wav2vec2-large-100k-voxpopuli-ft-Common_Voice_plus_TTS-Dataset_plus_Data_Augmentation-russian

Wav2vec2 Large 100k Voxpopuli fine-tuned in Russian using the Common Voice 7.0, MAILABS plus data augmentation

Wav2vec2 Large 100k Voxpopuli Wav2vec2 Large 100k Voxpopuli fine-tuned in Russian using the Common Voice 7.0, M-AILABS plus data augmentation method based on TTS and voice conversion.

Use this model

from transformers import AutoTokenizer, Wav2Vec2ForCTC
  
tokenizer = AutoTokenizer.from_pretrained("Edresson/wav2vec2-large-100k-voxpopuli-ft-Common_Voice_plus_TTS-Dataset_plus_Data_Augmentation-russian")

model = Wav2Vec2ForCTC.from_pretrained("Edresson/wav2vec2-large-100k-voxpopuli-ft-Common_Voice_plus_TTS-Dataset_plus_Data_Augmentation-russian")

Results

For the results check the paper

Example test with Common Voice Dataset

dataset = load_dataset("common_voice", "ru", split="test", data_dir="./cv-corpus-7.0-2021-07-21")

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"]))