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

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

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

Model Description

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

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

Voice Samples

default (M)

Finnish (suomi)

Finnish is a Finno-Ugric language primarily spoken in Finland. It belongs to the Uralic language family and is closely related to Estonian and Hungarian. Finnish has a unique phonetic system with a large number of vowel sounds and a notable absence of grammatical gender. It uses the Latin alphabet with additional characters like ä and ö. Finnish has a strong literary tradition and is known for its epic poem ‘Kalevala,’ which played a significant role in the development of Finnish national identity.

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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