Chinese_PRC Female TTS Model Tacotron2 DDC GST Encoding Trained on Baker Dataset at 22050Hz

Chinese_PRC (中文（中国）) female text-to-speech model trained at 22050 Hz and is available to synthesize the Chinese_PRC language.

Model Description

This Chinese_PRC (中文（中国）) female text-to-speech model is trained on the baker dataset at 22050 Hz and is available to synthesize the Chinese_PRC language. The model is based on the tacotron2-DDC-GST encoder.

pip install tts
tts --text "Hello, world!" --model_name tts_models/zh-CN/baker/tacotron2-DDC-GST

Voice Samples

default (F)

Chinese PRC

Baker Dataset

The Baker dataset is a collection of speech data consisting of various speakers and sentences. It is commonly used for training and evaluating speech recognition models.

Tacotron 2 DDC

Tacotron 2 is an exciting technology used for training audio models, specifically for text-to-speech synthesis. It’s like having a virtual voice that can read text aloud in a natural and human-like manner. Tacotron 2 uses deep learning algorithms to learn the patterns and nuances of human speech from large amounts of training data. It takes text as input and converts it into speech by generating a corresponding sequence of audio signals. The model learns how to pronounce words, intonations, and even subtle details like pauses and inflections, making the synthesized speech sound remarkably natural. Tacotron 2 has various applications, including creating voice-overs for videos, aiding individuals with speech disabilities, and even personalizing virtual assistants to have unique and expressive voices. Tacotron 2 with Double Decoder Consistency (DDC) is an advanced TTS model that addresses attention alignment issues during inference. It uses two decoders with different reduction factors to improve alignment performance. DDC enhances Tacotron 2’s architecture, which includes an encoder, attention module, decoder, and Postnet. By measuring consistency between the decoders, DDC mitigates attention problems caused by out-of-domain words or long input texts. It provides more accurate and natural-sounding speech synthesis.