In this work, we propose DiffWave, a versatile diffusion probabilistic model
for conditional and unconditional waveform generation. The model is
non-autoregressive, and converts the white noise signal into structured
waveform through a Markov chain with a constant number of steps at synthesis.
It is efficiently trained by optimizing a variant of variational bound on the
data likelihood. DiffWave produces high-fidelity audios in different waveform
generation tasks, including neural vocoding conditioned on mel spectrogram,
class-conditional generation, and unconditional generation. We demonstrate that
DiffWave matches a strong WaveNet vocoder in terms of speech quality (MOS: 4.44
versus 4.43), while synthesizing orders of magnitude faster. In particular, it
significantly outperforms autoregressive and GAN-based waveform models in the
challenging unconditional generation task in terms of audio quality and sample
diversity from various automatic and human evaluations.

本文介绍了 DiffWave，这是一种用于条件和非条件波形生成的多功能扩散概率模型。该模型高效地通过 Markov 链将白噪声信号转化为结构化波形，并通过特定的数据似然变分界限进行优化训练。DiffWave 在各种波形生成任务中均能生成高保真度音频，并在音频质量方面显著优于其他自回归和 GAN-based 波形模型。

DiffWave：一种用于音频合成的通用扩散模型

DiffWave: A Versatile Diffusion Model for Audio Synthesis

This paper introduces WaveGrad, a conditional model for waveform generation
which estimates gradients of the data density. The model is built on prior work
on score matching and diffusion probabilistic models. It starts from a Gaussian
white noise signal and iteratively refines the signal via a gradient-based
sampler conditioned on the mel-spectrogram. WaveGrad offers a natural way to
trade inference speed for sample quality by adjusting the number of refinement
steps, and bridges the gap between non-autoregressive and autoregressive models
in terms of audio quality. We find that it can generate high fidelity audio
samples using as few as six iterations. Experiments reveal WaveGrad to generate
high fidelity audio, outperforming adversarial non-autoregressive baselines and
matching a strong likelihood-based autoregressive baseline using fewer
sequential operations. Audio samples are available at
this https URL

WaveGrad 是一种基于分数匹配和扩散概率模型生成波形的条件模型，它使用梅尔频谱来限制采样过程，并可通过调整迭代次数来平衡生成质量和速度。实验结果表明 WaveGrad 能够生成高保真度的音频样本，超越对抗样本和自回归模型，并通过少量的循环操作匹配了强度的似然自回归模型。

WaveGrad：用于波形生成的梯度估计

WaveGrad: Estimating Gradients for Waveform Generation

In this paper, we propose multi-band MelGAN, a much faster waveform
generation model targeting to high-quality text-to-speech. Specifically, we
improve the original MelGAN by the following aspects. First, we increase the
receptive field of the generator, which is proven to be beneficial to speech
generation. Second, we substitute the feature matching loss with the
multi-resolution STFT loss to better measure the difference between fake and
real speech. Together with pre-training, this improvement leads to both better
quality and better training stability. More importantly, we extend MelGAN with
multi-band processing: the generator takes mel-spectrograms as input and
produces sub-band signals which are subsequently summed back to full-band
signals as discriminator input. The proposed multi-band MelGAN has achieved
high MOS of 4.34 and 4.22 in waveform generation and TTS, respectively. With
only 1.91M parameters, our model effectively reduces the total computational
complexity of the original MelGAN from 5.85 to 0.95 GFLOPS. Our Pytorch
implementation, which will be open-resourced shortly, can achieve a real-time
factor of 0.03 on CPU without hardware specific optimization.

本研究提出了一种快速的多波段 MelGAN 模型，通过增加接收域、使用多分辨率 STFT 损失和多波段处理提高声音生成的质量和稳定性。结果表明，该模型在声波生成和 TTS 方面均取得了较高的 MOS 得分。