readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  metrics:
  - wer
  model-index:
  - name: north2
    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. -->
  
  # north2
  
  This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the None dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.1364
  - Wer: 0.0652
  
  ## 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: 2
  - eval_batch_size: 8
  - seed: 42
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - lr_scheduler_warmup_steps: 1000
  - num_epochs: 50
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch | Step   | Validation Loss | Wer    |
  |:-------------:|:-----:|:------:|:---------------:|:------:|
  | 5.6443        | 0.12  | 1000   | 3.3282          | 1.0    |
  | 3.3258        | 0.23  | 2000   | 3.3195          | 1.0    |
  | 2.6482        | 0.35  | 3000   | 0.9655          | 0.7012 |
  | 0.9607        | 0.47  | 4000   | 0.5836          | 0.4781 |
  | 0.7212        | 0.58  | 5000   | 0.4785          | 0.4026 |
  | 0.6256        | 0.7   | 6000   | 0.4246          | 0.3641 |
  | 0.5762        | 0.82  | 7000   | 0.3872          | 0.3363 |
  | 0.533         | 0.94  | 8000   | 0.3771          | 0.3308 |
  | 0.4982        | 1.05  | 9000   | 0.3422          | 0.3091 |
  | 0.4513        | 1.17  | 10000  | 0.3325          | 0.2950 |
  | 0.442         | 1.29  | 11000  | 0.3224          | 0.2892 |
  | 0.4195        | 1.4   | 12000  | 0.3043          | 0.2848 |
  | 0.4082        | 1.52  | 13000  | 0.3016          | 0.2709 |
  | 0.4107        | 1.64  | 14000  | 0.2859          | 0.2662 |
  | 0.3883        | 1.75  | 15000  | 0.2825          | 0.2602 |
  | 0.3827        | 1.87  | 16000  | 0.2666          | 0.2486 |
  | 0.3671        | 1.99  | 17000  | 0.2552          | 0.2406 |
  | 0.3272        | 2.11  | 18000  | 0.2629          | 0.2375 |
  | 0.323         | 2.22  | 19000  | 0.2554          | 0.2311 |
  | 0.3168        | 2.34  | 20000  | 0.2540          | 0.2299 |
  | 0.3158        | 2.46  | 21000  | 0.2409          | 0.2187 |
  | 0.3124        | 2.57  | 22000  | 0.2291          | 0.2137 |
  | 0.2938        | 2.69  | 23000  | 0.2331          | 0.2107 |
  | 0.2981        | 2.81  | 24000  | 0.2258          | 0.2052 |
  | 0.2869        | 2.92  | 25000  | 0.2226          | 0.2009 |
  | 0.2716        | 3.04  | 26000  | 0.2160          | 0.1887 |
  | 0.2398        | 3.16  | 27000  | 0.2163          | 0.1935 |
  | 0.2502        | 3.27  | 28000  | 0.2126          | 0.1860 |
  | 0.2436        | 3.39  | 29000  | 0.2068          | 0.1847 |
  | 0.2407        | 3.51  | 30000  | 0.2055          | 0.1818 |
  | 0.2375        | 3.63  | 31000  | 0.2010          | 0.1744 |
  | 0.2351        | 3.74  | 32000  | 0.1891          | 0.1674 |
  | 0.2323        | 3.86  | 33000  | 0.1937          | 0.1697 |
  | 0.2373        | 3.98  | 34000  | 0.1876          | 0.1620 |
  | 0.2064        | 4.09  | 35000  | 0.1916          | 0.1611 |
  | 0.1999        | 4.21  | 36000  | 0.1889          | 0.1569 |
  | 0.199         | 4.33  | 37000  | 0.1844          | 0.1548 |
  | 0.1961        | 4.44  | 38000  | 0.1810          | 0.1483 |
  | 0.2004        | 4.56  | 39000  | 0.1787          | 0.1468 |
  | 0.1986        | 4.68  | 40000  | 0.1702          | 0.1448 |
  | 0.1968        | 4.8   | 41000  | 0.1624          | 0.1409 |
  | 0.2001        | 4.91  | 42000  | 0.1574          | 0.1372 |
  | 0.1835        | 5.03  | 43000  | 0.1654          | 0.1375 |
  | 0.1638        | 5.15  | 44000  | 0.1695          | 0.1439 |
  | 0.1681        | 5.26  | 45000  | 0.1561          | 0.1348 |
  | 0.1643        | 5.38  | 46000  | 0.1657          | 0.1353 |
  | 0.1691        | 5.5   | 47000  | 0.1737          | 0.1345 |
  | 0.1625        | 5.61  | 48000  | 0.1562          | 0.1309 |
  | 0.1649        | 5.73  | 49000  | 0.1561          | 0.1342 |
  | 0.1652        | 5.85  | 50000  | 0.1529          | 0.1244 |
  | 0.1702        | 5.96  | 51000  | 0.1557          | 0.1245 |
  | 0.1466        | 6.08  | 52000  | 0.1636          | 0.1301 |
  | 0.1485        | 6.2   | 53000  | 0.1565          | 0.1227 |
  | 0.1512        | 6.32  | 54000  | 0.1536          | 0.1200 |
  | 0.1453        | 6.43  | 55000  | 0.1476          | 0.1225 |
  | 0.1455        | 6.55  | 56000  | 0.1471          | 0.1181 |
  | 0.1464        | 6.67  | 57000  | 0.1494          | 0.1205 |
  | 0.1469        | 6.78  | 58000  | 0.1417          | 0.1159 |
  | 0.1467        | 6.9   | 59000  | 0.1513          | 0.1162 |
  | 0.1412        | 7.02  | 60000  | 0.1485          | 0.1167 |
  | 0.1298        | 7.13  | 61000  | 0.1498          | 0.