readme: ---
  language:
  - vi
  license: apache-2.0
  tags:
  - automatic-speech-recognition
  - common_voice
  - generated_from_trainer
  datasets:
  - common_voice
  metrics:
  - wer
  model-index:
  - name: wav2vec2-common_voice-vi
    results:
    - task:
        name: Automatic Speech Recognition
        type: automatic-speech-recognition
      dataset:
        name: COMMON_VOICE - VI
        type: common_voice
        config: vi
        split: train+validation
        args: 'Config: vi, Training split: train+validation, Eval split: test'
      metrics:
      - name: Wer
        type: wer
        value: 1.0
  ---
  
  <!-- 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-common_voice-vi
  
  This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the COMMON_VOICE - VI dataset.
  It achieves the following results on the evaluation set:
  - Loss: 28.9053
  - Wer: 1.0
  
  ## 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: 1
  - eval_batch_size: 8
  - seed: 42
  - gradient_accumulation_steps: 200
  - total_train_batch_size: 200
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - lr_scheduler_warmup_steps: 500
  - num_epochs: 15.0
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  
  
  ### Framework versions
  
  - Transformers 4.25.1
  - Pytorch 1.12.1+cu116
  - Datasets 2.6.1
  - Tokenizers 0.13.1
  