readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  datasets:
  - common_voice
  model-index:
  - name: wav2vec2-large-multilang-cv-ru
    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-multilang-cv-ru
  
  This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the common_voice dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.9734
  - Wer: 0.7037
  
  ## 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: 0.0005
  - 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: 30
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch | Step  | Validation Loss | Wer    |
  |:-------------:|:-----:|:-----:|:---------------:|:------:|
  | 6.0328        | 0.79  | 500   | 3.0713          | 1.0    |
  | 1.9426        | 1.58  | 1000  | 1.2048          | 0.9963 |
  | 1.1285        | 2.37  | 1500  | 0.9825          | 0.9282 |
  | 0.9462        | 3.15  | 2000  | 0.8836          | 0.8965 |
  | 0.8274        | 3.94  | 2500  | 0.8134          | 0.8661 |
  | 0.7106        | 4.73  | 3000  | 0.8033          | 0.8387 |
  | 0.6545        | 5.52  | 3500  | 0.8309          | 0.8366 |
  | 0.6013        | 6.31  | 4000  | 0.7667          | 0.8240 |
  | 0.5599        | 7.1   | 4500  | 0.7740          | 0.8160 |
  | 0.5027        | 7.89  | 5000  | 0.7796          | 0.8188 |
  | 0.4588        | 8.68  | 5500  | 0.8204          | 0.7968 |
  | 0.4448        | 9.46  | 6000  | 0.8277          | 0.7738 |
  | 0.4122        | 10.25 | 6500  | 0.8292          | 0.7776 |
  | 0.3816        | 11.04 | 7000  | 0.8548          | 0.7907 |
  | 0.3587        | 11.83 | 7500  | 0.8245          | 0.7805 |
  | 0.3374        | 12.62 | 8000  | 0.8371          | 0.7701 |
  | 0.3214        | 13.41 | 8500  | 0.8311          | 0.7822 |
  | 0.3072        | 14.2  | 9000  | 0.8940          | 0.7674 |
  | 0.2929        | 14.98 | 9500  | 0.8788          | 0.7604 |
  | 0.257         | 15.77 | 10000 | 0.8911          | 0.7633 |
  | 0.2592        | 16.56 | 10500 | 0.8673          | 0.7604 |
  | 0.2392        | 17.35 | 11000 | 0.9582          | 0.7810 |
  | 0.232         | 18.14 | 11500 | 0.9340          | 0.7423 |
  | 0.2252        | 18.93 | 12000 | 0.8874          | 0.7320 |
  | 0.2079        | 19.72 | 12500 | 0.9436          | 0.7483 |
  | 0.2003        | 20.5  | 13000 | 0.9573          | 0.7638 |
  | 0.194         | 21.29 | 13500 | 0.9361          | 0.7308 |
  | 0.188         | 22.08 | 14000 | 0.9704          | 0.7221 |
  | 0.1754        | 22.87 | 14500 | 0.9668          | 0.7265 |
  | 0.1688        | 23.66 | 15000 | 0.9680          | 0.7246 |
  | 0.162         | 24.45 | 15500 | 0.9443          | 0.7066 |
  | 0.1617        | 25.24 | 16000 | 0.9664          | 0.7265 |
  | 0.1504        | 26.03 | 16500 | 0.9505          | 0.7189 |
  | 0.1425        | 26.81 | 17000 | 0.9536          | 0.7112 |
  | 0.134         | 27.6  | 17500 | 0.9674          | 0.7047 |
  | 0.1301        | 28.39 | 18000 | 0.9852          | 0.7066 |
  | 0.1314        | 29.18 | 18500 | 0.9766          | 0.7073 |
  | 0.1219        | 29.97 | 19000 | 0.9734          | 0.7037 |
  
  
  ### Framework versions
  
  - Transformers 4.19.2
  - Pytorch 1.11.0
  - Datasets 2.2.2
  - Tokenizers 0.12.1
  