readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  datasets:
  - common_voice
  model-index:
  - name: wav2vec2-xls-r-300m-Tr-med-CommonVoice8-Tr-med-CommonVoice8
    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-xls-r-300m-Tr-med-CommonVoice8-Tr-med-CommonVoice8
  
  This model is a fine-tuned version of [emre/wav2vec2-xls-r-300m-Tr-med-CommonVoice8](https://huggingface.co/emre/wav2vec2-xls-r-300m-Tr-med-CommonVoice8) on the common_voice dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.2708
  - Wer: 0.5010
  
  ## 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: 16
  - eval_batch_size: 8
  - seed: 42
  - gradient_accumulation_steps: 2
  - total_train_batch_size: 32
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - lr_scheduler_warmup_steps: 300
  - num_epochs: 5
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Wer    |
  |:-------------:|:-----:|:----:|:---------------:|:------:|
  | 2.0402        | 0.67  | 500  | 0.3354          | 0.5681 |
  | 0.7265        | 1.33  | 1000 | 0.3181          | 0.5444 |
  | 0.6858        | 2.0   | 1500 | 0.3044          | 0.5322 |
  | 0.6537        | 2.66  | 2000 | 0.2911          | 0.5217 |
  | 0.6337        | 3.33  | 2500 | 0.2874          | 0.5164 |
  | 0.6111        | 3.99  | 3000 | 0.2758          | 0.5059 |
  | 0.5815        | 4.66  | 3500 | 0.2708          | 0.5010 |
  
  
  ### Framework versions
  
  - Transformers 4.11.3
  - Pytorch 1.10.0+cu111
  - Datasets 1.18.1
  - Tokenizers 0.10.3
  