Join Our Discord (630+ Members)

Hungarian Female TTS Model Vits Encoding Trained on Css10 Dataset at 22050Hz

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

Follow AI Models on Google News

An easy & free way to support AI Models is to follow our google news feed! More followers will help us reach a wider audience!

Google News: AI Models

Subscribe or Contribute

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

Model Description

This Hungarian (magyar) female text-to-speech model is trained on the the CSS10 dataset at 22050 Hz and is available to synthesize the Hungarian language. The model is based on the VITS encoder.

pip install tts
tts --text "Hello, world!" --model_name tts_models/hu/css10/vits

Voice Samples

default (F)

Hungarian (magyar)

Hungarian is a Uralic language primarily spoken in Hungary and parts of neighboring countries. It is not closely related to any other major language in Europe and has a unique linguistic heritage. Hungarian has a complex grammatical structure and uses vowel harmony, similar to Finnish and Estonian. It uses the Latin alphabet with additional diacritic marks to represent specific sounds. Hungarian is known for its extensive word-building and agglutinative nature.

CSS10 Dataset

The CSS10 dataset is a collection of speech data comprising ten different speakers. It is commonly used for training and evaluating speech synthesis 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.

Related Posts

Trippie Redd AI Voice

Trippie Redd AI Voice

Introducing AI Trippie Redd’s latest collection of songs, with diverse music styles and multiple languages.

Mordecai RVC (3.6k Steps, 750 epochs) AI Voice

Mordecai RVC (3.6k Steps, 750 epochs) AI Voice

Discover AI Mordecai RVC’s new collection of songs, created with the help of a dynamic community of AI enthusiasts.

Twi_Asante male TTS Model vits Encoding Trained on openbible Dataset at 22050Hz

Twi_Asante male TTS Model vits Encoding Trained on openbible Dataset at 22050Hz

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