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Danish Female TTS Model Vits Encoding Trained on Cv Dataset at 22050Hz

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

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

Model Description

This Danish (dansk) female text-to-speech model is trained on the the Common Voice dataset at 22050 Hz and is available to synthesize the Danish language. The model is based on the VITS encoder.

pip install tts
tts --text "Hello, world!" --model_name tts_models/da/cv/vits

Voice Samples

default (F)

Danish (dansk)

Danish is a North Germanic language primarily spoken in Denmark and also recognized as a minority language in the German state of Schleswig-Holstein. It belongs to the Indo-European language family and is closely related to Swedish and Norwegian. Danish has a rich history and is known for its distinctive pronunciation, characterized by soft and mellow vowels. It uses the Danish alphabet, which is based on the Latin script and includes three additional letters: æ, ø, and å.

CV Dataset

The CV dataset is a speech dataset that is specifically designed for computer vision tasks, such as lip-reading or audio-visual analysis. It contains audio samples synchronized with visual 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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