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whisper-medium-mix-ja

Automatic Speech Recognition
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openai/whisper-medium

This model is a fine-tuned version of openai/whisper-medium on the vumichien/preprocessed_jsut_jsss_css10_common_voice_11 dataset. It achieves the following results on the evaluation set:

  • Loss: 0.2836
  • Wer: 8.7213
  • Cer: 5.4698

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: 32
  • eval_batch_size: 16
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_steps: 500
  • training_steps: 10000
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Wer Cer
0.1106 1.1 1000 0.1827 10.3480 6.4784
0.0487 2.2 2000 0.1799 9.4764 5.9127
0.0243 3.29 3000 0.1950 9.2111 5.8069
0.0106 4.39 4000 0.2113 8.9713 5.5756
0.0054 5.49 5000 0.2325 8.6470 5.4041
0.0031 6.59 6000 0.2462 8.7078 5.4409
0.0014 7.68 7000 0.2608 8.7145 5.4849
0.0009 8.78 8000 0.2695 8.6301 5.3876
0.0004 9.88 9000 0.2794 8.6064 5.3528
0.0003 10.98 10000 0.2836 8.7213 5.4698

Framework versions

  • Transformers 4.26.0.dev0
  • Pytorch 1.13.0+cu117
  • Datasets 2.7.1.dev0
  • Tokenizers 0.13.2
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Dataset used to train vumichien/whisper-medium-mix-ja

Evaluation results

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