readme: ---
  language:
  - tr
  tags:
  - automatic-speech-recognition
  - common_voice
  - generated_from_trainer
  datasets:
  - common_voice
  model-index:
  - name: wav2vec2-xlarge-...-common_voice-tr-demo
    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-xlarge-...-common_voice-tr-demo
  
  This model is a fine-tuned version of [facebook/wav2vec2-xlarge-xlsr-...](https://huggingface.co/facebook/wav2vec2-xlarge-xlsr-...) on the COMMON_VOICE - TR dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.2701
  - Wer: 0.2309
  - Cer: 0.0527
  
  ## 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: 0.00005
  - train_batch_size: 2
  - eval_batch_size: 8
  - seed: 42
  - distributed_type: multi-GPU
  - num_devices: 2
  - gradient_accumulation_steps: 8
  - 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: 30.0
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Wer    |
  |:-------------:|:-----:|:----:|:---------------:|:------:|
  | 0.4388        | 3.7   | 400  | 1.366          | 0.9701 |
  | 0.3766        | 7.4   | 800  | 0.4914          | 0.5374 |
  | 0.2295        | 11.11 | 1200 | 0.3934          | 0.4125 |
  | 0.1121        | 14.81 | 1600 | 0.3264          | 0.2904 |
  | 0.1473        | 18.51 | 2000 | 0.3103          | 0.2671 |
  | 0.1013        | 22.22 | 2400 | 0.2589          | 0.2324 |
  | 0.0704        | 25.92 | 2800 | 0.2826          | 0.2339 |
  | 0.0537        | 29.63 | 3200 | 0.2704          | 0.2309 |
  
  
  ### Framework versions
  
  - Transformers 4.12.0.dev0
  - Pytorch 1.8.1
  - Datasets 1.14.1.dev0
  - Tokenizers 0.10.3
  