readme: ---
  language:
  - ja
  license: other
  tags:
  - whisper-event
  - generated_from_trainer
  datasets:
  - Elite35P-Server/EliteVoiceProject
  metrics:
  - wer
  model-index:
  - name: Whisper Small Japanese Elite
    results:
    - task:
        name: Automatic Speech Recognition
        type: automatic-speech-recognition
      dataset:
        name: Elite35P-Server/EliteVoiceProject youtube
        type: Elite35P-Server/EliteVoiceProject
        config: youtube
        split: test
        args: youtube
      metrics:
      - name: Wer
        type: wer
        value: 31.536388140161726
  ---
  
  <!-- 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 Small Japanese Elite
  
  This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the Elite35P-Server/EliteVoiceProject youtube dataset.
  It achieves the following results on the evaluation set:
  - Loss: 1.1596
  - Wer: 31.5364
  
  ## 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: 16
  - eval_batch_size: 8
  - seed: 42
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: constant_with_warmup
  - lr_scheduler_warmup_steps: 100
  - training_steps: 10000
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch | Step  | Validation Loss | Wer     |
  |:-------------:|:-----:|:-----:|:---------------:|:-------:|
  | 0.0003        | 52.0  | 1000  | 0.8053          | 28.8410 |
  | 0.0           | 105.0 | 2000  | 0.8636          | 28.5714 |
  | 0.0           | 157.0 | 3000  | 0.9056          | 28.0323 |
  | 0.0           | 210.0 | 4000  | 0.9414          | 28.8410 |
  | 0.0           | 263.0 | 5000  | 0.9842          | 31.2668 |
  | 0.0           | 315.0 | 6000  | 1.0223          | 31.2668 |
  | 0.0           | 368.0 | 7000  | 1.0677          | 31.2668 |
  | 0.0           | 421.0 | 8000  | 1.1079          | 31.2668 |
  | 0.0           | 473.0 | 9000  | 1.1468          | 31.5364 |
  | 0.0           | 526.0 | 10000 | 1.1596          | 31.5364 |
  
  
  ### Framework versions
  
  - Transformers 4.26.0.dev0
  - Pytorch 1.13.1+cu117
  - Datasets 2.8.1.dev0
  - Tokenizers 0.13.2
  