readme: ---
  language:
  - el
  license: apache-2.0
  tags:
  - whisper-event
  - generated_from_trainer
  datasets:
  - mozilla-foundation/common_voice_11_0
  metrics:
  - wer
  model-index:
  - name: Whisper Medium Greek - Robust
    results:
    - task:
        type: automatic-speech-recognition
        name: Automatic Speech Recognition
      dataset:
        name: mozilla-foundation/common_voice_11_0 el
        type: mozilla-foundation/common_voice_11_0
        config: el
        split: test
        args: el
      metrics:
      - type: wer
        value: 17.709881129271917
        name: Wer
      - type: wer
        value: 13.25
        name: WER
    - task:
        type: automatic-speech-recognition
        name: Automatic Speech Recognition
      dataset:
        name: google/fleurs
        type: google/fleurs
        config: el_gr
        split: test
      metrics:
      - type: wer
        value: 39.59
        name: WER
  ---
  
  <!-- 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 Greek - Robust
  
  This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) on the mozilla-foundation/common_voice_11_0 el dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.2807
  - Wer: 17.7099
  
  **IMPORTANT** The model has been trained using *data augmentation* to improve its generalization capabilities and robustness. 
  The results on the eval set during training are biased towards data augmentation applied to evaluation data.
  
  **Results on eval set**
  
  - Mozilla CV 11.0 - Greek: 13.250 WER (using official script)
  - Google Fluers - Greek: 39.59 WER (using official script)
   
  ## 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: 8
  - eval_batch_size: 4
  - 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: 20000
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch | Step  | Validation Loss | Wer     |
  |:-------------:|:-----:|:-----:|:---------------:|:-------:|
  | 0.0407        | 4.69  | 2000  | 0.2484          | 20.8767 |
  | 0.0128        | 9.39  | 4000  | 0.2795          | 21.2017 |
  | 0.0041        | 14.08 | 6000  | 0.2744          | 19.1308 |
  | 0.0017        | 18.78 | 8000  | 0.2759          | 17.9978 |
  | 0.0005        | 23.47 | 10000 | 0.2751          | 18.5457 |
  | 0.0015        | 28.17 | 12000 | 0.2928          | 19.2051 |
  | 0.0004        | 32.86 | 14000 | 0.2819          | 18.2857 |
  | 0.0002        | 37.56 | 16000 | 0.2831          | 17.7285 |
  | 0.0007        | 42.25 | 18000 | 0.2776          | 17.8399 |
  | 0.0           | 46.95 | 20000 | 0.2792          | 17.0970 |
  
  
  ### Framework versions
  
  - Transformers 4.26.0.dev0
  - Pytorch 1.11.0+cu113
  - Datasets 2.7.1
  - Tokenizers 0.12.1
  