readme: ---
  language:
  - es
  license: apache-2.0
  tags:
  - automatic-speech-recognition
  - mozilla-foundation/common_voice_8_0
  - generated_from_trainer
  - es
  - robust-speech-event
  - hf-asr-leaderboard
  datasets:
  - mozilla-foundation/common_voice_8_0
  model-index:
  - name: xls-r-es-test
    results:
    - task:
        name: Automatic Speech Recognition
        type: automatic-speech-recognition
      dataset:
        name: Common Voice 8.0
        type: mozilla-foundation/common_voice_8_0
        args: es
      metrics:
      - name: Test WER
        type: wer
        value: 12.62
    - task:
        name: Automatic Speech Recognition
        type: automatic-speech-recognition
      dataset:
        name: Robust Speech Event - Dev Data
        type: speech-recognition-community-v2/dev_data
        args: es
      metrics:
      - name: Test WER
        type: wer
        value: 36.08
    - task:
        name: Automatic Speech Recognition
        type: automatic-speech-recognition
      dataset:
        name: Robust Speech Event - Test Data
        type: speech-recognition-community-v2/eval_data
        args: es
      metrics:
      - name: Test WER
        type: wer
        value: 39.19
  ---
  
  <!-- 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. -->
  
  # xls-r-es-test
  
  This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - ES dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.1304
  - WER: 0.1261
  - CER: 0.035
  
  ## 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: 7.5e-05
  - train_batch_size: 8
  - eval_batch_size: 8
  - seed: 42
  - gradient_accumulation_steps: 4
  - total_train_batch_size: 32
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - lr_scheduler_warmup_steps: 2000
  - num_epochs: 10.0
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch | Step  | Validation Loss | Wer    |
  |:-------------:|:-----:|:-----:|:---------------:|:------:|
  | 2.9613        | 0.07  | 500   | 2.9647          | 1.0    |
  | 2.604         | 0.14  | 1000  | 1.8300          | 0.9562 |
  | 1.177         | 0.21  | 1500  | 0.3652          | 0.3077 |
  | 1.0745        | 0.28  | 2000  | 0.2707          | 0.2504 |
  | 1.0103        | 0.35  | 2500  | 0.2338          | 0.2157 |
  | 0.9858        | 0.42  | 3000  | 0.2321          | 0.2129 |
  | 0.974         | 0.49  | 3500  | 0.2164          | 0.2031 |
  | 0.9699        | 0.56  | 4000  | 0.2078          | 0.1970 |
  | 0.9513        | 0.63  | 4500  | 0.2173          | 0.2139 |
  | 0.9657        | 0.7   | 5000  | 0.2050          | 0.1979 |
  | 0.9484        | 0.77  | 5500  | 0.2008          | 0.1919 |
  | 0.9317        | 0.84  | 6000  | 0.2012          | 0.1911 |
  | 0.9366        | 0.91  | 6500  | 0.2024          | 0.1976 |
  | 0.9242        | 0.98  | 7000  | 0.2062          | 0.2028 |
  | 0.9138        | 1.05  | 7500  | 0.1924          | 0.1863 |
  | 0.921         | 1.12  | 8000  | 0.1935          | 0.1836 |
  | 0.9117        | 1.19  | 8500  | 0.1887          | 0.1815 |
  | 0.9064        | 1.26  | 9000  | 0.1909          | 0.1839 |
  | 0.9118        | 1.32  | 9500  | 0.1869          | 0.1830 |
  | 0.9121        | 1.39  | 10000 | 0.1863          | 0.1802 |
  | 0.9048        | 1.46  | 10500 | 0.1845          | 0.1791 |
  | 0.8955        | 1.53  | 11000 | 0.1863          | 0.1774 |
  | 0.8947        | 1.6   | 11500 | 0.1907          | 0.1814 |
  | 0.9073        | 1.67  | 12000 | 0.1892          | 0.1853 |
  | 0.8927        | 1.74  | 12500 | 0.1821          | 0.1750 |
  | 0.8732        | 1.81  | 13000 | 0.1815          | 0.1768 |
  | 0.8761        | 1.88  | 13500 | 0.1822          | 0.1749 |
  | 0.8751        | 1.95  | 14000 | 0.1789          | 0.1715 |
  | 0.8889        | 2.02  | 14500 | 0.1819          | 0.1791 |
  | 0.8864        | 2.09  | 15000 | 0.1826          | 0.1794 |
  | 0.886         | 2.16  | 15500 | 0.1788          | 0.1776 |
  | 0.8915        | 2.23  | 16000 | 0.1756          | 0.1719 |
  | 0.8689        | 2.3   | 16500 | 0.1769          | 0.1711 |
  | 0.879         | 2.37  | 17000 | 0.1777          | 0.1739 |
  | 0.8692        | 2.44  | 17500 | 0.1765          | 0.1705 |
  | 0.8504        | 2.51  | 18000 | 0.1699          | 0.1652 |
  | 0.8728        | 2.58  | 18500 | 0.1705          | 0.1694 |
  | 0.8523        | 2.65  | 19000 | 0.1674          | 0.1645 |
  | 0.8513        | 2.72  | 19500 | 0.1661          | 0.1611 |
  | 0.8498        | 2.79  | 20000 | 0.1660          | 0.1631 |
  | 0.8432        | 2.86  | 20500 | 0.1636          | 0.1610 |
  | 0.8492        | 2.93  | 21000 | 0.1708          | 0.1688 |
  | 0.8561        | 3.0   | 21500 | 0.1663          | 0.1604 |
  | 0.842         | 3.07  | 