  While recent large-scale text-to-speech (TTS) models have achieved
significant progress, they still fall short in speech quality, similarity, and
prosody. Considering speech intricately encompasses various attributes (e.g.,
content, prosody, timbre, and acoustic details) that pose significant
challenges for generation, a natural idea is to factorize speech into
individual subspaces representing different attributes and generate them
individually. Motivated by it, we propose NaturalSpeech 3, a TTS system with
novel factorized diffusion models to generate natural speech in a zero-shot
way. Specifically, 1) we design a neural codec with factorized vector
quantization (FVQ) to disentangle speech waveform into subspaces of content,
prosody, timbre, and acoustic details; 2) we propose a factorized diffusion
model to generate attributes in each subspace following its corresponding
prompt. With this factorization design, NaturalSpeech 3 can effectively and
efficiently model intricate speech with disentangled subspaces in a
divide-and-conquer way. Experiments show that NaturalSpeech 3 outperforms the
state-of-the-art TTS systems on quality, similarity, prosody, and
intelligibility, and achieves on-par quality with human recordings.
Furthermore, we achieve better performance by scaling to 1B parameters and 200K
hours of training data.
