readme: ---
  language:
  - el
  license: apache-2.0
  tags:
  - whisper-event
  - generated_from_trainer
  - hf-asr-leaderboard
  - automatic-speech-recognition
  - greek
  datasets:
  - mozilla-foundation/common_voice_11_0
  - google/fleurs
  metrics:
  - wer
  model-index:
  - name: whisper-sm-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: 20.068722139673106
  ---
  
  
  # Whisper small (Greek) Trained on Interleaved Datasets
  
  This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on interleaved mozilla-foundation/common_voice_11_0 (el) and google/fleurs (el_gr) dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.4741
  - Wer: 20.0687
  
  ## Model description
  
  The model was developed during the Whisper Fine-Tuning Event in December 2022. 
  More details on the model can be found [in the original paper](https://cdn.openai.com/papers/whisper.pdf)
  
  ## Intended uses & limitations
  
  The model is fine-tuned for transcription in the Greek language. 
  
  ## Training and evaluation data
  
  This model was trained by interleaving the training and evaluation splits from two different datasets:  
  
  - mozilla-foundation/common_voice_11_0 (el)
  - google/fleurs (el_gr)
  
  
  ## Training procedure
  
  The python script used is a modified version of the script provided by Hugging Face and can be found [here](https://github.com/kamfonas/whisper-fine-tuning-event/blob/minor-mods-by-farsipal/run_speech_recognition_seq2seq_streaming.py)   
  
  ### Training hyperparameters
  
  The following hyperparameters were used during training:
  - learning_rate: 1e-05
  - train_batch_size: 16
  - eval_batch_size: 8
  - seed: 42
  - gradient_accumulation_steps: 4
  - total_train_batch_size: 64
  - 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     |
  |:-------------:|:-----:|:----:|:---------------:|:-------:|
  | 0.0186        | 4.98  | 1000 | 0.3619          | 21.0067 |
  | 0.0012        | 9.95  | 2000 | 0.4347          | 20.3009 |
  | 0.0005        | 14.93 | 3000 | 0.4741          | 20.0687 |
  | 0.0003        | 19.9  | 4000 | 0.4974          | 20.1152 |
  | 0.0003        | 24.88 | 5000 | 0.5066          | 20.2266 |
  
  
  ### Framework versions
  
  - Transformers 4.26.0.dev0
  - Pytorch 1.13.0
  - Datasets 2.7.1.dev0
  - Tokenizers 0.12.1
  