readme: ---
  language:
  - en
  license: apache-2.0
  tags:
  - en-asr-leaderboard
  - generated_from_trainer
  datasets:
  - mn367/radio-test-dataset
  metrics:
  - wer
  model-index:
  - name: Whisper Medium En
    results:
    - task:
        name: Automatic Speech Recognition
        type: automatic-speech-recognition
      dataset:
        name: Radio dataset
        type: mn367/radio-test-dataset
        args: 'config: en, split: test'
      metrics:
      - name: Wer
        type: wer
        value: 30.971856370442257
  ---
  
  <!-- 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. -->
  
  # Whisper Medium En
  
  This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) on the Radio dataset dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.6118
  - Wer: 30.9719
  
  ## 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-06
  - train_batch_size: 16
  - eval_batch_size: 8
  - seed: 42
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - lr_scheduler_warmup_steps: 500
  - training_steps: 1600
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Wer     |
  |:-------------:|:-----:|:----:|:---------------:|:-------:|
  | 3.6505        | 0.75  | 100  | 3.5819          | 58.0618 |
  | 2.7405        | 1.5   | 200  | 2.5030          | 47.7471 |
  | 1.6934        | 2.26  | 300  | 1.6058          | 36.4342 |
  | 0.8876        | 3.01  | 400  | 0.7653          | 39.4843 |
  | 0.6136        | 3.76  | 500  | 0.6374          | 35.2281 |
  | 0.4993        | 4.51  | 600  | 0.5950          | 34.9785 |
  | 0.3364        | 5.26  | 700  | 0.5776          | 28.6150 |
  | 0.4268        | 6.02  | 800  | 0.5640          | 29.7241 |
  | 0.3367        | 6.77  | 900  | 0.5706          | 29.8489 |
  | 0.2706        | 7.52  | 1000 | 0.5757          | 28.0189 |
  | 0.2932        | 8.27  | 1100 | 0.5789          | 28.3377 |
  | 0.2699        | 9.02  | 1200 | 0.5869          | 29.9875 |
  | 0.2434        | 9.77  | 1300 | 0.5976          | 30.1816 |
  | 0.267         | 10.53 | 1400 | 0.6020          | 30.5559 |
  | 0.2364        | 11.28 | 1500 | 0.6106          | 30.6391 |
  | 0.2095        | 12.03 | 1600 | 0.6118          | 30.9719 |
  
  
  ### Framework versions
  
  - Transformers 4.25.0.dev0
  - Pytorch 1.12.1+cu113
  - Datasets 2.7.0
  - Tokenizers 0.13.2
  