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: wav2vec2-60-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: 59.1
        name: Test WER
        args:
        - learning_rate: 0.0003
        - train_batch_size: 16
        - eval_batch_size: 8
        - seed: 42
        - gradient_accumulation_steps: 2
        - total_train_batch_size: 32
        - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
        - lr_scheduler_type: linear
        - lr_scheduler_warmup_steps: 200
        - num_epochs: 50
        - mixed_precision_training: Native AMP
      - type: cer
        value: 33.1
        name: Test CER
        args:
        - learning_rate: 0.0003
        - train_batch_size: 16
        - eval_batch_size: 8
        - seed: 42
        - gradient_accumulation_steps: 2
        - total_train_batch_size: 32
        - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
        - lr_scheduler_type: linear
        - lr_scheduler_warmup_steps: 200
        - num_epochs: 50
        - mixed_precision_training: Native AMP
  ---
  # wav2vec2-large-xlsr-53-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: 0.5913
  - Cer: 0.3310
  
  ## 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: 16
  - eval_batch_size: 8
  - seed: 42
  - gradient_accumulation_steps: 2
  - total_train_batch_size: 32
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - lr_scheduler_warmup_steps: 200
  - num_epochs: 50
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Wer    | Cer    |
  |:-------------:|:-----:|:----:|:---------------:|:------:|:------:|
  | 12.6045       | 8.33  | 100  | 8.4997          | 0.6978 | 0.3923 |
  | 1.3367        | 16.67 | 200  | 5.0015          | 0.6515 | 0.3556 |
  | 0.5344        | 25.0  | 300  | 9.3687          | 0.6393 | 0.3625 |
  | 0.2922        | 33.33 | 400  | 9.2381          | 0.6236 | 0.3432 |
  | 0.1867        | 41.67 | 500  | 6.2150          | 0.6035 | 0.3448 |
  | 0.1166        | 50.0  | 600  | 6.4496          | 0.5913 | 0.3310 |
  
  
  ### Framework versions
  
  - Transformers 4.15.0
  - Pytorch 1.10.0+cu111
  - Datasets 1.17.0
  - Tokenizers 0.10.3
  