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whisper-small-CV-Fleurs-lg-300

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

  • Cer: 0.0803
  • Loss: 0.7644
  • Wer: 0.2848

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: 4
  • eval_batch_size: 2
  • seed: 42
  • gradient_accumulation_steps: 2
  • total_train_batch_size: 8
  • optimizer: Use adamw_hf with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_ratio: 0.1
  • num_epochs: 30
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Cer Validation Loss Wer
0.9959 1.0 30487 0.4780 0.5805 1.0507
0.2917 2.0 60974 0.8109 0.4413 1.4684
0.1945 3.0 91461 0.1449 0.4016 0.4604
0.139 4.0 121948 0.0933 0.3980 0.3740
0.0986 5.0 152435 0.0910 0.4101 0.3542
0.0698 6.0 182922 0.0935 0.4245 0.3447
0.0515 7.0 213409 0.0824 0.4571 0.3246
0.0412 8.0 243896 0.0843 0.4769 0.3185
0.0362 9.0 274383 0.0812 0.4909 0.3106
0.0339 10.0 304870 0.0819 0.5001 0.3119
0.0301 11.0 335357 0.0848 0.5248 0.3142
0.0243 12.0 365844 0.0843 0.5246 0.3045
0.0199 13.0 396331 0.0801 0.5518 0.3004
0.0167 14.0 426818 0.0857 0.5877 0.3085
0.0143 15.0 457305 0.0806 0.5835 0.3024
0.0124 16.0 487792 0.0819 0.5982 0.2995
0.011 17.0 518279 0.0845 0.5933 0.3022
0.0099 18.0 548766 0.0866 0.6195 0.2996
0.0088 19.0 579253 0.0825 0.6577 0.2966
0.0079 20.0 609740 0.0843 0.6416 0.2991
0.0073 21.0 640227 0.0810 0.6536 0.2938
0.0065 22.0 670714 0.0829 0.6708 0.2990
0.006 23.0 701201 0.0867 0.6726 0.2978
0.0056 24.0 731688 0.0819 0.6944 0.2921
0.0053 25.0 762175 0.0824 0.6845 0.2942
0.0049 26.0 792662 0.0840 0.6856 0.2926
0.0046 27.0 823149 0.0829 0.6926 0.2914
0.0042 28.0 853636 0.0832 0.7022 0.2866
0.004 29.0 884123 0.0800 0.7230 0.2898
0.0037 30.0 914610 0.0824 0.7287 0.2925
0.0034 31.0 945097 0.0801 0.7363 0.2860
0.0033 32.0 975584 0.0803 0.7497 0.2866
0.0032 33.0 1006071 0.0814 0.7478 0.2827
0.0029 34.0 1036558 0.0791 0.7292 0.2845
0.0028 35.0 1067045 0.0829 0.7657 0.2891
0.0026 36.0 1097532 0.0803 0.7644 0.2848

Framework versions

  • Transformers 4.47.0
  • Pytorch 2.1.0+cu118
  • Datasets 3.2.0
  • Tokenizers 0.21.0
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