Whisper Small Medical

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

  • Loss: 0.6201
  • Wer: 44.2553

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-07
  • train_batch_size: 32
  • eval_batch_size: 16
  • seed: 42
  • distributed_type: multi-GPU
  • optimizer: Use adamw_torch 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: 50
  • training_steps: 100
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Wer
7.4337 25.0 50 0.6201 44.2553
5.7447 50.0 100 0.6113 51.2340

Framework versions

  • Transformers 4.48.0.dev0
  • Pytorch 2.1.0+cu118
  • Datasets 3.1.1.dev0
  • Tokenizers 0.21.0
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