readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  model-index:
  - name: wav2vec2-large-xlsr-53-torgo-demo-m01-nolm
    results: []
  duplicated_from: jeapaul/wav2vec2-large-xlsr-53-torgo-demo-m01-nolm
  ---
  
  <!-- 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-m01-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: 0.0161
  - Wer: 0.4768
  
  ## 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    |
  |:-------------:|:-----:|:-----:|:---------------:|:------:|
  | 3.3987        | 0.9   | 500   | 4.6666          | 1.0    |
  | 2.9362        | 1.8   | 1000  | 3.2475          | 1.0    |
  | 2.7871        | 2.7   | 1500  | 2.9266          | 1.0    |
  | 2.3782        | 3.6   | 2000  | 2.2303          | 1.1786 |
  | 1.4728        | 4.5   | 2500  | 1.1054          | 1.2930 |
  | 1.0335        | 5.41  | 3000  | 0.6886          | 1.1068 |
  | 0.7658        | 6.31  | 3500  | 0.4544          | 0.9401 |
  | 0.633         | 7.21  | 4000  | 0.3153          | 0.8427 |
  | 0.5852        | 8.11  | 4500  | 0.2553          | 0.7724 |
  | 0.4882        | 9.01  | 5000  | 0.2212          | 0.7450 |
  | 0.4167        | 9.91  | 5500  | 0.1687          | 0.7014 |
  | 0.3897        | 10.81 | 6000  | 0.1433          | 0.6791 |
  | 0.382         | 11.71 | 6500  | 0.1249          | 0.6434 |
  | 0.3367        | 12.61 | 7000  | 0.1056          | 0.6198 |
  | 0.2897        | 13.51 | 7500  | 0.0988          | 0.6172 |
  | 0.2457        | 14.41 | 8000  | 0.0885          | 0.5895 |
  | 0.243         | 15.32 | 8500  | 0.0759          | 0.5850 |
  | 0.2594        | 16.22 | 9000  | 0.0621          | 0.5615 |
  | 0.2461        | 17.12 | 9500  | 0.0583          | 0.5470 |
  | 0.2307        | 18.02 | 10000 | 0.0501          | 0.5449 |
  | 0.2333        | 18.92 | 10500 | 0.0510          | 0.5332 |
  | 0.1579        | 19.82 | 11000 | 0.0447          | 0.5295 |
  | 0.2097        | 20.72 | 11500 | 0.0392          | 0.5246 |
  | 0.1661        | 21.62 | 12000 | 0.0360          | 0.5075 |
  | 0.1576        | 22.52 | 12500 | 0.0287          | 0.5076 |
  | 0.1452        | 23.42 | 13000 | 0.0253          | 0.4988 |
  | 0.1316        | 24.32 | 13500 | 0.0220          | 0.4863 |
  | 0.1521        | 25.23 | 14000 | 0.0222          | 0.4865 |
  | 0.1319        | 26.13 | 14500 | 0.0187          | 0.4810 |
  | 0.1392        | 27.03 | 15000 | 0.0170          | 0.4809 |
  | 0.1619        | 27.93 | 15500 | 0.0171          | 0.4777 |
  | 0.1374        | 28.83 | 16000 | 0.0164          | 0.4766 |
  | 0.1081        | 29.73 | 16500 | 0.0161          | 0.4768 |
  
  
  ### Framework versions
  
  - Transformers 4.23.1
  - Pytorch 1.12.1+cu113
  - Datasets 2.0.0
  - Tokenizers 0.13.2
  