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whisper-small-ko-baseline

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metadata
library_name: transformers
language:
  - hi
license: apache-2.0
base_model: openai/whisper-small
tags:
  - hf-asr-leaderboard
  - generated_from_trainer
datasets:
  - aihub_adult_baseline
model-index:
  - name: whisper-small-ko-baseline
    results: []

whisper-small-ko-baseline

This model is a fine-tuned version of openai/whisper-small on the aihub adult speed changed dataset. It achieves the following results on the evaluation set:

  • Loss: 0.2914
  • Cer: 8.5820

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: 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: 50
  • num_epochs: 4
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Cer
0.2433 0.1289 100 0.2943 7.8712
0.1379 0.2579 200 0.2758 7.4248
0.1105 0.3868 300 0.2831 7.6010
0.1231 0.5158 400 0.2671 7.2192
0.0984 0.6447 500 0.2721 7.2603
0.0938 0.7737 600 0.2742 7.1840
0.0954 0.9026 700 0.2718 6.9901
0.0385 1.0316 800 0.2649 6.9549
0.0302 1.1605 900 0.2645 7.5129
0.0388 1.2895 1000 0.2722 7.0606
0.0338 1.4184 1100 0.2819 7.8889
0.0389 1.5474 1200 0.2725 7.7479
0.0335 1.6763 1300 0.2716 8.3647
0.0331 1.8053 1400 0.2751 7.6774
0.0343 1.9342 1500 0.2825 7.8008
0.0134 2.0632 1600 0.2739 6.9079
0.0127 2.1921 1700 0.2779 8.8287
0.0141 2.3211 1800 0.2822 7.1429
0.0113 2.4500 1900 0.2864 8.6407
0.0131 2.5790 2000 0.2797 10.5909
0.0103 2.7079 2100 0.2835 8.4880
0.0117 2.8369 2200 0.2828 11.5425
0.0116 2.9658 2300 0.2832 9.5747
0.0046 3.0948 2400 0.2862 8.8640
0.0045 3.2237 2500 0.2877 10.0388
0.0061 3.3527 2600 0.2886 8.9991
0.0055 3.4816 2700 0.2894 8.4704
0.0052 3.6106 2800 0.2904 8.4410
0.0059 3.7395 2900 0.2908 10.3266
0.0051 3.8685 3000 0.2913 9.3280
0.0047 3.9974 3100 0.2914 8.5820

Framework versions

  • Transformers 4.46.0.dev0
  • Pytorch 2.4.1+cu121
  • Datasets 3.0.1
  • Tokenizers 0.20.0