readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  model-index:
  - name: wav2vec2-xlsr-persian-50p
    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-xlsr-persian-50p
  
  This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the None dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.6846
  - Wer: 0.4339
  
  ## 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: 0.0001
  - train_batch_size: 8
  - eval_batch_size: 8
  - seed: 42
  - gradient_accumulation_steps: 2
  - total_train_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: 30
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Wer    |
  |:-------------:|:-----:|:----:|:---------------:|:------:|
  | No log        | 1.05  | 250  | 3.2104          | 1.0    |
  | 3.2437        | 2.11  | 500  | 2.9131          | 1.0    |
  | 3.2437        | 3.16  | 750  | 1.0335          | 0.7303 |
  | 1.4382        | 4.22  | 1000 | 0.8335          | 0.6155 |
  | 1.4382        | 5.27  | 1250 | 0.7640          | 0.5904 |
  | 0.6923        | 6.33  | 1500 | 0.6923          | 0.5468 |
  | 0.6923        | 7.38  | 1750 | 0.6627          | 0.5238 |
  | 0.5137        | 8.44  | 2000 | 0.6606          | 0.5112 |
  | 0.5137        | 9.49  | 2250 | 0.6600          | 0.5125 |
  | 0.4258        | 10.55 | 2500 | 0.6337          | 0.4939 |
  | 0.4258        | 11.6  | 2750 | 0.6454          | 0.4851 |
  | 0.362         | 12.66 | 3000 | 0.6481          | 0.4793 |
  | 0.362         | 13.71 | 3250 | 0.6487          | 0.4801 |
  | 0.3179        | 14.77 | 3500 | 0.6602          | 0.4668 |
  | 0.3179        | 15.82 | 3750 | 0.6757          | 0.4683 |
  | 0.2861        | 16.88 | 4000 | 0.6544          | 0.4591 |
  | 0.2861        | 17.93 | 4250 | 0.6659          | 0.4634 |
  | 0.2529        | 18.99 | 4500 | 0.6311          | 0.4556 |
  | 0.2529        | 20.04 | 4750 | 0.6574          | 0.4525 |
  | 0.235         | 21.1  | 5000 | 0.7019          | 0.4462 |
  | 0.235         | 22.15 | 5250 | 0.6783          | 0.4426 |
  | 0.2203        | 23.21 | 5500 | 0.6789          | 0.4361 |
  | 0.2203        | 24.26 | 5750 | 0.6779          | 0.4336 |
  | 0.2014        | 25.32 | 6000 | 0.6805          | 0.4406 |
  | 0.2014        | 26.37 | 6250 | 0.6918          | 0.4407 |
  | 0.1957        | 27.43 | 6500 | 0.6919          | 0.4360 |
  | 0.1957        | 28.48 | 6750 | 0.6795          | 0.4332 |
  | 0.1837        | 29.53 | 7000 | 0.6846          | 0.4339 |
  
  
  ### Framework versions
  
  - Transformers 4.11.3
  - Pytorch 1.10.0+cu113
  - Datasets 1.18.3
  - Tokenizers 0.10.3
  