readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  datasets:
  - librispeech_asr
  model-index:
  - name: wav2vec2-large-xls-r-300m-en-libri-more-steps
    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-en-libri-more-steps
  
  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: 1.7624
  - Wer: 0.8772
  - Cer: 0.3762
  
  ## 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: 0.001
  - 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: 100
  - num_epochs: 20
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Wer    | Cer    |
  |:-------------:|:-----:|:----:|:---------------:|:------:|:------:|
  | No log        | 1.94  | 33   | 2.9987          | 1.0    | 1.0    |
  | No log        | 3.88  | 66   | 2.8951          | 1.0    | 1.0    |
  | No log        | 5.82  | 99   | 2.8732          | 1.0    | 1.0    |
  | 3.781         | 7.76  | 132  | 2.6057          | 1.0    | 1.0    |
  | 3.781         | 9.71  | 165  | 1.9015          | 1.0154 | 0.5616 |
  | 3.781         | 11.65 | 198  | 1.5226          | 0.9263 | 0.4462 |
  | 2.2258        | 13.59 | 231  | 1.5116          | 0.8913 | 0.3967 |
  | 2.2258        | 15.53 | 264  | 1.5634          | 0.8922 | 0.3842 |
  | 2.2258        | 17.47 | 297  | 1.7016          | 0.8876 | 0.3796 |
  | 0.7946        | 19.41 | 330  | 1.7624          | 0.8772 | 0.3762 |
  
  
  ### Framework versions
  
  - Transformers 4.20.1
  - Pytorch 1.11.0+cpu
  - Datasets 1.18.3
  - Tokenizers 0.12.1
  