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whisper-large-v3-cit-do015-wd0-lr5e-06-1000

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metadata
language:
  - en
license: apache-2.0
base_model: openai/whisper-large-v3
tags:
  - generated_from_trainer
metrics:
  - wer
model-index:
  - name: ./openai/whisper-large-v3-cit-do015-wd0-lr5e-06-1000
    results: []

./openai/whisper-large-v3-cit-do015-wd0-lr5e-06-1000

This model is a fine-tuned version of openai/whisper-large-v3 on the SF 1000 dataset. It achieves the following results on the evaluation set:

  • Loss: 0.4753
  • Wer Ortho: 23.5867
  • Wer: 12.4052

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-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 1.1494 33.5283 21.6616
1.2689 0.8889 50 0.6362 28.0702 14.9090
1.2689 1.3333 75 0.5078 24.3275 12.2534
0.5452 1.7778 100 0.3860 23.1189 11.7602
0.5452 2.2222 125 0.3789 23.1969 11.1912
0.3251 2.6667 150 0.3691 24.0546 11.4568
0.3251 3.1111 175 0.3545 23.9376 11.5706
0.2441 3.5556 200 0.3701 25.3411 13.2018
0.2441 4.0 225 0.3564 21.4815 9.9393
0.1651 4.4444 250 0.3909 22.5731 10.3566
0.1651 4.8889 275 0.3708 24.6394 13.0121
0.1394 5.3333 300 0.3928 24.7563 13.2018
0.1394 5.7778 325 0.4097 24.6784 13.2018
0.1062 6.2222 350 0.4270 25.3021 13.4294
0.1062 6.6667 375 0.4133 24.2105 12.8225
0.0831 7.1111 400 0.4275 23.9766 13.0880
0.0831 7.5556 425 0.4592 23.1579 12.3293
0.065 8.0 450 0.4617 23.9376 12.5190
0.065 8.4444 475 0.4685 23.5088 12.4810
0.0558 8.8889 500 0.4753 23.5867 12.4052

Framework versions

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