readme: ---
  license: apache-2.0
  tags:
  - automatic-speech-recognition
  - NbAiLab/NPSC
  - generated_from_trainer
  model-index:
  - name: ''
    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. -->
  
  # 
  
  This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the NBAILAB/NPSC - 16K_MP3 dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.1957
  - Wer: 0.1697
  
  ## 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: 7.5e-05
  - train_batch_size: 16
  - eval_batch_size: 16
  - seed: 42
  - gradient_accumulation_steps: 4
  - total_train_batch_size: 64
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - lr_scheduler_warmup_steps: 2000
  - num_epochs: 20.0
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch | Step  | Validation Loss | Wer    |
  |:-------------:|:-----:|:-----:|:---------------:|:------:|
  | 4.4527        | 0.28  | 250   | 4.0144          | 1.0    |
  | 3.1828        | 0.56  | 500   | 3.1369          | 1.0    |
  | 2.9927        | 0.85  | 750   | 3.0183          | 1.0    |
  | 2.9591        | 1.13  | 1000  | 2.9991          | 1.0    |
  | 2.8989        | 1.41  | 1250  | 2.9000          | 1.0000 |
  | 2.4286        | 1.69  | 1500  | 1.7688          | 0.9550 |
  | 1.6765        | 1.98  | 1750  | 0.6842          | 0.4855 |
  | 1.4521        | 2.26  | 2000  | 0.5096          | 0.3736 |
  | 1.3589        | 2.54  | 2250  | 0.4479          | 0.3335 |
  | 1.3136        | 2.82  | 2500  | 0.4056          | 0.3123 |
  | 1.2856        | 3.11  | 2750  | 0.3870          | 0.2987 |
  | 1.2283        | 3.39  | 3000  | 0.3646          | 0.2828 |
  | 1.2053        | 3.67  | 3250  | 0.3499          | 0.2748 |
  | 1.2087        | 3.95  | 3500  | 0.3345          | 0.2603 |
  | 1.2002        | 4.24  | 3750  | 0.3320          | 0.2523 |
  | 1.1383        | 4.52  | 4000  | 0.3117          | 0.2439 |
  | 1.1364        | 4.8   | 4250  | 0.3198          | 0.2383 |
  | 1.158         | 5.08  | 4500  | 0.3071          | 0.2342 |
  | 1.108         | 5.37  | 4750  | 0.3011          | 0.2314 |
  | 1.1025        | 5.65  | 5000  | 0.2875          | 0.2289 |
  | 1.0697        | 5.93  | 5250  | 0.2926          | 0.2256 |
  | 1.0904        | 6.21  | 5500  | 0.2695          | 0.2245 |
  | 1.0802        | 6.5   | 5750  | 0.2602          | 0.2189 |
  | 1.0882        | 6.78  | 6000  | 0.2603          | 0.2168 |
  | 1.0881        | 7.06  | 6250  | 0.2540          | 0.2293 |
  | 1.0378        | 7.34  | 6500  | 0.2614          | 0.2193 |
  | 1.0397        | 7.63  | 6750  | 0.2707          | 0.2104 |
  | 1.0296        | 7.91  | 7000  | 0.2483          | 0.2119 |
  | 1.0249        | 8.19  | 7250  | 0.2483          | 0.2047 |
  | 1.013         | 8.47  | 7500  | 0.2487          | 0.2042 |
  | 1.0064        | 8.76  | 7750  | 0.2456          | 0.2016 |
  | 1.0668        | 9.04  | 8000  | 0.2397          | 0.1995 |
  | 1.0129        | 9.32  | 8250  | 0.2374          | 0.1994 |
  | 1.0164        | 9.6   | 8500  | 0.2206          | 0.1992 |
  | 0.975         | 9.89  | 8750  | 0.2247          | 0.1973 |
  | 0.9849        | 10.17 | 9000  | 0.2325          | 0.1953 |
  | 0.9826        | 10.45 | 9250  | 0.2301          | 0.1934 |
  | 0.9835        | 10.73 | 9500  | 0.2192          | 0.1942 |
  | 0.9676        | 11.02 | 9750  | 0.2266          | 0.1913 |
  | 0.9627        | 11.3  | 10000 | 0.2193          | 0.1921 |
  | 0.976         | 11.58 | 10250 | 0.2309          | 0.1882 |
  | 0.969         | 11.86 | 10500 | 0.2268          | 0.1886 |
  | 0.9611        | 12.15 | 10750 | 0.2322          | 0.1863 |
  | 0.9397        | 12.43 | 11000 | 0.2197          | 0.1844 |
  | 0.9601        | 12.71 | 11250 | 0.2211          | 0.1871 |
  | 0.9718        | 12.99 | 11500 | 0.2079          | 0.1898 |
  | 0.9347        | 13.28 | 11750 | 0.2054          | 0.1843 |
  | 0.9377        | 13.56 | 12000 | 0.2031          | 0.1842 |
  | 0.934         | 13.84 | 12250 | 0.2059          | 0.1806 |
  | 0.9295        | 14.12 | 12500 | 0.2122          | 0.1861 |
  | 0.935         | 14.41 | 12750 | 0.2072          | 0.1787 |
  | 0.9021        | 14.69 | 13000 | 0.2105          | 0.1781 |
  | 0.9193        | 14.97 | 13250 | 0.2035          | 0.1786 |
  | 0.9214        | 15.25 | 13500 | 0.2035          | 0.1766 |
  | 0.9048        | 15.54 | 13750 | 0.1964          | 0.1758 |
  | 0.9006        | 15.82 | 14000 | 0.1984          | 0.1757 |
  | 0.9027        | 16.1  | 14250 | 0.2022          | 0.1743 |
  | 0.9083        | 16.38 | 14500 | 0.1969          | 0.1744 |
  | 0.9761        | 16.67 | 14750 | 0.1963          | 0.1728 |
  | 0.9311        | 16.95 | 15000 | 0.1960          | 0.1737 |
  | 0.886         |