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This model is a fine-tuned version of openai/whisper-medium.en on the 500 SF 1000 dataset. It achieves the following results on the evaluation set:

  • Loss: 0.6792
  • Wer Ortho: 31.5962
  • Wer: 21.0621

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
1.6525 3.1746 100 1.1367 40.1968 29.4223
0.8573 6.3492 200 0.7964 30.8309 20.3803
0.6108 9.5238 300 0.7344 28.6808 18.9092
0.4957 12.6984 400 0.7017 29.1181 18.7298
0.4164 15.8730 500 0.6860 29.2274 18.8016
0.3577 19.0476 600 0.6802 29.3367 18.6939
0.3168 22.2222 700 0.6787 31.2682 20.7750
0.3023 25.3968 800 0.6792 31.5962 21.0621

Framework versions

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