readme: ---
  language:
  - fa
  license: apache-2.0
  tags:
  - whisper-event
  - generated_from_trainer
  datasets:
  - mozilla-foundation/common_voice_11_0
  metrics:
  - wer
  model-index:
  - name: Whisper Small Persian
    results:
    - task:
        name: Automatic Speech Recognition
        type: automatic-speech-recognition
      dataset:
        name: mozilla-foundation/common_voice_11_0 fa
        type: mozilla-foundation/common_voice_11_0
        config: fa
        split: test
        args: fa
      metrics:
      - name: Wer
        type: wer
        value: 35.51342198040141
  ---
  
  <!-- 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 Persian
  
  This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the mozilla-foundation/common_voice_11_0 fa dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.4278
  - Wer: 35.5134
  
  ## Model description
  
  More information needed
  
  ## Intended uses & limitations
  
  More information needed
  
  ## Training and evaluation data
  
  More information needed
  
  ## Training procedure
  
  ### Training hyperparameters
  
  The following hyperparameters were used during training:
  - learning_rate: 1e-05
  - train_batch_size: 32
  - eval_batch_size: 16
  - seed: 42
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: constant_with_warmup
  - lr_scheduler_warmup_steps: 50
  - training_steps: 1000
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Wer     |
  |:-------------:|:-----:|:----:|:---------------:|:-------:|
  | 0.1644        | 1.36  | 1000 | 0.4278          | 35.5134 |
  
  
  ### Framework versions
  
  - Transformers 4.26.0.dev0
  - Pytorch 2.0.0.dev20221210+cu117
  - Datasets 2.7.1.dev0
  - Tokenizers 0.13.2
  