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Whisper tiny custom

This model is a fine-tuned version of openai/whisper-medium on the custom dataset dataset. It achieves the following results on the evaluation set:

  • Loss: 0.0315
  • Wer Ortho: 9.2105
  • Wer: 7.2848

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: 16
  • eval_batch_size: 16
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: constant_with_warmup
  • lr_scheduler_warmup_steps: 50
  • training_steps: 500
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Wer Ortho Wer
1.6536 2.5 50 0.4681 57.8947 50.9934
0.0732 5.0 100 0.0820 19.7368 15.2318
0.0076 7.5 150 0.0396 9.2105 7.9470
0.0013 10.0 200 0.0336 9.2105 8.6093
0.0007 12.5 250 0.0356 7.8947 5.9603
0.0005 15.0 300 0.0339 7.8947 5.9603
0.0004 17.5 350 0.0326 7.8947 5.9603
0.0003 20.0 400 0.0323 7.8947 5.9603
0.0003 22.5 450 0.0320 9.2105 7.2848
0.0002 25.0 500 0.0315 9.2105 7.2848

Framework versions

  • Transformers 4.35.2
  • Pytorch 2.1.0+cu121
  • Datasets 2.16.1
  • Tokenizers 0.15.1
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