readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  model-index:
  - name: wav2vec2-large-xls-r-300m-gl-jupyter4
    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-gl-jupyter4
  
  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: 0.0970
  - Wer: 0.0636
  
  ## 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: 45
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Wer    |
  |:-------------:|:-----:|:----:|:---------------:|:------:|
  | 3.492         | 3.36  | 400  | 0.3109          | 0.3158 |
  | 0.194         | 6.72  | 800  | 0.1279          | 0.1454 |
  | 0.0794        | 10.08 | 1200 | 0.1210          | 0.1240 |
  | 0.0565        | 13.44 | 1600 | 0.1209          | 0.1150 |
  | 0.041         | 16.8  | 2000 | 0.1186          | 0.1107 |
  | 0.0343        | 20.17 | 2400 | 0.1143          | 0.0933 |
  | 0.0283        | 23.53 | 2800 | 0.1067          | 0.0900 |
  | 0.0231        | 26.89 | 3200 | 0.1076          | 0.0812 |
  | 0.0176        | 30.25 | 3600 | 0.1094          | 0.0780 |
  | 0.0169        | 33.61 | 4000 | 0.1041          | 0.0766 |
  | 0.0138        | 36.97 | 4400 | 0.1012          | 0.0711 |
  | 0.0109        | 40.33 | 4800 | 0.0985          | 0.0655 |
  | 0.0099        | 43.69 | 5200 | 0.0970          | 0.0636 |
  
  
  ### Framework versions
  
  - Transformers 4.11.3
  - Pytorch 1.9.1+cu111
  - Datasets 1.18.3
  - Tokenizers 0.10.3
  