readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  datasets:
  - common_voice
  model-index:
  - name: wav2vec2-base-ft-cv3-v3
    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-base-ft-cv3-v3
  
  This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the "mozilla-foundation/common_voice_3_0 english" dataset: "train" and "validation" splits are used for training while "test" split is used for validation.
  It achieves the following results on the evaluation set:
  - Loss: 0.5787
  - Wer: 0.2470
  
  ## 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: 5e-05
  - train_batch_size: 16
  - eval_batch_size: 16
  - seed: 42
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - num_epochs: 12
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch | Step  | Validation Loss | Wer    |
  |:-------------:|:-----:|:-----:|:---------------:|:------:|
  | 3.5935        | 0.1   | 500   | 3.0085          | 1.0    |
  | 1.6296        | 0.21  | 1000  | 1.0879          | 0.5895 |
  | 0.7154        | 0.31  | 1500  | 0.8224          | 0.4839 |
  | 0.6387        | 0.42  | 2000  | 0.7290          | 0.4302 |
  | 0.5322        | 0.52  | 2500  | 0.6864          | 0.4044 |
  | 0.497         | 0.63  | 3000  | 0.6294          | 0.3746 |
  | 0.4659        | 0.73  | 3500  | 0.6388          | 0.3745 |
  | 0.4452        | 0.84  | 4000  | 0.6122          | 0.3570 |
  | 0.4356        | 0.94  | 4500  | 0.5770          | 0.3443 |
  | 0.3976        | 1.05  | 5000  | 0.6145          | 0.3296 |
  | 0.3767        | 1.15  | 5500  | 0.6099          | 0.3325 |
  | 0.3704        | 1.25  | 6000  | 0.5998          | 0.3263 |
  | 0.3541        | 1.36  | 6500  | 0.6070          | 0.3250 |
  | 0.3592        | 1.46  | 7000  | 0.6076          | 0.3352 |
  | 0.3508        | 1.57  | 7500  | 0.5712          | 0.3239 |
  | 0.3437        | 1.67  | 8000  | 0.5729          | 0.3202 |
  | 0.352         | 1.78  | 8500  | 0.5465          | 0.3100 |
  | 0.34          | 1.88  | 9000  | 0.5418          | 0.3059 |
  | 0.4086        | 1.99  | 9500  | 0.5189          | 0.3053 |
  | 0.2968        | 2.09  | 10000 | 0.5373          | 0.3076 |
  | 0.2968        | 2.2   | 10500 | 0.5602          | 0.3061 |
  | 0.2956        | 2.3   | 11000 | 0.5651          | 0.3051 |
  | 0.2863        | 2.41  | 11500 | 0.5476          | 0.2982 |
  | 0.2852        | 2.51  | 12000 | 0.5579          | 0.2954 |
  | 0.292         | 2.61  | 12500 | 0.5451          | 0.2953 |
  | 0.2877        | 2.72  | 13000 | 0.5468          | 0.2905 |
  | 0.285         | 2.82  | 13500 | 0.5283          | 0.2908 |
  | 0.2872        | 2.93  | 14000 | 0.5240          | 0.2867 |
  | 0.3286        | 3.03  | 14500 | 0.5078          | 0.2846 |
  | 0.2526        | 3.14  | 15000 | 0.5373          | 0.2836 |
  | 0.2494        | 3.24  | 15500 | 0.5566          | 0.2861 |
  | 0.2534        | 3.35  | 16000 | 0.5378          | 0.2859 |
  | 0.2435        | 3.45  | 16500 | 0.5225          | 0.2813 |
  | 0.3144        | 3.56  | 17000 | 0.5203          | 0.2808 |
  | 0.2501        | 3.66  | 17500 | 0.5176          | 0.2785 |
  | 0.2469        | 3.76  | 18000 | 0.5022          | 0.2795 |
  | 0.242         | 3.87  | 18500 | 0.5228          | 0.2757 |
  | 0.242         | 3.97  | 19000 | 0.5024          | 0.2788 |
  | 0.2205        | 4.08  | 19500 | 0.5318          | 0.2729 |
  | 0.2149        | 4.18  | 20000 | 0.5492          | 0.2763 |
  | 0.2186        | 4.29  | 20500 | 0.5599          | 0.2769 |
  | 0.2191        | 4.39  | 21000 | 0.5493          | 0.2695 |
  | 0.218         | 4.5   | 21500 | 0.5385          | 0.2709 |
  | 0.2046        | 4.6   | 22000 | 0.5326          | 0.2718 |
  | 0.2064        | 4.71  | 22500 | 0.5591          | 0.2725 |
  | 0.2066        | 4.81  | 23000 | 0.5283          | 0.2700 |
  | 0.2102        | 4.92  | 23500 | 0.5456          | 0.2713 |
  | 0.3345        | 5.02  | 24000 | 0.5474          | 0.2698 |
  | 0.1891        | 5.12  | 24500 | 0.5466          | 0.2672 |
  | 0.1954        | 5.23  | 25000 | 0.5691          | 0.2731 |
  | 0.1971        | 5.33  | 25500 | 0.5595          | 0.2741 |
  | 0.1995        | 5.44  | 26000 | 0.5609          | 0.2716 |
  | 0.1911        | 5.54  | 26500 | 0.5513          | 0.2684 |
  | 0.1954        | 5.65  | 27000 | 0.5282          | 0.2683 |
  | 0.193         | 5.75  | 27500 | 0.5460          | 0.2644 |
  | 0.1974        | 5.86  | 28000 | 0.5415          | 0.2650 |
  | 0.1947        | 5.96  | 28500 | 0.5227          | 0.2656 |
  | 0.1836        | 6.07  | 29000 | 0.5361          | 0.2743 |
  | 0.1741        | 6.17  | 29500 | 0.5637          | 0.2649 |
  | 0.1776        | 6.27  | 30000 | 0.5705       