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Italian Female TTS Model Glow Tts Encoding Trained on Mai_female Dataset at 16000Hz

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

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

Model Description

This Italian (italiano) female 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 Glow-TTS encoder.

pip install tts
tts --text "Hello, world!" --model_name tts_models/it/mai_female/glow-tts

Voice Samples

default (F)

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 Female Dataset

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

Glow-TTS

Glow-TTS is an advanced technology used for training audio models, specifically for text-to-speech synthesis. It stands for Glow: Generative Flow for Text to Speech. Glow-TTS leverages the power of deep learning and generative models to transform written text into natural and high-quality speech. By employing complex neural network architectures, Glow-TTS learns the intricate relationships between text and corresponding speech patterns. This enables it to generate speech that sounds remarkably human-like, with clear enunciation, natural prosody, and convincing emotional nuances. Glow-TTS breaks down the complexities of speech generation into a sequence of mathematical operations, making it easier for machines to learn and mimic the intricate nature of human speech. The technology has numerous applications, including voice assistants, automated voiceovers, interactive systems, and more, where realistic and expressive speech synthesis is required.

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