readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  metrics:
  - wer
  model-index:
  - name: wav2vec2-libri-train360_2-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-train360_2-colab
  
  This model is a fine-tuned version of [GW12/wav2vec2-libri-train360-colab](https://huggingface.co/GW12/wav2vec2-libri-train360-colab) on the None dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.1024
  - Wer: 0.0959
  
  ## 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: 4
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch | Step  | Validation Loss | Wer    |
  |:-------------:|:-----:|:-----:|:---------------:|:------:|
  | 3.219         | 0.04  | 500   | 0.1976          | 0.1215 |
  | 0.0762        | 0.08  | 1000  | 0.2818          | 0.1324 |
  | 0.0824        | 0.12  | 1500  | 0.4541          | 0.1602 |
  | 0.0807        | 0.15  | 2000  | 0.1556          | 0.1162 |
  | 0.0799        | 0.19  | 2500  | 0.1618          | 0.1164 |
  | 0.0826        | 0.23  | 3000  | 0.3510          | 0.1379 |
  | 0.0809        | 0.27  | 3500  | 0.1486          | 0.1182 |
  | 0.0854        | 0.31  | 4000  | 0.1267          | 0.1177 |
  | 0.0817        | 0.35  | 4500  | 0.1581          | 0.1218 |
  | 0.0835        | 0.38  | 5000  | 0.1670          | 0.1251 |
  | 0.0841        | 0.42  | 5500  | 0.1576          | 0.1179 |
  | 0.0798        | 0.46  | 6000  | 0.2201          | 0.1300 |
  | 0.083         | 0.5   | 6500  | 0.1165          | 0.1179 |
  | 0.0878        | 0.54  | 7000  | 0.2640          | 0.1430 |
  | 0.0811        | 0.58  | 7500  | 0.1585          | 0.1288 |
  | 0.083         | 0.62  | 8000  | 0.3127          | 0.1370 |
  | 0.083         | 0.65  | 8500  | 0.4790          | 0.1449 |
  | 0.0775        | 0.69  | 9000  | 0.1651          | 0.1163 |
  | 0.0787        | 0.73  | 9500  | 1.6426          | 0.2083 |
  | 0.0781        | 0.77  | 10000 | 0.2307          | 0.1324 |
  | 0.0827        | 0.81  | 10500 | 0.1765          | 0.1318 |
  | 0.0816        | 0.85  | 11000 | 0.1679          | 0.1201 |
  | 0.0797        | 0.88  | 11500 | 0.2506          | 0.1508 |
  | 0.0813        | 0.92  | 12000 | 0.1893          | 0.1239 |
  | 0.0758        | 0.96  | 12500 | 0.1266          | 0.1147 |
  | 0.091         | 1.0   | 13000 | 0.1606          | 0.1180 |
  | 0.0677        | 1.04  | 13500 | 0.1107          | 0.1118 |
  | 0.0733        | 1.08  | 14000 | 0.1734          | 0.1565 |
  | 0.072         | 1.12  | 14500 | 0.1141          | 0.1126 |
  | 0.0731        | 1.15  | 15000 | 0.1125          | 0.1112 |
  | 0.0793        | 1.19  | 15500 | 0.1818          | 0.1146 |
  | 0.07          | 1.23  | 16000 | 0.2678          | 0.1265 |
  | 0.0658        | 1.27  | 16500 | 0.2909          | 0.1203 |
  | 0.0678        | 1.31  | 17000 | 0.3241          | 0.1280 |
  | 0.0681        | 1.35  | 17500 | 0.3243          | 0.1497 |
  | 0.0666        | 1.38  | 18000 | 0.2056          | 0.1150 |
  | 0.0667        | 1.42  | 18500 | 0.4678          | 0.1252 |
  | 0.0656        | 1.46  | 19000 | 0.1603          | 0.1138 |
  | 0.0662        | 1.5   | 19500 | 0.1554          | 0.1115 |
  | 0.0669        | 1.54  | 20000 | 0.1215          | 0.1101 |
  | 0.0681        | 1.58  | 20500 | 0.1118          | 0.1083 |
  | 0.0708        | 1.62  | 21000 | 0.1743          | 0.1146 |
  | 0.0673        | 1.65  | 21500 | 0.1509          | 0.1109 |
  | 0.0667        | 1.69  | 22000 | 0.3411          | 0.1495 |
  | 0.065         | 1.73  | 22500 | 0.1045          | 0.1067 |
  | 0.0644        | 1.77  | 23000 | 0.0999          | 0.1075 |
  | 0.0643        | 1.81  | 23500 | 0.1019          | 0.1073 |
  | 0.0675        | 1.85  | 24000 | 0.1196          | 0.1073 |
  | 0.0618        | 1.88  | 24500 | 0.1092          | 0.1086 |
  | 0.0626        | 1.92  | 25000 | 0.1256          | 0.1070 |
  | 0.0635        | 1.96  | 25500 | 0.1183          | 0.1069 |
  | 0.0621        | 2.0   | 26000 | 0.1180          | 0.1091 |
  | 0.0548        | 2.04  | 26500 | 0.1199          | 0.1048 |
  | 0.0548        | 2.08  | 27000 | 0.1215          | 0.1057 |
  | 0.0531        | 2.12  | 27500 | 0.1086          | 0.1036 |
  | 0.0548        | 2.15  | 28000 | 0.1103          | 0.1043 |
  | 0.054         | 2.19  | 28500 | 0.1078          | 0.1048 |
  | 0.0521        | 2.23  | 29000 | 0.1094          | 0.1039 |
  | 0.0534        | 2.27  | 29500 | 0.1058          | 0.1037 |
  | 0.0539        | 2.31  | 30000 | 0.1035          | 0.1026 |
  | 0.0516        | 2.35  | 30500 | 0.1009          | 0.1027 |
  |