readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  datasets:
  - common_voice
  model-index:
  - name: wav2vec2-large-xls-r-300m-russian-colab-beam_search_test
    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-xls-r-300m-russian-colab-beam_search_test
  
  This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the common_voice dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.7619
  - Wer: 0.4680
  
  ## 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: 0.0003
  - train_batch_size: 16
  - eval_batch_size: 8
  - 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: 800
  - num_epochs: 100
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Wer    |
  |:-------------:|:-----:|:----:|:---------------:|:------:|
  | 10.0158       | 4.16  | 100  | 5.4134          | 1.0    |
  | 4.0394        | 8.33  | 200  | 3.4304          | 1.0    |
  | 3.2721        | 12.49 | 300  | 3.2273          | 1.0    |
  | 3.1277        | 16.66 | 400  | 2.8023          | 0.9984 |
  | 1.3791        | 20.82 | 500  | 0.9888          | 0.8546 |
  | 0.3659        | 24.99 | 600  | 0.7602          | 0.6304 |
  | 0.1858        | 29.16 | 700  | 0.7965          | 0.6156 |
  | 0.1403        | 33.33 | 800  | 0.7998          | 0.5839 |
  | 0.1173        | 37.49 | 900  | 0.8353          | 0.5941 |
  | 0.0917        | 41.66 | 1000 | 0.8272          | 0.5522 |
  | 0.0743        | 45.82 | 1100 | 0.8342          | 0.5471 |
  | 0.063         | 49.99 | 1200 | 0.7988          | 0.5352 |
  | 0.0528        | 54.16 | 1300 | 0.7740          | 0.5201 |
  | 0.0456        | 58.33 | 1400 | 0.7636          | 0.5165 |
  | 0.0389        | 62.49 | 1500 | 0.7922          | 0.5161 |
  | 0.0329        | 66.66 | 1600 | 0.8035          | 0.5158 |
  | 0.0283        | 70.82 | 1700 | 0.7873          | 0.4832 |
  | 0.0255        | 74.99 | 1800 | 0.7853          | 0.4870 |
  | 0.0236        | 79.16 | 1900 | 0.8236          | 0.5045 |
  | 0.0202        | 83.33 | 2000 | 0.7661          | 0.4796 |
  | 0.0165        | 87.49 | 2100 | 0.7584          | 0.4680 |
  | 0.0156        | 91.66 | 2200 | 0.7685          | 0.4772 |
  | 0.0149        | 95.82 | 2300 | 0.7519          | 0.4696 |
  | 0.0126        | 99.99 | 2400 | 0.7619          | 0.4680 |
  
  
  ### Framework versions
  
  - Transformers 4.11.3
  - Pytorch 1.10.0+cu111
  - Datasets 1.18.3
  - Tokenizers 0.10.3
  