readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  model-index:
  - name: wav2vec2-ksponspeech
    results: []
    
  ---
  
  
  # wav2vec2-ksponspeech
  
  This model is a fine-tuned version of [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:
  
  - **WER(Word Error Rate)** for Third party test data : 0.373
  
  **For improving WER:**
  - Numeric / Character Unification
  - Decoding the word with the correct notation (from word based on pronounciation)
  - Uniform use of special characters (. / ?)
  - Converting non-existent words to existing words
  
  ## Model description
  
  Korean Wav2vec with Ksponspeech dataset.  
  
  This model was trained by two dataset :
  
  - Train1 : https://huggingface.co/datasets/Taeham/wav2vec2-ksponspeech-train (1 ~ 20000th data in Ksponspeech)
  - Train2 : https://huggingface.co/datasets/Taeham/wav2vec2-ksponspeech-train2 (20100 ~ 40100th data in Ksponspeech)
  - Validation : https://huggingface.co/datasets/Taeham/wav2vec2-ksponspeech-test (20000 ~ 20100th data in Ksponspeech)
  - Third party test : https://huggingface.co/datasets/Taeham/wav2vec2-ksponspeech-test (60000 ~ 20100th data in Ksponspeech)
  
  ### Hardward Specification
  - GPU : GEFORCE RTX 3080ti 12GB
  - CPU : Intel i9-12900k
  - RAM : 32GB
  
  ### Training hyperparameters
  
  The following hyperparameters were used during training:
  - learning_rate: 0.0003
  - train_batch_size: 4
  - eval_batch_size: 4
  - seed: 42
  - 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
  
  ### Framework versions
  
  - Transformers 4.19.4
  - Pytorch 1.11.0
  - Datasets 2.2.2
  - Tokenizers 0.12.1
  