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whisper-large-v2-jp

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metadata
language:
  - ja
license: apache-2.0
tags:
  - whisper-event
  - generated_from_trainer
datasets:
  - mozilla-foundation/common_voice_11_0
metrics:
  - wer
base_model: openai/whisper-large-v2
model-index:
  - name: Whisper Large V2 Japanese
    results:
      - task:
          type: automatic-speech-recognition
          name: Automatic Speech Recognition
        dataset:
          name: mozilla-foundation/common_voice_11_0 ja
          type: mozilla-foundation/common_voice_11_0
          config: ja
          split: test
          args: ja
        metrics:
          - type: wer
            value: 8.1166
            name: Wer
          - type: cer
            value: 5.0032
            name: Cer

openai/whisper-large-v2

This model is a fine-tuned version of openai/whisper-large-v2 on the mozilla-foundation/common_voice_11_0 dataset. It achieves the following results on the evaluation set:

  • Loss: 0.2352
  • Wer: 8.1166
  • Cer: 5.0032

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
  • training_steps: 10000
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Wer Cer
0.0897 0.1 1000 0.1884 11.0068 6.6992
0.0396 0.2 2000 0.1749 9.7399 5.9350
0.036 1.1 3000 0.1698 9.1419 5.6781
0.012 1.2 4000 0.1849 9.3041 5.7661
0.0151 2.09 5000 0.1879 9.1959 5.6761
0.0047 2.19 6000 0.2097 8.6706 5.4422
0.0046 3.09 7000 0.2040 8.8277 5.4717
0.0015 3.19 8000 0.2260 8.4949 5.3101
0.0013 4.09 9000 0.2339 8.3716 5.1471
0.0005 4.19 10000 0.2352 8.1166 5.0032

Framework versions

  • Transformers 4.26.0.dev0
  • Pytorch 1.13.0+cu117
  • Datasets 2.7.1.dev0
  • Tokenizers 0.13.2