readme: ---
  language:
  - pl
  license: apache-2.0
  tags:
  - whisper-event
  - generated_from_trainer
  datasets:
  - mozilla-foundation/common_voice_11_0
  metrics:
  - wer
  model-index:
  - name: Whisper Small PL
    results:
    - task:
        type: automatic-speech-recognition
        name: Automatic Speech Recognition
      dataset:
        name: Common Voice 11.0
        type: mozilla-foundation/common_voice_11_0
        config: pl
        split: test
        args: pl
      metrics:
      - type: wer
        value: 8.85
        name: WER
      - type: wer_without_norm
        value: 21.75
        name: WER unnormalized
      - type: cer
        value: 2.63
        name: CER
      - type: mer
        value: 8.76
        name: MER
    - task:
        type: automatic-speech-recognition
        name: Automatic Speech Recognition
      dataset:
        name: facebook/voxpopuli
        type: facebook/voxpopuli
        config: pl
        split: test
      metrics:
      - type: wer
        value: 12.18
        name: WER
      - type: wer_without_norm
        value: 32.17
        name: WER unnormalized
      - type: cer
        value: 6.99
        name: CER
      - type: mer
        value: 11.84
        name: MER
    - task:
        type: automatic-speech-recognition
        name: Automatic Speech Recognition
      dataset:
        name: google/fleurs
        type: google/fleurs
        config: pl_pl
        split: test
      metrics:
      - type: wer
        value: 12.77
        name: WER
      - type: wer_without_norm
        value: 32.37
        name: WER unnormalized
      - type: cer
        value: 5.87
        name: CER
      - type: mer
        value: 12.52
        name: MER
  ---
  
  <!-- 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 PL
  
  This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) on the Common Voice 11.0 dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.3739
  - Wer: 8.5898
  
  ## 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: linear
  - lr_scheduler_warmup_steps: 500
  - training_steps: 5000
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Wer    |
  |:-------------:|:-----:|:----:|:---------------:|:------:|
  | 0.0474        | 1.1   | 1000 | 0.2561          | 9.4612 |
  | 0.0119        | 3.09  | 2000 | 0.2901          | 8.9726 |
  | 0.0045        | 5.08  | 3000 | 0.3151          | 8.8870 |
  | 0.0007        | 7.07  | 4000 | 0.4218          | 8.6032 |
  | 0.0005        | 9.07  | 5000 | 0.3739          | 8.5898 |
  
  ### Evaluation results
  
  When tested on diffrent polish ASR datasets (splits: test), this model achieves the following results:
  
  | Dataset           | WER   | WER unnormalized | CER   | MER   |
  |:-----------------:|:-----:|:----------------:|:-----:|:-----:|
  |common_voice_11_0  | 8.85 | 21.75            | 2.63 | 8.76 | 
  
  ### Framework versions
  
  - Transformers 4.26.0.dev0
  - Pytorch 1.13.0+cu117
  - Datasets 2.7.1.dev0
  - Tokenizers 0.13.2
  