readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  datasets:
  - common_voice
  model-index:
  - name: wav2vec2-burak-new-300-v2-2
    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-burak-new-300-v2-2
  
  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 dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.6158
  - Wer: 0.3094
  
  ## 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: 32
  - eval_batch_size: 8
  - seed: 42
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - lr_scheduler_warmup_steps: 500
  - num_epochs: 241
  
  ### Training results
  
  | Training Loss | Epoch  | Step  | Validation Loss | Wer    |
  |:-------------:|:------:|:-----:|:---------------:|:------:|
  | 5.5201        | 8.62   | 500   | 3.1581          | 1.0    |
  | 2.1532        | 17.24  | 1000  | 0.6883          | 0.5979 |
  | 0.5465        | 25.86  | 1500  | 0.5028          | 0.4432 |
  | 0.3287        | 34.48  | 2000  | 0.4986          | 0.4024 |
  | 0.2571        | 43.1   | 2500  | 0.4920          | 0.3824 |
  | 0.217         | 51.72  | 3000  | 0.5265          | 0.3724 |
  | 0.1848        | 60.34  | 3500  | 0.5539          | 0.3714 |
  | 0.1605        | 68.97  | 4000  | 0.5689          | 0.3670 |
  | 0.1413        | 77.59  | 4500  | 0.5962          | 0.3501 |
  | 0.1316        | 86.21  | 5000  | 0.5732          | 0.3494 |
  | 0.1168        | 94.83  | 5500  | 0.5912          | 0.3461 |
  | 0.1193        | 103.45 | 6000  | 0.5766          | 0.3378 |
  | 0.0996        | 112.07 | 6500  | 0.5818          | 0.3403 |
  | 0.0941        | 120.69 | 7000  | 0.5986          | 0.3315 |
  | 0.0912        | 129.31 | 7500  | 0.5802          | 0.3280 |
  | 0.0865        | 137.93 | 8000  | 0.5878          | 0.3290 |
  | 0.0804        | 146.55 | 8500  | 0.5784          | 0.3228 |
  | 0.0739        | 155.17 | 9000  | 0.5791          | 0.3180 |
  | 0.0718        | 163.79 | 9500  | 0.5864          | 0.3146 |
  | 0.0681        | 172.41 | 10000 | 0.6104          | 0.3178 |
  | 0.0688        | 181.03 | 10500 | 0.5983          | 0.3160 |
  | 0.0657        | 189.66 | 11000 | 0.6228          | 0.3203 |
  | 0.0598        | 198.28 | 11500 | 0.6057          | 0.3122 |
  | 0.0597        | 206.9  | 12000 | 0.6094          | 0.3129 |
  | 0.0551        | 215.52 | 12500 | 0.6114          | 0.3127 |
  | 0.0507        | 224.14 | 13000 | 0.6056          | 0.3094 |
  | 0.0554        | 232.76 | 13500 | 0.6158          | 0.3094 |
  
  
  ### Framework versions
  
  - Transformers 4.22.1
  - Pytorch 1.12.1+cu113
  - Datasets 2.5.1
  - Tokenizers 0.12.1
  