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whisper-medium.en-cit-do015-wd0-lr1e-06-1000

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metadata
license: apache-2.0
base_model: openai/whisper-medium.en
tags:
  - generated_from_trainer
metrics:
  - wer
model-index:
  - name: whisper-medium.en-cit-do015-wd0-lr1e-06-1000
    results: []

whisper-medium.en-cit-do015-wd0-lr1e-06-1000

This model is a fine-tuned version of openai/whisper-medium.en on the None dataset. It achieves the following results on the evaluation set:

  • Loss: 0.6953
  • Wer Ortho: 26.2768
  • Wer: 14.7572

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: 4
  • eval_batch_size: 8
  • seed: 42
  • distributed_type: multi-GPU
  • gradient_accumulation_steps: 4
  • 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: 100
  • training_steps: 500
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Wer Ortho Wer
No log 0.4444 25 1.5811 45.2632 31.9044
1.7463 0.8889 50 1.3848 39.1033 27.0106
1.7463 1.3333 75 1.2178 35.7505 23.0273
1.3387 1.7778 100 1.0166 36.1014 23.4446
1.3387 2.2222 125 0.8784 31.9298 19.1958
0.988 2.6667 150 0.8340 30.8382 18.4750
0.988 3.1111 175 0.8027 30.3314 17.7162
0.8856 3.5556 200 0.7812 29.6686 17.4127
0.8856 4.0 225 0.7651 30.1365 17.6783
0.7927 4.4444 250 0.7515 29.2008 16.8816
0.7927 4.8889 275 0.7402 28.2651 15.6677
0.7482 5.3333 300 0.7300 27.9922 15.5159
0.7482 5.7778 325 0.7217 27.8752 15.6677
0.7275 6.2222 350 0.7153 27.4854 15.4021
0.7275 6.6667 375 0.7085 27.3684 15.3642
0.7003 7.1111 400 0.7041 26.6277 14.6813
0.7003 7.5556 425 0.7002 26.3158 14.7572
0.6763 8.0 450 0.6973 26.2378 14.6055
0.6763 8.4444 475 0.6963 26.4327 14.7951
0.6687 8.8889 500 0.6953 26.2768 14.7572

Framework versions

  • Transformers 4.42.3
  • Pytorch 1.13.1+cu117
  • Datasets 2.20.0
  • Tokenizers 0.19.1