readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  model-index:
  - name: wav2vec2-1b-npsc-nst-bokmaal-repaired
    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. -->
  
  # wav2vec2-1b-npsc-nst-bokmaal-repaired
  
  This model is a fine-tuned version of [facebook/wav2vec2-xls-r-1b](https://huggingface.co/facebook/wav2vec2-xls-r-1b) on the None dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.0666
  - Wer: 0.0349
  
  ## 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: 2e-05
  - train_batch_size: 12
  - eval_batch_size: 12
  - seed: 42
  - gradient_accumulation_steps: 2
  - total_train_batch_size: 24
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - lr_scheduler_warmup_steps: 2000
  - num_epochs: 40.0
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch | Step   | Validation Loss | Wer    |
  |:-------------:|:-----:|:------:|:---------------:|:------:|
  | 2.9199        | 0.04  | 500    | 2.7695          | 1.0    |
  | 0.7529        | 0.08  | 1000   | 0.6234          | 0.6100 |
  | 0.541         | 0.12  | 1500   | 0.4089          | 0.3949 |
  | 0.5274        | 0.16  | 2000   | 0.2323          | 0.2240 |
  | 0.4434        | 0.2   | 2500   | 0.1954          | 0.1917 |
  | 0.4111        | 0.24  | 3000   | 0.1781          | 0.1677 |
  | 0.403         | 0.28  | 3500   | 0.1509          | 0.1466 |
  | 0.3725        | 0.32  | 4000   | 0.1433          | 0.1369 |
  | 0.3868        | 0.36  | 4500   | 0.1436          | 0.1279 |
  | 0.3078        | 0.39  | 5000   | 0.1359          | 0.1316 |
  | 0.2742        | 0.43  | 5500   | 0.1301          | 0.1241 |
  | 0.2695        | 0.47  | 6000   | 0.1246          | 0.1173 |
  | 0.2477        | 0.51  | 6500   | 0.1177          | 0.1129 |
  | 0.2458        | 0.55  | 7000   | 0.1199          | 0.1125 |
  | 0.2469        | 0.59  | 7500   | 0.1128          | 0.1103 |
  | 0.2313        | 0.63  | 8000   | 0.1072          | 0.1050 |
  | 0.242         | 0.67  | 8500   | 0.1096          | 0.1055 |
  | 0.247         | 0.71  | 9000   | 0.1013          | 0.1018 |
  | 0.2093        | 0.75  | 9500   | 0.1052          | 0.1009 |
  | 0.2048        | 0.79  | 10000  | 0.1036          | 0.1002 |
  | 0.2308        | 0.83  | 10500  | 0.1048          | 0.0987 |
  | 0.1979        | 0.87  | 11000  | 0.1007          | 0.0945 |
  | 0.1944        | 0.91  | 11500  | 0.1009          | 0.0967 |
  | 0.2147        | 0.95  | 12000  | 0.0954          | 0.0924 |
  | 0.2053        | 0.99  | 12500  | 0.0972          | 0.1032 |
  | 0.1723        | 1.03  | 13000  | 0.0950          | 0.0908 |
  | 0.1679        | 1.07  | 13500  | 0.0923          | 0.0871 |
  | 0.1839        | 1.11  | 14000  | 0.0924          | 0.0879 |
  | 0.1822        | 1.14  | 14500  | 0.0902          | 0.0874 |
  | 0.1774        | 1.18  | 15000  | 0.0894          | 0.0859 |
  | 0.1924        | 1.22  | 15500  | 0.0859          | 0.0829 |
  | 0.1668        | 1.26  | 16000  | 0.0855          | 0.0829 |
  | 0.1733        | 1.3   | 16500  | 0.0841          | 0.0810 |
  | 0.1813        | 1.34  | 17000  | 0.0838          | 0.0808 |
  | 0.1764        | 1.38  | 17500  | 0.0840          | 0.0812 |
  | 0.1923        | 1.42  | 18000  | 0.0825          | 0.0815 |
  | 0.1664        | 1.46  | 18500  | 0.0818          | 0.0823 |
  | 0.171         | 1.5   | 19000  | 0.0814          | 0.0802 |
  | 0.1603        | 1.54  | 19500  | 0.0812          | 0.0804 |
  | 0.1691        | 1.58  | 20000  | 0.0784          | 0.0790 |
  | 0.1611        | 1.62  | 20500  | 0.0810          | 0.0790 |
  | 0.1583        | 1.66  | 21000  | 0.0818          | 0.0780 |
  | 0.1644        | 1.7   | 21500  | 0.0777          | 0.0785 |
  | 0.1619        | 1.74  | 22000  | 0.0781          | 0.0775 |
  | 0.1701        | 1.78  | 22500  | 0.0740          | 0.0792 |
  | 0.1672        | 1.82  | 23000  | 0.0775          | 0.0772 |
  | 0.1652        | 1.86  | 23500  | 0.0777          | 0.0771 |
  | 0.1526        | 1.89  | 24000  | 0.0782          | 0.0781 |
  | 0.1529        | 1.93  | 24500  | 0.0751          | 0.0758 |
  | 0.1645        | 1.97  | 25000  | 0.0746          | 0.0764 |
  | 0.1389        | 2.01  | 25500  | 0.0751          | 0.0753 |
  | 0.146         | 2.05  | 26000  | 0.0735          | 0.0740 |
  | 0.1528        | 2.09  | 26500  | 0.0743          | 0.0731 |
  | 0.1483        | 2.13  | 27000  | 0.0787          | 0.0751 |
  | 0.1393        | 2.17  | 27500  | 0.0760          | 0.0735 |
  | 0.1427        | 2.21  | 28000  | 0.0765          | 0.0747 |
  | 0.1399        | 2.25  | 28500  | 0.0727          | 0.0727 |
  | 0.1546        | 2.29  | 29000  | 0.0716          | 0.0731 |
  | 0.1427        | 2.33  | 29500  | 0.0720          | 0.0717 |
  | 0.1402       