readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  metrics:
  - wer
  model-index:
  - name: openai/whisper-large-v2
    results: []
  ---
  
  <!-- 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 None dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.8022
  - Wer: 20.0210
  
  ## 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
  - distributed_type: multi-GPU
  - num_devices: 4
  - gradient_accumulation_steps: 2
  - total_train_batch_size: 128
  - total_eval_batch_size: 32
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - lr_scheduler_warmup_steps: 200
  - training_steps: 500
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Wer     |
  |:-------------:|:-----:|:----:|:---------------:|:-------:|
  | 0.0029        | 8.33  | 100  | 0.6650          | 19.2872 |
  | 0.0005        | 16.67 | 200  | 0.7456          | 18.4486 |
  | 0.0003        | 25.0  | 300  | 0.7798          | 19.4969 |
  | 0.0002        | 33.33 | 400  | 0.7964          | 19.7065 |
  | 0.0002        | 41.67 | 500  | 0.8022          | 20.0210 |
  
  
  ### Framework versions
  
  - Transformers 4.26.0.dev0
  - Pytorch 1.13.1
  - Datasets 2.8.1.dev0
  - Tokenizers 0.13.2
  