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: Whisper Tiny PT
    results:
    - task:
        type: automatic-speech-recognition
        name: Automatic Speech Recognition
      dataset:
        name: Common Voice 11.0
        type: mozilla-foundation/common_voice_11_0
        config: pt
        split: test
        args: pt
      metrics:
      - type: wer
        value: 29.11
        name: WER
    - task:
        type: automatic-speech-recognition
        name: Automatic Speech Recognition
      dataset:
        name: google/fleurs
        type: google/fleurs
        config: pt_br
        split: test
      metrics:
      - type: wer
        value: 26.36
        name: WER
    - task:
        type: automatic-speech-recognition
        name: Automatic Speech Recognition
      dataset:
        name: mozilla-foundation/common_voice_9_0
        type: mozilla-foundation/common_voice_9_0
        config: pt
        split: test
      metrics:
      - type: wer
        value: 28.68
        name: WER
  ---
  
  <!-- 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. -->
  
  # Whisper Tiny PT
  
  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: 0.6077
  - Wer: 29.9844
  
  ## 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: 1e-05
  - train_batch_size: 64
  - 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: 5000
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Wer     |
  |:-------------:|:-----:|:----:|:---------------:|:-------:|
  | 0.4143        | 1.04  | 500  | 0.5325          | 32.7399 |
  | 0.2693        | 3.03  | 1000 | 0.4718          | 29.4867 |
  | 0.1724        | 5.01  | 1500 | 0.4758          | 28.7218 |
  | 0.0849        | 7.0   | 2000 | 0.5070          | 29.2211 |
  | 0.0659        | 8.04  | 2500 | 0.5223          | 29.3169 |
  | 0.0539        | 10.03 | 3000 | 0.5402          | 30.1458 |
  | 0.0376        | 12.02 | 3500 | 0.5755          | 29.9995 |
  | 0.0217        | 14.0  | 4000 | 0.6067          | 29.6565 |
  | 0.0168        | 15.04 | 4500 | 0.6082          | 29.8162 |
  | 0.0205        | 17.03 | 5000 | 0.6077          | 29.9844 |
  
  
  ### Framework versions
  
  - Transformers 4.26.0.dev0
  - Pytorch 1.13.0+cu116
  - Datasets 2.7.1.dev0
  - Tokenizers 0.13.2
  