readme: ---
  language:
  - en
  license: apache-2.0
  tags:
  - hf-asr-leaderboard
  - generated_from_trainer
  datasets:
  - atcosim_corpus
  metrics:
  - wer
  model-index:
  - name: atcosim_corpus
    results:
    - task:
        name: Automatic Speech Recognition
        type: automatic-speech-recognition
      dataset:
        name: atcosim_corpus
        type: atcosim_corpus
        args: 'split: test'
      metrics:
      - name: Wer
        type: wer
        value: 2.490946029502694
  ---
  
  <!-- 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. -->
  
  # atcosim_corpus
  
  This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on the atcosim_corpus dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.0623
  - Wer: 2.4909
  
  ## 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: 1e-05
  - train_batch_size: 2
  - eval_batch_size: 2
  - seed: 42
  - gradient_accumulation_steps: 8
  - 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
  - training_steps: 4000
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Wer    |
  |:-------------:|:-----:|:----:|:---------------:|:------:|
  | 0.0281        | 2.1   | 1000 | 0.0716          | 4.1957 |
  | 0.0051        | 4.19  | 2000 | 0.0650          | 2.7162 |
  | 0.0009        | 6.29  | 3000 | 0.0624          | 2.4733 |
  | 0.0005        | 8.39  | 4000 | 0.0623          | 2.4909 |
  
  
  ### Framework versions
  
  - Transformers 4.25.1
  - Pytorch 1.13.0+cu116
  - Datasets 2.6.1
  - Tokenizers 0.13.2
  