readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  datasets:
  - common_voice
  metrics:
  - wer
  model-index:
  - name: firstcolab
    results:
    - task:
        name: Automatic Speech Recognition
        type: automatic-speech-recognition
      dataset:
        name: common_voice
        type: common_voice
        config: th
        split: train+validation
        args: th
      metrics:
      - name: Wer
        type: wer
        value: 0.9641604986365407
  ---
  
  <!-- 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. -->
  
  # firstcolab
  
  This model is a fine-tuned version of [Thanapon1998/firstcolab](https://huggingface.co/Thanapon1998/firstcolab) on the common_voice dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.9090
  - Wer: 0.9642
  - Cer: 0.2127
  
  ## 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: 0.0001
  - train_batch_size: 32
  - eval_batch_size: 16
  - 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: 2
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Wer    | Cer    |
  |:-------------:|:-----:|:----:|:---------------:|:------:|:------:|
  | 1.5413        | 0.66  | 100  | 1.1761          | 0.9887 | 0.2718 |
  | 1.4052        | 1.32  | 200  | 1.0352          | 0.9809 | 0.2396 |
  | 1.2922        | 1.97  | 300  | 0.9090          | 0.9642 | 0.2127 |
  
  
  ### Framework versions
  
  - Transformers 4.24.0
  - Pytorch 1.12.1+cu113
  - Datasets 2.7.1
  - Tokenizers 0.13.2
  