readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  model-index:
  - name: wav2vec2-1b-npsc-nst
    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
  
  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: 0.0633
  - Wer: 0.0358
  
  ## 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    |
  |:-------------:|:-----:|:------:|:---------------:|:------:|
  | 3.2301        | 0.04  | 500    | 3.1633          | 0.9980 |
  | 3.0299        | 0.08  | 1000   | 3.0472          | 0.9958 |
  | 1.7041        | 0.12  | 1500   | 1.5009          | 0.7927 |
  | 0.9346        | 0.16  | 2000   | 0.5483          | 0.4676 |
  | 0.7126        | 0.2   | 2500   | 0.4635          | 0.3379 |
  | 0.4732        | 0.24  | 3000   | 0.2517          | 0.2402 |
  | 0.4312        | 0.28  | 3500   | 0.1964          | 0.1957 |
  | 0.373         | 0.32  | 4000   | 0.1775          | 0.1739 |
  | 0.3233        | 0.35  | 4500   | 0.1721          | 0.1597 |
  | 0.2865        | 0.39  | 5000   | 0.1507          | 0.1551 |
  | 0.2901        | 0.43  | 5500   | 0.1427          | 0.1413 |
  | 0.2656        | 0.47  | 6000   | 0.1399          | 0.1377 |
  | 0.2641        | 0.51  | 6500   | 0.1332          | 0.1310 |
  | 0.2489        | 0.55  | 7000   | 0.1309          | 0.1317 |
  | 0.2531        | 0.59  | 7500   | 0.1247          | 0.1291 |
  | 0.2524        | 0.63  | 8000   | 0.1247          | 0.1232 |
  | 0.2142        | 0.67  | 8500   | 0.1248          | 0.1270 |
  | 0.2233        | 0.71  | 9000   | 0.1121          | 0.1201 |
  | 0.2313        | 0.75  | 9500   | 0.1102          | 0.1161 |
  | 0.2207        | 0.79  | 10000  | 0.1117          | 0.1200 |
  | 0.2252        | 0.83  | 10500  | 0.1087          | 0.1162 |
  | 0.208         | 0.87  | 11000  | 0.1036          | 0.1129 |
  | 0.2054        | 0.91  | 11500  | 0.1009          | 0.1075 |
  | 0.2008        | 0.95  | 12000  | 0.0986          | 0.1072 |
  | 0.2019        | 0.98  | 12500  | 0.0983          | 0.1057 |
  | 0.1957        | 1.02  | 13000  | 0.0959          | 0.1032 |
  | 0.181         | 1.06  | 13500  | 0.0958          | 0.1031 |
  | 0.1883        | 1.1   | 14000  | 0.0912          | 0.1013 |
  | 0.1747        | 1.14  | 14500  | 0.0898          | 0.0995 |
  | 0.1821        | 1.18  | 15000  | 0.0920          | 0.0990 |
  | 0.1793        | 1.22  | 15500  | 0.0879          | 0.0960 |
  | 0.1853        | 1.26  | 16000  | 0.0908          | 0.0953 |
  | 0.1903        | 1.3   | 16500  | 0.0864          | 0.0959 |
  | 0.1709        | 1.34  | 17000  | 0.0873          | 0.0970 |
  | 0.1676        | 1.38  | 17500  | 0.0886          | 0.0950 |
  | 0.1837        | 1.42  | 18000  | 0.0842          | 0.0932 |
  | 0.1679        | 1.46  | 18500  | 0.0870          | 0.0922 |
  | 0.1782        | 1.5   | 19000  | 0.0847          | 0.0908 |
  | 0.1684        | 1.54  | 19500  | 0.0838          | 0.0897 |
  | 0.1748        | 1.58  | 20000  | 0.0834          | 0.0874 |
  | 0.1682        | 1.62  | 20500  | 0.0832          | 0.0876 |
  | 0.1646        | 1.65  | 21000  | 0.0814          | 0.0884 |
  | 0.1716        | 1.69  | 21500  | 0.0818          | 0.0874 |
  | 0.1661        | 1.73  | 22000  | 0.0808          | 0.0865 |
  | 0.1797        | 1.77  | 22500  | 0.0810          | 0.0866 |
  | 0.1754        | 1.81  | 23000  | 0.0775          | 0.0854 |
  | 0.1648        | 1.85  | 23500  | 0.0785          | 0.0855 |
  | 0.1393        | 1.89  | 24000  | 0.0805          | 0.0873 |
  | 0.1598        | 1.93  | 24500  | 0.0796          | 0.0861 |
  | 0.1544        | 1.97  | 25000  | 0.0775          | 0.0844 |
  | 0.1357        | 2.01  | 25500  | 0.0768          | 0.0815 |
  | 0.1436        | 2.05  | 26000  | 0.0778          | 0.0820 |
  | 0.1394        | 2.09  | 26500  | 0.0750          | 0.0815 |
  | 0.1541        | 2.13  | 27000  | 0.0755          | 0.0806 |
  | 0.1422        | 2.17  | 27500  | 0.0704          | 0.0771 |
  | 0.1395        | 2.21  | 28000  | 0.0695          | 0.0744 |
  | 0.1385        | 2.25  | 28500  | 0.0688          | 0.0720 |
  | 0.1297        | 2.29  | 29000  | 0.0715          | 0.0735 |
  | 0.1377        | 2.32  | 29500  | 0.0709          | 0.0722 |
  | 0.1385        | 2.36  | 30000  | 0.0670        