readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  datasets:
  - common_voice
  model-index:
  - name: wav2vec2-large-xls-r-300m-spanish-custom
    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-large-xls-r-300m-spanish-custom
  
  This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the common_voice dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.4426
  - Wer: 0.2117
  
  ## 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: 0.0003
  - 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: 30
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch | Step  | Validation Loss | Wer    |
  |:-------------:|:-----:|:-----:|:---------------:|:------:|
  | 4.2307        | 0.4   | 400   | 1.4431          | 0.9299 |
  | 0.7066        | 0.79  | 800   | 0.5928          | 0.4836 |
  | 0.4397        | 1.19  | 1200  | 0.4341          | 0.3730 |
  | 0.3889        | 1.58  | 1600  | 0.4063          | 0.3499 |
  | 0.3607        | 1.98  | 2000  | 0.3834          | 0.3235 |
  | 0.2866        | 2.37  | 2400  | 0.3885          | 0.3163 |
  | 0.2833        | 2.77  | 2800  | 0.3765          | 0.3140 |
  | 0.2692        | 3.17  | 3200  | 0.3849          | 0.3132 |
  | 0.2435        | 3.56  | 3600  | 0.3779          | 0.2984 |
  | 0.2404        | 3.96  | 4000  | 0.3756          | 0.2934 |
  | 0.2153        | 4.35  | 4400  | 0.3770          | 0.3075 |
  | 0.2087        | 4.75  | 4800  | 0.3819          | 0.3022 |
  | 0.1999        | 5.14  | 5200  | 0.3756          | 0.2959 |
  | 0.1838        | 5.54  | 5600  | 0.3827          | 0.2858 |
  | 0.1892        | 5.93  | 6000  | 0.3714          | 0.2999 |
  | 0.1655        | 6.33  | 6400  | 0.3814          | 0.2812 |
  | 0.1649        | 6.73  | 6800  | 0.3685          | 0.2727 |
  | 0.1668        | 7.12  | 7200  | 0.3832          | 0.2825 |
  | 0.1487        | 7.52  | 7600  | 0.3848          | 0.2788 |
  | 0.152         | 7.91  | 8000  | 0.3810          | 0.2787 |
  | 0.143         | 8.31  | 8400  | 0.3885          | 0.2856 |
  | 0.1353        | 8.7   | 8800  | 0.4103          | 0.2827 |
  | 0.1386        | 9.1   | 9200  | 0.4142          | 0.2874 |
  | 0.1222        | 9.5   | 9600  | 0.3983          | 0.2830 |
  | 0.1288        | 9.89  | 10000 | 0.4179          | 0.2781 |
  | 0.1199        | 10.29 | 10400 | 0.4035          | 0.2789 |
  | 0.1196        | 10.68 | 10800 | 0.4043          | 0.2746 |
  | 0.1169        | 11.08 | 11200 | 0.4105          | 0.2753 |
  | 0.1076        | 11.47 | 11600 | 0.4298          | 0.2686 |
  | 0.1124        | 11.87 | 12000 | 0.4025          | 0.2704 |
  | 0.1043        | 12.26 | 12400 | 0.4209          | 0.2659 |
  | 0.0976        | 12.66 | 12800 | 0.4070          | 0.2672 |
  | 0.1012        | 13.06 | 13200 | 0.4161          | 0.2720 |
  | 0.0872        | 13.45 | 13600 | 0.4245          | 0.2697 |
  | 0.0933        | 13.85 | 14000 | 0.4295          | 0.2684 |
  | 0.0881        | 14.24 | 14400 | 0.4011          | 0.2650 |
  | 0.0848        | 14.64 | 14800 | 0.3991          | 0.2675 |
  | 0.0852        | 15.03 | 15200 | 0.4166          | 0.2617 |
  | 0.0825        | 15.43 | 15600 | 0.4188          | 0.2639 |
  | 0.081         | 15.83 | 16000 | 0.4181          | 0.2547 |
  | 0.0753        | 16.22 | 16400 | 0.4103          | 0.2560 |
  | 0.0747        | 16.62 | 16800 | 0.4017          | 0.2498 |
  | 0.0761        | 17.01 | 17200 | 0.4159          | 0.2563 |
  | 0.0711        | 17.41 | 17600 | 0.4112          | 0.2603 |
  | 0.0698        | 17.8  | 18000 | 0.4335          | 0.2529 |
  | 0.073         | 18.2  | 18400 | 0.4120          | 0.2512 |
  | 0.0665        | 18.6  | 18800 | 0.4335          | 0.2496 |
  | 0.0657        | 18.99 | 19200 | 0.4143          | 0.2468 |
  | 0.0617        | 19.39 | 19600 | 0.4339          | 0.2435 |
  | 0.06          | 19.78 | 20000 | 0.4179          | 0.2438 |
  | 0.0613        | 20.18 | 20400 | 0.4251          | 0.2393 |
  | 0.0583        | 20.57 | 20800 | 0.4347          | 0.2422 |
  | 0.0562        | 20.97 | 21200 | 0.4246          | 0.2377 |
  | 0.053         | 21.36 | 21600 | 0.4198          | 0.2338 |
  | 0.0525        | 21.76 | 22000 | 0.4511          | 0.2427 |
  | 0.0499        | 22.16 | 22400 | 0.4482          | 0.2353 |
  | 0.0475        | 22.55 | 22800 | 0.4449          | 0.2329 |
  | 0.0465        | 22.95 | 23200 | 0.4364          | 0.2320 |
  | 0.0443        | 23.34 | 23600 | 0.4481          | 0.2304 |
  | 0.0458        | 23.74 | 24000 | 0.4