readme: ---
  language: pt
  datasets:
  - common_voice 
  - mls
  - cetuc
  - lapsbm
  - voxforge
  metrics:
  - wer
  tags:
  - audio
  - speech
  - wav2vec2
  - pt
  - portuguese-speech-corpus
  - automatic-speech-recognition
  - speech
  - PyTorch
  - hf-asr-leaderboard
  license: apache-2.0
  model-index:
  - name: Lucas Gris XLSR Wav2Vec2 Large 53 Brazilian Portuguese 
    results:
    - task: 
        name: Speech Recognition
        type: automatic-speech-recognition
      metrics:
         - name: Test WER
           type: wer
           value: 12.905054857823264%
  ---
  
  # Wav2vec 2.0 With Open Brazilian Portuguese Datasets
  
  This a the demonstration of a fine-tuned Wav2vec model for Brazilian Portuguese using the following datasets:
  
  - [CETUC](http://www02.smt.ufrj.br/~igor.quintanilha/alcaim.tar.gz): contains approximately 145 hours of Brazilian Portuguese speech distributed among 50 male and 50 female speakers, each pronouncing approximately 1,000 phonetically balanced sentences selected from the [CETEN-Folha](https://www.linguateca.pt/cetenfolha/) corpus.
  - [Multilingual Librispeech (MLS)](https://arxiv.org/abs/2012.03411): a massive dataset available in many languages. The MLS is based on audiobook recordings in public domain like [LibriVox](https://librivox.org/). The dataset contains a total of 6k hours of transcribed data in many languages. The set in Portuguese [used in this work](http://www.openslr.org/94/) (mostly Brazilian variant) has approximately 284 hours of speech, obtained from 55 audiobooks read by 62 speakers.
  - [VoxForge](http://www.voxforge.org/): is a project with the goal to build open datasets for acoustic models. The corpus contains approximately 100 speakers and 4,130 utterances of Brazilian Portuguese, with sample rates varying from 16kHz to 44.1kHz.
  - [Common Voice 6.1](https://commonvoice.mozilla.org/pt) (_only train_): is a project proposed by Mozilla Foundation with the goal to create a wide open dataset in different languages to train ASR models. In this project, volunteers donate and validate speech using the [oficial site](https://commonvoice.mozilla.org/pt). The set in Portuguese (mostly Brazilian variant) used in this work is the 6.1 version (pt_63h_2020-12-11) that contains about 50 validated hours and 1,120 unique speakers.
  - [Lapsbm](https://github.com/falabrasil/gitlab-resources): "Falabrasil - UFPA" is a dataset used by the Fala Brasil group to benchmark ASR systems in Brazilian Portuguese. Contains 35 speakers (10 females), each one pronouncing 20 unique sentences, totalling  700 utterances in Brazilian Portuguese. The audios were recorded in 22.05 kHz without environment control.
  
  These datasets were combined to build a larger Brazilian Portuguese dataset. All data was used for training except Common Voice dev/test sets, that were used for validation/test respectively.
  
  The original model was fine-tuned using [fairseq](https://github.com/pytorch/fairseq). This notebook uses a converted version of the original one. The link to the original fairseq model is available [here](https://drive.google.com/drive/folders/1XTKIUB4kp3oYOavwH97wq8IPFsxP5sNz?usp=sharing).
  
  This model was trained in 80k updates.
  
  #### Datasets in number of instances and number of frames
  
  The following image shows the overall distribution of the dataset:
  
  ![datasets](https://drive.google.com/uc?export=view&id=1DF2_PehB2pZlEJLcBA7yeZQ9EAuLGh_r)
  
  #### Transcription examples
  
  | Text                                                                                                       | Transcription                                                                                                |
  |------------------------------------------------------------------------------------------------------------|--------------------------------------------------------------------------------------------------------------|
  | É comum os usuários confundirem software livre com software livre                                          | É comum os __usuares__ __confunder em__ __softwerlivr__ com __softwerlivre__                                          |
  | Ele fez tanto ghostwriting que ele começa a se sentir como um fantasma também                              | Ele fez tanto __golstraitn__ que ele __começou__ a se sentir como um fantasma também                           |
  | Arnold apresentou um gráfico mostrando quantas cegonhas ele havia contado nos últimos dez anos             | Arnold apresentou um gráfico mostrando quantas __segonhas__ ele havia contado nos últimos dez anos            |
  | Mais cedo ou mais tarde eles descobrirão como ler esses hieróglifos                                        | Mais __sedo__ ou mais tarde eles descobriram como __de__ esses __ierogrôficos__                                 |
  | Viver juntos compartilhar objetivos e ter um bom relacionamento                                            | __E ver__ juntos __signafica__ viver juntos ou __fartlhar__ objetivos ter um bom __relacionamentoo__             |
  | Da mesma forma uma patente pode impedir q