readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  metrics:
  - wer
  model-index:
  - name: whisper-large-nya
    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. -->
  
  # whisper-large-nya
  
  This model is a fine-tuned version of [openai/whisper-large](https://huggingface.co/openai/whisper-large) on the None dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.4712
  - Wer: 21.5239
  
  ## 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: 2.5e-05
  - train_batch_size: 8
  - eval_batch_size: 4
  - 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: 5000
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Wer     |
  |:-------------:|:-----:|:----:|:---------------:|:-------:|
  | 0.2416        | 0.99  | 500  | 0.5146          | 34.7076 |
  | 0.1343        | 1.97  | 1000 | 0.4138          | 28.1748 |
  | 0.0792        | 2.96  | 1500 | 0.4268          | 31.3290 |
  | 0.0372        | 3.94  | 2000 | 0.4256          | 32.8057 |
  | 0.0246        | 4.93  | 2500 | 0.4354          | 22.0673 |
  | 0.0097        | 5.92  | 3000 | 0.4532          | 25.1742 |
  | 0.003         | 6.9   | 3500 | 0.4595          | 21.0396 |
  | 0.0005        | 7.89  | 4000 | 0.4586          | 21.3113 |
  | 0.0007        | 8.87  | 4500 | 0.4653          | 21.7129 |
  | 0.0002        | 9.86  | 5000 | 0.4712          | 21.5239 |
  
  
  ### Framework versions
  
  - Transformers 4.25.1
  - Pytorch 1.13.0+cu116
  - Datasets 2.8.0
  - Tokenizers 0.13.2
  