readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  model-index:
  - name: wav2vec2-large-xlsr-korean-demo-with-LM
    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-with-LM
  
  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: 0.3015
  - Wer: 0.2113
  
  ## 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    |
  |:-------------:|:-----:|:-----:|:---------------:|:------:|
  | 4.7496        | 1.08  | 400   | 3.1801          | 1.0    |
  | 1.4505        | 2.16  | 800   | 0.5090          | 0.5659 |
  | 0.566         | 3.23  | 1200  | 0.3600          | 0.4039 |
  | 0.4265        | 4.31  | 1600  | 0.3224          | 0.3639 |
  | 0.3611        | 5.39  | 2000  | 0.3152          | 0.3575 |
  | 0.3035        | 6.47  | 2400  | 0.2814          | 0.3054 |
  | 0.2863        | 7.55  | 2800  | 0.2749          | 0.2923 |
  | 0.247         | 8.63  | 3200  | 0.2787          | 0.2884 |
  | 0.232         | 9.7   | 3600  | 0.2924          | 0.2788 |
  | 0.2069        | 10.78 | 4000  | 0.2668          | 0.2694 |
  | 0.1922        | 11.86 | 4400  | 0.2873          | 0.2667 |
  | 0.1747        | 12.94 | 4800  | 0.2870          | 0.2589 |
  | 0.1755        | 14.02 | 5200  | 0.2778          | 0.2543 |
  | 0.1546        | 15.09 | 5600  | 0.3062          | 0.2621 |
  | 0.1456        | 16.17 | 6000  | 0.3043          | 0.2479 |
  | 0.1404        | 17.25 | 6400  | 0.2885          | 0.2443 |
  | 0.1308        | 18.33 | 6800  | 0.3274          | 0.2417 |
  | 0.125         | 19.41 | 7200  | 0.2922          | 0.2401 |
  | 0.1148        | 20.49 | 7600  | 0.2899          | 0.2300 |
  | 0.1129        | 21.56 | 8000  | 0.2963          | 0.2276 |
  | 0.1086        | 22.64 | 8400  | 0.2903          | 0.2209 |
  | 0.097         | 23.72 | 8800  | 0.3041          | 0.2220 |
  | 0.099         | 24.8  | 9200  | 0.2870          | 0.2168 |
  | 0.0905        | 25.88 | 9600  | 0.2992          | 0.2176 |
  | 0.0929        | 26.95 | 10000 | 0.2934          | 0.2115 |
  | 0.0827        | 28.03 | 10400 | 0.2945          | 0.2141 |
  | 0.0818        | 29.11 | 10800 | 0.3015          | 0.2113 |
  
  
  ### Usage
  
  
  
  
  ### Framework versions
  
  - Transformers 4.21.0
  - Pytorch 1.12.0+cu113
  - Datasets 2.4.0
  - Tokenizers 0.12.1
  