Mozilla to Represent Speech Synthesis System LPCNet

LPCNet uses DSP for LPC filtering (Linear Predictive Coding) and voice path modeling
21 November 2018   808

Mozilla reported on the new speech synthesis system LPCNet, which effectively translates text to speech while reducing resource demands. This is achieved through a combination of traditional digital signal processing methods (DSP, digital signal processing) with speech synthesis mechanisms based on a recurrent neural network.

The main problem of modern systems for real-time speech synthesis based on neural networks is high computational complexity. It does not allow to use them on smartphones and tablets.

LPCNet uses DSP for LPC filtering (Linear Predictive Coding) and voice path modeling. Then, instead of all the selected samples, the neural network receives only the forecast of each subsequent one. This frees the AI ​​from modeling the vocal tract and leaves it with only an adjustment to the problems in forecasting. Neural networks need only to monitor the accuracy of the forecast, and not to generate each sample in real time.

The technology can be used in other areas where you need to improve the quality of the voice signal. For example, for transmitting speech over low-speed communication channels, eliminating noise, filtering data and restoring fragments of speech lost during transmission.

LPCNet is written in C using a high-level framework for building Keras neural networks. A GTX 1080 Ti video card is desirable for operation. Ready-made models are available for download, but the system can be trained on your own data. LPCNet is distributed under the BSD license.

Mozilla's speech synthesis system is being developed as an alternative to WaveNet by Google. The WaveNet code was open to developers in March 2018.

Nvidia to Open SPADE Source Code

SPADE machine learning system creates realistic landscapes based on rough human sketches
15 April 2019   864

NVIDIA has released the source code for the SPADE machine learning system (GauGAN), which allows for the synthesis of realistic landscapes based on rough sketches, as well as training models associated with the project. The system was demonstrated in March at the GTC 2019 conference, but the code was published only yesterday. The developments are open under the non-free license CC BY-NC-SA 4.0 (Creative Commons Attribution-NonCommercial-ShareAlike 4.0), allowing use only for non-commercial purposes. The code is written in Python using the PyTorch framework.

Sketches are drawn up in the form of a segmented map that determines the placement of exemplary objects on the scene. The nature of the generated objects is set using color labels. For example, a blue fill turns into sky, blue into water, dark green into trees, light green into grass, light brown into stones, dark brown into mountains, gray into snow, a brown line into a road, and a blue line into the river. Additionally, based on the choice of reference images, the overall style of the composition and the time of day are determined. The proposed tool for creating virtual worlds can be useful to a wide range of specialists, from architects and urban planners to game developers and landscape designers.

Objects are synthesized by a generative-adversarial neural network (GAN), which, based on a schematic segmented map, creates realistic images by borrowing parts from a model previously trained on several million photographs. In contrast to the previously developed systems of image synthesis, the proposed method is based on the use of adaptive spatial transformation followed by transformation based on machine learning. Processing a segmented map instead of semantic markup allows you to achieve an exact match of the result and control the style.

To achieve realism, two competing neural networks are used: the generator and the discriminator (Discriminator). The generator generates images based on mixing elements of real photos, and the discriminator identifies possible deviations from real images. As a result, a feedback is formed, on the basis of which the generator begins to assemble more and more qualitative samples, until the discriminator ceases to distinguish them from the real ones.