readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  model-index:
  - name: wav2vec2-large-danish-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-large-danish-npsc-nst
  
  This model is a fine-tuned version of [chcaa/xls-r-300m-danish](https://huggingface.co/chcaa/xls-r-300m-danish) on the None dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.0587
  - Wer: 0.0669
  
  ## 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    |
  |:-------------:|:-----:|:------:|:---------------:|:------:|
  | 3.0474        | 0.05  | 500    | 2.9879          | 1.0    |
  | 1.7255        | 0.11  | 1000   | 1.1271          | 0.9732 |
  | 0.8932        | 0.16  | 1500   | 0.5474          | 0.5983 |
  | 0.7358        | 0.21  | 2000   | 0.4152          | 0.4584 |
  | 0.5939        | 0.26  | 2500   | 0.3455          | 0.3860 |
  | 0.5437        | 0.32  | 3000   | 0.3024          | 0.3473 |
  | 0.5128        | 0.37  | 3500   | 0.2708          | 0.3043 |
  | 0.4682        | 0.42  | 4000   | 0.2462          | 0.2841 |
  | 0.4392        | 0.47  | 4500   | 0.2374          | 0.2639 |
  | 0.4022        | 0.53  | 5000   | 0.2182          | 0.2457 |
  | 0.4106        | 0.58  | 5500   | 0.2066          | 0.2331 |
  | 0.3883        | 0.63  | 6000   | 0.1997          | 0.2267 |
  | 0.3701        | 0.69  | 6500   | 0.1862          | 0.2164 |
  | 0.3628        | 0.74  | 7000   | 0.1817          | 0.2051 |
  | 0.3468        | 0.79  | 7500   | 0.1716          | 0.1963 |
  | 0.3311        | 0.84  | 8000   | 0.1704          | 0.1924 |
  | 0.3329        | 0.9   | 8500   | 0.1620          | 0.1873 |
  | 0.3179        | 0.95  | 9000   | 0.1575          | 0.1811 |
  | 0.3162        | 1.0   | 9500   | 0.1519          | 0.1729 |
  | 0.296         | 1.05  | 10000  | 0.1503          | 0.1701 |
  | 0.2912        | 1.11  | 10500  | 0.1473          | 0.1673 |
  | 0.296         | 1.16  | 11000  | 0.1422          | 0.1649 |
  | 0.3615        | 1.21  | 11500  | 0.1373          | 0.1607 |
  | 0.276         | 1.26  | 12000  | 0.1376          | 0.1572 |
  | 0.2719        | 1.32  | 12500  | 0.1349          | 0.1540 |
  | 0.2717        | 1.37  | 13000  | 0.1306          | 0.1524 |
  | 0.2742        | 1.42  | 13500  | 0.1280          | 0.1515 |
  | 0.261         | 1.48  | 14000  | 0.1246          | 0.1480 |
  | 0.2532        | 1.53  | 14500  | 0.1266          | 0.1460 |
  | 0.2501        | 1.58  | 15000  | 0.1227          | 0.1435 |
  | 0.2705        | 1.63  | 15500  | 0.1202          | 0.1400 |
  | 0.2433        | 1.69  | 16000  | 0.1190          | 0.1396 |
  | 0.2526        | 1.74  | 16500  | 0.1182          | 0.1381 |
  | 0.2362        | 1.79  | 17000  | 0.1169          | 0.1363 |
  | 0.2359        | 1.84  | 17500  | 0.1166          | 0.1362 |
  | 0.2341        | 1.9   | 18000  | 0.1133          | 0.1336 |
  | 0.2347        | 1.95  | 18500  | 0.1121          | 0.1311 |
  | 0.2428        | 2.0   | 19000  | 0.1109          | 0.1293 |
  | 0.229         | 2.06  | 19500  | 0.1104          | 0.1275 |
  | 0.2307        | 2.11  | 20000  | 0.1084          | 0.1278 |
  | 0.2287        | 2.16  | 20500  | 0.1070          | 0.1260 |
  | 0.217         | 2.21  | 21000  | 0.1066          | 0.1242 |
  | 0.2056        | 2.27  | 21500  | 0.1058          | 0.1235 |
  | 0.2039        | 2.32  | 22000  | 0.1013          | 0.1211 |
  | 0.192         | 2.37  | 22500  | 0.1028          | 0.1198 |
  | 0.2129        | 2.42  | 23000  | 0.1035          | 0.1202 |
  | 0.1972        | 2.48  | 23500  | 0.1002          | 0.1197 |
  | 0.2136        | 2.53  | 24000  | 0.1014          | 0.1183 |
  | 0.2176        | 2.58  | 24500  | 0.0990          | 0.1181 |
  | 0.2029        | 2.64  | 25000  | 0.0975          | 0.1170 |
  | 0.2015        | 2.69  | 25500  | 0.0981          | 0.1168 |
  | 0.2           | 2.74  | 26000  | 0.0960          | 0.1145 |
  | 0.2018        | 2.79  | 26500  | 0.0966          | 0.1152 |
  | 0.1935        | 2.85  | 27000  | 0.0936          | 0.1144 |
  | 0.1944        | 2.9   | 27500  | 0.0931          | 0.1129 |
  | 0.1862        | 2.95  | 28000  | 0.0920          | 0.1126 |
  | 0.1805        | 3.0   | 28500  | 0.0941          | 0.1103 |
  | 0.1764        | 3.06  | 29000  | 0.0940          | 0.1103 |
  | 0.1859        | 3.11  | 29500  | 0.0915          | 0.1095 |
  | 0.1865        | 3.16  | 30000  |