readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  model-index:
  - name: wav2vec2-large-xlsr-53-torgo-demo-f01-nolm
    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-xlsr-53-torgo-demo-f01-nolm
  
  This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the None dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.0153
  - Wer: 0.4756
  
  ## 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: 0.0001
  - train_batch_size: 8
  - eval_batch_size: 8
  - seed: 42
  - 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    |
  |:-------------:|:-----:|:-----:|:---------------:|:------:|
  | 3.4166        | 0.81  | 500   | 4.5019          | 1.0    |
  | 3.1088        | 1.62  | 1000  | 3.0459          | 1.0    |
  | 2.8249        | 2.44  | 1500  | 3.0850          | 1.0    |
  | 2.625         | 3.25  | 2000  | 2.6827          | 1.3656 |
  | 1.9816        | 4.06  | 2500  | 1.6636          | 1.3701 |
  | 1.3036        | 4.87  | 3000  | 0.9710          | 1.2504 |
  | 0.9862        | 5.68  | 3500  | 0.6023          | 1.0519 |
  | 0.7012        | 6.49  | 4000  | 0.4404          | 0.9342 |
  | 0.6102        | 7.31  | 4500  | 0.3297          | 0.8491 |
  | 0.5463        | 8.12  | 5000  | 0.2403          | 0.7773 |
  | 0.4897        | 8.93  | 5500  | 0.1907          | 0.7335 |
  | 0.4687        | 9.74  | 6000  | 0.1721          | 0.7095 |
  | 0.41          | 10.55 | 6500  | 0.1382          | 0.6851 |
  | 0.3277        | 11.36 | 7000  | 0.1189          | 0.6598 |
  | 0.3182        | 12.18 | 7500  | 0.1040          | 0.6372 |
  | 0.3279        | 12.99 | 8000  | 0.0961          | 0.6274 |
  | 0.2735        | 13.8  | 8500  | 0.0806          | 0.5880 |
  | 0.3153        | 14.61 | 9000  | 0.0821          | 0.5748 |
  | 0.251         | 15.42 | 9500  | 0.0633          | 0.5437 |
  | 0.2           | 16.23 | 10000 | 0.0534          | 0.5316 |
  | 0.2134        | 17.05 | 10500 | 0.0475          | 0.5195 |
  | 0.1727        | 17.86 | 11000 | 0.0435          | 0.5146 |
  | 0.2143        | 18.67 | 11500 | 0.0406          | 0.5072 |
  | 0.1679        | 19.48 | 12000 | 0.0386          | 0.5057 |
  | 0.1836        | 20.29 | 12500 | 0.0359          | 0.4984 |
  | 0.1542        | 21.1  | 13000 | 0.0284          | 0.4914 |
  | 0.1672        | 21.92 | 13500 | 0.0289          | 0.4884 |
  | 0.1526        | 22.73 | 14000 | 0.0256          | 0.4867 |
  | 0.1263        | 23.54 | 14500 | 0.0247          | 0.4871 |
  | 0.133         | 24.35 | 15000 | 0.0194          | 0.4816 |
  | 0.1005        | 25.16 | 15500 | 0.0190          | 0.4798 |
  | 0.1372        | 25.97 | 16000 | 0.0172          | 0.4786 |
  | 0.1126        | 26.79 | 16500 | 0.0177          | 0.4773 |
  | 0.0929        | 27.6  | 17000 | 0.0173          | 0.4775 |
  | 0.1069        | 28.41 | 17500 | 0.0164          | 0.4773 |
  | 0.0932        | 29.22 | 18000 | 0.0153          | 0.4756 |
  
  
  ### Framework versions
  
  - Transformers 4.23.1
  - Pytorch 1.12.1+cu113
  - Datasets 2.0.0
  - Tokenizers 0.13.2
  