readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  model-index:
  - name: wav2vec2-large-xlsr-53-torgo-demo-m02-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-m02-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: 0.0260
  - Wer: 0.4968
  
  ## 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    |
  |:-------------:|:-----:|:-----:|:---------------:|:------:|
  | 3.4385        | 0.91  | 500   | 4.0160          | 1.0    |
  | 3.0413        | 1.81  | 1000  | 3.2881          | 1.0    |
  | 3.0011        | 2.72  | 1500  | 3.2401          | 1.0    |
  | 2.8653        | 3.62  | 2000  | 3.0338          | 1.0    |
  | 2.6386        | 4.53  | 2500  | 2.7806          | 1.0492 |
  | 2.5376        | 5.43  | 3000  | 2.5253          | 1.3647 |
  | 2.2722        | 6.34  | 3500  | 2.1425          | 1.3252 |
  | 1.627         | 7.25  | 4000  | 1.4101          | 1.3658 |
  | 1.2689        | 8.15  | 4500  | 0.9284          | 1.2448 |
  | 1.0197        | 9.06  | 5000  | 0.6370          | 1.1254 |
  | 0.8198        | 9.96  | 5500  | 0.4743          | 0.9947 |
  | 0.7357        | 10.87 | 6000  | 0.3423          | 0.8820 |
  | 0.5532        | 11.78 | 6500  | 0.2764          | 0.8203 |
  | 0.5133        | 12.68 | 7000  | 0.2158          | 0.7580 |
  | 0.4943        | 13.59 | 7500  | 0.1872          | 0.7195 |
  | 0.3741        | 14.49 | 8000  | 0.1529          | 0.6762 |
  | 0.3524        | 15.4  | 8500  | 0.1269          | 0.6527 |
  | 0.3086        | 16.3  | 9000  | 0.1049          | 0.6254 |
  | 0.3141        | 17.21 | 9500  | 0.0887          | 0.6012 |
  | 0.2879        | 18.12 | 10000 | 0.0829          | 0.5863 |
  | 0.3141        | 19.02 | 10500 | 0.0660          | 0.5688 |
  | 0.2609        | 19.93 | 11000 | 0.0732          | 0.5591 |
  | 0.2707        | 20.83 | 11500 | 0.0552          | 0.5434 |
  | 0.2307        | 21.74 | 12000 | 0.0524          | 0.5406 |
  | 0.1863        | 22.64 | 12500 | 0.0466          | 0.5281 |
  | 0.2211        | 23.55 | 13000 | 0.0426          | 0.5226 |
  | 0.1827        | 24.46 | 13500 | 0.0365          | 0.5129 |
  | 0.1782        | 25.36 | 14000 | 0.0356          | 0.5099 |
  | 0.1799        | 26.27 | 14500 | 0.0323          | 0.5049 |
  | 0.1481        | 27.17 | 15000 | 0.0300          | 0.5034 |
  | 0.1609        | 28.08 | 15500 | 0.0278          | 0.5030 |
  | 0.1752        | 28.99 | 16000 | 0.0269          | 0.4978 |
  | 0.1541        | 29.89 | 16500 | 0.0260          | 0.4968 |
  
  
  ### Framework versions
  
  - Transformers 4.23.1
  - Pytorch 1.12.1+cu113
  - Datasets 2.0.0
  - Tokenizers 0.13.2
  