English female text-to-speech model trained on the jenny dataset at 48000 Hz and is available to synthesize the English language.
Model Description
This English female text-to-speech model is trained on the the Jenny dataset at 48000 Hz and is available to synthesize the English language. The model is based on the jenny encoder.
pip install tts
tts --text "Hello, world!" --model_name tts_models/en/jenny/jenny
Voice Samples
default (F)
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.
Jenny Dataset
The Jenny dataset is a speech dataset consisting of recordings from a single speaker. It is commonly used for building speech synthesis systems or voice cloning applications.
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.
