readme: ---
  language:
  - tr
  tags:
  - automatic-speech-recognition
  - common_voice
  - generated_from_trainer
  datasets:
  - common_voice
  model-index:
  - name: wav2vec2-xls-r-phoneme-300m-tr
    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-phoneme-300m-tr
  
  This model is a fine-tuned version of [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: 0.6380
  - PER: 0.1664
  
  ## 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: 16
  - eval_batch_size: 8
  - seed: 42
  - distributed_type: multi-GPU
  - num_devices: 2
  - total_train_batch_size: 32
  - 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: 20.0
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | PER    |
  |:-------------:|:-----:|:----:|:---------------:|:------:|
  | 13.6687       | 0.92  | 100  | 12.4567         | 1.0    |
  | 3.4219        | 1.83  | 200  | 3.4704          | 1.0    |
  | 3.1846        | 2.75  | 300  | 3.2281          | 0.9935 |
  | 2.0076        | 3.67  | 400  | 1.7415          | 0.5222 |
  | 1.0244        | 4.59  | 500  | 1.0290          | 0.3323 |
  | 0.7095        | 5.5   | 600  | 0.8424          | 0.2859 |
  | 0.619         | 6.42  | 700  | 0.7389          | 0.2232 |
  | 0.3541        | 7.34  | 800  | 0.7049          | 0.2043 |
  | 0.2946        | 8.26  | 900  | 0.7065          | 0.2153 |
  | 0.2868        | 9.17  | 1000 | 0.6840          | 0.2115 |
  | 0.2245        | 10.09 | 1100 | 0.6714          | 0.1952 |
  | 0.1394        | 11.01 | 1200 | 0.6864          | 0.1954 |
  | 0.1288        | 11.93 | 1300 | 0.6696          | 0.2017 |
  | 0.1568        | 12.84 | 1400 | 0.6468          | 0.1843 |
  | 0.1269        | 13.76 | 1500 | 0.6736          | 0.1965 |
  | 0.1101        | 14.68 | 1600 | 0.6689          | 0.1915 |
  | 0.1388        | 15.6  | 1700 | 0.6690          | 0.1782 |
  | 0.0739        | 16.51 | 1800 | 0.6364          | 0.1734 |
  | 0.0897        | 17.43 | 1900 | 0.6480          | 0.1748 |
  | 0.0795        | 18.35 | 2000 | 0.6356          | 0.1695 |
  | 0.0823        | 19.27 | 2100 | 0.6382          | 0.1685 |
  
  
  ### Framework versions
  
  - Transformers 4.13.0.dev0
  - Pytorch 1.8.1
  - Datasets 1.16.2.dev0
  - Tokenizers 0.10.3
  