readme: ---
  language:
  - ur
  license: apache-2.0
  tags:
  - automatic-speech-recognition
  - robust-speech-event
  - hf-asr-leaderboard
  datasets:
  - mozilla-foundation/common_voice_8_0
  metrics:
  - wer
  - cer
  model-index:
  - name: wav2vec2-urdu-V8-Abid
    results:
    - task:
        type: automatic-speech-recognition
        name: Speech Recognition
      dataset:
        type: mozilla-foundation/common_voice_8_0
        name: Common Voice ur
        args: ur
      metrics:
      - type: wer
        value: 44.63
        name: Test WER
        args:
        - learning_rate: 7.5e-05
        - 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 AMPP
      - type: cer
        value: 18.82
        name: Test CER
        args:
        - learning_rate: 7.5e-05
        - 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 AMPP
  ---
  
  <!-- 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. -->
  
  # wav2vec2-60-Urdu-V8
  
  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:
  - Loss: 11.4832
  - Wer: 0.5729
  - Cer: 0.3170
  
  
  ### Training hyperparameters
  
  The following hyperparameters were used during training:
  - learning_rate: 7.5e-05
  - 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    |
  |:-------------:|:-----:|:----:|:---------------:|:------:|:------:|
  | 19.671        | 8.33  | 100  | 7.7671          | 0.8795 | 0.4492 |
  | 2.085         | 16.67 | 200  | 9.2759          | 0.6201 | 0.3320 |
  | 0.6633        | 25.0  | 300  | 8.7025          | 0.5738 | 0.3104 |
  | 0.388         | 33.33 | 400  | 10.2286         | 0.5852 | 0.3128 |
  | 0.2822        | 41.67 | 500  | 11.1953         | 0.5738 | 0.3174 |
  | 0.2293        | 50.0  | 600  | 11.4832         | 0.5729 | 0.3170 |
  
  
  ### Framework versions
  
  - Transformers 4.16.2
  - Pytorch 1.10.0+cu111
  - Datasets 1.18.3
  - Tokenizers 0.11.0
  