English TTS Model 109 Voices Vits Encoding Trained on Vctk Dataset at 22050Hz

English text-to-speech model containing 109 voices trained on the vctk dataset at 22050 Hz and is available to synthesize the en language.

Model Description

This English text-to-speech model contains 109 voices trained on the the VCTK dataset at 22050 Hz and is available to synthesize the en language. The model is based on the VITS encoder.

pip install tts
tts --text "Hello, world!" --model_name tts_models/en/vctk/vits

Voice Samples

ED\n (F)

p225 (F)

p226 (M)

English

English is a West Germanic language that originated in England and is now one of the most widely spoken languages in the world. It belongs to the Indo-European language family and is closely related to German and Dutch. English has a diverse vocabulary and is known for its global influence as a lingua franca. It uses the Latin alphabet with modifications, including the addition of letters such as ð and þ in Old English. English features a complex phonetic system with a wide range of vowel and consonant sounds.

VCTK Dataset

The VCTK dataset is a multi-speaker English speech dataset that contains recordings from a diverse set of speakers. It is commonly used for training and evaluating speech synthesis and voice conversion models.

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.