readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  metrics:
  - wer
  model-index:
  - name: whisper-large-v2-spanish
    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-v2-spanish
  
  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: 0.1466
  - Wer: 0.0855
  
  ## 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: 16
  - seed: 42
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - lr_scheduler_warmup_steps: 500
  - training_steps: 25000
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch | Step  | Validation Loss | Wer    |
  |:-------------:|:-----:|:-----:|:---------------:|:------:|
  | 0.1908        | 0.03  | 1000  | 0.2235          | 0.1154 |
  | 0.1888        | 0.07  | 2000  | 0.2132          | 0.1131 |
  | 0.167         | 0.1   | 3000  | 0.2115          | 0.1133 |
  | 0.1752        | 0.14  | 4000  | 0.2081          | 0.1146 |
  | 0.1656        | 0.17  | 5000  | 0.2002          | 0.1073 |
  | 0.1535        | 0.21  | 6000  | 0.1971          | 0.1086 |
  | 0.1854        | 0.24  | 7000  | 0.1927          | 0.1048 |
  | 0.1722        | 0.28  | 8000  | 0.1889          | 0.1043 |
  | 0.166         | 0.31  | 9000  | 0.1850          | 0.1022 |
  | 0.1277        | 0.35  | 10000 | 0.1820          | 0.1032 |
  | 0.1457        | 0.38  | 11000 | 0.1777          | 0.0998 |
  | 0.169         | 0.42  | 12000 | 0.1771          | 0.0982 |
  | 0.1612        | 0.45  | 13000 | 0.1724          | 0.0976 |
  | 0.1616        | 0.49  | 14000 | 0.1693          | 0.0956 |
  | 0.1556        | 0.52  | 15000 | 0.1671          | 0.0942 |
  | 0.1448        | 0.56  | 16000 | 0.1646          | 0.0930 |
  | 0.117         | 0.59  | 17000 | 0.1613          | 0.0914 |
  | 0.1441        | 0.62  | 18000 | 0.1596          | 0.0899 |
  | 0.148         | 0.66  | 19000 | 0.1571          | 0.0895 |
  | 0.1255        | 0.69  | 20000 | 0.1547          | 0.0874 |
  | 0.1479        | 0.73  | 21000 | 0.1525          | 0.0885 |
  | 0.1304        | 0.76  | 22000 | 0.1503          | 0.0861 |
  | 0.1111        | 0.8   | 23000 | 0.1486          | 0.0867 |
  | 0.1337        | 0.83  | 24000 | 0.1472          | 0.0854 |
  | 0.1289        | 0.87  | 25000 | 0.1466          | 0.0855 |
  
  
  ### Framework versions
  
  - Transformers 4.26.0.dev0
  - Pytorch 1.13.1+cu117
  - Datasets 2.8.1.dev0
  - Tokenizers 0.13.2
  