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- .gitignore +5 -0
- Dockerfile +2 -3
- Dockerfile_GPU +2 -3
- LICENSE +661 -21
- README_zh.md +41 -51
- app.py +89 -3
- bert_vits2/LICENSE +674 -0
- bert_vits2/README.md +5 -0
- bert_vits2/__init__.py +2 -0
- bert_vits2/attentions.py +352 -0
- bert_vits2/bert/chinese-roberta-wwm-ext-large/.gitattributes +9 -0
- bert_vits2/bert/chinese-roberta-wwm-ext-large/README.md +57 -0
- bert_vits2/bert/chinese-roberta-wwm-ext-large/added_tokens.json +1 -0
- bert_vits2/bert/chinese-roberta-wwm-ext-large/config.json +28 -0
- chinese_dialect_lexicons/zaonhe_2.ocd2 → bert_vits2/bert/chinese-roberta-wwm-ext-large/flax_model.msgpack +2 -2
- bert_vits2/bert/chinese-roberta-wwm-ext-large/pytorch_model.bin +3 -0
- bert_vits2/bert/chinese-roberta-wwm-ext-large/special_tokens_map.json +1 -0
- bert_vits2/bert/chinese-roberta-wwm-ext-large/tf_model.h5 +3 -0
- bert_vits2/bert/chinese-roberta-wwm-ext-large/tokenizer.json +0 -0
- bert_vits2/bert/chinese-roberta-wwm-ext-large/tokenizer_config.json +1 -0
- bert_vits2/bert/chinese-roberta-wwm-ext-large/vocab.txt +0 -0
- bert_vits2/bert_vits2.py +86 -0
- bert_vits2/commons.py +161 -0
- bert_vits2/models.py +677 -0
- bert_vits2/modules.py +459 -0
- bert_vits2/requirements.txt +15 -0
- bert_vits2/text/__init__.py +29 -0
- bert_vits2/text/chinese.py +194 -0
- bert_vits2/text/chinese_bert.py +60 -0
- bert_vits2/text/cleaner.py +29 -0
- bert_vits2/text/cmudict.rep +0 -0
- chinese_dialect_lexicons/jyutjyu_2.ocd2 → bert_vits2/text/cmudict_cache.pickle +2 -2
- bert_vits2/text/english.py +146 -0
- bert_vits2/text/english_bert_mock.py +5 -0
- bert_vits2/text/japanese.py +104 -0
- bert_vits2/text/opencpop-strict.txt +429 -0
- bert_vits2/text/symbols.py +52 -0
- bert_vits2/text/tone_sandhi.py +351 -0
- bert_vits2/transforms.py +192 -0
- bert_vits2/utils.py +292 -0
- chinese_dialect_lexicons/changzhou.json +0 -23
- chinese_dialect_lexicons/changzhou.ocd2 +0 -0
- chinese_dialect_lexicons/changzhou_3.json +0 -23
- chinese_dialect_lexicons/changzhou_3.ocd2 +0 -0
- chinese_dialect_lexicons/cixi_2.json +0 -23
- chinese_dialect_lexicons/cixi_2.ocd2 +0 -0
- chinese_dialect_lexicons/fuyang_2.json +0 -23
- chinese_dialect_lexicons/fuyang_2.ocd2 +0 -0
- chinese_dialect_lexicons/hangzhou_2.json +0 -19
- chinese_dialect_lexicons/hangzhou_2.ocd2 +0 -0
.gitignore
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**/__pycache__
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/Model/
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/bert_vits2/monotonic_align/monotonic_align.egg-info/
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/bert_vits2/monotonic_align/dist/
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/bert_vits2/monotonic_align/build/
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Dockerfile
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ENV DEBIAN_FRONTEND=noninteractive
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RUN apt-get update && \
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apt-get install -yq build-essential espeak-ng cmake wget && \
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apt-get clean && \
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RUN pip install torch --index-url https://download.pytorch.org/whl/cpu --no-cache-dir
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COPY requirements.txt /app
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EXPOSE 23456
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CMD ["gunicorn", "-c", "gunicorn_config.py", "app:app"]
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COPY . /app
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RUN pip install torch --index-url https://download.pytorch.org/whl/cpu --no-cache-dir
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EXPOSE 23456
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CMD ["gunicorn", "-c", "gunicorn_config.py", "app:app"]
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ENV DEBIAN_FRONTEND=noninteractive
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apt-get install -yq build-essential espeak-ng cmake wget && \
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apt-get clean && \
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RUN pip install torch --index-url https://download.pytorch.org/whl/cu117 --no-cache-dir
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RUN pip install -r requirements.txt --no-cache-dir
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RUN pip install gunicorn --no-cache-dir
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CMD ["gunicorn", "-c", "gunicorn_config.py", "app:app"]
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ENV DEBIAN_FRONTEND=noninteractive
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COPY . /app
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RUN apt-get update && \
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apt-get install -yq build-essential espeak-ng cmake wget && \
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apt-get clean && \
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RUN pip install torch --index-url https://download.pytorch.org/whl/cu117 --no-cache-dir
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RUN pip install -r requirements.txt --no-cache-dir
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RUN pip install gunicorn --no-cache-dir
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EXPOSE 23456
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CMD ["gunicorn", "-c", "gunicorn_config.py", "app:app"]
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LICENSE
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1 |
+
GNU AFFERO GENERAL PUBLIC LICENSE
|
2 |
+
Version 3, 19 November 2007
|
3 |
+
|
4 |
+
Copyright (C) 2007 Free Software Foundation, Inc. <https://fsf.org/>
|
5 |
+
Everyone is permitted to copy and distribute verbatim copies
|
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of this license document, but changing it is not allowed.
|
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Preamble
|
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|
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The GNU Affero General Public License is a free, copyleft license for
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software and other kinds of works, specifically designed to ensure
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cooperation with the community in the case of network server software.
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The licenses for most software and other practical works are designed
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to take away your freedom to share and change the works. By contrast,
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our General Public Licenses are intended to guarantee your freedom to
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share and change all versions of a program--to make sure it remains free
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software for all its users.
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When we speak of free software, we are referring to freedom, not
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price. Our General Public Licenses are designed to make sure that you
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have the freedom to distribute copies of free software (and charge for
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them if you wish), that you receive source code or can get it if you
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want it, that you can change the software or use pieces of it in new
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free programs, and that you know you can do these things.
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Developers that use our General Public Licenses protect your rights
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with two steps: (1) assert copyright on the software, and (2) offer
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you this License which gives you legal permission to copy, distribute
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and/or modify the software.
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A secondary benefit of defending all users' freedom is that
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improvements made in alternate versions of the program, if they
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receive widespread use, become available for other developers to
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incorporate. Many developers of free software are heartened and
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encouraged by the resulting cooperation. However, in the case of
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The GNU General Public License permits making a modified version and
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letting the public access it on a server without ever releasing its
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The GNU Affero General Public License is designed specifically to
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ensure that, in such cases, the modified source code becomes available
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provide the source code of the modified version running there to the
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users of that server. Therefore, public use of a modified version, on
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a publicly accessible server, gives the public access to the source
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code of the modified version.
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An older license, called the Affero General Public License and
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published by Affero, was designed to accomplish similar goals. This is
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a different license, not a version of the Affero GPL, but Affero has
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released a new version of the Affero GPL which permits relicensing under
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this license.
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The precise terms and conditions for copying, distribution and
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modification follow.
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TERMS AND CONDITIONS
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0. Definitions.
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"This License" refers to version 3 of the GNU Affero General Public License.
|
64 |
+
|
65 |
+
"Copyright" also means copyright-like laws that apply to other kinds of
|
66 |
+
works, such as semiconductor masks.
|
67 |
+
|
68 |
+
"The Program" refers to any copyrightable work licensed under this
|
69 |
+
License. Each licensee is addressed as "you". "Licensees" and
|
70 |
+
"recipients" may be individuals or organizations.
|
71 |
+
|
72 |
+
To "modify" a work means to copy from or adapt all or part of the work
|
73 |
+
in a fashion requiring copyright permission, other than the making of an
|
74 |
+
exact copy. The resulting work is called a "modified version" of the
|
75 |
+
earlier work or a work "based on" the earlier work.
|
76 |
+
|
77 |
+
A "covered work" means either the unmodified Program or a work based
|
78 |
+
on the Program.
|
79 |
+
|
80 |
+
To "propagate" a work means to do anything with it that, without
|
81 |
+
permission, would make you directly or secondarily liable for
|
82 |
+
infringement under applicable copyright law, except executing it on a
|
83 |
+
computer or modifying a private copy. Propagation includes copying,
|
84 |
+
distribution (with or without modification), making available to the
|
85 |
+
public, and in some countries other activities as well.
|
86 |
+
|
87 |
+
To "convey" a work means any kind of propagation that enables other
|
88 |
+
parties to make or receive copies. Mere interaction with a user through
|
89 |
+
a computer network, with no transfer of a copy, is not conveying.
|
90 |
+
|
91 |
+
An interactive user interface displays "Appropriate Legal Notices"
|
92 |
+
to the extent that it includes a convenient and prominently visible
|
93 |
+
feature that (1) displays an appropriate copyright notice, and (2)
|
94 |
+
tells the user that there is no warranty for the work (except to the
|
95 |
+
extent that warranties are provided), that licensees may convey the
|
96 |
+
work under this License, and how to view a copy of this License. If
|
97 |
+
the interface presents a list of user commands or options, such as a
|
98 |
+
menu, a prominent item in the list meets this criterion.
|
99 |
+
|
100 |
+
1. Source Code.
|
101 |
+
|
102 |
+
The "source code" for a work means the preferred form of the work
|
103 |
+
for making modifications to it. "Object code" means any non-source
|
104 |
+
form of a work.
|
105 |
+
|
106 |
+
A "Standard Interface" means an interface that either is an official
|
107 |
+
standard defined by a recognized standards body, or, in the case of
|
108 |
+
interfaces specified for a particular programming language, one that
|
109 |
+
is widely used among developers working in that language.
|
110 |
+
|
111 |
+
The "System Libraries" of an executable work include anything, other
|
112 |
+
than the work as a whole, that (a) is included in the normal form of
|
113 |
+
packaging a Major Component, but which is not part of that Major
|
114 |
+
Component, and (b) serves only to enable use of the work with that
|
115 |
+
Major Component, or to implement a Standard Interface for which an
|
116 |
+
implementation is available to the public in source code form. A
|
117 |
+
"Major Component", in this context, means a major essential component
|
118 |
+
(kernel, window system, and so on) of the specific operating system
|
119 |
+
(if any) on which the executable work runs, or a compiler used to
|
120 |
+
produce the work, or an object code interpreter used to run it.
|
121 |
+
|
122 |
+
The "Corresponding Source" for a work in object code form means all
|
123 |
+
the source code needed to generate, install, and (for an executable
|
124 |
+
work) run the object code and to modify the work, including scripts to
|
125 |
+
control those activities. However, it does not include the work's
|
126 |
+
System Libraries, or general-purpose tools or generally available free
|
127 |
+
programs which are used unmodified in performing those activities but
|
128 |
+
which are not part of the work. For example, Corresponding Source
|
129 |
+
includes interface definition files associated with source files for
|
130 |
+
the work, and the source code for shared libraries and dynamically
|
131 |
+
linked subprograms that the work is specifically designed to require,
|
132 |
+
such as by intimate data communication or control flow between those
|
133 |
+
subprograms and other parts of the work.
|
134 |
+
|
135 |
+
The Corresponding Source need not include anything that users
|
136 |
+
can regenerate automatically from other parts of the Corresponding
|
137 |
+
Source.
|
138 |
+
|
139 |
+
The Corresponding Source for a work in source code form is that
|
140 |
+
same work.
|
141 |
+
|
142 |
+
2. Basic Permissions.
|
143 |
+
|
144 |
+
All rights granted under this License are granted for the term of
|
145 |
+
copyright on the Program, and are irrevocable provided the stated
|
146 |
+
conditions are met. This License explicitly affirms your unlimited
|
147 |
+
permission to run the unmodified Program. The output from running a
|
148 |
+
covered work is covered by this License only if the output, given its
|
149 |
+
content, constitutes a covered work. This License acknowledges your
|
150 |
+
rights of fair use or other equivalent, as provided by copyright law.
|
151 |
+
|
152 |
+
You may make, run and propagate covered works that you do not
|
153 |
+
convey, without conditions so long as your license otherwise remains
|
154 |
+
in force. You may convey covered works to others for the sole purpose
|
155 |
+
of having them make modifications exclusively for you, or provide you
|
156 |
+
with facilities for running those works, provided that you comply with
|
157 |
+
the terms of this License in conveying all material for which you do
|
158 |
+
not control copyright. Those thus making or running the covered works
|
159 |
+
for you must do so exclusively on your behalf, under your direction
|
160 |
+
and control, on terms that prohibit them from making any copies of
|
161 |
+
your copyrighted material outside their relationship with you.
|
162 |
+
|
163 |
+
Conveying under any other circumstances is permitted solely under
|
164 |
+
the conditions stated below. Sublicensing is not allowed; section 10
|
165 |
+
makes it unnecessary.
|
166 |
+
|
167 |
+
3. Protecting Users' Legal Rights From Anti-Circumvention Law.
|
168 |
+
|
169 |
+
No covered work shall be deemed part of an effective technological
|
170 |
+
measure under any applicable law fulfilling obligations under article
|
171 |
+
11 of the WIPO copyright treaty adopted on 20 December 1996, or
|
172 |
+
similar laws prohibiting or restricting circumvention of such
|
173 |
+
measures.
|
174 |
+
|
175 |
+
When you convey a covered work, you waive any legal power to forbid
|
176 |
+
circumvention of technological measures to the extent such circumvention
|
177 |
+
is effected by exercising rights under this License with respect to
|
178 |
+
the covered work, and you disclaim any intention to limit operation or
|
179 |
+
modification of the work as a means of enforcing, against the work's
|
180 |
+
users, your or third parties' legal rights to forbid circumvention of
|
181 |
+
technological measures.
|
182 |
+
|
183 |
+
4. Conveying Verbatim Copies.
|
184 |
+
|
185 |
+
You may convey verbatim copies of the Program's source code as you
|
186 |
+
receive it, in any medium, provided that you conspicuously and
|
187 |
+
appropriately publish on each copy an appropriate copyright notice;
|
188 |
+
keep intact all notices stating that this License and any
|
189 |
+
non-permissive terms added in accord with section 7 apply to the code;
|
190 |
+
keep intact all notices of the absence of any warranty; and give all
|
191 |
+
recipients a copy of this License along with the Program.
|
192 |
+
|
193 |
+
You may charge any price or no price for each copy that you convey,
|
194 |
+
and you may offer support or warranty protection for a fee.
|
195 |
+
|
196 |
+
5. Conveying Modified Source Versions.
|
197 |
+
|
198 |
+
You may convey a work based on the Program, or the modifications to
|
199 |
+
produce it from the Program, in the form of source code under the
|
200 |
+
terms of section 4, provided that you also meet all of these conditions:
|
201 |
+
|
202 |
+
a) The work must carry prominent notices stating that you modified
|
203 |
+
it, and giving a relevant date.
|
204 |
+
|
205 |
+
b) The work must carry prominent notices stating that it is
|
206 |
+
released under this License and any conditions added under section
|
207 |
+
7. This requirement modifies the requirement in section 4 to
|
208 |
+
"keep intact all notices".
|
209 |
+
|
210 |
+
c) You must license the entire work, as a whole, under this
|
211 |
+
License to anyone who comes into possession of a copy. This
|
212 |
+
License will therefore apply, along with any applicable section 7
|
213 |
+
additional terms, to the whole of the work, and all its parts,
|
214 |
+
regardless of how they are packaged. This License gives no
|
215 |
+
permission to license the work in any other way, but it does not
|
216 |
+
invalidate such permission if you have separately received it.
|
217 |
+
|
218 |
+
d) If the work has interactive user interfaces, each must display
|
219 |
+
Appropriate Legal Notices; however, if the Program has interactive
|
220 |
+
interfaces that do not display Appropriate Legal Notices, your
|
221 |
+
work need not make them do so.
|
222 |
+
|
223 |
+
A compilation of a covered work with other separate and independent
|
224 |
+
works, which are not by their nature extensions of the covered work,
|
225 |
+
and which are not combined with it such as to form a larger program,
|
226 |
+
in or on a volume of a storage or distribution medium, is called an
|
227 |
+
"aggregate" if the compilation and its resulting copyright are not
|
228 |
+
used to limit the access or legal rights of the compilation's users
|
229 |
+
beyond what the individual works permit. Inclusion of a covered work
|
230 |
+
in an aggregate does not cause this License to apply to the other
|
231 |
+
parts of the aggregate.
|
232 |
+
|
233 |
+
6. Conveying Non-Source Forms.
|
234 |
+
|
235 |
+
You may convey a covered work in object code form under the terms
|
236 |
+
of sections 4 and 5, provided that you also convey the
|
237 |
+
machine-readable Corresponding Source under the terms of this License,
|
238 |
+
in one of these ways:
|
239 |
+
|
240 |
+
a) Convey the object code in, or embodied in, a physical product
|
241 |
+
(including a physical distribution medium), accompanied by the
|
242 |
+
Corresponding Source fixed on a durable physical medium
|
243 |
+
customarily used for software interchange.
|
244 |
+
|
245 |
+
b) Convey the object code in, or embodied in, a physical product
|
246 |
+
(including a physical distribution medium), accompanied by a
|
247 |
+
written offer, valid for at least three years and valid for as
|
248 |
+
long as you offer spare parts or customer support for that product
|
249 |
+
model, to give anyone who possesses the object code either (1) a
|
250 |
+
copy of the Corresponding Source for all the software in the
|
251 |
+
product that is covered by this License, on a durable physical
|
252 |
+
medium customarily used for software interchange, for a price no
|
253 |
+
more than your reasonable cost of physically performing this
|
254 |
+
conveying of source, or (2) access to copy the
|
255 |
+
Corresponding Source from a network server at no charge.
|
256 |
+
|
257 |
+
c) Convey individual copies of the object code with a copy of the
|
258 |
+
written offer to provide the Corresponding Source. This
|
259 |
+
alternative is allowed only occasionally and noncommercially, and
|
260 |
+
only if you received the object code with such an offer, in accord
|
261 |
+
with subsection 6b.
|
262 |
+
|
263 |
+
d) Convey the object code by offering access from a designated
|
264 |
+
place (gratis or for a charge), and offer equivalent access to the
|
265 |
+
Corresponding Source in the same way through the same place at no
|
266 |
+
further charge. You need not require recipients to copy the
|
267 |
+
Corresponding Source along with the object code. If the place to
|
268 |
+
copy the object code is a network server, the Corresponding Source
|
269 |
+
may be on a different server (operated by you or a third party)
|
270 |
+
that supports equivalent copying facilities, provided you maintain
|
271 |
+
clear directions next to the object code saying where to find the
|
272 |
+
Corresponding Source. Regardless of what server hosts the
|
273 |
+
Corresponding Source, you remain obligated to ensure that it is
|
274 |
+
available for as long as needed to satisfy these requirements.
|
275 |
+
|
276 |
+
e) Convey the object code using peer-to-peer transmission, provided
|
277 |
+
you inform other peers where the object code and Corresponding
|
278 |
+
Source of the work are being offered to the general public at no
|
279 |
+
charge under subsection 6d.
|
280 |
+
|
281 |
+
A separable portion of the object code, whose source code is excluded
|
282 |
+
from the Corresponding Source as a System Library, need not be
|
283 |
+
included in conveying the object code work.
|
284 |
+
|
285 |
+
A "User Product" is either (1) a "consumer product", which means any
|
286 |
+
tangible personal property which is normally used for personal, family,
|
287 |
+
or household purposes, or (2) anything designed or sold for incorporation
|
288 |
+
into a dwelling. In determining whether a product is a consumer product,
|
289 |
+
doubtful cases shall be resolved in favor of coverage. For a particular
|
290 |
+
product received by a particular user, "normally used" refers to a
|
291 |
+
typical or common use of that class of product, regardless of the status
|
292 |
+
of the particular user or of the way in which the particular user
|
293 |
+
actually uses, or expects or is expected to use, the product. A product
|
294 |
+
is a consumer product regardless of whether the product has substantial
|
295 |
+
commercial, industrial or non-consumer uses, unless such uses represent
|
296 |
+
the only significant mode of use of the product.
|
297 |
+
|
298 |
+
"Installation Information" for a User Product means any methods,
|
299 |
+
procedures, authorization keys, or other information required to install
|
300 |
+
and execute modified versions of a covered work in that User Product from
|
301 |
+
a modified version of its Corresponding Source. The information must
|
302 |
+
suffice to ensure that the continued functioning of the modified object
|
303 |
+
code is in no case prevented or interfered with solely because
|
304 |
+
modification has been made.
|
305 |
+
|
306 |
+
If you convey an object code work under this section in, or with, or
|
307 |
+
specifically for use in, a User Product, and the conveying occurs as
|
308 |
+
part of a transaction in which the right of possession and use of the
|
309 |
+
User Product is transferred to the recipient in perpetuity or for a
|
310 |
+
fixed term (regardless of how the transaction is characterized), the
|
311 |
+
Corresponding Source conveyed under this section must be accompanied
|
312 |
+
by the Installation Information. But this requirement does not apply
|
313 |
+
if neither you nor any third party retains the ability to install
|
314 |
+
modified object code on the User Product (for example, the work has
|
315 |
+
been installed in ROM).
|
316 |
+
|
317 |
+
The requirement to provide Installation Information does not include a
|
318 |
+
requirement to continue to provide support service, warranty, or updates
|
319 |
+
for a work that has been modified or installed by the recipient, or for
|
320 |
+
the User Product in which it has been modified or installed. Access to a
|
321 |
+
network may be denied when the modification itself materially and
|
322 |
+
adversely affects the operation of the network or violates the rules and
|
323 |
+
protocols for communication across the network.
|
324 |
+
|
325 |
+
Corresponding Source conveyed, and Installation Information provided,
|
326 |
+
in accord with this section must be in a format that is publicly
|
327 |
+
documented (and with an implementation available to the public in
|
328 |
+
source code form), and must require no special password or key for
|
329 |
+
unpacking, reading or copying.
|
330 |
+
|
331 |
+
7. Additional Terms.
|
332 |
+
|
333 |
+
"Additional permissions" are terms that supplement the terms of this
|
334 |
+
License by making exceptions from one or more of its conditions.
|
335 |
+
Additional permissions that are applicable to the entire Program shall
|
336 |
+
be treated as though they were included in this License, to the extent
|
337 |
+
that they are valid under applicable law. If additional permissions
|
338 |
+
apply only to part of the Program, that part may be used separately
|
339 |
+
under those permissions, but the entire Program remains governed by
|
340 |
+
this License without regard to the additional permissions.
|
341 |
+
|
342 |
+
When you convey a copy of a covered work, you may at your option
|
343 |
+
remove any additional permissions from that copy, or from any part of
|
344 |
+
it. (Additional permissions may be written to require their own
|
345 |
+
removal in certain cases when you modify the work.) You may place
|
346 |
+
additional permissions on material, added by you to a covered work,
|
347 |
+
for which you have or can give appropriate copyright permission.
|
348 |
+
|
349 |
+
Notwithstanding any other provision of this License, for material you
|
350 |
+
add to a covered work, you may (if authorized by the copyright holders of
|
351 |
+
that material) supplement the terms of this License with terms:
|
352 |
+
|
353 |
+
a) Disclaiming warranty or limiting liability differently from the
|
354 |
+
terms of sections 15 and 16 of this License; or
|
355 |
+
|
356 |
+
b) Requiring preservation of specified reasonable legal notices or
|
357 |
+
author attributions in that material or in the Appropriate Legal
|
358 |
+
Notices displayed by works containing it; or
|
359 |
+
|
360 |
+
c) Prohibiting misrepresentation of the origin of that material, or
|
361 |
+
requiring that modified versions of such material be marked in
|
362 |
+
reasonable ways as different from the original version; or
|
363 |
+
|
364 |
+
d) Limiting the use for publicity purposes of names of licensors or
|
365 |
+
authors of the material; or
|
366 |
+
|
367 |
+
e) Declining to grant rights under trademark law for use of some
|
368 |
+
trade names, trademarks, or service marks; or
|
369 |
+
|
370 |
+
f) Requiring indemnification of licensors and authors of that
|
371 |
+
material by anyone who conveys the material (or modified versions of
|
372 |
+
it) with contractual assumptions of liability to the recipient, for
|
373 |
+
any liability that these contractual assumptions directly impose on
|
374 |
+
those licensors and authors.
|
375 |
+
|
376 |
+
All other non-permissive additional terms are considered "further
|
377 |
+
restrictions" within the meaning of section 10. If the Program as you
|
378 |
+
received it, or any part of it, contains a notice stating that it is
|
379 |
+
governed by this License along with a term that is a further
|
380 |
+
restriction, you may remove that term. If a license document contains
|
381 |
+
a further restriction but permits relicensing or conveying under this
|
382 |
+
License, you may add to a covered work material governed by the terms
|
383 |
+
of that license document, provided that the further restriction does
|
384 |
+
not survive such relicensing or conveying.
|
385 |
+
|
386 |
+
If you add terms to a covered work in accord with this section, you
|
387 |
+
must place, in the relevant source files, a statement of the
|
388 |
+
additional terms that apply to those files, or a notice indicating
|
389 |
+
where to find the applicable terms.
|
390 |
+
|
391 |
+
Additional terms, permissive or non-permissive, may be stated in the
|
392 |
+
form of a separately written license, or stated as exceptions;
|
393 |
+
the above requirements apply either way.
|
394 |
+
|
395 |
+
8. Termination.
|
396 |
+
|
397 |
+
You may not propagate or modify a covered work except as expressly
|
398 |
+
provided under this License. Any attempt otherwise to propagate or
|
399 |
+
modify it is void, and will automatically terminate your rights under
|
400 |
+
this License (including any patent licenses granted under the third
|
401 |
+
paragraph of section 11).
|
402 |
+
|
403 |
+
However, if you cease all violation of this License, then your
|
404 |
+
license from a particular copyright holder is reinstated (a)
|
405 |
+
provisionally, unless and until the copyright holder explicitly and
|
406 |
+
finally terminates your license, and (b) permanently, if the copyright
|
407 |
+
holder fails to notify you of the violation by some reasonable means
|
408 |
+
prior to 60 days after the cessation.
|
409 |
+
|
410 |
+
Moreover, your license from a particular copyright holder is
|
411 |
+
reinstated permanently if the copyright holder notifies you of the
|
412 |
+
violation by some reasonable means, this is the first time you have
|
413 |
+
received notice of violation of this License (for any work) from that
|
414 |
+
copyright holder, and you cure the violation prior to 30 days after
|
415 |
+
your receipt of the notice.
|
416 |
+
|
417 |
+
Termination of your rights under this section does not terminate the
|
418 |
+
licenses of parties who have received copies or rights from you under
|
419 |
+
this License. If your rights have been terminated and not permanently
|
420 |
+
reinstated, you do not qualify to receive new licenses for the same
|
421 |
+
material under section 10.
|
422 |
+
|
423 |
+
9. Acceptance Not Required for Having Copies.
|
424 |
+
|
425 |
+
You are not required to accept this License in order to receive or
|
426 |
+
run a copy of the Program. Ancillary propagation of a covered work
|
427 |
+
occurring solely as a consequence of using peer-to-peer transmission
|
428 |
+
to receive a copy likewise does not require acceptance. However,
|
429 |
+
nothing other than this License grants you permission to propagate or
|
430 |
+
modify any covered work. These actions infringe copyright if you do
|
431 |
+
not accept this License. Therefore, by modifying or propagating a
|
432 |
+
covered work, you indicate your acceptance of this License to do so.
|
433 |
+
|
434 |
+
10. Automatic Licensing of Downstream Recipients.
|
435 |
+
|
436 |
+
Each time you convey a covered work, the recipient automatically
|
437 |
+
receives a license from the original licensors, to run, modify and
|
438 |
+
propagate that work, subject to this License. You are not responsible
|
439 |
+
for enforcing compliance by third parties with this License.
|
440 |
+
|
441 |
+
An "entity transaction" is a transaction transferring control of an
|
442 |
+
organization, or substantially all assets of one, or subdividing an
|
443 |
+
organization, or merging organizations. If propagation of a covered
|
444 |
+
work results from an entity transaction, each party to that
|
445 |
+
transaction who receives a copy of the work also receives whatever
|
446 |
+
licenses to the work the party's predecessor in interest had or could
|
447 |
+
give under the previous paragraph, plus a right to possession of the
|
448 |
+
Corresponding Source of the work from the predecessor in interest, if
|
449 |
+
the predecessor has it or can get it with reasonable efforts.
|
450 |
+
|
451 |
+
You may not impose any further restrictions on the exercise of the
|
452 |
+
rights granted or affirmed under this License. For example, you may
|
453 |
+
not impose a license fee, royalty, or other charge for exercise of
|
454 |
+
rights granted under this License, and you may not initiate litigation
|
455 |
+
(including a cross-claim or counterclaim in a lawsuit) alleging that
|
456 |
+
any patent claim is infringed by making, using, selling, offering for
|
457 |
+
sale, or importing the Program or any portion of it.
|
458 |
+
|
459 |
+
11. Patents.
|
460 |
+
|
461 |
+
A "contributor" is a copyright holder who authorizes use under this
|
462 |
+
License of the Program or a work on which the Program is based. The
|
463 |
+
work thus licensed is called the contributor's "contributor version".
|
464 |
+
|
465 |
+
A contributor's "essential patent claims" are all patent claims
|
466 |
+
owned or controlled by the contributor, whether already acquired or
|
467 |
+
hereafter acquired, that would be infringed by some manner, permitted
|
468 |
+
by this License, of making, using, or selling its contributor version,
|
469 |
+
but do not include claims that would be infringed only as a
|
470 |
+
consequence of further modification of the contributor version. For
|
471 |
+
purposes of this definition, "control" includes the right to grant
|
472 |
+
patent sublicenses in a manner consistent with the requirements of
|
473 |
+
this License.
|
474 |
+
|
475 |
+
Each contributor grants you a non-exclusive, worldwide, royalty-free
|
476 |
+
patent license under the contributor's essential patent claims, to
|
477 |
+
make, use, sell, offer for sale, import and otherwise run, modify and
|
478 |
+
propagate the contents of its contributor version.
|
479 |
+
|
480 |
+
In the following three paragraphs, a "patent license" is any express
|
481 |
+
agreement or commitment, however denominated, not to enforce a patent
|
482 |
+
(such as an express permission to practice a patent or covenant not to
|
483 |
+
sue for patent infringement). To "grant" such a patent license to a
|
484 |
+
party means to make such an agreement or commitment not to enforce a
|
485 |
+
patent against the party.
|
486 |
+
|
487 |
+
If you convey a covered work, knowingly relying on a patent license,
|
488 |
+
and the Corresponding Source of the work is not available for anyone
|
489 |
+
to copy, free of charge and under the terms of this License, through a
|
490 |
+
publicly available network server or other readily accessible means,
|
491 |
+
then you must either (1) cause the Corresponding Source to be so
|
492 |
+
available, or (2) arrange to deprive yourself of the benefit of the
|
493 |
+
patent license for this particular work, or (3) arrange, in a manner
|
494 |
+
consistent with the requirements of this License, to extend the patent
|
495 |
+
license to downstream recipients. "Knowingly relying" means you have
|
496 |
+
actual knowledge that, but for the patent license, your conveying the
|
497 |
+
covered work in a country, or your recipient's use of the covered work
|
498 |
+
in a country, would infringe one or more identifiable patents in that
|
499 |
+
country that you have reason to believe are valid.
|
500 |
+
|
501 |
+
If, pursuant to or in connection with a single transaction or
|
502 |
+
arrangement, you convey, or propagate by procuring conveyance of, a
|
503 |
+
covered work, and grant a patent license to some of the parties
|
504 |
+
receiving the covered work authorizing them to use, propagate, modify
|
505 |
+
or convey a specific copy of the covered work, then the patent license
|
506 |
+
you grant is automatically extended to all recipients of the covered
|
507 |
+
work and works based on it.
|
508 |
+
|
509 |
+
A patent license is "discriminatory" if it does not include within
|
510 |
+
the scope of its coverage, prohibits the exercise of, or is
|
511 |
+
conditioned on the non-exercise of one or more of the rights that are
|
512 |
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specifically granted under this License. You may not convey a covered
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+
work if you are a party to an arrangement with a third party that is
|
514 |
+
in the business of distributing software, under which you make payment
|
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+
to the third party based on the extent of your activity of conveying
|
516 |
+
the work, and under which the third party grants, to any of the
|
517 |
+
parties who would receive the covered work from you, a discriminatory
|
518 |
+
patent license (a) in connection with copies of the covered work
|
519 |
+
conveyed by you (or copies made from those copies), or (b) primarily
|
520 |
+
for and in connection with specific products or compilations that
|
521 |
+
contain the covered work, unless you entered into that arrangement,
|
522 |
+
or that patent license was granted, prior to 28 March 2007.
|
523 |
+
|
524 |
+
Nothing in this License shall be construed as excluding or limiting
|
525 |
+
any implied license or other defenses to infringement that may
|
526 |
+
otherwise be available to you under applicable patent law.
|
527 |
+
|
528 |
+
12. No Surrender of Others' Freedom.
|
529 |
+
|
530 |
+
If conditions are imposed on you (whether by court order, agreement or
|
531 |
+
otherwise) that contradict the conditions of this License, they do not
|
532 |
+
excuse you from the conditions of this License. If you cannot convey a
|
533 |
+
covered work so as to satisfy simultaneously your obligations under this
|
534 |
+
License and any other pertinent obligations, then as a consequence you may
|
535 |
+
not convey it at all. For example, if you agree to terms that obligate you
|
536 |
+
to collect a royalty for further conveying from those to whom you convey
|
537 |
+
the Program, the only way you could satisfy both those terms and this
|
538 |
+
License would be to refrain entirely from conveying the Program.
|
539 |
+
|
540 |
+
13. Remote Network Interaction; Use with the GNU General Public License.
|
541 |
+
|
542 |
+
Notwithstanding any other provision of this License, if you modify the
|
543 |
+
Program, your modified version must prominently offer all users
|
544 |
+
interacting with it remotely through a computer network (if your version
|
545 |
+
supports such interaction) an opportunity to receive the Corresponding
|
546 |
+
Source of your version by providing access to the Corresponding Source
|
547 |
+
from a network server at no charge, through some standard or customary
|
548 |
+
means of facilitating copying of software. This Corresponding Source
|
549 |
+
shall include the Corresponding Source for any work covered by version 3
|
550 |
+
of the GNU General Public License that is incorporated pursuant to the
|
551 |
+
following paragraph.
|
552 |
+
|
553 |
+
Notwithstanding any other provision of this License, you have
|
554 |
+
permission to link or combine any covered work with a work licensed
|
555 |
+
under version 3 of the GNU General Public License into a single
|
556 |
+
combined work, and to convey the resulting work. The terms of this
|
557 |
+
License will continue to apply to the part which is the covered work,
|
558 |
+
but the work with which it is combined will remain governed by version
|
559 |
+
3 of the GNU General Public License.
|
560 |
+
|
561 |
+
14. Revised Versions of this License.
|
562 |
+
|
563 |
+
The Free Software Foundation may publish revised and/or new versions of
|
564 |
+
the GNU Affero General Public License from time to time. Such new versions
|
565 |
+
will be similar in spirit to the present version, but may differ in detail to
|
566 |
+
address new problems or concerns.
|
567 |
+
|
568 |
+
Each version is given a distinguishing version number. If the
|
569 |
+
Program specifies that a certain numbered version of the GNU Affero General
|
570 |
+
Public License "or any later version" applies to it, you have the
|
571 |
+
option of following the terms and conditions either of that numbered
|
572 |
+
version or of any later version published by the Free Software
|
573 |
+
Foundation. If the Program does not specify a version number of the
|
574 |
+
GNU Affero General Public License, you may choose any version ever published
|
575 |
+
by the Free Software Foundation.
|
576 |
+
|
577 |
+
If the Program specifies that a proxy can decide which future
|
578 |
+
versions of the GNU Affero General Public License can be used, that proxy's
|
579 |
+
public statement of acceptance of a version permanently authorizes you
|
580 |
+
to choose that version for the Program.
|
581 |
+
|
582 |
+
Later license versions may give you additional or different
|
583 |
+
permissions. However, no additional obligations are imposed on any
|
584 |
+
author or copyright holder as a result of your choosing to follow a
|
585 |
+
later version.
|
586 |
+
|
587 |
+
15. Disclaimer of Warranty.
|
588 |
+
|
589 |
+
THERE IS NO WARRANTY FOR THE PROGRAM, TO THE EXTENT PERMITTED BY
|
590 |
+
APPLICABLE LAW. EXCEPT WHEN OTHERWISE STATED IN WRITING THE COPYRIGHT
|
591 |
+
HOLDERS AND/OR OTHER PARTIES PROVIDE THE PROGRAM "AS IS" WITHOUT WARRANTY
|
592 |
+
OF ANY KIND, EITHER EXPRESSED OR IMPLIED, INCLUDING, BUT NOT LIMITED TO,
|
593 |
+
THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR
|
594 |
+
PURPOSE. THE ENTIRE RISK AS TO THE QUALITY AND PERFORMANCE OF THE PROGRAM
|
595 |
+
IS WITH YOU. SHOULD THE PROGRAM PROVE DEFECTIVE, YOU ASSUME THE COST OF
|
596 |
+
ALL NECESSARY SERVICING, REPAIR OR CORRECTION.
|
597 |
+
|
598 |
+
16. Limitation of Liability.
|
599 |
+
|
600 |
+
IN NO EVENT UNLESS REQUIRED BY APPLICABLE LAW OR AGREED TO IN WRITING
|
601 |
+
WILL ANY COPYRIGHT HOLDER, OR ANY OTHER PARTY WHO MODIFIES AND/OR CONVEYS
|
602 |
+
THE PROGRAM AS PERMITTED ABOVE, BE LIABLE TO YOU FOR DAMAGES, INCLUDING ANY
|
603 |
+
GENERAL, SPECIAL, INCIDENTAL OR CONSEQUENTIAL DAMAGES ARISING OUT OF THE
|
604 |
+
USE OR INABILITY TO USE THE PROGRAM (INCLUDING BUT NOT LIMITED TO LOSS OF
|
605 |
+
DATA OR DATA BEING RENDERED INACCURATE OR LOSSES SUSTAINED BY YOU OR THIRD
|
606 |
+
PARTIES OR A FAILURE OF THE PROGRAM TO OPERATE WITH ANY OTHER PROGRAMS),
|
607 |
+
EVEN IF SUCH HOLDER OR OTHER PARTY HAS BEEN ADVISED OF THE POSSIBILITY OF
|
608 |
+
SUCH DAMAGES.
|
609 |
+
|
610 |
+
17. Interpretation of Sections 15 and 16.
|
611 |
+
|
612 |
+
If the disclaimer of warranty and limitation of liability provided
|
613 |
+
above cannot be given local legal effect according to their terms,
|
614 |
+
reviewing courts shall apply local law that most closely approximates
|
615 |
+
an absolute waiver of all civil liability in connection with the
|
616 |
+
Program, unless a warranty or assumption of liability accompanies a
|
617 |
+
copy of the Program in return for a fee.
|
618 |
+
|
619 |
+
END OF TERMS AND CONDITIONS
|
620 |
+
|
621 |
+
How to Apply These Terms to Your New Programs
|
622 |
+
|
623 |
+
If you develop a new program, and you want it to be of the greatest
|
624 |
+
possible use to the public, the best way to achieve this is to make it
|
625 |
+
free software which everyone can redistribute and change under these terms.
|
626 |
+
|
627 |
+
To do so, attach the following notices to the program. It is safest
|
628 |
+
to attach them to the start of each source file to most effectively
|
629 |
+
state the exclusion of warranty; and each file should have at least
|
630 |
+
the "copyright" line and a pointer to where the full notice is found.
|
631 |
+
|
632 |
+
<one line to give the program's name and a brief idea of what it does.>
|
633 |
+
Copyright (C) <year> <name of author>
|
634 |
+
|
635 |
+
This program is free software: you can redistribute it and/or modify
|
636 |
+
it under the terms of the GNU Affero General Public License as published
|
637 |
+
by the Free Software Foundation, either version 3 of the License, or
|
638 |
+
(at your option) any later version.
|
639 |
+
|
640 |
+
This program is distributed in the hope that it will be useful,
|
641 |
+
but WITHOUT ANY WARRANTY; without even the implied warranty of
|
642 |
+
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
|
643 |
+
GNU Affero General Public License for more details.
|
644 |
+
|
645 |
+
You should have received a copy of the GNU Affero General Public License
|
646 |
+
along with this program. If not, see <https://www.gnu.org/licenses/>.
|
647 |
+
|
648 |
+
Also add information on how to contact you by electronic and paper mail.
|
649 |
+
|
650 |
+
If your software can interact with users remotely through a computer
|
651 |
+
network, you should also make sure that it provides a way for users to
|
652 |
+
get its source. For example, if your program is a web application, its
|
653 |
+
interface could display a "Source" link that leads users to an archive
|
654 |
+
of the code. There are many ways you could offer source, and different
|
655 |
+
solutions will be better for different programs; see section 13 for the
|
656 |
+
specific requirements.
|
657 |
+
|
658 |
+
You should also get your employer (if you work as a programmer) or school,
|
659 |
+
if any, to sign a "copyright disclaimer" for the program, if necessary.
|
660 |
+
For more information on this, and how to apply and follow the GNU AGPL, see
|
661 |
+
<https://www.gnu.org/licenses/>.
|
README_zh.md
CHANGED
@@ -18,10 +18,11 @@
|
|
18 |
|
19 |
# Feature
|
20 |
|
21 |
-
- [x] VITS
|
22 |
-
- [x] VITS语音转换
|
23 |
- [x] HuBert-soft VITS模型
|
24 |
- [x] W2V2 VITS / emotional-vits维度情感模型
|
|
|
|
|
25 |
- [x] 加载多模型
|
26 |
- [x] 自动识别语言并处理,根据模型的cleaner设置语言类型识别的范围,支持自定义语言类型范围
|
27 |
- [x] 自定义默认参数
|
@@ -29,31 +30,6 @@
|
|
29 |
- [x] GPU加速推理
|
30 |
- [x] SSML语音合成标记语言(完善中...)
|
31 |
|
32 |
-
<details><summary>Update Logs</summary><pre><code>
|
33 |
-
<h2>2023.6.5</h2>
|
34 |
-
<p>更换音频编码使用的库,增加flac格式,增加中文对读简单数学公式的支持</p>
|
35 |
-
<h2>2023.5.24</h2>
|
36 |
-
<p>添加dimensional_emotion api,从文件夹加载多个npy文件,Docker添加了Linux/ARM64和Linux/ARM64/v8平台</p>
|
37 |
-
<h2>2023.5.15</h2>
|
38 |
-
<p>增加english_cleaner,需要额外安装espeak才能使用</p>
|
39 |
-
<h2>2023.5.12</h2>
|
40 |
-
<p>增加ssml支持,但仍需完善。重构部分功能,hubert_vits中的speaker_id改为id</p>
|
41 |
-
<h2>2023.5.2</h2>
|
42 |
-
<p>增加w2v2-vits/emotional-vits模型支持,修改了speakers映射表并添加了对应模型支持的语言</p>
|
43 |
-
<h2>2023.4.23</h2>
|
44 |
-
<p>增加api key鉴权,默认禁用,需要在config.py中启用</p>
|
45 |
-
<h2>2023.4.17</h2>
|
46 |
-
<p>修改单语言的cleaner需要标注才会clean,增加GPU加速推理,但需要手动安装gpu推理环境</p>
|
47 |
-
<h2>2023.4.12</h2>
|
48 |
-
<p>项目由MoeGoe-Simple-API更名为vits-simple-api,支持长文本批处理,增加长文本分段阈值max</p>
|
49 |
-
<h2>2023.4.7</h2>
|
50 |
-
<p>增加配置文件可自定义默认参数,本次更新需要手动更新config.py,具体使用方法见config.py</p>
|
51 |
-
<h2>2023.4.6</h2>
|
52 |
-
<p>加入自动识别语种选项auto,lang参数默认修改为auto,自动识别仍有一定缺陷,请自行选择</p>
|
53 |
-
<p>统一POST请求类型为multipart/form-data</p>
|
54 |
-
</code></pre></details>
|
55 |
-
|
56 |
-
|
57 |
|
58 |
## demo
|
59 |
|
@@ -493,17 +469,17 @@ def voice_dimensional_emotion(upload_path):
|
|
493 |
|
494 |
## VITS语音合成
|
495 |
|
496 |
-
| Name | Parameter | Is must | Default
|
497 |
-
| ------------- | --------- | ------- |
|
498 |
-
| 合成文本 | text | true |
|
499 |
-
| 角色id | id | false |
|
500 |
-
| 音频格式 | format | false |
|
501 |
-
| 文本语言 | lang | false |
|
502 |
-
| 语音长度/语速 | length | false |
|
503 |
-
| 噪声 | noise | false |
|
504 |
-
| sdp噪声 | noisew | false |
|
505 |
-
| 分段阈值 | max | false |
|
506 |
-
| 流式响应 | streaming | false | false
|
507 |
|
508 |
## VITS 语音转换
|
509 |
|
@@ -524,25 +500,39 @@ def voice_dimensional_emotion(upload_path):
|
|
524 |
| 噪声 | noise | true | | float | 样本噪声,控制合成的随机性。 |
|
525 |
| sdp噪声 | noisew | true | | float | 随机时长预测器噪声,控制音素发音长度。 |
|
526 |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
527 |
## Dimensional emotion
|
528 |
|
529 |
| Name | Parameter | Is must | Default | Type | Instruction |
|
530 |
| -------- | --------- | ------- | ------- | ---- | ----------------------------- |
|
531 |
| 上传音频 | upload | true | | file | 返回存储维度情感向量的npy文件 |
|
532 |
|
533 |
-
##
|
534 |
-
|
535 |
-
| Name | Parameter | Is must | Default
|
536 |
-
| ------------- | --------- | ------- |
|
537 |
-
| 合成文本 | text | true |
|
538 |
-
| 角色id | id | false |
|
539 |
-
| 音频格式 | format | false |
|
540 |
-
| 文本语言 | lang | false |
|
541 |
-
| 语音长度/语速 | length | false |
|
542 |
-
| 噪声 | noise | false |
|
543 |
-
| sdp噪声 | noisew | false |
|
544 |
-
| 分段阈值 | max | false |
|
545 |
-
|
|
546 |
|
547 |
## SSML语音合成标记语言
|
548 |
目前支持的元素与属性
|
|
|
18 |
|
19 |
# Feature
|
20 |
|
21 |
+
- [x] VITS语音合成,语音转换
|
|
|
22 |
- [x] HuBert-soft VITS模型
|
23 |
- [x] W2V2 VITS / emotional-vits维度情感模型
|
24 |
+
- [x] [vits_chinese](https://github.com/PlayVoice/vits_chinese)
|
25 |
+
- [x] [Bert-VITS2](https://github.com/Stardust-minus/Bert-VITS2)
|
26 |
- [x] 加载多模型
|
27 |
- [x] 自动识别语言并处理,根据模型的cleaner设置语言类型识别的范围,支持自定义语言类型范围
|
28 |
- [x] 自定义默认参数
|
|
|
30 |
- [x] GPU加速推理
|
31 |
- [x] SSML语音合成标记语言(完善中...)
|
32 |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
33 |
|
34 |
## demo
|
35 |
|
|
|
469 |
|
470 |
## VITS语音合成
|
471 |
|
472 |
+
| Name | Parameter | Is must | Default | Type | Instruction |
|
473 |
+
| ------------- | --------- | ------- | ------------------- | ----- | ------------------------------------------------------------ |
|
474 |
+
| 合成文本 | text | true | | str | 需要合成语音的文本。 |
|
475 |
+
| 角色id | id | false | 从`config.py`中获取 | int | 即说话人id。 |
|
476 |
+
| 音频格式 | format | false | 从`config.py`中获取 | str | 支持wav,ogg,silk,mp3,flac |
|
477 |
+
| 文本语言 | lang | false | 从`config.py`中获取 | str | auto为自动识别语言模式,也是默认模式。lang=mix时,文本应该用[ZH] 或 [JA] 包裹。方言无法自动识别。 |
|
478 |
+
| 语音长度/语速 | length | false | 从`config.py`中获取 | float | 调节语音长度,相当于调节语速,该数值越大语速越慢。 |
|
479 |
+
| 噪声 | noise | false | 从`config.py`中获取 | float | 样本噪声,控制合成的随机性。 |
|
480 |
+
| sdp噪声 | noisew | false | 从`config.py`中获取 | float | 随机时长预测器噪声,控制音素发音长度。 |
|
481 |
+
| 分段阈值 | max | false | 从`config.py`中获取 | int | 按标点符号分段,加起来大于max时为一段文本。max<=0表示不分段。 |
|
482 |
+
| 流式响应 | streaming | false | false | bool | 流式合成语音,更快的首包响应。 |
|
483 |
|
484 |
## VITS 语音转换
|
485 |
|
|
|
500 |
| 噪声 | noise | true | | float | 样本噪声,控制合成的随机性。 |
|
501 |
| sdp噪声 | noisew | true | | float | 随机时长预测器噪声,控制音素发音长度。 |
|
502 |
|
503 |
+
## W2V2-VITS
|
504 |
+
|
505 |
+
| Name | Parameter | Is must | Default | Type | Instruction |
|
506 |
+
| ------------- | --------- | ------- | ------------------- | ----- | ------------------------------------------------------------ |
|
507 |
+
| 合成文本 | text | true | | str | 需要合成语音的文本。 |
|
508 |
+
| 角色id | id | false | 从`config.py`中获取 | int | 即说话人id。 |
|
509 |
+
| 音频格式 | format | false | 从`config.py`中获取 | str | 支持wav,ogg,silk,mp3,flac |
|
510 |
+
| 文本语言 | lang | false | 从`config.py`中获取 | str | auto为自动识别语言模式,也是默认模式。lang=mix时,文本应该用[ZH] 或 [JA] 包裹。方言无法自动识别。 |
|
511 |
+
| 语音长度/语速 | length | false | 从`config.py`中获取 | float | 调节语音长度,相当于调节语速,该数值越大语速越慢 |
|
512 |
+
| 噪声 | noise | false | 从`config.py`中获取 | float | 样本噪声,控制合成的随机性。 |
|
513 |
+
| sdp噪声 | noisew | false | 从`config.py`中获取 | float | 随机时长预测器噪声,控制音素发音长度。 |
|
514 |
+
| 分段阈值 | max | false | 从`config.py`中获取 | int | 按标点符号分段,加起来大于max时为一段文本。max<=0表示不分段。 |
|
515 |
+
| 维度情感 | emotion | false | 0 | int | 范围取决于npy情感参考文件,如[innnky](https://huggingface.co/spaces/innnky/nene-emotion/tree/main)的all_emotions.npy模型范围是0-5457 |
|
516 |
+
|
517 |
## Dimensional emotion
|
518 |
|
519 |
| Name | Parameter | Is must | Default | Type | Instruction |
|
520 |
| -------- | --------- | ------- | ------- | ---- | ----------------------------- |
|
521 |
| 上传音频 | upload | true | | file | 返回存储维度情感向量的npy文件 |
|
522 |
|
523 |
+
## Bert-VITS2语音合成
|
524 |
+
|
525 |
+
| Name | Parameter | Is must | Default | Type | Instruction |
|
526 |
+
| ------------- | --------- | ------- | ------------------- | ----- | ------------------------------------------------------------ |
|
527 |
+
| 合成文本 | text | true | | str | 需要合成语音的文本。 |
|
528 |
+
| 角色id | id | false | 从`config.py`中获取 | int | 即说话人id。 |
|
529 |
+
| 音频格式 | format | false | 从`config.py`中获取 | str | 支持wav,ogg,silk,mp3,flac |
|
530 |
+
| 文本语言 | lang | false | 从`config.py`中获取 | str | 目前只有中文。 |
|
531 |
+
| 语音长度/语速 | length | false | 从`config.py`中获取 | float | 调节语音长度,相当于调节语速,该数值越大语速越慢。 |
|
532 |
+
| 噪声 | noise | false | 从`config.py`中获取 | float | 样本噪声,控制合成的随机性。 |
|
533 |
+
| sdp噪声 | noisew | false | 从`config.py`中获取 | float | 随机时长预测器噪声,控制音素发音长度。 |
|
534 |
+
| 分段阈值 | max | false | 从`config.py`中获取 | int | 按标点符号分段,加起来大于max时为一段文本。max<=0表示不分段。 |
|
535 |
+
| SDP/DP混合比 | sdp_ratio | false | 从`config.py`中获取 | int | SDP在合成时的占比,理论上此比率越高,合成的语音语调方差越大。 |
|
536 |
|
537 |
## SSML语音合成标记语言
|
538 |
目前支持的元素与属性
|
app.py
CHANGED
@@ -48,9 +48,10 @@ def index():
|
|
48 |
kwargs = {
|
49 |
"speakers": tts.voice_speakers,
|
50 |
"speakers_count": tts.speakers_count,
|
51 |
-
"vits_speakers_count":tts.
|
52 |
-
"w2v2_speakers_count":tts.
|
53 |
-
"w2v2_emotion_count":tts.
|
|
|
54 |
}
|
55 |
return render_template("index.html", **kwargs)
|
56 |
|
@@ -370,6 +371,7 @@ def ssml():
|
|
370 |
|
371 |
|
372 |
@app.route('/voice/dimension-emotion', methods=["POST"])
|
|
|
373 |
def dimensional_emotion():
|
374 |
if request.method == "POST":
|
375 |
try:
|
@@ -394,6 +396,90 @@ def dimensional_emotion():
|
|
394 |
return send_file(path_or_file=audio, mimetype=file_type, download_name=fname)
|
395 |
|
396 |
|
|
|
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|
397 |
@app.route('/voice/check', methods=["GET", "POST"])
|
398 |
def check():
|
399 |
try:
|
|
|
48 |
kwargs = {
|
49 |
"speakers": tts.voice_speakers,
|
50 |
"speakers_count": tts.speakers_count,
|
51 |
+
"vits_speakers_count": tts.vits_speakers_count,
|
52 |
+
"w2v2_speakers_count": tts.w2v2_speakers_count,
|
53 |
+
"w2v2_emotion_count": tts.w2v2_emotion_count,
|
54 |
+
"bert_vits2_speakers_count": tts.bert_vits2_speakers_count
|
55 |
}
|
56 |
return render_template("index.html", **kwargs)
|
57 |
|
|
|
371 |
|
372 |
|
373 |
@app.route('/voice/dimension-emotion', methods=["POST"])
|
374 |
+
@require_api_key
|
375 |
def dimensional_emotion():
|
376 |
if request.method == "POST":
|
377 |
try:
|
|
|
396 |
return send_file(path_or_file=audio, mimetype=file_type, download_name=fname)
|
397 |
|
398 |
|
399 |
+
@app.route('/voice/bert-vits2', methods=["GET", "POST"])
|
400 |
+
@require_api_key
|
401 |
+
def voice_bert_vits2_api():
|
402 |
+
try:
|
403 |
+
if request.method == "GET":
|
404 |
+
text = request.args.get("text", "")
|
405 |
+
id = int(request.args.get("id", app.config.get("ID", 0)))
|
406 |
+
format = request.args.get("format", app.config.get("FORMAT", "wav"))
|
407 |
+
# lang = request.args.get("lang", app.config.get("LANG", "auto"))
|
408 |
+
lang = "ZH"
|
409 |
+
length = float(request.args.get("length", app.config.get("LENGTH", 1)))
|
410 |
+
noise = float(request.args.get("noise", app.config.get("NOISE", 0.5)))
|
411 |
+
noisew = float(request.args.get("noisew", app.config.get("NOISEW", 0.6)))
|
412 |
+
sdp_ratio = float(request.args.get("sdp_ratio", 0.2))
|
413 |
+
max = int(request.args.get("max", app.config.get("MAX", 50)))
|
414 |
+
elif request.method == "POST":
|
415 |
+
content_type = request.headers.get('Content-Type')
|
416 |
+
if content_type == 'application/json':
|
417 |
+
data = request.get_json()
|
418 |
+
else:
|
419 |
+
data = request.form
|
420 |
+
text = data.get("text", "")
|
421 |
+
id = int(data.get("id", app.config.get("ID", 0)))
|
422 |
+
format = data.get("format", app.config.get("FORMAT", "wav"))
|
423 |
+
# lang = data.get("lang", app.config.get("LANG", "auto"))
|
424 |
+
lang = "ZH"
|
425 |
+
length = float(data.get("length", app.config.get("LENGTH", 1)))
|
426 |
+
noise = float(data.get("noise", app.config.get("NOISE", 0.667)))
|
427 |
+
noisew = float(data.get("noisew", app.config.get("NOISEW", 0.8)))
|
428 |
+
sdp_ratio = float(data.get("noisew", app.config.get("SDP_RATIO", 0.2)))
|
429 |
+
max = int(data.get("max", app.config.get("MAX", 50)))
|
430 |
+
except Exception as e:
|
431 |
+
logger.error(f"[Bert-VITS2] {e}")
|
432 |
+
return make_response("parameter error", 400)
|
433 |
+
|
434 |
+
logger.info(f"[Bert-VITS2] id:{id} format:{format} lang:{lang} length:{length} noise:{noise} noisew:{noisew} sdp_ratio:{sdp_ratio}")
|
435 |
+
logger.info(f"[Bert-VITS2] len:{len(text)} text:{text}")
|
436 |
+
|
437 |
+
if check_is_none(text):
|
438 |
+
logger.info(f"[Bert-VITS2] text is empty")
|
439 |
+
return make_response(jsonify({"status": "error", "message": "text is empty"}), 400)
|
440 |
+
|
441 |
+
if check_is_none(id):
|
442 |
+
logger.info(f"[Bert-VITS2] speaker id is empty")
|
443 |
+
return make_response(jsonify({"status": "error", "message": "speaker id is empty"}), 400)
|
444 |
+
|
445 |
+
if id < 0 or id >= tts.bert_vits2_speakers_count:
|
446 |
+
logger.info(f"[Bert-VITS2] speaker id {id} does not exist")
|
447 |
+
return make_response(jsonify({"status": "error", "message": f"id {id} does not exist"}), 400)
|
448 |
+
|
449 |
+
# 校验模型是否支持输入的语言
|
450 |
+
speaker_lang = tts.voice_speakers["BERT-VITS2"][id].get('lang')
|
451 |
+
if lang.upper() != "AUTO" and lang.upper() != "MIX" and len(speaker_lang) != 1 and lang not in speaker_lang:
|
452 |
+
logger.info(f"[Bert-VITS2] lang \"{lang}\" is not in {speaker_lang}")
|
453 |
+
return make_response(jsonify({"status": "error", "message": f"lang '{lang}' is not in {speaker_lang}"}), 400)
|
454 |
+
|
455 |
+
# 如果配置文件中设置了LANGUAGE_AUTOMATIC_DETECT则强制将speaker_lang设置为LANGUAGE_AUTOMATIC_DETECT
|
456 |
+
if app.config.get("LANGUAGE_AUTOMATIC_DETECT", []) != []:
|
457 |
+
speaker_lang = app.config.get("LANGUAGE_AUTOMATIC_DETECT")
|
458 |
+
|
459 |
+
fname = f"{str(uuid.uuid1())}.{format}"
|
460 |
+
file_type = f"audio/{format}"
|
461 |
+
task = {"text": text,
|
462 |
+
"id": id,
|
463 |
+
"format": format,
|
464 |
+
"length": length,
|
465 |
+
"noise": noise,
|
466 |
+
"noisew": noisew,
|
467 |
+
"sdp_ratio": sdp_ratio,
|
468 |
+
"max": max,
|
469 |
+
"lang": lang,
|
470 |
+
"speaker_lang": speaker_lang}
|
471 |
+
|
472 |
+
if app.config.get("SAVE_AUDIO", False):
|
473 |
+
logger.debug(f"[Bert-VITS2] {fname}")
|
474 |
+
|
475 |
+
|
476 |
+
t1 = time.time()
|
477 |
+
audio = tts.bert_vits2_infer(task, fname)
|
478 |
+
t2 = time.time()
|
479 |
+
logger.info(f"[Bert-VITS2] finish in {(t2 - t1):.2f}s")
|
480 |
+
return send_file(path_or_file=audio, mimetype=file_type, download_name=fname)
|
481 |
+
|
482 |
+
|
483 |
@app.route('/voice/check', methods=["GET", "POST"])
|
484 |
def check():
|
485 |
try:
|
bert_vits2/LICENSE
ADDED
@@ -0,0 +1,674 @@
|
|
|
|
|
|
|
|
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|
1 |
+
GNU GENERAL PUBLIC LICENSE
|
2 |
+
Version 3, 29 June 2007
|
3 |
+
|
4 |
+
Copyright (C) 2007 Free Software Foundation, Inc. <https://fsf.org/>
|
5 |
+
Everyone is permitted to copy and distribute verbatim copies
|
6 |
+
of this license document, but changing it is not allowed.
|
7 |
+
|
8 |
+
Preamble
|
9 |
+
|
10 |
+
The GNU General Public License is a free, copyleft license for
|
11 |
+
software and other kinds of works.
|
12 |
+
|
13 |
+
The licenses for most software and other practical works are designed
|
14 |
+
to take away your freedom to share and change the works. By contrast,
|
15 |
+
the GNU General Public License is intended to guarantee your freedom to
|
16 |
+
share and change all versions of a program--to make sure it remains free
|
17 |
+
software for all its users. We, the Free Software Foundation, use the
|
18 |
+
GNU General Public License for most of our software; it applies also to
|
19 |
+
any other work released this way by its authors. You can apply it to
|
20 |
+
your programs, too.
|
21 |
+
|
22 |
+
When we speak of free software, we are referring to freedom, not
|
23 |
+
price. Our General Public Licenses are designed to make sure that you
|
24 |
+
have the freedom to distribute copies of free software (and charge for
|
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+
them if you wish), that you receive source code or can get it if you
|
26 |
+
want it, that you can change the software or use pieces of it in new
|
27 |
+
free programs, and that you know you can do these things.
|
28 |
+
|
29 |
+
To protect your rights, we need to prevent others from denying you
|
30 |
+
these rights or asking you to surrender the rights. Therefore, you have
|
31 |
+
certain responsibilities if you distribute copies of the software, or if
|
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+
you modify it: responsibilities to respect the freedom of others.
|
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+
|
34 |
+
For example, if you distribute copies of such a program, whether
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+
gratis or for a fee, you must pass on to the recipients the same
|
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+
freedoms that you received. You must make sure that they, too, receive
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+
or can get the source code. And you must show them these terms so they
|
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+
know their rights.
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Developers that use the GNU GPL protect your rights with two steps:
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(1) assert copyright on the software, and (2) offer you this License
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giving you legal permission to copy, distribute and/or modify it.
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For the developers' and authors' protection, the GPL clearly explains
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that there is no warranty for this free software. For both users' and
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authors' sake, the GPL requires that modified versions be marked as
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changed, so that their problems will not be attributed erroneously to
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authors of previous versions.
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Some devices are designed to deny users access to install or run
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use, which is precisely where it is most unacceptable. Therefore, we
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have designed this version of the GPL to prohibit the practice for those
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products. If such problems arise substantially in other domains, we
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stand ready to extend this provision to those domains in future versions
|
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+
of the GPL, as needed to protect the freedom of users.
|
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+
Finally, every program is threatened constantly by software patents.
|
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States should not allow patents to restrict development and use of
|
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+
software on general-purpose computers, but in those that do, we wish to
|
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avoid the special danger that patents applied to a free program could
|
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make it effectively proprietary. To prevent this, the GPL assures that
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patents cannot be used to render the program non-free.
|
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The precise terms and conditions for copying, distribution and
|
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modification follow.
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|
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+
TERMS AND CONDITIONS
|
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|
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+
0. Definitions.
|
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|
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"This License" refers to version 3 of the GNU General Public License.
|
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"Copyright" also means copyright-like laws that apply to other kinds of
|
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"The Program" refers to any copyrightable work licensed under this
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License. Each licensee is addressed as "you". "Licensees" and
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"recipients" may be individuals or organizations.
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To "modify" a work means to copy from or adapt all or part of the work
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exact copy. The resulting work is called a "modified version" of the
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A "covered work" means either the unmodified Program or a work based
|
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on the Program.
|
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|
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To "propagate" a work means to do anything with it that, without
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permission, would make you directly or secondarily liable for
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infringement under applicable copyright law, except executing it on a
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distribution (with or without modification), making available to the
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public, and in some countries other activities as well.
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To "convey" a work means any kind of propagation that enables other
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|
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|
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The "source code" for a work means the preferred form of the work
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for making modifications to it. "Object code" means any non-source
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+
A "Standard Interface" means an interface that either is an official
|
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standard defined by a recognized standards body, or, in the case of
|
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interfaces specified for a particular programming language, one that
|
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+
is widely used among developers working in that language.
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|
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+
The "System Libraries" of an executable work include anything, other
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+
than the work as a whole, that (a) is included in the normal form of
|
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packaging a Major Component, but which is not part of that Major
|
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+
Component, and (b) serves only to enable use of the work with that
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+
Major Component, or to implement a Standard Interface for which an
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+
implementation is available to the public in source code form. A
|
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+
"Major Component", in this context, means a major essential component
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(kernel, window system, and so on) of the specific operating system
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(if any) on which the executable work runs, or a compiler used to
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+
produce the work, or an object code interpreter used to run it.
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The "Corresponding Source" for a work in object code form means all
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the source code needed to generate, install, and (for an executable
|
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+
work) run the object code and to modify the work, including scripts to
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control those activities. However, it does not include the work's
|
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+
System Libraries, or general-purpose tools or generally available free
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The Corresponding Source need not include anything that users
|
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Source.
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The Corresponding Source for a work in source code form is that
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|
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+
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|
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All rights granted under this License are granted for the term of
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+
permission to run the unmodified Program. The output from running a
|
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covered work is covered by this License only if the output, given its
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content, constitutes a covered work. This License acknowledges your
|
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+
rights of fair use or other equivalent, as provided by copyright law.
|
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+
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+
You may make, run and propagate covered works that you do not
|
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+
convey, without conditions so long as your license otherwise remains
|
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+
in force. You may convey covered works to others for the sole purpose
|
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+
of having them make modifications exclusively for you, or provide you
|
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+
with facilities for running those works, provided that you comply with
|
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+
the terms of this License in conveying all material for which you do
|
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+
not control copyright. Those thus making or running the covered works
|
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+
for you must do so exclusively on your behalf, under your direction
|
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+
and control, on terms that prohibit them from making any copies of
|
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+
your copyrighted material outside their relationship with you.
|
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+
|
175 |
+
Conveying under any other circumstances is permitted solely under
|
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+
the conditions stated below. Sublicensing is not allowed; section 10
|
177 |
+
makes it unnecessary.
|
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+
|
179 |
+
3. Protecting Users' Legal Rights From Anti-Circumvention Law.
|
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+
|
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+
No covered work shall be deemed part of an effective technological
|
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+
measure under any applicable law fulfilling obligations under article
|
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+
11 of the WIPO copyright treaty adopted on 20 December 1996, or
|
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+
similar laws prohibiting or restricting circumvention of such
|
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+
measures.
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|
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+
When you convey a covered work, you waive any legal power to forbid
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circumvention of technological measures to the extent such circumvention
|
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+
modification of the work as a means of enforcing, against the work's
|
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+
users, your or third parties' legal rights to forbid circumvention of
|
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+
technological measures.
|
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+
|
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+
4. Conveying Verbatim Copies.
|
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|
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+
You may convey verbatim copies of the Program's source code as you
|
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+
receive it, in any medium, provided that you conspicuously and
|
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appropriately publish on each copy an appropriate copyright notice;
|
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keep intact all notices stating that this License and any
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non-permissive terms added in accord with section 7 apply to the code;
|
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keep intact all notices of the absence of any warranty; and give all
|
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|
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You may charge any price or no price for each copy that you convey,
|
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and you may offer support or warranty protection for a fee.
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+
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|
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|
210 |
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You may convey a work based on the Program, or the modifications to
|
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+
produce it from the Program, in the form of source code under the
|
212 |
+
terms of section 4, provided that you also meet all of these conditions:
|
213 |
+
|
214 |
+
a) The work must carry prominent notices stating that you modified
|
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it, and giving a relevant date.
|
216 |
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|
217 |
+
b) The work must carry prominent notices stating that it is
|
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+
released under this License and any conditions added under section
|
219 |
+
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|
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"keep intact all notices".
|
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+
|
222 |
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c) You must license the entire work, as a whole, under this
|
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License to anyone who comes into possession of a copy. This
|
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+
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|
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|
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+
regardless of how they are packaged. This License gives no
|
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+
permission to license the work in any other way, but it does not
|
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+
invalidate such permission if you have separately received it.
|
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|
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+
d) If the work has interactive user interfaces, each must display
|
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Appropriate Legal Notices; however, if the Program has interactive
|
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|
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|
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|
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A compilation of a covered work with other separate and independent
|
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works, which are not by their nature extensions of the covered work,
|
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|
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|
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"aggregate" if the compilation and its resulting copyright are not
|
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used to limit the access or legal rights of the compilation's users
|
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|
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in an aggregate does not cause this License to apply to the other
|
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parts of the aggregate.
|
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+
|
245 |
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6. Conveying Non-Source Forms.
|
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|
247 |
+
You may convey a covered work in object code form under the terms
|
248 |
+
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|
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+
machine-readable Corresponding Source under the terms of this License,
|
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in one of these ways:
|
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|
252 |
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a) Convey the object code in, or embodied in, a physical product
|
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|
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+
customarily used for software interchange.
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|
257 |
+
b) Convey the object code in, or embodied in, a physical product
|
258 |
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(including a physical distribution medium), accompanied by a
|
259 |
+
written offer, valid for at least three years and valid for as
|
260 |
+
long as you offer spare parts or customer support for that product
|
261 |
+
model, to give anyone who possesses the object code either (1) a
|
262 |
+
copy of the Corresponding Source for all the software in the
|
263 |
+
product that is covered by this License, on a durable physical
|
264 |
+
medium customarily used for software interchange, for a price no
|
265 |
+
more than your reasonable cost of physically performing this
|
266 |
+
conveying of source, or (2) access to copy the
|
267 |
+
Corresponding Source from a network server at no charge.
|
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|
269 |
+
c) Convey individual copies of the object code with a copy of the
|
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written offer to provide the Corresponding Source. This
|
271 |
+
alternative is allowed only occasionally and noncommercially, and
|
272 |
+
only if you received the object code with such an offer, in accord
|
273 |
+
with subsection 6b.
|
274 |
+
|
275 |
+
d) Convey the object code by offering access from a designated
|
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+
place (gratis or for a charge), and offer equivalent access to the
|
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Corresponding Source in the same way through the same place at no
|
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further charge. You need not require recipients to copy the
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Corresponding Source along with the object code. If the place to
|
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|
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may be on a different server (operated by you or a third party)
|
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that supports equivalent copying facilities, provided you maintain
|
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clear directions next to the object code saying where to find the
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Corresponding Source. Regardless of what server hosts the
|
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Corresponding Source, you remain obligated to ensure that it is
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+
available for as long as needed to satisfy these requirements.
|
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+
|
288 |
+
e) Convey the object code using peer-to-peer transmission, provided
|
289 |
+
you inform other peers where the object code and Corresponding
|
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+
Source of the work are being offered to the general public at no
|
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+
charge under subsection 6d.
|
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+
|
293 |
+
A separable portion of the object code, whose source code is excluded
|
294 |
+
from the Corresponding Source as a System Library, need not be
|
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included in conveying the object code work.
|
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|
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A "User Product" is either (1) a "consumer product", which means any
|
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tangible personal property which is normally used for personal, family,
|
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|
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|
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|
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|
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+
typical or common use of that class of product, regardless of the status
|
304 |
+
of the particular user or of the way in which the particular user
|
305 |
+
actually uses, or expects or is expected to use, the product. A product
|
306 |
+
is a consumer product regardless of whether the product has substantial
|
307 |
+
commercial, industrial or non-consumer uses, unless such uses represent
|
308 |
+
the only significant mode of use of the product.
|
309 |
+
|
310 |
+
"Installation Information" for a User Product means any methods,
|
311 |
+
procedures, authorization keys, or other information required to install
|
312 |
+
and execute modified versions of a covered work in that User Product from
|
313 |
+
a modified version of its Corresponding Source. The information must
|
314 |
+
suffice to ensure that the continued functioning of the modified object
|
315 |
+
code is in no case prevented or interfered with solely because
|
316 |
+
modification has been made.
|
317 |
+
|
318 |
+
If you convey an object code work under this section in, or with, or
|
319 |
+
specifically for use in, a User Product, and the conveying occurs as
|
320 |
+
part of a transaction in which the right of possession and use of the
|
321 |
+
User Product is transferred to the recipient in perpetuity or for a
|
322 |
+
fixed term (regardless of how the transaction is characterized), the
|
323 |
+
Corresponding Source conveyed under this section must be accompanied
|
324 |
+
by the Installation Information. But this requirement does not apply
|
325 |
+
if neither you nor any third party retains the ability to install
|
326 |
+
modified object code on the User Product (for example, the work has
|
327 |
+
been installed in ROM).
|
328 |
+
|
329 |
+
The requirement to provide Installation Information does not include a
|
330 |
+
requirement to continue to provide support service, warranty, or updates
|
331 |
+
for a work that has been modified or installed by the recipient, or for
|
332 |
+
the User Product in which it has been modified or installed. Access to a
|
333 |
+
network may be denied when the modification itself materially and
|
334 |
+
adversely affects the operation of the network or violates the rules and
|
335 |
+
protocols for communication across the network.
|
336 |
+
|
337 |
+
Corresponding Source conveyed, and Installation Information provided,
|
338 |
+
in accord with this section must be in a format that is publicly
|
339 |
+
documented (and with an implementation available to the public in
|
340 |
+
source code form), and must require no special password or key for
|
341 |
+
unpacking, reading or copying.
|
342 |
+
|
343 |
+
7. Additional Terms.
|
344 |
+
|
345 |
+
"Additional permissions" are terms that supplement the terms of this
|
346 |
+
License by making exceptions from one or more of its conditions.
|
347 |
+
Additional permissions that are applicable to the entire Program shall
|
348 |
+
be treated as though they were included in this License, to the extent
|
349 |
+
that they are valid under applicable law. If additional permissions
|
350 |
+
apply only to part of the Program, that part may be used separately
|
351 |
+
under those permissions, but the entire Program remains governed by
|
352 |
+
this License without regard to the additional permissions.
|
353 |
+
|
354 |
+
When you convey a copy of a covered work, you may at your option
|
355 |
+
remove any additional permissions from that copy, or from any part of
|
356 |
+
it. (Additional permissions may be written to require their own
|
357 |
+
removal in certain cases when you modify the work.) You may place
|
358 |
+
additional permissions on material, added by you to a covered work,
|
359 |
+
for which you have or can give appropriate copyright permission.
|
360 |
+
|
361 |
+
Notwithstanding any other provision of this License, for material you
|
362 |
+
add to a covered work, you may (if authorized by the copyright holders of
|
363 |
+
that material) supplement the terms of this License with terms:
|
364 |
+
|
365 |
+
a) Disclaiming warranty or limiting liability differently from the
|
366 |
+
terms of sections 15 and 16 of this License; or
|
367 |
+
|
368 |
+
b) Requiring preservation of specified reasonable legal notices or
|
369 |
+
author attributions in that material or in the Appropriate Legal
|
370 |
+
Notices displayed by works containing it; or
|
371 |
+
|
372 |
+
c) Prohibiting misrepresentation of the origin of that material, or
|
373 |
+
requiring that modified versions of such material be marked in
|
374 |
+
reasonable ways as different from the original version; or
|
375 |
+
|
376 |
+
d) Limiting the use for publicity purposes of names of licensors or
|
377 |
+
authors of the material; or
|
378 |
+
|
379 |
+
e) Declining to grant rights under trademark law for use of some
|
380 |
+
trade names, trademarks, or service marks; or
|
381 |
+
|
382 |
+
f) Requiring indemnification of licensors and authors of that
|
383 |
+
material by anyone who conveys the material (or modified versions of
|
384 |
+
it) with contractual assumptions of liability to the recipient, for
|
385 |
+
any liability that these contractual assumptions directly impose on
|
386 |
+
those licensors and authors.
|
387 |
+
|
388 |
+
All other non-permissive additional terms are considered "further
|
389 |
+
restrictions" within the meaning of section 10. If the Program as you
|
390 |
+
received it, or any part of it, contains a notice stating that it is
|
391 |
+
governed by this License along with a term that is a further
|
392 |
+
restriction, you may remove that term. If a license document contains
|
393 |
+
a further restriction but permits relicensing or conveying under this
|
394 |
+
License, you may add to a covered work material governed by the terms
|
395 |
+
of that license document, provided that the further restriction does
|
396 |
+
not survive such relicensing or conveying.
|
397 |
+
|
398 |
+
If you add terms to a covered work in accord with this section, you
|
399 |
+
must place, in the relevant source files, a statement of the
|
400 |
+
additional terms that apply to those files, or a notice indicating
|
401 |
+
where to find the applicable terms.
|
402 |
+
|
403 |
+
Additional terms, permissive or non-permissive, may be stated in the
|
404 |
+
form of a separately written license, or stated as exceptions;
|
405 |
+
the above requirements apply either way.
|
406 |
+
|
407 |
+
8. Termination.
|
408 |
+
|
409 |
+
You may not propagate or modify a covered work except as expressly
|
410 |
+
provided under this License. Any attempt otherwise to propagate or
|
411 |
+
modify it is void, and will automatically terminate your rights under
|
412 |
+
this License (including any patent licenses granted under the third
|
413 |
+
paragraph of section 11).
|
414 |
+
|
415 |
+
However, if you cease all violation of this License, then your
|
416 |
+
license from a particular copyright holder is reinstated (a)
|
417 |
+
provisionally, unless and until the copyright holder explicitly and
|
418 |
+
finally terminates your license, and (b) permanently, if the copyright
|
419 |
+
holder fails to notify you of the violation by some reasonable means
|
420 |
+
prior to 60 days after the cessation.
|
421 |
+
|
422 |
+
Moreover, your license from a particular copyright holder is
|
423 |
+
reinstated permanently if the copyright holder notifies you of the
|
424 |
+
violation by some reasonable means, this is the first time you have
|
425 |
+
received notice of violation of this License (for any work) from that
|
426 |
+
copyright holder, and you cure the violation prior to 30 days after
|
427 |
+
your receipt of the notice.
|
428 |
+
|
429 |
+
Termination of your rights under this section does not terminate the
|
430 |
+
licenses of parties who have received copies or rights from you under
|
431 |
+
this License. If your rights have been terminated and not permanently
|
432 |
+
reinstated, you do not qualify to receive new licenses for the same
|
433 |
+
material under section 10.
|
434 |
+
|
435 |
+
9. Acceptance Not Required for Having Copies.
|
436 |
+
|
437 |
+
You are not required to accept this License in order to receive or
|
438 |
+
run a copy of the Program. Ancillary propagation of a covered work
|
439 |
+
occurring solely as a consequence of using peer-to-peer transmission
|
440 |
+
to receive a copy likewise does not require acceptance. However,
|
441 |
+
nothing other than this License grants you permission to propagate or
|
442 |
+
modify any covered work. These actions infringe copyright if you do
|
443 |
+
not accept this License. Therefore, by modifying or propagating a
|
444 |
+
covered work, you indicate your acceptance of this License to do so.
|
445 |
+
|
446 |
+
10. Automatic Licensing of Downstream Recipients.
|
447 |
+
|
448 |
+
Each time you convey a covered work, the recipient automatically
|
449 |
+
receives a license from the original licensors, to run, modify and
|
450 |
+
propagate that work, subject to this License. You are not responsible
|
451 |
+
for enforcing compliance by third parties with this License.
|
452 |
+
|
453 |
+
An "entity transaction" is a transaction transferring control of an
|
454 |
+
organization, or substantially all assets of one, or subdividing an
|
455 |
+
organization, or merging organizations. If propagation of a covered
|
456 |
+
work results from an entity transaction, each party to that
|
457 |
+
transaction who receives a copy of the work also receives whatever
|
458 |
+
licenses to the work the party's predecessor in interest had or could
|
459 |
+
give under the previous paragraph, plus a right to possession of the
|
460 |
+
Corresponding Source of the work from the predecessor in interest, if
|
461 |
+
the predecessor has it or can get it with reasonable efforts.
|
462 |
+
|
463 |
+
You may not impose any further restrictions on the exercise of the
|
464 |
+
rights granted or affirmed under this License. For example, you may
|
465 |
+
not impose a license fee, royalty, or other charge for exercise of
|
466 |
+
rights granted under this License, and you may not initiate litigation
|
467 |
+
(including a cross-claim or counterclaim in a lawsuit) alleging that
|
468 |
+
any patent claim is infringed by making, using, selling, offering for
|
469 |
+
sale, or importing the Program or any portion of it.
|
470 |
+
|
471 |
+
11. Patents.
|
472 |
+
|
473 |
+
A "contributor" is a copyright holder who authorizes use under this
|
474 |
+
License of the Program or a work on which the Program is based. The
|
475 |
+
work thus licensed is called the contributor's "contributor version".
|
476 |
+
|
477 |
+
A contributor's "essential patent claims" are all patent claims
|
478 |
+
owned or controlled by the contributor, whether already acquired or
|
479 |
+
hereafter acquired, that would be infringed by some manner, permitted
|
480 |
+
by this License, of making, using, or selling its contributor version,
|
481 |
+
but do not include claims that would be infringed only as a
|
482 |
+
consequence of further modification of the contributor version. For
|
483 |
+
purposes of this definition, "control" includes the right to grant
|
484 |
+
patent sublicenses in a manner consistent with the requirements of
|
485 |
+
this License.
|
486 |
+
|
487 |
+
Each contributor grants you a non-exclusive, worldwide, royalty-free
|
488 |
+
patent license under the contributor's essential patent claims, to
|
489 |
+
make, use, sell, offer for sale, import and otherwise run, modify and
|
490 |
+
propagate the contents of its contributor version.
|
491 |
+
|
492 |
+
In the following three paragraphs, a "patent license" is any express
|
493 |
+
agreement or commitment, however denominated, not to enforce a patent
|
494 |
+
(such as an express permission to practice a patent or covenant not to
|
495 |
+
sue for patent infringement). To "grant" such a patent license to a
|
496 |
+
party means to make such an agreement or commitment not to enforce a
|
497 |
+
patent against the party.
|
498 |
+
|
499 |
+
If you convey a covered work, knowingly relying on a patent license,
|
500 |
+
and the Corresponding Source of the work is not available for anyone
|
501 |
+
to copy, free of charge and under the terms of this License, through a
|
502 |
+
publicly available network server or other readily accessible means,
|
503 |
+
then you must either (1) cause the Corresponding Source to be so
|
504 |
+
available, or (2) arrange to deprive yourself of the benefit of the
|
505 |
+
patent license for this particular work, or (3) arrange, in a manner
|
506 |
+
consistent with the requirements of this License, to extend the patent
|
507 |
+
license to downstream recipients. "Knowingly relying" means you have
|
508 |
+
actual knowledge that, but for the patent license, your conveying the
|
509 |
+
covered work in a country, or your recipient's use of the covered work
|
510 |
+
in a country, would infringe one or more identifiable patents in that
|
511 |
+
country that you have reason to believe are valid.
|
512 |
+
|
513 |
+
If, pursuant to or in connection with a single transaction or
|
514 |
+
arrangement, you convey, or propagate by procuring conveyance of, a
|
515 |
+
covered work, and grant a patent license to some of the parties
|
516 |
+
receiving the covered work authorizing them to use, propagate, modify
|
517 |
+
or convey a specific copy of the covered work, then the patent license
|
518 |
+
you grant is automatically extended to all recipients of the covered
|
519 |
+
work and works based on it.
|
520 |
+
|
521 |
+
A patent license is "discriminatory" if it does not include within
|
522 |
+
the scope of its coverage, prohibits the exercise of, or is
|
523 |
+
conditioned on the non-exercise of one or more of the rights that are
|
524 |
+
specifically granted under this License. You may not convey a covered
|
525 |
+
work if you are a party to an arrangement with a third party that is
|
526 |
+
in the business of distributing software, under which you make payment
|
527 |
+
to the third party based on the extent of your activity of conveying
|
528 |
+
the work, and under which the third party grants, to any of the
|
529 |
+
parties who would receive the covered work from you, a discriminatory
|
530 |
+
patent license (a) in connection with copies of the covered work
|
531 |
+
conveyed by you (or copies made from those copies), or (b) primarily
|
532 |
+
for and in connection with specific products or compilations that
|
533 |
+
contain the covered work, unless you entered into that arrangement,
|
534 |
+
or that patent license was granted, prior to 28 March 2007.
|
535 |
+
|
536 |
+
Nothing in this License shall be construed as excluding or limiting
|
537 |
+
any implied license or other defenses to infringement that may
|
538 |
+
otherwise be available to you under applicable patent law.
|
539 |
+
|
540 |
+
12. No Surrender of Others' Freedom.
|
541 |
+
|
542 |
+
If conditions are imposed on you (whether by court order, agreement or
|
543 |
+
otherwise) that contradict the conditions of this License, they do not
|
544 |
+
excuse you from the conditions of this License. If you cannot convey a
|
545 |
+
covered work so as to satisfy simultaneously your obligations under this
|
546 |
+
License and any other pertinent obligations, then as a consequence you may
|
547 |
+
not convey it at all. For example, if you agree to terms that obligate you
|
548 |
+
to collect a royalty for further conveying from those to whom you convey
|
549 |
+
the Program, the only way you could satisfy both those terms and this
|
550 |
+
License would be to refrain entirely from conveying the Program.
|
551 |
+
|
552 |
+
13. Use with the GNU Affero General Public License.
|
553 |
+
|
554 |
+
Notwithstanding any other provision of this License, you have
|
555 |
+
permission to link or combine any covered work with a work licensed
|
556 |
+
under version 3 of the GNU Affero General Public License into a single
|
557 |
+
combined work, and to convey the resulting work. The terms of this
|
558 |
+
License will continue to apply to the part which is the covered work,
|
559 |
+
but the special requirements of the GNU Affero General Public License,
|
560 |
+
section 13, concerning interaction through a network will apply to the
|
561 |
+
combination as such.
|
562 |
+
|
563 |
+
14. Revised Versions of this License.
|
564 |
+
|
565 |
+
The Free Software Foundation may publish revised and/or new versions of
|
566 |
+
the GNU General Public License from time to time. Such new versions will
|
567 |
+
be similar in spirit to the present version, but may differ in detail to
|
568 |
+
address new problems or concerns.
|
569 |
+
|
570 |
+
Each version is given a distinguishing version number. If the
|
571 |
+
Program specifies that a certain numbered version of the GNU General
|
572 |
+
Public License "or any later version" applies to it, you have the
|
573 |
+
option of following the terms and conditions either of that numbered
|
574 |
+
version or of any later version published by the Free Software
|
575 |
+
Foundation. If the Program does not specify a version number of the
|
576 |
+
GNU General Public License, you may choose any version ever published
|
577 |
+
by the Free Software Foundation.
|
578 |
+
|
579 |
+
If the Program specifies that a proxy can decide which future
|
580 |
+
versions of the GNU General Public License can be used, that proxy's
|
581 |
+
public statement of acceptance of a version permanently authorizes you
|
582 |
+
to choose that version for the Program.
|
583 |
+
|
584 |
+
Later license versions may give you additional or different
|
585 |
+
permissions. However, no additional obligations are imposed on any
|
586 |
+
author or copyright holder as a result of your choosing to follow a
|
587 |
+
later version.
|
588 |
+
|
589 |
+
15. Disclaimer of Warranty.
|
590 |
+
|
591 |
+
THERE IS NO WARRANTY FOR THE PROGRAM, TO THE EXTENT PERMITTED BY
|
592 |
+
APPLICABLE LAW. EXCEPT WHEN OTHERWISE STATED IN WRITING THE COPYRIGHT
|
593 |
+
HOLDERS AND/OR OTHER PARTIES PROVIDE THE PROGRAM "AS IS" WITHOUT WARRANTY
|
594 |
+
OF ANY KIND, EITHER EXPRESSED OR IMPLIED, INCLUDING, BUT NOT LIMITED TO,
|
595 |
+
THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR
|
596 |
+
PURPOSE. THE ENTIRE RISK AS TO THE QUALITY AND PERFORMANCE OF THE PROGRAM
|
597 |
+
IS WITH YOU. SHOULD THE PROGRAM PROVE DEFECTIVE, YOU ASSUME THE COST OF
|
598 |
+
ALL NECESSARY SERVICING, REPAIR OR CORRECTION.
|
599 |
+
|
600 |
+
16. Limitation of Liability.
|
601 |
+
|
602 |
+
IN NO EVENT UNLESS REQUIRED BY APPLICABLE LAW OR AGREED TO IN WRITING
|
603 |
+
WILL ANY COPYRIGHT HOLDER, OR ANY OTHER PARTY WHO MODIFIES AND/OR CONVEYS
|
604 |
+
THE PROGRAM AS PERMITTED ABOVE, BE LIABLE TO YOU FOR DAMAGES, INCLUDING ANY
|
605 |
+
GENERAL, SPECIAL, INCIDENTAL OR CONSEQUENTIAL DAMAGES ARISING OUT OF THE
|
606 |
+
USE OR INABILITY TO USE THE PROGRAM (INCLUDING BUT NOT LIMITED TO LOSS OF
|
607 |
+
DATA OR DATA BEING RENDERED INACCURATE OR LOSSES SUSTAINED BY YOU OR THIRD
|
608 |
+
PARTIES OR A FAILURE OF THE PROGRAM TO OPERATE WITH ANY OTHER PROGRAMS),
|
609 |
+
EVEN IF SUCH HOLDER OR OTHER PARTY HAS BEEN ADVISED OF THE POSSIBILITY OF
|
610 |
+
SUCH DAMAGES.
|
611 |
+
|
612 |
+
17. Interpretation of Sections 15 and 16.
|
613 |
+
|
614 |
+
If the disclaimer of warranty and limitation of liability provided
|
615 |
+
above cannot be given local legal effect according to their terms,
|
616 |
+
reviewing courts shall apply local law that most closely approximates
|
617 |
+
an absolute waiver of all civil liability in connection with the
|
618 |
+
Program, unless a warranty or assumption of liability accompanies a
|
619 |
+
copy of the Program in return for a fee.
|
620 |
+
|
621 |
+
END OF TERMS AND CONDITIONS
|
622 |
+
|
623 |
+
How to Apply These Terms to Your New Programs
|
624 |
+
|
625 |
+
If you develop a new program, and you want it to be of the greatest
|
626 |
+
possible use to the public, the best way to achieve this is to make it
|
627 |
+
free software which everyone can redistribute and change under these terms.
|
628 |
+
|
629 |
+
To do so, attach the following notices to the program. It is safest
|
630 |
+
to attach them to the start of each source file to most effectively
|
631 |
+
state the exclusion of warranty; and each file should have at least
|
632 |
+
the "copyright" line and a pointer to where the full notice is found.
|
633 |
+
|
634 |
+
<one line to give the program's name and a brief idea of what it does.>
|
635 |
+
Copyright (C) <year> <name of author>
|
636 |
+
|
637 |
+
This program is free software: you can redistribute it and/or modify
|
638 |
+
it under the terms of the GNU General Public License as published by
|
639 |
+
the Free Software Foundation, either version 3 of the License, or
|
640 |
+
(at your option) any later version.
|
641 |
+
|
642 |
+
This program is distributed in the hope that it will be useful,
|
643 |
+
but WITHOUT ANY WARRANTY; without even the implied warranty of
|
644 |
+
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
|
645 |
+
GNU General Public License for more details.
|
646 |
+
|
647 |
+
You should have received a copy of the GNU General Public License
|
648 |
+
along with this program. If not, see <https://www.gnu.org/licenses/>.
|
649 |
+
|
650 |
+
Also add information on how to contact you by electronic and paper mail.
|
651 |
+
|
652 |
+
If the program does terminal interaction, make it output a short
|
653 |
+
notice like this when it starts in an interactive mode:
|
654 |
+
|
655 |
+
<program> Copyright (C) <year> <name of author>
|
656 |
+
This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'.
|
657 |
+
This is free software, and you are welcome to redistribute it
|
658 |
+
under certain conditions; type `show c' for details.
|
659 |
+
|
660 |
+
The hypothetical commands `show w' and `show c' should show the appropriate
|
661 |
+
parts of the General Public License. Of course, your program's commands
|
662 |
+
might be different; for a GUI interface, you would use an "about box".
|
663 |
+
|
664 |
+
You should also get your employer (if you work as a programmer) or school,
|
665 |
+
if any, to sign a "copyright disclaimer" for the program, if necessary.
|
666 |
+
For more information on this, and how to apply and follow the GNU GPL, see
|
667 |
+
<https://www.gnu.org/licenses/>.
|
668 |
+
|
669 |
+
The GNU General Public License does not permit incorporating your program
|
670 |
+
into proprietary programs. If your program is a subroutine library, you
|
671 |
+
may consider it more useful to permit linking proprietary applications with
|
672 |
+
the library. If this is what you want to do, use the GNU Lesser General
|
673 |
+
Public License instead of this License. But first, please read
|
674 |
+
<https://www.gnu.org/licenses/why-not-lgpl.html>.
|
bert_vits2/README.md
ADDED
@@ -0,0 +1,5 @@
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1 |
+
# Bert-VITS2
|
2 |
+
|
3 |
+
VITS2 Backbone with bert
|
4 |
+
## 成熟的旅行者/开拓者/舰长/博士/sensei/猎魔人/喵喵露/V应该参阅代码自己学习如何训练。
|
5 |
+
### 严禁将此项目用于一切违反《中华人民共和国宪法》,《中华人民共和国刑法》,《中华人民共和国治安管理处罚法》和《中华人民共和国民法典》之用途。
|
bert_vits2/__init__.py
ADDED
@@ -0,0 +1,2 @@
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1 |
+
from bert_vits2.bert_vits2 import Bert_VITS2
|
2 |
+
from bert_vits2 import text
|
bert_vits2/attentions.py
ADDED
@@ -0,0 +1,352 @@
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|
1 |
+
import math
|
2 |
+
import torch
|
3 |
+
from torch import nn
|
4 |
+
from torch.nn import functional as F
|
5 |
+
from bert_vits2 import commons
|
6 |
+
from torch.nn.utils import weight_norm, remove_weight_norm
|
7 |
+
|
8 |
+
|
9 |
+
class LayerNorm(nn.Module):
|
10 |
+
def __init__(self, channels, eps=1e-5):
|
11 |
+
super().__init__()
|
12 |
+
self.channels = channels
|
13 |
+
self.eps = eps
|
14 |
+
|
15 |
+
self.gamma = nn.Parameter(torch.ones(channels))
|
16 |
+
self.beta = nn.Parameter(torch.zeros(channels))
|
17 |
+
|
18 |
+
def forward(self, x):
|
19 |
+
x = x.transpose(1, -1)
|
20 |
+
x = F.layer_norm(x, (self.channels,), self.gamma, self.beta, self.eps)
|
21 |
+
return x.transpose(1, -1)
|
22 |
+
|
23 |
+
|
24 |
+
@torch.jit.script
|
25 |
+
def fused_add_tanh_sigmoid_multiply(input_a, input_b, n_channels):
|
26 |
+
n_channels_int = n_channels[0]
|
27 |
+
in_act = input_a + input_b
|
28 |
+
t_act = torch.tanh(in_act[:, :n_channels_int, :])
|
29 |
+
s_act = torch.sigmoid(in_act[:, n_channels_int:, :])
|
30 |
+
acts = t_act * s_act
|
31 |
+
return acts
|
32 |
+
|
33 |
+
|
34 |
+
class Encoder(nn.Module):
|
35 |
+
def __init__(self, hidden_channels, filter_channels, n_heads, n_layers, kernel_size=1, p_dropout=0., window_size=4,
|
36 |
+
isflow=True, **kwargs):
|
37 |
+
super().__init__()
|
38 |
+
self.hidden_channels = hidden_channels
|
39 |
+
self.filter_channels = filter_channels
|
40 |
+
self.n_heads = n_heads
|
41 |
+
self.n_layers = n_layers
|
42 |
+
self.kernel_size = kernel_size
|
43 |
+
self.p_dropout = p_dropout
|
44 |
+
self.window_size = window_size
|
45 |
+
if isflow:
|
46 |
+
cond_layer = torch.nn.Conv1d(256, 2 * hidden_channels * n_layers, 1)
|
47 |
+
self.cond_pre = torch.nn.Conv1d(hidden_channels, 2 * hidden_channels, 1)
|
48 |
+
self.cond_layer = weight_norm(cond_layer, name='weight')
|
49 |
+
self.gin_channels = 256
|
50 |
+
self.cond_layer_idx = self.n_layers
|
51 |
+
if 'gin_channels' in kwargs:
|
52 |
+
self.gin_channels = kwargs['gin_channels']
|
53 |
+
if self.gin_channels != 0:
|
54 |
+
self.spk_emb_linear = nn.Linear(self.gin_channels, self.hidden_channels)
|
55 |
+
# vits2 says 3rd block, so idx is 2 by default
|
56 |
+
self.cond_layer_idx = kwargs['cond_layer_idx'] if 'cond_layer_idx' in kwargs else 2
|
57 |
+
# print(self.gin_channels, self.cond_layer_idx)
|
58 |
+
assert self.cond_layer_idx < self.n_layers, 'cond_layer_idx should be less than n_layers'
|
59 |
+
self.drop = nn.Dropout(p_dropout)
|
60 |
+
self.attn_layers = nn.ModuleList()
|
61 |
+
self.norm_layers_1 = nn.ModuleList()
|
62 |
+
self.ffn_layers = nn.ModuleList()
|
63 |
+
self.norm_layers_2 = nn.ModuleList()
|
64 |
+
for i in range(self.n_layers):
|
65 |
+
self.attn_layers.append(MultiHeadAttention(hidden_channels, hidden_channels, n_heads, p_dropout=p_dropout,
|
66 |
+
window_size=window_size))
|
67 |
+
self.norm_layers_1.append(LayerNorm(hidden_channels))
|
68 |
+
self.ffn_layers.append(
|
69 |
+
FFN(hidden_channels, hidden_channels, filter_channels, kernel_size, p_dropout=p_dropout))
|
70 |
+
self.norm_layers_2.append(LayerNorm(hidden_channels))
|
71 |
+
|
72 |
+
def forward(self, x, x_mask, g=None):
|
73 |
+
attn_mask = x_mask.unsqueeze(2) * x_mask.unsqueeze(-1)
|
74 |
+
x = x * x_mask
|
75 |
+
for i in range(self.n_layers):
|
76 |
+
if i == self.cond_layer_idx and g is not None:
|
77 |
+
g = self.spk_emb_linear(g.transpose(1, 2))
|
78 |
+
g = g.transpose(1, 2)
|
79 |
+
x = x + g
|
80 |
+
x = x * x_mask
|
81 |
+
y = self.attn_layers[i](x, x, attn_mask)
|
82 |
+
y = self.drop(y)
|
83 |
+
x = self.norm_layers_1[i](x + y)
|
84 |
+
|
85 |
+
y = self.ffn_layers[i](x, x_mask)
|
86 |
+
y = self.drop(y)
|
87 |
+
x = self.norm_layers_2[i](x + y)
|
88 |
+
x = x * x_mask
|
89 |
+
return x
|
90 |
+
|
91 |
+
|
92 |
+
class Decoder(nn.Module):
|
93 |
+
def __init__(self, hidden_channels, filter_channels, n_heads, n_layers, kernel_size=1, p_dropout=0.,
|
94 |
+
proximal_bias=False, proximal_init=True, **kwargs):
|
95 |
+
super().__init__()
|
96 |
+
self.hidden_channels = hidden_channels
|
97 |
+
self.filter_channels = filter_channels
|
98 |
+
self.n_heads = n_heads
|
99 |
+
self.n_layers = n_layers
|
100 |
+
self.kernel_size = kernel_size
|
101 |
+
self.p_dropout = p_dropout
|
102 |
+
self.proximal_bias = proximal_bias
|
103 |
+
self.proximal_init = proximal_init
|
104 |
+
|
105 |
+
self.drop = nn.Dropout(p_dropout)
|
106 |
+
self.self_attn_layers = nn.ModuleList()
|
107 |
+
self.norm_layers_0 = nn.ModuleList()
|
108 |
+
self.encdec_attn_layers = nn.ModuleList()
|
109 |
+
self.norm_layers_1 = nn.ModuleList()
|
110 |
+
self.ffn_layers = nn.ModuleList()
|
111 |
+
self.norm_layers_2 = nn.ModuleList()
|
112 |
+
for i in range(self.n_layers):
|
113 |
+
self.self_attn_layers.append(
|
114 |
+
MultiHeadAttention(hidden_channels, hidden_channels, n_heads, p_dropout=p_dropout,
|
115 |
+
proximal_bias=proximal_bias, proximal_init=proximal_init))
|
116 |
+
self.norm_layers_0.append(LayerNorm(hidden_channels))
|
117 |
+
self.encdec_attn_layers.append(
|
118 |
+
MultiHeadAttention(hidden_channels, hidden_channels, n_heads, p_dropout=p_dropout))
|
119 |
+
self.norm_layers_1.append(LayerNorm(hidden_channels))
|
120 |
+
self.ffn_layers.append(
|
121 |
+
FFN(hidden_channels, hidden_channels, filter_channels, kernel_size, p_dropout=p_dropout, causal=True))
|
122 |
+
self.norm_layers_2.append(LayerNorm(hidden_channels))
|
123 |
+
|
124 |
+
def forward(self, x, x_mask, h, h_mask):
|
125 |
+
"""
|
126 |
+
x: decoder input
|
127 |
+
h: encoder output
|
128 |
+
"""
|
129 |
+
self_attn_mask = commons.subsequent_mask(x_mask.size(2)).to(device=x.device, dtype=x.dtype)
|
130 |
+
encdec_attn_mask = h_mask.unsqueeze(2) * x_mask.unsqueeze(-1)
|
131 |
+
x = x * x_mask
|
132 |
+
for i in range(self.n_layers):
|
133 |
+
y = self.self_attn_layers[i](x, x, self_attn_mask)
|
134 |
+
y = self.drop(y)
|
135 |
+
x = self.norm_layers_0[i](x + y)
|
136 |
+
|
137 |
+
y = self.encdec_attn_layers[i](x, h, encdec_attn_mask)
|
138 |
+
y = self.drop(y)
|
139 |
+
x = self.norm_layers_1[i](x + y)
|
140 |
+
|
141 |
+
y = self.ffn_layers[i](x, x_mask)
|
142 |
+
y = self.drop(y)
|
143 |
+
x = self.norm_layers_2[i](x + y)
|
144 |
+
x = x * x_mask
|
145 |
+
return x
|
146 |
+
|
147 |
+
|
148 |
+
class MultiHeadAttention(nn.Module):
|
149 |
+
def __init__(self, channels, out_channels, n_heads, p_dropout=0., window_size=None, heads_share=True,
|
150 |
+
block_length=None, proximal_bias=False, proximal_init=False):
|
151 |
+
super().__init__()
|
152 |
+
assert channels % n_heads == 0
|
153 |
+
|
154 |
+
self.channels = channels
|
155 |
+
self.out_channels = out_channels
|
156 |
+
self.n_heads = n_heads
|
157 |
+
self.p_dropout = p_dropout
|
158 |
+
self.window_size = window_size
|
159 |
+
self.heads_share = heads_share
|
160 |
+
self.block_length = block_length
|
161 |
+
self.proximal_bias = proximal_bias
|
162 |
+
self.proximal_init = proximal_init
|
163 |
+
self.attn = None
|
164 |
+
|
165 |
+
self.k_channels = channels // n_heads
|
166 |
+
self.conv_q = nn.Conv1d(channels, channels, 1)
|
167 |
+
self.conv_k = nn.Conv1d(channels, channels, 1)
|
168 |
+
self.conv_v = nn.Conv1d(channels, channels, 1)
|
169 |
+
self.conv_o = nn.Conv1d(channels, out_channels, 1)
|
170 |
+
self.drop = nn.Dropout(p_dropout)
|
171 |
+
|
172 |
+
if window_size is not None:
|
173 |
+
n_heads_rel = 1 if heads_share else n_heads
|
174 |
+
rel_stddev = self.k_channels ** -0.5
|
175 |
+
self.emb_rel_k = nn.Parameter(torch.randn(n_heads_rel, window_size * 2 + 1, self.k_channels) * rel_stddev)
|
176 |
+
self.emb_rel_v = nn.Parameter(torch.randn(n_heads_rel, window_size * 2 + 1, self.k_channels) * rel_stddev)
|
177 |
+
|
178 |
+
nn.init.xavier_uniform_(self.conv_q.weight)
|
179 |
+
nn.init.xavier_uniform_(self.conv_k.weight)
|
180 |
+
nn.init.xavier_uniform_(self.conv_v.weight)
|
181 |
+
if proximal_init:
|
182 |
+
with torch.no_grad():
|
183 |
+
self.conv_k.weight.copy_(self.conv_q.weight)
|
184 |
+
self.conv_k.bias.copy_(self.conv_q.bias)
|
185 |
+
|
186 |
+
def forward(self, x, c, attn_mask=None):
|
187 |
+
q = self.conv_q(x)
|
188 |
+
k = self.conv_k(c)
|
189 |
+
v = self.conv_v(c)
|
190 |
+
|
191 |
+
x, self.attn = self.attention(q, k, v, mask=attn_mask)
|
192 |
+
|
193 |
+
x = self.conv_o(x)
|
194 |
+
return x
|
195 |
+
|
196 |
+
def attention(self, query, key, value, mask=None):
|
197 |
+
# reshape [b, d, t] -> [b, n_h, t, d_k]
|
198 |
+
b, d, t_s, t_t = (*key.size(), query.size(2))
|
199 |
+
query = query.view(b, self.n_heads, self.k_channels, t_t).transpose(2, 3)
|
200 |
+
key = key.view(b, self.n_heads, self.k_channels, t_s).transpose(2, 3)
|
201 |
+
value = value.view(b, self.n_heads, self.k_channels, t_s).transpose(2, 3)
|
202 |
+
|
203 |
+
scores = torch.matmul(query / math.sqrt(self.k_channels), key.transpose(-2, -1))
|
204 |
+
if self.window_size is not None:
|
205 |
+
assert t_s == t_t, "Relative attention is only available for self-attention."
|
206 |
+
key_relative_embeddings = self._get_relative_embeddings(self.emb_rel_k, t_s)
|
207 |
+
rel_logits = self._matmul_with_relative_keys(query / math.sqrt(self.k_channels), key_relative_embeddings)
|
208 |
+
scores_local = self._relative_position_to_absolute_position(rel_logits)
|
209 |
+
scores = scores + scores_local
|
210 |
+
if self.proximal_bias:
|
211 |
+
assert t_s == t_t, "Proximal bias is only available for self-attention."
|
212 |
+
scores = scores + self._attention_bias_proximal(t_s).to(device=scores.device, dtype=scores.dtype)
|
213 |
+
if mask is not None:
|
214 |
+
scores = scores.masked_fill(mask == 0, -1e4)
|
215 |
+
if self.block_length is not None:
|
216 |
+
assert t_s == t_t, "Local attention is only available for self-attention."
|
217 |
+
block_mask = torch.ones_like(scores).triu(-self.block_length).tril(self.block_length)
|
218 |
+
scores = scores.masked_fill(block_mask == 0, -1e4)
|
219 |
+
p_attn = F.softmax(scores, dim=-1) # [b, n_h, t_t, t_s]
|
220 |
+
p_attn = self.drop(p_attn)
|
221 |
+
output = torch.matmul(p_attn, value)
|
222 |
+
if self.window_size is not None:
|
223 |
+
relative_weights = self._absolute_position_to_relative_position(p_attn)
|
224 |
+
value_relative_embeddings = self._get_relative_embeddings(self.emb_rel_v, t_s)
|
225 |
+
output = output + self._matmul_with_relative_values(relative_weights, value_relative_embeddings)
|
226 |
+
output = output.transpose(2, 3).contiguous().view(b, d, t_t) # [b, n_h, t_t, d_k] -> [b, d, t_t]
|
227 |
+
return output, p_attn
|
228 |
+
|
229 |
+
def _matmul_with_relative_values(self, x, y):
|
230 |
+
"""
|
231 |
+
x: [b, h, l, m]
|
232 |
+
y: [h or 1, m, d]
|
233 |
+
ret: [b, h, l, d]
|
234 |
+
"""
|
235 |
+
ret = torch.matmul(x, y.unsqueeze(0))
|
236 |
+
return ret
|
237 |
+
|
238 |
+
def _matmul_with_relative_keys(self, x, y):
|
239 |
+
"""
|
240 |
+
x: [b, h, l, d]
|
241 |
+
y: [h or 1, m, d]
|
242 |
+
ret: [b, h, l, m]
|
243 |
+
"""
|
244 |
+
ret = torch.matmul(x, y.unsqueeze(0).transpose(-2, -1))
|
245 |
+
return ret
|
246 |
+
|
247 |
+
def _get_relative_embeddings(self, relative_embeddings, length):
|
248 |
+
max_relative_position = 2 * self.window_size + 1
|
249 |
+
# Pad first before slice to avoid using cond ops.
|
250 |
+
pad_length = max(length - (self.window_size + 1), 0)
|
251 |
+
slice_start_position = max((self.window_size + 1) - length, 0)
|
252 |
+
slice_end_position = slice_start_position + 2 * length - 1
|
253 |
+
if pad_length > 0:
|
254 |
+
padded_relative_embeddings = F.pad(
|
255 |
+
relative_embeddings,
|
256 |
+
commons.convert_pad_shape([[0, 0], [pad_length, pad_length], [0, 0]]))
|
257 |
+
else:
|
258 |
+
padded_relative_embeddings = relative_embeddings
|
259 |
+
used_relative_embeddings = padded_relative_embeddings[:, slice_start_position:slice_end_position]
|
260 |
+
return used_relative_embeddings
|
261 |
+
|
262 |
+
def _relative_position_to_absolute_position(self, x):
|
263 |
+
"""
|
264 |
+
x: [b, h, l, 2*l-1]
|
265 |
+
ret: [b, h, l, l]
|
266 |
+
"""
|
267 |
+
batch, heads, length, _ = x.size()
|
268 |
+
# Concat columns of pad to shift from relative to absolute indexing.
|
269 |
+
x = F.pad(x, commons.convert_pad_shape([[0, 0], [0, 0], [0, 0], [0, 1]]))
|
270 |
+
|
271 |
+
# Concat extra elements so to add up to shape (len+1, 2*len-1).
|
272 |
+
x_flat = x.view([batch, heads, length * 2 * length])
|
273 |
+
x_flat = F.pad(x_flat, commons.convert_pad_shape([[0, 0], [0, 0], [0, length - 1]]))
|
274 |
+
|
275 |
+
# Reshape and slice out the padded elements.
|
276 |
+
x_final = x_flat.view([batch, heads, length + 1, 2 * length - 1])[:, :, :length, length - 1:]
|
277 |
+
return x_final
|
278 |
+
|
279 |
+
def _absolute_position_to_relative_position(self, x):
|
280 |
+
"""
|
281 |
+
x: [b, h, l, l]
|
282 |
+
ret: [b, h, l, 2*l-1]
|
283 |
+
"""
|
284 |
+
batch, heads, length, _ = x.size()
|
285 |
+
# padd along column
|
286 |
+
x = F.pad(x, commons.convert_pad_shape([[0, 0], [0, 0], [0, 0], [0, length - 1]]))
|
287 |
+
x_flat = x.view([batch, heads, length ** 2 + length * (length - 1)])
|
288 |
+
# add 0's in the beginning that will skew the elements after reshape
|
289 |
+
x_flat = F.pad(x_flat, commons.convert_pad_shape([[0, 0], [0, 0], [length, 0]]))
|
290 |
+
x_final = x_flat.view([batch, heads, length, 2 * length])[:, :, :, 1:]
|
291 |
+
return x_final
|
292 |
+
|
293 |
+
def _attention_bias_proximal(self, length):
|
294 |
+
"""Bias for self-attention to encourage attention to close positions.
|
295 |
+
Args:
|
296 |
+
length: an integer scalar.
|
297 |
+
Returns:
|
298 |
+
a Tensor with shape [1, 1, length, length]
|
299 |
+
"""
|
300 |
+
r = torch.arange(length, dtype=torch.float32)
|
301 |
+
diff = torch.unsqueeze(r, 0) - torch.unsqueeze(r, 1)
|
302 |
+
return torch.unsqueeze(torch.unsqueeze(-torch.log1p(torch.abs(diff)), 0), 0)
|
303 |
+
|
304 |
+
|
305 |
+
class FFN(nn.Module):
|
306 |
+
def __init__(self, in_channels, out_channels, filter_channels, kernel_size, p_dropout=0., activation=None,
|
307 |
+
causal=False):
|
308 |
+
super().__init__()
|
309 |
+
self.in_channels = in_channels
|
310 |
+
self.out_channels = out_channels
|
311 |
+
self.filter_channels = filter_channels
|
312 |
+
self.kernel_size = kernel_size
|
313 |
+
self.p_dropout = p_dropout
|
314 |
+
self.activation = activation
|
315 |
+
self.causal = causal
|
316 |
+
|
317 |
+
if causal:
|
318 |
+
self.padding = self._causal_padding
|
319 |
+
else:
|
320 |
+
self.padding = self._same_padding
|
321 |
+
|
322 |
+
self.conv_1 = nn.Conv1d(in_channels, filter_channels, kernel_size)
|
323 |
+
self.conv_2 = nn.Conv1d(filter_channels, out_channels, kernel_size)
|
324 |
+
self.drop = nn.Dropout(p_dropout)
|
325 |
+
|
326 |
+
def forward(self, x, x_mask):
|
327 |
+
x = self.conv_1(self.padding(x * x_mask))
|
328 |
+
if self.activation == "gelu":
|
329 |
+
x = x * torch.sigmoid(1.702 * x)
|
330 |
+
else:
|
331 |
+
x = torch.relu(x)
|
332 |
+
x = self.drop(x)
|
333 |
+
x = self.conv_2(self.padding(x * x_mask))
|
334 |
+
return x * x_mask
|
335 |
+
|
336 |
+
def _causal_padding(self, x):
|
337 |
+
if self.kernel_size == 1:
|
338 |
+
return x
|
339 |
+
pad_l = self.kernel_size - 1
|
340 |
+
pad_r = 0
|
341 |
+
padding = [[0, 0], [0, 0], [pad_l, pad_r]]
|
342 |
+
x = F.pad(x, commons.convert_pad_shape(padding))
|
343 |
+
return x
|
344 |
+
|
345 |
+
def _same_padding(self, x):
|
346 |
+
if self.kernel_size == 1:
|
347 |
+
return x
|
348 |
+
pad_l = (self.kernel_size - 1) // 2
|
349 |
+
pad_r = self.kernel_size // 2
|
350 |
+
padding = [[0, 0], [0, 0], [pad_l, pad_r]]
|
351 |
+
x = F.pad(x, commons.convert_pad_shape(padding))
|
352 |
+
return x
|
bert_vits2/bert/chinese-roberta-wwm-ext-large/.gitattributes
ADDED
@@ -0,0 +1,9 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
*.bin.* filter=lfs diff=lfs merge=lfs -text
|
2 |
+
*.lfs.* filter=lfs diff=lfs merge=lfs -text
|
3 |
+
*.bin filter=lfs diff=lfs merge=lfs -text
|
4 |
+
*.h5 filter=lfs diff=lfs merge=lfs -text
|
5 |
+
*.tflite filter=lfs diff=lfs merge=lfs -text
|
6 |
+
*.tar.gz filter=lfs diff=lfs merge=lfs -text
|
7 |
+
*.ot filter=lfs diff=lfs merge=lfs -text
|
8 |
+
*.onnx filter=lfs diff=lfs merge=lfs -text
|
9 |
+
*.msgpack filter=lfs diff=lfs merge=lfs -text
|
bert_vits2/bert/chinese-roberta-wwm-ext-large/README.md
ADDED
@@ -0,0 +1,57 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
---
|
2 |
+
language:
|
3 |
+
- zh
|
4 |
+
tags:
|
5 |
+
- bert
|
6 |
+
license: "apache-2.0"
|
7 |
+
---
|
8 |
+
|
9 |
+
# Please use 'Bert' related functions to load this model!
|
10 |
+
|
11 |
+
## Chinese BERT with Whole Word Masking
|
12 |
+
For further accelerating Chinese natural language processing, we provide **Chinese pre-trained BERT with Whole Word Masking**.
|
13 |
+
|
14 |
+
**[Pre-Training with Whole Word Masking for Chinese BERT](https://arxiv.org/abs/1906.08101)**
|
15 |
+
Yiming Cui, Wanxiang Che, Ting Liu, Bing Qin, Ziqing Yang, Shijin Wang, Guoping Hu
|
16 |
+
|
17 |
+
This repository is developed based on:https://github.com/google-research/bert
|
18 |
+
|
19 |
+
You may also interested in,
|
20 |
+
- Chinese BERT series: https://github.com/ymcui/Chinese-BERT-wwm
|
21 |
+
- Chinese MacBERT: https://github.com/ymcui/MacBERT
|
22 |
+
- Chinese ELECTRA: https://github.com/ymcui/Chinese-ELECTRA
|
23 |
+
- Chinese XLNet: https://github.com/ymcui/Chinese-XLNet
|
24 |
+
- Knowledge Distillation Toolkit - TextBrewer: https://github.com/airaria/TextBrewer
|
25 |
+
|
26 |
+
More resources by HFL: https://github.com/ymcui/HFL-Anthology
|
27 |
+
|
28 |
+
## Citation
|
29 |
+
If you find the technical report or resource is useful, please cite the following technical report in your paper.
|
30 |
+
- Primary: https://arxiv.org/abs/2004.13922
|
31 |
+
```
|
32 |
+
@inproceedings{cui-etal-2020-revisiting,
|
33 |
+
title = "Revisiting Pre-Trained Models for {C}hinese Natural Language Processing",
|
34 |
+
author = "Cui, Yiming and
|
35 |
+
Che, Wanxiang and
|
36 |
+
Liu, Ting and
|
37 |
+
Qin, Bing and
|
38 |
+
Wang, Shijin and
|
39 |
+
Hu, Guoping",
|
40 |
+
booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: Findings",
|
41 |
+
month = nov,
|
42 |
+
year = "2020",
|
43 |
+
address = "Online",
|
44 |
+
publisher = "Association for Computational Linguistics",
|
45 |
+
url = "https://www.aclweb.org/anthology/2020.findings-emnlp.58",
|
46 |
+
pages = "657--668",
|
47 |
+
}
|
48 |
+
```
|
49 |
+
- Secondary: https://arxiv.org/abs/1906.08101
|
50 |
+
```
|
51 |
+
@article{chinese-bert-wwm,
|
52 |
+
title={Pre-Training with Whole Word Masking for Chinese BERT},
|
53 |
+
author={Cui, Yiming and Che, Wanxiang and Liu, Ting and Qin, Bing and Yang, Ziqing and Wang, Shijin and Hu, Guoping},
|
54 |
+
journal={arXiv preprint arXiv:1906.08101},
|
55 |
+
year={2019}
|
56 |
+
}
|
57 |
+
```
|
bert_vits2/bert/chinese-roberta-wwm-ext-large/added_tokens.json
ADDED
@@ -0,0 +1 @@
|
|
|
|
|
1 |
+
{}
|
bert_vits2/bert/chinese-roberta-wwm-ext-large/config.json
ADDED
@@ -0,0 +1,28 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
{
|
2 |
+
"architectures": [
|
3 |
+
"BertForMaskedLM"
|
4 |
+
],
|
5 |
+
"attention_probs_dropout_prob": 0.1,
|
6 |
+
"bos_token_id": 0,
|
7 |
+
"directionality": "bidi",
|
8 |
+
"eos_token_id": 2,
|
9 |
+
"hidden_act": "gelu",
|
10 |
+
"hidden_dropout_prob": 0.1,
|
11 |
+
"hidden_size": 1024,
|
12 |
+
"initializer_range": 0.02,
|
13 |
+
"intermediate_size": 4096,
|
14 |
+
"layer_norm_eps": 1e-12,
|
15 |
+
"max_position_embeddings": 512,
|
16 |
+
"model_type": "bert",
|
17 |
+
"num_attention_heads": 16,
|
18 |
+
"num_hidden_layers": 24,
|
19 |
+
"output_past": true,
|
20 |
+
"pad_token_id": 0,
|
21 |
+
"pooler_fc_size": 768,
|
22 |
+
"pooler_num_attention_heads": 12,
|
23 |
+
"pooler_num_fc_layers": 3,
|
24 |
+
"pooler_size_per_head": 128,
|
25 |
+
"pooler_type": "first_token_transform",
|
26 |
+
"type_vocab_size": 2,
|
27 |
+
"vocab_size": 21128
|
28 |
+
}
|
chinese_dialect_lexicons/zaonhe_2.ocd2 → bert_vits2/bert/chinese-roberta-wwm-ext-large/flax_model.msgpack
RENAMED
@@ -1,3 +1,3 @@
|
|
1 |
version https://git-lfs.github.com/spec/v1
|
2 |
-
oid sha256:
|
3 |
-
size
|
|
|
1 |
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:a46a510fe646213c728b80c9d0d5691d05235523d67f9ac3c3ce4e67deabf926
|
3 |
+
size 1302196529
|
bert_vits2/bert/chinese-roberta-wwm-ext-large/pytorch_model.bin
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:4ac62d49144d770c5ca9a5d1d3039c4995665a080febe63198189857c6bd11cd
|
3 |
+
size 1306484351
|
bert_vits2/bert/chinese-roberta-wwm-ext-large/special_tokens_map.json
ADDED
@@ -0,0 +1 @@
|
|
|
|
|
1 |
+
{"unk_token": "[UNK]", "sep_token": "[SEP]", "pad_token": "[PAD]", "cls_token": "[CLS]", "mask_token": "[MASK]"}
|
bert_vits2/bert/chinese-roberta-wwm-ext-large/tf_model.h5
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:72d18616fb285b720cb869c25aa9f4d7371033dfd5d8ba82aca448fdd28132bf
|
3 |
+
size 1302594480
|
bert_vits2/bert/chinese-roberta-wwm-ext-large/tokenizer.json
ADDED
The diff for this file is too large to render.
See raw diff
|
|
bert_vits2/bert/chinese-roberta-wwm-ext-large/tokenizer_config.json
ADDED
@@ -0,0 +1 @@
|
|
|
|
|
1 |
+
{"init_inputs": []}
|
bert_vits2/bert/chinese-roberta-wwm-ext-large/vocab.txt
ADDED
The diff for this file is too large to render.
See raw diff
|
|
bert_vits2/bert_vits2.py
ADDED
@@ -0,0 +1,86 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
import numpy as np
|
2 |
+
import torch
|
3 |
+
|
4 |
+
from bert_vits2 import utils, commons
|
5 |
+
from bert_vits2.models import SynthesizerTrn
|
6 |
+
from bert_vits2.text import symbols, cleaned_text_to_sequence, get_bert
|
7 |
+
from bert_vits2.text.cleaner import clean_text
|
8 |
+
from utils.nlp import sentence_split, cut
|
9 |
+
|
10 |
+
|
11 |
+
class Bert_VITS2:
|
12 |
+
def __init__(self, model, config, device=torch.device("cpu")):
|
13 |
+
self.hps_ms = utils.get_hparams_from_file(config)
|
14 |
+
self.n_speakers = getattr(self.hps_ms.data, 'n_speakers', 0)
|
15 |
+
self.speakers = [item[0] for item in
|
16 |
+
sorted(list(getattr(self.hps_ms.data, 'spk2id', {'0': 0}).items()), key=lambda x: x[1])]
|
17 |
+
self.net_g = SynthesizerTrn(
|
18 |
+
len(symbols),
|
19 |
+
self.hps_ms.data.filter_length // 2 + 1,
|
20 |
+
self.hps_ms.train.segment_size // self.hps_ms.data.hop_length,
|
21 |
+
n_speakers=self.hps_ms.data.n_speakers,
|
22 |
+
**self.hps_ms.model).to(device)
|
23 |
+
_ = self.net_g.eval()
|
24 |
+
self.device = device
|
25 |
+
self.load_model(model)
|
26 |
+
|
27 |
+
def load_model(self, model):
|
28 |
+
utils.load_checkpoint(model, self.net_g, None, skip_optimizer=True)
|
29 |
+
|
30 |
+
def get_speakers(self):
|
31 |
+
return self.speakers
|
32 |
+
|
33 |
+
def get_text(self, text, language_str, hps):
|
34 |
+
norm_text, phone, tone, word2ph = clean_text(text, language_str)
|
35 |
+
# print([f"{p}{t}" for p, t in zip(phone, tone)])
|
36 |
+
phone, tone, language = cleaned_text_to_sequence(phone, tone, language_str)
|
37 |
+
|
38 |
+
if hps.data.add_blank:
|
39 |
+
phone = commons.intersperse(phone, 0)
|
40 |
+
tone = commons.intersperse(tone, 0)
|
41 |
+
language = commons.intersperse(language, 0)
|
42 |
+
for i in range(len(word2ph)):
|
43 |
+
word2ph[i] = word2ph[i] * 2
|
44 |
+
word2ph[0] += 1
|
45 |
+
bert = get_bert(norm_text, word2ph, language_str)
|
46 |
+
|
47 |
+
assert bert.shape[-1] == len(phone)
|
48 |
+
|
49 |
+
phone = torch.LongTensor(phone)
|
50 |
+
tone = torch.LongTensor(tone)
|
51 |
+
language = torch.LongTensor(language)
|
52 |
+
|
53 |
+
return bert, phone, tone, language
|
54 |
+
|
55 |
+
def infer(self, text, sdp_ratio, noise_scale, noise_scale_w, length_scale, sid):
|
56 |
+
bert, phones, tones, lang_ids = self.get_text(text, "ZH", self.hps_ms)
|
57 |
+
with torch.no_grad():
|
58 |
+
x_tst = phones.to(self.device).unsqueeze(0)
|
59 |
+
tones = tones.to(self.device).unsqueeze(0)
|
60 |
+
lang_ids = lang_ids.to(self.device).unsqueeze(0)
|
61 |
+
bert = bert.to(self.device).unsqueeze(0)
|
62 |
+
x_tst_lengths = torch.LongTensor([phones.size(0)]).to(self.device)
|
63 |
+
speakers = torch.LongTensor([int(sid)]).to(self.device)
|
64 |
+
audio = self.net_g.infer(x_tst, x_tst_lengths, speakers, tones, lang_ids, bert, sdp_ratio=sdp_ratio
|
65 |
+
, noise_scale=noise_scale, noise_scale_w=noise_scale_w, length_scale=length_scale)[
|
66 |
+
0][0, 0].data.cpu().float().numpy()
|
67 |
+
|
68 |
+
torch.cuda.empty_cache()
|
69 |
+
return audio
|
70 |
+
|
71 |
+
def get_audio(self, voice, auto_break=False):
|
72 |
+
text = voice.get("text", None)
|
73 |
+
sdp_ratio = voice.get("sdp_ratio", 0.2)
|
74 |
+
noise_scale = voice.get("noise", 0.5)
|
75 |
+
noise_scale_w = voice.get("noisew", 0.6)
|
76 |
+
length_scale = voice.get("length", 1)
|
77 |
+
sid = voice.get("id", 0)
|
78 |
+
max = voice.get("max", 50)
|
79 |
+
# sentence_list = sentence_split(text, max, "ZH", ["zh"])
|
80 |
+
sentence_list = cut(text, max)
|
81 |
+
audios = []
|
82 |
+
for sentence in sentence_list:
|
83 |
+
audio = self.infer(sentence, sdp_ratio, noise_scale, noise_scale_w, length_scale, sid)
|
84 |
+
audios.append(audio)
|
85 |
+
audio = np.concatenate(audios)
|
86 |
+
return audio
|
bert_vits2/commons.py
ADDED
@@ -0,0 +1,161 @@
|
|
|
|
|
|
|
|
|
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|
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|
|
|
|
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|
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|
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|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
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|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
import math
|
2 |
+
import numpy as np
|
3 |
+
import torch
|
4 |
+
from torch import nn
|
5 |
+
from torch.nn import functional as F
|
6 |
+
|
7 |
+
|
8 |
+
def init_weights(m, mean=0.0, std=0.01):
|
9 |
+
classname = m.__class__.__name__
|
10 |
+
if classname.find("Conv") != -1:
|
11 |
+
m.weight.data.normal_(mean, std)
|
12 |
+
|
13 |
+
|
14 |
+
def get_padding(kernel_size, dilation=1):
|
15 |
+
return int((kernel_size * dilation - dilation) / 2)
|
16 |
+
|
17 |
+
|
18 |
+
def convert_pad_shape(pad_shape):
|
19 |
+
l = pad_shape[::-1]
|
20 |
+
pad_shape = [item for sublist in l for item in sublist]
|
21 |
+
return pad_shape
|
22 |
+
|
23 |
+
|
24 |
+
def intersperse(lst, item):
|
25 |
+
result = [item] * (len(lst) * 2 + 1)
|
26 |
+
result[1::2] = lst
|
27 |
+
return result
|
28 |
+
|
29 |
+
|
30 |
+
def kl_divergence(m_p, logs_p, m_q, logs_q):
|
31 |
+
"""KL(P||Q)"""
|
32 |
+
kl = (logs_q - logs_p) - 0.5
|
33 |
+
kl += 0.5 * (torch.exp(2. * logs_p) + ((m_p - m_q) ** 2)) * torch.exp(-2. * logs_q)
|
34 |
+
return kl
|
35 |
+
|
36 |
+
|
37 |
+
def rand_gumbel(shape):
|
38 |
+
"""Sample from the Gumbel distribution, protect from overflows."""
|
39 |
+
uniform_samples = torch.rand(shape) * 0.99998 + 0.00001
|
40 |
+
return -torch.log(-torch.log(uniform_samples))
|
41 |
+
|
42 |
+
|
43 |
+
def rand_gumbel_like(x):
|
44 |
+
g = rand_gumbel(x.size()).to(dtype=x.dtype, device=x.device)
|
45 |
+
return g
|
46 |
+
|
47 |
+
|
48 |
+
def slice_segments(x, ids_str, segment_size=4):
|
49 |
+
ret = torch.zeros_like(x[:, :, :segment_size])
|
50 |
+
for i in range(x.size(0)):
|
51 |
+
idx_str = ids_str[i]
|
52 |
+
idx_end = idx_str + segment_size
|
53 |
+
ret[i] = x[i, :, idx_str:idx_end]
|
54 |
+
return ret
|
55 |
+
|
56 |
+
|
57 |
+
def rand_slice_segments(x, x_lengths=None, segment_size=4):
|
58 |
+
b, d, t = x.size()
|
59 |
+
if x_lengths is None:
|
60 |
+
x_lengths = t
|
61 |
+
ids_str_max = x_lengths - segment_size + 1
|
62 |
+
ids_str = (torch.rand([b]).to(device=x.device) * ids_str_max).to(dtype=torch.long)
|
63 |
+
ret = slice_segments(x, ids_str, segment_size)
|
64 |
+
return ret, ids_str
|
65 |
+
|
66 |
+
|
67 |
+
def get_timing_signal_1d(
|
68 |
+
length, channels, min_timescale=1.0, max_timescale=1.0e4):
|
69 |
+
position = torch.arange(length, dtype=torch.float)
|
70 |
+
num_timescales = channels // 2
|
71 |
+
log_timescale_increment = (
|
72 |
+
math.log(float(max_timescale) / float(min_timescale)) /
|
73 |
+
(num_timescales - 1))
|
74 |
+
inv_timescales = min_timescale * torch.exp(
|
75 |
+
torch.arange(num_timescales, dtype=torch.float) * -log_timescale_increment)
|
76 |
+
scaled_time = position.unsqueeze(0) * inv_timescales.unsqueeze(1)
|
77 |
+
signal = torch.cat([torch.sin(scaled_time), torch.cos(scaled_time)], 0)
|
78 |
+
signal = F.pad(signal, [0, 0, 0, channels % 2])
|
79 |
+
signal = signal.view(1, channels, length)
|
80 |
+
return signal
|
81 |
+
|
82 |
+
|
83 |
+
def add_timing_signal_1d(x, min_timescale=1.0, max_timescale=1.0e4):
|
84 |
+
b, channels, length = x.size()
|
85 |
+
signal = get_timing_signal_1d(length, channels, min_timescale, max_timescale)
|
86 |
+
return x + signal.to(dtype=x.dtype, device=x.device)
|
87 |
+
|
88 |
+
|
89 |
+
def cat_timing_signal_1d(x, min_timescale=1.0, max_timescale=1.0e4, axis=1):
|
90 |
+
b, channels, length = x.size()
|
91 |
+
signal = get_timing_signal_1d(length, channels, min_timescale, max_timescale)
|
92 |
+
return torch.cat([x, signal.to(dtype=x.dtype, device=x.device)], axis)
|
93 |
+
|
94 |
+
|
95 |
+
def subsequent_mask(length):
|
96 |
+
mask = torch.tril(torch.ones(length, length)).unsqueeze(0).unsqueeze(0)
|
97 |
+
return mask
|
98 |
+
|
99 |
+
|
100 |
+
@torch.jit.script
|
101 |
+
def fused_add_tanh_sigmoid_multiply(input_a, input_b, n_channels):
|
102 |
+
n_channels_int = n_channels[0]
|
103 |
+
in_act = input_a + input_b
|
104 |
+
t_act = torch.tanh(in_act[:, :n_channels_int, :])
|
105 |
+
s_act = torch.sigmoid(in_act[:, n_channels_int:, :])
|
106 |
+
acts = t_act * s_act
|
107 |
+
return acts
|
108 |
+
|
109 |
+
|
110 |
+
def convert_pad_shape(pad_shape):
|
111 |
+
l = pad_shape[::-1]
|
112 |
+
pad_shape = [item for sublist in l for item in sublist]
|
113 |
+
return pad_shape
|
114 |
+
|
115 |
+
|
116 |
+
def shift_1d(x):
|
117 |
+
x = F.pad(x, convert_pad_shape([[0, 0], [0, 0], [1, 0]]))[:, :, :-1]
|
118 |
+
return x
|
119 |
+
|
120 |
+
|
121 |
+
def sequence_mask(length, max_length=None):
|
122 |
+
if max_length is None:
|
123 |
+
max_length = length.max()
|
124 |
+
x = torch.arange(max_length, dtype=length.dtype, device=length.device)
|
125 |
+
return x.unsqueeze(0) < length.unsqueeze(1)
|
126 |
+
|
127 |
+
|
128 |
+
def generate_path(duration, mask):
|
129 |
+
"""
|
130 |
+
duration: [b, 1, t_x]
|
131 |
+
mask: [b, 1, t_y, t_x]
|
132 |
+
"""
|
133 |
+
device = duration.device
|
134 |
+
|
135 |
+
b, _, t_y, t_x = mask.shape
|
136 |
+
cum_duration = torch.cumsum(duration, -1)
|
137 |
+
|
138 |
+
cum_duration_flat = cum_duration.view(b * t_x)
|
139 |
+
path = sequence_mask(cum_duration_flat, t_y).to(mask.dtype)
|
140 |
+
path = path.view(b, t_x, t_y)
|
141 |
+
path = path - F.pad(path, convert_pad_shape([[0, 0], [1, 0], [0, 0]]))[:, :-1]
|
142 |
+
path = path.unsqueeze(1).transpose(2, 3) * mask
|
143 |
+
return path
|
144 |
+
|
145 |
+
|
146 |
+
def clip_grad_value_(parameters, clip_value, norm_type=2):
|
147 |
+
if isinstance(parameters, torch.Tensor):
|
148 |
+
parameters = [parameters]
|
149 |
+
parameters = list(filter(lambda p: p.grad is not None, parameters))
|
150 |
+
norm_type = float(norm_type)
|
151 |
+
if clip_value is not None:
|
152 |
+
clip_value = float(clip_value)
|
153 |
+
|
154 |
+
total_norm = 0
|
155 |
+
for p in parameters:
|
156 |
+
param_norm = p.grad.data.norm(norm_type)
|
157 |
+
total_norm += param_norm.item() ** norm_type
|
158 |
+
if clip_value is not None:
|
159 |
+
p.grad.data.clamp_(min=-clip_value, max=clip_value)
|
160 |
+
total_norm = total_norm ** (1. / norm_type)
|
161 |
+
return total_norm
|
bert_vits2/models.py
ADDED
@@ -0,0 +1,677 @@
|
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|
|
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|
|
|
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|
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|
1 |
+
import math
|
2 |
+
import torch
|
3 |
+
from torch import nn
|
4 |
+
from torch.nn import functional as F
|
5 |
+
|
6 |
+
from bert_vits2 import commons
|
7 |
+
from bert_vits2 import modules
|
8 |
+
from bert_vits2 import attentions
|
9 |
+
|
10 |
+
from torch.nn import Conv1d, ConvTranspose1d, AvgPool1d, Conv2d
|
11 |
+
from torch.nn.utils import weight_norm, remove_weight_norm, spectral_norm
|
12 |
+
|
13 |
+
from bert_vits2.commons import init_weights, get_padding
|
14 |
+
from bert_vits2.text import symbols, num_tones, num_languages
|
15 |
+
|
16 |
+
|
17 |
+
class DurationDiscriminator(nn.Module): # vits2
|
18 |
+
def __init__(self, in_channels, filter_channels, kernel_size, p_dropout, gin_channels=0):
|
19 |
+
super().__init__()
|
20 |
+
|
21 |
+
self.in_channels = in_channels
|
22 |
+
self.filter_channels = filter_channels
|
23 |
+
self.kernel_size = kernel_size
|
24 |
+
self.p_dropout = p_dropout
|
25 |
+
self.gin_channels = gin_channels
|
26 |
+
|
27 |
+
self.drop = nn.Dropout(p_dropout)
|
28 |
+
self.conv_1 = nn.Conv1d(in_channels, filter_channels, kernel_size, padding=kernel_size // 2)
|
29 |
+
# self.norm_1 = modules.LayerNorm(filter_channels)
|
30 |
+
self.conv_2 = nn.Conv1d(filter_channels, filter_channels, kernel_size, padding=kernel_size // 2)
|
31 |
+
# self.norm_2 = modules.LayerNorm(filter_channels)
|
32 |
+
self.dur_proj = nn.Conv1d(1, filter_channels, 1)
|
33 |
+
|
34 |
+
self.pre_out_conv_1 = nn.Conv1d(2 * filter_channels, filter_channels, kernel_size, padding=kernel_size // 2)
|
35 |
+
self.pre_out_norm_1 = modules.LayerNorm(filter_channels)
|
36 |
+
self.pre_out_conv_2 = nn.Conv1d(filter_channels, filter_channels, kernel_size, padding=kernel_size // 2)
|
37 |
+
self.pre_out_norm_2 = modules.LayerNorm(filter_channels)
|
38 |
+
|
39 |
+
# if gin_channels != 0:
|
40 |
+
# self.cond = nn.Conv1d(gin_channels, in_channels, 1)
|
41 |
+
|
42 |
+
self.output_layer = nn.Sequential(
|
43 |
+
nn.Linear(filter_channels, 1),
|
44 |
+
nn.Sigmoid()
|
45 |
+
)
|
46 |
+
|
47 |
+
def forward_probability(self, x, x_mask, dur, g=None):
|
48 |
+
dur = self.dur_proj(dur)
|
49 |
+
x = torch.cat([x, dur], dim=1)
|
50 |
+
x = self.pre_out_conv_1(x * x_mask)
|
51 |
+
# x = torch.relu(x)
|
52 |
+
# x = self.pre_out_norm_1(x)
|
53 |
+
# x = self.drop(x)
|
54 |
+
x = self.pre_out_conv_2(x * x_mask)
|
55 |
+
# x = torch.relu(x)
|
56 |
+
# x = self.pre_out_norm_2(x)
|
57 |
+
# x = self.drop(x)
|
58 |
+
x = x * x_mask
|
59 |
+
x = x.transpose(1, 2)
|
60 |
+
output_prob = self.output_layer(x)
|
61 |
+
return output_prob
|
62 |
+
|
63 |
+
def forward(self, x, x_mask, dur_r, dur_hat, g=None):
|
64 |
+
x = torch.detach(x)
|
65 |
+
# if g is not None:
|
66 |
+
# g = torch.detach(g)
|
67 |
+
# x = x + self.cond(g)
|
68 |
+
x = self.conv_1(x * x_mask)
|
69 |
+
# x = torch.relu(x)
|
70 |
+
# x = self.norm_1(x)
|
71 |
+
# x = self.drop(x)
|
72 |
+
x = self.conv_2(x * x_mask)
|
73 |
+
# x = torch.relu(x)
|
74 |
+
# x = self.norm_2(x)
|
75 |
+
# x = self.drop(x)
|
76 |
+
|
77 |
+
output_probs = []
|
78 |
+
for dur in [dur_r, dur_hat]:
|
79 |
+
output_prob = self.forward_probability(x, x_mask, dur, g)
|
80 |
+
output_probs.append(output_prob)
|
81 |
+
|
82 |
+
return output_probs
|
83 |
+
|
84 |
+
|
85 |
+
class TransformerCouplingBlock(nn.Module):
|
86 |
+
def __init__(self,
|
87 |
+
channels,
|
88 |
+
hidden_channels,
|
89 |
+
filter_channels,
|
90 |
+
n_heads,
|
91 |
+
n_layers,
|
92 |
+
kernel_size,
|
93 |
+
p_dropout,
|
94 |
+
n_flows=4,
|
95 |
+
gin_channels=0,
|
96 |
+
share_parameter=False
|
97 |
+
):
|
98 |
+
|
99 |
+
super().__init__()
|
100 |
+
self.channels = channels
|
101 |
+
self.hidden_channels = hidden_channels
|
102 |
+
self.kernel_size = kernel_size
|
103 |
+
self.n_layers = n_layers
|
104 |
+
self.n_flows = n_flows
|
105 |
+
self.gin_channels = gin_channels
|
106 |
+
|
107 |
+
self.flows = nn.ModuleList()
|
108 |
+
|
109 |
+
self.wn = attentions.FFT(hidden_channels, filter_channels, n_heads, n_layers, kernel_size, p_dropout,
|
110 |
+
isflow=True, gin_channels=self.gin_channels) if share_parameter else None
|
111 |
+
|
112 |
+
for i in range(n_flows):
|
113 |
+
self.flows.append(
|
114 |
+
modules.TransformerCouplingLayer(channels, hidden_channels, kernel_size, n_layers, n_heads, p_dropout,
|
115 |
+
filter_channels, mean_only=True, wn_sharing_parameter=self.wn,
|
116 |
+
gin_channels=self.gin_channels))
|
117 |
+
self.flows.append(modules.Flip())
|
118 |
+
|
119 |
+
def forward(self, x, x_mask, g=None, reverse=False):
|
120 |
+
if not reverse:
|
121 |
+
for flow in self.flows:
|
122 |
+
x, _ = flow(x, x_mask, g=g, reverse=reverse)
|
123 |
+
else:
|
124 |
+
for flow in reversed(self.flows):
|
125 |
+
x = flow(x, x_mask, g=g, reverse=reverse)
|
126 |
+
return x
|
127 |
+
|
128 |
+
|
129 |
+
class StochasticDurationPredictor(nn.Module):
|
130 |
+
def __init__(self, in_channels, filter_channels, kernel_size, p_dropout, n_flows=4, gin_channels=0):
|
131 |
+
super().__init__()
|
132 |
+
filter_channels = in_channels # it needs to be removed from future version.
|
133 |
+
self.in_channels = in_channels
|
134 |
+
self.filter_channels = filter_channels
|
135 |
+
self.kernel_size = kernel_size
|
136 |
+
self.p_dropout = p_dropout
|
137 |
+
self.n_flows = n_flows
|
138 |
+
self.gin_channels = gin_channels
|
139 |
+
|
140 |
+
self.log_flow = modules.Log()
|
141 |
+
self.flows = nn.ModuleList()
|
142 |
+
self.flows.append(modules.ElementwiseAffine(2))
|
143 |
+
for i in range(n_flows):
|
144 |
+
self.flows.append(modules.ConvFlow(2, filter_channels, kernel_size, n_layers=3))
|
145 |
+
self.flows.append(modules.Flip())
|
146 |
+
|
147 |
+
self.post_pre = nn.Conv1d(1, filter_channels, 1)
|
148 |
+
self.post_proj = nn.Conv1d(filter_channels, filter_channels, 1)
|
149 |
+
self.post_convs = modules.DDSConv(filter_channels, kernel_size, n_layers=3, p_dropout=p_dropout)
|
150 |
+
self.post_flows = nn.ModuleList()
|
151 |
+
self.post_flows.append(modules.ElementwiseAffine(2))
|
152 |
+
for i in range(4):
|
153 |
+
self.post_flows.append(modules.ConvFlow(2, filter_channels, kernel_size, n_layers=3))
|
154 |
+
self.post_flows.append(modules.Flip())
|
155 |
+
|
156 |
+
self.pre = nn.Conv1d(in_channels, filter_channels, 1)
|
157 |
+
self.proj = nn.Conv1d(filter_channels, filter_channels, 1)
|
158 |
+
self.convs = modules.DDSConv(filter_channels, kernel_size, n_layers=3, p_dropout=p_dropout)
|
159 |
+
if gin_channels != 0:
|
160 |
+
self.cond = nn.Conv1d(gin_channels, filter_channels, 1)
|
161 |
+
|
162 |
+
def forward(self, x, x_mask, w=None, g=None, reverse=False, noise_scale=1.0):
|
163 |
+
x = torch.detach(x)
|
164 |
+
x = self.pre(x)
|
165 |
+
if g is not None:
|
166 |
+
g = torch.detach(g)
|
167 |
+
x = x + self.cond(g)
|
168 |
+
x = self.convs(x, x_mask)
|
169 |
+
x = self.proj(x) * x_mask
|
170 |
+
|
171 |
+
if not reverse:
|
172 |
+
flows = self.flows
|
173 |
+
assert w is not None
|
174 |
+
|
175 |
+
logdet_tot_q = 0
|
176 |
+
h_w = self.post_pre(w)
|
177 |
+
h_w = self.post_convs(h_w, x_mask)
|
178 |
+
h_w = self.post_proj(h_w) * x_mask
|
179 |
+
e_q = torch.randn(w.size(0), 2, w.size(2)).to(device=x.device, dtype=x.dtype) * x_mask
|
180 |
+
z_q = e_q
|
181 |
+
for flow in self.post_flows:
|
182 |
+
z_q, logdet_q = flow(z_q, x_mask, g=(x + h_w))
|
183 |
+
logdet_tot_q += logdet_q
|
184 |
+
z_u, z1 = torch.split(z_q, [1, 1], 1)
|
185 |
+
u = torch.sigmoid(z_u) * x_mask
|
186 |
+
z0 = (w - u) * x_mask
|
187 |
+
logdet_tot_q += torch.sum((F.logsigmoid(z_u) + F.logsigmoid(-z_u)) * x_mask, [1, 2])
|
188 |
+
logq = torch.sum(-0.5 * (math.log(2 * math.pi) + (e_q ** 2)) * x_mask, [1, 2]) - logdet_tot_q
|
189 |
+
|
190 |
+
logdet_tot = 0
|
191 |
+
z0, logdet = self.log_flow(z0, x_mask)
|
192 |
+
logdet_tot += logdet
|
193 |
+
z = torch.cat([z0, z1], 1)
|
194 |
+
for flow in flows:
|
195 |
+
z, logdet = flow(z, x_mask, g=x, reverse=reverse)
|
196 |
+
logdet_tot = logdet_tot + logdet
|
197 |
+
nll = torch.sum(0.5 * (math.log(2 * math.pi) + (z ** 2)) * x_mask, [1, 2]) - logdet_tot
|
198 |
+
return nll + logq # [b]
|
199 |
+
else:
|
200 |
+
flows = list(reversed(self.flows))
|
201 |
+
flows = flows[:-2] + [flows[-1]] # remove a useless vflow
|
202 |
+
z = torch.randn(x.size(0), 2, x.size(2)).to(device=x.device, dtype=x.dtype) * noise_scale
|
203 |
+
for flow in flows:
|
204 |
+
z = flow(z, x_mask, g=x, reverse=reverse)
|
205 |
+
z0, z1 = torch.split(z, [1, 1], 1)
|
206 |
+
logw = z0
|
207 |
+
return logw
|
208 |
+
|
209 |
+
|
210 |
+
class DurationPredictor(nn.Module):
|
211 |
+
def __init__(self, in_channels, filter_channels, kernel_size, p_dropout, gin_channels=0):
|
212 |
+
super().__init__()
|
213 |
+
|
214 |
+
self.in_channels = in_channels
|
215 |
+
self.filter_channels = filter_channels
|
216 |
+
self.kernel_size = kernel_size
|
217 |
+
self.p_dropout = p_dropout
|
218 |
+
self.gin_channels = gin_channels
|
219 |
+
|
220 |
+
self.drop = nn.Dropout(p_dropout)
|
221 |
+
self.conv_1 = nn.Conv1d(in_channels, filter_channels, kernel_size, padding=kernel_size // 2)
|
222 |
+
self.norm_1 = modules.LayerNorm(filter_channels)
|
223 |
+
self.conv_2 = nn.Conv1d(filter_channels, filter_channels, kernel_size, padding=kernel_size // 2)
|
224 |
+
self.norm_2 = modules.LayerNorm(filter_channels)
|
225 |
+
self.proj = nn.Conv1d(filter_channels, 1, 1)
|
226 |
+
|
227 |
+
if gin_channels != 0:
|
228 |
+
self.cond = nn.Conv1d(gin_channels, in_channels, 1)
|
229 |
+
|
230 |
+
def forward(self, x, x_mask, g=None):
|
231 |
+
x = torch.detach(x)
|
232 |
+
if g is not None:
|
233 |
+
g = torch.detach(g)
|
234 |
+
x = x + self.cond(g)
|
235 |
+
x = self.conv_1(x * x_mask)
|
236 |
+
x = torch.relu(x)
|
237 |
+
x = self.norm_1(x)
|
238 |
+
x = self.drop(x)
|
239 |
+
x = self.conv_2(x * x_mask)
|
240 |
+
x = torch.relu(x)
|
241 |
+
x = self.norm_2(x)
|
242 |
+
x = self.drop(x)
|
243 |
+
x = self.proj(x * x_mask)
|
244 |
+
return x * x_mask
|
245 |
+
|
246 |
+
|
247 |
+
class TextEncoder(nn.Module):
|
248 |
+
def __init__(self,
|
249 |
+
n_vocab,
|
250 |
+
out_channels,
|
251 |
+
hidden_channels,
|
252 |
+
filter_channels,
|
253 |
+
n_heads,
|
254 |
+
n_layers,
|
255 |
+
kernel_size,
|
256 |
+
p_dropout,
|
257 |
+
gin_channels=0):
|
258 |
+
super().__init__()
|
259 |
+
self.n_vocab = n_vocab
|
260 |
+
self.out_channels = out_channels
|
261 |
+
self.hidden_channels = hidden_channels
|
262 |
+
self.filter_channels = filter_channels
|
263 |
+
self.n_heads = n_heads
|
264 |
+
self.n_layers = n_layers
|
265 |
+
self.kernel_size = kernel_size
|
266 |
+
self.p_dropout = p_dropout
|
267 |
+
self.gin_channels = gin_channels
|
268 |
+
self.emb = nn.Embedding(len(symbols), hidden_channels)
|
269 |
+
nn.init.normal_(self.emb.weight, 0.0, hidden_channels ** -0.5)
|
270 |
+
self.tone_emb = nn.Embedding(num_tones, hidden_channels)
|
271 |
+
nn.init.normal_(self.tone_emb.weight, 0.0, hidden_channels ** -0.5)
|
272 |
+
self.language_emb = nn.Embedding(num_languages, hidden_channels)
|
273 |
+
nn.init.normal_(self.language_emb.weight, 0.0, hidden_channels ** -0.5)
|
274 |
+
self.bert_proj = nn.Conv1d(1024, hidden_channels, 1)
|
275 |
+
|
276 |
+
self.encoder = attentions.Encoder(
|
277 |
+
hidden_channels,
|
278 |
+
filter_channels,
|
279 |
+
n_heads,
|
280 |
+
n_layers,
|
281 |
+
kernel_size,
|
282 |
+
p_dropout,
|
283 |
+
gin_channels=self.gin_channels)
|
284 |
+
self.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1)
|
285 |
+
|
286 |
+
def forward(self, x, x_lengths, tone, language, bert, g=None):
|
287 |
+
x = (self.emb(x) + self.tone_emb(tone) + self.language_emb(language) + self.bert_proj(bert).transpose(1,
|
288 |
+
2)) * math.sqrt(
|
289 |
+
self.hidden_channels) # [b, t, h]
|
290 |
+
x = torch.transpose(x, 1, -1) # [b, h, t]
|
291 |
+
x_mask = torch.unsqueeze(commons.sequence_mask(x_lengths, x.size(2)), 1).to(x.dtype)
|
292 |
+
|
293 |
+
x = self.encoder(x * x_mask, x_mask, g=g)
|
294 |
+
stats = self.proj(x) * x_mask
|
295 |
+
|
296 |
+
m, logs = torch.split(stats, self.out_channels, dim=1)
|
297 |
+
return x, m, logs, x_mask
|
298 |
+
|
299 |
+
|
300 |
+
class ResidualCouplingBlock(nn.Module):
|
301 |
+
def __init__(self,
|
302 |
+
channels,
|
303 |
+
hidden_channels,
|
304 |
+
kernel_size,
|
305 |
+
dilation_rate,
|
306 |
+
n_layers,
|
307 |
+
n_flows=4,
|
308 |
+
gin_channels=0):
|
309 |
+
super().__init__()
|
310 |
+
self.channels = channels
|
311 |
+
self.hidden_channels = hidden_channels
|
312 |
+
self.kernel_size = kernel_size
|
313 |
+
self.dilation_rate = dilation_rate
|
314 |
+
self.n_layers = n_layers
|
315 |
+
self.n_flows = n_flows
|
316 |
+
self.gin_channels = gin_channels
|
317 |
+
|
318 |
+
self.flows = nn.ModuleList()
|
319 |
+
for i in range(n_flows):
|
320 |
+
self.flows.append(
|
321 |
+
modules.ResidualCouplingLayer(channels, hidden_channels, kernel_size, dilation_rate, n_layers,
|
322 |
+
gin_channels=gin_channels, mean_only=True))
|
323 |
+
self.flows.append(modules.Flip())
|
324 |
+
|
325 |
+
def forward(self, x, x_mask, g=None, reverse=False):
|
326 |
+
if not reverse:
|
327 |
+
for flow in self.flows:
|
328 |
+
x, _ = flow(x, x_mask, g=g, reverse=reverse)
|
329 |
+
else:
|
330 |
+
for flow in reversed(self.flows):
|
331 |
+
x = flow(x, x_mask, g=g, reverse=reverse)
|
332 |
+
return x
|
333 |
+
|
334 |
+
|
335 |
+
class PosteriorEncoder(nn.Module):
|
336 |
+
def __init__(self,
|
337 |
+
in_channels,
|
338 |
+
out_channels,
|
339 |
+
hidden_channels,
|
340 |
+
kernel_size,
|
341 |
+
dilation_rate,
|
342 |
+
n_layers,
|
343 |
+
gin_channels=0):
|
344 |
+
super().__init__()
|
345 |
+
self.in_channels = in_channels
|
346 |
+
self.out_channels = out_channels
|
347 |
+
self.hidden_channels = hidden_channels
|
348 |
+
self.kernel_size = kernel_size
|
349 |
+
self.dilation_rate = dilation_rate
|
350 |
+
self.n_layers = n_layers
|
351 |
+
self.gin_channels = gin_channels
|
352 |
+
|
353 |
+
self.pre = nn.Conv1d(in_channels, hidden_channels, 1)
|
354 |
+
self.enc = modules.WN(hidden_channels, kernel_size, dilation_rate, n_layers, gin_channels=gin_channels)
|
355 |
+
self.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1)
|
356 |
+
|
357 |
+
def forward(self, x, x_lengths, g=None):
|
358 |
+
x_mask = torch.unsqueeze(commons.sequence_mask(x_lengths, x.size(2)), 1).to(x.dtype)
|
359 |
+
x = self.pre(x) * x_mask
|
360 |
+
x = self.enc(x, x_mask, g=g)
|
361 |
+
stats = self.proj(x) * x_mask
|
362 |
+
m, logs = torch.split(stats, self.out_channels, dim=1)
|
363 |
+
z = (m + torch.randn_like(m) * torch.exp(logs)) * x_mask
|
364 |
+
return z, m, logs, x_mask
|
365 |
+
|
366 |
+
|
367 |
+
class Generator(torch.nn.Module):
|
368 |
+
def __init__(self, initial_channel, resblock, resblock_kernel_sizes, resblock_dilation_sizes, upsample_rates,
|
369 |
+
upsample_initial_channel, upsample_kernel_sizes, gin_channels=0):
|
370 |
+
super(Generator, self).__init__()
|
371 |
+
self.num_kernels = len(resblock_kernel_sizes)
|
372 |
+
self.num_upsamples = len(upsample_rates)
|
373 |
+
self.conv_pre = Conv1d(initial_channel, upsample_initial_channel, 7, 1, padding=3)
|
374 |
+
resblock = modules.ResBlock1 if resblock == '1' else modules.ResBlock2
|
375 |
+
|
376 |
+
self.ups = nn.ModuleList()
|
377 |
+
for i, (u, k) in enumerate(zip(upsample_rates, upsample_kernel_sizes)):
|
378 |
+
self.ups.append(weight_norm(
|
379 |
+
ConvTranspose1d(upsample_initial_channel // (2 ** i), upsample_initial_channel // (2 ** (i + 1)),
|
380 |
+
k, u, padding=(k - u) // 2)))
|
381 |
+
|
382 |
+
self.resblocks = nn.ModuleList()
|
383 |
+
for i in range(len(self.ups)):
|
384 |
+
ch = upsample_initial_channel // (2 ** (i + 1))
|
385 |
+
for j, (k, d) in enumerate(zip(resblock_kernel_sizes, resblock_dilation_sizes)):
|
386 |
+
self.resblocks.append(resblock(ch, k, d))
|
387 |
+
|
388 |
+
self.conv_post = Conv1d(ch, 1, 7, 1, padding=3, bias=False)
|
389 |
+
self.ups.apply(init_weights)
|
390 |
+
|
391 |
+
if gin_channels != 0:
|
392 |
+
self.cond = nn.Conv1d(gin_channels, upsample_initial_channel, 1)
|
393 |
+
|
394 |
+
def forward(self, x, g=None):
|
395 |
+
x = self.conv_pre(x)
|
396 |
+
if g is not None:
|
397 |
+
x = x + self.cond(g)
|
398 |
+
|
399 |
+
for i in range(self.num_upsamples):
|
400 |
+
x = F.leaky_relu(x, modules.LRELU_SLOPE)
|
401 |
+
x = self.ups[i](x)
|
402 |
+
xs = None
|
403 |
+
for j in range(self.num_kernels):
|
404 |
+
if xs is None:
|
405 |
+
xs = self.resblocks[i * self.num_kernels + j](x)
|
406 |
+
else:
|
407 |
+
xs += self.resblocks[i * self.num_kernels + j](x)
|
408 |
+
x = xs / self.num_kernels
|
409 |
+
x = F.leaky_relu(x)
|
410 |
+
x = self.conv_post(x)
|
411 |
+
x = torch.tanh(x)
|
412 |
+
|
413 |
+
return x
|
414 |
+
|
415 |
+
def remove_weight_norm(self):
|
416 |
+
print('Removing weight norm...')
|
417 |
+
for l in self.ups:
|
418 |
+
remove_weight_norm(l)
|
419 |
+
for l in self.resblocks:
|
420 |
+
l.remove_weight_norm()
|
421 |
+
|
422 |
+
|
423 |
+
class DiscriminatorP(torch.nn.Module):
|
424 |
+
def __init__(self, period, kernel_size=5, stride=3, use_spectral_norm=False):
|
425 |
+
super(DiscriminatorP, self).__init__()
|
426 |
+
self.period = period
|
427 |
+
self.use_spectral_norm = use_spectral_norm
|
428 |
+
norm_f = weight_norm if use_spectral_norm == False else spectral_norm
|
429 |
+
self.convs = nn.ModuleList([
|
430 |
+
norm_f(Conv2d(1, 32, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))),
|
431 |
+
norm_f(Conv2d(32, 128, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))),
|
432 |
+
norm_f(Conv2d(128, 512, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))),
|
433 |
+
norm_f(Conv2d(512, 1024, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))),
|
434 |
+
norm_f(Conv2d(1024, 1024, (kernel_size, 1), 1, padding=(get_padding(kernel_size, 1), 0))),
|
435 |
+
])
|
436 |
+
self.conv_post = norm_f(Conv2d(1024, 1, (3, 1), 1, padding=(1, 0)))
|
437 |
+
|
438 |
+
def forward(self, x):
|
439 |
+
fmap = []
|
440 |
+
|
441 |
+
# 1d to 2d
|
442 |
+
b, c, t = x.shape
|
443 |
+
if t % self.period != 0: # pad first
|
444 |
+
n_pad = self.period - (t % self.period)
|
445 |
+
x = F.pad(x, (0, n_pad), "reflect")
|
446 |
+
t = t + n_pad
|
447 |
+
x = x.view(b, c, t // self.period, self.period)
|
448 |
+
|
449 |
+
for l in self.convs:
|
450 |
+
x = l(x)
|
451 |
+
x = F.leaky_relu(x, modules.LRELU_SLOPE)
|
452 |
+
fmap.append(x)
|
453 |
+
x = self.conv_post(x)
|
454 |
+
fmap.append(x)
|
455 |
+
x = torch.flatten(x, 1, -1)
|
456 |
+
|
457 |
+
return x, fmap
|
458 |
+
|
459 |
+
|
460 |
+
class DiscriminatorS(torch.nn.Module):
|
461 |
+
def __init__(self, use_spectral_norm=False):
|
462 |
+
super(DiscriminatorS, self).__init__()
|
463 |
+
norm_f = weight_norm if use_spectral_norm == False else spectral_norm
|
464 |
+
self.convs = nn.ModuleList([
|
465 |
+
norm_f(Conv1d(1, 16, 15, 1, padding=7)),
|
466 |
+
norm_f(Conv1d(16, 64, 41, 4, groups=4, padding=20)),
|
467 |
+
norm_f(Conv1d(64, 256, 41, 4, groups=16, padding=20)),
|
468 |
+
norm_f(Conv1d(256, 1024, 41, 4, groups=64, padding=20)),
|
469 |
+
norm_f(Conv1d(1024, 1024, 41, 4, groups=256, padding=20)),
|
470 |
+
norm_f(Conv1d(1024, 1024, 5, 1, padding=2)),
|
471 |
+
])
|
472 |
+
self.conv_post = norm_f(Conv1d(1024, 1, 3, 1, padding=1))
|
473 |
+
|
474 |
+
def forward(self, x):
|
475 |
+
fmap = []
|
476 |
+
|
477 |
+
for l in self.convs:
|
478 |
+
x = l(x)
|
479 |
+
x = F.leaky_relu(x, modules.LRELU_SLOPE)
|
480 |
+
fmap.append(x)
|
481 |
+
x = self.conv_post(x)
|
482 |
+
fmap.append(x)
|
483 |
+
x = torch.flatten(x, 1, -1)
|
484 |
+
|
485 |
+
return x, fmap
|
486 |
+
|
487 |
+
|
488 |
+
class MultiPeriodDiscriminator(torch.nn.Module):
|
489 |
+
def __init__(self, use_spectral_norm=False):
|
490 |
+
super(MultiPeriodDiscriminator, self).__init__()
|
491 |
+
periods = [2, 3, 5, 7, 11]
|
492 |
+
|
493 |
+
discs = [DiscriminatorS(use_spectral_norm=use_spectral_norm)]
|
494 |
+
discs = discs + [DiscriminatorP(i, use_spectral_norm=use_spectral_norm) for i in periods]
|
495 |
+
self.discriminators = nn.ModuleList(discs)
|
496 |
+
|
497 |
+
def forward(self, y, y_hat):
|
498 |
+
y_d_rs = []
|
499 |
+
y_d_gs = []
|
500 |
+
fmap_rs = []
|
501 |
+
fmap_gs = []
|
502 |
+
for i, d in enumerate(self.discriminators):
|
503 |
+
y_d_r, fmap_r = d(y)
|
504 |
+
y_d_g, fmap_g = d(y_hat)
|
505 |
+
y_d_rs.append(y_d_r)
|
506 |
+
y_d_gs.append(y_d_g)
|
507 |
+
fmap_rs.append(fmap_r)
|
508 |
+
fmap_gs.append(fmap_g)
|
509 |
+
|
510 |
+
return y_d_rs, y_d_gs, fmap_rs, fmap_gs
|
511 |
+
|
512 |
+
|
513 |
+
class ReferenceEncoder(nn.Module):
|
514 |
+
'''
|
515 |
+
inputs --- [N, Ty/r, n_mels*r] mels
|
516 |
+
outputs --- [N, ref_enc_gru_size]
|
517 |
+
'''
|
518 |
+
|
519 |
+
def __init__(self, spec_channels, gin_channels=0):
|
520 |
+
|
521 |
+
super().__init__()
|
522 |
+
self.spec_channels = spec_channels
|
523 |
+
ref_enc_filters = [32, 32, 64, 64, 128, 128]
|
524 |
+
K = len(ref_enc_filters)
|
525 |
+
filters = [1] + ref_enc_filters
|
526 |
+
convs = [weight_norm(nn.Conv2d(in_channels=filters[i],
|
527 |
+
out_channels=filters[i + 1],
|
528 |
+
kernel_size=(3, 3),
|
529 |
+
stride=(2, 2),
|
530 |
+
padding=(1, 1))) for i in range(K)]
|
531 |
+
self.convs = nn.ModuleList(convs)
|
532 |
+
# self.wns = nn.ModuleList([weight_norm(num_features=ref_enc_filters[i]) for i in range(K)])
|
533 |
+
|
534 |
+
out_channels = self.calculate_channels(spec_channels, 3, 2, 1, K)
|
535 |
+
self.gru = nn.GRU(input_size=ref_enc_filters[-1] * out_channels,
|
536 |
+
hidden_size=256 // 2,
|
537 |
+
batch_first=True)
|
538 |
+
self.proj = nn.Linear(128, gin_channels)
|
539 |
+
|
540 |
+
def forward(self, inputs, mask=None):
|
541 |
+
N = inputs.size(0)
|
542 |
+
out = inputs.view(N, 1, -1, self.spec_channels) # [N, 1, Ty, n_freqs]
|
543 |
+
for conv in self.convs:
|
544 |
+
out = conv(out)
|
545 |
+
# out = wn(out)
|
546 |
+
out = F.relu(out) # [N, 128, Ty//2^K, n_mels//2^K]
|
547 |
+
|
548 |
+
out = out.transpose(1, 2) # [N, Ty//2^K, 128, n_mels//2^K]
|
549 |
+
T = out.size(1)
|
550 |
+
N = out.size(0)
|
551 |
+
out = out.contiguous().view(N, T, -1) # [N, Ty//2^K, 128*n_mels//2^K]
|
552 |
+
|
553 |
+
self.gru.flatten_parameters()
|
554 |
+
memory, out = self.gru(out) # out --- [1, N, 128]
|
555 |
+
|
556 |
+
return self.proj(out.squeeze(0))
|
557 |
+
|
558 |
+
def calculate_channels(self, L, kernel_size, stride, pad, n_convs):
|
559 |
+
for i in range(n_convs):
|
560 |
+
L = (L - kernel_size + 2 * pad) // stride + 1
|
561 |
+
return L
|
562 |
+
|
563 |
+
|
564 |
+
class SynthesizerTrn(nn.Module):
|
565 |
+
"""
|
566 |
+
Synthesizer for Training
|
567 |
+
"""
|
568 |
+
|
569 |
+
def __init__(self,
|
570 |
+
n_vocab,
|
571 |
+
spec_channels,
|
572 |
+
segment_size,
|
573 |
+
inter_channels,
|
574 |
+
hidden_channels,
|
575 |
+
filter_channels,
|
576 |
+
n_heads,
|
577 |
+
n_layers,
|
578 |
+
kernel_size,
|
579 |
+
p_dropout,
|
580 |
+
resblock,
|
581 |
+
resblock_kernel_sizes,
|
582 |
+
resblock_dilation_sizes,
|
583 |
+
upsample_rates,
|
584 |
+
upsample_initial_channel,
|
585 |
+
upsample_kernel_sizes,
|
586 |
+
n_speakers=256,
|
587 |
+
gin_channels=256,
|
588 |
+
use_sdp=True,
|
589 |
+
n_flow_layer=4,
|
590 |
+
n_layers_trans_flow=3,
|
591 |
+
flow_share_parameter=False,
|
592 |
+
use_transformer_flow=True,
|
593 |
+
**kwargs):
|
594 |
+
|
595 |
+
super().__init__()
|
596 |
+
self.n_vocab = n_vocab
|
597 |
+
self.spec_channels = spec_channels
|
598 |
+
self.inter_channels = inter_channels
|
599 |
+
self.hidden_channels = hidden_channels
|
600 |
+
self.filter_channels = filter_channels
|
601 |
+
self.n_heads = n_heads
|
602 |
+
self.n_layers = n_layers
|
603 |
+
self.kernel_size = kernel_size
|
604 |
+
self.p_dropout = p_dropout
|
605 |
+
self.resblock = resblock
|
606 |
+
self.resblock_kernel_sizes = resblock_kernel_sizes
|
607 |
+
self.resblock_dilation_sizes = resblock_dilation_sizes
|
608 |
+
self.upsample_rates = upsample_rates
|
609 |
+
self.upsample_initial_channel = upsample_initial_channel
|
610 |
+
self.upsample_kernel_sizes = upsample_kernel_sizes
|
611 |
+
self.segment_size = segment_size
|
612 |
+
self.n_speakers = n_speakers
|
613 |
+
self.gin_channels = gin_channels
|
614 |
+
self.n_layers_trans_flow = n_layers_trans_flow
|
615 |
+
self.use_spk_conditioned_encoder = kwargs.get("use_spk_conditioned_encoder", True)
|
616 |
+
self.use_sdp = use_sdp
|
617 |
+
self.use_noise_scaled_mas = kwargs.get("use_noise_scaled_mas", False)
|
618 |
+
self.mas_noise_scale_initial = kwargs.get("mas_noise_scale_initial", 0.01)
|
619 |
+
self.noise_scale_delta = kwargs.get("noise_scale_delta", 2e-6)
|
620 |
+
self.current_mas_noise_scale = self.mas_noise_scale_initial
|
621 |
+
if self.use_spk_conditioned_encoder and gin_channels > 0:
|
622 |
+
self.enc_gin_channels = gin_channels
|
623 |
+
self.enc_p = TextEncoder(n_vocab,
|
624 |
+
inter_channels,
|
625 |
+
hidden_channels,
|
626 |
+
filter_channels,
|
627 |
+
n_heads,
|
628 |
+
n_layers,
|
629 |
+
kernel_size,
|
630 |
+
p_dropout,
|
631 |
+
gin_channels=self.enc_gin_channels)
|
632 |
+
self.dec = Generator(inter_channels, resblock, resblock_kernel_sizes, resblock_dilation_sizes, upsample_rates,
|
633 |
+
upsample_initial_channel, upsample_kernel_sizes, gin_channels=gin_channels)
|
634 |
+
self.enc_q = PosteriorEncoder(spec_channels, inter_channels, hidden_channels, 5, 1, 16,
|
635 |
+
gin_channels=gin_channels)
|
636 |
+
if use_transformer_flow:
|
637 |
+
self.flow = TransformerCouplingBlock(inter_channels, hidden_channels, filter_channels, n_heads,
|
638 |
+
n_layers_trans_flow, 5, p_dropout, n_flow_layer,
|
639 |
+
gin_channels=gin_channels, share_parameter=flow_share_parameter)
|
640 |
+
else:
|
641 |
+
self.flow = ResidualCouplingBlock(inter_channels, hidden_channels, 5, 1, n_flow_layer,
|
642 |
+
gin_channels=gin_channels)
|
643 |
+
self.sdp = StochasticDurationPredictor(hidden_channels, 192, 3, 0.5, 4, gin_channels=gin_channels)
|
644 |
+
self.dp = DurationPredictor(hidden_channels, 256, 3, 0.5, gin_channels=gin_channels)
|
645 |
+
|
646 |
+
if n_speakers > 1:
|
647 |
+
self.emb_g = nn.Embedding(n_speakers, gin_channels)
|
648 |
+
else:
|
649 |
+
self.ref_enc = ReferenceEncoder(spec_channels, gin_channels)
|
650 |
+
|
651 |
+
def infer(self, x, x_lengths, sid, tone, language, bert, noise_scale=.667, length_scale=1, noise_scale_w=0.8,
|
652 |
+
max_len=None, sdp_ratio=0, y=None):
|
653 |
+
# x, m_p, logs_p, x_mask = self.enc_p(x, x_lengths, tone, language, bert)
|
654 |
+
# g = self.gst(y)
|
655 |
+
if self.n_speakers > 0:
|
656 |
+
g = self.emb_g(sid).unsqueeze(-1) # [b, h, 1]
|
657 |
+
else:
|
658 |
+
g = self.ref_enc(y.transpose(1, 2)).unsqueeze(-1)
|
659 |
+
x, m_p, logs_p, x_mask = self.enc_p(x, x_lengths, tone, language, bert, g=g)
|
660 |
+
logw = self.sdp(x, x_mask, g=g, reverse=True, noise_scale=noise_scale_w) * (sdp_ratio) + self.dp(x, x_mask,
|
661 |
+
g=g) * (
|
662 |
+
1 - sdp_ratio)
|
663 |
+
w = torch.exp(logw) * x_mask * length_scale
|
664 |
+
w_ceil = torch.ceil(w)
|
665 |
+
y_lengths = torch.clamp_min(torch.sum(w_ceil, [1, 2]), 1).long()
|
666 |
+
y_mask = torch.unsqueeze(commons.sequence_mask(y_lengths, None), 1).to(x_mask.dtype)
|
667 |
+
attn_mask = torch.unsqueeze(x_mask, 2) * torch.unsqueeze(y_mask, -1)
|
668 |
+
attn = commons.generate_path(w_ceil, attn_mask)
|
669 |
+
|
670 |
+
m_p = torch.matmul(attn.squeeze(1), m_p.transpose(1, 2)).transpose(1, 2) # [b, t', t], [b, t, d] -> [b, d, t']
|
671 |
+
logs_p = torch.matmul(attn.squeeze(1), logs_p.transpose(1, 2)).transpose(1,
|
672 |
+
2) # [b, t', t], [b, t, d] -> [b, d, t']
|
673 |
+
|
674 |
+
z_p = m_p + torch.randn_like(m_p) * torch.exp(logs_p) * noise_scale
|
675 |
+
z = self.flow(z_p, y_mask, g=g, reverse=True)
|
676 |
+
o = self.dec((z * y_mask)[:, :, :max_len], g=g)
|
677 |
+
return o, attn, y_mask, (z, z_p, m_p, logs_p)
|
bert_vits2/modules.py
ADDED
@@ -0,0 +1,459 @@
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|
|
|
|
1 |
+
import copy
|
2 |
+
import math
|
3 |
+
import numpy as np
|
4 |
+
import scipy
|
5 |
+
import torch
|
6 |
+
from torch import nn
|
7 |
+
from torch.nn import functional as F
|
8 |
+
|
9 |
+
from torch.nn import Conv1d, ConvTranspose1d, AvgPool1d, Conv2d
|
10 |
+
from torch.nn.utils import weight_norm, remove_weight_norm
|
11 |
+
|
12 |
+
from bert_vits2 import commons
|
13 |
+
from bert_vits2.commons import init_weights, get_padding
|
14 |
+
from bert_vits2.transforms import piecewise_rational_quadratic_transform
|
15 |
+
from bert_vits2.attentions import Encoder
|
16 |
+
|
17 |
+
LRELU_SLOPE = 0.1
|
18 |
+
|
19 |
+
|
20 |
+
class LayerNorm(nn.Module):
|
21 |
+
def __init__(self, channels, eps=1e-5):
|
22 |
+
super().__init__()
|
23 |
+
self.channels = channels
|
24 |
+
self.eps = eps
|
25 |
+
|
26 |
+
self.gamma = nn.Parameter(torch.ones(channels))
|
27 |
+
self.beta = nn.Parameter(torch.zeros(channels))
|
28 |
+
|
29 |
+
def forward(self, x):
|
30 |
+
x = x.transpose(1, -1)
|
31 |
+
x = F.layer_norm(x, (self.channels,), self.gamma, self.beta, self.eps)
|
32 |
+
return x.transpose(1, -1)
|
33 |
+
|
34 |
+
|
35 |
+
class ConvReluNorm(nn.Module):
|
36 |
+
def __init__(self, in_channels, hidden_channels, out_channels, kernel_size, n_layers, p_dropout):
|
37 |
+
super().__init__()
|
38 |
+
self.in_channels = in_channels
|
39 |
+
self.hidden_channels = hidden_channels
|
40 |
+
self.out_channels = out_channels
|
41 |
+
self.kernel_size = kernel_size
|
42 |
+
self.n_layers = n_layers
|
43 |
+
self.p_dropout = p_dropout
|
44 |
+
assert n_layers > 1, "Number of layers should be larger than 0."
|
45 |
+
|
46 |
+
self.conv_layers = nn.ModuleList()
|
47 |
+
self.norm_layers = nn.ModuleList()
|
48 |
+
self.conv_layers.append(nn.Conv1d(in_channels, hidden_channels, kernel_size, padding=kernel_size // 2))
|
49 |
+
self.norm_layers.append(LayerNorm(hidden_channels))
|
50 |
+
self.relu_drop = nn.Sequential(
|
51 |
+
nn.ReLU(),
|
52 |
+
nn.Dropout(p_dropout))
|
53 |
+
for _ in range(n_layers - 1):
|
54 |
+
self.conv_layers.append(nn.Conv1d(hidden_channels, hidden_channels, kernel_size, padding=kernel_size // 2))
|
55 |
+
self.norm_layers.append(LayerNorm(hidden_channels))
|
56 |
+
self.proj = nn.Conv1d(hidden_channels, out_channels, 1)
|
57 |
+
self.proj.weight.data.zero_()
|
58 |
+
self.proj.bias.data.zero_()
|
59 |
+
|
60 |
+
def forward(self, x, x_mask):
|
61 |
+
x_org = x
|
62 |
+
for i in range(self.n_layers):
|
63 |
+
x = self.conv_layers[i](x * x_mask)
|
64 |
+
x = self.norm_layers[i](x)
|
65 |
+
x = self.relu_drop(x)
|
66 |
+
x = x_org + self.proj(x)
|
67 |
+
return x * x_mask
|
68 |
+
|
69 |
+
|
70 |
+
class DDSConv(nn.Module):
|
71 |
+
"""
|
72 |
+
Dialted and Depth-Separable Convolution
|
73 |
+
"""
|
74 |
+
|
75 |
+
def __init__(self, channels, kernel_size, n_layers, p_dropout=0.):
|
76 |
+
super().__init__()
|
77 |
+
self.channels = channels
|
78 |
+
self.kernel_size = kernel_size
|
79 |
+
self.n_layers = n_layers
|
80 |
+
self.p_dropout = p_dropout
|
81 |
+
|
82 |
+
self.drop = nn.Dropout(p_dropout)
|
83 |
+
self.convs_sep = nn.ModuleList()
|
84 |
+
self.convs_1x1 = nn.ModuleList()
|
85 |
+
self.norms_1 = nn.ModuleList()
|
86 |
+
self.norms_2 = nn.ModuleList()
|
87 |
+
for i in range(n_layers):
|
88 |
+
dilation = kernel_size ** i
|
89 |
+
padding = (kernel_size * dilation - dilation) // 2
|
90 |
+
self.convs_sep.append(nn.Conv1d(channels, channels, kernel_size,
|
91 |
+
groups=channels, dilation=dilation, padding=padding
|
92 |
+
))
|
93 |
+
self.convs_1x1.append(nn.Conv1d(channels, channels, 1))
|
94 |
+
self.norms_1.append(LayerNorm(channels))
|
95 |
+
self.norms_2.append(LayerNorm(channels))
|
96 |
+
|
97 |
+
def forward(self, x, x_mask, g=None):
|
98 |
+
if g is not None:
|
99 |
+
x = x + g
|
100 |
+
for i in range(self.n_layers):
|
101 |
+
y = self.convs_sep[i](x * x_mask)
|
102 |
+
y = self.norms_1[i](y)
|
103 |
+
y = F.gelu(y)
|
104 |
+
y = self.convs_1x1[i](y)
|
105 |
+
y = self.norms_2[i](y)
|
106 |
+
y = F.gelu(y)
|
107 |
+
y = self.drop(y)
|
108 |
+
x = x + y
|
109 |
+
return x * x_mask
|
110 |
+
|
111 |
+
|
112 |
+
class WN(torch.nn.Module):
|
113 |
+
def __init__(self, hidden_channels, kernel_size, dilation_rate, n_layers, gin_channels=0, p_dropout=0):
|
114 |
+
super(WN, self).__init__()
|
115 |
+
assert (kernel_size % 2 == 1)
|
116 |
+
self.hidden_channels = hidden_channels
|
117 |
+
self.kernel_size = kernel_size,
|
118 |
+
self.dilation_rate = dilation_rate
|
119 |
+
self.n_layers = n_layers
|
120 |
+
self.gin_channels = gin_channels
|
121 |
+
self.p_dropout = p_dropout
|
122 |
+
|
123 |
+
self.in_layers = torch.nn.ModuleList()
|
124 |
+
self.res_skip_layers = torch.nn.ModuleList()
|
125 |
+
self.drop = nn.Dropout(p_dropout)
|
126 |
+
|
127 |
+
if gin_channels != 0:
|
128 |
+
cond_layer = torch.nn.Conv1d(gin_channels, 2 * hidden_channels * n_layers, 1)
|
129 |
+
self.cond_layer = torch.nn.utils.weight_norm(cond_layer, name='weight')
|
130 |
+
|
131 |
+
for i in range(n_layers):
|
132 |
+
dilation = dilation_rate ** i
|
133 |
+
padding = int((kernel_size * dilation - dilation) / 2)
|
134 |
+
in_layer = torch.nn.Conv1d(hidden_channels, 2 * hidden_channels, kernel_size,
|
135 |
+
dilation=dilation, padding=padding)
|
136 |
+
in_layer = torch.nn.utils.weight_norm(in_layer, name='weight')
|
137 |
+
self.in_layers.append(in_layer)
|
138 |
+
|
139 |
+
# last one is not necessary
|
140 |
+
if i < n_layers - 1:
|
141 |
+
res_skip_channels = 2 * hidden_channels
|
142 |
+
else:
|
143 |
+
res_skip_channels = hidden_channels
|
144 |
+
|
145 |
+
res_skip_layer = torch.nn.Conv1d(hidden_channels, res_skip_channels, 1)
|
146 |
+
res_skip_layer = torch.nn.utils.weight_norm(res_skip_layer, name='weight')
|
147 |
+
self.res_skip_layers.append(res_skip_layer)
|
148 |
+
|
149 |
+
def forward(self, x, x_mask, g=None, **kwargs):
|
150 |
+
output = torch.zeros_like(x)
|
151 |
+
n_channels_tensor = torch.IntTensor([self.hidden_channels])
|
152 |
+
|
153 |
+
if g is not None:
|
154 |
+
g = self.cond_layer(g)
|
155 |
+
|
156 |
+
for i in range(self.n_layers):
|
157 |
+
x_in = self.in_layers[i](x)
|
158 |
+
if g is not None:
|
159 |
+
cond_offset = i * 2 * self.hidden_channels
|
160 |
+
g_l = g[:, cond_offset:cond_offset + 2 * self.hidden_channels, :]
|
161 |
+
else:
|
162 |
+
g_l = torch.zeros_like(x_in)
|
163 |
+
|
164 |
+
acts = commons.fused_add_tanh_sigmoid_multiply(
|
165 |
+
x_in,
|
166 |
+
g_l,
|
167 |
+
n_channels_tensor)
|
168 |
+
acts = self.drop(acts)
|
169 |
+
|
170 |
+
res_skip_acts = self.res_skip_layers[i](acts)
|
171 |
+
if i < self.n_layers - 1:
|
172 |
+
res_acts = res_skip_acts[:, :self.hidden_channels, :]
|
173 |
+
x = (x + res_acts) * x_mask
|
174 |
+
output = output + res_skip_acts[:, self.hidden_channels:, :]
|
175 |
+
else:
|
176 |
+
output = output + res_skip_acts
|
177 |
+
return output * x_mask
|
178 |
+
|
179 |
+
def remove_weight_norm(self):
|
180 |
+
if self.gin_channels != 0:
|
181 |
+
torch.nn.utils.remove_weight_norm(self.cond_layer)
|
182 |
+
for l in self.in_layers:
|
183 |
+
torch.nn.utils.remove_weight_norm(l)
|
184 |
+
for l in self.res_skip_layers:
|
185 |
+
torch.nn.utils.remove_weight_norm(l)
|
186 |
+
|
187 |
+
|
188 |
+
class ResBlock1(torch.nn.Module):
|
189 |
+
def __init__(self, channels, kernel_size=3, dilation=(1, 3, 5)):
|
190 |
+
super(ResBlock1, self).__init__()
|
191 |
+
self.convs1 = nn.ModuleList([
|
192 |
+
weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[0],
|
193 |
+
padding=get_padding(kernel_size, dilation[0]))),
|
194 |
+
weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[1],
|
195 |
+
padding=get_padding(kernel_size, dilation[1]))),
|
196 |
+
weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[2],
|
197 |
+
padding=get_padding(kernel_size, dilation[2])))
|
198 |
+
])
|
199 |
+
self.convs1.apply(init_weights)
|
200 |
+
|
201 |
+
self.convs2 = nn.ModuleList([
|
202 |
+
weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1,
|
203 |
+
padding=get_padding(kernel_size, 1))),
|
204 |
+
weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1,
|
205 |
+
padding=get_padding(kernel_size, 1))),
|
206 |
+
weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1,
|
207 |
+
padding=get_padding(kernel_size, 1)))
|
208 |
+
])
|
209 |
+
self.convs2.apply(init_weights)
|
210 |
+
|
211 |
+
def forward(self, x, x_mask=None):
|
212 |
+
for c1, c2 in zip(self.convs1, self.convs2):
|
213 |
+
xt = F.leaky_relu(x, LRELU_SLOPE)
|
214 |
+
if x_mask is not None:
|
215 |
+
xt = xt * x_mask
|
216 |
+
xt = c1(xt)
|
217 |
+
xt = F.leaky_relu(xt, LRELU_SLOPE)
|
218 |
+
if x_mask is not None:
|
219 |
+
xt = xt * x_mask
|
220 |
+
xt = c2(xt)
|
221 |
+
x = xt + x
|
222 |
+
if x_mask is not None:
|
223 |
+
x = x * x_mask
|
224 |
+
return x
|
225 |
+
|
226 |
+
def remove_weight_norm(self):
|
227 |
+
for l in self.convs1:
|
228 |
+
remove_weight_norm(l)
|
229 |
+
for l in self.convs2:
|
230 |
+
remove_weight_norm(l)
|
231 |
+
|
232 |
+
|
233 |
+
class ResBlock2(torch.nn.Module):
|
234 |
+
def __init__(self, channels, kernel_size=3, dilation=(1, 3)):
|
235 |
+
super(ResBlock2, self).__init__()
|
236 |
+
self.convs = nn.ModuleList([
|
237 |
+
weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[0],
|
238 |
+
padding=get_padding(kernel_size, dilation[0]))),
|
239 |
+
weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[1],
|
240 |
+
padding=get_padding(kernel_size, dilation[1])))
|
241 |
+
])
|
242 |
+
self.convs.apply(init_weights)
|
243 |
+
|
244 |
+
def forward(self, x, x_mask=None):
|
245 |
+
for c in self.convs:
|
246 |
+
xt = F.leaky_relu(x, LRELU_SLOPE)
|
247 |
+
if x_mask is not None:
|
248 |
+
xt = xt * x_mask
|
249 |
+
xt = c(xt)
|
250 |
+
x = xt + x
|
251 |
+
if x_mask is not None:
|
252 |
+
x = x * x_mask
|
253 |
+
return x
|
254 |
+
|
255 |
+
def remove_weight_norm(self):
|
256 |
+
for l in self.convs:
|
257 |
+
remove_weight_norm(l)
|
258 |
+
|
259 |
+
|
260 |
+
class Log(nn.Module):
|
261 |
+
def forward(self, x, x_mask, reverse=False, **kwargs):
|
262 |
+
if not reverse:
|
263 |
+
y = torch.log(torch.clamp_min(x, 1e-5)) * x_mask
|
264 |
+
logdet = torch.sum(-y, [1, 2])
|
265 |
+
return y, logdet
|
266 |
+
else:
|
267 |
+
x = torch.exp(x) * x_mask
|
268 |
+
return x
|
269 |
+
|
270 |
+
|
271 |
+
class Flip(nn.Module):
|
272 |
+
def forward(self, x, *args, reverse=False, **kwargs):
|
273 |
+
x = torch.flip(x, [1])
|
274 |
+
if not reverse:
|
275 |
+
logdet = torch.zeros(x.size(0)).to(dtype=x.dtype, device=x.device)
|
276 |
+
return x, logdet
|
277 |
+
else:
|
278 |
+
return x
|
279 |
+
|
280 |
+
|
281 |
+
class ElementwiseAffine(nn.Module):
|
282 |
+
def __init__(self, channels):
|
283 |
+
super().__init__()
|
284 |
+
self.channels = channels
|
285 |
+
self.m = nn.Parameter(torch.zeros(channels, 1))
|
286 |
+
self.logs = nn.Parameter(torch.zeros(channels, 1))
|
287 |
+
|
288 |
+
def forward(self, x, x_mask, reverse=False, **kwargs):
|
289 |
+
if not reverse:
|
290 |
+
y = self.m + torch.exp(self.logs) * x
|
291 |
+
y = y * x_mask
|
292 |
+
logdet = torch.sum(self.logs * x_mask, [1, 2])
|
293 |
+
return y, logdet
|
294 |
+
else:
|
295 |
+
x = (x - self.m) * torch.exp(-self.logs) * x_mask
|
296 |
+
return x
|
297 |
+
|
298 |
+
|
299 |
+
class ResidualCouplingLayer(nn.Module):
|
300 |
+
def __init__(self,
|
301 |
+
channels,
|
302 |
+
hidden_channels,
|
303 |
+
kernel_size,
|
304 |
+
dilation_rate,
|
305 |
+
n_layers,
|
306 |
+
p_dropout=0,
|
307 |
+
gin_channels=0,
|
308 |
+
mean_only=False):
|
309 |
+
assert channels % 2 == 0, "channels should be divisible by 2"
|
310 |
+
super().__init__()
|
311 |
+
self.channels = channels
|
312 |
+
self.hidden_channels = hidden_channels
|
313 |
+
self.kernel_size = kernel_size
|
314 |
+
self.dilation_rate = dilation_rate
|
315 |
+
self.n_layers = n_layers
|
316 |
+
self.half_channels = channels // 2
|
317 |
+
self.mean_only = mean_only
|
318 |
+
|
319 |
+
self.pre = nn.Conv1d(self.half_channels, hidden_channels, 1)
|
320 |
+
self.enc = WN(hidden_channels, kernel_size, dilation_rate, n_layers, p_dropout=p_dropout,
|
321 |
+
gin_channels=gin_channels)
|
322 |
+
self.post = nn.Conv1d(hidden_channels, self.half_channels * (2 - mean_only), 1)
|
323 |
+
self.post.weight.data.zero_()
|
324 |
+
self.post.bias.data.zero_()
|
325 |
+
|
326 |
+
def forward(self, x, x_mask, g=None, reverse=False):
|
327 |
+
x0, x1 = torch.split(x, [self.half_channels] * 2, 1)
|
328 |
+
h = self.pre(x0) * x_mask
|
329 |
+
h = self.enc(h, x_mask, g=g)
|
330 |
+
stats = self.post(h) * x_mask
|
331 |
+
if not self.mean_only:
|
332 |
+
m, logs = torch.split(stats, [self.half_channels] * 2, 1)
|
333 |
+
else:
|
334 |
+
m = stats
|
335 |
+
logs = torch.zeros_like(m)
|
336 |
+
|
337 |
+
if not reverse:
|
338 |
+
x1 = m + x1 * torch.exp(logs) * x_mask
|
339 |
+
x = torch.cat([x0, x1], 1)
|
340 |
+
logdet = torch.sum(logs, [1, 2])
|
341 |
+
return x, logdet
|
342 |
+
else:
|
343 |
+
x1 = (x1 - m) * torch.exp(-logs) * x_mask
|
344 |
+
x = torch.cat([x0, x1], 1)
|
345 |
+
return x
|
346 |
+
|
347 |
+
|
348 |
+
class ConvFlow(nn.Module):
|
349 |
+
def __init__(self, in_channels, filter_channels, kernel_size, n_layers, num_bins=10, tail_bound=5.0):
|
350 |
+
super().__init__()
|
351 |
+
self.in_channels = in_channels
|
352 |
+
self.filter_channels = filter_channels
|
353 |
+
self.kernel_size = kernel_size
|
354 |
+
self.n_layers = n_layers
|
355 |
+
self.num_bins = num_bins
|
356 |
+
self.tail_bound = tail_bound
|
357 |
+
self.half_channels = in_channels // 2
|
358 |
+
|
359 |
+
self.pre = nn.Conv1d(self.half_channels, filter_channels, 1)
|
360 |
+
self.convs = DDSConv(filter_channels, kernel_size, n_layers, p_dropout=0.)
|
361 |
+
self.proj = nn.Conv1d(filter_channels, self.half_channels * (num_bins * 3 - 1), 1)
|
362 |
+
self.proj.weight.data.zero_()
|
363 |
+
self.proj.bias.data.zero_()
|
364 |
+
|
365 |
+
def forward(self, x, x_mask, g=None, reverse=False):
|
366 |
+
x0, x1 = torch.split(x, [self.half_channels] * 2, 1)
|
367 |
+
h = self.pre(x0)
|
368 |
+
h = self.convs(h, x_mask, g=g)
|
369 |
+
h = self.proj(h) * x_mask
|
370 |
+
|
371 |
+
b, c, t = x0.shape
|
372 |
+
h = h.reshape(b, c, -1, t).permute(0, 1, 3, 2) # [b, cx?, t] -> [b, c, t, ?]
|
373 |
+
|
374 |
+
unnormalized_widths = h[..., :self.num_bins] / math.sqrt(self.filter_channels)
|
375 |
+
unnormalized_heights = h[..., self.num_bins:2 * self.num_bins] / math.sqrt(self.filter_channels)
|
376 |
+
unnormalized_derivatives = h[..., 2 * self.num_bins:]
|
377 |
+
|
378 |
+
x1, logabsdet = piecewise_rational_quadratic_transform(x1,
|
379 |
+
unnormalized_widths,
|
380 |
+
unnormalized_heights,
|
381 |
+
unnormalized_derivatives,
|
382 |
+
inverse=reverse,
|
383 |
+
tails='linear',
|
384 |
+
tail_bound=self.tail_bound
|
385 |
+
)
|
386 |
+
|
387 |
+
x = torch.cat([x0, x1], 1) * x_mask
|
388 |
+
logdet = torch.sum(logabsdet * x_mask, [1, 2])
|
389 |
+
if not reverse:
|
390 |
+
return x, logdet
|
391 |
+
else:
|
392 |
+
return x
|
393 |
+
|
394 |
+
|
395 |
+
class TransformerCouplingLayer(nn.Module):
|
396 |
+
def __init__(self,
|
397 |
+
channels,
|
398 |
+
hidden_channels,
|
399 |
+
kernel_size,
|
400 |
+
n_layers,
|
401 |
+
n_heads,
|
402 |
+
p_dropout=0,
|
403 |
+
filter_channels=0,
|
404 |
+
mean_only=False,
|
405 |
+
wn_sharing_parameter=None,
|
406 |
+
gin_channels=0
|
407 |
+
):
|
408 |
+
assert channels % 2 == 0, "channels should be divisible by 2"
|
409 |
+
super().__init__()
|
410 |
+
self.channels = channels
|
411 |
+
self.hidden_channels = hidden_channels
|
412 |
+
self.kernel_size = kernel_size
|
413 |
+
self.n_layers = n_layers
|
414 |
+
self.half_channels = channels // 2
|
415 |
+
self.mean_only = mean_only
|
416 |
+
|
417 |
+
self.pre = nn.Conv1d(self.half_channels, hidden_channels, 1)
|
418 |
+
self.enc = Encoder(hidden_channels, filter_channels, n_heads, n_layers, kernel_size, p_dropout, isflow=True,
|
419 |
+
gin_channels=gin_channels) if wn_sharing_parameter is None else wn_sharing_parameter
|
420 |
+
self.post = nn.Conv1d(hidden_channels, self.half_channels * (2 - mean_only), 1)
|
421 |
+
self.post.weight.data.zero_()
|
422 |
+
self.post.bias.data.zero_()
|
423 |
+
|
424 |
+
def forward(self, x, x_mask, g=None, reverse=False):
|
425 |
+
x0, x1 = torch.split(x, [self.half_channels] * 2, 1)
|
426 |
+
h = self.pre(x0) * x_mask
|
427 |
+
h = self.enc(h, x_mask, g=g)
|
428 |
+
stats = self.post(h) * x_mask
|
429 |
+
if not self.mean_only:
|
430 |
+
m, logs = torch.split(stats, [self.half_channels] * 2, 1)
|
431 |
+
else:
|
432 |
+
m = stats
|
433 |
+
logs = torch.zeros_like(m)
|
434 |
+
|
435 |
+
if not reverse:
|
436 |
+
x1 = m + x1 * torch.exp(logs) * x_mask
|
437 |
+
x = torch.cat([x0, x1], 1)
|
438 |
+
logdet = torch.sum(logs, [1, 2])
|
439 |
+
return x, logdet
|
440 |
+
else:
|
441 |
+
x1 = (x1 - m) * torch.exp(-logs) * x_mask
|
442 |
+
x = torch.cat([x0, x1], 1)
|
443 |
+
return x
|
444 |
+
|
445 |
+
x1, logabsdet = piecewise_rational_quadratic_transform(x1,
|
446 |
+
unnormalized_widths,
|
447 |
+
unnormalized_heights,
|
448 |
+
unnormalized_derivatives,
|
449 |
+
inverse=reverse,
|
450 |
+
tails='linear',
|
451 |
+
tail_bound=self.tail_bound
|
452 |
+
)
|
453 |
+
|
454 |
+
x = torch.cat([x0, x1], 1) * x_mask
|
455 |
+
logdet = torch.sum(logabsdet * x_mask, [1, 2])
|
456 |
+
if not reverse:
|
457 |
+
return x, logdet
|
458 |
+
else:
|
459 |
+
return x
|
bert_vits2/requirements.txt
ADDED
@@ -0,0 +1,15 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
Cython
|
2 |
+
librosa==0.9.1
|
3 |
+
matplotlib==3.3.1
|
4 |
+
numpy
|
5 |
+
phonemizer
|
6 |
+
scipy
|
7 |
+
tensorboard
|
8 |
+
torch
|
9 |
+
torchvision
|
10 |
+
Unidecode
|
11 |
+
amfm_decompy
|
12 |
+
jieba
|
13 |
+
transformers
|
14 |
+
pypinyin
|
15 |
+
cn2an
|
bert_vits2/text/__init__.py
ADDED
@@ -0,0 +1,29 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
from bert_vits2.text.symbols import *
|
2 |
+
from .chinese_bert import get_bert_feature as zh_bert
|
3 |
+
from .english_bert_mock import get_bert_feature as en_bert
|
4 |
+
|
5 |
+
_symbol_to_id = {s: i for i, s in enumerate(symbols)}
|
6 |
+
|
7 |
+
|
8 |
+
def cleaned_text_to_sequence(cleaned_text, tones, language):
|
9 |
+
'''Converts a string of text to a sequence of IDs corresponding to the symbols in the text.
|
10 |
+
Args:
|
11 |
+
text: string to convert to a sequence
|
12 |
+
Returns:
|
13 |
+
List of integers corresponding to the symbols in the text
|
14 |
+
'''
|
15 |
+
phones = [_symbol_to_id[symbol] for symbol in cleaned_text]
|
16 |
+
tone_start = language_tone_start_map[language]
|
17 |
+
tones = [i + tone_start for i in tones]
|
18 |
+
lang_id = language_id_map[language]
|
19 |
+
lang_ids = [lang_id for i in phones]
|
20 |
+
return phones, tones, lang_ids
|
21 |
+
|
22 |
+
|
23 |
+
def get_bert(norm_text, word2ph, language):
|
24 |
+
lang_bert_func_map = {
|
25 |
+
'ZH': zh_bert,
|
26 |
+
'EN': en_bert
|
27 |
+
}
|
28 |
+
bert = lang_bert_func_map[language](norm_text, word2ph)
|
29 |
+
return bert
|
bert_vits2/text/chinese.py
ADDED
@@ -0,0 +1,194 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
import os
|
2 |
+
import re
|
3 |
+
|
4 |
+
import cn2an
|
5 |
+
from pypinyin import lazy_pinyin, Style
|
6 |
+
|
7 |
+
from bert_vits2.text.symbols import punctuation
|
8 |
+
from bert_vits2.text.tone_sandhi import ToneSandhi
|
9 |
+
|
10 |
+
current_file_path = os.path.dirname(__file__)
|
11 |
+
pinyin_to_symbol_map = {line.split("\t")[0]: line.strip().split("\t")[1] for line in
|
12 |
+
open(os.path.join(current_file_path, 'opencpop-strict.txt')).readlines()}
|
13 |
+
|
14 |
+
import jieba.posseg as psg
|
15 |
+
|
16 |
+
rep_map = {
|
17 |
+
':': ',',
|
18 |
+
';': ',',
|
19 |
+
',': ',',
|
20 |
+
'。': '.',
|
21 |
+
'!': '!',
|
22 |
+
'?': '?',
|
23 |
+
'\n': '.',
|
24 |
+
"·": ",",
|
25 |
+
'、': ",",
|
26 |
+
'...': '…',
|
27 |
+
'$': '.',
|
28 |
+
'“': "'",
|
29 |
+
'”': "'",
|
30 |
+
'‘': "'",
|
31 |
+
'’': "'",
|
32 |
+
'(': "'",
|
33 |
+
')': "'",
|
34 |
+
'(': "'",
|
35 |
+
')': "'",
|
36 |
+
'《': "'",
|
37 |
+
'》': "'",
|
38 |
+
'【': "'",
|
39 |
+
'】': "'",
|
40 |
+
'[': "'",
|
41 |
+
']': "'",
|
42 |
+
'—': "-",
|
43 |
+
'~': "-",
|
44 |
+
'~': "-",
|
45 |
+
'「': "'",
|
46 |
+
'」': "'",
|
47 |
+
|
48 |
+
}
|
49 |
+
|
50 |
+
tone_modifier = ToneSandhi()
|
51 |
+
|
52 |
+
|
53 |
+
def replace_punctuation(text):
|
54 |
+
text = text.replace("嗯", "恩").replace("呣", "母")
|
55 |
+
pattern = re.compile('|'.join(re.escape(p) for p in rep_map.keys()))
|
56 |
+
|
57 |
+
replaced_text = pattern.sub(lambda x: rep_map[x.group()], text)
|
58 |
+
|
59 |
+
replaced_text = re.sub(r'[^\u4e00-\u9fa5' + "".join(punctuation) + r']+', '', replaced_text)
|
60 |
+
|
61 |
+
return replaced_text
|
62 |
+
|
63 |
+
|
64 |
+
def g2p(text):
|
65 |
+
pattern = r'(?<=[{0}])\s*'.format(''.join(punctuation))
|
66 |
+
sentences = [i for i in re.split(pattern, text) if i.strip() != '']
|
67 |
+
phones, tones, word2ph = _g2p(sentences)
|
68 |
+
assert sum(word2ph) == len(phones)
|
69 |
+
assert len(word2ph) == len(text) # Sometimes it will crash,you can add a try-catch.
|
70 |
+
phones = ['_'] + phones + ["_"]
|
71 |
+
tones = [0] + tones + [0]
|
72 |
+
word2ph = [1] + word2ph + [1]
|
73 |
+
return phones, tones, word2ph
|
74 |
+
|
75 |
+
|
76 |
+
def _get_initials_finals(word):
|
77 |
+
initials = []
|
78 |
+
finals = []
|
79 |
+
orig_initials = lazy_pinyin(
|
80 |
+
word, neutral_tone_with_five=True, style=Style.INITIALS)
|
81 |
+
orig_finals = lazy_pinyin(
|
82 |
+
word, neutral_tone_with_five=True, style=Style.FINALS_TONE3)
|
83 |
+
for c, v in zip(orig_initials, orig_finals):
|
84 |
+
initials.append(c)
|
85 |
+
finals.append(v)
|
86 |
+
return initials, finals
|
87 |
+
|
88 |
+
|
89 |
+
def _g2p(segments):
|
90 |
+
phones_list = []
|
91 |
+
tones_list = []
|
92 |
+
word2ph = []
|
93 |
+
for seg in segments:
|
94 |
+
pinyins = []
|
95 |
+
# Replace all English words in the sentence
|
96 |
+
seg = re.sub('[a-zA-Z]+', '', seg)
|
97 |
+
seg_cut = psg.lcut(seg)
|
98 |
+
initials = []
|
99 |
+
finals = []
|
100 |
+
seg_cut = tone_modifier.pre_merge_for_modify(seg_cut)
|
101 |
+
for word, pos in seg_cut:
|
102 |
+
if pos == 'eng':
|
103 |
+
continue
|
104 |
+
sub_initials, sub_finals = _get_initials_finals(word)
|
105 |
+
sub_finals = tone_modifier.modified_tone(word, pos,
|
106 |
+
sub_finals)
|
107 |
+
initials.append(sub_initials)
|
108 |
+
finals.append(sub_finals)
|
109 |
+
|
110 |
+
# assert len(sub_initials) == len(sub_finals) == len(word)
|
111 |
+
initials = sum(initials, [])
|
112 |
+
finals = sum(finals, [])
|
113 |
+
#
|
114 |
+
for c, v in zip(initials, finals):
|
115 |
+
raw_pinyin = c + v
|
116 |
+
# NOTE: post process for pypinyin outputs
|
117 |
+
# we discriminate i, ii and iii
|
118 |
+
if c == v:
|
119 |
+
assert c in punctuation
|
120 |
+
phone = [c]
|
121 |
+
tone = '0'
|
122 |
+
word2ph.append(1)
|
123 |
+
else:
|
124 |
+
v_without_tone = v[:-1]
|
125 |
+
tone = v[-1]
|
126 |
+
|
127 |
+
pinyin = c + v_without_tone
|
128 |
+
assert tone in '12345'
|
129 |
+
|
130 |
+
if c:
|
131 |
+
# 多音节
|
132 |
+
v_rep_map = {
|
133 |
+
"uei": 'ui',
|
134 |
+
'iou': 'iu',
|
135 |
+
'uen': 'un',
|
136 |
+
}
|
137 |
+
if v_without_tone in v_rep_map.keys():
|
138 |
+
pinyin = c + v_rep_map[v_without_tone]
|
139 |
+
else:
|
140 |
+
# 单音节
|
141 |
+
pinyin_rep_map = {
|
142 |
+
'ing': 'ying',
|
143 |
+
'i': 'yi',
|
144 |
+
'in': 'yin',
|
145 |
+
'u': 'wu',
|
146 |
+
}
|
147 |
+
if pinyin in pinyin_rep_map.keys():
|
148 |
+
pinyin = pinyin_rep_map[pinyin]
|
149 |
+
else:
|
150 |
+
single_rep_map = {
|
151 |
+
'v': 'yu',
|
152 |
+
'e': 'e',
|
153 |
+
'i': 'y',
|
154 |
+
'u': 'w',
|
155 |
+
}
|
156 |
+
if pinyin[0] in single_rep_map.keys():
|
157 |
+
pinyin = single_rep_map[pinyin[0]] + pinyin[1:]
|
158 |
+
|
159 |
+
assert pinyin in pinyin_to_symbol_map.keys(), (pinyin, seg, raw_pinyin)
|
160 |
+
phone = pinyin_to_symbol_map[pinyin].split(' ')
|
161 |
+
word2ph.append(len(phone))
|
162 |
+
|
163 |
+
phones_list += phone
|
164 |
+
tones_list += [int(tone)] * len(phone)
|
165 |
+
return phones_list, tones_list, word2ph
|
166 |
+
|
167 |
+
|
168 |
+
def text_normalize(text):
|
169 |
+
numbers = re.findall(r'\d+(?:\.?\d+)?', text)
|
170 |
+
for number in numbers:
|
171 |
+
text = text.replace(number, cn2an.an2cn(number), 1)
|
172 |
+
text = replace_punctuation(text)
|
173 |
+
return text
|
174 |
+
|
175 |
+
|
176 |
+
def get_bert_feature(text, word2ph):
|
177 |
+
from bert_vits2.text import chinese_bert
|
178 |
+
return chinese_bert.get_bert_feature(text, word2ph)
|
179 |
+
|
180 |
+
|
181 |
+
if __name__ == '__main__':
|
182 |
+
from bert_vits2.text import get_bert_feature
|
183 |
+
|
184 |
+
text = "啊!但是《原神》是由,米哈\游自主, [研发]的一款全.新开放世界.冒险游戏"
|
185 |
+
text = text_normalize(text)
|
186 |
+
print(text)
|
187 |
+
phones, tones, word2ph = g2p(text)
|
188 |
+
bert = get_bert_feature(text, word2ph)
|
189 |
+
|
190 |
+
print(phones, tones, word2ph, bert.shape)
|
191 |
+
|
192 |
+
# # 示例用法
|
193 |
+
# text = "这是一个示例文本:,你好!这是一个测试...."
|
194 |
+
# print(g2p_paddle(text)) # 输出: 这是一个示例文本你好这是一个测试
|
bert_vits2/text/chinese_bert.py
ADDED
@@ -0,0 +1,60 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
import config
|
2 |
+
import torch
|
3 |
+
from transformers import AutoTokenizer, AutoModelForMaskedLM
|
4 |
+
from logger import logger
|
5 |
+
|
6 |
+
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
|
7 |
+
|
8 |
+
try:
|
9 |
+
logger.info("Loading chinese-roberta-wwm-ext-large...")
|
10 |
+
tokenizer = AutoTokenizer.from_pretrained(config.ABS_PATH + "/bert_vits2/bert/chinese-roberta-wwm-ext-large")
|
11 |
+
model = AutoModelForMaskedLM.from_pretrained(config.ABS_PATH + "/bert_vits2/bert/chinese-roberta-wwm-ext-large").to(
|
12 |
+
device)
|
13 |
+
logger.info("Loading finished.")
|
14 |
+
except Exception as e:
|
15 |
+
logger.error(e)
|
16 |
+
logger.error(f"Please download model from hfl/chinese-roberta-wwm-ext-large.")
|
17 |
+
|
18 |
+
|
19 |
+
def get_bert_feature(text, word2ph):
|
20 |
+
with torch.no_grad():
|
21 |
+
inputs = tokenizer(text, return_tensors='pt')
|
22 |
+
for i in inputs:
|
23 |
+
inputs[i] = inputs[i].to(device)
|
24 |
+
res = model(**inputs, output_hidden_states=True)
|
25 |
+
res = torch.cat(res['hidden_states'][-3:-2], -1)[0].cpu()
|
26 |
+
|
27 |
+
assert len(word2ph) == len(text) + 2
|
28 |
+
word2phone = word2ph
|
29 |
+
phone_level_feature = []
|
30 |
+
for i in range(len(word2phone)):
|
31 |
+
repeat_feature = res[i].repeat(word2phone[i], 1)
|
32 |
+
phone_level_feature.append(repeat_feature)
|
33 |
+
|
34 |
+
phone_level_feature = torch.cat(phone_level_feature, dim=0)
|
35 |
+
|
36 |
+
return phone_level_feature.T
|
37 |
+
|
38 |
+
|
39 |
+
if __name__ == '__main__':
|
40 |
+
# feature = get_bert_feature('你好,我是说的道理。')
|
41 |
+
import torch
|
42 |
+
|
43 |
+
word_level_feature = torch.rand(38, 1024) # 12个词,每个词1024维特征
|
44 |
+
word2phone = [1, 2, 1, 2, 2, 1, 2, 2, 1, 2, 2, 1, 2, 2, 2, 2, 2, 1, 1, 2, 2, 1, 2, 2, 2, 2, 1, 2, 2, 2, 2, 2, 1, 2,
|
45 |
+
2, 2, 2, 1]
|
46 |
+
|
47 |
+
# 计算总帧数
|
48 |
+
total_frames = sum(word2phone)
|
49 |
+
print(word_level_feature.shape)
|
50 |
+
print(word2phone)
|
51 |
+
phone_level_feature = []
|
52 |
+
for i in range(len(word2phone)):
|
53 |
+
print(word_level_feature[i].shape)
|
54 |
+
|
55 |
+
# 对每个词重复word2phone[i]次
|
56 |
+
repeat_feature = word_level_feature[i].repeat(word2phone[i], 1)
|
57 |
+
phone_level_feature.append(repeat_feature)
|
58 |
+
|
59 |
+
phone_level_feature = torch.cat(phone_level_feature, dim=0)
|
60 |
+
print(phone_level_feature.shape) # torch.Size([36, 1024])
|
bert_vits2/text/cleaner.py
ADDED
@@ -0,0 +1,29 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
from bert_vits2.text import chinese, cleaned_text_to_sequence
|
2 |
+
|
3 |
+
language_module_map = {
|
4 |
+
'ZH': chinese
|
5 |
+
}
|
6 |
+
|
7 |
+
|
8 |
+
def clean_text(text, language):
|
9 |
+
language_module = language_module_map[language]
|
10 |
+
norm_text = language_module.text_normalize(text)
|
11 |
+
phones, tones, word2ph = language_module.g2p(norm_text)
|
12 |
+
return norm_text, phones, tones, word2ph
|
13 |
+
|
14 |
+
|
15 |
+
def clean_text_bert(text, language):
|
16 |
+
language_module = language_module_map[language]
|
17 |
+
norm_text = language_module.text_normalize(text)
|
18 |
+
phones, tones, word2ph = language_module.g2p(norm_text)
|
19 |
+
bert = language_module.get_bert_feature(norm_text, word2ph)
|
20 |
+
return phones, tones, bert
|
21 |
+
|
22 |
+
|
23 |
+
def text_to_sequence(text, language):
|
24 |
+
norm_text, phones, tones, word2ph = clean_text(text, language)
|
25 |
+
return cleaned_text_to_sequence(phones, tones, language)
|
26 |
+
|
27 |
+
|
28 |
+
if __name__ == '__main__':
|
29 |
+
pass
|
bert_vits2/text/cmudict.rep
ADDED
The diff for this file is too large to render.
See raw diff
|
|
chinese_dialect_lexicons/jyutjyu_2.ocd2 → bert_vits2/text/cmudict_cache.pickle
RENAMED
@@ -1,3 +1,3 @@
|
|
1 |
version https://git-lfs.github.com/spec/v1
|
2 |
-
oid sha256:
|
3 |
-
size
|
|
|
1 |
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:b9b21b20325471934ba92f2e4a5976989e7d920caa32e7a286eacb027d197949
|
3 |
+
size 6212655
|
bert_vits2/text/english.py
ADDED
@@ -0,0 +1,146 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
import pickle
|
2 |
+
import os
|
3 |
+
import re
|
4 |
+
from g2p_en import G2p
|
5 |
+
|
6 |
+
from bert_vits2.text import symbols
|
7 |
+
|
8 |
+
current_file_path = os.path.dirname(__file__)
|
9 |
+
CMU_DICT_PATH = os.path.join(current_file_path, 'cmudict.rep')
|
10 |
+
CACHE_PATH = os.path.join(current_file_path, 'cmudict_cache.pickle')
|
11 |
+
_g2p = G2p()
|
12 |
+
|
13 |
+
arpa = {'AH0', 'S', 'AH1', 'EY2', 'AE2', 'EH0', 'OW2', 'UH0', 'NG', 'B', 'G', 'AY0', 'M', 'AA0', 'F', 'AO0', 'ER2',
|
14 |
+
'UH1', 'IY1', 'AH2', 'DH', 'IY0', 'EY1', 'IH0', 'K', 'N', 'W', 'IY2', 'T', 'AA1', 'ER1', 'EH2', 'OY0', 'UH2',
|
15 |
+
'UW1', 'Z', 'AW2', 'AW1', 'V', 'UW2', 'AA2', 'ER', 'AW0', 'UW0', 'R', 'OW1', 'EH1', 'ZH', 'AE0', 'IH2', 'IH',
|
16 |
+
'Y', 'JH', 'P', 'AY1', 'EY0', 'OY2', 'TH', 'HH', 'D', 'ER0', 'CH', 'AO1', 'AE1', 'AO2', 'OY1', 'AY2', 'IH1',
|
17 |
+
'OW0', 'L', 'SH'}
|
18 |
+
|
19 |
+
|
20 |
+
def post_replace_ph(ph):
|
21 |
+
rep_map = {
|
22 |
+
':': ',',
|
23 |
+
';': ',',
|
24 |
+
',': ',',
|
25 |
+
'。': '.',
|
26 |
+
'!': '!',
|
27 |
+
'?': '?',
|
28 |
+
'\n': '.',
|
29 |
+
"·": ",",
|
30 |
+
'、': ",",
|
31 |
+
'...': '…',
|
32 |
+
'v': "V"
|
33 |
+
}
|
34 |
+
if ph in rep_map.keys():
|
35 |
+
ph = rep_map[ph]
|
36 |
+
if ph in symbols:
|
37 |
+
return ph
|
38 |
+
if ph not in symbols:
|
39 |
+
ph = 'UNK'
|
40 |
+
return ph
|
41 |
+
|
42 |
+
|
43 |
+
def read_dict():
|
44 |
+
g2p_dict = {}
|
45 |
+
start_line = 49
|
46 |
+
with open(CMU_DICT_PATH) as f:
|
47 |
+
line = f.readline()
|
48 |
+
line_index = 1
|
49 |
+
while line:
|
50 |
+
if line_index >= start_line:
|
51 |
+
line = line.strip()
|
52 |
+
word_split = line.split(' ')
|
53 |
+
word = word_split[0]
|
54 |
+
|
55 |
+
syllable_split = word_split[1].split(' - ')
|
56 |
+
g2p_dict[word] = []
|
57 |
+
for syllable in syllable_split:
|
58 |
+
phone_split = syllable.split(' ')
|
59 |
+
g2p_dict[word].append(phone_split)
|
60 |
+
|
61 |
+
line_index = line_index + 1
|
62 |
+
line = f.readline()
|
63 |
+
|
64 |
+
return g2p_dict
|
65 |
+
|
66 |
+
|
67 |
+
def cache_dict(g2p_dict, file_path):
|
68 |
+
with open(file_path, 'wb') as pickle_file:
|
69 |
+
pickle.dump(g2p_dict, pickle_file)
|
70 |
+
|
71 |
+
|
72 |
+
def get_dict():
|
73 |
+
if os.path.exists(CACHE_PATH):
|
74 |
+
with open(CACHE_PATH, 'rb') as pickle_file:
|
75 |
+
g2p_dict = pickle.load(pickle_file)
|
76 |
+
else:
|
77 |
+
g2p_dict = read_dict()
|
78 |
+
cache_dict(g2p_dict, CACHE_PATH)
|
79 |
+
|
80 |
+
return g2p_dict
|
81 |
+
|
82 |
+
|
83 |
+
eng_dict = get_dict()
|
84 |
+
|
85 |
+
|
86 |
+
def refine_ph(phn):
|
87 |
+
tone = 0
|
88 |
+
if re.search(r'\d$', phn):
|
89 |
+
tone = int(phn[-1]) + 1
|
90 |
+
phn = phn[:-1]
|
91 |
+
return phn.lower(), tone
|
92 |
+
|
93 |
+
|
94 |
+
def refine_syllables(syllables):
|
95 |
+
tones = []
|
96 |
+
phonemes = []
|
97 |
+
for phn_list in syllables:
|
98 |
+
for i in range(len(phn_list)):
|
99 |
+
phn = phn_list[i]
|
100 |
+
phn, tone = refine_ph(phn)
|
101 |
+
phonemes.append(phn)
|
102 |
+
tones.append(tone)
|
103 |
+
return phonemes, tones
|
104 |
+
|
105 |
+
|
106 |
+
def text_normalize(text):
|
107 |
+
|
108 |
+
return text
|
109 |
+
|
110 |
+
|
111 |
+
def g2p(text):
|
112 |
+
phones = []
|
113 |
+
tones = []
|
114 |
+
words = re.split(r"([,;.\-\?\!\s+])", text)
|
115 |
+
for w in words:
|
116 |
+
if w.upper() in eng_dict:
|
117 |
+
phns, tns = refine_syllables(eng_dict[w.upper()])
|
118 |
+
phones += phns
|
119 |
+
tones += tns
|
120 |
+
else:
|
121 |
+
phone_list = list(filter(lambda p: p != " ", _g2p(w)))
|
122 |
+
for ph in phone_list:
|
123 |
+
if ph in arpa:
|
124 |
+
ph, tn = refine_ph(ph)
|
125 |
+
phones.append(ph)
|
126 |
+
tones.append(tn)
|
127 |
+
else:
|
128 |
+
phones.append(ph)
|
129 |
+
tones.append(0)
|
130 |
+
|
131 |
+
word2ph = [1 for i in phones]
|
132 |
+
|
133 |
+
phones = [post_replace_ph(i) for i in phones]
|
134 |
+
return phones, tones, word2ph
|
135 |
+
|
136 |
+
|
137 |
+
if __name__ == "__main__":
|
138 |
+
# print(get_dict())
|
139 |
+
# print(eng_word_to_phoneme("hello"))
|
140 |
+
print(g2p("In this paper, we propose 1 DSPGAN, a GAN-based universal vocoder."))
|
141 |
+
# all_phones = set()
|
142 |
+
# for k, syllables in eng_dict.items():
|
143 |
+
# for group in syllables:
|
144 |
+
# for ph in group:
|
145 |
+
# all_phones.add(ph)
|
146 |
+
# print(all_phones)
|
bert_vits2/text/english_bert_mock.py
ADDED
@@ -0,0 +1,5 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
import torch
|
2 |
+
|
3 |
+
|
4 |
+
def get_bert_feature(norm_text, word2ph):
|
5 |
+
return torch.zeros(1024, sum(word2ph))
|
bert_vits2/text/japanese.py
ADDED
@@ -0,0 +1,104 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
# modified from https://github.com/CjangCjengh/vits/blob/main/text/japanese.py
|
2 |
+
import re
|
3 |
+
import sys
|
4 |
+
|
5 |
+
import pyopenjtalk
|
6 |
+
|
7 |
+
from bert_vits2.text import symbols
|
8 |
+
|
9 |
+
# Regular expression matching Japanese without punctuation marks:
|
10 |
+
_japanese_characters = re.compile(
|
11 |
+
r'[A-Za-z\d\u3005\u3040-\u30ff\u4e00-\u9fff\uff11-\uff19\uff21-\uff3a\uff41-\uff5a\uff66-\uff9d]')
|
12 |
+
|
13 |
+
# Regular expression matching non-Japanese characters or punctuation marks:
|
14 |
+
_japanese_marks = re.compile(
|
15 |
+
r'[^A-Za-z\d\u3005\u3040-\u30ff\u4e00-\u9fff\uff11-\uff19\uff21-\uff3a\uff41-\uff5a\uff66-\uff9d]')
|
16 |
+
|
17 |
+
# List of (symbol, Japanese) pairs for marks:
|
18 |
+
_symbols_to_japanese = [(re.compile('%s' % x[0]), x[1]) for x in [
|
19 |
+
('%', 'パーセント')
|
20 |
+
]]
|
21 |
+
|
22 |
+
# List of (consonant, sokuon) pairs:
|
23 |
+
_real_sokuon = [(re.compile('%s' % x[0]), x[1]) for x in [
|
24 |
+
(r'Q([↑↓]*[kg])', r'k#\1'),
|
25 |
+
(r'Q([↑↓]*[tdjʧ])', r't#\1'),
|
26 |
+
(r'Q([↑↓]*[sʃ])', r's\1'),
|
27 |
+
(r'Q([↑↓]*[pb])', r'p#\1')
|
28 |
+
]]
|
29 |
+
|
30 |
+
# List of (consonant, hatsuon) pairs:
|
31 |
+
_real_hatsuon = [(re.compile('%s' % x[0]), x[1]) for x in [
|
32 |
+
(r'N([↑↓]*[pbm])', r'm\1'),
|
33 |
+
(r'N([↑↓]*[ʧʥj])', r'n^\1'),
|
34 |
+
(r'N([↑↓]*[tdn])', r'n\1'),
|
35 |
+
(r'N([↑↓]*[kg])', r'ŋ\1')
|
36 |
+
]]
|
37 |
+
|
38 |
+
|
39 |
+
def post_replace_ph(ph):
|
40 |
+
rep_map = {
|
41 |
+
':': ',',
|
42 |
+
';': ',',
|
43 |
+
',': ',',
|
44 |
+
'。': '.',
|
45 |
+
'!': '!',
|
46 |
+
'?': '?',
|
47 |
+
'\n': '.',
|
48 |
+
"·": ",",
|
49 |
+
'、': ",",
|
50 |
+
'...': '…',
|
51 |
+
'v': "V"
|
52 |
+
}
|
53 |
+
if ph in rep_map.keys():
|
54 |
+
ph = rep_map[ph]
|
55 |
+
if ph in symbols:
|
56 |
+
return ph
|
57 |
+
if ph not in symbols:
|
58 |
+
ph = 'UNK'
|
59 |
+
return ph
|
60 |
+
|
61 |
+
|
62 |
+
def symbols_to_japanese(text):
|
63 |
+
for regex, replacement in _symbols_to_japanese:
|
64 |
+
text = re.sub(regex, replacement, text)
|
65 |
+
return text
|
66 |
+
|
67 |
+
|
68 |
+
def preprocess_jap(text):
|
69 |
+
'''Reference https://r9y9.github.io/ttslearn/latest/notebooks/ch10_Recipe-Tacotron.html'''
|
70 |
+
text = symbols_to_japanese(text)
|
71 |
+
sentences = re.split(_japanese_marks, text)
|
72 |
+
marks = re.findall(_japanese_marks, text)
|
73 |
+
text = []
|
74 |
+
for i, sentence in enumerate(sentences):
|
75 |
+
if re.match(_japanese_characters, sentence):
|
76 |
+
p = pyopenjtalk.g2p(sentence)
|
77 |
+
text += p.split(" ")
|
78 |
+
|
79 |
+
if i < len(marks):
|
80 |
+
text += [marks[i].replace(' ', '')]
|
81 |
+
return text
|
82 |
+
|
83 |
+
|
84 |
+
def text_normalize(text):
|
85 |
+
return text
|
86 |
+
|
87 |
+
|
88 |
+
def g2p(norm_text):
|
89 |
+
phones = preprocess_jap(norm_text)
|
90 |
+
phones = [post_replace_ph(i) for i in phones]
|
91 |
+
|
92 |
+
tones = [0 for i in phones]
|
93 |
+
word2ph = [1 for i in phones]
|
94 |
+
return phones, tones, word2ph
|
95 |
+
|
96 |
+
|
97 |
+
if __name__ == '__main__':
|
98 |
+
for line in open("../../../Downloads/transcript_utf8.txt").readlines():
|
99 |
+
text = line.split(":")[1]
|
100 |
+
phones, tones, word2ph = g2p(text)
|
101 |
+
for p in phones:
|
102 |
+
if p == "z":
|
103 |
+
print(text, phones)
|
104 |
+
sys.exit(0)
|
bert_vits2/text/opencpop-strict.txt
ADDED
@@ -0,0 +1,429 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
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|
|
|
|
|
|
|
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|
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|
|
|
|
|
|
|
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|
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|
|
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|
|
|
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|
|
|
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|
|
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|
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|
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|
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|
|
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|
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|
|
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|
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|
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|
|
|
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|
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|
|
|
|
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|
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|
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|
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|
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|
|
|
|
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|
|
|
|
|
|
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|
|
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|
|
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|
|
|
|
|
|
|
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|
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|
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|
|
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|
|
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|
|
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|
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|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
a AA a
|
2 |
+
ai AA ai
|
3 |
+
an AA an
|
4 |
+
ang AA ang
|
5 |
+
ao AA ao
|
6 |
+
ba b a
|
7 |
+
bai b ai
|
8 |
+
ban b an
|
9 |
+
bang b ang
|
10 |
+
bao b ao
|
11 |
+
bei b ei
|
12 |
+
ben b en
|
13 |
+
beng b eng
|
14 |
+
bi b i
|
15 |
+
bian b ian
|
16 |
+
biao b iao
|
17 |
+
bie b ie
|
18 |
+
bin b in
|
19 |
+
bing b ing
|
20 |
+
bo b o
|
21 |
+
bu b u
|
22 |
+
ca c a
|
23 |
+
cai c ai
|
24 |
+
can c an
|
25 |
+
cang c ang
|
26 |
+
cao c ao
|
27 |
+
ce c e
|
28 |
+
cei c ei
|
29 |
+
cen c en
|
30 |
+
ceng c eng
|
31 |
+
cha ch a
|
32 |
+
chai ch ai
|
33 |
+
chan ch an
|
34 |
+
chang ch ang
|
35 |
+
chao ch ao
|
36 |
+
che ch e
|
37 |
+
chen ch en
|
38 |
+
cheng ch eng
|
39 |
+
chi ch ir
|
40 |
+
chong ch ong
|
41 |
+
chou ch ou
|
42 |
+
chu ch u
|
43 |
+
chua ch ua
|
44 |
+
chuai ch uai
|
45 |
+
chuan ch uan
|
46 |
+
chuang ch uang
|
47 |
+
chui ch ui
|
48 |
+
chun ch un
|
49 |
+
chuo ch uo
|
50 |
+
ci c i0
|
51 |
+
cong c ong
|
52 |
+
cou c ou
|
53 |
+
cu c u
|
54 |
+
cuan c uan
|
55 |
+
cui c ui
|
56 |
+
cun c un
|
57 |
+
cuo c uo
|
58 |
+
da d a
|
59 |
+
dai d ai
|
60 |
+
dan d an
|
61 |
+
dang d ang
|
62 |
+
dao d ao
|
63 |
+
de d e
|
64 |
+
dei d ei
|
65 |
+
den d en
|
66 |
+
deng d eng
|
67 |
+
di d i
|
68 |
+
dia d ia
|
69 |
+
dian d ian
|
70 |
+
diao d iao
|
71 |
+
die d ie
|
72 |
+
ding d ing
|
73 |
+
diu d iu
|
74 |
+
dong d ong
|
75 |
+
dou d ou
|
76 |
+
du d u
|
77 |
+
duan d uan
|
78 |
+
dui d ui
|
79 |
+
dun d un
|
80 |
+
duo d uo
|
81 |
+
e EE e
|
82 |
+
ei EE ei
|
83 |
+
en EE en
|
84 |
+
eng EE eng
|
85 |
+
er EE er
|
86 |
+
fa f a
|
87 |
+
fan f an
|
88 |
+
fang f ang
|
89 |
+
fei f ei
|
90 |
+
fen f en
|
91 |
+
feng f eng
|
92 |
+
fo f o
|
93 |
+
fou f ou
|
94 |
+
fu f u
|
95 |
+
ga g a
|
96 |
+
gai g ai
|
97 |
+
gan g an
|
98 |
+
gang g ang
|
99 |
+
gao g ao
|
100 |
+
ge g e
|
101 |
+
gei g ei
|
102 |
+
gen g en
|
103 |
+
geng g eng
|
104 |
+
gong g ong
|
105 |
+
gou g ou
|
106 |
+
gu g u
|
107 |
+
gua g ua
|
108 |
+
guai g uai
|
109 |
+
guan g uan
|
110 |
+
guang g uang
|
111 |
+
gui g ui
|
112 |
+
gun g un
|
113 |
+
guo g uo
|
114 |
+
ha h a
|
115 |
+
hai h ai
|
116 |
+
han h an
|
117 |
+
hang h ang
|
118 |
+
hao h ao
|
119 |
+
he h e
|
120 |
+
hei h ei
|
121 |
+
hen h en
|
122 |
+
heng h eng
|
123 |
+
hong h ong
|
124 |
+
hou h ou
|
125 |
+
hu h u
|
126 |
+
hua h ua
|
127 |
+
huai h uai
|
128 |
+
huan h uan
|
129 |
+
huang h uang
|
130 |
+
hui h ui
|
131 |
+
hun h un
|
132 |
+
huo h uo
|
133 |
+
ji j i
|
134 |
+
jia j ia
|
135 |
+
jian j ian
|
136 |
+
jiang j iang
|
137 |
+
jiao j iao
|
138 |
+
jie j ie
|
139 |
+
jin j in
|
140 |
+
jing j ing
|
141 |
+
jiong j iong
|
142 |
+
jiu j iu
|
143 |
+
ju j v
|
144 |
+
jv j v
|
145 |
+
juan j van
|
146 |
+
jvan j van
|
147 |
+
jue j ve
|
148 |
+
jve j ve
|
149 |
+
jun j vn
|
150 |
+
jvn j vn
|
151 |
+
ka k a
|
152 |
+
kai k ai
|
153 |
+
kan k an
|
154 |
+
kang k ang
|
155 |
+
kao k ao
|
156 |
+
ke k e
|
157 |
+
kei k ei
|
158 |
+
ken k en
|
159 |
+
keng k eng
|
160 |
+
kong k ong
|
161 |
+
kou k ou
|
162 |
+
ku k u
|
163 |
+
kua k ua
|
164 |
+
kuai k uai
|
165 |
+
kuan k uan
|
166 |
+
kuang k uang
|
167 |
+
kui k ui
|
168 |
+
kun k un
|
169 |
+
kuo k uo
|
170 |
+
la l a
|
171 |
+
lai l ai
|
172 |
+
lan l an
|
173 |
+
lang l ang
|
174 |
+
lao l ao
|
175 |
+
le l e
|
176 |
+
lei l ei
|
177 |
+
leng l eng
|
178 |
+
li l i
|
179 |
+
lia l ia
|
180 |
+
lian l ian
|
181 |
+
liang l iang
|
182 |
+
liao l iao
|
183 |
+
lie l ie
|
184 |
+
lin l in
|
185 |
+
ling l ing
|
186 |
+
liu l iu
|
187 |
+
lo l o
|
188 |
+
long l ong
|
189 |
+
lou l ou
|
190 |
+
lu l u
|
191 |
+
luan l uan
|
192 |
+
lun l un
|
193 |
+
luo l uo
|
194 |
+
lv l v
|
195 |
+
lve l ve
|
196 |
+
ma m a
|
197 |
+
mai m ai
|
198 |
+
man m an
|
199 |
+
mang m ang
|
200 |
+
mao m ao
|
201 |
+
me m e
|
202 |
+
mei m ei
|
203 |
+
men m en
|
204 |
+
meng m eng
|
205 |
+
mi m i
|
206 |
+
mian m ian
|
207 |
+
miao m iao
|
208 |
+
mie m ie
|
209 |
+
min m in
|
210 |
+
ming m ing
|
211 |
+
miu m iu
|
212 |
+
mo m o
|
213 |
+
mou m ou
|
214 |
+
mu m u
|
215 |
+
na n a
|
216 |
+
nai n ai
|
217 |
+
nan n an
|
218 |
+
nang n ang
|
219 |
+
nao n ao
|
220 |
+
ne n e
|
221 |
+
nei n ei
|
222 |
+
nen n en
|
223 |
+
neng n eng
|
224 |
+
ni n i
|
225 |
+
nian n ian
|
226 |
+
niang n iang
|
227 |
+
niao n iao
|
228 |
+
nie n ie
|
229 |
+
nin n in
|
230 |
+
ning n ing
|
231 |
+
niu n iu
|
232 |
+
nong n ong
|
233 |
+
nou n ou
|
234 |
+
nu n u
|
235 |
+
nuan n uan
|
236 |
+
nun n un
|
237 |
+
nuo n uo
|
238 |
+
nv n v
|
239 |
+
nve n ve
|
240 |
+
o OO o
|
241 |
+
ou OO ou
|
242 |
+
pa p a
|
243 |
+
pai p ai
|
244 |
+
pan p an
|
245 |
+
pang p ang
|
246 |
+
pao p ao
|
247 |
+
pei p ei
|
248 |
+
pen p en
|
249 |
+
peng p eng
|
250 |
+
pi p i
|
251 |
+
pian p ian
|
252 |
+
piao p iao
|
253 |
+
pie p ie
|
254 |
+
pin p in
|
255 |
+
ping p ing
|
256 |
+
po p o
|
257 |
+
pou p ou
|
258 |
+
pu p u
|
259 |
+
qi q i
|
260 |
+
qia q ia
|
261 |
+
qian q ian
|
262 |
+
qiang q iang
|
263 |
+
qiao q iao
|
264 |
+
qie q ie
|
265 |
+
qin q in
|
266 |
+
qing q ing
|
267 |
+
qiong q iong
|
268 |
+
qiu q iu
|
269 |
+
qu q v
|
270 |
+
qv q v
|
271 |
+
quan q van
|
272 |
+
qvan q van
|
273 |
+
que q ve
|
274 |
+
qve q ve
|
275 |
+
qun q vn
|
276 |
+
qvn q vn
|
277 |
+
ran r an
|
278 |
+
rang r ang
|
279 |
+
rao r ao
|
280 |
+
re r e
|
281 |
+
ren r en
|
282 |
+
reng r eng
|
283 |
+
ri r ir
|
284 |
+
rong r ong
|
285 |
+
rou r ou
|
286 |
+
ru r u
|
287 |
+
rua r ua
|
288 |
+
ruan r uan
|
289 |
+
rui r ui
|
290 |
+
run r un
|
291 |
+
ruo r uo
|
292 |
+
sa s a
|
293 |
+
sai s ai
|
294 |
+
san s an
|
295 |
+
sang s ang
|
296 |
+
sao s ao
|
297 |
+
se s e
|
298 |
+
sen s en
|
299 |
+
seng s eng
|
300 |
+
sha sh a
|
301 |
+
shai sh ai
|
302 |
+
shan sh an
|
303 |
+
shang sh ang
|
304 |
+
shao sh ao
|
305 |
+
she sh e
|
306 |
+
shei sh ei
|
307 |
+
shen sh en
|
308 |
+
sheng sh eng
|
309 |
+
shi sh ir
|
310 |
+
shou sh ou
|
311 |
+
shu sh u
|
312 |
+
shua sh ua
|
313 |
+
shuai sh uai
|
314 |
+
shuan sh uan
|
315 |
+
shuang sh uang
|
316 |
+
shui sh ui
|
317 |
+
shun sh un
|
318 |
+
shuo sh uo
|
319 |
+
si s i0
|
320 |
+
song s ong
|
321 |
+
sou s ou
|
322 |
+
su s u
|
323 |
+
suan s uan
|
324 |
+
sui s ui
|
325 |
+
sun s un
|
326 |
+
suo s uo
|
327 |
+
ta t a
|
328 |
+
tai t ai
|
329 |
+
tan t an
|
330 |
+
tang t ang
|
331 |
+
tao t ao
|
332 |
+
te t e
|
333 |
+
tei t ei
|
334 |
+
teng t eng
|
335 |
+
ti t i
|
336 |
+
tian t ian
|
337 |
+
tiao t iao
|
338 |
+
tie t ie
|
339 |
+
ting t ing
|
340 |
+
tong t ong
|
341 |
+
tou t ou
|
342 |
+
tu t u
|
343 |
+
tuan t uan
|
344 |
+
tui t ui
|
345 |
+
tun t un
|
346 |
+
tuo t uo
|
347 |
+
wa w a
|
348 |
+
wai w ai
|
349 |
+
wan w an
|
350 |
+
wang w ang
|
351 |
+
wei w ei
|
352 |
+
wen w en
|
353 |
+
weng w eng
|
354 |
+
wo w o
|
355 |
+
wu w u
|
356 |
+
xi x i
|
357 |
+
xia x ia
|
358 |
+
xian x ian
|
359 |
+
xiang x iang
|
360 |
+
xiao x iao
|
361 |
+
xie x ie
|
362 |
+
xin x in
|
363 |
+
xing x ing
|
364 |
+
xiong x iong
|
365 |
+
xiu x iu
|
366 |
+
xu x v
|
367 |
+
xv x v
|
368 |
+
xuan x van
|
369 |
+
xvan x van
|
370 |
+
xue x ve
|
371 |
+
xve x ve
|
372 |
+
xun x vn
|
373 |
+
xvn x vn
|
374 |
+
ya y a
|
375 |
+
yan y En
|
376 |
+
yang y ang
|
377 |
+
yao y ao
|
378 |
+
ye y E
|
379 |
+
yi y i
|
380 |
+
yin y in
|
381 |
+
ying y ing
|
382 |
+
yo y o
|
383 |
+
yong y ong
|
384 |
+
you y ou
|
385 |
+
yu y v
|
386 |
+
yv y v
|
387 |
+
yuan y van
|
388 |
+
yvan y van
|
389 |
+
yue y ve
|
390 |
+
yve y ve
|
391 |
+
yun y vn
|
392 |
+
yvn y vn
|
393 |
+
za z a
|
394 |
+
zai z ai
|
395 |
+
zan z an
|
396 |
+
zang z ang
|
397 |
+
zao z ao
|
398 |
+
ze z e
|
399 |
+
zei z ei
|
400 |
+
zen z en
|
401 |
+
zeng z eng
|
402 |
+
zha zh a
|
403 |
+
zhai zh ai
|
404 |
+
zhan zh an
|
405 |
+
zhang zh ang
|
406 |
+
zhao zh ao
|
407 |
+
zhe zh e
|
408 |
+
zhei zh ei
|
409 |
+
zhen zh en
|
410 |
+
zheng zh eng
|
411 |
+
zhi zh ir
|
412 |
+
zhong zh ong
|
413 |
+
zhou zh ou
|
414 |
+
zhu zh u
|
415 |
+
zhua zh ua
|
416 |
+
zhuai zh uai
|
417 |
+
zhuan zh uan
|
418 |
+
zhuang zh uang
|
419 |
+
zhui zh ui
|
420 |
+
zhun zh un
|
421 |
+
zhuo zh uo
|
422 |
+
zi z i0
|
423 |
+
zong z ong
|
424 |
+
zou z ou
|
425 |
+
zu z u
|
426 |
+
zuan z uan
|
427 |
+
zui z ui
|
428 |
+
zun z un
|
429 |
+
zuo z uo
|
bert_vits2/text/symbols.py
ADDED
@@ -0,0 +1,52 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
punctuation = ['!', '?', '…', ",", ".", "'", '-']
|
2 |
+
pu_symbols = punctuation + ["SP", "UNK"]
|
3 |
+
pad = '_'
|
4 |
+
|
5 |
+
# chinese
|
6 |
+
zh_symbols = ['E', 'En', 'a', 'ai', 'an', 'ang', 'ao', 'b', 'c', 'ch', 'd', 'e', 'ei', 'en', 'eng', 'er', 'f', 'g', 'h',
|
7 |
+
'i', 'i0', 'ia', 'ian', 'iang', 'iao', 'ie', 'in', 'ing', 'iong', 'ir', 'iu', 'j', 'k', 'l', 'm', 'n',
|
8 |
+
'o',
|
9 |
+
'ong',
|
10 |
+
'ou', 'p', 'q', 'r', 's', 'sh', 't', 'u', 'ua', 'uai', 'uan', 'uang', 'ui', 'un', 'uo', 'v', 'van', 've',
|
11 |
+
'vn',
|
12 |
+
'w', 'x', 'y', 'z', 'zh',
|
13 |
+
"AA", "EE", "OO"]
|
14 |
+
num_zh_tones = 6
|
15 |
+
|
16 |
+
# japanese
|
17 |
+
ja_symbols = ['I', 'N', 'U', 'a', 'b', 'by', 'ch', 'cl', 'd', 'dy', 'e', 'f', 'g', 'gy', 'h', 'hy', 'i', 'j', 'k', 'ky',
|
18 |
+
'm', 'my', 'n', 'ny', 'o', 'p', 'py', 'r', 'ry', 's', 'sh', 't', 'ts', 'u', 'V', 'w', 'y', 'z']
|
19 |
+
num_ja_tones = 1
|
20 |
+
|
21 |
+
# English
|
22 |
+
en_symbols = ['aa', 'ae', 'ah', 'ao', 'aw', 'ay', 'b', 'ch', 'd', 'dh', 'eh', 'er', 'ey', 'f', 'g', 'hh', 'ih', 'iy',
|
23 |
+
'jh', 'k', 'l', 'm', 'n', 'ng', 'ow', 'oy', 'p', 'r', 's',
|
24 |
+
'sh', 't', 'th', 'uh', 'uw', 'V', 'w', 'y', 'z', 'zh']
|
25 |
+
num_en_tones = 4
|
26 |
+
|
27 |
+
# combine all symbols
|
28 |
+
normal_symbols = sorted(set(zh_symbols + ja_symbols + en_symbols))
|
29 |
+
symbols = [pad] + normal_symbols + pu_symbols
|
30 |
+
sil_phonemes_ids = [symbols.index(i) for i in pu_symbols]
|
31 |
+
|
32 |
+
# combine all tones
|
33 |
+
num_tones = num_zh_tones + num_ja_tones + num_en_tones
|
34 |
+
|
35 |
+
# language maps
|
36 |
+
language_id_map = {
|
37 |
+
'ZH': 0,
|
38 |
+
"JA": 1,
|
39 |
+
"EN": 2
|
40 |
+
}
|
41 |
+
num_languages = len(language_id_map.keys())
|
42 |
+
|
43 |
+
language_tone_start_map = {
|
44 |
+
'ZH': 0,
|
45 |
+
"JA": num_zh_tones,
|
46 |
+
"EN": num_zh_tones + num_ja_tones
|
47 |
+
}
|
48 |
+
|
49 |
+
if __name__ == '__main__':
|
50 |
+
a = set(zh_symbols)
|
51 |
+
b = set(en_symbols)
|
52 |
+
print(sorted(a & b))
|
bert_vits2/text/tone_sandhi.py
ADDED
@@ -0,0 +1,351 @@
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|
|
|
1 |
+
# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
|
2 |
+
#
|
3 |
+
# Licensed under the Apache License, Version 2.0 (the "License");
|
4 |
+
# you may not use this file except in compliance with the License.
|
5 |
+
# You may obtain a copy of the License at
|
6 |
+
#
|
7 |
+
# http://www.apache.org/licenses/LICENSE-2.0
|
8 |
+
#
|
9 |
+
# Unless required by applicable law or agreed to in writing, software
|
10 |
+
# distributed under the License is distributed on an "AS IS" BASIS,
|
11 |
+
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
12 |
+
# See the License for the specific language governing permissions and
|
13 |
+
# limitations under the License.
|
14 |
+
from typing import List
|
15 |
+
from typing import Tuple
|
16 |
+
|
17 |
+
import jieba
|
18 |
+
from pypinyin import lazy_pinyin
|
19 |
+
from pypinyin import Style
|
20 |
+
|
21 |
+
|
22 |
+
class ToneSandhi():
|
23 |
+
def __init__(self):
|
24 |
+
self.must_neural_tone_words = {
|
25 |
+
'麻烦', '麻利', '鸳鸯', '高粱', '骨头', '骆驼', '马虎', '首饰', '馒头', '馄饨', '风筝',
|
26 |
+
'难为', '队伍', '阔气', '闺女', '门道', '锄头', '铺盖', '铃铛', '铁匠', '钥匙', '里脊',
|
27 |
+
'里头', '部分', '那么', '道士', '造化', '迷糊', '连累', '这么', '这个', '运气', '过去',
|
28 |
+
'软和', '转悠', '踏实', '跳蚤', '跟头', '趔趄', '财主', '豆腐', '讲究', '记性', '记号',
|
29 |
+
'认识', '规矩', '见识', '裁缝', '补丁', '衣裳', '衣服', '衙门', '街坊', '行李', '行当',
|
30 |
+
'蛤蟆', '蘑菇', '薄荷', '葫芦', '葡萄', '萝卜', '荸荠', '苗条', '苗头', '苍蝇', '芝麻',
|
31 |
+
'舒服', '舒坦', '舌头', '自在', '膏药', '脾气', '脑袋', '脊梁', '能耐', '胳膊', '胭脂',
|
32 |
+
'胡萝', '胡琴', '胡同', '聪明', '耽误', '耽搁', '耷拉', '耳朵', '老爷', '老实', '老婆',
|
33 |
+
'老头', '老太', '翻腾', '罗嗦', '罐头', '编辑', '结实', '红火', '累赘', '糨糊', '糊涂',
|
34 |
+
'精神', '粮食', '簸箕', '篱笆', '算计', '算盘', '答应', '笤帚', '笑语', '笑话', '窟窿',
|
35 |
+
'窝囊', '窗户', '稳当', '稀罕', '称呼', '秧歌', '秀气', '秀才', '福气', '祖宗', '砚台',
|
36 |
+
'码头', '石榴', '石头', '石匠', '知识', '眼睛', '眯缝', '眨巴', '眉毛', '相声', '盘算',
|
37 |
+
'白净', '痢疾', '痛快', '疟疾', '疙瘩', '疏忽', '畜生', '生意', '甘蔗', '琵琶', '琢磨',
|
38 |
+
'琉璃', '玻璃', '玫瑰', '玄乎', '狐狸', '状元', '特务', '牲口', '牙碜', '牌楼', '爽快',
|
39 |
+
'爱人', '热闹', '烧饼', '烟筒', '烂糊', '点心', '炊帚', '灯笼', '火候', '漂亮', '滑溜',
|
40 |
+
'溜达', '温和', '清楚', '消息', '浪头', '活泼', '比方', '正经', '欺负', '模糊', '槟榔',
|
41 |
+
'棺材', '棒槌', '棉花', '核桃', '栅栏', '柴火', '架势', '枕头', '枇杷', '机灵', '本事',
|
42 |
+
'木头', '木匠', '朋友', '月饼', '月亮', '暖和', '明白', '时候', '新鲜', '故事', '收拾',
|
43 |
+
'收成', '提防', '挖苦', '挑剔', '指甲', '指头', '拾掇', '拳头', '拨弄', '招牌', '招呼',
|
44 |
+
'抬举', '护士', '折腾', '扫帚', '打量', '打算', '打点', '打扮', '打听', '打发', '扎实',
|
45 |
+
'扁担', '戒指', '懒得', '意识', '意思', '情形', '悟性', '怪物', '思量', '怎么', '念头',
|
46 |
+
'念叨', '快活', '忙活', '志气', '心思', '得罪', '张罗', '弟兄', '开通', '应酬', '庄稼',
|
47 |
+
'干事', '帮手', '帐篷', '希罕', '师父', '师傅', '巴结', '巴掌', '差事', '工夫', '岁数',
|
48 |
+
'屁股', '尾巴', '少爷', '小气', '小伙', '将就', '对头', '对付', '寡妇', '家伙', '客气',
|
49 |
+
'实在', '官司', '学问', '学生', '字号', '嫁妆', '媳妇', '媒人', '婆家', '娘家', '委屈',
|
50 |
+
'姑娘', '姐夫', '妯娌', '妥当', '妖精', '奴才', '女婿', '头发', '太阳', '大爷', '大方',
|
51 |
+
'大意', '大夫', '多少', '多么', '外甥', '壮实', '地道', '地方', '在乎', '困难', '嘴巴',
|
52 |
+
'嘱咐', '嘟囔', '嘀咕', '喜欢', '喇嘛', '喇叭', '商量', '唾沫', '哑巴', '哈欠', '哆嗦',
|
53 |
+
'咳嗽', '和尚', '告诉', '告示', '含糊', '吓唬', '后头', '名字', '名堂', '合同', '吆喝',
|
54 |
+
'叫唤', '口袋', '厚道', '厉害', '千斤', '包袱', '包涵', '匀称', '勤快', '动静', '动弹',
|
55 |
+
'功夫', '力气', '前头', '刺猬', '刺激', '别扭', '利落', '利索', '利害', '分析', '出息',
|
56 |
+
'凑合', '凉快', '冷战', '冤枉', '冒失', '养活', '关系', '先生', '兄弟', '便宜', '使唤',
|
57 |
+
'佩服', '作坊', '体面', '位置', '似的', '伙计', '休息', '什么', '人家', '亲戚', '亲家',
|
58 |
+
'交情', '云彩', '事情', '买卖', '主意', '丫头', '丧气', '两口', '东西', '东家', '世故',
|
59 |
+
'不由', '不在', '下水', '下巴', '上头', '上司', '丈夫', '丈人', '一辈', '那个', '菩萨',
|
60 |
+
'父亲', '母亲', '咕噜', '邋遢', '费用', '冤家', '甜头', '介绍', '荒唐', '大人', '泥鳅',
|
61 |
+
'幸福', '熟悉', '计划', '扑腾', '蜡烛', '姥爷', '照顾', '喉咙', '吉他', '弄堂', '蚂蚱',
|
62 |
+
'凤凰', '拖沓', '寒碜', '糟蹋', '倒腾', '报复', '逻辑', '盘缠', '喽啰', '牢骚', '咖喱',
|
63 |
+
'扫把', '惦记'
|
64 |
+
}
|
65 |
+
self.must_not_neural_tone_words = {
|
66 |
+
"男子", "女子", "分子", "原子", "量子", "莲子", "石子", "瓜子", "电子", "人人", "虎虎"
|
67 |
+
}
|
68 |
+
self.punc = ":,;。?!“”‘’':,;.?!"
|
69 |
+
|
70 |
+
# the meaning of jieba pos tag: https://blog.csdn.net/weixin_44174352/article/details/113731041
|
71 |
+
# e.g.
|
72 |
+
# word: "家里"
|
73 |
+
# pos: "s"
|
74 |
+
# finals: ['ia1', 'i3']
|
75 |
+
def _neural_sandhi(self, word: str, pos: str,
|
76 |
+
finals: List[str]) -> List[str]:
|
77 |
+
|
78 |
+
# reduplication words for n. and v. e.g. 奶奶, 试试, 旺旺
|
79 |
+
for j, item in enumerate(word):
|
80 |
+
if j - 1 >= 0 and item == word[j - 1] and pos[0] in {
|
81 |
+
"n", "v", "a"
|
82 |
+
} and word not in self.must_not_neural_tone_words:
|
83 |
+
finals[j] = finals[j][:-1] + "5"
|
84 |
+
ge_idx = word.find("个")
|
85 |
+
if len(word) >= 1 and word[-1] in "吧呢啊呐噻嘛吖嗨呐哦哒额滴哩哟喽啰耶喔诶":
|
86 |
+
finals[-1] = finals[-1][:-1] + "5"
|
87 |
+
elif len(word) >= 1 and word[-1] in "的地得":
|
88 |
+
finals[-1] = finals[-1][:-1] + "5"
|
89 |
+
# e.g. 走了, 看着, 去过
|
90 |
+
# elif len(word) == 1 and word in "了着过" and pos in {"ul", "uz", "ug"}:
|
91 |
+
# finals[-1] = finals[-1][:-1] + "5"
|
92 |
+
elif len(word) > 1 and word[-1] in "们子" and pos in {
|
93 |
+
"r", "n"
|
94 |
+
} and word not in self.must_not_neural_tone_words:
|
95 |
+
finals[-1] = finals[-1][:-1] + "5"
|
96 |
+
# e.g. 桌上, 地下, 家里
|
97 |
+
elif len(word) > 1 and word[-1] in "上下里" and pos in {"s", "l", "f"}:
|
98 |
+
finals[-1] = finals[-1][:-1] + "5"
|
99 |
+
# e.g. 上来, 下去
|
100 |
+
elif len(word) > 1 and word[-1] in "来去" and word[-2] in "上下进出回过起开":
|
101 |
+
finals[-1] = finals[-1][:-1] + "5"
|
102 |
+
# 个做量词
|
103 |
+
elif (ge_idx >= 1 and
|
104 |
+
(word[ge_idx - 1].isnumeric() or
|
105 |
+
word[ge_idx - 1] in "几有两半多各整每做是")) or word == '个':
|
106 |
+
finals[ge_idx] = finals[ge_idx][:-1] + "5"
|
107 |
+
else:
|
108 |
+
if word in self.must_neural_tone_words or word[
|
109 |
+
-2:] in self.must_neural_tone_words:
|
110 |
+
finals[-1] = finals[-1][:-1] + "5"
|
111 |
+
|
112 |
+
word_list = self._split_word(word)
|
113 |
+
finals_list = [finals[:len(word_list[0])], finals[len(word_list[0]):]]
|
114 |
+
for i, word in enumerate(word_list):
|
115 |
+
# conventional neural in Chinese
|
116 |
+
if word in self.must_neural_tone_words or word[
|
117 |
+
-2:] in self.must_neural_tone_words:
|
118 |
+
finals_list[i][-1] = finals_list[i][-1][:-1] + "5"
|
119 |
+
finals = sum(finals_list, [])
|
120 |
+
return finals
|
121 |
+
|
122 |
+
def _bu_sandhi(self, word: str, finals: List[str]) -> List[str]:
|
123 |
+
# e.g. 看不懂
|
124 |
+
if len(word) == 3 and word[1] == "不":
|
125 |
+
finals[1] = finals[1][:-1] + "5"
|
126 |
+
else:
|
127 |
+
for i, char in enumerate(word):
|
128 |
+
# "不" before tone4 should be bu2, e.g. 不怕
|
129 |
+
if char == "不" and i + 1 < len(word) and finals[i +
|
130 |
+
1][-1] == "4":
|
131 |
+
finals[i] = finals[i][:-1] + "2"
|
132 |
+
return finals
|
133 |
+
|
134 |
+
def _yi_sandhi(self, word: str, finals: List[str]) -> List[str]:
|
135 |
+
# "一" in number sequences, e.g. 一零零, 二一零
|
136 |
+
if word.find("一") != -1 and all(
|
137 |
+
[item.isnumeric() for item in word if item != "一"]):
|
138 |
+
return finals
|
139 |
+
# "一" between reduplication words shold be yi5, e.g. 看一看
|
140 |
+
elif len(word) == 3 and word[1] == "一" and word[0] == word[-1]:
|
141 |
+
finals[1] = finals[1][:-1] + "5"
|
142 |
+
# when "一" is ordinal word, it should be yi1
|
143 |
+
elif word.startswith("第一"):
|
144 |
+
finals[1] = finals[1][:-1] + "1"
|
145 |
+
else:
|
146 |
+
for i, char in enumerate(word):
|
147 |
+
if char == "一" and i + 1 < len(word):
|
148 |
+
# "一" before tone4 should be yi2, e.g. 一段
|
149 |
+
if finals[i + 1][-1] == "4":
|
150 |
+
finals[i] = finals[i][:-1] + "2"
|
151 |
+
# "一" before non-tone4 should be yi4, e.g. 一天
|
152 |
+
else:
|
153 |
+
# "一" 后面如果是标点,还读一声
|
154 |
+
if word[i + 1] not in self.punc:
|
155 |
+
finals[i] = finals[i][:-1] + "4"
|
156 |
+
return finals
|
157 |
+
|
158 |
+
def _split_word(self, word: str) -> List[str]:
|
159 |
+
word_list = jieba.cut_for_search(word)
|
160 |
+
word_list = sorted(word_list, key=lambda i: len(i), reverse=False)
|
161 |
+
first_subword = word_list[0]
|
162 |
+
first_begin_idx = word.find(first_subword)
|
163 |
+
if first_begin_idx == 0:
|
164 |
+
second_subword = word[len(first_subword):]
|
165 |
+
new_word_list = [first_subword, second_subword]
|
166 |
+
else:
|
167 |
+
second_subword = word[:-len(first_subword)]
|
168 |
+
new_word_list = [second_subword, first_subword]
|
169 |
+
return new_word_list
|
170 |
+
|
171 |
+
def _three_sandhi(self, word: str, finals: List[str]) -> List[str]:
|
172 |
+
if len(word) == 2 and self._all_tone_three(finals):
|
173 |
+
finals[0] = finals[0][:-1] + "2"
|
174 |
+
elif len(word) == 3:
|
175 |
+
word_list = self._split_word(word)
|
176 |
+
if self._all_tone_three(finals):
|
177 |
+
# disyllabic + monosyllabic, e.g. 蒙古/包
|
178 |
+
if len(word_list[0]) == 2:
|
179 |
+
finals[0] = finals[0][:-1] + "2"
|
180 |
+
finals[1] = finals[1][:-1] + "2"
|
181 |
+
# monosyllabic + disyllabic, e.g. 纸/老虎
|
182 |
+
elif len(word_list[0]) == 1:
|
183 |
+
finals[1] = finals[1][:-1] + "2"
|
184 |
+
else:
|
185 |
+
finals_list = [
|
186 |
+
finals[:len(word_list[0])], finals[len(word_list[0]):]
|
187 |
+
]
|
188 |
+
if len(finals_list) == 2:
|
189 |
+
for i, sub in enumerate(finals_list):
|
190 |
+
# e.g. 所有/人
|
191 |
+
if self._all_tone_three(sub) and len(sub) == 2:
|
192 |
+
finals_list[i][0] = finals_list[i][0][:-1] + "2"
|
193 |
+
# e.g. 好/喜欢
|
194 |
+
elif i == 1 and not self._all_tone_three(sub) and finals_list[i][0][-1] == "3" and \
|
195 |
+
finals_list[0][-1][-1] == "3":
|
196 |
+
|
197 |
+
finals_list[0][-1] = finals_list[0][-1][:-1] + "2"
|
198 |
+
finals = sum(finals_list, [])
|
199 |
+
# split idiom into two words who's length is 2
|
200 |
+
elif len(word) == 4:
|
201 |
+
finals_list = [finals[:2], finals[2:]]
|
202 |
+
finals = []
|
203 |
+
for sub in finals_list:
|
204 |
+
if self._all_tone_three(sub):
|
205 |
+
sub[0] = sub[0][:-1] + "2"
|
206 |
+
finals += sub
|
207 |
+
|
208 |
+
return finals
|
209 |
+
|
210 |
+
def _all_tone_three(self, finals: List[str]) -> bool:
|
211 |
+
return all(x[-1] == "3" for x in finals)
|
212 |
+
|
213 |
+
# merge "不" and the word behind it
|
214 |
+
# if don't merge, "不" sometimes appears alone according to jieba, which may occur sandhi error
|
215 |
+
def _merge_bu(self, seg: List[Tuple[str, str]]) -> List[Tuple[str, str]]:
|
216 |
+
new_seg = []
|
217 |
+
last_word = ""
|
218 |
+
for word, pos in seg:
|
219 |
+
if last_word == "不":
|
220 |
+
word = last_word + word
|
221 |
+
if word != "不":
|
222 |
+
new_seg.append((word, pos))
|
223 |
+
last_word = word[:]
|
224 |
+
if last_word == "不":
|
225 |
+
new_seg.append((last_word, 'd'))
|
226 |
+
last_word = ""
|
227 |
+
return new_seg
|
228 |
+
|
229 |
+
# function 1: merge "一" and reduplication words in it's left and right, e.g. "听","一","听" ->"听一听"
|
230 |
+
# function 2: merge single "一" and the word behind it
|
231 |
+
# if don't merge, "一" sometimes appears alone according to jieba, which may occur sandhi error
|
232 |
+
# e.g.
|
233 |
+
# input seg: [('听', 'v'), ('一', 'm'), ('听', 'v')]
|
234 |
+
# output seg: [['听一听', 'v']]
|
235 |
+
def _merge_yi(self, seg: List[Tuple[str, str]]) -> List[Tuple[str, str]]:
|
236 |
+
new_seg = []
|
237 |
+
# function 1
|
238 |
+
for i, (word, pos) in enumerate(seg):
|
239 |
+
if i - 1 >= 0 and word == "一" and i + 1 < len(seg) and seg[i - 1][
|
240 |
+
0] == seg[i + 1][0] and seg[i - 1][1] == "v":
|
241 |
+
new_seg[i - 1][0] = new_seg[i - 1][0] + "一" + new_seg[i - 1][0]
|
242 |
+
else:
|
243 |
+
if i - 2 >= 0 and seg[i - 1][0] == "一" and seg[i - 2][
|
244 |
+
0] == word and pos == "v":
|
245 |
+
continue
|
246 |
+
else:
|
247 |
+
new_seg.append([word, pos])
|
248 |
+
seg = new_seg
|
249 |
+
new_seg = []
|
250 |
+
# function 2
|
251 |
+
for i, (word, pos) in enumerate(seg):
|
252 |
+
if new_seg and new_seg[-1][0] == "一":
|
253 |
+
new_seg[-1][0] = new_seg[-1][0] + word
|
254 |
+
else:
|
255 |
+
new_seg.append([word, pos])
|
256 |
+
return new_seg
|
257 |
+
|
258 |
+
# the first and the second words are all_tone_three
|
259 |
+
def _merge_continuous_three_tones(
|
260 |
+
self, seg: List[Tuple[str, str]]) -> List[Tuple[str, str]]:
|
261 |
+
new_seg = []
|
262 |
+
sub_finals_list = [
|
263 |
+
lazy_pinyin(
|
264 |
+
word, neutral_tone_with_five=True, style=Style.FINALS_TONE3)
|
265 |
+
for (word, pos) in seg
|
266 |
+
]
|
267 |
+
assert len(sub_finals_list) == len(seg)
|
268 |
+
merge_last = [False] * len(seg)
|
269 |
+
for i, (word, pos) in enumerate(seg):
|
270 |
+
if i - 1 >= 0 and self._all_tone_three(
|
271 |
+
sub_finals_list[i - 1]) and self._all_tone_three(
|
272 |
+
sub_finals_list[i]) and not merge_last[i - 1]:
|
273 |
+
# if the last word is reduplication, not merge, because reduplication need to be _neural_sandhi
|
274 |
+
if not self._is_reduplication(seg[i - 1][0]) and len(
|
275 |
+
seg[i - 1][0]) + len(seg[i][0]) <= 3:
|
276 |
+
new_seg[-1][0] = new_seg[-1][0] + seg[i][0]
|
277 |
+
merge_last[i] = True
|
278 |
+
else:
|
279 |
+
new_seg.append([word, pos])
|
280 |
+
else:
|
281 |
+
new_seg.append([word, pos])
|
282 |
+
|
283 |
+
return new_seg
|
284 |
+
|
285 |
+
def _is_reduplication(self, word: str) -> bool:
|
286 |
+
return len(word) == 2 and word[0] == word[1]
|
287 |
+
|
288 |
+
# the last char of first word and the first char of second word is tone_three
|
289 |
+
def _merge_continuous_three_tones_2(
|
290 |
+
self, seg: List[Tuple[str, str]]) -> List[Tuple[str, str]]:
|
291 |
+
new_seg = []
|
292 |
+
sub_finals_list = [
|
293 |
+
lazy_pinyin(
|
294 |
+
word, neutral_tone_with_five=True, style=Style.FINALS_TONE3)
|
295 |
+
for (word, pos) in seg
|
296 |
+
]
|
297 |
+
assert len(sub_finals_list) == len(seg)
|
298 |
+
merge_last = [False] * len(seg)
|
299 |
+
for i, (word, pos) in enumerate(seg):
|
300 |
+
if i - 1 >= 0 and sub_finals_list[i - 1][-1][-1] == "3" and sub_finals_list[i][0][-1] == "3" and not \
|
301 |
+
merge_last[i - 1]:
|
302 |
+
# if the last word is reduplication, not merge, because reduplication need to be _neural_sandhi
|
303 |
+
if not self._is_reduplication(seg[i - 1][0]) and len(
|
304 |
+
seg[i - 1][0]) + len(seg[i][0]) <= 3:
|
305 |
+
new_seg[-1][0] = new_seg[-1][0] + seg[i][0]
|
306 |
+
merge_last[i] = True
|
307 |
+
else:
|
308 |
+
new_seg.append([word, pos])
|
309 |
+
else:
|
310 |
+
new_seg.append([word, pos])
|
311 |
+
return new_seg
|
312 |
+
|
313 |
+
def _merge_er(self, seg: List[Tuple[str, str]]) -> List[Tuple[str, str]]:
|
314 |
+
new_seg = []
|
315 |
+
for i, (word, pos) in enumerate(seg):
|
316 |
+
if i - 1 >= 0 and word == "儿" and seg[i - 1][0] != "#":
|
317 |
+
new_seg[-1][0] = new_seg[-1][0] + seg[i][0]
|
318 |
+
else:
|
319 |
+
new_seg.append([word, pos])
|
320 |
+
return new_seg
|
321 |
+
|
322 |
+
def _merge_reduplication(
|
323 |
+
self, seg: List[Tuple[str, str]]) -> List[Tuple[str, str]]:
|
324 |
+
new_seg = []
|
325 |
+
for i, (word, pos) in enumerate(seg):
|
326 |
+
if new_seg and word == new_seg[-1][0]:
|
327 |
+
new_seg[-1][0] = new_seg[-1][0] + seg[i][0]
|
328 |
+
else:
|
329 |
+
new_seg.append([word, pos])
|
330 |
+
return new_seg
|
331 |
+
|
332 |
+
def pre_merge_for_modify(
|
333 |
+
self, seg: List[Tuple[str, str]]) -> List[Tuple[str, str]]:
|
334 |
+
seg = self._merge_bu(seg)
|
335 |
+
try:
|
336 |
+
seg = self._merge_yi(seg)
|
337 |
+
except:
|
338 |
+
print("_merge_yi failed")
|
339 |
+
seg = self._merge_reduplication(seg)
|
340 |
+
seg = self._merge_continuous_three_tones(seg)
|
341 |
+
seg = self._merge_continuous_three_tones_2(seg)
|
342 |
+
seg = self._merge_er(seg)
|
343 |
+
return seg
|
344 |
+
|
345 |
+
def modified_tone(self, word: str, pos: str,
|
346 |
+
finals: List[str]) -> List[str]:
|
347 |
+
finals = self._bu_sandhi(word, finals)
|
348 |
+
finals = self._yi_sandhi(word, finals)
|
349 |
+
finals = self._neural_sandhi(word, pos, finals)
|
350 |
+
finals = self._three_sandhi(word, finals)
|
351 |
+
return finals
|
bert_vits2/transforms.py
ADDED
@@ -0,0 +1,192 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
import torch
|
2 |
+
from torch.nn import functional as F
|
3 |
+
|
4 |
+
import numpy as np
|
5 |
+
|
6 |
+
DEFAULT_MIN_BIN_WIDTH = 1e-3
|
7 |
+
DEFAULT_MIN_BIN_HEIGHT = 1e-3
|
8 |
+
DEFAULT_MIN_DERIVATIVE = 1e-3
|
9 |
+
|
10 |
+
|
11 |
+
def piecewise_rational_quadratic_transform(inputs,
|
12 |
+
unnormalized_widths,
|
13 |
+
unnormalized_heights,
|
14 |
+
unnormalized_derivatives,
|
15 |
+
inverse=False,
|
16 |
+
tails=None,
|
17 |
+
tail_bound=1.,
|
18 |
+
min_bin_width=DEFAULT_MIN_BIN_WIDTH,
|
19 |
+
min_bin_height=DEFAULT_MIN_BIN_HEIGHT,
|
20 |
+
min_derivative=DEFAULT_MIN_DERIVATIVE):
|
21 |
+
if tails is None:
|
22 |
+
spline_fn = rational_quadratic_spline
|
23 |
+
spline_kwargs = {}
|
24 |
+
else:
|
25 |
+
spline_fn = unconstrained_rational_quadratic_spline
|
26 |
+
spline_kwargs = {
|
27 |
+
'tails': tails,
|
28 |
+
'tail_bound': tail_bound
|
29 |
+
}
|
30 |
+
|
31 |
+
outputs, logabsdet = spline_fn(
|
32 |
+
inputs=inputs,
|
33 |
+
unnormalized_widths=unnormalized_widths,
|
34 |
+
unnormalized_heights=unnormalized_heights,
|
35 |
+
unnormalized_derivatives=unnormalized_derivatives,
|
36 |
+
inverse=inverse,
|
37 |
+
min_bin_width=min_bin_width,
|
38 |
+
min_bin_height=min_bin_height,
|
39 |
+
min_derivative=min_derivative,
|
40 |
+
**spline_kwargs
|
41 |
+
)
|
42 |
+
return outputs, logabsdet
|
43 |
+
|
44 |
+
|
45 |
+
def searchsorted(bin_locations, inputs, eps=1e-6):
|
46 |
+
bin_locations[..., -1] += eps
|
47 |
+
return torch.sum(
|
48 |
+
inputs[..., None] >= bin_locations,
|
49 |
+
dim=-1
|
50 |
+
) - 1
|
51 |
+
|
52 |
+
|
53 |
+
def unconstrained_rational_quadratic_spline(inputs,
|
54 |
+
unnormalized_widths,
|
55 |
+
unnormalized_heights,
|
56 |
+
unnormalized_derivatives,
|
57 |
+
inverse=False,
|
58 |
+
tails='linear',
|
59 |
+
tail_bound=1.,
|
60 |
+
min_bin_width=DEFAULT_MIN_BIN_WIDTH,
|
61 |
+
min_bin_height=DEFAULT_MIN_BIN_HEIGHT,
|
62 |
+
min_derivative=DEFAULT_MIN_DERIVATIVE):
|
63 |
+
inside_interval_mask = (inputs >= -tail_bound) & (inputs <= tail_bound)
|
64 |
+
outside_interval_mask = ~inside_interval_mask
|
65 |
+
|
66 |
+
outputs = torch.zeros_like(inputs)
|
67 |
+
logabsdet = torch.zeros_like(inputs)
|
68 |
+
|
69 |
+
if tails == 'linear':
|
70 |
+
unnormalized_derivatives = F.pad(unnormalized_derivatives, pad=(1, 1))
|
71 |
+
constant = np.log(np.exp(1 - min_derivative) - 1)
|
72 |
+
unnormalized_derivatives[..., 0] = constant
|
73 |
+
unnormalized_derivatives[..., -1] = constant
|
74 |
+
|
75 |
+
outputs[outside_interval_mask] = inputs[outside_interval_mask]
|
76 |
+
logabsdet[outside_interval_mask] = 0
|
77 |
+
else:
|
78 |
+
raise RuntimeError('{} tails are not implemented.'.format(tails))
|
79 |
+
|
80 |
+
outputs[inside_interval_mask], logabsdet[inside_interval_mask] = rational_quadratic_spline(
|
81 |
+
inputs=inputs[inside_interval_mask],
|
82 |
+
unnormalized_widths=unnormalized_widths[inside_interval_mask, :],
|
83 |
+
unnormalized_heights=unnormalized_heights[inside_interval_mask, :],
|
84 |
+
unnormalized_derivatives=unnormalized_derivatives[inside_interval_mask, :],
|
85 |
+
inverse=inverse,
|
86 |
+
left=-tail_bound, right=tail_bound, bottom=-tail_bound, top=tail_bound,
|
87 |
+
min_bin_width=min_bin_width,
|
88 |
+
min_bin_height=min_bin_height,
|
89 |
+
min_derivative=min_derivative
|
90 |
+
)
|
91 |
+
|
92 |
+
return outputs, logabsdet
|
93 |
+
|
94 |
+
|
95 |
+
def rational_quadratic_spline(inputs,
|
96 |
+
unnormalized_widths,
|
97 |
+
unnormalized_heights,
|
98 |
+
unnormalized_derivatives,
|
99 |
+
inverse=False,
|
100 |
+
left=0., right=1., bottom=0., top=1.,
|
101 |
+
min_bin_width=DEFAULT_MIN_BIN_WIDTH,
|
102 |
+
min_bin_height=DEFAULT_MIN_BIN_HEIGHT,
|
103 |
+
min_derivative=DEFAULT_MIN_DERIVATIVE):
|
104 |
+
if torch.min(inputs) < left or torch.max(inputs) > right:
|
105 |
+
raise ValueError('Input to a transform is not within its domain')
|
106 |
+
|
107 |
+
num_bins = unnormalized_widths.shape[-1]
|
108 |
+
|
109 |
+
if min_bin_width * num_bins > 1.0:
|
110 |
+
raise ValueError('Minimal bin width too large for the number of bins')
|
111 |
+
if min_bin_height * num_bins > 1.0:
|
112 |
+
raise ValueError('Minimal bin height too large for the number of bins')
|
113 |
+
|
114 |
+
widths = F.softmax(unnormalized_widths, dim=-1)
|
115 |
+
widths = min_bin_width + (1 - min_bin_width * num_bins) * widths
|
116 |
+
cumwidths = torch.cumsum(widths, dim=-1)
|
117 |
+
cumwidths = F.pad(cumwidths, pad=(1, 0), mode='constant', value=0.0)
|
118 |
+
cumwidths = (right - left) * cumwidths + left
|
119 |
+
cumwidths[..., 0] = left
|
120 |
+
cumwidths[..., -1] = right
|
121 |
+
widths = cumwidths[..., 1:] - cumwidths[..., :-1]
|
122 |
+
|
123 |
+
derivatives = min_derivative + F.softplus(unnormalized_derivatives)
|
124 |
+
|
125 |
+
heights = F.softmax(unnormalized_heights, dim=-1)
|
126 |
+
heights = min_bin_height + (1 - min_bin_height * num_bins) * heights
|
127 |
+
cumheights = torch.cumsum(heights, dim=-1)
|
128 |
+
cumheights = F.pad(cumheights, pad=(1, 0), mode='constant', value=0.0)
|
129 |
+
cumheights = (top - bottom) * cumheights + bottom
|
130 |
+
cumheights[..., 0] = bottom
|
131 |
+
cumheights[..., -1] = top
|
132 |
+
heights = cumheights[..., 1:] - cumheights[..., :-1]
|
133 |
+
|
134 |
+
if inverse:
|
135 |
+
bin_idx = searchsorted(cumheights, inputs)[..., None]
|
136 |
+
else:
|
137 |
+
bin_idx = searchsorted(cumwidths, inputs)[..., None]
|
138 |
+
|
139 |
+
input_cumwidths = cumwidths.gather(-1, bin_idx)[..., 0]
|
140 |
+
input_bin_widths = widths.gather(-1, bin_idx)[..., 0]
|
141 |
+
|
142 |
+
input_cumheights = cumheights.gather(-1, bin_idx)[..., 0]
|
143 |
+
delta = heights / widths
|
144 |
+
input_delta = delta.gather(-1, bin_idx)[..., 0]
|
145 |
+
|
146 |
+
input_derivatives = derivatives.gather(-1, bin_idx)[..., 0]
|
147 |
+
input_derivatives_plus_one = derivatives[..., 1:].gather(-1, bin_idx)[..., 0]
|
148 |
+
|
149 |
+
input_heights = heights.gather(-1, bin_idx)[..., 0]
|
150 |
+
|
151 |
+
if inverse:
|
152 |
+
a = (((inputs - input_cumheights) * (input_derivatives
|
153 |
+
+ input_derivatives_plus_one
|
154 |
+
- 2 * input_delta)
|
155 |
+
+ input_heights * (input_delta - input_derivatives)))
|
156 |
+
b = (input_heights * input_derivatives
|
157 |
+
- (inputs - input_cumheights) * (input_derivatives
|
158 |
+
+ input_derivatives_plus_one
|
159 |
+
- 2 * input_delta))
|
160 |
+
c = - input_delta * (inputs - input_cumheights)
|
161 |
+
|
162 |
+
discriminant = b.pow(2) - 4 * a * c
|
163 |
+
assert (discriminant >= 0).all()
|
164 |
+
|
165 |
+
root = (2 * c) / (-b - torch.sqrt(discriminant))
|
166 |
+
outputs = root * input_bin_widths + input_cumwidths
|
167 |
+
|
168 |
+
theta_one_minus_theta = root * (1 - root)
|
169 |
+
denominator = input_delta + ((input_derivatives + input_derivatives_plus_one - 2 * input_delta)
|
170 |
+
* theta_one_minus_theta)
|
171 |
+
derivative_numerator = input_delta.pow(2) * (input_derivatives_plus_one * root.pow(2)
|
172 |
+
+ 2 * input_delta * theta_one_minus_theta
|
173 |
+
+ input_derivatives * (1 - root).pow(2))
|
174 |
+
logabsdet = torch.log(derivative_numerator) - 2 * torch.log(denominator)
|
175 |
+
|
176 |
+
return outputs, -logabsdet
|
177 |
+
else:
|
178 |
+
theta = (inputs - input_cumwidths) / input_bin_widths
|
179 |
+
theta_one_minus_theta = theta * (1 - theta)
|
180 |
+
|
181 |
+
numerator = input_heights * (input_delta * theta.pow(2)
|
182 |
+
+ input_derivatives * theta_one_minus_theta)
|
183 |
+
denominator = input_delta + ((input_derivatives + input_derivatives_plus_one - 2 * input_delta)
|
184 |
+
* theta_one_minus_theta)
|
185 |
+
outputs = input_cumheights + numerator / denominator
|
186 |
+
|
187 |
+
derivative_numerator = input_delta.pow(2) * (input_derivatives_plus_one * theta.pow(2)
|
188 |
+
+ 2 * input_delta * theta_one_minus_theta
|
189 |
+
+ input_derivatives * (1 - theta).pow(2))
|
190 |
+
logabsdet = torch.log(derivative_numerator) - 2 * torch.log(denominator)
|
191 |
+
|
192 |
+
return outputs, logabsdet
|
bert_vits2/utils.py
ADDED
@@ -0,0 +1,292 @@
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|
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|
|
|
|
|
|
|
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|
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|
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|
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|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
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|
|
|
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|
|
|
|
|
|
|
|
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|
|
|
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|
|
|
|
|
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|
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|
|
|
|
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|
|
|
|
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|
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|
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|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
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|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
import os
|
2 |
+
import glob
|
3 |
+
import sys
|
4 |
+
import argparse
|
5 |
+
import logging
|
6 |
+
import json
|
7 |
+
import subprocess
|
8 |
+
import numpy as np
|
9 |
+
from scipy.io.wavfile import read
|
10 |
+
import torch
|
11 |
+
|
12 |
+
MATPLOTLIB_FLAG = False
|
13 |
+
|
14 |
+
logging.basicConfig(stream=sys.stdout, level=logging.DEBUG)
|
15 |
+
logger = logging
|
16 |
+
|
17 |
+
|
18 |
+
def load_checkpoint(checkpoint_path, model, optimizer=None, skip_optimizer=False):
|
19 |
+
assert os.path.isfile(checkpoint_path)
|
20 |
+
checkpoint_dict = torch.load(checkpoint_path, map_location='cpu')
|
21 |
+
iteration = checkpoint_dict['iteration']
|
22 |
+
learning_rate = checkpoint_dict['learning_rate']
|
23 |
+
if optimizer is not None and not skip_optimizer and checkpoint_dict['optimizer'] is not None:
|
24 |
+
optimizer.load_state_dict(checkpoint_dict['optimizer'])
|
25 |
+
elif optimizer is None and not skip_optimizer:
|
26 |
+
# else: #Disable this line if Infer ,and enable the line upper
|
27 |
+
new_opt_dict = optimizer.state_dict()
|
28 |
+
new_opt_dict_params = new_opt_dict['param_groups'][0]['params']
|
29 |
+
new_opt_dict['param_groups'] = checkpoint_dict['optimizer']['param_groups']
|
30 |
+
new_opt_dict['param_groups'][0]['params'] = new_opt_dict_params
|
31 |
+
optimizer.load_state_dict(new_opt_dict)
|
32 |
+
saved_state_dict = checkpoint_dict['model']
|
33 |
+
if hasattr(model, 'module'):
|
34 |
+
state_dict = model.module.state_dict()
|
35 |
+
else:
|
36 |
+
state_dict = model.state_dict()
|
37 |
+
new_state_dict = {}
|
38 |
+
for k, v in state_dict.items():
|
39 |
+
try:
|
40 |
+
# assert "emb_g" not in k
|
41 |
+
# print("load", k)
|
42 |
+
new_state_dict[k] = saved_state_dict[k]
|
43 |
+
assert saved_state_dict[k].shape == v.shape, (saved_state_dict[k].shape, v.shape)
|
44 |
+
except:
|
45 |
+
print("error, %s is not in the checkpoint" % k)
|
46 |
+
new_state_dict[k] = v
|
47 |
+
if hasattr(model, 'module'):
|
48 |
+
model.module.load_state_dict(new_state_dict, strict=False)
|
49 |
+
else:
|
50 |
+
model.load_state_dict(new_state_dict, strict=False)
|
51 |
+
# print("load ")
|
52 |
+
logger.info("Loaded checkpoint '{}' (iteration {})".format(
|
53 |
+
checkpoint_path, iteration))
|
54 |
+
return model, optimizer, learning_rate, iteration
|
55 |
+
|
56 |
+
|
57 |
+
def save_checkpoint(model, optimizer, learning_rate, iteration, checkpoint_path):
|
58 |
+
logger.info("Saving model and optimizer state at iteration {} to {}".format(
|
59 |
+
iteration, checkpoint_path))
|
60 |
+
if hasattr(model, 'module'):
|
61 |
+
state_dict = model.module.state_dict()
|
62 |
+
else:
|
63 |
+
state_dict = model.state_dict()
|
64 |
+
torch.save({'model': state_dict,
|
65 |
+
'iteration': iteration,
|
66 |
+
'optimizer': optimizer.state_dict(),
|
67 |
+
'learning_rate': learning_rate}, checkpoint_path)
|
68 |
+
|
69 |
+
|
70 |
+
def summarize(writer, global_step, scalars={}, histograms={}, images={}, audios={}, audio_sampling_rate=22050):
|
71 |
+
for k, v in scalars.items():
|
72 |
+
writer.add_scalar(k, v, global_step)
|
73 |
+
for k, v in histograms.items():
|
74 |
+
writer.add_histogram(k, v, global_step)
|
75 |
+
for k, v in images.items():
|
76 |
+
writer.add_image(k, v, global_step, dataformats='HWC')
|
77 |
+
for k, v in audios.items():
|
78 |
+
writer.add_audio(k, v, global_step, audio_sampling_rate)
|
79 |
+
|
80 |
+
|
81 |
+
def latest_checkpoint_path(dir_path, regex="G_*.pth"):
|
82 |
+
f_list = glob.glob(os.path.join(dir_path, regex))
|
83 |
+
f_list.sort(key=lambda f: int("".join(filter(str.isdigit, f))))
|
84 |
+
x = f_list[-1]
|
85 |
+
print(x)
|
86 |
+
return x
|
87 |
+
|
88 |
+
|
89 |
+
def plot_spectrogram_to_numpy(spectrogram):
|
90 |
+
global MATPLOTLIB_FLAG
|
91 |
+
if not MATPLOTLIB_FLAG:
|
92 |
+
import matplotlib
|
93 |
+
matplotlib.use("Agg")
|
94 |
+
MATPLOTLIB_FLAG = True
|
95 |
+
mpl_logger = logging.getLogger('matplotlib')
|
96 |
+
mpl_logger.setLevel(logging.WARNING)
|
97 |
+
import matplotlib.pylab as plt
|
98 |
+
import numpy as np
|
99 |
+
|
100 |
+
fig, ax = plt.subplots(figsize=(10, 2))
|
101 |
+
im = ax.imshow(spectrogram, aspect="auto", origin="lower",
|
102 |
+
interpolation='none')
|
103 |
+
plt.colorbar(im, ax=ax)
|
104 |
+
plt.xlabel("Frames")
|
105 |
+
plt.ylabel("Channels")
|
106 |
+
plt.tight_layout()
|
107 |
+
|
108 |
+
fig.canvas.draw()
|
109 |
+
data = np.fromstring(fig.canvas.tostring_rgb(), dtype=np.uint8, sep='')
|
110 |
+
data = data.reshape(fig.canvas.get_width_height()[::-1] + (3,))
|
111 |
+
plt.close()
|
112 |
+
return data
|
113 |
+
|
114 |
+
|
115 |
+
def plot_alignment_to_numpy(alignment, info=None):
|
116 |
+
global MATPLOTLIB_FLAG
|
117 |
+
if not MATPLOTLIB_FLAG:
|
118 |
+
import matplotlib
|
119 |
+
matplotlib.use("Agg")
|
120 |
+
MATPLOTLIB_FLAG = True
|
121 |
+
mpl_logger = logging.getLogger('matplotlib')
|
122 |
+
mpl_logger.setLevel(logging.WARNING)
|
123 |
+
import matplotlib.pylab as plt
|
124 |
+
import numpy as np
|
125 |
+
|
126 |
+
fig, ax = plt.subplots(figsize=(6, 4))
|
127 |
+
im = ax.imshow(alignment.transpose(), aspect='auto', origin='lower',
|
128 |
+
interpolation='none')
|
129 |
+
fig.colorbar(im, ax=ax)
|
130 |
+
xlabel = 'Decoder timestep'
|
131 |
+
if info is not None:
|
132 |
+
xlabel += '\n\n' + info
|
133 |
+
plt.xlabel(xlabel)
|
134 |
+
plt.ylabel('Encoder timestep')
|
135 |
+
plt.tight_layout()
|
136 |
+
|
137 |
+
fig.canvas.draw()
|
138 |
+
data = np.fromstring(fig.canvas.tostring_rgb(), dtype=np.uint8, sep='')
|
139 |
+
data = data.reshape(fig.canvas.get_width_height()[::-1] + (3,))
|
140 |
+
plt.close()
|
141 |
+
return data
|
142 |
+
|
143 |
+
|
144 |
+
def load_wav_to_torch(full_path):
|
145 |
+
sampling_rate, data = read(full_path)
|
146 |
+
return torch.FloatTensor(data.astype(np.float32)), sampling_rate
|
147 |
+
|
148 |
+
|
149 |
+
def load_filepaths_and_text(filename, split="|"):
|
150 |
+
with open(filename, encoding='utf-8') as f:
|
151 |
+
filepaths_and_text = [line.strip().split(split) for line in f]
|
152 |
+
return filepaths_and_text
|
153 |
+
|
154 |
+
|
155 |
+
def get_hparams(init=True):
|
156 |
+
parser = argparse.ArgumentParser()
|
157 |
+
parser.add_argument('-c', '--config', type=str, default="./configs/base.json",
|
158 |
+
help='JSON file for configuration')
|
159 |
+
parser.add_argument('-m', '--model', type=str, required=True,
|
160 |
+
help='Model name')
|
161 |
+
|
162 |
+
args = parser.parse_args()
|
163 |
+
model_dir = os.path.join("./logs", args.model)
|
164 |
+
|
165 |
+
if not os.path.exists(model_dir):
|
166 |
+
os.makedirs(model_dir)
|
167 |
+
|
168 |
+
config_path = args.config
|
169 |
+
config_save_path = os.path.join(model_dir, "config.json")
|
170 |
+
if init:
|
171 |
+
with open(config_path, "r") as f:
|
172 |
+
data = f.read()
|
173 |
+
with open(config_save_path, "w") as f:
|
174 |
+
f.write(data)
|
175 |
+
else:
|
176 |
+
with open(config_save_path, "r") as f:
|
177 |
+
data = f.read()
|
178 |
+
config = json.loads(data)
|
179 |
+
|
180 |
+
hparams = HParams(**config)
|
181 |
+
hparams.model_dir = model_dir
|
182 |
+
return hparams
|
183 |
+
|
184 |
+
|
185 |
+
def clean_checkpoints(path_to_models='logs/44k/', n_ckpts_to_keep=2, sort_by_time=True):
|
186 |
+
"""Freeing up space by deleting saved ckpts
|
187 |
+
|
188 |
+
Arguments:
|
189 |
+
path_to_models -- Path to the model directory
|
190 |
+
n_ckpts_to_keep -- Number of ckpts to keep, excluding G_0.pth and D_0.pth
|
191 |
+
sort_by_time -- True -> chronologically delete ckpts
|
192 |
+
False -> lexicographically delete ckpts
|
193 |
+
"""
|
194 |
+
import re
|
195 |
+
ckpts_files = [f for f in os.listdir(path_to_models) if os.path.isfile(os.path.join(path_to_models, f))]
|
196 |
+
name_key = (lambda _f: int(re.compile('._(\d+)\.pth').match(_f).group(1)))
|
197 |
+
time_key = (lambda _f: os.path.getmtime(os.path.join(path_to_models, _f)))
|
198 |
+
sort_key = time_key if sort_by_time else name_key
|
199 |
+
x_sorted = lambda _x: sorted([f for f in ckpts_files if f.startswith(_x) and not f.endswith('_0.pth')],
|
200 |
+
key=sort_key)
|
201 |
+
to_del = [os.path.join(path_to_models, fn) for fn in
|
202 |
+
(x_sorted('G')[:-n_ckpts_to_keep] + x_sorted('D')[:-n_ckpts_to_keep])]
|
203 |
+
del_info = lambda fn: logger.info(f".. Free up space by deleting ckpt {fn}")
|
204 |
+
del_routine = lambda x: [os.remove(x), del_info(x)]
|
205 |
+
rs = [del_routine(fn) for fn in to_del]
|
206 |
+
|
207 |
+
|
208 |
+
def get_hparams_from_dir(model_dir):
|
209 |
+
config_save_path = os.path.join(model_dir, "config.json")
|
210 |
+
with open(config_save_path, "r", encoding='utf-8') as f:
|
211 |
+
data = f.read()
|
212 |
+
config = json.loads(data)
|
213 |
+
|
214 |
+
hparams = HParams(**config)
|
215 |
+
hparams.model_dir = model_dir
|
216 |
+
return hparams
|
217 |
+
|
218 |
+
|
219 |
+
def get_hparams_from_file(config_path):
|
220 |
+
with open(config_path, "r", encoding='utf-8') as f:
|
221 |
+
data = f.read()
|
222 |
+
config = json.loads(data)
|
223 |
+
|
224 |
+
hparams = HParams(**config)
|
225 |
+
return hparams
|
226 |
+
|
227 |
+
|
228 |
+
def check_git_hash(model_dir):
|
229 |
+
source_dir = os.path.dirname(os.path.realpath(__file__))
|
230 |
+
if not os.path.exists(os.path.join(source_dir, ".git")):
|
231 |
+
logger.warn("{} is not a git repository, therefore hash value comparison will be ignored.".format(
|
232 |
+
source_dir
|
233 |
+
))
|
234 |
+
return
|
235 |
+
|
236 |
+
cur_hash = subprocess.getoutput("git rev-parse HEAD")
|
237 |
+
|
238 |
+
path = os.path.join(model_dir, "githash")
|
239 |
+
if os.path.exists(path):
|
240 |
+
saved_hash = open(path).read()
|
241 |
+
if saved_hash != cur_hash:
|
242 |
+
logger.warn("git hash values are different. {}(saved) != {}(current)".format(
|
243 |
+
saved_hash[:8], cur_hash[:8]))
|
244 |
+
else:
|
245 |
+
open(path, "w").write(cur_hash)
|
246 |
+
|
247 |
+
|
248 |
+
def get_logger(model_dir, filename="train.log"):
|
249 |
+
global logger
|
250 |
+
logger = logging.getLogger(os.path.basename(model_dir))
|
251 |
+
logger.setLevel(logging.DEBUG)
|
252 |
+
|
253 |
+
formatter = logging.Formatter("%(asctime)s\t%(name)s\t%(levelname)s\t%(message)s")
|
254 |
+
if not os.path.exists(model_dir):
|
255 |
+
os.makedirs(model_dir)
|
256 |
+
h = logging.FileHandler(os.path.join(model_dir, filename))
|
257 |
+
h.setLevel(logging.DEBUG)
|
258 |
+
h.setFormatter(formatter)
|
259 |
+
logger.addHandler(h)
|
260 |
+
return logger
|
261 |
+
|
262 |
+
|
263 |
+
class HParams():
|
264 |
+
def __init__(self, **kwargs):
|
265 |
+
for k, v in kwargs.items():
|
266 |
+
if type(v) == dict:
|
267 |
+
v = HParams(**v)
|
268 |
+
self[k] = v
|
269 |
+
|
270 |
+
def keys(self):
|
271 |
+
return self.__dict__.keys()
|
272 |
+
|
273 |
+
def items(self):
|
274 |
+
return self.__dict__.items()
|
275 |
+
|
276 |
+
def values(self):
|
277 |
+
return self.__dict__.values()
|
278 |
+
|
279 |
+
def __len__(self):
|
280 |
+
return len(self.__dict__)
|
281 |
+
|
282 |
+
def __getitem__(self, key):
|
283 |
+
return getattr(self, key)
|
284 |
+
|
285 |
+
def __setitem__(self, key, value):
|
286 |
+
return setattr(self, key, value)
|
287 |
+
|
288 |
+
def __contains__(self, key):
|
289 |
+
return key in self.__dict__
|
290 |
+
|
291 |
+
def __repr__(self):
|
292 |
+
return self.__dict__.__repr__()
|
chinese_dialect_lexicons/changzhou.json
DELETED
@@ -1,23 +0,0 @@
|
|
1 |
-
{
|
2 |
-
"name": "Changzhou dialect to IPA",
|
3 |
-
"segmentation": {
|
4 |
-
"type": "mmseg",
|
5 |
-
"dict": {
|
6 |
-
"type": "ocd2",
|
7 |
-
"file": "changzhou.ocd2"
|
8 |
-
}
|
9 |
-
},
|
10 |
-
"conversion_chain": [
|
11 |
-
{
|
12 |
-
"dict": {
|
13 |
-
"type": "group",
|
14 |
-
"dicts": [
|
15 |
-
{
|
16 |
-
"type": "ocd2",
|
17 |
-
"file": "changzhou.ocd2"
|
18 |
-
}
|
19 |
-
]
|
20 |
-
}
|
21 |
-
}
|
22 |
-
]
|
23 |
-
}
|
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|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
chinese_dialect_lexicons/changzhou.ocd2
DELETED
Binary file (96.1 kB)
|
|
chinese_dialect_lexicons/changzhou_3.json
DELETED
@@ -1,23 +0,0 @@
|
|
1 |
-
{
|
2 |
-
"name": "Changzhou dialect to IPA",
|
3 |
-
"segmentation": {
|
4 |
-
"type": "mmseg",
|
5 |
-
"dict": {
|
6 |
-
"type": "ocd2",
|
7 |
-
"file": "changzhou.ocd2"
|
8 |
-
}
|
9 |
-
},
|
10 |
-
"conversion_chain": [
|
11 |
-
{
|
12 |
-
"dict": {
|
13 |
-
"type": "group",
|
14 |
-
"dicts": [
|
15 |
-
{
|
16 |
-
"type": "ocd2",
|
17 |
-
"file": "changzhou.ocd2"
|
18 |
-
}
|
19 |
-
]
|
20 |
-
}
|
21 |
-
}
|
22 |
-
]
|
23 |
-
}
|
|
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|
chinese_dialect_lexicons/changzhou_3.ocd2
DELETED
Binary file (96.1 kB)
|
|
chinese_dialect_lexicons/cixi_2.json
DELETED
@@ -1,23 +0,0 @@
|
|
1 |
-
{
|
2 |
-
"name": "Cixi dialect to IPA",
|
3 |
-
"segmentation": {
|
4 |
-
"type": "mmseg",
|
5 |
-
"dict": {
|
6 |
-
"type": "ocd2",
|
7 |
-
"file": "cixi.ocd2"
|
8 |
-
}
|
9 |
-
},
|
10 |
-
"conversion_chain": [
|
11 |
-
{
|
12 |
-
"dict": {
|
13 |
-
"type": "group",
|
14 |
-
"dicts": [
|
15 |
-
{
|
16 |
-
"type": "ocd2",
|
17 |
-
"file": "cixi.ocd2"
|
18 |
-
}
|
19 |
-
]
|
20 |
-
}
|
21 |
-
}
|
22 |
-
]
|
23 |
-
}
|
|
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|
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|
|
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|
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|
chinese_dialect_lexicons/cixi_2.ocd2
DELETED
Binary file (98 kB)
|
|
chinese_dialect_lexicons/fuyang_2.json
DELETED
@@ -1,23 +0,0 @@
|
|
1 |
-
{
|
2 |
-
"name": "Fuyang dialect to IPA",
|
3 |
-
"segmentation": {
|
4 |
-
"type": "mmseg",
|
5 |
-
"dict": {
|
6 |
-
"type": "ocd2",
|
7 |
-
"file": "fuyang.ocd2"
|
8 |
-
}
|
9 |
-
},
|
10 |
-
"conversion_chain": [
|
11 |
-
{
|
12 |
-
"dict": {
|
13 |
-
"type": "group",
|
14 |
-
"dicts": [
|
15 |
-
{
|
16 |
-
"type": "ocd2",
|
17 |
-
"file": "fuyang.ocd2"
|
18 |
-
}
|
19 |
-
]
|
20 |
-
}
|
21 |
-
}
|
22 |
-
]
|
23 |
-
}
|
|
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|
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|
chinese_dialect_lexicons/fuyang_2.ocd2
DELETED
Binary file (83.7 kB)
|
|
chinese_dialect_lexicons/hangzhou_2.json
DELETED
@@ -1,19 +0,0 @@
|
|
1 |
-
{
|
2 |
-
"name": "Hangzhounese to IPA",
|
3 |
-
"segmentation": {
|
4 |
-
"type": "mmseg",
|
5 |
-
"dict": {
|
6 |
-
"type": "ocd2",
|
7 |
-
"file": "hangzhou.ocd2"
|
8 |
-
}
|
9 |
-
},
|
10 |
-
"conversion_chain": [{
|
11 |
-
"dict": {
|
12 |
-
"type": "group",
|
13 |
-
"dicts": [{
|
14 |
-
"type": "ocd2",
|
15 |
-
"file": "hangzhou.ocd2"
|
16 |
-
}]
|
17 |
-
}
|
18 |
-
}]
|
19 |
-
}
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
chinese_dialect_lexicons/hangzhou_2.ocd2
DELETED
Binary file (427 kB)
|
|