readme: ---
  language:
  - ur
  license: apache-2.0
  tags:
  - hf-asr-leaderboard
  - generated_from_trainer
  datasets:
  - mozilla-foundation/common_voice_11_0
  metrics:
  - wer
  model-index:
  - name: Whisper Small UR 
    results:
    - task:
        name: Automatic Speech Recognition
        type: automatic-speech-recognition
      dataset:
        name: Common Voice 11.0
        type: mozilla-foundation/common_voice_11_0
        args: 'config: ur, split: test'
      metrics:
      - name: Wer
        type: wer
        value: 41.698656429942424
  ---
  
  <!-- 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. -->
  
  # Whisper Small UR - Muhammad Abdullah
  
  This model is a fine-tuned version of [openai/whisper-Small](https://huggingface.co/openai/whisper-large) on the Common Voice 11.0 dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.9758
  - Wer: 41.6987
  
  ### Training hyperparameters
  
  The following hyperparameters were used during training:
  - learning_rate: 1e-05
  - train_batch_size: 10
  - eval_batch_size: 8
  - seed: 42
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - lr_scheduler_warmup_steps: 500
  - training_steps: 3500
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Wer     |
  |:-------------:|:-----:|:----:|:---------------:|:-------:|
  | 0.0074        | 9.62  | 1000 | 0.8238          | 42.0345 |
  | 0.0003        | 19.23 | 2000 | 0.9381          | 42.6583 |
  | 0.0002        | 28.85 | 3000 | 0.9758          | 41.6987 |
  
  
  ### Framework versions
  
  - Transformers 4.25.0.dev0
  - Pytorch 1.12.1+cu113
  - Datasets 2.7.0
  - Tokenizers 0.13.2
  