  Deep learning models are mostly used in an offline inference fashion.
However, this strongly limits the use of these models inside audio generation
setups, as most creative workflows are based on real-time digital signal
processing. Although approaches based on recurrent networks can be naturally
adapted to this buffer-based computation, the use of convolutions still poses
some serious challenges. To tackle this issue, the use of causal streaming
convolutions have been proposed. However, this requires specific complexified
training and can impact the resulting audio quality.
  In this paper, we introduce a new method allowing to produce non-causal
streaming models. This allows to make any convolutional model compatible with
real-time buffer-based processing. As our method is based on a post-training
reconfiguration of the model, we show that it is able to transform models
trained without causal constraints into a streaming model. We show how our
method can be adapted to fit complex architectures with parallel branches. To
evaluate our method, we apply it on the recent RAVE model, which provides
high-quality real-time audio synthesis. We test our approach on multiple music
and speech datasets and show that it is faster than overlap-add methods, while
having no impact on the generation quality. Finally, we introduce two
open-source implementation of our work as Max/MSP and PureData externals, and
as a VST audio plugin. This allows to endow traditional digital audio
workstation with real-time neural audio synthesis on a laptop CPU.
