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openai/whisper-small

This model is a fine-tuned version of openai/whisper-small on the pphuc25/EngMed dataset. It achieves the following results on the evaluation set:

  • Loss: 0.0002
  • Wer: 18.0420
  • Cer: 13.7394

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
0.7947 1.0 386 0.3297 31.2590 24.7156
0.4419 2.0 772 0.1631 46.7252 46.6811
0.2375 3.0 1158 0.1046 36.4448 33.2804
0.1131 4.0 1544 0.0643 35.7395 32.0761
0.0974 5.0 1930 0.0458 32.6501 28.0269
0.0582 6.0 2316 0.0345 32.8258 26.8794
0.049 7.0 2702 0.0251 30.1177 25.6116
0.0551 8.0 3088 0.0171 26.8918 22.6404
0.0219 9.0 3474 0.0133 24.6637 19.5561
0.0183 10.0 3860 0.0092 21.5865 16.2971
0.0097 11.0 4246 0.0066 27.2316 23.4164
0.0122 12.0 4632 0.0044 18.0929 14.6218
0.0049 13.0 5018 0.0019 19.5776 16.1094
0.0034 14.0 5404 0.0011 17.0281 13.6385
0.001 15.0 5790 0.0006 17.5356 13.4349
0.0031 16.0 6176 0.0004 18.5909 13.6354
0.0006 17.0 6562 0.0003 16.7808 12.7811
0.0002 18.0 6948 0.0002 18.2195 13.7038
0.0006 19.0 7334 0.0002 18.3330 13.9521
0.0001 20.0 7720 0.0002 18.0420 13.7394

Framework versions

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