wav2vec2-large-xlsr-coraa-portuguese/README.md

language, datasets, metrics, tags, license, model-index

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