readme: ---
  language:
  - tr
  license: apache-2.0
  tags:
  - automatic-speech-recognition
  - common_voice
  - generated_from_trainer
  datasets:
  - common_voice
  model-index:
  - name: wav2vec2-xls-r-common_voice-tr-ft
    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-common_voice-tr-ft
  
  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 - TR dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.3736
  - Wer: 0.2930
  - Cer: 0.0708
  
  ## 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: 0.0005
  - train_batch_size: 12
  - eval_batch_size: 8
  - seed: 42
  - distributed_type: multi-GPU
  - num_devices: 8
  - total_train_batch_size: 96
  - total_eval_batch_size: 64
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - lr_scheduler_warmup_steps: 500
  - num_epochs: 100.0
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Wer    | Cer    |
  |:-------------:|:-----:|:----:|:---------------:|:------:|:------:|
  | 0.5462        | 13.51 | 500  | 0.4423          | 0.4807 | 0.1188 |
  | 0.342         | 27.03 | 1000 | 0.3781          | 0.3954 | 0.0967 |
  | 0.2272        | 40.54 | 1500 | 0.3816          | 0.3595 | 0.0893 |
  | 0.1805        | 54.05 | 2000 | 0.3943          | 0.3487 | 0.0854 |
  | 0.1318        | 67.57 | 2500 | 0.3818          | 0.3262 | 0.0801 |
  | 0.1213        | 81.08 | 3000 | 0.3777          | 0.3113 | 0.0758 |
  | 0.0639        | 94.59 | 3500 | 0.3788          | 0.2953 | 0.0716 |
  
  
  ### Framework versions
  
  - Transformers 4.14.1
  - Pytorch 1.8.0
  - Datasets 1.17.0
  - Tokenizers 0.10.3
  