readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  datasets:
  - common_voice_9_0
  model-index:
  - name: wav2vec2-large-xls-r-300m-pt-colab
    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-pt-colab
  
  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_9_0 dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.2975
  - Wer: 0.1736
  
  ## 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: 16
  - eval_batch_size: 8
  - seed: 42
  - gradient_accumulation_steps: 2
  - 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: 500
  - num_epochs: 30
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch | Step  | Validation Loss | Wer    |
  |:-------------:|:-----:|:-----:|:---------------:|:------:|
  | 5.179         | 0.49  | 400   | 1.4554          | 0.9349 |
  | 0.7545        | 0.98  | 800   | 0.5594          | 0.5174 |
  | 0.4485        | 1.47  | 1200  | 0.3964          | 0.3749 |
  | 0.4118        | 1.96  | 1600  | 0.3547          | 0.3172 |
  | 0.3282        | 2.45  | 2000  | 0.3372          | 0.3061 |
  | 0.3199        | 2.94  | 2400  | 0.3466          | 0.2910 |
  | 0.2847        | 3.44  | 2800  | 0.3651          | 0.3310 |
  | 0.2713        | 3.93  | 3200  | 0.3509          | 0.3016 |
  | 0.2414        | 4.42  | 3600  | 0.3451          | 0.2908 |
  | 0.2473        | 4.91  | 4000  | 0.3253          | 0.2747 |
  | 0.2168        | 5.4   | 4400  | 0.3243          | 0.2680 |
  | 0.219         | 5.89  | 4800  | 0.3067          | 0.2540 |
  | 0.196         | 6.38  | 5200  | 0.3268          | 0.2824 |
  | 0.1934        | 6.87  | 5600  | 0.3252          | 0.2736 |
  | 0.1808        | 7.36  | 6000  | 0.3422          | 0.2737 |
  | 0.177         | 7.85  | 6400  | 0.3292          | 0.2707 |
  | 0.1626        | 8.34  | 6800  | 0.3089          | 0.2524 |
  | 0.1605        | 8.83  | 7200  | 0.3062          | 0.2471 |
  | 0.1505        | 9.32  | 7600  | 0.3229          | 0.2474 |
  | 0.1491        | 9.82  | 8000  | 0.3098          | 0.2491 |
  | 0.1433        | 10.31 | 8400  | 0.3449          | 0.2681 |
  | 0.1431        | 10.8  | 8800  | 0.3439          | 0.2532 |
  | 0.1349        | 11.29 | 9200  | 0.3112          | 0.2413 |
  | 0.1236        | 11.78 | 9600  | 0.3248          | 0.2378 |
  | 0.1253        | 12.27 | 10000 | 0.3393          | 0.2394 |
  | 0.1195        | 12.76 | 10400 | 0.3050          | 0.2336 |
  | 0.1194        | 13.25 | 10800 | 0.3494          | 0.2550 |
  | 0.1125        | 13.74 | 11200 | 0.3332          | 0.2395 |
  | 0.1063        | 14.23 | 11600 | 0.3134          | 0.2365 |
  | 0.1044        | 14.72 | 12000 | 0.3101          | 0.2303 |
  | 0.0999        | 15.21 | 12400 | 0.3162          | 0.2248 |
  | 0.0986        | 15.71 | 12800 | 0.3183          | 0.2260 |
  | 0.0958        | 16.2  | 13200 | 0.3300          | 0.2279 |
  | 0.0907        | 16.69 | 13600 | 0.3136          | 0.2260 |
  | 0.0875        | 17.18 | 14000 | 0.3492          | 0.2203 |
  | 0.0823        | 17.67 | 14400 | 0.3214          | 0.2259 |
  | 0.0839        | 18.16 | 14800 | 0.3194          | 0.2145 |
  | 0.0783        | 18.65 | 15200 | 0.3122          | 0.2180 |
  | 0.0789        | 19.14 | 15600 | 0.3158          | 0.2127 |
  | 0.0732        | 19.63 | 16000 | 0.3076          | 0.2109 |
  | 0.0715        | 20.12 | 16400 | 0.3216          | 0.2150 |
  | 0.0649        | 20.61 | 16800 | 0.2958          | 0.2051 |
  | 0.0647        | 21.1  | 17200 | 0.3022          | 0.2014 |
  | 0.0649        | 21.59 | 17600 | 0.3045          | 0.2033 |
  | 0.0621        | 22.09 | 18000 | 0.3194          | 0.2035 |
  | 0.0561        | 22.58 | 18400 | 0.3197          | 0.2022 |
  | 0.0582        | 23.07 | 18800 | 0.3109          | 0.1978 |
  | 0.0533        | 23.56 | 19200 | 0.3121          | 0.1932 |
  | 0.0515        | 24.05 | 19600 | 0.3125          | 0.1939 |
  | 0.0484        | 24.54 | 20000 | 0.3081          | 0.1908 |
  | 0.0485        | 25.03 | 20400 | 0.3042          | 0.1896 |
  | 0.0444        | 25.52 | 20800 | 0.3038          | 0.1886 |
  | 0.0426        | 26.01 | 21200 | 0.2985          | 0.1868 |
  | 0.0415        | 26.5  | 21600 | 0.3066          | 0.1858 |
  | 0.0398        | 26.99 | 22000 | 0.3117          | 0.1828 |
  | 0.0397        | 27.48 | 22400 | 0.2980          | 0.1795 |
  | 0.0394        | 27.97 | 22800 | 0.2950          | 0.1791 |
  | 0.0364        | 28.47 | 23200 | 0.3025          | 0.1773 |
  | 0.0365        | 28.96 | 23600 | 0.3022          | 0.1747 |
  | 0.0376        | 29.45 | 24000 | 0.2978