readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  - whisper-event
  datasets:
  - vumichien/preprocessed_jsut_jsss_css10_common_voice_11
  metrics:
  - wer
  - cer
  model-index:
  - name: openai/whisper-large-v2
    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: 7.6453
        name: Wer
      - type: cer
        value: 4.7187
        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: 11.69
        name: WER
      - type: cer
        value: 7.12
        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-large-v2
  
  This model is a fine-tuned version of [openai/whisper-large-v2](https://huggingface.co/openai/whisper-large-v2) on the vumichien/preprocessed_jsut_jsss_css10_common_voice_11 dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.2284
  - Wer: 7.6453
  - Cer: 4.7187
  
  ## 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: 8
  - eval_batch_size: 8
  - seed: 42
  - gradient_accumulation_steps: 2
  - total_train_batch_size: 16
  - 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    |
  |:-------------:|:-----:|:-----:|:---------------:|:-------:|:------:|
  | 0.1912        | 0.55  | 1000  | 0.1828          | 11.2314 | 7.0357 |
  | 0.1329        | 1.1   | 2000  | 0.1618          | 9.4172  | 5.9028 |
  | 0.0912        | 1.65  | 3000  | 0.1616          | 8.9257  | 5.4711 |
  | 0.0576        | 2.2   | 4000  | 0.1664          | 8.5861  | 5.3055 |
  | 0.0449        | 2.74  | 5000  | 0.1642          | 8.4510  | 5.2930 |
  | 0.02          | 3.29  | 6000  | 0.1799          | 8.1537  | 5.0354 |
  | 0.019         | 3.84  | 7000  | 0.1801          | 8.125   | 5.0827 |
  | 0.0067        | 4.39  | 8000  | 0.2003          | 7.8412  | 4.8133 |
  | 0.006         | 4.94  | 9000  | 0.2071          | 7.5811  | 4.7023 |
  | 0.0022        | 5.49  | 10000 | 0.2284          | 7.6453  | 4.7187 |
  
  
  ### Framework versions
  
  - Transformers 4.26.0.dev0
  - Pytorch 1.13.0+cu117
  - Datasets 2.7.1.dev0
  - Tokenizers 0.13.2
  