We introduce a novel token-based text-to-speech (TTS) model with 0.8B parameters, trained on a mix of real and synthetic data totaling 650k hours, to address issues like pronunciation accuracy and style consistency. This model integrates a latent variable sequence with enhanced acoustic information into the TTS system, reducing errors and style changes. Our training includes data augmentation for improved timbre consistency, and we use a few-shot voice conversion model to generate diverse voices. This approach enables learning of one-to-many mappings in speech, ensuring both diversity and timbre consistency. Our model outperforms VALL-E in pronunciation, style maintenance, and timbre continuity.

This paper presents CrossSinger, a cross-lingual singing voice synthesizer based on Xiaoicesing2. It tackles the challenge of creating a multi-singer high-fidelity singing voice synthesis system with cross-lingual capabilities using only monolingual singers during training. The system unifies language representation, incorporates language information, and removes singer biases. Experimental results show that CrossSinger can synthesize high-quality songs for different singers in various languages, including code-switching cases.

This paper presents XiaoiceSing2, an enhanced singing voice synthesis system that addresses over-smoothing issues in middle- and high-frequency areas of mel-spectrograms. It employs a generative adversarial network (GAN) with improved model architecture to capture finer details.