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German Male TTS Model Vits Neon Encoding Trained on Css10 Dataset at 22050Hz

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

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

Model Description

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

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

Voice Samples

default (M)

German (Deutsch)

German is a West Germanic language primarily spoken in Germany, Austria, Switzerland, and Liechtenstein. It belongs to the Indo-European language family and is closely related to English, Dutch, and the Scandinavian languages. German has a rich literary tradition and is known for its compound words and grammatical complexity. It uses the Latin alphabet and includes umlauts (ä, ö, ü) and the Eszett (ß) as additional characters. German is classified as a Germanic language within the larger West Germanic branch.

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.

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