readme: ---
  language:
  - el
  license: apache-2.0
  tags:
  - hf-asr-leaderboard
  - whisper-large
  - mozilla-foundation/common_voice_11_0
  - greek
  - whisper-event
  - generated_from_trainer
  - whisper-event
  datasets:
  - mozilla-foundation/common_voice_11_0
  - google/fleurs
  metrics:
  - wer
  model-index:
  - name: whisper-lg-el-intlv-xs
    results:
    - task:
        name: Automatic Speech Recognition
        type: automatic-speech-recognition
      dataset:
        name: mozilla-foundation/common_voice_11_0
        type: mozilla-foundation/common_voice_11_0
        config: el
        split: test
      metrics:
      - name: Wer
        type: wer
        value: 9.8997
  ---
  
  # whisper-lg-el-intlv-xs
  
  This model is a fine-tuned version of [openai/whisper-large-v2](https://huggingface.co/openai/whisper-large-v2) on the mozilla-foundation/common_voice_11_0,google/fleurs el,el_gr dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.2913
  - Wer: 9.8997
  
  ## 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: 3.5e-06
  - train_batch_size: 8
  - eval_batch_size: 8
  - seed: 42
  - gradient_accumulation_steps: 4
  - 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: 10000
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch | Step  | Validation Loss | Wer     |
  |:-------------:|:-----:|:-----:|:---------------:|:-------:|
  | 0.0311        | 2.49  | 1000  | 0.1809          | 10.5498 |
  | 0.0074        | 4.98  | 2000  | 0.2470          | 10.2805 |
  | 0.0019        | 7.46  | 3000  | 0.3008          | 10.0297 |
  | 0.0011        | 9.95  | 4000  | 0.2913          | 9.8997  |
  | 0.0009        | 12.44 | 5000  | 0.3092          | 10.1876 |
  | 0.0005        | 14.93 | 6000  | 0.3495          | 10.1969 |
  | 0.0002        | 17.41 | 7000  | 0.3659          | 10.2526 |
  | 0.0001        | 19.9  | 8000  | 0.3846          | 10.2619 |
  | 0.0001        | 22.39 | 9000  | 0.3941          | 10.2897 |
  | 0.0001        | 24.88 | 10000 | 0.3990          | 10.3269 |
  
  
  ### Framework versions
  
  - Transformers 4.26.0.dev0
  - Pytorch 1.13.0+cu117
  - Datasets 2.7.1.dev0
  - Tokenizers 0.13.2
  