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Commit 719799a2 authored by lidc's avatar lidc
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增加了pytorch框架下的音频处理模型FastSpeech和ECAPA-TDNN的测试代码

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PyTorch/Speech/FastSpeech2/hifigan/__init__.py 0 → 100644
View file @ 719799a2
from .models import Generator
class AttrDict(dict):
def __init__(self, *args, **kwargs):
super(AttrDict, self).__init__(*args, **kwargs)
self.__dict__ = self
\ No newline at end of file
PyTorch/Speech/FastSpeech2/hifigan/config.json 0 → 100644
View file @ 719799a2
{
"resblock": "1",
"num_gpus": 0,
"batch_size": 16,
"learning_rate": 0.0002,
"adam_b1": 0.8,
"adam_b2": 0.99,
"lr_decay": 0.999,
"seed": 1234,
"upsample_rates": [8,8,2,2],
"upsample_kernel_sizes": [16,16,4,4],
"upsample_initial_channel": 512,
"resblock_kernel_sizes": [3,7,11],
"resblock_dilation_sizes": [[1,3,5], [1,3,5], [1,3,5]],
"segment_size": 8192,
"num_mels": 80,
"num_freq": 1025,
"n_fft": 1024,
"hop_size": 256,
"win_size": 1024,
"sampling_rate": 22050,
"fmin": 0,
"fmax": 8000,
"fmax_for_loss": null,
"num_workers": 4,
"dist_config": {
"dist_backend": "nccl",
"dist_url": "tcp://localhost:54321",
"world_size": 1
}
}
PyTorch/Speech/FastSpeech2/hifigan/models.py 0 → 100644
View file @ 719799a2
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn import Conv1d, ConvTranspose1d
from torch.nn.utils import weight_norm, remove_weight_norm
LRELU_SLOPE = 0.1
def init_weights(m, mean=0.0, std=0.01):
classname = m.__class__.__name__
if classname.find("Conv") != -1:
m.weight.data.normal_(mean, std)
def get_padding(kernel_size, dilation=1):
return int((kernel_size * dilation - dilation) / 2)
class ResBlock(torch.nn.Module):
def __init__(self, h, channels, kernel_size=3, dilation=(1, 3, 5)):
super(ResBlock, self).__init__()
self.h = h
self.convs1 = nn.ModuleList(
[
weight_norm(
Conv1d(
channels,
channels,
kernel_size,
1,
dilation=dilation[0],
padding=get_padding(kernel_size, dilation[0]),
)
),
weight_norm(
Conv1d(
channels,
channels,
kernel_size,
1,
dilation=dilation[1],
padding=get_padding(kernel_size, dilation[1]),
)
),
weight_norm(
Conv1d(
channels,
channels,
kernel_size,
1,
dilation=dilation[2],
padding=get_padding(kernel_size, dilation[2]),
)
),
]
)
self.convs1.apply(init_weights)
self.convs2 = nn.ModuleList(
[
weight_norm(
Conv1d(
channels,
channels,
kernel_size,
1,
dilation=1,
padding=get_padding(kernel_size, 1),
)
),
weight_norm(
Conv1d(
channels,
channels,
kernel_size,
1,
dilation=1,
padding=get_padding(kernel_size, 1),
)
),
weight_norm(
Conv1d(
channels,
channels,
kernel_size,
1,
dilation=1,
padding=get_padding(kernel_size, 1),
)
),
]
)
self.convs2.apply(init_weights)
def forward(self, x):
for c1, c2 in zip(self.convs1, self.convs2):
xt = F.leaky_relu(x, LRELU_SLOPE)
xt = c1(xt)
xt = F.leaky_relu(xt, LRELU_SLOPE)
xt = c2(xt)
x = xt + x
return x
def remove_weight_norm(self):
for l in self.convs1:
remove_weight_norm(l)
for l in self.convs2:
remove_weight_norm(l)
class Generator(torch.nn.Module):
def __init__(self, h):
super(Generator, self).__init__()
self.h = h
self.num_kernels = len(h.resblock_kernel_sizes)
self.num_upsamples = len(h.upsample_rates)
self.conv_pre = weight_norm(
Conv1d(80, h.upsample_initial_channel, 7, 1, padding=3)
)
resblock = ResBlock
self.ups = nn.ModuleList()
for i, (u, k) in enumerate(zip(h.upsample_rates, h.upsample_kernel_sizes)):
self.ups.append(
weight_norm(
ConvTranspose1d(
h.upsample_initial_channel // (2 ** i),
h.upsample_initial_channel // (2 ** (i + 1)),
k,
u,
padding=(k - u) // 2,
)
)
)
self.resblocks = nn.ModuleList()
for i in range(len(self.ups)):
ch = h.upsample_initial_channel // (2 ** (i + 1))
for j, (k, d) in enumerate(
zip(h.resblock_kernel_sizes, h.resblock_dilation_sizes)
):
self.resblocks.append(resblock(h, ch, k, d))
self.conv_post = weight_norm(Conv1d(ch, 1, 7, 1, padding=3))
self.ups.apply(init_weights)
self.conv_post.apply(init_weights)
def forward(self, x):
x = self.conv_pre(x)
for i in range(self.num_upsamples):
x = F.leaky_relu(x, LRELU_SLOPE)
x = self.ups[i](x)
xs = None
for j in range(self.num_kernels):
if xs is None:
xs = self.resblocks[i * self.num_kernels + j](x)
else:
xs += self.resblocks[i * self.num_kernels + j](x)
x = xs / self.num_kernels
x = F.leaky_relu(x)
x = self.conv_post(x)
x = torch.tanh(x)
return x
def remove_weight_norm(self):
print("Removing weight norm...")
for l in self.ups:
remove_weight_norm(l)
for l in self.resblocks:
l.remove_weight_norm()
remove_weight_norm(self.conv_pre)
remove_weight_norm(self.conv_post)
\ No newline at end of file
PyTorch/Speech/FastSpeech2/index.html 0 → 100644
View file @ 719799a2
<!DOCTYPE html>
<html>
<head>
<meta charset="utf-8" />
<meta name="viewport" content="width=device-width,initial-scale=1" />
<title>FastSpeech 2 Audio Samples</title>
<style>
body {
margin: 0% 15%;
padding: 50px 30px;
background: #fff;
color: #111;
font-size: 17px;
font-family: sans-serif;
font-weight: 400;
line-height: 1.8;
overflow-x: hidden;
-webkit-font-smoothing: antialiased;
}
h1 {
font-size: 1.75em;
}
h2 {
margin-bottom: 0.4em;
}
hr {
height: 0.5px;
border-width: 0;
color: lightgray;
background-color: lightgray
}
table {
width: 100%;
}
audio {
width: 100%;
}
</style>
</head>
<body>
<h1>FastSpeech 2 Audio Samples</h1>
<p>
<h2>English Single-Speaker TTS</h2>
<div>
<b>Dataset</b>: <a href="https://keithito.com/LJ-Speech-Dataset/">LJSpeech</a>
</div>
<div>
<b>Checkpoint</b>: <a
href="https://drive.google.com/file/d/1r3fYhnblBJ8hDKDSUDtidJ-BN-xAM9pe/view?usp=sharing">link</a>
</div>
<div>
<b>Config</b>: <a href="https://github.com/ming024/FastSpeech2/tree/master/config/LJSpeech">link</a>
</div>
<div>
<b>Vocoder</b>: <a href="https://github.com/jik876/hifi-gan">HiFi-GAN (LJSpeech)</a>
</div>
<hr>
<table>
<tr>
<th style="text-align: center">Extremely Long Sentence</th>
</tr>
<tr>
<td style="text-align: center"><audio controls="controls">
<source src="./demo/LJSpeech/LJSpeech_long_sentence.wav" autoplay />
</audio></td>
</tr>
</table>
<div><b>Text</b>: <em>Advanced text-to-speech models such as FastSpeech can synthesize speech significantly faster
than previous autoregressive models with comparable quality. The training of FastSpeech model relies on an
autoregressive teacher model for duration prediction and knowledge distillation , which can ease the one-to-many
mapping problem in TTS. However, FastSpeech has several disadvantages: 1) the teacher-student distillation
pipeline is complicated, 2) the duration extracted from the teacher model is not accurate enough, and the target
mel-spectrograms distilled from teacher model suffer from information loss due to data simplification, both of
which limit the voice quality. In this paper, we propose FastSpeech 2, which addresses the issues in FastSpeech
and better solves the one-to-many mapping problem in TTS by 1) directly training the model with ground-truth
target instead of the simplified output from teacher, and 2) introducing more variation information of speech as
conditional inputs. Specifically, we extract duration, pitch and energy from speech waveform and directly take
them as conditional inputs during training and use predicted values during inference. We further design FastSpeech
2s, which is the first attempt to directly generate speech waveform from text in parallel, enjoying the benefit of
full end-to-end training and even faster inference than FastSpeech. Experimental results show that 1) FastSpeech 2
and 2s outperform FastSpeech in voice quality with much simplified training pipeline and reduced training time; 2)
FastSpeech 2 and 2s can match the voice quality of autoregressive models while enjoying much faster inference
speed.
</em></div>
<hr>
<table>
<tr>
<th style="text-align: center">LJ042-0094 (Ground-Truth) </th>
<th style="text-align: center">LJ042-0094 (Synthesized) </th>
</tr>
<tr>
<td style="text-align: center"><audio controls="controls">
<source src="./demo/LJSpeech/LJ042-0094_ground-truth.wav" autoplay />
</audio></td>
<td style="text-align: center"><audio controls="controls">
<source src="./demo/LJSpeech/LJ042-0094_synthesized.wav" autoplay />
</audio></td>
</tr>
</table>
<div><b>Text</b>: <em>the soviet authorities denied oswald permission</em></div>
<hr>
<table>
<tr>
<th style="text-align: center">LJ021-0108 (Ground-Truth) </th>
<th style="text-align: center">LJ021-0108 (Synthesized) </th>
</tr>
<tr>
<td style="text-align: center"><audio controls="controls">
<source src="./demo/LJSpeech/LJ021-0108_ground-truth.wav" autoplay />
</audio></td>
<td style="text-align: center"><audio controls="controls">
<source src="./demo/LJSpeech/LJ021-0108_synthesized.wav" autoplay />
</audio></td>
</tr>
</table>
<div><b>Text</b>: <em>from the standpoint of the good of the industries themselves, as well as the general public
interest,</em></div>
<hr>
<table>
<tr>
<th style="text-align: center">LJ030-0031 (Ground-Truth) </th>
<th style="text-align: center">LJ030-0031 (Synthesized) </th>
</tr>
<tr>
<td style="text-align: center"><audio controls="controls">
<source src="./demo/LJSpeech/LJ030-0031_ground-truth.wav" autoplay />
</audio></td>
<td style="text-align: center"><audio controls="controls">
<source src="./demo/LJSpeech/LJ030-0031_synthesized.wav" autoplay />
</audio></td>
</tr>
</table>
<div><b>Text</b>: <em>secret service agents formed a cordon to keep the press and photographers from impeding their
passage and scanned the
crowd for threatening movements.</em></div>
<hr>
<table>
<tr>
<th style="text-align: center">LJ001-0012 (Ground-Truth) </th>
<th style="text-align: center">LJ001-0012 (Synthesized) </th>
</tr>
<tr>
<td style="text-align: center"><audio controls="controls">
<source src="./demo/LJSpeech/LJ001-0012_ground-truth.wav" autoplay />
</audio></td>
<td style="text-align: center"><audio controls="controls">
<source src="./demo/LJSpeech/LJ001-0012_synthesized.wav" autoplay />
</audio></td>
</tr>
</table>
<div><b>Text</b>: <em>especially as no more time is occupied, or cost incurred, in casting, setting, or printing
beautiful letters</em></div>
<hr>
<table>
<tr>
<th style="text-align: center">LJ011-0202 (Ground-Truth) </th>
<th style="text-align: center">LJ011-0202 (Synthesized) </th>
</tr>
<tr>
<td style="text-align: center"><audio controls="controls">
<source src="./demo/LJSpeech/LJ011-0202_ground-truth.wav" autoplay />
</audio></td>
<td style="text-align: center"><audio controls="controls">
<source src="./demo/LJSpeech/LJ011-0202_synthesized.wav" autoplay />
</audio></td>
</tr>
</table>
<div><b>Text</b>: <em>the uncle claimed her. the husband resisted.</em></div>
<hr>
<table>
<tr>
<th style="text-align: center">LJ006-0114 (Ground-Truth) </th>
<th style="text-align: center">LJ006-0114 (Synthesized) </th>
</tr>
<tr>
<td style="text-align: center"><audio controls="controls">
<source src="./demo/LJSpeech/LJ006-0114_ground-truth.wav" autoplay />
</audio></td>
<td style="text-align: center"><audio controls="controls">
<source src="./demo/LJSpeech/LJ006-0114_synthesized.wav" autoplay />
</audio></td>
</tr>
</table>
<div><b>Text</b>: <em>they bought their offices from one another, and were thus considered to have a vested interest
in them.</em></div>
<hr>
<table>
<tr>
<th style="text-align: center">LJ009-0038 (Ground-Truth) </th>
<th style="text-align: center">LJ009-0038 (Synthesized) </th>
</tr>
<tr>
<td style="text-align: center"><audio controls="controls">
<source src="./demo/LJSpeech/LJ009-0038_ground-truth.wav" autoplay />
</audio></td>
<td style="text-align: center"><audio controls="controls">
<source src="./demo/LJSpeech/LJ009-0038_synthesized.wav" autoplay />
</audio></td>
</tr>
</table>
<div><b>Text</b>: <em>in the center of the chapel was the condemned pew, a large dock-like erection painted
black.</em></div>
<hr>
<table>
<tr>
<th style="text-align: center">LJ006-0161 (Ground-Truth) </th>
<th style="text-align: center">LJ006-0161 (Synthesized) </th>
</tr>
<tr>
<td style="text-align: center"><audio controls="controls">
<source src="./demo/LJSpeech/LJ006-0161_ground-truth.wav" autoplay />
</audio></td>
<td style="text-align: center"><audio controls="controls">
<source src="./demo/LJSpeech/LJ006-0161_synthesized.wav" autoplay />
</audio></td>
</tr>
</table>
<div><b>Text</b>: <em>again, a turnkey deposed that his chief did not enter the wards more than once a fortnight.</em>
</div>
<hr>
<table>
<tr>
<th style="text-align: center">LJ027-0127 (Ground-Truth) </th>
<th style="text-align: center">LJ027-0127 (Synthesized) </th>
</tr>
<tr>
<td style="text-align: center"><audio controls="controls">
<source src="./demo/LJSpeech/LJ027-0127_ground-truth.wav" autoplay />
</audio></td>
<td style="text-align: center"><audio controls="controls">
<source src="./demo/LJSpeech/LJ027-0127_synthesized.wav" autoplay />
</audio></td>
</tr>
</table>
<div><b>Text</b>: <em>while neglecting to maintain his unity of ideal in the case of nearly all the numerous species
of snakes, he should have
added a tiny rudiment in the case of the python</em></div>
<hr>
<table>
<tr>
<th style="text-align: center">LJ050-0158 (Ground-Truth) </th>
<th style="text-align: center">LJ050-0158 (Synthesized) </th>
</tr>
<tr>
<td style="text-align: center"><audio controls="controls">
<source src="./demo/LJSpeech/LJ050-0158_ground-truth.wav" autoplay />
</audio></td>
<td style="text-align: center"><audio controls="controls">
<source src="./demo/LJSpeech/LJ050-0158_synthesized.wav" autoplay />
</audio></td>
</tr>
</table>
<div><b>Text</b>: <em>the department hopes to design a practical system which will fully meet the needs of the
protective research section of
the secret service.</em></div>
</p>
<hr>
<p>
<h2>Mandarin Multi-Speaker TTS</h2>
<div>
<b>Dataset</b>: <a href="http://www.aishelltech.com/aishell_3">AISHELL-3</a>
</div>
<div>
<b>Checkpoint</b>: <a
href="https://drive.google.com/file/d/1uYWd5JlaK-fochQ2JFgIP_wOEkoLPLqs/view?usp=sharing">link</a>
</div>
<div>
<b>Config</b>: <a href="https://github.com/ming024/FastSpeech2/tree/master/config/AISHELL3">link</a>
</div>
<div>
<b>Vocoder</b>: <a href="https://github.com/jik876/hifi-gan">HiFi-GAN (universal)</a>
</div>
<hr>
<table>
<tr>
<th style="text-align: center">SSB08630110 (Ground-Truth) </th>
<th style="text-align: center">SSB08630110 (Synthesized) </th>
</tr>
<tr>
<td style="text-align: center"><audio controls="controls">
<source src="./demo/AISHELL3/SSB08630110_ground-truth.wav" autoplay />
</audio></td>
<td style="text-align: center"><audio controls="controls">
<source src="./demo/AISHELL3/SSB08630110_synthesized.wav" autoplay />
</audio></td>
</tr>
</table>
<div><b>Speaker</b>: <em>SSB0863</em></div>
<div><b>Text</b>: <em>放首歌来听，丁当的歌 (fang4 shou3 ge1 lai2 ting1 sp ding1 dang1 de5 ge1)</em></div>
<hr>
<table>
<tr>
<th style="text-align: center">SSB07940279 (Ground-Truth) </th>
<th style="text-align: center">SSB07940279 (Synthesized) </th>
</tr>
<tr>
<td style="text-align: center"><audio controls="controls">
<source src="./demo/AISHELL3/SSB07940279_ground-truth.wav" autoplay />
</audio></td>
<td style="text-align: center"><audio controls="controls">
<source src="./demo/AISHELL3/SSB07940279_synthesized.wav" autoplay />
</audio></td>
</tr>
</table>
<div><b>Speaker</b>: <em>SSB0794</em></div>
<div><b>Text</b>: <em>中国，今日有望创下历史单，日，出行人次最高 (zhong1 guo2 sp jin1 ri4 you3 wang4 chuang4 xia4 li4 shi3 dan1 sp ri4 sp
chu1 xing2 ren2 ci4 zui4 gao1)</em></div>
<hr>
<table>
<tr>
<th style="text-align: center">SSB00800003 (Ground-Truth) </th>
<th style="text-align: center">SSB00800003 (Synthesized) </th>
</tr>
<tr>
<td style="text-align: center"><audio controls="controls">
<source src="./demo/AISHELL3/SSB00800003_ground-truth.wav" autoplay />
</audio></td>
<td style="text-align: center"><audio controls="controls">
<source src="./demo/AISHELL3/SSB00800003_synthesized.wav" autoplay />
</audio></td>
</tr>
</table>
<div><b>Speaker</b>: <em>SSSB0080</em></div>
<div><b>Text</b>: <em>中新网七月十四日电 (zhong1 xin1 wang3 qi1 yue4 shi2 si4 ri4 dian4)</em></div>
<hr>
<table>
<tr>
<th style="text-align: center">SSB15930399 (Ground-Truth) </th>
<th style="text-align: center">SSB15930399 (Synthesized) </th>
</tr>
<tr>
<td style="text-align: center"><audio controls="controls">
<source src="./demo/AISHELL3/SSB15930399_ground-truth.wav" autoplay />
</audio></td>
<td style="text-align: center"><audio controls="controls">
<source src="./demo/AISHELL3/SSB15930399_synthesized.wav" autoplay />
</audio></td>
</tr>
</table>
<div><b>Speaker</b>: <em>SSB1593</em></div>
<div><b>Text</b>: <em>也就是说汤米踩过点 (ye3 jiu4 shi4 shuo1 tang1 mi3 cai3 guo4 dian3)</em></div>
<hr>
<table>
<tr>
<th style="text-align: center">SSB16070080 (Ground-Truth) </th>
<th style="text-align: center">SSB16070080 (Synthesized) </th>
</tr>
<tr>
<td style="text-align: center"><audio controls="controls">
<source src="./demo/AISHELL3/SSB16070080_ground-truth.wav" autoplay />
</audio></td>
<td style="text-align: center"><audio controls="controls">
<source src="./demo/AISHELL3/SSB16070080_synthesized.wav" autoplay />
</audio></td>
</tr>
</table>
<div><b>Speaker</b>: <em>SSB1607</em></div>
<div><b>Text</b>: <em>但阵营之间差距拉大 (dan4 zhen4 ying2 zhi1 jian1 cha1 ju4 la1 da4)</em></div>
<hr>
<table>
<tr>
<th style="text-align: center">SSB10200388 (Ground-Truth) </th>
<th style="text-align: center">SSB10200388 (Synthesized) </th>
</tr>
<tr>
<td style="text-align: center"><audio controls="controls">
<source src="./demo/AISHELL3/SSB10200388_ground-truth.wav" autoplay />
</audio></td>
<td style="text-align: center"><audio controls="controls">
<source src="./demo/AISHELL3/SSB10200388_synthesized.wav" autoplay />
</audio></td>
</tr>
</table>
<div><b>Speaker</b>: <em>SSSB1020</em></div>
<div><b>Text</b>: <em>我抱着一大堆纸箱回了家 (wo3 bao4 zhe5 yi2 da4 dui1 zhi3 xiang1 hui2 le5 jia1)</em></div>
<hr>
<table>
<tr>
<th style="text-align: center">SSB05900363 (Ground-Truth) </th>
<th style="text-align: center">SSB05900363 (Synthesized) </th>
</tr>
<tr>
<td style="text-align: center"><audio controls="controls">
<source src="./demo/AISHELL3/SSB05900363_ground-truth.wav" autoplay />
</audio></td>
<td style="text-align: center"><audio controls="controls">
<source src="./demo/AISHELL3/SSB05900363_synthesized.wav" autoplay />
</audio></td>
</tr>
</table>
<div><b>Speaker</b>: <em>SSB0590</em></div>
<div><b>Text</b>: <em>我们不能分头行动 (wo3 men2 bu4 neng2 fen1 tou2 xing2 dong4)</em></div>
<hr>
<table>
<tr>
<th style="text-align: center">SSB04700028 (Ground-Truth) </th>
<th style="text-align: center">SSB04700028 (Synthesized) </th>
</tr>
<tr>
<td style="text-align: center"><audio controls="controls">
<source src="./demo/AISHELL3/SSB04700028_ground-truth.wav" autoplay />
</audio></td>
<td style="text-align: center"><audio controls="controls">
<source src="./demo/AISHELL3/SSB04700028_synthesized.wav" autoplay />
</audio></td>
</tr>
</table>
<div><b>Speaker</b>: <em>SSB0470</em></div>
<div><b>Text</b>: <em>毕节市的景点有什么 (bi4 jie2 shi4 de5 jing2 dian2 you3 shen2 me5)</em></div>
<hr>
<table>
<tr>
<th style="text-align: center">SSB06290079 (Ground-Truth) </th>
<th style="text-align: center">SSB06290079 (Synthesized) </th>
</tr>
<tr>
<td style="text-align: center"><audio controls="controls">
<source src="./demo/AISHELL3/SSB06290079_ground-truth.wav" autoplay />
</audio></td>
<td style="text-align: center"><audio controls="controls">
<source src="./demo/AISHELL3/SSB06290079_synthesized.wav" autoplay />
</audio></td>
</tr>
</table>
<div><b>Speaker</b>: <em>SSB0629</em></div>
<div><b>Text</b>: <em>你以为 (ni2 yi3 wei2)</em></div>
<hr>
<table>
<tr>
<th style="text-align: center">SSB08220199 (Ground-Truth) </th>
<th style="text-align: center">SSB08220199 (Synthesized) </th>
</tr>
<tr>
<td style="text-align: center"><audio controls="controls">
<source src="./demo/AISHELL3/SSB08220199_ground-truth.wav" autoplay />
</audio></td>
<td style="text-align: center"><audio controls="controls">
<source src="./demo/AISHELL3/SSB08220199_synthesized.wav" autoplay />
</audio></td>
</tr>
</table>
<div><b>Speaker</b>: <em>SSB0822</em></div>
<div><b>Text</b>: <em>该职位员工可以，居住在西雅，图或，洛，杉矶 (gai1 zhi2 wei4 yuan2 gong1 ke2 yi3 sp ju1 zhu4 zai4 xi1 ya3 sp tu2 huo4
sp luo4 sp shan3 ji1)</em></div>
</p>
<hr>
<p>
<h2>English Multi-Speaker TTS</h2>
<div>
<b>Dataset</b>: <a href="https://research.google/tools/datasets/libri-tts/">LibriTTS</a>
</div>
<div>
<b>Checkpoint</b>: <a
href="https://drive.google.com/file/d/1M6BxJtTxYW56dG1Myz9MqZmG_OXJLUqy/view?usp=sharing">link</a>
</div>
<div>
<b>Config</b>: <a href="https://github.com/ming024/FastSpeech2/tree/master/config/LibriTTS">link</a>
</div>
<div>
<b>Vocoder</b>: <a href="https://github.com/jik876/hifi-gan">HiFi-GAN (universal)</a>
</div>
<hr>
<table>
<tr>
<th style="text-align: center">207_131203_000011_000000 (Ground-Truth) </th>
<th style="text-align: center">207_131203_000011_000000 (Synthesized) </th>
</tr>
<tr>
<td style="text-align: center"><audio controls="controls">
<source src="./demo/LibriTTS/207_131203_000011_000000_ground-truth.wav" autoplay />
</audio></td>
<td style="text-align: center"><audio controls="controls">
<source src="./demo/LibriTTS/207_131203_000011_000000_synthesized.wav" autoplay />
</audio></td>
</tr>
</table>
<div><b>Speaker</b>: <em>207</em></div>
<div><b>Text</b>: <em>his mother, however, was a little shy of the company for him, and besides she could not always
spare him.</em></div>
<hr>
<table>
<tr>
<th style="text-align: center">2299_6524_000057_000000 (Ground-Truth) </th>
<th style="text-align: center">2299_6524_000057_000000 (Synthesized) </th>
</tr>
<tr>
<td style="text-align: center"><audio controls="controls">
<source src="./demo/LibriTTS/2299_6524_000057_000000_ground-truth.wav" autoplay />
</audio></td>
<td style="text-align: center"><audio controls="controls">
<source src="./demo/LibriTTS/2299_6524_000057_000000_synthesized.wav" autoplay />
</audio></td>
</tr>
</table>
<div><b>Speaker</b>: <em>2299</em></div>
<div><b>Text</b>: <em>on the arrival at the hut to my chagrin we found it filled with snow.</em></div>
<hr>
<table>
<tr>
<th style="text-align: center">2388_153731_000003_000000 (Ground-Truth) </th>
<th style="text-align: center">2388_153731_000003_000000 (Synthesized) </th>
</tr>
<tr>
<td style="text-align: center"><audio controls="controls">
<source src="./demo/LibriTTS/2388_153731_000003_000000_ground-truth.wav" autoplay />
</audio></td>
<td style="text-align: center"><audio controls="controls">
<source src="./demo/LibriTTS/2388_153731_000003_000000_synthesized.wav" autoplay />
</audio></td>
</tr>
</table>
<div><b>Speaker</b>: <em>2388</em></div>
<div><b>Text</b>: <em>the story of the first scientific observation of the corona and the prominences is thrillingly
interesting, and in fact dramatic.</em></div>
<hr>
<table>
<tr>
<th style="text-align: center">3615_14677_000014_000000 (Ground-Truth) </th>
<th style="text-align: center">3615_14677_000014_000000 (Synthesized) </th>
</tr>
<tr>
<td style="text-align: center"><audio controls="controls">
<source src="./demo/LibriTTS/3615_14677_000014_000000_ground-truth.wav" autoplay />
</audio></td>
<td style="text-align: center"><audio controls="controls">
<source src="./demo/LibriTTS/3615_14677_000014_000000_synthesized.wav" autoplay />
</audio></td>
</tr>
</table>
<div><b>Speaker</b>: <em>3615</em></div>
<div><b>Text</b>: <em>beat three eggs with a pinch of salt; add one pint of milk and two thirds of a cup of
flour.</em></div>
<hr>
<table>
<tr>
<th style="text-align: center">4744_31668_000003_000001 (Ground-Truth) </th>
<th style="text-align: center">4744_31668_000003_000001 (Synthesized) </th>
</tr>
<tr>
<td style="text-align: center"><audio controls="controls">
<source src="./demo/LibriTTS/4744_31668_000003_000001_ground-truth.wav" autoplay />
</audio></td>
<td style="text-align: center"><audio controls="controls">
<source src="./demo/LibriTTS/4744_31668_000003_000001_synthesized.wav" autoplay />
</audio></td>
</tr>
</table>
<div><b>Speaker</b>: <em>4744</em></div>
<div><b>Text</b>: <em>far from morbid naturally, she did her best to deny the thought, and so simple and unartificial
was her type of mind that for weeks together she would wholly lose it.</em></div>
<hr>
<table>
<tr>
<th style="text-align: center">480_127525_000006_000000 (Ground-Truth) </th>
<th style="text-align: center">480_127525_000006_000000 (Synthesized) </th>
</tr>
<tr>
<td style="text-align: center"><audio controls="controls">
<source src="./demo/LibriTTS/480_127525_000006_000000_ground-truth.wav" autoplay />
</audio></td>
<td style="text-align: center"><audio controls="controls">
<source src="./demo/LibriTTS/480_127525_000006_000000_synthesized.wav" autoplay />
</audio></td>
</tr>
</table>
<div><b>Speaker</b>: <em>480</em></div>
<div><b>Text</b>: <em>i tried and found by experiment that the tide kept sweeping us westward until i had laid her
head due east, or just about right angles to the way we ought to go.</em></div>
<hr>
<table>
<tr>
<th style="text-align: center">5126_27504_000011_000003 (Ground-Truth) </th>
<th style="text-align: center">5126_27504_000011_000003 (Synthesized) </th>
</tr>
<tr>
<td style="text-align: center"><audio controls="controls">
<source src="./demo/LibriTTS/5126_27504_000011_000003_ground-truth.wav" autoplay />
</audio></td>
<td style="text-align: center"><audio controls="controls">
<source src="./demo/LibriTTS/5126_27504_000011_000003_synthesized.wav" autoplay />
</audio></td>
</tr>
</table>
<div><b>Speaker</b>: <em>5126</em></div>
<div><b>Text</b>: <em>it looked as if our luck was dead out, and we began to think our chance of getting across the
border to queensland, and clear out of the colony that way, looked worse every day.</em></div>
<hr>
<table>
<tr>
<th style="text-align: center">6098_57837_000008_000000 (Ground-Truth) </th>
<th style="text-align: center">6098_57837_000008_000000 (Synthesized) </th>
</tr>
<tr>
<td style="text-align: center"><audio controls="controls">
<source src="./demo/LibriTTS/6098_57837_000008_000000_ground-truth.wav" autoplay />
</audio></td>
<td style="text-align: center"><audio controls="controls">
<source src="./demo/LibriTTS/6098_57837_000008_000000_synthesized.wav" autoplay />
</audio></td>
</tr>
</table>
<div><b>Speaker</b>: <em>6098</em></div>
<div><b>Text</b>: <em>word was sent of their predicament to the nearest fort, and lieutenant pershing was sent with a
small detachment to their rescue.</em></div>
<hr>
<table>
<tr>
<th style="text-align: center">6544_231862_000065_000001 (Ground-Truth) </th>
<th style="text-align: center">6544_231862_000065_000001 (Synthesized) </th>
</tr>
<tr>
<td style="text-align: center"><audio controls="controls">
<source src="./demo/LibriTTS/6544_231862_000065_000001_ground-truth.wav" autoplay />
</audio></td>
<td style="text-align: center"><audio controls="controls">
<source src="./demo/LibriTTS/6544_231862_000065_000001_synthesized.wav" autoplay />
</audio></td>
</tr>
</table>
<div><b>Speaker</b>: <em>6544</em></div>
<div><b>Text</b>: <em>denzil did look, and uttered a second cry more startling than the first.</em></div>
<hr>
<table>
<tr>
<th style="text-align: center">968_122545_000053_000000 (Ground-Truth) </th>
<th style="text-align: center">968_122545_000053_000000 (Synthesized) </th>
</tr>
<tr>
<td style="text-align: center"><audio controls="controls">
<source src="./demo/LibriTTS/968_122545_000053_000000_ground-truth.wav" autoplay />
</audio></td>
<td style="text-align: center"><audio controls="controls">
<source src="./demo/LibriTTS/968_122545_000053_000000_synthesized.wav" autoplay />
</audio></td>
</tr>
</table>
<div><b>Speaker</b>: <em>968</em></div>
<div><b>Text</b>: <em>the bee fought the window angrily, up and down, up and down, for several minutes; then found the
open glass and whirled
out into the sunshine, joyfully.</em></div>
</p>
</body>
</html>
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PyTorch/Speech/FastSpeech2/lexicon/librispeech-lexicon.txt 0 → 100644
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PyTorch/Speech/FastSpeech2/lexicon/pinyin-lexicon-r.txt 0 → 100644
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a2 a2
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a4 a4
a5 a5
ai1 ai1
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ai3 ai3
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ang1 ang1
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ceng1 c eng1
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ceng3 c eng3
ceng4 c eng4
ceng5 c eng5
cha1 ch a1
cha2 ch a2
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chai1 ch ai1
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chan1 ch an1
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chan3 ch an3
chan4 ch an4
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chang1 ch ang1
chang2 ch ang2
chang3 ch ang3
chang4 ch ang4
chang5 ch ang5
chao1 ch ao1
chao2 ch ao2
chao3 ch ao3
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che1 ch e1
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che3 ch e3
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chen1 ch en1
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chen5 ch en5
cheng1 ch eng1
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chi4 ch iii4
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chong1 ch ong1
chong2 ch ong2
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chong4 ch ong4
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chu1 ch u1
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chuai1 ch uai1
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chuan3 ch uan3
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chuan5 ch uan5
chuang1 ch uang1
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chuang3 ch uang3
chuang4 ch uang4
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chun5 ch uen5
chuo1 ch uo1
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chuo4 ch uo4
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ci1 c ii1
ci2 c ii2
ci3 c ii3
ci4 c ii4
ci5 c ii5
cong1 c ong1
cong2 c ong2
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cong4 c ong4
cong5 c ong5
cou1 c ou1
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cou3 c ou3
cou4 c ou4
cou5 c ou5
cu1 c u1
cu2 c u2
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cuan1 c uan1
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cuan4 c uan4
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cui1 c uei1
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cuo1 c uo1
cuo2 c uo2
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cuo4 c uo4
cuo5 c uo5
da1 d a1
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dai1 d ai1
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dao1 d ao1
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ding1 d ing1
ding2 d ing2
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ding4 d ing4
ding5 d ing5
diu1 d iou1
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diu5 d iou5
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ei1 ei1
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eng1 eng1
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eng3 eng3
eng4 eng4
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r1 er1
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r4 er4
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er1 er1
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fan1 f an1
fan2 f an2
fan3 f an3
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fei3 f ei3
fei4 f ei4
fei5 f ei5
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gan1 g an1
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gen1 g en1
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geng1 g eng1
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gua5 g ua5
guai1 g uai1
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guai3 g uai3
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guang1 g uang1
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guang3 g uang3
guang4 g uang4
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gui1 g uei1
gui2 g uei2
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gui4 g uei4
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gun1 g uen1
gun2 g uen2
gun3 g uen3
gun4 g uen4
gun5 g uen5
guo1 g uo1
guo2 g uo2
guo3 g uo3
guo4 g uo4
guo5 g uo5
ha1 h a1
ha2 h a2
ha3 h a3
ha4 h a4
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tai4 t ai4
tai5 t ai5
tan1 t an1
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tan3 t an3
tan4 t an4
tan5 t an5
tang1 t ang1
tang2 t ang2
tang3 t ang3
tang4 t ang4
tang5 t ang5
tao1 t ao1
tao2 t ao2
tao3 t ao3
tao4 t ao4
tao5 t ao5
te1 t e1
te2 t e2
te3 t e3
te4 t e4
te5 t e5
tei1 t ei1
tei2 t ei2
tei3 t ei3
tei4 t ei4
tei5 t ei5
teng1 t eng1
teng2 t eng2
teng3 t eng3
teng4 t eng4
teng5 t eng5
ti1 t i1
ti2 t i2
ti3 t i3
ti4 t i4
ti5 t i5
tian1 t ian1
tian2 t ian2
tian3 t ian3
tian4 t ian4
tian5 t ian5
tiao1 t iao1
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tiao3 t iao3
tiao4 t iao4
tiao5 t iao5
tie1 t ie1
tie2 t ie2
tie3 t ie3
tie4 t ie4
tie5 t ie5
ting1 t ing1
ting2 t ing2
ting3 t ing3
ting4 t ing4
ting5 t ing5
tong1 t ong1
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tong3 t ong3
tong4 t ong4
tong5 t ong5
tou1 t ou1
tou2 t ou2
tou3 t ou3
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tou5 t ou5
tu1 t u1
tu2 t u2
tu3 t u3
tu4 t u4
tu5 t u5
tuan1 t uan1
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tuan3 t uan3
tuan4 t uan4
tuan5 t uan5
tui1 t uei1
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tui5 t uei5
tun1 t uen1
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tun3 t uen3
tun4 t uen4
tun5 t uen5
tuo1 t uo1
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tuo3 t uo3
tuo4 t uo4
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wa1 w ua1
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wa3 w ua3
wa4 w ua4
wa5 w ua5
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wai3 w uai3
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wai5 w uai5
wan1 w uan1
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wang3 w uang3
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wu1 w u1
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xian1 x ian1
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xiang1 x iang1
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xiang3 x iang3
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xiao3 x iao3
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xie1 x ie1
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xin1 x in1
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xiong1 x iong1
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xiu1 x iou1
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xu1 x v1
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xuan3 x van3
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xun3 x vn3
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ya3 y ia3
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yang1 y iang1
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yang3 y iang3
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yao1 y iao1
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yao3 y iao3
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ye1 y ie1
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ying1 y ing1
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yo1 y iou1
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yo3 y iou3
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yong1 y iong1
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yong3 y iong3
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you1 y iou1
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za4 z a4
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zhang1 zh ang1
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zhang3 zh ang3
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zhao1 zh ao1
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zheng1 zh eng1
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zheng3 zh eng3
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zhong1 zh ong1
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zhong3 zh ong3
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zhou1 zh ou1
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zhou3 zh ou3
zhou4 zh ou4
zhou5 zh ou5
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zhuan1 zh uan1
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zhuan5 zh uan5
zhuang1 zh uang1
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zhui1 zh uei1
zhui2 zh uei2
zhui3 zh uei3
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zhui5 zh uei5
zhun1 zh uen1
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zhun3 zh uen3
zhun4 zh uen4
zhun5 zh uen5
zhuo1 zh uo1
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zhuo3 zh uo3
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zi1 z ii1
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zi4 z ii4
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zou1 z ou1
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zou3 z ou3
zou4 z ou4
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zu1 z u1
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zu3 z u3
zu4 z u4
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zuan1 z uan1
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zui1 z uei1
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zui3 z uei3
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zui5 z uei5
zun1 z uen1
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zun3 z uen3
zun4 z uen4
zun5 z uen5
zuo1 z uo1
zuo2 z uo2
zuo3 z uo3
zuo4 z uo4
zuo5 z uo5
ar1 a1 rr
ar2 a2 rr
ar3 a3 rr
ar4 a4 rr
ar5 a5 rr
air1 ai1 rr
air2 ai2 rr
air3 ai3 rr
air4 ai4 rr
air5 ai5 rr
anr1 an1 rr
anr2 an2 rr
anr3 an3 rr
anr4 an4 rr
anr5 an5 rr
angr1 ang1 rr
angr2 ang2 rr
angr3 ang3 rr
angr4 ang4 rr
angr5 ang5 rr
aor1 ao1 rr
aor2 ao2 rr
aor3 ao3 rr
aor4 ao4 rr
aor5 ao5 rr
bar1 b a1 rr
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bar3 b a3 rr
bar4 b a4 rr
bar5 b a5 rr
bair1 b ai1 rr
bair2 b ai2 rr
bair3 b ai3 rr
bair4 b ai4 rr
bair5 b ai5 rr
banr1 b an1 rr
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banr5 b an5 rr
bangr1 b ang1 rr
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bangr3 b ang3 rr
bangr4 b ang4 rr
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baor1 b ao1 rr
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baor3 b ao3 rr
baor4 b ao4 rr
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beir1 b ei1 rr
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benr1 b en1 rr
benr2 b en2 rr
benr3 b en3 rr
benr4 b en4 rr
benr5 b en5 rr
bengr1 b eng1 rr
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bengr3 b eng3 rr
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bir1 b i1 rr
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bir3 b i3 rr
bir4 b i4 rr
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bianr1 b ian1 rr
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bianr3 b ian3 rr
bianr4 b ian4 rr
bianr5 b ian5 rr
biaor1 b iao1 rr
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biaor3 b iao3 rr
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bier1 b ie1 rr
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bier3 b ie3 rr
bier4 b ie4 rr
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binr1 b in1 rr
binr2 b in2 rr
binr3 b in3 rr
binr4 b in4 rr
binr5 b in5 rr
bingr1 b ing1 rr
bingr2 b ing2 rr
bingr3 b ing3 rr
bingr4 b ing4 rr
bingr5 b ing5 rr
bor1 b o1 rr
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bor3 b o3 rr
bor4 b o4 rr
bor5 b o5 rr
bur1 b u1 rr
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bur3 b u3 rr
bur4 b u4 rr
bur5 b u5 rr
car1 c a1 rr
car2 c a2 rr
car3 c a3 rr
car4 c a4 rr
car5 c a5 rr
cair1 c ai1 rr
cair2 c ai2 rr
cair3 c ai3 rr
cair4 c ai4 rr
cair5 c ai5 rr
canr1 c an1 rr
canr2 c an2 rr
canr3 c an3 rr
canr4 c an4 rr
canr5 c an5 rr
cangr1 c ang1 rr
cangr2 c ang2 rr
cangr3 c ang3 rr
cangr4 c ang4 rr
cangr5 c ang5 rr
caor1 c ao1 rr
caor2 c ao2 rr
caor3 c ao3 rr
caor4 c ao4 rr
caor5 c ao5 rr
cer1 c e1 rr
cer2 c e2 rr
cer3 c e3 rr
cer4 c e4 rr
cer5 c e5 rr
cenr1 c en1 rr
cenr2 c en2 rr
cenr3 c en3 rr
cenr4 c en4 rr
cenr5 c en5 rr
cengr1 c eng1 rr
cengr2 c eng2 rr
cengr3 c eng3 rr
cengr4 c eng4 rr
cengr5 c eng5 rr
char1 ch a1 rr
char2 ch a2 rr
char3 ch a3 rr
char4 ch a4 rr
char5 ch a5 rr
chair1 ch ai1 rr
chair2 ch ai2 rr
chair3 ch ai3 rr
chair4 ch ai4 rr
chair5 ch ai5 rr
chanr1 ch an1 rr
chanr2 ch an2 rr
chanr3 ch an3 rr
chanr4 ch an4 rr
chanr5 ch an5 rr
changr1 ch ang1 rr
changr2 ch ang2 rr
changr3 ch ang3 rr
changr4 ch ang4 rr
changr5 ch ang5 rr
chaor1 ch ao1 rr
chaor2 ch ao2 rr
chaor3 ch ao3 rr
chaor4 ch ao4 rr
chaor5 ch ao5 rr
cher1 ch e1 rr
cher2 ch e2 rr
cher3 ch e3 rr
cher4 ch e4 rr
cher5 ch e5 rr
chenr1 ch en1 rr
chenr2 ch en2 rr
chenr3 ch en3 rr
chenr4 ch en4 rr
chenr5 ch en5 rr
chengr1 ch eng1 rr
chengr2 ch eng2 rr
chengr3 ch eng3 rr
chengr4 ch eng4 rr
chengr5 ch eng5 rr
chir1 ch iii1 rr
chir2 ch iii2 rr
chir3 ch iii3 rr
chir4 ch iii4 rr
chir5 ch iii5 rr
chongr1 ch ong1 rr
chongr2 ch ong2 rr
chongr3 ch ong3 rr
chongr4 ch ong4 rr
chongr5 ch ong5 rr
chour1 ch ou1 rr
chour2 ch ou2 rr
chour3 ch ou3 rr
chour4 ch ou4 rr
chour5 ch ou5 rr
chur1 ch u1 rr
chur2 ch u2 rr
chur3 ch u3 rr
chur4 ch u4 rr
chur5 ch u5 rr
chuair1 ch uai1 rr
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chuair3 ch uai3 rr
chuair4 ch uai4 rr
chuair5 ch uai5 rr
chuanr1 ch uan1 rr
chuanr2 ch uan2 rr
chuanr3 ch uan3 rr
chuanr4 ch uan4 rr
chuanr5 ch uan5 rr
chuangr1 ch uang1 rr
chuangr2 ch uang2 rr
chuangr3 ch uang3 rr
chuangr4 ch uang4 rr
chuangr5 ch uang5 rr
chuir1 ch uei1 rr
chuir2 ch uei2 rr
chuir3 ch uei3 rr
chuir4 ch uei4 rr
chuir5 ch uei5 rr
chunr1 ch uen1 rr
chunr2 ch uen2 rr
chunr3 ch uen3 rr
chunr4 ch uen4 rr
chunr5 ch uen5 rr
chuor1 ch uo1 rr
chuor2 ch uo2 rr
chuor3 ch uo3 rr
chuor4 ch uo4 rr
chuor5 ch uo5 rr
cir1 c ii1 rr
cir2 c ii2 rr
cir3 c ii3 rr
cir4 c ii4 rr
cir5 c ii5 rr
congr1 c ong1 rr
congr2 c ong2 rr
congr3 c ong3 rr
congr4 c ong4 rr
congr5 c ong5 rr
cour1 c ou1 rr
cour2 c ou2 rr
cour3 c ou3 rr
cour4 c ou4 rr
cour5 c ou5 rr
cur1 c u1 rr
cur2 c u2 rr
cur3 c u3 rr
cur4 c u4 rr
cur5 c u5 rr
cuanr1 c uan1 rr
cuanr2 c uan2 rr
cuanr3 c uan3 rr
cuanr4 c uan4 rr
cuanr5 c uan5 rr
cuir1 c uei1 rr
cuir2 c uei2 rr
cuir3 c uei3 rr
cuir4 c uei4 rr
cuir5 c uei5 rr
cunr1 c uen1 rr
cunr2 c uen2 rr
cunr3 c uen3 rr
cunr4 c uen4 rr
cunr5 c uen5 rr
cuor1 c uo1 rr
cuor2 c uo2 rr
cuor3 c uo3 rr
cuor4 c uo4 rr
cuor5 c uo5 rr
dar1 d a1 rr
dar2 d a2 rr
dar3 d a3 rr
dar4 d a4 rr
dar5 d a5 rr
dair1 d ai1 rr
dair2 d ai2 rr
dair3 d ai3 rr
dair4 d ai4 rr
dair5 d ai5 rr
danr1 d an1 rr
danr2 d an2 rr
danr3 d an3 rr
danr4 d an4 rr
danr5 d an5 rr
dangr1 d ang1 rr
dangr2 d ang2 rr
dangr3 d ang3 rr
dangr4 d ang4 rr
dangr5 d ang5 rr
daor1 d ao1 rr
daor2 d ao2 rr
daor3 d ao3 rr
daor4 d ao4 rr
daor5 d ao5 rr
der1 d e1 rr
der2 d e2 rr
der3 d e3 rr
der4 d e4 rr
der5 d e5 rr
deir1 d ei1 rr
deir2 d ei2 rr
deir3 d ei3 rr
deir4 d ei4 rr
deir5 d ei5 rr
denr1 d en1 rr
denr2 d en2 rr
denr3 d en3 rr
denr4 d en4 rr
denr5 d en5 rr
dengr1 d eng1 rr
dengr2 d eng2 rr
dengr3 d eng3 rr
dengr4 d eng4 rr
dengr5 d eng5 rr
dir1 d i1 rr
dir2 d i2 rr
dir3 d i3 rr
dir4 d i4 rr
dir5 d i5 rr
diar1 d ia1 rr
diar2 d ia2 rr
diar3 d ia3 rr
diar4 d ia4 rr
diar5 d ia5 rr
dianr1 d ian1 rr
dianr2 d ian2 rr
dianr3 d ian3 rr
dianr4 d ian4 rr
dianr5 d ian5 rr
diaor1 d iao1 rr
diaor2 d iao2 rr
diaor3 d iao3 rr
diaor4 d iao4 rr
diaor5 d iao5 rr
dier1 d ie1 rr
dier2 d ie2 rr
dier3 d ie3 rr
dier4 d ie4 rr
dier5 d ie5 rr
dingr1 d ing1 rr
dingr2 d ing2 rr
dingr3 d ing3 rr
dingr4 d ing4 rr
dingr5 d ing5 rr
diur1 d iou1 rr
diur2 d iou2 rr
diur3 d iou3 rr
diur4 d iou4 rr
diur5 d iou5 rr
dongr1 d ong1 rr
dongr2 d ong2 rr
dongr3 d ong3 rr
dongr4 d ong4 rr
dongr5 d ong5 rr
dour1 d ou1 rr
dour2 d ou2 rr
dour3 d ou3 rr
dour4 d ou4 rr
dour5 d ou5 rr
dur1 d u1 rr
dur2 d u2 rr
dur3 d u3 rr
dur4 d u4 rr
dur5 d u5 rr
duanr1 d uan1 rr
duanr2 d uan2 rr
duanr3 d uan3 rr
duanr4 d uan4 rr
duanr5 d uan5 rr
duir1 d uei1 rr
duir2 d uei2 rr
duir3 d uei3 rr
duir4 d uei4 rr
duir5 d uei5 rr
dunr1 d uen1 rr
dunr2 d uen2 rr
dunr3 d uen3 rr
dunr4 d uen4 rr
dunr5 d uen5 rr
duor1 d uo1 rr
duor2 d uo2 rr
duor3 d uo3 rr
duor4 d uo4 rr
duor5 d uo5 rr
er1 e1 rr
er2 e2 rr
er3 e3 rr
er4 e4 rr
er5 e5 rr
eir1 ei1 rr
eir2 ei2 rr
eir3 ei3 rr
eir4 ei4 rr
eir5 ei5 rr
enr1 en1 rr
enr2 en2 rr
enr3 en3 rr
enr4 en4 rr
enr5 en5 rr
engr1 eng1 rr
engr2 eng2 rr
engr3 eng3 rr
engr4 eng4 rr
engr5 eng5 rr
far1 f a1 rr
far2 f a2 rr
far3 f a3 rr
far4 f a4 rr
far5 f a5 rr
fanr1 f an1 rr
fanr2 f an2 rr
fanr3 f an3 rr
fanr4 f an4 rr
fanr5 f an5 rr
fangr1 f ang1 rr
fangr2 f ang2 rr
fangr3 f ang3 rr
fangr4 f ang4 rr
fangr5 f ang5 rr
feir1 f ei1 rr
feir2 f ei2 rr
feir3 f ei3 rr
feir4 f ei4 rr
feir5 f ei5 rr
fenr1 f en1 rr
fenr2 f en2 rr
fenr3 f en3 rr
fenr4 f en4 rr
fenr5 f en5 rr
fengr1 f eng1 rr
fengr2 f eng2 rr
fengr3 f eng3 rr
fengr4 f eng4 rr
fengr5 f eng5 rr
for1 f o1 rr
for2 f o2 rr
for3 f o3 rr
for4 f o4 rr
for5 f o5 rr
four1 f ou1 rr
four2 f ou2 rr
four3 f ou3 rr
four4 f ou4 rr
four5 f ou5 rr
fur1 f u1 rr
fur2 f u2 rr
fur3 f u3 rr
fur4 f u4 rr
fur5 f u5 rr
gar1 g a1 rr
gar2 g a2 rr
gar3 g a3 rr
gar4 g a4 rr
gar5 g a5 rr
gair1 g ai1 rr
gair2 g ai2 rr
gair3 g ai3 rr
gair4 g ai4 rr
gair5 g ai5 rr
ganr1 g an1 rr
ganr2 g an2 rr
ganr3 g an3 rr
ganr4 g an4 rr
ganr5 g an5 rr
gangr1 g ang1 rr
gangr2 g ang2 rr
gangr3 g ang3 rr
gangr4 g ang4 rr
gangr5 g ang5 rr
gaor1 g ao1 rr
gaor2 g ao2 rr
gaor3 g ao3 rr
gaor4 g ao4 rr
gaor5 g ao5 rr
ger1 g e1 rr
ger2 g e2 rr
ger3 g e3 rr
ger4 g e4 rr
ger5 g e5 rr
geir1 g ei1 rr
geir2 g ei2 rr
geir3 g ei3 rr
geir4 g ei4 rr
geir5 g ei5 rr
genr1 g en1 rr
genr2 g en2 rr
genr3 g en3 rr
genr4 g en4 rr
genr5 g en5 rr
gengr1 g eng1 rr
gengr2 g eng2 rr
gengr3 g eng3 rr
gengr4 g eng4 rr
gengr5 g eng5 rr
gongr1 g ong1 rr
gongr2 g ong2 rr
gongr3 g ong3 rr
gongr4 g ong4 rr
gongr5 g ong5 rr
gour1 g ou1 rr
gour2 g ou2 rr
gour3 g ou3 rr
gour4 g ou4 rr
gour5 g ou5 rr
gur1 g u1 rr
gur2 g u2 rr
gur3 g u3 rr
gur4 g u4 rr
gur5 g u5 rr
guar1 g ua1 rr
guar2 g ua2 rr
guar3 g ua3 rr
guar4 g ua4 rr
guar5 g ua5 rr
guair1 g uai1 rr
guair2 g uai2 rr
guair3 g uai3 rr
guair4 g uai4 rr
guair5 g uai5 rr
guanr1 g uan1 rr
guanr2 g uan2 rr
guanr3 g uan3 rr
guanr4 g uan4 rr
guanr5 g uan5 rr
guangr1 g uang1 rr
guangr2 g uang2 rr
guangr3 g uang3 rr
guangr4 g uang4 rr
guangr5 g uang5 rr
guir1 g uei1 rr
guir2 g uei2 rr
guir3 g uei3 rr
guir4 g uei4 rr
guir5 g uei5 rr
gunr1 g uen1 rr
gunr2 g uen2 rr
gunr3 g uen3 rr
gunr4 g uen4 rr
gunr5 g uen5 rr
guor1 g uo1 rr
guor2 g uo2 rr
guor3 g uo3 rr
guor4 g uo4 rr
guor5 g uo5 rr
har1 h a1 rr
har2 h a2 rr
har3 h a3 rr
har4 h a4 rr
har5 h a5 rr
hair1 h ai1 rr
hair2 h ai2 rr
hair3 h ai3 rr
hair4 h ai4 rr
hair5 h ai5 rr
hanr1 h an1 rr
hanr2 h an2 rr
hanr3 h an3 rr
hanr4 h an4 rr
hanr5 h an5 rr
hangr1 h ang1 rr
hangr2 h ang2 rr
hangr3 h ang3 rr
hangr4 h ang4 rr
hangr5 h ang5 rr
haor1 h ao1 rr
haor2 h ao2 rr
haor3 h ao3 rr
haor4 h ao4 rr
haor5 h ao5 rr
her1 h e1 rr
her2 h e2 rr
her3 h e3 rr
her4 h e4 rr
her5 h e5 rr
heir1 h ei1 rr
heir2 h ei2 rr
heir3 h ei3 rr
heir4 h ei4 rr
heir5 h ei5 rr
henr1 h en1 rr
henr2 h en2 rr
henr3 h en3 rr
henr4 h en4 rr
henr5 h en5 rr
hengr1 h eng1 rr
hengr2 h eng2 rr
hengr3 h eng3 rr
hengr4 h eng4 rr
hengr5 h eng5 rr
hongr1 h ong1 rr
hongr2 h ong2 rr
hongr3 h ong3 rr
hongr4 h ong4 rr
hongr5 h ong5 rr
hour1 h ou1 rr
hour2 h ou2 rr
hour3 h ou3 rr
hour4 h ou4 rr
hour5 h ou5 rr
hur1 h u1 rr
hur2 h u2 rr
hur3 h u3 rr
hur4 h u4 rr
hur5 h u5 rr
huar1 h ua1 rr
huar2 h ua2 rr
huar3 h ua3 rr
huar4 h ua4 rr
huar5 h ua5 rr
huair1 h uai1 rr
huair2 h uai2 rr
huair3 h uai3 rr
huair4 h uai4 rr
huair5 h uai5 rr
huanr1 h uan1 rr
huanr2 h uan2 rr
huanr3 h uan3 rr
huanr4 h uan4 rr
huanr5 h uan5 rr
huangr1 h uang1 rr
huangr2 h uang2 rr
huangr3 h uang3 rr
huangr4 h uang4 rr
huangr5 h uang5 rr
huir1 h uei1 rr
huir2 h uei2 rr
huir3 h uei3 rr
huir4 h uei4 rr
huir5 h uei5 rr
hunr1 h uen1 rr
hunr2 h uen2 rr
hunr3 h uen3 rr
hunr4 h uen4 rr
hunr5 h uen5 rr
huor1 h uo1 rr
huor2 h uo2 rr
huor3 h uo3 rr
huor4 h uo4 rr
huor5 h uo5 rr
jir1 j i1 rr
jir2 j i2 rr
jir3 j i3 rr
jir4 j i4 rr
jir5 j i5 rr
jiar1 j ia1 rr
jiar2 j ia2 rr
jiar3 j ia3 rr
jiar4 j ia4 rr
jiar5 j ia5 rr
jianr1 j ian1 rr
jianr2 j ian2 rr
jianr3 j ian3 rr
jianr4 j ian4 rr
jianr5 j ian5 rr
jiangr1 j iang1 rr
jiangr2 j iang2 rr
jiangr3 j iang3 rr
jiangr4 j iang4 rr
jiangr5 j iang5 rr
jiaor1 j iao1 rr
jiaor2 j iao2 rr
jiaor3 j iao3 rr
jiaor4 j iao4 rr
jiaor5 j iao5 rr
jier1 j ie1 rr
jier2 j ie2 rr
jier3 j ie3 rr
jier4 j ie4 rr
jier5 j ie5 rr
jinr1 j in1 rr
jinr2 j in2 rr
jinr3 j in3 rr
jinr4 j in4 rr
jinr5 j in5 rr
jingr1 j ing1 rr
jingr2 j ing2 rr
jingr3 j ing3 rr
jingr4 j ing4 rr
jingr5 j ing5 rr
jiongr1 j iong1 rr
jiongr2 j iong2 rr
jiongr3 j iong3 rr
jiongr4 j iong4 rr
jiongr5 j iong5 rr
jiur1 j iou1 rr
jiur2 j iou2 rr
jiur3 j iou3 rr
jiur4 j iou4 rr
jiur5 j iou5 rr
jur1 j v1 rr
jur2 j v2 rr
jur3 j v3 rr
jur4 j v4 rr
jur5 j v5 rr
juanr1 j van1 rr
juanr2 j van2 rr
juanr3 j van3 rr
juanr4 j van4 rr
juanr5 j van5 rr
juer1 j ve1 rr
juer2 j ve2 rr
juer3 j ve3 rr
juer4 j ve4 rr
juer5 j ve5 rr
junr1 j vn1 rr
junr2 j vn2 rr
junr3 j vn3 rr
junr4 j vn4 rr
junr5 j vn5 rr
kar1 k a1 rr
kar2 k a2 rr
kar3 k a3 rr
kar4 k a4 rr
kar5 k a5 rr
kair1 k ai1 rr
kair2 k ai2 rr
kair3 k ai3 rr
kair4 k ai4 rr
kair5 k ai5 rr
kanr1 k an1 rr
kanr2 k an2 rr
kanr3 k an3 rr
kanr4 k an4 rr
kanr5 k an5 rr
kangr1 k ang1 rr
kangr2 k ang2 rr
kangr3 k ang3 rr
kangr4 k ang4 rr
kangr5 k ang5 rr
kaor1 k ao1 rr
kaor2 k ao2 rr
kaor3 k ao3 rr
kaor4 k ao4 rr
kaor5 k ao5 rr
ker1 k e1 rr
ker2 k e2 rr
ker3 k e3 rr
ker4 k e4 rr
ker5 k e5 rr
keir1 k ei1 rr
keir2 k ei2 rr
keir3 k ei3 rr
keir4 k ei4 rr
keir5 k ei5 rr
kenr1 k en1 rr
kenr2 k en2 rr
kenr3 k en3 rr
kenr4 k en4 rr
kenr5 k en5 rr
kengr1 k eng1 rr
kengr2 k eng2 rr
kengr3 k eng3 rr
kengr4 k eng4 rr
kengr5 k eng5 rr
kongr1 k ong1 rr
kongr2 k ong2 rr
kongr3 k ong3 rr
kongr4 k ong4 rr
kongr5 k ong5 rr
kour1 k ou1 rr
kour2 k ou2 rr
kour3 k ou3 rr
kour4 k ou4 rr
kour5 k ou5 rr
kur1 k u1 rr
kur2 k u2 rr
kur3 k u3 rr
kur4 k u4 rr
kur5 k u5 rr
kuar1 k ua1 rr
kuar2 k ua2 rr
kuar3 k ua3 rr
kuar4 k ua4 rr
kuar5 k ua5 rr
kuair1 k uai1 rr
kuair2 k uai2 rr
kuair3 k uai3 rr
kuair4 k uai4 rr
kuair5 k uai5 rr
kuanr1 k uan1 rr
kuanr2 k uan2 rr
kuanr3 k uan3 rr
kuanr4 k uan4 rr
kuanr5 k uan5 rr
kuangr1 k uang1 rr
kuangr2 k uang2 rr
kuangr3 k uang3 rr
kuangr4 k uang4 rr
kuangr5 k uang5 rr
kuir1 k uei1 rr
kuir2 k uei2 rr
kuir3 k uei3 rr
kuir4 k uei4 rr
kuir5 k uei5 rr
kunr1 k uen1 rr
kunr2 k uen2 rr
kunr3 k uen3 rr
kunr4 k uen4 rr
kunr5 k uen5 rr
kuor1 k uo1 rr
kuor2 k uo2 rr
kuor3 k uo3 rr
kuor4 k uo4 rr
kuor5 k uo5 rr
lar1 l a1 rr
lar2 l a2 rr
lar3 l a3 rr
lar4 l a4 rr
lar5 l a5 rr
lair1 l ai1 rr
lair2 l ai2 rr
lair3 l ai3 rr
lair4 l ai4 rr
lair5 l ai5 rr
lanr1 l an1 rr
lanr2 l an2 rr
lanr3 l an3 rr
lanr4 l an4 rr
lanr5 l an5 rr
langr1 l ang1 rr
langr2 l ang2 rr
langr3 l ang3 rr
langr4 l ang4 rr
langr5 l ang5 rr
laor1 l ao1 rr
laor2 l ao2 rr
laor3 l ao3 rr
laor4 l ao4 rr
laor5 l ao5 rr
ler1 l e1 rr
ler2 l e2 rr
ler3 l e3 rr
ler4 l e4 rr
ler5 l e5 rr
leir1 l ei1 rr
leir2 l ei2 rr
leir3 l ei3 rr
leir4 l ei4 rr
leir5 l ei5 rr
lengr1 l eng1 rr
lengr2 l eng2 rr
lengr3 l eng3 rr
lengr4 l eng4 rr
lengr5 l eng5 rr
lir1 l i1 rr
lir2 l i2 rr
lir3 l i3 rr
lir4 l i4 rr
lir5 l i5 rr
liar1 l ia1 rr
liar2 l ia2 rr
liar3 l ia3 rr
liar4 l ia4 rr
liar5 l ia5 rr
lianr1 l ian1 rr
lianr2 l ian2 rr
lianr3 l ian3 rr
lianr4 l ian4 rr
lianr5 l ian5 rr
liangr1 l iang1 rr
liangr2 l iang2 rr
liangr3 l iang3 rr
liangr4 l iang4 rr
liangr5 l iang5 rr
liaor1 l iao1 rr
liaor2 l iao2 rr
liaor3 l iao3 rr
liaor4 l iao4 rr
liaor5 l iao5 rr
lier1 l ie1 rr
lier2 l ie2 rr
lier3 l ie3 rr
lier4 l ie4 rr
lier5 l ie5 rr
linr1 l in1 rr
linr2 l in2 rr
linr3 l in3 rr
linr4 l in4 rr
linr5 l in5 rr
lingr1 l ing1 rr
lingr2 l ing2 rr
lingr3 l ing3 rr
lingr4 l ing4 rr
lingr5 l ing5 rr
liur1 l iou1 rr
liur2 l iou2 rr
liur3 l iou3 rr
liur4 l iou4 rr
liur5 l iou5 rr
lor1 l o1 rr
lor2 l o2 rr
lor3 l o3 rr
lor4 l o4 rr
lor5 l o5 rr
longr1 l ong1 rr
longr2 l ong2 rr
longr3 l ong3 rr
longr4 l ong4 rr
longr5 l ong5 rr
lour1 l ou1 rr
lour2 l ou2 rr
lour3 l ou3 rr
lour4 l ou4 rr
lour5 l ou5 rr
lur1 l u1 rr
lur2 l u2 rr
lur3 l u3 rr
lur4 l u4 rr
lur5 l u5 rr
luanr1 l uan1 rr
luanr2 l uan2 rr
luanr3 l uan3 rr
luanr4 l uan4 rr
luanr5 l uan5 rr
luer1 l ve1 rr
luer2 l ve2 rr
luer3 l ve3 rr
luer4 l ve4 rr
luer5 l ve5 rr
lver1 l ve1 rr
lver2 l ve2 rr
lver3 l ve3 rr
lver4 l ve4 rr
lver5 l ve5 rr
lunr1 l uen1 rr
lunr2 l uen2 rr
lunr3 l uen3 rr
lunr4 l uen4 rr
lunr5 l uen5 rr
luor1 l uo1 rr
luor2 l uo2 rr
luor3 l uo3 rr
luor4 l uo4 rr
luor5 l uo5 rr
lvr1 l v1 rr
lvr2 l v2 rr
lvr3 l v3 rr
lvr4 l v4 rr
lvr5 l v5 rr
mar1 m a1 rr
mar2 m a2 rr
mar3 m a3 rr
mar4 m a4 rr
mar5 m a5 rr
mair1 m ai1 rr
mair2 m ai2 rr
mair3 m ai3 rr
mair4 m ai4 rr
mair5 m ai5 rr
manr1 m an1 rr
manr2 m an2 rr
manr3 m an3 rr
manr4 m an4 rr
manr5 m an5 rr
mangr1 m ang1 rr
mangr2 m ang2 rr
mangr3 m ang3 rr
mangr4 m ang4 rr
mangr5 m ang5 rr
maor1 m ao1 rr
maor2 m ao2 rr
maor3 m ao3 rr
maor4 m ao4 rr
maor5 m ao5 rr
mer1 m e1 rr
mer2 m e2 rr
mer3 m e3 rr
mer4 m e4 rr
mer5 m e5 rr
meir1 m ei1 rr
meir2 m ei2 rr
meir3 m ei3 rr
meir4 m ei4 rr
meir5 m ei5 rr
menr1 m en1 rr
menr2 m en2 rr
menr3 m en3 rr
menr4 m en4 rr
menr5 m en5 rr
mengr1 m eng1 rr
mengr2 m eng2 rr
mengr3 m eng3 rr
mengr4 m eng4 rr
mengr5 m eng5 rr
mir1 m i1 rr
mir2 m i2 rr
mir3 m i3 rr
mir4 m i4 rr
mir5 m i5 rr
mianr1 m ian1 rr
mianr2 m ian2 rr
mianr3 m ian3 rr
mianr4 m ian4 rr
mianr5 m ian5 rr
miaor1 m iao1 rr
miaor2 m iao2 rr
miaor3 m iao3 rr
miaor4 m iao4 rr
miaor5 m iao5 rr
mier1 m ie1 rr
mier2 m ie2 rr
mier3 m ie3 rr
mier4 m ie4 rr
mier5 m ie5 rr
minr1 m in1 rr
minr2 m in2 rr
minr3 m in3 rr
minr4 m in4 rr
minr5 m in5 rr
mingr1 m ing1 rr
mingr2 m ing2 rr
mingr3 m ing3 rr
mingr4 m ing4 rr
mingr5 m ing5 rr
miur1 m iou1 rr
miur2 m iou2 rr
miur3 m iou3 rr
miur4 m iou4 rr
miur5 m iou5 rr
mor1 m o1 rr
mor2 m o2 rr
mor3 m o3 rr
mor4 m o4 rr
mor5 m o5 rr
mour1 m ou1 rr
mour2 m ou2 rr
mour3 m ou3 rr
mour4 m ou4 rr
mour5 m ou5 rr
mur1 m u1 rr
mur2 m u2 rr
mur3 m u3 rr
mur4 m u4 rr
mur5 m u5 rr
nar1 n a1 rr
nar2 n a2 rr
nar3 n a3 rr
nar4 n a4 rr
nar5 n a5 rr
nair1 n ai1 rr
nair2 n ai2 rr
nair3 n ai3 rr
nair4 n ai4 rr
nair5 n ai5 rr
nanr1 n an1 rr
nanr2 n an2 rr
nanr3 n an3 rr
nanr4 n an4 rr
nanr5 n an5 rr
nangr1 n ang1 rr
nangr2 n ang2 rr
nangr3 n ang3 rr
nangr4 n ang4 rr
nangr5 n ang5 rr
naor1 n ao1 rr
naor2 n ao2 rr
naor3 n ao3 rr
naor4 n ao4 rr
naor5 n ao5 rr
ner1 n e1 rr
ner2 n e2 rr
ner3 n e3 rr
ner4 n e4 rr
ner5 n e5 rr
neir1 n ei1 rr
neir2 n ei2 rr
neir3 n ei3 rr
neir4 n ei4 rr
neir5 n ei5 rr
nenr1 n en1 rr
nenr2 n en2 rr
nenr3 n en3 rr
nenr4 n en4 rr
nenr5 n en5 rr
nengr1 n eng1 rr
nengr2 n eng2 rr
nengr3 n eng3 rr
nengr4 n eng4 rr
nengr5 n eng5 rr
nir1 n i1 rr
nir2 n i2 rr
nir3 n i3 rr
nir4 n i4 rr
nir5 n i5 rr
nianr1 n ian1 rr
nianr2 n ian2 rr
nianr3 n ian3 rr
nianr4 n ian4 rr
nianr5 n ian5 rr
niangr1 n iang1 rr
niangr2 n iang2 rr
niangr3 n iang3 rr
niangr4 n iang4 rr
niangr5 n iang5 rr
niaor1 n iao1 rr
niaor2 n iao2 rr
niaor3 n iao3 rr
niaor4 n iao4 rr
niaor5 n iao5 rr
nier1 n ie1 rr
nier2 n ie2 rr
nier3 n ie3 rr
nier4 n ie4 rr
nier5 n ie5 rr
ninr1 n in1 rr
ninr2 n in2 rr
ninr3 n in3 rr
ninr4 n in4 rr
ninr5 n in5 rr
ningr1 n ing1 rr
ningr2 n ing2 rr
ningr3 n ing3 rr
ningr4 n ing4 rr
ningr5 n ing5 rr
niur1 n iou1 rr
niur2 n iou2 rr
niur3 n iou3 rr
niur4 n iou4 rr
niur5 n iou5 rr
nongr1 n ong1 rr
nongr2 n ong2 rr
nongr3 n ong3 rr
nongr4 n ong4 rr
nongr5 n ong5 rr
nour1 n ou1 rr
nour2 n ou2 rr
nour3 n ou3 rr
nour4 n ou4 rr
nour5 n ou5 rr
nur1 n u1 rr
nur2 n u2 rr
nur3 n u3 rr
nur4 n u4 rr
nur5 n u5 rr
nuanr1 n uan1 rr
nuanr2 n uan2 rr
nuanr3 n uan3 rr
nuanr4 n uan4 rr
nuanr5 n uan5 rr
nuer1 n ve1 rr
nuer2 n ve2 rr
nuer3 n ve3 rr
nuer4 n ve4 rr
nuer5 n ve5 rr
nver1 n ve1 rr
nver2 n ve2 rr
nver3 n ve3 rr
nver4 n ve4 rr
nver5 n ve5 rr
nuor1 n uo1 rr
nuor2 n uo2 rr
nuor3 n uo3 rr
nuor4 n uo4 rr
nuor5 n uo5 rr
nvr1 n v1 rr
nvr2 n v2 rr
nvr3 n v3 rr
nvr4 n v4 rr
nvr5 n v5 rr
or1 o1 rr
or2 o2 rr
or3 o3 rr
or4 o4 rr
or5 o5 rr
our1 ou1 rr
our2 ou2 rr
our3 ou3 rr
our4 ou4 rr
our5 ou5 rr
par1 p a1 rr
par2 p a2 rr
par3 p a3 rr
par4 p a4 rr
par5 p a5 rr
pair1 p ai1 rr
pair2 p ai2 rr
pair3 p ai3 rr
pair4 p ai4 rr
pair5 p ai5 rr
panr1 p an1 rr
panr2 p an2 rr
panr3 p an3 rr
panr4 p an4 rr
panr5 p an5 rr
pangr1 p ang1 rr
pangr2 p ang2 rr
pangr3 p ang3 rr
pangr4 p ang4 rr
pangr5 p ang5 rr
paor1 p ao1 rr
paor2 p ao2 rr
paor3 p ao3 rr
paor4 p ao4 rr
paor5 p ao5 rr
peir1 p ei1 rr
peir2 p ei2 rr
peir3 p ei3 rr
peir4 p ei4 rr
peir5 p ei5 rr
penr1 p en1 rr
penr2 p en2 rr
penr3 p en3 rr
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zhour2 zh ou2 rr
zhour3 zh ou3 rr
zhour4 zh ou4 rr
zhour5 zh ou5 rr
zhur1 zh u1 rr
zhur2 zh u2 rr
zhur3 zh u3 rr
zhur4 zh u4 rr
zhur5 zh u5 rr
zhuar1 zh ua1 rr
zhuar2 zh ua2 rr
zhuar3 zh ua3 rr
zhuar4 zh ua4 rr
zhuar5 zh ua5 rr
zhuair1 zh uai1 rr
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zhuair3 zh uai3 rr
zhuair4 zh uai4 rr
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zhuangr2 zh uang2 rr
zhuangr3 zh uang3 rr
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zhuangr5 zh uang5 rr
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zhuir2 zh uei2 rr
zhuir3 zh uei3 rr
zhuir4 zh uei4 rr
zhuir5 zh uei5 rr
zhunr1 zh uen1 rr
zhunr2 zh uen2 rr
zhunr3 zh uen3 rr
zhunr4 zh uen4 rr
zhunr5 zh uen5 rr
zhuor1 zh uo1 rr
zhuor2 zh uo2 rr
zhuor3 zh uo3 rr
zhuor4 zh uo4 rr
zhuor5 zh uo5 rr
zir1 z ii1 rr
zir2 z ii2 rr
zir3 z ii3 rr
zir4 z ii4 rr
zir5 z ii5 rr
zongr1 z ong1 rr
zongr2 z ong2 rr
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zour4 z ou4 rr
zour5 z ou5 rr
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zur5 z u5 rr
zuanr1 z uan1 rr
zuanr2 z uan2 rr
zuanr3 z uan3 rr
zuanr4 z uan4 rr
zuanr5 z uan5 rr
zuir1 z uei1 rr
zuir2 z uei2 rr
zuir3 z uei3 rr
zuir4 z uei4 rr
zuir5 z uei5 rr
zunr1 z uen1 rr
zunr2 z uen2 rr
zunr3 z uen3 rr
zunr4 z uen4 rr
zunr5 z uen5 rr
zuor1 z uo1 rr
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zuor3 z uo3 rr
zuor4 z uo4 rr
zuor5 z uo5 rr
PyTorch/Speech/FastSpeech2/model/__init__.py 0 → 100644
View file @ 719799a2
from .fastspeech2 import FastSpeech2
from .loss import FastSpeech2Loss
from .optimizer import ScheduledOptim
\ No newline at end of file
PyTorch/Speech/FastSpeech2/model/fastspeech2.py 0 → 100644
View file @ 719799a2
import os
import json
import torch
import torch.nn as nn
import torch.nn.functional as F
from transformer import Encoder, Decoder, PostNet
from .modules import VarianceAdaptor
from utils.tools import get_mask_from_lengths
class FastSpeech2(nn.Module):
""" FastSpeech2 """
def __init__(self, preprocess_config, model_config):
super(FastSpeech2, self).__init__()
self.model_config = model_config
self.encoder = Encoder(model_config)
self.variance_adaptor = VarianceAdaptor(preprocess_config, model_config)
self.decoder = Decoder(model_config)
self.mel_linear = nn.Linear(
model_config["transformer"]["decoder_hidden"],
preprocess_config["preprocessing"]["mel"]["n_mel_channels"],
)
self.postnet = PostNet()
self.speaker_emb = None
if model_config["multi_speaker"]:
with open(
os.path.join(
preprocess_config["path"]["preprocessed_path"], "speakers.json"
),
"r",
) as f:
n_speaker = len(json.load(f))
self.speaker_emb = nn.Embedding(
n_speaker,
model_config["transformer"]["encoder_hidden"],
)
def forward(
self,
speakers,
texts,
src_lens,
max_src_len,
mels=None,
mel_lens=None,
max_mel_len=None,
p_targets=None,
e_targets=None,
d_targets=None,
p_control=1.0,
e_control=1.0,
d_control=1.0,
):
src_masks = get_mask_from_lengths(src_lens, max_src_len)
mel_masks = (
get_mask_from_lengths(mel_lens, max_mel_len)
if mel_lens is not None
else None
)
output = self.encoder(texts, src_masks)
if self.speaker_emb is not None:
output = output + self.speaker_emb(speakers).unsqueeze(1).expand(
-1, max_src_len, -1
)
(
output,
p_predictions,
e_predictions,
log_d_predictions,
d_rounded,
mel_lens,
mel_masks,
) = self.variance_adaptor(
output,
src_masks,
mel_masks,
max_mel_len,
p_targets,
e_targets,
d_targets,
p_control,
e_control,
d_control,
)
output, mel_masks = self.decoder(output, mel_masks)
output = self.mel_linear(output)
postnet_output = self.postnet(output) + output
return (
output,
postnet_output,
p_predictions,
e_predictions,
log_d_predictions,
d_rounded,
src_masks,
mel_masks,
src_lens,
mel_lens,
)
\ No newline at end of file
PyTorch/Speech/FastSpeech2/model/loss.py 0 → 100644
View file @ 719799a2
import torch
import torch.nn as nn
class FastSpeech2Loss(nn.Module):
""" FastSpeech2 Loss """
def __init__(self, preprocess_config, model_config):
super(FastSpeech2Loss, self).__init__()
self.pitch_feature_level = preprocess_config["preprocessing"]["pitch"][
"feature"
]
self.energy_feature_level = preprocess_config["preprocessing"]["energy"][
"feature"
]
self.mse_loss = nn.MSELoss()
self.mae_loss = nn.L1Loss()
def forward(self, inputs, predictions):
(
mel_targets,
_,
_,
pitch_targets,
energy_targets,
duration_targets,
) = inputs[6:]
(
mel_predictions,
postnet_mel_predictions,
pitch_predictions,
energy_predictions,
log_duration_predictions,
_,
src_masks,
mel_masks,
_,
_,
) = predictions
src_masks = ~src_masks
mel_masks = ~mel_masks
log_duration_targets = torch.log(duration_targets.float() + 1)
mel_targets = mel_targets[:, : mel_masks.shape[1], :]
mel_masks = mel_masks[:, :mel_masks.shape[1]]
log_duration_targets.requires_grad = False
pitch_targets.requires_grad = False
energy_targets.requires_grad = False
mel_targets.requires_grad = False
if self.pitch_feature_level == "phoneme_level":
pitch_predictions = pitch_predictions.masked_select(src_masks)
pitch_targets = pitch_targets.masked_select(src_masks)
elif self.pitch_feature_level == "frame_level":
pitch_predictions = pitch_predictions.masked_select(mel_masks)
pitch_targets = pitch_targets.masked_select(mel_masks)
if self.energy_feature_level == "phoneme_level":
energy_predictions = energy_predictions.masked_select(src_masks)
energy_targets = energy_targets.masked_select(src_masks)
if self.energy_feature_level == "frame_level":
energy_predictions = energy_predictions.masked_select(mel_masks)
energy_targets = energy_targets.masked_select(mel_masks)
log_duration_predictions = log_duration_predictions.masked_select(src_masks)
log_duration_targets = log_duration_targets.masked_select(src_masks)
mel_predictions = mel_predictions.masked_select(mel_masks.unsqueeze(-1))
postnet_mel_predictions = postnet_mel_predictions.masked_select(
mel_masks.unsqueeze(-1)
)
mel_targets = mel_targets.masked_select(mel_masks.unsqueeze(-1))
mel_loss = self.mae_loss(mel_predictions, mel_targets)
postnet_mel_loss = self.mae_loss(postnet_mel_predictions, mel_targets)
pitch_loss = self.mse_loss(pitch_predictions, pitch_targets)
energy_loss = self.mse_loss(energy_predictions, energy_targets)
duration_loss = self.mse_loss(log_duration_predictions, log_duration_targets)
total_loss = (
mel_loss + postnet_mel_loss + duration_loss + pitch_loss + energy_loss
)
return (
total_loss,
mel_loss,
postnet_mel_loss,
pitch_loss,
energy_loss,
duration_loss,
)
PyTorch/Speech/FastSpeech2/model/modules.py 0 → 100644
View file @ 719799a2
import os
import json
import copy
import math
from collections import OrderedDict
import torch
import torch.nn as nn
import numpy as np
import torch.nn.functional as F
from utils.tools import get_mask_from_lengths, pad
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
class VarianceAdaptor(nn.Module):
"""Variance Adaptor"""
def __init__(self, preprocess_config, model_config):
super(VarianceAdaptor, self).__init__()
self.duration_predictor = VariancePredictor(model_config)
self.length_regulator = LengthRegulator()
self.pitch_predictor = VariancePredictor(model_config)
self.energy_predictor = VariancePredictor(model_config)
self.pitch_feature_level = preprocess_config["preprocessing"]["pitch"][
"feature"
]
self.energy_feature_level = preprocess_config["preprocessing"]["energy"][
"feature"
]
assert self.pitch_feature_level in ["phoneme_level", "frame_level"]
assert self.energy_feature_level in ["phoneme_level", "frame_level"]
pitch_quantization = model_config["variance_embedding"]["pitch_quantization"]
energy_quantization = model_config["variance_embedding"]["energy_quantization"]
n_bins = model_config["variance_embedding"]["n_bins"]
assert pitch_quantization in ["linear", "log"]
assert energy_quantization in ["linear", "log"]
with open(
os.path.join(preprocess_config["path"]["preprocessed_path"], "stats.json")
) as f:
stats = json.load(f)
pitch_min, pitch_max = stats["pitch"][:2]
energy_min, energy_max = stats["energy"][:2]
if pitch_quantization == "log":
self.pitch_bins = nn.Parameter(
torch.exp(
torch.linspace(np.log(pitch_min), np.log(pitch_max), n_bins - 1)
),
requires_grad=False,
)
else:
self.pitch_bins = nn.Parameter(
torch.linspace(pitch_min, pitch_max, n_bins - 1),
requires_grad=False,
)
if energy_quantization == "log":
self.energy_bins = nn.Parameter(
torch.exp(
torch.linspace(np.log(energy_min), np.log(energy_max), n_bins - 1)
),
requires_grad=False,
)
else:
self.energy_bins = nn.Parameter(
torch.linspace(energy_min, energy_max, n_bins - 1),
requires_grad=False,
)
self.pitch_embedding = nn.Embedding(
n_bins, model_config["transformer"]["encoder_hidden"]
)
self.energy_embedding = nn.Embedding(
n_bins, model_config["transformer"]["encoder_hidden"]
)
def get_pitch_embedding(self, x, target, mask, control):
prediction = self.pitch_predictor(x, mask)
if target is not None:
embedding = self.pitch_embedding(torch.bucketize(target, self.pitch_bins))
else:
prediction = prediction * control
embedding = self.pitch_embedding(
torch.bucketize(prediction, self.pitch_bins)
)
return prediction, embedding
def get_energy_embedding(self, x, target, mask, control):
prediction = self.energy_predictor(x, mask)
if target is not None:
embedding = self.energy_embedding(torch.bucketize(target, self.energy_bins))
else:
prediction = prediction * control
embedding = self.energy_embedding(
torch.bucketize(prediction, self.energy_bins)
)
return prediction, embedding
def forward(
self,
x,
src_mask,
mel_mask=None,
max_len=None,
pitch_target=None,
energy_target=None,
duration_target=None,
p_control=1.0,
e_control=1.0,
d_control=1.0,
):
log_duration_prediction = self.duration_predictor(x, src_mask)
if self.pitch_feature_level == "phoneme_level":
pitch_prediction, pitch_embedding = self.get_pitch_embedding(
x, pitch_target, src_mask, p_control
)
x = x + pitch_embedding
if self.energy_feature_level == "phoneme_level":
energy_prediction, energy_embedding = self.get_energy_embedding(
x, energy_target, src_mask, p_control
)
x = x + energy_embedding
if duration_target is not None:
x, mel_len = self.length_regulator(x, duration_target, max_len)
duration_rounded = duration_target
else:
duration_rounded = torch.clamp(
(torch.round(torch.exp(log_duration_prediction) - 1) * d_control),
min=0,
)
x, mel_len = self.length_regulator(x, duration_rounded, max_len)
mel_mask = get_mask_from_lengths(mel_len)
if self.pitch_feature_level == "frame_level":
pitch_prediction, pitch_embedding = self.get_pitch_embedding(
x, pitch_target, mel_mask, p_control
)
x = x + pitch_embedding
if self.energy_feature_level == "frame_level":
energy_prediction, energy_embedding = self.get_energy_embedding(
x, energy_target, mel_mask, p_control
)
x = x + energy_embedding
return (
x,
pitch_prediction,
energy_prediction,
log_duration_prediction,
duration_rounded,
mel_len,
mel_mask,
)
class LengthRegulator(nn.Module):
"""Length Regulator"""
def __init__(self):
super(LengthRegulator, self).__init__()
def LR(self, x, duration, max_len):
output = list()
mel_len = list()
for batch, expand_target in zip(x, duration):
expanded = self.expand(batch, expand_target)
output.append(expanded)
mel_len.append(expanded.shape[0])
if max_len is not None:
output = pad(output, max_len)
else:
output = pad(output)
return output, torch.LongTensor(mel_len).to(device)
def expand(self, batch, predicted):
out = list()
for i, vec in enumerate(batch):
expand_size = predicted[i].item()
out.append(vec.expand(max(int(expand_size), 0), -1))
out = torch.cat(out, 0)
return out
def forward(self, x, duration, max_len):
output, mel_len = self.LR(x, duration, max_len)
return output, mel_len
class VariancePredictor(nn.Module):
"""Duration, Pitch and Energy Predictor"""
def __init__(self, model_config):
super(VariancePredictor, self).__init__()
self.input_size = model_config["transformer"]["encoder_hidden"]
self.filter_size = model_config["variance_predictor"]["filter_size"]
self.kernel = model_config["variance_predictor"]["kernel_size"]
self.conv_output_size = model_config["variance_predictor"]["filter_size"]
self.dropout = model_config["variance_predictor"]["dropout"]
self.conv_layer = nn.Sequential(
OrderedDict(
[
(
"conv1d_1",
Conv(
self.input_size,
self.filter_size,
kernel_size=self.kernel,
padding=(self.kernel - 1) // 2,
),
),
("relu_1", nn.ReLU()),
("layer_norm_1", nn.LayerNorm(self.filter_size)),
("dropout_1", nn.Dropout(self.dropout)),
(
"conv1d_2",
Conv(
self.filter_size,
self.filter_size,
kernel_size=self.kernel,
padding=1,
),
),
("relu_2", nn.ReLU()),
("layer_norm_2", nn.LayerNorm(self.filter_size)),
("dropout_2", nn.Dropout(self.dropout)),
]
)
)
self.linear_layer = nn.Linear(self.conv_output_size, 1)
def forward(self, encoder_output, mask):
out = self.conv_layer(encoder_output)
out = self.linear_layer(out)
out = out.squeeze(-1)
if mask is not None:
out = out.masked_fill(mask, 0.0)
return out
class Conv(nn.Module):
"""
Convolution Module
"""
def __init__(
self,
in_channels,
out_channels,
kernel_size=1,
stride=1,
padding=0,
dilation=1,
bias=True,
w_init="linear",
):
"""
:param in_channels: dimension of input
:param out_channels: dimension of output
:param kernel_size: size of kernel
:param stride: size of stride
:param padding: size of padding
:param dilation: dilation rate
:param bias: boolean. if True, bias is included.
:param w_init: str. weight inits with xavier initialization.
"""
super(Conv, self).__init__()
self.conv = nn.Conv1d(
in_channels,
out_channels,
kernel_size=kernel_size,
stride=stride,
padding=padding,
dilation=dilation,
bias=bias,
)
def forward(self, x):
x = x.contiguous().transpose(1, 2)
x = self.conv(x)
x = x.contiguous().transpose(1, 2)
return x
PyTorch/Speech/FastSpeech2/model/optimizer.py 0 → 100644
View file @ 719799a2
import torch
import numpy as np
class ScheduledOptim:
""" A simple wrapper class for learning rate scheduling """
def __init__(self, model, train_config, model_config, current_step):
self._optimizer = torch.optim.Adam(
model.parameters(),
betas=train_config["optimizer"]["betas"],
eps=train_config["optimizer"]["eps"],
weight_decay=train_config["optimizer"]["weight_decay"],
)
self.n_warmup_steps = train_config["optimizer"]["warm_up_step"]
self.anneal_steps = train_config["optimizer"]["anneal_steps"]
self.anneal_rate = train_config["optimizer"]["anneal_rate"]
self.current_step = current_step
self.init_lr = np.power(model_config["transformer"]["encoder_hidden"], -0.5)
def step_and_update_lr(self):
self._update_learning_rate()
self._optimizer.step()
def zero_grad(self):
# print(self.init_lr)
self._optimizer.zero_grad()
def load_state_dict(self, path):
self._optimizer.load_state_dict(path)
def _get_lr_scale(self):
lr = np.min(
[
np.power(self.current_step, -0.5),
np.power(self.n_warmup_steps, -1.5) * self.current_step,
]
)
for s in self.anneal_steps:
if self.current_step > s:
lr = lr * self.anneal_rate
return lr
def _update_learning_rate(self):
""" Learning rate scheduling per step """
self.current_step += 1
lr = self.init_lr * self._get_lr_scale()
for param_group in self._optimizer.param_groups:
param_group["lr"] = lr
PyTorch/Speech/FastSpeech2/prepare_align.py 0 → 100644
View file @ 719799a2
import argparse
import yaml
from preprocessor import ljspeech, aishell3, libritts
def main(config):
if "LJSpeech" in config["dataset"]:
ljspeech.prepare_align(config)
if "AISHELL3" in config["dataset"]:
aishell3.prepare_align(config)
if "LibriTTS" in config["dataset"]:
libritts.prepare_align(config)
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("config", type=str, help="path to preprocess.yaml")
args = parser.parse_args()
config = yaml.load(open(args.config, "r"), Loader=yaml.FullLoader)
main(config)
PyTorch/Speech/FastSpeech2/preprocess.py 0 → 100644
View file @ 719799a2
import argparse
import yaml
from preprocessor.preprocessor import Preprocessor
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("config", type=str, help="path to preprocess.yaml")
args = parser.parse_args()
config = yaml.load(open(args.config, "r"), Loader=yaml.FullLoader)
preprocessor = Preprocessor(config)
preprocessor.build_from_path()
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