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Italian Male TTS Model Vits Encoding Trained on Mai_male Dataset at 16000Hz

Italian (italiano) male text-to-speech model trained at 16000 Hz and is available to synthesize the Italian language.

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Italian (italiano) male text-to-speech model trained at 16000 Hz and is available to synthesize the Italian language.

Model Description

This Italian (italiano) male text-to-speech model is trained on the the MAI dataset at 16000 Hz and is available to synthesize the Italian language. The model is based on the VITS encoder.

pip install tts
tts --text "Hello, world!" --model_name tts_models/it/mai_male/vits

Voice Samples

default (M)

Italian (italiano)

Italian is a Romance language that originated in Italy and is now spoken by millions of people worldwide. It belongs to the Indo-European language family and is closely related to other Romance languages such as Spanish, French, and Portuguese. Italian has a rich cultural heritage and is known for its musicality and expressive gestures. It uses the Latin alphabet and features distinctive phonetic properties, including double consonants and vowel lengthening.

MAI Male Dataset

The MAI Male dataset is a subset of the MAI dataset that includes speech recordings from male speakers. It can be used for specific research or applications that require male voice 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.

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