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whisper-medium-mix-ja

Automatic Speech Recognition
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metadata
license: apache-2.0
tags:
  - whisper-event
  - generated_from_trainer
datasets:
  - vumichien/preprocessed_jsut_jsss_css10_common_voice_11
metrics:
  - wer
base_model: openai/whisper-medium
model-index:
  - name: Whisper Medium 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: 8.7213
            name: Wer
          - type: cer
            value: 5.4698
            name: Cer
      - task:
          type: automatic-speech-recognition
          name: Automatic Speech Recognition
        dataset:
          name: google/fleurs
          type: google/fleurs
          config: ja_jp
          split: test
        metrics:
          - type: wer
            value: 12.825163229350192
            name: WER
          - type: cer
            value: 7.797336057522297
            name: CER

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