readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  model-index:
  - name: wav2vec2-1b-npsc-nst-bokmaal
    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
  
  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: 0.0636
  - Wer: 0.0345
  
  ## 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    |
  |:-------------:|:-----:|:------:|:---------------:|:------:|
  | 2.9289        | 0.04  | 500    | 2.7971          | 1.0    |
  | 0.7651        | 0.08  | 1000   | 0.5766          | 0.5701 |
  | 0.518         | 0.12  | 1500   | 0.3660          | 0.3564 |
  | 0.5481        | 0.16  | 2000   | 0.2730          | 0.2557 |
  | 0.4286        | 0.2   | 2500   | 0.2282          | 0.2385 |
  | 0.4543        | 0.24  | 3000   | 0.1852          | 0.1843 |
  | 0.4064        | 0.28  | 3500   | 0.1574          | 0.1544 |
  | 0.3308        | 0.32  | 4000   | 0.1558          | 0.1404 |
  | 0.3005        | 0.36  | 4500   | 0.1417          | 0.1349 |
  | 0.2898        | 0.4   | 5000   | 0.1304          | 0.1301 |
  | 0.2739        | 0.44  | 5500   | 0.1316          | 0.1258 |
  | 0.2429        | 0.48  | 6000   | 0.1222          | 0.1221 |
  | 0.2405        | 0.52  | 6500   | 0.1224          | 0.1147 |
  | 0.2681        | 0.56  | 7000   | 0.1162          | 0.1119 |
  | 0.2382        | 0.6   | 7500   | 0.1251          | 0.1338 |
  | 0.2193        | 0.64  | 8000   | 0.1121          | 0.1087 |
  | 0.2235        | 0.68  | 8500   | 0.1058          | 0.1059 |
  | 0.2124        | 0.72  | 9000   | 0.1106          | 0.1060 |
  | 0.2076        | 0.76  | 9500   | 0.1023          | 0.1003 |
  | 0.2082        | 0.8   | 10000  | 0.0985          | 0.1037 |
  | 0.2272        | 0.84  | 10500  | 0.0990          | 0.1019 |
  | 0.1903        | 0.88  | 11000  | 0.0982          | 0.0978 |
  | 0.2044        | 0.92  | 11500  | 0.0966          | 0.0991 |
  | 0.1976        | 0.96  | 12000  | 0.0925          | 0.0958 |
  | 0.1966        | 1.0   | 12500  | 0.0924          | 0.0929 |
  | 0.1959        | 1.04  | 13000  | 0.0888          | 0.0918 |
  | 0.1727        | 1.08  | 13500  | 0.0901          | 0.0943 |
  | 0.1853        | 1.12  | 14000  | 0.0934          | 0.0890 |
  | 0.1791        | 1.16  | 14500  | 0.0935          | 0.0914 |
  | 0.1872        | 1.2   | 15000  | 0.0851          | 0.0883 |
  | 0.1821        | 1.24  | 15500  | 0.0857          | 0.0873 |
  | 0.1756        | 1.28  | 16000  | 0.0884          | 0.0890 |
  | 0.1666        | 1.32  | 16500  | 0.0871          | 0.0849 |
  | 0.1708        | 1.36  | 17000  | 0.0837          | 0.0863 |
  | 0.1653        | 1.4   | 17500  | 0.0831          | 0.0849 |
  | 0.1734        | 1.44  | 18000  | 0.0808          | 0.0850 |
  | 0.1643        | 1.48  | 18500  | 0.0814          | 0.0835 |
  | 0.1635        | 1.52  | 19000  | 0.0810          | 0.0816 |
  | 0.1611        | 1.56  | 19500  | 0.0827          | 0.0829 |
  | 0.1633        | 1.6   | 20000  | 0.0797          | 0.0820 |
  | 0.1626        | 1.64  | 20500  | 0.0789          | 0.0820 |
  | 0.1618        | 1.68  | 21000  | 0.0766          | 0.0817 |
  | 0.1634        | 1.72  | 21500  | 0.0783          | 0.0832 |
  | 0.1761        | 1.76  | 22000  | 0.0787          | 0.0817 |
  | 0.1518        | 1.8   | 22500  | 0.0775          | 0.0817 |
  | 0.1492        | 1.84  | 23000  | 0.0785          | 0.0802 |
  | 0.1652        | 1.88  | 23500  | 0.0759          | 0.0783 |
  | 0.1545        | 1.92  | 24000  | 0.0758          | 0.0788 |
  | 0.1548        | 1.96  | 24500  | 0.0774          | 0.0816 |
  | 0.1641        | 2.0   | 25000  | 0.0734          | 0.0780 |
  | 0.1506        | 2.04  | 25500  | 0.0718          | 0.0748 |
  | 0.1474        | 2.08  | 26000  | 0.0748          | 0.0746 |
  | 0.137         | 2.12  | 26500  | 0.0736          | 0.0744 |
  | 0.1483        | 2.16  | 27000  | 0.0744          | 0.0763 |
  | 0.1472        | 2.2   | 27500  | 0.0728          | 0.0736 |
  | 0.143         | 2.24  | 28000  | 0.0708          | 0.0767 |
  | 0.1467        | 2.28  | 28500  | 0.0721          | 0.0737 |
  | 0.1286        | 2.32  | 29000  | 0.0701          | 0.0748 |
  | 0.1407        | 2.36  | 29500  | 0.0695          | 0.0740 |
  | 0.1377        | 2.4   | 30000  