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whisper-medium-medical

Automatic Speech Recognition
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whisper
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metadata
license: apache-2.0
base_model: openai/whisper-medium
tags:
  - generated_from_trainer
datasets:
  - yashtiwari/PaulMooney-Medical-ASR-Data
metrics:
  - wer
model-index:
  - name: Whisper Medium Medical
    results:
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: Medical ASR
          type: yashtiwari/PaulMooney-Medical-ASR-Data
        metrics:
          - name: Wer
            type: wer
            value: 16.051170649287954

Visualize in Weights & Biases

Whisper Medium Medical

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

  • Loss: 0.0567
  • Wer: 16.0512

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-05
  • train_batch_size: 32
  • eval_batch_size: 8
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_steps: 50
  • training_steps: 500
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Wer
0.4817 0.5405 100 0.1982 12.8651
0.104 1.0811 200 0.0839 10.3065
0.0549 1.6216 300 0.0643 15.9063
0.0245 2.1622 400 0.0610 14.0961
0.012 2.7027 500 0.0567 16.0512

Framework versions

  • Transformers 4.42.4
  • Pytorch 2.2.0+cu121
  • Datasets 2.20.0
  • Tokenizers 0.19.1