readme: ---
  license: cc0-1.0
  tags:
  - generated_from_trainer
  model-index:
  - name: wav2vec2-large-voxrex-npsc-nst-bokmaal-fixed
    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-voxrex-npsc-nst-bokmaal-fixed
  
  This model is a fine-tuned version of [KBLab/wav2vec2-large-voxrex](https://huggingface.co/KBLab/wav2vec2-large-voxrex) on the None dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.0482
  - Wer: 0.0493
  
  ## 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: 16
  - eval_batch_size: 16
  - seed: 42
  - gradient_accumulation_steps: 2
  - total_train_batch_size: 32
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - lr_scheduler_warmup_steps: 2000
  - num_epochs: 15.0
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch | Step   | Validation Loss | Wer    |
  |:-------------:|:-----:|:------:|:---------------:|:------:|
  | 6.2179        | 0.05  | 500    | 4.1309          | 0.9998 |
  | 2.9345        | 0.11  | 1000   | 2.9238          | 1.0    |
  | 0.7252        | 0.16  | 1500   | 0.3986          | 0.3914 |
  | 0.5085        | 0.21  | 2000   | 0.2691          | 0.2844 |
  | 0.397         | 0.26  | 2500   | 0.2210          | 0.2347 |
  | 0.3632        | 0.32  | 3000   | 0.1961          | 0.2042 |
  | 0.3393        | 0.37  | 3500   | 0.1793          | 0.1848 |
  | 0.32          | 0.42  | 4000   | 0.1637          | 0.1682 |
  | 0.3074        | 0.48  | 4500   | 0.1520          | 0.1592 |
  | 0.2941        | 0.53  | 5000   | 0.1435          | 0.1532 |
  | 0.2706        | 0.58  | 5500   | 0.1391          | 0.1468 |
  | 0.2704        | 0.64  | 6000   | 0.1340          | 0.1385 |
  | 0.2526        | 0.69  | 6500   | 0.1274          | 0.1336 |
  | 0.2508        | 0.74  | 7000   | 0.1191          | 0.1290 |
  | 0.2372        | 0.79  | 7500   | 0.1215          | 0.1252 |
  | 0.2416        | 0.85  | 8000   | 0.1177          | 0.1246 |
  | 0.2269        | 0.9   | 8500   | 0.1109          | 0.1191 |
  | 0.2373        | 0.95  | 9000   | 0.1060          | 0.1202 |
  | 0.2355        | 1.01  | 9500   | 0.1047          | 0.1186 |
  | 0.2112        | 1.06  | 10000  | 0.1081          | 0.1123 |
  | 0.2099        | 1.11  | 10500  | 0.1089          | 0.1109 |
  | 0.1878        | 1.16  | 11000  | 0.1009          | 0.1102 |
  | 0.2194        | 1.22  | 11500  | 0.0964          | 0.1121 |
  | 0.204         | 1.27  | 12000  | 0.0953          | 0.1051 |
  | 0.184         | 1.32  | 12500  | 0.0934          | 0.1051 |
  | 0.1896        | 1.38  | 13000  | 0.0968          | 0.1037 |
  | 0.1849        | 1.43  | 13500  | 0.0917          | 0.1038 |
  | 0.1829        | 1.48  | 14000  | 0.0907          | 0.1002 |
  | 0.1781        | 1.53  | 14500  | 0.0898          | 0.0997 |
  | 0.1838        | 1.59  | 15000  | 0.0873          | 0.1002 |
  | 0.1868        | 1.64  | 15500  | 0.0883          | 0.0938 |
  | 0.1888        | 1.69  | 16000  | 0.0850          | 0.0928 |
  | 0.1767        | 1.75  | 16500  | 0.0851          | 0.0915 |
  | 0.1759        | 1.8   | 17000  | 0.0864          | 0.0932 |
  | 0.1778        | 1.85  | 17500  | 0.0842          | 0.0913 |
  | 0.1612        | 1.91  | 18000  | 0.0831          | 0.0873 |
  | 0.163         | 1.96  | 18500  | 0.0797          | 0.0940 |
  | 0.1618        | 2.01  | 19000  | 0.0798          | 0.0902 |
  | 0.1576        | 2.06  | 19500  | 0.0818          | 0.0883 |
  | 0.1585        | 2.12  | 20000  | 0.0808          | 0.0879 |
  | 0.1519        | 2.17  | 20500  | 0.0804          | 0.0868 |
  | 0.1504        | 2.22  | 21000  | 0.0790          | 0.0834 |
  | 0.1518        | 2.28  | 21500  | 0.0772          | 0.0832 |
  | 0.1521        | 2.33  | 22000  | 0.0757          | 0.0820 |
  | 0.1459        | 2.38  | 22500  | 0.0778          | 0.0815 |
  | 0.1436        | 2.43  | 23000  | 0.0777          | 0.0808 |
  | 0.1475        | 2.49  | 23500  | 0.0736          | 0.0800 |
  | 0.1448        | 2.54  | 24000  | 0.0758          | 0.0809 |
  | 0.1602        | 2.59  | 24500  | 0.0721          | 0.0802 |
  | 0.1417        | 2.65  | 25000  | 0.0716          | 0.0795 |
  | 0.145         | 2.7   | 25500  | 0.0719          | 0.0791 |
  | 0.1456        | 2.75  | 26000  | 0.0721          | 0.0813 |
  | 0.1391        | 2.8   | 26500  | 0.0687          | 0.0780 |
  | 0.1431        | 2.86  | 27000  | 0.0727          | 0.0770 |
  | 0.24          | 2.91  | 27500  | 0.0699          | 0.0769 |
  | 0.1333        | 2.96  | 28000  | 0.0701          | 0.0770 |
  | 0.1305        | 3.02  | 28500  | 0.0715          | 0.0756 |
  | 0.1366        | 3.07  | 29000  | 0.0700          | 0.0764 |
  | 0.1354        | 3.12  | 29500  | 0.0674          | 0.0743 |
  |