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 7
    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: it, split: test'
      metrics:
      - name: Wer
        type: wer
        value: 97.56655574043262)
  ---
  # Whisper Tiny it 7
  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: 2.137834		
  - Wer: 97.566556
  
  ## Model description
  
  This model is the openai whisper small transformer adapted for Italian audio to text transcription. 
  As part of the hyperparameter tuning process weight decay set to 0.1, attention dropout, encoder dropout and decoder dropout have been set to 0.1,
  the learning rate has been set to 1e-6, the number of decoder attention heads and encoder attention heads have been set to 8
  however, it did not improved the performance on the evaluation set.
  
  ## 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.
  
  
  ## 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-06
  - 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
  ### Training results
  | Training Loss | Epoch | Step | Validation Loss | Wer     |
  |:-------------:|:-----:|:----:|:---------------:|:-------:|
  | 1.7353        | 3.82  | 4000 | 2.1378          | 97.5666 |
  ### Framework versions
  - Transformers 4.26.0.dev0
  - Pytorch 1.12.1+cu113
  - Datasets 2.7.1
  - Tokenizers 0.13.2