readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  metrics:
  - wer
  model-index:
  - name: wav2vec2-burak-new-v10-small
    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-v10-small
  
  This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the None dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.3345
  - Wer: 0.2030
  
  ## 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: 4
  - total_train_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: 271
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch  | Step  | Validation Loss | Wer    |
  |:-------------:|:------:|:-----:|:---------------:|:------:|
  | 6.1239        | 9.43   | 500   | 3.1263          | 1.0    |
  | 1.7776        | 18.87  | 1000  | 0.3793          | 0.4838 |
  | 0.5275        | 28.3   | 1500  | 0.2654          | 0.3379 |
  | 0.3605        | 37.74  | 2000  | 0.2704          | 0.2953 |
  | 0.2802        | 47.17  | 2500  | 0.2610          | 0.2911 |
  | 0.2348        | 56.6   | 3000  | 0.2717          | 0.2677 |
  | 0.2101        | 66.04  | 3500  | 0.2736          | 0.2691 |
  | 0.1805        | 75.47  | 4000  | 0.2782          | 0.2595 |
  | 0.1644        | 84.91  | 4500  | 0.2873          | 0.2491 |
  | 0.1469        | 94.34  | 5000  | 0.3040          | 0.2381 |
  | 0.138         | 103.77 | 5500  | 0.3205          | 0.2429 |
  | 0.1247        | 113.21 | 6000  | 0.3217          | 0.2264 |
  | 0.118         | 122.64 | 6500  | 0.3148          | 0.2244 |
  | 0.1116        | 132.08 | 7000  | 0.3114          | 0.2209 |
  | 0.1045        | 141.51 | 7500  | 0.3151          | 0.2175 |
  | 0.0988        | 150.94 | 8000  | 0.3096          | 0.2092 |
  | 0.0925        | 160.38 | 8500  | 0.3357          | 0.2230 |
  | 0.0898        | 169.81 | 9000  | 0.3220          | 0.2099 |
  | 0.0848        | 179.25 | 9500  | 0.3372          | 0.2209 |
  | 0.0831        | 188.68 | 10000 | 0.3030          | 0.2030 |
  | 0.0796        | 198.11 | 10500 | 0.3297          | 0.2127 |
  | 0.0747        | 207.55 | 11000 | 0.3312          | 0.2134 |
  | 0.0777        | 216.98 | 11500 | 0.3231          | 0.2168 |
  | 0.0724        | 226.42 | 12000 | 0.3248          | 0.2078 |
  | 0.0705        | 235.85 | 12500 | 0.3277          | 0.2023 |
  | 0.0691        | 245.28 | 13000 | 0.3262          | 0.1996 |
  | 0.0661        | 254.72 | 13500 | 0.3356          | 0.1996 |
  | 0.0678        | 264.15 | 14000 | 0.3345          | 0.2030 |
  
  
  ### Framework versions
  
  - Transformers 4.25.1
  - Pytorch 1.13.0+cu116
  - Datasets 2.7.1
  - Tokenizers 0.13.2
  