readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  datasets:
  - common_voice_11_0
  metrics:
  - wer
  model-index:
  - name: wav2vec2-common_voice-ur-demo-dist
    results:
    - task:
        name: Automatic Speech Recognition
        type: automatic-speech-recognition
      dataset:
        name: common_voice_11_0
        type: common_voice_11_0
        config: ur
        split: test
        args: ur
      metrics:
      - name: Wer
        type: wer
        value: 0.5251625890368535
  ---
  
  <!-- 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-common_voice-ur-demo-dist
  
  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_11_0 dataset.
  It achieves the following results on the evaluation set:
  - Loss: inf
  - Wer: 0.5252
  
  ## 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: 4
  - eval_batch_size: 8
  - seed: 42
  - distributed_type: multi-GPU
  - num_devices: 2
  - total_train_batch_size: 8
  - total_eval_batch_size: 16
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - lr_scheduler_warmup_steps: 500
  - num_epochs: 15.0
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch | Step  | Validation Loss | Wer    |
  |:-------------:|:-----:|:-----:|:---------------:|:------:|
  | 6.0956        | 0.11  | 100   | inf             | 1.0    |
  | 3.4569        | 0.22  | 200   | inf             | 1.0    |
  | 3.0492        | 0.32  | 300   | inf             | 0.9973 |
  | 3.0042        | 0.43  | 400   | inf             | 0.9993 |
  | 1.7725        | 0.54  | 500   | inf             | 0.9112 |
  | 1.875         | 0.65  | 600   | inf             | 0.8314 |
  | 1.2135        | 0.75  | 700   | inf             | 0.8312 |
  | 1.0577        | 0.86  | 800   | inf             | 0.7337 |
  | 1.4374        | 0.97  | 900   | inf             | 0.7513 |
  | 1.0388        | 1.08  | 1000  | inf             | 0.7077 |
  | 0.8839        | 1.18  | 1100  | inf             | 0.6833 |
  | 0.8233        | 1.29  | 1200  | inf             | 0.6503 |
  | 0.7636        | 1.4   | 1300  | inf             | 0.6851 |
  | 0.8722        | 1.51  | 1400  | inf             | 0.6185 |
  | 0.6055        | 1.61  | 1500  | inf             | 0.6085 |
  | 0.7535        | 1.72  | 1600  | inf             | 0.6130 |
  | 0.4796        | 1.83  | 1700  | inf             | 0.5901 |
  | 0.739         | 1.94  | 1800  | inf             | 0.5643 |
  | 0.4362        | 2.05  | 1900  | inf             | 0.5895 |
  | 0.66          | 2.15  | 2000  | inf             | 0.5468 |
  | 0.5365        | 2.26  | 2100  | inf             | 0.5337 |
  | 0.4798        | 2.37  | 2200  | inf             | 0.5412 |
  | 0.5259        | 2.48  | 2300  | inf             | 0.5764 |
  | 0.5697        | 2.58  | 2400  | inf             | 0.5408 |
  | 0.7113        | 2.69  | 2500  | inf             | 0.6217 |
  | 0.6562        | 2.8   | 2600  | inf             | 0.5362 |
  | 0.3337        | 2.91  | 2700  | inf             | 0.5397 |
  | 0.392         | 3.01  | 2800  | inf             | 0.5299 |
  | 0.4472        | 3.12  | 2900  | inf             | 0.5332 |
  | 0.3124        | 3.23  | 3000  | inf             | 0.5202 |
  | 0.6489        | 3.34  | 3100  | inf             | 0.5360 |
  | 0.2983        | 3.44  | 3200  | inf             | 0.5146 |
  | 0.287         | 3.55  | 3300  | inf             | 0.5170 |
  | 0.5538        | 3.66  | 3400  | inf             | 0.5304 |
  | 0.3668        | 3.77  | 3500  | inf             | 0.4904 |
  | 0.6103        | 3.88  | 3600  | inf             | 0.5044 |
  | 0.2878        | 3.98  | 3700  | inf             | 0.5208 |
  | 0.4004        | 4.09  | 3800  | inf             | 0.5121 |
  | 0.3397        | 4.2   | 3900  | inf             | 0.5394 |
  | 0.3226        | 4.31  | 4000  | inf             | 0.4968 |
  | 0.2259        | 4.41  | 4100  | inf             | 0.4905 |
  | 0.254         | 4.52  | 4200  | inf             | 0.4879 |
  | 0.3353        | 4.63  | 4300  | inf             | 0.4836 |
  | 0.3923        | 4.74  | 4400  | inf             | 0.4746 |
  | 0.3685        | 4.84  | 4500  | inf             | 0.4933 |
  | 0.2538        | 4.95  | 4600  | inf             | 0.4721 |
  | 0.2082        | 5.06  | 4700  | inf             | 0.4727 |
  | 0.3077        | 5.17  | 4800  | inf             | 0.4689 |
  | 0.2114        | 5.27  | 4900  | inf             | 0.4725 |
  | 0.2047        | 5.38  | 5000  | inf             | 0.4756 |
  | 0.1977        | 5.49  | 5100  | inf             | 0.4716 |
  | 0.2005        | 5.6   | 5200  | inf             | 0.4675 |
  | 0.1636        | 5.71  | 5300  | inf             | 0.4673 |
  | 0.3709        | 5.81  | 5400  | inf             | 0.4