readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  datasets:
  - common_voice
  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 dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.3637
  - Wer: 0.2982
  
  ## 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    |
  |:-------------:|:-----:|:-----:|:---------------:|:------:|
  | 4.591         | 1.15  | 400   | 0.9128          | 0.6517 |
  | 0.5049        | 2.31  | 800   | 0.4596          | 0.4437 |
  | 0.2871        | 3.46  | 1200  | 0.3964          | 0.3905 |
  | 0.2077        | 4.61  | 1600  | 0.3958          | 0.3744 |
  | 0.1695        | 5.76  | 2000  | 0.4040          | 0.3720 |
  | 0.1478        | 6.92  | 2400  | 0.3866          | 0.3651 |
  | 0.1282        | 8.07  | 2800  | 0.3987          | 0.3674 |
  | 0.1134        | 9.22  | 3200  | 0.4128          | 0.3688 |
  | 0.1048        | 10.37 | 3600  | 0.3928          | 0.3561 |
  | 0.0938        | 11.53 | 4000  | 0.4048          | 0.3619 |
  | 0.0848        | 12.68 | 4400  | 0.4229          | 0.3555 |
  | 0.0798        | 13.83 | 4800  | 0.3974          | 0.3468 |
  | 0.0688        | 14.98 | 5200  | 0.3870          | 0.3503 |
  | 0.0658        | 16.14 | 5600  | 0.3875          | 0.3351 |
  | 0.061         | 17.29 | 6000  | 0.4133          | 0.3417 |
  | 0.0569        | 18.44 | 6400  | 0.3915          | 0.3414 |
  | 0.0526        | 19.6  | 6800  | 0.3957          | 0.3231 |
  | 0.0468        | 20.75 | 7200  | 0.4110          | 0.3301 |
  | 0.0407        | 21.9  | 7600  | 0.3866          | 0.3186 |
  | 0.0384        | 23.05 | 8000  | 0.3976          | 0.3193 |
  | 0.0363        | 24.21 | 8400  | 0.3910          | 0.3177 |
  | 0.0313        | 25.36 | 8800  | 0.3656          | 0.3109 |
  | 0.0293        | 26.51 | 9200  | 0.3712          | 0.3092 |
  | 0.0277        | 27.66 | 9600  | 0.3613          | 0.3054 |
  | 0.0249        | 28.82 | 10000 | 0.3783          | 0.3015 |
  | 0.0234        | 29.97 | 10400 | 0.3637          | 0.2982 |
  
  
  ### Framework versions
  
  - Transformers 4.11.3
  - Pytorch 1.10.0+cu102
  - Datasets 1.13.3
  - Tokenizers 0.10.3
  