readme: ---
  language:
  - ja
  license: apache-2.0
  tags:
  - whisper-event
  - generated_from_trainer
  datasets:
  - mozilla-foundation/common_voice_11_0
  metrics:
  - wer
  model-index:
  - name: Whisper Medium Japanese
    results:
    - task:
        name: Automatic Speech Recognition
        type: 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:
      - name: Wer
        type: wer
        value: 62.6897432259895
  ---
  
  <!-- 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. -->
  
  # Whisper Medium Japanese
  
  This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) on the mozilla-foundation/common_voice_11_0 ja dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.2165
  - Wer: 62.6897
  
  ## 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: 2
  - eval_batch_size: 1
  - 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: 5000
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Wer     |
  |:-------------:|:-----:|:----:|:---------------:|:-------:|
  | 0.2264        | 0.2   | 1000 | 0.3102          | 79.3588 |
  | 0.3195        | 0.4   | 2000 | 0.2830          | 78.1955 |
  | 0.3905        | 0.6   | 3000 | 0.2508          | 72.9181 |
  | 0.2478        | 0.8   | 4000 | 0.2407          | 68.8466 |
  | 0.0922        | 1.1   | 5000 | 0.2165          | 62.6897 |
  
  
  ### Framework versions
  
  - Transformers 4.26.0.dev0
  - Pytorch 1.13.0+cu117
  - Datasets 2.7.1.dev0
  - Tokenizers 0.13.2
  