readme: ---
  language:
  - ur
  license: apache-2.0
  tags:
  - automatic-speech-recognition
  - hf-asr-leaderboard
  - robust-speech-event
  datasets:
  - mozilla-foundation/common_voice_7_0
  metrics:
  - wer
  - cer
  model-index:
  - name: wav2vec2-urdu
    results:
    - task:
        type: automatic-speech-recognition
        name: Speech Recognition
      dataset:
        type: mozilla-foundation/common_voice_7_0
        name: Common Voice ur
        args: ur
      metrics:
      - type: wer
        value: 52.4
        name: Test WER
        args:
        - learning_rate: 0.0003
        - train_batch_size: 64
        - eval_batch_size: 8
        - seed: 42
        - gradient_accumulation_steps: 2
        - total_train_batch_size: 128
        - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
        - lr_scheduler_type: linear
        - lr_scheduler_warmup_steps: 100
        - num_epochs: 100
        - mixed_precision_training: Native AMP
      - type: cer
        value: 26.46
        name: Test CER
        args:
        - learning_rate: 0.0003
        - train_batch_size: 64
        - eval_batch_size: 8
        - seed: 42
        - gradient_accumulation_steps: 2
        - total_train_batch_size: 128
        - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
        - lr_scheduler_type: linear
        - lr_scheduler_warmup_steps: 100
        - num_epochs: 100
        - mixed_precision_training: Native AMP
      - type: wer
        value: 45.63
        name: Test WER LM CV8
      - type: cer
        value: 20.45
        name: Test CER LM CV8
  ---
  
  <!-- 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-large-xls-r-300m-Urdu
  
  This model is a fine-tuned version of [Harveenchadha/vakyansh-wav2vec2-urdu-urm-60](https://huggingface.co/Harveenchadha/vakyansh-wav2vec2-urdu-urm-60) on the common_voice dataset.
  It achieves the following results on the evaluation set:
  - Wer: 0.5747
  - Cer: 0.3268
  
  ## Model description
  The training and valid dataset is 0.58 hours. It was hard to train any model on lower number of so I decided to take vakyansh-wav2vec2-urdu-urm-60 checkpoint and finetune the wav2vec2 model.  
  
  ## Training procedure
  Trained on Harveenchadha/vakyansh-wav2vec2-urdu-urm-60 due to lesser number of samples.
  
  ### Training hyperparameters
  
  The following hyperparameters were used during training:
  - learning_rate: 0.0003
  - train_batch_size: 64
  - eval_batch_size: 8
  - seed: 42
  - gradient_accumulation_steps: 2
  - total_train_batch_size: 128
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - lr_scheduler_warmup_steps: 100
  - num_epochs: 100
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Wer    | Cer    |
  |:-------------:|:-----:|:----:|:---------------:|:------:|:------:|
  | 4.3054        | 16.67 | 50   | 9.0055          | 0.8306 | 0.4869 |
  | 2.0629        | 33.33 | 100  | 9.5849          | 0.6061 | 0.3414 |
  | 0.8966        | 50.0  | 150  | 4.8686          | 0.6052 | 0.3426 |
  | 0.4197        | 66.67 | 200  | 12.3261         | 0.5817 | 0.3370 |
  | 0.294         | 83.33 | 250  | 11.9653         | 0.5712 | 0.3328 |
  | 0.2329        | 100.0 | 300  | 7.6846          | 0.5747 | 0.3268 |
  
  
  ### Framework versions
  
  - Transformers 4.16.0.dev0
  - Pytorch 1.10.1+cu102
  - Datasets 1.17.1.dev0
  - Tokenizers 0.11.0
  