readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  model-index:
  - name: wav2vec2-xls-r-phone-mfa_korean
    results: []
  language:
  - ko
  metrics:
  - wer
  pipeline_tag: automatic-speech-recognition
  ---
  
  <!-- 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-xls-r-300m_phoneme-mfa_korean
  
  Creator & Uploader: Jooyoung Lee (excalibur12@snu.ac.kr)
  
  This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on a phonetically balanced native Korean read-speech corpus.
  
  # Training and Evaluation Data
  
  Training Data
  - Data Name: Phonetically Balanced Native Korean Read-speech Corpus
  - Num. of Samples: 54,000
  - Audio Length: 108 Hours
  
  Evaluation Data
  - Data Name: Phonetically Balanced Native Korean Read-speech Corpus
  - Num. of Samples: 6,000
  - Audio Length: 12 Hours
  
  # Training Hyperparameters
  
  The following hyperparameters were used during training:
  - learning_rate: 0.0001
  - train_batch_size: 8
  - eval_batch_size: 8
  - seed: 42
  - gradient_accumulation_steps: 2
  - total_train_batch_size: 16
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - lr_scheduler_warmup_ratio: 0.2
  - num_epochs: 20 (EarlyStopping: patience: 5 epochs max)
  - mixed_precision_training: Native AMP
  
  # Evaluation Result
  
  Phone Error Rate 3.88%
  
  # Output Examples
  ![output_examples](./output_examples.png)
  
  # MFA-IPA Phoneset Tables
  
  ## Vowels
  ![mfa_ipa_chart_vowels](./mfa_ipa_chart_vowels.png)
  
  ## Consonants
  ![mfa_ipa_chart_consonants](./mfa_ipa_chart_consonants.png)
  
  ## Experimental Results
  Official implementation of the paper (in review)  
  Major error patterns of L2 Korean speech from five different L1s: Chinese (ZH), Vietnamese (VI), Japanese (JP), Thai (TH), English (EN)  
  ![Experimental Results](./ICPHS2023_table2.png)
  
  # Framework versions
  
  - Transformers 4.21.3
  - Pytorch 1.12.1
  - Datasets 2.4.0
  - Tokenizers 0.12.1