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Sep29-Mixat-whisper-lg-3-transliteration-0.1trainasval

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whisper
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Sep29-Mixat-whisper-lg-3-transliteration-0.1trainasval / README.md
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metadata
license: apache-2.0
base_model: openai/whisper-large-v3
tags:
  - generated_from_trainer
metrics:
  - wer
model-index:
  - name: Sep29-Mixat-whisper-lg-3-transliteration-0.1trainasval
    results: []

Sep29-Mixat-whisper-lg-3-transliteration-0.1trainasval

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

  • Loss: 0.7875
  • Wer: 39.7972

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: 8
  • eval_batch_size: 8
  • seed: 42
  • gradient_accumulation_steps: 2
  • 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: 500
  • num_epochs: 100
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Wer
0.7619 0.4762 100 0.5335 54.2752
0.4908 0.9524 200 0.4641 49.9738
0.3846 1.4286 300 0.4498 43.4342
0.389 1.9048 400 0.4382 42.3151
0.2851 2.3810 500 0.4605 42.1927
0.2723 2.8571 600 0.4651 42.0878
0.202 3.3333 700 0.4855 40.9862
0.1731 3.8095 800 0.4809 41.3184
0.1243 4.2857 900 0.5475 40.6540
0.0988 4.7619 1000 0.5303 40.7064
0.0742 5.2381 1100 0.5775 40.6889
0.0531 5.7143 1200 0.5825 40.5316
0.0482 6.1905 1300 0.5976 41.3534
0.0368 6.6667 1400 0.6118 41.0911
0.0312 7.1429 1500 0.6439 42.0353
0.0242 7.6190 1600 0.6332 42.0528
0.0239 8.0952 1700 0.6684 39.3251
0.018 8.5714 1800 0.6527 42.3326
0.019 9.0476 1900 0.6736 40.7239
0.0153 9.5238 2000 0.6701 42.3326
0.0168 10.0 2100 0.7033 43.6790
0.0134 10.4762 2200 0.7028 40.2868
0.0141 10.9524 2300 0.6997 43.9063
0.0112 11.4286 2400 0.7055 42.1927
0.0118 11.9048 2500 0.7112 40.4266
0.0091 12.3810 2600 0.7509 41.5982
0.0106 12.8571 2700 0.7075 42.7872
0.0072 13.3333 2800 0.7263 43.3992
0.0096 13.8095 2900 0.7365 42.5249
0.0086 14.2857 3000 0.7722 42.0353
0.0099 14.7619 3100 0.7480 40.9862
0.0112 15.2381 3200 0.7422 40.9512
0.0076 15.7143 3300 0.7749 41.3709
0.0087 16.1905 3400 0.7505 39.9545
0.0073 16.6667 3500 0.7583 41.8780
0.0072 17.1429 3600 0.7541 41.0911
0.0063 17.6190 3700 0.7516 40.9337
0.0074 18.0952 3800 0.7798 41.2135
0.0067 18.5714 3900 0.7780 40.4791
0.0078 19.0476 4000 0.7596 41.2660
0.0061 19.5238 4100 0.7660 39.6048
0.0071 20.0 4200 0.7699 40.9862
0.0045 20.4762 4300 0.7855 41.4583
0.0055 20.9524 4400 0.7875 39.7972

Framework versions

  • Transformers 4.43.4
  • Pytorch 2.4.1
  • Datasets 3.0.0
  • Tokenizers 0.19.1