readme: ---
  language:
  - uk
  license: apache-2.0
  tags:
  - automatic-speech-recognition
  - common_voice
  - generated_from_trainer
  datasets:
  - common_voice
  model-index:
  - name: wav2vec2-xls-r-300m-uk
    results:
    - task: 
        name: Speech Recognition
        type: automatic-speech-recognition
      dataset:
        name: Common Voice uk
        type: common_voice
        args: uk
      metrics:
         - name: Test WER
           type: wer
           value: 27.99
  ---
  
  <!-- 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-xls-r-300m-uk
  
  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.  
  Notebook for training is located in this repository: [https://github.com/robinhad/wav2vec2-xls-r-ukrainian](https://github.com/robinhad/wav2vec2-xls-r-ukrainian).  
  It achieves the following results on the evaluation set:
  - Loss: 0.4165
  - Wer: 0.2799
  - Cer: 0.0601
  
  ## 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: 8
  - eval_batch_size: 8
  - seed: 42
  - gradient_accumulation_steps: 20
  - total_train_batch_size: 160
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - lr_scheduler_warmup_steps: 500
  - num_epochs: 500
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch  | Step  | Cer    | Validation Loss | Wer    |
  |:-------------:|:------:|:-----:|:------:|:---------------:|:------:|
  | 4.3982        | 9.3    | 400   | 0.1437 | 0.5218          | 0.6507 |
  | 0.229         | 18.6   | 800   | 0.0848 | 0.3679          | 0.4048 |
  | 0.1054        | 27.9   | 1200  | 0.0778 | 0.3813          | 0.3670 |
  | 0.0784        | 37.21  | 1600  | 0.0747 | 0.3839          | 0.3550 |
  | 0.066         | 46.51  | 2000  | 0.0736 | 0.3970          | 0.3443 |
  | 0.0603        | 55.8   | 2400  | 0.0722 | 0.3702          | 0.3393 |
  | 0.0539        | 65.11  | 2800  | 0.0724 | 0.3762          | 0.3388 |
  | 0.0497        | 74.41  | 3200  | 0.0713 | 0.3623          | 0.3414 |
  | 0.0432        | 83.71  | 3600  | 0.0725 | 0.3847          | 0.3346 |
  | 0.0438        | 93.02  | 4000  | 0.0750 | 0.4058          | 0.3393 |
  | 0.0413        | 102.32 | 4400  | 0.0727 | 0.3957          | 0.3363 |
  | 0.039         | 111.62 | 4800  | 0.0718 | 0.3865          | 0.3330 |
  | 0.0356        | 120.92 | 5200  | 0.0711 | 0.3860          | 0.3319 |
  | 0.0336        | 130.23 | 5600  | 0.0700 | 0.3902          | 0.3242 |
  | 0.034         | 139.53 | 6000  | 0.0732 | 0.3930          | 0.3337 |
  | 0.0273        | 148.83 | 6400  | 0.0748 | 0.3912          | 0.3375 |
  | 0.027         | 158.14 | 6800  | 0.0752 | 0.4266          | 0.3434 |
  | 0.028         | 167.44 | 7200  | 0.0708 | 0.3895          | 0.3227 |
  | 0.0241        | 176.73 | 7600  | 0.0727 | 0.3967          | 0.3294 |
  | 0.0241        | 186.05 | 8000  | 0.0712 | 0.4058          | 0.3255 |
  | 0.0209        | 195.34 | 8400  | 0.0702 | 0.4102          | 0.3233 |
  | 0.0206        | 204.64 | 8800  | 0.0699 | 0.4075          | 0.3194 |
  | 0.0172        | 213.94 | 9200  | 0.0695 | 0.4222          | 0.3191 |
  | 0.0166        | 223.25 | 9600  | 0.0678 | 0.3860          | 0.3135 |
  | 0.0156        | 232.55 | 10000 | 0.0677 | 0.4035          | 0.3117 |
  | 0.0149        | 241.85 | 10400 | 0.0677 | 0.3951          | 0.3087 |
  | 0.0142        | 251.16 | 10800 | 0.0674 | 0.3972          | 0.3097 |
  | 0.0134        | 260.46 | 11200 | 0.0675 | 0.4069          | 0.3111 |
  | 0.0116        | 269.76 | 11600 | 0.0697 | 0.4189          | 0.3161 |
  | 0.0119        | 279.07 | 12000 | 0.0648 | 0.3902          | 0.3008 |
  | 0.0098        | 288.37 | 12400 | 0.0652 | 0.4095          | 0.3002 |
  | 0.0091        | 297.67 | 12800 | 0.0644 | 0.3892          | 0.2990 |
  | 0.0094        | 306.96 | 13200 | 0.0647 | 0.4026          | 0.2983 |
  | 0.0081        | 316.28 | 13600 | 0.0646 | 0.4303          | 0.2978 |
  | 0.0079        | 325.57 | 14000 | 0.0643 | 0.4044          | 0.2980 |
  | 0.0072        | 334.87 | 14400 | 0.0655 | 0.3828          | 0.2999 |
  | 0.0081        | 344.18 | 14800 | 0.0668 | 0.4108          | 0.3046 |
  | 0.0088        | 353.48 | 15200 | 0.0654 | 0.4019          | 0.2993 |
  | 0.0088        | 362.78 | 15600 | 0.0681 | 0.4073          | 0.3091 |
  | 0.0079        | 372.09 | 16000 | 0.0667 | 0.4204          | 0.3055 |
  | 0.0072        | 381.39 | 16400 | 0.0656 | 0.4030          | 0.3028 |
  | 0.0073        | 390.69 | 16800 | 0.0677 | 0.4032          | 0.3081 |
  | 0.0069        | 399.99 | 17200 | 0.0669 | 0.4130          | 0.3021 |
  | 0.0063        | 409.3  | 17600 | 0.0651 | 0.4072          | 0.2979 |
  | 0.0059        | 418.6  | 18000 | 0.0640 | 