readme: ---
  tags:
  - generated_from_trainer
  model-index:
  - name: exp12-reducedTorgoOnly-predComparison
    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. -->
  
  # exp12-reducedTorgoOnly-predComparison
  
  This model is a fine-tuned version of [yongjian/wav2vec2-large-a](https://huggingface.co/yongjian/wav2vec2-large-a) on the None dataset.
  It achieves the following results on the evaluation set:
  - Loss: 1.2016
  - Wer: 1.0412
  
  ## 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: 4
  - 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: 1000
  - num_epochs: 30
  
  ### Training results
  
  | Training Loss | Epoch | Step  | Validation Loss | Wer    |
  |:-------------:|:-----:|:-----:|:---------------:|:------:|
  | 35.9644       | 0.92  | 500   | 3.6081          | 1.0084 |
  | 3.2094        | 1.83  | 1000  | 2.7519          | 1.0    |
  | 2.8848        | 2.75  | 1500  | 2.7494          | 1.0014 |
  | 2.7505        | 3.66  | 2000  | 2.5622          | 1.2840 |
  | 2.6354        | 4.58  | 2500  | 2.3878          | 1.2819 |
  | 2.3473        | 5.49  | 3000  | 2.0214          | 1.2666 |
  | 2.0339        | 6.41  | 3500  | 1.8040          | 1.2394 |
  | 1.7779        | 7.33  | 4000  | 1.5898          | 1.2289 |
  | 1.5254        | 8.24  | 4500  | 1.7275          | 1.2080 |
  | 1.4553        | 9.16  | 5000  | 1.3815          | 1.1786 |
  | 1.3222        | 10.07 | 5500  | 1.3647          | 1.1835 |
  | 1.1964        | 10.99 | 6000  | 1.2442          | 1.1528 |
  | 1.1169        | 11.9  | 6500  | 1.5896          | 1.2059 |
  | 1.0342        | 12.82 | 7000  | 1.3880          | 1.1766 |
  | 0.989         | 13.74 | 7500  | 1.2111          | 1.1396 |
  | 0.9109        | 14.65 | 8000  | 1.3362          | 1.1137 |
  | 0.8875        | 15.57 | 8500  | 1.2594          | 1.1326 |
  | 0.8053        | 16.48 | 9000  | 1.1858          | 1.1242 |
  | 0.7566        | 17.4  | 9500  | 1.1987          | 1.1117 |
  | 0.7284        | 18.32 | 10000 | 1.2963          | 1.0998 |
  | 0.7345        | 19.23 | 10500 | 1.1835          | 1.0865 |
  | 0.6424        | 20.15 | 11000 | 1.1564          | 1.0907 |
  | 0.6323        | 21.06 | 11500 | 1.2123          | 1.0851 |
  | 0.5871        | 21.98 | 12000 | 1.2736          | 1.0691 |
  | 0.5788        | 22.89 | 12500 | 1.2094          | 1.0768 |
  | 0.5368        | 23.81 | 13000 | 1.1626          | 1.0398 |
  | 0.5357        | 24.73 | 13500 | 1.1960          | 1.0607 |
  | 0.5407        | 25.64 | 14000 | 1.1724          | 1.0586 |
  | 0.491         | 26.56 | 14500 | 1.1877          | 1.0426 |
  | 0.4866        | 27.47 | 15000 | 1.2227          | 1.0593 |
  | 0.5011        | 28.39 | 15500 | 1.2033          | 1.0440 |
  | 0.4634        | 29.3  | 16000 | 1.2016          | 1.0412 |
  
  
  ### Framework versions
  
  - Transformers 4.23.1
  - Pytorch 1.12.1+cu113
  - Datasets 1.18.3
  - Tokenizers 0.13.2
  