readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  model-index:
  - name: wav2vec2-base-vios-commonvoice
    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. -->
  
  # wav2vec2-base-vios-commonvoice
  
  This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the None dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.3823
  - Wer: 0.2401
  
  ## 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: 5e-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
  - num_epochs: 15
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch | Step  | Validation Loss | Wer    |
  |:-------------:|:-----:|:-----:|:---------------:|:------:|
  | 1.2268        | 0.66  | 500   | 0.8746          | 0.5939 |
  | 0.8728        | 1.32  | 1000  | 0.6435          | 0.4554 |
  | 0.6899        | 1.99  | 1500  | 0.5655          | 0.3995 |
  | 0.5842        | 2.65  | 2000  | 0.5267          | 0.3694 |
  | 0.5371        | 3.31  | 2500  | 0.4980          | 0.3431 |
  | 0.4921        | 3.97  | 3000  | 0.4781          | 0.3276 |
  | 0.4508        | 4.64  | 3500  | 0.4434          | 0.3134 |
  | 0.433         | 5.3   | 4000  | 0.4348          | 0.2963 |
  | 0.404         | 5.96  | 4500  | 0.4248          | 0.2874 |
  | 0.3834        | 6.62  | 5000  | 0.4163          | 0.2775 |
  | 0.3784        | 7.28  | 5500  | 0.4104          | 0.2751 |
  | 0.3669        | 7.95  | 6000  | 0.4143          | 0.2724 |
  | 0.3462        | 8.61  | 6500  | 0.4131          | 0.2699 |
  | 0.3364        | 9.27  | 7000  | 0.4070          | 0.2617 |
  | 0.3249        | 9.93  | 7500  | 0.4076          | 0.2603 |
  | 0.3154        | 10.6  | 8000  | 0.3998          | 0.2577 |
  | 0.3117        | 11.26 | 8500  | 0.3930          | 0.2505 |
  | 0.3101        | 11.92 | 9000  | 0.4003          | 0.2492 |
  | 0.298         | 12.58 | 9500  | 0.3960          | 0.2496 |
  | 0.2968        | 13.24 | 10000 | 0.3877          | 0.2469 |
  | 0.29          | 13.91 | 10500 | 0.3870          | 0.2456 |
  | 0.2921        | 14.57 | 11000 | 0.3823          | 0.2401 |
  
  
  ### Framework versions
  
  - Transformers 4.19.2
  - Pytorch 1.11.0+cu113
  - Datasets 2.2.2
  - Tokenizers 0.12.1
  