char2wav   6

Char2Wav: End-to-End Speech Synthesis | OpenReview
Abstract: We present Char2Wav, an end-to-end model for speech synthesis. Char2Wav has two components: a reader and a neural vocoder. The reader is an encoder-decoder model with attention. The encoder is a bidirectional recurrent neural network that accepts text or phonemes as inputs, while the decoder is a recurrent neural network (RNN) with attention that produces vocoder acoustic features. Neural vocoder refers to a conditional extension of SampleRNN which generates raw waveform samples from intermediate representations. Unlike traditional models for speech synthesis, Char2Wav learns to produce audio directly from text.
speech-synthesis  deep-learning  char2wav  e2e 
april 2018 by arsyed
sotelo/parrot: RNN-based generative models for speech.
"This repo has the code for our ICLR submission:

Jose Sotelo, Soroush Mehri, Kundan Kumar, João Felipe Santos, Kyle Kastner, Aaron Courville, Yoshua Bengio. Char2Wav: End-to-End Speech Synthesis."
rnn  sampleRNN  speech-synthesis  char2wav  code 
november 2017 by arsyed
Generative Model-Based Text-to-Speech Synthesis - YouTube
Heiga Zen, Google Abstract: Recent progress in generative modeling has improved the naturalness of synthesized speech significantly. In this talk I will summ...
tts  google  wavenet  char2wav 
april 2017 by rryan

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code  deep-learning  e2e  google  nn  rnn  samplernn  speech-synthesis  speech  tts  wavenet 

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