Yoruba Male TTS Model Vits Encoding Trained on Openbible Dataset at 22050Hz

Yoruba (Yorùbá) male text-to-speech model trained at 22050 Hz and is available to synthesize the Yoruba language.

Model Description

This Yoruba (Yorùbá) male text-to-speech model is trained on the openbible dataset at 22050 Hz and is available to synthesize the Yoruba language. The model is based on the VITS encoder.

pip install tts
tts --text "Hello, world!" --model_name tts_models/yor/openbible/vits

Voice Samples

default (M)

Yoruba (Yorùbá)

Yoruba is a Niger-Congo language spoken primarily in southwestern Nigeria. It is one of the largest languages in Africa, with millions of speakers. Yoruba has a tonal system with three tones and a rich oral tradition. It is written using a modified Latin alphabet and features diacritic marks to represent specific sounds. Yoruba is known for its proverbs, folktales, and contributions to music and religion.

OpenBible Dataset

The OpenBible dataset is a speech dataset that includes recordings of Bible passages read by various speakers. It is commonly used for developing applications related to biblical text processing or speech analysis.

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.