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This model is a fine-tuned version of openai/whisper-large-v3 on the 3382 FULL dataset. It achieves the following results on the evaluation set:

  • Loss: 0.5210
  • Wer Ortho: 28.1904
  • Wer: 19.8548

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: 3e-06
  • train_batch_size: 8
  • eval_batch_size: 8
  • seed: 42
  • distributed_type: multi-GPU
  • gradient_accumulation_steps: 2
  • total_train_batch_size: 16
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_steps: 200
  • training_steps: 1000
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Wer Ortho Wer
0.8814 0.5249 100 0.6343 33.7614 24.7007
0.636 1.0499 200 0.5703 30.8376 22.5417
0.5203 1.5748 300 0.5425 30.0474 21.4292
0.4733 2.0997 400 0.5259 29.0695 20.3451
0.4026 2.6247 500 0.5176 28.3880 19.8548
0.3779 3.1496 600 0.5205 28.0917 19.8171
0.3365 3.6745 700 0.5146 28.2003 20.1094
0.3354 4.1995 800 0.5220 27.7558 19.6097
0.3016 4.7244 900 0.5229 28.4077 19.8171
0.2875 5.2493 1000 0.5210 28.1904 19.8548

Framework versions

  • Transformers 4.44.0
  • Pytorch 1.13.1+cu117
  • Datasets 2.20.0
  • Tokenizers 0.19.1
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