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metadata
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

Whisper Small dysarthric Dutch

This model is a fine-tuned version of 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