readme: ---
  license: apache-2.0
  tags:
  - whisper-event
  - generated_from_trainer
  datasets:
  - data/copas
  metrics:
  - wer
  model-index:
  - name: Whisper Small dysarthric Dutch
    results:
    - task:
        name: Automatic Speech Recognition
        type: automatic-speech-recognition
      dataset:
        name: data/copas copas-full
        type: data/copas
        config: copas-full
        split: test
        args: copas-full
      metrics:
      - name: Wer
        type: wer
        value: 22.87060529177238
  ---
  
  <!-- 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 Small dysarthric Dutch
  
  This model is a fine-tuned version of [qmeeus/whisper-small-nl](https://huggingface.co/qmeeus/whisper-small-nl) on the data/copas copas-full dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.4891
  - Wer: 22.8706
  
  ## 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: 32
  - seed: 42
  - gradient_accumulation_steps: 2
  - total_train_batch_size: 64
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - lr_scheduler_warmup_steps: 500
  - training_steps: 10000
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch | Step  | Validation Loss | Wer     |
  |:-------------:|:-----:|:-----:|:---------------:|:-------:|
  | 0.1493        | 2.02  | 500   | 0.3960          | 28.9779 |
  | 0.0383        | 5.02  | 1000  | 0.4041          | 26.5132 |
  | 0.0264        | 8.01  | 1500  | 0.4274          | 25.5890 |
  | 0.0155        | 11.01 | 2000  | 0.4437          | 24.7735 |
  | 0.0041        | 14.01 | 2500  | 0.4454          | 25.0453 |
  | 0.0044        | 17.01 | 3000  | 0.4444          | 23.9761 |
  | 0.0044        | 20.01 | 3500  | 0.4394          | 23.4868 |
  | 0.0022        | 23.01 | 4000  | 0.4415          | 22.8525 |
  | 0.0034        | 26.01 | 4500  | 0.4602          | 23.6499 |
  | 0.0027        | 29.01 | 5000  | 0.4577          | 23.3780 |
  | 0.0072        | 32.01 | 5500  | 0.4573          | 23.3962 |
  | 0.0002        | 35.01 | 6000  | 0.4673          | 23.1062 |
  | 0.0001        | 38.01 | 6500  | 0.4723          | 22.9975 |
  | 0.0001        | 41.01 | 7000  | 0.4770          | 23.0881 |
  | 0.0           | 44.01 | 7500  | 0.4807          | 23.0518 |
  | 0.0           | 47.01 | 8000  | 0.4835          | 22.9612 |
  | 0.0           | 50.01 | 8500  | 0.4857          | 22.9250 |
  | 0.0           | 53.0  | 9000  | 0.4874          | 22.9069 |
  | 0.0           | 56.0  | 9500  | 0.4887          | 22.9069 |
  | 0.0           | 59.0  | 10000 | 0.4891          | 22.8706 |
  
  
  ### Framework versions
  
  - Transformers 4.26.0.dev0
  - Pytorch 1.12.1+cu116
  - Datasets 2.4.0
  - Tokenizers 0.12.1
  