readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  datasets:
  - librispeech_asr
  model-index:
  - name: xlsr-english
    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. -->
  
  # xlsr-english
  
  This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the librispeech_asr dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.3098
  - Wer: 0.1451
  
  ## 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    |
  |:-------------:|:-----:|:----:|:---------------:|:------:|
  | 3.2453        | 2.37  | 400  | 0.5789          | 0.4447 |
  | 0.3736        | 4.73  | 800  | 0.3737          | 0.2850 |
  | 0.1712        | 7.1   | 1200 | 0.3038          | 0.2136 |
  | 0.117         | 9.47  | 1600 | 0.3016          | 0.2072 |
  | 0.0897        | 11.83 | 2000 | 0.3158          | 0.1920 |
  | 0.074         | 14.2  | 2400 | 0.3137          | 0.1831 |
  | 0.0595        | 16.57 | 2800 | 0.2967          | 0.1745 |
  | 0.0493        | 18.93 | 3200 | 0.3192          | 0.1670 |
  | 0.0413        | 21.3  | 3600 | 0.3176          | 0.1644 |
  | 0.0322        | 23.67 | 4000 | 0.3079          | 0.1598 |
  | 0.0296        | 26.04 | 4400 | 0.2978          | 0.1511 |
  | 0.0235        | 28.4  | 4800 | 0.3098          | 0.1451 |
  
  
  ### Framework versions
  
  - Transformers 4.11.3
  - Pytorch 1.10.0+cu113
  - Datasets 1.18.3
  - Tokenizers 0.10.3
  