| \documentclass{article} | |
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| \newcommand{\cnnbasic}[4]{#4\mathfont{C}{}^{{#1}}_{{#2} \star {#3}}} | |
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| \newcommand\copyrighttext{\footnotesize \copyright 2018 IEEE. Personal use of this material is permitted. | |
| Permission from IEEE must be obtained for all other uses, in any current or future | |
| media, including reprinting/republishing this material for advertising or promotional | |
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| lists, or reuse of any copyrighted component of this work in other works. | |
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| (current page.north) {\fbox{\parbox{\dimexpr\textwidth-\fboxsep-\fboxrule\relax}{\copyrighttext}}}; | |
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| \title{Efficiently Trainable Text-to-Speech System Based on \\Deep Convolutional Networks With Guided Attention} | |
| \twoauthors {Hideyuki Tachibana,~~Katsuya Uenoyama}{PKSHA Technology, Inc.\\Bunkyo, Tokyo, Japan\\{\large\url{h_tachibana@pkshatech.com}}}{Shunsuke Aihara}{Independent Researcher\\Shinjuku, Tokyo, Japan\\{\large\url{aihara@argmax.jp}}}\copyrightnotice{\copyright\ IEEE 2018.} | |
| \toappear{Published as a conference paper at ICASSP 2018, Calgary, Canada} | |
| \begin{document} | |
| \ninept | |
| \maketitle | |
| \mycopyrightnotice | |
| \begin{abstract} | |
| This paper describes a novel text-to-speech (TTS) technique based on deep convolutional neural networks (CNN), | |
| without use of any recurrent units. | |
| Recurrent neural networks (RNN) have become a standard technique to model sequential data recently, | |
| and this technique has been used in some cutting-edge neural TTS techniques. | |
| However, training RNN components often requires a very powerful computer, | |
| or a very long time, typically several days or weeks. | |
| Recent other studies, on the other hand, have shown that CNN-based sequence synthesis can be much faster than RNN-based techniques, | |
| because of high parallelizability. | |
| The objective of this paper is to show that an alternative neural TTS based only on CNN | |
| alleviate these economic costs of training. | |
| In our experiment, the proposed Deep Convolutional TTS was sufficiently trained overnight (15 hours), | |
| using an ordinary gaming PC equipped with two GPUs, while the quality of the synthesized speech was almost acceptable. | |
| \end{abstract} | |
| \begin{keywords} | |
| Text-to-speech, deep learning, convolutional neural network, attention, sequence-to-sequence learning. | |
| \end{keywords} | |
| \section{Introduction}\label{sec:intro} | |
| Text-to-speech (TTS) is getting more and more common recently, | |
| and is getting to be a basic user interface for many systems. | |
| To further promote the use of TTS in various systems, | |
| it is significant to develop a manageable, maintainable, and extensible TTS component that is accessible to speech non-specialists, | |
| enterprising individuals and small teams. | |
| Traditional TTS systems, however, are not necessarily friendly for them, | |
| since they are typically composed of many domain-specific modules. | |
| For example, a typical parametric TTS system is an elaborate integration of many modules e.g.{} a text analyzer, an $F_0$ generator, | |
| a spectrum generator, a pause estimator, and a vocoder that synthesize a waveform from these data, etc. | |
| Deep learning~\cite{dlbook} may integrate these internal building blocks into a single model, | |
| and connects the input and the output directly. This type of technique is sometimes called `end-to-end' learning. | |
| Although such a technique is sometimes criticized as `a black box,' | |
| an end-to-end TTS system named Tacotron~\cite{Tacotron2017}, | |
| which directly estimates a spectrogram from an input text, | |
| has achieved promising performance recently, | |
| without using hand-engineered parametric models based on domain-specific knowledge. | |
| Tacotron, however, has a drawback that it uses many recurrent units which are quite costly to train. | |
| It is almost infeasible for ordinary labs who do not have luxurious machines to study and extend it further. | |
| In fact, some people tried to implement open clones of Tacotron~\cite{tacotron:open1,tacotron:open2,tacotron:open3,tacotron:open4}, | |
| but they are struggling to reproduce the speech of satisfactory quality as clear as the original results. | |
| The purpose of this paper is to show Deep Convolutional TTS (DCTTS), a novel fully convolutional neural TTS. | |
| The architecture is largely similar to Tacotron~\cite{Tacotron2017}, | |
| but is based on a fully convolutional sequence-to-sequence learning model similar to the literature \cite{cs2s}. | |
| We show that this fully convolutional TTS actually works in a reasonable setting. | |
| The contribution of this article is twofold: | |
| (1) Propose a fully CNN-based TTS system | |
| which can be trained much faster than an | |
| RNN-based state-of-the-art neural TTS system, while the sound quality is still acceptable. | |
| (2) An idea to rapidly train the attention module, which we call `guided attention,' is also shown. | |
| \subsection{Related Work} | |
| \vspace{-5pt} | |
| \subsubsection{Deep Learning and TTS} | |
| Recently, deep learning-based TTS systems have been intensively studied, | |
| and surprisingly high quality results are obtained in some of recent studies. | |
| The TTS systems based on deep neural networks include Zen's work~\cite{zenicassp}, | |
| the studies based on RNN e.g.{}~\cite{rnntts1,rnntts2,rnntts3,rnntts4}, and recently proposed techniques e.g.{} | |
| WaveNet~\cite[sec.~3.2]{wavenet}, Char2Wav~\cite{char2wav}, DeepVoice1\&2~\cite{deepvoice,deepvoice2}, | |
| and Tacotron~\cite{Tacotron2017}. | |
| Some of them tried to reduce the dependency on hand-engineered internal modules. | |
| The most extreme technique in this trend would be Tacotron~\cite{Tacotron2017}, | |
| which depends only on mel and linear spectrograms, and not on any other speech features e.g.~$F_0$. | |
| Our method is close to Tacotron in a sense that it depends only on these spectral representations of audio signals. | |
| Most of the existing methods above use RNN, a natural technique of time series prediction. | |
| An exception is WaveNet, which is fully convolutional. | |
| Our method is also based only on CNN but our usage of CNN would be different from WaveNet, | |
| as WaveNet is a kind of a vocoder, or a back-end, | |
| which synthesizes a waveform from some conditioning information that is given by front-end components. | |
| On the other hand, ours is rather a front-end (and most of back-end processing). | |
| We use CNN to synthesize a spectrogram, from which a simple vocoder can synthesize a waveform. | |
| \vspace{-5pt} | |
| \subsubsection{Sequence to Sequence $($seq2seq$)$ Learning} | |
| Recently, recurrent neural networks (RNN) have become a standard technique for mapping a sequence to another sequence, | |
| especially in the field of natural language processing, e.g.{} machine translation~\cite{cho,seq2seq}, dialogue system~\cite{dialoguele,dialogue}, etc. | |
| See also~\cite[sec.~10.4]{dlbook}. | |
| RNN-based seq2seq, however, has some disadvantages. | |
| Firstly, a vanilla encode-decoder model cannot encode too long sequence into a fixed-length vector effectively. | |
| This problem has been resolved by a mechanism called `attention'~\cite{nmt}, | |
| and the attention mechanism now has become a standard idea of seq2seq learning techniques; see also~\cite[sec.~12.4.5.1]{dlbook}. | |
| Another problem is that RNN typically requires much time to train, | |
| since it is less suited for parallel computation using GPUs. | |
| To overcome this problem, several researchers proposed the use of CNN instead of RNN, e.g.~\cite{Kim2014,Zhang2015a,Kalchbrenner2016,Dauphin2016a,qrnn}. | |
| Some studies have shown that CNN-based alternative networks can be trained much faster, | |
| and sometimes can even outperform the RNN-based techniques. | |
| Gehring et~al.~\cite{cs2s} recently united these two improvements of seq2seq learning. | |
| They proposed an idea on how to use attention mechanism in a CNN-based seq2seq learning model, | |
| and showed that the method is quite effective for machine translation. | |
| The method we proposed is based on the similar idea to the literature~\cite{cs2s}. | |
| \section{Preliminary} | |
| \tsume | |
| \subsection{Basic Knowledge of the Audio Spectrograms} | |
| An audio waveform and a complex spectrogram $Z = \{Z_{\freq t}\}\in \mathbb{C}^{\Freq' \times T'}$ are | |
| mutually transformed by linear maps called Short-term Fourier Transform (STFT) and inverse STFT, | |
| where $\Freq'$ and $T'$ denote the number of frequency bins and temporal bins, respectively. | |
| It is common to consider only the magnitude $|Z| = \{|Z_{\freq t}|\} \in \mathbb{R}^{\Freq' \times T'}$, | |
| since it still has useful information for many purposes, | |
| and that $|Z|$ and $Z$ are almost the same in the sense that there exist many phase estimation | |
| (i.e.,{} $|Z|$ to $Z$ estimation, and therefore, waveform synthesis) techniques, | |
| e.g.{} the famous Griffin\&Lim algorithm (GLA)~\cite{gla}; see also e.g.~\cite{wileyphase}. | |
| We always use RTISI-LA~\cite{Zhu2007}, an online GLA, to synthesize a waveform. | |
| In this paper, we always normalize STFT spectrograms as $|Z| \gets (|Z|/\max(|Z| ))^\gamma$, | |
| and convert back $|Z| \gets |Z|^{\eta/\gamma}$ when we finally need to synthesize the waveform, | |
| where $\gamma, \eta$ are pre- and post-emphasis factors. | |
| It is also common to consider a mel spectrogram $S \in \mathbb{R}^{\Freq \times T'}$, $(\Freq \ll \Freq')$, | |
| obtained by applying a mel filter-bank to $|Z|$. | |
| This is a standard dimension reduction technique for speech processing. | |
| In this paper, we also reduce the temporal dimensionality from $T'$ to $\lceil T'/4 \rceil =: T$ | |
| by picking up a time frame every four time frames, | |
| to accelerate the training of Text2Mel shown below. | |
| We also normalize mel spectrograms as $S \gets (S/\max(S))^\gamma$. | |
| \subsection{Notation: Convolution and Highway Activation}\label{notation} | |
| In this paper, we denote the 1D convolution layer~\cite{convnet} by a space saving notation | |
| $\cnnbasic{o \gets i}{k}{\dilate}{} (X)$, | |
| where | |
| $ i $ is the size of input channel, | |
| $ o $ is the size of output channel, | |
| $ k $ is the size of kernel, | |
| $ \dilate $ is the dilation factor, | |
| and an argument $X$ is a tensor having three dimensions ({\it batch, channel, temporal}). | |
| The stride of convolution is always $1$. | |
| Convolution layers are preceded by appropriately-sized zero padding, whose size is suitably determined by a simple arithmetic | |
| so that the length of the sequence is kept constant. | |
| Let us similarly denote the 1D {\it deconvolution} | |
| layer as $\dcnn{o \gets i}{k}{\dilate}{} (X)$. | |
| The stride of deconvolution is always $2$ in this paper. | |
| Let us write a layer composition operator as $\cdot \minicirc \cdot$, and | |
| let us write networks like | |
| $\mathfont{F} \minicirc \mathfont{ReLU} \minicirc \mathfont{G} (X) | |
| := \mathfont{F}(\mathfont{ReLU} ( \mathfont{G} (X))),$ | |
| and | |
| $(\mathfont{F} \minicirc \mathfont{G})^2(X) | |
| := \mathfont{F} \minicirc \mathfont{G} \minicirc \mathfont{F} \minicirc \mathfont{G} (X)$, etc. | |
| $\mathfont{ReLU}$ is an element-wise activation function defined by $\mathfont{ReLU}(x) = \max(x, 0)$. | |
| Convolution layers are sometimes followed by a Highway Network~\cite{highway}-like gated activation, | |
| which is advantageous in very deep networks: | |
| $ | |
| \mathfont{Highway}(X; \mathfont{L}) = \sigma(H_1) \odot \mathfont{ReLU}(H_2) + (1 - \sigma(H_1)) \odot X, | |
| $ | |
| where $H_1, H_2$ are tensors of the same shape as $X$, and are output as $[H_1, H_2]= \mathfont{L}(X)$ by a layer $\mathfont{L}$. | |
| The operator $\odot$ is the element-wise product, and $\sigma$ is the element-wise sigmoid function. | |
| Hereafter let us denote | |
| $ | |
| \cnn{\channels \gets \channels}{k}{\dilate}{}(X) := \mathfont{Highway} (X; \cnnbasic{2d \gets d}{k}{\dilate}{})$. | |
| \tsume | |
| \section{Proposed Network} | |
| \begin{figure}[!t] | |
| \centering | |
| \begin{minipage}[t]{0.95\linewidth} | |
| \centering | |
| \centerline{\includegraphics[width=0.85\columnwidth]{fig_0.pdf}} | |
| \end{minipage} | |
| \vspace{-10pt} | |
| \caption{{ Network architecture.}}\label{fig:network} | |
| \vspace{-5pt} | |
| \begin{minipage}[t]{0.99\linewidth} | |
| { | |
| \footnotesize | |
| \hrulefill | |
| $ | |
| \textstyle | |
| \mathsf{TextEnc}(L) := | |
| ( | |
| \cnn{2d \gets 2d}{1}{1}{1} | |
| )^2 \minicirc | |
| ( | |
| \cnn{2d \gets 2d}{3}{1}{1} | |
| )^2 \minicirc | |
| ( | |
| \cnn{2d \gets 2d}{3}{27}{1} \minicirc | |
| \cnn{2d \gets 2d}{3}{9}{1} \minicirc | |
| \cnn{2d \gets 2d}{3}{3}{1} \minicirc | |
| \cnn{2d \gets 2d}{3}{1}{1} | |
| )^2 \minicirc | |
| \resizetensor{2d \gets 2d}{1}{1}{} \minicirc | |
| \resizetensor{2d \gets e}{1}{1}{\relu\minicirc}\minicirc | |
| \mathsf{CharEmbed}^{e\hyphen\text{dim}} | |
| (L).\label{eq:textenc} | |
| $ | |
| \hrulefill | |
| $ | |
| \mathsf{AudioEnc}(S) := | |
| ( | |
| \cnn{d \gets d}{3}{3}{1} | |
| )^2 \minicirc | |
| ( | |
| \cnn{d \gets d}{3}{27}{1} \minicirc | |
| \cnn{d \gets d}{3}{9}{1} \minicirc | |
| \cnn{d \gets d}{3}{3}{1} \minicirc | |
| \cnn{d \gets d}{3}{1}{1} | |
| )^2 \minicirc | |
| \resizetensor{d \gets d}{1}{1}{} \minicirc | |
| \resizetensor{d \gets d}{1}{1}{\relu\minicirc} \minicirc | |
| \resizetensor{d \gets \Freq}{1}{1}{\relu\minicirc} | |
| (S).\label{eq:audioenc} | |
| $ | |
| \hrulefill | |
| $ | |
| \mathsf{AudioDec}(R') := | |
| \resizetensor{\Freq \gets d}{1}{1}{\sigma\minicirc} \minicirc | |
| ( | |
| \cnnbasic{d \gets d}{1}{1}{\relu\minicirc} | |
| )^3 \minicirc | |
| ( | |
| \cnn{d \gets d}{3}{1}{1} | |
| )^2 \minicirc | |
| ( | |
| \cnn{d \gets d}{3}{27}{1} \minicirc | |
| \cnn{d \gets d}{3}{9}{1} \minicirc | |
| \cnn{d \gets d}{3}{3}{1} \minicirc | |
| \cnn{d \gets d}{3}{1}{1} | |
| ) \minicirc | |
| \resizetensor{d \gets 2d}{1}{1}{} | |
| (R').\label{eq:audiodec} | |
| $ | |
| \hrulefill | |
| $ | |
| \mathsf{SSRN}(Y) := | |
| \resizetensor{\Freq' \gets \Freq'}{1}{1}{\sigma\minicirc} \minicirc | |
| ( | |
| \cnnbasic{\Freq' \gets \Freq'}{1}{1}{\relu \minicirc} | |
| )^2 \minicirc | |
| \resizetensor{\Freq' \gets 2c}{1}{1}{} \minicirc | |
| ( | |
| \cnn{2c \gets 2c}{3}{1}{} | |
| )^2 \minicirc | |
| \resizetensor{2c \gets c}{1}{1}{}\minicirc | |
| ( | |
| \cnn{c \gets c}{3}{3}{}\minicirc | |
| \cnn{c \gets c}{3}{1}{}\minicirc | |
| \dcnn{c \gets c}{2}{1}{} | |
| )^2\minicirc | |
| ( | |
| \cnn{c \gets c}{3}{3}{}\minicirc | |
| \cnn{c \gets c}{3}{1}{} | |
| )\minicirc | |
| \resizetensor{c \gets \Freq}{1}{1}{} | |
| (Y).\label{eq:ssrn} | |
| $ | |
| \hrulefill | |
| } | |
| \end{minipage} | |
| \vspace{-10pt} | |
| \caption{{ Details of each component. For notation, see section~\ref{notation}.}}\label{fig:network-detail} | |
| \end{figure} | |
| Since some literature~\cite{Tacotron2017,stackgan} suggest that the staged synthesis from low- to high-resolution | |
| has advantages over the direct synthesis of high-resolution data, | |
| we synthesize the spectrograms using the following two networks. | |
| (1) Text2Mel, which synthesizes a mel spectrogram from an input text, | |
| and (2) Spectrogram Super-resolution Network (SSRN), which synthesizes a full STFT spectrogram from a coarse mel spectrogram. | |
| Fig.~\ref{fig:network} shows the overall architecture. | |
| \subsection{Text2Mel: Text to Mel Spectrogram Network} | |
| We first consider to synthesize a coarse mel spectrogram from a text. This is the main part of the proposed method. | |
| This module consists of four submodules: Text Encoder, Audio Encoder, Attention, and Audio Decoder. | |
| The network $\mathfont{TextEnc}$ first encodes the input sentence | |
| $L = [l_1, \dots, l_N] \in \mathfont{Char}^N$ | |
| consisting of $N$ characters, into the two matrices $K$ (key), $V$ (value) $\in \mathbb{R}^{d \times N}$, where $d$ the dimension of encoded characters. | |
| On the other hand, the network $\mathfont{AudioEnc}$ encodes the coarse mel spectrogram $S = S_{1:\Freq,1:T} \in \mathbb{R}^{\Freq \times T}$, | |
| of speech of length $T$, | |
| into a matrix $Q$ (query) $\in \mathbb{R}^{d \times T}$. | |
| \vspace{-2pt}\begin{equation} | |
| (K, V) = \mathfont{TextEnc}(L), ~~ | |
| Q = \mathfont{AudioEnc}(S_{1:\Freq,1:T}). | |
| \end{equation}\vspace{-2pt}An attention matrix $A \in \mathbb{R}^{N \times T}$, defined as follows, | |
| evaluates how strongly the $n$-th character $l_n$ and the $t$-th mel spectrum $S_{1:\Freq,t}$ are related. | |
| \vspace{-2pt}\begin{equation} | |
| A = \mathfont{softmax}_{n\hyphen\text{axis}}(K\trans Q/\sqrt{d}). | |
| \end{equation}\vspace{-2pt}$A_{nt} \sim 1 $ implies that | |
| the module is focusing on $n$-th character $l_n$ at time $t$, | |
| and it will focus on $l_n$ or $l_{n+1}$ (or others nearby), at the subsequent time $t+1$. | |
| Whatever, let us expect those are encoded in $n$-th column of $V$. | |
| Thus a matrix $R \in \mathbb{R}^{d\times T}$, which is the `seed' of the subsequent mel spectra $S_{1:\Freq,2:T+1}$, | |
| is obtained as | |
| \vspace{-2pt}\begin{equation} | |
| R = \att{Q}{K}{V} := V A. \text{~~~~(Note: matrix product.)} | |
| \end{equation}\vspace{-2pt}The resultant $R$ is concatenated with the encoded audio $Q$, as $R' = [R, Q]$, | |
| because we found it beneficial in our pilot study. | |
| Then, the Audio Decoder module estimates a mel spectrogram | |
| from the seed matrix $R' \in \mathbb{R}^{2d\times T}$ as follows, | |
| \vspace{-2pt}\begin{equation}Y_{1:\Freq,2: T+1} = \mathfont{AudioDec}(R'). | |
| \end{equation}\vspace{-2pt}The resultant $Y_{1:\Freq, 2:T+1}$ needs to approximate the temporally-shifted ground truth $S_{1:\Freq, 2:T+1}$. | |
| The error is evaluated by a loss function $\mathcal{L}_\text{spec} (Y_{1:\Freq, 2:T+1} | S_{1:\Freq, 2:T+1} )$, | |
| and is back-propagated to the network parameters. | |
| The loss function was the sum of L1 loss and a function $\mathcal{D}_\text{bin}$ which we call binary divergence, | |
| \vspace{-7pt}\begin{align}\mathcal{D}_\text{bin} (Y|S) | |
| &:= \mathbb{E}_{\freq t} \left[ | |
| - S_{\freq t} \log \frac{Y_{\freq t}}{S_{\freq t}} | |
| - (1 - S_{\freq t}) \log \frac{1 - Y_{\freq t}}{1 - S_{\freq t}} | |
| \right] \nonumber \\ | |
| &= \mathbb{E}_{\freq t} [- S_{\freq t} \hat{Y}_{\freq t} + \log(1 + \exp \hat{Y}_{\freq t}) ] + \mathrm{const.},\label{divergence} | |
| \end{align} | |
| \vspace{-2pt}where $\hat{Y}_{\freq t} = \mathrm{logit}(Y_{\freq t})$. | |
| Since the gradient is non-vanishing, i.e., | |
| $ | |
| \partial \mathcal{D}_\text{bin} (Y|S)/\partial \hat{Y}_{\freq t} \propto Y_{\freq t} - S_{\freq t} | |
| $, | |
| it is advantageous for gradient-based training. | |
| It is easily verified that the spectrogram error is non-negative, | |
| $\mathcal{L}_\text{spec}(Y|S) = \mathcal{D}_\text{bin} (Y|S) + \mathbb{E}[|Y_{\freq t} - S_{\freq t}|] \ge 0$, | |
| and the equality holds iff $Y=S$. | |
| \subsubsection{Details of TextEnc, AudioEnc, and AudioDec} | |
| The networks are fully convolutional, and are not dependent on any recurrent units. | |
| In order to take into account the long contextual information, | |
| we used {\it dilated convolution}~\cite{dilatedcnn,wavenet,Kalchbrenner2016} | |
| instead of RNN. | |
| The top equation of Fig.~\ref{fig:network-detail} is the content of $\mathsf{TextEnc}$. | |
| It consists of a character embedding and several 1D {\it non-causal} convolution layers. | |
| In the literature~\cite{Tacotron2017} an RNN-based component named `CBHG' was used, but we found this convolutional network also works well. | |
| $\mathsf{AudioEnc}$ and $\mathsf{AudioDec}$, shown in Fig.~\ref{fig:network-detail}, are composed of 1D {\it causal} convolution layers. | |
| These convolution should be causal because the output of $\mathsf{AudioDec}$ is feedbacked to the input of $\mathsf{AudioEnc}$ at the synthesis stage. | |
| \subsection{Spectrogram Super-resolution Network (SSRN)} | |
| We finally synthesize a full spectrogram $|Z| \in \mathbb{R}^{\Freq' \times 4T}$, | |
| from the obtained coarse mel spectrogram $Y \in \mathbb{R}^{\Freq \times T}$, | |
| using the spectrogram super-resolution network (SSRN). | |
| Upsampling frequency from $\Freq$ to $\Freq'$ is fairly simple. | |
| We can achieve that by increasing the convolution channels of a 1D convolutional network. | |
| Upsampling of the temporal axis is not done the same way. | |
| Instead, | |
| we quadruple the length of the sequence from $T$ to $4T=T'$, | |
| by applying `deconvolution' layers of stride size 2 twice. | |
| The bottom equation of Fig.~\ref{fig:network-detail} shows $\mathsf{SSRN}$. | |
| In this paper, all convolutions of SSRN are non-causal, since we do not consider online processing. | |
| The loss function is the same as Text2Mel: the sum of L1 distance and $\mathcal{D}_\text{bin}$, defined by \eqref{divergence}, | |
| between the synthesized spectrogram $\mathsf{SSRN}(S)$ and the ground truth $|Z|$. | |
| \tsume | |
| \section{Guided Attention} | |
| \tsume | |
| \subsection{Guided Attention Loss: Motivation, Method and Effects} | |
| In general, an attention module is quite costly to train. | |
| Therefore, if there is some prior knowledge, incorporating them into the model may be a help to alleviate the heavy training. | |
| We show that the following simple method helps to train the attention module. | |
| In TTS, the possible attention matrix $A$ lies in the very small subspace of $\mathbb{R}^{N\times T}$. | |
| This is because of the rough correspondence of the order of the characters and the audio segments. | |
| That is, when one reads a text, it is natural to assume that the text position $n$ progresses nearly linearly to the time $t$, | |
| i.e., $n \sim a t$, where $a \sim N/T$. | |
| This is a noticeable difference of TTS from other seq2seq learning techniques such as machine translation, | |
| where an attention module needs to resolve the word alignment between two languages that have very different syntax, e.g.{} English and Japanese. | |
| Based on this idea, we introduce `guided attention loss,' which prompts the attention matrix $A$ to be `nearly diagonal,' | |
| $ | |
| \mathcal{L}_\text{att}(A) = \mathbb{E}_{nt}[ A_{nt} W_{nt} ], | |
| $ | |
| where | |
| $W_{nt} = 1 - \exp\{-{(n/N - t/T)^2}/{2g^2}\}.$ | |
| In this paper, we set $g=0.2$. | |
| If $A$ is far from diagonal (e.g., reading the characters in the random order), | |
| it is strongly penalized by the loss function. | |
| This subsidiary loss function is simultaneously optimized with the main loss $\mathcal{L}_\text{spec}$ with the equal weight. | |
| \begin{figure}[!t] | |
| \vspace{-10pt} | |
| \centering | |
| \begin{minipage}[t]{0.99\linewidth} | |
| \centering | |
| \centerline{\includegraphics[width=0.99\columnwidth]{fig_1.pdf}} | |
| \vspace{-10pt} | |
| \caption{ | |
| { | |
| Comparison of the attention matrix $A$, trained with and without the guided attention loss $\mathcal{L}_\text{att}(A)$. | |
| (Left) Without, and (Right) with the guided attention. | |
| The test text is {\it ``icassp stands for the international conference on acoustics, speech, and signal processing."} | |
| We did not use the heuristics described in section~\ref{sec:fia}. | |
| } | |
| } | |
| \label{fig:attention} | |
| \end{minipage} | |
| \vspace{-10pt} | |
| \end{figure} | |
| Although this measure is based on quite a rough assumption, it improved the training efficiency. | |
| In our experiment, if we added the guided attention loss to the objective, | |
| the term began decreasing only after $\sim$100 iterations. | |
| After $\sim$5K iterations, the attention became roughly correct, not only for training data, but also for new input texts. | |
| On the other hand, without the guided attention loss, it required much more iterations. | |
| It began learning after $\sim$10K iterations, | |
| and it required $\sim$50K iterations to look at roughly correct positions, but the attention matrix was still vague. | |
| Fig.~\ref{fig:attention} compares the attention matrix, trained with and without guided attention loss. | |
| \tsume | |
| \subsection{Forcibly Incremental Attention at the Synthesis Stage}\label{sec:fia} | |
| At the synthesis stage, the attention matrix $A$ sometimes fails to focus on the correct characters. | |
| Typical errors we observed were (1) skipping several letters, and (2) repeating a same word twice or more. | |
| In order to make the system more robust, | |
| we heuristically modified the matrix $A$ to be `nearly diagonal,' by a simple rule as follows. | |
| We observed this method sometimes alleviated such failures. | |
| {\it | |
| Let $n_t$ be the position of the character which is read at time $t$, i.e., $n_t = \mathrm{argmax}_n A_{nt}$. | |
| Comparing the current position $n_t$ and the previous position $n_{t-1}$, | |
| if the difference $n_t - n_{t-1}$ is outside of the range | |
| $-1 \le n_t - n_{t-1} \le 3$, the current attention is forcibly set as | |
| $A_{nt} = \delta_{n, n_{t-1}+1}$ $($Kronecker delta$)$, | |
| to increment the attention target as $n_t = n_{t-1} + 1$. | |
| } | |
| \tsume | |
| \section{Experiment} | |
| \tsume | |
| \subsection{Experimental Setup} | |
| \begin{table}[t] | |
| \centering | |
| \caption{{ Parameter Settings.}} | |
| \label{tab:params} | |
| { | |
| \footnotesize | |
| \begin{tabular}{l|l} \hline | |
| Sampling rate of audio signals & 22050 Hz\\ \hline | |
| STFT window function& Hanning\\ \hline | |
| STFT window length and shift & 1024 ($\sim$46.4 [ms]), 256 ($\sim$11.6[ms])\\ \hline | |
| STFT spectrogram size $\Freq' \times 4T$ & $513 \times 4T$ ($T$ depends on audio clip) \\ \hline | |
| Mel spectrogram size $\Freq \times T$ & $80 \times T$ ($T$ depends on audio clip) \\ \hline | |
| Dimension $e$, $d$ and $c$ & 128, 256, 512 \\ \hline | |
| ADAM parameters $(\alpha, \beta_1, \beta_2, \varepsilon)$ & $(2 \times 10^{-4}, 0.5, 0.9, 10^{-6})$ \\ \hline | |
| Minibatch size & 16 \\ \hline | |
| Emphasis factors $(\gamma, \eta)$ & (0.6, 1.3) \\ \hline | |
| RTISI-LA window and iteration & 100, 10 \\ \hline | |
| Character set, $\mathsf{Char}$ & \texttt{a-z,.'-} and \texttt{Space} and \texttt{NULL} \\ \hline | |
| \end{tabular} | |
| } | |
| \vspace{-7pt} | |
| \centering | |
| \caption{{ Comparison of MOS (95\% confidence interval), training time, and iterations (Text2Mel/SSRN), | |
| of an open Tacotron~\cite{tacotron:open3} and the proposed method (DCTTS). | |
| The digits with * were excerpted from the repository~\cite{tacotron:open3}}. | |
| } | |
| \label{tab:comparison} | |
| { | |
| \footnotesize | |
| \begin{tabular}{l|rr|r} \hline | |
| Method & Iteration & Time & MOS (95\% CI) \\ \hline \hline | |
| Open Tacotron~\cite{tacotron:open3} & | |
| 877K${}^*$ & 12 days${}^*$ & $2.07 \pm 0.62$ \\ \hline | |
| DCTTS | |
| & 20K/\phantom{9}40K & $\sim$2 hours & $1.74 \pm 0.52$ \\ \hline | |
| DCTTS | |
| & 90K/150K & $\sim$7 hours & $2.63 \pm 0.75$ \\ \hline | |
| DCTTS | |
| & 200K/340K & $\sim$15 hours & $2.71 \pm 0.66$ \\ \hline | |
| DCTTS | |
| & 540K/900K & $\sim$40 hours & $2.61 \pm 0.62$ \\ \hline | |
| \end{tabular} | |
| } | |
| \vspace{-5pt} | |
| \end{table} | |
| We used LJ Speech Dataset ~\cite{ljspeech} to train the networks. | |
| This is a public domain speech dataset | |
| consisting of $\sim$13K pairs of text and speech, without phoneme-level alignment, $\sim$24 hours in total. | |
| These speech data have a~little reverberation. | |
| We preprocessed the texts by spelling out some of abbreviations and numeric expressions, | |
| decapitalizing the capitals, and removing less frequent characters not shown in Table~\ref{tab:params}, | |
| where \texttt{NULL} is a dummy character for zero-padding. | |
| We implemented our neural networks using Chainer~2.0~\cite{chainer}. | |
| We trained the models using a household gaming PC equipped with two GPUs. | |
| The main memory of the machine was 62GB, which is much larger than the audio dataset. | |
| Both GPUs were NVIDIA GeForce GTX 980 Ti, with 6 GB memories. | |
| For simplicity, we trained Text2Mel and SSRN independently and asynchronously using different GPUs. | |
| All network parameters were initialized using He's Gaussian initializer~\cite{henorm}. | |
| Both networks were trained by the ADAM optimizer~\cite{adam}. | |
| When training SSRN, we randomly extracted short sequences of length $T=64$ for each iteration to save memory usage. | |
| To reduce the disk access, we reduced the frequency of creating the snapshot of parameters to only once per 5K iterations. | |
| Other parameters are shown in Table~\ref{tab:params}. | |
| As it is not easy for us to reproduce the original results of Tacotron, | |
| we instead used a ready-to-use model~\cite{tacotron:open3} for comparison, | |
| which seemed to produce the most reasonable sounds in the open implementations. | |
| It is reported that this model was trained using LJ Dataset | |
| for 12 days (877K iterations) on a GTX 1080 Ti, newer GPU than ours. | |
| Note, this iteration is still much less than the original Tacotron, which was trained for more than 2M iterations. | |
| We evaluated mean opinion scores (MOS) of both methods by crowdsourcing on Amazon Mechanical Turk using crowdMOS toolkit~\cite{crowdmos}. | |
| We used 20 sentences from {\it Harvard Sentences} List 1\&2. | |
| The audio data were synthesized using five methods shown in Table~\ref{tab:comparison}. | |
| The crowdworkers rated these 100 clips from 1 (Bad) to 5 (Excellent). | |
| Each worker is supposed to rate at least 10 clips. | |
| To obtain more responses of higher quality, we set a few incentives shown in the literature. | |
| The results were statistically processed using the method shown in the literature~\cite{crowdmos}. | |
| \subsection{Result and Discussion} | |
| \begin{figure}[t] | |
| \begin{minipage}[t]{0.99\linewidth} | |
| \centering | |
| \vspace{-5pt} | |
| \centerline{\includegraphics[width=0.99\columnwidth]{fig_2.pdf}} | |
| \vspace{-15pt} | |
| \caption{ | |
| { | |
| (Top) Attention, (middle) mel, and (bottom) linear STFT spectrogram, synthesized by the proposed method, after 15 hours training. | |
| The input text is {\it``icassp stands for the international conference on acoustics, speech and signal processing."} (90 chars) | |
| }}\label{fig:result} | |
| \end{minipage} | |
| \vspace{-5pt} | |
| \end{figure} | |
| In our setting, the training throughput was $\sim$3.8 minibatch/s (Text2Mel) and $\sim$6.4 minibatch/s (SSRN). | |
| This implies that we can iterate the updating formulae of Text2Mel 200K times in 15 hours. | |
| Fig.~\ref{fig:result} shows an example of attention, synthesized mel and full spectrograms, after 15 hours training. | |
| It shows that the method can almost correctly focus on the correct characters, and synthesize quite clear spectrograms. | |
| More samples are available at the author's web page.\footnote{\url{https://github.com/tachi-hi/tts_samples}} | |
| In our crowdsourcing experiment, 31 subjects evaluated our data. | |
| After the automatic screening by the toolkit~\cite{crowdmos}, 560 scores rated by 6 subjects were selected for final statistics calculation. | |
| Table~\ref{tab:comparison} compares the performance of our proposed method (DCTTS) and an open Tacotron. | |
| Our MOS (95\% confidence interval) was $2.71 \pm 0.66$ (15 hours training) while the Tacotron's was $2.07 \pm 0.62$. | |
| Although it is not a strict comparison since the frameworks and the machines were different, | |
| it would be still concluded that our proposed method is quite rapidly trained to the satisfactory level compared to Tacotron. | |
| Note that the MOS were below the level reported in~\cite{Tacotron2017}. | |
| The reasons may be threefold: (1) the limited number of iterations, | |
| (2) SSRN needs to synthesize the spectrograms from less information than~\cite{Tacotron2017}, | |
| and (3) the reverberation of the training data. | |
| \tsume | |
| \section{Summary and Future Work} | |
| This paper described a novel TTS technique based on deep convolutional neural networks, | |
| and a technique to train the attention module rapidly. | |
| In our experiment, the proposed Deep Convolutional TTS was trained overnight ($\sim$15 hours), | |
| using an ordinary gaming PC equipped with two GPUs, while the quality of the synthesized speech was almost acceptable. | |
| Although the audio quality is far from perfect yet, | |
| it may be improved | |
| by tuning some hyper-parameters thoroughly, and | |
| by applying some techniques developed in the deep learning community. | |
| We believe this method will encourage further development of the applications based on speech synthesis. | |
| We can expect that this simple neural TTS may be extended to many other purposes | |
| e.g.{} emotional/non-linguistic/personalized speech synthesis, etc., | |
| by further studies. | |
| In addition, since a neural TTS has become lighter, the studies on more integrated speech systems | |
| e.g.{} some multimodal systems, | |
| may have become more feasible. | |
| These issues should be worked out in the future. | |
| \tsume | |
| \section{Acknowledgement} | |
| The authors would like to thank the OSS contributors | |
| and the data creators (LibriVox contributors and @keithito). | |
| \bibliographystyle{IEEEbib} | |
| { | |
| \bibliography{mybib} | |
| } | |
| \end{document} | |