readme: ---
  license: apache-2.0
  tags:
  - whisper-event
  - generated_from_trainer
  datasets:
  - vumichien/preprocessed_jsut_jsss_css10_common_voice_11
  metrics:
  - wer
  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
  ---
  
  <!-- This model card has been generated automatically according to the information the Trainer had access to. You
  should probably proofread and complete it, then remove this comment. -->
  
  # openai/whisper-medium
  
  This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/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
  