readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  model-index:
  - name: wav2vec2-Arabizi-gpu-colab-similar-to-german-param
    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-Arabizi-gpu-colab-similar-to-german-param
  
  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: 0.5609
  - Wer: 0.4042
  
  ## 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: 2
  - eval_batch_size: 8
  - seed: 42
  - gradient_accumulation_steps: 6
  - total_train_batch_size: 12
  - 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    |
  |:-------------:|:-----:|:----:|:---------------:|:------:|
  | 4.6416        | 2.83  | 400  | 2.8983          | 1.0    |
  | 1.4951        | 5.67  | 800  | 0.6272          | 0.6097 |
  | 0.6419        | 8.51  | 1200 | 0.5491          | 0.5069 |
  | 0.4767        | 11.35 | 1600 | 0.5152          | 0.4553 |
  | 0.3899        | 14.18 | 2000 | 0.5436          | 0.4475 |
  | 0.3342        | 17.02 | 2400 | 0.5400          | 0.4431 |
  | 0.2982        | 19.85 | 2800 | 0.5599          | 0.4248 |
  | 0.2738        | 22.69 | 3200 | 0.5401          | 0.4103 |
  | 0.2563        | 25.53 | 3600 | 0.5710          | 0.4198 |
  | 0.2443        | 28.37 | 4000 | 0.5609          | 0.4042 |
  
  
  ### Framework versions
  
  - Transformers 4.11.3
  - Pytorch 1.10.0+cu113
  - Datasets 1.18.3
  - Tokenizers 0.10.3
  