metadata

language: pt
datasets:
  - CORAA
metrics:
  - wer
tags:
  - audio
  - speech
  - wav2vec2
  - pt
  - portuguese-speech-corpus
  - automatic-speech-recognition
  - hf-asr-leaderboard
  - speech
  - PyTorch
license: apache-2.0
model-index:
  - name: Edresson Casanova XLSR Wav2Vec2 Large 53 Portuguese
    results:
      - task:
          name: Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: CORAA
          type: CORAA
          args: pt
        metrics:
          - name: Test CORAA WER
            type: wer
            value: 25.26
      - task:
          name: Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: Common Voice 7
          type: mozilla-foundation/common_voice_7_0
          args: pt
        metrics:
          - name: Test WER on Common Voice 7
            type: wer
            value: 20.08

Wav2vec 2.0 trained with CORAA Portuguese Dataset

This a the demonstration of a fine-tuned Wav2vec model for Portuguese using the following CORAA dataset

Use this model


from transformers import AutoTokenizer, Wav2Vec2ForCTC
  
tokenizer = AutoTokenizer.from_pretrained("Edresson/wav2vec2-large-xlsr-coraa-portuguese")

model = Wav2Vec2ForCTC.from_pretrained("Edresson/wav2vec2-large-xlsr-coraa-portuguese")

Results

For the results check the CORAA article

Example test with Common Voice Dataset

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

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"]))