readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  metrics:
  - wer
  model-index:
  - name: wav2vec2-burak-new-300-v2-9-medium
    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-v2-9-medium
  
  This model is a fine-tuned version of [facebook/wav2vec2-xls-r-1b](https://huggingface.co/facebook/wav2vec2-xls-r-1b) on the None dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.3098
  - Wer: 0.1789
  
  ## 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    |
  |:-------------:|:------:|:-----:|:---------------:|:------:|
  | 3.2366        | 9.43   | 500   | 0.3980          | 0.4652 |
  | 0.5066        | 18.87  | 1000  | 0.2423          | 0.2719 |
  | 0.2559        | 28.3   | 1500  | 0.2482          | 0.2443 |
  | 0.1869        | 37.74  | 2000  | 0.2537          | 0.2395 |
  | 0.1498        | 47.17  | 2500  | 0.2877          | 0.2361 |
  | 0.1271        | 56.6   | 3000  | 0.2681          | 0.2237 |
  | 0.1145        | 66.04  | 3500  | 0.2788          | 0.2189 |
  | 0.1043        | 75.47  | 4000  | 0.2800          | 0.2264 |
  | 0.094         | 84.91  | 4500  | 0.2992          | 0.2244 |
  | 0.0844        | 94.34  | 5000  | 0.2864          | 0.2209 |
  | 0.0776        | 103.77 | 5500  | 0.2758          | 0.2175 |
  | 0.0714        | 113.21 | 6000  | 0.2792          | 0.2051 |
  | 0.0666        | 122.64 | 6500  | 0.2945          | 0.2175 |
  | 0.0601        | 132.08 | 7000  | 0.2865          | 0.2092 |
  | 0.0579        | 141.51 | 7500  | 0.3168          | 0.2175 |
  | 0.0532        | 150.94 | 8000  | 0.3110          | 0.2292 |
  | 0.0474        | 160.38 | 8500  | 0.3070          | 0.2175 |
  | 0.0446        | 169.81 | 9000  | 0.3206          | 0.2223 |
  | 0.0409        | 179.25 | 9500  | 0.3017          | 0.2106 |
  | 0.037         | 188.68 | 10000 | 0.3157          | 0.2092 |
  | 0.0344        | 198.11 | 10500 | 0.3222          | 0.2058 |
  | 0.0345        | 207.55 | 11000 | 0.3047          | 0.2017 |
  | 0.0309        | 216.98 | 11500 | 0.3023          | 0.1913 |
  | 0.03          | 226.42 | 12000 | 0.2963          | 0.1920 |
  | 0.0268        | 235.85 | 12500 | 0.3036          | 0.1872 |
  | 0.0249        | 245.28 | 13000 | 0.2926          | 0.1844 |
  | 0.0227        | 254.72 | 13500 | 0.3045          | 0.1865 |
  | 0.021         | 264.15 | 14000 | 0.3098          | 0.1789 |
  
  
  ### Framework versions
  
  - Transformers 4.25.1
  - Pytorch 1.13.0+cu116
  - Datasets 2.7.1
  - Tokenizers 0.13.2
  