readme: ---
  language:
  - tr
  license: apache-2.0
  tags:
  - automatic-speech-recognition
  - common_voice
  - generated_from_trainer
  datasets:
  - common_voice
  model-index:
  - name: wav2vec2-common_voice-tr-demo-dist
    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-common_voice-tr-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 - TR dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.3934
  - Wer: 0.3305
  
  ## 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    |
  |:-------------:|:-----:|:----:|:---------------:|:------:|
  | 3.5459        | 0.23  | 100  | 3.6773          | 1.0    |
  | 3.2247        | 0.46  | 200  | 3.1491          | 0.9999 |
  | 2.3457        | 0.69  | 300  | 2.4236          | 1.0041 |
  | 0.9149        | 0.92  | 400  | 0.9471          | 0.7684 |
  | 0.6622        | 1.15  | 500  | 0.7518          | 0.6863 |
  | 0.7205        | 1.38  | 600  | 0.6387          | 0.6402 |
  | 0.6978        | 1.61  | 700  | 0.5611          | 0.5739 |
  | 0.5317        | 1.84  | 800  | 0.5061          | 0.5418 |
  | 0.5222        | 2.07  | 900  | 0.4839          | 0.5344 |
  | 0.4467        | 2.3   | 1000 | 0.5060          | 0.5339 |
  | 0.3196        | 2.53  | 1100 | 0.4619          | 0.5213 |
  | 0.276         | 2.76  | 1200 | 0.4595          | 0.5020 |
  | 0.3569        | 2.99  | 1300 | 0.4339          | 0.4901 |
  | 0.2236        | 3.22  | 1400 | 0.4602          | 0.4887 |
  | 0.293         | 3.45  | 1500 | 0.4376          | 0.4639 |
  | 0.1677        | 3.68  | 1600 | 0.4371          | 0.4605 |
  | 0.1838        | 3.91  | 1700 | 0.4116          | 0.4589 |
  | 0.1225        | 4.14  | 1800 | 0.4144          | 0.4495 |
  | 0.2301        | 4.37  | 1900 | 0.4250          | 0.4567 |
  | 0.1931        | 4.6   | 2000 | 0.4081          | 0.4470 |
  | 0.1427        | 4.83  | 2100 | 0.4295          | 0.4482 |
  | 0.361         | 5.06  | 2200 | 0.4374          | 0.4445 |
  | 0.3272        | 5.29  | 2300 | 0.4088          | 0.4258 |
  | 0.3686        | 5.52  | 2400 | 0.4087          | 0.4258 |
  | 0.3087        | 5.75  | 2500 | 0.4100          | 0.4371 |
  | 0.4637        | 5.98  | 2600 | 0.4038          | 0.4219 |
  | 0.1485        | 6.21  | 2700 | 0.4361          | 0.4197 |
  | 0.1341        | 6.44  | 2800 | 0.4217          | 0.4132 |
  | 0.1185        | 6.67  | 2900 | 0.4244          | 0.4097 |
  | 0.1588        | 6.9   | 3000 | 0.4212          | 0.4181 |
  | 0.0697        | 7.13  | 3100 | 0.3981          | 0.4073 |
  | 0.0491        | 7.36  | 3200 | 0.3992          | 0.4010 |
  | 0.088         | 7.59  | 3300 | 0.4206          | 0.4022 |
  | 0.0731        | 7.82  | 3400 | 0.3998          | 0.3841 |
  | 0.2767        | 8.05  | 3500 | 0.4195          | 0.3829 |
  | 0.1725        | 8.28  | 3600 | 0.4167          | 0.3946 |
  | 0.1242        | 8.51  | 3700 | 0.4177          | 0.3821 |
  | 0.1133        | 8.74  | 3800 | 0.3993          | 0.3802 |
  | 0.1952        | 8.97  | 3900 | 0.4132          | 0.3904 |
  | 0.1399        | 9.2   | 4000 | 0.4010          | 0.3795 |
  | 0.047         | 9.43  | 4100 | 0.4128          | 0.3703 |
  | 0.049         | 9.66  | 4200 | 0.4319          | 0.3670 |
  | 0.0994        | 9.89  | 4300 | 0.4118          | 0.3631 |
  | 0.1209        | 10.11 | 4400 | 0.4296          | 0.3722 |
  | 0.0484        | 10.34 | 4500 | 0.4130          | 0.3615 |
  | 0.2065        | 10.57 | 4600 | 0.3958          | 0.3668 |
  | 0.133         | 10.8  | 4700 | 0.4102          | 0.3679 |
  | 0.0622        | 11.03 | 4800 | 0.4137          | 0.3585 |
  | 0.0999        | 11.26 | 4900 | 0.4042          | 0.3583 |
  | 0.0346        | 11.49 | 5000 | 0.4183          | 0.3573 |
  | 0.072         | 11.72 | 5100 | 0.4060          | 0.3530 |
  | 0.0365        | 11.95 | 5200 | 0.3968          | 0.3483 |
  | 0.0615        | 12.18 | 5300 | 0.3958          | 0.3485 |
  | 0.1067        | 12.41 | 5400 | 0.3987          | 0.3453 |
  | 0.0253        | 12.64 | 5500 | 0.4182          | 0.3405 |
  | 0.0636        | 12.87 | 5600 | 0.4199          | 0.3458 |
  | 0.0506        | 13.1  | 5700 | 0.4056          | 0.3412 |
  | 0.0944        | 13.33 | 5800 | 0.4061          | 0.3381 |
  | 0.1187        | 13.56 | 5900 | 0.4113          | 0.33