metadata
language: ru
datasets:
- Common Voice
metrics:
- wer
tags:
- audio
- speech
- wav2vec2
- ru
- russian-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 M-AILABS in Russian
results:
- task:
name: Speech Recognition
type: automatic-speech-recognition
metrics:
- name: Test Common Voice 7.0 WER
type: wer
value: 24.8
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
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
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"]))