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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: 16.9843
  • Cer: 13.2128

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
0.7745 1.0 386 0.3281 23.8481 18.9940
0.4222 2.0 772 0.1559 34.1276 31.2181
0.2229 3.0 1158 0.0890 41.8389 41.5395
0.0959 4.0 1544 0.0530 43.5633 41.1019
0.0783 5.0 1930 0.0374 35.9313 35.5561
0.0491 6.0 2316 0.0241 31.5489 24.3995
0.0384 7.0 2702 0.0182 26.3650 22.6028
0.0454 8.0 3088 0.0122 25.1452 20.9192
0.0153 9.0 3474 0.0076 21.1914 17.8663
0.013 10.0 3860 0.0059 20.7264 16.9106
0.0049 11.0 4246 0.0035 18.8548 14.8285
0.0028 12.0 4632 0.0026 16.8447 12.9932
0.0045 13.0 5018 0.0017 20.3620 16.0350
0.0015 14.0 5404 0.0010 18.4088 13.8127
0.0021 15.0 5790 0.0006 16.3526 13.1850
0.0042 16.0 6176 0.0004 16.0627 12.6793
0.0009 17.0 6562 0.0003 16.4933 13.0223
0.0003 18.0 6948 0.0002 16.9122 12.9176
0.0006 19.0 7334 0.0002 16.8802 13.1395
0.0001 20.0 7720 0.0002 16.9843 13.2128

Framework versions

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