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WaveNet: A Generative Model for Raw Audio

Talking Machines Allowing people to converse with machines is a long-standing dream of human-computer interaction. The ability of computers to understand natural speech has been revolutionised in the last few years by the application of deep neural networks (e.g., Google Voice Search). However, generating speech with computers — a process usually referred to as speech synthesis or text-to-speech (TTS) — is still largely based on so-called concatenative TTS, where a very large database of short speech fragments are recorded from a single speaker and then recombined to form complete utterances. This has led to a great demand for parametric TTS, where all the information required to generate the data is stored in the parameters of the model, and the contents and characteristics of the speech can be controlled via the inputs to the model. WaveNet changes this paradigm by directly modelling the raw waveform of the audio signal, one sample at a time. Related:  enrico.a

StartPage by Ixquick Search Engine Attention and Augmented Recurrent Neural Networks — Distill WaveNet and Other Synthetic Voices  |  Cloud Text-to-Speech API  |  Google Cloud The Text-to-Speech API creates raw audio data of natural, human speech. That is, it creates audio that sounds like a person talking. When you send a synthesis request to the Text-to-Speech API, you must specify a voice that 'speaks' the words. There are a wide selection of custom voices available for you to pick from in the Text-to-Speech API. The voices differ by language, gender, and accent (for some languages). The voices offered from Text-to-Speech API can also differ in how they are produced, the synthetic speech technology used to create the machine model of the voice. The Cloud Text-to-Speech API also offers a group of premium voices generated using a WaveNet model, the same technology used to produce speech for Google Assistant, Google Search, and Google Translate. A WaveNet generates speech that sounds more natural than other text-to-speech systems. Figure 1. Unlike most other text-to-speech systems, a WaveNet model creates raw audio waveforms from scratch. Example 1.

DiskStation Manager - Knowledge Base | Synology Inc. Overview When you purchase a new Synology NAS, your existing data can be moved from the old Synology NAS to the newly acquired one. This simple process is called "migration" but needs to be performed with care, so please read the instructions below to avoid any accidental data loss due to human error. Depending on your Synology product or individual setup, there are several methods to perform migration. Contents 1. 1.1 Source and target Synology NAS Performing migration moves data and drives from one Synology NAS to another. 1.2 Always back up your data The migration procedures mentioned in this article allow you to keep most of your data. Note: Performing migration requires Synology Assistant 5.0 and DSM 5.0 or higher. 2. This section provides steps to perform migration. Once you are prepared, please see the sections below for instructions on how to perform migration. 2.1 Migrating between two identical Synology NAS models Before you start:Prepare a temporary SATA hard drive. Important: Note!

Human and Artificial Intelligence May Be Equally Impossible to Understand Dmitry Malioutov can’t say much about what he built. As a research scientist at IBM, Malioutov spends part of his time building machine learning systems that solve difficult problems faced by IBM’s corporate clients. One such program was meant for a large insurance corporation. It was a challenging assignment, requiring a sophisticated algorithm. When it came time to describe the results to his client, though, there was a wrinkle. “We couldn’t explain the model to them because they didn’t have the training in machine learning.” In fact, it may not have helped even if they were machine learning experts. As exciting as their performance gains have been, though, there’s a troubling fact about modern neural networks: Nobody knows quite how they work. Take, for example, an episode recently reported by machine learning researcher Rich Caruana and his colleagues. The neural networks were right more often than any of the other methods. Also in Artificial Intelligence Robots Can’t Dance Aaron M.

Google AI Blog O câncer em uma lata: este é um dos produtos mais tóxicos e quase todos consomem sem saber! Você certamente conhece a batatinha tipo chips. Trata-se de um produto muito apreciado, distribuído em larga escala por todo o mundo. Ela normalmente é vendida em caprichadas e vistosas embalagens de plástico, mas também é comum a embalagem em lata. Apesar de ser muito vendida e apreciada, é um alimento muito prejudicial. Entenda: na primeira fase de produção das batatas chips, os ingredientes são comuns e podem ser consumidos, como: arroz, trigo, flocos de milho e flocos de batata. Eles são misturados para formar um tipo de massa fininha. Em seguida, inicia-se o processo de moldar as batatas. Depois que elas estão na forma que as conhecemos, são levadas para assar numa temperatura altíssima. Na última etapa, antes da embalagem, é a dos temperos. Uma máquina sopra nas batatas para eliminar o excesso de gordura, depois as chips recebem sabores artificiais em pó, como bacon, queijo, cebola... O perigo está justamente no aquecimento das batatinhas industrializadas. Além disso, ela:

The Neural Network Zoo - The Asimov Institute With new neural network architectures popping up every now and then, it’s hard to keep track of them all. Knowing all the abbreviations being thrown around (DCIGN, BiLSTM, DCGAN, anyone?) can be a bit overwhelming at first. So I decided to compose a cheat sheet containing many of those architectures. One problem with drawing them as node maps: it doesn’t really show how they’re used. It should be noted that while most of the abbreviations used are generally accepted, not all of them are. Composing a complete list is practically impossible, as new architectures are invented all the time. For each of the architectures depicted in the picture, I wrote a very, very brief description. Feed forward neural networks (FF or FFNN) and perceptrons (P) are very straight forward, they feed information from the front to the back (input and output, respectively). Rosenblatt, Frank. Radial basis function (RBF) networks are FFNNs with radial basis functions as activation functions. Hopfield, John J.

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