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 Large v2 Portuguese
    results:
    - task:
        name: Automatic Speech Recognition
        type: automatic-speech-recognition
      dataset:
        name: mozilla-foundation/common_voice_11_0 pt
        type: mozilla-foundation/common_voice_11_0
        config: pt
        split: test
        args: pt
      metrics:
      - name: Wer
        type: wer
        value: 5.590020342630419
  ---
  
  # Whisper Large V2 Portuguese 🇧🇷🇵🇹
  
  Bem-vindo ao **whisper large-v2** para transcrição em português 👋🏻
  
  Transcribe Portuguese audio to text with the highest precision.
  
  - Loss: 0.282
  - Wer: 5.590
  
  This model is a fine-tuned version of [openai/whisper-large-v2](https://huggingface.co/openai/whisper-large-v2) on the [mozilla-foundation/common_voice_11](https://huggingface.co/datasets/mozilla-foundation/common_voice_11_0) dataset. If you want a lighter model, you may be interested in [jlondonobo/whisper-medium-pt](https://huggingface.co/jlondonobo/whisper-medium-pt). It achieves faster inference with almost no difference in WER.
  
  ### Comparable models
  Reported **WER** is based on the evaluation subset of Common Voice.
  | Model                                            | WER    | # Parameters |
  |--------------------------------------------------|:--------:|:------------:|
  | [jlondonobo/whisper-large-v2-pt](https://huggingface.co/jlondonobo/whisper-large-v2-pt)                     | **5.590** 🤗  | 1550M       |
  | [openai/whisper-large-v2](https://huggingface.co/openai/whisper-large-v2)                            | 6.300   | 1550M       |
  | [jlondonobo/whisper-medium-pt](https://huggingface.co/jlondonobo/whisper-medium-pt)                            | 6.579   | 769M       |
  | [jonatasgrosman/wav2vec2-large-xlsr-53-portuguese](https://huggingface.co/jonatasgrosman/wav2vec2-large-xlsr-53-portuguese) | 11.310  | 317M       |
  | [Edresson/wav2vec2-large-xlsr-coraa-portuguese](https://huggingface.co/Edresson/wav2vec2-large-xlsr-coraa-portuguese)    | 20.080 | 317M       |
  
  
  ### Training hyperparameters
  We used the following hyperparameters for training:
  - `learning_rate`: 1e-05
  - `train_batch_size`: 16
  - `eval_batch_size`: 8
  - `seed`: 42
  - `gradient_accumulation_steps`: 2
  - `total_train_batch_size`: 32
  - `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    |
  |:-------------:|:-----:|:----:|:---------------:|:------:|
  | 0.0828        | 1.09  | 1000 | 0.1868          | 6.778 |
  | 0.0241        | 3.07  | 2000 | 0.2057          | 6.109 |
  | 0.0084        | 5.06  | 3000 | 0.2367          | 6.029 |
  | 0.0015        | 7.04  | 4000 | 0.2469          | 5.709 |
  | 0.0009        | 9.02  | 5000 | 0.2821          | 5.590 🤗|
  
  
  ### Framework versions
  
  - Transformers 4.26.0.dev0
  - Pytorch 1.13.0+cu117
  - Datasets 2.7.1.dev0
  - Tokenizers 0.13.2
  