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openai/whisper-small

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

  • Loss: 0.7117
  • Wer: 25.4037
  • Cer: 15.3838

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 Cer
0.6088 1.0 194 0.5708 42.7234 27.2387
0.2668 2.0 388 0.5730 30.4536 18.2579
0.1536 3.0 582 0.5971 30.4330 19.1535
0.1118 4.0 776 0.6131 25.2700 15.6419
0.0878 5.0 970 0.6541 28.3657 17.5805
0.0574 6.0 1164 0.6766 25.7431 16.3089
0.0417 7.0 1358 0.6932 29.3737 18.7464
0.0426 8.0 1552 0.6984 24.9820 16.0750
0.0225 9.0 1746 0.6977 25.9282 16.3107
0.0199 10.0 1940 0.7123 24.4678 15.1725
0.0134 11.0 2134 0.7021 24.6220 15.4254
0.0089 12.0 2328 0.6977 24.7557 15.4289
0.0057 13.0 2522 0.6929 24.0975 14.9784
0.0048 14.0 2716 0.7050 24.1078 15.4462
0.0009 15.0 2910 0.7049 24.1078 15.4964
0.0002 16.0 3104 0.7080 23.6553 15.0391
0.0002 17.0 3298 0.7083 25.3625 15.4063
0.0001 18.0 3492 0.7106 25.3214 15.3630
0.0002 19.0 3686 0.7114 25.3831 15.3752
0.0003 20.0 3880 0.7117 25.4037 15.3838

Framework versions

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