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metadata
language:
  - en
license: apache-2.0
base_model: openai/whisper-base.en
tags:
  - generated_from_trainer
datasets:
  - Hani89/medical_asr_recording_dataset
metrics:
  - wer
model-index:
  - name: English Whisper Model
    results:
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: Medical
          type: Hani89/medical_asr_recording_dataset
          args: 'split: test'
        metrics:
          - name: Wer
            type: wer
            value: 8.218579234972678

English Whisper Model

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

  • Loss: 0.1241
  • Wer: 8.2186

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: 16
  • 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: 500
  • training_steps: 3000
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Wer
0.0573 3.0030 1000 0.1333 8.6703
0.0073 6.0060 2000 0.1185 8.2914
0.0009 9.0090 3000 0.1241 8.2186

Framework versions

  • Transformers 4.42.3
  • Pytorch 2.1.2
  • Datasets 2.20.0
  • Tokenizers 0.19.1