Zongheng Yang

TL;DR: Tacotron 2, a neural network architecture for speech synthesis directly from text that is composed of a recurrent sequence-to-sequence feature prediction network that maps character embeddings to mel-scale spectrograms, followed by a modified WaveNet model acting as a vocoder to synthesize time-domain waveforms from those Spectrograms is described.

TL;DR: Tacotron as mentioned in this paper is an end-to-end generative text to speech model that synthesizes speech directly from characters, given pairs, the model can be trained completely from scratch with random initialization.

TL;DR: Tacotron 2 as mentioned in this paper uses a recurrent sequence-to-sequence feature prediction network that maps character embeddings to mel-scale spectrograms, followed by a modified WaveNet model acting as a vocoder to synthesize timedomain waveforms.

TL;DR: Ray as mentioned in this paper is a distributed system that implements a unified interface that can express both task-parallel and actor-based computations, supported by a single dynamic execution engine and employs a distributed scheduler and a distributed and fault-tolerant store to manage the control state.

TL;DR: Tacotron is presented, an end-to-end generative text- to-speech model that synthesizes speech directly from characters that achieves a 3.82 subjective 5-scale mean opinion score on US English, outperforming a production parametric system in terms of naturalness.