readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  model-index:
  - name: wav2vec2-large-xlsr-53-torgo-demo-m04-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-m04-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: 0.0179
  - Wer: 0.4609
  
  ## 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    |
  |:-------------:|:-----:|:-----:|:---------------:|:------:|
  | 3.3456        | 0.88  | 500   | 4.3417          | 1.0    |
  | 2.9307        | 1.75  | 1000  | 2.9493          | 1.0    |
  | 2.741         | 2.63  | 1500  | 2.9803          | 1.0    |
  | 2.2291        | 3.5   | 2000  | 1.9442          | 1.1343 |
  | 1.3963        | 4.38  | 2500  | 1.0394          | 1.3114 |
  | 0.9695        | 5.25  | 3000  | 0.6343          | 1.0388 |
  | 0.7907        | 6.13  | 3500  | 0.4243          | 0.9229 |
  | 0.6529        | 7.01  | 4000  | 0.3108          | 0.8083 |
  | 0.5078        | 7.88  | 4500  | 0.2490          | 0.7440 |
  | 0.5039        | 8.76  | 5000  | 0.1810          | 0.7036 |
  | 0.4288        | 9.63  | 5500  | 0.1525          | 0.6676 |
  | 0.3999        | 10.51 | 6000  | 0.1335          | 0.6371 |
  | 0.319         | 11.38 | 6500  | 0.1227          | 0.6016 |
  | 0.3104        | 12.26 | 7000  | 0.1064          | 0.5910 |
  | 0.3006        | 13.13 | 7500  | 0.0924          | 0.5816 |
  | 0.3059        | 14.01 | 8000  | 0.0929          | 0.5604 |
  | 0.2689        | 14.89 | 8500  | 0.0692          | 0.5547 |
  | 0.2416        | 15.76 | 9000  | 0.0638          | 0.5338 |
  | 0.1964        | 16.64 | 9500  | 0.0594          | 0.5233 |
  | 0.1868        | 17.51 | 10000 | 0.0535          | 0.5151 |
  | 0.1963        | 18.39 | 10500 | 0.0534          | 0.4941 |
  | 0.2159        | 19.26 | 11000 | 0.0440          | 0.4890 |
  | 0.2059        | 20.14 | 11500 | 0.0432          | 0.4836 |
  | 0.1701        | 21.02 | 12000 | 0.0364          | 0.4793 |
  | 0.1748        | 21.89 | 12500 | 0.0308          | 0.4763 |
  | 0.1673        | 22.77 | 13000 | 0.0266          | 0.4745 |
  | 0.1473        | 23.64 | 13500 | 0.0280          | 0.4726 |
  | 0.1245        | 24.52 | 14000 | 0.0234          | 0.4700 |
  | 0.1396        | 25.39 | 14500 | 0.0243          | 0.4658 |
  | 0.1428        | 26.27 | 15000 | 0.0235          | 0.4637 |
  | 0.116         | 27.15 | 15500 | 0.0192          | 0.4620 |
  | 0.1366        | 28.02 | 16000 | 0.0182          | 0.4603 |
  | 0.0974        | 28.9  | 16500 | 0.0186          | 0.4604 |
  | 0.1439        | 29.77 | 17000 | 0.0179          | 0.4609 |
  
  
  ### Framework versions
  
  - Transformers 4.23.1
  - Pytorch 1.12.1+cu113
  - Datasets 2.0.0
  - Tokenizers 0.13.2
  