Nvidia to Open SPADE Source Code

SPADE machine learning system creates realistic landscapes based on rough human sketches
15 April 2019   1117

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.

MelNet Algorithm to Simulate Person's Voice

It analyzes the spectrograms of the audio tracks of the usual TED Talks, notes the speech characteristics of the speaker and reproduces short replicas
11 June 2019   318

Facebook AI Research team has developed a MelNet algorithm that synthesizes speech with characteristics specific to a particular person. For example, it learned to imitate the voice of Bill Gates.

MelNet analyzes the spectrograms of the audio tracks of the usual TED Talks, notes the speech characteristics of the speaker and reproduces short replicas.

Just the length of the replicas limits capabilities of the algorithm. It reproduces short phrases very close to the original. However, the person's intonation changes when he speaks on different topics, with different moods, different pitches. The algorithm is not yet able to imitate this, therefore long sentences sound artificially.

MIT Technology Review notes that even such an algorithm can greatly affect services like voice bots. There just all communication is reduced to an exchange of short remarks.

A similar approach - analysis of speech spectrograms - was used by scientists from Google AI when working on the Translatotron algorithm. This AI is able to translate phrases from one language to another, preserving the peculiarities of the speaker's speech.