readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  metrics:
  - wer
  model-index:
  - name: wav2vec2-libri-train500-colab
    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-libri-train500-colab
  
  This model is a fine-tuned version of [GW12/wav2vec2-libri-train360_2-colab](https://huggingface.co/GW12/wav2vec2-libri-train360_2-colab) on the None dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.2348
  - Wer: 0.1600
  
  ## 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: 8
  - 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: 5
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch | Step  | Validation Loss | Wer    |
  |:-------------:|:-----:|:-----:|:---------------:|:------:|
  | 0.1452        | 0.03  | 500   | 0.2791          | 0.1920 |
  | 0.1544        | 0.05  | 1000  | 0.2897          | 0.1980 |
  | 0.1654        | 0.08  | 1500  | 0.3320          | 0.2121 |
  | 0.1807        | 0.11  | 2000  | 0.3317          | 0.2182 |
  | 0.1697        | 0.13  | 2500  | 0.3250          | 0.2124 |
  | 0.1761        | 0.16  | 3000  | 0.3158          | 0.2102 |
  | 0.1697        | 0.19  | 3500  | 0.3335          | 0.2114 |
  | 0.169         | 0.22  | 4000  | 0.3160          | 0.2168 |
  | 0.1715        | 0.24  | 4500  | 0.2950          | 0.2105 |
  | 0.1666        | 0.27  | 5000  | 0.2936          | 0.2079 |
  | 0.1734        | 0.3   | 5500  | 0.3106          | 0.2098 |
  | 0.1664        | 0.32  | 6000  | 0.2997          | 0.2048 |
  | 0.1688        | 0.35  | 6500  | 0.3352          | 0.2123 |
  | 0.1641        | 0.38  | 7000  | 0.3181          | 0.2069 |
  | 0.1709        | 0.4   | 7500  | 0.2950          | 0.2049 |
  | 0.1601        | 0.43  | 8000  | 0.2976          | 0.2041 |
  | 0.1616        | 0.46  | 8500  | 0.3217          | 0.2051 |
  | 0.158         | 0.48  | 9000  | 0.3133          | 0.2110 |
  | 0.1635        | 0.51  | 9500  | 0.3158          | 0.2081 |
  | 0.1555        | 0.54  | 10000 | 0.3234          | 0.2130 |
  | 0.166         | 0.56  | 10500 | 0.3494          | 0.2183 |
  | 0.1606        | 0.59  | 11000 | 0.2910          | 0.2020 |
  | 0.1607        | 0.62  | 11500 | 0.3231          | 0.2103 |
  | 0.1629        | 0.65  | 12000 | 0.2999          | 0.2048 |
  | 0.1604        | 0.67  | 12500 | 0.3150          | 0.2029 |
  | 0.1555        | 0.7   | 13000 | 0.3163          | 0.2062 |
  | 0.1577        | 0.73  | 13500 | 0.3695          | 0.2138 |
  | 0.1609        | 0.75  | 14000 | 0.3334          | 0.2048 |
  | 0.155         | 0.78  | 14500 | 0.2887          | 0.2004 |
  | 0.1549        | 0.81  | 15000 | 0.3375          | 0.2069 |
  | 0.1537        | 0.83  | 15500 | 0.2979          | 0.2038 |
  | 0.1515        | 0.86  | 16000 | 0.2683          | 0.1984 |
  | 0.1548        | 0.89  | 16500 | 0.2873          | 0.2016 |
  | 0.152         | 0.91  | 17000 | 0.3142          | 0.2010 |
  | 0.1501        | 0.94  | 17500 | 0.2751          | 0.1963 |
  | 0.1511        | 0.97  | 18000 | 0.2951          | 0.1988 |
  | 0.1465        | 1.0   | 18500 | 0.2909          | 0.1969 |
  | 0.1333        | 1.02  | 19000 | 0.2910          | 0.1951 |
  | 0.1289        | 1.05  | 19500 | 0.3146          | 0.1931 |
  | 0.1283        | 1.08  | 20000 | 0.2859          | 0.1916 |
  | 0.1306        | 1.1   | 20500 | 0.2870          | 0.1923 |
  | 0.131         | 1.13  | 21000 | 0.2864          | 0.1929 |
  | 0.1268        | 1.16  | 21500 | 0.2850          | 0.1929 |
  | 0.1298        | 1.18  | 22000 | 0.2780          | 0.1908 |
  | 0.1253        | 1.21  | 22500 | 0.2775          | 0.1924 |
  | 0.1285        | 1.24  | 23000 | 0.3637          | 0.2051 |
  | 0.1238        | 1.26  | 23500 | 0.2821          | 0.1914 |
  | 0.13          | 1.29  | 24000 | 0.3083          | 0.1954 |
  | 0.1333        | 1.32  | 24500 | 0.3033          | 0.1937 |
  | 0.1281        | 1.35  | 25000 | 0.2995          | 0.1947 |
  | 0.122         | 1.37  | 25500 | 0.3041          | 0.1935 |
  | 0.127         | 1.4   | 26000 | 0.3235          | 0.1976 |
  | 0.1254        | 1.43  | 26500 | 0.3084          | 0.1917 |
  | 0.128         | 1.45  | 27000 | 0.3172          | 0.1952 |
  | 0.1212        | 1.48  | 27500 | 0.2766          | 0.1898 |
  | 0.1248        | 1.51  | 28000 | 0.2936          | 0.1905 |
  | 0.1225        | 1.53  | 28500 | 0.2746          | 0.1872 |
  | 0.1245        | 1.56  | 29000 | 0.2790          | 0.1886 |
  | 0.1278        | 1.59  | 29500 | 0.2785          | 0.1895 |
  | 0.1251        | 1.61  | 30000 | 0.2583          | 0.1867 |
  | 0.1186        | 1.64  | 30500 | 0.2899          | 0.1894 |
  |