Deep Voice 3: Scaling Text-to-Speech with Convolutional Sequence Learning
We present Deep Voice 3, a fully-convolutional attention-based neural text-to-speech (TTS) system. Deep Voice 3 matches state-of-the-art neural speech synthesis systems in naturalness while training ten times faster. We scale Deep Voice 3 to data set sizes unprecedented for TTS, training on more than eight hundred hours of audio from over two thousand speakers. In addition, we identify common error modes of attention-based speech synthesis networks, demonstrate how to mitigate them, and compare several different waveform synthesis methods. We also describe how to scale inference to ten million queries per day on one single-GPU server.
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Colab
Tasks
Datasets
LibriSpeech
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Methods
Adam •
Convolution •
Deep Voice 3 •
Dense Connections •
Dilated Causal Convolution •
Dropout •
DV3 Attention Block •
DV3 Convolution Block •
GLU •
Gradient Clipping •
Griffin-Lim Algorithm •
L1 Regularization •
Mixture of Logistic Distributions •
ReLU •
Residual Connection •
Scaled Dot-Product Attention •
Softmax •
Softsign Activation •
WaveNet •
Weight Normalization
