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This model is a fine-tuned version of openai/whisper-medium.en on the 200 SF 200 dataset. It achieves the following results on the evaluation set:

  • Loss: 0.8389
  • Wer Ortho: 35.1676
  • Wer: 23.8967

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-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: 800
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Wer Ortho Wer
1.5629 8.0 100 1.1016 41.3994 29.9964
0.7012 16.0 200 0.8286 36.0787 25.1525
0.4369 24.0 300 0.8091 36.1152 25.3678
0.3073 32.0 400 0.8257 34.7303 23.7890
0.2298 40.0 500 0.8354 34.8397 22.8920
0.1938 48.0 600 0.8306 35.4592 23.4661
0.1658 56.0 700 0.8359 35.5321 23.8967
0.1534 64.0 800 0.8389 35.1676 23.8967

Framework versions

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