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whisper-large-v3-medical

Automatic Speech Recognition
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metadata
library_name: transformers
license: apache-2.0
base_model: openai/whisper-large-v3
tags:
  - whisper-event
  - generated_from_trainer
datasets:
  - OUTCOMESAI/medical_speech_corpus
metrics:
  - wer
model-index:
  - name: Whisper Large V3 Medical
    results:
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: OUTCOMESAI/medical_speech_corpus en
          type: OUTCOMESAI/medical_speech_corpus
        metrics:
          - name: Wer
            type: wer
            value: 3.2635854592980795

Whisper Large V3 Medical

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

  • Loss: 0.1453
  • Wer: 3.2636

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: 64
  • eval_batch_size: 32
  • 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: 200
  • training_steps: 5000
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Wer
4.2439 0.1530 200 0.2935 4.5078
3.3374 0.3060 400 0.2734 4.6961
3.0833 0.4591 600 0.2673 4.2733
1.8243 0.6121 800 0.2681 4.4373
1.1288 0.7651 1000 0.2549 4.2771
0.8199 0.9181 1200 0.2412 4.2041
0.681 1.0712 1400 0.2311 4.1054
0.5798 1.2242 1600 0.2192 4.0093
0.5233 1.3772 1800 0.2072 3.8927
0.463 1.5302 2000 0.1992 3.8197
0.428 1.6832 2200 0.1951 3.7748
0.3944 1.8363 2400 0.1866 3.6775
0.3682 1.9893 2600 0.1792 3.6044
0.3543 2.1423 2800 0.1725 3.5301
0.3368 2.2953 3000 0.1714 3.4904
0.3136 2.4484 3200 0.1648 3.4571
0.3121 2.6014 3400 0.1604 3.4238
0.2959 2.7544 3600 0.1561 3.3956
0.2912 2.9074 3800 0.1538 3.3738
0.2767 3.0604 4000 0.1511 3.3456
0.2848 3.2135 4200 0.1487 3.3200
0.274 3.3665 4400 0.1475 3.2841
0.2694 3.5195 4600 0.1464 3.2828
0.2731 3.6725 4800 0.1455 3.2687
0.2677 3.8256 5000 0.1453 3.2636

Framework versions

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