Wav2vec2 Large 100k Voxpopuli Ft Common Voice Plus TTS Dataset Russian

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

This project fine-tunes Wav2vec2 Large 100k Voxpopuli in Russian using the Common Voice 7.0 and M-AILABS, aiming to improve the performance of automatic speech recognition in the Russian language.

🚀 Quick Start

Prerequisites

• The model is based on the transformers library, so make sure you have it installed.
• You also need torchaudio for audio processing.

Installation

You can install the required libraries using pip:

pip install transformers torchaudio

✨ Features

• Fine-tuned for Russian: This model is fine-tuned on the Russian language using the Common Voice 7.0 and M-AILABS datasets, which can better adapt to Russian speech characteristics.
• High-performance Speech Recognition: It can achieve a relatively low Word Error Rate (WER) in Russian speech recognition tasks.

📦 Installation

As mentioned above, you can install the necessary libraries via pip:

pip install transformers torchaudio

💻 Usage Examples

Basic Usage

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")

Advanced Usage

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"]))

📚 Documentation

Model Information

Results

For the results check the paper

📄 License

This model is licensed under the apache-2.0 license.