readme: ---
  language:
  - it
  license: apache-2.0
  tags:
  - hf-asr-leaderboard
  - generated_from_trainer
  datasets:
  - mozilla-foundation/common_voice_11_0
  metrics:
  - wer
  model-index:
  - name: Whisper Tiny It 3 - Gianluca Ruberto
    results:
    - task:
        name: Automatic Speech Recognition
        type: automatic-speech-recognition
      dataset:
        name: Common Voice 11.0
        type: mozilla-foundation/common_voice_11_0
        config: it
        split: test[:10%]
        args: 'config: hi, split: test'
      metrics:
      - name: Wer
        type: wer
        value: 43.233499722684414
  ---
  
  
  # Whisper Tiny It 3 - Gianluca Ruberto
  
  This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on the Common Voice 11.0 dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.711673		
  - Wer: 43.233500
  
  ## Model description
  
  This model is the openai whisper small transformer adapted for Italian audio to text transcription. This model has weight decay set to 0.1 to cope with overfitting.
  
  ## Intended uses & limitations
  
  The model is available through its [HuggingFace web app](https://huggingface.co/spaces/GIanlucaRub/whisper-it)
  
  ## Training and evaluation data
  
  Data used for training is the initial 10% of train and validation of [Italian Common Voice](https://huggingface.co/datasets/mozilla-foundation/common_voice_11_0/viewer/it/train) 11.0 from Mozilla Foundation.
  The dataset used for evaluation is the initial 10% of test of Italian Common Voice.
  Weight decay showed to have slightly better result also on the evaluation dataset.
  
  ## Training procedure
  
  After loading the pre trained model, it has been trained on the dataset.
  
  ### Training hyperparameters
  
  The following hyperparameters were used during training:
  - learning_rate: 1e-05
  - train_batch_size: 16
  - eval_batch_size: 8
  - 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: 4000
  - mixed_precision_training: Native AMP
  - weight_decay: 0.1
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Wer     |
  |:-------------:|:-----:|:----:|:---------------:|:-------:|
  | 0.5837        | 0.95  | 1000 | 0.790374        | 50.2981 |
  | 0.4183        | 1.91  | 2000 | 0.730100        | 45.4174 |
  | 0.3147        | 2.86  | 3000 | 0.713152        | 44.3150 |
  | 0.2670        | 3.82  | 4000 | 0.711673        | 43.2335 |
  
  
  ### Framework versions
  
  - Transformers 4.26.0.dev0
  - Pytorch 1.12.1+cu113
  - Datasets 2.7.1
  - Tokenizers 0.13.2
  