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whisper-medium-mn-4

This model is a fine-tuned version of openai/whisper-medium on the None dataset. It achieves the following results on the evaluation set:

  • Loss: 0.6015
  • Wer: 33.0293
  • Cer: 10.9236

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: 32
  • eval_batch_size: 16
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_steps: 500
  • training_steps: 15000
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Wer Cer
0.0362 4.26 1000 0.4204 40.2720 13.8389
0.0087 8.51 2000 0.4712 37.4918 12.9175
0.0044 12.77 3000 0.4893 36.3393 12.4727
0.0033 17.02 4000 0.5159 35.8423 12.2933
0.0017 21.28 5000 0.5183 35.2797 12.1104
0.0016 25.53 6000 0.5422 35.4326 11.7454
0.0011 29.79 7000 0.5361 34.5314 11.5196
0.0004 34.04 8000 0.5406 34.0998 11.3650
0.0006 38.3 9000 0.5540 33.8650 11.2912
0.0002 42.55 10000 0.5748 34.0889 11.5333
0.0003 46.81 11000 0.5771 34.5641 11.4895
0.0 51.06 12000 0.5809 33.4335 11.2070
0.0 55.32 13000 0.5941 33.2095 11.0009
0.0 59.57 14000 0.6015 33.0293 10.9236
0.0 63.83 15000 0.6045 33.0347 10.9125

Framework versions

  • Transformers 4.26.0.dev0
  • Pytorch 1.13.0+cu117
  • Datasets 2.7.1.dev0
  • Tokenizers 0.13.2
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Datasets used to train bayartsogt/whisper-medium-mn-4

Evaluation results