Speeding Up Speech Synthesis In Diffusion Models By Reducing Data
  Distribution Recovery Steps Via Content Transfer
Peter Ochieng
Diffusion based vocoders have been criticised for being slow due to the many
steps required during sampling. Moreover, the model's loss function that is
popularly implemented is designed such that the target is the original input
$x_0$ or error $\epsilon_0$. For early time steps of the reverse process, this
results in large prediction errors, which can lead to speech distortions and
increase the learning time. We propose a setup where the targets are the
different outputs of forward process time steps with a goal to reduce the
magnitude of prediction errors and reduce the training time. We use the
different layers of a neural network (NN) to perform denoising by training them
to learn to generate representations similar to the noised outputs in the
forward process of the diffusion. The NN layers learn to progressively denoise
the input in the reverse process until finally the final layer estimates the
clean speech. To avoid 1:1 mapping between layers of the neural network and the
forward process steps, we define a skip parameter $\tau>1$ such that an NN
layer is trained to cumulatively remove the noise injected in the $\tau$ steps
in the forward process. This significantly reduces the number of data
distribution recovery steps and, consequently, the time to generate speech. We
show through extensive evaluation that the proposed technique generates
high-fidelity speech in competitive time that outperforms current
state-of-the-art tools. The proposed technique is also able to generalize well
to unseen speech.