readme: ---
  language:
  - pl
  license: apache-2.0
  tags:
  - whisper-event
  - generated_from_trainer
  datasets:
  - mozilla-foundation/common_voice_11_0
  - google/fleurs
  metrics:
  - wer
  model-index:
  - name: Whisper Medium 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: 8.71
        name: WER
      - type: wer_without_norm
        value: 22.0
        name: WER unnormalized
      - type: cer
        value: 2.41
        name: CER
      - type: mer
        value: 8.65
        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: 11.99
        name: WER
      - type: wer_without_norm
        value: 30.9
        name: WER unnormalized
      - type: cer
        value: 6.54
        name: CER
      - type: mer
        value: 11.68
        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: 10.89
        name: WER
      - type: wer_without_norm
        value: 30.7
        name: WER unnormalized
      - type: cer
        value: 4.04
        name: CER
      - type: mer
        value: 10.8
        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 Medium PL
  
  This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) on the Common Voice 11.0 and the FLEURS datasets.
  It achieves the following results on the evaluation set:
  - Loss: 0.3947
  - Wer: 8.6872
  
  ## 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: 4
  - eval_batch_size: 1
  - seed: 42
  - gradient_accumulation_steps: 8
  - 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: 500
  - training_steps: 8000
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Wer     |
  |:-------------:|:-----:|:----:|:---------------:|:-------:|
  | 0.0805        | 0.48  | 500  | 0.2556          | 10.4888 |
  | 0.0685        | 0.96  | 1000 | 0.2462          | 10.7608 |
  | 0.0356        | 1.45  | 1500 | 0.2561          | 9.6728  |
  | 0.0337        | 1.93  | 2000 | 0.2327          | 9.6459  |
  | 0.017         | 2.41  | 2500 | 0.2444          | 9.9464  |
  | 0.0179        | 2.9   | 3000 | 0.2554          | 9.6476  |
  | 0.0056        | 3.38  | 3500 | 0.3001          | 9.3638  |
  | 0.007         | 3.86  | 4000 | 0.2809          | 9.2245  |
  | 0.0033        | 4.34  | 4500 | 0.3235          | 9.3437  |
  | 0.0024        | 4.83  | 5000 | 0.3148          | 9.0633  |
  | 0.0008        | 5.31  | 5500 | 0.3416          | 9.0112  |
  | 0.0011        | 5.79  | 6000 | 0.3876          | 9.1858  |
  | 0.0004        | 6.27  | 6500 | 0.3745          | 8.7292  |
  | 0.0003        | 6.76  | 7000 | 0.3704          | 9.0314  |
  | 0.0003        | 7.24  | 7500 | 0.3929          | 8.6553  |
  | 0.0002        | 7.72  | 8000 | 0.3947          | 8.6872  |
  
  
  ### Framework versions
  
  - Transformers 4.26.0.dev0
  - Pytorch 1.13.0+cu117
  - Datasets 2.7.1.dev0
  - Tokenizers 0.13.2
  