exp_004_base_multistage_urdu

This model is a fine-tuned version of sharjeel103/whisper-base-urdu on an unknown dataset. It achieves the following results on the evaluation set:

  • Loss: 0.3947
  • Wer: 65.7765
  • Wer Ortho: 68.6697
  • Cer: 21.6281

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: 5e-06
  • train_batch_size: 32
  • eval_batch_size: 16
  • seed: 42
  • gradient_accumulation_steps: 2
  • total_train_batch_size: 64
  • optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_steps: 1000
  • training_steps: 10000

Training results

Training Loss Epoch Step Validation Loss Wer Wer Ortho Cer
5.0263 0.6410 500 1.5195 95.4066 95.7445 33.6187
3.5885 1.2821 1000 0.9330 89.3228 90.2797 30.3680
3.0302 1.9231 1500 0.7431 82.6594 84.2995 27.0128
2.6802 2.5641 2000 0.6501 79.3803 81.3448 26.0944
2.4353 3.2051 2500 0.5907 76.0507 78.3277 24.5965
2.2853 3.8462 3000 0.5492 74.3293 76.6987 24.3492
2.1399 4.4872 3500 0.5184 72.7590 75.1236 23.5756
2.0097 5.1282 4000 0.4915 70.1810 72.8504 22.8648
1.9407 5.7692 4500 0.4697 69.6855 72.3975 22.6328
1.8625 6.4103 5000 0.4527 68.6736 71.3502 22.4363
1.7877 7.0513 5500 0.4359 68.1740 70.8474 22.0212
1.7349 7.6923 6000 0.4235 68.3125 71.0136 22.4257
1.6679 8.3333 6500 0.4130 66.2846 69.0105 21.4575
1.6108 8.9744 7000 0.4019 66.2804 69.1560 21.8611
1.5329 9.6154 7500 0.3947 65.7765 68.6697 21.6281
1.5234 10.2564 8000 0.3889 66.0621 68.8651 21.4800
1.5123 10.8974 8500 0.3841 66.1796 69.0396 21.5778
1.4779 11.5385 9000 0.3809 65.9949 68.8900 21.5524
1.4631 12.1795 9500 0.3787 65.9739 68.8277 21.4915
1.4256 12.8205 10000 0.3778 65.8857 68.7238 21.5002

Framework versions

  • Transformers 5.0.0
  • Pytorch 2.10.0+cu128
  • Datasets 3.6.0
  • Tokenizers 0.22.2
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