  Generating sound effects that humans want is an important topic. However,
there are few studies in this area for sound generation. In this study, we
investigate generating sound conditioned on a text prompt and propose a novel
text-to-sound generation framework that consists of a text encoder, a Vector
Quantized Variational Autoencoder (VQ-VAE), a decoder, and a vocoder. The
framework first uses the decoder to transfer the text features extracted from
the text encoder to a mel-spectrogram with the help of VQ-VAE, and then the
vocoder is used to transform the generated mel-spectrogram into a waveform. We
found that the decoder significantly influences the generation performance.
Thus, we focus on designing a good decoder in this study. We begin with the
traditional autoregressive decoder, which has been proved as a state-of-the-art
method in previous sound generation works. However, the AR decoder always
predicts the mel-spectrogram tokens one by one in order, which introduces the
unidirectional bias and accumulation of errors problems. Moreover, with the AR
decoder, the sound generation time increases linearly with the sound duration.
To overcome the shortcomings introduced by AR decoders, we propose a
non-autoregressive decoder based on the discrete diffusion model, named
Diffsound. Specifically, the Diffsound predicts all of the mel-spectrogram
tokens in one step and then refines the predicted tokens in the next step, so
the best-predicted results can be obtained after several steps. Our experiments
show that our proposed Diffsound not only produces better text-to-sound
generation results when compared with the AR decoder but also has a faster
generation speed, e.g., MOS: 3.56 \textit{v.s} 2.786, and the generation speed
is five times faster than the AR decoder.
