 245  | 1.5867          | 0.7045 |
  | 3.9785        | 8.0   | 280  | 1.3179          | 0.6082 |
  | 1.2266        | 9.0   | 315  | 1.2431          | 0.6066 |
  | 1.2266        | 10.0  | 350  | 1.1791          | 0.5384 |
  | 1.2266        | 11.0  | 385  | 1.0994          | 0.5298 |
  | 0.3916        | 12.0  | 420  | 1.1552          | 0.5196 |
  | 0.3916        | 13.0  | 455  | 1.1495          | 0.5486 |
  | 0.3916        | 14.0  | 490  | 1.1340          | 0.5290 |
  | 0.2488        | 15.0  | 525  | 1.2208          | 0.5525 |
  | 0.2488        | 16.0  | 560  | 1.1682          | 0.5024 |
  | 0.2488        | 17.0  | 595  | 1.1479          | 0.5008 |
  | 0.1907        | 18.0  | 630  | 1.1735          | 0.4882 |
  | 0.1907        | 19.0  | 665  | 1.2302          | 0.4914 |
  | 0.1461        | 20.0  | 700  | 1.2497          | 0.4890 |
  | 0.1461        | 21.0  | 735  | 1.2434          | 0.4914 |
  | 0.1461        | 22.0  | 770  | 1.2031          | 0.5031 |
  | 0.1147        | 23.0  | 805  | 1.2451          | 0.4976 |
  | 0.1147        | 24.0  | 840  | 1.2746          | 0.4937 |
  | 0.1147        | 25.0  | 875  | 1.2405          | 0.4828 |
  | 0.0892        | 26.0  | 910  | 1.2228          | 0.4929 |
  | 0.0892        | 27.0  | 945  | 1.2642          | 0.4898 |
  | 0.0892        | 28.0  | 980  | 1.2586          | 0.4843 |
  | 0.0709        | 29.0  | 1015 | 1.2518          | 0.4788 |
  | 0.0709        | 30.0  | 1050 | 1.2537          | 0.4765 |
  
  
  ### Framework versions
  
  - Transformers 4.20.1
  - Pytorch 1.12.0+cu113
  - Datasets 2.4.0
  - Tokenizers 0.12.1
  