readme: ---
  license: cc0-1.0
  tags:
  - generated_from_trainer
  model-index:
  - name: wav2vec2-large-voxrex-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-voxrex-npsc-nst
  
  This model is a fine-tuned version of [KBLab/wav2vec2-large-voxrex](https://huggingface.co/KBLab/wav2vec2-large-voxrex) on the None dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.0475
  - Wer: 0.0514
  
  ## 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    |
  |:-------------:|:-----:|:------:|:---------------:|:------:|
  | 3.3888        | 0.05  | 500    | 3.2558          | 1.0    |
  | 2.7683        | 0.11  | 1000   | 2.4163          | 1.0000 |
  | 0.6279        | 0.16  | 1500   | 0.3610          | 0.3608 |
  | 0.5093        | 0.21  | 2000   | 0.2610          | 0.2776 |
  | 0.4024        | 0.26  | 2500   | 0.2219          | 0.2303 |
  | 0.3705        | 0.32  | 3000   | 0.1940          | 0.2043 |
  | 0.3588        | 0.37  | 3500   | 0.1806          | 0.1822 |
  | 0.3312        | 0.42  | 4000   | 0.1611          | 0.1736 |
  | 0.3062        | 0.47  | 4500   | 0.1571          | 0.1619 |
  | 0.2838        | 0.53  | 5000   | 0.1482          | 0.1552 |
  | 0.2896        | 0.58  | 5500   | 0.1406          | 0.1482 |
  | 0.2704        | 0.63  | 6000   | 0.1311          | 0.1467 |
  | 0.263         | 0.69  | 6500   | 0.1258          | 0.1406 |
  | 0.2574        | 0.74  | 7000   | 0.1252          | 0.1343 |
  | 0.252         | 0.79  | 7500   | 0.1162          | 0.1279 |
  | 0.2355        | 0.84  | 8000   | 0.1161          | 0.1275 |
  | 0.2381        | 0.9   | 8500   | 0.1095          | 0.1247 |
  | 0.2354        | 0.95  | 9000   | 0.1106          | 0.1250 |
  | 0.234         | 1.0   | 9500   | 0.1044          | 0.1186 |
  | 0.2094        | 1.05  | 10000  | 0.1052          | 0.1157 |
  | 0.2088        | 1.11  | 10500  | 0.1026          | 0.1158 |
  | 0.2123        | 1.16  | 11000  | 0.0998          | 0.1120 |
  | 0.3087        | 1.21  | 11500  | 0.0971          | 0.1108 |
  | 0.1995        | 1.26  | 12000  | 0.0973          | 0.1085 |
  | 0.1989        | 1.32  | 12500  | 0.0928          | 0.1063 |
  | 0.1993        | 1.37  | 13000  | 0.0920          | 0.1064 |
  | 0.1996        | 1.42  | 13500  | 0.0904          | 0.1050 |
  | 0.1917        | 1.48  | 14000  | 0.0895          | 0.1051 |
  | 0.1857        | 1.53  | 14500  | 0.0889          | 0.1038 |
  | 0.1871        | 1.58  | 15000  | 0.0867          | 0.1054 |
  | 0.2047        | 1.63  | 15500  | 0.0866          | 0.1017 |
  | 0.1845        | 1.69  | 16000  | 0.0865          | 0.1007 |
  | 0.178         | 1.74  | 16500  | 0.0835          | 0.0999 |
  | 0.1741        | 1.79  | 17000  | 0.0838          | 0.0985 |
  | 0.1737        | 1.84  | 17500  | 0.0833          | 0.0966 |
  | 0.1713        | 1.9   | 18000  | 0.0799          | 0.0963 |
  | 0.1703        | 1.95  | 18500  | 0.0802          | 0.0950 |
  | 0.1735        | 2.0   | 19000  | 0.0785          | 0.0926 |
  | 0.1619        | 2.06  | 19500  | 0.0785          | 0.0930 |
  | 0.1707        | 2.11  | 20000  | 0.0787          | 0.0928 |
  | 0.17          | 2.16  | 20500  | 0.0765          | 0.0902 |
  | 0.1604        | 2.21  | 21000  | 0.0772          | 0.0918 |
  | 0.1576        | 2.27  | 21500  | 0.0745          | 0.0912 |
  | 0.1529        | 2.32  | 22000  | 0.0741          | 0.0906 |
  | 0.1435        | 2.37  | 22500  | 0.0751          | 0.0888 |
  | 0.1526        | 2.42  | 23000  | 0.0734          | 0.0892 |
  | 0.1471        | 2.48  | 23500  | 0.0746          | 0.0886 |
  | 0.1553        | 2.53  | 24000  | 0.0727          | 0.0872 |
  | 0.1641        | 2.58  | 24500  | 0.0720          | 0.0862 |
  | 0.1495        | 2.64  | 25000  | 0.0707          | 0.0868 |
  | 0.1498        | 2.69  | 25500  | 0.0719          | 0.0864 |
  | 0.1438        | 2.74  | 26000  | 0.0703          | 0.0853 |
  | 0.1532        | 2.79  | 26500  | 0.0710          | 0.0854 |
  | 0.1435        | 2.85  | 27000  | 0.0690          | 0.0847 |
  | 0.1486        | 2.9   | 27500  | 0.0683          | 0.0882 |
  | 0.1359        | 2.95  | 28000  | 0.0673          | 0.0839 |
  | 0.1309        | 3.0   | 28500  | 0.0687          | 0.0843 |
  | 0.1312        | 3.06  | 29000  | 0.0696          | 0.0865 |
  | 0.1387        | 3.11  | 29500  | 0.0667          | 0.0857 |
  | 0.1327        | 3.16  | 300