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openai/whisper-medium

This model is a fine-tuned version of openai/whisper-medium on the Hanhpt23/GermanMed dataset. It achieves the following results on the evaluation set:

  • Loss: 5.5611
  • Wer: 107.4053

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: 8
  • 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: 100
  • num_epochs: 20

Training results

Training Loss Epoch Step Validation Loss Wer
3.0873 1.0 765 3.7193 114.9828
2.0404 2.0 1530 3.8002 105.8553
1.1104 3.0 2295 4.2393 106.7738
0.6064 4.0 3060 4.4641 113.7773
0.3464 5.0 3825 4.8067 106.8886
0.2769 6.0 4590 5.0650 106.6590
0.2327 7.0 5355 5.0613 111.1366
0.181 8.0 6120 5.2019 104.7072
0.17 9.0 6885 5.3335 123.8806
0.1518 10.0 7650 5.3649 156.4294
0.1405 11.0 8415 5.4181 107.6349
0.1229 12.0 9180 5.4629 102.0666
0.1335 13.0 9945 5.4842 106.0850
0.1016 14.0 10710 5.4736 105.4535
0.119 15.0 11475 5.4178 109.1848
0.0979 16.0 12240 5.4872 106.7738
0.0919 17.0 13005 5.5066 105.7979
0.0835 18.0 13770 5.5156 117.8530
0.0979 19.0 14535 5.5140 109.9885
0.1001 20.0 15300 5.5611 107.4053

Framework versions

  • Transformers 4.41.1
  • Pytorch 2.3.0
  • Datasets 2.19.1
  • Tokenizers 0.19.1
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