readme: ---
  language:
  - ja
  license: other
  tags:
  - whisper-event
  - generated_from_trainer
  datasets:
  - Elite35P-Server/EliteVoiceProject
  metrics:
  - wer
  model-index:
  - name: Whisper Base Japanese Elite
    results:
    - task:
        name: Automatic Speech Recognition
        type: automatic-speech-recognition
      dataset:
        name: Elite35P-Server/EliteVoiceProject twitter
        type: Elite35P-Server/EliteVoiceProject
        config: twitter
        split: test
        args: twitter
      metrics:
      - name: Wer
        type: wer
        value: 17.073170731707318
  ---
  
  <!-- 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 Base Japanese Elite
  
  This model is a fine-tuned version of [openai/whisper-base](https://huggingface.co/openai/whisper-base) on the Elite35P-Server/EliteVoiceProject twitter dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.4385
  - Wer: 17.0732
  
  ## 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: 32
  - seed: 42
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: constant_with_warmup
  - lr_scheduler_warmup_steps: 200
  - training_steps: 10000
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch  | Step  | Validation Loss | Wer     |
  |:-------------:|:------:|:-----:|:---------------:|:-------:|
  | 0.0002        | 111.0  | 1000  | 0.2155          | 9.7561  |
  | 0.0001        | 222.0  | 2000  | 0.2448          | 12.1951 |
  | 0.0           | 333.0  | 3000  | 0.2674          | 13.4146 |
  | 0.0           | 444.0  | 4000  | 0.2943          | 15.8537 |
  | 0.0           | 555.0  | 5000  | 0.3182          | 17.0732 |
  | 0.0           | 666.0  | 6000  | 0.3501          | 18.9024 |
  | 0.0           | 777.0  | 7000  | 0.3732          | 16.4634 |
  | 0.0           | 888.0  | 8000  | 0.4025          | 17.0732 |
  | 0.0           | 999.0  | 9000  | 0.4178          | 20.1220 |
  | 0.0           | 1111.0 | 10000 | 0.4385          | 17.0732 |
  
  
  ### Framework versions
  
  - Transformers 4.26.0.dev0
  - Pytorch 1.13.1+cu117
  - Datasets 2.8.1.dev0
  - Tokenizers 0.13.2
  