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Bengali Female TTS Model Vits Female Encoding Trained on Custom Dataset at 22050Hz

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

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

Model Description

This Bengali (বাংলা) female text-to-speech model is trained on the a custom dataset at 22050 Hz and is available to synthesize the Bengali language. The model is based on the VITS encoder.

pip install tts
tts --text "Hello, world!" --model_name tts_models/bn/custom/vits-female

Voice Samples

default (F)

Bengali (বাংলা)

Bengali, also known as Bangla, is an Eastern Indo-Aryan language predominantly spoken in Bangladesh and the Indian state of West Bengal. It has a rich literary heritage and is one of the most widely spoken languages in the world, with over 230 million native speakers. Bengali is written using the Bengali script, a descendant of the ancient Brahmi script. It has a unique phonetic feature known as the retroflex consonants, which are produced by curling the tongue tip backward.

Custom Dataset

The custom dataset refers to a user-defined or specific dataset that is created for a particular task or project. It can include speech recordings and associated metadata tailored to the specific requirements of the project.

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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