readme: ---
  language: pt
  datasets:
  - Common Voice 
  metrics:
  - wer
  tags:
  - audio
  - speech
  - wav2vec2
  - pt
  - portuguese-speech-corpus
  - automatic-speech-recognition
  - speech
  - PyTorch
  license: apache-2.0
  model-index:
  - name: Edresson Casanova Wav2vec2 Large 100k Voxpopuli fine-tuned with Common Voice and TTS-Portuguese Corpus in Portuguese 
    results:
    - task: 
        name: Speech Recognition
        type: automatic-speech-recognition
      metrics:
         - name: Test Common Voice 7.0 WER
           type: wer
           value: 20.39
  ---
  
  # Wav2vec2 Large 100k Voxpopuli fine-tuned with Common Voice and TTS-Portuguese Corpus in Portuguese 
  
  [Wav2vec2 Large 100k Voxpopuli](https://huggingface.co/facebook/wav2vec2-large-100k-voxpopuli) fine-tuned in Portuguese using the Common Voice 7.0 and TTS-Portuguese Corpus.
  
  
  
  # Use this model
  
  ```python
  
  from transformers import AutoTokenizer, Wav2Vec2ForCTC
    
  tokenizer = AutoTokenizer.from_pretrained("Edresson/wav2vec2-large-100k-voxpopuli-ft-Common-Voice_plus_TTS-Dataset-portuguese")
  
  model = Wav2Vec2ForCTC.from_pretrained("Edresson/wav2vec2-large-100k-voxpopuli-ft-Common-Voice_plus_TTS-Dataset-portuguese")
  ```
  # Results
  For the results check the [paper](https://arxiv.org/abs/2204.00618)
  
  # Example test with Common Voice Dataset
  
  
  ```python
  dataset = load_dataset("common_voice", "pt", split="test", data_dir="./cv-corpus-6.1-2020-12-11")
  
  resampler = torchaudio.transforms.Resample(orig_freq=48_000, new_freq=16_000)
  
  def map_to_array(batch):
      speech, _ = torchaudio.load(batch["path"])
      batch["speech"] = resampler.forward(speech.squeeze(0)).numpy()
      batch["sampling_rate"] = resampler.new_freq
      batch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower().replace("â€™", "'")
      return batch
  ```
  
  ```python
  ds = dataset.map(map_to_array)
  result = ds.map(map_to_pred, batched=True, batch_size=1, remove_columns=list(ds.features.keys()))
  print(wer.compute(predictions=result["predicted"], references=result["target"]))
  ```
  
  