readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  model-index:
  - name: urdumodel
    results: []
  metrics:
  - wer
  - cer
  ---
  
  <!-- 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. -->
  
  # urdumodel
  
  This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the None dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.4939
  - Wer: 0.3698
  - Cer: 0.1465
  
  ## Model description
  
  More information needed
  
  ## Intended uses & limitations
  
  More information needed
  
  ## Training and evaluation data
  
  For training 95 hours of audio data is used. For evaluation test set of common voice 10.0 is used. 
  
  ## Training procedure
  All the code is available here
  https://github.com/talhaanwarch/Urdu-ASR
  ### Training hyperparameters
  
  The following hyperparameters were used during training:
  - learning_rate: 0.0003
  - train_batch_size: 24
  - eval_batch_size: 24
  - seed: 42
  - gradient_accumulation_steps: 4
  - total_train_batch_size: 96
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - num_epochs: 20
  - mixed_precision_training: Native AMP
  
  # Model score on test
  When I train I got different WER and CER score on test set, but when I tested locally
  I got WER of 0.27 without language model and 0.22 with language model.
  
  
  ### Framework versions
  
  - Transformers 4.21.1
  - Pytorch 1.12.0
  - Datasets 2.4.0
  - Tokenizers 0.12.1
  