readme: ---
  language:
  - pt
  license: apache-2.0
  tags:
  - whisper-event
  - generated_from_trainer
  datasets:
  - mozilla-foundation/common_voice_11_0
  metrics:
  - wer
  model-index:
  - name: Sussurrar
    results:
    - task:
        name: Automatic Speech Recognition
        type: automatic-speech-recognition
      dataset:
        name: Common Voice 11.0
        type: mozilla-foundation/common_voice_11_0
        config: pt
        split: test
        args: pt
      metrics:
      - name: Wer
        type: wer
        value: 26.260504201680675
  ---
  
  <!-- 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. -->
  
  # Sussurrar
  
  This model is a fine-tuned version of [openai/whisper-base](https://huggingface.co/openai/whisper-base) on the Common Voice 11.0 dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.4367
  - Wer: 26.2605
  
  ## Model description
  
  The model is fine-tuned for ASR in Portuguese. We decided to train in Portuguese because it is a very common language, yet does not have many resources in terms of NLP.
  
  ## Intended uses & limitations
  
  The model is used for Automatic Speach Recognition. It is fine-tuned in the Portuguese language. 
  
  ## Training and evaluation data
  
  Trained and evaluated on the Common Voice 11 Portuguese data.
  
  ## Training procedure
  
  ### 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
  - training_steps: 2000
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Wer     |
  |:-------------:|:-----:|:----:|:---------------:|:-------:|
  | 0.4076        | 0.1   | 200  | 0.5182          | 32.4930 |
  | 0.3462        | 0.2   | 400  | 0.4912          | 29.0266 |
  | 0.3283        | 0.3   | 600  | 0.4671          | 27.0308 |
  | 0.3579        | 0.4   | 800  | 0.4662          | 26.6457 |
  | 0.2766        | 0.5   | 1000 | 0.4639          | 26.7157 |
  | 0.2147        | 1.03  | 1200 | 0.4470          | 26.7857 |
  | 0.1877        | 1.13  | 1400 | 0.4382          | 26.4006 |
  | 0.192         | 1.23  | 1600 | 0.4430          | 26.3655 |
  | 0.1894        | 1.33  | 1800 | 0.4349          | 26.4006 |
  | 0.1725        | 1.43  | 2000 | 0.4367          | 26.2605 |
  
  
  ### Framework versions
  
  - Transformers 4.26.0.dev0
  - Pytorch 1.13.0+cu116
  - Datasets 2.8.1.dev0
  - Tokenizers 0.13.2
  