readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  datasets:
  - fleurs
  metrics:
  - wer
  model-index:
  - name: wav2vec2-large-xls-r-300m-kr-jw4169
    results:
    - task:
        name: Automatic Speech Recognition
        type: automatic-speech-recognition
      dataset:
        name: fleurs
        type: fleurs
        config: ko_kr
        split: train
        args: ko_kr
      metrics:
      - name: Wer
        type: wer
        value: 0.519593179778642
  ---
  
  <!-- 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-xls-r-300m-kr-jw4169
  
  This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the fleurs dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.9752
  - Wer: 0.5196
  
  ## 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: 4
  - total_train_batch_size: 16
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - lr_scheduler_warmup_steps: 500
  - num_epochs: 30
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Wer    |
  |:-------------:|:-----:|:----:|:---------------:|:------:|
  | 35.084        | 1.39  | 200  | 6.8536          | 1.0    |
  | 4.853         | 2.78  | 400  | 4.6246          | 1.0    |
  | 4.5491        | 4.17  | 600  | 4.3815          | 1.0    |
  | 2.799         | 5.55  | 800  | 1.7402          | 0.8642 |
  | 1.3872        | 6.94  | 1000 | 1.2019          | 0.7448 |
  | 0.9599        | 8.33  | 1200 | 1.0594          | 0.7134 |
  | 0.675         | 9.72  | 1400 | 0.9321          | 0.6404 |
  | 0.4775        | 11.11 | 1600 | 0.9088          | 0.5911 |
  | 0.3479        | 12.5  | 1800 | 0.9430          | 0.6010 |
  | 0.2712        | 13.89 | 2000 | 0.8948          | 0.5854 |
  | 0.2283        | 15.28 | 2200 | 0.9009          | 0.5495 |
  | 0.1825        | 16.67 | 2400 | 0.9079          | 0.5501 |
  | 0.161         | 18.06 | 2600 | 0.9518          | 0.5390 |
  | 0.1394        | 19.44 | 2800 | 0.9529          | 0.5399 |
  | 0.1266        | 20.83 | 3000 | 0.9505          | 0.5283 |
  | 0.1102        | 22.22 | 3200 | 0.9748          | 0.5328 |
  | 0.101         | 23.61 | 3400 | 0.9593          | 0.5316 |
  | 0.0907        | 25.0  | 3600 | 0.9832          | 0.5292 |
  | 0.0833        | 26.39 | 3800 | 0.9773          | 0.5181 |
  | 0.0781        | 27.78 | 4000 | 0.9736          | 0.5163 |
  | 0.0744        | 29.17 | 4200 | 0.9752          | 0.5196 |
  
  
  ### Framework versions
  
  - Transformers 4.25.0.dev0
  - Pytorch 1.10.0+cu102
  - Datasets 2.6.1
  - Tokenizers 0.13.1
  