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This model is a fine-tuned version of openai/whisper-medium.en on the 300 SF 1000 dataset. It achieves the following results on the evaluation set:

  • Loss: 0.75
  • Wer Ortho: 33.0175
  • Wer: 21.3491

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: 500
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Wer Ortho Wer
1.6187 5.2632 100 1.1230 41.5087 30.1041
0.7964 10.5263 200 0.8076 31.8878 20.2368
0.5424 15.7895 300 0.7627 31.7055 20.0933
0.4204 21.0526 400 0.7495 33.1268 21.2056
0.3666 26.3158 500 0.75 33.0175 21.3491

Framework versions

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