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: 24.555998550199348
  ---
  
  <!-- 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: 0.4242
  - Wer: 24.5560
  
  ## 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: 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     |
  |:-------------:|:-----:|:-----:|:---------------:|:-------:|
  | 0.3363        | 2.02  | 500   | 0.3762          | 29.7934 |
  | 0.0945        | 5.02  | 1000  | 0.3418          | 27.6912 |
  | 0.0332        | 8.01  | 1500  | 0.3353          | 26.1689 |
  | 0.0147        | 11.01 | 2000  | 0.3476          | 26.1327 |
  | 0.0071        | 14.01 | 2500  | 0.3623          | 25.9333 |
  | 0.0034        | 17.01 | 3000  | 0.3789          | 25.2084 |
  | 0.0024        | 20.01 | 3500  | 0.3827          | 24.8641 |
  | 0.0026        | 23.01 | 4000  | 0.3877          | 25.3171 |
  | 0.0021        | 26.01 | 4500  | 0.3933          | 25.4259 |
  | 0.0014        | 29.01 | 5000  | 0.3941          | 25.0997 |
  | 0.0008        | 32.01 | 5500  | 0.4014          | 25.0997 |
  | 0.0004        | 35.01 | 6000  | 0.4035          | 24.8278 |
  | 0.0003        | 38.01 | 6500  | 0.4080          | 24.9184 |
  | 0.0003        | 41.01 | 7000  | 0.4120          | 24.8097 |
  | 0.0002        | 44.01 | 7500  | 0.4151          | 24.6104 |
  | 0.0002        | 47.01 | 8000  | 0.4176          | 24.3929 |
  | 0.0002        | 50.01 | 8500  | 0.4200          | 24.5198 |
  | 0.0001        | 53.0  | 9000  | 0.4230          | 24.5198 |
  | 0.0001        | 56.0  | 9500  | 0.4252          | 24.4291 |
  | 0.0001        | 59.0  | 10000 | 0.4242          | 24.5560 |
  
  
  ### Framework versions
  
  - Transformers 4.26.0.dev0
  - Pytorch 1.12.1+cu116
  - Datasets 2.4.0
  - Tokenizers 0.12.1
  