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Edresson/
wav2vec2-large-100k-voxpopuli-ft-Common-Voice_plus_TTS-Dataset-russian

Name: wav2vec2-large-100k-voxpopuli-ft-Common-Voice_plus_TTS-Dataset-russian
Availability: InStock

transformers

pytorch

wav2vec2

automatic-speech-recognition

audio

speech

russian-speech-corpus

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Wav2vec2 Large 100k Voxpopuli fine-tuned with Common Voice and M-AILABS in Russian

Wav2vec2 Large 100k Voxpopuli fine-tuned in Russian using the Common Voice 7.0 and M-AILABS.

Use this model

python

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

model = Wav2Vec2ForCTC.from_pretrained("Edresson/wav2vec2-large-100k-voxpopuli-ft-Common-Voice_plus_TTS-Dataset-russian")

Results

For the results check the paper

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"]))