Bulgarian Female TTS Model Vits Encoding Trained on Cv Dataset at 22050Hz

Bulgarian (български) female text-to-speech model trained at 22050 Hz and is available to synthesize the Bulgarian language.

Model Description

This Bulgarian (български) female text-to-speech model is trained on the the Common Voice dataset at 22050 Hz and is available to synthesize the Bulgarian language. The model is based on the VITS encoder.

pip install tts
tts --text "Hello, world!" --model_name tts_models/bg/cv/vits

Voice Samples

default (F)

Bulgarian (български)

Bulgarian is a South Slavic language that originated in Bulgaria, a country located in Southeast Europe. It belongs to the Indo-European language family and specifically to the Slavic branch. Bulgarian is known for its rich literary tradition dating back to the 9th century. It uses the Cyrillic alphabet for writing. One interesting fact about Bulgarian is that it is mutually intelligible with Macedonian, another South Slavic language.

CV Dataset

The CV dataset is a speech dataset that is specifically designed for computer vision tasks, such as lip-reading or audio-visual analysis. It contains audio samples synchronized with visual data.

VITS (VQ-VAE-Transformer)

VITS, also known as VQ-VAE-Transformer, is an advanced technique used for training audio models. It combines different components to create powerful models that can understand and generate human-like speech. VITS works by breaking down audio into tiny pieces called vectors, which are like puzzle pieces that represent different parts of the sound. These vectors are then put together using a special algorithm that helps the model learn patterns and understand the structure of the audio. It’s similar to how we put together jigsaw puzzles to form a complete picture. With VITS, the model can not only recognize and understand different speech sounds but also generate new sounds that sound very similar to human speech. This technology has a wide range of applications, from creating realistic voice assistants to helping people with speech impairments communicate more effectively.