Denoising diffusion probabilistic models (DDPMs) have recently achieved
leading performances in many generative tasks. However, the inherited iterative
sampling process costs hinder their applications to text-to-speech deployment.
Through the preliminary study on diffusion model parameterization, we find that
previous gradient-based TTS models require hundreds or thousands of iterations
to guarantee high sample quality, which poses a challenge for accelerating
sampling. In this work, we propose ProDiff, on progressive fast diffusion model
for high-quality text-to-speech. Unlike previous work estimating the gradient
for data density, ProDiff parameterizes the denoising model by directly
predicting clean data to avoid distinct quality degradation in accelerating
sampling. To tackle the model convergence challenge with decreased diffusion
iterations, ProDiff reduces the data variance in the target site via knowledge
distillation. Specifically, the denoising model uses the generated
mel-spectrogram from an N-step DDIM teacher as the training target and distills
the behavior into a new model with N/2 steps. As such, it allows the TTS model
to make sharp predictions and further reduces the sampling time by orders of
magnitude. Our evaluation demonstrates that ProDiff needs only 2 iterations to
synthesize high-fidelity mel-spectrograms, while it maintains sample quality
and diversity competitive with state-of-the-art models using hundreds of steps.
ProDiff enables a sampling speed of 24x faster than real-time on a single
NVIDIA 2080Ti GPU, making diffusion models practically applicable to
text-to-speech synthesis deployment for the first time. Our extensive ablation
studies demonstrate that each design in ProDiff is effective, and we further
show that ProDiff can be easily extended to the multi-speaker setting. Audio
samples are available at https://ProDiff.github.io/.

本文提出了一种名为 ProDiff 的文本到语音模型，它利用渐进式快速扩散模型直接预测干净数据来减少扰动模型迭代次数，从而提高高质量的音频语音合成速度。使用基于 GPU 硬件的 ProDiff 模型可以实现 24 倍于实时速度的语音合成，相对于其他使用数百步骤的模型，它只需要进行 2 次模型迭代就能合成高保真和多样化的语音合成样本.